Effects of Increasing the Category Resolution of the Sea Ice Thickness Distribution in a Coupled Climate Model on Arctic and Antarctic Sea Ice Mean State. Issue 10 (4th October 2022)

• Record Type: Journal Article
• Authors: Smith, Madison M.; Holland, Marika M.; Petty, Alek A.; Light, Bonnie; Bailey, David A.
• Is Part Of: Journal of geophysical research. Volume 127:Issue 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-04
• Subjects: sea ice -- global climate model -- polar oceanography -- remote sensing; Oceanography -- Periodicals; 551.4605
• DOI: 10.1029/2022JC019044
• Languages: English
• ISSNs: 2169-9275

Abstract

Abstract: Many modern sea ice models used in global climate models represent the subgrid‐scale heterogeneity in sea ice thickness with an ice thickness distribution (ITD), which improves model realism by representing the significant impact of the high spatial heterogeneity of sea ice thickness on thermodynamic and dynamic processes. Most models default to five thickness categories. However, little has been done to explore the effects of the resolution of this distribution (number of categories) on sea‐ice feedbacks in a coupled model framework and resulting representation of the sea ice mean state. Here, we explore this using sensitivity experiments in CESM2 with the standard 5 ice thickness categories and 15 ice thickness categories. Increasing the resolution of the ITD in a run with preindustrial climate forcing results in substantially thicker Arctic sea ice year‐round. Analyses show that this is a result of the ITD influence on ice strength. With 15 ITD categories, weaker ice occurs for the same average thickness, resulting in a higher fraction of ridged sea ice. In contrast, the higher resolution of thin ice categories results in enhanced heat conduction and bottom growth and leads to only somewhat increased winter Antarctic sea ice volume. The spatial resolution of the ICESat‐2 satellite mission provides a new opportunity to compare model outputs with observations of seasonal evolution of the ITD in the Arctic (ICESat‐2; 2018–2021). Comparisons highlight significant differences from the ITD modeled with both runs over this period, likely pointing to underlying issues contributing to the representation of average thickness. Plain Language Summary: The sea ice thickness is a key property of the sea ice cover, and is highly variable in both the Arctic and Antarctic. The thickness influences the growth and melt of sea ice, as well as the process of ice piling up (referred to as ridging). One of the simplifying assumptions used in simulating sea ice in global climate models is representing the variation in ice thickness with a set number of thickness categories. Typically, most models use five categories. Here, we test the implication of using a higher number of categories (15) on the simulation of sea ice. More thickness categories in the model results in significantly more simulated Arctic sea ice, but only slight increases in Antarctic sea ice. This is primarily because the model estimates that the ice is weaker, and so more of it piles up to result in thicker ice in the Arctic. Modeled variability in ice thickness is also compared with thicknesses from satellite observations (ICESat‐2). In the current version of the model, increasing the number of thickness categories does not improve the comparison with observations. This is the first comparison of thickness observations with models on this scale, and highlights areas for future work. Key Points: Higher resolution of the sea ice thickness distribution increases simulated Arctic sea ice thickness, with little change in Antarctic sea ice The impact has a bigger effect on dynamic processes than thermodynamic processes Comparison with subgrid‐scale thickness observations from ICESat‐2 suggests targeting ridging for improvement