Science, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
DSpace
UBCV
Izett, Robert William
Doctor of Philosophy - PhD
University of British Columbia
This PhD thesis focuses on the development of new tools for measuring marine net community production (NCP), an important ecological variable quantifying the metabolic balance between photosynthesis and community-wide respiration. A common approach to estimating NCP exploits the seawater oxygen-to-argon ratio (O₂/Ar) and derived biological O₂ saturation anomaly, ΔO₂/Ar, as a tracer of net biological production. Using ship-based mass spectrometry, ΔO₂/Ar can be measured at high-resolution, enabling surface water NCP quantification from evaluations of the mixed layer O₂ budget. However, resulting NCP estimates may be biased by the vertical mixing flux of low- or high-O₂ water into the ocean’s surface, while the requirement of mass spectrometry to measure ΔO₂/Ar has largely constrained ship-based NCP quantification to ocean regions sampled by research vessels. These challenges have limited our ability to accurately observe NCP variability, particularly in coastal or under-sampled waters. This thesis addresses these limitations by combining high-resolution, underway sampling in the Subarctic Northeast Pacific and Canadian Arctic Ocean with instrumentation development and numerical analyses to yield new tools for NCP quantification from ship-based observations. Using measurements of nitrous oxide within and below the mixed layer and the output from a numerical ocean circulation model, I evaluate two approaches for correcting surface NCP estimates for biases caused by vertical mixing fluxes of O₂ in coastal and offshore waters. Applying these approaches produces new NCP estimates that reveal elevated productivity along the continental margin of the Subarctic Pacific and near regions of strong nutrient input from glaciers and wind-driven mixing in the Arctic. I also develop a new approach for NCP quantification based on surface seawater O₂ and nitrogen (N₂) observations obtained using a custom-built autonomous ship-based measurement system. This approach combines high-resolution O₂/N₂ sampling and computations with a one-dimensional gas model to derive a new NCP tracer, ΔO₂/N₂'. Using numerical simulations and field data, ΔO₂/N₂' is shown to be nearly equivalent to ΔO₂/Ar throughout the study region. The tools developed in this thesis can improve the accuracy and coverage of NCP observations, facilitating improved evaluations of the climate-dependent sensitivity of NCP and its relationship to other ocean ecosystem processes.
IMPROVED ESTIMATES OF NET COMMUNITY PRODUCTION IN THE SUBARCTIC PACIFIC AND CANADIAN ARCTIC OCEAN USING SHIP-BASED AUTONOMOUS MEASUREMENTS AND COMPUTATIONAL APPROACHES by Robert Izett B. Sc., The University of Victoria, 2015 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILISOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Oceanography) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2021 © Robert Izett, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Improved estimates of net community production in the Subarctic Pacific and Canadian Arctic Ocean using ship-based autonomous measurements and computational approaches submitted by Robert Izett in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Oceanography. Examining Committee: Philippe Tortell, Earth, Ocean and Atmospheric Sciences, University of British Columbia Supervisor Roberta Hamme, School of Earth and Ocean Sciences, University of Victoria Supervisory Committee Member Kristin Orians, Earth, Ocean and Atmospheric Sciences, University of British Columbia University Examiner Bernard Laval, Civil Engineering, University of British Columbia University Examiner Laurie Juranek, College of Earth, Ocean and Atmospheric Sciences, Oregon State University External Examiner Additional Supervisory Committee Members: Susan Allen, Earth, Ocean and Atmospheric Sciences, University of British Columbia Supervisory Committee Member Debby Ianson, Institute of Ocean Sciences, Department of Fisheries and Oceans Canada Supervisory Committee Member iii Abstract This PhD thesis focuses on the development of new tools for measuring marine net community production (NCP), an important ecological variable quantifying the metabolic balance between photosynthesis and community-wide respiration. A common approach to estimating NCP exploits the seawater oxygen-to-argon ratio (O2/Ar) and derived biological O2 saturation anomaly, ΔO2/Ar, as a tracer of net biological production. Using ship-based mass spectrometry, ΔO2/Ar can be measured at high-resolution, enabling surface water NCP quantification from evaluations of the mixed layer O2 budget. However, resulting NCP estimates may be biased by the vertical mixing flux of low- or high-O2 water into the ocean’s surface, while the requirement of mass spectrometry to measure ΔO2/Ar has largely constrained ship-based NCP quantification to ocean regions sampled by research vessels. These challenges have limited our ability to accurately observe NCP variability, particularly in coastal or under-sampled waters. This thesis addresses these limitations by combining high-resolution, underway sampling in the Subarctic Northeast Pacific and Canadian Arctic Ocean with instrumentation development and numerical analyses to yield new tools for NCP quantification from ship-based observations. Using measurements of nitrous oxide within and below the mixed layer and the output from a numerical ocean circulation model, I evaluate two approaches for correcting surface NCP estimates for biases caused by vertical mixing fluxes of O2 in coastal and offshore waters. Applying these approaches produces new NCP estimates that reveal elevated productivity along the continental margin of the Subarctic Pacific and near regions of strong nutrient input from glaciers and wind-driven mixing in the Arctic. I also develop a new approach for NCP quantification based on surface seawater O2 and nitrogen (N2) observations obtained using a custom-built autonomous ship-based measurement system. This approach combines high-resolution O2/N2 sampling and computations with a one-dimensional gas model to derive a new NCP tracer, ΔO2/N2'. Using numerical simulations and field data, ΔO2/N2' is shown to be nearly equivalent to ΔO2/Ar throughout the study region. The tools developed in this thesis can improve the accuracy and coverage of NCP observations, facilitating improved evaluations of the climate-dependent sensitivity of NCP and its relationship to other ocean ecosystem processes. iv Lay Summary As climate change is having significant effects on many ocean processes and ecosystems, it is necessary to monitor rates of biological primary productivity. One metric for describing primary productivity is net community production (NCP), a variable that reflects an ocean region’s ability to support marine life and remove carbon dioxide from the atmosphere. Presently, NCP measurements are subject to significant uncertainty and require expensive instrumentation. These challenges have limited our understanding of NCP in many ocean regions, making it difficult to assess its link to climate variability and other ecosystem processes. This thesis investigates new approaches to quantify NCP in the ocean, addressing some sources of uncertainty, and developing a new method for measuring NCP from cheaper and user-friendly instrumentation. Critically, these approaches can significantly improve our capacity to accurately assess NCP in space and time, which should enable better predictions of the ocean’s response to on-going climate change. v Preface A version of Chapter 2 has been published as: Izett, R. W., Manning, C. C., Hamme, R. C. & Tortell, P. D. (2018). Refined estimates of net community production in the Subarctic Northeast Pacific derived from ΔO2/Ar measurements with N2O-based corrections for vertical mixing. Global Biogeochemical Cycles, 32. 326–350. https://doi.org/10.1002/2017GB00579. Data from this publication are available on the PANGAEA database (https://doi.org/10.1594/PANGAEA.885867). I performed all of the analyses and writing of the manuscript, with input from the co-authors. I collected and analyzed the underway field data and discrete gas samples, with assistance from L. Fenwick. Ancillary data were provided by the Institute of Ocean Sciences, DFO Canada. Chapter 3 has been prepared with the intent of publication, and will be submitted for peer-review after the completion of this thesis. Ship-board gas data from this chapter are openly available at the Polar Data Catalogue (http://doi.org/10.5884/13242) and will be archived with supporting data after publication. I performed the analyses and writing of the manuscript with guidance from L. Castro de la Guardia, M. Chanona, P. Myers, S. Waterman and P. Tortell. P. Tortell and I collected the underway ship-board data, and ancillary field data were provided by the Amundsen Science group of U. Laval. The numerical model simulations were designed and performed by P. Myers. A version of Chapter 4 has been published as: Izett, R. W., & Tortell, P. D. (2021). ΔO2/N2' as a tracer of mixed layer net community production: Theoretical considerations and proof-of-concept. Limnology and Oceanography: Methods. https://doi.org/10.1002/lom3.10440. All content is original work with guidance and input from P. Tortell. I developed the one-dimensional mixed layer model described in the chapter, and performed the subsequent simulations, experiments and analyses, and developed the N2' calculation approach. P. Tortell proposed the name “N2-prime”. I completed the writing with feedback from P. Tortell. Original software scripts, including code for using the one-dimensional mixed layer model and performing vi N2' calculations, were written by me and are made available at https://doi.org/10.5281/zenodo.4024925. A version of Chapter 5 has been published as: Izett, R. W. & Tortell, P. D. (2020). The Pressure of In-situ Gases Instrument (PIGI) for autonomous ship-board measurement of dissolved O2 and N2 in surface ocean waters. Oceanography, 33(2), 156-162. https://doi.org/10.5670/oceanog.2020.214. Supporting information and materials for the manuscript, including Matlab code for processing the data produced by the instrument system, LabVIEW data acquisition software and detailed assembly and deployment instructions are available with the publication and at https://doi.org/10.5281/zenodo.4024951. I designed and tested the instrument system with input from P. Tortell, C. McNeil, R. McCulloch, J. Unger, D. Lovrity and M. Barry. I collected and analyzed the laboratory, field and calibration data with support from W. Burt, Z. Zheng, R. Hamme, E. Raftery, A. Bourbonnias, H. Westbrook and scientists at the Institute of Ocean Sciences. I wrote the manuscript with input from P. Tortell. Chapter 6 has been prepared with the intent of publication. It was submitted for publication in June 2021, and was undergoing peer revision at the time of thesis submission. Data from this work will be publicly archived after publication. I performed the analyses and writing of the manuscript with guidance from R. Hamme, C. McNeil, C. Manning, A. Bourbonnais and P. Tortell. Underway ship-board data were collected by W. Burt and me, with ancillary field data provided by R. Hamme, A. Bourbonnais, the Amundsen Science group of U. Laval and the Institute of Ocean Sciences. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ........................................................................................................................ vii List of Tables .............................................................................................................................. xiii List of Figures ............................................................................................................................. xiv List of Common Terms and Abbreviations ........................................................................... xviii Acknowledgements ......................................................................................................................xx Chapter 1: Introduction ................................................................................................................1 1.1 Rationale for studying marine net community production ................................................ 1 1.2 Context for studying NCP in the Subarctic NE Pacific and Canadian Arctic Ocean ....... 3 1.3 Current approaches to estimating NCP ............................................................................. 5 1.3.1 Challenges limiting accurate ship-board NCP estimation .......................................... 7 1.4 Thesis Objectives .............................................................................................................. 9 1.4.1 Chapter 2: Refined estimates of net community production in the Subarctic Northeast Pacific derived from ΔO2/Ar measurements with N2O-based corrections for vertical mixing ......................................................................................................................... 9 1.4.2 Chapter 3: Impact of mixing dynamics and freshwater input on net community production in Canadian Arctic and Subarctic waters: Insights from in-situ measurements and numerical simulations ................................................................ 10 1.4.3 Chapter 4: ΔO2/N2' as a tracer of mixed layer net community production: Theoretical considerations and proof-of-concept ........................................................................ 10 1.4.4 Chapter 5: The Pressure of In-situ gases Instrument (PIGI) for autonomous ship-board measurement of O2 and N2 in surface ocean waters ................................................. 11 1.4.5 Chapter 6: Field evaluation of ΔO2/N2 and ΔO2/N2' as tracers of mixed layer net community production in the Subarctic Northeast Pacific and Canadian Arctic Ocean .................................................................................................................................. 11 1.4.6 Conclusions .............................................................................................................. 11 viii 1.4.7 Appendices ............................................................................................................... 12 Chapter 2: Refined estimates of net community production in the Subarctic Northeast Pacific derived from ΔO2/Ar measurements with N2O-based corrections for vertical mixing ............................................................................................................................................13 2.1 Overview ......................................................................................................................... 13 2.2 Introduction ..................................................................................................................... 13 2.3 Methods ........................................................................................................................... 16 2.3.1 Study area ................................................................................................................. 16 2.3.2 Underway O2/Ar measurements ............................................................................... 17 2.3.3 Nitrous oxide sample collection ............................................................................... 18 2.3.4 Calculations of net community production .............................................................. 18 2.3.5 Calculations of apparent mixing coefficients, Κmix .................................................. 21 2.3.6 Ancillary hydrographic data and calculations .......................................................... 22 2.4 Results ............................................................................................................................. 23 2.4.1 Hydrographic conditions and phytoplankton biomass ............................................. 23 2.4.2 O2 and N2O ............................................................................................................... 25 2.4.3 Apparent mixing coefficients ................................................................................... 26 2.4.4 Net community production ....................................................................................... 28 2.5 Discussion ....................................................................................................................... 30 2.5.1 Methodological considerations and assumptions ..................................................... 31 2.5.1.1 Considerations for the [O2]B/[N2O]B supply ratio term ..................................... 31 2.5.1.2 Mixed layer nitrification .................................................................................... 32 2.5.1.3 Thermal correction for [N2O]B ......................................................................... 35 2.5.1.4 Ar saturation below the mixed layer ................................................................. 37 2.5.2 Discussion of the approach limitations and uncertainties ........................................ 38 2.5.2.1 Scenarios where the N2O correction approach is invalid .................................. 38 2.5.2.2 Alternatives to the N2O correction approach .................................................... 40 2.5.2.3 Additional limitations of in-situ NCP estimates ............................................... 41 2.5.3 Correction factor and Kmix ........................................................................................ 43 2.5.4 Spatial patterns of NCP ............................................................................................ 45 ix 2.6 Conclusions ..................................................................................................................... 49 Chapter 3: Impact of vertical mixing and freshwater input on summertime net community production in Canadian Arctic and Subarctic waters: Insights from in situ measurements and numerical simulations ..........................................................................................................51 3.1 Overview ......................................................................................................................... 51 3.2 Introduction ..................................................................................................................... 51 3.3 Methods ........................................................................................................................... 54 3.3.1 Underway gas measurements and bioflux-NCP calculations ................................... 56 3.3.2 Ancillary sampling and datasets ............................................................................... 58 3.3.3 Vertical mixing calculations and NCP corrections .................................................. 59 3.3.3.1 Numerical model description and mixing rate estimates .................................. 59 3.3.3.2 Mixing correction calculations .......................................................................... 60 3.3.4 Model evaluation and comparisons .......................................................................... 61 3.4 Results ............................................................................................................................. 62 3.4.1 NEMO model evaluation .......................................................................................... 62 3.4.2 Observations: Biogeochemical properties and the physical environment ................ 64 3.4.3 Sub-mesoscale variability in ΔO2/Ar ....................................................................... 68 3.4.4 Mixing rates and vertical O2 gradients ..................................................................... 69 3.4.5 Net community production and mixing corrections ................................................. 70 3.5 Discussion ....................................................................................................................... 73 3.5.1 Evaluating the mixing correction ............................................................................. 74 3.5.1.1 Strengths and weaknesses of the approach ....................................................... 74 3.5.1.2 Spatial variability in the mixing correction ....................................................... 77 3.5.2 Variability in net community production ................................................................. 79 3.5.2.1 Environmental drivers of NCP .......................................................................... 80 3.5.2.2 Contrasting influence of freshwater on NCP .................................................... 82 3.5.2.3 Statistical prediction of NCP variability ........................................................... 87 3.6 Conclusions ..................................................................................................................... 89 x Chapter 4: ΔO2/N2' as a tracer of mixed layer net community production: Theoretical considerations and proof-of-concept ..........................................................................................91 4.1 Overview ......................................................................................................................... 91 4.2 Introduction ..................................................................................................................... 91 4.3 Procedures ....................................................................................................................... 94 4.3.1 One-dimensional mixed layer physical gas model ................................................... 94 4.3.2 Model simulation runs .............................................................................................. 96 4.3.3 Derivation of N2' ....................................................................................................... 98 4.3.4 Evaluation against field measurements .................................................................. 100 4.4 Assessment .................................................................................................................... 101 4.4.1 Model evaluation and sensitivity tests .................................................................... 101 4.4.2 Predicting ΔO2/Ar and ΔΟ2/Ν2 differences ............................................................ 104 4.4.3 ΔO2/N2' as an analog for ΔO2/Ar ............................................................................ 106 4.4.4 Uncertainty in ΔO2/N2' ........................................................................................... 108 4.5 Discussion and recommendations ................................................................................. 111 Chapter 5: The Pressure of In-situ Gases Instrument (PIGI) for autonomous ship-board measurement of dissolved O2 and N2 in surface ocean waters...............................................115 5.1 Overview ....................................................................................................................... 115 5.2 Background ................................................................................................................... 115 5.3 System overview ........................................................................................................... 116 5.4 Materials and costs ........................................................................................................ 119 5.5 Field applications and laboratory and in-situ testing ..................................................... 120 5.6 Recommendations and future modifications ................................................................. 125 5.7 Conclusions ................................................................................................................... 126 Chapter 6: Field evaluation of ΔO2/N2 and ΔO2/N2' as tracers of mixed layer net community production in the Subarctic Northeast Pacific and Canadian Arctic Ocean .......................127 6.1 Overview ....................................................................................................................... 127 6.2 Introduction ................................................................................................................... 127 6.3 Methods ......................................................................................................................... 129 6.3.1 Deployments ........................................................................................................... 129 xi 6.3.2 Underway ΔO2/Ar and ΔO2/N2 observations .......................................................... 131 6.3.2.1 Underway gas measurements and data processing ......................................... 131 6.3.2.2 Derivation of ΔO2/N2 ...................................................................................... 133 6.3.2.3 Underway signal calibration and discrete gas samples ................................... 134 6.3.3 Ancillary datasets ................................................................................................... 135 6.3.4 N2': Correcting N2 for solubility and bubble effects ............................................... 136 6.3.5 Calculations of net community production ............................................................ 138 6.4 Results ........................................................................................................................... 138 6.4.1 Underway signal calibration ................................................................................... 139 6.4.2 Surface water hydrography, physical forcing and gas saturation anomalies .......... 140 6.4.2.1 Gas observations in the Subarctic Northeast Pacific ....................................... 141 6.4.2.2 Gas observations in the Canadian Arctic ........................................................ 143 6.4.3 Comparison of underway ΔO2/Ar, ΔO2/N2 and ΔO2/N2' ........................................ 145 6.4.4 Net community production estimates ..................................................................... 146 6.5 Discussion ..................................................................................................................... 148 6.5.1 Uncertainty and artifacts in surface ΔO2/N2 observations ...................................... 148 6.5.1.1 Bubble entrainment during elevated sea states ............................................... 149 6.5.1.2 Additional sources of analytical uncertainty ................................................... 150 6.5.2 Explaining differences between ΔO2/Ar and ΔO2/N2 ............................................ 152 6.5.3 Evaluating ΔO2/N2' as an NCP tracer in field studies ............................................ 154 6.5.3.1 Remaining biases between ΔO2/Ar and ΔO2/N2'............................................. 156 6.6 Conclusions ................................................................................................................... 157 Chapter 7: Conclusion ...............................................................................................................158 7.1 Summary and Implications ............................................................................................ 158 7.1.1 Correcting ship-board NCP estimates for vertical O2 mixing biases ..................... 158 7.1.2 NCP derivation from underway ΔO2/N2 measurements ......................................... 159 7.2 Future work ................................................................................................................... 160 7.3 Concluding remarks ...................................................................................................... 163 xii References ...................................................................................................................................164 Appendices ..................................................................................................................................181 Appendix A - Supporting Information for Chapter 2 ............................................................. 181 A.1 Derivation of mixed layer nitrification equations .................................................. 181 A.2 Error analysis for NCP estimates with N2O corrections ........................................ 181 Appendix B - Supporting Information for Chapter 3 ............................................................. 190 B.1 Regional definitions and hydrography ................................................................... 190 B.2 Uncertainty in Fmix,O2 and NCP calculations .......................................................... 190 B.2.1 Temporal and spatial uncertainty in dO2B/dZ and κZ ...................................... 190 B.2.2 Numerical uncertainty in κZ ............................................................................ 191 B.2.3 Total uncertainty in Fmix,O2 .............................................................................. 192 Appendix C - Supporting Information for Chapter 4 ............................................................. 199 C.1 One-dimensional mixed layer gas model ............................................................... 199 C.2 N2' budget, Quasi-steady-state conditions and the O2 re-equilibration timescale ... 201 C.2.1 Derivations ...................................................................................................... 201 C.2.2 Quasi-steady-state ΔN2/Ar conditions ............................................................. 204 C.3 Additional controlled simulations .......................................................................... 205 C.4 Uncertainty analyses ............................................................................................... 207 C.4.1 ΔN2' parameter uncertainty ............................................................................. 207 C.4.2 Assumption uncertainty ................................................................................... 208 C.4.3 Uncertainty from N2-fixation .......................................................................... 208 C.4.4 Total uncertainty ............................................................................................. 209 Appendix D - Supporting Information for Chapter 6 ............................................................. 215 D.1 Surface and subsurface gas data ............................................................................. 215 D.1.1 Underway optode and GTD data processing ................................................... 215 D.1.2 N2 derivation ................................................................................................... 217 D.1.3 Discrete gas sampling: N2,sat calibration and subsurface ΔN2/Ar.................... 218 D.2 Effects of seawater warming on GTD readings ..................................................... 219 D.3 Historic environmental datasets ............................................................................. 221 D.4 Uncertainty in ΔO2/Ar, ΔO2/N2 and ΔO2/N2' ......................................................... 222 xiii List of Tables Table 2.1 The potential effects of surface nitrification on mixed layer N2O concentrations, NCP estimates, and mixing coefficients ........................................................................... 36 Table 2.2 Influence of air-sea heat flux on mixed layer N2O budgets during one N2O residence time prior to sampling .............................................................................................. 37 Table 3.1 Summary of along-track NCP and hydrographic observations from the 2015, 2018 and 2019 CCGS Amundsen cruises ......................................................................... 73 Table 3.2 Statistics for the multiple linear regression model of cor-NCP in the central and eastern North American Arctic ................................................................................ 88 Table 4.1 Time-variable one-dimensional model parameters and their corresponding values in the OSP-FM simulation ........................................................................................... 98 Table 5.1 Abbreviated list of expenses of the PIGI system ................................................... 120 Table 5.2 List of PIGI system deployments between 2016 and 2021 .................................... 123 Table 6.1 Summary of NCP estimates, differences between NCP terms and other biases in NCP calculations during the 2018 and 2019 NEP-summer, NEP-winter and Arctic cruises ............................................................................................................................... 147 Table A.1 Summary table of corrected NCP estimates, and the magnitude of the N2O correction for the 2016 Line P and La Perouse cruises........................................................... 185 Table A.2 Summary of uncertainties in underway NCP estimates with N2O-based mixing corrections .............................................................................................................. 186 Table B.1 Regionally averaged sources of uncertainty in model-based Fmix,O2 calculations . 193 Table C.1 Summary of experimental one-dimensional model simulation conditions ............ 210 Table C.2 Sources of uncertainty in ΔO2/N2', determined through Monte Carlo and sensitivity analyses on a realistic one-dimensional model simulation of OSP ....................... 210 Table C.3 Summary of biases and uncertainty in NCP calculations based on ΔO2/Ar, ΔO2/N2 and ΔO2/N2' ............................................................................................................ 210 Table D.1 Average environmental conditions and mixing terms applied in N2' calculations applied to data from the August 2018 Line P (NEP-summer), February 2019 Line P (NEP-winter) and summer 2019 CCGS Amundsen (Arctic) cruises .................... 224 xiv List of Figures Figure 1.1 Maps of the Subarctic Northeast Pacific (a) and central and eastern Canadian Arctic and Subarctic Ocean (b) study regions ...................................................................... 3 Figure 2.1 Maps of the Line P and La Perouse survey regions showing the locations of underway and discrete data collection ...................................................................................... 17 Figure 2.2 Mixed layer Chl a (a-c) and NO3-+NO2- (d-f) concentrations along the 2016 Line P and La Perouse transects. ......................................................................................... 24 Figure 2.3 The relationship between [N2O]B and [O2]B concentrations in the upper water column from Line P and La Perouse cruises between 2012 and 2016 (a-b), estimates of the vertical [O2]B gradient below the mixed layer in 2016 (c), and sensitivity of NCP calculations to differing estimates of the vertical mixing terms (d) ........................ 26 Figure 2.4 Vertical mixing coefficients derived from profile measurements of N2O (a-b) along the 2016 Line P and La Perouse cruise tracks with corresponding estimates of the vertical [N2O]B gradient below the mixed layer (c-d) ............................................. 28 Figure 2.5 Uncorrected and N2O-corrected NCP estimates from the 2016 winter (a-b), spring (c-e), and summer (f-h) Line P and La Perouse cruises ............................................... 30 Figure 2.6 Spatial distribution of NCP from the 2016 winter (a), spring (b), and summer (c) Line P and La Perouse cruises.......................................................................................... 43 Figure 2.7 Relationships between NCP and Chl a (a,d), SST (b,e) and mixed layer depth (c,f) from the 2016 Line P and La Perouse cruises.......................................................... 49 Figure 3.1 The central and eastern North American Arctic study area, showing the 2015 (a), 2018 (b) and 2019 (c) CCGS Amundsen cruise tracks ............................................ 56 Figure 3.2 Comparison of upper ocean density (a-b), mixed layer depth (c), Brunt-Väisälä frequency (d), and eddy diffusivity (e) between CTD-based observations and NEMO-derived output .......................................................................................................... 64 Figure 3.3 Distributions of ΔO2/Ar and hydrographic properties along the 2015 CCGS Amundsen cruise track ............................................................................................. 66 Figure 3.4 Distributions of ΔΟ2/Ar and hydrographic properties along the 2018 CCGS Amundsen cruise track ............................................................................................. 67 Figure 3.5 Distributions of ΔΟ2/Ar and hydrographic properties along the 2019 CCGS Amundsen cruise track ............................................................................................. 68 Figure 3.6 Mean summertime NEMO-derived eddy diffusivity at the base of the mixed layer (a-c) and the distribution of the subsurface vertical O2B gradient (d) in the central and eastern North American Arctic ................................................................................ 70 Figure 3.7 The distribution of corrected and uncorrected NCP along the 2015, 2018, and 2019 CCGS Amundsen cruise tracks ................................................................................ 72 xv Figure 3.8 Distribution of cor-NCP and subsurface nitrate and nitrite in the northern CAA ... 82 Figure 3.9 Distributions of average summertime mixed layer cor-NCP (a), Chl a (b), absorption at 443 nm (c), salinity (d) and select river properties (b-d) in the central and eastern Canadian Arctic in 2015, 2018 and 2019 ................................................................. 86 Figure 3.10 Statistical predictions of cor-NCP in the central and eastern Canadian Arctic ....... 89 Figure 4.1 A schematic of the one-dimensional mixed layer model with labelled terms ......... 96 Figure 4.2 Comparison of modeled and observed ΔN2 (a) and ΔO2 (b) over a two-year cycle at OSP, and the sensitivity of results to different model parameterizations (c) ......... 103 Figure 4.3 ΔN2/Ar results from one-dimensional model simulations of OSP in the Subarctic Northeast Pacific. ................................................................................................... 105 Figure 4.4 Evaluation of N2' in one-dimensional model simulations of OSP (a and b), and ship-board measurements (c). ........................................................................................ 108 Figure 4.5 Difference between ΔO2/Ar and ΔO2/N2 (a) and corresponding biases in NCP estimates (b) from a one-dimensional simulation of OSP ..................................... 111 Figure 5.1 A schematic (a) and photographs (b-e) of the flow-through PIGI system ............. 118 Figure 5.2 A screenshot of the PIGI LabVIEW interface ....................................................... 119 Figure 5.3 Field calibration of PIGI-derived O2 (a) and N2 (b) against discrete samples, and estimates of instrument response times within the system (c-d)............................ 124 Figure 5.4 One day of field observations from side-by-side deployments of PIGI and MIMS systems near a fjord in the Canadian Arctic .......................................................... 125 Figure 6.1 Maps of ship tracks in the Subarctic NE Pacific (a), and the eastern Canadian Arctic (b) showing the locations where underway ΔO2/Ar and ΔO2/N2 were compared . 131 Figure 6.2 Calibration of underway O2 (a) and N2,sat (b) against discrete samples obtained from 2018 and 2019 Line P and Arctic cruises .............................................................. 140 Figure 6.3 Spatial survey of ΔO2 and salinity (a), ΔO2/Ar and ΔO2/N2 (b), and wind speed and net SST change (c) along the August 2018 Line P (NEP-summer) cruise track ... 142 Figure 6.4 Spatial survey of ΔO2 and salinity (a), ΔO2/Ar and ΔO2/N2 (b), and wind speed and net SST change (c) along the February 2019 Line P (NEP-winter) cruise track. .. 143 Figure 6.5 Spatial survey of ΔO2 and salinity (a), ΔO2/Ar and ΔO2/N2 (b), and wind speed and net SST change (c) along the summer 2019 CCGS Amundsen (Arctic-summer) cruise track........................................................................................................................ 144 Figure 6.6 The relationship between ΔO2/Ar and ΔO2/N2 or ΔO2/N2' during the NEP-summer, NEP-winter and Arctic cruises ............................................................................... 146 Figure 6.7 Comparison of NCP estimates derived from ΔO2/Ar, ΔO2/N2 and ΔO2/N2' measurements obtained during the 2018 and 2019 NEP-summer, NEP-winter and Arctic cruises ......................................................................................................... 148 xvi Figure 6.8 A subset of the 2019 Arctic cruise track depicting the occurrence of transient ΔO2/Ar – ΔO2/N2 decoupling resulting from differential response times of the optode, GTD and MIMS .............................................................................................................. 152 Figure A.1 Comparison of N2O concentrations measured in 5 m Niskin bottles, and samples obtained from the underway seawater system of the CCGS J. P. Tully ................ 187 Figure A.2 Surface [N2O]B concentrations measured during the 2016 winter (a-b), spring (c-e) and summer (f-h) Line P and La Perouse cruises .................................................. 187 Figure A.3 Example profiles of O2 (a-b) and N2O (c-d) and the subsurface relationship between [O2]B and [N2O]B (e) from 2016 Line P cruises ..................................................... 188 Figure A.4 Subsurface O2B gradients in the 25 m below the mixed layer, measured from CTD casts during the 2016 winter (a-b), spring (c-e) and summer (f-h) Line P and La Perouse cruises ....................................................................................................... 189 Figure A.5 The relationship between Coastal Bakun upwelling index values and derived Kmix averaged over the on-shelf regions of the 2016 Line P and La Perouse cruises .... 189 Figure B.1 Central and eastern North American Arctic study sub-regions defined using NEMO model-derived properties (a-d), bathymetry (e) and satellite-based Chl a (f) ....... 194 Figure B.2 Distribution of surface temperature (a) and salinity (b), mixed layer (c) and pycnocline depths (d), mixed layer O2 residence time (e), and the subsurface O2B gradient (f) around the central and eastern Canadian Arctic sampling domain. .... 195 Figure B.3 Average wind speed (a-c), ice cover (d-f), and time since ice melt during the 2015 (left), 2018 (middle) and 2019 (right) CCGS Amundsen cruises .......................... 196 Figure B.4 Comparison of along-track CTD and model-based O2 concentrations (panels (a) and (b), respectively) at each CTD sampling station along the 2015, 2018 and 2019 CCGS Amundsen cruise tracks ......................................................................................... 197 Figure B.5 Distribution of high NEMO-based κZ identified as the proportion of pixels with enhanced diffusivity (κZ > 10-2 m2 s-1) in the central and eastern North American Arctic...................................................................................................................... 198 Figure C.1 The differential physical properties of O2, Ar and N2 across a range of temperatures and salinities (a-d), and the relationship between ΔO2, ΔN2/Ar and the absolute differences between ΔO2/Ar and ΔO2/N2 (e). ........................................................ 211 Figure C.2 The combined effects of wind speed and mixing on quasi-steady-state surface ΔN2/Ar derived from one-dimensional model simulations ................................................. 212 Figure C.3 Surface ΔN2/Ar derived in experimental one-dimensional model simulations ..... 213 Figure C.4 Surface ΔN2 and ΔAr in a realistic one-dimensional model simulation of OSP, with corresponding forcing parameters .......................................................................... 214 Figure D.1 The impact of seawater warming between the intake and laboratory on GTD pressure measurement .......................................................................................................... 224 xvii Figure D.2 Along-track surface water ΔAr and ΔN2 during the 2018 and 2019 Subarctic NE Pacific summer (a), winter (b) and Arctic (c) cruises ............................................ 225 Figure D.3 Environmental data along the August 2018 Line P (NEP-summer; left), February 2019 Line P (NEP-winter; middle) and summer 2019 Arctic (right) cruise tracks prior to ship based sampling ........................................................................................... 226 Figure D.4 The distribution of surface ΔN2/Ar (left) and ΔN2'/Ar (right) by region in each of the August 2019 Line P (NEP-summer; a-b), February 2019 Line P (NEP-winter; c-d) and summer 2019 Arctic (e-f) cruises .................................................................... 227 Figure D.5 The distribution of subsurface ΔN2/Ar (ΔN2/Ardeep) in the NE Pacific (a) and Arctic (b) ........................................................................................................................... 228 Figure D.6 Comparison of multi-year in-situ wind speed (left) and SST (right) against gridded dataset observations in the Arctic (top) and NE Pacific (bottom) ......................... 229 xviii List of Common Terms and Abbreviations ΔC Supersaturation anomaly of gas C ΔO2/Ar Biological O2 saturation anomaly; also ΔO2/N2 or ΔO2/N2' Ar Argon BB Baffin Bay CAA Canadian Arctic Archipelago Chl a Chlorophyll a concentration Csat Saturation state of gas C DS Davis Strait Fmix,O2 Vertical O2 mixing flux GTD Gas Tension Device HB Hudson Bay HS Hudson Strait LS Labrador Sea MIMS Membrane Inlet Mass Spectrometer ML Mixed layer MLD Mixed layer depth N+N Nitrate and nitrite concentration; also NO3- + NO2- N2 Nitrogen N2' Nitrogen-prime N2O Nitrous oxide N2OB Biological fraction of N2O concentration NA North Atlantic Ocean NCP Net community production NEP Northeast Pacific Ocean O2 Oxygen O2B Biological fraction of O2 concentration OSP Ocean Station Papa PIGI Pressure of In-situ Gases Instrument xix QCS Queen Charlotte Sound QMG Queen Maud Gulf SSS Sea surface salinity SST Sea surface temperature WCVI West Coast of Vancouver Island xx Acknowledgements First and foremost, I would like to acknowledge that most of the research presented in this thesis was completed on the unceded territories of the xʷməθkʷəy̓əm (Musqueam), Sḵwx̱wú7mesh (Squamish), and Sel̓íl̓witulh (Tsleil-Waututh) Nations. I would also like to acknowledge the work that was conducted within Inuit Nunangat. I am grateful to the stewards of the land and sea. I would like to say a huge thank you to my advisor, Philippe Tortell. Thank you, Phil, for your constant guidance, encouragement and support; for the amazing opportunities you bestowed upon me; and for your friendship over the past six years. I would also like to thank my supervisory committee, Susan Allen, Roberta Hamme and Debby Ianson. I have very-much valued and appreciated your thorough and thoughtful feedback on my thesis. Thank you to my colleagues and co-workers, and to all of the scientists with whom I had the pleasure of conducting field work. I am especially grateful to Karl Purcell, Amanda Timmerman, Theresa Venello, Jade Shiller, Marie Robert and Doug Yelland for making my time at sea memorable and enjoyable. Speaking of my time at sea, I am grateful to Goldfish and Ginger Ale for getting me through the sickness! I would also like to thank the UBC EOAS office and technical staff, especially Joern Unger; the Amundsen Science and Institute of Ocean Sciences staff and technicians; and the crews of the CCGS Tully and CCGS Amundsen. I am also grateful to NSERC, Polar Knowledge Canada, MEOPAR, ArcticNet and the UBC EOAS Department for funding my research and training. Thank you to the many close friends and lab mates I have leaned upon, learned from and shared many fond memories with over the past several years. I am especially thankful to Will Burt, Florian Lüskow, Jake Lerner, Patrick Pata, Ross McCulloch, Sarah Rosengard, Cara Manning, Zarah Zheng, Kate Schuler, Yayla Sezginer, Brandon McNabb, Ana Franco, Alysia Herr, Lian Kwong, Sam James, other members of the Tornado lab and ESB office 2041 (past and present), and many other EOAS grad students. xxi I cannot thank my family enough. Thanks for your unwavering support, encouragement and love. Thank you Mum, Dad and Jonathan for always believing in my abilities and supporting me through my academic (and non-academic) journey every step of the way. Thank you Maaike and Jonathan for being role models and sources of wisdom and guidance; but not for making this PhD journey look so easy! Thank you, Kelly – for everything. Thank you for putting up with my long absences and working hours, my quirks and obsessions, and for embracing a life filled with ocean-themed home décor and nature documentaries, walks on the beach picking up trash, and dinner conversations about (dissolved) gas, helicopters, polar bears and the Arctic. Thank you for your unconditional love and support. Finally, thank you, the reader, for taking an interest in my work. I do hope you enjoy it! 1 Chapter 1: Introduction 1.1 Rationale for studying marine net community production The primary production of organic carbon serves a foundational role for a variety of ecosystem services. Marine net community production (NCP) is a key ecological metric reflecting primary production and quantifying the metabolic balance between photosynthesis and community-wide respiration. Positive NCP reflects a system that is net autotrophic (i.e. excess accumulation of organic matter), while negative values reflect a net heterotrophic state. Over seasonal to annual timescales, NCP is functionally equivalent to the rate of new production (i.e. primary production fuelled by nutrients produced outside of the sunlit upper ocean; Dugdale & Goering, 1967) and carbon-export from the upper ocean (Laws, 1991), thus constraining the capacity of an ocean region to sequester carbon dioxide (CO2) from the atmosphere (Volk & Hoffert, 1985) and sustain biomass production by fish and other upper trophic level organisms (e.g. Ware & Thomson, 2005). As such, NCP is an important process regulating global climate, and has considerable commercial, cultural, and ecological value (Jin et al., 2020). Atmospheric warming and climate change have resulted in wide-spread increases in oceanic heat and freshwater content, declining sea ice extent and thickness at high latitudes, and concomitant changes to mixing and density stratification (Carmack et al., 2016; Li et al., 2020; Niemi et al., 2019; Whitney & Freeland, 1999). These global-scale changes occur to differing degrees in various ocean regions, and their impacts on NCP are highly uncertain, in part due to the sparsity of relevant in-situ observations. Yet, as variability in NCP is linked to the physical environment through the seasonal availability of light and nutrients in surface waters, changes to the distributions of heat, freshwater and ice cover will likely have significant impacts on marine biogeochemistry and biological productivity. Manifestations of these perturbations have already been observed in a range of ocean regions, but such responses are likely to be heterogeneous in space and time. For example, declining productivity has been linked to multi-year warming in many ocean regions (Polovina et al., 2008), and a warm water anomaly was implicated for a short-term decline in NCP (Yang et al., 2018) and elevated marine bird and mammal mortality (Cavole et al., 2016) in the Subarctic Pacific. Elsewhere, Arctic sea ice retreat has triggered a region-wide increase in primary production (Arrigo & van Dijken, 2015), and changes to the timing and extent 2 of spring and fall high-productivity (i.e. blooms) events (Ardyna et al., 2014; Renaut et al., 2018). In order to better understand and predict such climate-dependent shifts in marine productivity and biological carrying-capacity, it is crucial to observe NCP variability on ecologically relevant space and time scales. Presently, several methodological and conceptual challenges limit our ability to accurately quantify NCP on various scales. As described below, these challenges relate to the current requirement for complex and expensive instrumentation for ship-board NCP derivation, and the need to account for vertical mixing fluxes in deriving NCP estimates from surface water measurements. The central objective of this thesis is to address these challenges, and improve the methods for oceanic NCP determination from ship-board surveys. To this end, I have developed and applied new observational and numerical methods to better constrain spatial and temporal patterns in NCP in two distinct oceanic regions: The Subarctic Northeast (NE) Pacific and the central and eastern Canadian Arctic and Subarctic Ocean (Fig. 1.1). Below, I provide some context for studying NCP in NE Pacific and Arctic waters before describing current approaches for quantifying NCP. I conclude this introduction by outlining the specific objectives of this thesis and summarizing the goals and methods of the subsequent chapters. 3 Figure 1.1 Maps of the Subarctic Northeast Pacific (a) and central and eastern Canadian Arctic and Subarctic Ocean (b) study regions. Geographic sub-domains are distinguished and labelled. Additional labels correspond with: Ocean Station Papa (OSP), Queen Charlotte Sound (QCS), West Coast of Vancouver Island (WCVI), Baffin Bay (BB), Canadian Arctic Archipelago (CAA), DS (Davis Strait), HS (Hudson Strait), HB (Hudson Bay), LS (Labrador Sea) and NA (North Atlantic). White-to-indigo and green-to-white shading represent water depth (0-4500 m) and elevation (0-1000 m), respectively. Land colours do not necessarily represent ground cover. The black contour identifies the 500 m isobath. For panel (b), corresponding names in Inuktitut are given in Fig. 3.1. 1.2 Context for studying NCP in the Subarctic NE Pacific and Canadian Arctic Ocean The Subarctic NE Pacific and Canadian Arctic Ocean are both characterized by large seasonality and spatial heterogeneity in physical and biogeochemical properties, causing primary production to vary significantly across different oceanographic domains. I therefore further distinguish these regions into the following sub-domains, which are labelled in Figure 1.1: onshore NE Pacific (<500 m water depth; i.e. on-shelf or near-shore); offshore NE Pacific (>500 m water depth; i.e. off-shelf); North Atlantic Ocean and Labrador Sea (NA/LS); Baffin Bay and Davis 4 Strait (BB/DS); Hudson Bay and Strait (HB/HS); the Canadian Arctic Archipelago (CAA) and the NA/LS and BB/DS continental shelf (<500 m water depth). In broad terms, both the Subarctic NE Pacific and Arctic Ocean presently experience spring and early summer productivity blooms, enabled through the relaxation of light limitation. These precede the onset of a modestly productive late summer, when productivity becomes constrained by macro- and micronutrient limitations (Harrison et al., 1999; Tremblay et al., 2015). Nearshore waters of the NEP are sustained by nutrient supply via seasonal upwelling and mixing over the continental shelf (Whitney et al., 2005), while primary productivity in offshore waters is typically low due to iron (summer) or light (winter) limitation (Maldonado et al., 1999). In the Canadian Arctic Ocean, the timing and distribution of primary production during the short sunlit part of the year has important implications for the region’s ecology (Michel et al., 2006), and for Inuit whose cultural practices are linked to the ocean. While wintertime productivity is constrained by low levels of solar radiation and the seasonal ice-capping of the ocean, the onset of melting initiates large under-ice and pelagic spring blooms (Ardyna et al., 2020; Arrigo et al., 2012; Tremblay et al., 2012). Nutrient replenishment via convection during the preceding winter and wind-induced and tidal mixing are important for nourishing these blooms and sustaining productivity during the ensuing ice-free periods (Tremblay et al., 2015). Additional nutrient fluxes from fluvial or glacial sources may also promote high levels of summertime productivity in coastal waters (Arrigo et al., 2017; Hopwood et al., 2018). On longer timescales, marine productivity is likely to respond to changes in the physical environment. Recent climate-driven changes in the Subarctic NE Pacific include the warming and freshening of surface waters, and resulting declines in nutrient resupply via near-surface mixing (Bond et al., 2015; Di Lorenzo & Mantua, 2016; Whitney & Freeland, 1999). Limited time-series measurements at Ocean Station Papa (OSP) in the central Subarctic NE Pacific currently suggest a strong biological signature of CO2 drawdown (Emerson & Stump, 2010; Takahashi et al., 2009; Yang et al., 2017). However, routine in-situ NCP monitoring with broad spatial coverage in oceanic and coastal waters has been lacking over the past several decades. As a result, we cannot adequately address the impact of environmental changes on biological productivity. The Line P and La Perouse ocean monitoring programs, run by the Department of Fisheries and Oceans Canada (DFO), provide an ideal platform from which observations of NCP can be made in both 5 coastal and offshore waters several times per year. Leveraging these programs presents a significant opportunity to collect long-term information on NCP dynamics. In the Arctic, environmental conditions and ecological processes are evolving rapidly under ongoing shifts in the regional climate (e.g. Niemi et al., 2019). Indeed, the variety of responses to declining sea ice coverage, water column warming and an accelerating hydrological cycle – including intensified surface stratification, mixed layer light levels, and wind-induced mixing (Arrigo et al., 2008; Carmack et al., 2016; Jackson et al., 2010; Overland & Wang, 2013; Rainville et al., 2011) – could result in complex controls on primary productivity. While waters of the Canadian Arctic are regarded as a net sink of CO2 (Ahmed et al., 2019; Ahmed & Else, 2019), the contribution of biological productivity to this drawdown is highly uncertain, due, in part, to under-sampling and high interannual variability. Establishing robust tools for determining NCP in these ecologically-significant and complex seascapes is therefore critical for predicting the implications of future climate change on Arctic ecology and culture. However, the difficulties conducting ship-based research in the Arctic (e.g. imposed by ice restrictions on the timing and extent of oceanographic expeditions and the limited availability of dedicated scientific ships) mean that data coverage is sparse. For the past decade and a half, the Canadian ArcticNet program has conducted annual summer research cruises to the Canadian Arctic, helping to increase our understanding of on-going oceanographic change over broad sections of the Canadian Arctic ocean. Until recently, these surveys have not included explicit NCP measurements. 1.3 Current approaches to estimating NCP Many current approaches to estimating marine NCP are based on measuring in-situ inventories of one or more biologically-active compounds, including oxygen (O2), inorganic carbon or nitrate. In the case of O2, for example, the net biological impact on surface water concentrations reflects the balance between production through photosynthesis and removal through respiration. Historically, NCP estimation was constrained by the need for discrete sample collection on research vessels (e.g. Emerson et al., 1995), thereby limiting data coverage. However, recent advancements in sensor technology have enabled autonomous biogeochemical observations from surface moorings (e.g. Emerson & Stump, 2010; Johnson, 2010; Körtzinger et al., 2008), floats and gliders (Bushinsky & Emerson, 2015; Johnson et al., 2017; Plant et al., 2016; 6 Yang et al., 2017), and underway (i.e. continuous), ship-board sampling (see below), affording the opportunity to observe NCP variability over vastly enhanced space and time scales. At present, mooring-based NCP time-series are only available at a handful of deep-ocean sites, while NCP estimation from in-situ floats and gliders remains subject to considerable uncertainty (Gordon et al., 2020). Underway, ship-board observations thus remain an important tool to expand the spatial and temporal coverage of oceanic NCP measurements. One common approach to estimating NCP from ship-board sampling exploits the ratio of O2-to-argon (Ar) as a tracer of biological production. Argon is a biologically-inert gas, with solubility and diffusivity properties that are nearly identical to O2 (Craig & Hayward, 1987). As such, the seawater O2/Ar ratio is largely unaffected by physical perturbations to the O2 saturation state resulting from bubble injection or temperature- and salinity-induced changes. The so-called “biological saturation anomaly”, ΔO2/Ar (Eq. 1.1), thus quantifies the saturation state of O2 attributed to net biological productivity, and is derived by normalizing the seawater O2/Ar ratio (i.e. [O2/Ar]meas) to the equilibrium gas ratio (i.e. [O2/Ar]eq). By evaluating the ocean’s mixed layer ΔO2/Ar mass balance budget, NCP is equated to the sea-to-air flux of biologically produced excess O2, following Kaiser et al. (2005) (Eq. 1.2). ΔO2 Ar⁄ = ([O2 Ar⁄ ]meas[O2 Ar⁄ ]eq – 1) ×100 % (1.1) NCP = ΔO2 Ar⁄ × [O2]eq × kO2 (1.2) Here, [O2]eq is the equilibrium concentration of O2, derived from seawater temperature and salinity measurements, and kO2 is the gas transfer velocity, derived from a wind-speed dependent parameterization. Following this approach, NCP has been estimated in a number of ocean regions using discrete (e.g. Emerson et al., 1995; Giesbrecht et al., 2012; Hamme et al., 2010; Reuer et al., 2007) and continuous ΔO2/Ar sampling. Developments in ship-board mass spectrometry (Cassar et al., 2009; Kaiser et al., 2005; Tortell, 2005) have enabled high-resolution, underway ΔO2/Ar-based NCP estimation on sub-kilometer scales, and this approach has been applied broadly in recent 7 years (e.g. Burt et al., 2018; Eveleth et al., 2017b; Hamme et al., 2012; Juranek et al., 2019; Lockwood et al., 2012; Manning et al., 2017; Stanley et al., 2010; Ulfsbo et al., 2014). 1.3.1 Challenges limiting accurate ship-board NCP estimation Despite the increasing application of underway ΔO2/Ar measurements, significant challenges remain in producing accurate estimates of NCP from these measurements. One such challenge is the requirement of mass spectrometers to obtain high-resolution ΔO2/Ar measurements. These instruments can be cost-prohibitive to many research groups and require considerable expertise and attention to maintain and run at sea. The deployment of mass spectrometers is therefore largely confined to research cruises with dedicated personnel, thereby limiting the coverage of NCP estimates to well-sampled ocean regions. Recent developments of stable O2 sensors (e.g. O2 optodes; Tengberg et al., 2006) and Gas Tension Devices (GTDs; McNeil et al., 2006a; Reed et al., 2018) capable of high-sampling frequencies provides impetus for the adoption of an alternative system for NCP estimation. In contrast to mass spectrometers, optodes and GTDs are relatively inexpensive, simple to use, and can be run in a fully-autonomous (i.e. unattended) configuration for extended deployments. Dissolved nitrogen (N2) concentrations can be derived from concurrent optode and GTD measurements (McNeil et al., 1995), permitting high-resolution observations of a N2-based biological O2 saturation anomaly (i.e. ΔΟ2/Ν2, following Eq. 1.1). In principle, ΔΟ2/Ν2 can be used to derive NCP estimates, following Eq. 1.2, but this approach has yet to be widely applied and rigorously evaluated. Although N2 has similar physical properties to O2, it is about half as soluble as O2 – and therefore more susceptible to bubble-induced supersaturation – and less sensitive to temperature-induced changes in solubility. As a result, ΔO2/N2 is an imperfect biological tracer. If physical differences between ΔO2/Ar and ΔO2/N2 can be reconciled, a combined optode/GTD measurement system could be a viable alternative for expanding NCP estimation from a range of sampling platforms, including research vessels, volunteer observing ships (VOS) and autonomous surface vehicles. An additional challenge to all approaches to estimating NCP from the O2 mass balance is the need to correct surface O2 observations for vertical mixing biases. Throughout the Subarctic Pacific and Arctic Ocean, subsurface O2 undersaturation or O2 maxima commonly develop through 8 the respiratory decomposition of organic matter or the net photosynthetic production of organic matter at depth (e.g. Bianucci et al., 2011; Martin et al., 2010; Shulenberger & Reid, 1981; Tabata, 1961). Several physical processes, including entrainment during mixed layer deepening, vertical diapycnal mixing, or advection, can transport these sub-surface O2 signatures into the mixed layer, thus influencing the O2 mass balance (e.g. Bushinsky & Emerson, 2018; Tortell et al., 2012). When O2 is undersaturated (supersaturated) beneath the mixed layer, failure to account for the mixing supply of this relatively low-O2 (high-O2) water into the surface can bias interpretations of surface ΔΟ2/Ar or ΔΟ2/Ν2 signals, leading to an under (over)-estimation of derived NCP. Such mixing effects are particularly problematic in physically dynamic coastal waters, limiting the applicability of ΔO2/Ar or ΔO2/N2 as NCP tracers in these regions. In some cases, coastal waters may falsely appear to be net heterotrophic (i.e. having negative ΝCP) unless a mixing correction is made. While this problem is widely acknowledged (Hamme & Emerson, 2006; Jonsson et al., 2013; Teeter et al., 2018), a correction for the vertical mixing flux is often neglected, and allegedly biased data are often excluded from NCP calculations (e.g. Giesbrecht et al., 2012; Juranek et al., 2019; Reuer et al., 2007). This practice has likely limited our understanding of NCP dynamics in some ocean regions, including coastal waters that may contribute disproportionately to overall ocean carbon cycling (Borges et al., 2005). Recent modeling work by Cassar et al. (2014) has suggested that measurements of nitrous oxide (N2O), within and below the mixed layer, may be used to trace vertical O2 fluxes, and ultimately correct NCP estimates. This approach is based on the stoichiometric relationship between N2O and O2 through the thermocline, which reflects the strong imprint of nitrification (i.e. ammonium oxidation to nitrate). Nitrous oxide-based corrections have been shown to be effective in model computations, but they also require field validation across a range of oceanic systems. In some regions, such as the Arctic Ocean, additional nitrogen cycle processes (e.g. denitrification and anammox) cause the O2-N2O stoichiometry to be more complex (Fenwick et al., 2017), thus precluding the use of N2O as a mixing tracer. Under such conditions, numerical approaches based on oceanic mixing rates derived from general circulation models may provide an alternative means of addressing this bias in NCP estimates. While validation against field data is necessary, such models are increasingly being used to investigate a range of physical and biogeochemical processes (Castro de la Guardia et al., 2019; Grivault et al., 2018; Hughes et al., 2017; Marchese 9 et al., 2019). The combination of numerical models with field observations will allow us to fill gaps in traditional oceanographic monitoring, and better constrain the processes affecting oceanic O2 distributions. 1.4 Thesis Objectives The overall goal of this dissertation is to expand the application and accuracy of NCP estimates in the Subarctic NE Pacific and Canadian Arctic Ocean through the combined use of various observational and numerical approaches. In particular, Chapters 2 and 3 address the biases in NCP estimates resulting from vertical O2 mixing fluxes, while Chapters 4-6 describe the development and deployment of an O2/N2 measurement system to increase NCP coverage using relatively simple and robust autonomous sensors. Measurements obtained using underway ship-based instrumentation, including membrane inlet mass spectrometers (MIMS), O2 optodes, and gas tension devices, are complemented by discrete geochemical and hydrographic sampling, numerical model output and estimates of environmental conditions from satellite and reanalysis products. The combined application of the tools described here can facilitate a new wave of NCP estimation using robust observational and numerical approaches. Below, I briefly describe the specific objectives and methods of each chapter of this dissertation, explaining how each contributes to my overarching research goal. Details of publications and associated contributions arising from this thesis are included in the preface. 1.4.1 Chapter 2: Refined estimates of net community production in the Subarctic Northeast Pacific derived from ΔO2/Ar measurements with N2O-based corrections for vertical mixing In this chapter, I assess the accuracy of a geochemical approach for refining marine NCP estimates for biases associated with vertical O2 mixing fluxes. I combine underway observations of ΔO2/Ar, obtained via ship-board MIMS sampling on five research cruises in the Subarctic NE Pacific, with discrete surface and depth-resolved O2 and N2O measurements to derive high-resolution NCP estimates across a coastal to open ocean gradient. This work builds on previously published theoretical work (Cassar et al. 2014) by providing an important field-based evaluation of N2O as a tracer of vertical mixing fluxes in the surface O2 mass balance. The results demonstrate 10 the utility of this tracer, and the importance of vertical mixing corrections for obtaining accurate NCP estimates in physically dynamic coastal waters. In evaluating the tracer, I provide recommendations for future applications, and assess the validity of the approach in other ocean regions. 1.4.2 Chapter 3: Impact of mixing dynamics and freshwater input on net community production in Canadian Arctic and Subarctic waters: Insights from in-situ measurements and numerical simulations This chapter evaluates a method for refining NCP estimates for vertical mixing fluxes using the output from a numerical ocean model. I present observations from three oceanographic cruises in central and eastern Canadian Arctic and Subarctic waters, examining the influence of mixing as both a source of bias in NCP calculations and a driver of surface productivity. Estimates of near-surface vertical mixing rates, predicted in a physical circulation model, are combined with ship-board underway ΔO2/Ar and depth-resolved O2 observations to produce high-resolution, refined NCP estimates. The results are subsequently used to document the coupling between vertical mixing dynamics, freshwater input and surface NCP across a range of oceanographic regions. This work demonstrates the use of numerical model results to improve NCP estimates, and contributes important new measurements in a poorly studied region, providing insight into future changes in biological productivity in response to rapidly changing marine conditions. 1.4.3 Chapter 4: ΔO2/N2' as a tracer of mixed layer net community production: Theoretical considerations and proof-of-concept In this chapter, I develop a theoretical justification for the use of ΔO2/N2 as an alternative NCP tracer to ΔO2/Ar. I use a one-dimensional mixed layer model to assess the drivers of inert gas variability in oceanic surface waters and assess differences in ΔΟ2/Ar and ΔO2/N2 resulting from several physical forcing mechanisms. Based on this modeling work, I propose a new tracer, ΔO2/N2', which is shown to be a near-analog for ΔO2/Ar in both numerical and field settings. I describe how the new tracer can be derived from underway O2 and N2 observations and simple mixed layer budget calculations using readily available environmental data. By quantifying the uncertainty in ΔO2/N2', and providing recommendations for its application in field settings, this 11 work provides an important proof-of-concept for a new method of NCP estimation, which can be applied to in-situ surveys on a variety of sampling platforms. 1.4.4 Chapter 5: The Pressure of In-situ gases Instrument (PIGI) for autonomous ship-board measurement of O2 and N2 in surface ocean waters This chapter describes and evaluates a custom-built, autonomous measurement system for high-resolution O2 and N2 measurements. The key goal is to assess the suitability of the system for underway NCP estimates from surface O2/N2 measurements, and to provide detailed and accessible documentation for the construction and deployment of the system. To achieve this, I describe the system, which contains commercial optode and GTD instrumentation, assess the results from extensive field and laboratory testing, and provide open-source hardware designs and data processing software. I further describe how the system can be deployed unattended on research vessels or volunteer observing ships to supplement mass spectrometry-based ΔO2/Ar sampling, and thereby improve the coverage of NCP estimation. 1.4.5 Chapter 6: Field evaluation of ΔO2/N2 and ΔO2/N2' as tracers of mixed layer net community production in the Subarctic Northeast Pacific and Canadian Arctic Ocean In this chapter, I further evaluate the potential of autonomous ΔO2/N2 sampling to supplement or replace ΔO2/Ar for NCP derivation. Using field data from three simultaneous deployments of underway MIMS and optode/GTD measurement systems, I demonstrate both the field applicability of the NCP tracer ΔO2/N2' (Chapter 4) and O2/N2 measurement system (Chapter 5) across a range of hydrographic regimes in Subarctic NE Pacific and Artic Ocean waters. The results highlight the significant utility of the new approach, while also identifying some limitations and remaining challenges. I provide recommendations for future field studies employing these new tools and argue for their widespread uptake in order to significantly expand NCP coverage from a range of underway platforms. 1.4.6 Conclusions In this final chapter, I summarize the key results from my PhD thesis work and provide 12 recommendations for continuing research. 1.4.7 Appendices The appendices of this dissertation are intended to provide context and supporting information for the materials presented in Chapters 2-6. The materials include mathematical derivations, descriptions of sources of uncertainty, and supporting figures and tables. Additional materials containing instrumentation instructions and software scripts for performing analyses described herein are archived online at https://doi.org/10.5281/zenodo.4024925 (Ch. 4, 6) and https://doi.org/10.5281/zenodo.4024951 (Ch. 5). 13 Chapter 2: Refined estimates of net community production in the Subarctic Northeast Pacific derived from ΔO2/Ar measurements with N2O-based corrections for vertical mixing 2.1 Overview I present the first field application of a N2O-based approach to correct for vertical mixing in the estimation of net community production (NCP) from mixed layer O2 measurements. Using new ship-based observations of N2O and biological oxygen saturation anomalies (ΔO2/Ar) from the Subarctic Northeast Pacific, I provide refined mixed layer NCP estimates across contrasting hydrographic regimes and a comprehensive assessment of the methodological considerations and limitations of the approach. Increased vertical mixing coefficients at the base of the mixed layer, derived using N2O measurements, corresponded with periods of heightened wind speed and coastal upwelling. Corrections were most significant in coastal regions where the vertical supply of low-O2 water can otherwise falsely imply net heterotrophy from negative ΔO2/Ar measurements. After correcting for the mixing flux, all coastal stations showed autotrophic signatures, with maximum NCP exceeding 100 mmol O2 m-2 day-1 in the spring and summer. Vertical fluxes were lower in off-shelf waters, but often contributed more than 50 % to corrected NCP. At some oceanic stations, however, the co-occurrence of N2O minima and O2 maxima resulted in biased (over-estimated) N2O corrections. Evaluating vertical fluxes in these regions remains a challenge for ship-based studies. Nonetheless, the refined NCP estimates show better coherence with surface chlorophyll, temperature, and mixed layer depth than uncorrected values. Potential mixed layer N2O production introduces some uncertainty in the approach, but errors are likely to be small. This work provides rationale for the adoption of the N2O correction to refine NCP estimates, particularly in coastal waters. 2.2 Introduction Marine organic carbon production via photosynthesis regulates higher trophic level biomass (Ware & Thomson, 2005) and influences the strength of the ocean’s biological pump (Volk & Hoffert, 1985). Accurate quantification of primary production is thus critical for understanding the functioning of marine ecosystems and their response to environmental change. As a currency of 14 life, oxygen (O2) can be used as a tracer of biological production in the ocean. The net community production (NCP) of O2 represents the balance between primary production and community respiration (both autotrophic and heterotrophic), and is equivalent to carbon export at steady state and on inter-annual timescales (Laws, 1991). A number of field studies have previously quantified NCP by examining the mixed layer O2 mass balance (e.g. Emerson et al., 1997; Kaiser et al., 2005; Reuer et al., 2007). One common approach is to normalize O2 concentrations to argon (Ar), a biologically inert gas, with solubility and diffusivity properties that are nearly identical to O2 (Craig & Hayward, 1987). This normalization removes the influence of physical processes, such as bubble injection and temperature change, that affect O2 saturation state. Following Kaiser et al. (2005), steady state NCP (mmol O2 m-2 day-1) is equated to the air-sea flux of biologically-produced O2: NCP = ΔO2 Ar⁄ × [O2]eq × kO2 (2.1) where ΔO2/Ar is the biological O2 saturation anomaly (%; Eq. 2.2), [O2]eq is the O2 equilibrium concentration (mmol m-3), and kO2 is the gas transfer velocity of O2 (m day-1). Developments in sea-going mass spectrometry over the past decade (Cassar et al., 2009; Kaiser et al., 2005; Tortell, 2005) have made it possible to obtain high-resolution estimates of NCP using continuous ship-board measurements of O2/Ar. High spatial sampling resolution is achieved, and biases associated with discrete methods for measuring primary production are avoided, making this approach ideal for open-ocean environments. A number of groups have now applied ship-based ΔO2/Ar measurements in a variety of regions, greatly expanding the spatial resolution of NCP data (e.g. Hamme et al., 2012; Lockwood et al., 2012; Manning et al., 2017; Tortell et al., 2012, 2015; Ulfsbo et al., 2014). Despite significant advances in NCP measurements, current estimates remain limited by uncertainty in the contribution of vertical mixing to the surface O2 budget (Hamme & Emerson, 2006). When O2 is undersaturated (supersaturated) beneath the mixed layer, failure to account for the mixing supply of this low-O2 (high-O2) water, through mixed layer entrainment or vertical diffusivity and advection, can lead to an under-estimation (over-estimation) of derived NCP. This problem has been widely recognized, particularly in coastal regions, and limits the applicability of 15 ΔO2/Ar as a productivity tracer (Hamme & Emerson, 2006; Jonsson et al., 2013; Teeter et al., 2018). In some cases, such regions may falsely appear to be net heterotrophic unless a mixing correction is made. Recent modeling work by Cassar et al. (2014) has suggested that measurements of nitrous oxide (N2O), within and below the mixed layer, may be used to trace vertical mixing, and correct ΔO2/Ar-based NCP estimates. Nitrous oxide is largely produced in the oceans during organic matter remineralization, as a by-product of ammonium oxidation to nitrate (i.e. nitrification) (e.g. Ward, 2000). As a result, a consistent relationship between the N2O surplus and O2 deficit is observed in much of the world’s sub-surface oceans (Cohen & Gordon, 1979; Nevison et al., 2003; Yoshinari, 1976). Vertical mixing, including advection, diffusion, and entrainment, can therefore provide a source of water that is both undersaturated in O2 and supersaturated in N2O. Such a signature has been observed in surface waters of upwelling margins globally (Bange et al., 1996; Capelle & Tortell, 2016). It has generally been believed that nitrification is photo-inhibited and primarily constrained to subsurface waters (Horrigan et al., 1981). Mixed layer N2O supersaturation is thus assumed to largely reflect vertical mixing, such that surface N2O measurements can, in theory, be used to track the upward transport of low-O2 water. There have not yet been any direct tests of this method and its assumptions with field data. In this chapter, I present new NCP estimates from the Subarctic NE Pacific Ocean, using N2O-corrected ship-based measurements of ΔO2/Ar across a range of coastal (on-shelf) and open ocean (off-shelf) waters. Surface productivity in the off-shelf waters of the Subarctic Pacific is limited by iron (Fe) availability during the summer (Harrison et al., 1999), while the persistence of a steep halocline and strong density stratification (Tabata, 1975) limits exchange between the surface and subsurface. In contrast, coastal waters of this region are characterized by complex physical dynamics, including seasonal upwelling and mixing over heterogeneous bottom topography (Whitney et al., 2005). These physical processes drive high productivity, but also introduce uncertainty in ΔO2/Ar-based NCP estimates. This study thus presents an opportunity to evaluate O2-based estimates of NCP across contrasting oceanographic regimes. My new observations provide refined estimates of NCP in the Subarctic Pacific, and demonstrate that neglecting the physical contributions to the mixed layer O2 budget can lead to significant under-estimates in some cases. I also evaluate the seasonal and spatial trends in NCP, and present an in-16 depth evaluation of the limitations, uncertainties, and methodological considerations of the N2O correction approach. This work builds on that of Cassar et al. (2014) and provides guidance for future studies. 2.3 Methods 2.3.1 Study area I collected data on five oceanographic cruises in the Subarctic NE Pacific on the CCGS John P. Tully, between February and September, 2016 (Fig. 2.1). Line P cruises to Ocean Station Papa (OSP; 50 °N, 145 °W) were conducted in winter (February), spring (June), and summer (August), covering a transect from the coastal Juan de Fuca Strait (JF) and southern West Coast Vancouver Island (WCVI) to the open ocean waters of the Gulf of Alaska. Coastal waters around Vancouver Island, including the JF and WCVI regions, were surveyed extensively during cruises in May and September, as part of the La Perouse monitoring program, run by Fisheries and Oceans Canada. The WCVI region is generally influenced by the seasonal timing and intensity of summer upwelling and winter downwelling (Bylhouwer et al., 2013). During the summer and fall, local currents near the mouth of the JF region enhance upward transport of low-O2, nutrient-rich deep water onto the shelf (Crawford & Peña, 2013). In the open ocean waters of the NE Pacific, strong winter winds drive upper ocean turbulence and mixed layer deepening, whereas increased solar radiation and weaker winds in the summer cause shoaling (Whitney & Freeland, 1999). In the spring, water column stratification enhances phytoplankton growth and nutrient drawdown prior to the onset of off-shelf summer macronutrient and Fe-limitation, and subsequent re-supply via turbulent winter mixing (Peña & Varela, 2007; Whitney & Freeland, 1999). Year-round, deep mixing in the off-shelf waters is restricted by the presence of a steep halocline. 17 Figure 2.1 Maps of the Line P and La Perouse survey regions showing the locations of underway and discrete data collection. The grey and black lines depict representative Line P and La Perouse cruise tracks, respectively, and the markers represent the locations of N2O profile sampling. The contour lines on the inset represent the 100, 500, 1000 and 2000 m isobaths. JF, WCVI, and QCS denote the Juan de Fuca canyon, West Coast Vancouver Island, and Queen Charlotte Sound regions, respectively. P4, P8, P12, P16, P20, and OSP (P26) represent the major Line P stations, and LB, LC, LD and LG refer to transects along the La Perouse sampling grid. Note that the ship tracks varied slightly between cruises, and that discrete surface samples for N2O measurements were collected at higher spatial resolution than the plot markers (not shown). The inset shows an expanded view of the JF and southern WCVI regions. The open squares indicate the locations at which Bakun Upwelling Index values were obtained (Fig. A.5). 2.3.2 Underway O2/Ar measurements Continuous measurements of mixed layer O2/Ar were obtained using membrane inlet mass spectrometry (MIMS) (Tortell et al., 2011), with a Hiden Analytical HAL20 quadrupole mass spectrometer. Seawater from the ship’s underway seawater loop (intake at ~5 m) was circulated at constant flow rate through rigid polypropylene tubing, past a 0.007” thick water-impermeable silicone membrane. To minimize changes in the membrane gas permeability, which can affect measurements of O2 and Ar, I maintained constant temperature (10 oC) of inflowing seawater by circulating the water through a 20-foot heat exchange coil immersed in a water bath. Measurements were obtained every ~20 seconds as the ratio of ion currents at the mass-to-charge ratios of 32 and 40, and averaged into one-minute bins. Seawater standards, used to correct for instrument drift and to derive biological O2 saturation anomalies (see section 2.3.4), consisted of air-equilibrated, 0.2 µm filtered seawater in 4-L polycarbonate bottles. The bottles were incubated at ambient sea surface temperature, and gently bubbled using an air pump to obtain gas equilibration with the atmosphere. While bubbling can introduce supersaturation in O2, it negligibly affects the O2/Ar ratio. At two-hour intervals, I 18 measured the standards for two minutes, using the same flow rate and sampling temperature as during the underway data acquisition. 2.3.3 Nitrous oxide sample collection Discrete samples for N2O analysis were collected on all cruises from Niskin bottles. Depth profiles were obtained at various stations along the respective cruise tracks (markers in Fig. 2.1), while surface measurements (5 m Niskin bottles) of N2O supersaturation were collected at higher spatial resolution. During the August Line P trip, additional samples for surface N2O analysis were obtained in the main laboratory directly from the ship’s underway seawater system. These samples and corresponding Niskin samples (obtained at the same station from 5 m Niskin bottles) showed good agreement (average RMSE of 0.5 µmol m-3; Fig. A.1), and no statistically significant difference was observed between paired samples (n = 12). Underway surface N2O samples obtained between stations were therefore included in my dataset. For all N2O measurements, duplicate subsamples were collected into 60 mL glass vials using flexible silicone tubing. The vials were overfilled three volumes avoiding bubble contamination, immediately poisoned with 50 µL saturated mercuric chloride (HgCl2), and stored at 4 oC in the dark until analysis (within three months). Nitrous oxide was measured in the laboratory using an automated purge-and-trap gas chromatography, mass spectrometry system (Capelle et al., 2015). Measurement precision was 3.5 %, based on the average percent-standard deviation of 125 triplicate air-equilibrated standards processed at the same time as the seawater samples. 2.3.4 Calculations of net community production Uncorrected mixed layer NCP was calculated following Eq. 2.1, where the biological O2 saturation anomaly is defined as ΔO2 Ar⁄ = [O2 Ar⁄ ]meas[O2 Ar⁄ ]eq – 1. (2.2) The “meas” and “eq” subscripts refer to the seawater and atmospheric equilibrium ratios measured from the underway seawater system and the bubbled air-standards, respectively. 19 I calculated corrected NCP (mmol O2 m-2 day-1) following the approach of Cassar et al. (2014), which combines the mixed layer O2 and N2O budgets. NCP = kO2× (ΔO2 Ar⁄ × [O2]eq – kN2OkO2 × ∂[O2]B∂[N2O]B × [N2O]B) (2.3) Here, kN2O is the gas transfer velocity for N2O (m day-1), ∂[O2]B∂[N2O]B is the vertical gradient of biological-O2 to biological-N2O concentrations (mmol O2 (mmol N2O)-1), and [N2O]B is the surface biological-N2O saturation concentration (mmol N2O m-3). Cassar et al. (2014) assumed a constant kN2OkO2 ratio of 0.92, but I independently calculated the ratio (range 0.87-0.94) for each observation based on Schmidt number parameterizations reported in Wanninkhof (2014). The approach assumes that N2O production (through nitrification) is photo-inhibited in the mixed layer, but I discuss this assumption below (section 2.5.1.2). The NCP correction terms (i.e. the second part of Eq. 2.3), obtained at each station with surface N2O measurements (mean spatial resolution of approximately 102, 37, and 47 km in winter, spring and summer respectively), were linearly interpolated to the resolution of the underway ΔO2/Ar data (mean spatial resolution of 0.2 km). The interpolated correction terms were thus applied to Eq. 2.1 to obtain high-resolution estimates of NCP along the entire cruise tracks. I assume steady-state in biological O2 concentrations, and that horizontal fluxes of O2 can be neglected in calculating NCP (see sections 2.5.2.3 and A.2 for an analysis of these assumptions). As measurements of ΔO2/Ar reflect processes occurring over the residence time of O2 within the mixed layer (between approximately one and four weeks), I applied the approach of Reuer et al. (2007), with corrections by Teeter et al. (2018), to calculate weighted gas transfer velocities for O2 and N2O over a 30-day period prior to my observations. I used the gas transfer velocity parameterization of Ho et al. (2006) based on 6-hourly CCMP v2 wind vector analysis product data (retrieved from http://www.remss.com/measurements/ccmp/; Atlas et al., 2011). The [O2]B and [N2O]B concentrations (mmol m-3) represent the biological saturation concentrations of the respective gases. Within the mixed layer, [O2]B is defined as 20 [O2]B = ΔO2 Ar⁄ × [O2]eq (2.4) where ΔO2/Ar (Eq. 2.2) accounts for bubble injection and physically-induced changes in solubility. Below the mixed layer, where I do not have Ar data, I assume that Ar concentrations are at saturation for the calculation of [O2]B (this assumption is evaluated in section 2.5.1.4). Since there is no similar inert analog for N2O, I follow the recommendation of Cassar et al. (2014) and apply a thermal correction to surface measurements to account for solubility changes resulting from recent heat flux. Using this correction, mixed layer [N2O]B is calculated as [N2O]B = [N2O]meas – [N2O]eq – [N2O]thermal. (2.5) Here, [N2O]thermal is derived following the equation of Keeling and Shertz (1992), with kinetic corrections and delay as per Jin et al. (2007). [N2O]thermal = 11.3 × (–∂[N2O]eq∂T × Qcp×ρ × 86,400 s1 day) × 1kN2O (2.6) The term in parentheses represents the thermally-induced sea-to-air flux of N2O (mmol N2O m-2 day-1). Rapid temperature changes cause a disequilibrium between observed surface N2O and the saturation concentration. The thermally-induced flux of N2O should be equal to the disequilibrium concentration gradient, multiplied by the gas transfer velocity of N2O. I thus divide the thermal flux by kN2O to obtain the concentration offset, [N2O]thermal, produced by solubility effects averaged over the N2O residence time (defined as MLD/kN2O) before the measurement time. The negative operator in Eq. 2.6 converts the air-to-sea heat flux (Q, W m-2, the sum of latent heat, sensible heat, longwave and shortwave radiative fluxes) and positive heat capacity (cp, J kg-1 C-1; multiplied by sea surface density, ρ, kg m-3) into a sea-to-air N2O flux. Here, Q and cp were estimated as localized mean values over the mixed layer N2O residence time from NCEP/NCAR Reanalysis 1 surface flux (6-hr resolution data retrieved from https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html; Kalnay et al., 1996) and Level 3 MODIS Aqua sea surface temperature (1-day, 9-km resolution mean of 11 μ daytime and 21 nighttime data retrieved from https://oceancolor.gsfc.nasa.gov/cgi/l3; NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group, 2014a, 2014b). Below the mixed layer, no thermal correction for N2O is made in the calculation of [N2O]B (i.e. [N2O]thermal = 0 in Eq. 2.5). The solubility equations of Garcia and Gordon (1992, 1993), and Weiss and Price (1980) were used to derive the saturation concentrations of O2 and N2O, respectively, from sea surface temperature (SST) and sea surface salinity (SSS) measurements. In these calculations, I accounted for changes in atmospheric N2O concentrations using observations from Barrow, AK (data provided by the NOAA ESRL Global Monitoring Division HATS Network and retrieved from https://www.esrl.noaa.gov/gmd/dv/iadv/; Hall et al., 2007). The vertical [O2]B-[N2O]B gradient (hereafter referred to as the “supply ratio”) represents the stoichiometry of vertical mixing, with respect to biological O2 and N2O. Cassar et al. (2014) used a discrete two-point slope (∂[O2]B∂[N2O]B=[O2]Bdeep – [O2]BML[N2O]Bdeep – [N2O]BML), based on end-members within (“ML”) and below (“deep”) the mixed layer. I found that this approach produced significant variability in the NCP correction (discussed in section 2.5.1.1). I thus utilized depth profile measurements to estimate the supply ratio from the slope of the [O2]B versus [N2O]B relationship in the water column from the base of the mixed layer to 150 m below (MLD to MLD+150 m). The supply ratio was therefore characterized independently for each cruise as the slope through the respective pooled dataset of all profile measurements in the 150 m below the mixed layer. As discussed below (section 2.5.1.1), the magnitude of the NCP correction is sensitive to the approach used to estimate the supply ratio term, but is largely insensitive to the depth of integration or the threshold criterion of the mixed layer depth estimate (see section 2.3.6). 2.3.5 Calculations of apparent mixing coefficients, Κmix To evaluate the NCP corrections, I calculated an apparent mixing coefficient (Κmix, m2 s-1) based on the mixed layer N2O budget. The coefficient reflects all vertical advection, diffusion, and entrainment processes over the mixed layer N2O residence time. Neglecting lateral fluxes and assuming that N2O is produced only below the euphotic zone (see section 2.5.1.2), the mixing coefficient at steady state is calculated from the surface N2O mass balance as: 22 Kmix= kN2O × [N2O]B∂[N2O]B ∂Z⁄ × 1 day 86,400 s (2.7) where Κmix×(∂[N2O]B ∂Z⁄ ) is the vertical mixing flux of [N2O]B (mmol m-2 s-1) and kN2O×[N2O]B is the air-sea flux (mmol m-2 day-1). The vertical gradient of [N2O]B, ∂[N2O]B ∂Z⁄ (mmol m-4), was estimated at each station with profile N2O measurements as the depth-dependent regression slope of all N2O measurements in the 150 m below the mixed layer. 2.3.6 Ancillary hydrographic data and calculations Ancillary hydrographic data were collected and provided by the Institute of Ocean Sciences of the Department of Fisheries and Oceans Canada. Continuous underway measurements of SST and SSS were obtained from a thermosalinograph (Sea-Bird CTD SBE-21) connected to the ship’s surface seawater supply. Depth profiles of temperature and salinity were obtained from CTD casts using a Sea-Bird CTD (SBE-911-plus) mounted on the Rosette frame. I used a density-difference criterion of 0.125 kg m-3 to define the mixed layer depth (MLD). Water column measurements of O2 (at depths corresponding with my N2O samples) were made by discrete Winkler titrations, using an automated titration system (Metrohm Dosimat 876) (Carpenter, 1965). Chlorophyll a (Chl a) samples, collected onto 25 mm GF/F filters prior to extraction in 90 % acetone, were measured on a Turner 10AU fluorometer before and after acidification to correct for phaeopigments (Welschmeyer, 1994). Nitrate and nitrite (NO3-+NO2-) samples were measured spectrophotometrically using an Astoria autoanalyzer (Barwell-Clarke & Whitney, 1996). Higher resolution (1-m depth intervals) O2 measurements were made using a SBE-43 O2 sensor mounted on the CTD platform. These data were used to evaluate the gradient of biological O2 (∂[O2]B ∂Z⁄ , mmol m-4) below the mixed layer at each station along the Line P and La Perouse transects. The gradient was taken as the slope of the regression line through each station’s dataset in the 25 m interval below the mixed layer (i.e. ∂[O2]B ∂Z⁄ |25). I selected an integration depth of 25 m as it represents the depth range immediately below the mixed layer, and the direct water 23 source for vertical mixing to the surface. As I explain in section 2.5.2.1, I use these O2 gradients to diagnose situations where the N2O correction could not be applied. 2.4 Results In the presentation of my results, I divide the sampling area into two distinct hydrographic regions, based on the spatial patterns I observed in NCP, Chl a and nutrient concentrations. The off-shelf region consists of those portions of the Line P transect west of P4, while the on-shelf (coastal) region includes all of the La Perouse track, and the Line P transect east of and including station P4. This separation corresponds approximately with the location of the continental slope (500-1000 m isobaths; Figs. 1.1, 2.1), and is similar to previous divisions of the Line P transect (Whitney et al., 1998) and other physical representations of the continental shelf region (Crawford & Thomson, 1991). I first present observations of hydrography and plankton biomass across these two regions (section 2.4.1). Then I examine the relationship between O2 and N2O, and assess how N2O data can be used to derive mixing coefficients to correct NCP estimates (sections 2.4.2–2.4.3). I finish by examining the spatial patterns of NCP across the survey region in relation to other oceanographic variables (section 2.4.4). Limitations of the N2O correction approach are addressed in the discussion (section 2.5.1). 2.4.1 Hydrographic conditions and phytoplankton biomass The observed spatial and temporal variability in mixed layer Chl a generally followed typical patterns for the study area, with the highest Chl a in the on-shelf region (up to 38 mg m-3 during the August Line P cruise), and lower values (~0.2-0.8 mg m-3) in much of the off-shelf region (Fig. 2.2a-c). I observed the expected seasonal cycle in surface Chl a in the coastal waters, with values increasing from <1.2 mg m-3 in February to >10 mg m-3 in May and June, and >30 mg m-3 in August. In the off-shelf region, winter and spring concentrations (~0.2-0.8 mg m-3) were usually greater than in August (<0.4 mg m-3 west of station P8). Peaks in Chl a were observed near OSP during the spring and summer. The maximum Chl concentration observed at OSP on the June cruise (1.2 mg m-3) was unusually high (relative to the long-term summer average of ~0.4 mg m-3). Dissolved NO3-+NO2- showed maximum mixed layer concentrations (>10 μmol L-1) closest to shore in all three seasons (Fig. 2.2d-f). In the oceanic waters, concentrations were highest in 24 February, ranging from ~5 µmol L-1 at the boundary between the on-shelf and off-shelf zone (approximately 80 km from shore), to nearly 14 µmol L-1 at OSP (Fig. 2.2d). June and August had a similar spatial pattern, but concentrations decreased through the spring and summer. In general, spring and summer nutrient concentrations in the southern shelf region were low beyond 40 km off-shelf (<6 µmol L-1), but increased sharply towards the coast to values above 10 µmol L-1 (Fig. 2.2e, f). Between stations P4 and P14 (~400 km from shore), I observed near-depletion (<0.5 µmol L-1) of mixed layer NO3-+NO2- in June. In August, the off-shelf depletion extended to over 600 km. Beyond the regions of macronutrient depletion, concentrations increased but never reached maximum values observed in the winter. The elevated summer macronutrient concentrations in off-shelf waters are taken as evidence of Fe-limitation (Varela & Harrison, 1999). Figure 2.2 Mixed layer Chl a (a-c) and NO3-+NO2- (d-f) concentrations along the 2016 Line P and La Perouse transects. Data are separated into the off-shelf (a, d) and on-shelf (b, e) regions of the Line P transect, and the La Perouse cruise (c, f). Note that the winter on-shelf symbols in panel (b) are obscured behind the spring symbols, and that the x- and y-scales differ among some of the panels. 25 2.4.2 O2 and N2O Depth profiles of O2 and N2O in the upper water column exhibited a linear relationship at all stations. My measurements, made in 2016, are consistent with those from previous Line P and La Perouse cruises dating back to 2012 (Capelle & Tortell, 2016) (Fig. 2.3a). Both O2 and N2O typically exhibited homogeneous distributions and supersaturation in the mixed layer ([O2]B and [N2O]B > 0; surface [N2O]B data are presented in Fig. A.2). With the exception of some off-shelf stations during spring and summer, N2O supersaturation increased monotonically below the mixed layer, while O2 became significantly undersaturated (Fig. 2.3a and A.3; deviations are discussed below in section 2.5.2.1). From all available profile data (2012-present), I thus derived an inverse linear relationship between [O2]B and [N2O]B, which yielded a narrow distribution in the subsurface-to-surface (MLD to MLD+150 m) supply ratio term (Fig. 2.3b). Based on 2016 profile data, the calculated supply ratios ranged from -3.2x104 to -1.0x104 (mean -1.5x104) mmol O2 (mmol N2O)-1. These values fall well within the range I observed along Line P since 2012 (-1.2x105 to -6.6x103 mmol O2 (mmol N2O)-1), with 99 % of 2016 gradients being within one standard deviation of the 2012-2016 mean (-1.8x104 ± 1.3x104 mmol O2 (mmol N2O)-1). Estimates of the [O2]B gradient in the 25 m below the mixed layer ranged from –8.4 to 1.1 mmol O2 m-4 (Fig. 2.3c). Thus, at some stations, I observed increasing or near-constant [O2]B concentrations below the mixed layer (∂[O2]B ∂Z⁄ |25  -0.1). All of the positive values (i.e. higher O2 concentrations below the mixed layer) occurred during the spring or summer in the off-shelf region. In contrast, only negative gradients were observed in the coastal regions during all seasons (Fig. A.4). Where N2O profiles were obtained, I also observed N2O deficits (i.e. ∂[N2O]B ∂Z⁄ |25 < 0) coincident with the O2 maxima, and therefore negative linear [O2]B/[N2O]B gradients with depth (Fig. A.3). The supply ratio for these profiles was not significantly different than the other stations or seasons. 26 Figure 2.3 The relationship between [N2O]B and [O2]B concentrations in the upper water column from Line P and La Perouse cruises between 2012 and 2016 (a-b), estimates of the vertical [O2]B gradient below the mixed layer in 2016 (c), and sensitivity of NCP calculations to differing estimates of the vertical mixing terms (d). Panel (a) includes [N2O]B and [O2]B from all 2016 measurements to 150 m (white) and 500 m (black) below the mixed layer, and previous observations to 500 m below the mixed layer (2012-2015; grey). The dashed lines indicate 100 % saturation. Panel (b) shows the distribution of supply ratio estimates derived from 2016 and 2012-2015 data, calculated for each station in the 150 m below the mixed layer (see text for further explanation). The solid horizontal line denotes a slope of -1x104 mmol O2 (mmol N2O)-1, representing the global approximate average stoichiometry of O2/N2O. Panel (c) shows estimates of the subsurface [O2]B gradient by season (2016 cruises only), defined in the 25 m below the mixed layer. The absolute sensitivity of NCP estimates to the supply ratio term, the method of estimating it, and the correction approach is shown in panel (d). Corrected NCP was estimated using a supply ratio derived in the 150 m below the mixed layer, 500 m below, using the global stoichiometric average value, and a 2-point slope. I also evaluated NCP by making corrections using a literature-derived eddy diffusivity value (Cronin et al., 2015) and my estimates of ∂[O2]B ∂Z⁄ |25 (see section 2.5.2.2 for more details). In panels (b-d), the boxes show the 25th, 50th, and 75th percentile values (bottom, middle and top lines, respectively). Whiskers extend to the 1st and 99th percentile ranges, and crosses represent outlying data beyond this range. The lower limit on (b) was restricted to -4x104 mmol O2 (mmol N2O)-1, and the scale on (d) was limited to ±300 mmol O2 m-2 day-1. 2.4.3 Apparent mixing coefficients Based on my N2O measurements, I derived apparent mixing coefficients (representing vertical advection, diffusion, and entrainment processes) between 1.1x10-4 and 1.7x10-3 (mean 27 6.7x10-4) m2 s-1 (Fig. 2.4). Values are presented only where stations showed the expected negative O2 gradient below the mixed layer (see sections 2.4.4 and 2.5.2.1). Similarly, I did not calculate a Kmix value at several stations where I observed undersaturated N2O concentrations in the mixed layer (Fig. A.2). The largest Kmix values were observed in the on-shelf regions following periods of intense spring and summer upwelling (mean 8.2x10-4 m2 s-1; Fig. A.5). By comparison, mixing coefficients were lower in most of the off-shelf region during the same seasons (mean 3.2x10-4 m2 s-1), except following periods of increased wind speed (e.g. P8 in August). In the winter, I observed mixed layer N2O undersaturation at P20 and P26, resulting in derived Kmix < 0 that cannot be used to correct NCP estimates. Elsewhere off-shelf, Kmix had intermediate values in February (mean 2.7x10-4 m2 s-1), and the lowest value at the coastal P4 station (2.6x10-4 m2 s-1), relative to the other Line P cruises. 28 Figure 2.4 Vertical mixing coefficients derived from profile measurements of N2O (a-b) along the 2016 Line P and La Perouse cruise tracks with corresponding estimates of the vertical [N2O]B gradient below the mixed layer (c-d). Panels (a) and (c), and (b) and (d) represent the off- and on-shelf regions, respectively. The asterisks in (a) represent the OSP summer and autumn mean eddy diffusivity (1x10-4 and 3x10-4 m2 s-1, respectively) estimated by Cronin et al. (2015). Note that my Kmix accounts for vertical diffusivity, advection and entrainment fluxes, while these latter values do not include advection and entrainment. The horizontal dashed lines represent the upper and lower Kmix values after accounting for mixed layer nitrification (section 2.5.1.2). The y-axis in (a) and (b) is on a logarithmic scale. Panels (c) and (d) show the subsurface [N2O]B gradients (derived in the 150 m below the mixed layer) which are used to estimate Kmix. Values in all panels are only presented for stations where I obtained profile measurements of N2O; Kmix is shown only where the NCP correction was not biased by a subsurface maximum in O2 concentrations (section 2.5.2.1). 2.4.4 Net community production Figure 2.5 shows the spatial distribution of NCP during the winter (a-b), spring (c-e), and summer (f-h) cruises, with and without the N2O correction. I present both uncorrected and corrected estimates, noting that some of the corrected values are biased by undersaturated surface [N2O]B, or subsurface [N2O]B deficits (and [O2]B maxima), which yield (nonsensical) negative mixing terms (discussed below in section 2.5.2.1). My final NCP estimates are thus compiled from a combination of corrected and uncorrected values, with uncorrected NCP selected for regions where the N2O correction could not be applied. In total, the N2O correction could not be applied 29 at 19 out of 137 locations (<15 % of data points). A summary of mean and standard deviation values for final NCP, and the N2O correction is presented in Table A.1. The N2O correction always increased estimated NCP over uncorrected values. The magnitude of the correction factor was generally highest in the on-shelf region during the summer, with an average of 75 mmol O2 m-2 d-1 difference (3-190 mmol O2 m-2 d-1 range) between corrected and uncorrected data. These corrections are equivalent to 23-327 % of corrected NCP values. Prior to correction, several stations in the southern on-shelf region had negative NCP values in spring and summer, suggesting net heterotrophic conditions. After correction, however, all estimates became positive, implying net autotrophy. The absolute magnitude of the correction was lower in the off-shelf waters, with mean values of 23, 25, and 31 mmol O2 m-2 d-1, during the winter, spring and summer respectively, but the relative contributions to corrected NCP were still high, (81, 52, 74 %, on average, respectively). I observed negative NCP near OSP during the winter. However, the observation of undersaturated N2O in the mixed layer at these stations meant that a correction could not be applied. True NCP here is likely higher than uncorrected values, because my uncorrected value may be biased low by mixing of low-O2 water from below (I discuss other correction approaches in section 2.5.2.2). In the spring, NCP was slightly higher at OSP relative to other oceanic stations, consistent with the coincident positive anomaly in Chl a (Fig. 2.2a). The final NCP estimates show a spatial gradient characterized by generally low values in the off-shelf region, and highest values on-shelf (Figs. 2.5, 2.6). For each spring and summer cruise, high NCP was observed in the southern WCVI area, with maximum values of 162, 182, 396, and 189 mmol O2 m-2 day-1 in May, June, August, and September, respectively. Wintertime measurements in the same region revealed significantly lower NCP, at just ~24 mmol O2 m-2 day-1 (maximum), and a weaker spatial gradient between coastal and oceanic stations. The repeat sampling of this region suggests that NCP is persistently high over the on-shelf section of the Line P transect, and between the LB and LC lines of the La Perouse survey during the spring and summer (Fig. 2.6). The off-shelf extent of this high productivity region varies seasonally, with elevated NCP extending to ~50 km (126 oW) from the coast in June, and ~100 km of the coast (127 oW) in August. Further north on the La Perouse track, I observed increased NCP on the transects adjacent to the coast (May; Fig. 2.6b) and in QCS (September; Fig. 2.6c). Off-shelf, 30 during the spring and summer, NCP was mostly low, except for the high anomalies observed at OSP and between stations P16 and P20 in the spring. Relatively high NCP was also observed between P8 and P16 during the winter. Figure 2.5 Uncorrected and N2O-corrected NCP estimates from the 2016 winter (a-b), spring (c-e), and summer (f-h) Line P and La Perouse cruises. The left, middle and right panels represent observations from the off-shelf Line P, on-shelf Line P and La Perouse transects, respectively. The dashed black lines trace final NCP estimates, taken as the unbiased N2O-corrected values (black markers) or uncorrected values (open-face markers) when corrected values were biased (asterisks). See text, section 2.5.2.1 for details of biased corrections. The Line P data are presented as the average of the outbound and inbound transects and the La Perouse data show all stations arranged by their proximity to shore. The dashed vertical grey lines show the locations of major stations along Line P, while the solid horizontal line indicates 0 NCP. Note the difference in scale between the off-shelf (a, c, f) and on-shelf regions (b, d, e, g, h). 2.5 Discussion To my knowledge, this work represents the first field application of the Cassar et al. (2014) method of N2O-based corrections for ΔO2/Ar-derived NCP estimates. This approach is significant in enabling more robust productivity estimates in physically dynamic regions, where vertical 31 mixing is a non-negligible term in the mixed layer O2 mass balance. Below, I discuss my results in terms of both methodological and practical considerations (section 2.5.1), and the interpretation of spatial and temporal patterns in NCP (section 2.5.4). In section 2.5.2, I outline some key limitations of, and alternatives to, the current approach and provide a discussion of uncertainties. Appendix A contains a detailed explanation of the uncertainties. 2.5.1 Methodological considerations and assumptions I begin by addressing methodological considerations of the NCP correction, expanding on the work of Cassar et al. (2014), with emphasis on the implications for field studies. I focus on the use of different approaches to derive the O2/N2O supply ratio term, the possible impact of mixed layer nitrification, thermal solubility effects on mixed layer N2O, and potential deviations from subsurface Ar saturation. I summarize by discussing the limitations of the current approach, outlining remaining uncertainties and providing recommendations for future applications. 2.5.1.1 Considerations for the [O2]B/[N2O]B supply ratio term Cassar et al. (2014) estimated the subsurface-to-surface supply ratio of O2/N2O based on a two-point slope, with end members within and below the mixed layer. Using this approach, I obtained some supply ratio terms that resulted in unrealistic NCP values (<-300 or >400 mmol O2 m-2 day-1; Fig. 2.3d) based on the range of previous estimates in the study area (see section 2.5.4). The two-point slope approach is highly sensitive to the selected depth of the end members (i.e. the depth of discrete Niskin bottle sampling), and is thus susceptible to even minor irregularities in the profile data. For these reasons, I used an alternative approach, estimating the supply ratio as the multi-point slope through the pooled datasets of [O2]B and [N2O]B in the 150 m below the MLD for each respective cruise. The depth-dependent increase in [N2O]B and corresponding decrease in [O2]B that I observed at most stations yielded a narrow range of supply ratio estimates, and is consistent with nitrification being the dominant source of N2O in the study area (Capelle & Tortell, 2016; Fenwick & Tortell, 2018; Grundle et al., 2012). My supply ratio estimates are also consistent in space and time along the Line P and La Perouse transects, and the difference between vertical [O2]B/[N2O]B gradients for individual profiles during a single cruise was not statistically significant. This result justifies 32 the use of a pooled dataset for each cruise. I selected an integration depth of 150 m as it extends fully beyond the maximum depths of the mixed layer and euphotic zone observed during the 2016 cruises, thereby capturing the subsurface regions of increased N2O production. A similar depth of integration for vertical mixing (120 m) was used by Ianson & Allen (2002) in a physical model of the WCVI region. The supply ratios derived by the multi-point slope approach did not produce any unrealistic negative NCP values (Fig. 2.3d), and my estimates from the 2012-2016 data agree well with O2/N2O gradients reported in the region, and with the global average of ~-1x104 mmol O2 (mmol N2O)-1 (Cohen & Gordon, 1979; Nevison et al., 2003; Rees et al., 1997). In the absence of depth profile data, a gradient of -1x104 mmol O2 (mmol N2O)-1 may therefore constitute a suitable alternative in most regions. Indeed, I observed only small differences between NCP estimated using the data-derived supply ratio terms, and the global average stoichiometric value (mean difference of 11.3 mmol O2 m-2 day-1; Fig. 2.3d), with the largest deviations occurring in coastal waters with high surface [N2O]B concentrations. Moreover, the NCP correction is generally insensitive to the depth of integration of the supply ratio term, provided that sufficient points are selected to accurately estimate a regression slope and that a region of subsurface nitrification is included (i.e. using integration depths that extend beyond the euphotic zone). When the gradient was estimated as the slope integrated over the 500 m below the MLD, for example, I observed a mean deviation of 6.6 mmol O2 m-2 day from the corrected NCP estimates (Fig. 2.3d). Similarly, I found that varying the MLD estimates by ± 5 m resulted in less than 3.8 % variation in the supply ratio terms (<1 mmol O2 m-2 day-1 NCP). This result demonstrates that my analyses are not overly sensitive to the choice of a 0.125 kg m-3 density difference criterion to define the base of the mixed layer. In all cases, the spatial patterns of NCP remain similar using different slope-derived supply ratio terms (data not shown), but the amplitude of the correction changes slightly. As I describe below in section 2.5.2, caution should nonetheless be exercised before applying the N2O based approach in some scenarios. 2.5.1.2 Mixed layer nitrification Nitrous oxide is produced mainly through nitrification, as a by-product of the oxidation of ammonium (AO) to NO2- and NO3-, or through incomplete denitrification under low O2 conditions 33 (Codispoti et al., 2005; Ward, 2000). It is believed that nitrification is photo-inhibited in surface waters (Horrigan et al., 1981), while high mixed layer O2 concentrations are not favorable for denitrification. The net result is that N2O has typically been assumed to be produced mainly in subsurface waters (e.g. Dore & Karl, 1996) and this assumption is applied in the N2O correction (Eq. 2.3). Yet, recent work suggests that N2O production may occur within the euphotic zone (Grundle et al., 2013; Smith et al., 2014; Zamora & Oschlies, 2014). Cassar et al. (2014) suggested that mixed layer N2O production would lead to an overestimate of the vertical mixing term, and over-correction of NCP. Below, I attempt to quantify the potential significance of this limitation. During nitrification, [N2O]B increases, while ΔO2/Ar decreases as O2 is consumed through AO. A similar net change occurs through mixing, such that the contribution of mixing, and subsequently the NCP correction, is over-estimated when nitrification is neglected. I define NCP as net O2 production resulting from gross photosynthesis, minus community respiration of organic carbon, such that only respiratory processes affecting the organic carbon pool are considered. As such, nitrification is not an inherent component of my definition of NCP, and the NCP equation (Eq. 2.3) can be adjusted, accounting for the effects of this process on both O2 and N2O (refer to section A.1 of Appendix A for the derivation). NCP = kO2× (ΔO2 Ar⁄ × [O2]eq – kN2OkO2×∂[O2]B∂[N2O]B×[N2O]B) – N × (1 – ΔN2OΔO2×∂[O2]B∂[N2O]B) (2.8) Here, N is the nitrification rate in terms of O2 consumption (≤ 0 mmol O2 m-2 day-1), and ΔN2OΔO2 is the mixed layer N2O stoichiometric yield of nitrification (≤ 0 mmol N2O (mmol O2)-1). Applying Eq. 2.8, I calculate the potential impact of mixed layer nitrification on my 2016 NCP estimates, considering only those locations where the NCP correction was not biased (see section 2.5.2.1). Grundle et al. (2013) reported mean mixed layer nitrification rates of ~20 nM NH4+ day-1 along the off-shelf Line P transect, which is equivalent to O2 consumption of 0.04 mmol O2 m-3 day-1, based on the stoichiometry of the reaction: NH4+ + 2O2 → NO3- + H2O + 2H+. Since rates of nitrification vary geographically, I use this regional value in my calculations (Table 2.1), recognizing that it falls within the range of globally reported rate estimates for near-surface 34 waters (0-~130 nM NH4+ day-1; Dore & Karl, 1996; Santoro et al., 2010; Ward, 2005). Based on 2016 mixed layer depths from my data, I derived mixed layer-integrated nitrification rates (N in Eq. 2.8) between -0.4 and -3.6 mmol O2 m-2 day-1. The impact of surface water nitrification on the N2O correction approach depends on the N2O yield of this process. The stoichiometric N2O yield of nitrification (ΔN2OΔO2) varies widely under different substrate concentrations (Frame & Casciotti, 2010), and will often differ from that of vertical mixing (∂[N2O]B∂[O2]B; the reciprocal of the supply ratio). In the case where these two terms are the same, nitrification will have no net effect on the NCP correction, since Eq. 2.8 simplifies to Eq. 2.3. For the other (more likely) scenario, I derived lower and upper bounds on my calculations, using low and high N2O yields (-1.4x10-4 and -3.8x10-4 mol N2O (mol O2)-1, respectively; Santoro et al., 2011; Yoshida et al., 1989) reported under O2 and NO2- concentrations similar to those observed in my study area. The results of my calculations show that nitrification could produce between 0.04 and 0.26 μmol m-3 N2O in the mixed layer, which is equivalent to 0.4-2.2 % of mean 2016 surface concentrations (Table 2.1). These values translate into net reductions of NCP between 0.3 and 11.2 mmol O2 m-2 day-1, through the combined influence of altered O2 and N2O concentrations. On one hand, nitrification acts to decrease apparent NCP by consuming O2 (Eq. 2.3). Conversely, nitrification increases the surface N2O saturation anomaly, thereby leading to an overestimate of the mixing correction term (i.e. leading to over-correction of NCP). In my calculations, I found that nitrification would increase NCP by between 0.4 to 3.6 mmol O2 m-2 day-1 through its effects on surface O2, and decrease NCP by between 0.8 and 14.9 mmol O2 m-2 day-1 through increased N2O. These independent effects sum to produce the net influence on NCP (presented in Table 2.1). Similarly, Kmix (Eq. A.1) could be between 0.7 and 58 % lower than values calculated without consideration of nitrification (Fig. 2.4). I thus conclude that the effect of nitrification on NCP estimates could be substantial in the study region under conditions where background NCP and mixing rates are low (e.g. in open ocean waters). Indeed, at the upper limit, the bias in NCP derived from mixed layer nitrification could be as much as 42 % of the apparent NCP. By comparison, in productive coastal waters, the effect of surface nitrification tends to introduce only a small error (<4 % in spring and summer). Direct 35 observations of nitrification rates are required to better constrain the influence of this process on surface NCP estimates. In the absence of such data, I continue to support the assumption made by Cassar et al. (2014), since the uncertainty estimated here (mean 5.5 mmol O2 m-2 day-1, or 16 % of estimated NCP based on high N2O yields) is of similar magnitude, and typically smaller than errors in the gas transfer parameterizations (mean 4.4 mmol O2 m-2 day-1; section A.2 of Appendix A). I note, however, that NCP measurements made by this approach may still constitute an upper bound on true values. 2.5.1.3 Thermal correction for [N2O]B Following Cassar et al. (2014), I applied a thermal adjustment to mixed layer measurements of [N2O]B (Eqs. 2.5, 2.6) to account for solubility effects. Recent warming (cooling) prior to sampling decreases (increases) N2O solubility and increases (decreases) the apparent saturation anomaly, causing a N2O efflux (influx) at the surface. Since the residence time of gases in the mixed layer is typically 2-3 weeks, atmospheric gas exchange lags behind changes in gas solubility. As a result of warming (cooling), mixed layer [N2O]B would appear over (under)-saturated, and the NCP correction would be too large (small). Figure A.2 shows [N2O]B with and without the thermal adjustment, and the resulting effect on NCP is proportional to the difference in observed concentrations. Using the unbiased corrected NCP estimates, I find that the effect of the N2O thermal correction is generally small in absolute magnitude (<11.4 mmol O2 m-2 day-1), but proportionately large (up to 25 %), in low productivity, off-shelf regions (Table 2.2). For air-sea heat fluxes exceeding 100 W m-2 in magnitude (spring and summer), estimated NCP would be more than 7.8 mmol O2 m-2 day higher in the absence of thermal adjustments. When NCP is relatively low, as in much of the off-shelf regions, such large heat fluxes can contribute >20 % bias in final NCP estimates. I thus recommend the continued application of this correction for NCP estimates in off-shelf waters, despite the relatively large errors associated with estimates of surface heat fluxes (see Appendix A, Table A.2). By comparison, the thermal correction makes a relatively small (mean ~9 %) contribution to the overall uncertainty in coastal NCP, due to the large magnitude of NCP estimates here, but should still be included since thermal fluxes are likely to vary widely in space and time. 36 Table 2.1 The potential effects of surface nitrification on mixed layer N2O concentrations, NCP estimates, and mixing coefficients. I estimated the proportional contribution of in-situ nitrification to surface N2O concentrations (N2O produced) based on literature-derived values of mean ammonium oxidation rates and N2O yields of nitrification (see text for details). The effects on NCP and Kmix were estimated following Eqs. 2.8 and A.1. Mixed layer depths and N2O residence times (τ) represent the average values for the off-shelf and on-shelf regions respectively, in each season. The two rows of calculations for each region represent values based on the low (shaded grey) and high N2O yield estimates respectively. Percent values (%) represent the proportional change in a variable relative to values estimated when nitrification was neglected. Region 2016 Data Calculations MLD τ Mean N2O ∂[O2]B∂[N2O]B ∂[N2O]B∂Z Mixed Layer Nitrification rate N2O Produced Change in NCP Change in Kmix [m] [days] [µmol m-3] [mmol mmol-1] [mmol m-4] [mmol O2 m-2 d-1] [µmol m-3] [ %] [mmol O2 m-2 d-1] [ %] [m2 s-1] [ %] Winter Off 90.5 16.5 11.95 -1.08x104 1.07x10-4 -3.6 0.09 0.7 -1.7 -6.2 -5.3x10-5 -20.5 0.25 2.1 -11.2 -41.9 -1.5x10-4 -57.8 On 69.0 16.3 11.47 -2.8 0.09 0.8 -1.3 -6.1 -4.0x10-5 -15.6 0.25 2.2 -8.6 -41.1 -1.1x10-4 -43.9 Spring Off 28.1 10.8 10.78 -1.40x104 9.55x10-5 -1.1 0.06 0.5 -1.0 -2.2 -1.8x10-5 -7.3 0.16 1.5 -4.9 -10.7 -5.2x10-5 -20.5 On 14.1 8.8 12.79 -0.6 0.05 0.4 -0.5 -0.7 -9.2x10-6 -1.3 0.13 1.0 -2.4 -3.4 -2.6x10-5 -3.7 Summer Off 25.5 8.3 9.40 -1.29x104 1.05x10-4 -1.0 0.04 0.5 -0.8 -1.7 -1.5x10-5 -4.3 0.13 1.3 -4.0 -9.2 -4.3x10-5 -12.1 On 10.9 16.8 16.15 -0.4 0.09 0.6 -0.3 -0.3 -6.5x10-6 -0.7 0.26 1.6 -1.7 -1.6 -1.8x10-5 -1.9 37 Table 2.2 Influence of air-sea heat flux on mixed layer N2O budgets during one N2O residence time prior to sampling. The air-sea heat flux, Q, is defined in Eq. 2.6. The effect of thermally-derived N2O changes on NCP was estimated by comparing calculations with and without the thermal N2O correction: ΔNCP represents the change in NCP if the thermal adjustment is not made. Negative heat fluxes represent ocean cooling which induce negative sea-air N2O fluxes according to Eq. 2.6. Region Air-Sea Heat Flux Sea-Air Thermal N2O Flux ΔNCP [W m-2] [µmol m-2 day-1] [mmol O2 m-2 d-1] [ %] Winter Off -18.8 -0.13 -1.4 -5.3 On -17.0 -0.11 -1.2 -5.6 Spring Off 126.7 0.75 11.4 25.2 On 110.3 0.61 8.2 11.6 Summer Off 120.4 0.57 7.8 18.3 On 123.6 0.65 8.0 7.5 2.5.1.4 Ar saturation below the mixed layer In the calculation of the vertical [O2]B term, the NCP correction approach assumes that subsurface Ar concentrations are at saturation. Departures from these conditions impact the derived supply-ratio term. Work in the North Pacific by Hamme & Emerson (2002), and Emerson et al. (2012) suggests that subsurface Ar concentrations rarely deviate from saturation by more than ±4 %. In the absence of corresponding profile measurements of Ar, I re-calculated all supply ratio terms, assuming ΔAr = ±0.04, to estimate the upper limit on the potential uncertainty in ∂[O2]B∂[N2O]B. I found that gradients were up to 3.2 % steeper (shallower) when Ar was supersaturated (undersaturated). This source of error is small compared with the larger uncertainties in the gas transfer velocity parameterization and supply ratio terms (see Appendix A, section A.2), and I thus suggest that deviations in Ar from equilibrium can be neglected without a significant influence on the overall results. 38 2.5.2 Discussion of the approach limitations and uncertainties 2.5.2.1 Scenarios where the N2O correction approach is invalid There are several scenarios for which NCP corrections based on the O2/N2O supply ratio may not be suitable. In much of the Arctic Ocean, for example, a complex N-cycle (Brown et al., 2015) and lack of a consistent relationship between O2 and N2O (Fenwick et al., 2017) invalidate this approach, and likely preclude the use of N2O corrections. Furthermore, regions where the subsurface [O2]B/[N2O]B supply ratio does not represent the relevant gradient for vertical O2 mixing pose a limitation to the correction approach of Cassar et al. (2014). Principally, this scenario occurs where subsurface O2 maxima co-occur with localized minima in N2O, and a negative relationship between [O2]B and [N2O]B persists. Here, the intuitive NCP correction should be to lower uncorrected values, since the upward supply of relatively high-O2 water from beneath the mixed layer would introduce a positive ΔΟ2/Αr anomaly. However, since the supply ratio stoichiometry of [O2]B/[N2O]B remains negative, a false positive correction term is derived (i.e. corrected NCP > uncorrected NCP), leading to an over-estimation of NCP if Eq. 2.3 is applied. I observed this “biased” scenario at a number of off-shelf stations in June and August, and chose to present uncorrected data for these regions in the absence of a suitable correction factor (see section 2.5.2.2 below). The same limitation would also occur in regions where [N2O]B and [O2]B both decline below the mixed layer (∂[O2]B ∂[N2O]B⁄ > 0), as might be seen where near-surface oxygen minimum zones result in N2O loss through denitrification (e.g. Farías et al., 2009). In these cases, the positive supply ratio would yield a negative correction term, even though vertical mixing would have introduced a low-ΔO2/Ar anomaly. It is thus evident that the NCP approach applied here is invalidated if N2O decreases below the mixed layer. In general, the approach works well when subsurface [N2O]B increases, and [O2]B decreases, as was the case at all coastal stations (where I found the largest correction terms) and much of the off-shelf region. Yet, my extensive O2 and N2O profile measurements show that the approach, as described by Cassar et al. (2014), is not likely to be valid everywhere. At several stations, I observed deficits in subsurface N2O that corresponded with relative maxima in O2 (Fig. A.3). Although I lack profile measurements of N2O at every sampling station, I can utilize the CTD-derived O2 measurements to diagnose this limitation. By independently evaluating the depth-39 dependent O2 slope in the 25 m below the mixed layer ( ∂[O2]B ∂Z⁄ |25) in every profile, I observed a number of stations with positive gradients in the off-shelf region during the spring and summer (Fig. 2.3 and A.4). Where profiles of N2O existed, corresponding deficits in N2O, resulted in a negative O2/N2O gradient that was not significantly different than all other stations (Fig. A.3e). The observation of a consistent negative supply ratio term in all profile data resulted in positive correction terms, and over-estimation (i.e. positive bias) of true NCP when the N2O-based approach was used. It is important to note that the O2 minima/N2O maxima that I describe here are shallow features, localized to the waters just below the mixed layer (Fig. A.3). Beyond these depths, I typically observed declining O2, and increasing N2O concentrations. For this reason, the ∂[N2O]B ∂Z⁄ values I use to derive Kmix terms (integrated down to 150 m below the mixed layer) are not explicitly negative for the regions where the NCP correction is biased (Fig. 2.4c-d). I simply lack the depth-resolution of N2O measurements to better constrain the relevant slope over a shallower depth range. I find the presence of these N2O minima to be somewhat counter-intuitive, and difficult to explain, since O2 concentrations are clearly high enough to prevent N2O loss through denitrification. Finally, I also note that the current approach breaks down at other stations where mixed layer N2O is undersaturated ([N2O]B < 0; e.g. P20-P26 in February). This scenario results in a nonsensical negative mixing correction term. I therefore suggest that stations that do not exhibit significantly negative subsurface [O2]B gradients, or where surface N2O is undersaturated, should be precluded from the [O2]B/[N2O]B supply ratio-based NCP correction. I use an upper ∂[O2]B ∂Z⁄ |25 cut-off threshold of -0.1 mmol O2 m-4 (Fig. A.4) as mixing fluxes using this value are small (< 4 mmol O2 m-2 day-1) for the mixing rates I observe off-shelf. If O2 profiles are not available to diagnose the suitability of the N2O correction, this limitation may apply in oceanic regions with strong density stratification, and deep euphotic zones, where subsurface primary productivity may generate an O2 maximum, or when water masses containing signatures of high productivity are subducted beneath surface layers. This scenario is prevalent in much of the North (tropical to mid-latitude) Pacific during the summer time (Shulenberger & Reid, 1981). Yet, strong density stratification in these regions will likely act to minimize the impact of vertical mixing fluxes, thus limiting the potential importance of NCP 40 corrections (Giesbrecht et al., 2012). Nonetheless, future studies should focus on O2 and N2O dynamics in these waters, to understand the cause of subsurface N2O deficits, and to evaluate whether N2O-based corrections could be feasible. 2.5.2.2 Alternatives to the N2O correction approach As an alternative to the N2O approach, a correction can be made using estimates of a mixing rate and observed subsurface O2 gradients (i.e. – Kmix×∂[O2]B ∂Z⁄ ). When I apply this correction to my data using average off-shelf Kmix values for the respective seasons, I obtained correction terms exceeding 75 mmol O2 m-2 day-1 at stations P20-P26 in February, and final NCP of ~50 mmol O2 m-2 day-1. These elevated NCP values are more than two-fold higher than previous estimates (e.g. Giesbrecht et al., 2012; Palevsky et al., 2016; Yang et al., 2017). I therefore believe these values to be the unrealistic result of a combination of very strong subsurface O2 gradients, and over-estimation of Kmix using the off-shelf (station P8-P16) average value. Indeed, the subsurface [N2O]B gradients at stations P20 and P26 were steeper than at the other off-shelf stations (Fig. 2.4c), such that the Kmix terms are likely to be smaller than predicted by the average value. Similarly, off-shelf ∂[N2O]B ∂Z⁄ differs between stations in the summer, and average Kmix values may be a poor representation of the regional mixing rates. My study clearly demonstrates the spatial and temporal variability of oceanic mixing rates (Fig. 2.4). I thus caution against the use of non-site specific and literature-derived estimates of Kmix, or eddy diffusivity when deriving NCP corrections. To examine the sensitivity of estimates to the choice of mixing rates used for corrections, I recalculated corrected NCP using the mean summertime eddy diffusivity value of Cronin et al. (2015) (1x10-4 m2 s-1) and my independent subsurface O2 gradient estimates (Fig. 2.3d). On average, these estimates differed from the N2O-corrected values by 32.4 mmol O2 m-2 day-1 (~50 % mean). This result underscores the importance of independent estimates of mixing rates for each respective station. In the absence of such direct estimates, I suggest the use of uncorrected NCP as the most viable option at present. This practice seems reasonable given the low apparent contribution of mixing fluxes to surface ΔO2/Ar in the summertime (Giesbrecht et al. 2012; Palevsky et al., 2016; Yang et al. 2017). In coastal regions, where mixing corrections are largest, the O2 and N2O profiles permit the use of the N2O-based 41 correction approach. In summary, my analyses highlight the importance of examining the shape of the O2 profiles before applying the N2O-based NCP correction. 2.5.2.3 Additional limitations of in-situ NCP estimates Additional limitations and uncertainties (see section A.2 for a detailed error analysis) of the present approach are inherent to the majority of ship-based estimates of NCP from surface ΔΟ2/Ar measurements. Specifically, NCP derived from O2 mass balance equations (Eqs. 2.1 and A.3) assumes steady-state surface O2 concentrations and negligible horizontal fluxes. The present approach provides corrections for vertical mixing (diffusion, entrainment, and advection), but makes no attempt to rectify potential fluxes from these additional sources. In a Southern Ocean Lagrangian study, Hamme et al. (2012) observed absolute changes in ΔO2/Ar up to 0.6 %, partially attributable to diurnal cycles in productivity. I found that ΔO2/Ar varied by 3 % (from ~3.5-6.5 %) during a one-day occupation of OSP in June, with lower variability (mean 0.1 % day-1 off-shelf and 0.5 % day-1 on-shelf; overall maximum 2.5 % day-1) observed between outbound and inbound sampling of the same locations along the Line P cruises. Thus, while the timing of sampling (i.e. during the diel cycles) has an effect on the magnitude of ΔO2/Ar measurements, the uncertainty in NCP attributed to temporal changes in ΔO2/Ar (~0.6 and 1.1 mmol O2 m-2 day-1 off-shelf and on-shelf respectively) is smaller than errors associated with gas flux parametrizations (~4.4 mmol O2 m-2 day-1). Off-shore, lateral advection is likely to be negligible, but near the coast, a combination of high current speeds and patchiness in phytoplankton biomass may make horizontal fluxes significant. Using net current velocity data (data provided by the NASA Physical Oceanography Distributed Active Archive Center and retrieved from http://dx.doi.org/10.5067/OSCAR-03D01; Bonjean & Lagerloef, 2002; ESR, 2009), and derived estimates of the surface lateral [O2]B gradient (from underway ΔO2/Ar and TSG data), I estimated mean horizontal fluxes of ~1.6 and 22.7 mmol O2 m-2 day-1 off-shelf and on-shelf, respectively. This level of uncertainty applies to measurements of NCP in Eulerian studies. However, in a Lagrangian sense, lateral advection influences the location assigned to a given NCP measurement but not the rate itself, so these flux estimates are in many cases an exaggeration of the influence of lateral advection on underway NCP estimates. 42 Finally, the relatively low spatial resolution of my surface N2O measurements (mean of 102, 37, and 47 km in winter, spring and summer cruises respectively) also limits the application of this approach on highly-resolved space scales. I obtained estimates of NCP on the sub-kilometer scale (as in Fig. 2.6) by linearly interpolating the N2O correction term to the resolution of my underway ΔΟ2/Αr measurements. However, I estimate a mean uncertainty in these values of 5.5 mmol O2 m-2 day-1 by comparing estimates derived through linear and nearest-neighbour interpolation schemes (see Appendix A for more details). This additional uncertainty motivates the need for improved resolution of N2O measurements. My comparison between samples obtained from Niskin and the ship’s seawater supply (Fig. A.1) shows that high quality N2O measurements can be derived without the need for discrete, on-station bottle sampling, suggesting good possibilities for continuous underway analysis. Nitrous oxide measurements are not common on most oceanographic field programs, and existing data are largely based on discrete sampling. However, recent developments in instrumentation (Arévalo-Martínez et al., 2013; Grefe & Kaiser, 2014) should provide additional impetus for the development of ship-based underway N2O measurements to complement MIMS-based ΔO2/Ar data. This endeavour would constitute an important next step in the application of the correction approach, enabling sub-kilometer spatial resolution of N2O-corrected NCP estimates. Overall, in spite of the above-mentioned limitations and uncertainties, the N2O approach I evaluate here represents an improvement over existing techniques for evaluating NCP from ship-based measurements of ΔΟ2/Αr. Specifically, the total mean analytical and parameterization uncertainty in my data is 9.2 mmol O2 m-2 day-1 (~34 and 21 % of off- and on-shelf estimates), with the correction factor contributing roughly 4.3 and 13.7 mmol O2 m-2 day-1 in each region (Table A.2). Although the methodological uncertainties are large in some cases (combined maximum of ~16 and 26 mmol O2 m-2 day-1 off- and on-shelf), these represent conservative estimates. Moving forward, constraining the rate of upper ocean N2O production, improving reanalysis products for the estimation of oceanic heat flux, and reconciling surface measurements of ΔO2/Ar for lateral advection would significantly improve confidence in N2O-derived vertical mixing correction terms, making this an important tool for resolving spatial and temporal patterns in NCP. 43 Figure 2.6 Spatial distribution of NCP from the 2016 winter (a), spring (b), and summer (c) Line P and La Perouse cruises. The outbound and inbound Line P data are overlaid on one another. The N2O correction was linearly interpolated to the resolution of underway MIMS ΔO2/Ar data. 2.5.3 Correction factor and Kmix Previous studies that have corrected for mixing processes on the mixed layer O2 mass balance have used literature values for eddy diffusivity coefficients (Bushinsky & Emerson, 2015; Castro-Morales et al., 2013; Weeding & Trull, 2014). These indirectly-obtained values may obscure potentially significant variability in both space and time (see above, section 2.5.2.2). Moreover, additional means of quantifying advection or entrainment are required. Some approaches have relied on model-derived mixing fluxes to evaluate all three terms (e.g. Plant et al., 2016). The N2O-based approach does so empirically. In general, the magnitude of Kmix (Fig. 2.4) reflects regional and temporal differences in upwelling, water column stratification and wind-induced mixed layer turbulence. I observed the largest apparent mixing coefficients (and NCP correction factors) in the on-shelf region following periods of upwelling (based on Bakun Upwelling Index values at 48 oN, 125 oW and 50 οΝ, 131 οW provided by the Pacific Fisheries Environmental Laboratory and retrieved from 44 http://www.pfeg.noaa.gov/products/PFEL/modeled/indices/upwelling/NA/data_download.html; Bakun, 1973). Indeed, the mean on-shelf Kmix values strongly correlate with the regional upwelling index (R2 = 63 %; Fig. A.5). This result, and the occurrence of elevated on-shelf surface [N2O]B concentrations during the spring and summer upwelling periods (Fig. A.2), is supported by previous observations showing that upwelling is the dominant N2O transport process over the continental shelf along the WCVI (Capelle & Tortell, 2016). Strong stratification in off-shelf regions, resulting from high solar radiation and mixed layer shoaling, is likely responsible for the smaller mixing fluxes and coefficients in this region, during the summer. Higher winter and spring Kmix at the off-shelf stations, relative to summer, is likely due to increased wind speeds during that season (data not shown). The relative decline in Kmix from P12 to P26 in the spring is mostly attributed to reduced wind intensities along the same gradient. Although wind speeds are highest during the winter, strong temperature stratification, caused by the persistent thermocline located just below the mixed layer at this time of year (data not shown), resists particularly large mixing rates. The low winter-time Kmix values at the coastal Line P station, P4, likely reflects the net downwelling that occurs during this time (Fig. A.5a). The observation of undersaturated N2O in the winter waters at OSP, and profiles with negative N2O gradients (positive O2 gradients) in the spring and summer make it difficult to estimate mixing at some locations. Overall, my estimates of Kmix are consistent with the multi-year OSP time-series of Cronin et al. (2015), who used heat and salt budgets to estimate vertical diffusivities. The results of their work suggest an annual range of mixing rates from ~1x10-5 to 1x10-2 m2 s-1. Derived estimates, based on N2O observations (with and without considerations for potential mixed layer nitrification) are fully encompassed in the range reported by Cronin et al., as well as the direct depth-resolved measurements of eddy diffusivity at OSP (~1x10-5-1x10-3 m2 s-1 below the mixed layer) of Rousseau et al. (2010). My Kmix also show reasonable seasonal and spatial patterns. The increase in mixing coefficients closer to shore (in spring and summer) fits the global dataset of Whalen et al. (2012), who identified elevated mixing rates over rough bottom topography, and near the coast. Indeed, the on-shelf WCVI region is characterized by heterogeneous bottom topography, with numerous canyons, that can result in intensified mixing (Allen & Durrieu de Madron, 2009). This result indicates that the N2O correction approach can provide significant insight into physical processes occurring in near-surface ocean waters. 45 It is important to note that my Kmix estimates represent the combined fluxes due to diffusion, advection and entrainment, whereas other estimates typically express mixing rates in terms of diffusivity coefficients. While I do not evaluate the relative contributions of each process to the total vertical flux, others (e.g. Yang et al., 2017) have shown that vertical diffusivity dominates over entrainment or advection in the off-shelf waters. In coastal waters, during upwelling events, however, vertical advection is likely to play a considerable role. It is therefore understandable that my Kmix values are comparable to, but on the upper range of, values derived from other datasets, particularly in regions impacted by upwelling, or subject to significant entrainment. 2.5.4 Spatial patterns of NCP The data presented here contribute to our knowledge of NCP in the Subarctic Pacific by providing refined estimates across broad spatial scales and contrasting oceanographic regimes. Much of our understanding of NCP in the Subarctic NE Pacific comes from previous studies in the vicinity of OSP (e.g. Bushinsky & Emerson, 2015; Emerson & Stump, 2010; Fassbender et al., 2016; Lockwood et al., 2012; Palevsky et al., 2016; Plant et al., 2016) or at discrete locations along the Line P transect (Giesbrecht et al., 2012; Hamme et al., 2010). By comparison, fewer studies have quantified NCP in the coastal regions of the Subarctic Pacific, owing to the difficulties in constraining vertical mixing fluxes, limiting our understanding of this ecologically-significant regime. Here, I compare my corrected NCP estimates to values reported elsewhere along the Line P transect, and discuss the apparent spatial patterns in the context of observed biological and physical forcing. My NCP estimates for the oceanic regions are consistent with those previously derived from a mixed layer O2 mass balance approach in the Subarctic Pacific, but the upper range of my values exceeds any previous estimates (~50, 81, and 180 mmol O2 m-2 day-1 in winter, spring and summer, respectively). At OSP, for example, previous spring and summer O2-based NCP estimates have ranged from ~-10-60 mmol O2 m-2 day-1 (Bushinsky & Emerson, 2015; Fassbender et al., 2016; Giesbrecht et al., 2012; Plant et al., 2016). My corrected results from OSP measurements (58 mmol O2 m-2 day-1 in spring), are at the upper range of estimates of these studies, and are similar to the anomalously high NCP (~65 mmol O2 m-2 day-1) observed following a large atmospheric dust deposition event in 2008 (see below; Hamme et al., 2010). Only one previous study (Lockwood et 46 al., 2012) reported summertime rates (~84 mmol O2 m-2 day-1) higher than my estimates. Likewise, along the rest of the off-shelf Line P transect, many of my springtime NCP estimates (>40 mmol O2 m-2 day-1) exceed, or represent the upper range of any previously measured values (~10-48 mmol O2 m-2 day-1; Giesbrecht et al., 2012; Hamme et al., 2010; Palevsky et al., 2016). While inter-annual variability in NCP partially explains differences between my observations and those of previous studies, it is clear that the contribution of mixing fluxes is also significant. The difference between my results and previous studies is particularly evident in the coastal waters over the continental shelf. At station P4, my average corrected NCP value in August (180 mmol O2 m-2 day-1) is more than four-fold higher than the previous summertime estimates of Hamme et al. (2010) and Giesbrecht et al. (2012). Notably, my uncorrected NCP estimate for this station agrees well with these prior measurements, suggesting that the discrepancy results from an underestimation of NCP in previous studies that have neglected the influence of vertical mixing. This result emphasizes the importance of accounting for negative O2 fluxes due to subsurface-to-surface mixing, especially in coastal regimes. Net community production estimates derived from previous studies without a correction for mixing should thus be considered as a lower bound on true values, particularly near to the coast. To my knowledge, only Bushinsky & Emerson (2015), Palevsky et al. (2016), Plant et al. (2016), and Yang et al. (2017) have included physical vertical transport terms when evaluating NCP from O2 measurements in the Subarctic Pacific. Only Palevsky et al. evaluated spatial patterns in NCP from ship-based data, using model-derived corrections for mixing. Giesbrecht et al. (2012) considered mixing, but found vertical fluxes to be negligible in the spring and summer, and therefore excluded any explicit corrections. This result supports my choice to not apply corrections in off-shelf waters where the observed N2O gradients result in biased mixing coefficients (see section 2.5.2.2). Beyond the consideration of absolute values, my NCP estimates show spatial and temporal patterns that are consistent with expectations for the Subarctic NE Pacific. Specifically, my data broadly reflect trends in phytoplankton biomass (using Chl a as a proxy), and demonstrate variability that suggests environmental control from surface nutrient concentrations, seawater temperature, and light availability. The strong NCP gradient from high on-shelf, to relatively low off-shelf production in spring and summer is an example of this concept. In the off-shelf waters of 47 the Line P transect, my measurements likely reflect a progression from wintertime light-limitation, to a N- and Fe-limited regime in the late summer, based on insights derived from previous studies (Boyd et al., 1996; Harrison et al., 1999; Peña & Varela, 2007). The on-shelf waters show typical spring and summer blooms that exceed wintertime production, but these trends are not observed in the uncorrected NCP data, due to the effects of summer upwelling and deep tidal mixing (e.g. in the JdF region) that partially mask in-situ O2 production. In some cases, these mixing processes result in apparent net heterotrophy over parts of the continental shelf. Across broad spatial and temporal scales, my corrected NCP estimates show strong correlations with Chl a, SST and mixed layer depth (proxy for mixed layer light levels and stratification) (Fig. 2.7a-c), except under conditions of presumed macro- or micronutrient limitation. I infer N-limitation in the spring and summer from near-depleted macronutrient concentrations (~50-400 km off-shelf in June, and ~50-700 km in August; Fig. 2.2d, e), and summertime Fe-limitation west of these regions where the significant increase in NO3-+NO2- concentrations is indicative of previously-observed HNLC conditions (Harrison et al., 1999). Significantly, I found that the correlations between NCP and the ancillary data were weaker when I used the uncorrected data (based on the R2 values of empirical best-fit equations; Fig. 2.7). This result suggests that the corrected values provide a better estimate of the broad spatial patterns and environmental controls on NCP across the survey region. The relationships I observed between NCP and Chl a, SST, and MLD are consistent with results from previous studies in other oceanic systems (Eppley, 1972; Li & Cassar, 2016; Tortell et al., 2015). My results also show some localized NCP patterns, such as the wintertime net heterotrophy at OSP (P26), and increased NCP during the spring and summer in the off-shelf waters. While the mechanism driving heterotrophy in Subarctic gyres is currently debated, this phenomenon has been observed at OSP previously (Bushinsky & Emerson, 2015; Fassbender et al., 2016; Plant et al., 2016). It should be noted, however, that I could not apply a mixing correction at station P26 during the winter, and therefore my NCP represents a lower bound. In contrast, OSP appeared to experience unusually high phytoplankton biomass and NCP in the spring of 2016, coincident with an anomalous increase in Chl a (Fig. 2.2a). Intermittent Fe fertilization events have been proposed as a mechanism for elevating phytoplankton production at OSP. For example, enhanced aerosol deposition (associated with the eruption of Aleutian volcanoes) during summer 2008 was 48 implicated as a source of Fe to the Subarctic Pacific that stimulated enhanced phytoplankton productivity (Hamme et al., 2010). To examine the potential impact of aerosol deposition events at OSP, I evaluated the time-series of aerosol optical thickness data, derived from MODIS Aqua satellite products, prior to my observations in June 2016 (data provided by the NASA Goddard Earth Sciences Data and Information Services Center Giovanni database and retrieved from https://giovanni.gsfc.nasa.gov/giovanni/; Acker & Leptoukh, 2007). This analysis suggested increased aerosol concentrations near OSP during the preceding month (data not shown), providing a potential explanation for the elevated Chl a and NCP. Other regions of high off-shelf productivity (e.g. ~200-500 km off-shelf in February and ~250-600 km in June; Figs. 2.5, 2.6) may be attributed to periodic nutrient inputs via coastal eddies (Johnson et al., 2005), periods of mixed layer deepening and entrainment of high subsurface nutrient concentrations, elevated SST, or enhanced stratification. In support of this hypothesis, satellite-based altimetry showed sea surface height anomalies (data provided by the NASA Physical Oceanography Distributed Active Archive Center and retrieved from http://dx.doi.org/10.5067/SLREF-CDRV1; Zlotnicki et al., 2016) indicative of anti-cyclonic eddies near the Line P transect in June and August. 49 Figure 2.7 Relationships between NCP and Chl a (a,d), SST (b,e) and mixed layer depth (c,f) from the 2016 Line P and La Perouse cruises. The upper and lower panels represent corrected and uncorrected NCP estimates, respectively. The numbers in each panel represent the correlation coefficients (R2) for the respective empirical fits. The grey asterisks represent data from regions of presumed N- or Fe-limitation; these data were excluded from the correlations. 2.6 Conclusions Current estimates of ship-based NCP, particularly in coastal waters, will often be biased if investigators neglect vertical mixing fluxes when evaluating surface O2 budgets. I present refined NCP estimates from five coastal and oceanic cruises in the Subarctic Northeast Pacific, derived using the first field application of a N2O-based approach to correct for vertical mixing. My NCP values exceed most past measurements in the study region (due to a combination of natural variability and considerations of a mixing correction), and show strong correlations with Chl a concentrations, SST and mixed layer depth, providing insight into the environmental controls on biological productivity. I also provide a comprehensive assessment of the N2O approach in field settings, making recommendations for future applications in the NE Pacific study area and other oceanographic regions. I find that potential surface N2O production (from nitrification) likely 50 introduces only a small over-bias in NCP derived by this approach, although simultaneous measurements of surface N2O production rates and nitrification yields are required to fully assess the impact of this process. I also discuss scenarios where the N2O approach should be applied cautiously, or omitted altogether. In particular, N2O-based corrections are problematic in locations with subsurface minima in [N2O]B and corresponding [O2]B maxima. Quantifying the magnitude of vertical fluxes from ship-based observations remains a challenge in these regions, since independent and site-specific estimates of vertical mixing rates are necessary. Nonetheless, I conclude that the N2O correction approach is robust across a range of oceanographic settings, especially in coastal regions where mixing fluxes constitute a substantial part of mixed layer O2 budgets and where studies of NCP are generally sparse. These coastal waters contribute disproportionately to ocean biogeochemical cycles, and are often characterized by significant fine-scale variability, highlighting the utility of underway ship-board ΔO2/Ar measurements to estimate surface water NCP. In light of recent anomalous conditions in the Subarctic Pacific such as large volcanic eruptions (Hamme et al., 2010), the warm-water anomaly (the “blob”; Bond et al., 2015), and observed longer-term trends in sea surface warming, freshening, and deoxygenation (Crawford & Peña, 2013; Whitney & Freeland, 1999), it is important to quantify marine productivity on ecologically relevant spatial and temporal scales. The method employed here has the potential to provide unrivalled surface coverage of NCP estimates corrected for physical mixing. Developing technologies for underway, high-resolution measurements of surface N2O concentrations, and reducing the uncertainties around key assumptions of the method would constitute significant progress in applying this technique. Subsequently, the accumulation of a large NCP database would help to produce regionally-tuned satellite algorithms providing basin-scale coverage, and enabling better assessment of the future evolution of the ocean’s capacity for biomass production and carbon sequestration. 51 Chapter 3: Impact of vertical mixing and freshwater input on summertime net community production in Canadian Arctic and Subarctic waters: Insights from in situ measurements and numerical simulations 3.1 Overview I present ΔO2/Ar-based estimates of mixed layer net community production (NCP) from three summertime cruises in the central and eastern North American Arctic and Subarctic Ocean. Coupling high resolution ship-based data with in-situ hydrographic measurements and output from a regional configuration of an ocean circulation model, I correct the NCP estimates for vertical mixing contributions to the surface O2 budget. Large positive NCP corrections were derived in regions of strong wind-driven mixing, such as the Labrador Sea (Inuktitut: Lâbradorip Imappinga), and physically dynamic waters of the Canadian Arctic Archipelago. In contrast, corrections were small in the density-stratified Baffin Bay (Saknirutiak Imanga) and well-mixed Hudson Strait (Ikirasarjuaq). After accounting for mixing fluxes on the O2 mass balance, elevated NCP was apparent in the southern section of the survey area and in nearshore regions influenced by glacial meltwater and recent ice retreat. Low NCP and isolated net heterotrophy were observed in Baffin Bay, and near strong freshwater and organic matter sources in Hudson Bay (Tasiujarjuar) and the Queen Maud Gulf (Ugjulik). These results demonstrate a heterogeneous distribution of NCP across the region, and ~55 % of the apparent variability could be explained by a multiple liner regression model based on hydrographic and satellite data. Overall, the results reflect varying contributions of vertical nutrient supply, freshwater input and sea ice dynamics in controlling the net metabolic state of surface waters. This work demonstrates the utility of combined ship-board measurements and numerical models to constrain rates of biological productivity in under-sampled high latitude waters. 3.2 Introduction Arctic and Subarctic oceanic waters contain highly productive ecosystems that contribute significantly to the global carbon cycle (e.g. Ahmed & Else, 2019; Fennel et al., 2018), and have sustained the livelihoods of northern Indigenous communities for thousands of years. Strong 52 variability in these marine ecosystems is driven by seasonal cycles in primary production (PP) associated with large-amplitude changes in mixed layer nutrient inventories, solar irradiance, and sea ice dynamics (Tremblay et al., 2015). On longer timescales, atmospheric warming and the acceleration of the polar hydrological cycle is enhancing water column stratification (e.g. Carmack et al., 2016) and reducing sea-ice extent and thickness (e.g. Stroeve & Notz, 2018), with significant potential impacts on marine biogeochemistry (e.g. Barber et al., 2015), mixing dynamics (e.g. Rainville et al., 2011) and underwater light fields (Nicolaus et al., 2012). These perturbations have already manifested as an increase in annual Arctic-wide PP (Arrigo & van Dijken, 2015). The magnitude and environmental sensitivity of PP to ongoing environmental change is likely to differ across polar and sub-polar regions. In the central and eastern North American Arctic and Subarctic (Fig. 3.1), PP is linked to sea ice dynamics (e.g. Ji et al., 2013), and the replenishment of surface water nutrients via wind- and tide-induced vertical mixing, wintertime convection and freshwater input during the spring and summer (Randelhoff et al., 2020; Tremblay et al., 2015). Enhanced stratification may limit vertical nutrient supply and reduce PP in some regions, while potentially stimulating light-limited production in areas of deeper convection (Ardyna et al., 2011). Elsewhere, expanding ice-free conditions will likely alter the timing of bloom events (Kikuchi et al., 2020), or enhance mid-to-late season productivity through increases in wind-driven mixing (Ardyna et al., 2014; Castro de la Guardia et al., 2019; Crawford et al., 2020; Juranek et al., 2019). In addition, freshwater input derived from glacial meltwater and river runoff will likely have varied impacts on the coastal and oceanic domains, as the contributions of these sources to marine nutrient and organic carbon budgets differ among glaciated, non-glaciated and vegetated environments (Bhatia et al., 2013a, 2013b; Kanna et al., 2020; Martin et al., 2020; Pain et al., 2020). While productivity may be enhanced near regions of strong glacial discharge (Arrigo et al., 2017; Hopwood et al., 2018), increased salinity-stratification may result in reduced PP further away from the continental margins (Kwiatkowski et al., 2019). In order to evaluate and predict the response of Arctic and Subarctic ecosystems to these climate-dependent influences, it is necessary to monitor PP on ecologically relevant space and time scales. At ecosystem scales, PP is often quantified as net community production (NCP), a variable representing the metabolic balance between photosynthesis and community respiration. This productivity metric can be evaluated at high-resolution using underway ship-based measurements 53 of seawater oxygen (O2) and argon (Ar). Argon is a biologically inert gas analog for O2 with near-identical solubility properties. The resulting tracer, ΔO2/Ar, is largely insensitive to physically-induced changes in O2 saturation (Craig & Hayward, 1987), and thus reflects only water column photosynthesis and respiration. The development of ship-board mass spectrometry has enabled the use of high-resolution ΔO2/Ar measurements to examine surface ocean biological oxygen cycling and NCP across a range of oceanic regions (Cassar et al., 2009; Kaiser et al., 2005; Tortell, 2005), including Arctic waters (e.g. Burgers et al., 2020; Juranek et al., 2019; Ulfsbo et al., 2014). In this approach, NCP is quantified from the mixed layer O2 mass balance, neglecting the influence of vertical mixing (or advection) across the base of the mixed layer. While this assumption is robust during periods of strong near-surface stratification, vertical O2 fluxes can significantly bias interpretations of ΔO2/Ar as a mixed layer NCP tracer in physically dynamic regions (Hamme & Emerson, 2006; Teeter et al., 2018). The resulting uncertainty may prevent calculations of NCP altogether (e.g. Giesbrecht et al., 2012; Juranek et al., 2019; Li & Cassar, 2016), leading to gaps in our understanding of PP variability. The apparent paradox between the role of mixing in stimulating surface water PP and simultaneously biasing ΔO2/Ar measurements necessitates a quantitative assessment of the vertical mixing influence on NCP calculations. Yet, our understanding of the space-time distribution of mixing rates remains limited, particularly in Arctic waters. Sparse observations suggest that ice capping and strong stratification cause mixing rates in the Arctic interior basins to be one to two orders of magnitude lower than other ocean regions (range ~10-7-10-6 m2 s-1; e.g. Fer, 2009; Rainville & Winsor, 2008). Closer to the coast and in regions of rough topography, mixing rates may be elevated up to around 10-5-10-3 m2 s-1 (e.g. Chanona et al., 2018; Chanona & Waterman, 2020), consistent with observations around the continental margins (e.g. Polzin et al., 1997; Whalen et al., 2012). Given the high spatial and temporal variability in ocean mixing rates and difficulties associated with observing them directly, indirect proxies for estimating the influence of mixing on surface ΔO2/Ar are required. In many temperate ocean regions where O2 and nitrous oxide (N2O) are stoichiometrically coupled through nitrification, vertical O2 mixing fluxes can be inferred from observations of O2/N2O (Cassar et al., 2014; Ch. 2). However, this approach is not feasible in the Arctic Ocean where a complex nitrogen cycle complicates the relationship between O2 and N2O 54 (Brown et al., 2015; Fenwick et al., 2017). In the absence of direct mixing rate measurements or a robust chemical tracer for vertical O2 fluxes, numerical models may provide alternative means of estimating the influence of vertical mixing on water column biogeochemical mass balances. The predictive skill and spatial resolution of such physical models has increased significantly in recent years (Fox-Kemper et al., 2019), presenting new opportunities to assess the influence of mixing processes on NCP. However, numerical predictions of biological properties (e.g. net biological O2 production) remain uncertain (e.g. Fennel et al., 2019; Vancoppenolle et al., 2013) such that a combination of observations and models are needed to understand the physical mechanisms driving PP variability (Marchese et al., 2019). In this chapter, I present observations from three cruises in North American Arctic and Subarctic waters, documenting the coupling between vertical mixing, freshwater input and surface NCP. I use high-resolution shipboard observations of ΔO2/Ar, combined with CTD-derived O2 measurements, and vertical mixing rates derived from a numerical model to characterize patterns in NCP across a range of hydrographic regions. From these results, I examine the influence of vertical mixing as a source of bias in NCP calculations and a driver of surface productivity, while also exploring the relationship between NCP and freshwater input. These results contribute important baseline NCP measurements in a poorly studied region, and provide insight into the processes that may alter biological productivity under rapidly changing conditions in polar and subpolar marine waters. 3.3 Methods I present ship-board data from three cruises in the central and eastern North American Arctic (Fig. 3.1). Sampling occurred on the CCGS Amundsen during segments of the 2015 (10 July – 20 August), 2018 (24 July – 8 September) and 2019 (5 July – 15 August) ArcticNet expeditions. These data were supplemented with additional archived CTD measurements obtained from ArcticNet expeditions between 2002 and 2016. In the presentation of results, I distinguish various sub-regions of the study area based on differences in hydrography and bathymetry (Fig. 3.1d; details in Appendix B). Following recommendations from Wong et al. (2020), and after consultation with the Inuit Tapiriit Kanatami organization (https://www.itk.ca/inuit-nunangat-map/), I include place names written in Inuktitut 55 (indicated in italics) alongside corresponding English names. The sections are: North Atlantic and Labrador Sea (NA/LS) (Lâbradorip Imappinga), Baffin Bay and Davis Strait (BB/DS) (Saknirutiak Imanga and Qikiqtaaluup Ikinga), Hudson Strait (HS) (Ikirasarjuaq), Hudson Bay (HB) (Tasiujarjuar), Canadian Arctic Archipelago (CAA) and the continental shelf of NA/LS and DS/BB with bottom depths <250 m. The CAA, as defined here, spans waters of northwest Greenland and the Kitikmeot and Qikiqtani regions of Nunavut, including (from south to north) the Queen Maud Gulf (QMG) (Ugjulik), Lancaster Sound (Tallurutiup Imanga) Jones Sound, North Water Polynya (Pikialasorsuaq), Nares Strait, and Kennedy Channel. 56 Figure 3.1 The central and eastern North American Arctic study area, showing the 2015 (a), 2018 (b) and 2019 (c) CCGS Amundsen cruise tracks. The distance along each track is represented by white (start) to red (end) shading. The white and red diamond markers represent the start and end of the transects, respectively. The background colour represents the mean satellite-derived Chl a concentration during each year’s sampling period (approximately July – Sept.). Panel (d) shows the boundaries of different sub-regions, defined on the basis of hydrographic and bathymetric features. The black markers represent the locations of CTD profiles obtained during the 2015, 2018 and 2019 cruises (a-c) or from additional ArcticNet sampling between 2002 and 2017 (d). Observations obtained from Argo floats between 2015 and 2019 are identified with red markers in (d). Labels in (c) are in Inuktitut, with corresponding English names in (a) and (b). Several major glaciers are identified with white stars in panel (d). 3.3.1 Underway gas measurements and bioflux-NCP calculations Seawater ΔO2/Ar measurements were obtained using a Hiden Analytical membrane inlet mass spectrometer (MIMS), following the protocols described in Tortell et al. (2011). Briefly, the MIMS was run continuously with the sampling cuvette connected to the ship’s seawater supply 57 (nominal intake ~7.5 m depth within the mixed layer in all sampled regions). The ratio of seawater O2 and Ar ([O2/Ar]sw) was measured every ~10 seconds and normalized to the ratio measured in a 0.2 μm-filtered seawater standard ([O2/Ar]std), which was aerated continuously using an air pump and held near ambient sea surface temperature in a flow-through water bath. Standards were automatically introduced into the sampling cuvette for a period of five minutes every 45-90 minutes. The measured O2/Ar ratio of these standards was interpolated to the frequency of seawater observations to derive the biological O2 saturation anomaly, ΔO2/Ar (i.e. ΔO2 Ar⁄ = ([O2 Ar⁄ ]sw [O2 Ar⁄ ]std⁄ -1) × 100 %). Measurements of seawater and standards were made at constant temperature and flow rate by circulating water through a 6-m heat-exchange coil submerged in a water bath before introduction in the MIMS cuvette. I estimated NCP from the underway ΔΟ2/Αr measurements following Kaiser et al. (2005) and Reuer et al. (2007). In this approach, NCP is derived from the diffusive sea-to-air flux of biologically-produced excess O2 weighted over the mixed layer O2 residence time, τO2, neglecting vertical mixing fluxes (i.e. bioflux-NCP; mmol O2 m-2 d-1; Teeter et al., 2018). bioflux-NCP = kO2,wt×ΔO2 Ar⁄100 %×[O2]eq (3.1) The [O2]eq term (mmol m-3) is the surface temperature- and salinity-dependent concentration of O2 at equilibrium with one standard atmosphere (Garcia & Gordon, 1993), and ΔO2/Ar × [O2]eq represents the biological fraction of the mixed layer O2 inventory (i.e. O2B; mmol m-3). The kO2,wt term (m d-1) represents the time and wind speed-weighted O2 gas transfer velocity (Reuer et al., 2007; Teeter et al., 2018), calculated from wind speed and ice data over the 30-day time period before shipboard measurements. Along-track kO2,wt was determined from unweighted values, kO2, calculated backwards in time in 0.25 day increments, and scaled to the fractional ice coverage, f (i.e. kO2 = kO2,open × (1 – f), where kO2,open is the O2 gas transfer velocity in ice-free waters), following the parameterization of Butterworth & Miller (2016), which was developed for partially ice-covered waters. Wind speed and ice cover data for these calculations were obtained from the North American Regional Analysis (NARR; Mesinger et al., 2006) and Advanced Microwave Scanning Radiometer v2 (AMSR-2; Spreen et al., 2008) products, respectively, and matched to the sampling 58 grid through a nearest-neighbour three-dimensional (time, latitude, longitude) interpolation. Mixed layer depth and surface hydrography (see below) were assumed constant over the 30-day kO2 weighting period. The effect of these assumptions on kO2 calculations is small. 3.3.2 Ancillary sampling and datasets Ancillary ship-board data were provided by the Amundsen Science Group (Amundsen Science Data Collection, 2020b, 2020a). Underway sea surface temperature (SST) and salinity (SSS) were measured using a SeaBird SBE45 thermosalinograph, while Chlorophyll a (Chl a; mg m-3) fluorescence, taken as a proxy for phytoplankton biomass, was measured using a WetLabs WetStar fluorometer. These measurements were obtained from this ship’s seawater line, and all underway signals (MIMS and ancillary) were binned to one-minute intervals. Underway Chl a was calibrated using discrete samples collected from the CTD/Rosette or seawater supply line (see below). Estimates of sea surface Chl a concentration were also obtained from satellite products (8-day average) parameterized specifically for the Arctic region, as described in Lewis et al. (2016). The 2019 satellite Chl a data were from the NASA L3 VIIRS-SNPP Ocean Color product (OCx algorithm; https://oceancolor.gsfc.nasa.gov/l3/), as the Artic-specific product was not available at the time of writing. Depth-resolved measurements were collected during each cruise (Fig. 3.1) for observations of seawater hydrography, nutrient, O2 and Chl a concentrations. A SeaBird SBE-911plus CTD package was used to obtain depth profiles of temperature and salinity, while O2 concentrations (mmol m-3) were measured using a Seabird SBE-43 sensor, calibrated against discrete samples analyzed by Winkler titration. Using these hydrographic measurements, the mixed layer depth (MLD; m) was estimated based on a density-difference criterion of 0.125 kg m-3 from values in the upper 5 m. I also estimated the base of the pycnocline as the depth below the MLD where the vertical density gradient fell below 0.05 kg m-4. These criteria predicted the MLD and pycnocline depth with a high degree of skill, based on a visual inspection of density profiles throughout the study region. Nitrate and nitrite (N+N; μmol L-1) concentrations were measured using a Satlantic ISUS mounted on the Rosette frame and calibrated using discrete Niskin bottle samples measured on board the ship using a Bran+Luebbe autoanalyzer 3. Depth-resolved Chl a fluorescence, 59 measured using a CTD-mounted Seapoint fluorometer, was calibrated against discrete samples collected for Chl a biomass determination (mg m-3). Finally, N+N and dissolved organic carbon (DOC) data were obtained from various rivers sampled near the cruise tracks between 2015 and 2019. Data from rivers in the CAA were provided by the Canadian Arctic Archipelago Rivers Program (K. Brown personal communication, 2020), while DOC data from HB were provided by U. Manitoba (D. Capelle, personal communication, 2021). The 2015 CAA data are archived at doi.org/10.1594/PANGAEA.908497 (Brown et al., 2019), and this database will be updated with the more recent dataset. 3.3.3 Vertical mixing calculations and NCP corrections 3.3.3.1 Numerical model description and mixing rate estimates To quantify the influence of vertical mixing on the ML O2B mass balance, I obtained estimates of the vertical eddy diffusivity coefficient below the ML (κZ, m2 s-1) from a simulation of a coupled ocean-sea ice model based on the Nucleus for European Modelling of the Ocean (NEMO, version 3.4; Madec & The NEMO team, 2008). The physical ocean model was configured for the Arctic and North Atlantic (ANHA4 configuration, simulation EPM014 with explicit tidal forcing), and coupled to the Louvain-la-neauve ice module (version 2; Vancoppenolle et al., 2009). The ANHA4 domain, described in more detail in Hu et al. (2018) and Castro de la Guardia et al. (2019), provides five-day output of physical and hydrographic fields across the sampling region with a nominal 0.25-degree horizontal resolution and vertical discretization in 50 depth bins (1 m resolution at surface, increasing non-linearly to >100 m at depth). Briefly, the simulation was forced with boundary conditions from the Global Ocean Reanalysis and Simulations (GLORYS2v3; Masina et al., 2017), atmospheric data from the ERA-Interim reanalysis (Dee et al., 2011), river and glacier freshwater input from Dai et al. (2009) and Bamber et al. (2012), and tidal data from TOPEX/Poseidon Global Inverse Solution TPXO 8 (Egbert & Erofeeva, 2002). In the model, eddy diffusivity, κZ, is initialized with a background value of 10-5 m2 s-1. Thereafter, κZ is derived from a prognostic equation for turbulent kinetic energy (TKE) using a closure assumption to prevent very large and very small values. This TKE scheme has a dependency on the Richardson number (Ri) and sea ice concentration. Under unstable water column conditions (diagnosed as Ri < 0.2 in the model), the TKE scheme is 60 overridden, and a maximum κZ value is set. Thus, at a given model time step, κZ can range from a minimum of 10-6 m2 s-1 under non-turbulent water column conditions (Ri > 0.2) and 100 % sea ice cover, to a maximum value of 101 m2 s-1 under unstable water column conditions. The enhanced diffusion is aimed at rapidly restoring the water column stability, which helps maintain the numerical stability of the model, but produces an unrealistic upper bound of κZ. For this reason, I selectively excluded grid points where unrealistic enhanced diffusion was suspected, based on five-day average κZ output >10-2 m2 s-1 (i.e. at least one 1080-second model time-point during a five-day period with κZ = 101 m2 s-1; Fig. B.5). 3.3.3.2 Mixing correction calculations The vertical O2B flux at the base of the ML (Fmix,O2; mmol O2 m-2 d-1; Eq 2) was derived as the product of the model-based κZ and an observed O2B concentration gradient below the ML (dO2B/dZ, mmol Ο2 m-4). As I lacked subsurface Ar observations, O2B below the ML was calculated as the difference between the observed and equilibrium dissolved O2 concentrations, derived from CTD measurements (i.e. O2B = O2 – [O2]eq). Corrected-NCP (cor-NCP) was then estimated from the ML O2B budget by subtracting Fmix,O2 from bioflux-NCP. Fmix,O2 = κΖ × dO2BdZ (3.2) cor-NCP = bioflux-NCP − Fmix,O2 (3.3) Model-derived κZ was matched to the ΔO2/Ar observations by obtaining values from the NEMO simulation output within a 10-km radius of the locations of the underway sampling data. I estimated “underway κZ” as the average of these values over a depth range (integration depth, dZ) from the base of the model MLD to 10-m below the base of the model pycnocline (both calculated using the same density criteria described above), and averaged over one O2 residence time, τO2 (typically one to four weeks; Table 3.1), prior to the ship-board observations. Final underway κZ was filtered by removing outliers in each sub-region, defined as data occurring outside of 1.5-times the interquartile range of values. The dO2B/dZ gradient was estimated at each CTD/Rosette 61 sampling station as the slope of O2B over dZ, defined using observed MLD and pycnocline depths. As no CTD measurements were obtained in HS and HB during 2015, that year’s dataset was supplemented with summertime dO2B/dZ estimates from ArcticNet sampling between 2002 and 2016 (markers in Fig. 3.1d). On-station values were spatially interpolated to the resolution of the underway ΔΟ2/Αr data after removing outliers, using same criteria as for κZ (depicted in Fig. B.2f). Final underway Fmix,O2 was derived as the product of underway κZ and dO2B/dZ, smoothed to a 5-km running mean. My approach was intended to capture the general mixing effects on ML O2 along the cruise tracks. Spatial averaging was applied to better reflect the mean conditions encountered by a moving water mass over τO2. The Fmix,O2 results are sensitive to the depth and time integration scales of the calculations, but the general trends are consistent over a range of reasonable values (details in section 3.5.1). The lower limit of dZ was extended below the pycnocline to account for potential variability in the pycnocline depth over τΟ2 (~5 to 12 m based on variability in the NEMO simulation over a two-week period). With this approach, dZ was defined to represent the maximum vertical extent of surface mixing, accounting for potential variability in the subsurface O2B gradient over τO2. The calculations are more sensitive to the upper limit of dZ, rather than the lower. 3.3.4 Model evaluation and comparisons To evaluate the output fields of the NEMO model and justify the use of the model-derived κZ values in the NCP correction, model-predicted density and related terms (MLD, pycnocline depth and the Brunt-Väisälä frequency, N) were compared against values obtained from field sampling in the upper 75 m of the water column. The ML and pycnocline depths were calculated from model density, following the definitions in section 3.3.2, and N was calculated as √−gρ×∂ρ∂z, where g, ρ and ∂ρ∂z are the acceleration due to gravity, model density, and density gradient, respectively. These model-derived values were compared against observations from ~800 CTD profiles from ArcticNet sampling between 2002 and 2019, and ~2000 profiles from 75 Argo floats present within the vicinity of the sampling area between 2015 and 2019 (black and red markers in Fig. 3.1, respectively). Values were compared by matching the model output to the time and location of each independent profile. As N describes water column stability and is a component of 62 the model’s determination of κZ (further details in Appendix B), comparisons of this term between the model and observations provide insights into the accuracy of the model-derived mixing rates. Comparisons of density between the model and observations reflect the model’s accuracy in predicting the distribution of ocean properties and in describing vertical water column structure. I also evaluated the model vertical eddy diffusivity over a depth range of 75 to 250 m using estimates of an internal wave-driven diffusivity parameterized from CTD observations between 2002 and 2016 (Chanona et al., 2018). Note that these independent estimates of κZ were not available in the upper 75 m, and the evaluation was performed for subsurface layers of the water column, somewhat below those relevant to surface O2 mixing fluxes. 3.4 Results 3.4.1 NEMO model evaluation The model and observations agreed well throughout most of the study area, with the model generally capturing the spatial and temporal density distribution along the cruise-tracks (Fig. 3.2a, b). Across the entire region, a direct comparison between modelled and observed (from ArcticNet CTD measurements between 2002 and 2019 and Argo sampling) upper ocean (<75 m) density yielded a root mean square error (RMSE) of <2.5 kg m-3. This relatively high RMSE value (compared with a corresponding observed density range of ~10 kg m-3) is biased by larger discrepancies between the model and observations occurring in some coastal waters. Larger offsets, identified as profile stations having observation-model differences in upper ocean density and Brunt-Väisälä frequency (N) at the base of the mixed layer exceeding 3 kg m-3 and 0.02 s-1 (relative difference ~20 %), respectively, occurred primarily adjacent to land or glaciers (red markers in Fig. 3.2d representing 15 % of the dataset). Excluding measurements obtained at these locations, the RMSE of the density comparison is ~0.7 kg m-3, and the model output and observations showed a strong correlation (R2 = 0.7) with a slope not significantly different from unity (0.98 ± 0.03; p < 0.05). This result suggests minimal bias across most of the study region. Similar results were obtained in evaluations of the ML and pycnocline depths. At ~90 % of stations, N values at the base of the ML differed by less than 0.02 s-1 between the model and observations, suggesting that the model predictions of stratification, and therefore mixing, should be reasonably accurate in most regions. It is notable, however, that at most stations, N at the base 63 of the mixed layer was lower in the model than in observations (median difference 0.006 s-1), indicating that the model predicts weaker stratification, and may thus have a slight positive bias in mixing rates, particularly in coastal waters where offsets are larger. The larger discrepancies between the model and observations in coastal waters are unsurprising given the sensitivity of the simulation output to modelled bathymetry, which is most uncertain in these nearshore regions (see section 3.5.1). Offsets may also result from a mismatch between discrete point CTD observations and more coarsely averaged model output (each grid cell represents 0.25-degree and five-day means). The model also performed reasonably well in simulating the observed spatial distribution of κZ, representing differences between sub-regions (Fig. 3.2e). Specifically, the model captured the elevated mixing rates in the NA/LS relative to the adjacent BB/DS and CAA regions. Model-based κΖ estimates in the CAA were generally within the range of values reported elsewhere (Hughes et al., 2017; Kagan et al., 2010), although modelled mixing rates in all regions were somewhat lower than empirical estimates. The median offset between modelled and observed κZ ranged from -3x10-6 to 5x10-5 m2 s-1 (mean ~2x10-5 m2 s-1). Given the ~two-fold uncertainty in the in-situ parameterization (Chanona et al., 2018) and its tendency to slightly over-estimate mixing (Chanona & Waterman, 2020), the magnitude of the offsets suggests a reasonable agreement between the observations and the model output. 64 Figure 3.2 Comparison of upper ocean density (a-b), mixed layer depth (c), Brunt-Väisälä frequency (d), and eddy diffusivity (e) between CTD-based observations and NEMO-derived output. Panels (a) and (b) show the along-track CTD and NEMO-derived density, respectively, at each CTD sampling station from 2015 (left), 2018 (middle) and 2019 (right) cruises. Corresponding estimates of the MLD are shown in (c). Panel (d) shows the observed-model difference in the Brunt-Väisälä frequency (N) at the base of the ML derived from the 2015, 2018 and 2019 cruises, 2002-2016 ArcticNet sampling, and Argo floats. Markers with red outlines identify locations where the average upper ocean density differed by >3 kg m-3 and N differed by >0.02 s-1 between CTD and NEMO-based values. For panels (a)-(d), the output from the NEMO simulation was matched to the time and location of in-situ sampling. Panel (e) shows the regional distributions of eddy diffusivity values obtained from the model (black) and those parameterized from CTD observations (grey) from Chanona et al. (2018). Values represent κZ estimates between 75 and 250 m. 3.4.2 Observations: Biogeochemical properties and the physical environment The spatial distribution of satellite-derived surface Chl a concentration (Fig. 3.1) varied between the three sampling years, but was generally consistent with the summertime decadal mean 65 (Fig. B.1f). Chlorophyll a concentrations during the sampling periods were elevated in the ice-free regions of the North Water Polynya (2015 and 2018 only) and Kennedy Channel, HS, eastern HB and over the continental shelves, but low in BB and highly variable throughout the NA/LS and CAA. Shipboard underway Chl a fluorescence reflected the patterns observed in the satellite imagery (Figs. 3.3-3.5e,f), and also showed some coherence with ΔO2/Ar, SSS and SST, as described below. Average wind speeds over each year’s sampling period showed a latitudinal gradient, with generally higher values (~5-10 m s-1) in the southern section (NA/LS) of the study region and reduced wind speeds (typically <6 m s-1) throughout northern BB/DS and the CAA (Fig. B.3). Wind speeds were notably elevated in 2018, with average values in the NA/LS, BB/DS, HS and HB regions approximately 1-2 m s-1 higher than in 2015 and 2019. Conditions in the CAA were similar across each sampling year. Mostly ice-free conditions were present during the sampling in the southern regions (NA/LS and southern BB/DS), while areas of intermediate-to-high coverage existed to the north (Fig. B.3). Sea ice cover was typically highest in eastern BB, HB and the CAA, but was somewhat anomalous in 2015 relative to the other sampling years. During 2015, the average ice extent in western HB and eastern BB/DS was elevated, while ice cover throughout the remainder of BB/DS and northern LS was considerably lower than in the other years. In most regions, waters were largely ice-free (<15 % aerial ice coverage) for at least two weeks prior to sampling, but in some regions (e.g. NA/LS, HS and HB in 2015, Lancaster Sound in 2018, and NA/LS, BB/DS and parts of the northern CAA in 2019), ice melt did not occur until closer to the time of sampling (Figs. 3.3-3.5e; Table 3.1). Surface hydrography (SST and SSS) varied between regions and was highly heterogeneous along the respective cruise tracks (Figs. 3.3-3.5c,d; B.1-B.2). Surface temperature and salinity showed latitudinal gradients, with highest values in the NA/LS and decreasing values through BB towards the CAA. Sharp gradients in SSS also occurred throughout the region, corresponding with water mass variability and strong hydrographic fronts associated with freshwater input. These patterns were particularly notable in HB, the Queen Maud Gulf (QMG) and the northern CAA in the underway surface measurements (Figs. 3.3-3.5), and the observed (Fig. 3.9d) and NEMO-based (Fig. B.1) summertime surface salinity climatologies. Particularly low SSS occurred near 66 prominent glaciers (CAA and shelf; markers in Fig. 3.1d and labelled in Figs. 3.3-3.5) and rivers (QMG and HB; markers in Fig. 3.9d), with values often falling below 25 PSU. Strong salinity fronts in BB (e.g. ~7200 km in 2015) likely reflect recent ice melt, while the east-west gradient reflects circulation from Greenland (elevated S) and the CAA outflow (lower S). Figure 3.3 Distributions of ΔO2/Ar and hydrographic properties along the 2015 CCGS Amundsen cruise track. The coloured bar at the top of (a) represents the different sampling regions denoted in Fig. 3.1d, and the vertical light grey patches represent sampling over the continental shelf. In (a) and (b), the red markers represent negative ΔO2/Ar. Red markers in (e) represent discrete mixed layer Chl a measurements obtained at CTD stations, and the grey line represents satellite-derived Chl a interpolated onto the cruise track. The light blue shading in (e) shows the number of days before the cruise sampling when ice levels fell below 15 %. Values less than zero indicate that ice coverage did not fall below that threshold prior to sampling. 67 Figure 3.4 Distributions of ΔΟ2/Ar and hydrographic properties along the 2018 CCGS Amundsen cruise track. Gaps in the ΔO2/Ar data occurred when the ship’s underway seawater supply became blocked by ice. Refer to Fig. 3.3 caption for further details. Note the differences in y-axis scaling in Figs. 3.3-3.5. 68 Figure 3.5 Distributions of ΔΟ2/Ar and hydrographic properties along the 2019 CCGS Amundsen cruise track. Gaps in the ΔO2/Ar data correspond with periods of instrument malfunction. Refer to Fig. 3.3 caption for further details. Note the differences in y-axis scaling in Figs. 3.3-3.5. 3.4.3 Sub-mesoscale variability in ΔO2/Ar Surface ΔO2/Ar measurements exhibited significant variability across the three sampling years (Figs. 3.3-3.5a,b). The highest values occurred in 2015 and 2019, where ΔO2/Ar often exceeded ~8 % in shelf, CAA and HS waters. In contrast, ΔO2/Ar was typically low (within ~2 % of equilibrium) throughout BB/DS during each cruise and had intermediate values in NA/LS. I observed only a few sections of negative ΔO2/Ar (red markers in Figs. 3.3-3.5a,b), indicating net 69 autotrophy across the majority of the study region. The most significant negative ΔO2/Ar excursions occurred in HS, where ΔO2/Ar reached minimum values of ~ -7%. Strong spatial variability in ΔO2/Ar was associated with surface hydrographic fronts. For example, sharp SST and SSS gradients (>5 oC and >5 PSU over several km) in the BB, shelf and CAA regions of each cruise often corresponded with elevated ΔΟ2/Ar. The most striking of these features occurred in BB (~7200 km in 2015; Fig. 3.3) and throughout the northern CAA (~6500-9000 km in 2019; Fig. 3.5), where SSS fell below 25 PSU and ΔO2/Ar increased by ~5-10 % over short spatial scales (order ~20 km). The frontal features encountered in 2019 occurred adjacent to the Devon Ice Cap (5300 and 8500 km), Humboldt Glacier (6800 km), Petermann Glacier (7800 km) and Sydkapp Ice Cap (8800 km). In contrast, the low SSS in QMG and HS corresponded with typically low ΔΟ2/Αr. Large-amplitude ΔO2/Ar variability was also observed along two overlapping longitudinal transects in HS, where values ranged from ~-7 to >15 % over ~100 km and corresponded with strong peaks in Chl a. Both properties were somewhat lower on the eastward (second) transect, and this likely reflects the influence of enhanced air-sea exchange and gas re-equilibration during a storm that occurred during the second transect (maximum wind speeds >10 m s-1; data not shown). 3.4.4 Mixing rates and vertical O2 gradients The model-derived mixing rates (κZ) at the base of ML varied by over two orders of magnitude across the study area (Fig. 3.6a-c). Mixing was highly variable across the CAA and continental shelves, with rates ranging from 1x10-5 to ~2x10-3 m2 s-1 over short distances. Average summertime κZ was typically highest (>10-3 m2 s-1) in the NA/LS, HS and shelf regions, an order of magnitude lower in BB, and nearly two orders lower in the southwestern CAA. Mixing throughout the NA/LS, HS, HB and southern BB/DS was notably elevated in 2018 relative to the other sampling years, and 10-year summertime average (Fig. B.1). Subsurface O2B gradients (dO2B/dZ) varied from ~-4 to 3 mmol O2 m-4 (Figs. 3.6d, B.2e). Negative values reflect O2 consumption below the mixed layer, as is observed in many oceanic waters, whereas positive values reflect subsurface O2 maxima. I also observed many instances with low dO2B/dZ values <±1 mmol O2 m-4, reflecting relatively well mixed upper ocean O2 profiles. 70 There were no apparent regional differences in dO2B/dZ, although more positive values were observed in BB/DS and the CAA than other regions. Figure 3.6 Mean summertime NEMO-derived eddy diffusivity at the base of the mixed layer (a-c) and the distribution of the subsurface vertical O2B gradient (d) in the central and eastern North American Arctic. Panels (a)-(c) represent model output corresponding with the timing of the 2015 (a), 2018 (b) and 2019 (c) cruises. Values represent the average κZ in the 10 m below the MLD over each cruise’s sampling period. Data in panel (d) were calculated from CTD profiles obtained between 2002 and 2019. 3.4.5 Net community production and mixing corrections Figure 3.7 presents the along-track distribution of uncorrected NCP (bioflux-NCP) and corrected NCP (cor-NCP) in each sampling year, while Fig. 3.9 shows the three-year average spatial distribution of cor-NCP. Table 3.1 compiles the cor-NCP results with other hydrographic properties by sampling year and region. 71 Bioflux-NCP (black lines in Fig. 3.7) ranged from ~-50 to 150 mmol O2 m-2 d-1 across all sampling years. The largest negative values were observed in HS (2015), implying strong net heterotrophy in that region. Other regions, including parts of BB/DS and the CAA, also exhibited low or negative bioflux-NCP in each sampling year. The highest bioflux-NCP values were observed in localized parts of HS and HB, while the largest regionally-averaged values (up to ~50 mmol O2 m-2 d-1) occurred in the NA/LS (2015 and 2018). Strong mesoscale variability in bioflux-NCP was also observed in HS, HB and the CAA, where values spanned >100 mmol O2 m-2 d-1 over several hundred kilometers. Corrected NCP (red lines in Fig. 3.7) was generally higher than bioflux-NCP, except in regions of positive dO2B/dZ. Mixing corrections (–Fmix,O2) ranged from ~-20 to 100 mmol O2 m-2 d-1, and were largest (>50 mmol O2 m-2 d-1) in the CAA (2018 and 2019) and LS (2015 and 2018). I observed negative mixing corrections throughout parts of BB/DS and the CAA in each sampling year. Notably, some regions of apparent net heterotrophy persisted even after applying the mixing correction. For example, cor-NCP remained negative in the HS region during 2015, and additional sampling in this region in July 2018 revealed subsaturated surface O2 (data not shown, and ΔO2/Ar data unavailable). These results suggest that the waters of eastern HS may experience persistent net heterotrophy, or may be influenced by upwelling. Other isolated regions of negative cor-NCP occurred throughout BB and the CAA. Overall, cor-NCP ranged from ~-40 to 175 mmol O2 m-2 d-1, with the highest values occurring in HS and the CAA. Notable sub-mesoscale hotspots of elevated cor-NCP (>100 mmol O2 m-2 d-1) occurred throughout the northern CAA (Kennedy Channel and Nares Strait; ~6800-8000 km in Fig. 3.7c) and near the Devon Ice Cap (~8500 km along-track in 2015, 10500 km in 2018, and ~5000, 8600 and 9200 km in 2019). Additional small-scale features occurred in western Lancaster Sound (~9000 km in 2015) and on the Greenland shelf (~2000 km in 2019). Across all years, cor-NCP was typically higher in NA/LS than in BB/DS, where values were generally <20 mmol O2 m-2 d-1. 72 Figure 3.7 The distribution of corrected (“cor”) and uncorrected (“bioflux”) NCP along the 2015, 2018, and 2019 CCGS Amundsen cruise tracks. The red and black lines represent cor-NCP and bioflux-NCP, respectively, while the blue shading represents the uncertainty in Fmix,O2 (see details in appendix B). Panel (d) shows the compiled three-year cor-NCP dataset, separated by region. The black line in each distribution represents the median value, and the width of the bars represents the proportion of observations at a given y-axis value. Colour bars at the top of panels (a)-(c) represent the along-track sampling regions, with colours corresponding to the regions represented in (d). The vertical grey shading represents sampling that occurred over the shelf regions. 73 Table 3.1 Summary of along-track NCP and hydrographic observations from the 2015, 2018 and 2019 CCGS Amundsen cruises. Values represent the average and standard deviation in each sub-region. Blank cells indicate that no data were obtained in a given region. NA/LS BB/DS HS HB CAA Shelf cor-NCP 2015 54.5 ± 25.4 4.9 ± 9.6 62.3 ± 47.4 5.0 ± 12 12.3 ± 23.4 26 ± 16.6 [mmol m-2 d-1] 2018 33.9 ± 14 13.3 ± 9.3 14.8 ± 20.2 12.4 ± 12.7 2019 13.0 ± 15.8 8.0 ± 14.5 24.9 ± 27.9 12.0 ± 6.4 Chl a 2015 1.0 ± 0.7 0.3 ± 1.7 0.9 ± 1.4 0.3 ± 0.3 0.3 ± 0.2 0.5 ± 0.6 [mg m-3] 2018 1.2 ± 1.0 0.4 ± 0.9 0.2 ± 0.9 1.1 ± 1.1 2019 0.3 ± 0.7 0.3 ± 0.4 0.4 ± 0.5 0.9 ± 0.7 log10(κZ ) 2015 -3.5 ± 3.8 -4.3 ± 4.4 -3.6 ± 3.8 -4.7 ± 4.8 -4.6 ± 3.7 -3.8 ± 4.0 [m2 s-1] 2018 -3.7 ± 4.3 -4.1 ± 4.0 -4.3 ± 3.6 -4.1 ± 3.6 2019 -4.2 ± 4.7 -4.2 ± 4.3 -4.1 ± 3.6 -4 ± 3.9 dO2B/dZ 2015 -1.3 ± 0.2 0.7 ± 1.3 -0.2 ± 0.4 -0.5 ± 0.6 -0.6 ± 0.9 -0.9 ± 1.2 [mmol m-4] 2018 -0.9 ± 0.8 -0.4 ± 0.6 -0.4 ± 1 -0.4 ± 0.7 2019 1.0 0.2 ± 1.4 -0.5 ± 1.3 -0.2 ± 0.8 Days since ice < 15 % 2015 63 ± 3 38 ± 10 36 ± 19 17 ± 6 27 ± 10 44 ± 10 2018 70 ± 5 54 ± 19 38 ± 21 56 ± 21 [days] 2019 65 ± 2 46 ± 21 40 ± 35 40 ± 23 O2 residence time 2015 5 ± 2 19 ± 13 4 ± 2 32 ± 17 31 ± 21 9 ± 6 2018 8 ± 2 8 ± 3 9 ± 5 7 ± 4 [days] 2019 13 ± 2 9 ± 8 10 ± 8 13 ± 12 3.5 Discussion The results presented here, combining high resolution ship-board measurements with discrete hydrographic data, numerical model output and remote sensing, allowed us to correct ΔΟ2/Ar-derived NCP estimates for vertical mixing biases, and to examine several drivers of biological productivity. These observations provide insights into the spatial coupling between NCP and various forcing mechanisms, including vertical mixing, seasonal ice-melt and freshwater input. Below, I discuss the strengths and limitations of the mixing correction applied in the current study (section 3.5.1.1), and interpret the spatial variability in this correction (section 3.5.1.2) and the resulting NCP estimates (sections 3.5.2). In appendix B, additional details are provided on the uncertainty in the model-derived mixing corrections. 74 3.5.1 Evaluating the mixing correction 3.5.1.1 Strengths and weaknesses of the approach The range of mixing flux corrections (–Fmix,O2 from ~-20 to 100 mmol O2 m-2 d-1) derived here is consistent with estimates from offshore and dynamic coastal waters of the Subarctic Pacific (~5 to 190 mmol O2 m-2 d-1 in Ch. 2), but is higher than corrections derived in other studies (e.g. <~20 mmol O2 m-2 d-1 in Castro-Morales et al., 2013; Haskell et al., 2016; Manning et al., 2017). No previous attempts have been made to correct ΔO2/Ar-based NCP estimates for vertical mixing biases in Arctic waters, reflecting the challenges associated with such corrections in this region. For example, complex nitrogen cycling in many Arctic marine waters (Fenwick et al., 2017) precludes the use of O2/N2O as a mixing tracer (Ch. 2). Moreover, the Fmix,O2 parameterization of Haskell et al. (2016) attributes vertical mixing solely to wind-induced horizontal shear, and does not capture the more dominant mechanisms of near-surface mixing, such as buoyancy fluxes, tidal flow and internal wave breaking through interactions with bottom topography or ice (Chanona et al., 2018; Hughes et al., 2017; Marsden et al., 1994). Additional approaches to estimating Fmix,O2 have relied on literature-derived estimates (Castro-Morales et al., 2013) or direct measurements of κz from in-situ microstructure observations (Manning et al., 2017), which yield only discrete cor-NCP coverage. These limitations highlight the need for alternative approaches to estimate NCP mixing biases. The model-based approach described here is able to resolve along-track mixing variability and can, therefore, be a powerful tool for evaluating biases on surface ΔO2/Ar observations. The output from the NEMO simulation generally agreed well with independent observations of density, Brunt-Väisälä frequency (N) and κZ estimates across most of the study region (Fig. 3.2), demonstrating that the model performed well at predicting the water column structure in most (but not all) regions. These results are complemented by previous evaluations of similar NEMO configurations showing good predictive skill in the Canadian Arctic (Grivault et al., 2018; Hughes et al., 2017). Moving forward, evaluations of the model’s predictions of mixing variability should involve a more comprehensive comparison of N throughout the study region, since model-derived κZ is directly related to N (see Appendix B). Larger discrepancies between the model and observations in coastal waters may be partially attributed to the relatively low spatial resolution of the ANHA4 simulation (average lateral 75 resolution of ~15 km in the CAA) and inaccurate representation of complex bathymetry or environmental forcing in the model. Model bathymetry is constrained by the poor coverage of bathymetric observations in Arctic waters, and recent seafloor mapping has exposed differences between the simulated (based on the ETOPO1 database) and true seafloor, particularly in narrow channels and fjords (Jakobsson et al., 2020). Freshwater input and atmospheric properties are also highly heterogeneous, and inaccurate forcing of these variables will cause model biases (particularly in coastal regions where freshwater influence is most significant), which are reflected in the comparison of observed and modelled properties. A higher resolution configuration of the NEMO model (ANHA12; 1/12th-degree grid with average lateral resolution of ~4 km in the CAA) may improve determination of fine-scale coastal features (Hughes et al., 2017), but remains susceptible to biases in the model bathymetry and forcing. Thus, while it is not clear that a higher-resolution model is necessary for the present application, the approach described here will undoubtedly benefit from improvements in the parameterizations of bathymetry and environmental forcing in the model. Another limitation is the influence of water mass advection, which is significant in parts of the CAA (Münchow, 2016) and makes it difficult to directly match productivity and mixing dynamics occurring over an O2 residence time. Such limitations, however, are difficult to assess in the present study, and reflect a broader challenge of the use of ΔO2/Ar as a productivity tracer in dynamic waters. Nonetheless, use of the 0.25-degree grid and spatial-averaging of model-derived κZ was intended to represent the general mixing dynamics relevant to NCP calculations integrated over τΟ2. The present approach is also sensitive to several assumptions in the calculation of Fmix,O2. The largest potential errors in this term stem from variability in κZ and dO2B/dZ over τO2 and dZ (details in Appendix B). As time-variability cannot be adequately resolved using cruise-based CTD observations, the approach requires the assumption of constant dO2B/dZ over τO2 (i.e. steady-state O2B budget), which is likely invalid in some regions. This issue is not unique to my correction approach and can only be fully reconciled using time-series observations of subsurface O2. I attempted to address this limitation and produce more generalized along-track patterns by removing outlier dO2B/dZ values in each sub-region and smoothing interpolated underway estimates. Nonetheless, capturing the time variability in dO2B/dZ remains a significant source of uncertainty in deriving an NCP mixing correction, contributing up to ~35 mmol O2 m-2 d-1 of error 76 in underway cor-NCP estimates (regional average error ~0.3-12 mmol O2 m-2 d-1). Errors resulting from uncertainty in dZ can also be significant. Previous work has applied dZ values ranging from ~7 m (Haskell et al., 2016) to the thickness of the oxycline (Castro-Morales et al., 2013), but no apparent consensus has been reached on the appropriate integration depth scale. I derived κΖ and dO2B/dZ from the base of the ML to just below the base of the pycnocline (average dZ of ~13 ± 6 m). This definition spans a depth range that is important for upper ocean stratification and should thus represent spatial variability in water column stability better than a static dZ. Evaluating dO2B/dZ over a range of dZ values (e.g. MLD to pycnocline, MLD +5 m, MLD x2) contributed an average uncertainty in Fmix,O2 of ~2-15 mmol O2 m-2 d-1 (underway values up to ~30 mmol O2 m-2 d-1). The time and depth variability in κZ, assessed using the model output, contributed a further uncertainty of ~2-20 mmol O2 m-2 d-1 (underway up to ~28 mmol O2 m-2 d-1). Finally, the Fmix,O2 calculations are also subject to numerical uncertainty in κZ (i.e. errors in the model-output resulting from parameterizations), which is difficult to constrain. The model-observation comparisons of density and N can provide some insight into the model accuracy, and by propagating observed-model biases in N, I estimated that numerical errors in κZ (~2x10-5 to 1x10-4 m2 s-1; see Appendix B) can contribute up to ~22 mmol O2 m-2 d-1 uncertainty in Fmix,O2. The largest of these errors occur in the northern CAA (average ~10 mmol O2 m-2 d-1, compared with typical regional averages <6 mmol O2 m-2 d-1). Future work should thus focus on better constraining oceanic mixing rates in dynamic regions through more model-observation comparisons similar to the ones presented here. Importantly, while the total underway uncertainty in Fmix,O2 may be significant in some locations (up to ~45 mmol O2 m-2 d-1 in NA/LS and small sections of the CAA), consistent spatial patterns in cor-NCP are produced under a range of realistic variability in κZ, dO2B/dZ, and dZ (blue shading in Fig. 3.7 representing total error in Fmix,O2 with full details in Appendix B), with average regional uncertainties being ~4-28 mmol O2 m-2 d-1. Moreover, it is noteworthy that errors resulting from the dO2B/dZ term typically contribute the largest source of uncertainty in calculations, except in NA/LS where strong temporal variability in κZ was most important. In future work, output from a biogeochemical model coupled to the physical domain can be used to evaluate dO2B/dZ, and therefore reduce some sources of uncertainty in Fmix,O2. At present, however, model-derived estimates of biogeochemical properties are less robust than those of physical parameters, as 77 highlighted by a significantly weaker, albeit significant, relationship between model- and CTD-based O2 (R2 = 0.13, RMSE = 35 mmol O2 m-3; Fig. B.4). 3.5.1.2 Spatial variability in the mixing correction Application of the O2 mixing correction significantly affected the interpretation of the derived NCP results in some regions (Fig. 3.7). For example, in the NA/LS and CAA, mixing corrections often exceeded 50 mmol O2 m-2 d-1, representing a large proportion of bioflux-NCP. Elsewhere (e.g. BB and parts of the CAA), cor-NCP values were sometimes lower than bioflux-NCP, reflecting subsurface O2 maxima. These features – classified as “subsurface Chlorophyll maxima” – are commonly found throughout the Arctic due to near-surface nutrient depletion (Martin et al., 2010), and they contribute to a positive mixing bias in surface ΔO2/Ar. In these waters, calculations of mixed layer NCP can significantly under-represent water column productivity (see section 3.5.2.1), presenting a limitation of underway ΔO2/Ar sampling. In other regions, mixing corrections were largely insignificant. Collectively, these results highlight the importance evaluating vertical mixing fluxes for the interpretation of surface ΔO2/Ar data. The strong positive mixing corrections in the NA/LS and CAA reflect the combination of high vertical mixing rates (κZ) and strong subsurface O2 gradients in those regions (Fig. 3.6). In the NA/LS, strong near-surface vertical mixing is broadly associated with relatively high wind speeds over ice-free waters (Fig. B.3), which results in a significant momentum transfer and turbulent shear production at the base of the ML (Rainville et al., 2011). Wind speed and κZ were significantly correlated in NA/LS and BB/DS (R2 = 0.47, p < 0.05), and elevated summertime near-surface κZ throughout the NA/LS in 2018 corresponded with higher surface wind speeds at that time. In contrast, high mixing rates in the CAA and other coastal waters are associated more with strong tidal flow (Hannah et al., 2009), internal wave breaking over varied topography (Chanona et al., 2018) and nearshore buoyancy fluxes (Hughes et al., 2017). Mixing corrections in parts of HS were small, despite average κZ being similar in magnitude to values observed in the NA/LS and continental shelves. Although CTD observations were not available for HS during the time of the underway sampling, previous summertime observations revealed a relatively weak dO2B/dZ in parts of the region (Figs. 3.6, B.2 Table 3.1), implying that the upper water column may be well mixed with respect to O2. Indeed, HS is influenced by strong 78 tidal flows (e.g. Arbic et al., 2007), and the associated currents are likely responsible for maintaining relatively homogeneous O2 profiles. In BB/DS, the southern CAA and HB, the weak near-surface mixing rates, and resulting small mixing corrections, broadly reflect strong salinity stratification in these regions. Both the southern CAA and HB are highly influenced by riverine freshwater input (see section 3.5.2.2, below), and the combination of low-salinity outflow water from these regions, and local ice melt contribute to the relatively brackish surface conditions and shallow mixed layers in eastern BB/DS (e.g. Tang et al., 2004). Finally, ice coverage, which was particularly high near the time of sampling in HB in 2015, will have dampened wind-induced vertical mixing in these regions. In addition to vertical mixing corrections associated with diapycnal turbulent fluxes, it is possible that vertical advection (upwelling) may also bias surface ΔO2/Ar observations, as is commonly the case along continental margins (e.g. Teeter et al., 2018). This process may be most relevant in the northern CAA, where typically prevailing southerly winds (Samelson & Barbour, 2008) cause upwelling on the eastern side of Nares St. and the North Water Polynya during reduced ice periods (Shroyer et al., 2017). Yet, simulated vertical velocities obtained from the model suggest that advection fluxes throughout the study region are only important nearshore, and would be generally at least one order of magnitude smaller than fluxes associated with diapycnal mixing (model κZ range ~1x10-5 to 3x10-3 m2 s-1, compared with vertical advection velocities integrated over the pycnocline of <1x10-6 to ~1x10-4 m2 s-1; data not shown). In regions associated with strong riverine input, such as the Queen Maud Gulf (see section 3.5.2.2, below), upwelling driven by estuarine circulation may also cause a negative-bias in surface ΔO2/Ar. However, that upwelling signal likely occurs closer to shore than the location of the underway sampling (minimum sampling distance from land ~50 km), while the typically weak vertical O2 gradients in that region (Fig. 3.6d) suggests that resulting vertical O2 fluxes would be small. Finally, isolated regions of upwelling (or mixing) around icebergs (Smith et al., 2013) or ice edges (Mundy et al., 2009) can occur on smaller spatial scales than can be resolved by the model, and may therefore represent a source of bias in underway ΔO2/Ar sampling that cannot be reconciled with the present approach. For these reasons, and the fact that upwelling velocities cannot be evaluated against observations as readily as κZ, the influence of upwelling on the surface O2 budget has been neglected in the present study. Future work should, however, attempt to extend the present analysis to account for 79 this potentially important flux, particularly as upwelling may become more important under diminished sea ice conditions (Carmack & Chapman, 2003). 3.5.2 Variability in net community production The observed range of bioflux-NCP (~-50 to 150 mmol O2 m-2 d-1) is larger than previously reported estimates based on surface ΔO2/Ar surveys across the North American Arctic ocean, owing, in part, to the poor coverage of observations in these waters. Published estimates of bioflux-NCP in the Bering and Beaufort Seas and BB are typically below ~20 mmol O2 m-2 d-1 (Burgers et al., 2020; Ji et al., 2019; Juranek et al., 2019), and lower (<10 mmol O2 m-2 d-1) in the Arctic basins (e.g. Ulfsbo et al., 2014). Values exceeding ~60 mmol O2 m-2 d-1 have only been recorded in “hotspots” along the continental shelves of the Bering and Chukchi Seas (Juranek et al., 2019), although PP, measured by other metrics, is perennially high in the North Water Polynya (Pikialasorsuaq) (e.g. Tremblay et al., 2006), and other parts of the CAA and Greenland Shelf (e.g. Codispoti et al., 2013). The upper limit of cor-NCP estimates observed here (~175 mmol O2 m-2 d-1) thus exceeds most previous NCP observations from Arctic waters, but is similar to rates reported in the highly productive Western Antarctic Peninsula (e.g. Eveleth et al., 2017b) and continental shelf of the Subarctic Northeast Pacific (e.g. Ch. 2). In the context of previous work, this study is therefore significant for two reasons. First, no previous attempts have been made to correct bioflux-NCP for mixing influences on surface ΔO2/Ar measurements in the Arctic, leading to potential biases in the resulting NCP estimates. Second, my analyses identified high-NCP regions of the eastern and central North American Arctic, where few prior measurements existed, thus expanding the distribution of NCP estimates and improving our understanding of spatial NCP patterns on regional scales. The cor-NCP distribution I observed reflects environmental, hydrographic and biogeochemical variability across the sub-regions of the study area, with differing responses to nutrient supply, ice melt and input from glaciers and rivers. In broad terms, underway cor-NCP corresponded with fluorescence- and satellite-derived Chl a, with typically high values of both metrics in the northern CAA, NA/LS and HS, and lower values in BB/DS and the southern CAA. The linear regression between average cor-NCP and satellite Chl a during the sampling years (Fig. 3.9a,b) yielded a statistically significant R2 value of ~0.25 (p < 0.05). The relationship with 80 bioflux-NCP was not significant. Although the residence time of Chl a and ΔO2/Ar-NCP differ (days versus weeks), this result is notable as it suggests that first-order relationships between NCP and Chl a are better reflected in the cor-NCP estimates than bioflux-NCP. 3.5.2.1 Environmental drivers of NCP Although the NA/LS and BB/DS are both characterized by relatively wide, deep basins, with mostly ice-free conditions during the sampling periods, productivity was noticeably lower in BB/DS (Figs. 3.7, 3.9a). In NA/LS, wind-induced mixing is a key mechanism for nutrient re-supply to the MLD, elevating productivity throughout the entire growing season (Castro de la Guardia et al., 2019; Crawford et al., 2020) and promoting sporadic late-season blooms (Ardyna et al., 2014). In BB, nutrient-poor Arctic outflow waters and weaker vertical mixing restrict nutrient replenishment, so that a single bloom and subsequent decline in PP typically prevail following the initial retreat of sea ice (e.g. Burgers et al., 2020). The data presented here is consistent with these general patterns, with cor-NCP, surface Chl a, and mean wind speeds all being higher in NA/LS compared with BB/DS. Although intense under-ice, ice-edge or pelagic blooms can form as ice thins and melts (Arrigo et al., 2012; Niebauer, 1991), such blooms are typically short-lived (Ji et al., 2013), and the observations of low underway ΔO2/Ar and Chl a concentrations in BB/DS suggest post-bloom conditions. Indeed, as sampling in BB always occurred more than one O2 residence time (~1-2 weeks on average in BB/DS and NA/LS) after ice levels fell below 15 % in that region (Table 3.1), it is likely that surface ventilation erased any bloom-derived ΔO2/Ar signatures before my observations. During the post-bloom phase, a higher proportion (up to 70 %) of NCP occurs below the ML in the subsurface Chl a maximum (Burgers et al., 2020). This subsurface productivity reflects surface nutrient limitation (Martin et al., 2010; Tremblay et al., 2015) and the effects of salinity stratification in limiting summertime re-supply. Unfortunately, the underway sampling was unable to capture these features, but the low ML cor-NCP and positive dO2B/dZ observed in BB is consistent with these previous observations. A somewhat different pattern occurred near a strong salinity front in the 2015 BB transect, where ice levels fell below 15 % within 15-30 days of sampling (~7100 km along-track in Fig. 3.3). Interestingly, although surface Chl a exceeded 5 mg m-3 and ΔO2/Ar spiked to ~10 %, neither bioflux-NCP nor cor-NCP estimates were elevated in that segment of the cruise track. As my NCP 81 calculations reflect productivity over the previous O2 residence time, these observations likely reflect the onset of an ice-edge bloom, with low productivity occurring prior to the time of sampling. In contrast to the NA/LS and BB/DS, cor-NCP in the shallower basins and coastal waters (HS, HB, CAA and shelf regions) reflected stronger controls from freshwater fluxes (section 3.5.2.2), ice dynamics and nutrient input associated with vertical mixing via strong tides and glacial plumes. The range of cor-NCP estimates in these regions was considerably higher than in NA/LS and BB/DS, particularly in the northern CAA and HS, where the highest (up to ~175 mmol O2 m-2 d-1) and lowest (~-40 mmol O2 m-2 d-1) cor-NCP occurred. For example, cor-NCP in the northern CAA (north of ~78 oN and ~6000-8000 km along-track in 2019) was almost always above ~40 mmol O2 m-2 d-1, and frequently exceeded 100 mmol O2 m-2 d-1 (Fig. 3.8a). While relatively low surface Chl a and N+N concentrations (~1-2 mg m-3 and <1 mg m-3, respectively; Fig. 3.8c) indicate that sampling occurred after any phytoplankton blooms, the ML O2 residence (~10-40 days; Fig. B.2d) is comparable to the timeframe of ice retreat in that region (Table 3.1), suggesting that high NCP could reflect either residual signatures from an ice algae bloom, or pelagic productivity occurring after the onset of ice retreat but prior to the timing of the ship-board measurements. Similar productivity dynamics may have occurred in the Jones Sound region (~8500-9000 km along track in 2019) and at the western extent of Lancaster Sound (~9000 km in 2015 and marker h in Fig. 3.8a), where NCP was also elevated. In all of these sub-regions of the northern CAA, there is strong potential for nutrient input from glaciers or via tide-induced vertical mixing. While nutrient upwelling can significantly enhance primary production immediately adjacent to tide-water glaciers (e.g. Hopwood et al., 2018), nutrient rich subsurface waters may also be supplied to the surface via upwelling near icebergs, or tidal mixing over sills (Meire et al., 2017). These processes may therefore lead to the accumulation of nutrients under-ice, pre-conditioning the water column for a large bloom, or they may constitute a more continuous source of nutrients following ice break-up. 82 Figure 3.8 Distribution of cor-NCP and subsurface nitrate and nitrite in the northern CAA. Panel (a) shows the spatial distribution of estimates from all three cruises, with labels identifying “hotspots” of elevated cor-NCP. Labels A-F correspond with sampling in 2019, while labels g and h correspond with 2015 and 2018. Labels A, D, E and F identify underway sampling immediately adjacent to large glaciers. The black arrows show the approximate direction of travel in 2019. Data in (b) and (c) represent 2019 observations only. Panel (b) shows the along-track cor-NCP (black) and bioflux-NCP (grey) between the points represented by the red star and box. Panel (c) shows the along-track subsurface N+N measured at CTD stations, denoted by the light dashed lines. The black contour lines represent salinity. 3.5.2.2 Contrasting influence of freshwater on NCP The Arctic is heavily influenced by rivers (Carmack et al., 2016), which export large quantities of fresh water and terrestrial material to the adjacent coastal zones and deeper Arctic basins (e.g. Forest et al., 2007; McClelland et al., 2016). The glaciers of Greenland and Ellesmere 83 and Devon Islands also represent an increasingly important source of freshwater with variable inputs of organic and inorganic micro- and macronutrients (e.g. Bhatia et al., 2013b; Kanna et al., 2020; Martin et al., 2020). My results suggest that freshwater input can have different effects on ML NCP across various hydrographic regimes. In this study, the influence of freshwater on ML properties was observed as relatively low surface salinity (Figs. 3.3-3.5d, 3.9d) near regions of strong freshwater discharge in the CAA, Hudson Bay and W. Greenland Shelf (markers in Fig. 3.9d represent the July-August freshwater discharge forcing in the NEMO simulation). Notably, cor-NCP was low (near zero and sometimes negative) in the QMG and HB regions, but elevated in the northern CAA and W. Greenland (Figs. 3.7, 3.9a). These differences can be attributed to the contrasting freshwater sources (e.g., non-glacial vs glacial) and adjacent groundcover (e.g., vegetated versus non-vegetated) or topography (Fig. 3.9a) across the different coastal zones. The QMG is surrounded by many non-glacial rivers (e.g. Alkire et al., 2017; Brown et al., 2020), with large tributaries spread over lowlands of thinly vegetated groundcover and permafrost (Walker et al., 2005). These rivers, and similar drainage basins throughout the Arctic, constitute a summertime source of organic matter, but typically a low source of inorganic nutrients to the ocean (Alkire et al., 2017; Holmes et al., 2012). Geochemical river sampling during the 2015, 2018 and 2019 cruises revealed the highest freshwater concentrations of dissolved organic carbon (DOC) in rivers surrounding the QMG (up to ~6 mg C L-1), with much lower concentrations (<1 mg C L-1) further north in the CAA (markers in Fig. 3.9c represent relative river DOC concentration). Corresponding river N+N exhibited contrasting patterns (markers in Fig. 3.9b), consistent with observations reported by Alkire et al. (2017). These measurements are also reflected in satellite-based observations of elevated coloured dissolved organic matter (taken by-proxy as the absorption at 443 nm; Pefanis et al., 2020), which were high in QMG and HB (Fig. 3.9c). I thus propose that the low cor-NCP and shift towards localized heterotrophic conditions surrounding the brackish QMG and HB regions may reflect a signature of community respiration driven by allochthonous organic material, as opposed to carbon produced locally through marine primary production. While particulate carbon rapidly sinks beneath the ML, DOC sinks slowly and is typically remineralized in the surface (Hansell, 2013). Although sampling in QMG (mid-August, 2015 and 2018) and eastern HB (mid-July, 2015) occurred after the timing of typical peak river volume discharge in mid-June and early July, respectively (river gauge data from the Canadian 84 Hydrographic Service not shown), this hypothesis is supported by recent work by Capelle et al. (2020), which documented the dominant role of terrestrially derived DOC remineralization in driving summertime CO2 accumulation in HB waters. Similarly, my findings of low NCP and near-heterotrophy in QMG are consistent with Ahmed & Else (2019), who observed weak sea-to-air CO2 fluxes (representing excess surface water CO2) in the QMG during the time of the 2015 sampling. Finally, the observations of non-zero surface seawater N+N and generally ice-free conditions during the sampling in QMG suggest that local NCP should not have been limited by macronutrient or light availability. Future work should attempt to document the relative influence of locally produced versus imported organic material in driving the balance between autotrophy and heterotrophy in Arctic coastal waters. In contrast to the QMG and HB lowlands, the coastlines of the northern CAA and W. Greenland are mountainous (Fig. 3.9a), glaciated and sparsely vegetated (Walker et al., 2005). Non-vegetated glaciated regions typically contribute small inputs of dissolved organic carbon (Bhatia et al., 2013b; Pain et al., 2020), but may be significant sources of inorganic nutrients (Bhatia et al., 2013a; Martin et al., 2020) to marine surface waters. In my survey of the northern CAA, I identified several cor-NCP hotspots adjacent to the Devon Ice Cap and Humboldt Glacier (stars in Fig. 3.1d), which are labelled in Figure 3.8 (labels A, F, g, h). Corresponding subsurface N+N and salinity fields from 2019 CTD profiles (Fig. 3.8c) showed evidence of buoyant mixing plumes, identified as tongues of relatively high N+N and salinity near the ocean surface. These signatures have been observed immediately adjacent to tidewater glaciers (within ~20 km of the terminus), with the magnitude of the upwelling flux being dependent on the depth of freshwater discharge, and inversely related to the sampling distance from the glacier (Hopwood et al., 2018; Meire et al., 2017). Although I lacked CTD observations corresponding with the cor-NCP peaks (>100 mmol O2 m-2 d-1) near Devon Island in 2015 and 2018 (label h in Fig. 3.8), a similar mechanism may have occurred. Sampling adjacent to the Greenland coast also showed relatively high cor-NCP (>50 mmol m-2 d-1 at ~2000 km along-track in Fig. 3.7), but CTD observations from that section of the transect do not show the same evidence of buoyant plumes. It is possible that my sampling, which occurred >60 km from the nearest glacier, may have been too far away to directly observe the mixing plume in CTD measurements. 85 Notably, as the mixing plumes associated with glacial discharge simultaneously entrain low-O2 water into the ML, the bioflux-NCP results did not always reveal similar productivity hotspots. I also did not detect high cor-NCP (nor bioflux-NCP) adjacent to the Petermann Glacier, despite a nearby CTD profile suggesting upwelling of subsurface waters (label D in Fig. 3.8). As discussed above (section 3.5.1), this discrepancy may reflect the inaccurate representation of bathymetry in the ANHA4 simulation; the model’s bathymetry classifies the marine region that I sampled adjacent to the glacier as being above ground, owing, in part, to the recent extreme retreat of the Petermann Glacier (Jakobsson et al., 2018). Finally, while I associate the cor-NCP features described above with processes occurring immediately adjacent to freshwater sources, previous work has suggested that freshwater export from Greenland may stimulate productivity as far away as ~200-500 km in the NA/LS (e.g. Arrigo et al., 2017). It is thus possible that the elevated cor-NCP I observed in NA/LS may reflect this process. 86 Figure 3.9 Distributions of average summertime mixed layer cor-NCP (a), Chl a (b), absorption at 443 nm (c), salinity (d) and select river properties (b-d) in the central and eastern Canadian Arctic in 2015, 2018 and 2019. Panel (a) shows the gridded average (0.25 o resolution) cor-NCP from 2015, 2018 and 2019 cruises, with topography, represented by the black-to-white (low-to-high) colour shading, overlaid. Panels (b)-(d) show the distributions of mean summertime satellite-derived Chl a concentration, absorption at 443 nm and salinity, respectively. Absorption data were taken as a proxy for coloured dissolved organic matter (Pefanis et al., 2020), and were obtained from the NASA L3 MODIS-Aqua Ocean Color product (https://oceancolor.gsfc.nasa.gov/l3/). Salinity data were from the World Ocean Database (https://www.nodc.noaa.gov/OC5/WOD/pr_wod.html). The relative concentrations of nitrate and nitrite (N+N) and dissolved organic carbon (DOC) measured in rivers sampled near the cruise tracks are shown as red and black markers in (b) and (c), respectively. Red markers represent samples obtained at the same time as the 2015, 2018 or 2019 cruises, while black markers represent summertime samples obtained during a different sampling period. The size of the markers represents the concentration. The black markers in (d) represent the river forcing for the NEMO model, with the marker size corresponding with the relative discharge rate based on Dai et al. (2009) and Bamber et al. (2012). 87 3.5.2.3 Statistical prediction of NCP variability Using observations from all CTD/Rosette profile stations in the study, I performed a stepwise multiple linear regression (MLR) to predict the variability in cor-NCP. The final model had an RMSE of ~18 mmol O2 m-2 d-1 (R2 = 0.50, p <0.05) and revealed significant first-order relationships (in order of significance) between NCP and Chl a, SST, the time since ice retreat, and N+N below the ML (Table 3.2; Fig. 3.10). These results are largely consistent with the sources of NCP variability described above, and further point to a key role of vertical nutrient supply in stimulating elevated PP. In this model, I excluded κΖ as a potential regressor since that term is used to derive underway cor-NCP. However, since subsurface N+N data were available only at CTD stations I was unable to apply the model equation to underway observations. I therefore performed a second stepwise MLR analysis by including κZ, producing a second model in which κΖ, Chl a, SST and the time since ice retreat were significant predictor variables. This model had an RMSE (~19 mmol O2 m-2 d-1) that was nearly identical to the model parameterized without eddy diffusivity. Applying this model to the underway data yielded an RMSE between measured high-resolution cor-NCP and the predicted NCP of ~20 mmol O2 m-2 d-1 across all years and regions (R2 = 0.55, p < 0.05). The model performed best in BB/DS, and was most biased in HS, likely resulting from the lack of CTD station measurements in that region. Interestingly, the lack of a freshwater term in both final MLR models resulted in over- and under-estimates of NCP in the QMG and the northern CAA, respectively (Fig. 3.10c). Going forward, additional NCP surveys should enable development of a more robust statistical model based on a larger number of observations. Nonetheless, the preliminary results offer some mechanistic insights into the controls on NCP variability in the study region, with the potential to improve NCP predictions. In a direct comparison of the cor-NCP estimates with net ML O2 production values from a biogeochemical model coupled to the NEMO simulation (Biogeochemistry with Light Iron and Nutrient limitation and Gases Version 0; BLING; Galbraith et al., 2010), I obtained an RMSE of ~40 mmol O2 m-2 d-1 (R2 = 0.3, p < 0.05). This result implies that the statistical approach performed better than the numerical model in capturing NCP distributions. Moreover, it is noteworthy that the cor-NCP dataset corresponded better with the BLING-based productivity metric than bioflux-NCP (Fig. 3.10c). This result is particularly encouraging in NA/LS and parts of the CAA where the mixing corrections were largest. As model 88 developments continue, the apparent relationship between NCP and various freshwater sources (section 3.5.2.2) may help to refine model-based estimates in regions like the QMG and HB, where the BLING model currently significantly over-estimates NCP. Table 3.2 Statistics for the multiple linear regression model of cor-NCP in the central and eastern North American Arctic. Standardized coefficients were determined by performing the MLR analysis using regressors standardized around their mean values and were used to rank the terms from most significant (left) to least (right). Values in parentheses represent the standard error for each coefficient. All variables have p-values less than 0.05. Variable (Chl a) 1/2 [mg m-3] SST [oC] (days since ice <50 %)2 [days] subsurface N+N [μmol L-1] Intercept Coefficient 24.3 (5.5) -3.2 (0.8) 0.003 (0.0008) 4.0 (1.6) 4.2 (4.4) Standardized coef. 9.4 (4.2) -7.4 (3.6) 7.2 (3.6) 5.2 (4) 23.3 (3.5) R2 versus measured cor-NCP 0.34 0.15 0.10 0.20 89 Figure 3.10 Statistical predictions of cor-NCP in the central and eastern Canadian Arctic. Panel (a) depicts the performance of an empirical multiple linear regression model (MLR; x-axis) of cor-NCP. Two regression models were developed using data from CTD stations (black markers representing model excluding κZ as a regressor) and applied to underway observations (coloured markers for a model including κZ). The dashed line represents the 1:1 fit. Numbers in the figure legend represent the RMSE for each subset of the data, while the label at the bottom right represents the RMSE between the MLR and measured underway cor-NCP values. Panel (b) shows the offset between measured cor-NCP and values predicted by the underway MLR model (light grey) or in a biogeochemical model coupled to the NEMO simulation (blue). Panel (c) shows along-track NCP estimates derived from different metrics. 3.6 Conclusions The approach described and evaluated here can be used across a range of oceanographic environments to refine high-resolution NCP estimates for vertical mixing fluxes. Application of this approach to three years of Arctic and Subarctic ΔO2/Ar data demonstrated the importance of accounting for mixing biases on surface ΔΟ2/Αr observations in some regions. The resulting NCP dataset (cor-NCP) revealed a highly heterogeneous distribution of surface water productivity, which differed from that of bioflux-NCP. Variability in cor-NCP showed a coherent relationship with nutrient supply, sea ice dynamics and other hydrographic properties, enabling construction of 90 a statistical model explaining ~55 % of the apparent NCP variability. The results also suggest different effects of freshwater fluxes on surface water NCP in various regions, with the potential to either stimulate productivity or local respiration through direct nutrient inputs. I observed indirect evidence for allochthonous DOC supply as a driver of low NCP and net heterotrophy in some regions, such as parts of HB and the QMG. In contrast, NCP showed the opposite response to freshwater inputs in waters of the northern CAA influenced by indirect macronutrient supply from glaciers. From a methodological perspective, this work demonstrates the potential to combine numerical model simulations with in-situ sampling. Such studies remain limited in application (e.g. Marchese et al., 2019), but offer important opportunities to infer key biogeochemical processes in a rapidly changing ocean environment. Further efforts should be made to broadly evaluate the approach used here. Developments in numerical accuracy and computational efficiency should enable the use of higher-resolution models to infer physical processes in dynamic coastal waters, while expanding observations of oceanic mixing rates will enable better evaluation of the output. Under this framework, new synergies between the modelling and observational communities can develop, leading to the refinement of both physical and biogeochemical models for improved prediction of future ocean conditions. The model evaluation presented here provides some insight into the current skill of the NEMO model framework, and the MLR results described above could be expanded to improve NCP prediction in subsequent simulations. Combining the present approach with new observational tools for estimating NCP from autonomous ΔO2/N2 measurements on various underway platforms (e.g. described in Chapters 4-6) can significantly expand NCP coverage, helping to integrate biogeochemical and ecological observations over a range of relevant time and space scales. Additional in-situ and multi-year sampling will be required to ground-truth satellite-based studies predicting changes in productivity under an increasingly ice-free regime (Arrigo & van Dijken, 2015). Results from this work should be examined in the context of broader ecological variability, while also considering valuable sources of Indigenous Knowledge on land or sea use changes. Such an integrated approach will be required to better evaluate and predict the ecological and social impacts of environmental change across Arctic and Subarctic regions. 91 Chapter 4: ΔO2/N2' as a tracer of mixed layer net community production: Theoretical considerations and proof-of-concept 4.1 Overview I present a method to estimate net community production (NCP) from underway ΔO2/N2 observations. This approach, based on high-resolution measurements using relatively low-cost O2-optode and Gas Tension Device (GTD) instrumentation, provides an alternative to NCP derivation from mass spectrometric analysis of ΔO2/Ar. To fully exploit ΔΟ2/Ν2 as an NCP tracer, it is necessary to account for differences in surface water argon (Ar) and nitrogen (Ν2) saturation states. Using a one-dimensional mixed layer model, evaluated against field observations, I examined the divergence between ΔO2/Ar and ΔΟ2/Ν2 resulting from various physical processes. Results show that changes in sea surface temperature, bubble injection, and vertical mixing can decouple mixed layer ΔO2/Ar and ΔΟ2/Ν2, while biological N2-fixation has a negligible effect. Based on readily available environmental data, I developed a framework to correct for Ar and N2 solubility differences, yielding a new tracer, ΔΟ2/Ν2', that is a near-analog of ΔO2/Ar. Through model simulations and field measurements, I show that ΔΟ2/Ν2' provides an excellent approximation to ΔO2/Ar, and that uncertainty and biases in ΔO2/N2' are small relative to other errors in NCP calculations. This work demonstrates the potential for ΔO2/N2' measurements to significantly expand NCP estimates from a variety of sampling platforms. 4.2 Introduction Net community production (NCP) quantifies the balance between mixed layer gross photosynthesis and community-wide respiration, serving as an important metric of the metabolic state of an ocean region (e.g. Laws, 1991). As such, the spatial and temporal distribution of marine NCP has significant implications for food web dynamics and global biogeochemical cycles. To understand and predict the response of marine systems to future environmental change, it is therefore important to quantify NCP on ecologically-relevant time and space scales. A common approach for deriving high resolution oceanic NCP estimates employs ship-based mass spectrometry for underway measurements of the seawater oxygen-to-argon ratio 92 (O2/Ar; e.g. Kaiser et al., 2005; Tortell, 2005). This approach relies on the nearly identical solubility properties of O2 and its biologically-inert analog Ar, which make O2/Ar largely insensitive to temperature or salinity-dependent solubility changes, or bubble injection processes (Craig & Hayward, 1987; Fig. C.1). The biological saturation anomaly (ΔO2/Ar, Eq. 4.1) provides a specific tracer for net biological O2 production, and is derived by normalizing measured O2/Ar ([O2/Ar]sw) to the seawater equilibrium ratio ([O2/Ar]eq). Δ O2 Ar⁄ = ([O2 Ar⁄ ]sw[O2 Ar⁄ ]eq− 1) ×100 % (4.1) Net community production is then equated to the air-sea flux of biologically-produced excess O2 (Kaiser et al., 2005). This approach has been applied to obtain broad spatial coverage of NCP estimates from ship-based surveys, improving our understanding of marine carbon cycling (e.g. recent publications include Eveleth et al., 2017a; Juranek et al., 2019; Palevsky et al., 2016; and see Ch. 2). However, the expense of mass spectrometers and the technical expertise required to operate these instruments may be prohibitive to some research groups. Additionally, mass spectrometers typically have significant power requirements and many are not capable of unattended deployments, thus limiting their use to scientific research vessels with dedicated infrastructure and personnel. Yet, in order to predict ecosystem-level responses to climate change impacts on marine biogeochemical and biological processes, it is crucial that we significantly increase capacity for NCP observations. The development of O2 optodes (Tengberg et al., 2006) and Gas Tension Devices (GTD; Reed et al., 2018) capable of stable and accurate in-situ measurements provides new opportunities for autonomous NCP surveys on research vessels, ships of opportunity, or autonomous surface vehicles. Using observations of O2 from the optode, and the seawater total dissolved gas pressure (i.e. the sum of all gas partial pressures) from the GTD, seawater nitrogen (N2) concentrations can be estimated (McNeil et al., 1995). As the temperature- and salinity-dependency of N2 solubility properties is similar to that of O2 (Fig. C.1a-c), NCP could, in principle, be approximated from N2-based calculations of the biological O2 saturation anomaly (i.e. ΔO2/N2, following Eq. 4.1). However, given that the absolute solubility of N2 is approximately half that of O2 or Ar (Fig. C.1d), ΔO2/N2 may be an imperfect biological tracer in some situations, unless corrections can be made. 93 To date, O2 and N2 measurements have been combined to estimate NCP time-series from optode and GTD deployments on moorings and floats (e.g. Bushinsky & Emerson, 2015; Emerson & Stump, 2010; Yang et al., 2017). These applications employ simultaneous observations of seawater hydrography, wind speed and surface N2 to constrain the physical mixed layer budget and isolate an NCP signal from O2 measurements without the need for Ar or N2 normalization. Thus far, estimates of NCP from ship-based O2 and N2 measurements have only been obtained from depth profiles (McNeil et al., 2006b) or in-ice measurements (Zhou et al., 2014). To my knowledge, no previous work has derived NCP from underway ΔO2/N2 surveys, or evaluated the potential application of this mixed layer productivity tracer. A key challenge in the use of ΔO2/N2 as an NCP tracer is the need to account for divergence in mixed layer ΔO2/Ar and ΔO2/N2 resulting from the solubility difference between Ar and N2. As N2 is approximately half as soluble in water as O2 and Ar, it is more susceptible to bubbled-induced supersaturation. Moreover, differences in the temperature-sensitivity of O2/Ar and O2/N2 can induce differential responses to surface warming or cooling. Finally, N2, unlike Ar, is not entirely inert, as its concentration can be altered by N2-fixation, denitrification, and anammox. If differences between Ar and N2 supersaturation anomalies (ΔAr and ΔN2, respectively) are sufficiently large and unaccounted for, interpretations of NCP estimated from ΔO2/N2 could be biased, with significant implications for the interpretation of oceanic trophic status and net metabolic state. In this chapter, I develop a new NCP tracer based on seawater ΔO2/N2 measurements as an alternative to ΔO2/Ar. In this proof-of-concept study, I present simulations from a simple one-dimensional model, evaluated against in-situ N2 and O2 measurements, to determine differences between ΔO2/Ar and ΔO2/N2 resulting from ΔAr and ΔN2 divergence in surface waters. Based on the simulations, I developed a new tracer, N2' (“N2-prime”), which accounts for excess ΔN2 relative to ΔAr. I present model results and field data demonstrating the accuracy of ΔO2/N2' as an alternative to ΔO2/Ar measurements, and evaluate the uncertainty in the resulting NCP calculations. The results demonstrate the potential for underway ΔO2/N2' to expand coverage of NCP estimates in oceanic waters. In Chapter 6 and Appendix C, I provide details and software code for the application of the N2' approach to field surveys. 94 4.3 Procedures 4.3.1 One-dimensional mixed layer physical gas model I developed a one-dimensional mixed layer (ML) box model to evaluate the main physical mechanisms driving surface water divergence between ΔAr and ΔN2, and to calculate N2' (described below). Figure 4.1 shows a schematic of the model, and Table 4.1 lists the time-variable model terms and their range of values. Further details of the modelling framework are presented in Appendix C (section C.1), and annotated code for performing simulations and N2' calculations is provided at https://doi.org/10.5281/zenodo.4024925. The following section (section 4.3.2) describes model forcing parameters used in the simulations presented below. I employed a two-box domain with prescribed hydrographic properties in the subsurface and ML (see below). The following ML mass balance was applied to O2, Ar and N2. d(C × MLD)dt= Fd + FC + FP + Fk + Fw + Fe + J (4.2) Here, MLD is the mixed layer depth, d(C × MLD)dt is the change in gas concentration during each model time step, and J represents biological production of O2 or N2 (J set to zero for Ar). The Fd, FC and FP terms are the air-sea gas exchange fluxes via diffusion and small and large bubbles, respectively, based on the parameterization of Liang et al. (2013). This parameterization has been evaluated for N2 and other similar gases (Emerson & Bushinsky 2016; and see section 4.4.1) and is applied here assuming that temperature-dependent Ar diffusion rates exceed those of N2 by approximately 30 % (Fig. C.1). Mixing in the model reflects the sum of diapycnal mixing (FK), upwelling (Fw) and entrainment (Fe) of water from below the ML. Time-variable rate terms for these fluxes were derived from a prescribed eddy diffusivity coefficient (κZ), calculated Ekman pumping velocity (ω, proportional to the curl of wind stress; details in section C.1 of Appendix C) and the rate of ML deepening (dMLD/dt) between model time steps, respectively. Mixing fluxes were derived as the product of the rate terms and the vertical gas gradient (i.e. dC/dZ = (Cdeep – Csurf) / dZ, where Cdeep is the subsurface gas concentration) in the case of diapycnal mixing, or concentration difference between boxes (i.e. Cdeep – Csurf) for upwelling and entrainment. Flux equations are presented in Table 4.1 and Appendix C (section C.1). 95 I defined the two-box model to represent adjacent upper ocean layers of near-constant gas concentrations, separated by strong vertical stratification. In this formulation, “surf” (upper box) represents the mixed layer, and “deep” (lower box) represents a layer immediately below the pycnocline. The dZ term represents the distance between these layers (i.e. the thickness of the pycnocline) over which gas concentrations and hydrographic properties vary sharply (Fig. 4.1). Mixing in the model thus occurs between the base of the mixed layer, and top of the pycnocline, both of which vary in time. This approach assumes a constant gas gradient over dZ. My model is similar to the one-dimensional components of Emerson & Stump (2010) and Ianson et al. (2012); however, unlike the approach used by Ianson et al., the model used here includes time-variable mixing rates (κZ) and upwelling, and approximates mixing over a depth range more similar to that used in Emerson & Stump. Gas concentrations were initialized at their equilibrium values (Ceq), which were calculated from simulated surface water temperature and salinity and sea level pressure (SLP) using the solubility equations of Garcia & Gordon (1993) and Hamme & Emerson (2004). Unless stated otherwise, individual surface gas supersaturation anomalies were normalized to their respective equilibrium concentrations at ambient SLP (i.e. ΔC = C (Ceq×SLP1 atm)⁄ ). Subsurface N2 (ΔN2,deep) was derived from subsurface Ar (ΔArdeep) and ΔΝ2/Αr (ΔN2/Ardeep), taken from published data or prescribed in the model (see below). The simulations neglect lateral fluxes as lateral gas gradients are typically small, and not relevant to underway surveys which measure gas concentrations that have been modified along a Lagrangian flow path (Teeter et al., 2018). 96 Figure 4.1 A schematic of the one-dimensional mixed layer model with labelled terms. C represents either O2, Ar or N2. The dashed line represents a simplified density profile with mixing occurring across the pycnocline. The thick arrows represent fluxes, and thin arrows represent depth horizons. All terms in the diagram are time-variable, and are parameterized as described in the text and Table 4.1. {Fk and FW} and [Fe] identify terms that were combined or excluded from the simplified N2' budget. 4.3.2 Model simulation runs I performed two main simulations with the gas model to examine the primary drivers of ΔAr and ΔN2 divergence under realistic oceanographic conditions. The simulations were intended to mimic a two-year cycle (2011-2012) at Ocean Station Papa (OSP) in the Subarctic Northeast Pacific, with additional variations as described below. Wind speed velocity (u10), sea surface temperature (SST) and sea level pressure (SLP) forcing data were obtained from the NOAA mooring at OSP (provided by NOAA PMEL at www.pmel.noaa.gov/ocs/data/disdel/). I applied time-variable MLD, dZ and subsurface temperature and salinity values based on hydrographic observations on the mooring line. The range of MLD and dZ values in the OSP simulations is presented in Table 4.1, and the density criteria used to define the ML and pycnocline depths is included in Appendix C. The surface biological O2 production term was set to the mean annual cycle at OSP based on Fassbender et al. (2016), and surface N2 production set to zero, unless otherwise stated. Initial conditions were set to mooring observations on 1 January, 2011, and the first 90 days of output were omitted so that the results were independent of these conditions. This 90-day period corresponds with the upper limit of the surface O2 re-equilibration timescale, 97 defined below (Eq. 4.4). Calculations were performed in 0.25-day time increments, although the results were not sensitive to lowering this value. The two simulations were designed to represent contrasting mixing scenarios. In a full mixing scenario (denoted “OSP-FM”), I applied time-variable κz by extrapolating results from Cronin et al. (2015) onto the model domain, and calculated ω at OSP from wind stress vectors derived from the Cross-Calibrated Multi-Parameter (CCMP) wind speed product (details in Appendix C, section C.1). Following Bushinsky & Emerson (2015), I applied κZ estimates that were intermediate between values derived from the heat- and salt-budgets in Cronin et al. Subsurface O2 concentrations (O2,deep), Ar (ΔArdeep) and ΔN2/Ardeep were linearly interpolated from observations obtained below the pycnocline at OSP in February, June and August, and smoothed to a 30-day running average. I applied climatological average subsurface Ar and N2 values based on archived data from Hamme et al. (2019; provided at https://doi.org/10.1575/1912/bco-dmo.744563), and 2011-2012 O2 data provided by the Institute of Ocean Sciences, Department of Fisheries and Oceans Canada. Argon and N2 concentrations in the lower box (Ardeep and N2,deep) were calculated from these values and gas equilibrium concentrations derived from mooring-based subsurface temperature and salinity measurements. In the simulation without mixing (denoted “OSP-NM”), all mixing terms (Fe, Fw, Fe) were set to zero. Values in Table 4.1 correspond with the OSP-FM simulation. I performed sensitivity analyses by comparing the simulated OSP-FM gas time-series with in-situ observations to determine appropriate parameterizations for a bubble scaling factor, β (i.e. FC + FP = β × (FC + FP)) and the integration depth (dZ) of vertical mixing fluxes. From these analyses, I found that a β value of 0.5 produced best results (i.e. closest agreement between the model and observations), consistent with results from Yang et al. (2017) and Emerson et al. (2019). I also found that a dZ value equivalent to the thickness of the pycnocline resulted in strong agreement between the model and observations, but results were generally insensitive to ±10 m variability in this term (see section 4.4.1). Additional simulations were performed to evaluate ΔAr and ΔN2 divergence under controlled, experimental conditions, and these are presented in Appendix C (section C.3). 98 Table 4.1 Time-variable one-dimensional model parameters and their corresponding values in the OSP-FM simulation. Fluxes represent those of N2, with positive values reflecting an influx into the upper box. Additional terms not included in the table are: kd (air-sea diffusive gas coefficient; m d-1), Ceq (equilibrium gas concentration; mmol m-3); SLP (sea level pressure; atm); β (bubble-flux scaling coefficient; unitless); dC/dZ (subsurface gas gradient; mmol m-4); Cdeep (subsurface gas concentration; mmol m-3). Refer to the text for further details on these terms. Term Description Value MLD Mixed layer depth Depth of upper box 20 – 115 m dZ Integration depth dZ = (Pycnocline depth - MLD) 10 – 25 m Fd Diffusive air-sea flux Fd = kd (Ceq ×SLP1 atm− C); kd from Liang et al. 2013 -2.7 – 0.3 mmol N2 m-2 d-1 FC Small bubble air-sea flux FC = β × FC; FC from Liang et al. 2013 0 – 0.9 mmol N2 m-2 d-1 FP Large bubble air-sea flux FP = β × FP; FP from Liang et al. 2013 -0.6 – 0.5 mmol N2 m-2 d-1 Fk Diapycnal mixing flux Fk = κZ ×dCdZ 0 – 0.4 mmol N2 m-2 d-1 Fw Upwelling flux Fw = ω × (Cdeep − C); ω > 0 0 – 0.05 mmol N2 m-2 d-1 Fe Entrainment flux (excluded from N2' budget) Fe =dMLDdt× (Cdeep − C); dMLDdt > 0 0 – 1.0 mmol N2 m-2 d-1 κZ Eddy diffusivity coefficient Obtained from Cronin et al. 2015 8x10-5 – 4x10-4 m2 s-1 ω Upwelling velocity Calculated from wind stress curl 0 – 1x10-6 m s-1 J Biological production JN2 = 0 or prescribed as N2-fixation JO2 = prescribed (Fassbender et al., 2016) JAr = 0 0 – 0.6 mmol N2 m-2 d-1 4.3.3 Derivation of N2' Based on the simulations, I developed an approach to correct for differences in ΔO2/Ar and ΔO2/N2 resulting from differential solubility effects and physical fluxes of Ar and N2. This framework yields a new tracer, N2' (“N2-prime”), which converts N2 into an Ar analog. In deriving N2', a slightly simplified version of the gas budget (Eq. 4.2) is used to predict the difference between ΔN2 and ΔAr, which is then subtracted from observed ΔN2 values (either simulated or from field measurements). I modified Eq. 4.2 by combining all vertical mixing processes into a 99 single term, expressing the rates of diapycnal mixing and advection with a single coefficient, κ (κ = κΖ + ω × dZ). I excluded entrainment from the simplified N2' budget because temporal variability in MLD (i.e. dMLD/dt) cannot be readily estimated from ship-based sampling. The simplified budget is represented by d(C × MLD)dt= Fd + FC + FP +Cdeep−CdZ× κ. (4.3) This budget is evaluated for Ar and N2 over one mixed layer O2 re-equilibration timescale, TO2, prior to each model time-step, or field measurement. TO2 =−ln (0.01)× MLD(kT+κdZ) (4.4) The TO2 term (derived from an analytical solution to Eq. 4.3 in section C.2) is approximately five-fold longer than the more commonly used mixed layer O2 residence time, τO2, (MLD/kT, where kT is the O2 gas transfer velocity), in order to fully account for residual differences between ΔN2 and ΔAr. If N2' calculations are performed over τO2 instead of TO2, differences between Ar and N2 are not fully reconciled, and residual differences between ΔO2/Ar and ΔO2/N2 (or ΔO2/N2') will persist. As in the full simulations, I evaluated Eq. 4.3 by setting the starting gas concentrations to equilibrium values and performing calculations in 0.25-day increments. ΔN2' (Eq. 4.5) was obtained by subtracting the derived difference between ΔAr and ΔN2 (ΔN2est – ΔArest) from the corresponding ΔN2 value predicted by the full model simulations (ΔN2true). ΔO2/N2' was then recalculated from ΔN2' following Eq. 4.1. ΔN2′ = ΔN2true − (ΔN2est − ΔArest) (4.5) In field studies, ΔN2true will be the measured gas supersaturation condition, and ΔN2est and ΔArest are predicted by the simplified gas budget (Eq. 4.3) using environmental data over one ΤO2 period before observations. As such, N2' evaluations are applied to each independent underway 100 gas measurement. Weighted wind-speed based estimates of kT, following Reuer et al. (2007) with modifications by Teeter et al. (2018), should be applied when evaluating TO2. Since estimates of u10, SST and SLP can be obtained from readily available reanalysis products, I evaluated Eq. 4.3 using the same environmental forcing data as in the full model. However, time-histories of MLD, surface salinity, and sub-surface conditions (deep temperature, deep salinity, κ, ΔN2/Ardeep) are more difficult to estimate. For compatibility with field applications, Eq. 4.3 was therefore evaluated by holding these terms constant at values corresponding with the time of ΔN2true (i.e. the time of field sampling). Moreover, given the paucity of in-situ subsurface noble gas measurements, I set ΔArdeep to 0 % in all N2' calculations, and held ΔN2/Ardeep constant at values from the full model run corresponding with the time of ΔN2true. Below (section 4.4.4) I evaluate errors in ΔN2' and ΔO2/N2' resulting from these simplifying assumptions. 4.3.4 Evaluation against field measurements To evaluate the modelling approach, I compared N2 and O2 from the OSP-FM simulations against a two-year cycle of surface measurements at OSP. In-situ observations were obtained from deployments of an optode and GTD on the NOAA PMEL mooring (data provided at https://www.ncei.noaa.gov/access/ocean-carbon-data-system/oceans/Moorings/Papa_145W_50N.html; Emerson et al., 2017). Additional discrete measurements of O2 and N2 were obtained by Rosette sampling on Line P cruises in February, June and May. Oxygen data were provided by the Institute of Ocean Sciences, and N2 data were from Hamme et al. (2019). I also present a subset of field data collected in ice-free waters of central Baffin Bay on board the CCGS Amundsen, providing a brief field-evaluation of the present approach. Continuous measurements of ΔO2/Ar and ΔO2/N2 were obtained using a membrane inlet mass spectrometer (MIMS; Tortell 2005) and a flow-through optode/GTD system (see Ch. 5) connected to the ship’s seawater supply line. Inflowing water was drawn from the mixed layer, and underway gas measurements were calibrated using discrete samples collected from surface Niskin bottles. 101 4.4 Assessment In evaluating ΔO2/N2' as a mixed layer NCP tracer, I first evaluated the model output against a time-series of in-situ O2 and N2 observations at OSP. Using results from the OSP-FM runs and additional simulations presented in Appendix C (section C.3), I then examined the role of different physical processes in decoupling ΔO2/Ar and ΔΟ2/N2, which I quantify using the term ΔN2/Ar (i.e. ([N2/Ar]/[N2/Ar]eq – 1) × 100 %). As discussed below, results from these analyses provide confidence in the ΔN2' approach, which I show to be capable of minimizing offsets between ΔN2 and ΔAr. I conclude by demonstrating strong coherence between ΔO2/N2' and ΔO2/Ar in field observations, and identifying the main sources of uncertainty associated with the use of this approach. I present an in-depth field evaluation of ΔO2/N2' measurements for NCP derivation across a range of oceanographic conditions in Ch. 6. 4.4.1 Model evaluation and sensitivity tests As shown in Fig. 4.2, the model calculations forced with real environmental data and time-variable mixing terms agree well with mooring-based ΔN2 and ΔO2 observations at OSP. The model reproduces both short- (e.g. SLP variability and wind events) and long-term (e.g. seasonal SST and mixing variability) responses of ΔN2 to environmental drivers (see next section), with an overall root mean square error (RMSE) between the model and data of ~1.0 % (range of ΔN2 deviations ~0 – 2.4 %). Moreover, the modelled ΔO2, which was forced with a mean annual NCP time-series and subsurface O2 observations at OSP, captures the observed annual cycle with similarly good agreement (RMSE = 1.2 %; range 0 – 3.1 %). Residual differences between the model and observations can be explained by several factors, including the potential effects of lateral advection (Emerson & Stump, 2010), which are neglected in the simulations, and the use of smoothed mixed layer and forcing conditions. Interpolation of sparse ΔArdeep and ΔN2/Ardeep observations from discrete gas sampling may not fully represent subsurface conditions and mixing fluxes across the entire modelled seasonal cycle, while uncertainty in estimates of the mixing coefficients, κZ (on the order of ~10-4 m2 s-1; Cronin et al., 2015) also likely contributes to model-observation offsets. Indeed, the poorest alignment between model and observed ΔN2 occurred during the fall to early winter (days ~260-390) and late winter to early spring (days ~460-510), when mixing rates were relatively high (Fig. 4.3c), but no 102 subsurface gas observations were available. Nonetheless, it is important to note that the model run with realistic mixing fluxes better replicated the full seasonal cycles of ΔN2 and ΔO2 than the simulation run without mixing (light grey line in Fig. 4.2a,b). The main exception to this result occurred between days ~460 and 510, when ΔN2 from the OSP-NM run agreed better with observations than corresponding results from the OSP-FM run. This result likely reflects an over-estimate of subsurface ΔN2,deep at that time in the OSP-FM run. I tested the model sensitivity to flux parameterizations by performing additional simulations in which the mixing terms (κZ, dZ, entrainment) and bubble scaling coefficient (β) were modified from the OSP-FM full mixing run (Fig. 4.2c). Overall, I find that the model skill at reproducing observed ΔN2 at OSP is most sensitive to the exclusion of bubble processes and vertical flux terms, particularly during winter. This result is consistent with previous work (Emerson et al., 2019; Emerson & Bushinsky, 2016; Hamme & Severinghaus, 2007). In contrast, the model sensitivity to mixing and bubble fluxes is relatively low during summer months (June to August), when different model runs varied by less than 0.25 % across all modified simulations. This result supports the application of this approach during time-periods when NCP and carbon export are typically elevated, and when in-situ sampling from research vessels is most common. Based on the comparisons of modeled and observed N2, I conclude that the model captures the main variability of N2 saturation in oceanic surface waters. Although the model cannot be equally evaluated for Ar due to a lack of time-series observations, these conclusions likely apply to this gas as well. Importantly, the strong agreement between the model results and observations over a two-year cycle justifies the air-sea flux and mixing parameterizations used in the model simulations, providing confidence in the N2' approach, which is assessed below (section 4.4). 103 Figure 4.2 Comparison of modeled and observed ΔN2 (a) and ΔO2 (b) over a two-year cycle at OSP, and the sensitivity of results to different model parameterizations (c). Model results are from the full mixing (OSP-FM; thin black line) and no mixing (OSP-NM; dashed line) simulations, and observations were obtained from the NOAA PMEL mooring (thick grey lines) and Rosette sampling (red markers). The thick, light grey section of the mooring ΔN2 time-series in (a) represents a period of the record where data may be biased by sampling artifacts at the beginning of a new GTD deployment (S. Emerson personal communication, 2020). Note that discrete ΔΝ2 observations represent climatological mean values for February, June and August, while ΔO2 data represent samples obtained in 2011 and 2012. Panel (c) shows a sensitivity analysis in which parameterizations in the OSP full mixing run were modified as indicated by the labels on the x-axis. The sensitivity was evaluated during summer (left axis) and winter (right axis; note the difference in scale). Values on the y-axes represent the mean increase in the RMSE between the base (i.e. OSP-FM) and modified runs (error bars depict the range of values), and the numbers below the x-axis labels represent the average RMSE between each modified model run and the real mooring N2 data from OSP. 104 4.4.2 Predicting ΔO2/Ar and ΔΟ2/Ν2 differences The OSP simulations allowed me to evaluate the combined influence of different physical factors on ΔN2/Ar and test the N2' framework under typical oceanographic conditions. I note that ΔN2/Ar is not sensitive to ΔO2, and thus not dependent on modelled biological O2 production. Positive ΔN2/Ar values reflect ΔN2 > ΔAr and ΔO2/Ar > ΔO2/N2. I found that SST variability and differential gas exchange rates exerted the strongest controls on ΔN2/Ar (Fig. 4.3). Indeed, ΔAr and ΔN2 (Fig. C.4) and ΔN2/Ar (in the run without mixing) followed seasonal changes in SST, with transient modifications. During periods of net seasonal cooling (warming), the decline (increase) in ΔN2/Ar reflects nitrogen’s slower rate of re-equilibration via diffusion (Fig. C.1). These effects are less pronounced in the OSP-FM simulation due to the role of mixing in maintaining high wintertime ΔN2/Ar. Short-term modifications occur in response to SLP variability, enhanced bubble processes (increase ΔN2/Ar, ΔN2 and ΔAr) during elevated wind conditions or rapid temperature changes which cause ΔN2/Ar disequilibria resulting from nitrogen’s weaker temperature-dependent solubility. These results are reflected in additional controlled simulations presented in Appendix C (Fig. C.3) and are consistent with Hamme & Emerson (2002), who demonstrated the first-order control of temperature changes and bubble-mediated gas exchange in controlling surface inert gas conditions. Variable SLP can cause significant short-term changes in individual gas anomalies, but does not impact ΔN2/Ar. The OSP simulations also demonstrated mixing effects on gas conditions, with vertical fluxes elevating surface ΔN2/Ar, particularly during autumn and winter periods. The mixing effect was most significant at higher κ and during MLD deepening (Fig. 4.3c,d), when surface ΔN2/Ar approached ΔN2/Ardeep. Except during periods of reduced summertime mixing when ΔN2/Ar was equivalent in both mixing runs (annotated in Fig. 4.3), ΔN2/Ar was always higher in the OSP-FM run than in OSP-NM. Notably, mixing acts to shift the seasonal baseline of surface ΔN2/Ar, onto which SST and u10 changes are superimposed. Variability in the physical responses of ΔN2/Ar will ultimately be sensitive to the relative magnitude of SST changes, u10, κZ and ΔN2/Ardeep, as demonstrated in the controlled simulations described in Appendix C. Indeed, a quasi-steady-state condition (thick grey lines in Fig. 4.3), predicted by the ML budget described in Eq. 4.3 (derived in Eq. C.5.11 in Appendix C), demonstrates that ΔN2/Ar will be weighted between the bubble-induced ratio of N2 and Ar and 105 ΔN2/Ardeep by the relative strength of kT and κ/dZ (Fig. C.2). However, ΔN2/Ar seldom achieves a quasi-steady-state condition, reflecting the high variability in environmental forcing, and the fact that thermal disequilibrium can persist for a long period under low wind speeds. Fortunately, the evaluation of the simulations demonstrates that the model, upon which the N2' calculations are based, captures the effects of various fluxes on gas saturation states. Below, I evaluate the efficacy of the N2' approach in reducing offsets between ΔO2/Ar and ΔO2/N2. Figure 4.3 ΔN2/Ar results from one-dimensional model simulations of OSP in the Subarctic Northeast Pacific. Panel (a) shows ΔN2/Ar in runs with (solid line) and without (dotted line) mixing, while panels (b)-(d) represent the forcing data. The horizontal axes represent the 2011 year-day (Jan. 2011 – Jan. 2013). The thick grey line in (a) depicts the quasi-steady-state condition (Eq. C.5.11) for the simulation with mixing. The double-ended arrows in (a) identify summertime periods of weak mixing between June and September, and correspond with results in Fig. 4.4. 106 4.4.3 ΔO2/N2' as an analog for ΔO2/Ar In testing the performance of the N2' approach, I applied the same environmental forcing data (SST, u10 and SLP) as in the full model simulations, but assumed constant values backwards in time for MLD, surface salinity, κ, and ΔN2/Ardeep for compatibility with field studies. The results indicate that N2' successfully corrects for differences in surface water ΔAr and ΔN2, reducing biases between ΔO2/Ar and ΔO2/N2 across all simulations (Fig. 4.4a,b). In the OSP-FM simulations, median ΔN2'/Ar was ~0.05 (range -0.1 to 0.2 %; Table C.3), demonstrating that differences between ΔO2/Ar and ΔO2/N2 can be corrected using simple ML budget computations performed over an estimated O2 re-equilibration time, TO2. The remaining ΔN2'/Ar disequilibria is mostly attributable to the simplifying assumptions in the N2' approach, which I describe in the following section. The largest remaining disequilibria occurred during the autumn months of the full mixing run when vertical entrainment was significant (~days 270-330 and >600 in Fig. 4.5), and in early summer (~days 160-200 and ~525-565) when the N2' budget was unable to resolve the relatively strong mixing occurring prior to this time. Despite these offsets, my results demonstrate the utility of N2' in reducing differences between ΔO2/Ar and ΔO2/N2 observations. Given the sparsity of oceanic mixing rate estimates and subsurface Ar and N2 measurements, the mixing flux terms in the N2' model may be difficult to constrain. I therefore performed an additional set of N2' calculations on the OSP-FM simulation to test the approach when vertical mixing fluxes are neglected by setting κ to 0 m2 s-1 in the N2' calculations. In these calculations, ΔN2true in Eq. 4.5 was still the value predicted in the OSP-FM simulation, but ΔN2est – ΔArest was calculated by setting κ to 0 in Eq. 4.3. The resulting value, denoted as ΔN2'(κ=0)/Ar, was consistently smaller than uncorrected ΔN2/Ar, but was unable to fully reconcile ΔΑr and ΔN2 differences in most of the simulation (Fig. 4.4). However, in a subset of the run corresponding with mid-June to September (labelled bars in Fig. 4.3), N2'(κ=0) successfully reduced ΔN2'/Ar to a median value of ~0.05 % (i.e. the same value as if a non-zero κ was applied). This result is promising for in-situ applications in stratified ocean regions, when vertical mixing is small relative to other gas flux terms. The model results also show that ΔO2/N2' performed significantly better than either ΔO2 or uncorrected ΔO2/N2 in reproducing ΔO2/Ar, suggesting that ΔO2/N2' offers a robust analog for 107 ΔO2/Ar. Indeed, ΔO2/N2' derived from model simulations was tightly correlated to ΔO2/Ar, with a linear regression slope that was not significantly different from unity, and an RMSE of 0.03 %. Further support for the use of O2/N2' comes from field measurements shown in Fig. 4.4c. Results obtained from simultaneous deployment of a membrane inlet mass spectrometer (MIMS) and a combined optode/GTD system demonstrate strong coherence between ΔO2/Ar and ΔO2/N2' during an underway ship-board survey (see Ch. 6). As in the simulations, the field data show that ΔO2/N2' provides a significantly better analog for ΔO2/Ar than either ΔO2/N2 or ΔO2 alone. Note that in this particular example, ΔO2/N2 and ΔO2/N2' are opposite in sign: ΔO2/N2' and ΔO2/Ar are positive, while ΔO2/N2 is negative. As a result, NCP determination from uncorrected ΔO2/N2 would falsely imply net heterotrophy in surface waters. 108 Figure 4.4 Evaluation of N2' in one-dimensional model simulations of OSP (a and b), and ship-board measurements (c). Panels (a) and (b) show the distribution of ΔΝ2/Αr and ΔN2'/Ar in the simulations with and without mixing, respectively. Note that values of zero indicate a perfect match with Ar for either N2 or N2'. The numbers above each box represent the median ΔN2/Ar (black) or ΔN2'/Ar (red and grey) values. The right hand boxes represent a subset of the OSP simulation results obtained under stratified summer conditions (annotated in Fig. 4.3a). Panel (c) depicts the strong coherence between ΔO2/N2' (red) and ΔO2/Ar (light blue) during one day of ship-board measurements on a cruise in Baffin Bay. Note the difference in scales on the left (ΔO2/C data) and right (ΔO2) axes. 4.4.4 Uncertainty in ΔO2/N2' Uncertainty in ΔO2/N2' arises from the simplifying assumptions in the N2' calculations and uncertainty in each of the parameters (u10, SST, SLP, Fd, FC, FP, β, MLD, κ and ΔN2/Ardeep) in Eq. 4.3. I provide full details of the assessment of the ΔO2/N2' uncertainty in section C.4 of Appendix C, and summarize the results here. Error terms are represented in Figure 4.5, and Tables C.2 and C.3. 109 In evaluating Eq. 4.3 (i.e. the simplified ML gas budget), values for MLD, surface salinity, κ, and ΔN2/Ardeep are assumed constant for compatibility with field studies. By performing additional N2' calculations on the OSP-FM simulation in which these terms were permitted to vary, I found that the contribution of these simplifying assumptions to uncertainty in ΔO2/N2' was ~0.05 %. Errors associated with these assumptions can only be reduced by quantifying the time-variability of the relevant terms, using data from profiling floats near to cruise tracks (if available), additional reanalysis or climatological products, or numerical model output (see below). To evaluate the magnitude of the parameterization errors, I performed a Monte Carlo analysis on the full mixing OSP-FM simulation by randomly varying each of the input variables around their estimated parameter uncertainty. This analysis yielded a combined absolute parameterization error in ΔO2/N2' of 0.06 %, with the largest contributions coming from the SST product. This result is unsurprising given the strong influence of SST variability in driving intra-annual variability in gas conditions. The bubble terms (FC, FP, β) contributed relatively small errors, due to the low prevalence of elevated wind speeds (Fig. 4.3). An additional consideration is the influence of biological processes on ΔN2/Ar. Unlike Ar, N2 concentrations can be altered by several bacterially-mediated processes, including surface N2-fixation, and subsurface or sedimentary denitrification and anammox. In nitrate-deplete waters of the subtropical and tropical ocean where N2-fixation is most significant, the upper range of N2-fixation rates is ~0.6 mmol N2 m-2 d-1 (Deutsch et al., 2007), and mean rates in temperate and polar waters range from about <0.01 to 0.24 mmol N2 m-2 d-1 (Blais et al., 2012; Sipler et al., 2017; Tang et al., 2020). In additional model runs forced with a constant N2-fixation rate of 0.6 mmol N2 m-2 d-1, simulated ΔN2 differed from values in the base runs (no N2 fixation) by less than 0.05 %, while steady-state ΔN2 only exceeded 0.05 % at wind speeds below 6 m s-1 in simulations excluding mixing. Given that N2-fixation rates are significantly lower than air-sea gas fluxes over most ocean regions, any ΔN2 anomalies produced by biological processes are rapidly erased during periods of elevated u10 (Shigemitsu et al. 2016). I thus conclude that N2-fixation will not have a significant impact on ΔO2/N2'-based NCP estimates in most ocean regions. The influence of denitrification and anammox on surface ΔO2/N2 will also be small and indirect, as these processes elevate subsurface N2 (and therefore ΔN2/Ardeep), without directly altering surface N2. The contribution of these processes to excess N2 into the mixed layer may only 110 be prominent in regions where O2 and nitrate depletion occurs in the upper water column (e.g. Arabian Sea, Eastern Tropical North and South Pacific, suboxic inlets and estuaries; Chang et al., 2012; Manning et al., 2010) or overlying shallow continental shelves where benthic and sedimentary denitrification occur (DeVries et al., 2013). Given an upper limit of subsurface ΔN2/Ar of ~2-2.5 % in these regions, surface ΔN2/Ar anomalies will be less than 2.5 % (Fig. C.2). However, subsurface ΔN2/Ar measurements can be applied in N2' calculations to minimize these biases. Other processes not evaluated in the present study, such as freshwater input, lateral mixing and ice melt/formation, may also cause divergence between ΔAr and ΔN2, but their contributions to surface ΔN2/Ar disequilibria are largely unknown, and the resulting uncertainty in ΔO2/N2-based NCP estimates is likely to be small in most ocean regions. As the model evaluation suggests, the framework I presented here captures the main drivers of inert gas and N2 variability in oceanic waters, thus justifying the terms included in the N2' model. Additional fluxes may be larger in coastal or polar regions, but NCP biases resulting from vertical O2 fluxes or diel O2 variability are also likely to be more significant in these regions (see below). Overall, I approximate the total absolute uncertainty in ΔO2/N2' (including the upper limit of uncertainty due to N2 fixation) to be less than 0.3 % (Fig. 4.5a). This value is smaller than the offset between ΔΟ2/Αr and uncorrected ΔO2/N2 predicted in the simulations, and other sources of error in NCP calculations (Table C.3), most notably errors in gas transfer parameterizations (Fig. 4.5b). 111 Figure 4.5 Difference between ΔO2/Ar and ΔO2/N2 (a) and corresponding biases in NCP estimates (b) from a one-dimensional simulation of OSP. Results are shown for the full-mixing simulation. Uncertainty in ΔO2/N2' is represented as shaded regions around model-derived ΔO2/Ar – ΔO2/N2' values (red line). The light grey, dark grey and light blue patches represent the combined parameterization, assumption and total uncertainty in ΔO2/N2', respectively. Mean uncertainty estimates are summarized in Table C.2. Panel (b) shows the absolute NCP bias (i.e. difference from ΔO2/Ar-based NCP) calculated from O2/N2 (black) and O2/N2' (red) in the OSP-FM simulation. The red patch represents the NCP bias including the propagated total uncertainty in ΔO2/N2'. The dashed line shows the NCP bias incurred by a 20 % uncertainty in the gas transfer velocity, kO2. 4.5 Discussion and recommendations From the numerical simulations, I was able to resolve physical factors decoupling ΔO2/Ar and ΔO2/N2 on various timescales, including bubble fluxes, temperature-induced changes in gas saturation state and vertical mixing. For a realistic range of ΔO2 and ΔN2/Ar in the ocean, absolute differences between ΔO2/Ar and ΔO2/N2 may exceed 2 % (Figs. C.1-C.3). Such decoupling propagates as errors in NCP calculations based on O2 and N2 observations, and in the case of 112 positive ΔN2/Ar anomalies, NCP estimates would be biased low. In the OSP-FM simulation, ΔO2/Ar – ΔO2/N2 biases of ~0-0.5 % introduce absolute NCP errors up to ~8 mmol O2 m-2 d-1 (Fig. 4.5b). These are similar to those resulting from the ~20 % uncertainty in kO2 (Wanninkhof, 2014), diel O2 variability (up to ~5 to >100 mmol O2 m-2 d-1; Wang et al., 2020) and vertical mixing of subsurface O2-deplete waters (<50 mmol O2 m-2 d-1 in offshore waters; see Ch. 2). These limitations motivate the need to correct ΔO2/N2 observations for excess physical N2 saturation, particularly in offshore waters where other biases are smaller and even small ΔO2/N2 biases may lead to false conclusions regarding the metabolic status of surface waters (i.e. implied net heterotrophy from negative ΔO2/N2 versus autotrophy from positive ΔO2/Ar; Figs. 4.4c and C.1e). My analyses demonstrate that simple ML budget calculations, based on readily available environmental data, can be used to account for differences between ΔO2/Ar and ΔO2/N2, yielding a new term, N2', which traces physically-induced changes in mixed layer O2. The resulting variable, ΔO2/N2', is a robust NCP tracer in both model and field settings, with uncertainties that are generally smaller than other error sources in NCP calculations. I thus conclude that underway ΔΟ2/Ν2 measurements, combined with careful application of the N2' calculations described here, can serve as an effective alternative to ΔO2/Ar-based NCP sampling across a wide range of oceanic conditions. Recent advances in relatively inexpensive optode and GTD technology have made robust, high-resolution ΔO2/N2 sampling feasible, providing potential avenues to expand NCP derivation from widespread deployments of low-cost and user-friendly instrument systems (see Ch. 5). Underway optode/GTD systems can be readily deployed on research vessels, and also integrated into opportunistic sampling platforms, including volunteer observing ship and autonomous surface vehicles, providing enhanced coverage of ΔO2/N2' measurements into regions and times of year that are typically under-sampled. The resulting datasets will facilitate studies of the mechanistic drivers of NCP across a range of scales. In field studies, derivation of N2' requires measurements of surface gas concentrations (N2), hydrographic data (temperature and salinity) and best estimates of the MLD and the mixing terms κ and ΔN2/Ardeep, which will commonly be assumed constant backwards in time. The MLD can be derived by interpolating between CTD casts or using Argo profiles (if available) and 113 climatological datasets. Information on surface ocean conditions (SST, u10, and SLP) during the TO2 prior to gas measurements can be obtained from reanalysis or satellite products. The mixing terms, κ and ΔN2/Ardeep, may be more difficult to constrain in field studies as observations of these terms are sparse. Recent work has provided several approaches to approximating κ from direct or indirect estimates of turbulent dissipation rates (Chanona et al., 2018; Whalen et al., 2012), and by proxy relationships with temperature and salinity (Cronin et al. 2015) or nitrous oxide (see Ch. 2). In the absence of direct measurements, it is also possible to estimate κ and ΔN2/Ardeep from archived datasets (e.g. Hamme et al. 2019) or circulation models (e.g. Hughes et al., 2017; Shigemitsu et al., 2016). However, as demonstrated in simulations with weak or no mixing, vertical fluxes have a relatively small impact on surface ΔN2/Ar when mixing rates are on the order of ~10-5 m2 s-1 (or less), or when ΔN2/Ardeep is less than ~0.25 % (Figs. 4.3, C.2-C.3). Indeed, during simulated summer periods (labelled in Fig. 4.3a), when κZ was <~5x10-5 m2 s-1, ΔΝ2/Αr converged on similar values for both mixing scenarios of the OSP runs. These results imply that mixing terms can potentially be neglected in N2' corrections under conditions of moderate to strong stratification or weak mixing (e.g. surface κZ ~1x10-5 to 5x10-5 m2 s-1). Such conditions exist during periods of summer productivity when NCP estimates are often of greatest interest. In dynamic coastal waters where vertical mixing may contribute to significant divergence between ΔO2/Ar and ΔO2/Ν2, particularly in regions of subsurface denitrification, the resulting NCP biases may still be small compared with other errors resulting from vertical O2 fluxes (>50 mmol O2 m-2 d-1; Ch. 2, 3) and diel variability. Finally, calculations of N2' will depend on the air-sea exchange model employed in the budget evaluations. I used the bubble-mediated model of Liang et al. (2013), which has been evaluated against in-situ N2 and noble gas measurements (here, and in Emerson et al., 2019; Emerson & Bushinsky, 2016), and was parameterized for weakly-soluble gases similar to N2 and O2. When possible, I recommend that future studies employ air-sea exchange parameterizations that have been validated for the region of interest. Overall, this work demonstrates the feasibility of deriving ΔO2/N2'-based NCP estimates from underway O2 and N2 measurements and relatively simple computations. Field application of ΔO2/N2' will depend on the accuracy of environmental data products, and assumptions about the time-variability of mixed layer hydrography. This approach is expected to be most accurate in 114 stratified waters and during summer conditions, when surface productivity is elevated, and mixing contributions to ΔN2/Ar decoupling are minimal. In most ocean regions, biological N2 cycling will have a negligible impact on NCP estimates derived from underway ΔO2/N2'. Even when differences between ΔO2/Ar and ΔO2/N2' cannot be reduced to zero, N2' is still a valuable tracer for minimizing NCP errors based on O2/N2 measurements. The approach I describe here thus has the potential to greatly expand NCP coverage from research vessels, volunteer observing platforms and/or autonomous surface vehicles, enabling better quantification and prediction of oceanic metabolism across a range of space and time spaces. By making the model codes and sample calculations freely available (https://doi.org/10.5281/zenodo.4024925), I hope to expand the use of this approach by other research groups. 115 Chapter 5: The Pressure of In-situ Gases Instrument (PIGI) for autonomous ship-board measurement of dissolved O2 and N2 in surface ocean waters 5.1 Overview I describe an autonomous flow-through system capable of continuous, unattended measurements of oxygen (O2) and nitrogen (N2) concentrations in surface seawater. The derived biological O2 saturation anomaly, ΔO2/N2, can be used to estimate mixed layer net community production (NCP) in place of ΔO2/Ar-based estimates obtained via mass spectrometry. The “Pressure of In-situ Gases Instrument” (PIGI) consists of two parallel flow-through chambers; the first contains a buoyancy-driven debubbler, while the second houses an Aanderaa optode and Pro-Oceanus Gas Tension Device (GTD). Custom-designed software is provided to visualize and record observations in real-time, and post-process data. The system has been tested in the laboratory and on more than 15 deployments in various ocean regions. The PIGI has average optode and GTD response times of ~1.1 and 1.6 min., respectively, shows good calibrated accuracy based on comparisons with discrete samples, and derived ΔO2/N2 exhibits strong coherence with independent ΔO2/Ar measurements from mass spectrometry. I conclude with recommendations for successful field use and potential future modifications to support a range of deployments strategies. Overall, the system can be used as a cost-effective tool to increasing global coverage of NCP estimates from research vessels, volunteer observing ships and land-based observatories. 5.2 Background Marine net community production (NCP; i.e. photosynthesis minus respiration) is a critical ecological variable that constrains the ocean’s capacity for biomass production and carbon export via the biological pump. Ocean metabolism is reflected in surface O2 variability, such that NCP can be estimated from in-situ O2 measurements obtained along ship tracks, at fixed sites, and on autonomous profilers (e.g. Emerson et al., 2008; Kaiser et al., 2005; Yang et al., 2017). However, physical processes, including temperature-dependent solubility changes and bubble injection, also influence O2 budgets, and these effects can bias NCP estimates. For this reason, underway ship-based measurements of the biological O2 saturation anomaly (ΔO2/Ar) have become increasingly 116 common as a tracer of mixed layer NCP, where argon (Ar) is a biologically-inert O2 analog that corrects for physically-induced changes in gas saturation states (Craig & Hayward, 1987). Presently, underway measurements of ΔO2/Ar are obtained using ship-based mass spectrometry (MS), and this technique has yielded many important datasets. Yet, these instruments may be cost-prohibitive to some researchers and require significant power and operator oversight, making MS poorly suited for long-term data collection without significant support infrastructure. This limits the capacity for truly autonomous NCP derivation from underway platforms and volunteer observing ships (VOS). Recent technological advances in O2 optodes and Gas Tension Devices (GTD) (Reed et al., 2018; Tengberg et al., 2006) have enabled high-resolution, unattended measurements of seawater O2 and nitrogen (N2) (McNeil et al., 2005). Additional work presented in Chapters 4 and 6 has evaluated the potential to estimate NCP from O2/N2 ratios (i.e. ΔO2/N2) in a manner analogous to ΔO2/Ar. Although the solubility properties of N2 and O2 somewhat differ, and N2 is subject to minor biological influences (e.g. N2-fixation), ΔO2/N2 approximates ΔO2/Ar under many conditions. Oxygen and N2 measurements have been combined to infer gas dynamics (Tortell et al., 2015; Zhou et al., 2014) and NCP from refined O2 budgets (Emerson et al., 2019), but NCP derivation from underway ΔO2/N2 remains largely under-exploited. The development of a robust system for continuous O2/N2 measurement thus has the potential to significantly expand oceanic NCP estimates. In this chapter, I describe a system for autonomous, flow-through O2/N2 measurements. I provide an overview of this system, named PIGI (“Pressure of In-situ Gases Instrument”), with validation results from field deployments and laboratory testing, followed by recommendations for successful field use. Software, and detailed assembly, deployment and data processing instructions are provided at https://doi.org/10.5281/zenodo.4024951. 5.3 System overview The PIGI is an autonomous measurement system consisting of an electronics box, and a flow-through wet box connected to a continuous seawater supply. The wet box (Fig. 5.1) comprises a primary chamber (~600 mL) containing a custom-built buoyancy-driven debubbler, and an instrument loop containing an Aanderaa Data Instruments Optode 4330 (standard sensing foil), and a Pro-Oceanus Systems Inc. mini-TDGP GTD. Seawater entering the system from the ship’s 117 supply first passes through the debubbler to minimize measurement contamination by trapped air. Bubble-free water is then pulled from the primary chamber and past the gas sensors using a centrifugal pump installed downstream of the instruments. In the instrument loop, seawater first passes the GTD sensing membrane through a manufacturer-supplied plenum before entering a flow-through cell (~250 mL) containing the optode. Best results (i.e. reduced data noise and optimal instrument response times) are obtained when seawater flows directly onto the sensing faces. Water exiting both chambers merges to form a single outflow line, and check valves are installed to prevent recirculation or drainage of the centrifugal pump head. Discrete calibration samples can be obtained via a separate line at the outflow of the instrument loop. Flow measurements from both chambers facilitate system monitoring and quality control. Flow rates through the instrument loop should be held constant within 10 % of a nominal value, with target rates of ~1.5-3 L min-1 providing optimal data quality. The wet box components can be mounted directly onto a wall or grating (Fig. 5.1c) or installed on a base plate secured within a Pelican case (Fig. 5.1b) for protection and easy transport. In either configuration, the system can be disassembled for easy cleaning (see video at https://doi.org/10.5281/zenodo.4024951) and should be oriented vertically (intake at bottom) so that entrained bubbles escape via the top of the primary chamber (Fig. 5.1a). The electronics box runs LabVIEW and AutoIt programs to automate data acquisition and instrument control. AutoIt is used to restart data collection at user-defined intervals. The LabVIEW program (Fig. 5.2) displays and logs data at a frequency of up to 1 Hz, and automatically turns off the instruments pump to prevent damage during interruptions of seawater flow to the system (e.g. during sea ice blockages). Uncalibrated gas signals (optode O2 and GTD total dissolved gas pressure), ancillary measurements (temperature and flow rates) and raw sensor signals (e.g. optode phase shift) are saved to a continuously updated ascii file. Deployment metadata and instrument settings are logged separately at the beginning of each acquisition. The LabVIEW program interfaces with the instruments (e.g. turn pump on/off, set sampling rate), acquires ship GPS information, and performs satellite data transmissions during remote deployments with an optional Iridium transducer module. Data acquisition software and post-processing Matlab scripts are provided at https://doi.org/10.5281/zenodo.4024951. 118 Figure 5.1 A schematic (a) and photographs (b-e) of the flow-through PIGI system. Panels (b), (d) and (e) show the system installed in a Pelican case, while (c) shows a wall-mounted version at the Hakai Institute’s Quadra Island Ecological Observatory. Seawater flow paths are represented by blue lines in (a). Labelled parts in (a)-(c) are: seawater inflow (1), debubbler (2), primary chamber (3), primary chamber flow meter (4), seawater outflow (5), TDGP-mini plenum (6), TDGP-mini GTD (7), optode chamber (8), optode (sensing foil shown in white) (9), centrifugal pump (10), instruments flow meter (11), discrete sampling line (12) and Pelican case (13). Not all components are shown in (c). Deployments at Base Prat, Antarctica and on the CCGS Amundsen are shown in (d) and (e), respectively. The white and red arrows show the locations of the electronics box and seawater supply. A MIMS system is depicted by the blue arrow in (e). 119 Figure 5.2 A screenshot of the PIGI LabVIEW interface. Data are displayed in real-time in the figure panels, and comments corresponding with observations can be recorded to the data file in real-time. 5.4 Materials and costs The PIGI system can be constructed from readily available materials with relatively basic knowledge of electronic systems. The components of the flow-through chambers are machined from durable PVC and acrylic. The electronics box contains an Intel Nuc mini-processor, data acquisition board, DC relays, and power supplies. I provide a summarized list of expenses in Table 5.1, and a comprehensive parts list (including recommended suppliers) with assembly instructions at https://doi.org/10.5281/zenodo.4024951. Notably, the cost of the optode and GTD sensors (combined <$12,000 USD) is significantly less than a MS system. (1) Acquisition settings are specified by the user. Deployment metadata are recorded to a separate file. (2) Operators select the input data channels and specify a sampling rate. (3) The instrument pump can be controlled manually or automatically.(4) The system can be integrated with an iridium transmission module. (5) Underway data are visualized in real-time, and raw instrument signals are displayed. (6) Underway comments can be recorded in real-time and saved directly to output data files.123456120 The system consists of an Aanderaa optode 4330 and Pro-Oceanus TDGP-mini, but other sensors such as Aanderaa units designed for shallow water deployments (e.g. models 4531 or 4835; ~$2700 and $4775 USD, respectively) or the RBRcoda T.ODO O2 sensor package (~$4500 USD) can be incorporated with only minor adjustments for cost-savings. Although users require a LabVIEW license (Table 5.1) to modify the software I provide, I have also produced a stand-alone version of the data acquisition program that can be used with the free run-time LabVIEW engine. Data may also be acquired or processed using alternative open-source software (e.g. Python, Inlino; Haëntjens & Boss, 2020), and a description of the workflow required for autonomous operation is included at https://doi.org/10.5281/zenodo.4024951. Table 5.1 Abbreviated list of expenses of the PIGI system. Full details are provided at https://doi.org/10.5281/zenodo.4024951. Costs are based on quotations obtained in June/July 2020 and exclude local taxes, shipping and machining expenses. The cost of the wet box includes the gas sensors and additional flow-through parts, while the electronics box expenses include a Windows operating system required to run the automation software, which is quoted separately. Component Approx. Cost (USD) Calibrated Aanderaa optode 4330 (with cable) 6,750 Pro-Oceanus mini-TDGP GTD (with cable) 5,000 Wet box total 13,500 Electronics box total 1,650 System total 15,150 LabVIEW Base license (optional) 400 (1-yr) / 3,500 (life) Matlab license (optional) 860 (1-yr) / 2,150 (life) 5.5 Field applications and laboratory and in-situ testing Various iterations of the PIGI system have been tested on 19 field deployments since 2016 (Table 5.2). My system has been deployed frequently on La Perouse and Line P cruises in the Subarctic NE Pacific, while a second unit has collected data continuously at the Hakai Institute’s Quadra Island Ecological Observatory for over a year (Fig. 5.1). I have deployed a third system throughout the Canadian Arctic and at a coastal station in Antarctica. During field deployments, the system was evaluated for underway data accuracy, ease-of-use and integration with existing instrumentation. Salinity-compensated optode O2 measurements 121 (see section 5.6) were calibrated using discrete samples obtained from the PIGI sampling line (Fig. 5.1) or surface Rosette bottles, and analyzed by Winkler titration. I observed linear relationships between sensor and discrete O2 (Fig. 5.3a), with offsets in the uncalibrated data typical of Aanderaa optodes whose accuracy decays over time (Bittig et al., 2018). The strong linearity between sensor and discrete data enables calibration with an average accuracy of 1 %, which is required for successful field deployments (Emerson et al., 2019). Optode and GTD-derived N2 also showed strong coherence with discrete Niskin bottle samples analyzed by mass spectrometry (Fig. 5.3b) and can be validated to within ~1.2 %. The GTD signal shows significantly less drift than optode measurements such that routine calibration with mass spectrometry samples may not be necessary. These results are consistent across multiple deployments and demonstrate that PIGI measurements are not biased by sampling artifacts. Moreover, GTD pressure measurements on air-equilibrated freshwater circulated through the PIGI trace atmospheric pressure within ~0.1 % across multiple instruments and laboratory tests. I thus conclude that my system is able to accurately determine both O2 and N2 concentrations in seawater, provided that necessary calibrations are conducted (see below). I performed laboratory tests to evaluate PIGI response times by circulating water with contrasting gas compositions (air-equilibrated freshwater and a 1:1 solution of carbonated soda and N2-bubbled deionized water) through the primary and instrument chambers at various temperatures and flow rates (Fig. 5.3c). I determined response times (e-folding time, t63) of O2 and gas tension signals in the PIGI system ranging from ~0.9 to 1.6 min. and 1.0 to 2.2 min., respectively, based on the time when the water source changed until signals re-stabilized. These are similar to those of a membrane inlet mass spectrometry (MIMS) system used to measure Ο2/Αr (Tortell, 2005), and thus permit observations of small-scale oceanic features (Figs. 5.3c, 5.4). Notably, the instrument response times are shorter at higher temperatures and faster flow rates (lower range of error bars in Fig. 5.3c), and are shorter than response times for ΔO2/Ar measurements obtained from equilibrator inlet mass spectrometry systems (~7 min.; Cassar et al., 2009). The reported response times reflect the time required to flush the wet box chambers and the water residence time within the optode cell. As a result, the optode response in the PIGI system is longer than the manufacturers specifications (~25 sec.) or values reported in different systems (e.g. 122 Bittig et al., 2014). Despite this, the GTD signal, with a response time similar to the manufacture specification of 1.5 min, still lags behind O2 (Fig. 5.4a), so that additional data processing is required when encountering very strong frontal features. Nonetheless, my testing demonstrated that flow rates greater than 1.5 L min-1 through the instrument loop (water residence times <1 and <10 sec. within the GTD plenum and optode chamber, respectively) produces high-quality data with good spatial resolution. Increasing the seawater flow rates should further improve instrument response times. Testing also showed that the inclusion of a debubbler significantly reduced data noise and contamination from bubbles entrained in the ship’s seawater lines. Bubble effects are particularly problematic during elevated sea states, and, to a lesser extent, in calmer conditions if the seawater intake depth is shallow. Commonly-used Vortex debubblers increase the water residence time in flow-through systems, thereby leading to excess gas dissolution between the seawater intake and measurement interface. In the PIGI, I thus designed an upwards-orienting debubbler that enables rapid escape of bubbles via buoyant rising (Fig. 5.1). The sampling line leading to the optode and GTD is installed at the bottom of the debubbling chamber so that bubble-free water is drawn towards the sensors. This approach is very effective in diverting bubbles away from the instruments, even during high sea states. Anomalously high O2 and N2 signals can still be measured during elevated sea states, but these result from bubble dissolution in the seawater lines upstream of the PIGI system (see Ch. 6). The installation of the pump downstream of the gas sensors and direction of flow onto the instruments’ sensing faces (Fig. 5.1) are important for minimizing response times and maintaining neutral hydrostatic pressure within the system. Flow rate changes through the instrument loop, following pulses in the discharge from the ship’s seawater supply, induce hydrostatic effects on GTD measurements analogous to pressure changes during depth profiling (Reed et al. 2018). The PIGI system was designed to minimize these flow-dependent effects and includes flow rate monitoring for diagnosing such artifacts. Laboratory tests show that GTD response times following such disturbances range between ~10 min. and 1 hr, depending on flow rate (Fig. 5.3d). For this reason, it is important to maintain system flow rates as high and constant as possible. I also performed side-by-side field deployments of PIGI and MIMS systems (Fig. 5.1e). A subset of results (Fig. 5.4) demonstrates strong coherence between ΔO2/N2 and ΔO2/Ar across 123 small-scale hydrographic frontal features in Canadian Arctic waters. Offsets between the NCP tracers result from the relatively slow GTD signal, and from the slightly different solubility properties of N2 and Ar. These offsets can be minimized, however, through simple time-response corrections on underway signals (e.g. Bittig et al., 2014; Hamme et al., 2015) and careful evaluation of physical contributions to excess N2 saturation (Chapters 4 and 6). These results show significant promise for the wide-spread application of PIGI systems to increase spatial coverage of NCP estimates. Refer to Ch. 6 for a more comprehensive comparison of MIMS-based ΔO2/Ar and PIGI-based ΔO2/N2 signals. Table 5.2 List of PIGI system deployments between 2016 and 2021. Cruise IDs are included, where available, in the first column. Program / Cruise Ship Location Duration (days) Purpose Line P (2016-001) La Perouse (2016-047) Line P (2016-006) Line P (2016-008) Line P (2017-001) La Perouse (2017-005) Line P (2017-006) CCGS J. P. Tully NE Pacific / West coast Vancouver Island ~100 (total) testing 2018 ArcticNet Amundsen Expedition (1802 and 1803) CCGS Amundsen Labrador Sea, Baffin Bay, Canadian Arctic Archipelago 42 (July-Sept., 2018) NCP survey, testing Line P (2017-008) La Perouse (2017-009) La Perouse (2018-039) Line P (2018-026) Line P (2018-040) Line P (2019-001) Line P (2019-006) La Perouse (2019-023) CCGS J. P. Tully, CCGS Laurier NE Pacific / West coast Vancouver Island >100 (total) NCP survey Hakai Institute coastal monitoring N/A Quadra Island Ecological Observatory June 2019 – present NCP time-series 2019 ArcticNet Amundsen Expedition (1902) CCGS Amundsen Baffin Bay, Canadian Arctic Archipelago 43 (July-Aug. 2019) NCP survey W. Antarctic greenhouse gas survey N/A Base Prat, Greenwich Island, Antarctica (Chile) 60 (Jan.-Mar., 2020) NCP time-series 124 Figure 5.3 Field calibration of PIGI-derived O2 (a) and N2 (b) against discrete samples, and estimates of instrument response times within the system (c-d). Black markers in (a) and (b) represent samples obtained from Niskin bottles, and coloured markers (blue = Arctic; red = Subarctic NE Pacific) are for samples obtained from the PIGI system discrete sampling line. Sensor data have been adjusted for offsets using the discrete samples. The insets in (a) and (b) show the residuals between sensor-derived and discrete gas data, with the y-axis representing the proportion of data points. N2 saturation (N2,sat) represents the seawater concentration normalized to the equilibrium concentration at one standard atmosphere. Panel (c) shows the instrument response times in the PIGI system determined in the laboratory as the time required for re-equilibration after a change in the inflowing sample water composition. The response time of a MIMS system, determined under the same conditions, is shown for reference. Error bars represent the range of values during repeat experiments and at different instrument flow rates (1-3 L min-1). Panel (d) shows the response time of the mini-TDGP in the PIGI to changes in hydrostatic pressure caused by altering the flow rate through the system. The x-axis corresponds with the flow rate through the instrument chamber during re-equilibration, and error bars represent the standard deviation of values at 4, 10 and 18 oC. 125 Figure 5.4 One day of field observations from side-by-side deployments of PIGI and MIMS systems near a fjord in the Canadian Arctic. Panel (a) shows the calibrated optode and GTD measurements (normalized to the O2 equilibrium concentration and 1013.25 mbar, respectively), with GTD signals seen to lag behind O2 near strong fronts. Derived NCP tracers, ΔO2/N2 and ΔO2/Ar, are presented in (b) and hydrographic data is shown in (c). 5.6 Recommendations and future modifications Throughout the system development and testing, I made modifications to the PIGI hardware and software to improve overall performance and ease of deployment. From my field and laboratory testing of previous versions of the system, I found that the present configuration maximizes data quality. Importantly, PIGI was designed for unattended use by operators with little or no previous experience with the relevant instruments. I suggest that future deployments of optode/GTD systems follow similar principles to those discussed here, while considering several recommendations for additional improvements. Accurate quantification of NCP from O2/N2 measurement systems will require ancillary temperature and salinity (T/S) datasets and O2 calibrations to achieve a desired accuracy of 1 %. The latter can be achieved through Winkler analyses on discrete seawater samples, or in-air 126 measurements following the approach used on BGC Argo floats (Bittig & Körtzinger, 2015). Nitrogen derivation relies on accurate O2 and GTD pressure observations, which can be calibrated from in-air measurements made prior to and following deployments. Future designs of the PIGI may incorporate an air-pumping system (Bushinsky & Emerson, 2013), so that air-based calibrations could be performed during deployments. Moreover, T/S data are necessary for performing post-processing calculations on PIGI measurements, including O2 salinity-compensation and the determination of gas partial pressures. While optodes include a temperature sensor, salinity measurements must be obtained from separate sensors. If T/S measurements are not made elsewhere on the deployment platform, a salinity sensor can be incorporated in the PIGI instrument loop. Future systems may also benefit from a smaller optode chamber, which would decrease the optode response time (at a given flow rate) by reducing the optode chamber flushing time. I note, however, that the response time of the GTD in the current system remains most limiting to higher resolution ΔO2/N2 measurements. In addition, seawater should be pumped rapidly through the primary chamber to reduce the overall system response time and minimize the impact of potential bubble dissolution. To limit hydrostatic pressure effects on GTD measurements, a flow-controller may be installed in the instrument loop. In its absence, a stable flow rate through the instrument loop should be measured and maintained in order to minimize excursions in the GTD data. 5.7 Conclusions In this chapter, I describe an automated, user-friendly optode/GTD system capable of high-resolution and accurate O2/N2 measurements from a continuous seawater supply. The system has been tested and deployed under a wide range of laboratory and oceanographic conditions and has produced high-quality data from deployments on various platforms. Based on insights from these deployments, I provide recommendations for successful PIGI operation and, and include detailed designs for the system hardware and software at https://doi.org/10.5281/zenodo.4024951. The system I describe here may be deployed on research vessels, VOS platforms or at land-based field stations with a continuous seawater supply. Wide-spread deployment of the underway PIGI system, as described in Ch. 6, would lead to a significant expansion of global NCP measurements, providing better understanding of large-scale oceanic responses to on-going climate variability. 127 Chapter 6: Field evaluation of ΔO2/N2 and ΔO2/N2' as tracers of mixed layer net community production in the Subarctic Northeast Pacific and Canadian Arctic Ocean 6.1 Overview I compared field measurements of the biological O2 saturation anomalies, ΔO2/Ar and ΔO2/N2, from simultaneous deployments of a membrane inlet mass spectrometer and optode/gas tension device (GTD) in the Subarctic Northeast Pacific and Canadian Arctic Ocean. These data were used to evaluate ΔO2/N2 as an alternative mixed layer net community production (NCP) tracer to ΔO2/Ar. I observed strong spatial coherence between the two tracers, with small offsets resulting from differences in the solubility properties of Ar and N2 and their sensitivity to vertical mixing fluxes. Larger offsets between ΔO2/Ar and ΔO2/N2 were observed across hydrographic fronts and in elevated sea states, resulting from the differential time-response of the optode and GTD and bubble dissolution in the ship’s seawater lines. I used a simple numerical framework to correct for physical sources of divergence between N2 and Ar, producing estimates of the tracer ΔO2/N2', which more closely approximated ΔO2/Ar across much of the survey regions. An exception occurred in coastal Arctic waters, where ΔO2/N2 performed just as well as ΔO2/N2'. My results demonstrate a significant potential to derive NCP from underway O2/N2 measurements across a range of oceanic regions. Mixed layer NCP estimated from ΔO2/Ar and ΔO2/N2' agreed to within ~2 mmol O2 m-2 d-1, on average, with offsets typically smaller than other errors in NCP calculations. I suggest that optode/GTD systems could replace mass spectrometers for autonomous NCP derivation under many conditions, thereby presenting opportunities to significantly expand global NCP coverage from various underway platforms. 6.2 Introduction Marine net community production (NCP) represents the difference between gross photosynthesis and community-wide respiration, exerting a first-order control on the ocean’s capacity to support upper trophic level biomass and sequester atmospheric carbon dioxide via the biological pump (Volk & Hoffert, 1985; Ware & Thomson, 2005). Accurately quantifying NCP is 128 therefore important for understanding a variety of ecologically and economically important ocean processes, and for predicting climate-dependent shifts in marine biogeochemical cycles. Oceanic responses to climate change are likely to alter marine biological production (e.g. Bhatt et al., 2014; Moore et al., 2018), but our capacity to predict these changes is limited, in part, by poor data coverage. Multi-year NCP time-series are only available from a handful of deep ocean sites (Emerson, 2014), and many ship-based studies provide only sparse coverage in well-sampled ocean regions. New observational tools are required to facilitate NCP quantification on global scales and thereby enable predictions of its climate-related variability. A common approach to estimating NCP involves quantification of the mixed layer O2 mass balance, using O2 measurements from ship-board surveys, moorings, profiling floats or gliders (e.g. Bushinsky & Emerson, 2015; Emerson & Stump, 2010; Kaiser et al., 2005; Palevsky & Nicholson, 2018; Yang et al., 2017). Since the O2 saturation state is sensitive to both biological and physical processes, including temperature and salinity-dependent solubility effects and bubble injection, O2 measurements alone are insufficient to accurately resolve NCP. To address this limitation, O2 concentrations can be normalized to argon (Ar), a biologically inert gas with solubility properties that are virtually identical to O2 (Craig & Hayward, 1987). The so-called “biological O2 saturation anomaly”, ΔO2/Ar (Eq. 6.1), defined by normalizing the seawater O2/Ar ratio ([O2 Ar⁄ ]sw) to the equilibrium ratio ([O2 Ar⁄ ]eq), thus isolates the biological processes affecting O2. Δ O2 Ar⁄ = ([O2 Ar⁄ ]sw[O2 Ar⁄ ]eq− 1) × 100 % (6.1) In recent years, ship-based mass spectrometry has been employed to provide high-resolution coverage of NCP estimates from underway ΔO2/Ar measurements (Kaiser et al., 2005), yielding an improved understanding of the distribution of NCP (e.g. Eveleth et al., 2017b; Juranek et al., 2019; Kavanaugh et al., 2014). However, the requirement for mass spectrometry generally limits deployments to research ships, while the expense of these instruments and the expertise required to deploy them at sea may be prohibitive to some research groups. Truly autonomous measurements on research vessels, volunteer observing ships (VOS), or in-situ platforms such as unmanned surface vehicles (USV) would significantly expand the global coverage of NCP 129 estimates, helping to integrate these data with upper trophic level processes and observations of climatic variability across a range of scales. Recent work has demonstrated that the seawater O2/N2 ratio, derived from simultaneous deployments of autonomous O2 optode and gas tension device (GTD) sensors, may be used as an alternative to O2/Ar measurements for high-resolution NCP estimates (see chapter 4). Observations from these instruments can be used to derive seawater nitrogen (N2) concentrations (McNeil et al., 2005), and therefore calculate the biological O2 saturation anomaly from in-situ O2/N2 (i.e. ΔO2/N2, following Eq. 6.1). Relative to ΔO2/Ar, however, ΔO2/N2 does not fully account for physical effects on O2, due to differences in the solubility properties of O2 and N2. Chapter 4 described an approach to correct for physical offsets between ΔO2/N2 and ΔO2/Ar, using readily available environmental data and a modeling framework. The approach yields a new tracer, ΔO2/N2' (denoted “N2-prime”) which provides an analog for ΔO2/Ar. In principle, ΔO2/N2' holds significant promise as an NCP tracer under a range of oceanic conditions. To date, however, this approach has not been evaluated in the field, and no studies have reported NCP estimates derived from underway ΔO2/N2 or ΔO2/N2'. In this chapter, I present an in-situ evaluation of ΔΟ2/N2 and ΔO2/N2' as NCP tracers, using observations from three research cruises in the Subarctic Northeast Pacific and Canadian Arctic Ocean. My measurements allow me to compare ΔO2/N2, ΔO2/N2' and ΔO2/Ar over broad spatial scales and contrasting hydrographic regimes, and to evaluate the accuracy of the associated O2/N2-based NCP estimates. I demonstrate the strong potential utility of ΔO2/N2' as an alternative to ΔO2/Ar for autonomous NCP measurements in a range of oceanic environments, while also identifying conditions where uncorrected ΔO2/N2 provides a useful NCP tracer. I evaluate potential errors in the estimation of NCP based on O2/N2 and provide recommendations for future deployments of optode/GTD systems as a tool for oceanic productivity measurements. 6.3 Methods 6.3.1 Deployments I present data from three research cruises conducted during 2018 and 2019; two in the Subarctic NE Pacific (September 2018 and February 2019; Fig. 6.1a) and one in the eastern and central Canadian Arctic Ocean, spanning the Labrador Sea (LS), Baffin Bay (BB) and the 130 Canadian Arctic Archipelago (CAA; July – August 2019; Fig. 6.1b). I use the identifications “NEP-summer”, “NEP-winter” and “Arctic-summer” to distinguish the datasets. The two Subarctic Northeast Pacific (NEP) expeditions were conducted on board the CCGS J. P. Tully, as part of the Line P monitoring program (Department of Fisheries and Oceans Canada, cruise IDs 2018-040 and 2019-001, respectively), while the Arctic deployment was conducted on the CCGS Amundsen, as part of the ArcticNet program (Leg 2, cruise ID 1902). On all cruises, gas and hydrographic observations were obtained using underway optode/GTD, membrane inlet mass spectrometer (MIMS) and thermosalinograph systems (sections 6.3.2 and 6.3.3). Seawater was pumped from nominal intake depths of ~5 m (NEP cruises) and ~7.5 m (Arctic cruise) to the ship’s laboratories and directed to the respective instruments. Pumped water was always obtained from within the mixed layer (Fig. D.3). I omitted data corresponding with instances of instrument malfunction and periods of seawater flow interruption resulting from ice blockages in the ship’s pumping system. 131 Figure 6.1 Maps of ship tracks in the Subarctic NE Pacific (a), and the eastern Canadian Arctic (b) showing the locations where underway ΔO2/Ar and ΔO2/N2 were compared. The approximate along-track distance (in km) is indicated for reference. In panel (a), black lines correspond with the NEP-winter track, while the red line represents the NEP-summer track, displaced north by 0.5-degrees for visibility. The thick grey lines in (a) represent regions that experienced elevated sea states and high surface winds. In both panels, pale grey shading represents the approximate continental shelf regions (<500 m bathymetry), and the sampling sub-regions are distinguished by colour outlines around the cruise track lines. OSP = Ocean Station Papa, QCS = Queen Charlotte Sound, LS = Labrador Sea, BB = Baffin Bay, CAA = Canadian Arctic Archipelago. 6.3.2 Underway ΔO2/Ar and ΔO2/N2 observations 6.3.2.1 Underway gas measurements and data processing Measurements of O2/Ar were made using a membrane inlet mass spectrometer (MIMS) following Tortell (2005). Briefly, seawater was circulated at a constant flow rate and temperature through a cuvette equipped with a 0.18 mm thick silicon membrane interfaced to the vacuum inlet 132 of the mass spectrometer. Measurements of the mass-to-charge ratios at 32 (O2) and 40 (Ar) AMU were obtained at approximately 20 second intervals. Air standards, consisting of filtered seawater (<0.2 µm) incubated at ambient sea surface temperature and gently bubbled using an aquarium air pump, were run periodically by automatically switching the inflow water source every 45-90 minutes. The seawater and air standard O2/Ar ratios ([O2/Ar]sw and [O2/Ar]eq, respectively) were used to derive underway ΔO2/Ar (following Eq. 6.1) by linearly interpolating between air standard measurements. On the NEP-summer cruise, final underway ΔO2/Ar was obtained by calibrating the continuous measurements against discrete samples obtained from Rosette sampling (sample collection and processing details in section 6.3.2.3). The underway data were linearly calibrated, with offsets between calibrated and uncalibrated signals ranging from ~0 % (at ΔO2/Ar values near equilibrium) to ~3 % (at ΔO2/Ar values of ~10 %). No calibrations were performed on the NEP-winter or Arctic datasets. Underway O2 and N2 data were derived (section 6.3.2.2) from seawater measurements obtained using a custom-built optode/GTD system described in Chapter 5. The system recorded O2 concentration (µmol L-1) and total dissolved gas pressure (i.e. the sum of all gas partial pressures; TP, mbar) at ~10 second intervals from an Aanderaa Data Instruments AS optode 4330F and Pro-Oceanus Systems Inc. mini-TDGP gas tension device, respectively. Debubbled seawater from the ship’s supply line was pumped rapidly and at constant flow rate passed the instruments (~2 L min-1) to minimize the system residence time and hydrostatic pressure effects on the GTD measurements. A flow-through head (water residence time <1 sec.) was installed on the face of the GTD to direct water onto the instrument’s Teflon membrane. The optode was submerged in a 0.25 L flow-through cell (residence time <10 sec.) and oriented so that water flowed onto the sensing foil. To improve the alignment between the optode/GTD, MIMS and hydrographic data (section 6.3.3), and account for the slower response time of the GTD relative to the other gas sensors, I filtered the underway data (O2, TP, ΔO2/Ar, temperature, salinity) before deriving ΔO2/N2 (section 6.3.2.2). All raw signals were low-pass filtered (frequency 0.5 min-1) and subsequently median-binned into two-minute intervals. Following Hamme et al. (2015; their Eq. 2), I then applied a cumulative filter to all underway data (excluding GTD measurements) to replicate the rate of O2 diffusion across the GTD membrane. I used a time-constant of 1/τGTD (where τGTD is the GTD’s 133 temperature-dependent response time; ~1-2 min, per Ch. 5), extrapolated to the ambient seawater temperature. Finally, I adjusted for time offsets between sensors by manually aligning measurement peaks. Unless stated otherwise, all results presented below are based on this processing. 6.3.2.2 Derivation of ΔO2/N2 Detailed handling and calibration procedures for O2, N2 and ΔO2/N2 are presented in Appendix D (section D.1), and briefly summarized here. I corrected raw TP signals (TPcor) for measurement bias (average offset <1 mbar assessed as the difference between in-air GTD readings and atmospheric pressure) and for the effects of seawater warming between the intake and laboratory. Raw optode O2 measurements were salinity-corrected (O2cor) following Uchida et al. (2008) and Bittig et al. (2018), and calibrated against discrete samples obtained from the outflow of the optode/GTD system or from Niskin bottles (details in section 6.3.2.3) to derive final O2 concentrations (O2cal). Calibrated oxygen saturation (O2,satcal, %) was derived by dividing O2cal by the O2 solubility (Garcia & Gordon, 1993) calculated using ambient sea level and water vapour pressures (PSLP and PH2O) and sea surface salinity and temperature data from a thermosalinograph installed near the ship’s intake (i.e. O2,satcal =O2calO2,eq × PSLP − PH2O1013.25 mbar − PH2O × 100 % ). I derived the N2 partial pressure (pN2) following McNeil et al. (2005) by subtracting the partial pressures of O2 (obtained from O2,satcal following Eq. 6.2), Ar and water vapour from TPcor measurements (Eq. D.7). The Ar saturation state (Arsat), and thus partial pressure, was estimated from MIMS ΔO2/Ar and optode O2,satcal by re-arranging Eq. 6.1. The saturation state of all remaining gases (including carbon dioxide and Ar when MIMS data were unavailable) was assumed to be equivalent to N2, following McNeil et al. (2005). The N2 saturation level (N2,sat), and resulting ΔO2/N2 (both %), were calculated following N2,sat =pN2χN2×(PSLP−PH2O)×100 % (6.2) 134 ΔO2 N2⁄ = (O2,satcalN2,sat− 1) × 100 % (6.3) where χN2 is the atmospheric dry mole fraction of N2 (=0.78084). The equilibrium concentrations of N2 and Ar were derived from the equations of Hamme & Emerson (2004) using temperature and salinity measurements made near the ship’s intake. Discrete samples from mixed layer Niskin bottles were used to calibrate the underway N2 measurements (details in section 6.3.2.3). In discussing my results, I present calibrated data and omit the “cal” and “cor” superscripts. Gases (O2, Ar or N2) are represented by their saturation anomalies (i.e. ΔC = (Csat 100⁄ −1)×100 %) referenced at ambient PSLP. I represent offsets between ΔΟ2/Αr and ΔO2/N2 based on their absolute difference (i.e. ΔΟ2/Αr – ΔO2/N2), and by using the tracer ΔN2/Ar (calculated following Eq. 6.3 convention), which is independent of the O2 saturation state. Gas ratios (ΔO2/Ar, ΔO2/N2 and ΔN2/Ar) are not influenced by ambient PSLP. 6.3.2.3 Underway signal calibration and discrete gas samples On each cruise, optode measurements were calibrated against discrete samples obtained from the outlet of the optode/GTD system. Discrete samples were analyzed by Winkler titration (automated colorimetric and potentiometric endpoint determinations during the NEP and Arctic cruises, respectively). I derived a best-fit linear regression between calibration samples and corresponding optode measurements (averaged within 2 minutes of sampling) and adjusted the calibrated O2 for differences in concentration between samples obtained from the laboratory and mixed layer Niskin bottles (Eq. 6.3). While I did not observe any evidence of O2 consumption in the ship’s seawater lines (Juranek et al., 2010), calibrating the optode signal against discrete laboratory and Niskin samples should correct for such potential sampling artifacts in subsequent studies. Since optode sensitivity drifts during storage (and in some cases during deployments; Bittig et al., 2018; D’Asaro & McNeil, 2013), I performed calibrations using discrete samples obtained over a range of hydrographic conditions and O2 concentrations throughout the duration of each deployment. A single calibration was derived for each separate cruise dataset. During the NEP-summer and Arctic cruises, underway N2,sat data were calibrated using discrete gas samples obtained from Niskin bottles fired within the mixed layer as close to the depth 135 of the seawater intake as possible. Discrete N2,sat was calculated using N2/Ar ratios measured in the bottle samples and corresponding underway Arsat. I obtained broad coverage of discrete samples across the Arctic cruise track, but reduced coverage during NEP-summer, and no samples from NEP-winter. All samples were collected in a manner that avoided bubble contamination and were preserved with saturated mercuric chloride solution before analysis by mass spectrometry, following Emerson et al. (1999) (NEP-summer samples) and Kana et al. (1994) (Arctic samples). ΔO2/Ar was also measured in the discrete NEP-summer samples for calibration of the underway MIMS data. While no calibrations of the underway ΔO2/Ar data were performed on the NEP-winter or Arctic cruises, calibrations of N2,sat based on discrete N2/Ar measurements and underway MIMS- and optode-derived Arsat should make the comparison of ΔO2/Ar and ΔO2/N2 intrinsically consistent. Discrete gas samples for subsurface N2/Ar analyses were also collected during the NEP-summer and Arctic cruises (Fig. D.5). Samples were collected at various depths and analyzed as described above. Subsurface N2/Ar data were used in calculating N2' (section 3.3.4). Detailed discrete gas sampling and analysis procedures are presented in section D.1.3 of Appendix D. 6.3.3 Ancillary datasets Continuous measurements of sea surface temperature (SST) and salinity were made using a thermosalinograph installed near the ships’ seawater intakes (Sea-Bird SBE-21 and SBE-45/SBE-38 thermometer, on the CCGS Tully and CCGS Amundsen, respectively). Depth profiles of temperature and salinity were obtained from CTD casts (Sea-Bird SBE-911plus), and the mixed layer depth (MLD) was defined based on a 0.125 kg m-3 density difference from the mean value in the upper 5 m (Thomson & Fine, 2003). I performed a two-dimensional spatial interpolation of MLD between CTD stations to estimate the MLD along the entire cruise tracks. Hydrographic data were provided by the Institute of Ocean Sciences (Department of Fisheries and Oceans Canada; http://www.waterproperties.ca/linep/) and the Amundsen Science group of U. Laval (Amundsen Science Data Collection, 2020a, 2020b). I obtained wind speed data at 10-m elevation from the CCMPv2 vector (Atlas et al., 2011), and NARR reanalysis (Mesinger et al., 2006) products for the NEP and Arctic, respectively. Gridded SST were from the NOAA High Resolution OI v2 dataset (Reynolds et al., 2007), and 136 PSLP and PH2O data from the NCEP/NCAR Reanalysis products (Kalnay et al., 1996). Sea ice fractional coverage was from the AMSR-2 product (Spreen et al., 2008). The environmental data (excluding sea ice) were calibrated for my study regions using linear equations derived by comparing the gridded data with a multi-year ensemble of observations from moorings, buoys and ships in coastal and offshore waters (details in Appendix D). The wind speed data were adjusted by <1.2 m s-1, on average, across both regions, while SST and PSLP gridded data were adjusted by ~0.2 oC (NEP) and 0.6 oC (Arctic) and 3 mbar, respectively. I performed a nearest-neighbour interpolation of the adjusted gridded data to the cruise tracks at the time of data collection, and backwards in time by 60 days to determine the environmental histories of the sampling regions prior to the ship’s arrival (Fig. D.3). The interpolated data were used to perform N2' calculations (section 6.3.4). The average uncertainty in the wind speed, SST and SLP data, estimated as the mean deviation from ship-board measurements at the time of sampling was ~3 m s-1, 0.7 oC and 2 mbar, respectively. 6.3.4 N2': Correcting N2 for solubility and bubble effects Following Chapter 4, I derived the term N2' to reconcile physical differences in Ar and N2 supersaturation (ΔAr and ΔN2) resulting from differential solubility properties, air-sea exchange rates, bubble effects and vertical mixing and advection. Briefly, I calculated the expected Ar and N2 supersaturation difference (ΔN2mod – ΔArmod) at the time of my measurements using a simple one-dimensional model forced with gridded environmental data over one O2 re-equilibration timescale prior to the ship-board observations (TΟ2; see below). The model evaluates the mixed layer (ML) evolution of gas C, N2 or Ar, following: d(C×MLD)dt = Fd+FC+FP+Cdeep−CdZ× κZ. (6.4) The first three terms on the right represent the air-to-sea flux via diffusive (Fd), and bubble-mediated (FC + FP; denoting fully collapsing and partially dissolving bubbles) processes, and the last term represents the vertical mixing flux. I used the models of Liang et al. (2013) and Butterworth & Miller (2016) to estimate air-sea exchange terms in ice-free and partially ice-covered waters, respectively. The term Cdeep−CdZ (mmol m-4) represents the vertical gradient of Ar or 137 N2 between the surface and a given depth interval, dZ (equal to the thickness of the pycnocline, as described in Ch. 4; Fig. 4.1), below the mixed layer, and κZ (m2 d-1) is the vertical mixing rate. Subsurface Ar (Ardeep) was set to the equilibrium concentration at subsurface temperature and salinity conditions derived by interpolating CTD measurements along the cruise track, and N2,deep was derived from subsurface N2/Ar data (i.e. ΔN2/Ardeep) and Ardeep. In the NEP-summer and Arctic cruises, ΔN2/Ardeep was measured from N2/Ar samples (Fig. D.5; details in section 6.3.2.3 and D.1.3), while archived data from Hamme et al. (2018, 2019) were used to characterize the deep gas conditions in NEP-winter (data provided at https://doi.org/10.1575/1912/bco-dmo.744563). κZ was derived from vertically-resolved nitrous oxide (N2O) measurements in the NEP (following Ch. 2) and from NEMO circulation model simulations of the Arctic and N. Atlantic (following Ch. 3). In performing the N2' calculations, I subdivided my survey regions into smaller zones (described in section 6.3), applying mean ΔN2/Ardeep and κZ values in each of these sub-regions (Table D.1), except in the Arctic where I used along-track κZ values based on the NEMO model output. I evaluated the sensitivity of the N2' calculations to these parameterizations in section 6.5.3. I calculated TO2, the O2 re-equilibration time, as -ln(0.01) × MLD / (kT + κZ/dZ) (where kT is combined diffusive and large bubble-induced O2 gas transfer velocity; see Chapter 4), and evaluated the budget by setting starting ML gas concentrations to Ceq. The re-equilibration timescale is approximately five-fold larger than the ML O2 residence time (τO2 = MLD / kT; Fig. D.3), in order to fully erase the initial conditions invoked in the N2' model calculations. I assumed a constant MLD based on interpolated values from CTD casts. As discussed in Chapter 4 (sections 4.4.3-4.4.4), this assumption may be problematic during periods of significant MLD changes (e.g. spring and autumn), but the uncertainty incurred in N2' calculations remains small relative to other NCP errors. In each time step, I calculated Ceq from time-variable SST and PSLP, and constant salinity (based on ship-board measurements). The modelled difference between Ar and N2 supersaturation (ΔCmod) at the time of my cruise observations was then subtracted from optode/GTD-derived ΔN2 observations to obtain ΔN2', before re-evaluating ΔO2/N2 following Eq. 6.3. ΔN2' = ΔN2obs – (ΔN2mod – ΔΑrmod) (6.5) 138 For full details and evaluation of the model calculations, refer to Chapter 4. Matlab scripts with examples are available at https://doi.org/10.5281/zenodo.4024925. 6.3.5 Calculations of net community production I estimated NCP (mmol m-2 d-1) as the product of the biological O2 saturation anomaly, the O2 equilibrium concentration (at 1013.25 mbar), and the O2 gas transfer velocity (kO2, m d-1). NCP = kO2 × (ΔO2 C⁄ ) × [O2]eq (6.6) In this approach, C represents either Ar, N2 or N2', and NCP is equivalent to the diffusive sea-air flux of biologically-produced excess O2 (Teeter et al., 2018). I used the diffusive gas transfer velocity parameterizations from Liang et al. (2013) and Butterworth & Miller (2016) (scaled to the fraction of ice-free water, f, (i.e. kO2=kO2× (1 − f)) in the NEP and Arctic, respectively, and weighted values over a 30 day period following Teeter et al. (2018). Adjusting the length of the weighting period to account for shorter or longer ML O2 residence times does not have a significant effect on kO2. Below, I present NCP estimates calculated using the three different metrics of the biological O2 saturation anomaly: ΔO2/Ar-NCP, ΔO2/N2-NCP and ΔO2/N2'-NCP. 6.4 Results My combined observations provide broad spatial coverage across a variety of oceanographic regimes throughout the Subarctic NEP and Canadian Arctic. In these contrasting regions, various physical and biological controls are expected to exert different relative effects on gas saturation anomalies. Together these data allow me to examine a range of conditions under which measurements of ΔO2/N2 or ΔO2/N2' can be used for accurate NCP derivation. In describing my results, I divided the cruise tracks into distinct sub-regions (Fig. 6.1). In the NEP, I differentiate between offshore (>500 m water depth) and nearshore waters. I only present results from the offshore section of the return (eastward) transect of the NEP-summer cruise due to periodic instrument problems on the outbound leg. In the NEP winter cruise, sampling 139 within the nearshore region occurred in the Queen Charlotte Sound (QCS; >2700 km along-track). In the Arctic, I identify nearshore waters as those occurring over the Baffin Bay continental shelf (all regions overlying the continental shelf <6000 km along-track; identified by shading in Fig. 6.1b) and in the Canadian Arctic Archipelago (CAA), and offshore waters of Baffin Bay (BB; excluding shelf regions <6000 km along-track). 6.4.1 Underway signal calibration I observed a strong linear relationship between optode and Winkler-based O2 concentrations (R2 = 0.99 for each cruise), enabling calibration of the underway data with a root mean square error (RMSE) of <0.75 % (Fig. 6.2a). As discrete samples were collected periodically throughout the duration of each deployment over a broad range of hydrographic conditions and O2 concentrations (bars in Fig. 6.2a), I believe that the final O2 observations are accurate for the conditions encountered on each cruise. I also observed a strong linear relationship between underway and discrete N2,sat (R2 = 0.90, RMSE = 0.90 % in the pooled NEP-summer and Arctic dataset; Fig. 6.2b). Given the accuracy of O2 (calibrated to <1 %) and TP (±0.1 %, per Pro-Oceanus Systems Inc.) measurements, this calibration validates the underway N2,sat data within an accuracy of ~1 %. Moreover, as discrete N2 was derived using N2/Ar ratios measured in the bottle samples and MIMS-based Arsat, this result suggests a similar accuracy for the underway ΔO2/Ar data. Since the NEP-summer and winter deployments used the same gas sensors, and I observed no drift in the GTD measurements over time (i.e. constant offset against atmospheric pressure), I applied the NEP-summer N2 calibration to both datasets. Although the range of TP and N2,sat conditions I encountered differed between the cruises (bars in Fig. 6.2b), calibrations based on the NEP-summer, Arctic or pooled datasets resulted in derived ΔO2/N2 that varied by less than 0.4 %. Across all cruises, the maximum difference between calibrated and uncalibrated N2,sat was ~2 % at the highest saturation levels (mean ~0.2 %), suggesting that biases in ΔO2/N2 and derived NCP should be small even without N2 calibrations. However, given that underway N2,sat derivation is sensitive to the degree of seawater warming between the ship’s intake and GTD measurement interface (details in sections D.2 and D.4 of Appendix D), TP measurements should be made as 140 close as possible to the ship’s seawater intake to minimize this source of error if calibrations are unavailable. Figure 6.2 Calibration of underway O2 (a) and N2,sat (b) against discrete samples obtained from 2018 and 2019 Line P and Arctic cruises. Discrete O2 samples were obtained from the seawater supply line, while N2 samples were taken from Niskin bottles fired in the mixed layer. Underway sensor data (x-axis) represent the mean signal during a two-minute interval around the time of discrete sample collection. Error bars represent one standard deviation of the mean of duplicate discrete data; the standard deviation of the underway data is too small to resolve on the figure. The dashed lines show the 1:1 fit, and the bars at the bottom of each panel show the range of observed underway O2 and N2 on each cruise, excluding data during storm-impacted segments. The lowest Arctic O2 calibration point (black diamond ~260 µmol L-1) represents an air-equilibrated standard obtained by bubbling seawater at ~18 oC before manually circulating through the optode/GTD system at the end of the Arctic cruise. 6.4.2 Surface water hydrography, physical forcing and gas saturation anomalies I observed substantial spatial variability in surface hydrography (represented by sea surface salinity) across the study regions, and large temporal variability in physical forcing (wind speed, temperature changes and sea ice cover) prior to the cruise measurements. These factors contributed to large variations in mixed layer gas saturation states (ΔO2/Ar, ΔO2/N2, ΔO2, ΔAr and ΔN2). Figures 6.3-6.5 summarize underway observations as well as wind speed and SST change (ΔSST) over the mixed layer residence time of O2, τO2. Figures D.2 and D.3 (Appendix D) present the along-track ΔN2 and ΔAr observations and environmental histories, respectively. 141 Prior to my observations, wind speed and SST were highly variable in the NEP and Arctic, while Arctic sea ice coverage showed a declining trend (Fig. D.3). On average, wind speeds during and prior to the NEP-winter cruise were higher and more variable than during the summer cruises (Figs. 6.3-6.5d; D.3). Several high-wind events (wind speed >10 m s-1) occurred near the time of my winter sampling, while wind speeds during the summer deployments were mostly below 10 m s-1. Throughout most of the NEP-winter transect, surface waters experienced net cooling before the cruise, with the greatest cooling (~3 oC over τO2) observed in nearshore waters. The summertime NEP cruise also experienced net cooling of around 2 oC over τO2. In contrast, warming was recorded over nearly the entire Arctic cruise track (ΔSST range -2 – 7 oC; Fig. 6.5d), and cooling was observed in only several isolated regions. 6.4.2.1 Gas observations in the Subarctic Northeast Pacific Gas distributions and hydrographic properties differed between the summer and winter NEP cruises (Figs. 6.3-6.4, D.2-D.3). Along the offshore NEP-summer track, I generally observed little spatial variability in all gases and hydrographic properties (Fig. 6.3). ΔO2, ΔAr and ΔN2 were all within ~2 % of saturation, and ΔO2/Ar and ΔO2/N2 strongly mirrored patterns in ΔO2. ΔO2/Ar typically exceeded ΔO2/N2 (i.e. ΔN2 > ΔAr), and values ranged from ~ -0.75 % to 2 %. During NEP-winter, I observed extremely high ΔO2, ΔAr and ΔN2 (up to ~40-50 %; Fig. 6.4a, D.2b) corresponding with periods of very rough sea states and elevated wind speeds in offshore waters (grey shaded areas in Fig. 6.4). In these stormy regions, ΔO2/N2 was highly erratic and exhibited some extreme low values (minimum -18 %; Fig. 6.4c), while ΔO2/Ar showed little variability. Throughout the remainder of the winter transect, ΔO2 was mostly undersaturated, and ΔAr and ΔN2 were nearer to saturation (±5 %). ΔO2/Ar almost always exceeded ΔO2/N2, and both tracers had an overall range of ~-20 to 2 %, strongly reflecting ΔO2 variability. The lowest and most variable ΔO2 values occurred in the nearshore QCS archipelago, corresponding with strong hydrographic fronts (Fig. 6.4a). The largest offset between ΔO2/Ar and ΔO2/N2 also occurred in the QCS region, where ΔN2 was up to ~2.5 % higher than ΔAr. 142 Figure 6.3 Spatial survey of ΔO2 and salinity (a), ΔO2/Ar and ΔO2/N2 (b), and wind speed and net SST change (c) along the August 2018 Line P (NEP-summer) cruise track. Net ΔSST was calculated as the change in SST during one mixed layer τO2 prior to the cruise time, based on SST from NOAA reanalysis products (section 6.3.4). Wind speed data were taken from the ship’s sensors (1-day mean) and from the CCMP Reanalysis product data (10-day mean). The transect was completed over ~3 days. 143 Figure 6.4 Spatial survey of ΔO2 and salinity (a), ΔO2/Ar and ΔO2/N2 (b), and wind speed and net SST change (c) along the February 2019 Line P (NEP-winter) cruise track. Vertical light blue bars identify stormy sections of the transect. As described in the text, data from these regions were excluded from my analyses. The locations of the offshore and QCS regions are indicated by coloured bars in panels (a) and (c), corresponding with those in Fig. 6.1. The transect was completed over ~15 days. Refer to Fig. 6.3 caption for further details. 6.4.2.2 Gas observations in the Canadian Arctic Gas saturation anomalies during the Arctic cruise contrasted those observed in the NEP. Most notably, all gases (O2, Ar and N2) were supersaturated and highly variable along most of the transect. ΔO2/Ar typically exceeded ΔO2/N2 (ΔN2 > ΔAr), and both were typically positive (Fig. 6.5c). As in the NEP, the distributions of ΔΟ2/Αr and ΔO2/N2 followed patterns in ΔO2, with the 144 highest values (~10-30 %) occurring in the shelf and CAA regions (~2000 and >6000 km along-track). Additional variability in ΔO2, ΔO2/Ar and ΔO2/N2 corresponded with significant salinity features in the vicinity of freshwater sources from glacier run-off and river discharge throughout the shelf and CAA (e.g. sharp drops in salinity ~5400, 6400, 6800, 7800 and 8800 km; Fig. 6.5a). ΔAr and ΔN2 (Fig. D.2c) were also higher and more variable in the Arctic than in the NEP (excluding storm-affected regions). Figure 6.5 Spatial survey of ΔO2 and salinity (a), ΔO2/Ar and ΔO2/N2 (b), and wind speed (NARR database) and net SST change (c) along the summer 2019 CCGS Amundsen (Arctic-summer) cruise track. The locations of the different regions are indicated by coloured bars in panels (a) and (c). The transect was completed over ~40 days. Refer to Fig. 6.3 caption for further details. 145 6.4.3 Comparison of underway ΔO2/Ar, ΔO2/N2 and ΔO2/N2' The distribution of ΔO2/Ar and ΔO2/N2 (excluding the storm-impacted sections of the NEP) exhibited high spatial coherence on all cruises (Figs. 6.3-6.5c, 6.6). The linear slope of the ΔO2/Ar versus ΔO2/N2 relationship was 1.08 ± 0.04 (R2 = 0.96) for the pooled dataset. These results demonstrate, to first order, the potential to use ΔO2/N2 as an NCP tracer in place of ΔO2/Ar. In addition, ΔO2/N2 showed greater spatial coherence with ΔO2/Ar than ΔO2 alone (pooled dataset slope = 1.54 ± 0.03), supporting the continued normalization of O2 measurements by a gas analog for NCP derivation. Despite the good agreement between ΔO2/Ar and ΔO2/N2, small differences between the tracers remain. Figures 6.6a-c and D.4 show the cruise-wide and regional offsets between ΔO2/Ar and ΔΟ2/Ν2 before and after applying the N2' calculations. I observed median differences between ΔO2/Ar and ΔO2/N2 of 0.3, 1.1, and 0.3 % in the NEP-summer, -winter and Arctic datasets, respectively. Maximum deviations were up to ~12 % (excluding storm-impacted data with wind speeds exceeding ~10 m s-1), and the range of values was ~18 %. As discussed in section 6.5.1, the most extreme values likely represent sampling artifacts rather than true differences between ΔAr and ΔN2. Applying the N2' corrections increased the alignment with ΔO2/Ar on regional and sub-regional scales. Median offsets between ΔO2/Ar and ΔO2/N2' were reduced to ~0.02 % (NEP-summer), 0.1 % (NEP-winter) and -0.04 % (Arctic). The largest reduction in the bias between tracers occurred during the winter NEP cruise, while throughout the Arctic, ΔO2/N2 and ΔO2/N2' were more similar. Overall, the direct comparison between all pooled ΔO2/Ar and ΔO2/N2' observations had a linear regression slope not significantly different from unity (1.01 ± 0.03 with R2 of 0.96). 146 Figure 6.6 The relationship between ΔO2/Ar and ΔO2/N2 or ΔO2/N2' during the NEP-summer, NEP-winter and Arctic cruises. Panels (a)-(c) show the differences between ΔO2/Ar and ΔO2/N2, before (black) and after (red outline) N2' corrections. The light blue distributions in (b) represent storm-impacted data of the NEP-winter cruise. The numbers in the figure legends represent the median differences obtained from calculations using different N2 terms. The x-axes in panels (a) - (c) have been truncated to exclude data occurring in less than 0.05 % of observations. Direct correlations between ΔO2/Ar and ΔO2 (grey), ΔO2/N2 (black) and ΔO2/N2' (red) are shown in panel (d). The linear slope value for each fit is included in the panel legend. 6.4.4 Net community production estimates I did not compare ΔO2/Ar-, ΔO2/N2- and ΔO2/N2'-based NCP values on a point-by-point basis, since results are influenced by the differential response times of the gas sensors (details in section 6.5.1). Rather, I compared NCP estimates after binning the respective datasets into 20-km intervals. Table 6.1 and Figure 6.7 summarize the derived NCP values by sub-region. I observed the largest range of NCP values in the nearshore (QCS) waters of the Subarctic NE Pacific, and highest values (up to ~30 mmol O2 m-2 d-1) in the Arctic shelf and CAA. Derived 147 values were mostly negative during NEP-winter, and predominantly positive in both summer cruises. The strong negative values in the QCS likely reflect biases from vertical mixing, more-so than significant heterotrophy. Estimates derived from uncorrected ΔO2/N2 resulted in a higher frequency of negative values compared with corresponding estimates based on ΔO2/Ar (15 % of pooled dataset), but application of N2' significantly reduced this frequency (to 4 %) in all sub-regions. My observations thus suggest that ΝCP estimated from ΔO2/Ar and ΔO2/N2' provided consistent representation of the metabolic state of surface waters on regional scales. Moreover, ΔO2/N2'-NCP values calculated in each sub-region typically better matched ΔO2/Ar-based estimates, with median offsets being <2 mmol O2 m-2 d-1 (versus <9 mmol O2 m-2 d-1 for ΔO2/N2-NCP). As shown in Fig. 6.7b, where y-axis values represent the proportion of the dataset with NCP biases smaller than or equal to a given x-value, N2' calculations reduced biases in derived NCP across the dataset. In contrast to the NEP cruises, however, NCP estimates derived ΔO2/N2' and ΔO2/N2 were nearly equivalent in parts of the Arctic (particularly in shelf and CAA waters), suggesting that N2' was less important for reducing NCP biases. Table 6.1 Summary of NCP estimates, differences between NCP terms and other biases in NCP calculations during the 2018 and 2019 NEP-summer, NEP-winter and Arctic cruises. Numbers represent the median values (± standard deviation). Values in parentheses (“NCP difference” row) represent the percent of NCP observations that have opposite signs (e.g. ΔO2/Ar-NCP >0 corresponding with ΔO2/N2-NCP <0). The O2 mixing bias was estimated from N2O and O2 measurements (following Ch. 2) in the NEP cruises, and from κZ and O2 profiles obtained from NEMO model simulations and CTD profiles in the Arctic (following Ch. 3). The NCP error due to ~20-40 % uncertainty in the O2 gas transfer velocity term (kO2) represents mean values in each sampling region based on ΔO2/Ar-NCP. Cruise: NEP-summer NEP-winter Arctic Region: Offshore Offshore QCS BB Shelf & CAA [mmol O2 m-2 d-1] NCP O2/Ar 4.8 ± 3.5 -4.4 ± 5.1 -24.5 ± 12.0 2.0 ± 1.4 6.5± 6.4 O2/N2' 4.2 ± 4.8 -6.4 ± 4.8 -24.8 ± 13.8 2.6 ± 2.0 6.6 ± 7.2 O2/N2 1.7 ± 4.8 -8.7 ± 5.3 -32.7 ± 12.8 1.6 ± 2.1 5.9 ± 6.8 NCP difference (% wrong sign) O2/Ar – O2/N2' 1.1 ± 1.0 (10) 2.0 ± 2.5 (2) 1.9 ± 1.7 (0) 0.6 ± 0.9 (5) 0.8 ± 2.1 (3) O2/Ar – O2/N2 3.0 ± 1.4 (30) 4.1 ± 3.6 (5) 8.9 ± 2.2 (0) 0.8 ± 0.7 (0) 1.1 ± 1.9 (1) Mixing bias 4.8 ± 2.3 8.4 ± 3.2 92.9 5.8 ± 6.2 6.3 ± 12.1 Error due to kO2 uncertainty 1.5 ± 1.0 1.7 ± 1.4 6.7 ± 3.3 0.7 ± 0.4 2.5 ± 2.0 148 Figure 6.7 Comparison of NCP estimates derived from ΔO2/Ar, ΔO2/N2 and ΔO2/N2' measurements obtained during the 2018 and 2019 NEP-summer, NEP-winter and Arctic cruises. Panel (a) shows the distribution of NCP estimated using each biological O2 saturation anomaly term by cruise and sub-region. NCP estimates were binned to 20-km intervals and have not been corrected for vertical mixing biases on surface O2, which would influence all NCP estimates to the same extent. The white line represents median value for each distribution. Panel (b) shows the cumulative frequency distribution of biases in ΔO2/N2'- and ΔO2/N2-based NCP estimates relative to ΔO2/Ar-NCP. Y-axis values represent the proportion of the dataset with NCP biases lower than a given x-axis value. The blue patches represent errors in ΔO2/N2'-NCP, assessed as the combined analytical uncertainty and sensitivity to the κZ and ΔN2/Ardeep terms. The grey patch represents NCP biases incurred by a 20-40 % uncertainty in the O2 gas transfer velocity (kO2). 6.5 Discussion 6.5.1 Uncertainty and artifacts in surface ΔO2/N2 observations A key result of this work is that ΔO2/Ar and ΔO2/N2 show strong spatial coherence across all cruise transects, excluding regions in the Subarctic Pacific impacted by winter storms. Despite 149 this overall agreement, important differences exist between these measurements, leading to ΔN2/Ar disequilibria and offsets in derived NCP estimates. These can be attributed to physical processes – including solubility changes and bubble and mixing effects – that cause ΔAr and ΔN2 to diverge (section 6.5.2), and to analytical or measurement uncertainty which must also be considered. 6.5.1.1 Bubble entrainment during elevated sea states The anomalous gas observations in parts of the offshore Subarctic NE Pacific winter data represent a limitation of the present application of O2/N2-based NCP estimates. Several lines of evidence suggest that the exceedingly high ΔO2, ΔAr and ΔN2 and strongly negative ΔO2/N2 I observed in the wintertime offshore Subarctic NEP (Figs. 6.4, D.2b) reflect artifacts from bubble entrainment in the ship’s seawater supply during periods of elevated sea states. Following Vagle et al. (2010), application of the ideal gas law (n = f × (χ× pSLP) / (R × SST)) shows that dissolution of bubbles with an air-volume fraction (f) of only ~4 % is sufficient to yield the elevated ΔO2, ΔAr and ΔN2 values observed during the cruise (up to ~45 %). In contrast, observed noble gas and N2 supersaturation seldom exceed ~6 % in the subarctic ocean (Emerson & Bushinsky, 2016; Hamme et al., 2019; Steiner et al., 2007), even under extreme hurricane-force winds (wind speeds up to 57 m s-1; D’Asaro & McNeil, 2008), and most air-sea exchange parameterizations predict bubble-induced supersaturation anomalies of O2, N2 and Ar of less than ~6 % at wind speeds below 20 m s-1. Only extremely rapid warming (>10 oC over several days) could yield such strong positive supersaturation anomalies of all three gases, but I observed net cooling before the winter cruise (Fig. 6.4d). Finally, such anomalies do not reflect sampling artifacts in the optode/GTD system, since it was designed to successfully divert bubbles from the instrument interfaces (Ch. 5). I thus conclude that the anomalously high gas supersaturation values I observed during the NEP-winter cruise resulted from bubble dissolution in the ~100 m of piping between the intake and laboratory. As O2 and Ar have virtually identical solubilities, the bubble effect on ΔO2/Ar is small (albeit not entirely negligible for high rates of bubble dissolution). In contrast, the reduced solubility of N2 relative to O2 results in elevated N2 sensitivity to bubble dissolution and a large negative ΔO2/N2 anomaly. Based on these observations, I suggest that O2/N2 measurements should be excluded from NCP surveys when significant bubble dissolution in the ship’s underway sampling system is 150 suspected. More generally, the set of criteria I used to discard ΔO2/N2 data (i.e. strongly negative ΔO2/N2 corresponding with ΔN2 > ΔO2 and both >~5 %) should be applicable to many ocean regions that experience modest short-term temperature changes. It is important to note that negative ΔO2/N2 does not imply poor data quality, per se. The near-shore waters of the NEP-winter cruise provide an example of negative ΔO2/N2 (Fig. 6.4c) likely resulting from significant vertical fluxes of O2-deplete water, rather than bubble entrainment artifacts. Observations should thus be considered in light of the environmental forcing histories prior to sampling, and of additional gas fluxes that may produce negative ΔO2/N2. Moreover, my data quality criteria may not identify impacts of smaller bubble dissolution signatures, but such effects could be diagnosed by careful inspection of the underway data for stochastic N2 variability relative to O2. Indeed, as the underway sampling suggests, the spatial and temporal variability of O2 is typically much greater than that of N2 or Ar. Overall, bubble artifacts can likely be reduced by installing the optode/GTD system closer to the seawater intake, thereby reducing the length of internal tubing in which bubbles can dissolve. This issue should also be less important on larger ships which are less susceptible to rolling and pitching during storm activity, or on ships with deeper intakes where the chance of entraining air in the underway seawater system is reduced. 6.5.1.2 Additional sources of analytical uncertainty Outside of the storm-impacted sections, I observed median offsets between ΔO2/Ar and ΔO2/N2 less than ~1.5 % in all cruise regions. Given the calibrated accuracy of the underway O2 data, and the validation of N2,sat and ΔO2/Ar (section 6.4.1), I believe that these median signals largely represent real physical differences between ΔO2/Ar and ΔO2/N2. However, the full range of differences between ΔO2/Ar and ΔO2/N2 extended from ~-6 to 12 %. This large range is attributable to some sources of measurement uncertainty and the sensitivity of N2 calculations to various assumptions, which contribute to a mean uncertainty in ΔO2/N2 of ~1.3 %. By comparison, the absolute uncertainty in underway ΔO2/Ar, is ~0.75 %, and the combined error in ΔO2/Ar-ΔO2/N2 is ~1.5 % (details in section D.2.4 of Appendix D). More extreme biases between ΔO2/Ar and ΔO2/N2 are attributed to other sampling artifacts, resulting from differences in the response times of the respective gas sensors. 151 Although my data were filtered to the response time of the GTD (i.e. the slowest sensor) and adjusted for signal offsets before performing subsequent calculations, considerable transient biases between ΔO2/Ar and ΔO2/N2 remain when comparing individual data points. These offsets were most noticeable in waters with strong hydrographic gradients (e.g. near continental shelf, ice-covered or glacial regions), as demonstrated in Figure 6.8, which shows a subsection of the Arctic transect through a coastal fjord (~8800 km in Figs. 6.1, 6.5). Although there is clear coherence between ΔO2/Ar and ΔO2/N2 (Fig. 6.8b), the slow GTD response produces transient differences between ΔO2/Ar and ΔO2/N2 around hydrographic fronts. This problem, which is more significant in the unfiltered data, manifests as a large range in ΔO2/Ar – ΔO2/N2 and extended tails in Fig. 6.6a-c. The smoothing I applied reduces these transient signals but does not eliminate them altogether. Additional smoothing and filtering of the data may reduce such offsets, but the spatial features of the resulting datasets would be significantly dampened. Outside of strong frontal regions, or where ΔO2/Ar and ΔO2/N2 are low, this issue is less significant. In future studies of ΔO2/N2-NCP, I recommend the data processing procedures outlined in section 6.3.2. Additional data treatment may be required to selectively remove signals clearly impacted by the slower response of the GTD relative to the optode. This procedure could, for example, be facilitated by comparing ΔO2/N2 and SST signals. The O2/N2 measurement system used in this study was designed to optimize GTD response times (Ch. 5), so the issues described here reflect current instrumentation limitations and are unavoidable without further data filtering. Overall, this problem, while not insurmountable, should motivate continued development of GTD technology with more rapid response times. 152 Figure 6.8 A subset of the 2019 Arctic cruise track depicting the occurrence of transient ΔO2/Ar – ΔO2/N2 decoupling resulting from differential response times of the optode, GTD and MIMS. Panel (a) shows the difference between the unfiltered (grey) and filtered (black) ΔO2/Ar and ΔO2/N2 datasets. The histogram shows the corresponding distribution of offsets. In panels (b) and (c), the thick, lighter-coloured lines represent unfiltered data, while the bold lines are the filtered signals. Note that the ΔO2/N2 data in (b) were shifted up by 2 % so signals could be distinguished from ΔO2/Ar. SST is shown for reference in (d). 6.5.2 Explaining differences between ΔO2/Ar and ΔO2/N2 In Chapter 4, I developed a one-dimensional model to evaluate the contributions of various physical processes to divergence between ΔΟ2/Αr and ΔO2/N2 (i.e. ΔN2/Ar disequilibrium) at OSP. These simulations demonstrated the coupling between seasonal SST trends and ΔN2/Ar variability in temperate waters. Rapid SST changes and short-term elevated wind events or SLP variability were shown to induce more transient responses, while combined bubble and vertical mixing fluxes contributed to seasonal alterations in baseline ΔN2/Ar, leading to higher winter values. As I discuss below, the field observations presented here are consistent with these results, and with surface inert gas observations reported elsewhere (Hamme et al., 2017; Hamme & Emerson, 2006). 153 In the offshore NEP-summer waters, surface ΔN2/Ar values of ~0.25 % (Fig. D.4) predominantly reflect the influence of summertime warming in increasing gas saturation states, with gas re-equilibration under low-to-moderate wind speeds (typically < 10 m s-1) maintaining ΔN2 greater than ΔAr. In contrast, the ΔN2/Ar anomalies along the offshore section of the NEP-winter cruise (median ~0.5 %) likely reflect cooling and stronger bubble and, to some extent, mixing fluxes, which respectively lower and elevate gas saturation states and ΔΝ2/Αr. Despite lacking ΔN2/Ardeep observations from the winter cruise (archived observations suggest a value close to 0.5 %; Hamme et al., 2019), I did measure relatively strong offshore mixing rates (κZ ~4x10-4 m2 s-1; Table D.1), implying that mixing contributed to the observed ΔN2/Ar disequilibrium. The influence of vertical mixing was also likely significant in the QCS waters, where I observed the largest ΔN2/Ar disequilibria. In this region, sedimentary or deep-water denitrification, which has been observed in coastal fjords and inland channels throughout British Columbia (Bourbonnais et al., 2013; Manning et al., 2010) may have elevated ΔN2/Ardeep, resulting in the vertical supply of microbially N2-enriched water into surface waters. As the success of the N2' calculations suggest (section 6.5.3), the influence of vertical mixing on surface ΔN2/Ar across all sampling regions in NEP-winter was important, and my combined estimates of κZ and ΔN2/Ardeep capture this process. Enhanced mixing of O2-deplete subsurface water in the nearshore QCS region is also reflected in strong negative surface ΔO2 observations in that region (Fig. 6.4a). In the Arctic, the elevated ΔAr and ΔN2 I observed (up to 10 %; Fig. D.2) exceed most previously reported gas observations in the region (Eveleth et al., 2014; Hamme et al., 2017), but such conditions can be produced in mixed layer model simulations replicating the conditions encountered before my sampling in northern Baffin Bay (BB) (warming up to 9 oC over ~30 days and mean wind speeds ~4 m s-1; simulation results based on the model in Ch. 4 not shown). The resulting positive ΔN2/Ar disequilibria (median ~0.5 % in BB; Fig. D.4) is consistent with values of ~0.3–0.7 % predicted in these numerical simulations, reflecting the dominant influence of seasonal warming. Although warming increases both ΔAr and ΔN2, Ar re-equilibrates more rapidly than N2, causing ΔN2 > ΔAr. The mixing contribution was less important (mean BB κZ ~1x10-4 m2 s-1). Elsewhere in the Arctic, the high spatial heterogeneity of ΔO2, ΔAr and ΔN2 likely reflects variability in ice cover and environmental forcing via air-sea exchange, SST changes and mixing. 154 6.5.3 Evaluating ΔO2/N2' as an NCP tracer in field studies Across the dataset, estimates of the biological O2 saturation anomaly based on O2/N2 measurements were able to replicate the high-resolution heterogeneity in surface productivity captured by the MIMS-based ΔO2/Ar surveys (Figs. 6.3-6.5). However, offsets between ΔO2/Ar and uncorrected ΔO2/N2 manifested as important deviations in NCP estimates derived from the respective tracers (Fig. 6.7). Most importantly, in a portion of the dataset (~15 % of the pooled data), ΔO2/N2-NCP predicted net heterotrophic conditions (negative NCP), whereas ΔO2/Ar-NCP was positive (Table 6.1). Notwithstanding sources of analytical errors (section 6.5.1), these offsets are largely attributable to physical processes which cause ΔN2 and ΔAr to diverge. If uncorrected, these effects can have significant consequences for the interpretation of oceanic productivity and trophic status, particularly if NCP estimates are integrated over an annual cycle. Fortunately, such biases can be corrected to a large extent, using the new tracer ΔO2/N2' (Chapter 4). When I apply these corrections, derived ΔO2/N2' approached ΔO2/Ar across all cruise regions and sub-regions, and the linear relationship between these tracers was not significantly different than one (Fig. 6.6). On regional scales, ΔO2/N2'-NCP was roughly equivalent to ΔO2/Ar-NCP and was typically more accurate than ΔO2/N2-NCP (Fig. 6.7). Indeed, in all sampling sub-regions, the median difference between ΔO2/Ar- and ΔO2/N2'-NCP was lower than corresponding differences with ΔO2/N2-NCP (Table 6.1). While the N2' calculations were unable to fully eliminate NCP biases (see section 6.5.3.1), my results nonetheless demonstrate the ability of ΔO2/N2' to reduce NCP estimation errors. For example, offsets between ΔΟ2/Ar- and ΔO2/N2'-NCP (red lines in Fig. 6.7b) were typically smaller than NCP errors associated with vertical O2 mixing fluxes (Table 6.1) or the ~20-40 % uncertainty in the O2 gas transfer velocity, kO2 (Bender et al., 2011; Wanninkhof, 2014). Biases in the ΔO2/N2-NCP dataset (black lines in Fig. 6.7b) were more frequently equivalent to or greater than these other errors. Median mixing biases, estimated from N2O-based measurements in the NEP (following Ch. 2), or by combining NEMO model- and CTD-derived κZ and O2 observations in the Arctic (following Ch. 3), ranged from ~2 to >20 mmol O2 m-2 d-1, with higher values in nearshore regions. Errors associated with a 20 % uncertainty in kO2 were up to ~12 mmol O2 m-2 d-1 (mean ~3 mmol O2 m-2 d-1). Excluding observations where the ΔO2/Ar – ΔO2/N2 offset was >1.5 % (i.e. likely resulting from analytical uncertainties and sampling artifacts), biases between uncorrected ΔO2/N2- and ΔO2/Ar-NCP exceeded the magnitude of the 155 corresponding air-sea and O2 mixing flux errors in ~40 % and 5 % of the dataset, respectively. These values were reduced to ~10 % and 1 % after applying N2' calculations, suggesting that uncertainty in ΔO2/N2' will be typically smaller than other main sources of error in NCP calculations. Moreover, ΔO2/N2'-NCP more frequently predicted the same metabolic status as ΔO2/Ar-based estimates in all sub-regions (~96 % of all observations; Table 6.1). These results demonstrate the strong potential of the N2' approach for reducing biases in NCP estimates derived from O2/N2 surveys, even if offsets between ΔO2/Ar and ΔO2/N2' cannot be fully reconciled. The relative success the N2' calculations varied by sub-region. In the NEP (summer and winter), ΔO2/N2' represented a significant improvement over uncorrected ΔO2/N2. In parts of the Arctic, however, the benefit of N2' is somewhat less clear, as median and pair-wise offsets between NCP calculations were similar in both the ΔO2/N2 and ΔO2/N2' datasets. Yet, my analyses suggest that ΔO2/N2' is a less ambiguous NCP tracer, particularly in Baffin Bay where estimates based on ΔO2/N2 had the opposite sign to ΔΟ2/Αr-NCP in ~5 % of the dataset (compared with no values in the ΔO2/N2' dataset). In the coastal waters of the Arctic shelf and CAA, where the vertical O2 mixing bias may overwhelm the ML mass balance (see Ch. 3), N2' calculations are subject to more uncertainty (see below, section 6.5.3.1), and may therefore not be necessary. Indeed, in the shelf and CAA regions, median NCP biases based on ΔΟ2/N2' and ΔO2/N2 (~0.8 ± 2.1 and 1.1 ± 1.9 mmol O2 m-2 d-1, respectively) were smaller than the mean mixing biases of ~6.2 ± 12.1 mmol O2 m-2 d-1. In the nearshore waters of the NEP (QCS region), the O2 mixing bias (up to ~90 mmol O2 m-2 d-1) constituted a major component of the ML O2 mass balance and was higher than NCP biases between the various tracers (~9 mmol O2 m-2 d-1). However, N2' calculations still significantly reduced the large errors between ΔO2/Ar- and ΔO2/N2-NCP. Thus, while N2' can reduce NCP errors in both coastal and offshore waters, it may remain more important for future work to address challenges relating to the vertical mixing of O2 in such dynamic regions. In contrast, calculation of ΔO2/N2' will likely be necessary to accurately reflect the true metabolic state of low productivity offshore waters and evaluate seasonal changes in ocean productivity. In all of my sampling regions, much of the remaining biases between ΔO2/Ar- and ΔO2/N2'-NCP can be attributed to simplifications in the N2' calculations, which are discussed in Chapter 4 and summarized for the present study below. 156 6.5.3.1 Remaining biases between ΔO2/Ar and ΔO2/N2' Notwithstanding sources of analytical error (sections 6.5.1 and D.4), remaining biases between ΔO2/Ar and ΔO2/N2' arise from errors in ΔΟ2/N2' computations, which have been estimated at ~0.3 % (Chapter 4). That work demonstrated the ability of the N2' budget to capture the dominant processes driving N2 evolution in oceanic waters of the offshore NEP, but in my dataset, errors are most significant in nearshore regions where the negligence of lateral fluxes likely oversimplifies ΔN2/Ar dynamics. For example, lateral advection or mixing, freshwater input and ice processes may contribute to divergence between the NCP tracers, while significant water mass transport would produce uncertainty in the environmental histories that I ascribe to my underway observations. These processes are difficult to constrain in simple modeling approaches, and the physical and biological effects of ice melt on seawater ΔN2/Ar signatures are uncertain (Zhou et al., 2014). Moreover, as I lack broad coverage of direct κZ and ΔN2/Ardeep observations, misrepresentation of these terms may have contributed to errors in my ΔO2/N2' calculations. However, sensitivity analyses in which I set κZ to 0 m2 s-1 or varied κZ and ΔN2/Ardeep (by 5x10-5 m2 s-1 and 0.25 %, respectively), produced ΔO2/N2'-NCP estimates that were typically better than, or equivalent to, ΔO2/N2-based values (blue shading in Fig. 6.7b), suggesting that even sparse κZ or ΔN2/Ardeep measurements can improve the performance of ΔO2/N2' as an NCP tracer. In the absence of direct observations, κZ may be derived from numerical simulations or geochemical (e.g. Ch. 2) and hydrographic proxies (Cronin et al., 2015; Haskell et al., 2016), while ΔN2/Ardeep can be approximated using archived datasets (Hamme et al., 2019). Future improvements in the accuracy of ΔO2/N2' will require further refinement of gas flux parameterizations and environmental datasets, particularly in polar regions where current re-analysis products are less accurate (Fig. D.8), and partial ice-cover complicates air-sea flux parameterizations (Islam et al., 2016). Climatological or ancillary datasets (e.g. obtained from Argo floats) of subsurface hydrographic conditions or MLD may be used to better evaluate the time-variability of ML gas evolution, while air-sea flux parameterizations should be validated for the relevant study region. These considerations would enable application of the present approach in unattended optode/GTD deployments from VOSs or USVs where subsurface observations are presently not feasible. 157 6.6 Conclusions In this chapter, I evaluated a new approach for deriving NCP estimates from underway O2/N2 surveys, building on work presented in Chapter 4 describing the approach in a model framework. The current study constitutes the first published NCP results based on underway ship-board O2/N2 data, and a first attempt at comparing ΔO2/Ar-NCP and ΔO2/N2-NCP. My results demonstrate the potential to accurately derive NCP from underway O2/N2 observations obtained from optode and GTD measurements in a range of ocean regions, including coastal and offshore waters of polar and subpolar seas. These observations, combined with simple computations of the new tracer, ΔO2/N2', can be used to replicate ΔO2/Ar-based NCP estimates. In some cases, however, NCP estimates based on uncorrected ΔO2/N2 may be sufficiently accurate, and future work should endeavour to address other current limitations in NCP calculations (e.g. vertical O2 mixing flux biases) or further validate the present approach. In all future applications, gas sensors should be installed as near to the seawater intake as possible (during ship-board use) and calibrated before and after deployments (at minimum) to optimize data accuracy. Successful applications of ΔO2/N2' will also rely on accurate parameterization of the environmental conditions for the study region of interest and careful data handling to minimize analytical errors in the measurements of O2/N2. These endeavours will be aided by the continued development of reanalysis data products and gas sensor technology. Widespread application of the approach evaluated here has the potential to significantly expand global coverage of NCP measurements from relatively inexpensive autonomous surface O2 and N2 measurements. Indeed, future surveys should incorporate optode/GTD instrumentation or autonomous measurement systems (e.g. Ch. 5) into existing sampling infrastructure on research vessels, VOS (e.g. container ships, cruise ships, sailboats) and in-situ USVs to provide unprecedented spatial and temporal coverage of NCP estimates, and improved integration with other autonomous oceanographic and ecological observations. Given the anticipated impacts of climate change on marine biological productivity, such observations will be crucial for evaluating future variability in biogeochemical and environmental conditions and predicting associated ecosystem-level responses across a range of oceanic environments. 158 Chapter 7: Conclusion Accurate measurements of marine net community production (NCP) are necessary for assessing the ecological impacts of climate-driven shifts unfolding across many ocean regions, including the Subarctic Northeast Pacific and Canadian Arctic. Over the past two decades, new ship-board instruments and measurement techniques have enabled high-resolution observation of NCP variability, yet the resulting data are limited by several methodological and conceptual challenges. In physically dynamic and highly productive coastal waters, vertical fluxes of O2 across the base of the mixed layer can cause significant biases in the mixed layer NCP tracer, ΔO2/Ar. Moreover, the requirement of ship-board mass spectrometry for underway ΔO2/Ar measurements has largely restricted observations to research vessels, limiting the scope and coverage of NCP measurements. These challenges, in turn, limit our ability to understand the environmental controls on NCP, and link NCP variability to other key marine ecological processes. This dissertation contributes new methodological and conceptual approaches to improve the accuracy of NCP estimates in a range of oceanic regimes, and provides insights into the mechanistic controls on NCP variability in Subarctic and Arctic marine waters. Below, I summarize the key findings and implications of this research, and provide recommendations for future work. 7.1 Summary and Implications 7.1.1 Correcting ship-board NCP estimates for vertical O2 mixing biases In Chapters 2 and 3, I evaluated geochemical and numerical approaches to refine ΔO2/Ar-based NCP estimates for the vertical mixing of O2 across the base of the mixed layer. The results presented in Ch. 2 constitute the first field evaluation of a tracer-based method for deriving mixing corrections using coupled surface and subsurface O2 and nitrous oxide (N2O) measurements (Cassar et al., 2014). Using high-resolution surface ΔO2/Ar observations and discrete measurements of O2 and N2O from ship-board Rosette sampling on five cruises in coastal and offshore waters of the Subarctic Northeast Pacific, I demonstrated the significant utility of N2O as a tracer of NCP vertical mixing biases. In Chapter 3, I employed a different approach for evaluating mixing corrections, using output from a numerical model alongside underway ship-board 159 observations. Results presented in this chapter, based on summertime sampling in the central and eastern Canadian Arctic Ocean, demonstrate the potential for refining high-resolution NCP estimates using combined numerical simulations and in-situ observations when direct geochemical or hydrographic approaches (e.g. those based on N2O measurements) are invalid. Overall, the research in Chapters 2-3 constitutes an important advancement in our ability to describe NCP in dynamic waters and yields important new NCP observations across a range of oceanic regimes. Both studies demonstrate the importance of considering vertical O2 fluxes in interpreting ΔO2/Ar observations and deriving NCP from evaluations of the mixed layer O2 budget. In previous work, ΔO2/Ar data have been discarded when strong mixing biases were suspected (i.e. indicated by negative ΔO2/Ar), significantly limiting data coverage in many high-productivity regions (e.g. coastal upwelling regimes). In this thesis, however, refined NCP estimates reveal net autotrophy (i.e. positive NCP) in most sampled regions, consistent with views that coastal waters contribute significantly to global carbon cycling. In the Subarctic Northeast Pacific, a coast-to-open ocean gradient in the vertical mixing bias was revealed, and refined NCP results showed significantly elevated (>100 mmol O2 m-2 d-1) rates over the continental shelf of British Columbia, and periodic macro- and micronutrient limitation in offshore waters. In contrast, NCP in the Canadian Arctic Ocean was highly heterogeneous, with high-productivity (>50 mmol O2 m-2 d-1) in waters influenced by the vertical supply of nutrients mediated by wind-driven mixing or glacial discharge plumes, and low NCP or periodic heterotrophy near strong freshwater sources of terrestrially derived organic carbon. These new results provide important insights into NCP dynamics in both study regions, and its response to various environmental factors, aiding our understanding of potential future NCP variability under ongoing climate change. 7.1.2 NCP derivation from underway ΔO2/N2 measurements Chapters 4-6 describe a new approach for NCP derivation based on underway observations of a nitrogen-based biological O2 saturation anomaly, ΔO2/N2. Work presented in these chapters builds a framework for ΔO2/N2-based NCP estimation by employing numerical, laboratory and field studies and instrumentation development. Numerical work, based on a simple one-dimensional model, explores the differential responses of ΔO2/Ar and ΔO2/N2 to variable environmental forcing, leading to the development of a new in-situ NCP tracer, ΔO2/N2', which 160 corrects for excess N2 supersaturation resulting from bubble processes, seawater temperature changes and vertical mixing (Ch. 4). Based on this proof-of-concept study, a new ship-board measurement system (i.e. the Pressure of In-situ Gases Instrument, PIGI) has been developed for autonomous, high-resolution surface water ΔO2/N2 observations from measurements with an optode and Gas Tension Device (GTD) (Ch. 5). Following significant design modifications and field and laboratory testing, the system is shown to be capable of producing accurate measurements of seawater O2 and N2 from unattended deployments on research vessels or at land-based observatories. Side-by-side field deployments of a membrane inlet mass spectrometer (MIMS) and the PIGI system enable the direct comparison of underway ΔO2/Ar and ΔO2/N2 measurements in Chapter 6. The results from three cruises in the Northeast Pacific and eastern Canadian Arctic demonstrate strong coherence between the tracers, with small, but important offsets leading to differences in NCP estimates derived from ΔO2/Ar and ΔO2/N2. After applying simple numerical model calculations, NCP derived from ΔO2/N2' matches closely with estimates based on ΔO2/Ar. In some ocean regions, such as the Canadian Arctic Archipelago, however, differences between ΔO2/Ar and ΔO2/N2 may be sufficiently small that NCP estimates based on uncorrected ΔO2/N2 are nearly equivalent to those derived from ΔO2/Ar. In total, NCP derived from ΔO2/N2 or ΔO2/N2' matches estimates based on ΔO2/Ar to within the error of other main sources of uncertainty in NCP estimates. Overall, the research presented in Ch. 4-6 demonstrates the potential to significantly expand global coverage of NCP estimates from underway ΔO2/N2 observations, reducing the need for mass spectrometric ΔO2/Ar measurements. Future deployments of autonomous optode/GTD sensor packages on research ships, volunteer observing platforms (e.g. container ships) or autonomous surface vehicles have the potential to greatly expand observations of the surface water biological O2 saturation anomaly, facilitating widespread NCP measurements. 7.2 Future work The research presented in this thesis constitutes an important advancement in our ability to assess marine NCP variability, yet additional work will be required to address outstanding limitations and further validate the tools presented here. Below, I summarize what I believe to be important avenues for future research. 161 Several key sources of uncertainty in the derivation of NCP from ship-board gas surveys remain. Among these, the influence of diel (i.e. daily) variability in surface water O2 saturation states, water mass advection and uncertainty in the O2 gas transfer velocity are likely most significant. Diel variability, caused by day-night cycles in mixed layer productivity and community-wide respiration, is less problematic in lower productivity offshore regions but may be significant in coastal waters where ΔO2/Ar (or ΔΟ2/Ν2) fluctuations can exceed ~5-10 % (Rosengard et al., 2020; Wang et al., 2020; Ch. 2). This issue makes the timing of sampling an important consideration for the interpretation of the mixed layer O2 budget, and it may be necessary to adjust ship-board sampling strategies to obtain observations over a 12- to 24-hr cycle (Wang et al., 2020). Moreover, as surface ΔO2/Ar or ΔO2/N2 reflects NCP along a Lagrangian (i.e. moving) flow path over the previous mixed layer O2 residence time (Teeter et al., 2018), water mass advection makes it challenging to ascribe productivity, vertical mixing processes or air-sea fluxes to the specific location at which ship-board measurements were made. Since the evolution of surface N2O supersaturation should be consistent with that of O2 in a moving water mass, this issue is somewhat less problematic for the application of N2O as a vertical mixing tracer. However, the use of numerical model output to assess vertical mixing dynamics may be expanded to incorporate Lagrangian particle tracking to better identify the historical mixing dynamics and spatial footprint of O2 evolution along a flow path. This endeavour will be most useful in nearshore waters where advection can be significant. Finally, reducing uncertainty in NCP estimates will require better constraints on the O2 gas transfer velocity derived from wind speed reanalysis products, which remains an important source of error in calculations (Wanninkhof, 2014). The tools developed in this thesis – namely new approaches for correcting mixed layer NCP for vertical mixing fluxes and deriving NCP from surface ΔO2/N2 measurements – should be further evaluated. For example, the efficacy of N2O as a mixing tracer should be assessed in waters where subsurface or sedimentary denitrification may influence water column O2/N2O stoichiometry. Similarly, the vertical mixing rates derived from N2O measurements or numerical model output should be compared against one another in ocean regions where both are feasible, and evaluated against direct estimates of ocean mixing derived from microstructure observations when possible. The numerical modeling approach to estimating vertical O2 fluxes should be further evaluated and refined by testing the sensitivity of the method to mixing parameterizations and 162 representations of bathymetry and environmental forcing in the model. In addition, the use of ΔO2/N2 or ΔO2/N2' as mixed layer NCP tracers should be further evaluated against ΔO2/Ar in a range of ocean regions, including tropical and subtropical waters where the effects of lower wind speeds and seasonal heat fluxes and nitrogen fixation may result in different decoupling between ΔAr and ΔN2, or in the Southern Ocean where decoupling may be stronger. Finally, it will remain important to compare NCP derived from underway ΔO2/Ar or ΔO2/N2 measurements against alternative methods for estimating primary production or carbon export (e.g. as in Burgers et al., 2020; Giesbrecht et al., 2012; Rosengard et al., 2020; Timmerman & Hamme, 2021). In doing so, future research should also attempt to better quantify the proportion of NCP occurring below the mixed layer, particularly in ocean regions where subsurface productivity maxima exist, such as in Baffin Bay. Such evaluations are needed to assess methodological constraints in underway sampling and the potential decoupling between surface NCP and oceanic carbon uptake and export. Notwithstanding the need for further methodological refinement, as described above, the tools presented in this thesis will facilitate improved monitoring of NCP in a variety of ocean regions. Using these tools, it will be possible to amass new NCP datasets from various platforms – including research vessels, volunteer observing ships, and autonomous surface vehicles – or land-based observatories. For example, recent technological developments have enabled continuous measurements of surface seawater N2O concentrations (e.g. Grefe & Kaiser, 2014), and the simultaneous deployment of these systems with ΔO2/Ar or ΔO2/N2 measurement systems will facilitate the collection of high-resolution NCP datasets, corrected for vertical mixing. Ultimately, the accumulation of large NCP datasets, spanning a variety of ocean regimes and time periods, will facilitate the development of improved statistical algorithms predicting NCP from more commonly-measured variables. For example, existing global algorithms, based on remote sensing observations (e.g. Li & Cassar, 2016; Westberry et al., 2012) can be further refined for individual ocean regions (e.g. Subarctic Northeast Pacific and Canadian Arctic) using new datasets and statistical techniques (e.g. machine learning methods), while in-situ observations should be more widely used to evaluate and refine predictions of biological productivity in numerical models. Improved algorithms will be vital for predicting synoptic scale NCP variability, increasing our understanding of climate-dependent shifts in key marine ecological processes. The resulting 163 data will present new opportunities to investigate the links between NCP and phytoplankton community structure, the distribution of key fish species, or variability in other oceanographic or environmental conditions. 7.3 Concluding remarks The new tools for ship-board NCP measurements developed and openly shared in this thesis and corresponding publications (see Preface) will complement the expanding global array of autonomous biogeochemical observation systems. Going forward, research vessels will remain important infrastructure for oceanographic research, but these sampling platforms will be increasingly complemented by new technologies and methods for in-situ NCP derivation from floats, gliders and moorings. To fully exploit the full suite of emerging tools, open data access, software sharing, and technology transfer will be vital to integrate and validate observations from multiple observing systems. These endeavours, complemented by the formation of cross-discipline synergies between the observation and modeling communities will enable fruitful research output with significant implications for improving our understanding of the ocean in the global climate system. 164 References Acker, J. G., & Leptoukh, G. (2007). Online Analysis Enhances Use of NASA Earth Science Data. Eos Transitions AGU, 88(2), 14 and 17. Ahmed, M., & Else, B. G. T. (2019). The Ocean CO2 Sink in the Canadian Arctic Archipelago: A Present Day Budget and Past Trends Due to Climate Change. Geophysical Research Letters, 46. https://doi.org/10.1029/2019GL083547 Ahmed, M., Else, B. G. T., Burgers, T. M., & Papakyriakou, T. (2019). Variability of Surface Water pCO 2 in the Canadian Arctic Archipelago From 2010 to 2016. Journal of Geophysical Research: Oceans, 124(3), 1876–1896. https://doi.org/10.1029/2018JC014639 Alkire, M. B., Jacobson, A. D., Lehn, G. O., Macdonald, R. W., & Rossi, M. W. (2017). On the geochemical heterogeneity of rivers draining into the straits and channels of the Canadian Arctic Archipelago. Journal of Geophysical Research: Biogeosciences, 122(10), 2527–2547. https://doi.org/10.1002/2016JG003723 Allen, S. E., & Durrieu de Madron, X. (2009). A review of the role of submarine canyons in deep-ocean exchange with the shelf. Ocean Science, 5, 607–620. https://doi.org/10.5194/os-5-607-2009 Amundsen Science Data Collection. (2020a). CTD-Rosette data collected by the CCGS Amundsen in the Canadian Arctic. Arcticnet Inc., arcticnet Inc., Québec, Canada. Processed data. Version 1. Archived at www.polardata.ca, Canadian Cryospheric Information Network (CCIN), Waterloo, Canada. Waterloo, Canada: Canadian Cryospheric Information Network (CCIN). https://doi.org/https://doi.org/10.5884/12713. Accessed May 2020. Amundsen Science Data Collection. (2020b). TSG data collected by the CCGS Amundsen in the Canadian Arctic. Arcticnet Inc., arcticnet Inc., Québec, Canada. Processed data. Version 1. Archived at www.polardata.ca, Canadian Cryospheric Information Network (CCIN), Waterloo, Canada. https://doi.org/https://doi.org/10.5884/12715. Accessed May 2020 Arbic, B. K., St-Laurent, P., Sutherland, G., & Garrett, C. (2007). On the resonance and influence of the tides in Ungava Bay and Hudson Strait. Geophysical Research Letters, 34(17), L17606. https://doi.org/10.1029/2007GL030845 Ardyna, M., Gosselin, M., Michel, C., Poulin, M., & Tremblay, J. (2011). Environmental forcing of phytoplankton community structure and function in the Canadian High Arctic: contrasting oligotrophic and eutrophic regions. Marine Ecology Progress Series, 442, 37–57. https://doi.org/10.3354/meps09378 Ardyna, M., Babin, M., Gosselin, M., Devred, E., Rainville, L., & Tremblay J.-E. (2014). Recent Arctic Ocean sea ice loss triggers novel fall phytoplankton blooms. Geophysical Research Letters, 41, 6207–6212. https://doi.org/10.1002/2014GL061047.Received Ardyna, M., Mundy, C. J., Mayot, N., Matthes, L. C., Oziel, L., Horvat, C., et al. (2020). Under-Ice Phytoplankton Blooms: Shedding Light on the “Invisible” Part of Arctic Primary Production. Frontiers in Marine Science, 7. https://doi.org/10.3389/fmars.2020.608032 Arévalo-Martínez, D. L., Beyer, M., Krumbholz, M., Piller, I., Kock, A., Steinhoff, T., et al. (2013). A new method for continuous measurements of oceanic and atmospheric N2O, CO and CO2: performance of off-axis integrated cavity output spectroscopy (OA-ICOS) coupled to non-dispersive infrared detection (NDIR). Ocean Science, 9, 1071–1087. https://doi.org/10.5194/os-9-1071-2013 Arrigo, K. R., & van Dijken, G. L. (2015). Continued increases in Arctic Ocean primary production. Progress in Oceanography, 136, 60–70. https://doi.org/10.1016/j.pocean.2015.05.002 Arrigo, K. R., van Dijken, G., & Pabi, S. (2008). Impact of a shrinking Arctic ice cover on marine primary production. Geophysical Research Letters, 35(19), 1–6. https://doi.org/10.1029/2008GL035028 Arrigo, K. R., Perovich, D. K., Pickart, R. S., Brown, Z. W., Van Dijken, G. L., Lowry, K. E., et al. (2012). Massive phytoplankton blooms under arctic sea ice. Science, 336(6087), 1408. https://doi.org/10.1126/science.1215065 165 Arrigo, K. R., van Dijken, G. L., Castelao, R. M., Luo, H., Rennermalm, Å. K., Tedesco, M., et al. (2017). Melting glaciers stimulate large summer phytoplankton blooms in southwest Greenland waters. Geophysical Research Letters, 44(12), 6278–6285. https://doi.org/10.1002/2017GL073583 Atlas, R., Hoffman, R. N., Ardizzone, J., Leidner, S. M., Jusem, J. C., Smith, D. K., & Gombos, D. (2011). A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bulletin of the American Meteorological Society, 92, 157–174. https://doi.org/10.1175/2010BAMS2946.1 Bakun, A. (1973). Coastal upwelling indices, west coast of North America, 1946-71. U.S. Dept. of Commerce, NOAA Tech. Rep., NMFS SSRF-671, 1–103. Bamber, J., van den Broeke, M., Ettema, J., Lenaerts, J., & Rignot, E. (2012). Recent large increases in freshwater fluxes from Greenland into the North Atlantic. Geophysical Research Letters, 39(19), n/a-n/a. https://doi.org/10.1029/2012GL052552 Bange, H., Rapsomanikis, S., & Andreae, M. (1996). Nitrous oxide in coastal waters. Global Biogeochemical Cycles, 10(1), 197–207. https://doi.org/10.1029/95GB03834 Barber, D. G., Hop, H., Mundy, C. J., Else, B., Dmitrenko, I. A., Tremblay, J. E., et al. (2015). Selected physical, biological and biogeochemical implications of a rapidly changing Arctic Marginal Ice Zone. Progress in Oceanography, 139, 122–150. https://doi.org/10.1016/j.pocean.2015.09.003 Barwell-Clarke, J., & Whitney, F. (1996). Institute of Ocean Sciences nutrient methods and analysis. Canadian Technical Report of Hydrography and Ocean Sciences, (182), 1–42. Bender, M. L., Kinter, S., Cassar, N., & Wanninkhof, R. (2011). Evaluating gas transfer velocity parameterizations using upper ocean radon distributions. Journal of Geophysical Research, 116(C02010), 1–11. https://doi.org/10.1029/2009JC005805 Bhatia, M. P., Kujawinski, E. B., Das, S. B., Breier, C. F., Henderson, P. B., & Charette, M. A. (2013a). Greenland meltwater as a significant and potentially bioavailable source of iron to the ocean. Nature Geoscience, 6(4), 274–278. https://doi.org/10.1038/ngeo1746 Bhatia, M. P., Das, S. B., Xu, L., Charette, M. A., Wadham, J. L., & Kujawinski, E. B. (2013b). Organic carbon export from the Greenland ice sheet. Geochimica et Cosmochimica Acta, 109, 329–344. https://doi.org/10.1016/j.gca.2013.02.006 Bhatt, U. S., Walker, D. A., Walsh, J. E., Carmack, E. C., Frey, K. E., Meier, W. N., et al. (2014). Implications of arctic sea ice decline for the earth system. Annual Review of Environment and Resources, 39, 57–89. https://doi.org/10.1146/annurev-environ-122012-094357 Bianucci, L., Denman, K. L., & Ianson, D. (2011). Low oxygen and high inorganic carbon on the Vancouver Island Shelf. Journal of Geophysical Research, 116(C07011). https://doi.org/10.1029/2010JC006720 Bittig, H. C., & Körtzinger, A. (2015). Tackling oxygen optode drift: Near-surface and in-air oxygen optode measurements on a float provide an accurate in situ reference. Journal of Atmospheric and Oceanic Technology, 32, 1536–1543. https://doi.org/10.1175/JTECH-D-14-00162.1 Bittig, H. C., Fiedler, B., Scholz, R., Krahmann, G., & Körtzinger, A. (2014). Time response of oxygen optodes on profiling platforms and its dependence on flow speed and temperature. Limnology and Oceanography: Methods, 12, 617–636. https://doi.org/10.4319/lom.2014.12.617 Bittig, H. C., Körtzinger, A., Neill, C., van Ooijen, E., Plant, J. N., Hahn, J., et al. (2018). Oxygen Optode Sensors: Principle, Characterization, Calibration, and Application in the Ocean. Frontiers in Marine Science, 4(429), 1–25. https://doi.org/10.3389/fmars.2017.00429 Blais, M., Tremblay, J. É., Jungblut, A. D., Gagnon, J., Martin, J., Thaler, M., & Lovejoy, C. (2012). Nitrogen fixation and identification of potential diazotrophs in the Canadian Arctic. Global Biogeochemical Cycles, 26(3). https://doi.org/10.1029/2011GB004096 Bond, N. A., Cronin, M. F., Freeland, H., & Mantua, N. (2015). Causes and impacts of the 2014 warm anomaly in 166 the NE Pacific. Geophysical Research Letters, 42, 3414–3420. https://doi.org/10.1002/2015GL063306 Bonjean, F., & Lagerloef, G. S. E. (2002). Diagnostic model and analysis of the surface currents in the tropical Pacific Ocean. Journal of Physical Oceanography, 32, 2938–2954. Borges, A. V., Delille, B., & Frankignoulle, M. (2005). Budgeting sinks and sources of CO2 in the coastal ocean: Diversity of ecosystems counts. Geophysical Research Letters, 32(14). https://doi.org/10.1029/2005GL023053 Bourbonnais, A., Lehmann, M. F., Hamme, R. C., Manning, C. C., & Juniper, S. K. (2013). Nitrate elimination and regeneration as evidenced by dissolved inorganic nitrogen isotopes in Saanich Inlet, a seasonally anoxic fjord. Marine Chemistry, 157, 194–207. https://doi.org/10.1016/j.marchem.2013.09.006 Boyd, P. W., Muggli, D. L., Varela, D. E., Goldblatt, R. H., Chretien, R., Orians, K. J., & Harrison, P. J. (1996). In vitro iron enrichment experiments in NE subarctic Pacific. Marine Ecology Progress Series, 136, 179–193. Brown, K. A., Williams, W. J., Carmack, E. C., Fiske, G., François, R., McLennan, D., & Peucker-Ehrenbrink, B. (2019). Canadian Arctic Archipelago Rivers Project Geochemical Data 2014-2016. PANGAEA. https://doi.org/https://doi.org/10.1594/PANGAEA.908497 Brown, K. A., Williams, W. J., Carmack, E. C., Fiske, G., François, R., McLennan, D., & Peucker-Ehrenbrink, B. (2020). Geochemistry of Small Canadian Arctic Rivers with Diverse Geological and Hydrological Settings. Journal of Geophysical Research: Biogeosciences, 125(1), 1–21. https://doi.org/10.1029/2019JG005414 Brown, Z. W., Casciotti, K. L., Pickart, R. S., Swift, J. H., & Arrigo, K. R. (2015). Aspects of the marine nitrogen cycle of the Chukchi Sea shelf and Canada Basin. Deep Sea Research II, 118, 73–87. https://doi.org/10.1016/j.dsr2.2015.02.009 Burgers, T. M., Tremblay, J.-É., Else, B. G. T., & Papakyriakou, T. N. (2020). Estimates of net community production from multiple approaches surrounding the spring ice-edge bloom in Baffin Bay. Elementa: Science of the Anthropocene, 8. https://doi.org/10.1525/elementa.013 Burt, W. J., Westberry, T. K., Behrenfeld, M. J., Zeng, C., Izett, R. W., & Tortell, P. D. (2018). Carbon : Chlorophyll ratios and net primary productivity of Subarctic Pacific surface waters derived from autonomous shipboard sensors. Global Biogeochemical Cycles, 32. https://doi.org/10.1002/2017GB005783 Bushinsky, S. M., & Emerson, S. (2013). A method for in-situ calibration of Aanderaa oxygen sensors on surface moorings. Marine Chemistry, 155, 22–28. https://doi.org/10.1016/j.marchem.2013.05.001 Bushinsky, S. M., & Emerson, S. (2015). Marine biological production from in situ oxygen measurements on a profiling float in the subarctic Pacific Ocean. Global Biogeochemical Cycles, 29, 2050–2060. https://doi.org/10.1002/2015GB005251 Bushinsky, S. M., & Emerson, S. R. (2018). Biological and physical controls on the oxygen cycle in the Kuroshio Extension from an array of profiling floats. Deep-Sea Research Part I, 141, 51–70. https://doi.org/10.1016/j.dsr.2018.09.005 Butterworth, B. J., & Miller, S. D. (2016). Air-sea exchange of carbon dioxide in the Southern Ocean and Antarctic marginal ice zone. Geophysical Research Letters, 43, 7223–7230. https://doi.org/10.1002/2016GL069581 Bylhouwer, B., Ianson, D., & Kohfeld, K. (2013). Changes in the onset and intensity of wind-driven upwelling and downwelling along the North American Pacific coast. Journal of Geophysical Research Oceans, 118, 2565–2580. https://doi.org/10.1002/jgrc.20194 Capelle, D. W., & Tortell, P. D. (2016). Factors controlling methane and nitrous-oxide variability in the southern British Columbia coastal upwelling system. Marine Chemistry, 179(2016), 56–67. https://doi.org/10.1016/j.marchem.2016.01.011 Capelle, D. W., Dacey, J. W., & Tortell, P. D. (2015). An automated, high through-put method for accurate and precise measurements of dissolved nitrous-oxide and methane concentrations in natural waters. Limnology and Oceanography: Methods, 13, 345–355. https://doi.org/10.1002/lom3.10029 167 Capelle, D. W., Kuzyk, Z. Z. A., Papakyriakou, T., Guéguen, C., Miller, L. A., & Macdonald, R. W. (2020). Effect of terrestrial organic matter on ocean acidification and CO2 flux in an Arctic shelf sea. Progress in Oceanography, 185(April). https://doi.org/10.1016/j.pocean.2020.102319 Carmack, E., & Chapman, D. C. (2003). Wind-driven shelf/basin exchange on an Arctic shelf: The joint roles of ice cover extent and shelf-break bathymetry. Geophysical Research Letters, 30(14), 3–6. https://doi.org/10.1029/2003GL017526 Carmack, E. C., Yamamoto-Kawai, M., Haine, T. W. N., Bacon, S., Bluhm, B. A., Lique, C., et al. (2016). Freshwater and its role in the Arctic Marine System: Sources, disposition, storage, export, and physical and biogeochemical consequences in the Arctic and global oceans. Journal of Geophysical Research G: Biogeosciences, 121(3), 675–717. https://doi.org/10.1002/2015JG003140 Carpenter, J. H. (1965). The Chesapeake Bay Institute Technique for the Winkler Dissolved Oxygen Method. Limnology and Oceanography, 10(1), 141–143. https://doi.org/10.4319/lo.1965.10.1.0141 Cassar, N., Barnett, B. A., Bender, M. L., Kaiser, J., Hamme, R. C., & Tilbrook, B. (2009). Continuous high-frequency dissolved O2/Ar Measurements by Equilibrator Inlet Mass Spectrometry. Analytical Chemistry, 81(5), 1855–1864. https://doi.org/10.1021/ac802300u Cassar, N., Nevison, C. D., & Manizza, M. (2014). Correcting oceanic O2/Ar‐net community production estimates for vertical mixing using N2O observations. Geophysical Research Letters, 41, 8961–8970. https://doi.org/10.1002/2014GL062040 Castro de la Guardia, L., Garcia-Quintana, Y., Claret, M., Hu, X., Galbraith, E. D., & Myers, P. G. (2019). Assessing the Role of High-Frequency Winds and Sea Ice Loss on Arctic Phytoplankton Blooms in an Ice-Ocean-Biogeochemical Model. Journal of Geophysical Research: Biogeosciences, 124(9), 2728–2750. https://doi.org/10.1029/2018JG004869 Castro-Morales, K., Cassar, N., Shoosmith, D. R., & Kaiser, J. (2013). Biological production in the Bellingshausen Sea from oxygen-to-argon ratios and oxygen triple isotopes. Biogeosciences, 10, 2273–2291. https://doi.org/10.5194/bg-10-2273-2013 Cavole, L., Demko, A., Diner, R., Giddings, A., Koester, I., Pagniello, C., et al. (2016). Biological Impacts of the 2013–2015 Warm-Water Anomaly in the Northeast Pacific: Winners, Losers, and the Future. Oceanography, 29(2). https://doi.org/10.5670/oceanog.2016.32 Chang, B. X., Devo, A. H., & Emerson, S. R. (2012). Fixed nitrogen loss from the eastern tropical North Pacific and Arabian Sea oxygen deficient zones determined from measurements of N2:Ar. Global Biogeochemical Cycles, 26(3), 1–8. https://doi.org/10.1029/2011GB004207 Chanona, M., & Waterman, S. (2020). Temporal Variability of Internal Wave-Driven Mixing in Two Distinct Regions of the Arctic Ocean. Journal of Geophysical Research: Oceans, 125(10), 1–23. https://doi.org/10.1029/2020JC016181 Chanona, M., Waterman, S., & Gratton, Y. (2018). Variability of Internal Wave-Driven Mixing and Stratification in Canadian Arctic Shelf and Shelf-Slope Waters. Journal of Geophysical Research: Oceans, 123(12), 9178–9195. https://doi.org/10.1029/2018jc014342 Codispoti, L. A., Yoshinari, T., & Devol, A. H. (2005). Suboxic respiration in the oceanic water column. In Respiration in Aquatic Ecosystems (pp. 225–247). Oxford, UK: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198527084.003.0012 Codispoti, L. A., Kelly, V., Thessen, A., Matrai, P., Suttles, S., Hill, V., et al. (2013). Synthesis of primary production in the Arctic Ocean: III. Nitrate and phosphate based estimates of net community production. Progress in Oceanography, 110, 126–150. https://doi.org/10.1016/j.pocean.2012.11.006 Cohen, Y., & Gordon, L. I. (1979). Nitrous oxide production in the Ocean. Journal of Geophysical Research, 84(C1), 347–353. Craig, H., & Hayward, T. (1987). Oxygen Supersaturation in the Ocean: Biological Versus Physical Contributions. 168 Science, 235(4785), 199–202. https://doi.org/10.1126/science.235.4785.199 Crawford, A. D., Krumhardt, K. M., Lovenduski, N. S., van Dijken, G. L., & Arrigo, K. R. (2020). Summer High‐Wind Events and Phytoplankton Productivity in the Arctic Ocean. Journal of Geophysical Research Oceans, 1–17. https://doi.org/10.1029/2020JC016565 Crawford, W. R., & Peña, M. A. (2013). Declining Oxygen on the British Columbia Continental Shelf. Atmosphere-Ocean, 51(1), 88–103. https://doi.org/10.1080/07055900.2012.753028 Crawford, W. R., & Thomson, R. E. (1991). Physical oceanography of the western Canadian continental shelf. Continental Shelf Research, 11(8–10), 669–683. https://doi.org/10.1016/0278-4343(91)90073-F Cronin, M. F., Pellan, N. A., Emerson, S. R., & Crawford, W. R. (2015). Estimating diffusivity from the mixed layer heat and salt balances in the North Pacific. Journal of Geophysical Research: Oceans, 120, 7346–7362. https://doi.org/10.1002/2015JC011010 D’Asaro, E., & McNeil, C. (2008). Air-sea gas exchange at extreme wind speeds measured by autonomous oceanographic floats. Journal of Marine Systems, 74(1–2), 722–736. https://doi.org/10.1016/j.jmarsys.2008.02.006 D’Asaro, E. A., & McNeil, C. (2013). Calibration and stability of oxygen sensors on autonomous floats. Journal of Atmospheric and Oceanic Technology, 30(8), 1896–1906. https://doi.org/10.1175/JTECH-D-12-00222.1 Dai, A., Qian, T., Trenberth, K. E., & Milliman, J. D. (2009). Changes in Continental Freshwater Discharge from 1948 to 2004. Journal of Climate, 22(10), 2773–2792. https://doi.org/10.1175/2008JCLI2592.1 Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597. https://doi.org/10.1002/qj.828 Deutsch, C., Sarmiento, J. L., Sigman, D. M., Gruber, N., & Dunne, J. P. (2007). Spatial coupling of nitrogen inputs and losses in the ocean. Nature, 445(7124), 163–167. https://doi.org/10.1038/nature05392 DeVries, T., Deutsch, C., Rafter, P. A., & Primeau, F. (2013). Marine denitrification rates determined from a global 3-D inverse model. Biogeosciences, 10(4), 2481–2496. https://doi.org/10.5194/bg-10-2481-2013 Dore, J. E., & Karl, D. M. (1996). Nitrification in the euphotic zone as a source for nitrite, nitrate, and nitrous oxide at Station ALOHA. Limnology and Oceanography, 41(8), 1619–1628. Dugdale, R. C., & Goering, J. J. (1967). Uptake of new and regenerated forms of nitrogen in primary productivity. Limnology and Oceanography, 12(2), 196–206. https://doi.org/10.4319/lo.1967.12.2.0196 Egbert, G. D., & Erofeeva, S. Y. (2002). Efficient Inverse Modeling of Barotropic Ocean Tides. Journal of Atmospheric and Oceanic Technology, 19(2), 183–204. https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2 Emerson, S. (2014). Annual net community production and the biological carbon flux in the ocean. Global Biogeochemical Cycles, 14–28. https://doi.org/10.1002/2013GB004680 Emerson, S., & Bushinsky, S. (2016). The role of bubbles during air-sea gas exchange. Journal of Geophysical Research: Oceans, 121(6), 4360–4376. https://doi.org/10.1002/2016JC011744 Emerson, S., & Stump, C. (2010). Net biological oxygen production in the ocean-II: Remote in situ measurements of O2 and N2 in subarctic pacific surface waters. Deep-Sea Research Part I, 57, 1255–1265. https://doi.org/10.1016/j.dsr.2010.06.001 Emerson, S., Quay, P. D., Stump, C., Wilbur, D., & Schudlich, R. (1995). Chemical tracers of productivity and respiration in the subtropical Pacific Ocean. Journal of Geophysical Research, 100(C8), 15873–15887. https://doi.org/10.1029/95JC01333 Emerson, S., Quay, P., Karl, D., Winn, C., Tupas, L., & Landry, M. (1997). Experimental determination of the organic carbon flux from open-ocean surface waters. Nature, 389, 951–954. https://doi.org/10.1038/40111 169 Emerson, S., Stump, C., Wilbur, D., & Quay, P. (1999). Accurate measurement of O2, N2, and Ar gases in water and the solubility of N2. Marine Chemistry, 64(4), 337–347. https://doi.org/10.1016/S0304-4203(98)00090-5 Emerson, S., Stump, C., & Nicholson, D. (2008). Net biological oxygen production in the ocean: Remote in situ measurements of O2 and N2 in surface waters. Global Biogeochemical Cycles, 22(GB3023), 1–13. https://doi.org/10.1029/2007GB003095 Emerson, S., Ito, T., & Hamme, R. C. (2012). Argon supersaturation indicates low decadal-scale vertical mixing in the ocean thermocline. Geophysical Research Letters, 39(18). https://doi.org/10.1029/2012GL053054 Emerson, S., White, M. R. T., Stump, C., & Bushinsky, S. M. (2017). Salinity and other variables collected from time series observations using Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer and other instruments from MOORINGS_PAPA_145W_50N. NOAA National Centers for Environmental Information. Dataset. https://doi.org/https://doi.org/10.3334/cdiac/otg.tsm_papa_145w_50n_o2_n2. Emerson, S., Yang, B., White, M., & Cronin, M. (2019). Air-Sea Gas Transfer: Determining Bubble Fluxes With In Situ N2 Observations. Journal of Geophysical Research: Oceans, 124(4), 2716–2727. https://doi.org/10.1029/2018JC014786 Eppley, R. W. (1972). Temperature and phytoplankton growth in the sea. Fishery Bulletin, 70, 1063–1085. ESR. (2009). OSCAR third degree resolution ocean surface currents. Ver. 1. PO.DAAC, CA, USA. Retrieved from http://dx.doi.org/10.5067/OSCAR-03D01 Eveleth, R., Timmermans, M.-L., & Cassar, N. (2014). Physical and biological controls on oxygen saturation variability in the upper Arctic Ocean. Journal of Geophysical Research: Oceans, 119(11), 7420–7432. https://doi.org/https://doi.org/10.1002/2014JC009816 Eveleth, R., Cassar, N., Doney, S. C., Munro, D. R., & Sweeney, C. (2017a). Biological and physical controls on O2/Ar, Ar and pCO2 variability at the Western Antarctic Peninsula and in the Drake Passage. Deep-Sea Research Part II, 139, 77–88. https://doi.org/10.1016/j.dsr2.2016.05.002 Eveleth, R., Cassar, N., Sherrell, R. M., Ducklow, H., Meredith, M. P., Venables, H. J., et al. (2017b). Ice melt influence on summertime net community production along the Western Antarctic Peninsula. Deep-Sea Research Part II, 139, 89–102. https://doi.org/10.1016/j.dsr2.2016.07.016 Farías, L., Castro-González, M., Cornejo, M., Charpentier, J., Faúndez, J., Boontanon, N., & Yoshida, N. (2009). Denitrification and nitrous oxide cycling within the upper oxycline of the eastern tropical South Pacific oxygen minimum zone. Limnology and Oceanography, 54(1), 132–144. https://doi.org/10.4319/lo.2009.54.1.0132 Fassbender, A. J., Sabine, C. L., & Cronin, M. F. (2016). Net community production and calcification from 7 years of NOAA Station Papa Morring measurements. Global Biogeochemical Cycles, 30, 250–267. https://doi.org/10.1002/2015GB005205 Fennel, K., Alin, S., Barbero, L., Evans, W., Bourgeois, T., Cooley, S., et al. (2018). Carbon cycling in the North American coastal ocean: A synthesis. Biogeosciences Discussions, 1–36. https://doi.org/10.5194/bg-2018-420 Fennel, K., Gehlen, M., Brasseur, P., Brown, C. W., Ciavatta, S., Cossarini, G., et al. (2019). Advancing marine biogeochemical and ecosystem reanalyses and forecasts as tools for monitoring and managing ecosystem health. Frontiers in Marine Science, 6(MAR), 1–9. https://doi.org/10.3389/fmars.2019.00089 Fenwick, L., & Tortell, P. D. (2018). Methane and nitrous oxide distributions in coastal and open ocean waters of the Northeast Subarctic Pacific during 2015–2016. Marine Chemistry, 200(February), 45–56. https://doi.org/10.1016/j.marchem.2018.01.008 Fenwick, L. A., Capelle, D., Damm, E., Zimmermann, S., Williams, W. J., Vagle, S., & Tortell, P. D. (2017). Methane and nitrous oxide distributions across the North American Arctic Ocean during summer, 2015. Journal of Geophysical Research: Oceans, 122, 390–412. https://doi.org/10.1002/2016JC012493 Fer, I. (2009). Weak Vertical Diffusion Allows Maintenance of Cold Halocline in the Central Arctic. Atmospheric 170 and Oceanic Science Letters, 2(3), 148–152. https://doi.org/10.1080/16742834.2009.11446789 Forest, A., Sampei, M., Hattori, H., Makabe, R., Sasaki, H., Fukuchi, M., et al. (2007). Particulate organic carbon fluxes on the slope of the Mackenzie Shelf (Beaufort Sea): Physical and biological forcing of shelf-basin exchanges. Journal of Marine Systems, 68(1–2), 39–54. https://doi.org/10.1016/j.jmarsys.2006.10.008 Fox-Kemper, B., Adcroft, A., Böning, C. W., Chassignet, E. P., Curchitser, E., Danabasoglu, G., et al. (2019). Challenges and Prospects in Ocean Circulation Models. Frontiers in Marine Science, 6. https://doi.org/10.3389/fmars.2019.00065 Frame, C. H., & Casciotti, K. L. (2010). Biogeochemical controls and isotopic signatures of nitrous oxide production by a marine ammonia-oxidizing bacterium. Biogeosciences Discussions, 7, 2695–2709. https://doi.org/10.5194/bg-7-2695-2010 Galbraith, E. D., Gnanadesikan, A., Dunne, J. P., & Hiscock, M. R. (2010). Regional impacts of iron-light colimitation in a global biogeochemical model. Biogeosciences, 7(3), 1043–1064. https://doi.org/10.5194/bg-7-1043-2010 Garcia, H. E., & Gordon, L. I. (1992). Oxygen solubility in Seawater: better fitting equations. Limnology and Oceanography, 37(6), 1307–1312. https://doi.org/10.2307/2837876 Garcia, H. E., & Gordon, L. I. (1993). Erratum: Oxygen solubility in seawater: better fitting equations. Limnology and Oceanography, 38, 656. Gaspar, P., Grégoris, Y., & Lefevre, J.-M. (1990). A simple eddy kinetic energy model for simulations of the oceanic vertical mixing: Tests at station Papa and long-term upper ocean study site. Journal of Geophysical Research, 95(C9), 16179. https://doi.org/10.1029/JC095iC09p16179 Giesbrecht, K. E., Hamme, R. C., & Emerson, S. R. (2012). Biological productivity along Line P in the subarctic northeast Pacific: In situ versus incubation-based methods. Global Biogeochemical Cycles, 26(GB3028). https://doi.org/10.1029/2012GB004349 Gordon, C., Fennel, K., Richards, C., Shay, L. K., & Brewster, J. K. (2020). Can ocean community production and respiration be determined by measuring high-frequency oxygen profiles from autonomous floats? Biogeosciences, 17, 4119–4134. https://doi.org/10.5194/bg-2020-119 Grefe, I., & Kaiser, J. (2014). Equilibrator-based measurements of dissolved nitrous oxide in the surface ocean using an integrated cavity output laser absorption spectrometer. Ocean Science, 10, 501–512. https://doi.org/10.5194/os-10-501-2014 Grivault, N., Hu, X., & Myers, P. G. (2018). Impact of the Surface Stress on the Volume and Freshwater Transport Through the Canadian Arctic Archipelago From a High-Resolution Numerical Simulation. Journal of Geophysical Research: Oceans, 123(12), 9038–9060. https://doi.org/10.1029/2018JC013984 Grundle, D. S., Maranger, R., & Juniper, S. K. (2012). Upper Water Column Nitrous Oxide Distributions in the Northeast Subarctic Pacific Ocean. Atmosphere-Ocean, 50(4), 475–486. https://doi.org/10.1080/07055900.2012.727779 Grundle, D. S., Juniper, S. K., & Giesbrecht, K. E. (2013). Euphotic zone nitrification in the NE subarctic Pacific: Implications for measurements of new production. Marine Chemistry, 155(2013), 113–123. https://doi.org/10.1016/j.marchem.2013.06.004 Haëntjens, N., & Boss, E. (2020). Inlinino: A modylar software data logger for oceanography. Oceanography, 33(1), 80–84. https://doi.org/https://doi.org/10.5670/oceanog.2020.112 Hall, B. D., Dutton, G. S., & Elkins, J. W. (2007). The NOAA nitrous oxide standard scale for atmospheric observations. Journal of Geophysical Research Atmospheres, 112(D09305). https://doi.org/10.1029/2006JD007954 Hamme, R. C., & Emerson, S. R. (2002). Mechanisms controlling the global oceanic distribution of the inert gases argon, nitrogen and neon. Geophysical Research Letters, 29(23), 1–4. https://doi.org/10.1029/2002GL015273 171 Hamme, R. C., & Emerson, S. R. (2004). The solubility of neon, nitrogen and argon in distilled water and seawater. Deep-Sea Research Part I, 51, 1517–1528. https://doi.org/10.1016/j.dsr.2004.06.009 Hamme, R. C., & Emerson, S. R. (2006). Constraining bubble dynamics and mixing with dissolved gases: Implications for productivity measurements by oxygen mass balance. Journal of Marine Research, 64, 73–95. https://doi.org/10.1357/002224006776412322 Hamme, R. C., & Emerson, S. R. (2013). Deep-sea nutrient loss inferred from the marine dissolved N2/Ar ratio. Geophysical Research Letters, 40(6), 1149–1153. https://doi.org/10.1002/grl.50275 Hamme, R. C., & Severinghaus, J. P. (2007). Trace gas disequilibria during deep-water formation. Deep-Sea Research Part I: Oceanographic Research Papers, 54(6), 939–950. https://doi.org/10.1016/j.dsr.2007.03.008 Hamme, R. C., Webley, P. W., Crawford, W. R., Whitney, F. A., Degrandpre, M. D., Emerson, S. R., et al. (2010). Volcanic ash fuels anomalous plankton bloom in subarctic northeast Pacific. Geophysical Research Letters, 37(L19604). https://doi.org/10.1029/2010GL044629 Hamme, R. C., Cassar, N., Lance, V. P., Vaillancourt, R. D., Bender, M. L., Strutton, P. G., et al. (2012). Dissolved O2/Ar and other methods reveal rapid changes in productivity during a Lagrangian experiment in the Southern Ocean. Journal of Geophysical Research, 117(C00F12). https://doi.org/10.1029/2011JC007046 Hamme, R. C., Berry, J. E., Klymak, J. M., & Denman, K. L. (2015). In situ O2 and N2 measurements detect deep-water renewal dynamics in seasonally-anoxic Saanich Inlet. Continental Shelf Research, 106, 107–117. https://doi.org/10.1016/j.csr.2015.06.012 Hamme, R. C., Emerson, S. R., Severinghaus, J. P., Long, M. C., & Yashayaev, I. (2017). Using Noble Gas Measurements to Derive Air-Sea Process Information and Predict Physical Gas Saturations. Geophysical Research Letters, 44(19), 9901–9909. https://doi.org/10.1002/2017GL075123 Hamme, R. C., Jenkins, W. J., Emerson, S. R., & Nicholson, D. P. (2018). A compilation of dissolved noble gas and N2/Ar ratio measurements collected from 1999-2016 in locations spanning the globe. 2018-08-24. https://doi.org/10.1575/1912/bco-dmo.744563 Hamme, R. C., Nicholson, D. P., Jenkins, W. J., & Emerson, S. R. (2019). Using Noble Gases to Assess the Ocean’s Carbon Pumps. Annual Review of Marine Science, 11(18), 1–29. https://doi.org/10.1146/annurev-marine-121916-063604 Hannah, C. G., Dupont, F., & Dunphy, M. D. (2009). Polynyas and tidal currents in the Canadian Arctic Archipelago. Arctic, 62(1), 83–95. https://doi.org/10.14430/arctic115 Hansell, D. A. (2013). Recalcitrant Dissolved Organic Carbon Fractions. Annual Review of Marine Science, 5(1), 421–445. https://doi.org/10.1146/annurev-marine-120710-100757 Harrison, P. J., Boyd, P. W., Varela, D. E., Takeda, S., Shiomoto, A., & Odate, T. (1999). Comparison of factors controlling phytoplankton productivity in the NE and NW subarctic Pacific gyres. Progress in Oceanography, 43(1999), 205–234. Hartmann, D. L. (2016). The ocean general circulation and climate. In Global Physical Climatology (pp. 195–231). San Diego, CA: Academic Press. Haskell, W. Z., Prokopenko, M. G., Stanley, R. H. R., & Knapp, A. N. (2016). Estimates of vertical turbulent mixing used to determine a vertical gradient in net and gross oxygen production in the oligotrophic South Pacific Gyre. Geophysical Research Letters, 43(14), 7590–7599. https://doi.org/10.1002/2016GL069523 Ho, D. T., Law, C. S., Smith, M. J., Schlosser, P., Harvey, M., & Hill, P. (2006). Measurements of air-sea gas exchange at high wind speeds in the Southern Ocean: Implications for global parameterizations. Geophysical Research Letters, 33(L1661), 1–6. https://doi.org/10.1029/2006GL026817 Holmes, R. M., McClelland, J. W., Peterson, B. J., Tank, S. E., Bulygina, E., Eglinton, T. I., et al. (2012). Seasonal and Annual Fluxes of Nutrients and Organic Matter from Large Rivers to the Arctic Ocean and Surrounding Seas. Estuaries and Coasts, 35(2), 369–382. https://doi.org/10.1007/s12237-011-9386-6 172 Hopwood, M. J., Carroll, D., Browning, T. J., Meire, L., Mortensen, J., Krisch, S., & Achterberg, E. P. (2018). Non-linear response of summertime marine productivity to increased meltwater discharge around Greenland. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-05488-8 Horrigan, S. G., Carlucci, A. F., & Williams, P. M. (1981). Light inhibition of nitrification in sea-surface films. Journal of Marine Research, 39(3), 557–565. Hu, X., Sun, J., Chan, T. O., & Myers, P. G. (2018). Thermodynamic and dynamic ice thickness contributions in the Canadian Arctic Archipelago in NEMO-LIM2 numerical simulations. The Cryosphere, 12(4), 1233–1247. https://doi.org/10.5194/tc-12-1233-2018 Hughes, K. G., Klymak, J. M., Hu, X., & Myers, P. G. (2017). Water mass modification and mixing rates in a 1/12° simulation of the Canadian Arctic Archipelago. Journal of Geophysical Research: Oceans, 122(2), 803–820. https://doi.org/10.1002/2016JC012235 Ianson, D., & Allen, S. E. (2002). A two-dimensional nitrogen and carbon flux model in a coastal upwelling region. Global Biogeochemical Cycles, 16(1). https://doi.org/10.1029/2001GB001451 Islam, F., DeGrandpre, M., Beatty, C., Krishfield, R., & Toole, J. (2016). Gas exchange of CO2 and O2 in partially ice-covered regions of the Arctic Ocean investigated using in situ sensors. IOP Conference Series: Earth and Environmental Science, 35(012018). https://doi.org/10.1088/1755-1315/35/1/012018 Jackson, J. M., Carmack, E. C., McLaughlin, F. A., Allen, S. E., & Ingram, R. G. (2010). Identification, characterization, and change of the near-surface temperature maximum in the Canada Basin, 1993-2008. Journal of Geophysical Research, 115(C05021), 1–16. https://doi.org/10.1029/2009JC005265 Jakobsson, M., Hogan, K. A., Mayer, L. A., Mix, A., Jennings, A., Stoner, J., et al. (2018). The Holocene retreat dynamics and stability of Petermann Glacier in northwest Greenland. Nature Communications, 9(1), 2104. https://doi.org/10.1038/s41467-018-04573-2 Jakobsson, M., Mayer, L. A., Bringensparr, C., Castro, C. F., Mohammad, R., Johnson, P., et al. (2020). The International Bathymetric Chart of the Arctic Ocean Version 4.0. Scientific Data, 7(1), 176. https://doi.org/10.1038/s41597-020-0520-9 Ji, B. Y., Sandwith, Z. O., Williams, W. J., Diaconescu, O., Ji, R., Li, Y., et al. (2019). Variations in Rates of Biological Production in the Beaufort Gyre as the Arctic Changes: Rates From 2011 to 2016. Journal of Geophysical Research: Oceans, 124(6), 3628–3644. https://doi.org/10.1029/2018JC014805 Ji, R., Jin, M., & Varpe, Ø. (2013). Sea ice phenology and timing of primary production pulses in the Arctic Ocean. Global Change Biology, 19(3), 734–741. https://doi.org/10.1111/gcb.12074 Jin, D., Hoagland, P., & Buesseler, K. O. (2020). The value of scientific research on the ocean’s biological carbon pump. Science of the Total Environment, 749, 141357. https://doi.org/10.1016/j.scitotenv.2020.141357 Jin, X., Najjar, R. G., Louanchi, F., & Doney, S. C. (2007). A modeling study of the seasonal oxygen budget of the global ocean. Journal of Geophysical Research: Oceans, 112(5), 1–19. https://doi.org/10.1029/2006JC003731 Johnson, K. S. (2010). Simultaneous measurements of nitrate, oxygen, and carbon dioxide on oceanographic moorings: Observing the Redfield ratio in real time. Limnology and Oceanography, 55(2), 615–627. https://doi.org/10.4319/lo.2009.55.2.0615 Johnson, K. S., Plant, J. N., Dunne, J. P., Talley, L. D., & Sarmiento, J. L. (2017). Annual nitrate drawdown observed by SOCCOM profiling floats and the relationship to annual net community production. Journal of Geophysical Research: Oceans, 122(8), 6668–6683. https://doi.org/10.1002/2017JC012839 Johnson, W. K., Miller, L. A., Sutherland, N. E., & Wong, C. S. (2005). Iron transport by mesoscale Haida eddies in the Gulf of Alaska. Deep Sea Research II, 52, 933–953. https://doi.org/10.1016/j.dsr2.2004.08.017 Jonsson, B. F., Doney, S. C., Dunne, J., & Bender, M. (2013). Evaluation of the Southern Ocean O2/Ar-based NCP estimates in a model framework. Journal of Geophysical Research: Biogeosciences, 118, 385–399. https://doi.org/10.1002/jgrg.20032 173 Juranek, L., Takahashi, T., Mathis, J., & Pickart, R. (2019). Significant Biologically Mediated CO2 Uptake in the Pacific Arctic During the Late Open Water Season. Journal of Geophysical Research: Oceans, 124(2), 1–23. https://doi.org/10.1029/2018JC014568 Juranek, L. W., Hamme, R. C., Kaiser, J., Wanninkhof, R., & Quay, P. D. (2010). Evidence of O2 consumption in underway seawater lines : Implications for air-sea O2 and CO2 fluxes. Geophysical Research Letters, 37(L01601), 1–5. https://doi.org/10.1029/2009GL040423 Kagan, B. A., Sofina, E. V., & Timofeev, A. A. (2010). On diapycnal mixing induced by internal tidal waves in the Arctic Ocean. Izvestiya, Atmospheric and Oceanic Physics, 46(2), 224–231. https://doi.org/10.1134/S0001433810020106 Kaiser, J., Reuer, M. K., Barnett, B., & Bender, M. L. (2005). Marine productivity estimates from continuous O 2 /Ar ratio measurements by membrane inlet mass spectrometry. Geophysical Research Letters, 32(19), 1–5. https://doi.org/10.1029/2005GL023459 Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., et al. (1996). The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society, 77, 437–471. https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2 Kana, T. M., Darkangelo, C., Hunt, M. D., Oldham, J. B., Bennett, G. E., & Cornwell, J. C. (1994). Membrane inlet mass spectrometer for rapid high-precision determination of N2, O2, and Ar in environmental water samples. Analytical Chemistry, 66(23), 4166–4170. https://doi.org/10.1021/ac00095a009 Kanna, N., Sugiyama, S., Fukamachi, Y., Nomura, D., & Nishioka, J. (2020). Iron Supply by Subglacial Discharge Into a Fjord Near the Front of a Marine-Terminating Glacier in Northwestern Greenland. Global Biogeochemical Cycles, 34(10). https://doi.org/10.1029/2020GB006567 Kavanaugh, M. T., Emerson, S. R., Hales, B., Lockwood, D. M., Quay, P. D., & Letelier, R. M. (2014). Physicochemical and biological controls on primary and net community production across northeast Pacific seascapes. Limnology and Oceanography, 59(6), 2013–2027. https://doi.org/10.4319/lo.2014.59.6.2013 Keeling, R. F., & Shertz, S. R. (1992). Seasonal and interannual variations in atmospheric oxygen and implications for the global carbon cycle. Nature, 358(6389), 723–727. https://doi.org/10.1038/358723a0 Kikuchi, G., Abe, H., Hirawake, T., & Sampei, M. (2020). Distinctive spring phytoplankton bloom in the Bering Strait in 2018: A year of historically minimum sea ice extent. Deep Sea Research Part II: Topical Studies in Oceanography, 181–182, 104905. https://doi.org/10.1016/j.dsr2.2020.104905 Körtzinger, A., Send, U., Wallace, D. W. R., Karstensen, J., & de Grandpre, M. (2008). Seasonal cycle of O2 and pCO2 in the central Labrador Sea: Atmospheric, biological, and physical implications. Global Biogeochemical Cycles, 22(GB1014), 1–16. https://doi.org/10.1029/2007GB003029 Kwiatkowski, L., Naar, J., Bopp, L., Aumont, O., Defrance, D., & Couespel, D. (2019). Decline in Atlantic Primary Production Accelerated by Greenland Ice Sheet Melt. Geophysical Research Letters, 46(20), 11347–11357. https://doi.org/10.1029/2019GL085267 Large, W. G., & Pond, S. (1981). Open Ocean Momentum Flux Measurements in Moderate to Strong Winds. Journal of Physical Oceanography, 11(3), 324–336. https://doi.org/10.1175/1520-0485(1981)011<0324:OOMFMI>2.0.CO;2 Laws, E. A. (1991). Photosynthetic quotients, new production and net community production in the open ocean. Deep Sea Research, 38(1), 143–167. Lewis, K. M., Mitchell, B. G., van Dijken, G. L., & Arrigo, K. R. (2016). Regional chlorophyll a algorithms in the Arctic Ocean and their effect on satellite-derived primary production estimates. Deep-Sea Research Part II, 130, 14–27. https://doi.org/10.1016/j.dsr2.2016.04.020 Li, G., Cheng, L., Zhu, J., Trenberth, K. E., Mann, M. E., & Abraham, J. P. (2020). Increasing ocean stratification over the past half-century. Nature Climate Change, 10, 1116–1123. Li, Z., & Cassar, N. (2016). Satellite estimates of net community production based on O2/Ar observations and 174 comparison to other estimates. Global Biogeochemical Cycles, 30. https://doi.org/10.1002/2015GB005314 Liang, J.-H. H., Deutsch, C., McWilliams, J. C., Baschek, B., Sullivan, P. P., & Chiba, D. (2013). Parameterizing bubble-mediated air-sea gas exchange and its effect on ocean ventilation. Global Biogeochemical Cycles, 27(3), 894–905. https://doi.org/10.1002/gbc.20080 Lockwood, D., Quay, P. D., Kavanaugh, M. T., Juranek, L. W., & Feely, R. A. (2012). High-resolution estimates of net community production and air-sea CO2 flux in the northeast Pacific. Global Biogeochemical Cycles, 26(GB4010). https://doi.org/10.1029/2012GB004380 Di Lorenzo, E., & Mantua, N. (2016). Multi-year persistence of the 2014/2015 North Pacific marine heatwave. Nature Climate Change, 6(1042–1048). https://doi.org/10.1038/NCLIMATE3082 Madec, G., & The NEMO team. (2008). NEMO ocean engine. Note Du Pôle de Modélisation, Institut Pierre‐Simon Laplace (IPSL), France. Madec, G., Delecluse, P., Imbard, M., & Levy, C. (2008). OPA 8.1: Ocean General Circulation Model Reference Manual. Maldonado, M. T., Boyd, P. W., Harrison, P. J., & Price, N. M. (1999). Co-limitation of phytoplankton growth by light and Fe during winter in the NE subarctic Pacific Ocean. Deep Sea Research II, 46(11), 2475–2485. https://doi.org/10.1016/S0967-0645(99)00072-7 Manning, C. C., Hamme, R. C., & Bourbonnais, A. (2010). Impact of deep-water renewal events on fixed nitrogen loss from seasonally-anoxic Saanich Inlet. Marine Chemistry, 122(1), 1–10. https://doi.org/doi.org/10.1016/j.marchem.2010.08.002 Manning, C. C., Stanley, R. H. R., Nicholson, D. P., Smith, J. M., Pennington, J. T., Fewings, M. R., et al. (2017). Impact of recently upwelled water on productivity investigated using in situ and incubation-based methods in Monterey Bay. Journal of Geophysical Research: Oceans, 122, 1901–1926. https://doi.org/10.1002/2016JC012306 Marchese, C., Castro de la Guardia, L., Myers, P. G., & Bélanger, S. (2019). Regional differences and inter-annual variability in the timing of surface phytoplankton blooms in the Labrador Sea. Ecological Indicators, 96(September 2018), 81–90. https://doi.org/10.1016/j.ecolind.2018.08.053 Marsden, R. F., Ingram, R. G., & Legendre, L. (1994). Currents under land-fast ice in the Canadian Arctic Archipelago Part 2: Vertical mixing. Journal of Marine Research, 52(6), 1037–1049. https://doi.org/10.1357/0022240943076812 Martin, J., Tremblay, J., Gagnon, J., Tremblay, G., Lapoussière, A., Jose, C., et al. (2010). Prevalence, structure and properties of subsurface chlorophyll maxima in Canadian Arctic waters. Marine Ecology Progress Series, 412, 69–84. https://doi.org/10.3354/meps08666 Martin, J. B., Pain, A. J., Martin, E. E., Rahman, S., & Ackerman, P. (2020). Comparisons of Nutrients Exported From Greenlandic Glacial and Deglaciated Watersheds. Global Biogeochemical Cycles, 34(12). https://doi.org/10.1029/2020GB006661 Masina, S., Storto, A., Ferry, N., Valdivieso, M., Haines, K., Balmaseda, M., et al. (2017). An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project. Climate Dynamics, 49(3), 813–841. https://doi.org/10.1007/s00382-015-2728-5 McClelland, J. W., Holmes, R. M., Peterson, B. J., Raymond, P. A., Striegl, R. G., Zhulidov, A. V., et al. (2016). Particulate organic carbon and nitrogen export from major Arctic rivers. Global Biogeochemical Cycles, 30(5), 629–643. https://doi.org/10.1002/2015GB005351 McNeil, C., Katz, D., Wanninkhof, R., & Johnson, B. (2005). Continuous shipboard sampling of gas tension , oxygen and nitrogen. Deep-Sea Research Part I, 52(9), 1767–1785. https://doi.org/10.1016/j.dsr.2005.04.003 McNeil, C., D’Asaro, E., Johnson, B., & Horn, M. (2006a). A gas tension device with response times of minutes. Journal of Atmospheric and Oceanic Technology, 23(11), 1539–1558. https://doi.org/10.1175/JTECH1974.1 175 McNeil, C., Katz, D. R., Ward, B., McGillis, W. R., & Johnson, B. D. (2006b). A method to estimate net community metabolism from profiles of dissolved O2 and N2. Hydrobiologia, 571(1), 181–190. https://doi.org/10.1007/s10750-006-0236-7 McNeil, C. L., Johnson, B. D., & Farmer, D. M. (1995). In situ measurement of dissolved nitrogen and oxygen in the ocean. Deep-Sea Research Part I, 42(5), 819–826. https://doi.org/10.1016/0967-0637(95)97829-W Meire, L., Mortensen, J., Meire, P., Sejr, M. K., Rysgaard, S., Nygaard, R., et al. (2017). Marine-terminating glaciers sustain high productivity in Greenland fjords, (June), 5344–5357. https://doi.org/10.1111/gcb.13801 Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P. C., Ebisuzaki, W., et al. (2006). North American Regional Reanalysis. Bulletin of the American Meteorological Society, 87(3), 343–360. https://doi.org/10.1175/BAMS-87-3-343 Michel, C., Ingram, R. G., & Harris, L. R. (2006). Variability in oceanographic and ecological processes in the Canadian Arctic Archipelago. Progress in Oceanography, 71(2–4), 379–401. https://doi.org/10.1016/j.pocean.2006.09.006 Moore, J. K., Fu, W., Primeau, F., Britten, G. L., Lindsay, K., Long, M., et al. (2018). Sustained climate warming drives declining marine biological productivity. Science, 359(6380), 113–1143. https://doi.org/10.1126/science.aao6379 Münchow, A. (2016). Volume and Freshwater Flux Observations from Nares Strait to the West of Greenland at Daily Time Scales from 2003 to 2009. Journal of Physical Oceanography, 46(1), 141–157. https://doi.org/10.1175/JPO-D-15-0093.1 Mundy, C. J., Gosselin, M., Ehn, J., Gratton, Y., Rossnagel, A., Barber, D. G., et al. (2009). Contribution of under-ice primary production to an ice-edge upwelling phytoplankton bloom in the Canadian Beaufort Sea. Geophysical Research Letters, 36(17), L17601. https://doi.org/10.1029/2009GL038837 NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group, . (2014a). Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Daytime 11µ SST Data. NASA, OB.DAAC. https://doi.org/http://dx.doi.org/10.5067/MODSA-1D9D4 NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group, . (2014b). Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Nightime 11µ SST Data. NSAS OB.DAAC. https://doi.org/http://dx.doi.org/10.5067/MODSA-1D9N4 Nevison, C., Butler, J. H., & Elkins, J. W. (2003). Global distribution of N2O and the ΔN2O-AOU yield in the subsurface ocean. Global Biogeochemical Cycles, 17(4). https://doi.org/10.1029/2003GB002068 Nicolaus, M., Katlein, C., Maslanik, J., & Hendricks, S. (2012). Changes in Arctic sea ice result in increasing light transmittance and absorption. Geophysical Research Letters, 39(24), 2012GL053738. https://doi.org/10.1029/2012GL053738 Niebauer, H. J. (1991). Bio-physical oceanographic interactions at the edge of the Arctic ice pack. Journal of Marine Systems, 2(1–2), 209–232. https://doi.org/10.1016/0924-7963(91)90025-P Niemi, A., Ferguson, S., Hedges, K., Melling, H., Michel, C., Ayles, B., et al. (2019). State of Canada’s Arctic Seas. Canadian Technical Report of Fisheries and Aquatic Sciences, 3344, 1–189. Retrieved from http://www.dfo-mpo.gc.ca/oceans/soto-rceo/arctic-arctique/publications/technical-report/summary-sommaire-eng.html Overland, J. E., & Wang, M. (2013). When will the summer Arctic be nearly sea ice free? Geophysical Research Letters, 40(10), 2097–2101. https://doi.org/10.1002/grl.50316 Pain, A. J., Martin, J. B., Martin, E. E., Rahman, S., & Ackermann, P. (2020). Differences in the Quantity and Quality of Organic Matter Exported From Greenlandic Glacial and Deglaciated Watersheds. Global Biogeochemical Cycles, 34(10), 1–20. https://doi.org/10.1029/2020GB006614 Palevsky, H., & Nicholson, D. (2018). The North Atlantic Biological Pump: Insights from the Ocean Observatories Initiative Irminger Sea Array. Oceanography, 31(1), 42–49. https://doi.org/10.5670/oceanog.2018.108 176 Palevsky, H. I., Quay, P. D., Lockwood, D. E., & Nicholson, D. P. (2016). The annual cycle of gross primary production, net community production, and export efficiency across the North Pacific Ocean. Global Biogeochemical Cycles, 30(2), 361–380. https://doi.org/10.1002/2015GB005318 Pefanis, V., Losa, S. N., Losch, M., Janout, M. A., & Bracher, A. (2020). Amplified Arctic Surface Warming and Sea Ice Loss Due to Phytoplankton and Colored Dissolved Material. Geophysical Research Letters, 47(21). https://doi.org/10.1029/2020GL088795 Peña, M. A., & Varela, D. E. (2007). Seasonal and interannual variability in phytoplankton and nutrient dynamics along Line P in the NE subarctic Pacific. Progress in Oceanography, 75(2), 200–222. https://doi.org/10.1016/j.pocean.2007.08.009 Plant, J. N., Johnson, K. S., Sakamoto, C. M., Jannasch, H. W., Coletti, L. J., Riser, S. C., & Swift, D. D. (2016). Net community production at Ocean Station Papa observed with nitrate and oxygen sensors on profiling floats. Global Biogeochemical Cycles, 30, 859–879. https://doi.org/10.1002/2015GB005349.Received Polovina, J. J., Howell, E. A., & Abecassis, M. (2008). Ocean’s least productive waters are expanding. Geophysical Research Letters, 35(L03618), 1–5. https://doi.org/10.1029/2007GL031745 Polzin, K. L., Toole, J. M., Ledwell, J. R., & Schmitt, R. W. (1997). Spatial Variability of Turbulent Mixing in the Abyssal Ocean. Science, 276(5309), 93–96. https://doi.org/10.1126/science.276.5309.93 Rainville, L., & Winsor, P. (2008). Mixing across the Arctic Ocean: Microstructure observations during the Beringia 2005 Expedition. Geophysical Research Letters, 35(8), 2–6. https://doi.org/10.1029/2008GL033532 Rainville, L., Lee, C. M., & Woodgate, R. A. (2011). Impact of wind-driven mixing in the Arctic Ocean. Oceanography, 24(3), 136–145. https://doi.org/10.5670/oceanog.2011.65 Randelhoff, A., Holding, J., Janout, M., Sejr, M. K., Babin, M., Tremblay, J. É., & Alkire, M. B. (2020). Pan-Arctic Ocean Primary Production Constrained by Turbulent Nitrate Fluxes. Frontiers in Marine Science, 7(March), 1–15. https://doi.org/10.3389/fmars.2020.00150 Reed, A., McNeil, C., D’Asaro, E., Altabet, M., Bourbonnais, A., & Johnson, B. (2018). A gas tension device for the mesopelagic zone. Deep-Sea Research Part I, 139(2018), 68–78. https://doi.org/10.1016/j.dsr.2018.07.007 Rees, A. P., Owens, N. J. P., & Upstill-Goddard, R. C. (1997). Nitrous oxide in the Bellingshausen Sea and Drake Passage. Journal of Geophysical Research, 102(C2), 3383–3391. https://doi.org/10.1029/96JC03350 Renaut, S., Devred, E., & Babin, M. (2018). Northward Expansion and Intensification of Phytoplankton Growth During the Early Ice-Free Season in Arctic. Geophysical Research Letters, 45(19), 10,590-10,598. https://doi.org/10.1029/2018GL078995 Reuer, M. K., Barnett, B. A., Bender, M. L., Falkowski, P. G., & Hendricks, M. B. (2007). New estimates of Southern Ocean biological production rates from O2/Ar ratios and the triple isotope composition of O2. Deep Sea Research I, 54(2007), 951–974. https://doi.org/10.1016/j.dsr.2007.02.007 Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., & Schlax, M. G. (2007). Daily High-Resolution-Blended Analyses for Sea Surface Temperature. Journal of Climate, 20, 5473-5496. Rosengard, S. Z., Izett, R. W., Burt, W. J., Schuback, N., & Tortell, P. D. (2020). Decoupling of O 2 / Ar and particulate organic carbon dynamics in nearshore surface ocean waters. Biogeosciences, 17, 3277–3298. Retrieved from https://doi.org/10.5194/bg-17-3277-2020 Rousseau, S., Kunze, E., Dewey, R., Bartlett, K., & Dower, J. (2010). On Turbulence Production by Swimming Marine Organisms in the Open Ocean and Coastal Waters. Journal of Physical Oceanography, 40, 2107–2121. https://doi.org/10.1175/2010JPO4415.1 Samelson, R. M., & Barbour, P. L. (2008). Low-Level Jets, Orographic Effects, and Extreme Events in Nares Strait: A Model-Based Mesoscale Climatology. Monthly Weather Review, 136(12), 4746–4759. https://doi.org/10.1175/2007MWR2326.1 Santoro, A. E., Casciotti, K. L., & Francis, C. A. (2010). Activity, abundance and diversity of nitrifying archaea and 177 bacteria in the central California Current. Environmental Microbiology, 12(7), 1989–2006. https://doi.org/10.1111/j.1462-2920.2010.02205.x Santoro, A. E., Buchwald, C., McIlvin, M. R., & Casciotti, K. L. (2011). Isotopic Signature of N2O Produced by Marine Ammonia-Oxidizing Archaea. Science, 333(6047), 1282–1285. Shigemitsu, M., Gruber, N., Oka, A., & Yamanaka, Y. (2016). Potential use of the N2/Ar ratio as a constraint on the oceanic fixed nitrogen loss. Global Biogeochemical Cycles, 30, 576–594. https://doi.org/10.1111/1462-2920.13280 Shroyer, E. L., Padman, L., Samelson, R. M., Munchow, A., & Stearns, L. A. (2017). Seasonal control of Petermann Gletscher ice-shelf melt by the ocean’s response to sea-ice cover in Nares Strait. Journal of Glaciology, 63(238), 324–330. https://doi.org/10.1017/jog.2016.140 Shulenberger, E., & Reid, J. L. (1981). The Pacific shallow oxygen maximum, deep chlorophyll maximum, and primary productivity, reconsidered. Deep Sea Research, 28(9), 901–919. https://doi.org/10.1016/0198-0149(81)90009-1 Sipler, R. E., Gong, D., Baer, S. E., Sanderson, M. P., Roberts, Q. N., Mulholland, M. R., & Bronk, D. A. (2017). Preliminary estimates of the contribution of Arctic nitrogen fixation to the global nitrogen budget. Limnology and Oceanography Letters, 2(5), 159–166. https://doi.org/10.1002/lol2.10046 Smith, J. M., Casciotti, K. L., Chavez, F. P., & Francis, C. A. (2014). Differential contributions of archaeal ammonia oxidizer ecotypes to nitrification in coastal surface waters. The ISME Journal, 8(8), 1704–1714. https://doi.org/10.1038/ismej.2014.11 Smith, K. L., Sherman, A. D., Shaw, T. J., & Sprintall, J. (2013). Icebergs as Unique Lagrangian Ecosystems in Polar Seas. Annual Review of Marine Science, 5(1), 269–287. https://doi.org/10.1146/annurev-marine-121211-172317 Spreen, G., Kaleschke, L., & Heygster, G. (2008). Sea ice remote sensing using AMSR-E 89-GHz channels. Journal of Geophysical Research, 113(C2), C02S03. https://doi.org/10.1029/2005JC003384 Stanley, R. H. R., Kirkpatrick, J. B., Cassar, N., Barnett, B. A., & Bender, M. L. (2010). Net community production and gross primary production rates in the western equatorial Pacific. Global Biogeochemical Cycles, 24(GB4001). https://doi.org/10.1029/2009GB003651 Steiner, N., Vagle, S., Denman, K. L., & McNeil, C. (2007). Oxygen and nitrogen cycling in the northeast Pacific - Simulations and observations at Station Papa in 2003/2004. Journal of Marine Research, 65(3), 441–469. https://doi.org/10.1357/002224007781567658 Stroeve, J., & Notz, D. (2018). Changing state of Arctic sea ice across all seasons. Environmental Research Letters, 13(10), 103001. https://doi.org/10.1088/1748-9326/aade56 Sullivan, P. P., Romero, L., McWilliams, J. C., & Melville, W. K. (2012). Transient Evolution of Langmuir Turbulence in Ocean Boundary Layers Driven by Hurricane Winds and Waves. Journal of Physical Oceanography, 42(11), 1959–1980. https://doi.org/10.1175/JPO-D-12-025.1 Tabata, S. (1961). Temporal Changes of Salinity, Temperature, and Dissolved Oxygen Content of the Water at Station “P” in the Northeast Pacific Ocean, and Some of Their Determining Factors. Journal of the Fisheries Research Board of Canada, 18(6), 1073–1124. https://doi.org/10.1139/f61-066 Tabata, S. (1975). The general circulation of the Pacific Ocean and a brief account of the oceanographic structure of the north pacific ocean. Atmosphere, 13, 1034–1054. https://doi.org/10.1080/00046973.1976.9648398 Takahashi, T., Sutherland, S. C., Wanninkhof, R., Sweeney, C., Feely, R. A., Chipman, D. W., et al. (2009). Climatological mean and decadal change in surface ocean pCO2, and net sea-air CO2flux over the global oceans. Deep-Sea Research Part II: Topical Studies in Oceanography, 56(8–10), 554–577. https://doi.org/10.1016/j.dsr2.2008.12.009 Tang, C. C. L., Ross, C. K., Yao, T., Petrie, B., DeTracey, B. M., & Dunlap, E. (2004). The circulation, water masses and sea-ice of Baffin Bay. Progress in Oceanography, 63(4), 183–228. 178 https://doi.org/10.1016/j.pocean.2004.09.005 Tang, W., Cerdán-García, E., Berthelot, H., Polyviou, D., Wang, S., Baylay, A., et al. (2020). New insights into the distributions of nitrogen fixation and diazotrophs revealed by high-resolution sensing and sampling methods. ISME Journal. https://doi.org/10.1038/s41396-020-0703-6 Teeter, L., Hamme, R. C., Ianson, D., & Bianucci, L. (2018). Accurate Estimation of Net Community Production From O2/Ar Measurements. Global Biogeochemical Cycles, 32, 1163–1181. https://doi.org/10.1029/2017GB005874 Tengberg, A., Hovdenes, J., Andersson, H. J., Brocandel, O., Diaz, R., Hebert, D., et al. (2006). Evaluation of a lifetime-based optode to measure oxygen in aquatic systems. Limnology and Oceanography: Methods, 4(2), 7–17. https://doi.org/10.4319/lom.2006.4.7 Thomson, R. E., & Fine, I. V. (2003). Estimating Mixed Layer Depth from Oceanic Profile Data. Journal of Atmospheric and Oceanic Technology, 20(2), 319–329. https://doi.org/10.1175/1520-0426(2003)020<0319:EMLDFO>2.0.CO;2 Timmerman, A. H. ., & Hamme, R. C. (2021). Consistent Relationships Among Productivity Rate Methods in the NE Subarctic Pacific. Global Biogeochemical Cycles, 1–18. https://doi.org/10.1029/2020gb006721 Tortell, P. D. (2005). Dissolved gas measurements in oceanic waters made by membrane inlet mass spectrometry. Limnology and Oceanography: Methods, 3, 24–37. https://doi.org/10.4319/lom.2005.3.24 Tortell, P. D., Guéguen, C., Long, M. C., Payne, C. D., Lee, P., & DiTullio, G. R. (2011). Spatial variability and temporal dynamics of surface water pCO2, ΔO2/Ar and dimethylsulfide in the Ross Sea, Antarctica. Deep Sea Research I, 58(3), 241–259. https://doi.org/10.1016/j.dsr.2010.12.006 Tortell, P. D., Merzouk, A., Ianson, D., Pawlowicz, R., & Yelland, D. R. (2012). Influence of regional climate forcing on surface water pCO2, O2/Ar and dimethylsulfide (DMS) along the southern British Columbia coast. Continental Shelf Research, 47(2012), 119–132. https://doi.org/10.1016/j.csr.2012.07.007 Tortell, P. D., Bittig, H. C., Körtzinger, A., Jones, E. M., & Hoppema, M. (2015). Biological and physical controls on N2, O2, and CO2 distributions in contrasting Southern Ocean surface waters. Global Biogeochemical Cycles, 29, 994–1013. https://doi.org/10.1002/2014GB004975 Tremblay, J.-E., Robert, D., Varela, D. E., Lovejoy, C., Darnis, G., Nelson, R. J., & Sastri, A. R. (2012). Current state and trends in Canadian Arctic marine ecosystems: I. Primary production. Climatic Change, 115, 161–178. https://doi.org/10.1007/s10584-012-0496-3 Tremblay, J.-E., Anderson, L. G., Matrai, P., Coupel, P., Bélanger, S., Michel, C., & Reigstad, M. (2015). Global and regional drivers of nutrient supply, primary production and CO2 drawdown in the changing Arctic Ocean. Progress in Oceanography, 139, 171–196. https://doi.org/10.1016/j.pocean.2015.08.009 Tremblay, J.-É., Hattori, H., Michel, C., Ringuette, M., Mei, Z.-P., Lovejoy, C., et al. (2006). Trophic structure and pathways of biogenic carbon flow in the eastern North Water Polynya. Progress in Oceanography, 71(2–4), 402–425. https://doi.org/10.1016/j.pocean.2006.10.006 Uchida, H., Kawano, T., Kaneko, I., & Fukasawa, M. (2008). In situ calibration of optode-based oxygen sensors. Journal of Atmospheric and Oceanic Technology, 25(12), 2271–2281. https://doi.org/10.1175/2008JTECHO549.1 Uchida, H., Johnson, G. C., & McTaggart, K, E. (2010). CTD oxygen sensor calibration procedures. IOCCP No. 14. Retrieved from http://www.pmel.noaa.gov/pubs/PDF/uchi3533/uchi3533.pdf Ulfsbo, A., Cassar, N., Korhonen, M., van Heuven, S., Hoppema, M., Kattner, G., & Anderson, L. G. (2014). Late summer net community production in the central Arctic Ocean using multiple approaches. Global Biogeochemical Cycles, 28, 1129–1148. https://doi.org/10.1002/2014GB004833 Vagle, S., McNeil, C., & Steiner, N. (2010). Upper ocean bubble measurements from the NE Pacific and estimates of their role in air-sea gas transfer of the weakly soluble gases nitrogen and oxygen. Journal of Geophysical Research: Oceans, 115(12), 1–16. https://doi.org/10.1029/2009JC005990 179 Vancoppenolle, M., Fichefet, T., Goosse, H., Bouillon, S., Madec, G., & Maqueda, M. A. M. (2009). Simulating the mass balance and salinity of Arctic and Antarctic sea ice. 1. Model description and validation. Ocean Modelling, 27(1–2), 33–53. https://doi.org/10.1016/j.ocemod.2008.10.005 Vancoppenolle, M., Bopp, L., Madec, G., Dunne, J., Ilyina, T., Halloran, P. R., & Steiner, N. (2013). Future Arctic Ocean primary productivity from CMIP5 simulations: Uncertain outcome, but consistent mechanisms. Global Biogeochemical Cycles, 27(3), 605–619. https://doi.org/10.1002/gbc.20055 Varela, D. E., & Harrison, P. J. (1999). Seasonal variability in nitrogenous nutrition of phytoplankton assemblages in the northeastern subarctic Pacific Ocean. Deep Sea Research II, 46, 2505–2538. Voermans, J. J., Babanin, A. V., Thomson, J., Smith, M. M., & Shen, H. H. (2019). Wave Attenuation by Sea Ice Turbulence. Geophysical Research Letters, 46(12), 6796–6803. https://doi.org/10.1029/2019GL082945 Volk, T., & Hoffert, M. I. (1985). Ocean Carbon Pumps: Analysis of Relative Strengths and Efficiencies in Ocean-Driven Atmospheric CO2 Changes. In E. T. Sundquist & W. S. Broecker (Eds.), The Carbon Cycle and Atmospheric CO2: Natural Variations Archean to Present (pp. 99–110). AGU. Walker, D. A., Raynolds, M. K., Daniëls, F. J. A., Einarsson, E., Elvebakk, A., Gould, W. A., et al. (2005). The Circumpolar Arctic vegetation map. Journal of Vegetation Science, 16(3), 267–282. https://doi.org/10.1111/j.1654-1103.2005.tb02365.x Wang, S., Kranz, S. A., Kelly, T. B., Song, H., Stukel, M. R., & Cassar, N. (2020). Lagrangian Studies of Net Community Production: The Effect of Diel and Multiday Nonsteady State Factors and Vertical Fluxes on O 2 /Ar in a Dynamic Upwelling Region. Journal of Geophysical Research: Biogeosciences, 125(6), 1–19. https://doi.org/10.1029/2019jg005569 Wanninkhof, R. (2014). Relationship between wind speed and gas exchange over the ocean revisited. Limnology and Oceanography: Methods, 12(6), 351–362. https://doi.org/10.1029/92JC00188 Ward, B. B. (2000). Nitrification and the marine nitrogen cycle. In D. Kirchman (Ed.), Microbial Ecology (pp. 427–454). New York, New York: Wiley-Liss. Ward, B. B. (2005). Temporal variability in nitrification rates and related biogeochemical factors in Monterey Bay, California, USA. Marine Ecology Progress Series, 292, 97–109. https://doi.org/10.3354/meps292097 Ware, D. M., & Thomson, R. E. (2005). Bottom-Up Ecosystem Trophic Dynamics Determine Fish Production in the Northeast Pacific. Science, 308, 1280–1284. https://doi.org/10.1126/science.1109049 Weeding, B., & Trull, T. W. (2014). Hourly oxygen and total gas tension measurements at the Southern Ocean Time Series site reveal winter ventilation and spring net community production. Journal of Geophysical Research: Oceans, 119, 348–358. https://doi.org/10.1002/2013JC009302 Weiss, R. F., & Price, B. A. (1980). Nitrous oxide solubility in water and seawater. Marine Chemistry, 8(4), 347–359. https://doi.org/10.1016/0304-4203(80)90024-9 Welschmeyer, N. A. (1994). Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnology and Oceanography, 39(8), 1985–1992. https://doi.org/10.4319/lo.1994.39.8.1985 Westberry, T. K., Williams, P. J. L. B., & Behrenfeld, M. J. (2012). Global net community production and the putative net heterotrophy of the oligotrophic oceans. Global Biogeochemical Cycles, 26(4). https://doi.org/10.1029/2011GB004094 Whalen, C. B., Talley, L. D., & MacKinnon, J. A. (2012). Spatial and temporal variability of global ocean mixing inferred from Argo profiles. Geophysical Research Letters, 39(18), 1–5. https://doi.org/10.1029/2012GL053196 Whitney, F. A., & Freeland, H. J. (1999). Variability in upper-ocean water properties in the NE Pacific Ocean. Deep Sea Research II, 46(11–12), 2351–2370. Whitney, F. A., Wong, C. S., & Boyd, P. W. (1998). Interannual variability in nitrate supply to surface waters of the Northeast Pacific Ocean. Marine Ecology Progress Series, 170, 15–23. https://doi.org/10.3354/meps170015 180 Whitney, F. A., Crawford, W. R., & Harrison, P. J. (2005). Physical processes that enhance nutrient transport and primary productivity in the coastal and open ocean of the subarctic NE Pacific. Deep Sea Research II, 52(2005), 681–706. https://doi.org/10.1016/j.dsr2.2004.12.023 Wong, C., Ballegooyen, K., Ignace, L., Johnson, M. J. (Gùdia), & Swanson, H. (2020). Towards reconciliation: 10 Calls to Action to natural scientists working in Canada. FACETS, 5(1), 769–783. https://doi.org/10.1139/FACETS-2020-0005 Yang, B., Emerson, S. R., & Bushinsky, S. M. (2017). Annual net community production in the subtropical Pacific Ocean from in-situ oxygen measurements on profiling floats. Global Biogeochemical Cycles, 31, 728–744. https://doi.org/10.1002/2016GB005545 Yang, B., Emerson, S. R., & Peña, M. A. (2018). The effect of the 2013-2016 high temperature anomaly in the subarctic Northeast Pacific (the “Blob”) on net community production. Biogeosciences, 15, 6747–6759. Yoshida, N., Morimoto, H., Hirano, M., Koike, I., Matsuo, S., Wada, E., et al. (1989). Nitrification rates and 15N abundances of N2O and NO3- in the western North Pacific. Nature, 342, 895–897. https://doi.org/10.1038/342895a0 Yoshinari, T. (1976). Nitrous oxide in the sea. Marine Chemistry, 4(1976), 189–202. https://doi.org/10.1016/0304-4203(76)90007-4 Yu, L., & Weller, R. A. (2007). Objectively Analyzed Air-Sea Heat Fluxes for the Global Ice-Free Oceans (1981-2005). Bulletin of the American Meteorological Society, 88(4), 527–539. https://doi.org/10.1175/BAMS-88-4-527 Zamora, L. M., & Oschlies, A. (2014). Surface nitrification: A major uncertainty in marine N2O emissions. Geophysical Research Letters, 41(12), 4247–4253. https://doi.org/10.1002/2014GL060556 Zhou, J., Delille, B., Brabant, F., & Tison, J. L. (2014). Insights into oxygen transport and net community production in sea ice from oxygen, nitrogen and argon concentrations. Biogeosciences, 11, 5007–5020. https://doi.org/10.5194/bg-11-5007-2014 Zlotnicki, V., Qu, Z., & Willis, J. (2016). JPL MEaSUREs Gridded Sea Surface Height Anomalies Version 1609. Ver. 1609. PO.DAAC, CA, USA. Retrieved from http://dx.doi.org/10.5067/SLREF-CDRV1. 181 Appendices Appendix A - Supporting Information for Chapter 2 A.1 Derivation of mixed layer nitrification equations Assuming that high O2 concentrations at the surface preclude denitrification, mixed layer N2O cycling is dominated by air-sea gas exchange, vertical mixing, and nitrification. If mixed layer nitrification rates are non-zero, the N2O budget is written as, MLD ×dN2Odt⁄ = –kN2O×[N2O]B + Kmix×∂[N2O]B∂Z + N×ΔN2OΔO2 (A.1) where MLD is the mixed layer depth (m), N is the nitrification rate in terms of O2 consumption (≤0 mmol O2 m-2 day-1; negative as O2 is consumed during nitrification), and ΔN2OΔO2 is the mixed layer N2O stoichiometric yield of nitrification (≤0 mmol N2O (mmol O2)-1). Similarly, the O2 budget is adjusted by adding N, when defining NCP to only include production and respiratory processes that affect the organic carbon pool. Since respiration through nitrification does not directly alter organic carbon budgets, I assume that NCP inherently excludes nitrification. MLD ×dO2dt⁄ = NCP – kO2× ΔO2 Ar⁄ ×[O2]eq+ Kmix×∂[O2]B∂Z+ N (A.2) Other definitions of NCP may include nitrification, such that the O2 budget would not be adjusted. Following the approach of Cassar et al. (2014), and assuming steady state for O2 and N2O, the new NCP equation (Eq. 2.8) is derived by multiplying Eq. A.1 by the subsurface-to-surface supply ratio (∂[O2]B∂[N2O]B; ≤0 mmol O2 (mmol N2O)-1), and subtracting the product from Eq. A.2. NCP = kO2× (ΔO2 Ar⁄ ×[O2]eq –kN2OkO2×∂[O2]B∂[N2O]B× [N2O]B) – N× (1–ΔN2OΔO2×∂[O2]B∂[N2O]B) (A.3) A.2 Error analysis for NCP estimates with N2O corrections This section provides a technical description of the sources of uncertainty in my NCP estimates and N2O corrections. Uncertainty in NCP was derived following a standard form: 182 NCP = NCP(Xi) (A.4) δNCPi=∂NCP∂Xi× δXi (A.5) δNCPtotal= √∑ (δNCPi)2i (A.6) where 𝑋𝑖 represents all components (k660, ScO2, ScN2O ΔO2/Ar, [O2]eq, [N2O]B, and ∂[O2]B∂[N2O]B) of the NCP equation (Eq. 2.3), ∂NCP∂Xi is the partial derivative of NCP with respect to each component, δNCP (mmol m-2 day-1) is the absolute error in total NCP, and 𝛿𝑋𝑖 (units of Xi) is the absolute error of each component. The δNCPi term represents the uncertainty in NCP attributed to component Xi. Uncertainty in Kmix was estimated using an analogous approach. Table A.2 shows the mean percent variability in each component term (i.e. δXi/X̅i; %) and the absolute uncertainties in NCP attributed to the specified terms (i.e. δNCPi; mmol O2 m-2 day-1). Component errors were calculated according to Eq. A.5, where δXi was estimated by summing in-quadrature all of the errors from parameterization, analytical/measurement, and averaging. Total errors were evaluated by combing the contributing component uncertainties. The methodological (e.g. assumption) uncertainties were calculated as described in Ch. 2. The parameterization errors in the k660, Sc, [O2]eq, [N2O]eq terms were obtained from Ho et al. (2006), Wanninkhof (Wanninkhof, 2014), Garcia and Gordon (1992), and Weiss and Price (1980), respectively. Analytical uncertainty in the underway ΔO2/Ar measurements was obtained from Tortell et al. (2005), and determined for [N2O]meas as the average standard deviation of 125 triplicate air-equilibrated standards processed at the same time as the seawater samples. For CCMP wind data, I used a mean wind speed error of 0.5 m s-1 (Atlas et al., 2011). Averaging errors were taken as the standard deviation of all measurements within each one-minute bin, or as the mean standard deviation of duplicate measurements (i.e. [N2O]meas). I used a conservative uncertainty of 15 % for calculations of the thermal heat flux ([N2O]therm) based on NCEP/NCAR Reanalysis 1 data (Yu & Weller, 2007). The error in the supply ratio, and vertical ([O2]B and [N2O]B) gradient terms was taken as the confidence interval of the regression slope through the pooled data sets, and as the mean confidence interval of individual slope estimates, respectively. Uncertainty in the 183 estimation of the mixed layer depth is also included in these errors, by assuming a potential variability of ± 5m. I also evaluated the errors associated with violations of the steady state (d[O2]B dt ⁄ = 0) and zero-horizontal flux (Fhoriz) assumptions, and include these in the estimates of total NCP uncertainty. For the steady state assumption, I evaluated on-station ΔO2/Ar from a 1-day occupation of OSP during the June Line P cruise (this was the longest period sampling at a single location), and observed a mean rate of change 3.5 % day-1. I also compared outbound and inbound (to/from OSP) ΔO2/Ar data during the Line P cruises, with mean rates of ΔO2/Ar change of 0.1 % day-1 in offshore stations, and 0.5 % near to the coast (maximum 2.8 % day-1). The potential variability in ΔΟ2/Αr derived from these non-steady state conditions contributes only ~0.6 and 1.1 mmol O2 m-2 day-1, on average, to the uncertainty off- and on-shelf NCP respectively. To determine the uncertainty due to a violation of the horizontal advection assumption, I obtained surface current speed data from the Physical Oceanography Distributed Active Archive Center (Bonjean & Lagerloef, 2002; ESR, 2009; https://podaac.jpl.nasa.gov/), and determined approximate horizontal [O2]B gradients from the underway data. Maximum current speeds were ~0.4 m s-1, and maximum horizontal [O2]B gradients were 0.01 mmol m-4. From this approach, I derive mean horizontal fluxes of ~1.6 and 22.7 mmol O2 m-2 day-1 in the off-shelf and on-shelf regions, respectively. I estimated the uncertainty associated with interpolating the on-station N2O NCP correction terms to the resolution of the underway O2/Ar measurements (i.e. Fig. 2.6), but do not include these values in the estimation of total error. Data in Fig. 2.6 were obtained by linearly interpolating the correction term along the distance-to-shore axis. To estimate the error in this, I calculated the difference between data obtained by this approach, and data interpolated using a nearest-neighbour scheme. I observed a maximum difference of 73 mmol O2 m-2 day-1 (mean 5.5 mmol O2 m-2 day-1), with values generally being larger between coastal stations. Values approached 0 mmol O2 m-2 day-1 near to stations. Combining all error estimates, I can examine the relative importance of different sources of uncertainty to my final NCP estimates. The analysis shows that uncertainties in the gas transfer velocities (k660 x ScO2 and k660 x ScN2O), [N2O]B and the supply ratio term dominated. Most of the error in the [N2O]B term derives from uncertainty in the thermal flux estimates (Yu and Weller, 184 2007). The total uncertainty in final NCP, attributed only to the equation components (i.e. excluding methodological and assumption uncertainties), is ~4.4 and 14.1 mmol O2 m-2 day-1, in oceanic and coastal regions, respectively. This equates to ~34 and 21 % of observed signals in the respective regions. When the conservative methodological and assumption uncertainties are included, the total error could be up to ~17 mmol O2 m-2 day-1 offshore, and 29 mmol O2 m-2 day-1 in the coastal region. The total uncertainty in uncorrected NCP, ~2.1 (offshore) and 6.4 (onshore) mmol O2 m-2 day-1, is consistent with Jonsson et al. (2013), and the uncertainty I derive in Kmix terms (mean 32 % of the observed signals) is an improvement on Bushinsky & Emerson (2015) and Yang et al. (2017), who report uncertainty in their literature-derived diffusivity values between 50-100 %. 185 Table A.1 Summary table of corrected NCP estimates, and the magnitude of the N2O correction for the 2016 Line P and La Perouse cruises. All values are reported in units of mmol O2 m-2 day-1. Data presented here correspond with Fig. 2.5. Values for the major Line P stations represent the average of outbound and inbound transects to/from OSP. SD values represent one standard deviation of all observations within the respective regions. N/A indicates where data were not available. NCP corrections where the N2O approach was found to be biased are represented by the lighter font and italicized numbers (see section 2.5.2). Data where the N2O correction was not applied were excluded from the total mean off- and on-shelf statistics of the N2O correction. Region / Station Final NCP NCP Correction Mean SD Min. Max. Mean SD Min. Max. Line P Winter P4 20.8 4.4 17.8 23.9 25.6 2.6 23.8 27.4 P8 33.4 N/A 26.2 N/A P12 28.0 4.1 25.1 30.9 20.7 0.5 20.3 21.0 P16 23.2 N/A 24.4 N/A P20 2.9 4.8 -0.6 6.3 -0.3 1.4 -1.3 0.8 P26 -6.0 N/A -7.6 N/A Off 18.7 17.4 -6.5 49.7 22.9 13.3 4.8 44.7 On 20.9 3.1 17.8 23.9 22.6 5.6 16.5 27.4 Spring P4 44.8 9.4 38.2 51.4 31.4 8.3 25.6 37.3 P8 7.6 3.6 5.0 10.1 19.5 6.6 14.8 24.2 P12 60.2 8.3 54.3 66.1 38.6 12.8 29.6 47.6 P16 64.0 10.6 56.5 71.5 36.1 6.9 31.2 41.0 P20 38.2 7.5 32.8 43.5 15.5 5.7 11.5 19.5 P26 57.6 N/A 13.8 N/A Off 35.7 20.0 2.3 81.2 24.6 13.0 6.9 50.4 On 72.0 53.8 23.8 182.4 62.5 54.4 16.5 177.8 Summer P4 180.6 57.7 139.8 221.4 90.4 28.1 70.5 110.3 P8 23.1 31.0 1.1 45.0 54.3 12.4 45.5 63.0 P12 3.1 2.8 1.1 5.1 21.3 2.3 19.7 22.9 P16 17.2 9.4 10.5 23.8 16.9 10.2 9.7 24.1 P20 1.8 0.2 1.7 2.0 17.0 1.6 15.9 18.1 P26 6.3 N/A 46.4 N/A Off 20.9 36.3 0.8 180.2 31.0 26.8 6.3 100.2 On 159.3 127.2 13.6 396.1 93.7 55.1 33.8 190.3 La Perouse Spring 74.1 46.6 1.7 162.3 48.5 30.4 14.3 133.0 Summer 65.5 43.1 9.5 188.9 66.6 42.4 3.8 157.5 186 Table A.2 Summary of uncertainties in underway NCP estimates with N2O-based mixing corrections. Values in the first two sections represent the mean uncertainties in total NCP attributed to various methodological considerations and calculation assumptions (see section 2.5.1). Section three shows the percent variability (i.e. δXi/X̅i, Eq. A.5; %) in each term of the NCP equation (Eq. 2.3), with the respective effects on corrected NCP propagated to section four (i.e. δNCPi). The total composite uncertainties are shown in Section 5 (i.e. δNCPtotal). In Section five, the numbers in brackets under Corrected NCP represent the total error including the methodological/assumption uncertainties. The two sets of numbers, separated by the ‘/’, on rows one, two, four and five represent the mean off-shelf and on-shelf errors, respectively. Unless otherwise indicated, numbers are in NCP units (mmol O2 m-2 day-1). 1. Methodological Uncertainties [mmol O2 m-2 day-1] Mixed layer nitrification N2O thermal adjustment Subsurface Ar saturation 11.2/8.6 11.4/8.2 0.8/2.0 2. Assumption Uncertainties [mmol O2 m-2 day-1] d[O2]B/dt Fhoriz Interpolation 0.6/1.1 1.6/22.7 5.5 3. Percent Variability in NCP Components [%] k660 ScO2 ScN2O ΔO2/Ar [O2]eq [N2O]meas [N2O]eq [N2O]therm ∂[O2]B∂[N2O]B 19.3 2.9 2.5 6.8 4.1 3.5 4.5 15.0 14.1 4. Contribution of Components to Total NCP Uncertainty [mmol O2 m-2 day-1] k660 ScO2 ScN2O ΔO2/Ar [O2]eq [N2O]meas [N2O]eq [N2O]therm ∂[O2]B∂[N2O]B 2.1/6.7 0.3/1.0 0.4/1.6 0.8/2.2 0.4/1.5 0.5/2.2 0.5/2.8 2.3/9.3 2.4/8.1 5. Total Uncertainty [mmol O2 m-2 day-1] Kmix [m2 s-1] Uncorrected NCP Correction term Corrected NCP 1.6x10-4 2.1/6.4 4.3/13.7 4.4/14.1 (16.7/29.3) 187 Figure A.1 Comparison of N2O concentrations measured in 5 m Niskin bottles, and samples obtained from the underway seawater system of the CCGS J. P. Tully. Samples from the ship’s continuous seawater supply (nominal intake ~5 m) were obtained at approximately the same time as Niskin samples. Error bars represent one standard deviation of duplicate samples. No significant difference was found between paired samples (n=12; p > 0.1). Figure A.2 Surface [N2O]B concentrations measured during the 2016 winter (a-b), spring (c-e) and summer (f-h) Line P and La Perouse cruises. The left, middle and right panels represent observations from the off-shelf Line P, on-shelf Line P and La Perouse transects, respectively. Thermally adjusted values (filled symbols) include the N2O thermal flux correction (Eq. 2.6). Note the difference in y-scale between off-shore and on-shelf data. 188 Figure A.3 Example profiles of O2 (a-b) and N2O (c-d) and the subsurface relationship between [O2]B and [N2O]B (e) from 2016 Line P cruises. Typical profiles of measured (labelled “meas.”) O2 and N2O concentrations and biologically-induced disequilibria (“bio.”) show measurements from the upper 500 m at stations P8 (a, b), and P26 (c, d), during the August, 2016 Line P cruise. Panels (a) and (b) represent typical profiles with O2 maxima occurring within the mixed layer, while panels (c) and (d) represent profiles with O2 maxima beneath the surface. Panel (e) shows the resulting relationships between [O2]B and [N2O]B. The dashed lines in (e) represent the derived supply ratio terms for each profile. Despite the different forms of the O2 and N2O profiles shown in (c) and (d), the derived O2 / N2O gradient was not significantly different from that measured at other stations or cruises. As discussed in the text, profiles with subsurface O2 maxima present a significant challenge to the application of the N2O correction approach. 189 Figure A.4 Subsurface O2B gradients in the 25 m below the mixed layer, measured from CTD casts during the 2016 winter (a-b), spring (c-e) and summer (f-h) Line P and La Perouse cruises. The left, middle and right panels represent observations from the off-shelf Line P, on-shelf Line P and La Perouse transects, respectively. The grey shaded area shows values above a threshold of -0.1 mmol O2 m-4. Gradients greater than this threshold (asterisk markers) represent profiles where the NCP correction was not made. Figure A.5 The relationship between Coastal Bakun upwelling index values and derived Kmix averaged over the on-shelf regions of the 2016 Line P and La Perouse cruises. Spring and summer points are labelled (M = May; J = June; A = August; S = September). Upwelling values (Bakun, 1973) were taken as the average index value integrated over the mixed layer residence time of N2O prior to sampling. I obtained an R2 of 0.63 from an exponential line fit through the data. 190 Appendix B - Supporting Information for Chapter 3 B.1 Regional definitions and hydrography I used 10-year average output fields of surface temperature, salinity, mixed layer depth and eddy diffusivity from the NEMO EMP014 simulation (details in Ch. 3), satellite Chlorophyll a (Chl a) observations and bathymetry data from the ETOPO1 database to define rough boundaries between the sub-regions discussed in Ch. 3 (Fig. B.1). While there is strong variability within each region (Fig. B.2), discernable patterns corresponding with geographic features are evident, enabling the demarcation the six distinct sub-regions: North Atlantic and Labrador Sea (NA/LS), Baffin Bay and Davis Strait (BB/DS), Hudson Strait (HS), Hudson Bay (HB), the Canadian Arctic Archipelago (CAA) and the continental shelf of NA/LS and BB/DS. Wind speed and ice coverage also varied between regions, but the patterns were less consistent across the three sampling years (Fig. B.3). B.2 Uncertainty in Fmix,O2 and NCP calculations B.2.1 Temporal and spatial uncertainty in dO2B/dZ and κZ I estimated the total error in Fmix,O2 (section B.2.3) as the combined uncertainty in dO2B/dZ and κZ, including potential numerical uncertainty (section B.2.2). Regionally averaged errors are presented in Table B.1, and underway errors are shown as blue shading in Fig. 3.7 (Chapter 3). For each term, I determined their respective uncertainties along the cruise tracks (i.e. “underway” uncertainty) resulting from time and depth variability, and summed the propagated estimates in-quadrature to obtain the total Fmix,O2 error. Time variability reflects uncertainty in the steady state assumption of Fmix,O2 and cor-NCP calculations, while depth variability reflects errors resulting from uncertainty in dZ. I estimated the temporal variability of dO2B/dZ by evaluating that term in a biogeochemical model (BLING) coupled to the NEMO framework. At all CTD sampling locations along each cruise track, I calculated the standard deviation in BLING-derived dO2B/dZ over one O2 residence time prior to ship-board sampling, deriving regional uncertainties of ~0.1 to 0.6 mmol O2 m-4. Although O2 is presently not represented as well as non-biotic properties in numerical simulations 191 (compare Figs. B.4 and 3.2, and R2 values of 0.7 and 0.13 for direct evaluations of density and O2, respectively, between modelled and observed values), the model is still a useful tool for diagnosing variability in dO2B/dZ in the absence of time-series observations. As biogeochemical model predictions improve, their output should be used to better constrain the spatial and temporal variability in dO2B/dZ in future studies. The average temporal variability in model-derived κZ, taken as the standard deviation in that term over the O2 residence time at all underway sampling locations, ranged from ~2x10-5 to 2x10-4 m2 s-1. The errors in κZ and dO2B/dZ due to dZ were estimated as the standard deviation of values over a range of dZ (MLD to base of pycnocline, MLD +5 m, MLD ×2) at all underway locations or CTD sampling stations, respectively. This analysis yielded errors in κZ and dO2B/dZ of ~1x10-6 to 5x10-5 m2 s-1 and 0.4 to 0.9 mmol O2 m-4, respectively. B.2.2 Numerical uncertainty in κZ The model-observation comparisons (Ch. 3, section 3.4.1) provide some insights into the accuracy of the model-based estimates of κZ, which are also subject to numerical uncertainties and potential biases. However, the numerical uncertainties in κZ are difficult to constrain. Here, I estimate the potential numerical uncertainty in κZ, and describe its impact on Fmix,O2 calculations in section B.2.3. Since the vertical resolution of the NEMO domain (minimum 1 m at the surface, and increasing to >100 m at depth) exceeds the resolution of turbulent ocean mixing (<1 m), the NEMO framework parameterizes κZ from a turbulent kinetic energy closure scheme (Gaspar et al., 1990; with modifications from Madec et al., 2008) in which κZ is proportional to a vertical eddy diffusivity coefficient (Cκ), turbulent kinetic energy (TKE), and the inverse of the Brunt-Väisälä frequency (N) via the mixing length scale and Richardson number-dependency of the Prandl number (Madec & The NEMO team, 2008). The TKE closure, in turn, is dependent on N and the same coefficients. Thus, the comparison of N between the model and observations (Fig. 3.2) can help to identify biases in the model-based κZ values. In comparing N at the base of the mixed layer, model-derived values typically exceeded observed values, with a median offset of ~0.006 s-1 (relative offset ~10 %) in most regions, and exceeding ~0.02 s-1 (20 %) in some coastal waters. Assuming that these offsets propagate as similar errors in model-derived κZ, mean potential biases 192 of ~2x10-5 and 5x10-5 m2 s-1 are derived for non-coastal (e.g. NA/LS and BB/DS) and coastal (e.g. HS, HB, CAA and shelf) regions. These values are similar in magnitude to the offsets observed between model- and CTD-derived κZ (Fig. 3.2e), which ranged from ~3x10-6 to 5x10-5 m2 s-1. Other sources of numerical errors in κZ can arise from uncertainty in the TKE parameterization, but these errors are difficult to constrain given the lack of studies evaluating the TKE closure scheme in the present study area. Errors in the Cκ coefficient, for example, may vary by as much as a factor of approximately two, based on a range of variability presented in various ocean regions (Gaspar et al. 1990), potentially contributing further uncertainty in κZ on the order of ~10-4 m2 s-1, or greater (computed as the mean of 2×κZ across the entire study region). However, these errors likely over-estimate the total parameterization error in model-derived κZ. Taking the combined biases between modelled and observed κZ and N, I thus estimate that numerical errors in κZ likely range from ~2x10-5 to 1x10-4 m2 s-1 which is comparable to errors resulting from spatial and temporal variability in κZ (see above and Table B.1). As discussed in Ch. 3, the model is also sensitive to the environmental forcing and parameterizations of bathymetry, so that developments in the resolution and accuracy of these properties should improve model performance. Moving forward, a comprehensive assessment of the uncertainty in model-derived κZ should involve a comparison of results across an ensemble of model experiments to assess the robustness of the output to variations in TKE parameterizations, model forcing and model resolution. Additional comparisons with in-situ observations of κZ will also help to better constrain numerical errors. B.2.3 Total uncertainty in Fmix,O2 Following a standard form of error propagation (see Eqs. A.4-A.6; Appendix 2), I combined all individual errors’ contributions to uncertainty in Fmix,O2 in-quadrature, yielding regional average total uncertainties of ~3 to 28 mmol O2 m-2 d-1 (underway uncertainty up to ~45 mmol O2 m-2 d-1). In most regions, uncertainty in the dO2B/dZ term constituted the largest component of the total error in the mixing correction, and errors resulting from numerical uncertainty in κZ were typically less important. It is notable that in the NA/LS, strong time-variability in κZ resulted in the largest regional Fmix,O2 error, while uncertainty in dO2B/dZ (time and depth-dependent) was most important in the shelf and CAA regions. 193 Given uncertainties in the remaining NCP calculation terms described in Chapter 2 and Appendix A, I estimated the average total error in cor-NCP to be ~8 to 30 mmol O2 m-2 d-1 (underway uncertainty up to ~50 mmol O2 m-2 d-1). Table B.1 Regionally averaged sources of uncertainty in model-based Fmix,O2 calculations. Uncertainty in the dO2B/dZ and κZ terms, and the resulting error in Fmix,O2, was estimated along the cruise track as described in section B.2, before averaging by region. Temporal errors (“time” column) in dO2B/dZ were estimated using O2 output from a BLING model matched to the location of each CTD profile station, while errors resulting from dZ (“dZ” column) variability were determined from in-situ CTD sampling. Errors in κZ were assessed as the temporal and spatial variability in NEMO-derived values along the cruise tracks. The total error in Fmix,O2 was assessed by summing the individual underway spatial, temporal and numerical (“num” column) dO2B/dZ and κZ errors in quadrature. Note that errors in κZ (middle section) are scaled to 10-5 m2 s-1. error in: dO2B/dZ κZ Fmix,O2 [mmol m-4] [m2 s-1] (x10-5) [mmol O2 m-2 d-1] due to: time dZ total time dZ num. total dO2B/dZ κZ total time dZ time dZ num. NA/LS 0.3 0.4 0.5 21.9 4.4 2.8 5.2 5.3 9.9 20.1 4.0 2.4 27.8 BB/DS 0.5 0.8 0.9 3.5 0.6 2.1 2.2 3.9 8.5 2.6 0.4 1.6 11.2 HS 0.2 0.4 0.4 20.0 4.8 7.7 9.1 5.1 7.7 9.8 2.4 3.9 15.5 HB 0.1 0.9 0.9 2.2 0.1 5.1 5.1 0.3 2.2 0.7 0.1 1.6 3.1 CAA 0.6 0.6 0.8 1.6 4.8 8.1 9.4 11.8 10.9 1.0 4.1 5.8 12.4 Shelf 0.3 0.4 0.5 10.4 2.1 14.2 14.4 9.0 14.1 4.6 0.9 5.9 19.4 All regions 0.3 0.6 0.7 9.9 2.8 6.7 7.6 5.9 8.9 6.5 2.0 3.5 14.9 194 Figure B.1 Central and eastern North American Arctic study sub-regions defined using NEMO model-derived properties (a-d), bathymetry (e) and satellite-based Chl a (f). All values represent the climatological summertime average between 2010 and 2019. The thick black lines show the boundaries between region, which are labelled in panel (d). The continental shelf region is shown in lighter grey. 195 Figure B.2 Distribution of surface temperature (a) and salinity (b), mixed layer (c) and pycnocline depths (d), mixed layer O2 residence time (e), and the subsurface O2B gradient (f) around the central and eastern Canadian Arctic sampling domain. Values in panels (a)-(d) and (f) were derived from CTD-profiles in each sub-region between 2002 and 2019, while τΟ2 was based on CTD-derived MLD and wind speed data obtained at the time of sampling in 2015, 2018 and 2019 only. The lower and upper limits of the boxes represent the first and third quartile ranges of each distribution, respectively, with the middle line depicting the median value. Outliers (coloured dots in panels a-d, and red in panel e) represent values outside of 1.5-times the interquartile range. Outliers in the dO2B/dZ gradient distribution were excluded from my analyses. Box colours represent different sampling regions, with the convention following Fig. 3.1 in the Ch. 3. 196 Figure B.3 Average wind speed (a-c), ice cover (d-f), and time since ice melt during the 2015 (left), 2018 (middle) and 2019 (right) CCGS Amundsen cruises. Note that ice cover data are plotted on a logarithmic colour scale. Panels (g)-(h) show the number of days since ice coverage fell below 15 %, relative to the median date of cruise sampling in each year, which is indicated above the figures. Negative values represent ice breakup after the median date. 197 Figure B.4 Comparison of along-track CTD and model-based O2 concentrations (panels (a) and (b), respectively) at each CTD sampling station along the 2015, 2018 and 2019 CCGS Amundsen cruise tracks. The model values were obtained from a biogeochemical model (BLING) coupled to the NEMO physical framework and matched to the time and location of CTD stations during the respective cruises. A direct comparison of CTD- and model-based values yielded a RMSE of 35 mmol O2 m-3 (R2 = 0.13), implying that the model is not as well validated as it is for density-based fields, which had an R2 of 0.7 (see Fig. 3.2). 198 Figure B.5 Distribution of high NEMO-based κZ identified as the proportion of pixels with enhanced diffusivity (κZ > 10-2 m2 s-1) in the central and eastern North American Arctic. The proportion of pixels was determined by summing all occurrences of κZ above this threshold over the duration of each sampling time period in 2015, 2018 and 2019. Panel (d) shows the average cruise-time proportion interpolated onto the respective cruise tracks, which are represented by black lines in (a)-(c). 199 Appendix C - Supporting Information for Chapter 4 C.1 One-dimensional mixed layer gas model As per Eq. 4.2, I applied the following one-dimensional (1D) mixed layer (ML) budget to O2, Ar and N2: d(C × MLD)dt= Fd + FC + FP + Fk + Fw + Fe + J (C.1.1) All terms have units of mmol m-2 d-1, and are described in Ch. 4 and Table 4.1. To summarize, MLD is the mixed layer depth (m), and d(C × MLD)dt (mmol m-2 d-1) represents the change in the ML gas inventory over time. Fd represents the diffusive air-sea exchange flux and FC and FP represent air-sea fluxes via complete injection from small bubbles, or partial exchange from large bubbles, respectively). Mixing is represented by the sum of vertical diapycnal mixing (Fk), upwelling (Fw) and entrainment during ML deepening (Fe). J is the biological production of O2 or N2 (J set to zero for Ar). For ice-free waters, I used the model of Liang et al. (2013) to parameterize the air-sea exchange terms (Fd, Fc and FP). Additional work based on the Liang et al. model has found that a scaling coefficient, β, of the bubble terms (FC and FP) is required to better-match GTD-derived N2 observations with a modelled time-series at Ocean Station Papa (Bushinsky & Emerson, 2015; Emerson et al., 2019; Yang et al., 2017). Following that approach, I determined an appropriate β value of 0.5 by tuning the model to best match observations (further details in Ch. 4). Equation C1.1 can be expanded as: d(C × MLD)dt= [kd × (Ceq ×SLP1 atm− C)] + [β × Fc] + [β × kp × (Ceq × (1 + ΔP) ×SLP1 atm− C)] + [κZ ×dCdZ] + [ω × (Cdeep − C)] + [dMLDdt× (Cdeep − C)] + J (C.1.2) where kd and kp are the diffusive and bubble-mediated air-sea exchange coefficients (both m d-1) and ΔP (mbar) is the supersaturation caused by partially-dissolving bubbles, as parameterized in 200 Liang et al. (2013). Ceq (mmol m-3) is the gas solubility at equilibrium (Garcia & Gordon, 1993; Hamme & Emerson, 2004), and SLP represents the sea level pressure (atm). In Eq. C.1.2, and throughout, I do not account for water vapour pressure in defining the SLP terms (i.e. SLP−pH2O1 atm−pH2O simplified to SLP1 atm, where pH2O is the vapour pressure), because the impact on Ceq is negligible (<0.04 % for N2 over the range of SLP and temperature in my simulations), and this assumption does not affect ΔN2/Ar calculations. The mixing terms are represented by the eddy diffusivity coefficient (κZ; m2 s-1 converted to m2 d-1), advection velocity (ω; m s-1 converted to m d-1; Eq. C.1.3), the rate of mixed layer deepening (dMLD/dt; m d-1), the surface-to-subsurface gas gradient (dC/dZ = (Cdeep – C)/dZ; mmol m-4; where dZ is the integration depth, equivalent to the thickness of the pycnocline as described in Ch. 4) and the deep gas concentration (Cdeep; mmol m-3). All mixing terms are varied in time, unless otherwise stated. In the simulations described in Ch. 4, MLD, pycnocline depth and dZ values were calculated from density observations on the Ocean Station Papa mooring line (details in Ch. 4), using a density difference criterion (0.125 kg m-3 difference from average upper 5 m values) or a density gradient criterion (first depth where density gradient falls below 0.05 kg m-4) for the ML and pycnocline depths, respectively. In the absence of moored observations, these terms may be parameterized from climatological datasets, Argo float profiles (if available), or by interpolating between infrequent year-round sampling periods. I obtained estimates of κZ from the literature (Cronin et al., 2015; Fig. 4.3) and set the entrainment term to zero when the ML shoaled. The advection (Ekman pumping) velocity, ω, was calculated from the curl (∇) of the wind stress (τ), derived from gridded wind speed vectors (Eq. C.1.3), following Hartmann (2016). ω = ∇ × (τρ0×𝑓) =∂∂x(τyρ0×𝑓) −∂∂y(τxρ0×𝑓) (C.1.3) Here, τx and τy represent the eastward and northward components of the wind stress, derived from the respective u- and v-components of the surface wind speed vectors (τi = ρair × CD × ui2, where ρair is the density of air, and CD is the drag coefficient, defined as a piece-wise function of wind speed velocity following Large & Pond, 1981; Sullivan et al., 2012). In Eq. C.1.3, f is the Coriolis 201 parameter (f = 2 × Ω × sin(φ), where Ω is 7.2921 x 10−5 rad. s-1 and φ is latitude), and ρ0 is the surface seawater density. Wind speed vectors were obtained from the Cross-Calibrated Multi-Parameter (CCMP) product (details below), and density was derived from observations as described in Ch. 4. The advection term was set to zero when ω was negative (i.e. ω only represents upwelling). In the simulations described in Ch. 4, Eqs. C.1.1 – C.1.3 were evaluated over a two-year cycle at Ocean Station Papa (OSP; 50 oN, 145 oW) in the Subarctic Northeast Pacific. Matlab code and sample calculations with the one-dimensional gas model is provided in an O2N2_NCP_toolbox on Zenodo (https://doi.org/10.5281/zenodo.4024925). The codes allow users to define forcing input (e.g. environmental data, initial conditions, and mixing coefficients) and air-sea flux parameterizations, and explore the impact of various environmental conditions on O2 and N2 ML saturation states. C.2 N2' budget, Quasi-steady-state conditions and the O2 re-equilibration timescale C.2.1 Derivations To derive N2', I simplified the ML budget described in Eq. C.1 by combining all mixing processes into a single term. I set Fe to zero, and combined the remaining mixing fluxes with a single coefficient, κ (κ = κZ + ω × dZ; m2 s-1 converted to m2 d-1): Fk + Fw + Fe ≈ dCdZ× κ (C.2) Combining Eqs. C.1.2 and C.2 yields the simplified budget used to perform N2' calculations (Eq. 4.3), which was evaluated for N2 and Ar over one O2 re-equilibration time, ΤO2 (Eq. C.6.3), prior to observations. d(C × MLD)dt= [kd × (Ceq ×SLP1 atm− C)] + [β × Fc] + [β × kp × (Ceq × (1 + ΔP) ×SLP1 atm− C)] + [dCdZ× κ] (C.3) Using this budget, I calculated the expected divergence between Ar and N2 saturation states at the end of the ΤO2 time period, and subtracted this difference from the true ΔN2 (Eq. 4.5). The N2' calculation was repeated for all time points in the full model simulations. In field studies, ΔN2true 202 is the measured N2 supersaturation, and calculations are performed over the O2 re-equilibration timeframe prior to each observation. The analytical solution to Eq. C.3 describes the quasi-steady-state gas condition, and can be derived by further simplifying the gas budget. I approximated the total air-sea exchange by combining bubble processes and diffusive exchange into a single term (e.g. as in Eq. 2 in Liang et al., 2013): Fd + FC + FP ≈ −kT × (C − Ceq × (1 + Δeq) ×SLP1 atm) (C.4) Here kT represents the pooled diffusive and bubble-mediated gas transfer coefficients (i.e. kd + β × kP) and Δeq is the bubble-induced steady-state gas supersaturation. kT, kB, and Δeq were all defined following Liang et al. (2013). The analytical solution can subsequently be derived from Eq. C.4 by discretizing the simplified gas budget into sufficiently short time increments, dt, so that MLD, kT, Δeq, Cdeep and κ can be considered constant. I note that Eq. C.4 is an over-simplification of air-sea exchange processes involving bubbles, but this approximation simplifies the derivation of the analytical solution and matches the net air-sea flux predicted by the full parameterization (as in Eq. C.1) within ~2 % over a range of wind speeds (0-25 m s-1) and gas saturation states (90-110 %). The resulting O2 re-equilibration time-scale, TO2 (see below) should be sufficiently long to capture the full history of environmental processes driving ΔAr and ΔN2 divergence. Below, I derive the analytical solution to the simplified budget through the following steps: 1) Expand Eq. C.3 with simplified gas flux terms. dCdt=−kTMLD× (C − Ceq × (1 + Δeq) ×SLP1 atm) +Cdeep−CdZ×κMLD (C.5.1) 2) Simplify kT/MLD and κ/(dZ × MLD) as K1 and K2, respectively, and re-write the equation by combining like-terms. dCdt= −(K1 + K2) × C + K1 × Ceq × (1 + Δeq) ×SLP1 atm+ K2 × Cdeep (C.5.2) 203 3) Further simplify by expressing (K1 + K2) as P, (K1 × Ceq × (1 + Δeq) ×SLP1 atm+ K2 × Cdeep) as Q, and dC/dt as C'. Apply an integration factor of ePt. C′ = −P × C + Q (C.5.3) C′ × ePt + P × C × ePt = Q × ePt (C.5.4) 4) Note that d/dt(C × ePt) = C′ × ePt + P × C × ePt by the product rule. ddt(C × ePt) = Q × ePt (C.5.5) C × ePt = Q × ∫ ePt dt (C.5.6) 5) Since P and Q (defined above in step 3) are considered constant over dt, Eq. C.5.6 can be integrated simply. In Eq. C.5.7, R is the integration constant. C =QP+ R × e−Pt (C.5.7) 6) Finally, by setting C(t=0) to be the initial condition, C0, I derive a single analytical solution, by re-substituting the simplifying terms (P and Q defined above in step 3) for the gas flux terms. R = C0 −QP (C.5.8) C =kT×Ceq×(1+Δeq)×SLP1 atm+κdZ ×CdeepkT+κdZ + (C0 −kT×Ceq×(1+Δeq)×SLP1 atm+κdZ×CdeepkT+κdZ) × e−(kTMLD+κdZ × MLD)t (C.5.9) When mixing is negligible (i.e. κ ≈0 m2 d-1), Eq. C.5.9 simplifies to C = Ceq × (1 + Δeq) ×SLP1 atm+ (C0 − Ceq × (1 + Δeq) ×SLP1 atm) × e−(kTMLD)t (C.5.10) 204 The analytical solution is useful for understanding quasi-steady-state gas conditions, and the rate of response to perturbations. For example, the quasi-steady-state gas concentration, CSS, can be estimated from Eq. C.5.9 as t approaches infinity, and C approaches the value of the first term: CSS = kT×Ceq×(1+Δeq)×SLP1 atm+κdZ×CdeepkT+κdZ (C.5.11) Subsequently, the ML O2 re-equilibration timescale, ΤO2, can be estimated as the time required for air-sea gas exchange and vertical mixing processes to re-establish the quasi-steady-state condition. I thus estimated ΤO2 by setting (C – CSS) / (C0 – CSS) to be 0.01 (i.e. C – CSS << C0 – CSS) and t to ΤO2: C = CSS + (C0−CSS) × e−(kTMLD+κdZ × MLD)×TO2 (C.6.1) C−CSSC0−CSS= 0.01 = e−(kTMLD+κdZ × MLD)×TO2 (C.6.2) TO2 =−ln (0.01)× MLD(kT+κdZ) (C.6.3) The MLD O2 re-equilibration time calculated by this approach represents several MLD O2 residence times (τO2), which is typically approximated by MLD/kT. Calculating ΤO2 by this approach reflects the contribution of vertical mixing (proportional to κ) in reducing O2 cycling in surface waters, and more fully represents the timescale of gas re-equilibration within the MLD. This timescale is more appropriate for N2' calculations relevant to NCP derivation within the ML. Indeed, when the N2' calculations are performed over a timescale represented by MLD/kT, differences between Ar and N2 relevant to NCP calculations are not fully reconciled. In ocean environments, where MLD, kT and κ vary in time, ΤO2 should be estimated using 30- to 60-day weighted values for these terms, where possible, or from ship-based observations. C.2.2 Quasi-steady-state ΔN2/Ar conditions Evaluating Eq. C.5.11 for Ar and N2 enables calculation of the quasi-steady-state ΔN2/Ar condition. Analysis of those conditions (Fig. C.2) reveals that bubble processes, mediated by high 205 wind speeds, can induce the largest ΔN2/Ar disequilibria (excluding disequilibria resulting from rapid temperature changes), with values potentially exceeding 1.5 %. Bubble processes always produce positive ΔN2/Ar, but these effects can be dampened or enhanced by vertical mixing. Indeed, the model predicts that bubble effects are dampened as mixing rates increase and/or ΔN2/Ardeep decreases (which may be caused by warming beneath the ML, or trapping of a low or negative ΔN2/Ar signal previously formed at the surface), potentially resulting in negative ΔN2/Ar. Without vertical mixing, ΔN2/Ar is set by the relative bubble-induced supersaturation states (Δeq) of N2 and Ar (grey lines in Fig. C.2). In the absence of bubble processes, the quasi-steady-state ΔN2/Ar approaches ΔN2/Ardeep (evaluate Eq. C.5.11 for Δeq set to 0 %). Thus, the quasi-steady-state ΔN2/Ar will be weighted between these two end members by the relative strength of kT and κ/dZ. This analysis illustrates the combined importance of bubble processes and vertical mixing in setting baseline ΔN2/Ar in marine surface waters. C.3 Additional controlled simulations I performed additional numerical experiments using the 1D model to examine ΔN2/Ar responses to controlled environmental forcing. These simulations, denoted with the prefix ‘Ex’, were designed to represent the impacts of extreme temperature (SST) and wind speed (u10) variability in ice-free (runs Ex-IF 1, Ex-IF 2, and Ex-IF 3) and partially ice-covered (run Ex-IC 1) waters. In these experimental runs, initial conditions (temperature and salinity profiles) were derived from ship-board CTD observations at OSP (runs Ex-IF 1–3) and in northern Baffin Bay (Ex-IC 1) (data provided by the Institute of Ocean Sciences, DFO Canada at www.waterproperties.ca/linep and by Amundsen Science Data Collection, 2020a at www.polardata.ca). I introduced two rapid step-changes in u10 and SST based on observed variability in Subarctic NE Pacific and Arctic field studies (unpublished results). Salinity, MLD and subsurface properties were derived from the initial conditions and held constant throughout the runs. For each set of conditions, I performed three runs: (a) no mixing (FM = 0); (b) dampened mixing (κZ = 10-5 m2 s-1); (c) full mixing (κZ = 10-4 m2 s-1). I set the value of ΔN2/Ardeep to 1.5 % based on the observed upper range of ΔN2/Ar below the ML in most ocean basins (Hamme et al., 2019). The simulation forcing conditions are summarized in Table C.1. 206 The experimental simulations reveal the expected effects of elevated wind speed and strong mixing in determining surface ΔN2/Ar conditions (Fig. C.3) and controlling the rate of re-equilibration to steady-state, as predicted by the equation for TO2 (Eq. C.6.3). In reality, however, air-sea exchange rates will often exceed turnover via mixing in many ocean regions. Thus, mixing may cause baseline shifts in surface ΔN2/Ar, but elevated wind events should still dominate on most timescales. Temperature impacts on saturation states can enhance or dampen ΔN2/Ar disequilibria over both long (i.e. seasonal) and shorter (days to weeks) time-scales. While bubble processes only produce positive ΔN2/Ar, temperature changes (ΔSST) can sometimes lead to negative ΔN2/Ar as represented in the Ex-IF 2 and Ex-IF 3 simulations (Fig. C.3b, c). For example, a transient response to rapid warming on day 12 of these simulations (cooling on day 65) first caused ΔN2/Ar to decrease (increase) as a result of the greater SST-dependent solubility of Ar. Subsequently, when SST stabilizes or the rate of ΔSST decreases, ΔN2/Ar increases (decreases), as Ar re-equilibrates more rapidly via diffusive air-sea exchange. These results reveal a key difference in the response of ML ΔN2/Ar to rapid and slow/long-term (i.e. seasonal) ΔSST, as can be seen by comparing the non-mixing Ex-IF2 (Fig. C.3b) and OSP simulations (Fig. 4.3). While the rapid ΔSST induced in Ex-IF2 (4 oC over ~2 days) is likely unrealistic, the simulation demonstrates a short-lived ΔN2/Ar response that occurs when ΔSST occurs more rapidly than ventilation. Conversely, in the OSP simulation, the same magnitude of ΔSST occurs over ~60 days, such that air-sea exchange rates typically exceed the rate of ΔSST. This limits the transient ΔN2/Ar response, causing ΔN2/Ar to follow the seasonal SST trend. Ultimately, SST-induced disequilibria can persist as long as the rate of temperature-induced solubility change exceeds the rate of ventilation. As shown in Ex-IC (Fig. C.3d), sea ice dampens u10 effects and accentuates SST and mixing controls on gas saturation states by reducing surface ventilation. These effects cause ΔN2/Ar disequilibria to persist for extended time periods, with the exact response depending on the gas exchange model employed and the fraction of ice cover. Sensitivity analyses with different gas exchange parameterizations produced the same general results of persistent ΔN2/Ar disequilibrium, albeit with slightly variable re-equilibration times (not shown). Suppression of 207 bubble effects under ice-covered conditions reflects the reduction in sea states and wave breaking activity (Voermans et al., 2019). C.4 Uncertainty analyses C.4.1 ΔN2' parameter uncertainty I performed a Monte Carlo analysis on the OSP full mixing (OSP-FM) simulation to determine the contributions of individual and combined parameterization uncertainty to errors in ΔO2/N2'. This analysis was conducted by randomly varying each of the input variables (wind speed, SST, SLP, MLD), air-sea flux (Fd, FP, FC, β) and mixing terms (κ, ΔN2/Ardeep) in the N2' ML budget (Eq. C.4 above, and Eq. 4.3) around their estimated uncertainties (Table C.2), and performing 100 (for individual parameter errors) or 1000 (for combined errors) iterations of N2' calculations. I used uncertainties of ± 2.5 m s-1, ± 0.75 oC and ± 2 mbar in the wind speed, SST and SLP data based on a comparison of in-situ observations (from ships, moorings and weather buoys) with various gridded products. For observations based in the Subarctic NE Pacific, the Cross-Calibrated Multi-Parameter (CCMP) wind speed product (provided by Remote Sensing Systems at www.remss.com; Atlas et al., 2011), NOAA High Resolution SST Dataset (provided by NOAA ESRL at https://psl.noaa.gov/; Reynolds et al., 2007), and NCEP/NCAR reanalysis 2 SLP (provided by NOAA ESRL at psl.noaa.gov; Kalnay et al., 1996) products compared best with observations from moorings in coastal and off-shore waters. The air-sea flux terms (Fd, FP and FC) depend on the choice of gas transfer parameterization. I used the bubble-mediated model of Liang et al. (2013), as it was parameterized for O2 and N2, and is considered valid for gases with similarly low solubility. Over a range of wind speeds and temperatures, I estimated the relative uncertainty in these terms to be ~18-24 %, based on the standard deviation of values derived using several gas transfer parameterizations. These errors are roughly consistent with Wanninkhof (2014), who estimated a mean total error in the air-sea gas flux of ~20 %. I therefore followed Bushinsky & Emerson (2015) by assigning a relative error of 15 % to Fd, FP and FC in the Monte Carlo analysis, since the Wanninkhof estimate of 20 % includes uncertainty in wind speed data as well. Finally, I ascribed an error of ±5 m to estimates of the MLD, following Ch. 2, an error of ±0.14 to β based on Emerson et al. (2019), and uncertainties of ±5x10-5 m2 s-1 and 0.5 % to κZ and ΔN2/Ardeep, respectively. 208 Overall, uncertainty in the sea surface temperature product (mean 0.05 %) contributes the largest individual errors to ΔO2/N2'. This result is unsurprising as seasonal SST variability, which drives diffusive air-sea exchange, contributes to strong variability in ΔN2/Ar (see Ch. 4). Errors in the mixing coefficient term were next largest (mean 0.04 %), while errors in the bubble-flux terms (β, FC, FP) were generally small, due to the low prevalence of high wind speeds. Errors associated with these terms were typically larger during autumn to spring, when wind speeds were elevated. The combined uncertainty from all parameterizations is 0.06 %, on average, across both OSP simulation. C.4.2 Assumption uncertainty The N2' approach assumes constant values of MLD, salinity, κ, and ΔN2/Ardeep, based on the availability of data products and at-sea measurements (see Ch. 4 for details). To evaluate the uncertainty in N2' resulting from these assumptions, I compared calculations on the OSP full mixing simulation in which these values were held constant against calculations in which they were allowed to vary based on values in the full model simulations. I found mean errors in ΔO2/N2' from these assumptions of 0.05 % (range ~0.001 – 0.3; Table C.2). This value can only be reduced through accurate estimation of the time-history of MLD, salinity κ, and ΔN2/Ardeep terms, although this may not be feasible for many field measurement programs. C.4.3 Uncertainty from N2-fixation I estimated the potential uncertainty in N2' and ΔO2/N2' due to N2-fixation by applying constant rates of N2 removal (see Ch. 4) in 1D model simulations without vertical mixing over a range of constant u10 and SST. I compared the steady-state ΔN2 from these runs against values obtained with N2-fixation rates of zero. For the upper range of N2-fixation observed in the ocean, I calculate a maximum steady-state deviation of ~0.3 % at very low wind speeds, but values are always less than 0.05 % above wind speeds of 6 m s-1 (not shown). Applying a constant N2-fixation rate equivalent to the global observed maximum to the OSP full mixing run resulted in deviations of less than 0.05 % in N2 saturation when compared against the run excluding N2-fixation. I thus conclude that N2-fixation will have a negligible effect on ΔO2/N2' calculations in most oceanic waters. 209 C.4.4 Total uncertainty The error analyses produce a total average uncertainty in ΔO2/N2' of 0.1 % (range 0.04 – 0.3; Table C.2) resulting from the parameterization and assumption errors. On average, this value is smaller than the offset between ΔO2/Ar and uncorrected ΔO2/N2. The range of uncertainty in ΔO2/N2' (including potential contributions from N2-fixation) is represented in Fig. 4.5, which presents ΔO2/Ar – ΔO2/N2' from the OSP simulation (full mixing). Since the assumption errors contribute the largest proportion to total ΔN2' uncertainty, the total error in ΔO2/N2' is smallest when these values are small during periods of reduced MLD, κ and ΔN2/Ardeep variability. In field studies, the error approximated here and potential uncertainty from in-situ O2 and N2 measurement accuracy will contribute to total uncertainty in ΔO2/N2'. I exclude the RMSE derived from the N2 validation against observations (1.0 %; see Ch. 4) from the total estimated N2' uncertainty, as this likely over estimates the relative differences between Ar and N2. 210 Table C.1 Summary of experimental one-dimensional model simulation conditions. The three numbers listed for SST and u10 correspond with values used during each of three forcing time segments (days 0-12, 12-65, and 65-120 in Fig. C.3). OSP = Ocean Station Papa; BB = Baffin Bay. Simulation (duration) deep and initial conditions SST [oC] u10 [m s-1] ice [%] κZ [m2 s-1] ΔΝ2/Αrdeep [%] Ex-IF 1 OSP CTD profile 10, 10, 10 7, 15, 7 0 Run a: 0 Run b: 10-5 Run c: 10-4 Run a: 0 Run b: 1.5 Run c: 1.5 Ex-IF 2 OSP CTD profile 10, 14, 10 7, 7, 7 0 Ex-IF 3 OSP CTD profile 6, 10, 14 7, 15, 7 0 Ex-IC 1 BB CTD profile 0, 4, 8 7, 15, 7 50 Table C.2 Sources of uncertainty in ΔO2/N2', determined through Monte Carlo and sensitivity analyses on a realistic one-dimensional model simulation of OSP. Reported uncertainty (right column) represents the range and mean (in parentheses) of values. The total uncertainty presented at the bottom of the upper section was derived by summing in quadrature each of the underlined errors. Parameter Parameter Error Absolute ΔO2/N2' uncertainty [%] Sea surface temperature, SST 0.75 oC 0.03 – 0.06 (0.05) Mixing coefficient, κ 5x10-5 m2 s-1 0.004 – 0.1 (0.04) Bubbled flux scaling coefficient, β 0.14 <0.001 – 0.04 (0.02) Wind speed, u10 2.5 m s-1 0.002 – 0.02 (0.01) Partial bubble flux, FP 15 % (relative) <0.001 – 0.02 (0.004) Subsurface ΔN2/Ar, ΔN2/Ardeep 0.5 % 0.001 – 0.01 (0.004) Diffusive air-sea flux, Fd 15 % (relative) 0.001 – 0.01 (0.003) Sea level pressure, SLP 2 mbar <0.001 – 0.003 (0.002) Small bubble flux, FC 15 % (relative) <0.001– 0.005 (0.001) Mixed layer depth, MLD 5 m <0.001 – 0.006 (0.001) Combined parameterization 0.04 – 0.2 (0.06) All assumptions 0.001 – 0.3 (0.05) Surface N2-fixation <0.05 Total 0.04 – 0.3 (0.1) Table C.3 Summary of biases and uncertainty in NCP calculations based on ΔO2/Ar, ΔO2/N2 and ΔO2/N2'. NCP errors were calculated from gas and environmental conditions in the corresponding model runs. “Ex” represents the additional controlled experiments described in section C.3, while “OSP” refers to the realistic simulations presented in Ch. 4. Parameter Parameter value [%] Range (mean) NCP error [mmol O2 m-2 d-1] N2 validation 1.0 N/A Ex ΔO2/Ar – ΔO2/N2 0 – 1.1 (0.4) 0 – 18 OSP ΔO2/Ar – ΔO2/N2 0 – 0.5 (0.2) 0 – 8 Ex ΔO2/Ar – ΔO2/N2' 0 – 0.5 (0.01) 0 – 9 Real ΔO2/Ar – ΔO2/N2' O2 gas transfer velocity, kO2 0 – 0.2 (0.04) 20 0 – 3 0 – 8 Offshore mixing Nearshore mixing Diel ΔO2/Ar variability N/A N/A N/A 0 – 50 (Ch. 2) 60 – 190 (Ch. 2) 0 – 26 (Wang et al., 2020) 211 Figure C.1 The differential physical properties of O2, Ar and N2 across a range of temperatures and salinities (a-d), and the relationship between ΔO2, ΔN2/Ar and the absolute differences between ΔO2/Ar and ΔO2/N2 (e). In (a)-(d), O2, Ar and N2 are represented by the dotted grey, black and solid grey lines, respectively. Panels (a) and (b) represent the solubility-temperature and -salinity dependence of each gas, respectively, panel (c) presents their air-sea diffusion exchange coefficients, and panel (d) shows the Henry's Law constant for each gas in one standard atmosphere of dry air. Lines represent average values over a salinity range of 0-35 PSU (panels a, c and d) or a temperature range of -2 to 10 oC (panel b). Panel (e) shows the relationship between ΔO2, ΔN2/Ar and the absolute differences between ΔO2/Ar and ΔO2/N2 (at ΔAr = 0 %) over a realistic range of surface ΔO2 in the ocean, and ΔN2/Ar produced in the model simulations. The grey outlined region identifies scenarios where ΔO2/Ar and ΔO2/N2 have opposite signs. The vertical dashed lines represent the range of ΔΝ2'/Αr in the OSP simulations. 212 Figure C.2 The combined effects of wind speed and mixing on quasi-steady-state surface ΔN2/Ar derived from one-dimensional model simulations. Colour scaling represents the quasi-steady-state surface ΔN2/Ar predicted by the model at SST and salinity values of 10 oC, and 34 PSU, respectively, based on Eq. C.5.11. The thick grey line represents the average bubble-induced steady-state condition without mixing over a 0-25 oC SST range (right y-axis). Panels show a gradient from weakest (a) to strongest (c) mixing, with the mixing coefficient, κ, given at the top left of each panel. 213 Figure C.3 Surface ΔN2/Ar derived in experimental one-dimensional model simulations. The three black lines in each panel represent the different mixing scenarios (legend in panel a), and the thick light grey lines depict expected quasi-steady-state ΔN2/Ar values resulting from bubble-induced supersaturation in each model run (grey lines in Fig. C.2). Arrows above the panels represent step changes of increasing or decreasing wind speed (light grey) and SST (black), per the imposed experimental conditions presented in Table C.1 The locations of the arrows reflect the approximate time when step changes occurred. The bottom panel presents results in simulations containing 50 % ice cover. 214 Figure C.4 Surface ΔN2 and ΔAr in a realistic one-dimensional model simulation of OSP, with corresponding forcing parameters. In panel (a), solid lines represent output from the full-mixing simulation, and dotted lines represent output from simulations with no mixing. The x-axes represent 2011 year-day. Refer to Fig. 4.3 for further details. 215 Appendix D - Supporting Information for Chapter 6 D.1 Surface and subsurface gas data D.1.1 Underway optode and GTD data processing Several processing steps are required to derive final, calibrated datasets from raw optode and GTD sensor data. I describe the steps here, and provide some sample Matlab code for performing calculations on underway data (available at https://doi.org/10.5281/zenodo.4024951, with link to https://github.com/rizett/PIGI_system). Optode: 1. Raw O2 concentration is calculated by the optode software from a sensed phase shift and measured seawater temperature, following a Stern-Volmer relationship (Uchida et al., 2010). O2′ =(P0Pc−1)KSV (D.1) KSV = c0 + c1 × T + c2 × T2 (D.1a) P0 = c3 + c4 × T (D.1b) Pc = c5 + c6 × Pr (D.1c) c0-c6 are foil-specific coefficients characterized by a multi-point O2 calibration provided by the manufacturer, T is the temperature reading, and Pr is the phase shift. 2. O2 sensed by the optode reflects the partial pressure of O2 in seawater and is therefore insensitive to ambient salinity. To determine accurate O2 concentrations, O2′ from Eq. D.1 must therefore be salinity-compensated following Bittig et al. (2018). O2cor =1013.25− pH2O(S=0)1013.25− pH2O(S)× Scorr × O2′ (D.2) pH2O is the vapour pressure at 0 salinity (pH2O(S=0)) and ambient salinity (pH2O(S)), respectively, and Scorr is the salinity-dependent component of the O2 solubility (Garcia & 216 Gordon, 1993) (i.e. exp{S × (B0 + B1 × T + B2 × T2 + B3 × T3) + C0 × S2}, where B and C are coefficients, and T is the scaled temperature, defined in Garcia & Gordon) 3. Sensor-derived O2cor (µmol L-1) was calibrated against discrete samples analyzed via Winkler titration. O2cal = b + m × O2cor + offset (D.3) b and m are the linear regression coefficients. The offset term represents the difference between O2 measured in corresponding surface Niskin bottles and O2 measured in samples obtained from the seawater supply line. All coefficients in Eq. D.3 are unique to each cruise dataset (see Fig. 6.2). 4. O2 saturation (O2,sat, %) was calculated from O2cal and equilibrium O2 solubility, using TSG temperature and salinity (O2,eq). O2,satcal =O2calO2,eq(S,T)×(pSLPatm−PH2O)(1013.25 mbar−PH2O)×100 % (D.4) Here, pSLPatm is the ambient sea level pressure. GTD: 1. An atmospheric bias offset correction, derived by comparing in-air GTD measurements with atmospheric pressure data (pSLPatm) prior to and after each deployment, was first applied to underway TP (mbar) observations in order to account for potential sensor drift. TP = TPraw + offset (D.5) offset = pSLPatm − TPraw, in air (D.5a) The average offset was <1 mbar. The in-air GTD measurements were recorded outside at the University of British Columbia campus and onboard the research ships. pSLPatm data were taken as the altitude-adjusted hourly sea level pressure reported by the Vancouver International Airport or as measurements made by ship-board sensors. 2. I then corrected GTD readings for the effects of warming within the seawater supply lines of the ship (see section D.2). Warming was estimated as the difference between the water 217 temperature measured by a thermosalinograph (TSG) installed adjacent to the optode/GTD system (or by the optode’s temperature sensor when a TSG was not installed nearby), and temperature measured by a thermosalinograph at the ship’s intake. TPcor = TP − TPincrease (D.6) D.1.2 N2 derivation I derived N2 concentrations from the O2,satcal and TPcor data following McNeil et al. (2005). Unlike McNeil et al., who assume that Ar and N2 saturation states are equal, I estimated Ar saturation (Arsat = [Ar]sw/[Ar]eq, where [Ar]eq is the solubility concentration of Ar at ambient sea level pressure), and hence the partial pressure of Ar (pAr), from MIMS ΔO2/Ar and optode O2,satcal (rearrange Eq. 6.1). The error associated with the McNeil et al. assumption is, however, minimal (<1 %; McNeil et al. 1995). I did not explicitly account for the contribution of carbon dioxide (CO2) to TP, since these measurements were not made on all cruises. However, other gases – including CO2, the noble gases, nitrous oxide and methane – occur in trace quantities (<< 1% of TP). They are assumed to be saturated to the same degree as N2, which is accounted for in the denominator of Eq. D.7 (see McNeil et al., 2005). The partial pressure of N2 (pN2, units mbar) was thus derived by subtracting the partial pressures of O2 (pO2cal), water vapour (pH2O, assumed 100 % saturated), and Ar from TPcor following pN2=TPcor − pO2cal − pAr − pH2O1+ (1 − χN2 − χO2 − χAr) χN2⁄ (D.7) where χN2, χO2, and χAr are the atmospheric dry mole fractions of N2 (=0.78084), O2 (=0.20946), and Ar (=0.00934) respectively. The N2 saturation level (%) was calculated as: N2,sat =pN2χN2×(pSLPatm−pH2Oatm)×100 % (D.8) 218 where pSLPatm and pH2Oatm are the atmospheric sea level and vapour pressures (both mbar) derived from NCEP/NCAR reanalysis data products. pO2cal and pAr in Eq. D.7 were calculated following Eq. D.8. I derived [N2]eq and [Ar]eq from the solubility equations of Hamme & Emerson (2004), using hydrographic data obtained near the ship’s intake. N2,sat, calculated from Eq. D.8, was calibrated using discrete samples obtained from mixed layer Niskin bottles (see below, section D.1.3). Matlab codes for the data processing steps described above are provided at the https://doi.org/10.5281/zenodo.4024925 and http://github.com/rizett/O2N2_NCP_toolbox repositories. D.1.3 Discrete gas sampling: N2,sat calibration and subsurface ΔN2/Ar Samples for discrete gas analyses were collected from Niskin bottles fired throughout the water column during the NEP-summer and NEP-winter cruises. Discrete N2,sat was calculated using N2/Ar ratios measured in bottle samples obtained from the mixed layer and corresponding Arsat derived from the underway MIMS and optode data. During the NEP-summer cruise, samples were obtained from the surface (5 m), and from one depth below the mixed layer. Duplicate samples were collected into pre-evacuated flasks containing crystalized mercuric chloride (HgCl2), using CO2-flushed tubing to fill the bottles to half-way. The water was equilibrated with the headspace at a known temperature and then removed. Headspace O2/N2/Ar ratios were cryogenically purified to remove H2O and CO2 and analyzed against standards of similar composition on a Thermo Scientific MAT 253 isotope ratio mass spectrometer. Corrections were made for incomplete gas equilibration with the headspace and for differential ionization efficiencies in the mass spectrometer based on the concentration of O2. The pooled standard deviation of duplicates for N2/Ar from the NEP-summer cruise was 0.06 %. Further details on collection and analysis procedures are described in Hamme & Emerson (2013) and in Emerson et al. (1999). During the Arctic cruise, subsurface samples for N2/Ar analyses were collected at low spatial resolution (markers in Fig. D.5b), following the same procedure applied for discrete sample collection in the NEP-summer cruise. An additional set of surface samples (drawn from Niskin bottles fired at ~7 m), used to calibrate the underway N2 data, were collected at higher spatial 219 resolution across the survey region. These samples, obtained in duplicate, were collected into 12 mL screw-capped Labco exetainers with chlorobutyl septa. Samples were obtained from Niskin bottles by overflowing at least three volumes into submerged exetainers. Τhe samples were capped (while submerged), and poisoned with 0.02 mL of a concentrated HgCl2 solution, using a needle and syringe. Samples were stored underwater at room temperature, until analysis using a Bay Instruments membrane inlet mass spectrometer (MIMS) (Kana et al., 1994). During analysis, samples were transferred to a water bath close to in-situ temperature and equilibrated for at least two hours prior to measurement on the MIMS. Following Kana et al. (1994), water was extracted from the exetainers by inserting a length of stainless-steel tubing to the bottom of the sample vials. Water was then pumped at constant flow rate (2 mL min-1) through a 20 oC water bath, before passing the MIMS inlet. A liquid nitrogen trap between the MIMS inlet and ion sources was used to remove CO2 and water vapour. Two air equilibrated seawater standards (20 oC and 5 oC) were run after every five samples. The average N2/Ar standard deviation on all samples was ~0.3 %. In both the NEP-summer and Arctic cruises, the discrete surface samples were used to calibrate the underway N2,sat signal. ΔN2/Ardeep in the N2'' calculations was derived from the discrete subsurface samples as the mean ΔN2/Ar value in the 25 m below the MLD (Fig. D.5). On each cruise, I applied average ΔN2/Ardeep values in each sub-region based on all samples obtained along the respective sections of the cruise track. In the NEP-Winter dataset, I obtained estimates of ΔN2/Ardeep from the archived dataset of Hamme et al. (2019) (diamonds in Fig. D.5a). D.2 Effects of seawater warming on GTD readings On the CCGS J. P. Tully and CCGS Amundsen, I observed seawater warming between the intake of the seawater supply and the location where the optode/GTD system was installed (Fig. D.1a). This warming occurs as relatively cool water from the ocean surface passes through approximately 50-100 m of tubing in the ship’s hull. Warming was determined by comparing the sea surface temperature (SST) measured by a TSG installed at the seawater intake and that recorded by a second TSG adjacent to the optode/GTD system (or by the optode when a second TSG was not present). The observed warming was approximately 0.3 oC (NEP-Summer), 0.5 oC (NEP-Winter) and 1.5 oC (Arctic-Summer). 220 In the absence of changes in absolute gas concentrations, seawater warming will increase the saturation state of all dissolved gases (and hence, partial pressures), causing TP readings from the GTD to be higher than true dissolved pressure in the ocean’s surface. To estimate this bias, I first calculated the degree of saturation increase (δCsat) for each of the main dissolved gases (C = O2, N2, Ar) using their solubility equations (Ceq) and assuming constant salinity (S) between the intake and laboratory. Csat,lab = δCsat + Csat,intake (D.8a) δCsat =ClabCeq(SSTlab,S)×100 % − CintakeCeq(SSTintake,S)×100 % (D.8b) Assuming Clab = Cintake = C, and expressing C as the product of the gas’ saturation state, Csat, and equilibrium solubility, Ceq (i.e. C = Csat,intake100 %×Ceq(SSTintake,S)) reduces Eq. D.8b to δCsat = Csat,intake ×Ceq(SSTintake,S)−Ceq(SSTlab,S)Ceq(SSTlab,S). (D.8c) For O2 and Ar, Csat,intake was obtained from the calibrated optode and MIMS (ΔO2/Ar) data. I set N2,sat,intake to be equal to Arsat,intake. I then expressed the expected change in gas saturation state (δCsat) as a change in partial pressure (δpC) δpC = δCsat×𝜒𝐶 × (pSLPatm − pH2Oatm). (D.9) As TP is the sum of partial pressures for all gases (pC), the expected increase in TP due to warming (TPincrease) was evaluated by summing δpC for O2, Ar and N2. The expected increase in water vapour pressure (δpH2O) was calculated from temperature measurements at the intake and laboratory and added to TPincrease. TPincrease=Σ(δpC)+δpH2O (D.10) Raw GTD measurements of TP were corrected by subtracting TPincrease from GTD TP (Eq. D.6). For the warming observed along my cruise tracks, median TP corrections (TPincrease) were 5, 16 and 18 mbar, during NEP-summer, NEP-winter and Arctic-summer, respectively (Fig. D.1b). 221 Note that the optode O2,sat data are corrected for potential increase in O2 saturation state between the seawater intake and sensors by calculating O2,sat from calibrated O2 concentration data and SST measured at the ship’s intake (see above, section D.1). O2 data, therefore, do not require further temperature corrections. D.3 Historic environmental datasets Historic (i.e. prior to ship-based observations) sea surface temperature (SST), sea ice and sea level pressure data were obtained from the NOAA High Resolution OI V2, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and the NCEP/NCAR reanalysis 2 data products, respectively. Wind speed data were obtained from the Cross-Calibrated Multi-Platform wind vector analysis version 2 (CCMPv2; NE Pacific) and North American Regional Reanalysis (NARR; Arctic) products. I obtained data from both study regions during the 60 days prior to cruise sampling, and performed a nearest-neighbour interpolation of the three-dimensional grids onto my cruise track to create 0.25-day historic records corresponding with each underway cruise observation (Fig. D.3). I compared gridded observations against multi-year measurements of wind speed and SST, covering large geographic ranges in NE Pacific and Arctic waters (Fig. D.6). In-situ measurements were obtained from underway ship-based observations on the CCGS Tully and CCGS Amundsen, moorings (NOAA PMEL Ocean Station Papa and Environment and Climate Change Canada coastal British Columbia moorings), buoys (International Arctic Buoy Program) and the Ocean Networks Canada Cambridge Bay observatory. Based on these comparisons, I applied best-fit linear regression corrections separately to gridded data in the NE Pacific and Arctic to scale the historic datasets to best-match in-situ observations. The root mean square errors of these fits were 2.5 m s-1 and 1.8 m s-1 for wind speed, and 0.5 and 1.3 oC for SST in the NEP and Arctic, respectively. The offset between uncorrected gridded data and in-situ observations was <1.5 m s-1 and <0.7 oC, on average, for wind speed and SST, respectively, in both study regions. A comparison of SLP between ship-based and gridded data resulted in an average adjustment of <3 mbar in each dataset (results not shown). 222 D.4 Uncertainty in ΔO2/Ar, ΔO2/N2 and ΔO2/N2' Uncertainty in ΔO2/N2 resulting from N2 measurements arises from biases in gas TP measurements and N2 calculation assumptions (e.g. pCO2 and pAr variability, and warming between the seawater intake and measurement system). To estimate the contribution of these sources of uncertainty to total errors in ΔO2/N2, I performed sensitivity tests by varying each parameter (TP, pCO2, pAr and warming), and re-calculating ΔO2/N2. The error was taken as the mean deviation between my best estimates of ΔO2/N2 and the sensitivity values. Across a range of observations, adjusting pCO2 by ±400 ppm (i.e. pCO2 varied from 0 to 800 ppm in N2 calculations) contributed negligible uncertainty to derived ΔO2/N2 (average 0.001 %), while performing pN2 calculations by constraining pAr = pN2 (as in McNeil et al., 2005) instead of deriving pAr from MIMS-observations (section D.2.2), contributed only ~0.1 % uncertainty in final ΔΟ2/Ν2. Τhe largest source of error in N2 calculations arises from the estimation of warming-induced supersaturation. In my data, I observed warming between ~0 and 2 oC, corresponding with TP corrections of up to ~35 mbar (Fig. D.1). Neglecting to apply these corrections produced errors in ΔO2/N2 around 1.8 %, on average. As expected, the warming correction is most important in regions of cold ambient SST, where seawater warming between the ocean’s surface and laboratory can be extreme. I note, however, that the estimate of ~1.8 % uncertainty should be seen as an upper limit, since errors associated with the TP warming correction depend on the accuracy of temperature estimates between the intake and optode/GTD system, which is likely to be smaller than the signal itself. Applying a conservative ±0.25 oC error to estimates of the degree of warming results in mean TP offsets of 5 mbar, and uncertainty in ΔO2/N2 of 0.6 %, across all cruises. Installing the optode/GTD instrumentation nearer to the seawater intake will minimize these errors. Finally, errors in raw TP measurements by the GTD are on the order of ~0.1 % (~1 mbar, per Pro-Oceanus Systems Inc.), contributing a further ΔO2/N2 uncertainty of 0.1 %. Overall errors in ΔΟ2/Ν2, resulting from uncertainty in the N2 term are ~1.04 % on average. Calibration of N2 signals, however, reduces this uncertainty to the root mean square error of the linear calibration, 0.9 % (Fig. 6.2b). Uncertainty in O2 also contributes uncertainty to ΔO2/N2, and errors in measured O2 may be on the order of several percent due to drift in optode measurements (Bittig & Körtzinger, 2015; D’Asaro & McNeil, 2013). In my dataset, underway data were 223 calibrated to within 0.9 %, and showed strong linearity with discrete samples analyzed by Winkler titration (Fig. 6.2a). In total, considering the calibrated accuracies of the underway O2 and N2 signals, I estimate a mean uncertainty in ΔO2/N2 of ~1.3 %. This is of similar magnitude to errors in underway ΔO2/Ar (~0.75 %), which are described in Appendix 2. Combining these terms in quadrature, yields a conservative uncertainty in ΔO2/Ar – ΔO2/N2 (roughly equivalent to ΔN2/Ar) of ~1.5 %. I estimated the uncertainty in ΔO2/N2' by performing sensitivity analyses on N2' calculations in which I adjusted the ΔN2/Ardeep and κΖ terms. For the ΔO2/N2' data presented in Ch. 6, I applied average ΔN2/Ardeep and κZ values (Table D.1) in the different sub-regions of each cruise based on subsurface N2/Ar sampling (section D.1.3) and mixing rate estimates as described in Ch. 6. In the Arctic, I applied along-track κZ values based on NEMO model output. For the sensitivity analyses, additional N2' computations were performed by varying κZ and ΔN2/Ardeep by 0.25 % and 5x10-5 m2 s-1, respectively, following Chapter 4, or by neglecting the mixing term altogether (i.e. κZ set to 0 m2 s-1). These analyses demonstrated a small sensitivity (<0.5 % in all cruises) of my results to the choice of ΔN2/Ardeep and κZ used in the calculations (Fig. 6.7). In almost all cases, each combination of ΔN2/Ardeep and κZ produced ΔN2'/Ar smaller than ΔN2/Ar (i.e. a smaller deficit between ΔO2/Ar and ΔO2/N2' than between ΔO2/Ar and ΔO2/N2). In Chapter 4, the uncertainty in ΔO2/N2' from a range of parameterization and assumption errors was estimated to be <0.3 % (average ~0.1 %). Combining this with the measurement errors in ΔO2/N2 and sensitivity of ΔO2/N2' described above yields an average error in my ΔO2/N2' estimates of 1.3 %. Notably, while N2' calculations contribute uncertainty in ΔO2/N', the largest contribution to the total error in this term comes from measurement uncertainty, which can be minimized through careful calibration of underway signals. 224 Table D.1 Average environmental conditions and mixing terms applied in N2' calculations applied to data from the August 2018 Line P (NEP-summer), February 2019 Line P (NEP-winter) and summer 2019 CCGS Amundsen (Arctic) cruises. Numbers represent the average and range of values encountered over the O2 re-equilibration period of the N2' calculations. Cruise Region Wind speed SST Ice cover κZ ΔN2/Ardeep [m s-1] [oC] [%] [m2 s-1] [%] NEP-summer Offshore 8.1 ± 1.6 14.6 ± 0.4 0 2.1x10-4 ± 8.3x10-5 -0.15 NEP-winter Offshore 9.4 ± 1.9 8.4 ± 0.2 0 4.0x10-4 ± 1.7x10-4 0.5 QCS 6.9 ± 1.0 7.5 ± 0.5 0 1.9x10-3 1.5 Arctic BB 3.4 ± 0.8 3.2 ± 0.7 39 ± 24 1.2x10-4 ± 1.1x10-4 0.9 Shelf & CAA 3.4 ± 0.4 2.2 ± 0.4 35 ± 14 1.0x10-4 ± 1.0x10-4 0.8 Figure D.1 The impact of seawater warming between the intake and laboratory on GTD pressure measurements. Panel (a) shows the observed warming between a TSG installed at the ships’ seawater intakes and the optode/GTD system, and panel (b) shows the expected TPincrease, for a given degree of warming, if all gases were at 100 % saturation (Csat,intake = 100 % in Eq. D.8), and pSLP was 1 atm (1013.25 mbar). I calculated mean expected TP increase of 5, 16 and 18 mbar for NEP-summer, NEP-winter and Arctic data, respectively, but apply Eqs. D.8-D.11 to all underway TP measurements uniquely, based on the high-resolution temperature measurements. 225 Figure D.2 Along-track surface water ΔAr and ΔN2 during the 2018 and 2019 Subarctic NE Pacific summer (a), winter (b) and Arctic (c) cruises. The light blue bars in (b) represent storm-impacted regions of the transect. 226 Figure D.3 Environmental data along the August 2018 Line P (NEP-summer; left), February 2019 Line P (NEP-winter; middle) and summer 2019 Arctic (right) cruise tracks prior to ship based sampling. Data not covered by the grey shaded area represent conditions within one mixed layer O2 residence time (calculated as MLD/kO2, where kO2 is the O2 gas transfer velocity) of the time of underway sampling. The along-track MLD (dashed black line) is overlaid on the middle row of each panel. Not the differences in colour scales for each panel. 227 Figure D.4 The distribution of surface ΔN2/Ar (left) and ΔN2'/Ar (right) by region in each of the August 2019 Line P (NEP-summer; a-b), February 2019 Line P (NEP-winter; c-d) and summer 2019 Arctic (e-f) cruises. 228 Figure D.5 The distribution of subsurface ΔN2/Ar (ΔN2/Ardeep) in the NE Pacific (a) and Arctic (b). In both panels, circle markers represent measurements obtained from below the mixed layer, while diamonds represent subsurface ΔN2/Ar from an archived dataset (Hamme et al., 2019) used to represent ΔN2/Ardeep in the NEP-winter dataset. The diamonds are displaced by 0.5-degree latitude from the sampling transect for display purposes. 229 Figure D.6 Comparison of multi-year in-situ wind speed (left) and SST (right) against gridded dataset observations in the Arctic (top) and NE Pacific (bottom). In-situ data were obtained from ship-based measurements on the CCGS Tully and CCGS Amundsen, and observations from moorings (NOAA PMEL Ocean Station Papa and Environment and Climate Change Canada coastal British Columbia moorings), buoys (International Arctic Buoy Program) and the Ocean Networks Canada Cambridge Bay observatory. The corresponding gridded data were obtained from the products described in the x-axis labels. The root mean square error (RMSE) of the respective fits are displayed in the upper left of each panel, while the mean adjustment to underway data is presented in the lower right. The red and blue lines represent the 1:1 and linear regression fits, respectively.
Thesis/Dissertation
eng
Oceanography
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Tortell, Philippe Daniel, 1972-
Improved estimates of net community production in the Subarctic Pacific and Canadian Arctic Ocean using ship-based autonomous measurements and computational approaches
Text