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In the absence of widespread snowfall observations over the Arctic Ocean, reanalysis products provide a wide range of estimates of time-evolving snowfall rates over Arctic sea ice, and it can be difficult to determine which product is most representative. In this work, Arctic snowfall rates retrieved from 2006 to 2016 CloudSat observations and snowfall products from three reanalyses are assessed. The products can be brought into encouraging agreement over the region on interannual time scales once differences in spatial representativeness and temporal sampling are accounted for. This motivates the use of CloudSat's snowfall product to calibrate reanalysis snowfall. The calibration is carried out for four Arctic quadrants and combined to produce regionally resolved and consistent estimates of interannually varying snowfall. Calibrated reanalysis snowfall inputs are then used to drive the NASA Eulerian Snow On Sea Ice Model, reducing the interproduct spread in the resulting simulated snow depths across the Arctic.

Plain Language Summary Snow on Arctic sea ice impacts global climate in many ways. Because the Arctic is a remote region, we have few direct measurements of snow depth on Arctic sea ice. We can use snow models, which take input of snowfall rates from numerical model-based products that incorporate observations, to estimate this snow depth. However, we are unsure which of these models best describes the actual amount of snowfall over the Arctic Ocean. In this study, we examine how well snowfall rates from satellite observations and model-based snowfall products agree over the Arctic Ocean. We find that the snowfall rates are broadly well correlated, for different seasons and years, despite the differences between how the satellite snowfall and the model-based snowfall are derived. We then calibrate the snowfall from each model-based product to better match the satellite snowfall and apply this calibration to products used to predict snow depth on sea ice. This calibration provides a measurable reduction of uncertainty and gives us a more confident estimate of snow depth that can be compared directly to in situ observations and used to better estimate related quantities like sea ice thickness.