Arctic-Wide Sea Ice Thickness Estimates From Combining Satellite Remote Sensing Data and a Dynamic Ice-Ocean Model with Data Assimilation During the CryoSat-2 Period

Exploiting the complementary character of CryoSat-2 and Soil Moisture and Ocean Salinity satellite sea ice thickness products, daily Arctic sea ice thickness estimates from October 2010 to December 2016 are generated by an Arctic regional ice-ocean model with satellite thickness assimilated. The assimilation is performed by a Local Error Subspace Transform Kalman filter coded in the Parallel Data Assimilation Framework. The new estimates can be generally thought of as combined model and satellite thickness (CMST). It combines the skill of satellite thickness assimilation in the freezing season with the model skill in the melting season, when neither CryoSat-2 nor Soil Moisture and Ocean Salinity sea ice thickness is available. Comparisons with in situ observations from the Beaufort Gyre Exploration Project, Ice Mass Balance Buoys, and the NASA Operation IceBridge demonstrate that CMST reproduces most of the observed temporal and spatial variations. Results also show that CMST compares favorably to the Pan-Arctic Ice-Ocean Modeling and Assimilation System product and even appears to correct known thickness biases in the Pan-Arctic Ice-Ocean Modeling and Assimilation System. Due to imperfect parameterizations in the sea ice model and satellite thickness retrievals, CMST does not reproduce the heavily deformed and ridged sea ice along the northern coast of the Canadian Arctic Archipelago and Greenland. With the new Arctic sea ice thickness estimates sea ice volume changes in recent years can be further assessed. Plain Language Summary Sea ice plays a crucial role in climate changes; however, sea ice thickness is difficult to measure directly from space. The novel satellite thickness products from CryoSat-2 and Soil Moisture and Ocean Salinity have complementary characters, which facilitate the assimilation into the model to generate a new Arctic thickness record in this study. Also, benefitting from the model dynamics and sea ice concentration assimilation, the new data can further cover the melting seasons when satellite thickness data are unavailable. Compared to the in situ observations, the new thickness data show some advantages over the statistically merged satellite product CS2SMOS and Pan-Arctic Ice-Ocean Modeling and Assimilation System thickness product.