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CNT Parameterization Based on the Observed INP Concentration during Arctic Summer Campaigns in a Marine Environment Cirisan, Ana; Girard, Eric; Blanchet, Jean-Pierre; Keita, Setigui Aboubacar; Gong, Wanmin; Irish, Vickie; Bertram, Allan K.
Abstract
Aerosol–cloud interactions present a large source of uncertainties in atmospheric and climate models. One of the main challenges to simulate ice clouds is to reproduce the right ice nucleating particle concentration. In this study, we derive a parameterization for immersion freezing according to the classical nucleation theory. Our objective was to constrain this parameterization with observations taken over the Canadian Arctic during the Amundsen summer 2014 and 2016 campaigns. We found a linear dependence of contact angle and temperature. Using this approach, we were able to reproduce the scatter in ice nucleated particle concentrations within a factor 5 of observed values with a small negative bias. This parameterization would be easy to implement in climate and atmospheric models, but its representativeness has to first be validated against other datasets.
Item Metadata
Title |
CNT Parameterization Based on the Observed INP Concentration during Arctic Summer Campaigns in a Marine Environment
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Creator | |
Publisher |
Multidisciplinary Digital Publishing Institute
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Date Issued |
2020-08-28
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Description |
Aerosol–cloud interactions present a large source of uncertainties in atmospheric and climate models. One of the main challenges to simulate ice clouds is to reproduce the right ice nucleating particle concentration. In this study, we derive a parameterization for immersion freezing according to the classical nucleation theory. Our objective was to constrain this parameterization with observations taken over the Canadian Arctic during the Amundsen summer 2014 and 2016 campaigns. We found a linear dependence of contact angle and temperature. Using this approach, we were able to reproduce the scatter in ice nucleated particle concentrations within a factor 5 of observed values with a small negative bias. This parameterization would be easy to implement in climate and atmospheric models, but its representativeness has to first be validated against other datasets.
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Subject | |
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Language |
eng
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Date Available |
2020-09-28
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Provider |
Vancouver : University of British Columbia Library
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Rights |
CC BY 4.0
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DOI |
10.14288/1.0394530
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URI | |
Affiliation | |
Citation |
Atmosphere 11 (9): 916 (2020)
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Publisher DOI |
10.3390/atmos11090916
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Peer Review Status |
Reviewed
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Scholarly Level |
Faculty
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DSpace
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Item Media
Item Citations and Data
Rights
CC BY 4.0