GSTDTAP
项目编号1820927
Parameterizing sub-grid Arctic snow-on-sea-ice processes in Earth System Models using MOSAiC field observations and realistic-resolution process models.
Glen Liston
主持机构Colorado State University
项目开始年2018
2018-09-01
项目结束日期2021-08-31
资助机构US-NSF
项目类别Standard Grant
项目经费600571(USD)
国家美国
语种英语
英文摘要This project will help answer one of the key questions being asked by the international Arctic climate-science community: What is the role of snow-on-sea-ice in controlling heat exchanges in the Arctic climate system? This work will educate young scientists in the combined issues of field measurements, high-resolution modeling, and representing high-resolution processes in regional and global models. In addition, it will collect high quality field measurements that will advance Arctic system science for years to come.

This research will synthesize three interrelated science contributions: (1) Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) field observations will identify governing physical processes, fluxes, and linkages within the Arctic climate system. (2) Realistic-resolution (meters-scale), physically based modeling tools (e.g., MicroMet, SnowModel, EnBal, SnowPack, SnowTran-3D, SnowDunes, SnowAssim, SeaIce-3D) will quantify the behavior, interactions, and sensitivities among the Arctic system components. (3) Field observations and high-resolution modeling will be integrated to define what is missing or poorly represented in Earth System Models and guide the parameterization of the associated snow-related processes and fluxes for the Community Earth System Model (CESM) and the Los Alamos Sea Ice Model (CICE).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/73376
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Glen Liston.Parameterizing sub-grid Arctic snow-on-sea-ice processes in Earth System Models using MOSAiC field observations and realistic-resolution process models..2018.
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