Evaluating Arctic sea ice and snow thickness simulations: methodological insights from MOSAiC and CMIP6 data

The Arctic sea ice cover and thickness have rapidly declined in recent years, with snow cover on sea ice playing a key role in driving this variability and trend. Arctic sea ice and snow properties also strongly regulate heat and momentum exchange between the ocean and atmosphere, making their accurate representation in climate models essential. Yet in situ observations of Arctic sea ice and snow thickness are scarce. The year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provided valuable, high-resolution measurements of these properties, but this dataset is short-term and localized compared to the observational products typically used for model evaluation. We examine whether free-running climate model simulations can be meaningfully compared to point observations, such as those from MOSAiC, to assess model performance. To address this, we employ multiple methodological approaches to generate representative seasonal cycles of simulated snow and sea ice thickness: a standard 30-year climatology, two proxy-year methods (based on sea ice area (SIA) and the Arctic Oscillation (AO) index), atmospherically nudged simulations, and a Monte Carlo random-year benchmark. We find that the SIA-based proxy method performs comparably to the 30-year climatology. In contrast, the AO-based proxy method reduces bias relative to the SIA method for snow thickness comparisons. However, both methods nevertheless fail to accurately reproduce the amplitude of the observed snow thickness cycle, suggesting unresolved processes in models. Overall, these findings show potential for snow and sea ice evaluation against localized measurements and demonstrate that proxy methods can provide viable alternatives when nudging or direct temporal overlaps are unavailable. Finally, this study highlights the need for an improved representation of modeled sea ice and snow processes to enhance the next generation of global climate models.