An Observationally Based Evaluation of Subgrid Scale Ice Thickness Distributions Simulated in a Large‐Scale Sea Ice‐Ocean Model of the Arctic Ocean. Issue 11 (9th November 2018)

• Record Type: Journal Article
• Authors: Ungermann, Mischa; Losch, Martin
• Is Part Of: Journal of geophysical research. Volume 123:Issue 11(2018)
• Page Start: 8052
• Page End: 8067
• Publication Date: 2018-11-09
• Subjects: MITgcm -- upward‐looking sonar -- airborne electromagnetic sounding -- regional -- local -- decadal variability -- modal thickness; Oceanography -- Periodicals; 551.4605
• DOI: 10.1029/2018JC014022
• Languages: English
• ISSNs: 2169-9275

Abstract

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. Plain Language Summary: Sea ice is part of the climate system: computer models of sea ice are necessary for climate simulation. Many processes that shape sea ice in the Arctic Ocean cannot be represented in a computer model. For example, a sea ice model cannot distinguish between different ice thicknesses that are observed in a given area, but requires a submodel to calculate these ice thickness distributions, a model within the model. Any submodel must be tested against observations before it can be used with confidence. Here we repeat previous tests of the ice thickness distribution submodel, which had some data limitations, with a larger data set of new observations and a sea ice model of the entire Arctic Ocean. We find that the submodel works very well in many cases. Unfortunately, the model simulations do not agree with observed changes that happen slowly over 20 years and they do not contain as many small fluctuations as the observations. As a by‐product we find that we can learn a lot about computer models when we not only compare mean ice thickness to observations, but also the most frequent ice thickness. Key Points: Recent observations allow to evaluate ice thickness distributions in the Arctic on regional to local scales A pan‐Arctic model simulates the observed regional and seasonal range of ice thickness distributions with some skill The model underestimates the decadal variability of ice thickness distributions