Science, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
DSpace
UBCV
Stevens, Samuel William
Doctor of Philosophy - PhD
University of British Columbia
Coastal oceans are disproportionately important compared to their limited size. Semi-enclosed coastal areas, in particular, are geomorphologically diverse regions that often support high primary productivity and can offer rich sheltered habitats for large marine communities. Due to their enclosed nature, these areas are uniquely vulnerable to the impacts of climate change and pollution as deep water masses can have long residence times, which can generate hypoxic conditions and cause an accumulation of long-lived contaminants, among other issues. Understanding the subsurface circulation of these areas is important to understanding and predicting the evolution of these conditions. However, the processes that drive circulation occur on time and space scales that span the entire coastal continuum, ranging from synoptic-scale processes at the ocean margin through to microscale processes in the interior basins. For this reason, semi-enclosed seas are often under-sampled relative to the multiple scales of variability that drive their circulation. Here, I present research on the subsurface circulation of two of North America's largest deep stratified marginal seas: the Salish Sea, with a particular focus on the Strait of Georgia; and the Gulf of St. Lawrence. These areas have diverse geographic features, yet share a number of oceanographic similarities, such as exhibiting rotationally-modified estuarine circulations. In these regions, I outline and quantify subsurface mixing dynamics, transit times, transit pathways, and advection speeds via observations, regional-scale model outputs, and simplified 1D models. One strong motivation for this research was to generate novel tools for oceanographers to study these regions and similar regions globally. I have approached this in a number of ways. For example, I make broad use of hydrographic data gathered via an extensive volunteer program in British Columbia, highlighting the utility and efficacy of citizen science as a scientific resource. Furthermore, over a three year period, I designed, developed, and deployed a new neutrally-buoyant float system, nicknamed "Swish floats", to provide low cost Langrangian measurements of subsurface dispersion. Finally, I spearheaded analyses in the first ever deliberate subsurface tracer release experiment in the Gulf of St. Lawrence.
Measuring Subsurface Circulation in Deep Stratified Marginal SeasbySamuel William StevensB.Sc. Oceanography, The University of Southampton, 2013A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDoctor of PhilosophyinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Oceanography)The University of British Columbia(Vancouver)February 2024© Samuel William Stevens, 2024The following individuals certify that they have read, and recommend to the Faculty of Graduateand Postdoctoral Studies for acceptance, the dissertation entitled:Measuring Subsurface Circulation in Deep Stratified Marginal Seassubmitted by Samuel William Stevens in partial fulfillment of the requirements for the degree ofDoctor of Philosophy in OceanographyExamining Committee:Rich Pawlowicz, Professor, Earth, Ocean and Atmospheric Sciences, UBCSupervisorSusan E. Allen, Professor, Earth, Ocean and Atmospheric Sciences, UBCSupervisory Committee MemberRoger Franc¸ois, Professor, Earth, Ocean and Atmospheric Sciences, UBCSupervisory Committee MemberRoger Beckie, Professor, Earth, Ocean and Atmospheric Sciences, UBCUniversity ExaminerSteven Weijs, Associate Professor, Civil Engineering, UBCUniversity ExamineriiAbstractCoastal oceans are disproportionately important compared to their limited size. Semi-enclosedcoastal areas, in particular, are geomorphologically diverse regions that often support high primaryproductivity and can offer rich sheltered habitats for large marine communities. Due to their en-closed nature, these areas are uniquely vulnerable to the impacts of climate change and pollutionas deep water masses can have long residence times, which can generate hypoxic conditions andcause an accumulation of long-lived contaminants, among other issues. Understanding the subsur-face circulation of these areas is important to understanding and predicting the evolution of theseconditions. However, the processes that drive circulation occur on time and space scales that spanthe entire coastal continuum, ranging from synoptic-scale processes at the ocean margin throughto microscale processes in the interior basins. For this reason, semi-enclosed seas are often under-sampled relative to the multiple scales of variability that drive their circulation.Here, I present research on the subsurface circulation of two of North America’s largest deepstratified marginal seas: the Salish Sea, with a particular focus on the Strait of Georgia; and the Gulfof St. Lawrence. These areas have diverse geographic features, yet share a number of oceanographicsimilarities, such as exhibiting rotationally-modified estuarine circulations. In these regions, I out-line and quantify subsurface mixing dynamics, transit times, transit pathways, and advection speedsvia observations, regional-scale model outputs, and simplified 1D models.One strong motivation for this research was to generate novel tools for oceanographers to studythese regions and similar regions globally. I have approached this in a number of ways. For exam-ple, I make broad use of hydrographic data gathered via an extensive volunteer program in BritishColumbia, highlighting the utility and efficacy of citizen science as a scientific resource. Further-more, over a three year period, I designed, developed, and deployed a new neutrally-buoyant floatsystem, nicknamed “Swish floats”, to provide low cost Langrangian measurements of subsurfacedispersion. Finally, I spearheaded analyses in the first ever deliberate subsurface tracer release ex-periment in the Gulf of St. Lawrence.iiiLay SummarySeas that are mostly surrounded by land are vulnerable to pollution and climate change becausewater is flushed out of them infrequently. Understanding how water moves below the surface ofthese seas is important but challenging, as we must account for conditions that vary over short dis-tances and periods of time, like tidal currents, while also considering conditions that vary over longdistances and periods of time, like seasonal temperature changes. Here, I use various techniques(some that I have developed myself) to measure underwater currents in the Salish Sea and the Gulfof St. Lawrence, two large, mostly enclosed seas in North America that have large rivers flowinginto them at the surface. These areas are both rich habitats for marine life that have been affectedby human activities. The findings in this research can help us better understand how pollution andclimate change are impacting these important ecosystems.ivPrefaceChapters 2-4 of this thesis were written as standalone papers:• Chapter 2 is published as: Stevens, S. W., Pawlowicz, R., and Allen, S. E. (2021). A Studyof Intermediate Water Circulation in the Strait of Georgia Using Tracer-Based, Eulerian, andLagrangian Methods. Journal of Physical Oceanography, 51(6), 1875-1893.• Chapter 3 is published as: Stevens, S. W., and Pawlowicz, R. (2023). Swish floats: an inex-pensive neutrally buoyant float to monitor dispersion in coastal seas. Journal of Atmosphericand Oceanic Technology, 40, 1493–1508.• A version of Chapter 4 is in review. The authors of this study are Samuel W. Stevens,Rich Pawlowicz, Toste Tanhua, Lennart Gerke, William A. Nesbitt, Adam Drozdowski, Joe¨lChasse´, and Douglas W. R. Wallace.A version of Appendix A was included as supplementary content to Stevens et al. (2021). Aversion of Appendix B was prepared with Rich Pawlowicz for inclusion in an unpublished technicaldocument in December 2022.This thesis contains my own original research, developed under the supervision of Rich Pawlow-icz and my supervisory committee and conducted in collaboration with a number of different re-searchers and research groups. In Chapter 2, the initial idea to perform an in-depth study of theStrait of Georgia intermediate circulation was posed by Rich Pawlowicz, while I developed the spe-cific research questions and analysis plan. This work uses model output from SalishSeaCast, whichwas developed by Susan Allen’s research group at UBC, and observations from the Canadian ForcesMaritime Experimental and Test Ranges, Hakai Institute, Pacific Salmon Foundation, Departmentof Fisheries and Oceans, and Ocean Networks Canada. All analyses were performed by myself,with input from Rich Pawlowicz in formulating a 1D pipe model.In Chapter 3, the initial idea to develop low-cost neutrally buoyant floats was a result of con-versations between Rich Pawlowicz and myself. The floats were designed with input from RichPawlowicz, and Joern Unger, who also manufactured the float components. Float testing, assembly,and ballasting were performed by me and Elli Cunningham with assistance from Grace Watts. Ivled all of the fieldwork efforts and all analyses were performed by myself, with input from RichPawlowicz in formulating the engineering equations used in float design.In Chapter 4, I describe results from the Gulf of St. Lawrence Deep Tracer Release Experiment,a jointly-funded Re´seau Que´bec maritime (RQM) and Marine Environmental Observation Predic-tion and Response Network (MEOPAR) initiative headed by Douglas Wallace and Toste Tanhua.My contribution to the project was to assist with fieldwork, process data, and write a study detail-ing the initial spreading of tracer. The research questions for this study were jointly developed bymyself, Rich Pawlowicz, and Douglas Wallace. All analyses were performed by myself, with inputfrom Rich Pawlowicz in formulating 1D pipe models. Douglas Wallace, Toste Tanhua, LennartGerke, William A. Nesbitt and I participated in the tracer release field campaign and Adam Droz-dowski and Joe¨l Chasse´ contributed 3D modeling efforts to assist with the tracer release surveys.viTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xx1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Project motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Geomorphological classification . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Circulation dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3.1 Estuary circulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3.2 Vertical and lateral transport . . . . . . . . . . . . . . . . . . . . . . . . . 61.4 Biogeochemical linkages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81.5 Measuring subsurface flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.5.1 Lagrangian and Eulerian classification . . . . . . . . . . . . . . . . . . . . 91.5.2 Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.6 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 A study of intermediate water circulation in the Strait of Georgia using tracer-based,Eulerian, and Lagrangian methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17vii2.2.1 Regional Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.2.2 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.3.1 Seasonality as an age tracer . . . . . . . . . . . . . . . . . . . . . . . . . 192.3.2 Seasonality tracer: renewal signal . . . . . . . . . . . . . . . . . . . . . . 212.3.3 Seasonality tracer: lateral circulation . . . . . . . . . . . . . . . . . . . . 262.3.4 Evaluation of SalishSeaCast model . . . . . . . . . . . . . . . . . . . . . 292.3.5 Eulerian Mean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.3.6 Lagrangian Particle Tracking . . . . . . . . . . . . . . . . . . . . . . . . . 332.3.7 Eddy Diffusivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Swish floats: an inexpensive neutrally buoyant float to monitor dispersion in coastalseas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.3 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453.3.1 GNSS Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453.3.2 Housing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453.3.3 Pressure testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483.3.4 Thermal expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493.3.5 Release Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503.3.6 Other design features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513.4 Principles of operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513.5 Ballasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523.5.1 Ballasting tank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523.5.2 Ballasting procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533.6 Uncertainty budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543.7 Float behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563.8 Ocean Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573.8.1 Deployments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573.8.2 Neutral buoyancy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603.8.3 Lateral motion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623.8.4 Vertical motion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633.8.5 Surface trajectories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66viii4 TReX Deep: a SF5CF3 tracer release experiment in the Gulf of St. Lawrence . . . . 694.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704.2.1 Study Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704.2.2 Gulf Of St. Lawrence Deep Tracer Release Experiment (TReX Deep), 2021 724.2.3 Data Analysis Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 744.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 754.3.1 Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 754.3.2 Diffusivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 784.4 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 825 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 855.1 Addressing Research Questions and Contributions to the Field . . . . . . . . . . . 855.2 Overarching conclusions and future work . . . . . . . . . . . . . . . . . . . . . . 88Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92A Strait of Georgia Dissolved Oxygen Analysis . . . . . . . . . . . . . . . . . . . . . . 113B Linking Contaminant Measurements to Circulation- Iona Outfall Modeling . . . . . 117C Swish Float Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126ixList of TablesTable 3.1 Uncertainty budget for depth targeting. Uncertainties are presented with a cov-erage factor k = 2 (Ridler et al., 2007), which corresponds to a 95% confidenceinterval assuming a normal distribution of the measurement errors. Depth uncer-tainty is provided for the intermediate waters of the Strait of Georgia (dρ/dz =0.11 kg m−3 10 m−1) and the Gulf of St. Lawrence in parentheses (dρ/dz= 0.18kg m−3 10 m−1). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55Table 4.1 Summary of advection-diffusion characteristics. Note that κ f (marked with anasterisk) is a mean value derived from injection and Leg 1 float deployments.Lateral tracer motion uncertainties are expressed as two standard deviations ofthe tracer spreading analyses to represent the 95% confidence interval (coveragefactor k= 2) assigned from a bootstrap analysis; lateral float motion uncertaintiesare the standard deviation (coverage factor of k = 1) for u⃗ f and κ f calculated viabootstrap analyses; vertical tracer motion uncertainty is assigned in Section 4.3.2. 76xList of FiguresFigure 1.1 Maps and circulation schematics of the Gulf of St. Lawrence and the Strait ofGeorgia. Red arrows and text denote research areas covered in this thesis. . . . 4Figure 2.1 A bathymetric map and circulation schematic of the Salish Sea. Dashed linesand bold font approximately separate and identify the following Strait of Geor-gia regions: Haro Strait (H.S., also shaded red to identify the IW formationregion); the southern basin (S.B.); and the northern basin (N.B.). JohnstoneStrait (J.S.), seen in the inset map, connects Discovery Passage to the Pacificin the northwest of this region. The island marked L.I. is Lasqueti Island. Theblack box in the inset map depicts the spatial extent of SalishSeaCast numericalmodeling, the red box depicts the limits of the main map. Schematic shows asimplified side-on view of regional circulation. Red region responds to HaroStrait region of main map, bold black arrows represent IW circulation, grayarrows represent other aspects of IW circulation. . . . . . . . . . . . . . . . . 16Figure 2.2 Solutions of the 1D advection-diffusion pipe model with a seasonal oscillationat x = 0 for varying γ . The form of the solution is set by γ = 2K/U with weak(a and d), intermediate (b and e) and strong (c and f) diffusion cases shown. Thefirst row of panels depict time/range contour plots, where the solid white lineshows the speed of advection U (1.5 cm s−1) and the dashed white line showsapparent propagation speed. The second row of panels show the spatial solutionat t = 0 in black lines and the envelopes of the oscillation in red. The coloredcontours represent the modeled oscillation of temperature. Plot g) is the realand imaginary components of k as a function of γ (Eqn. 2.3). The black verticallines represent γ = 20 km, γ = 100 km, and γ = 10000 km for the three abovecases. The blue shaded region represents the 56–75 km γ range calculated fromthe tracer advection speed and the Lagrangian diffusivity in Section 2.4. . . . . 22xiFigure 2.3 Seasonal cycle characteristics. a) Representative temperature climatologies andfitted seasonal sinusoids from two stations in the southern and northern basin.Scatter points represent the measurements from the two stations, solid lines rep-resent the sinusoidal cycles fitted to these climatologies, and dotted lines indi-cate the coldest Julian day of the year (φCD) derived from these seasonal cycles.The colors of the scatter points and lines correspond to the colored stations ininset map. Observations from the southern station span March 2016 to October2018 and from the northern station span February 2015 to January 2019. Fig-ure 2b) and c) show how the seasonal cycle characteristics from all stations inthe Strait vary as a function of depth. The sinusoid fitting algorithm (Section2.3.2) was performed on temperature climatologies from each depth level at allstations, so that each individual station has a φCD depth profile and an ampli-tude (At) depth profile. Each depth level of these profiles was normalized bysubtracting the overall mean (for that depth level) and dividing by the standarddeviation (for that depth level) to produce normalized property profiles (plottedin gray). The black line represents the mean of these normalized profiles, in-tersected with colored lines to denote upper (blue; 60 m–80 m), core (red; 90m–110 m), and lower (yellow; 120 m–140 m) IW. . . . . . . . . . . . . . . . . 23Figure 2.4 Seasonal cycle spatial variability for the Strait of Georgia. Hydrographic mea-surements span 1969–2019, though are primarily from the 2000–2019 timeperiod. a) A map detailing station locations denoted by the colored markers.Marker colors in a) correspond with the colored y-axis in b), e), and h) to de-note seasonal cycle properties as a function of along-strait distance from HaroStrait. The three rows of plots represent data from the the upper IW (60 m–80m), the core IW (90 m–110 m), and the deep IW (120 m–140 m), respectively.In c), d), f), g), i), and j) gray scatter points show the variability of the seasonalcycle phase shifts φCD and amplitudes At as a function of along-strait distancefrom Haro Strait; the error bars show the mean values ± the standard deviation(coverage factor k = 1), representing the uncertainty in the sinusoidal fittingalgorithm (Eqn. 2.4) calculated using a balanced bootstrap re-sampling tech-nique. For reference, the quantity aget is calculated aget = φCD− φHS, whereφHS is the earliest φCD measurement from Haro Strait. The solid red lines de-tail the linear trends in these properties. The yellow and blue lines in f) detailthe same trends for the Lagrangian transit time and SalishSeaCast model sea-sonality analyses, respectively. Trends are derived using a using a Theil-Senmethod. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25xiiFigure 2.5 Maps of seasonality tracer-based age (aget) from: a) & d) the upper IW (60m–80 m); b) & e) the core IW (90 m–110 m); and c) & f) the deep IW (120m–140 m). Panels in the first row depict aget derived from observational tem-perature climatologies, the second row from SalishSeaCast hindcast tempera-ture climatologies. Coral-colored arrows drawn in in a) and d) represent circu-lation estimated from a core method perspective, coral-colored stars representthe cores of the gyre-like circulation. Hydrographic station locations are plottedas black scatter points in a)–c). . . . . . . . . . . . . . . . . . . . . . . . . . . 27Figure 2.6 Maps of IW seasonal cycle amplitude At from: a) & d) the upper IW (60 m–80m); b) & e) the core IW (90 m–110 m); and c) & f) the deep IW (120 m–140m). Panels in the first row depict At derived from observational temperatureclimatologies, the second row from SalishSeaCast hindcast temperature clima-tologies. Hydrographic station locations are plotted as black scatter points ina)–c). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Figure 2.7 Map of Eulerian circulation. Map a) arrows show mean velocity field (U) andcontours show along-strait component of velocity calculated from SalishSea-Cast velocity hindcasts from November 2014 to June 2019. Inset map b) ar-rows shows long-term mean currents derived from cyclesonde measurements ofStacey et al. (1987) at 100 m from June 1984–January 1985 and three VENUSADCP records: Central node (September 2008–May 2020); East node (Septem-ber 2007–May 2020); and DDL node (May 2013–May 2020), where black ar-rows denote corresponding SalishSeaCast mean currents from the nearest gridcell. Data from between 50 m and 150 m were used to calculate ADCP meancurrents and velocity fields apart from the DDL node, where the mean is calcu-lated between 50 m and 140 m (roughly the seafloor depth). Map c) depicts theratio of eddy kinetic energy (EKE) to mean kinetic energy (MKE), KER. . . . 31Figure 2.8 Transit time and time-evolution of particle positions derived from the HaroStrait particle tracking experiment. Maps a) and f) describe the mean transittime horizontally and vertically, respectively, derived from Transit Time Distri-butions (TTDs). Dashed white lines depict the basin boundaries. Maps b)–e)and g)–j) detail the time-evolution of the released particles 20, 40, 80, and 160days after release. The particle concentration is calculated as the number of par-ticles contained in a grid cell normalized by the grid cell containing the largestnumber of particles, for each time-step. Particle release points are denoted by awhite marker and/or black cross. Grey seafloor in f)–j) is representative of depthalong the thalweg. The 10 m isobath of the Fraser River slopes is highlighted inb)–e) with a gray contour. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34xiiiFigure 2.9 Lagrangian statistics derived from particle trajectories. The red arrows in mapa) are a projection of the rotated along/across-strait coordinate axis with dis-tances marked in either direction. The bathymetry of the Strait is shown andan example trajectory is plotted in white; note that the axis is centered on theparticle release point. Plots b) and c) show the time-evolution of particle driftin both directions; the colored regions show the drift of all particles binned into500 m along-strait bins and the white line is the mean drift of all the particles.The remaining plots show the time-evolution of particle dispersion (d and e),kurtosis (f and g), and diffusivity (h and i). All time-series are truncated toshow only the first 200 days of the 365 day experiment as the system reachesquasi-steady state after 100–150 days. . . . . . . . . . . . . . . . . . . . . . . 36Figure 3.1 Swish float schematic with coarse measurements (cm; 2:1 vertical to horizontalscale; left), an internal view of a float (center); and a labeled photograph oftwo Swish floats (right). Note the previously deployed and recovered red float(rightmost) with a Temperature-Depth (TD) sensor attachment and a bridle toaid recovery from a vessel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46Figure 3.2 Swish float compression a) measured in pressure tank experiments as a functionof external pressure Pe (blue scatter points); and b) derived γm. . . . . . . . . . 50Figure 3.3 Results from the Strait of Georgia float deployments. In a) circular markers rep-resent pop-up positions colored by resurfacing timescale t, the gold pentagramthe deployment site. As a point of reference, grey lines represent 24 hours ofsurface drifter trajectories passing within 2 km of the deployment site (Pawlow-icz, 2021). Inset b) is a zoomed out map of the region and c) is a histogram offloat resurfacing timescales t. . . . . . . . . . . . . . . . . . . . . . . . . . . . 58Figure 3.4 Results from the Gulf of St. Lawrence float deployments. Map a) is a map ofthe deployment region where the black and grey pentagrams are the deploy-ment sites of two deployments in October 2021 and June 2022, respectively.In c) and d), circular markers represent pop-up positions and grey contours arebathymetry; b) and e) are histograms of float resurfacing timescales t (all arecolored by resurfacing timescale t). . . . . . . . . . . . . . . . . . . . . . . . 59xivFigure 3.5 Mean climatological ρ (solid black lines) and temperature (red lines) profilesfrom a) the Strait of Georgia and b) the Gulf of St. Lawrence; shading represents±σ limits. Dashed black lines are the variation in float density ρ f throughoutwater column calculated from Equation 3.15 for a typical float ballasted for neu-tral buoyancy at z=100 m; shading represents±2σ limits based on variability oftemperature profiles in the climatologies. Dotted black lines show the densityof a water parcel with the volume of a typical float, a constant compressiblitycoefficient of γw = −4.4× 10−6 dbar−1, and a variable thermal expansion co-efficient calculated for the water column (αw ≈ 1.5×10−4 °C−1). Grey shadedregions of the profile depicts depth ranges where the stability criterion (Equa-tion 3.14) is not achieved (δρ/δ z <δρ f /δ z), un-shaded depth ranges depictwhere it is achieved (δρ/δ z >δρ f /δ z). . . . . . . . . . . . . . . . . . . . . . 60Figure 3.6 Strait of Georgia float depth records from the June (D1; black), July (D2; blue),and September (D3; red) 2022 deployments. “Pair A” are denoted by darker redlines. Pink circles denote initial neutral buoyancy depth of floats. Plot a) showsthe entire drift depth record, plot b) shows only the initial descent. . . . . . . . 61Figure 3.7 Lateral transport of floats. In a), circular grey points show the float resurfac-ing distances. In b), circular grey points show the separation distances of floatpairs.√2Kt is also depicted for various values of K: K=170 m2 s−1 from theSwish floats are plotted with 95% confidence limits (grey shading); K=88–167m2 s−1 is the intermediate layer diffusivity estimated in Section 2.3.7; andK=4000–7000 m2 s−1 is the surface layer diffusivity estimated by Pawlowiczet al. (2019), plotted for reference. . . . . . . . . . . . . . . . . . . . . . . . . 63Figure 3.8 This figure shows three float frequency spectra from D3 (grey lines) in cyclesper hour (CPH). The dashed line is the GM76 spectrum, the solid vertical lineis f and the dot-dashed vertical line is N. . . . . . . . . . . . . . . . . . . . . 65Figure 3.9 Surface trajectories from resurfaced floats in a) the Strait of Georgia and c)the Gulf of St. Lawrence; scatter points along trajectories represent the GNSSposition fixes. Inset plot b) is surface separation of Strait of Georgia float pairsthat resurface <3 km from one another (grey lines) and separation√2KRt usingaverage diffusivity for the first 24 hrs KR =∼2312 m2 s−1 (black dashed line). . 67xvFigure 4.1 a) and b) Gulf of St. Lawrence maps with sampling stations; Leg 1 stations areblue, Leg 2 stations are red, x’s represent “boundary” stations (h < 279 m), andtriangles represent “interior” stations (h > 279 m). The tracer injection site ismarked by a violet star and the black contour lines represents the injection depthz=279 m bathymetric contour. Abbreviated location names are: TS, Tadoussac;QC, Quebec; AI; Anticosti Island; CS, Cabot Strait, NL, Newfoundland; BI,Belle Ille Strait. The Lower St. Lawrence Estuary (LSLE), immediately east ofTadoussac, is shaded gray. c) Hydrographic conditions from a CTD profile im-mediately following the tracer injection: red represents temperature (°C), blueis salinity (g kg−1), black is oxygen (µmol kg−1). Shading represents the 95%confidence limits of a October–December climatological hydrography from theAtlantic Zone Monitoring Program (AZMP) in the eastern Gulf between 1998and 2022. d) Histogram showing the number of samples in different z∗ =10 mdepth bins from Leg-1 and Leg-2 cruises. . . . . . . . . . . . . . . . . . . . . 71Figure 4.2 Interpolated maps of vertically integrated observed tracer I j from a) Leg 1 at8 months after injection; and d) Leg 2 at 12 months after injection. The goldstar represents the tracer injection site, the dashed gray line represents the An-ticosti Island-Newfoundland transect, gray x’s represent “boundary” stations(h< 279 m), and gray triangles represent “interior” stations (h> 279 m). Swishfloat were deployed at the injection site during Leg 1 [gold star in a)] and inthe Laurentian Channel during Leg 2 [black cross in d)]. Float resurfacing lo-cations are marked with black circles; circle diameter is a representation of flatresurfacing time, ranging from 6 days (largest diameter) to 36 days (smallestdiameter). Leg 1 b) and Leg 2 c) vertical profiles of SF5CF3 concentrationsin z∗ coordinates, solid gray lines and axes are hypsographies A(z∗) in m2 anddashed gray line is A0e−z∗/L. e) Leg 1 and f) Leg 2 are profiles of SF5CF3concentrations in σθ coordinates; the black dotted lines represent the injectionsurface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73xviFigure 4.3 Vertical tracer dispersion. Map a) shows the vertical distribution of tracer fromseven regional subsets c j(z∗), where the different regions (denoted by subscriptj) were selected using a K-mean clustering algorithm. The geographical ex-tent of each subset is represented on the map as translucent, colored shapes anddisplayed on each region is c j(z∗) tracer depth profile showing Leg 1 (blue)and Leg 2 (red) observations compiled from the corresponding area, with theinjection depth marked with a black dashed line. Also included are normal-ized regional hypsographies A˜ j(z) (black lines). The normalization involvesinitially shifting regional hypsographies A j(z) so that its minimum value be-comes zero, computed as A j(z)−min(A j(z)). This shifted data is then scaled toa range between 0 and 1, achieved by dividing by the range of the original data,max(A j(z))−min(A j(z)). The final step involves scaling this normalized datato align with the scale of the tracer concentration profiles c j(z∗), resulting in thenormalized hypsographies A˜ j(z). Plot b) shows profiles of normalized tracerconcentration r(zˆ) [dashed lines] from Leg 1 (blue) and Leg 2 (red) with er-ror bars denoting two standard deviations derived from a bootstrapping analysis(coverage factor k = 2). The wide, solid curves show the best fitting advection-diffusion model solutions based on the optimization routine explained in Sec-tion 4.3.2: note that the fitting regime for Leg 2 is weighted to emphasize thefit through the well-sampled shallow region zˆ < 0 m (see Eq. 4.10). Plot c)is similar to b), showing r and best-fitting 1D model solutions for boundary(green) and interior (teal) regions; (Leg 1 is plotted with lighter curves, Leg 2with darker curves) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77Figure 4.4 Along-channel tracer dispersion. Map a) shows the Laurentian Channel stationsfrom Leg 1 (blue points) and Leg 2 (red points) and an along-channel axis withx= 50 km spacing between points. Plot b) shows station-wise vertically inte-grated tracer I j as a function of along-channel distance: blue is used for Leg 1and red for Leg 2. The window mean curves represent a 50 km overlappingwindow for Leg 1 and a 75 km overlapping window for Leg 2, both with a 50%overlap. In c), I j(x,t) is estimated from the 1D pipe model (u, κh) and d) showsmodeled I j(t) time-series at points along the channel [line colors correspond tocolored axis markers in a)]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80xviiFigure A.1 Dissolved oxygen (DO) seasonal cycle characteristics: a) map detailing stationlocations denoted by the coloured markers, marker colours in a) correspondwith the coloured y-axis in b) to denote seasonal cycle as a function of along-strait excursion. Plots c)-e) detail seasonal cycle characteristics as a function ofalong-strait excursion, grey scatter points show the variability of the seasonalcycle phase shifts (i.e. the least oxic Julian year days), amplitudes, and meanDO as a function of along-strait excursion from Haro Strait. The solid red linesdetail the linear trends in these properties. . . . . . . . . . . . . . . . . . . . . 114Figure A.2 Dissolved oxygen tracer maps of a) IW age; and b) seasonal cycle amplitude.Annual dissolved oxygen climatologies were derived at 122 stations (marked byblack scatter points) in the IW layer core (90-110 m). A synthesis of dissolvedoxygen measurements from various observational programs are subject to thesame seasonal cycle fitting algorithm as temperature climatologies. . . . . . . 115Figure A.3 Seasonality tracer maps derived from a station-wise analysis of SalishSeaCastmodel fields. An IW layer mean temperature field was created using three avail-able depth levels at 77 m, 98 m, and 122 m. Model grid points nearest to hydro-graphic stations (marked by black scatter points) were selected from this fieldand annual temperature climatologies were created at 128 stations (marked byblack scatter points) in the IW layer. Map a) depicts IW age and b) depictsseasonal cycle amplitude. The spatial expression of these properties is similarto observations, supporting the notion that the model fields are representative ofthe measured properties. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116Figure B.1 Lagrangian tracking from Iona outfall source. Maps a) and f) describe the meantransit time horizontally and vertically, respectively, derived from Transit TimeDistributions (TTDs). Dashed white lines depict the basin boundaries. Mapsb)–e) and g)–j) detail the time-evolution of the released parcels 20, 40, 80, and160 days after release. The parcel concentration is calculated as the number ofparcels contained in a grid cell normalized by the grid cell containing the largestnumber of parcels, for each time-step. Parcel release points are denoted by awhite marker and/or black cross. Grey seafloor in f)–j) is representative of depthalong the thalweg. The 10 m isobath of the Fraser River slopes is highlighted inb)–e) with a gray contour. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121xviiiFigure B.2 Depth-integrated maps of modeled water column PBDE concentrations for var-ious different PBDE half-lives (bold typeface on maps). Concentrations arecalculated as the mass of PBDE in a grid cell from surface to seafloor, dividedby the volume of that grid cell from surface to seafloor. Values printed on mapsshow the concentrations for various stations of interest. . . . . . . . . . . . . 122Figure B.3 Cross-channel integrated maps of modeled water column PBDE concentrationsfor various different PBDE half-lives (bold typeface on maps). Concentrationsare calculated as the mass of PBDE in 1m deep grid cells spanning zonallyacross the Strait, divided by the volume of those grid cells spanning zonallyacross the strait. Values printed on maps show the maximum concentrations ina zonal grid cell at various stations of interest. Dashed lines indicate stationlocations and approximate boundaries of the northern and southern basin (NBand SB, respectively). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123Figure B.4 Profiles of modeled PBDE concentration for different PBDE half-lives at var-ious stations of interest. Scatter points show modeled values, profiles showvalues smoothed with a 10 m moving average. . . . . . . . . . . . . . . . . . . 124Figure B.5 Time-series of modeled PBDE concentration for different PBDE half-lives atvarious stations of interest. Legends show ratio of standard deviation/mean toassess the magnitude of variability in the signal. . . . . . . . . . . . . . . . . . 125Figure C.1 Climatology of stratification δρ/δ z near the Swish Float deployment zone.Archive hydrography is from the Chapter 2 CTD dataset. In-situ δρ/δ z av-eraged between 70 m and 120 m depth are plotted as circular markers for eachfloat deployment; float δρ f /δ z averaged at the same depths are plotted as starmarkers—note that for floats to be stable, δρ f /δ z < δρ/δ z. . . . . . . . . . . 127xixAcknowledgmentsFirst, I would like to express my sincere gratitude to Rich Pawlowicz, who has consistently andreliably provided support, guidance, and structure throughout my PhD studies while also offeringme the latitude to pursue my own personal and professional goals. Thank you for teaching me,fostering my passion for oceanography, and putting me in a position to produce research that Ican be proud of. I would also like to thank Susan Allen and Roger Franc¸ois for offering theirtime and friendly support, and for providing valuable insights that have improved my research andexpanded my perspective. A heartfelt thanks to Lora Pakhomova and Chris Payne for their supportand good company during fieldwork endeavours throughout beautiful British Columbia, and to ElliCunningham for all of the pivotal hard work and dedication to the Swish float program. I am gratefulto Re´seau Que´bec maritime, MEOPAR, and the TReX Deep team for providing me the opportunityand funding to work in the Gulf of St. Lawrence—particular thanks go to Doug Wallace for hissupport and enthusiasm. I would also like to acknowledge and thank Eleanor Frajka-Williams,Andrew Gowland, and Rod Johnson for their mentorship throughout my oceanography journey.xxChapter 1Introduction1.1 Project motivationRelative to their size, coastal oceans hold exceptional ecological, economical, and cultural signifi-cance to human society. They absorb a significant amount of CO2 (Cooley et al., 2009), have highprimary productivity (Ware and Thomson, 2005), and supply 95% of global fish catch (Stock et al.,2017). On smaller scales, it has been shown that the welfare of coastal communities is closelylinked to the health of adjacent marine environments, with evidence to suggest that this is partic-ularly true for Indigenous communities, where access to sustainable natural resources is importantfor community health (Donatuto et al., 2014; Bennett et al., 2018).Due to their geomorphology, coastal systems are uniquely vulnerable to shifts in external forc-ing and changes in the loading of dissolved and suspended constituents. This vulnerability is es-pecially important in marginal seas—coastal areas that are partially surrounded by land—as theirbathymetry restricts flow, leading to reduced oceanic exchange and longer flushing times. Directforcing from nearby human activities can pose immediate threats: wastewater discharge introducesorganic and inorganic pollutants (Walters et al., 2011; Johannessen et al., 2015; Sun et al., 2018)and contamination can occur via spills and vessel collisions (Passow and Overton, 2021; Ug˘urluet al., 2020). Overfishing (Steneck, 1998), aquaculture (Buschmann et al., 2009), and noise pol-lution (Bassett et al., 2012), can further stress these ecosystems, and dredging activities can causelong-term degradation (Eisma, 2005).The effects of broader-scale forcing can place further pressures on coastal systems. Changes inheat (Cheng et al., 2019), pH through acidification (Bauer et al., 2013; Beaupre´-Laperrie`re et al.,2020; Jarnı´kova´ et al., 2022), and oxygen content (Gilbert et al., 2005; Oschlies et al., 2018) impactregional physical and chemical properties. These changes, in turn, can have detrimental effects onecosystem structure (Jackson et al., 2001).Understanding the circulation dynamics of marginal seas—that is, the common pathways and1transit time of waters within these systems and the processes that drive these patterns—is an im-portant factor in understanding how the state of a system evolves over time. Achieving this under-standing is challenging, however, as coastal oceanographers must consider processes over a varietyof time and space scales within the coastal continuum, ranging from synoptic-scale processes at theocean margin through to finescale estuarine and nearshore processes in the interior basin. Thus, ob-serving systems tend to be sparse in coastal regions relative to the small scales of variability foundthere (De Mey-Fre´maux et al., 2019). For this reason, the circulation schemes of many marginalseas are still uncertain and the development of novel techniques to measure circulation is a pressinggoal for coastal oceanographers.In this thesis, I explore subsurface flows in the Salish Sea and the Gulf of St. Lawrence, twoof Canada’s largest marginal seas, using a range of experimental techniques, outputs from regionalmodels, and simplified numerical simulations. I provide new insights into the deep circulationpatterns of these areas using various existing methodologies. Further, I describe the development ofnovel techniques for observing subsurface flows in stratified coastal seas, including the developmentof a new neutrally buoyant float system specifically designed to measure dispersion in stratifiedcoastal regions. My research specifically targets subsurface transport in intermediate and deep layersand have separate dynamics to the surface layer. Monitoring these subsurface waters is importantas, despite generally being much larger in volume than the surface layer and playing a significantrole in the regional oceanography, we often know far less about their circulation as they are harderto observe.The motivations for studying these two specific regions are varied. On the west coast of NorthAmerica, the Strait of Georgia is a collection of basins and waterways in the Salish Sea, connected tothe Pacific Ocean at its southern extent via the Juan de Fuca Strait and partially at its northern extentvia the more-restricted Johnstone Strait (Figure 1.1; for an overview of regional oceanography,see Thomson, 1981). It is a region with high primary productivity and is an important habitat fora number of large mammal species (Preikshot et al., 2013). As a result of long residence times(Pawlowicz et al., 2007) and a large adjacent population of around 4 million people (with manymore residing in the neighboring Puget Sound region), it is critical to monitor contamination in thisregion via continued measurements and modeling, especially given recent concerns regarding thebuild up of a range of contaminants from wastewater outfalls (Johannessen et al., 2015; Sun et al.,2023).One important and broadly understudied aspect of this system is the circulation of intermediatewaters, which exist between 50 m–200 m and directly receive a significant portion of the region’swastewater via an outfall at 70 m–90 m depth. Prior to the research presented in Stevens et al. (2021)and Chapter 2, our knowledge of the circulation of this water mass was based on a short-term ob-servations from a handful of current moorings (Stacey et al., 1987) and hydrographic measurements(LeBlond et al., 1991; Pawlowicz et al., 2007), or various numerical simulations (Marinone and2Pond, 1996; Masson and Cummins, 2004). Thus, there was a pressing need to observe the dynam-ics of intermediate depth flows to understand and predict the dispersal of contaminants from thisoutfall.On the east coast of North America, the Gulf of St. Lawrence is a collection of large channelsthat drains the St. Lawrence River and is connected to the Atlantic Ocean primarily via Cabot Strait(Figure 1.1). Considered to be the largest estuarine system in the world (Jutras et al., 2023), it isa key shipping link between the Atlantic and the Great Lakes and an important habitat for manyresident and migratory marine mammals. The Gulf has broad similarities to the Strait of Georgia,with layered stratification and complex bathymetry; however, it differs in a few key aspects, such asits larger area and volume; and its seasonal variability in ice cover and subsurface stratification.Various aspects of the region’s deepwater circulation have been studied previously to providetransit times along the main channel (Bugden, 1988; Gilbert et al., 2005) and to simulate circulation(Han et al., 1999). Concerns have grown in recent years regarding the impacts of a long-termdeoxygenation signal in the regional bottom waters, as records show a 50% decrease in bottomwater dissolved oxygen concentration over the past century (Gilbert et al., 2005; Jutras et al., 2020)and a further abrupt decline in 2020 (Jutras et al., 2023). The deoxygenation signal enters the Gulfas a deep inflow of bottom waters at Cabot Strait. Thus, it is important to map the transport andmixing of these waters to predict how recent and future hypoxic events will impact the system.Further concerns surround the possibility of widespread contamination from a cargo vessel sinkingto the seafloor, which have been prompted by the recent sinking of the “Felicity Ace”, a cargo shipcarrying 4000 cars that foundered in the Atlantic. Currently, the specific transport pathways andtimescales that would allow for an accurate assessment of subsurface contaminant and/or inflowspreading are not well defined.Research into the circulation of these two marginal seas, along with explanations of the tech-niques I have developed to study these phenomena, make up the core focus of this document. Theremainder of this section is organized as follows: first, I classify the main geomorphological char-acteristics that define my study regions (Section 1.2). Then, I describe circulation patterns that arecommonly observed in stratified marginal seas (Section 1.3), outline the physical-biogeochemicalcoupling of these regions (Section 1.4), and summarize various techniques that oceanographers canuse to study these patterns (Section 1.5). Finally, I present the objectives that have guided theresearch included in this thesis (Section 1.6).1.2 Geomorphological classificationDrawing from the definition of deep marginal seas in Marsh and van Sebille (2021), I classify myareas of study as semi-enclosed coastal waters where exchange is limited with the adjacent ocean bybathymetry or geometry, depths are greater than 200 m, and water masses are layered and separatedby density gradients which inhibit vertical mixing. Such stratification typically originates from3HaroStraitStrait of GeorgiaJuan de Fuca StraitPacific waterFraser RiverDiscovery PassageTransit time?Mixing? Pathways?Northern basin exchange and circulation??North basin?TadoussacLower St. Lawrence Estuary Gulf of St. LawrenceAtlantic ShelfSurface layerCold Intermediate Layer (CIL)Deep layerSt. Lawrence RiverCabot StraitTransit time?Mixing? Pathways?Basin boundary processes??Figure 1.1: Maps and circulation schematics of the Gulf of St. Lawrence and the Strait ofGeorgia. Red arrows and text denote research areas covered in this thesis.4riverine or meltwater influx in areas known as Regions Of Freshwater Influence (ROFIs; Simp-son, 1997), where fresher, less dense water is introduced at the surface and overlays saltier, denseroceanic water.This specific geomorphology is often observed in fjordal regions and is especially pronouncedin basins where large volumes of freshwater discharge into bounded marginal seas, such as the Gulfof St. Lawrence (Han et al., 1999; Galbraith et al., 2022), the Baltic Sea (Axell, 1998), the Chiloe´Inner Sea (Strub et al., 2019), and the Salish Sea (MacCready et al., 2020), to name a few. Thesesystems exhibit varying degrees of “estuarine” characteristics, which is defined as any coastal inletwhere buoyancy forces alter the fluid density relative to the adjacent ocean (Valle-Levinson, 2010).1.3 Circulation dynamicsThe unique geomorphology, water properties, and external forcing of every marginal sea means thatno two systems have an identical circulation. There are, however, some commonalities between thedrivers and patterns of circulation we observe in these areas. I focus here on introducing some ofthe relevant circulation dynamics of this particular class of marginal seas.1.3.1 Estuary circulationThe work of Hansen and Rattray (1965) first described estuarine circulation, also commonly re-ferred to as an exchange flow (both are used synonymously in this thesis), as a flow regime in whichfreshwater input acts as a surface buoyancy source in a channel with lateral boundaries. This inputcreates a highly stratified interface in the vertical and causes along-channel density gradients in thehorizontal which are modified by turbulent mixing at the interface. The resulting pressure gradientdrives a shear flow, where fresh or brackish water flows seaward at the surface and dense shelf watertravels up-channel at depth. The magnitude of the shelf water inflow can be many times greater thanthe freshwater outflow. The rate of flushing due to the exchange flow has clear implications for theregional biogeochemistry, whereby strong exchange produces enhanced nutrient supply and rapidremoval of contaminants and weak exchange produces increased sedimentation trapping, nutrientrecycling, and hypoxia. We describe this bidirectional exchange flow as a two-layer system, char-acterized by two distinct horizontal layers. Some examples of two-layer estuaries are the SevernEstuary, UK (Uncles and Jordan, 1980) and the Hudson Estuary, USA (Ralston et al., 2008).Three-layer estuarine flow adds complexity to the two-layer model by introducing an interme-diate layer interposed between a surface and deep layer (i.e. Fig. 1.1). This intermediate layeris often the largest of the three layers by volume (representing 53% of the overall volume of theStrait of Georgia, for example; Pawlowicz et al., 2007), and is characterized by vertical gradients insalinity and temperature (Chapter 2). This intermediate layer can act as a buffer, reducing the directinteraction between the surface and bottom layers, and can be influenced by factors such as tidal5mixing, surface buoyancy flux, and variability in its constituent water masses (Masson, 2006).The three-layer model is observed in more bathymetrically complex estuarine systems such asthe Strait of Georgia (Pawlowicz et al., 2007, Fig. 1.1), the Gulf of St. Lawrence (seasonally;Smith et al., 2006), the Baltic Sea (Chubarenko and Demchenko, 2010), and the San Francisco Bay(Walters et al., 1985). In certain marginal seas with this configuration (such as the Strait of Georgia),the upper layer still consists of the fresher, less dense water flowing seaward, while the intermediatelayer retains the denser, saltier ocean water moving landward. In fjords and other basins that aresilled to a certain depth, the deep layer can be physically disconnected from its oceanic source exceptduring renewal events which require specific oceanic conditions to produce ventilation (Masoud andPawlowicz, 2023), leading to long residence times in deep water masses. Alternatively, in othermarginal seas with deep winter mixed layers (such as the Gulf of St. Lawrence), the deep water ismoving inland while the intermediate water is a remnant layer of winter mixing (Smith et al., 2006).1.3.2 Vertical and lateral transportThe exchange flow scheme described in the previous section provides a two-dimensional descriptionof flow in an estuary (e.g. Fig. 1.1). As one might expect, the time-varying, scale-dependentcirculation dynamics in deep, stratified marginal seas are more complex than are described by thissimplified framework. When considering transport in deeper water masses of marginal seas, thereare some common mechanisms that are superimposed onto the exchange flow signal to influencethe vertical and lateral movement of water and tracers.Seasonal variations in stratification, water mass structure, and buoyancy forcing can play im-portant roles in the vertical and horizontal transport in marginal seas. In seas such as the BlackSea (Tuzhilkin, 2008), the Gulf of St. Lawrence (Cyr et al., 2011), or the Sea of Okhotsk (Ro-gachev et al., 2000) it is common to see the formation of Cold Intermediate Layers (CILs), wherewinter mixed layers are capped at the surface by stratification caused by sea-ice melt, heat fluxes,and increase runoff at the onset of spring. In regions with significant surface freshwater input, theexchange circulation can be strongly modified by the seasonally-varying freshwater influx, which inturn can modify the strength of the exchange flow (Pawlowicz et al., 2007; Galbraith et al., 2022).In arid or semi-arid regions, some basins that experience high evaporation rates and minimal fresh-water input can exhibit “reverse estuary” characteristics, in which salinity increases further inlandand the estuarine exchange flow is reversed: some areas in which this can occur are the Red Sea(Sofianos and Johns, 2007) and Baja, California (de Velasco and Winant, 2004). Interestingly, re-cent numerical simulations of the Salish Sea and the adjacent shelf environment have suggestedthat, during specific downwelling and offshore river plume conditions, the Salish Sea and othergeomorphologically-similar marginal seas might briefly experience reverse estuary characteristics(Giddings and MacCready, 2017).Tides play a significant role in vertical and lateral circulation patterns in estuarine and coastal6environments. Tidal currents directly drive the movement of water masses during a tidal cycle,but also give rise to secondary effects that further complicate circulation dynamics. For example,tidal currents interact with the seabed, causing frictional stresses which lead to turbulence in thebottom boundary layer (Stacey and Ralston, 2005). Changes in tidal energy during spring-neapcycles modulates bottom boundary layer turbulence, causing variations in local mixing and watermass formation (Masson, 2006; Olson et al., 2020). Further, tidal flow generates internal tides (i.e.internal waves driven by tides moving over bottom topography) within a basin (Foreman et al.,1995; Gong et al., 2023), the breaking of which can increase rates of vertical mixing (Cyr et al.,2015). The generation of a tidal straining circulation, where water column stratification varies onsemi-diurnal or diurnal timescales, can drive an additional, vertically sheared circulation (Burchardet al., 2011; Geyer and MacCready, 2014).While the deeper waters of a inland sea are often insulated from the direct influence of windstress by a surface layer of sufficient thickness, the secondary impacts of wind stress can still playa role in subsurface transport. For example, it is common to see coastal upwelling in basins withlongitudinal winds when they are wider that the internal Rossby deformation radius LR = NH/ f ,where N is buoyancy frequency, H is depth, and f is the Coriolis parameter (Cushman-Roisin et al.,1994). Some examples of areas where coastal upwelling has been observed are basins in the Straitof Georgia (Moore-Maley and Allen, 2022), the Baltic Sea (Delpeche-Ellmann et al., 2017), and theGulf of St. Lawrence (Bourque and Kelley, 1995). Averaged over long timescales, marginal seaswith this geometry often exhibit varying degrees of geostrophically balanced circulation. In thesesystems, inflowing waters can be concentrated into boundary currents (Morris et al., 2021), withcounter-flow occurring on the opposite side of the basin (e.g. Chapter 2; Han et al., 1999; Ridenouret al., 2019; Liblik et al., 2022).In marginal seas, the turbulent, disorganized transport of water masses and tracers via stirringand mixing processes is an important mechanism for both vertical and lateral transport, particularlyin regions where stable stratification or strong fronts exist. Vertically, mixing rates can be influencedby factors such as internal wave breaking (MacKinnon and Gregg, 2005; Whalen et al., 2020),shear instabilities (Strang and Fernando, 2001), convection (Watson et al., 1999), and a combinationof various turbulent boundary layer processes (Skyllingstad et al., 2000; Trowbridge and Lentz,2018). Lateral mixing is driven by the movement, stirring, and dissipation of eddies on variousscales (Sundermeyer and Ledwell, 2001; Garrett, 2006), which can transport and mix heat, salt, andother tracers across considerable distances. Often, regional-scale numerical models do not resolveturbulent processes and so these mixing processes are incorporated using various observation-basedparameterizations (Fox-Kemper et al., 2019).71.4 Biogeochemical linkagesIdentifying the origins, movements, and ages of water masses in a marginal sea is a key step inunderstanding the distributions of biologically important chemical compounds and physical prop-erties. In turn, these distributions modulate the diversity and amount of lower trophic level produc-tivity (Del Bel Belluz et al., 2021; Suchy et al., 2019), which can govern the bottom-up ecology ofentire regions (Jarnı´kova´ et al., 2022). Thus, measuring and understanding the circulation of an areais necessary for a full description of physical-biological coupling in an ecosystem—this connectionwas aptly termed an “ecology of advection” by Carmack and Wassmann (2006).Marginal seas are ventilated via deep inflows originating from the adjacent continental shelf.The composition and properties of these source waters can vary due to various shelf and offshoreprocesses (Allen et al., 2001; Beutel, 2023) and these variations are communicated through themargin via ventilating waters. In general, these inflows are a source of heat, salt, nutrients, traceelements, and dissolved gases (e.g. oxygen) into the inland sea—generally these waters promoteproductivity (Reid et al., 2003), though they can also sometimes elevate regional concentrations oftoxic compounds (Kuang et al., 2022).As the inflow ages, oxygen is utilized by bacteria and other organisms via respiration and de-composition, and organic matter is remineralized (Sutton et al., 2013; Johannessen et al., 2021).These processes can impact the carbonate chemistry of the waters, leading to elevated CO2 and cor-rosive conditions (Cai et al., 2021). Sediment-bottom water interactions can lead to the exchange oftrace elements and sequestration or release of certain compounds (Sun et al., 2023; Shi et al., 2023).All of these biogeochemical processes occur in the moving medium of the deeper water masses, andtherefore have pronounced time and space variability.Anthropogenic drivers can imprint onto this natural state. This includes forcing from increasedanthropogenic CO2 (Ianson et al., 2016; Evans et al., 2022; Jarnı´kova´ et al., 2022) and heat (Itoh,2007; Liblik and Lips, 2019) content, which can have detrimental effects for local biology (Bed-narsˇek et al., 2021). Marginal seas are often naturally prone to hypoxia due to long residence times;however, the extent, frequency, and duration of these events can be exacerbated by enhanced nu-trient input, warming, and shifts in circulation patterns (Gilbert et al., 2005; Jutras et al., 2020;Carstensen and Conley, 2019; Fennel and Testa, 2019), which can have severe impacts on fisheriesand hatcheries (Chan et al., 2019). Interestingly, enhanced interior mixing combined with limitedflushing in the Strait of Georgia acts to buffer deoxygenation while simultaneously exacerbatingacidification, suggesting a local decoupling of these usually related phenomenons (Ianson et al.,2016).In semi-enclosed regions with long residence times, it is especially important to consider circu-lation dynamics when studying the transport and eventual fate of contaminants. Pollutants can beintroduced via wastewater, terrestrial runoff, industrial discharge, atmospheric deposition, or con-tamination events, which can quickly disperse or accumulate depending on local currents, mixing,8and sedimentation processes. Different contaminants can have varying short and long-term impactson an ecosystem such as deoxygenation via eutrophication (Rabalais et al., 2014), the contamina-tion of local fisheries (Meschke and Boyle, 2006), or long-term radiological risks associated withradioactive contamination (Qiao et al., 2021).1.5 Measuring subsurface flows1.5.1 Lagrangian and Eulerian classificationWe can generally classify the methods described in the following section into one of two categories:Eulerian or Lagrangian. Eulerian methods observe the flow field at fixed points in space, whereasLagrangian methods directly track the movement of fluid parcels over a sufficiently long time period(Neumann, 1968). Most commonly, Eulerian methods are associated with measurements from cur-rent meters; however, they can also be inferred from observations of density fields via geostrophicmethods. These methods offer observations from single locations with high temporal resolution.The drawback of this class of methods is that they offer more-limited spatial coverage, and there-fore have the potential to miss broader circulation patterns. Lagrangian methodologies are associ-ated with freely-floating instruments, such as Argo or RAFOS floats (Gould, 2005). This class ofmeasurements offers a three-dimensional view of ocean currents that can provide realistic estimatesof transport pathways, transit times, and volume transport, though are often complex to analyze,involving orders of magnitude more information than Eulerian observations (Davis, 1994). BothLagrangian and Eulerian frameworks provide unique opportunities to observe fluid flow, and themost complete descriptions of flow are often obtained from a combination of both methods.1.5.2 MethodologiesAt the ocean surface, transport can be measured using a variety of methods such as surface driftinginstruments (Pawlowicz et al., 2019; van Sebille et al., 2021; Lumpkin et al., 2017), radar systems(Halverson and Pawlowicz, 2016), remote sensing (Hamad et al., 2005), fluorescent dye releases(Shcherbina et al., 2015), and current meters. Producing a comprehensive, time and space-varyingdescription of subsurface circulation in any marginal sea is arguably more challenging as the oceansurface is opaque to electromagnetic radiation, making it hard to observe at any depth. Often, weare restricted to subsurface measurements at discrete points in space over periods of time, or morespatially-comprehensive measurements at a single point in time: it is rare that we are able to achieveboth.One method to measure subsurface circulation is to track the movement of a tracer througha system. Most commonly, oceanographers use conservative properties such as temperature orsalinity (LeBlond et al., 1991; Siedlecki et al., 2015; Holliday et al., 2020); provided we have an9adequate understanding of the tracer’s sources and sinks within a system, we can also use non-conserved properties such as oxygen (Gilbert et al., 2005; Jutras et al., 2023) or nutrients (Olsonet al., 2020; Moore-Maley and Allen, 2022). Transient tracers, which are man-made compounds thatare inadvertently introduced into the ocean, offer an additional means to trace the movement of watermasses. Some examples of transient tracers are isotopes created by atmospheric nuclear weaponstests in the 1950s and 1960s (Broecker et al., 1986); isotopes released from nuclear reprocessingfacilities (e.g. in the Baltic; Yi et al., 2013); and chlorofluorocarbons (CFCs; Smethie et al., 1988;Smethie, 1993), which were mainly introduced into the environment between 1950 and 2000.Another way we can use tracers to measure subsurface circulation is via the deliberate injectionand subsequent survey of a stable compound at a particular depth. Sulfur hexafluoride (SF6) wascommonly used as a tracer in earlier experiments since the first pilot study in the Santa Monica Basinduring the 1980’s (Ledwell et al., 1986); however, due to its growing background concentration andusefulness as a transient tracer, the usage of this compound has recently been phased out in favourof a chemically similar compound, SF5CF3 (Ho et al., 2008). By measuring the evolution of thehorizontal and vertical tracer distributions over a period of time, tracer releases have been usedto directly track transport pathways and mixing rates. Tracer release experiments are, however,expensive and logistically challenging, requiring specialist equipment to inject and monitor thetracer spreading.These experiments have been performed primarily in ocean basins (Ledwell et al., 1993; Polzinet al., 1997; Ledwell et al., 1998, 2000, 2011; Banyte et al., 2012; Rye et al., 2012; Watson et al.,2013; Zika et al., 2020), but have also been employed in lakes (Goudsmit et al., 1997), rivers (Hoet al., 2002; Caplow et al., 2004), fjords (Stigebrandt, 1979), and marginal seas such as the Baltic(Holtermann et al., 2012), the Gulf of Mexico (Ledwell et al., 2016), and the Gulf of St. Lawrence(Chapter 4). As the majority of tracer release experiments have focused on open ocean environmentswithout land boundaries, the analysis techniques to assess tracer spreading are not always directlyapplicable to semi-enclosed regions. This is particularly true of the 1D advection-diffusion modelsthat are commonly employed to estimate spreading rates, which require adaptations to incorporatethe geomorpohological aspects of an inland sea. Thus, there is a need to develop these models foruse in marginal-seas.Eulerian observations of velocity can be directly measured using Acoustic Doppler CurrentProfilers (ADCPs) and other current meters. Long-term records from moorings can can be used toderive mean circulation (e.g. in the Medditeranean Sea; Testor et al., 2005) or to assess periodicor episodic transport events, such has been done to assess seasonal circulation variability in theBaltic Sea (Suhhova et al., 2015) or intermittent renewal events in the Salish Sea (Masoud andPawlowicz, 2023). These moorings can be particularly useful when located at or near the entrancesto marginal seas, as they can be used to directly measure exchange with the adjacent shelf waters, ashas been performed to assess seasonally-varying exchange rates in Prince William Sound, Alaska10(Halverson et al., 2013). Additionally, current meters can be placed around the slopes of a basinto assess the cyclonic nature of the circulation—these types of measurements have been used toobserve the rotationally modified circulations of the Strait of Georgia (Stacey et al., 1987) and theGulf of St. Lawrence (Sheng, 2001), for example. Shipborne ADCPs can provide increased spatialcoverage to assess basin wide circulation (e.g. in the Bering Sea; Cokelet et al., 1996). In additionto measuring velocity, ADCPs can also be used to measure turbulence via the “variance method”(Wiles et al., 2006), where shear stress is estimated from the difference of velocity variance in theopposing ADCP beams. This method has been used to measure turbulence in the Salish Sea (Luet al., 2000) and the Clyde Sea (Lucas et al., 2014), among others.Another technique oceanographers can use to study circulation is to deploy and monitor La-grangian, free-floating instruments. We can glean useful information from these instruments bygridding drift velocities to provide Eulerian current estimates (G. Voet and Søiland, 2010), evalu-ating drift and separation rate to provide Lagrangian statistics of dispersion (LaCasce, 2008; vanSebille et al., 2018), or through more complex trajectory analyses (Maximenko et al., 2012; van Se-bille et al., 2012; Miron et al., 2019). While such studies typically focus on large ocean basins usingdata from the well-known Argo or RAFOS float programs (Gould, 2005; Wong et al., 2020), coastaland inland applications are less common due to the higher risk associated with deploying expensivedrifting instruments in confined areas. However, some efforts have been made in this direction;for example, neutrally-buoyant floats were used to study vertical flow components in Knight Inlet,British Columbia (D’Asaro and Lien, 2000; Klymak and Gregg, 2001), and small numbers of Argofloats have been deployed in some Arctic (Sandby et al., 2021) and larger marginal seas (Korotaevet al., 2006; Poulain et al., 2007). Surface circulation is increasingly measured with expendablesurface drifters, which are low-cost and can be deployed in large numbers (Pawlowicz et al., 2019;Pawlowicz, 2021; Novelli et al., 2017; Meyerju¨rgens et al., 2019; Page et al., 2019): such a systemfor subsurface waters would greatly improve our ability to perform Lagrangian measurements ofdeep flows.Numerical simulations are another key tool for understanding circulation in marginal seas.These simulations range in complexity from rudimentary 1D “pipe” models to highly-resolved,multi-scale, physics-based 3D models of a system. These numerical representations can be used tounderstand specific individual processes such as mixing and spreading (Holtermann et al., 2012),bulk exchange of water and tracers between regions (Pawlowicz et al., 2007; Wang et al., 2019),or overall circulation patterns (Rutherford and Fennel, 2018). In models with time-evolving veloc-ity fields, Lagrangian simulations can be performed to provide large datasets of trajectories, fromwhich a number of Lagrangian or Eulerian analyses can be performed (van Sebille et al., 2018;Beutel, 2023). Further, the time-evolution of model tracer fields can be used to track circulation(Olson et al., 2020; Moore-Maley and Allen, 2022). Various high-resolution 3D model systemshave been developed for the Salish Sea during the past decade, including SalishSeaCast (Soontiens11et al., 2016), LiveOcean (MacCready et al., 2021), and the Salish Sea Model (Khangaonkar et al.,2017). Similarly, various high-resolution 3D models exist for the Gulf of St. Lawrence, such asthe Regional Ice Ocean Prediction System (RIOPS; Smith et al., 2021) and the Coastal Ice-OceanPrediction System (CIOPS-E; Paquin et al., 2021, 2022; Le Corre et al., 2023). Similar systemsexist for coastal regions globally, for example in the Mediterranean Sea (Tonani et al., 2008) andthe Baltic Sea (Zhuang et al., 2011).1.6 Research objectivesNow that I have described the general features of estuarine circulation, and argued for the importantof understanding this circulation in a quantitative sense, the research questions addressed in thisthesis fall under two broad categories:1. Regional circulation: what are the main transport pathways and timescales of subsurfacewater masses in the Strait of Georgia and the Gulf of St. Lawrence?2. Method evaluation and development: how can I develop techniques to provide a more-varied array of tools with which to observe subsurface circulation in interior seas?The specific objectives of each research chapter are as follows:• Chapter 2: The intermediate layer of the Strait of Georgia receives a large volume of mu-nicipal wastewater from the population that surrounds it via a mid-depth wastewater outfall.However, the circulation of this water mass is yet undefined. In Chapter 2, I approach theintermediate circulation of the Strait of Georgia from a number different perspectives.First, I compile a large archive of hydrographic measurements to derive a novel “seasonality”tracer, which I use to age the intermediate waters of the Strait, infer mixing, and define amean circulation. I then derive the same tracer from a regional numerical model, finding thatthe model performs well in simulating this mean circulation. Next, I formulate and evaluate a1D pipe model of this seasonality tracer in the Strait and compare this tracer-based approachto Lagrangian particle tracking and observed and modeled velocity fields. I conclude byoffering a perspective on how the various approaches compare with each other and includean appendix (Appendix B) to discuss further work that has linked the circulation to regionalcontaminant distribution.• Chapter 3: Recent surface drifter studies have utilized modern asset-tracking technology tomeasure surface dispersion by deploying large numbers of inexpensive Global NavigationSatellite System (GNSS) tracked instruments. As yet, no such frameworks exist to studysubsurface flows. In Chapter 3, I adapt the expendable drifter concept for subsurface flows bydetailing the development of an new experimental neutrally-buoyant float system, nicknamed12“Swish floats”. I first provide details on the design, materials, properties, and laboratorytesting of the floats before presenting data from a handful of initial deployments in the Straitof Georgia and the Gulf of St. Lawrence. From these datasets, I analyze the vertical andhorizontal motion of the floats to provide regional dispersion statistics and internal wavespectra.• Chapter 4: Deliberate tracer release experiments have provided valuable information onmixing and spreading dynamics in a number of ocean basins. However; very few tracerrelease experiments have been performed with the direct goal of assessing circulation andmixing dynamics in marginal seas. In Chapter 4, I present the initial results from TracerRelease Experiment (TReX) Deep, the first subsurface tracer release experiment that has beenundertaken in the Gulf of St. Lawrence.In this experiment, an inert SF5CF3 tracer was injected into Gulf inflow waters alongside ex-perimental neutrally buoyant Swish Floats (see Chapter 3) to assess regional transport andmixing pathways. I map the time and space-evolving tracer distributions and model thespreading to approximate diffusivities. I then calculate along-channel transit times and assessthe spatially-varying vertical spreading, finding evidence of enhanced mixing at the slopes ofthe basin.The findings presented in this research make important contributions to our understanding ofsubsurface circulation in two of Canada’s largest marginal seas, providing previously unknownphysical context to regional biogeochemical processes. Furthermore, this research establishes anumber of novel techniques for oceanographers to assess subsurface circulation in stratified marginalseas globally.13Chapter 2A study of intermediate watercirculation in the Strait of Georgia usingtracer-based, Eulerian, and LagrangianmethodsIn LeBlond et al. (1991), the phase lag of seasonally varying properties were analysed at ahandful of stations distributed along the main axis of the Strait of Georgia to provide roughestimations of subsurface transit time. Thirty years later, it was suggested by Rich Pawlowiczthat it was worth revisiting this concept with a much larger observational dataset, especiallyconsidering the recent influx of hydographic data produced by the Pacific Salmon FoundationCitizen Science Program. The results presented in this chapter (published in Stevens et al., 2021),grew from the exploration of that initial idea, with the scope of study expanding significantly afterthe synthesis of results from SalishSeaCast, a regional numerical model.2.1 IntroductionThe Strait of Georgia, British Columbia, Canada, an important part of the Salish Sea, is a system ofdeep basins and waterways with an estuarine circulation (Fig. 2.1), the inflowing (lower) componentof which occurs in the intermediate depths of the Strait. The Strait is deep enough that this Inter-mediate Water (IW) layer is interposed between a dynamic surface layer (Pawlowicz et al., 2019)and a (mostly) stagnant deeper layer (Masson, 2006; Pawlowicz et al., 2007), and is thus shieldedfrom direct wind, solar, and bottom forcing, and in addition is wide enough for Coriolis effects to beimportant (Stacey et al., 1991). While this is a fjord-type estuary for which an overall estuarine ex-change scheme is well-known (Pawlowicz et al., 2007, 2019), details of transport pathways within14the intermediate water layer are still obscure. Understanding the circulation of intermediate watermasses is critical to understanding the regional oceanography, not only for its intrinsic interest butalso to be able to predict where objects or observed parameters of concern will travel over time.One practical motivation for understanding these pathways in the Strait of Georgia system is that amajor effluent outfall introduces contaminants into this layer and various regulatory agencies needto know more about the ultimate fate of these substances.A number of approaches are possible for determining the intermediate circulation. One tech-nique is the so-called “core method” (Wu¨st, 1935), an approach in which maps are made of par-ticular tracers, and arrows are drawn along tongues (i.e., crossing isopleths of these tracer fields).Tracers may be conservative (e.g., temperature or salinity), or may depict age (e.g., dissolved oxy-gen, or any number of biogeochemical parameters). However, these views of the circulation areoften qualitative since speeds are not well-known, and in certain cases a geostrophically controlledcirculation could be directed along isopleths rather than across them. Quantitative tracer-based ap-proaches include the “box-model”-based Knudsen relations for estuarine flow (Hansen and Rattray,1965; MacCready et al., 2018) or more complex variations thereof, applied locally by Pawlowicz(2001), Riche and Pawlowicz (2014), and MacCready et al. (2020); or optimal water mass analyses(Tomczak, 1981)—this latter approach was previously used along the thalweg of the Salish Sea asa whole by Masson (2006).A second approach is to consider the Eulerian mean velocity field, obtained by taking a long-term average of direct current measurements at fixed locations. This method could include meansderived from mooring programs, but also those inferred from an observed (mean) three-dimensionaldensity field using mathematical theories (e.g., geostrophic/β -spiral and inverse methods; Wunsch,1996) or the mean velocity fields in numerical models. Eulerian mean velocity fields can be prob-lematic for predicting tracer transport, since they ignore the turbulent nature of oceanic flow. Theturbulent flux of particular properties—which can be separated from the mean flux via a Reynoldsdecomposition and termed “eddy flux” (Davis, 1991)—depends on details of the time-varying cur-rent field, and is important to the transport of tracers in energetic regions of the ocean (Wunsch,1999). Numerical investigations have been attempted for the Salish Sea circulation on a numberof occasions, but have generally concentrated on circulation aspects closer to the Pacific entrance(Marinone and Pond, 1996; Masson and Cummins, 2004; MacCready et al., 2020).A third approach is Lagrangian, in which water parcels are tracked either directly by instruments(floats or drifters; Swallow and Worthington, 1961; Rossby et al., 1986) or indirectly by numericalintegration through time-varying velocity fields of numerical models (Daher et al., 2020; Oninket al., 2019). These approaches are perhaps the most realistic in terms of predicting where objectswithin the ocean will go, but are for various reasons the most difficult to analyze, involving ordersof magnitude more information (Davis, 1994). While producing results from the first two methodsgenerally requires few analytical steps, the analyses of Lagrangian summaries are often more com-15HaroStraitStrait of GeorgiaS.B.N.B.Juan de Fuca StraitPacific waterFraser RiverDiscovery PassageFigure 2.1: A bathymetric map and circulation schematic of the Salish Sea. Dashed lines andbold font approximately separate and identify the following Strait of Georgia regions:Haro Strait (H.S., also shaded red to identify the IW formation region); the southernbasin (S.B.); and the northern basin (N.B.). Johnstone Strait (J.S.), seen in the inset map,connects Discovery Passage to the Pacific in the northwest of this region. The islandmarked L.I. is Lasqueti Island. The black box in the inset map depicts the spatial extentof SalishSeaCast numerical modeling, the red box depicts the limits of the main map.Schematic shows a simplified side-on view of regional circulation. Red region respondsto Haro Strait region of main map, bold black arrows represent IW circulation, grayarrows represent other aspects of IW circulation.16plicated (LaCasce, 2008; van Sebille et al., 2018). These approaches have not yet been applied to thethe Strait of Georgia’s IW layer, although they have been used to study regional surface circulation(Pawlowicz et al., 2019; Pawlowicz, 2021) and Boundary Pass/Haro Strait circulation (Soontiensand Allen, 2017).Usually a particular investigation relies on only one of these approaches. Here, we take advan-tage of recently available resources to implement all three approaches to better understand the spa-tially varying circulation within the intermediate layer of the Strait of Georgia. The newly availableresources that allow for this intercomparison include observations from a Citizen Science programwhich has provided many thousands of hydrographic profiles between the years of 2015–2018,relatively well spread out in both time and space (Pawlowicz et al., 2020). These measurementsare used to derive, model, and track a temperature-based “seasonality” tracer, providing direct es-timates of water mass age within the intermediate layer; and to evaluate the accuracy of a newlyavailable high-resolution regional model, which provides time-varying hindcast estimates of hydro-graphic and velocity fields (Soontiens et al., 2016; Soontiens and Allen, 2017; Moore-Maley et al.,2016; Olson et al., 2020). Interpretation of the seasonality tracer is assisted by comparison witha 1D advective/diffusive “pipe” model, which provides a simplified but intuitive representation ofthe system. We then analyze the model velocity fields to estimate Eulerian currents and to seednumerical Lagrangian pseudo-drifters to directly predict the transport of IW water parcels. Usinginformation from these analyses, we identify gyres and regions of strong flow to create a compre-hensive overview of the Strait of Georgia intermediate circulation.2.2 Materials and Methods2.2.1 Regional OverviewThe Strait of Georgia (SoG), Canada, is part of the Salish Sea, a region within both the US andCanada lying on the inshore coast of Vancouver Island (Fig. 2.1). The SoG is about 30 km wide and200 km long, with a maximum depth of about 430 m and a mean depth of 150 m. Its water columncan be divided into three layers: a seaward flowing surface layer (0-50 m; Pawlowicz et al., 2019),an inflowing (on average) Intermediate Water (IW; 50–200 m; LeBlond et al., 1991; Pawlowiczet al., 2007), and a deep water layer (200 m–bottom) which is stagnant most of the year, but isventilated by (approximately) monthly pulses of oceanic water during the summer (LeBlond et al.,1991; Masson, 2002; Masoud and Pawlowicz, 2023). These water masses represent 27%, 53%, and20% of the overall volume of the SoG, respectively (Pawlowicz et al., 2007). By volume alone, theIW may therefore be the most important water mass in the system—especially considering it is thelayer into which most contaminants enter via effluent outfalls (Sun et al., 2018)—but it is least wellunderstood. Various other wide, layered coastal seas with subsurface intrusions to which the resultspresented here may broadly apply include the Baltic Sea (Lehmann et al., 2002); Prince William17Sound, Alaska (Niebauer et al., 1994); and the Gulf of St. Lawrence (Saucier et al., 2003).The Salish Sea estuarine circulation is driven by significant freshwater input, dominated by thatof the Fraser River (Griffin and LeBlond, 1990; Li et al., 1999; Riche and Pawlowicz, 2014). TheIW is formed in the Haro Strait area by tidal mixing between outflowing surface water, with a largeseasonal temperature cycle, and inflowing deeper Pacific water. Thus, the IW source water hasseasonally varying characteristics. Previously, the phase lag in seasonal temperature cycles from ahandful of stations in the southern basin has been used to drive a simple box model to estimate anIW residence time of 160 days (with an uncertainty range of 100 to 330 days) before being entrainedinto the surface layer (Pawlowicz et al., 2007).Historically, scientific efforts to investigate SoG circulation have targeted the more dynamicsouthern basin and thus a comprehensive understanding of the lateral IW circulation and transportpathways remains obscure. However, a relatively rapid inflowing eastern boundary current in thesouthern Strait was deduced from hydrographic observations (Pawlowicz et al., 2007), and a varietyof numerical models and observational programs have (inconsistently) depicted gyre-like horizontalcirculations within the deeper parts of the Strait (Waldichuk, 1957; Stacey et al., 1987; LeBlondet al., 1991; Marinone and Pond, 1996; Masson and Cummins, 2004). An internal deformationradius of ∼ 8 km suggests that the IW layer is strongly rotational (Stacey et al., 1991).2.2.2 Data SourcesHydrographic observations in the SoG are available from multiple sources, consisting primarily ofConductivity-Temperature-Depth (CTD) profiles and bottle measurements. Several regional moni-toring programs include quarterly or triannual shipboard surveys of the area carried out by govern-ment scientists, and a long-term (1968 to date) set of weekly CTD profiles at a central location inthe Strait obtained as a byproduct of sound-speed measurements required by the Canadian ForcesMaritime Experimental and Test Ranges (Masson and Cummins, 2007). In the southern Strait, aset of 48 hydrographic surveys were carried out over three years in 2003–2005 (Pawlowicz et al.,2007), and in the northern Strait a regular set of observations at one location has been carried outweekly since 2015 by the Hakai Foundation. However, the most comprehensive dataset for thispaper is obtained from the Pacific Salmon Foundation’s Citizen Science Oceanography Program(Pawlowicz et al., 2020). Some 80 stations over the entire Strait are occupied at nearly the same dayby 7–10 different patrols, about 20 times a year from 2015 to present (more than 1000 CTD profilesa year). These profiles extend only to 150 m, so that the deep waters of the Strait are not measured,but this depth range suffices for our investigation of the IW.We can derive Eulerian current velocity estimates at locations in the southern basin where long-term observations are available. As part of a cabled observatory maintained by Ocean NetworksCanada, three upward-facing Acoustic Doppler Current Profilers (ADCPs) measure currents every2–5 m throughout the water column. The ADCP records span a 7–12 year period between Septem-18ber 2008 to May 2020, albeit with some data gaps (Ayranci and Dashtgard, 2020). Additionally,cyclesonde and current meter measurements from Stacey et al. (1987) between June 1984–January1985 at 100 m are available from nine stations in the central Strait.Estimates of tracer fields and velocities in the Strait are also available from SalishSeaCast, a Nu-cleus for European Modelling of the Ocean (NEMO) 3.6 model configured for the Salish Sea (Olsonet al., 2020; Soontiens and Allen, 2017). SalishSeaCast is a baroclinic, three-dimensional, primitiveequation model with 40 vertical depth levels (1 m spacing near the surface, 27 m at the bottom)and an approximately 440 m x 500 m horizontal grid. The model domain encompasses the entireSalish Sea (black box on Fig. 2.1 inset map), with open boundaries at the mouth of Juan de FucaStrait at the west and Johnstone Strait at the north. It is forced by Environment and Climate ChangeCanada’s High Resolution Deterministic Prediction System (HRDPS) atmospheric fields (Milbrandtet al., 2016), a mix of measured and climatological rivers, modeled tracer concentrations at the west-ern boundary (Live Ocean Model; Davis et al., 2014; Siedlecki et al., 2015), observed climatologiesat the northern boundary (provided by the Hakai Institute), and modeled tides (Webtide, Foremanet al., 2000) tuned with tidal measurements and sea surface height observations. Vertical turbulenceis handled using turbulent kinetic energy closure and generalized length scale schemes.From model hindcasts (version v201905), we extract daily velocity and temperature fields span-ning November 2014 to January 2020 (inclusive). Pseudo-drifter trajectories using SalishSeaCasthourly velocity fields are calculated over a two year period (January 2016 to December 2017, inclu-sive) using the community-based Lagrangian numerical analysis tool, Ariane (Blanke and Raynaud,1997; van Sebille et al., 2018).2.3 Results2.3.1 Seasonality as an age tracerOur first circulation estimate is based on the assumption that water is generally transported in thedirection of greater age, where age represents the time since the water was last in contact with theatmosphere. A typical age tracer is based on dissolved oxygen concentration, but this technique isnot directly useful here because the temperature of the source water changes seasonally, so that theoxygen content also changes seasonally. Instead, we derive a new tracer based on seasonality usingthe conservative property of temperature. The seasonality tracer is analogous to anomaly tracers inopen-ocean studies (e.g., Sasaki et al., 2010), although the regularity of the seasonal cycle allowsus to more quantitatively and accurately estimate age. In addition, as we shall explain below, theseasonality tracer can also be used to model the relative importance of both advective and (eddy)diffusive processes in transport.Previous studies have noted that the IW temperature varies seasonally in an approximate si-nusoidal pattern (LeBlond et al., 1991; Pawlowicz et al., 2007). In the surface layer, the seasonal19temperature cycle is in phase with the atmospheric seasonal cycle—coolest in early February andwarmest in late August. However; the coldest and warmest times in the IW seasonal temperaturecycle occur later in the year. This delay increases with northward distance from Haro Strait, wherethese waters were last in contact with the atmosphere. Thus, water with a specific temperature,set by air/sea heat processes in the mixing regime of Haro Strait, subducts upon entering the SoGand then tends to keep a conserved temperature “identity” as water parcels move northward in theintermediate layer.Rather than trace any particular temperature, we then consider the phase of the seasonal cycleas a measure of water age. For example, if the coldest point of the seasonal cycle at a given stationoccurs in April, then we infer that it has taken about two months for the water to get there from thesource region. In addition to a delay in the timing of the temperature seasonal cycle, we will alsofind that the seasonal cycle amplitude will also decrease with age due to mixing with surroundingwaters on the northward path; the degree of decrease will allow us to estimate the importance ofmixing.The basic concept of the seasonality tracer can be demonstrated with a simple toy model. Con-sider the IW layer as a one-dimensional pipe, in which temperature T (x, t) for x, t ≥ 0 is governedby both (constant) advection U and diffusion with a diffusivity K:Tt +UTx = KTxx (2.1)where subscripts indicate partial derivatives. If we set a seasonally varying boundary condition atx = 0, T (0, t) = ∆T0 cos(ωt), then we have:T (x, t) = ∆T0 exp(−krx)cos(kix−ωt) (2.2)with kr = Re(k) and ki = Im(k), andk =(1γ){√1+2iωUγ−1}(2.3)with γ = 2K/U an advection/diffusion balance length scale. It is important to note that kr setsthe decay length scale with x of the input oscillation amplitude; ω/ki determines the propagationspeed of oscillation phase; U represents the mean propagation of our tracer through the pipe; and Krepresents all higher-order processes that aren’t encompassed in this mean advection term, which in-clude spatially-varying mean flow (e.g., lateral inhomogeneities) as well as time-varying influencesassociated with mixing and stirring.For a realistic simulation of the SoG, we set the length of the pipe X = 200 km, the frequencyof the oscillating input as ω = 2π/365 days (representing an annual cycle), and an advection speedU = 1.5 cm s−1 based on an approximate through-Strait transit time of 160 days. As γ varies,20different behaviors are possible: in the case that γ is small, advection dominates and the oscillatingsignal propagates down-pipe at the advection speed U with a constant amplitude (Fig. 2.2a and d).When γ is large and diffusion dominates, the oscillating input at x = 0 is mixed almost instantlythroughout the pipe and the apparent advection speed is greater than U with very little change inamplitude (Fig. 2.2c and f). For an intermediate γ where diffusion and advection are balanced, thesignal also propagates with an apparent advection speed that is greater than U , but in addition theamplitude of the oscillation decreases with x (Fig. 2.2b and e). In this case, the speed of phasepropagation is greater than the advective speed and, at the furthest extent of the pipe, the amplitudeof the inflow signal has decreased by ∼55% (Fig. 2.2b).Thus, the seasonality tracer has an advantage over simpler tracers since it can also indicate therelative strengths of mixing and advection. The amplitude decay scale is set by kr which peaks be-tween γ = 50–500 km (Fig. 2.2g). This result suggests that, for amplitude damping to occur withinthis system, the measured advection/diffusion length scale γ should fall within this range—note thatpeak γ= 200 km is similar to the SoG basin length scale of ∼200 km. This is an interesting coin-cidence that may be worth investigating in other basins. This behavior is relatively robust to somerealistic variations. For example, Pawlowicz et al. (2007) and Riche and Pawlowicz (2014) bothimply that U might vary seasonally by up to a factor of two. Some numerical experimentation withEqn. 2.1 in which U is varied seasonally above and below a mean value shows that the sinusoidalshape of seasonal cycles within the Strait becomes distorted by the presence of higher-frequencyharmonics. However; the phase of an annual sinusoid fitted to these distorted shapes remains al-most unchanged from that predicted using the time-invariant U , while the amplitude decay of thefitted sinusoids increases only slightly by an additional 10%.2.3.2 Seasonality tracer: renewal signalNow, we apply these ideas to our observations by fitting a sinusoidal cycle to a yearly temperatureclimatology at each location and depth in the Strait for which a long (>11 months) time series ofhydrographic data is available. A least-squares method is used to find coefficients of the function:Tobs(t) = At cos(2πt/L+φ) + b + ε (2.4)where Tobs are the observed temperatures, t represents the Julian year day upon which the data weremeasured, L represents the length of the annual cycle (i.e., 365.25 days), At the amplitude and φthe phase of the seasonal cycle, b represents the climatological mean, and ε is model misfit. It ismore intuitive to represent φ by an equivalent variable, the yearday of coldest water (φCD): typically,φCD ∼ 40 (i.e., in early February) for surface water at all locations. At is O(1◦C).Since the dataset contains both seasonal and interannual variability, there is some deviationaround the sinusoidal fits (Fig. 2.3a). An error analysis for the sinusoid fitting is carried out using21Figure 2.2: Solutions of the 1D advection-diffusion pipe model with a seasonal oscillation atx = 0 for varying γ . The form of the solution is set by γ = 2K/U with weak (a and d),intermediate (b and e) and strong (c and f) diffusion cases shown. The first row of panelsdepict time/range contour plots, where the solid white line shows the speed of advectionU (1.5 cm s−1) and the dashed white line shows apparent propagation speed. The secondrow of panels show the spatial solution at t = 0 in black lines and the envelopes of theoscillation in red. The colored contours represent the modeled oscillation of temperature.Plot g) is the real and imaginary components of k as a function of γ (Eqn. 2.3). The blackvertical lines represent γ = 20 km, γ = 100 km, and γ = 10000 km for the three abovecases. The blue shaded region represents the 56–75 km γ range calculated from the traceradvection speed and the Lagrangian diffusivity in Section 2.4.22Figure 2.3: Seasonal cycle characteristics. a) Representative temperature climatologies andfitted seasonal sinusoids from two stations in the southern and northern basin. Scatterpoints represent the measurements from the two stations, solid lines represent the sinu-soidal cycles fitted to these climatologies, and dotted lines indicate the coldest Julian dayof the year (φCD) derived from these seasonal cycles. The colors of the scatter points andlines correspond to the colored stations in inset map. Observations from the southern sta-tion span March 2016 to October 2018 and from the northern station span February 2015to January 2019. Figure 2b) and c) show how the seasonal cycle characteristics from allstations in the Strait vary as a function of depth. The sinusoid fitting algorithm (Section2.3.2) was performed on temperature climatologies from each depth level at all stations,so that each individual station has a φCD depth profile and an amplitude (At) depth profile.Each depth level of these profiles was normalized by subtracting the overall mean (forthat depth level) and dividing by the standard deviation (for that depth level) to producenormalized property profiles (plotted in gray). The black line represents the mean ofthese normalized profiles, intersected with colored lines to denote upper (blue; 60 m–80m), core (red; 90 m–110 m), and lower (yellow; 120 m–140 m) IW.a balanced bootstrap re-sampling technique (Efron and Tibshirani, 1994). The temperature clima-tologies for each station are randomly resampled 500 times while maintaining the same overalloccurrence of each sample, and the fitting process carried out for each permutation. Uncertaintiesare then taken as the standard deviation of the bootstrap replicates (coverage factor k = 1). Thisuncertainty, plotted as error bars in Fig. 2.4, is typically about 13 days in φCD and 0.1◦C in At .To assess whether the IW behaves uniformly at all depths, we can examine how the seasonalitytracer varies as a function of depth (Fig. 2.3). To focus on the vertical structure of the water mass,we normalize each profile by subtracting its mean and dividing by its standard deviation, centeringthe profiles around a zero value. Examining these normalized At profiles from all of the stations23(Fig. 2.3c), we find large amplitudes at the surface where the water column is influenced by theatmospheric seasonal cycle of warming and cooling. This influence decreases with increasing depthuntil ∼60 m, and then increases to a weak maxima at ∼ 100 m depth. This broad maxima inAt , coinciding with a depth range over which the phase is roughly constant (Fig. 2.3b), representsthe advected signal of IW present at this depth over a large portion of the hydrographic stations.Later we find there is some spatial variability in the depth of the maximum, thus for a thoroughrepresentation of the IW layer, we divide the water mass into upper IW (60 m–80 m), core IW (90m–110 m), and lower IW (120 m–140 m) and analyze each division separately.On a regional scale (Fig. 2.4) the seasonal cycles show a consistent spatial variation, with a gen-eral delay in φCD with along-strait (i.e., northward) distance from the formation zone. To calculatethe age of the water using the seasonality tracer (aget hereafter), the earliest φCD value from stationsin Haro Strait (between 40 and 65 days, depending on the depth level) was subtracted from φCD ateach station. The relationship between along-strait distance and aget is strongest in the deep IW(correlation coefficient R= 0.87) and weakest in the surface IW (R= 0.37). The gradient of this re-lationship (a linear fit, calculated using a Theil-Sen approach, Sen, 1968, uncertainties denote 95%confidence interval, coverage factor k = 2)), yields an approximate northward transport velocityof 1.5±0.07 cm s−1 in the upper IW, 2.0±0.10 cm s−1 in the core IW, and 1.9±0.10 cm s−1 inthe lower IW. When considering the constant advection term U in the 1D model (Eqn. 2.1), the25% difference in transport velocity between sub-layers equates to small changes in the modeledpropagation speed and amplitude damping signal (on the order of∼10%), but does not significantlychange the position of the kr maxima (Fig. 2.2g), thus it does not impact the advection/diffusionproperties of 1D system. An accompanying reduction in seasonal cycle At also occurs at all depths(Fig. 2.4): a reduction of 0.45±0.06 ◦C 100 km−1 (R= 0.79), 0.44±0.05 ◦C 100 km−1 (R= 0.82),and 0.37±0.05 ◦C 100 km−1 (R=0.83) for the upper, core, and lower IW, respectively, representinga 78–85% attenuation of At over the length of the strait. This measured attenuation is larger thanthat derived from the intermediate γ case of the 1D model (∼ 55%; Fig. 2.2e), indicating that, asmight be expected, the 1D toy model, although showing the essential behaviour, is not completelysatisfactory as a quantitative model of the Strait. Other issues that may be important include theeffective length of the Strait used—the curved cross-isobath paths (coral arrows) indicated in Fig.2.5a may be longer than the straight-line 200 km distance we have estimated. More importantly,the “leakage” of water out of the IW layer along the length of the Strait (which almost by definitionmust approach 100% in an estuarine system) suggests that in a better approximation the advectionspeed U should decrease northward (to zero at the northern end of the Strait). Numerical experimen-tation shows that such a decrease does significantly reduce the cycle amplitude in the northern Straitrelative to that calculated in our analytically convenient constant U approach; as long as diffusion isalso important the phase delays are not greatly affected.We can also employ this technique using other seasonally varying properties; an identical anal-24Figure 2.4: Seasonal cycle spatial variability for the Strait of Georgia. Hydrographic measure-ments span 1969–2019, though are primarily from the 2000–2019 time period. a) A mapdetailing station locations denoted by the colored markers. Marker colors in a) corre-spond with the colored y-axis in b), e), and h) to denote seasonal cycle properties as afunction of along-strait distance from Haro Strait. The three rows of plots represent datafrom the the upper IW (60 m–80 m), the core IW (90 m–110 m), and the deep IW (120m–140 m), respectively. In c), d), f), g), i), and j) gray scatter points show the variabilityof the seasonal cycle phase shifts φCD and amplitudes At as a function of along-straitdistance from Haro Strait; the error bars show the mean values ± the standard deviation(coverage factor k = 1), representing the uncertainty in the sinusoidal fitting algorithm(Eqn. 2.4) calculated using a balanced bootstrap re-sampling technique. For reference,the quantity aget is calculated aget = φCD−φHS, where φHS is the earliest φCD measure-ment from Haro Strait. The solid red lines detail the linear trends in these properties.The yellow and blue lines in f) detail the same trends for the Lagrangian transit time andSalishSeaCast model seasonality analyses, respectively. Trends are derived using a usinga Theil-Sen method.25ysis using dissolved oxygen produces similar results to our temperature-derived analysis (Supple-mentary Figs. 1 and 2). However, the mean temperature b from the sinusoidal fitting is almostconstant throughout the Strait (as one might expect for a conservative property), whereas the meandissolved oxygen level decreases with age. We then calculate a decrease of 0.69 ±0.18 µmol kg−1day−1 in the IW, representing oxygen utilization by remineralization processes (see Appendix A forfurther details on dissolved oxygen analysis).2.3.3 Seasonality tracer: lateral circulationThe strong relationship between along-strait distance and the two seasonality tracers—φCD and At(Fig. 2.4)—is the first-order description of IW renewal that is directly related to the features ofour 1D pipe model (Fig. 2.2). However, the scatter in φCD and At around the mean northwardtrend suggests that unresolved lateral variability may be present in the mean fields. Although theconceptual link to the 1D pipe model is now less direct, we will proceed by mapping these meanfields, and then assume that, as in the “core” method, a spatially-variable mean advection occursmostly across isopleths.To analyze data from the irregularly spaced hydrographic stations, we used a kriging interpo-lation method (Davis, 2002) to estimate the aget and At fields over a uniform horizontal grid ofdata points with a spacing of 0.1 km x 0.2 km over the deepwater areas of the Strait. Ordinarykriging is used with a spherical model for the semi-variogram of the derived data set and a best-fitalgorithm was used to derive the coefficients of the covariance function. The range coefficient (thatis, the decorrelation length scale) for all interpolations was ∼35 km; no nugget effect was applied.We then map the interpolated tracer fields to evaluate the 2D spatial expression of the circulation(Figs. 2.5 and 2.6).Just north of Haro Strait, the younger water of the southern inflow is transported primarily alongthe eastern boundary of the southern basin; IW ages in this boundary flow range from 10–40 days(Fig. 2.5a–c). This tongue appears at all three depth levels, extending as far north as Texada Island.The water west of this inflow on the shores of Vancouver Island is 20–40 days older.At the northernmost extent of the southern basin, just south of Texada Island, an aget of ∼70days is fairly uniform across the Strait, identifying the northern limit of the boundary current. Notethat a gyre-like pattern appears in the southern basin, in which regions of older water are surrounded(or nearly surrounded) by younger water, depicting a system with two interior cores (coral-coloredstars in Figs. 2.5a and d). The oldest waters are generally found at the north-east of the region(Fig. 2.5), and are 10–20 days older in the deep sub-layer compared to the surface sub-layer.The spatial maps also reveal a new intermediate feature. Originating in Discovery Pass, a sec-ond, weaker tongue of younger water extends southwards along the coast of Vancouver Island in theshallowest intermediate layer (Figs. 2.5a and d). Similar to Haro Strait, Discovery Pass is a regionwhere strong tidal mixing homogenizes the water column (Thomson, 1976). This feature suggests26Figure 2.5: Maps of seasonality tracer-based age (aget) from: a) & d) the upper IW (60 m–80m); b) & e) the core IW (90 m–110 m); and c) & f) the deep IW (120 m–140 m). Panelsin the first row depict aget derived from observational temperature climatologies, the sec-ond row from SalishSeaCast hindcast temperature climatologies. Coral-colored arrowsdrawn in in a) and d) represent circulation estimated from a core method perspective,coral-colored stars represent the cores of the gyre-like circulation. Hydrographic stationlocations are plotted as black scatter points in a)–c).27Figure 2.6: Maps of IW seasonal cycle amplitude At from: a) & d) the upper IW (60 m–80 m);b) & e) the core IW (90 m–110 m); and c) & f) the deep IW (120 m–140 m). Panels in thefirst row depict At derived from observational temperature climatologies, the second rowfrom SalishSeaCast hindcast temperature climatologies. Hydrographic station locationsare plotted as black scatter points in a)–c).28that these Discovery Passage transformation processes form intermediate waters that then enter thenorthern basin as an inflowing tongue of slightly shallower IW. The presence of this southwardtongue is responsible for the ∼ 25% decrease in the mean northward velocity of upper IW relativeto the values for core and deep IW (Fig. 2.4).Comparing spatial maps of At (Figs. 2.6a-c) with those of aget (Figs. 2.5a-c), we find that At(Fig. 2.6) decreases with age. At the southern entrance, amplitudes are as large as 1.5◦C, but theydecrease to <0.5 ◦C in the northern Strait. This signal is again found entering the southern basinpreferentially along the eastern boundary, which has At of 0.75–1.25 ◦C.The 1D pipe model does not incorporate the influence of the Discovery Pass inflow or any othersignal. We can, however, apply the advection/diffusion characteristics of the 1D model to help un-derstand the dynamics of the northern inflow. Based on our toy model (Fig. 2.2), we have intuited:(1) the amplitude of the seasonal signal is damped when advection and diffusion are balanced butstays roughly constant if one dominates; (2) if advection dominates, then the age progression (thatis, the aging of water as a function of along-strait distance) provides a good estimate of mean flow;and (3) if diffusion dominates, then the age progression will be far faster than the mean flow. Ap-plying this intuition to the seasonality observations, the overall decrease in At as aget progressessuggests that advection and diffusion are both important in understanding the overall northwardtransport. Furthermore, although both the southern boundary current and the overall northwardtransport appear similar in both aget and At plots, the northern tongue is only apparent in the agetmapping (Figs. 2.5a and d); there appears to be limited amplitude damping along the coast of Van-couver Island (Figs. 2.6a and d). This finding suggests that either advective or diffusive processesdominate southward transport along Vancouver Island, and that this inflow behaves differently fromthe advection/diffusion regime of the southern inflow.2.3.4 Evaluation of SalishSeaCast modelFor the Eulerian and Lagrangian analyses, we now consider the numerical SalishSeaCast modelsimulations. We first confirm that these simulations accurately portray the Strait by applying theseasonality tracer methodology to calculate aget and At from modeled temperature climatologies,which are compiled from daily hindcasts spanning November 2014 to January 2020, and comparingthese fields to the observations (Figs. 2.5 and 2.6). The model grid is highly resolved and nearlyuniformly distributed in space; no interpolation was performed on the model fields prior to mapping.To test the agreement between the observed seasonality fields and the modeled, full-resolutionseasonality fields, we first use a nearest neighbor scheme to map the (interpolated) observationaldata grid to the model data grid. We then calculate skill scores (SS) per Murphy (1988), wherewe compare the root mean squared difference between observed (X) and modeled (Y ) values to thevariance of the observations:29RMSD =[1n1mn∑i=1m∑j=1(Yi j−Xi j)2]1/2(2.5)SS = 1− RMSD2σ2X(2.6)where indices i and j represents the grid coordinates and σ represents standard deviation. A SS = 1indicates perfect agreement between the X and Y fields, while a SS > 0 is generally considered aminimum requirement for acceptable performance (Murphy and Epstein, 1989). For aget fields,SS =0.65 (upper), 0.72 (core), 0.48 (lower) and for At fields, SS =0.75 (upper), 0.42 (core), 0.11(lower), suggest that the model replicates the observations to an acceptable degree, performingbetter in the upper and core sub-layers than in the deep sub-layer.Qualitatively, the spatial expression of seasonality in the model depicts the same features asthose identified in the observational fields (Figs. 2.5 and 2.6). There is a general trend of increasingaget and decreasing At with along-strait distance and the inflowing tongues of younger water arepresent in both the basins. The northern tongue is most clearly expressed in the aget of the upperIW waters, as in our observational analysis, and is less obvious at deeper depths. The southern basininflowing tongue correlates with a corresponding feature in the At maps; however, this correlation isnot as obvious in the northern basin tongue. The oldest water is amassed in the northeast of the re-gion and regions of older/lower amplitude water are again surrounded by younger/higher amplitudewaters in the southern basin.However, there are also some notable discrepancies between the model and observational fields.For example, the aget of northeastern Strait waters are ∼ 20 days older in the model analysescompared to the observations (Figs. 2.5b and e), the At of the southern basin is larger in the modelanalyses compared to the observations (Figs. 2.6b and e), and the interior cores (coral-colored starsin Figs. 2.5a and d) are more well defined in the model analyses compared to the observations. Thesedisparities could be explained by a lack of observations in those regions rather than a model failing.Generally, the agreement between the model seasonality and that derived from observations is good,giving us confidence in the ability of SalishSeacast to accurately reflect the Strait’s circulation.2.3.5 Eulerian MeanTo assess the subsurface circulation from a Eulerian perspective, we can map IW currents fromfixed locations. Here, this mapping is achieved using estimates from SalishSeacast daily-averagedvelocity fields (Fig. 2.7a), in addition to measurements from three ADCP moorings and nine cy-clesondes/current meters in the southern basin (Fig. 2.7b). By projecting these velocities along avector in the along-channel direction we can classify the velocities as up-strait (from Haro Straittowards Discovery Passage) and down-strait (the opposite).30Figure 2.7: Map of Eulerian circulation. Map a) arrows show mean velocity field (U) andcontours show along-strait component of velocity calculated from SalishSeaCast veloc-ity hindcasts from November 2014 to June 2019. Inset map b) arrows shows long-termmean currents derived from cyclesonde measurements of Stacey et al. (1987) at 100 mfrom June 1984–January 1985 and three VENUS ADCP records: Central node (Septem-ber 2008–May 2020); East node (September 2007–May 2020); and DDL node (May2013–May 2020), where black arrows denote corresponding SalishSeaCast mean cur-rents from the nearest grid cell. Data from between 50 m and 150 m were used to calcu-late ADCP mean currents and velocity fields apart from the DDL node, where the meanis calculated between 50 m and 140 m (roughly the seafloor depth). Map c) depicts theratio of eddy kinetic energy (EKE) to mean kinetic energy (MKE), KER.31We separate the velocity fields U into a mean U and subtidal time-varying component U ′:U =U +U ′ (2.7)where the mean is calculated over five years of the hindcast dataset.The mean Eulerian current field U (Fig. 2.7a) has similar features to those depicted by theseasonality tracer. Ventilation from the south occurs via a strong eastern boundary current thattransports water up-strait at mean speeds as large as 16 cm s−1 (found within black rectangle inFig. 2.7a). Upon reaching Texada Island this inflow recirculates, producing a cyclonic system withinthe southern basin. Two cyclonic cells are present in the southern basin, one located near the centerof the basin and another located toward the north of the basin. A strong down-strait jet emanatesfrom Discovery Passage with mean velocities up to 19 cm s−1. The mean along-strait velocity fieldbecomes intermittent at, and north of, Texada Island.Model velocity estimates (in parentheses hereafter) in the southern basin largely agree withsparse observations from the ONC ADCP moorings, albeit with a somewhat smaller magnitude(Fig. 2.7b). Mean IW velocities from the East and DDL nodes are 14 (11) cm s−1 and 10 (5) cms−1, respectively, in an up-strait direction. Subsurface currents measured at the Central node areslower, with a mean velocity of 3 (1.5) cm s−1 in an approximately down-strait direction. Modelcurrents are also consistent with the more spatially comprehensive measurements of Stacey et al.(1987, Fig. 2.7b) and depict a gyre-like circulation with velocities ranging from 2.8–8.4 cm s−1(1.5–7.9 cm s−1).If we associate the mean velocity field U with advective transport and the fluctuating residualvelocity field U ′ with a (subtidal) diffusive contribution to transport, we can assess the relativecontribution of each by comparing the mean kinetic energy (MKE) and eddy kinetic energy (EKE)of the SalishSeaCast velocity fields:MKE =u2+ v22, EKE =u′2+ v′22(2.8)where U = (u,v) and U ′ = (u′,v′) are the horizontal velocity vectors. We can then calculate theratio of the components at each grid cell:KERij =EKEijMKEij(2.9)where subscript i denotes across-strait distance, and subscript j denotes along-strait distance, andmap KER for spatial comparison (Fig. 2.7c).The largest MKE contributions to the kinetic energy field are observed in the boundary currentregions (Fig. 2.7c), however the spatial extent of these features is limited; only 25% of the KERfield is <0.75. It should be noted that the boundary currents are also regions of high EKE, but32the contribution of EKE to the overall energy of these features is small. Outside of the boundarycurrent regions, EKE dominates in 61% of the KER field (KER >1.25) and 13% of the field has anintermediate KER (0.75–1.25).To evaluate the overall influence of advection and diffusion within the IW, we can sum the totalEKE and MKE in the system and compare the two:KET =∑ni=1∑mj=1 EKEij∑ni=1∑mj=1 MKEij(2.10)yielding a ratio KET=0.79. Note that this summation only includes points within the SoG; datafrom Haro Strait and surrounding inlets are discarded. This ratio suggests that while the influencesof diffusion and advection are similar, mean flows (i.e., advective features with high MKE) areslightly more important in transporting water and material at subtidal scales, even though eddy-diffusive transport (i.e., regions with high subtidal EKE) dominates in a large majority of the Strait.This finding provides insight into the linkages between the 1D and 3D model presented here: whilea KET ratio of around 1 suggests that the balanced diffusion/advection regime of the 1D modelscales up well as an overall representation of the system, is is clear from Fig. 2.7c that there is alarge amount of spatial variablity in KER, and that a balanced diffusion/advection regime is notapplicable on smaller spatial scales.It is important to note that tidal variability is ignored in the Eulerian analysis (though not in theLagrangian analysis; Section 2.3.6); however, while tidal flows are large in the shallower Haro Straitand Discovery Passage regions, they are generally small within the Strait of Georgia itself, and theinfluence of tides on the lateral dispersion of particles in the deeper basins of the Strait is thought tobe limited.2.3.6 Lagrangian Particle TrackingThe analyses above suggest that the IW circulation is dominated by a large northward flowingrenewal signal, consisting of a combination of advective and eddy-diffusive effects. We can furtherinvestigate the properties of this inflow from a Lagrangian perspective by tracking the dispersion ofnumerical particles from a Boundary Pass release point. Particles were introduced into the velocityfields every 60 minutes for an entire year at 50 m, 75 m, and 100 m. In total, 26,352 particles werereleased. The trajectories of all existing particles in the system were calculated every 20 minutesuntil they expired after 365 days. If a particle reached the oceanic boundaries of the model, itwas removed from the system. Each trajectory was smoothed using a 24 hour window-average tominimize tidal oscillations and decimated for computational ease. The mean daily positions of allof the particles were then spatially binned into 0.01◦ x 0.01◦ horizontal bins and 1 m depth bins.To calculate IW transit time fields from the Lagrangian information, which are comparable butnot identical to age estimates (Bolin and Rodhe, 1973), we can form a histogram of particle ages for33Figure 2.8: Transit time and time-evolution of particle positions derived from the Haro Straitparticle tracking experiment. Maps a) and f) describe the mean transit time horizontallyand vertically, respectively, derived from Transit Time Distributions (TTDs). Dashedwhite lines depict the basin boundaries. Maps b)–e) and g)–j) detail the time-evolutionof the released particles 20, 40, 80, and 160 days after release. The particle concentrationis calculated as the number of particles contained in a grid cell normalized by the gridcell containing the largest number of particles, for each time-step. Particle release pointsare denoted by a white marker and/or black cross. Grey seafloor in f)–j) is representativeof depth along the thalweg. The 10 m isobath of the Fraser River slopes is highlighted inb)–e) with a gray contour.any grid cell in the region; this is known as the Transit Time Distribution (van Sebille et al., 2018;Deleersnijder et al., 2001; Haine and Hall, 2002), or TTD. A TTD is the probability distribution thatdescribes the transit time of particles to reach any grid cell in the system by any pathway. The meanof the TTD for a grid cell defines the average transit time for particles to travel from their releasepoint to that grid cell, and mapping the mean transit times allows us to approximate IW age andinfer circulation dynamics (Fig. 2.8).34The mean transit time field (Fig. 2.8a) depicts rapid transport through the eastern boundary cur-rent, which advects particles in an along-strait fashion toward the northern basin. Particles tend tostay in an IW tongue centered at a depth of about 100 m (Fig. 2.8f), although they also appear in sur-face and deeper waters. Average IW ages within the boundary current range from 0–20 days at thesouthern entrance of the basin to 80 days at the basin boundary. There is also a clear but less rapidtransport of particles into the interior, though it is not immediately obvious whether these particlesenter the interior via the boundary current, directly from the south, or some combination of both.The transit time in the interior and western extents of the basin range from 60–120 days—generally∼60 days older than the adjacent boundary current waters. We can estimate the inflow propaga-tion speed from a Lagrangian perspective by considering the slope of the transit time/along-straitdistance relationship (yellow line in Fig. 2.4f), yielding a propagation speed of 1.5 cm s−1 for theintermediate layer.The variability in these features is illustrated by examining snapshots of particle distributionat different times since release. After 20 days, the particles are vertically distributed around therelease region by strong mixing (negating any dependence on initial release depth) and subduct tofill the IW layer in the southern Strait (Fig. 2.8b,g), although some remain near the source. A smallnumber of particles disperse rapidly and enter the northern basin. In 40 days they fill the entiresouthern basin, and begin to fill the northern Strait through Malaspina Strait, Sabine Channel, andthe main channel connecting the two basins west of Lasqueti Island (LI in Fig 2.1). After 80 daysboth basins are mostly filled, and the particles are uniformly distributed around the Strait after 160days.A particle budget analysis shows that the flux of particles through the upper (50 m) and lower(150 m) IW boundaries is highest in the initial week following release, where 19% of the activeparticles leave the IW through these boundaries—this flux is a result of strong mixing in BoundaryPass that vertically redistributes the particles (Fig. 2.8f). The particles then disperse through theSoG and the vertical flux of active particles out of the IW falls to 0.2% day−1, on average, whichoccurs primarily at the upper interface. This slow leakage of particles from the IW represents acombined signal of vertical mixing and upwelling (and downwelling, to a lesser degree) that is notcaptured in the 1D model; however, this small flux of particles represents an amount of verticalmixing that is unlikely to play a significant role in the diffusion/advection scheme presented in the1D model on timescales less than a number of months. We can calculate the “flushing time” as theamount of time for 63% of particles to leave the layer (Monsen et al., 2002). Here, the flushing timefor IW originating in Boundary Pass is 128 days, in agreement with other recent SoG flushing timeestimates of 125 days (MacCready et al., 2020).35Figure 2.9: Lagrangian statistics derived from particle trajectories. The red arrows in map a)are a projection of the rotated along/across-strait coordinate axis with distances markedin either direction. The bathymetry of the Strait is shown and an example trajectory isplotted in white; note that the axis is centered on the particle release point. Plots b) and c)show the time-evolution of particle drift in both directions; the colored regions show thedrift of all particles binned into 500 m along-strait bins and the white line is the mean driftof all the particles. The remaining plots show the time-evolution of particle dispersion (dand e), kurtosis (f and g), and diffusivity (h and i). All time-series are truncated to showonly the first 200 days of the 365 day experiment as the system reaches quasi-steady stateafter 100–150 days.2.3.7 Eddy DiffusivityWe can gain quantitative insight into the dispersion characteristics of the IW via statistical analysisof the Lagrangian particle trajectories (Fig. 2.9). Here, we derive single particle statistics pertainingto the movement of particles in a modified coordinate system: an along-strait/across-strait axis (asindicated on Fig. 2.9a). For the purposes of this analysis, trajectory data from Juan de Fuca Strait,surrounding inlets, or the surface and deep layers were discarded.Various statistical measures can be performed on Lagrangian data (see LaCasce (2008) for acomprehensive review of these techniques). The first-order moment of the particle displacements36describes the mean drift of the cloud’s centre of mass in x-direction:Mx(t) =1NN∑i=1[xi(t)− xi(0)] (2.11)The second-order moment of the displacements (the variance of the particles’ displacement,or “dispersion”) describes the spread of the cloud around its centre of mass in x-direction, and iscalculated:Dx(t) =1N−1N∑i=1(xi(t)− xi(0)−Mx(t))2 (2.12)The fourth-order moment, or kurtosis, describes the width of the displacement probability den-sity function (PDF) in x-direction:ku(x) =∑i(xi−Mx)4[∑i(xi−Mx)2]2(2.13)where Gaussian distributed PDFs have ku≈ 3 and PDFs with higher “tailedness” have values ku> 3.Finally, we calculate the relative horizontal diffusivity of the system as the time derivative of thedispersion in x-direction:K =12ddtDx (2.14)There is an increased spreading of particles in the along-strait direction. This spreading isapparent in the mean drift and dispersion characteristics (Figs. 2.9b–e)—on average, from theirrelease point in Haro Strait, particles drift 93 km along-strait and disperse over a 3000 km2 area,compared to 15 km across-strait drift and 250 km2 dispersion.This spreading is also reflected in the kurtosis characteristics of the displacements (Figs. 2.9f andg): kurtosis values, large initially where particles are grouped around the release point, fall to ku≈ 3after two weeks as particles disperse from the center of mass. In the across-strait direction, thekurtosis then increases to ku≈ 5—physically, this increase indicates that particles become groupedin the interior of the basins. In the along-strait direction, the kurtosis briefly increases to a ku ≈ 3maxima after 40 days, before decreasing to ku < 3 after 120 days; the final state of the system is aflat distribution of particles spanning the along-strait extent.The relative diffusivity measured here reflects the system’s ability to laterally spread particles(Figs. 2.9h and i). Peak across-strait diffusivity values of 160 m2 s−1 occur near the beginning of theexperiment, falling to zero after ∼20 days. In the along-strait direction, diffusivity values as largeas 560 m2 s−1 occur near the beginning of the experiment, falling to zero after ∼100 days. Recentobservational estimates of SoG surface layer diffusivity—4000 m2 s−1 at the surface (Pawlowiczet al., 2019) compared to 88–167 m2 s−1 for the IW—indicate that substances disperse more rapidly37at the surface, presumably due to the direct effects of wind and river inflow, than at depth.The reduction in diffusivity to ∼0 as a function of time (or, “filling timescale”) is partiallya product of the aspect ratio of the Strait, whereby particles take less time to spread across theStrait than along it due to the elongated shape of the basin. Thus, by assessing the diffusivitiesfrom t < filling timescales, we can approximate the diffusivity in either direction prior to the timewhen particles have maximally dispersed. The mean diffusivity values for across(along)-strait inthe first 20 (100) days is 83 (181) m2 s−1. Comparing the two measures, we see that the meandiffusivity values are around twice as large in the along-strait direction: the Eulerian mean velocityand KER fields (Fig. 2.7) indicate that the major advective (MKE) features are directed along-strait.This finding suggests that the along-strait diffusivity represents components of both advective anddiffusive transport, whereas across-strait diffusivity reflects mainly diffusive transport.2.4 DiscussionPrevious research on the SoG intermediate circulation has mainly focused on patterns of circulationin specific regions of the Strait (Stacey et al., 1987), or on the general estuarine exchange natureof the layer (Pawlowicz et al., 2007; Riche and Pawlowicz, 2014). The maps and transit times pre-sented in Figs. 2.5–2.9 aim to provide a more comprehensive overview of the mean IW circulationand to highlight the major pathways though which IW is transported. These findings are particu-larly significant when considering the subsurface circulation of the northern basin, where very littleinformation is currently available.The general northward progression of seasonal cycle phase, first noted by LeBlond et al. (1991),has been carefully re-evaluated for the whole Strait using a much more comprehensive dataset. Anew feature of this progression is a corresponding decay in cycle amplitude. We can use the mea-sured advection and diffusion properties to reconcile these findings within the context of the 1Dmodel. To calculate γ = 2K/U , we use peak along-strait relative diffusivity of k =560 m2 s−1to represent the unrestricted spreading of particles in an along-pipe direction (Fig. 2.9h) and theobserved and modeled propagation speeds (slopes of relationships in Fig. 2.4f) to estimate an ad-vection velocity range of U =1.5–2 cm s−1, producing γ range of 56–75 km (blue shaded regionin Fig. 2.2g). This length-scale is less than the modeled γ of 100 km (Fig. 2.2b) but within thetheoretical range necessary to produce a physically realistic simulation of the tracer behavior inthe Strait (Fig. 2.2g). This observation, in addition to the striking qualitative similarities betweenmeasured seasonality in the Strait (Fig. 2.4) and the intermediate diffusion-advection case in the 1Dpipe model (Fig. 2.2b), suggests that, in a time-averaged sense, the first-order along-strait renewalprocesses can be well-represented by a balanced advection-diffusion relationship (Eqn. 2.1). How-ever, it is clear from the various spatial analyses performed here (Figs. 2.5–2.8) that this schemeonly serves as a very general description of IW flow, and that multiple inflows and circulation cellscontribute to a higher-order variability in advection-diffusion characteristics that cannot be rendered38by a simple 1D approximation.If we assume that mean transport crosses isopleths of age and cycle amplitude, we find, overlaidon the general northward propagation within the Strait, an anti-clockwise circulation pattern, withfaster northward speeds on the eastern side of the Strait and slower (or even southward) velocities onthe western side. The U field derived from SalishSeaCast (Fig. 2.7a) also outlines an anti-clockwisegyre-like circulation in the southern basin in agreement with the long-term but sparse observationsof the ONC nodes and earlier current meter deployments (Fig. 2.7b; Stacey et al., 1987), with up-strait currents as large as 16 cm s−1 compared to down-strait currents up to 9 cm s−1. However;while the tracer and U fields indicate a gyre-like circulation is present, it should be noted that theKER field identifies only the eastern limb of this loop as a strongly advective feature. This findingsuggests that the eastern boundary current is the most strongly expressed feature of the circular flow,and that the rest of the gyre-like system is only expressed when we consider mean transport on longtimescales. Further analysis of Lagrangian trajectories might answer how prevalent the cycloniccirculation is on sub-interannual timescales—techniques such as most-likely path algorithms (vanSebille et al., 2012; Maximenko et al., 2012) or Lagrangian Coherent Structures analyses (Haller,2015), for example.No obvious transport pathway can be found between the southern and northern basins at inter-mediate depths. Circulation patterns in Malaspina Strait suggest some exchange between MalaspinaStrait and the southern basin (Fig. 2.7), but there is no clear communication between MalaspinaStrait and the interior of the northern basin, the boundary between which is silled at a depth of∼100 m. A measure of inter-basin exchange occurs between Lasqueti Island and Vancouver Island(Fig. 2.8b–e), though the passage of water through this channel is likely impeded by shallow sillsthat separate the two basins (Fig. 2.9a). Sabine Channel—a 240 m deep but very narrow corridorbetween Texada Island and Lasqueti Island—may then represent an important pathway between thetwo basins, though closer analysis of the transport through this channel is necessary to confirm thisnotion. Interestingly, there is no clear evidence of this inter-basin topographic barrier in the linearevolution of aget (∼ 150 km along-strait distance in Figure 2.4), suggesting that, regardless of thespecific transport pathways, the transport between basins is fairly efficient.Within the seasonality tracer framework we assume a constant intermediate transport. Thisassumption is supported by the strongly sinusoidal nature of seasonal cycles themselves (Fig. 2.4b,e, and h) and the robust linear relationship between northward excursion and seasonal cycle phase(Fig. 2.4c, d, f, g, i, and j), suggesting constant, near-constant, or frequent IW renewal from HaroStrait. This premise is opposed to that of an episodic signal of renewal, which would producetemperature signals with step-like variability that is not apparent in our dataset. However, thisassumption conflicts with studies that have linked subsurface inflow strength in the southern basinto discrete deep water renewal events (LeBlond et al., 1991; Masson, 2002). Therefore, we proposethat a constant inflow assumption is valid when investigating the time-averaged circulation of the39region, but the time-varying nature of the inflow should be considered when assessing circulationon interannual or shorter timescales. Evaluating the seasonal and annual inflow strength variabilityis an important next step in determining the dispersion of contaminants through the SoG on shortertimescales, and will be aided by the rapidly expanding regional monitoring and modeling efforts,though this analysis is beyond the scope of this study.The linear relationship between along-strait distance and both φCD and transit time (Fig. 2.4f),rendered in the toy model as a constant advection term U (Eqn. 2.1), suggests that the southerninflow ventilates the system steadily northward. This steady up-strait transport may not be an intu-itive conclusion when studying the bi-directional flow of the Eulerian mean current field (Fig. 2.7a);however, the KER field (Fig 2.7c) identifies the boundary current jets as the only strongly advectivefeatures, suggesting that these features are the only components of the flow field in which the meanflow is significantly expressed. As these features are oriented in the along-strait direction, we canconsider the along-strait trends in age and transit time (and our model term U) as the sum product ofall the processes that contribute to the mean northward propagation of the renewal signal, includingthe mean up-strait influence of advective features and the mean up-strait contribution of mixing andstirring. We can then class the residual variability—the spatiotemporal variability of the flow field,seen as the scatter in age around the aget trend and parameterized as K in the 1D model—as thehigher-order processes that impact IW transport.Finally, regional pollutant transport may be particularly sensitive to the subsurface circulation,as the majority of wastewater from the densely populated southern Strait region is incorporatedinto the IW directly via wastewater outfalls or indirectly via subducting water in Haro Strait. Asa result of elevated wastewater levels in the IW, various semi-conservative chemical tracers (suchas such as polybrominated diphenyl ethers, or PBDEs; Sun et al., 2018) are concentrated withinthe intermediate layer. Further study into the dispersion characteristics of these chemical tracersthroughout the IW may provide an approach to link the physical circulation with biogeochemicalvariability observed in the region. The Lagrangian analysis of the southern inflow (Figs. 2.8 and 2.9)suggests that small amounts of long-lived pollutants will spread quickly through the entire Strait inapproximately one month while bulk spreading occurs on the scale of 3–4 months, after which thesystem is in quasi-steady state.2.5 ConclusionsHere, we have used three different approaches to produce an intermediate circulation scheme forthe Strait of Georgia. We can use this three-pronged approach to compare the strengths and weak-nesses of the different methodologies. Tracer-based techniques incorporate the influence of bothadvection and diffusion into transport estimates, though separating the contributions of each to theoverall transport requires additional numerical analysis (e.g., Eqns. 2.1–2.3) which, in the case ofsome tracers, may not be possible. The analysis of Eulerian velocity fields can create simple, in-40tuitive maps of mean currents—indeed, it is easy for one to look at the arrows designating currentvelocities (as in Fig. 2.7) and draw simple pathlines to join them, giving a general sense of howwater is transported through the system—however, these maps can be misleading unless we weighthe influence of time-invariant features against the time variability of the flows (e.g., Fig. 2.7c). La-grangian techniques perhaps represent the most complete three-dimensional representation of theregional circulation, but are complicated observationally, numerically, and analytically. Of the threeapproaches, the seasonality tracer is distinct; while the analysis of velocity fields and Lagrangiantrajectories are commonplace in circulation studies, the seasonality tracer derived here has provideda novel technique for identifying intermediate pathways and mixing dynamics that could plausiblybe used to map circulation in other subducting water masses with cyclical variability. While thereis overlap in the findings of each method, no one single method provides a complete explanationand the resulting inter-comparison between results has provided a more rigorous characterization ofthe intermediate circulation, emphasizing that a multi-faceted approach to circulation problems isadvantageous, where possible.Estimates of intermediate water transit times have proven particularly useful to the study ofPBDE dispersal from a wastewater outfall in the southern basin (Sun et al., 2023). In applyingsome of the analysis methods detailed in this study to additional particle tracking simulations thatfocus on this wastewater source, we were able to better understand the sources, sinks, and eventualfate of PBDEs in the Strait of Georgia system. A subset of these analyses, which enhance ourunderstanding of PBDE sources, sinks, and dynamics in the Strait of Georgia, is documented inAppendix B for reference.41Chapter 3Swish floats: an inexpensive neutrallybuoyant float to monitor dispersion incoastal seasThe Ocean Dynamics Lab at UBC spent many years developing, testing and deploying expend-able drifters to measure surface dynamics. Given my focus on subsurface flows, one natural pointof discussion was how we might approximate the surface drifter concept for deeper waters. As noobvious counterpart existed, we began conceptualizing a simple instrument that might performas a subsurface analogue to expendable drifters. Initial designs drew interest from investigatorsinvolved in the Gulf of St. Lawrence Tracer Release Experiment (TReX), who provided an oppor-tunity to field test the instruments as part of a larger experiment to track dispersion in the Gulf.This chapter, (published as Stevens and Pawlowicz, 2023) details the development, properties,and data from initial deployments of these instruments.3.1 IntroductionThe direct measurement of subsurface ocean currents using neutrally buoyant floats has been a com-mon practice among oceanographers for decades (Gould, 2005). These free-floating instrumentstrack the movement of water parcels and can provide a kinematic description of a system. Follow-ing the neutrally buoyant “Swallow floats” of the 1950s (Swallow, 1955), floats have been deployedto measure the paths, structures and variability of features such as mesoscale eddies (Prater, 2002;Shoosmith et al., 2005), internal waves (D’Asaro, 2003; Goodman and Levine, 1990), Rossby waves(Hamilton, 2009), and basin-scale current systems (Bower et al., 2011; Lozier et al., 2022). Whenusing neutrally buoyant floats for these purposes, it is preferable to gather many individual obser-vations of a flow to improve the statistical significance of any determinations of a mean flow over42a period of time. Collecting a large number of these observations can be costly, however, and it istherefore desirable to find ways to reduce the cost of each individual measurement. While variouslow-cost surface drifter systems have been developed that implement inexpensive tracking devicesto measure surface flows (e.g. Pawlowicz et al., 2019; Pawlowicz, 2021; Novelli et al., 2017; Mey-erju¨rgens et al., 2019; Page et al., 2019), less attention has been given to the production of aninexpensive float system for subsurface flows.Many coastal and inland seas have deep circulation that is not well known and could benefitgreatly from an inexpensive measure of their internal dynamics. While the number of subsurfaceLagrangian observations in the open ocean has been growing in the past few decades as a resultof global initiatives such as the Argo program (Wong et al., 2020), observations in coastal andmarginal seas remain limited as they are often a more-complicated and riskier proposition. Thisis largely due to strong tides; challenges with float buoyancy control as a result of increased watercolumn density variability; and a heightened probability that expensive instrumentation will runaground or collide with ships and other vessels (Pawlowicz, 2021). Moreover, access to the usualframeworks for underwater telemetry such as acoustic sources or hydrophones (like those employedin RAFOS floats; Richardson et al., 1981), are often unavailable or hard to establish due to elevatedlevels of low-frequency ambient noise in areas of increased vessel traffic (e.g. Bassett et al., 2012).Examples of floats deployed for the specific purpose of coastal monitoring are scarce, thoughefforts have been made to develop floats to measure coastal upwelling (Hebert et al., 1997), andto deploy profiling floats in Arctic (Sandby et al., 2021) and marginal seas (e.g. the MediterraneanSea and the Black Sea; Korotaev et al., 2006; Poulain et al., 2007). Other modern Lagrangianmethodologies such as the release and recapture of magnetically attractive particles (Hrycik et al.,2013) offer comparable frameworks to track dispersion but require further development and test-ing on larger space and time scales. This lack of measurements highlights the utility of low-costLagrangian solutions where the financial risk is reduced and no single instrument is pivotal to themonitoring scheme.In this chapter we describe a simple neutrally buoyant satellite-tracked float that has been de-veloped to measure subsurface dispersion in coastal regions at a material cost of $300 CAD perunit. The initial motivation for the float design outlined here is the need to better understand flowsin a layer at depths of 50–200 m in the Strait of Georgia, Canada (Chapter 2), but similar prob-lems also arise in other regions where shipping traffic can result in a significant risk factor forunattended deployment of Lagrangian instruments with a surface element, and where layered flowsexist but bathymetry is complex enough that current meters are not always useful [e.g. the Gulfof St. Lawrence (Han et al., 1999); the Chiloe´ Inner Sea (Strub et al., 2019); and a number ofother fjordal areas worldwide]. Our floats share some passing similarities with the original Swal-low floats, thus they are nicknamed “Swallow-ish”, or Swish floats. Here, we describe the Swishfloat design, including the construction materials, engineering principles, and testing of the float43components; outline the equations that govern a float’s behavior during its drift period; discuss theballasting procedure; and present the results from initial deployments in coastal waters to evaluateperformance and provide examples of possible analysis techniques.3.2 ObjectivesThe scientific goal of the Swish floats is to measure subsurface flows for periods up to one month.To achieve this, the floats must sink to a pre-selected depth following deployment at a known timeand location, after which they become neutrally buoyant and drift with the prevailing currents for aperiod of time. Although their location is not known during this drift, after a predetermined time thefloats then jettison a sufficient mass to become positively buoyant, resurface, and automatically be-gin reporting a Global Navigation Satellite System (GNSS) derived position via a satellite telemetrylink. The exactly known time and distance between the deployment and the resurfacing positionprovides a single point Lagrangian displacement that is the integral of the in-situ motions whiledrifting at depth (Rossby and Dorson, 1983). The neutral buoyancy depth of the float during itsdrift is dependent on a number of factors, including the float mass, the float volume (which variesdynamically throughout the deployment due to in-situ pressure and temperature changes), and theprevailing isopycnal structure of the water column. The floats can be treated as single-use expend-able instruments or, in certain circumstances, can be recovered once resurfaced—in this case, thefloats can be used as a limited platform for internally-recording sensors and/or reused. Secondarily,the floats provide a surface trajectory upon resurfacing until the GNSS unit loses power.To achieve this scientific goal, the following design criteria were specified:1. Affordability and ease of construction: To increase the statistical significance of drift mea-surements, it is desirable to maximize the number of measurements and minimize the expenseassociated with collecting these measurements. Thus, at every level of the design process,simplicity of manufacturing and reduction of cost is emphasized. This specification promotesthe use of off-the-shelf components that require limited customization by the user.2. Reliability of operation: The float housing must be watertight and resistant to crushing attarget pressures; large enough to accommodate the necessary electronics; relatively resistantto the elastic effects of pressure and thermal expansion; resistant to plastic deformation; andresistant to corrosion. The resurfacing mechanism must be reliable and configurable for avariety of time-scales.3. Accuracy of positioning: As the primary goal of this float is to consider transport on time-scales up to one month, a simple point-to-point measure of float travel between deploymentat time = t and resurfacing at t +∆t suffices for a basic understanding of the advective anddiffusive characteristics of a system. This geolocation is easily achieved using commercially44available GNSS units. To allow GNSS communication, the float must be permeable to electro-magnetic radiation and the surface expression of the float must be upright with an appropriatefreeboard to ensure that the GNSS unit is orientated skyward with an unobstructed view.3.3 Design3.3.1 GNSS UnitThe modern technology that allows us to economically communicate the surface positions of thefloats is the now widely-available commercial asset tracker. These trackers can return position dataat frequent intervals using either cell-phone or low-earth-orbit satellite systems. We use the SPOTTrace (SPOT LLC, Louisiana) at a cost of $150–$190 CAD per unit. The units have a waterproofcase, are powered internally by 4 lithium AA cells, are 7× 5× 2 cm in size, and weigh 90 g. Theseunits have been tested at length during various surface drifter experiments (Pawlowicz et al., 2019;Blanken et al., 2020; Pawlowicz, 2021) and have been found to transmit reliable GNSS locations viathe Globalstar constellation of low-Earth-orbit satellites with lifetimes of weeks to months. Testson land find that the standard error in positions from the units is about 2.5 m, uncorrelated fromfix to fix. The systems can be programmed to transmit every 5, 10, 30, or 60 minutes and arevibration activated, meaning that they cannot be mounted in a stationary manner: to combat this,the GNSS units are hung by a line within the housing (see schematic in Fig. 3.1) so that when thefloat surfaces, the wave-induced movement of the hanging unit causes vibration due to contact withthe hull, activating the system. The units can be programmed for increased sensitivity to furtherpromote activation; however, in calm sea states, the GNSS transmission can pause for periods untilsurface wave energy increases sufficiently to reactivate the unit. The surface battery life of theseunits suggests that no significant activation occurs while the floats are submerged.3.3.2 HousingThe float housing design consists of a cylindrical hull and watertight top and bottom endcaps (Figure3.1). The hull must be wide enough to contain the satellite communications package and longenough relative to its width to float upright when surfaced. This requires a hull with an insidewidth of about 10 cm for our asset tracker, and thus a length of at least 50 cm or so (i.e., a volumeof about 5 L), as an initial size specification. In addition, the float must be sufficiently rigid sothat it will not crumple at the pressures of interest, nor should its volume decrease too quicklyunder pressure loading so that it would sink to the bottom uncrumpled. The compressibility of avolume V of seawater due to changes in pressure P is about γw = −4.4×10−6 dbar−1 and so floatcompressibilities should be of that magnitude or less. However, we also want to reduce materialcosts as much as possible, which implies that optimal design properties will tend towards to greatest4510.1664.460.8 2:17PVC endcapLarge D-ringsGTR Drop weightMonofilament lineBridleTD sensorSmall D-ringsAluminum endcapLatch mechanismGNSSunitInternal ballastO-ringgrooveFigure 3.1: Swish float schematic with coarse measurements (cm; 2:1 vertical to horizontalscale; left), an internal view of a float (center); and a labeled photograph of two Swishfloats (right). Note the previously deployed and recovered red float (rightmost) with aTemperature-Depth (TD) sensor attachment and a bridle to aid recovery from a vessel.compressibility within that design range.To select materials with the appropriate properties, we first approximate the overall compress-ibility of a float by calculating the contribution of each external component to the compressibility. Ifwe class the float cylindrical hull as an intermediate length thin-walled pressure vessel (i.e. length-/radius= 0.1–100 and radius≫hull wall thickness) we can model its pressure response as follows(ASME BPVC.VIII.1-2019, 2019):εΘ =−PREt(1− υ2)(3.1)εz =−PREt(12−υ)(3.2)γ =− 1Vh∂Vh∂P= 2εΘ+ εz =−PREt(5−4υ2)(3.3)46where γ is the compressibility coefficient, Vh is the hull volume, P is the external pressure; εΘ is thehoop strain; εz is the longitudinal stress; and R, t, E and υ are the radius, wall thickness, Young’smodulus, and Poisson ratio of the hull material, respectively.The endcap contribution to a float’s compressibility will be due primarily to the inward bowingof the endcaps due to axial stress. If we class the endcap as a simply-supported thin circular plateunder a uniformly distributed load (Budynas and Sadegh, 2020), we can model the endcap inwarddisplacement as a function of pressure. While the endcaps do not perfectly meet the criteria of a thinplate (i.e. thickness/radius=<0.1), this basic approximation will suffice for a first order estimate ofendcap bowing. For a cylinder of height H and an endcap with thickness l, we can integrate thecircular plate displacement function to obtain the endcap contribution to compressibility by inwardbowing as follows:γe =116PE(1−υ)(5+υ) R4Hl3(3.4)In addition to having an adequate rigidity, we also need to make sure that catastrophic failurewill not occur at the pressures of interest. The first mode of failure in a cylindrical pressure vesselwill likely be due to an elastic buckling of the hull (Sharp, 1981). We can calculate the bucklingpressure of the hull to determine the maximum useful depth of the floats:Pb =2E1−υ2( t2R)3(3.5)It has been shown in pressure tank experiments that this theoretical buckling pressure Pb of cylin-drical hulls is a good estimate of actual buckling pressure (Ikeda et al., 2013).After considering and testing various options, we decided to construct the hull from aluminum6061 T6 alloy tubing. This material was chosen as it is, among other things: inexpensive, purchasedfrom a local supplier for $13 CAD per foot; easily machined; easily sealed using a simple plugstyle endcap; relatively incompressible; and relatively resistant to mass changes due to corrosion(D’Asaro, 2003). While borosillicate glass is sometimes favored for float construction (Rossby andDorson, 1983) it is significantly more expensive, costing (locally) almost eight times as much asan aluminum tube of similar dimensions. Plastics such as polyvinyl chloride (PVC) and acryloni-trile butadiene (ABS) are less expensive materials that are sometimes used for low-cost pressurehousings; however, the relatively high compressibility of cylindrical hulls constructed from thesematerials in easily available thicknesses precludes them from being an effective float hull material,even though they may be resistant to buckling.A H=61 cm (2’; where appropriate, design measurements will be provided with imperial con-versions in parentheses as these are often the standard units used in purchasing and manufacturing)length of aluminum tube was chosen as roughly the shortest—and therefore least expensive andeasiest to handle—hull that could float upright at the surface. Note that we specify “tube” here as47opposed to “pipe”, the difference being that tubing has a lower manufacturing tolerance on the outerdiameter (OD), which we found more suitable for design and machining purposes, whereas pipe hasa lower tolerance on the inner diameter (ID).From Equations 3.1–3.3, it follows that doubling the hull wall thickness t or halving the hull ra-dius R will halve the hull compressibility γ; thus, it is necessary to carefully select the wall thicknessand the radius of the hull to construct a float with the desired compressibility. Although a number ofstandard wall thickness options ranging from t = 0.12–1.9 cm are available for tubes of this diame-ter, a wall thickness of t = 0.32 cm (1/8”) was judged to be best for the hulls. While thicker-walledtubes would produce less compressible hulls (which, as we show below, would have some advan-tages), we found hulls with t > 0.32 cm to be harder to machine in addition to being more expensive.The narrowest standard-sized t = 0.32 cm aluminum tube available from local suppliers that couldaccommodate the GNSS unit was nominally listed with an OD= 10.2 cm (4”) and an ID= 9.9 cm.Per Equations 3.1–3.3, a hull with r = 5.1 cm, t = 0.32 cm, E = 69×105 Pa, and υ = 0.33 (mate-rial properties from Dexter, 1972) yields an estimated compressibility of γ = −4.3×10−6 dbar−1.These hulls have a volume Vh = 4.93× 10−3m3 and a buckling pressure Pb = 472 dbar, which issufficient for most coastal regions.Flanged plug-style endcaps were manufactured with a recessed o-ring on the inner flange toseal the float and to allow endcap expansion without cracking or leaking (see Fig. 3.1). The topendcap is a 3.3 cm (1.3”) thick, 10.2 cm (4”) OD plug constructed from PVC and firmly seatedusing a latch mechanism composed of steel wire and a small hose clamp. It is constructed fromPVC to allow GNSS signal transmission through the housing. The bottom endcap is a 3.3 cm (1.3”)thick, 10.2 cm (4”) OD aluminum plug which is glued permanently shut upon assembly, designedto bolster the incompressibility of the float. For the PVC top endcap, γet = −0.2× 10−6 dbar−1,while for the aluminum bottom endcap, γeb = −0.001× 10−6 dbar−1. This gives a cumulativecompressibility for the Swish float of γd = γ+γet +γeb =−4.5×10−6 dbar−1, whereby the endcapscontribution to the overall compressibility is 5%. Note that the floats are slightly more compressiblethan seawater (γd < γw); we shall show that in a stably stratified ocean, this compressibility will besufficient to achieve neutral buoyancy. We provide an engineering drawing of a Swish float housingat https://doi.org/10.5281/zenodo.8173038.3.3.3 Pressure testingWhile Section 3.3.2 provides an approximation of housing compression and buckling that aids inthe selection of construction materials, it is beneficial for accurate ballasting to directly measurethese properties. To achieve this, the float housings were tested in a pressure tank facility wherethey were pressurized to at least 250 dbar to a) ensure the structural integrity of the floats to oceandepths of at least 240 m and b) to directly measure housing compressibility γm to compare with γd .We can directly measure the coefficient γm of a float by pressurizing the housing and measuring the48internal pressure (Pi) and absolute temperature (Ti) response as a function of external pressure (Pe).These measurements were achieved using an Arduino BMP180 sensor placed inside the housing.Assuming the air inside the cylinder behaves as an ideal gas, we can approximate γm for smalldisplacements as follows:γm =1V∂V∂Pe≈ 1∆Pe(∆TiTi− ∆PiPi)(3.6)The volume response and γm of eight individual housings were measured by pressure cycling eachhousing between 30 and 250–300 dbar a number of times (Fig. 3.2). The coefficient γm ranged from−8.0× 10−6 dbar−1 to −4.3× 10−6 dbar−1 with a mean of γm = −6.4× 10−6 dbar−1, which is30% larger than γd . As a directly measured property of the float housing, we use γm as the overallfloat compressibility during ballasting and analysis: note that this choice is supported by empiricalevidence from float deployments, whereby the use of γm in ballasting calculations produces accuratedepth targeting for the floats. The exact cause of the difference between γm and γd is unclear,though may relate to some property of the float construction that has not been accounted for inthe compressibility calculations, such as the seating of the endcaps when exposured to pressure.For example, our tests show that the latch mechanism, which acts to more completely seat the topendcap, increases γm by 0.3×10−6 dbar−1 (i.e. makes the float less compressible). This indicatesthat endcap and o-ring seating might play a role in the compressibility of the float that is not yetaccounted for in Equations 3.1–3.3.3.3.4 Thermal expansionOne drawback of using metal for the float hull, in contrast to glass, is that it can undergo significantthermal expansion. Thus, we must also consider the change in float volume due to this thermal ex-pansion. Making direct measurements of a float’s thermal expansion coefficient (α) is difficult; how-ever, the thermal expansion coefficient of a float can be estimated by taking the volume-weightedaverage of the coefficients of its external components:α =1V∂V∂T=Vaαa+VpαpVa+Vp(3.7)where T is the float temperature and subscripts a and p refer to the aluminum (αa = 71×10−6 °C−1)and PVC (αp = 80× 10−6 °C−1) components that are in direct contact with the water (Dexter,1972). For the Swish floats, this yields α = 71× 10−6 °C−1 which is functionally identical toαa due to the large proportion of external aluminum and similarity in αa and αp. The thermalexpansion coefficient of seawater varies primarily due to temperature, salinity, and pressure. For thewater masses targeted in this study, the thermal expansion coefficient of water would range fromα=40–100×10−6 °C−1 (Roquet et al., 2022).49Figure 3.2: Swish float compression a) measured in pressure tank experiments as a functionof external pressure Pe (blue scatter points); and b) derived γm.3.3.5 Release MechanismTo return the floats to the surface at the end of their deployments, a release mechanism is requiredto jettison the drop weight after a set duration. An inexpensive ($3–$15 CAD per unit), reliable,and commercially-available solution is a Galvanic Timed Release (GTR, also know as pop-up re-lease): a simple sacrificial link that consists of two plated cathodic wire eyes seated in preciselymachined anodic metal alloy cylinders (Fig. 3.1). Most commonly used for fishing, the GTRs elec-trochemically degrade when placed in seawater and release after a preset period of time that dependsprimarily on: a) the amount of galvanic material in the alloy cylinder, ranging in volume from VGT R0.4–4.8 cm3; and b) the temperature of the ambient water (warmer/cooler water increases/decreasesthe rate of degradation). The timescales of the GTRs range from 1 day to 30 days with a nominalmanufacturing accuracy of ±2.5% of the GTR duration. The drop weight weighs 1.1 kg and ismade from a 7.6 cm length of 5.1 cm diameter hot-rolled steel bar with a hole drilled through thecenter for ease of attachment; once released, the float will have significant positive buoyancy.Tests have shown that the in-situ lifespan of the GTRs is often less-predictable than the manu-facturer stated accuracy due to two main reasons: 1) the ambient water may be warmer or colderthan anticipated, decreasing or increasing the lifespan of a release, respectively; and 2) prior to de-ployment, the GTRs are submerged in saltwater for a varying amount of time during the ballasting50process, reducing the in-situ lifespan of the release.Another characteristic of the GTRs’ gradual loss of galvanic material is a reduction in the massof the float coupled with a loss in volume, and therefore buoyancy. The density of the galvanicalloy is approximately ρGT R = 1600 kg m−3, therefore any loss of galvanic material will increasethe buoyancy of the float. This buoyancy increase can be calculated as MGT R =VGT R(ρ−ρGT R) inseawater of density ρ , and ranges from 0.2 g for the shorter releases to 2.8 g for the longest releases.The consequences of this for float behavior will be discussed later.3.3.6 Other design featuresSome other important design features are listed here and labeled in Figure 3.1. Externally, stainlesssteel D-rings are fastened to the housing using a stainless steel hose clamp to serve as attachmentpoints and for ease of handling. The release mechanism is attached to these D-rings using nylonmono-filament line, which is then threaded through the drop weight and tied to a stainless steelwasher to secure the weight. The line is held in place on either side of the hull using two, smallerstainless steel D-rings attached with glue or epoxy resin.A handful of the floats were designed for recovery from a vessel—these floats are spray-paintedred for visibility, have a short wire lanyard and steel clip to attach small sensors, and have a 1.2 mlong wire bridle to aid recovery. Internally, a 7.6 cm long x 5.1 cm radius hot-rolled steel bar weigh-ing approximately 1.2 kg is glued to the inner-bottom endcap to ensure that the floats are upright atthe surface with the GNSS unit sufficiently above sea level when drifting; in this configuration, thefloats extend 18 cm above the waterline.3.4 Principles of operationAssuming conditions of hydrostatic balance, a float will be neutrally buoyant when its mass M f isequal to the mass Mt of water it displaces (Swift and Riser, 1994). This mass can be calculated asfollows:Mt(S,T,P) = M f (S,T,P) = M0+V0[ρc(S,T,P)−ρ0]+ρc(S,T,P)∆V (3.8)where P, S, T , and ρc(S,T,P) are the in-situ pressure, salinity, temperature, and density of theambient water and∆V =V0 [α(T0−T )+ γP] (3.9)The required float mass Equation 3.8 consists of three terms: the first term is the mass of afloat M0 that is neutrally buoyant in a fluid with density ρ0 (say, in a ballasting tank). The secondterm applies a mass correction using the initial float volume V0 and the difference between theballasting density ρ0 and the in-situ ocean density ρc(S,T,P) to correct the mass (note that small51corrections to these density terms are discussed in the following section). The third term is a furthermass correction that uses the ballasting tank temperature T0 and the in-situ ocean temperature Tand pressure P to account for in-situ changes in float volume ∆V due to compression and thermalexpansion. To implement Equation 3.8, the user must first ballast the float to neutral buoyancyin a fluid of known density, have an understanding of the compressibility and thermal expansionproperties of the float (previously discussed in Sections 3.3.2, 3.3.3, and 3.3.4), and have priorknowledge of the temperature and density structure of the body of water into which the floats arebeing deployed.3.5 Ballasting3.5.1 Ballasting tankPractically implementing Equation 3.8 requires careful ballasting of a float in a tank of fluid. As weshall discuss below, a target accuracy for this ballasting process is about 0.1 kg m−3. In contrast,the accuracy of ballasting calculations for ocean gliders are typically an order of magnitude lessstringent as they have a buoyancy engine which can change their effective density by O(10) kg m−3.The floats are ballasted using a 227 liter (60 gallon) drum of freshwater that is tuned to density closeto that of the target ocean by the addition of Instant Ocean, a readily available aquarium-grade saltmixture that dissolves freely into the freshwater. The temperature T0 and conductivity of the tankare measured frequently during the ballasting process using a CTD and tank density is derived fromthese measurements.As the ionic composition of Instant Ocean does not perfectly mimic that of seawater, its densitywill differ from that of seawater of the same conductivity. In the language of the TEOS-10 standard(Thermodynamic Equation of Seawater 2010; IOC et al., 2010) there is a Salinity Anomaly δSA,associated with the density error (δρA) that occurs when ρ0 is calculated from CTD measurementsusing formulas that are associated with the specific chemical composition of seawater. One problemwith commercial salt preparations is that their composition can vary widely (Atkinson et al., 1997)and so their TEOS-10 Salinity Anomaly is not known. Thus, we supplement the frequent CTDmeasurements by direct density determinations using an Anton Paar DMA 5000 benchtop densitymeter to measure the density of water samples to an accuracy of ±0.01 kg m−3. The differencebetween these measurements is used to calculate a calibration term ∆ρ . This term is applied tocalibrate the tank density ρ0 = ρ ′+∆ρ , where ρ ′ is the measured tank density.We found that this calibration varies not only between different commercial preparations, butalso between seawaters made from different batches of the same product. Seawaters made fromone batch had a measured salinity anomaly δSA= 0.05 g kg−1 leading to a density error δρA =0.04 kg m−3, whereas the seawaters made from a second batch had a salinity anomaly δSA =−0.20 g kg−1 leading to a density error δρA = −0.15 kg m−3. This indicates that δρA should be52measured regularly.In addition to inaccurate conductivity-derived densities of the ballast tank seawater, note that theTEOS-10 salinity anomaly for seawater in coastal areas may also be non-zero. Calculated valuesvary from δρA=0.02 kg m−3 to 0.3 kg m−3 in different coastal regions (Pawlowicz, 2015). A densityanomaly correction term δρA=0.02 kg m−3 was calculated for both the Strait of Georgia and Gulf ofSt. Lawrence intermediate waters based on the ionic composition of the relevant water masses andapplied to the in-situ density term ρc(S,T,P) = ρ ′(S,T,P)+δρA, where ρ ′(S,T,P) is the measuredor estimated in-situ density.Finally, experience has shown that tuning tank density ρ0 close to target density ρc(S,T,P)is desirable, as any error present in the ballasting procedure will scale as ρc(S,T,P)-ρ0 increases.Thus, it is necessary to use an artificial seawater in this procedure, in spite of the extra complicationsthat ensue, over using (say) tap water for which an equation of state is known to much greaterprecision.3.5.2 Ballasting procedureTo roughly ballast the float, links of small gauge scrap chain are added until the float mass is ap-proximately 5.5 kg. The float is placed into the ballasting tank and the mass of the float in water(Mw) is fine-tuned using 4.5 mm caliber steel ball-bearing (BB) pellets weighing 0.35± 0.003 geach until the float is neutrally buoyant within the tank. This neutral buoyancy point is identified byhanging the float from the underfloor weighing mechanism of a balance and iteratively adding/re-moving ballast inside the float until the float is approximately neutrally buoyant (i.e. the float weightin water Mw = 0). To confirm the neutral buoyancy point, a calibration mass of known weight inwater (Mc) is placed on top of the float; the float ballasting is complete when the combined weightMw +Mc = Mc. Note that using a balance with an readability of order 0.01 g enables the user todistinguish the differences in ballasting due to individual BB pellets.Then, the float mass in air M′ is measured with a benchtop balance with a readability of 0.5 g.To determine M0, a mass correction ∆M must be applied to M′ to account for the buoyancy of thefloat in air. This correction is ∆M =Vρa where V = 5.22×10−3 m3 is the approximate float volumebased on the geometry and ρa = 1.18 kg m−3 is the approximate density of air in the ballasting labbased on temperature and pressure measurements; the correction ∆M accounts for approximately6.2 g difference between the weight and the mass of the float.Once neutral buoyancy has been achieved, the float volume V0 is calculated as follows:V0 =(M′+∆M)(ρ ′+∆ρ)=M0ρ0(3.10)Equation 3.10 uses the calibrated float mass M0 ballasted to neutral buoyancy in a tank of water ofcalibrated density ρ0 to calculate the float volume V0 in laboratory conditions. We can then employ53Equations 3.8 and 3.9 to calculated the final mass of the float M f for a target depth with salinity S,temperature T , pressure P, and density ρc(S,T,P), taking into account the in-situ compressive andthermal affects on volume ∆V (Equation 3.9). Per Equations 3.8–3.9, we require the in-situ densityρc(S,T,P) and temperature T to ballast the floats in the laboratory. We can estimate these propertiesusing a recent CTD cast from the deployment region; in the case that no such data is available, weestimate ρc(S,T,P) from local property climatologies or from regional models.In practice, however, we have found that climatologies are often not accurate enough to revealthe actual water properties at deployment to sufficient precision. Thus, to fine-tune the ballastingto the in-situ physical conditions, a final ballasting step occurs whereby a CTD profile is collectedimmediately before a deployment, M f is recalculated, and the ballast is adjusted according to theinstantaneous in-situ measurements of ρc(S,T,P) and T . These mass adjustments can be made on-the-fly without the use of scales or balances by adjusting the number of steel BB pellets in the floathousing. In our deployments so far we have found that the final adjustments are generally <2 g,depending on the accuracy of estimates of ρc(S,T,P) and temperature T .3.6 Uncertainty budgetErrors in ballasting will be manifested as a mistargeting of the float in depth (Swift and Riser,1994). When preparing a float to target a certain depth, it is important to understand how theerrors will propagate in the ballasting Equations 3.8, 3.9, and 3.10 and the impact it will have ondepth targeting. To evaluate the impact of these errors, we calculated an uncertainty budget using aMonte-Carlo resampling error analysis (Table 3.1).For this analysis, we assigned uncertainty to terms in the ballasting equations; these uncertaintylimits are defined by the accuracy of our laboratory measurement techniques, the precision of ourmeasurement equipment, and the manufacturing tolerances of the hull. Equations 3.8, 3.9, and 3.10were then computed 1000 times with simulated errors in each of the terms to calculate the 95%confidence limits for our determinations of float mass and density. Doing this, we can isolate theimpact of uncertainty in each individual parameter in addition to combined uncertainty. Finally,we used an average layer density stratification from our two test regions—the Strait of Georgiaintermediate waters (0.11 kg m−3 per 10 m depth between 50–200 m; Chapter 2) and the Gulf ofSt. Lawrence intermediate waters (0.18 kg m−3 per 10 m depth between 50–150 m; Galbraith et al.,2022)—to estimate the neutral buoyancy depth variability based on this uncertainty.The uncertainty budget yields global 95% confidence limits of ±1.60 g and ±0.30 kg m−3, re-sulting in a neutral buoyancy depth variability of ± ∼29 m for the Strait of Georgia and ± ∼16 mfor the Gulf of St. Lawrence. The largest contributor to the global float error stems from the de-termination of V0. To produce the uncertainty estimate for V0, we determined V0 thirty times usingEquation 3.10 for three different floats while varying tank density ρ0 and calculated two standarddeviations (coverage factor k = 2) as an estimate for a 95% confidence limit for this determination54Table 3.1: Uncertainty budget for depth targeting. Uncertainties are presented with a coveragefactor k = 2 (Ridler et al., 2007), which corresponds to a 95% confidence interval assum-ing a normal distribution of the measurement errors. Depth uncertainty is provided for theintermediate waters of the Strait of Georgia (dρ/dz = 0.11 kg m−3 10 m−1) and the Gulfof St. Lawrence in parentheses (dρ/dz = 0.18 kg m−3 10 m−1).Parameter Explanation Value M uncertainty ρ uncertainty Depth uncertainty(g) (kg m−3) (m)M0 Benchtop scale 5508 g ±0.29 ±0.05 ±5 (3)readabilityρAP Anton Paar DMA 5000 1023.5 kg m−3 ±0.06 ±0.01 ±1 (1)readabilityV0 Observed error in 5380.6 cm−3 ±1.43 ±0.27 ±25 (15)volume determinationT0 Float internal 21.5 °C ±0.45 ±0.08 ±8 (5)temperature variabilityγm Manufacturing tolerance 4.9×10−6 dbar−1 ±0.49 ±0.09 ±9 (5)in hull r and tGlobal uncertainty Combined - ±1.60 ±0.30 ±29 (16)uncertainty(Table 3.1). While a small portion of this error originates from the laboratory measurements ofM0 and ρ0, a seemingly larger portion originates from uncontrolled uncertainty in the ballastingprocess.A likely origin of this uncertainty could be the trapping and formation of air bubbles around thehull. Various other studies (Rossby and Dorson, 1983; D’Asaro et al., 1996) have found that thepresence of air bubbles trapped and/or formed on float external components can cause a buoyancyvariability of a few grams, which would have a significant and unpredictable impact on determiningV0 that we are unable to control for using the simple ballasting setup described in Section 3.5.Other significant sources of uncertainty stem from temperature of the float in the ballasting tank, inwhich the T0 can lag ambient laboratory temperature by approximately 1 °C, and the manufacturingtolerances of the aluminum hull (OD=1.3 mm, t=0.3 mm), which produces uncertainty in γ .As discussed in Section 3.3.5, the GTRs degrade during the course of a deployment, causinga float to gain buoyancy and impacting its neutral buoyancy depth. The degradation of the shorterreleases used in the Strait of Georgia (one-day to one-week) result in a 0.2–0.9 g buoyancy gain anda shallowing of the neutral buoyancy depth of 3–15 m. The longer one-month release employed inthe Gulf of St. Lawrence, however, will cause a 2.8 g buoyancy gain and a shallowing of the neutralbuoyancy depth of 29 m.553.7 Float behaviorNeutrally buoyant floats can be classed as isopycnal or isobaric. This classification refers to theequilibrium surface on which they will settle: an ideal isopycnal float would settle and remain on aspecified density surface, whereas an ideal isobaric float would do the same on a specified pressuresurface. Upon reaching the equilibrium surface, three factors can cause the floats to deviate fromthat surface: internal waves, water mass changes, and variations in float mass (Garfield et al., 1999).The response of a float to any of these three forcings will depend on its physical properties and thebackground stratification, therefore we must have a strong understanding of these to understand afloat’s behavior during a deployment.In a coordinate system where z is the vertical position taken as positive upward, we can definethe position of a float at rest on an equilibrium surface as Ze = Z f = Zρ , where Ze is the depth ofthe equilibrium surface, Z f is the depth of the float, and Zρ is the depth of the equilibrium isopycnalρe. The response of a float to vertical perturbations from this equilibrium surface can be defined bythe ratio r = εr/εw, which compares the vertical displacement of the float from ρe (εr = Zρ −Z f )to the vertical displacement of ρe from the equilibrium depth (εw = Zρ −Ze). Following Goodmanand Levine (1990), we can then define r:r = εr/εw ≈ (1− s)/(1− s+N 2) (3.11)N =Ncg−1(3.12)N =√− gρdρdz(3.13)Here, s= γm/γw is a ratio comparing the float compressibility to the compressibility of seawater (γw)andN is the local buoyancy frequency N normalized by cg−1 ≈ 3.8 cyc h−1, where c is the speed ofsound in water and g = 9.81 m2 s−1 is acceleration due to gravity. From this formulation it followsthat a perfectly isopycnal float with an identical compressiblity to seawater would have the samevertical displacement as the isopycnal displacement (s = 1, r = 0) whereas a perfectly isobaric floatthat is much more incompressible than seawater would remain at the equilibrium depth regardlessof the isopycnal displacement (s≈ 0, r = 1). Floats that are more compressible than seawater (s> 1,r < 0) will have vertical displacements which are amplifications of isopycnal displacements.In the deployment regions of this study, the floats typically have a response ratio ranging fromr≈-0.5 to r≈-0.2 suggesting that the floats will behave isopycnally with a vertical response that willbe an amplification of isopycnal displacements. This strong response to isopycnal displacementsoccurs largely due to the slightly higher compressibillity of the floats compared to seawater (s =1.45), meaning that the floats will compress more than the ambient seawater for any given verticaldisplacement.56In more homogeneous water masses, there is a risk that the background density gradient δρ/δ zis less than the float density gradient δρ f /δ z. We can define a stability criterion:δρδ z>δρ f (S,T,P)δ z(3.14)whereδρ f (S,T,P)δ z=1Mt∆Vδ z=1MtV0[α(T0−T )+ γP]δ z(3.15)In water columns where this stability criterion is met, a float will be stable on an equilibrium surfacei.e. it will return to an equilibrium surface following vertical perturbations from that surface. Ifthe stability criterion is not achieved float will become unstable, resulting in uncontrolled verticalexcursions of the float from its intended target depth; the practical consequences of an unstablefloat is that it sinks to the seafloor. Therefore the stability criterion must be taken into accountwhen considering which regions and water masses to target and in the case that the user wouldtarget a surface where the float might become unstable, measures should be taken to reduce thecompressibillity of the float by, for example, increasing the wall thickness.3.8 Ocean PerformanceHere we present data from initial Swish float deployments to evaluate their performance and illus-trate the types of data that the floats produce.3.8.1 DeploymentsThree deployments of between four and six floats occurred in the Strait of Georgia, British Columbia,Canada in June (D1), July (D2), and September (D3) 2022 to initiate an ongoing regional monitor-ing program (Fig. 3.3). These floats were deployed immediately offshore of a large wastewatertreatment outfall with the goal of tracking pollutant dispersal in the regional intermediate layer(50–200 m depth; Chapter 2). The release mechanisms had timescales of one to five days and alldeployed floats resurfaced successfully. The GNSS units were configured to transmit every 5 or 10minutes—upon resurfacing, the units had an average battery life of 8.5 days. Of the 15 floats de-ployed in total, seven were designed to be retrieved at the surface and were recovered with a 100%success rate. Four of the remaining floats were found and returned by members of the public on thebeaches of British Columbia; this 50% recovery rate from public findings (to date) is in line withrecovery rates from surface drifter programs in the region (Pawlowicz et al., 2019).Additionally, to test the long-term reliability of the floats on scales up to a month, two deploy-ments of eight and six floats occurred in the Gulf of St. Lawrence, Canada in October 2021 and June2022, respectively (Fig. 3.4). These floats targeted the 110 m isobath to track the initial dispersionof a chemical tracer. The release mechanisms had timescales of one to five weeks and all but one57Figure 3.3: Results from the Strait of Georgia float deployments. In a) circular markers rep-resent pop-up positions colored by resurfacing timescale t, the gold pentagram the de-ployment site. As a point of reference, grey lines represent 24 hours of surface driftertrajectories passing within 2 km of the deployment site (Pawlowicz, 2021). Inset b) is azoomed out map of the region and c) is a histogram of float resurfacing timescales t.of the deployed floats resurfaced successfully. The GNSS units were configured to transmit everyhour and had an average post-resurfacing battery life of 12 days. Of the 14 floats deployed, fourwere recovered on beaches and returned by the general public; all four of the recovered floats wererecovered from the second deployment (Fig. 3.4c) on the Newfoundland coastline.Figure 3.5 shows average climatological temperature and density properties of the two deploy-ment regions of this study and indicates in which portions of the water column the stability criterion(Equation 3.14) is achieved. In the intermediate layers of the deployment regions, the floats are gen-erally stable due to the relatively large density stratification; this stability criterion was met in all ofthe above deployments for the targeted water masses and is only not achieved at intermediate depths58Figure 3.4: Results from the Gulf of St. Lawrence float deployments. Map a) is a map of thedeployment region where the black and grey pentagrams are the deployment sites of twodeployments in October 2021 and June 2022, respectively. In c) and d), circular markersrepresent pop-up positions and grey contours are bathymetry; b) and e) are histograms offloat resurfacing timescales t (all are colored by resurfacing timescale t).59Figure 3.5: Mean climatological ρ (solid black lines) and temperature (red lines) profiles froma) the Strait of Georgia and b) the Gulf of St. Lawrence; shading represents ±σ limits.Dashed black lines are the variation in float density ρ f throughout water column calcu-lated from Equation 3.15 for a typical float ballasted for neutral buoyancy at z=100 m;shading represents ±2σ limits based on variability of temperature profiles in the clima-tologies. Dotted black lines show the density of a water parcel with the volume of a typi-cal float, a constant compressiblity coefficient of γw =−4.4×10−6 dbar−1, and a variablethermal expansion coefficient calculated for the water column (αw ≈ 1.5× 10−4 °C−1).Grey shaded regions of the profile depicts depth ranges where the stability criterion(Equation 3.14) is not achieved (δρ/δ z <δρ f /δ z), un-shaded depth ranges depict whereit is achieved (δρ/δ z >δρ f /δ z).in 2.0% of the historical Strait of Georgia profiles and 0.8% of the historical Gulf of St. Lawrenceprofiles.3.8.2 Neutral buoyancyTo evaluate the in-situ depth variability, a handful of the floats deployed in the Strait of Georgia(Fig. 3.3) were equipped with Star-Oddi DST milliTD external temperature-depth (TD) sensors torecord depth every 30 seconds (Fig. 3.6). These 12 g, 13 mm x 39 mm (length x diameter) sensorswere attached prior to ballasting and are internally-logging, accurate to ±0.3 m, and have an onlinebattery life of >1 year. Note that as the sensors have a small volume (∼ 16 ml, or 0.3% of floatvolume) and are contained in a very incompressible ceramic housing, their contribution to float600 5 10 15 20 25dt (hours)050100150200Depth (m)a)0 15 30 45 60dt (minutes)b)Figure 3.6: Strait of Georgia float depth records from the June (D1; black), July (D2; blue),and September (D3; red) 2022 deployments. “Pair A” are denoted by darker red lines.Pink circles denote initial neutral buoyancy depth of floats. Plot a) shows the entire driftdepth record, plot b) shows only the initial descent.compression and thermal expansion is assumed to be negligible.The time and space variability of the isopycnal field experienced by each float will impactneutral buoyancy depth during its drift period; however, the initial neutral buoyancy depth of afloat (pink circles in Fig. 3.6) is a good indicator of ballasting accuracy due to the in-situ ballastingstep that fine-tunes a float’s ballast to the water column properties at the time of deployment. Theinitial neutral buoyancy depths of the six floats fitted with sensors range from 75–152 m, withan average depth of z=109 m and a standard deviation zσ =27 m. The floats deployed in D1 had awide range of neutral buoyancy depths (black lines in Fig. 3.6); following some improvements in theballasting protocol, neutral buoyancy depths in deployments D2 and D3 were less variable (z=103 m,zσ =20 m). These deviations in depth are comparable to the calculated uncertainty budget error fordepth targeting in the Strait of Georgia (29 m; Section 3.6 and Table 3.1), suggesting that we haveidentified the significant error sources. Further, this level of depth targeting error is comparable toother float designs that do not feature buoyancy compensating mechanisms (Garfield et al., 1999).In addition to monitoring the buoyant depth, these records also provide information on thefall and rise rate of the floats. The average float descent rate ranges from 1.6 m minute−1 to3.2 m minute−1, taking anywhere between 24 to 53 minutes to reach neutral buoyancy (Fig. 3.6b),while the ascent rate ranges from 39 m minute−1 to 73 m minute−1. It should be noted that rela-tively strong surface currents will be integrated into the Lagrangian displacement of a float, almostentirely during the slower descent period. The slowest sinking float cumulatively spends 6 minutesof its 22 hour deployment in the upper 20 m (Fig. 3.6b)—for an upper limit estimate of this effect, astrong 1 m s−1 current in the surface 20 m would displace this float 360 m, which is small comparedto the kilometer/tens of kilometers scale resurfacing displacements seen in these deployments.613.8.3 Lateral motionThe positions of the floats when they resurface can provide information about the dispersive char-acteristics of a system. Here, we achieve this for the Strait of Georgia intermediate water massby aggregating the individual deployments (Fig. 3.3) into a single dataset to represent the averagetransport and dispersion over the three-month observation period. To achieve this, we use single andmultiple particle statistics to calculate dispersion in both absolute and relative terms [see LaCasce(2008) for a detailed overview of Lagrangian statistics].We can calculate the mean float drift speed of n floats to provide an advection speed for thetargeted water mass:U =1nn∑i=1[xi− x0ti](3.16)where xi are the resurfacing positions and x0 is the deployment site and ti is the time interval untilsurfacing. The float drift speeds range from 1.9–7.9 cm sec−1 with U=5.2 cm sec−1 (or 4.5 kmday−1; Fig. 3.7), which is consistent with observed and modeled current speeds in the same region(Chapter 2).The relative dispersion describes the mean square separation of float pairs over time. As theresurfacing periods of the floats are not identical, we can match any floats that resurface withtimescales t± 12 hours. A limit of ±12 hours was imposed to roughly reflect the observed uncer-tainty in GTR lifespan, whereby floats with GTRs of the same nominal timescale would generallysurface within this limit. There are 43 unique pairs that fit this criterion. We calculate the relativedispersion:D(t) =12N(N−1)∑i ̸= j|xi(t)− x j(t)|2 (3.17)Here, t is the average resurfacing timescale of the float pair and i and j are the float indices. Therelative diffusivity is then:K =12ddtD (3.18)If we assume the diffusivity K is constant, the root-mean-square separation distance D shouldincrease with time t as√2Kt (Fig. 3.7b; Pawlowicz, 2021). The separation of the floats yieldsa relative diffusivity of K = 170 m2 s−1, which is comparable to diffusivities of 88–167 m2 s−1estimated from Lagrangian simulations of the regional intermediate water mass (Section 2.3.7).Certain float pairs deployed at the same time do not separate as much as expected (Fig. 3.7b).One such pair is “Pair A” (purple diamond on Fig. 3.7b) which were neutrally buoyant on approx-imately the same isobar (thick red lines in Fig. 3.6) and had a separation of 1 km after 22 hours,yielding a separation rate of just 1.2 cm s−1. It has been found that floats that equilibrate on the62Figure 3.7: Lateral transport of floats. In a), circular grey points show the float resurfacingdistances. In b), circular grey points show the separation distances of float pairs.√2Ktis also depicted for various values of K: K=170 m2 s−1 from the Swish floats are plottedwith 95% confidence limits (grey shading); K=88–167 m2 s−1 is the intermediate layerdiffusivity estimated in Section 2.3.7; and K=4000–7000 m2 s−1 is the surface layerdiffusivity estimated by Pawlowicz et al. (2019), plotted for reference.same equilibrium surface tend to stay closer together due to a reduced influence of vertical shear(Rossby et al., 2021). Pair A, along with other pairs that fall well below the predicted separationrate, are likely examples of this reduced isopycnal dispersion.3.8.4 Vertical motionVertical velocities in the ocean interior are typically very small compared to the horizontal velocityfield. Neutrally buoyant floats offer an ideal platform to observe these small vertical motions bymaintaining a near-zero relative horizontal velocity with respect to the surrounding water, effec-tively isolating the weaker vertical component of motion from the much larger horizontal velocities63(Rossby, 2007). When a float is on an equilibrium surface, dynamic vertical forcing is largely theresult of isopycnal displacement by the internal wave field (Goodman and Levine, 1990). The re-sponse of a float to this isopycnal displacement is determined by both the float properties and thewater properties (see Section 3.7). With a full description of these properties, it is possible to usethe vertical displacement of a float as a reliable measure of the internal wave field, including mo-tions such as diurnal and semidiurnal internal tides, near-inertial motions, and nonlinear internalwaves. Measuring an internal wave field directly through high-resolution observations of verticaldisplacements, as demonstrated here, is uncommon, as neutrally buoyant instruments are not oftenable to sample and/or telemeter their vertical position at high enough frequencies to resolve the fullbandwidth of the internal wave spectrum.The Garrett–Munk (GM) spectrum (Garrett and Munk, 1972, 1975) is an effective descriptionof the oceanic internal wave field that we can compare to the vertical motions of the Swish floats(Fig. 3.8). The GM model predicts that spectral energy will be concentrated in a band betweena lower limit of f = 2Ωsinϕ , the local Coriolis forcing frequency, and an upper limit of N, thelocal buoyancy frequency. The natural frequency of a float (ω0; Goodman and Levine, 1990),which represents the oscillation frequency of a float derived from the buoyancy force per unit massbetween the float and the water surrounding the float, is as follows:ω20 = N2+ρt +g2(γw− γm)−gα(dTdz)(3.19)As the float is isopycnal (Section 3.7), we expect its natural frequency ω0 to be approximatelyequal to the local buoyancy frequency N. We analyze three depth signals from D3 (red lines inFig. 3.6) using a multitaper power spectral density method to provide a power spectrum of verticaldisplacement (Fig. 3.8). Additionally, we can approximate the internal wave spectrum using theGM76 model (calculated using the GM MATLAB toolbox). The float spectra, which agree wellwith the GM76 estimate in spite of the coastal environment, are red in the frequency band betweenf = 0.06 cyc hr−1 and ω0 = 6.13 cyc hr−1. The local buoyancy frequency is N = 5.99 cyc hr−1(dot-dash line on Fig. 3.8). At higher frequencies the spectra rapidly fall through four orders ofmagnitude to the instrument noise floor. The float spectra from D1 and D2 (not shown) have similaragreement with their respective GM76 spectra.These results suggest that the Swish floats are able to measure the full dynamic range of inter-nal waves. They can achieve this as their response ratio is r < 0 (Section 3.7). This characteristicmeans that the floats will amplify isopycnal displacements, allowing them to capture weaker ver-tical motions effectively. Further, due to their isopycnal behavior, their natural frequency ω0 willapproximately match the local neutral buoyancy frequency N. Thus, the floats will respond in neardynamic equilibrium to the internal wave forcing frequencies band bounded by f and N (Goodmanand Levine, 1990).64Figure 3.8: This figure shows three float frequency spectra from D3 (grey lines) in cycles perhour (CPH). The dashed line is the GM76 spectrum, the solid vertical line is f and thedot-dashed vertical line is N.3.8.5 Surface trajectoriesWhile the primary goal of the Swish floats is to measure subsurface dispersion, upon resurfacingthe floats also produce a dataset of surface trajectories (Figs. 3.9a and 3.9c) which can be used toprovide information on surface transport characteristics. As the Swish floats are not designed to beoptimal Lagrangian surface followers, it is important to first evaluate how effectively a Swish floatfollows surface currents. This is necessary as drifters are subject to processes such as wind slip(Niiler and Paduan, 1995) and Stokes drift (Curcic et al., 2016) that influence their trajectories.A simple technique to evaluate the efficacy of a surface drifter is to calculate the drag ratio Rd ofthe drogued (i.e. submerged) cross-sectional area to the area above-water components (Niiler et al.,1995). The Swish floats have a drag ratio of Rd = 2.6; this is not dissimilar to some early undrogueddrifter designs (Niiler and Paduan, 1995), but falls well short of the canonical Rd = 40 ratio thatdefines a “modern drifter” (Niller et al., 1987). To improve the surface following capabilities of theSwish floats, future designs could feasibly incorporate a drogue to increase the underwater surfacearea of the float.While the Swish floats do not represent perfectly Lagrangian surface followers, they can stillprovide information on dispersion processes at the surface (Fig. 3.9b). Pair separation can be calcu-lated from Strait of Georgia float pairs that resurface from the same deployment with a timescale of±12 hours, which reflects the uncertainty of the the GTR lengths—the average separation distanceupon resurfacing from the 11 pairs matching this criteria is 2.3 km. Note that, given the separationlength and time scales reported for Strait of Georgia surface drifters, this pairing criteria is deemedstringent enough to ensure that resurfacing float pairs are initially in close proximity and will diverge65roughly as expected based on surface drifter pair separation (Pawlowicz et al., 2019).We can again employ Equations 3.17 and 3.18 to calculate surface relative dispersion and diffu-sivity, where t is equal to time since resurfacing. As all three deployments are used in this analysis,properties derived here are representative mean conditions over the June–September 2023 deploy-ment period. These surface-float pairs yield an absolute diffusivity of 2×104 m2 s−1 over the first24 hours post-resurfacing, which is smaller but comparable to absolute diffusivity measurementsfrom surface drifters in the same region (4–7×104 m2 s−1; Pawlowicz et al., 2019). The differencein surface diffusivity measured by the Swish flats compared to the surface drifters can likely beattributed to the different surface following characteristics of the two designs. Furthermore, thereare no float surface separation observations over winter during periods where separation rates mightbe enhanced.3.9 ConclusionsThe data presented in Section 3.8 suggest that Swish floats are an effective tool to investigate a num-ber of ocean processes in coastal regions. We have shown that the floats can be used as expendableunits to track lateral dispersion (Section 3.8.3), or as a limited platform for small internally-loggingsensors to acquire additional datasets and quality control information (Section 3.8.2 and 3.8.4), al-though the requirement for recovery does preclude such floats from being expendable instruments.The simplicity of the design means that the floats can be constructed from easily-available ma-terials; require limited resources to manufacture; can be assembled by hand; can be ballasted in anyworkshop that can house a 227 liter (60 gallon) drum; and can be easily deployed from most vessels.These properties might make them attractive to other researchers in coastal areas or enclosed bodiesof water such as estuaries or fjords—indeed, much of the initial Swish float testing was performedwith some success in the smaller fjords of British Columbia, Canada, proving that these instru-ments could feasibly be deployed in areas where the usage of more expensive Lagrangian floats isimpractical.However, this design simplicity does come with certain concessions, some significant ones be-ing the lack of a compensating mechanism to improve depth targeting via automated adjustmentsin the float volume/buoyancy, some form of underwater positioning that would enable the floats torecord subsurface trajectories (though this might be affordably achieved in certain regions by incor-porating recently available “fish-chip” hydrophones; Fischer et al., 2017), or a telemetry frameworkto transfer data collected by the float.Most importantly, these datasets can be acquired at a fraction of the cost of other float plat-forms—for context, the average material cost after building several dozen Swish floats is about$300 CAD each, compared to the $20,000 USD cost of a single profiling float in the Argo program(https://argo.ucsd.edu/faq). In this regard, Swish floats provide an opportunity to collect informa-tion in a similar fashion to any number of increasingly-common expendable surface drifter studies66Figure 3.9: Surface trajectories from resurfaced floats in a) the Strait of Georgia and c) theGulf of St. Lawrence; scatter points along trajectories represent the GNSS position fixes.Inset plot b) is surface separation of Strait of Georgia float pairs that resurface <3 kmfrom one another (grey lines) and separation√2KRt using average diffusivity for the first24 hrs KR =∼2312 m2 s−1 (black dashed line).67(e.g. Novelli et al., 2017; Pawlowicz et al., 2019; Page et al., 2019; van Sebille et al., 2021), wherelarge numbers of Lagrangian measurements are used to study regional kinematics.A number of deployments have occurred since September 2022 that are not detailed here. Dur-ing the course of these deployments, some operational improvements were enacted to assess ambientstratification and improve the likelihood of a successful deployment—these changes are detailed inAppendix C.68Chapter 4TReX Deep: a SF5CF3 tracer releaseexperiment in the Gulf of St. LawrenceDuring the development of the “Swish floats” (Chapter 3), I was invited on a research cruise aspart of the Gulf of St. Lawrence Deep Tracer Release Experiment (TReX). The goal of the cruisewas to survey the spread of an inert chemical tracer that had been injected into the Gulf eightmonths prior. My role on this survey was twofold: firstly, to deploy a handful of Swish floatsinto the core of the spreading tracer; and secondly, to map the tracer in real-time to help directthe survey sampling scheme. In performing this role, I produced initial tracer maps, spreadingrate estimates, and tracer inventories, and as a result of this effort was given the opportunityto synthesize these results into a study detailing the initial spreading of the tracer. This chapterconstitutes the results of that study, a version of which is currently in review.4.1 IntroductionThe Gulf of St. Lawrence drains over 25% of the world’s freshwater reserves into the western NorthAtlantic through its connection to the Great Lakes of North America and is increasingly exposed tonatural and human-induced drivers of environmental change (Beauchesne et al., 2020; Long et al.,2016). These changes include a notable shift in source water composition (Gilbert et al., 2005),leading to a Gulf-wide reduction in bottom water dissolved oxygen content (Jutras et al., 2020,2023). This hypoxia is approaching critical levels in some locations with adverse health effects forvarious organisms (e.g. Dupont-Prinet et al., 2013; Brown-Vuillemin et al., 2022). Given a 3–7 yearup-estuary transit time (Bugden, 1988; Gilbert, 2004), the full ramifications of this deoxygenationsignal on the St. Lawrence Estuary may not be evident until the coming years. Considering thatthe Gulf is a busy shipping region, further concerns surround the possibility of widespread contam-ination following marine traffic incidents. Currently, the specific transport pathways and timescales69that would allow for an accurate assessment of subsurface contaminant or inflow spreading are notwell defined.Obtaining accurate measurements of dispersion at depth can be challenging (Chapter 2). Oneresource-intensive but effective method of studying long-term dispersion involves releasing an inertchemical tracer and then tracking its movement (Ledwell et al., 1986). While surface tracer releaseexperiments often use short-lived fluorescent dyes to measure dispersion (e.g. Sundermeyer andLedwell, 2001; Shcherbina et al., 2015) subsurface dispersion studies generally inject and monitorlong-lived inert compounds (Ledwell et al., 1998; Ho et al., 2008; Ledwell et al., 2011; Banyteet al., 2012). Subsurface tracer release experiments in the Baltic (BATRE Holtermann et al., 2012)and Gulf of Mexico (Ledwell et al., 2016) have provided valuable insight into subsurface transporttimescales and the impact of basin geometry on spreading and mixing in inland basins.Here, we report initial results from the Gulf of St. Lawrence Deep Tracer Release Experiment(TReX Deep), the first deepwater tracer release experiment conducted in the Gulf of St. Lawrenceand one of the first in any marginal sea globally. This experiment aimed to track inflow transportpathways by spiking inflowing water at the entrance to the Gulf in October 2021 with trifluoromethylsulfur pentafluoride (SF5CF3) and subsequently measuring this tracer spread via ship surveys 8 and12 months later. The experiment also utilized neutrally buoyant “Swish floats” (Chapter 3) as aLagrangian measurement of dispersion. Along with tracer inventories, a comprehensive biogeo-chemical catalog of the system was obtained in order to relate transport processes to changes inother chemical parameters of interest. Our observations reveal the tracer flowing shoreward andalso undergoing significant lateral spreading. We also assess the variation in vertical tracer distri-bution to gauge spatially varying mixing processes, finding evidence that diapycnal mixing alongthe slopes plays an important role in basin-wide vertical transport. Finally, we predict the futureevolution of this tracer in the Gulf to motivate and inform future sampling efforts.4.2 Methods4.2.1 Study RegionThe Gulf of St. Lawrence is a collection of straits and channels in eastern North America (Fig. 4.1a)with an estuarine circulation driven primarily by outflow from the St. Lawrence River (Dickie andTrites, 1983). The estuary is relatively shallow until Tadoussac, after which it abruptly deepens intothe 1240 km long Laurentian Channel with depths greater than 300 m. This channel then emptiesinto the North Atlantic Ocean primarily through Cabot Strait via a 500 m deep channel that cutsthrough the shelf offshore. Belle Isle Strait to the north, which also links with the North Atlantic, isonly 50 m deep with limited flow.70Figure 4.1: a) and b) Gulf of St. Lawrence maps with sampling stations; Leg 1 stations are blue, Leg 2 stations are red, x’s represent“boundary” stations (h < 279 m), and triangles represent “interior” stations (h > 279 m). The tracer injection site is markedby a violet star and the black contour lines represents the injection depth z=279 m bathymetric contour. Abbreviated locationnames are: TS, Tadoussac; QC, Quebec; AI; Anticosti Island; CS, Cabot Strait, NL, Newfoundland; BI, Belle Ille Strait. TheLower St. Lawrence Estuary (LSLE), immediately east of Tadoussac, is shaded gray. c) Hydrographic conditions from a CTDprofile immediately following the tracer injection: red represents temperature (°C), blue is salinity (g kg−1), black is oxygen(µmol kg−1). Shading represents the 95% confidence limits of a October–December climatological hydrography from the AtlanticZone Monitoring Program (AZMP) in the eastern Gulf between 1998 and 2022. d) Histogram showing the number of samples indifferent z∗ =10 m depth bins from Leg-1 and Leg-2 cruises.71Within the Laurentian Channel, the deep circulation has a broad cyclonic tendency (Han et al.,1999). The Atlantic inflow core can be identified by an oxygen minimum at 200-250m in verticalprofiles (Fig. 4.1c). Climatological estuarine inflow rates at Cabot Strait range from 0.1 Sv insummer to 0.5 Sv in winter (Galbraith et al., 2019) and model simulations suggest a mean residencetime (defined as the average lifetime of a water parcel in the Gulf) of about 1 year (Rutherfordand Fennel, 2018). The surface outflow occurs in the upper 50 m, and between the fresher surfacelayer and the deep water is a Cold Intermediate Layer (CIL), formed locally during winter mixing(Smith et al., 2006). Diapycnal mixing rates derived from microstructure measurements near theLower St. Lawrence Estuary vary spatially, increasing from O(10−5) m2 s−1 in the basin interiorto O(10−4) m2 s−1 in the bottom boundary layer (Cyr et al., 2011) due to internal tide mixing (Cyret al., 2015).4.2.2 Gulf Of St. Lawrence Deep Tracer Release Experiment (TReX Deep), 2021On October 25th, 2021, the tracer was injected using GEOMAR’s Ocean Tracer Injection System(OTIS, initially developed at the Woods Hole Oceanographic Institution; Ledwell and Watson, 1991)as part of a larger multi-investigator Tracer Release Experiment (TReX) which took place over2021-2022 at various locations in the St. Lawrence estuary. The OTIS was towed behind the R/VCoriolis II at ∼1 knot while injecting a liquid emulsion of tracer through two 25 µm orifices. TheSF5CF3 tracer is a stable, non-toxic compound with a very low detection limit and an undetectablebackground concentration that has been used in various open ocean (e.g. Banyte et al., 2012) andmarginal sea (e.g. Holtermann et al., 2012; Ledwell et al., 2016) experiments. The target isopyncalwas σθ=27.26 kg m−3 at a depth of about 280 m.Two follow-up tracer surveys have been conducted to date. As winter ice cover from Decemberthrough April restricts sampling, the first mission (Leg 1) was a dedicated tracer survey that tookplace from June 12th to 22nd, 2022, collecting 515 samples at 111 stations with an average stationspacing of ∼15 km (Fig. 4.2a). Vertical sampling was concentrated ∼ ±50–100 m around the in-jection isopycnal to maximize the number of tracer-labeled samples (Fig. 4.1d). Tracer sampleswere collected with a CTD rosette into 200 ml glass syringes. Samples were analyzed on-boardusing a custom purge and trap gas chromatography system, which was standardized using a com-mercially prepared gaseous SF5CF3 standard. The analytical precision was 0.06 fmol kg−1 with aquantification limit of 0.1 fmol kg−1.The second survey (Leg 2), occurred from October 25th to November 10th, 2022, as part ofa Canada Department of Fisheries and Oceans Atlantic Zone Monitoring Program (AZMP) cruise,collecting 225 samples at 51 stations with an average station spacing of∼31 km. (Fig. 4.2d). Tracersample analysis protocols were identical to Leg 1. Due to logistical restrictions associated withthe standard monitoring program, the sampling resolution was reduced laterally and vertically incomparison to the first survey, though a larger geographic area was surveyed. The vertical resolution72Figure 4.2: Interpolated maps of vertically integrated observed tracer I j from a) Leg 1 at8 months after injection; and d) Leg 2 at 12 months after injection. The gold star rep-resents the tracer injection site, the dashed gray line represents the Anticosti Island-Newfoundland transect, gray x’s represent “boundary” stations (h < 279 m), and graytriangles represent “interior” stations (h > 279 m). Swish float were deployed at the in-jection site during Leg 1 [gold star in a)] and in the Laurentian Channel during Leg 2[black cross in d)]. Float resurfacing locations are marked with black circles; circle di-ameter is a representation of flat resurfacing time, ranging from 6 days (largest diameter)to 36 days (smallest diameter). Leg 1 b) and Leg 2 c) vertical profiles of SF5CF3 concen-trations in z∗ coordinates, solid gray lines and axes are hypsographies A(z∗) in m2 anddashed gray line is A0e−z∗/L. e) Leg 1 and f) Leg 2 are profiles of SF5CF3 concentrationsin σθ coordinates; the black dotted lines represent the injection surface.of samples was fairly uniform throughout the tracer labelled area of the water column, howeversampling below 300 m was limited (Fig. 4.1d).A total of 14 neutrally buoyant expendable “Swish floats” were deployed with resurfacingtimescales of one to six weeks during the deployment and Leg 1 cruises [Fig. 4.2a and d; seeChapter 3 for a description of float characteristics]. Each float provides a single point Lagrangiandisplacement at a low cost and were used to confirm trends in advection and horizontal diffusion.The precise equilibrium depth of the expendable floats is not known; however, given the float bal-73lasting and prevailing stratification, post-cruise analysis suggests that neutral buoyancy occurred at∼150 m.4.2.3 Data Analysis MethodsIn a bounded region such as the Gulf, isopycnal surfaces may have significant variability in depth.When measuring tracer concentration c(z), it is important to note that the concentration at a givendepth will be a combination of mixing processes, which are irreversible and the target of our ob-servations, and isopycnal heaving, which is reversible and unrelated to mixing, and a steady stateresponse balance with geostrophic flow, which is also reversible and unrelated to mixing. Thus, toremove effects of vertical displacements from heaving and large-scale changes in isopycnal depth,tracer measurements at a depth z associated with a measured potential density σθ (z) are mappedonto a new vertical coordinate z∗ (positive downwards) which defines the location of that densityin a reference profile σθ (z) using σθ (z) = σθ (z∗)Ledwell et al. (1998); Holtermann et al. (2012);Banyte et al. (2012). This reference profile is the mean potential density profile from observationsin the vicinity of Cabot Strait, the deepest area of the basin containing the densest water, and is cho-sen to encompass the entire density and depth range for all possible observations in the Gulf. Themapping generally results in vertical adjustments of less than 20 m. We grid the observations usinga log-linear interpolation scheme to create vertical tracer profiles c j(z∗) with 1 m vertical spacing,where c is concentration of SF5CF3 and j is an index of our individual tracer profiles. The log-linearinterpolation scheme used here is a linear interpolation scheme applied to concentration values inlog(c) coordinate space to reduce the effects of changes in curvature at the edges of the distribution.Then, to assess the lateral spreading of the tracer, we first calculate vertical integrals of the tracerprofiles from both surveys as follows:I j =∫ z∗tz∗bc j(z∗) dz∗ (4.1)where z∗b=400 m and z∗t =1 m. Kriging (Davis, 2002) is used to produce horizontally interpolated I jfields (Fig. 4.2a and d). Areas outside of the survey area were removed from the interpolated fields.Following Ledwell and Watson (1991), we create normalized, weight-averaged tracer profilesr(zˆ) to compare the vertical tracer distribution between surveys, where zˆ is a vertical coordinatereferenced to the injection depth zˆ = z∗−279 m. This normalization is achieved using the relation:r(z∗) =∑jWjc j(z∗)I j(4.2)whereWj =I j∑ j I j(4.3)is a weighting function using the integrated amount of observed tracer. We use a bootstrap resam-74pling technique to compute uncertainty ranges for r by creating 1000 random subsets of our profilesand calculating two standard deviations (coverage factor of k = 2; Ridler et al., 2007) to representthe 95% confidence intervals of the mean tracer profile.Finally, following Holtermann et al. (2012) [adapted from Ledwell and Watson (1991)], wecalculate tracer mass inventories (M) for the two surveys by integrating the mean sampled tracerconcentration c(z∗) over the area of the Gulf:M =∫ z∗sz∗bc(z∗)A(z∗) dz∗ (4.4)where basin geometry is accounted for with the hypsographic relationship for basin area A(z∗) (Figs.4.2b,c).To visualize Gulf-wide tracer distributions from the irregularly spaced stations, we used a krig-ing interpolation method (Davis, 2002) to estimate the I j fields. The interpolation occurs over auniform horizontal grid of data points with a spacing of 1.5 km × 1.5 km covering the the deep-water areas of the Gulf. Ordinary kriging is used with a spherical model for the semi-variogramof the derived data set and a best-fit algorithm was used to derive the coefficients of the covariancefunction. The range coefficient derived from this best fit algorithm for all interpolations was ∼135km; no nugget effect was applied. We then map the interpolated tracer fields to evaluate the 2Dspatial expression of the circulation (Fig. 4.2). Note that these interpolated fields are solely used inthe visualizations of Figure 4.2, and not in any analysis of the tracer spreading.4.3 Results4.3.1 ObservationsInjectionThe SF5CF3 tracer was injected in a 4.8 km streak in the northeastward region of Cabot Strait(Fig. 4.1a). Two independently logging frame-mounted conductivity-temperature-depth (CTD)sensors confirmed that the injection occurred at the target isopycnal σθ=27.26 kg m−3 within a±0.016 kg m−3 interval (two standard deviations to represent the 95% confidence intervals, ap-proximately equivalent to 3 m), centered at a depth z=279 m. Due to a fault in the OTIS pumpingcontrol, the exact amount of tracer injected is unclear. Initially, the OTIS was filled with 4.5 kg ofSF5CF3; however, a rupture in one of the pump’s internal membranes allowed tracer to escape intothe atmosphere, significantly reducing the actual amount injected. As we shall show, our tracer massbudgets estimate that subsequent tracer surveys found between 50g to 100g of SF5CF3. Despite thisdiscrepancy, we observed no evidence of tracer at depths other than the target depth aside from thatproduced by mixing and were able to calculate realistic spreading rates from the tracer observations.75u (cm s−1) κh (m2 s−1)Lateral Tracer MotionLeg 1 0.53±0.16 227±91Leg 2 0.50±0.23 157±67u⃗ f (cm s−1) κ f (m2 s−1)Lateral Float MotionInjection 2.1±0.9 480±167*Leg 1 1.7±1.2w (m yr−1) κv (m2 s−1)Vertical Tracer MotionLeg 1 9.5 1.0×10−5Leg 2 6.5 1.2×10−5Table 4.1: Summary of advection-diffusion characteristics. Note that κ f (marked with an as-terisk) is a mean value derived from injection and Leg 1 float deployments. Lateral tracermotion uncertainties are expressed as two standard deviations of the tracer spreading anal-yses to represent the 95% confidence interval (coverage factor k = 2) assigned from abootstrap analysis; lateral float motion uncertainties are the standard deviation (coveragefactor of k = 1) for u⃗ f and κ f calculated via bootstrap analyses; vertical tracer motionuncertainty is assigned in Section 4.3.2.Post-injection CTD measurements confirmed typical November conditions in Cabot Strait (Fig.4.1c). As proposed, the tracer effectively labeled the inflow waters of Atlantic origin, as indicatedby its location in the core of the oxygen minimum. Shorter timescale Swish floats deployed duringthe injection cruise resurfaced mainly around the injection site within a few weeks (Fig. 4.2a).However, floats with resurfacing timescales of a month or longer traveled northward with a meanspeed of 2.1 cm s−1, indicating the initial direction of post-injection transport was as expected basedon the general cyclonic circulation of the region.Leg 1 (8 Months Post-Injection)After the first winter, the tracer core was located 100 km northwest of the injection site in theLaurentian Channel (I j = 4×104 fmol m−2; Fig. 4.2a and 4.3a). The Leg 1 inventory (Eq. 4.4) wasM1 = 70.9 g, mostly in the Laurentian Channel; only 0.9 g was found in the northern Gulf region (i.e.north of the gray line in Fig. 4.2a). Isolated tracer patches were found at 300 km and 420 km alongthe channel and moderately high concentrations were observed near the entrance to Cabot Strait,suggesting some tracer may have exited the Gulf since injection via deepwater transport along thesouth side of the Laurentian Channel’s assumed cyclonic circulation. Swish floats deployed duringLeg 1 consistently resurfaced westward further into the Laurentian Channel at speeds of 1.7 cm s−1(Fig. 4.2d), suggesting that the tracer would continue to spread along-channel.The maximum measured SF5CF3 concentration was 0.75 fmol kg−1 at z∗ ≈290 m, equivalent76Figure 4.3: Vertical tracer dispersion. Map a) shows the vertical distribution of tracer fromseven regional subsets c j(z∗), where the different regions (denoted by subscript j) wereselected using a K-mean clustering algorithm. The geographical extent of each subsetis represented on the map as translucent, colored shapes and displayed on each regionis c j(z∗) tracer depth profile showing Leg 1 (blue) and Leg 2 (red) observations com-piled from the corresponding area, with the injection depth marked with a black dashedline. Also included are normalized regional hypsographies A˜ j(z) (black lines). The nor-malization involves initially shifting regional hypsographies A j(z) so that its minimumvalue becomes zero, computed as A j(z)−min(A j(z)). This shifted data is then scaledto a range between 0 and 1, achieved by dividing by the range of the original data,max(A j(z))−min(A j(z)). The final step involves scaling this normalized data to alignwith the scale of the tracer concentration profiles c j(z∗), resulting in the normalized hyp-sographies A˜ j(z). Plot b) shows profiles of normalized tracer concentration r(zˆ) [dashedlines] from Leg 1 (blue) and Leg 2 (red) with error bars denoting two standard deviationsderived from a bootstrapping analysis (coverage factor k = 2). The wide, solid curvesshow the best fitting advection-diffusion model solutions based on the optimization rou-tine explained in Section 4.3.2: note that the fitting regime for Leg 2 is weighted toemphasize the fit through the well-sampled shallow region zˆ < 0 m (see Eq. 4.10). Plotc) is similar to b), showing r and best-fitting 1D model solutions for boundary (green)and interior (teal) regions; (Leg 1 is plotted with lighter curves, Leg 2 with darker curves)77to σθ ≈27.29 kg m−3 (Fig. 4.2b and e). During Leg 1, a majority of the tracer appears concentratedin the interior of the Laurentian Channel (Fig. 4.2a). The vertical distribution was approximatelyGaussian with a full width at half maximum (FWHM) of 60 m in z∗ coordinates and 0.2 kg m−3in σθ coordinates. Spatial variations in vertical tracer distribution were observed (blue points inFig. 4.3a); notably, enhanced tracer spreading and shoaling was observed north of the injection sitewhere depths are generally shallower than the injection depth.Leg 2 (12 Months Post-Injection)The tracer core was observed near the injection site (I j=3.1×104 fmol m−2), but extended furtherwest along the Laurentian Channel, consistent with the Leg 1 Swish float trajectories (Fig. 4.2d).In spite of the different spatial distribution, the Leg 2 inventory was similar to that of Leg-1 withM2 =60.9 g, primarily (> 59 g) in the Laurentian Channel. Moderate tracer concentrations in CabotStrait (I j=1.4×104 fmol m−2) suggest some eastward motion may be occurring. However, the smalldecrease in total inventory is likely within the uncertainty of our estimates, given the availablespatial sampling. Therefore, the potential for deep-water advective loss appears to be minimal overthe duration of our experiment to date.The peak of the Leg 2 vertical distribution was observed at z∗ ≈290 m, σθ ≈27.29 kg m−3at a concentration of 0.69 fmol kg−1 (Fig. 4.2e and f). The vertical distribution was well-resolvedbetween z∗=100 m (σθ=26.70 kg m−3) and z∗=320 m (σθ=27.35 kg m−3); however, due to samplinglimitations, the distribution was poorly-resolved at z∗ > 320 m and σθ >27.35 kg m−3. Assuminga Gaussian distribution, we can estimate an approximate FWHM of 110 m in z∗ coordinates and0.34 kg m−3 in σθ coordinates. These values suggest increased overall vertical spreading sinceLeg 1, though again this vertical spreading was not spatially uniform, with limited spreading andshoaling occurring in Cabot Strait compared to other regions (Fig. 4.3a).4.3.2 Diffusivity AnalysisOur surveys show that tracer is mostly moving inshore, spreading both vertically and horizontally.To gain insight into these processes, we first interpret our observations using some simple advection-diffusion models, before considering the additional complications that arise from the relatively com-plex bathymetry of the region.Isopycnal SpreadingFirst, focusing on the Laurentian Channel where most tracer was found, we employ a 1D advection-diffusion “pipe” model to study along-channel isopycnal transport. This model is similar to the oneused in Section 2.3.1 for the Strait of Georgia:78∂c∂ t+∂∂x(uc−κh ∂c∂x) = 0 (4.5)where u is a constant advection velocity and κh is a constant horizontal diffusivity. In response toan initial “spike” input c j(t = 0) = Mδ (x), the tracer in this model spreads diffusively into a patchof width√2κht around an initial point that moves in time as x = ut:I′(x, t) =M√4πκhtexp(−(x−ut)24κht)1Y Mmρ(4.6)where x is distance in an along-channel coordinate system (Fig. 4.4), t is time since injection,Mm = 196.06 g mol−1 is the molar mass of SF5CF3, ρ = 1026 kg m3 is a density conversion factorfor the deep waters of the Laurentian Channel, and Y = 75 km is a channel width scale.We can then vary u and κh to find the best-fitting solution at known time t by minimizing thecost function:χ(x,κh,u) =∫ [I j(x)− I′(x)]2 dx (4.7)We use a bootstrap resampling technique to create 1000 random subsets of I j and apply the abovenonlinear optimization to find the best-fitting solution for each subset. From this subset of solutions,we express uncertainty as two standard deviations of u and κh using a coverage factor of k = 2 torepresent the 95% confidence interval(Ridler et al., 2007).Doing so results in estimated along-channel inflow advection velocities of u= 0.53±0.16 cm s−1and u = 0.50± 0.23 cm s−1, and horizontal diffusivities of κh = 227± 91 m2 s−1 and κh = 157±67 m2 s−1, as best estimates over the first 8 months and 12 months after the release, respectively (Ta-ble 4.1; Fig. 4.4b). As diffusivity estimates are scale-dependent (Richardson and Stommel, 1948),note that these estimates are on timescales of years and spatial scales of hundreds of kilometers.Some experimentation shows all dispersion estimates remain stable to moderate variations (±25%)in M. We can compare the tracer dispersion to the neutrally buoyant Swish floats by calculating floatdisplacement and pair separation (LaCasce, 2008). We estimate a float drift speed of ∼ 2 cm s−1and lateral relative diffusivity of ∼ 500 m2 s−1 (Table 4.1).Diapycnal SpreadingWhen analyzing tracer release experiments, it is often assumed that the vertical tracer distributionwill evolve according to a Gaussian spreading model, provided that vertical diffusivity is roughlyconstant throughout the tracer-labeled region of the water column (Ledwell et al., 1986; Ledwelland Watson, 1991). During the earlier stages of the TReX Deep experiment, we might expect themajority of the vertical mixing to be interior basin mixing associated with a nearly constant verticalmixing rate, leading to a vertical tracer distribution with a roughly Gaussian appearance; however,79Figure 4.4: Along-channel tracer dispersion. Map a) shows the Laurentian Channel stationsfrom Leg 1 (blue points) and Leg 2 (red points) and an along-channel axis with x= 50 kmspacing between points. Plot b) shows station-wise vertically integrated tracer I j as afunction of along-channel distance: blue is used for Leg 1 and red for Leg 2. The windowmean curves represent a 50 km overlapping window for Leg 1 and a 75 km overlappingwindow for Leg 2, both with a 50% overlap. In c), I j(x,t) is estimated from the 1D pipemodel (u, κh) and d) shows modeled I j(t) time-series at points along the channel [linecolors correspond to colored axis markers in a)].as the tracer spreads laterally, more tracer will reach the edges of the basin and be influenced byenhanced mixing rates in the bottom boundary layer, asymmetrically distorting the vertical tracerdistribution (Holtermann et al., 2012). Statistical tests suggest that the Leg 1 and Leg 2 distributionsare not strictly Gaussian, implying that a non-constant diffusivity, possibly as the result of boundarymixing effects. Nevertheless, the majority of the spreading measured during Leg 1 and, to a lesserdegree, Leg 2, follows a Gaussian spreading model, so we proceed by analyzing the vertical tracerdistributions from the standpoint of Gaussian spreading to gain first-order estimates into effectivevertical diffusivity in the Gulf.If we ignore horizontal dispersion and consider a vertical profile of tracer concentrations, weobserve the tracer spreading vertically (Fig. 4.2b, c, e, and f). It therefore seems reasonable touse a horizontally averaged vertical 1D advection-diffusion system to initially estimate the diapy-cnal diffusion at rate κv and vertical advection at a velocity w of a tracer c(zˆ, t) in a basin with ahypsography A(zˆ). The vertical velocity would model the effects of the estuarine upwelling.80An initial tracer distribution c(zˆ,0) = Mδ (zˆ)/A0 will then evolve diapycnally according to:∂ (cA)∂ t+∂∂ zˆ(wcA−κvA∂c∂ zˆ ) = 0 (4.8)To simplify, we assume vertical velocity w (positive downwards) and diffusivity κv are constant andthe hypsography near the injection depth can be parameterized as A(zˆ)≈ A0e−zˆ/L (dashed grey linesin Figs. 4.2b and c) for a scale depth L, where L is large for a steep basin, small for a wide shallowbasin, and is about 200 m for the Gulf (Fig. 4.2b); A0 corresponds to basin area at the injection depthA(zˆ = 0). By applying the transformation c(zˆ, t) = exp(−wt/L) c′(zˆ, t) and using standard methodsfor diffusion problems, we find a solution for the transformed concentration c′(zˆ, t) as follows:c′(zˆ, t) =MA0√4πκvtexp(−(zˆ−Wt)24κvt+wtL)(4.9)The concentration peak vertically shifts at speed W = w+ κv/L. With an upward estuarineadvection (negative w), the peak will also tend to move upwards if the basin has steep walls andweak diffusion (large L, small κv), but with more gradual slopes and greater diffusion (small L, largeκv), the peak of the tracer distribution peak may actually sink as the dilution due to the opening basinreduces concentrations above the injection. However, in this simplified system, the distribution willremain Gaussian, spreading into a vertical patch of thickness√2κvt; note that there will be anoverall decrease of the concentration at all levels due to the dilution that occurs as the tracer movesupwards into depth ranges with a greater surface area, with a time scale L/w.To estimate κv and w from our observations, we optimize our solution by minimizing the costfunction at known times t:χ(zˆ,κv,w) =∫ω[r′(zˆ)− r(zˆ)]2dzˆ (4.10)where r′ is c′ normalized by substituting model values into Eqs. 4.2–4.3. Here, an additionalweighting factor ω(zˆ) is defined as follows:ω(zˆ) =0 if n(zˆ) = 0,1 if n(zˆ)> 0,2 if n(zˆ)> 0 and zˆ < 0 (for Leg 2),where n(zˆ) is the histogram of observations at depth zˆ. This weighting emphasizes the fitting throughthe well-resolved shallow region for Leg 2.Through this approach we estimate a bulk basin-wide diapyncal diffusivity and vertical advec-tion velocity of κv = 1.0×10−5 m2 s−1 and w = 9.5 m yr−1 for Leg 1 and κv = 1.2×10−5 m2 s−1and w = 6.5 m yr−1 for Leg 2 (Table 4.1; Fig. 4.3b). The downward velocity is not due to the81dilution factor as κv/L ≈ 1.5 m yr−1—we shall consider the vertical advection signal and assignuncertainty to our estimates in the proceeding sections.Boundary And Interior SpreadingOur observations suggest that in areas where the target isopycnal approaches the seafloor, generallyobserved north of the Anticosti Island-Newfoundland transect, tracer has shoaled significantly incomparison to the deeper Laurentian Channel (Fig. 4.3). These values suggest that boundary regionsmay be undergoing a qualitatively different evolution due to mixing in a bottom boundary layer.Following Ledwell et al. (2016), we divide observations from each cruise into “boundary” and“interior” subsets to evaluate bottom boundary layer effects on vertical mixing. The boundary subsetincludes stations with I j > 0 fmol m−2 at a bathymetric depth h < 279 m, mainly north of theAnticosti Island-Newfoundland transect and on Laurentian Channel banks (x’s in Figs. 4.1a, 4.2aand d; 33 stations from Leg 1, 10 from Leg 2). The interior subset includes stations with I j >0 fmol m−2 at a bathymetric depth h > 279 m, mainly in the Laurentian Channel (triangles inFigs. 4.1a, 4.2a and d; 44 stations from Leg 1, 16 from Leg 2). Both subsets undergo the samenormalization and optimization as per Equations 4.2–4.10.Here, asymmetrical vertical spreading produces tracer profiles with significantly variable shapes(Fig. 4.3a and c). Due to this variability, any assignment of basin-wide uncertainty to κv will over-estimate the error in our 1D model fitting. Thus, we assign uncertainty to each of the boundary andinterior subsets individually using a bootstrap resampling technique to estimate uncertainty rangesfor the 1D model by creating 1000 random data subsets and calculating two standard deviations ofthese subsets (coverage factor of k = 2; Ridler et al., 2007) to attribute uncertainty to the modelcoefficients.Averaged over both legs, observations from the interior have a vertical (in the z∗ or zˆ coordinate)diffusivity of κin = 8.5± 3× 10−6 m2 s−1 and a positive (i.e. downwelling) advection velocity ofwin = 11.2±3 m yr−1, whereas observations from the boundary regions have a vertical diffusivityof κb = 1.5±1.0×10−5 m2 s−1 and a negative (i.e. upwelling) advection velocity of wb =−23.5±11 m yr−1 (Table 4.1; Fig. 4.3c). The increased uncertainty in the boundary subset reflects thevariable tracer profile shapes and reduced data coverage in these areas. However, these estimatessuggest that mixing and upwelling is enhanced in areas where the tracer contacts the slopes of thebasin.4.4 Discussion and ConclusionsIn October 2021, we injected an inert tracer into deep inflow waters entering the Gulf of St. Lawrencefrom the Atlantic. Measurements 9 and 12 months later show the tracer advecting inshore at speedsbroadly consistent with expectations during experiment planning, but also undergoing significant82dispersion that plays an important role in horizontal spreading. The tracer also exhibits down-ward vertical displacement and moderate dispersion in the basin interior with contrasting upwarddisplacement and enhanced dispersion along the boundaries. This observed vertical dispersion isconsiderably smaller than predictions from a high-resolution regional ocean model.A lateral diffusivity of ∼ 200 m2 s−1 aligns with that of subsurface layers in analogous basinssuch as the Baltic Sea (O[102 m2 s−1; Holtermann et al., 2012) and the Strait of Georgia (Section2.3.7; 88–167 m2 s−1). It is also consistent with apparent diffusivities in similar scale surface oceancirculation as described by Okubo (1971). Float dispersion exceeded that of the tracer, likely dueto vertical current shear and the floats’ shallower neutral buoyancy. Furthermore, given the floatdeployment locations and the Laurentian Channel’s mean cyclonic circulation (Han et al., 1999;Galbraith et al., 2019), larger float-derived currents are expected near the channel edges comparedto the more uniformly dispersed tracer (Fig. 4.2a and d). The predominance of lateral diffusivityover mean circulation observed here is an important confirmation of a characteristic recently notedin the Strait of Georgia (Chapter 2), with implications for the spread of contaminants in these coastalareas.The observed basin-wide diapycnal diffusivity of ∼ 1× 10−5 m2 s aligns with tracer measure-ments in the Baltic Sea (1–2×10−5 m2 s; Holtermann et al., 2012) and some larger-scale mid-depthocean experiments (1–1.5×10−5 m2 s; Ledwell et al., 2011; Banyte et al., 2012), but is betweenthree and thirty times smaller than measurements in various other basins and open-ocean mixinghot-spots (Ledwell and Watson, 1991; Ledwell et al., 2016; Rye et al., 2012; Watson et al., 2013).Our estimate also compares reasonably well with microstructure observations of 4×10−5 m2 s fromthe Lower St. Lawrence Estuary (Cyr et al., 2011), especially considering that these microstructuremeasurements were taken from an energetic mixing region (Cyr et al., 2015).Similar to other basins (Ledwell and Hickey, 1995; Ledwell et al., 2016), we observe local in-creases in diapycnal mixing rates near the basin slopes (Fig. 4.3a and c), likely due to turbulentprocesses in the bottom boundary layer (Polzin et al., 1997; Waterhouse et al., 2014) and isopyc-nal spreading through the boundary layer (Holtermann et al., 2012). Once the tracer contacts theboundary layer, diapycnal diffusivity increases dramatically (Ledwell and Bratkovich, 1995). InTReX Deep, horizontal tracer distributions suggest that initial contact with the basin slopes occuredat some point between the injection and Leg 1, indicating that boundary layer processes are indeedincorporated into our estimates of diapycnal diffusivity.Our observations also reveal asymmetrical vertical advection of the tracer in the Gulf, charac-terized by an interior deepening of the tracer peak and a shoaling peak near the basin boundaries(Fig. 4.3c). This result is contrary to the estuarine nature of the Gulf, where deep waters usuallyshoal. The magnitude of hypsographic correction κv/L is only a few meters per year, suggest-ing that diffusive processes alone cannot account for the interior deepening. The basin slopes areidentified as key upwelling regions and warrant close monitoring in future surveys. The simple83advection-diffusion models used here fall short of capturing this spatiotemporal complexity, indi-cating the need for models with space and/or time-varying diapycnal spreading parameters. Suchmodels could potentially account for the observed spreading asymmetry through a depth-variableκv, which may appear as a ”pseudo-velocity” that explains the upwelling or downwelling signal.Fortunately, opportunities still exist to understand this spreading in the Gulf using the 1D mod-els. To understand along-channel inflow dynamics, we can roughly simulate transport in the 1Dpipe model by substituting mean u and κh (Eq. 4.6). One point of regional interest is the inflowtransit time to the Lower St. Lawrence Estuary near Tadoussac, QC (Fig. 4.1; brown station atx=∼800 km on Figs. 4.4a and d), with current predictions ranging from 3 to 7 years (Bugden, 1988;Gilbert, 2004). Our simulations show tracer concentrations increasing at Tadoussac after approxi-mately 1.6 years and peaking at 4.0 years, with small amounts of dye still present up to a decadeafter injection (Fig. 4.4d). The 1D pipe model used here, while a basic approximation, could serveas a useful framework for modeling other tracers. Given the extensive TReX Deep measurementsof CFCs and key biogeochemical species, minor model adjustments to incorporate time-varyingboundary conditions (as in Section 2.3.1) and internal sources and sinks could yield realistic tracerdistribution approximations.Assuming stable Gulf dynamics over interannual timescales, we expect the tracer to spreadisopycnally through the Laurentian Channel, reaching Tadoussac by winter 2023 and peaking thereby late 2025. The tracer peak will sink and mix steadily in the interior of the basin; however,basin-wide diapycnal diffusivity may appear to increase as more tracer reaches the basin slopes andturbulent boundary layer processes further contribute to mixing and local shoaling of the tracer peak.Several factors, however, limit the certainty of these predictions, including the reduced samplingresolution of Leg 2 and the ambiguity around the initial tracer injection mass. For these reasons,continued monitoring of tracer spreading over the next few years are required to refine our mixingestimates; better understand and model the impacts of boundary mixing on the basin-wide transportof inflow waters; and provide a robust estimate of tracer flushing time, which will be vital in gaugingthe useful lifetime of this experiment.84Chapter 5ConclusionsThe research that I have presented here investigates the subsurface circulation of the Strait of Geor-gia and the Gulf of St. Lawrence, two distinct yet comparable areas of coastal ocean. To varyingdegrees in either region, I have outlined and quantified subsurface mixing dynamics, transit times,transit pathways, and advection speeds via observations, regional-scale model outputs, and simpli-fied 1D models. In the Strait of Georgia, this research depicts for the first time a mostly stochasticintermediate circulation with a few strongly expressed current features. In the Gulf of St. Lawrence,my findings refine the existing understanding of regional deep water circulation by providing a de-tailed quantification of lateral dispersion, transit times, and spatially-asymmetrical diapycnal mixingdynamics. One important thread in this research is that of the newly developed Swish floats, whichhave offered a useful Lagrangian measure of kinetics in both regions.The content of my PhD studies has stemmed from my involvement in various different projectsover the past six years. These projects have ranged in scope from smaller-scale local efforts with ahandful close collaborators (Chapter 2 and 3) through to larger, multi-investigator, multi-institutionstudies (Chapter 4). The wide-ranging goals of these projects, along with my interest in designingand building simple oceanographic instrumentation, has produced the broad exploration of inlandsubsurface circulation that has been presented in Chapters 2–4. In the following discussion, I willrevisit the initial research questions that were posed in Section 1.6 to guide this work, summarize mycontributions to the field, and make efforts to link the key findings of Chapters 2–4 into a cohesivenarrative.5.1 Addressing Research Questions and Contributions to the FieldRegional circulation: what are the main transport pathways and timescales of subsurface watermasses in the Strait of Georgia and the Gulf of St. Lawrence?• Strait of Georgia: intermediate waters form in Haro Strait and enter the southern basin of theStrait primarily via a boundary current along the eastern banks of the basin. This boundary85current transports water to the northern extent of the basin in one or two months, with someevidence of “leakage” from the boundary into the basin interior during transit. Upon reachingthe northern boundary of the basin, the water either recirculates along the western edge or istransported into the Strait’s northern basin, though the exact exchange mechanism betweenthe two basins is not clear. Overall, inflow transport to the northernmost extent of the Straittakes around four months at speeds of 1.5–2.0 cm s−1. The circulation of the northern basinis heavily influenced by the presence of a tidal jet emanating from Discovery Pass, whichmodifies the Haro Strait inflow.Particle tracking suggests that intermediate water tends to get mixed upward to the surfaceand that a large portion (∼20%) of the overall inflow vertical mixing occurs in Boundary Pass.Once the inflow leaves this region, the vertical flux of particles drops to a rate of around 0.2%day−1, as an average throughout the intermediate layer. Lateral intermediate layer diffusivitycalculated from these simulations is 88–167 m2 s−1.• Gulf of St. Lawrence: after entering the Gulf via Cabot Strait, the bulk mass of bottomwater is transported along the Laurentian Channel at a rate of around 0.5 cm s−1. Turbulentprocesses mix the inflow horizontally at a rate of approximately 200 m2 s−1 and vertically ata rate of 10−5 m2 s−1. When the inflow contacts the basin slopes, the water column’s verticaldiffusivity doubles and the center of mass shoals. Only a small volume of ventilating watersreach the northern extent of the Gulf, where vertical mixing and shoaling are relatively high.Method evaluation and development: what methods exist to effectively monitor subsurface flows ininterior seas, how do they inter-compare, and how can I further develop these techniques to providea more varied array of tools with which to observe subsurface circulation?• In the Strait of Georgia, a multi-method approach encompassing observations and modeloutput was employed to understand intermediate circulation. While this approach providedwide-ranging information on the circulation scheme in general, it had the additional benefitof allowing me to inter-compare the various methodological approaches.Tracer-based techniques offered insights into local advection and diffusion characteristics,but had limitations in separating these contributions. Eulerian velocity fields provided intu-itive maps of mean currents but required careful consideration of time variability. Lagrangiantechniques offered a comprehensive three-dimensional view but were complex to implement.Notably, the seasonality tracer, which was developed from a concept of LeBlond et al. (1991),emerged as a useful tool for mapping circulation and mixing in water masses with seasonalvariability. The inter-comparison between these methods led to a more rigorous characteriza-tion of the intermediate circulation and emphasized the benefits of a multi-method approach.86• The work of Chapters 2 and 3 represent some of the first published analysis of hydrographicdata from the Pacific Science Foundation Citizen Science Program (see https://sogdatacentre.ca/atlas/ for information; Pawlowicz et al., 2020). In this program, members of the publicvolunteer to gather hydrographic profiles from various stations around the Strait. Since 2015,this dataset has arguably been the most comprehensive oceanographic record of the Strait ofGeorgia, which highlights the utility and efficacy of citizen science as a scientific resource.• The Ocean Dynamics Laboratory at UBC is one of only a few groups globally that has im-plemented the use of commercial GNSS asset trackers to track surface circulation using in-expensive, expendable, easily-produced instrumentation. As a novel part of this undertaking,my goal was to replicate this framework for subsurface water masses by developing an inex-pensive neutrally-buoyant float—the first effort of its kind. This undertaking produced Swishfloats, which I designed, tested, and implemented over a three year period. The methodologyfor constructing, ballasting, and deploying the floats has been recorded in internal documentsat UBC, ensuring that this program can be continued locally in the Strait of Georgia and byoceanographers studying similar regions.The preliminary data from these floats have been used to calculate lateral diffusivities in theStrait and Gulf of 170 m2 s−1 and 500 m2 s−1, respectively, and advection speeds of 5 cm s−1and 2 cm s−1, respectively. Efforts were also made to assess the internal wave field in theStrait using float depth records, producing good agreement with theoretical Garrett-Munkinternal wave spectra, and to assess post-resurfacing surface dispersion, providing a value of2312 m 2 s−1 which is smaller but comparable to that derived from surface drifters (Pawlowiczet al., 2019). These results compare favorably to other local measures of the same variables,suggesting that the Swish floats work as intended.• While many tracer release experiments have been performed in open ocean environments,before TReX Deep only one other has been performed in deep stratified marginal seas asdefined here (the Baltic Sea; Holtermann et al., 2012). The TReX Deep project represents thefirst subsurface tracer release in the Gulf of St. Lawrence and adds to the small but growingliterature on tracer release experiments in inland seas (Holtermann et al., 2012; Ledwell et al.,2016).The inland tracer release differed from its open ocean counterparts in a number of importantways: first, a primary goal was to assess the lateral spreading along specific pathways, whichis not often the case in the open ocean; second, the bounded topography of the basin playedan important role in the spreading dynamics; third, the loss of tracer from the Gulf shouldprovide a direct measure of flushing time, which is often hard to achieve via other observa-tional methods (although calculating the regional tracer budget has proven challenging); andfourth, the vertical shift in the tracer center of mass has specific implications for the regional87exchange circulation. In Chapter 4, we formulate spreading models for inland tracer releasesthat could be used as foundational models in future experiments in other inland seas.In addressing the research questions above, I hope to have created frameworks that continueto contribute to the research field once my PhD studies are complete. For example, the informa-tion presented in Chapter 2 regarding Strait intermediate water transit times has been used by otherresearchers in the study of contaminant (Kuang et al., 2022), crustacean (Lewis et al., 2022), andcarbon (W. Evans, personal communications, September 18th, 2023) transport in the Strait of Geor-gia, in addition to the classification of regional ecosystem boundaries (Dosser et al., 2021).Furthermore, the Strait of Georgia Swish float monitoring program, which I initiated in early2022 to measure the spread of pollutants from the nearby Iona wastewater outfall, is slated to con-tinue at least until late 2024. While Chapter 3 details the development and initial findings from theprogram, I have hopes that the continuation of this program will further yield valuable informationregarding the spreading of wastewater contaminants in the Strait of Georgia.Finally, Chapter 4 represents the first study generated from a large, well-funded, multi-institution,international tracer release experiment. I have prepared this research to serve as a foundation forfuture studies in this ongoing experiment by providing benchmark estimates of spreading and transittimes, estimating the useful lifetime of the project, and posing a number of important questions todrive future research.5.2 Overarching conclusions and future workOne striking theme in this thesis is the breath of scale that is required for understanding the subsur-face circulation of a marginal sea. The water masses studied here not only have their own uniqueinternal dynamics, but also interact with the adjacent oceans while being coupled to an energeticsurface layer and a bottom boundary layer (or, at times, a less-dynamic bottom layer). The spectrumof processes that must be addressed to fully understand these systems spans broad temporal and spa-tial scales, emphasizing how hard it can be to fully understand these dynamics when observationsare limited to specific scales, as is often the case in inland waters.This concept is well-demonstrated in our research on the Gulf of St. Lawrence (Chapter 4),whereby understanding changing conditions in the Gulf interior begins with synoptic scale currentsin the Atlantic Ocean and ends with microscale mixing dynamics in the St. Lawrence Estuary.Looking ahead, I expect further insights into the Strait of Georgia’s intermediate flow will sim-ilarly require both broadening and narrowing our analytical scales. For example, a study of thetime-variability of intermediate ventilation strength might consider the effects of source water com-position as a driver for this variability. On the other end of the spectrum, any investigation into theeventual fate of the intermediate inflow will require an inventory of the fine-scale mixing processesthat contribute to its erosion.88Another common thread throughout this research is the importance of variability over the meanwhen considering ocean flows. While it is tempting to take all of the available spatial data from aregion and average it over time to provide an overview of a circulation system (indeed, this often acrucial first step in understanding a regional circulation), evidence from this thesis would suggestthat the instantaneous state of the system might not often (or ever) be represented by this meanpicture. This concept is outlined well in the Strait of Georgia model results of Figure 2.7, whichsuggests that the only current vectors in a) that would be consistently expressed are those foundin the purple (i.e. KER> 1) regions of c), with the large majority of the Strait exhibiting variablecurrents. Keeping in mind the importance of variable over mean flows in the Strait will be crucialto future evaluations of transport using the expanding Swish float dataset.We also observe this stochasticity in the Gulf of St. Lawrence deep waters. Our initial explo-ration of the TReX tracer release (Chapter 4) provides Gulf-wide estimates of spreading which, bynature, requires bulk averaging methods that likely obscure a portion of the system’s variability.Even so, we find that the diffusive mixing of the tracer is a strong driver of spreading compared toadvection, suggesting a similarly variable circulation to the Strait.This phenomenon has implications for the transport of tracers and contaminants in either system.Consider estimated tracer transit time distributions from Cabot Strait to the LSLE from the Gulf1D pipe model (Fig. 4.4d) or from Vancouver to the northern basin in our Strait of Georgia pipemodel (Fig. 4.6a, b, and c), and substitute our tracers for a pulse of dissolved pollutant, perhapsfrom a recently sunken vessel or a wastewater outfall. In a diffusion-dominated system (which iswhat we have observed in both regions), we would expect flatter pollutant transit time distributionswith long tails, where small amounts of tracer arrive quickly, linger for long periods, and have arelatively low peak in maximum pollutant concentration. Conversely, consider a system dominatedby advection: there would be very little contaminant observable until a large spike arrived andquickly dissipated. The extremes of either scenario would have vastly different ramifications forthe receiving ecosystem, based on the contaminant involved and the biological system in question.One possible realization for this concept pertains to a radical geoengineering proposal to directlyinject oxygen into the Gulf inflow waters to combat the growing regional hypoxia (Wallace, 2023).In this scenario, it is important that the injected oxygen would be distributed quickly and evenlythroughout the Gulf bottom waters, which would seemingly be the case given the strong diffusivitywe that we have measured in the system.It could be argued that one weakness of this thesis is its reliance on simplified 1D advection-diffusion models to represent very complex systems. On one hand, it is clear in Chapters 2 and 4 thatthe 1D representations, by nature, oversimplify the systems by neglecting lateral flow variability orspatially varying dispersion dynamics. However, these 1D models have served as valuable tools forinitial investigations, offering frameworks that were easily-tuned to provide estimates of transportmechanisms. Their simplicity also lent itself to fairly straightforward interpretation by allowing us89to isolate the effects of individual parameters on spreading estimates: this was particularly usefulwhen trying to understand the balance of advection and diffusion in the Strait intermediate layer(Section 2.3.1). Moreover, these models can now serve as a foundation upon which more complexmodels can be built, which will potentially be the case in future diapycnal tracer spreading analysesin the Gulf. Thus, while acknowledging their limitations, I would argue that the utility of 1D modelsin providing a first-order understanding of the system is an important aspect of this work.From a different perspective, the research presented in Chapters 2–4 relies heavily on La-grangian techniques which highlights their usefulness in deriving the dynamics of a system. Ar-guably, modern oceanography has excelled in developing methods to measure the state of the ocean,capturing instantaneous distributions of properties like heat and salt, but still lacks techniques formeasuring system dynamics, leaving gaps in our understanding of how these property distributionschange over time (Rossby, 2007). This may explain the growing interest in Lagrangian methodolo-gies, which is evident from several indicators over recent years. Consider the growth in publicationsusing surface drifter data: according to the Scripps Institution of Oceanography Lagrangian DrifterLaboratory, which maintains a list of 1,315 publications using such data since 1968 (accessed inOctober 2023, last updated in September 2022, https://gdp.ucsd.edu/ldl/drifter-bibliography/), morethan half of these studies were published since 2015. Additionally, the field has seen considerableprogress in the realms of numerical modeling as highlighted in a review by van Sebille et al. (2018),where millions of Lagrangian trajectories can be simulated using modern high-performance com-puting. Certainly, the rising interest in Lagrangian methodologies underscores the timeliness andrelevance of developing technologies such as the Swish floats described in this research.While I expect continued float deployments to yield further insights into wastewater dispersionin the Strait, I have been encouraged by the utility of the early Swish float data. For example, thefloat data have provided useful counterpoints to diffusivity and advection estimates in the Strait andGulf, comparing well to estimates obtained from more comprehensive observational datasets. Theirapplicability to other regions is already evidenced by their efficacy in both the Salish Sea and theGulf of St. Lawrence, suggesting that they could perform a similar role in other well-stratified en-vironments. However; there are some small design changes that would immediately improve theirefficacy. One seemingly simple improvement would be to redesign the float hull to reduce compress-ibility, which would allow deployment in less stratified conditions (see Appendix C). This could beachieved by reinforcing the float walls with structural rings (see D’Asaro, 2003, for a similar ex-ample) or using thicker walled hulls, though this has proven difficult with the available machiningfacilities at UBC. Other design changes (discussed in Section 3.9) entail additional sensing and/ortelemetry capabilities to produce and communicate underwater trajectories. The drawback to anyof these improvements is the inevitable increase in per-unit cost, which would diverge from theoriginal goal of building an inexpensive instrument.A further development that has proven challenging to date is to improve the sustainability of90the floats, which are deemed “expendable” but have in reality been recovered at fairly high rates.Certainly, enhancing the Swish float’s sustainability by reducing plastic usage in its constructionpresents a challenge. The top-cap material must meet multiple criteria: it needs to be strong, incom-pressible, cost-effective, non-degradable, and permeable to electromagnetic transmissions. Giventhese requirements, options for non-plastic materials are limited. One potential improvement couldbe incorporating a simple antenna into a metallic top-cap, eliminating the need for plastic compo-nents. In any case, any material changes would necessitate additional lab and field testing to confirmthat the float’s performance remains within acceptable limits.Another avenue to improve sustainability is to increase the rate at which we recover and reusethe floats. As far as is possible to tell, no resurfaced float has escaped the Strait of Georgia (thisoccurrence is unlikely given that none of the multitude of surface drifters deployed in the centralStrait reached the Pacific; Pawlowicz et al., 2019). At the time of writing, 41 floats have beendeployed in the Strait and 32 have been recovered (15 by hovercraft and 17 by members of thepublic), with continued deployments planned over the next 12 months. Recovery via hovercraft hasproven to be a 100% effective method, but of course comes with associated costs, both monetarilyand environmentally from fuel emissions. The 66% recovery rate of grounded floats (26 grounded,17 found) by members of the public exactly mirrors that of grounded surface drifters in the Strait(Pawlowicz et al., 2019), and components from these recovered floats can almost always be reusedin manufacturing new instruments. While assuming that that public will play a role in recoveringbeached instruments is perhaps not a long-term solution to this challenge, it is, for the time being, arelatively effective one.To conclude, it is important to specifically address how applicable the methods presented heremight be to other deep stratified marginal seas. As discussed previously, evidence would suggestthat Swish floats are transferable to different environments, assuming the regions are well-stratifiedor that the user can reduce their compressibility. The seasonality tracer presented in Chapter 2 couldcertainly be applied directly to subsurface layers with cyclical variability—one possible region forthis application might be the Sea of Okhotsk, where water temperatures vary seasonally at inter-mediate depths due to exchange with the North Pacific (Gladyshev et al., 2003). Finally, it is clearfrom Chapter 4 and many other studies that tracer release experiments offer a expansive frameworkto study circulation in any marine environment. One realization from this work, however, is that alarge majority of the extensive literature on the subject covers tracer releases in unbounded open-ocean environments and due to this, many of the commonly-used techniques to process and analyzetracer spreading did not translate seamlessly to semi-enclosed environments. 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S., Naveira-Garabato, A. C., Watson, A. J., Messias, M.-J.,and King, B. A. (2020). Tracking the spread of a passive tracer through Southern Ocean watermasses. Ocean Science, 16(2):323–336. → page 10112Appendix AStrait of Georgia Dissolved OxygenAnalysisThe dissolved oxygen (DO) seasonality analysis is conducted similarly to the temperature seasonal-ity analysis outlined in Chapter 2. Instead of temperature climatologies, DO climatologies are used.The DO measurements are sourced from the observational programs described in Section 2.2.2 andcombine sensor-derived DO profiles from CTD sampling with bottle measurements. These datawere collected from 119 stations in the Strait of Georgia (Figure A.1a).As organic material descends into deeper waters, respiration processes lead to a decrease indissolved oxygen. The seasonality analysis estimates an intermediate water (IW) oxygen utilizationrate of 0.69±0.18µmol kg−1 day−1 for the entire strait (Figure A.1e). This rate is slightly higherthan the estimates by Pawlowicz et al. (2007) for the southern strait’s deep waters and other fjordson the west coast of Vancouver Island, but significantly lower than rates in many other estuaries(Hopkinson and Smith, 2005). Assuming this rate is applicable to the Strait’s average depth belowthe 30 m thick euphotic zone, it corresponds to a deep-water areal respiration rate of approximately80mmol m−2 day−1. This aligns well with a euphotic-zone-only estimate of 60mmol m−2 day−1for net community production Wang et al. (2019), and supports the findings of (Johannessen et al.,2003) that most particulate matter in the Strait is biologically derived and largely sinks within theStrait, becoming trapped in sediments.113Figure A.1: Dissolved oxygen (DO) seasonal cycle characteristics: a) map detailing station locations denoted by the coloured markers,marker colours in a) correspond with the coloured y-axis in b) to denote seasonal cycle as a function of along-strait excursion.Plots c)-e) detail seasonal cycle characteristics as a function of along-strait excursion, grey scatter points show the variabilityof the seasonal cycle phase shifts (i.e. the least oxic Julian year days), amplitudes, and mean DO as a function of along-straitexcursion from Haro Strait. The solid red lines detail the linear trends in these properties.114Figure A.2: Dissolved oxygen tracer maps of a) IW age; and b) seasonal cycle amplitude.Annual dissolved oxygen climatologies were derived at 122 stations (marked by blackscatter points) in the IW layer core (90-110 m). A synthesis of dissolved oxygen mea-surements from various observational programs are subject to the same seasonal cyclefitting algorithm as temperature climatologies.115Figure A.3: Seasonality tracer maps derived from a station-wise analysis of SalishSeaCastmodel fields. An IW layer mean temperature field was created using three availabledepth levels at 77 m, 98 m, and 122 m. Model grid points nearest to hydrographicstations (marked by black scatter points) were selected from this field and annual tem-perature climatologies were created at 128 stations (marked by black scatter points) inthe IW layer. Map a) depicts IW age and b) depicts seasonal cycle amplitude. The spa-tial expression of these properties is similar to observations, supporting the notion thatthe model fields are representative of the measured properties.116Appendix BLinking Contaminant Measurements toCirculation- Iona Outfall ModelingAfter the average Strait of Georgia intermediate circulation scheme was outlined in Chapter 2, workbegan on linking this circulation to the various measurements of contaminants in the Strait. Inparticular, this focused on connecting measurements of PBDEs from the Iona wastewater outfalland various stations around the Strait with the newly identified circulation pathways. To understandthe spatial variability of these contaminants, a model was developed that integrated particle trackingwithin SalishSeaCast with estimates of PBDE scavenging to track the movement and removal ofPBDEs following their release from the Iona outfall. I provide below a straightforward descriptionof the components and workings of this model.The model is driven using velocity fields from SalishSeaCast, a regional oceanographic modelspecifically designed for the Salish Sea based on the NEMO regional ocean modeling engine, ver-sion 3.6. The model works by solving complex ocean physics equations and operates on a grid with898 by 398 horizontal cells, each about 500 meters apart. It also has 40 vertical layers in the watercolumn, with varying resolutions from the surface to the bottom. The model covers the Salish Seaarea and includes two open boundaries at Juan de Fuca (JdF) and Johnstone Strait. It incorporatesthe effects of eight tidal constituents and sea level predictions from NOAA. Over 150 rivers feedinto the model, with specific data for the Fraser River and a general monthly average for others.The model also takes into account atmospheric conditions like wind and solar radiation, using datafrom the High Resolution Deterministic Prediction System (Soontiens et al., 2016; Jarnı´kova´ et al.,2022).Similar to Section 2.2, Ariane particle tracking software was used to calculate virtual waterparcel trajectories in SalishSeaCast hindcast velocity fields. Ariane is a numerical tool developedfor analyzing ocean currents and water movement. This tool can be used to calculate the paths ofwater parcels (or particles) in 3D space, based on data from ocean circulation models. There are117some common misconceptions surrounding this type of particle tracking: firstly, it is important tounderstand that Ariane is an example of an “off-line” calculation, where trajectories are computedafter the underlying model run has been completed, not during. Secondly, note that the terms“particles” or “parcels” are used interchangeably and do not refer to physical entities with mass orvolume, but instead are virtual markers that perfectly follow the velocity vectors of the model inwhich they are seeded. For these reasons, understand that the particle tracking plays no role in theunderlying model calculations of velocity, volume transport etc.To evaluate the dispersion and removal of PBDE added to the Salish Sea from the Iona Islandeffluent outfall, virtual parcels were seeded every hour at the Iona outfall location at depths of 50 m,75 m, and 100 m for one year between January 1st 2016 and January 1st 2017. The parcel positionswere evaluated every 20 minutes and had a lifespan of one year following release before they wereremoved from the model. The three seeding depths are chosen as the Iona diffuser dischargeseffluent between 71 and 106 m depth, with the effluent plume rising to a buoyancy depth of about50m. When the particle tracking experiments were performed, the January 2017–December 2018period was the most recent two-year period available to perform the simulations. In total, the numberof virtual parcels (NT ) released from the Iona outfall totalled 26,352 (3 virtual parcels/hr x 8784hr/y).Once all of the trajectories had been calculated, each trajectory was smoothed using a 24 hourwindow-average for computational ease. The mean daily positions of all the parcels were then spa-tially binned into 0.01◦ × 0.01◦ grid cells to allow us for summations of parcels. Note that the PBDEmodel grid and the SalishSeaCast are not aligned, nor do they need to be; we create a grid to geo-graphically group parcels, so we do so using a higher-resolution that the underlying SalishSeaCastmodel grid. This is possible as Ariane does not move parcels between the grid cells of the under-lying model, but advects them freely throughout the underlying model domain by interpolating thevelocity field to the position of the virtual parcels.To trace the movement of dissolved PBDEs through the Strait using the virtual parcel trajecto-ries, it is necessary to assign a mass of contaminant to each parcel. This mass will not be constantdue to the scavenging of PBDEs by physical particulate matter (i.e. silt and biogenic material; Sunet al., 2023). We can model this removal as follows:α(t) = α0e−ti/H (B.1)where α is the PBDE mass of any given parcel, α0 is the initial PBDE mass upon release from theIona outfall, ti is the age of the parcel in days (i.e. days elapsed since the parcel was released) withsubscript i being an index for each individual parcel, and H is the PBDE half-life due to scavengingby particulate matter. Measurements show that approximately R= 29.6 kg yr−1 of PBDEs (allcongeners) are released at the Iona outfall, annually. From this, we can show that α0 = R/NT (i.e.the total PBDE load divided by the the number of parcels released in the model) and using equation118B.1, we can calculate α for every parcel for all t.At any given time, any grid cell can contain multiple parcels (Fig. B.2b–e and g–j) of differentage, depending of the path they followed to arrive at this particular grid cell: the distribution of allparcel ages in a individual cell is the Transit Time Distribution (N j(ti)), or TTD, for that cell. As inChapter 2, we can use this concept to calculate the mean age of parcels released at Iona (Fig. B.2aand b), which is calculated for every cell j in the by taking the mean of the TTD.We can then combine our information of the time-varying PBDE mass of a parcel to calculatethe PBDE concentration in any grid cell as follows:C( j) =∑∞ti=1 Nj(ti)α(ti)V(B.2)where V is the volume of grid cell.As the half-life H of PBDEs in not known, here we use a range of values and compare theresults to PBDE measurements in order to estimate the most appropriate value of H in this system.It should be noted that as we integrate the PBDE concentration over a long time period of time(2 years; 2016–2018) after which the released parcels have maximally dispersed and the systemis in approximate steady state, the results from this model represent the mean state of the systemaccording to different H removal time-scales.We can first assess the results of the model by looking at the spatial variability of PBDEsthroughout the region (Figs. B.2 and B.3). A removal half-life H= 1 day shows a small amountof northward PBDE transport from the Iona outfall as the contaminant is removed very quicklyfrom the system. A H= 5 days shows a similar northward dispersal of the contaminant, with highconcentrations (20 pg l−1–100 pg l−1) in Burrard inlet and Howe Sound. With a removal half-lifeH= 10 days, there is a measure of south-westward dispersal of the contaminant and low concentra-tions (2 pg l−1–10 pg l−1) are found in the interior of the southern basin and along the eastern shoreof Vancouver Island. At H<20 days, the PBDEs generally stay concentrated in the intermediatelayer. For H= 20 days, there is increased spreading of PBDEs northward and southward, in additionto an increased vertical spreading, with low contaminant concentrations (<5 pg l−1) appearing atthe surface of the southern basin and Haro Strait. At H>20 days, there is a build-up of PBDEs nearthe mouth of the Fraser River on Sturgeon Banks, where concentrations exceed 200 pg l−1, and atH= 30 days the lateral spreading of parcels leads to increased PBDE concentrations in the northernbasin and the Haro Strait region. For an (unrealistically long) H= 100 days, there are relatively highPBDE concentrations throughout the entire Salish Sea at all depths with significant accumulation inthe southern basin.The PBDE concentration at all of the stations is highly variable in time (Fig. B.5), with concen-trations generally decreasing with distance from the Iona source. At station S4-1.5, closest to theIona outfall, there is a pronounced fluctuation in PBDE levels, with spikes reaching up to 40 pg l−1,119which diminish in amplitude with increasing distance from the source, as seen at station NG. Thisstation also exhibits considerable fluctuations, but the peak concentrations are noticeably lower.Station HS, while still showing significant variability, has lower overall concentrations than the pre-ceding stations, indicative of the contaminants’ dispersion and removal processes in action. Lastly,station JF2, furthest from the source, presents the lowest concentration levels with less frequent andlower peaks. The moving-mean for station S4-1.5 suggests that some seasonal variability in thePBDE at this station signal might be present, with elevated concentrations in spring and winter.We can further evaluate the model from the perspective of various stations in the Salish Seawhere PBDE measurements are available. At S4-1.5, the nearest station to the Iona outfall, measure-ments from Sun et al. (2023) suggest elevated PBDE concentrations up to approximately 500 pg l−1in the intermediate layer during certain periods with similarly elevated concentrations at the seafloorduring other periods. At the same station, modeled PDBE depth profiles (Fig. B.4) suggest concen-trations as large as 100 pg l−1 in the intermediate (50 m–200 m) depth range (Fig. B.4). The depthvariability in modeled PDBE concentrations agree broadly with those from measured PBDE pro-files (Sun et al., 2023), although the modeled concentrations are significantly lower that observed.At NG, the modeled PBDE maxima is less pronounced and slightly elevated in the the intermedi-ate and deeper waters of the region. Conversely, stations south of the Iona outfall (HS and JF2)show elevated modeled PBDE concentrations in the surface layer with low concentrations at depth.The elevated modeled surface concentrations in the southern Salish Sea/deep concentrations in thenorthern Salish Sea represent the large-scale propagation of contaminants through the regional es-tuarine circulation. Compared to S4-1.5, there are limited measurements of PBDEs from all otherstations for model evaluation; however, one point of interest is that the modeled concentrations atthese stations are consistently lower than those from our limited observations.While the Iona outfall is considered the largest single PBDE input into the Strait (Johannessenet al., 2015), there are other inputs (atmospheric deposition, other smaller wastewater outfalls, sed-iment resuspension etc.) that are not accounted for in this model. Therefore, while the modelmay capture some amount of the spatial and temporal variability in PBDE concentrations in theSalish Sea, a portion of the variability is not accounted for. Observations have indicated that thePBDE concentrations in the Strait of Georgia are surprisingly high, especially in the southern basinnear Vancouver (Sun et al., 2023). These concentrations are comparable to those in other coastalzones heavily impacted by human activities. Interestingly, the PBDE load in the Strait is almostexclusively in the dissolved (primarily colloidal) form, contrasting with other coastal regions whereparticulate PBDEs are more dominant. The known and estimated direct sources of PBDEs, suchas wastewater treatment plants and atmospheric deposition, are insufficient to account for the highconcentrations observed in the Strait. This suggests that other, yet unidentified, mechanisms mightbe contributing to the high PBDE load. Continued monitoring and research are warranted to identifythese mechanisms and to refine the existing models.120Figure B.1: Lagrangian tracking from Iona outfall source. Maps a) and f) describe the meantransit time horizontally and vertically, respectively, derived from Transit Time Distri-butions (TTDs). Dashed white lines depict the basin boundaries. Maps b)–e) and g)–j)detail the time-evolution of the released parcels 20, 40, 80, and 160 days after release.The parcel concentration is calculated as the number of parcels contained in a grid cellnormalized by the grid cell containing the largest number of parcels, for each time-step.Parcel release points are denoted by a white marker and/or black cross. Grey seafloor inf)–j) is representative of depth along the thalweg. The 10 m isobath of the Fraser Riverslopes is highlighted in b)–e) with a gray contour.121Figure B.2: Depth-integrated maps of modeled water column PBDE concentrations for various different PBDE half-lives (bold typefaceon maps). Concentrations are calculated as the mass of PBDE in a grid cell from surface to seafloor, divided by the volume of thatgrid cell from surface to seafloor. Values printed on maps show the concentrations for various stations of interest.122Figure B.3: Cross-channel integrated maps of modeled water column PBDE concentrations for various different PBDE half-lives (boldtypeface on maps). Concentrations are calculated as the mass of PBDE in 1m deep grid cells spanning zonally across the Strait,divided by the volume of those grid cells spanning zonally across the strait. Values printed on maps show the maximum concen-trations in a zonal grid cell at various stations of interest. Dashed lines indicate station locations and approximate boundaries ofthe northern and southern basin (NB and SB, respectively).123Figure B.4: Profiles of modeled PBDE concentration for different PBDE half-lives at various stations of interest. Scatter points showmodeled values, profiles show values smoothed with a 10 m moving average.124Figure B.5: Time-series of modeled PBDE concentration for different PBDE half-lives at var-ious stations of interest. Legends show ratio of standard deviation/mean to assess themagnitude of variability in the signal.125Appendix CSwish Float OperationsFollowing the work of Chapter 3, deployments have continued as part of the Strait of Georgia Swishfloat program. During these deployments, there were periods of time where the ambient watercolumn density gradient δρ/δ z was less than the float density gradient δρ f /δ z. In one particularcase, this resulted in a failed deployment (D4 FAIL in Fig. C.1) where the floats became unstableand sank to the seafloor.As a result of this failed deployment, a routine was built into the float protocol to comparethe pre-deployment density gradient from in-situ CTD profiles to both the anticipated float densitygradient and to historical stratification data. The resultant figure (Fig. C.1) shows that, historicallyduring the period between January and April every year, the Strait water column stratification wasoccasionally too weak to deploy Swish floats (i.e. δρ f /δ z > δρ/δ z). This figure has provenuseful not only for its intended purpose as an in-situ assessment of stratification, but also as aoperational tool to help plan fieldwork, and to understand which periods of the year are most likely tosupport Swish float deployments. Note that some experimental tuning of γ (not shown) tells us that,to ensure year-round stability even in extreme (i.e. low δρ/δ z) conditions, float compressibility(γ = 6.4×10−6 db−1) must be reduced to that of seawater (γw = 4.4×10−6 db−1) or less.12650 100 150 200 250 300 350Day of year0.0050.010.0150.020.025d / dZ (70-120m) [kg m-3 m-1 ]CTD archive (<20km from Iona)14-day meanSOGtest 20220425 (50-80m)SOGD1 20220601SOGD2 20220725SOGD3 20220906SOGD4 FAIL 20230118SOGD5 20230301SOGD6 20230719Figure C.1: Climatology of stratification δρ/δ z near the Swish Float deployment zone.Archive hydrography is from the Chapter 2 CTD dataset. In-situ δρ/δ z averaged be-tween 70 m and 120 m depth are plotted as circular markers for each float deployment;float δρ f /δ z averaged at the same depths are plotted as star markers—note that for floatsto be stable, δρ f /δ z < δρ/δ z.127
Thesis/Dissertation
eng
Oceanography
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Pawlowicz, Rich
Measuring subsurface circulation in deep stratified marginal seas
Text