Evaluating Arctic Sea-Ice and Snow Thickness: A Proxy-Based Comparison of MOSAiC Data with CMIP6 Simulations

Abstract. The Arctic sea-ice cover and thickness have rapidly declined in the recent past. Snow cover on sea ice, acting as an insulating barrier, was shown to be instrumental in driving the variability and trends in sea-ice thickness. Because of this, the ability of climate models to realistically simulate the present-day annual cycles of Arctic sea-ice properties has become a central measure of model performance in Arctic-focused climate model intercomparisons. However, evaluating free-running model simulations usually requires multi-year observational datasets, which is challenged by the relatively short-term existing Arctic measurements particularly sea-ice and snow thickness. In this exploratory study, we propose a new methodology to improve the meaningfulness of sea ice and snow comparisons to model data. We make use of the exceptional year-long MOSAiC observations to examine the simulated Arctic sea-ice and snow thickness in 10 CMIP6 models. To perform meaningful comparisons with the modeled simulations, we define two “proxy years” selection methods based on sea-ice area and atmospheric criteria, when these conditions in the Arctic are similar to those during the MOSAiC year. We verify the capability of the proxy-year composites to capture the atmospheric and sea-ice variability, by comparing them with the sets of nudged simulations in which the atmospheric circulation observed during the MOSAiC year is directly imposed. Our results show that models tend to simulate similar annual cycles compared to the observations however, with an overestimation in amplitude for snow thickness and a misaligned phase of sea-ice thickness cycles. Overall, the study highlights that regardless of the specific modeled configurations and conditions within individual proxy years, biases in sea-ice and snow thickness remain consistent, even when wind conditions are imposed in the nudged model simulations. This highlights the necessity for a better representation of modeled processes driving the sea-ice and snow thicknesses which will be instrumental in the next generation of GCMs. This first MOSAiC-based assessment of the modeled snow and ice thickness, and the proposed proxy-year-based methodology, pave the way for further meaningful model evaluation.