Assessment of Arctic Sea Ice and Surface Climate Conditions in Nine CMIP6 Climate Models

• Main Authors: Henke, Martin, Ferreira, Celso M, Zhang, Jinlun, Ravens, Thomas, Miesse, Tyler Will, Cassalho, Felício
• Format: Other/Unknown Material
• Language: unknown
• Published: Authorea, Inc. 2022
• Subjects: Arctic; Laptev Sea; Wandel; Climate change; laptev; Sea ice
• Online Access: http://dx.doi.org/10.22541/essoar.167161257.71038395/v1

The observed retreat and anticipated further decline in Arctic sea ice hold strong climate, environmental, and societal implications. In predicting climate evolution, ensembles of coupled climate models have demonstrated appreciable accuracy in simulating sea ice area and volume trends throughout the historical period. However, individual climate models still show significant differences in simulating the sea ice thickness distribution. To better understand individual model performance in sea ice simulation, nine climate models previously identified to provide plausible sea ice decline and global temperature change were evaluated in comparison with Arctic satellite and reanalysis derived sea ice thickness data, sea ice extent records, and atmospheric reanalysis data of surface wind and air temperature. Assessment found that the simulated spatial distribution of historical sea ice thickness varies greatly between models and that several key limitations persist among models. Primarily, most models do not capture the thickest regimes of multi-year ice present in the Wandel and Lincoln Seas; those that do, often possess erroneous positive bias in other regions such as the Laptev Sea or along the Eurasian Arctic Shelf. From analysis, no model could be identified as performing best overall in simulating historic sea ice, as model bias varies regionally and seasonally. Nonetheless, the bias maps and statistical measures derived from this analysis should enhance understanding of the limitations of each climate model. This research is motivated in-part to inform future usage of coupled climate model projection for regional modeling efforts and enhance climate change preparedness and resilience in the Arctic.