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

Abstract

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.

Using Sea Surface Temperature Observations to Constrain Upper Ocean Properties in an Arctic Sea Ice‐Ocean Data Assimilation System

“…The thickness assimilation in winter preconditions the sea ice appropriately, so that the summer sea ice thickness is also simulated more accurately. The long sea ice memory is attributed to the relatively slow melting and freezing processes (Day et al, ; Mu, Losch, et al, ).…”

“…The atmospheric forcing data are the 23 ensemble forecasts of the UK Met Office Unified Model (UKMO UM; Bowler et al, ; obtained from http://tigge.ecmwf.int/). Further details about the model configuration can be found in Mu, Losch, et al ().…”

“…The NoSSTasim run assimilates the SSMIS sea ice concentration, the SMOS, and CryoSat‐2 sea ice thickness data as in Mu, Losch, et al (). In this run the model state vector for the assimilation includes only include sea ice concentration and sea ice thickness.…”

“…The localization radius is set to 12 grid points, corresponding to approximately 216 km. The uncertainties of the SSMIS sea ice concentration data accounting for measurement and representation errors are assumed to be uniform with 25% following Mu, Losch, et al (). The postassimilation process focuses on basic physical relationships among sea ice concentration, sea ice thickness, and ocean temperature.…”

“…Mu et al () introduced an ensemble ESTKF data assimilation scheme into the Massachusetts Institute of Technology general circulation model (MITgcm; Marshall et al, ) and assimilated sea ice concentration and thickness observations. They found that the sea ice thickness simulation substantially improved by the thickness assimilation, whereas the improvement in the simulated sea ice concentration was small.…”