An Observationally Based Evaluation of Subgrid Scale Ice Thickness Distributions Simulated in a Large-Scale Sea Ice-Ocean Model of the Arctic Ocean


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Martin.Losch [ at ] awi.de

Abstract

A key parameterization in sea ice models describes the subgrid scale ice thickness distribution. Based on only a few observations, the ice thickness distribution model was shown to be consistent with field data and to improve the simulation’s large-scale properties. The available submarine and airborne observations enable to evaluate in greater detail the ability of a pan-Arctic sea ice-ocean model with an ice thickness distribution parameterization to reproduce observed thickness distributions in different regions and seasons. Many observations are reproduced accurately. Some cases of poorly simulated modes and tails of the distributions are tentatively attributed to simplified thermodynamics and inaccurate deformation fields. Variability on decadal timescales, however, is generally underestimated. Thickness distributions in individual grid cells of the model show similar differences between regions and seasons as observed regional mean distributions, but the modeled grid-scale variability is lower than observed. Simulated modal thicknesses of first-year ice are only insufficiently different from those of multiyear ice. The modal thickness proves to be a useful metric for quantifying model biases in both dynamics and thermodynamics. In addition to improving basin-wide mean variables, the ice thickness distribution parameterization provides reliable and valuable additional subgrid scale data. At the same time the low climate sensitivity of the parameterization may affect longer simulations with strong climate change aspects.



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ISI/Scopus peer-reviewed
Publication Status
Published
Eprint ID
48416
DOI 10.1029/2018JC014022

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Ungermann, M. and Losch, M. (2018): An Observationally Based Evaluation of Subgrid Scale Ice Thickness Distributions Simulated in a Large-Scale Sea Ice-Ocean Model of the Arctic Ocean , Journal of Geophysical Research: Oceans, 123 , pp. 1-16 . doi: 10.1029/2018JC014022


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