The impact of assimilating sea ice thickness data derived from ESA's Soil Moisture and Ocean Salinity (SMOS) satellite together with Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data of the National Snow and Ice Data Center (NSIDC) in a coupled sea ice‐ocean model is examined. A period of 3 months from 1 November 2011 to 31 January 2012 is selected to assess the forecast skill of the assimilation system. The 24 h forecasts and longer forecasts are based on the Massachusetts Institute of Technology general circulation model (MITgcm), and the assimilation is performed by a localized Singular Evolutive Interpolated Kalman (LSEIK) filter. For comparison, the assimilation is repeated only with the SSMIS sea ice concentrations. By running two different assimilation experiments, and comparing with the unassimilated model, independent satellite‐derived data, and in situ observation, it is shown that the SMOS ice thickness assimilation leads to improved thickness forecasts. With SMOS thickness data, the sea ice concentration forecasts also agree better with observations, although this improvement is smaller.

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.

An ice mass balance buoy (IMB) monitors the evolution of snow and ice cover on seas, ice caps and lakes through the measurement of various variables. The crucial measurement of snow and ice thickness has been achieved using acoustic sounders in early devices but a more recently developed IMB called the Snow and Ice Mass Balance Array (SIMBA) measures vertical temperature profiles through the air-snow-ice-water column using a thermistor string. The determination of snow depth and ice thickness from SIMBA temperature profiles is presently a manual process. We present an automated algorithm to perform this task. The algorithm is based on heat flux continuation, limit ratio between thermal heat conductivity of snow and ice, and minimum resolution (±0.0625°C) of the temperature sensors. The algorithm results are compared with manual analyses, in situ borehole measurements and numerical model simulation. The bias and root mean square error between algorithm and other methods ranged from 1 to 9 cm for ice thickness counting 2%–7% of the mean observed values. The algorithm works well in cold condition but becomes less reliable in warmer conditions where the vertical temperature gradient is reduced.

Antarctic sea ice variations are affected by moisture and heat from low‐ and mid‐latitudes, more than 90% of which are transported by atmospheric rivers (ARs). This study employs the ERA5 reanalysis and satellite observations to detect all the ARs over the Antarctic sea ice and analyze the general contributions of ARs on sea ice changes from 1979 to 2020. Though AR frequency is low in all seasons, ARs give rise to intense sea ice reduction at a rate of more than 10%/day in marginal ice zone. Thermodynamic processes of sea ice dominate the AR‐induced variations, associated with anomalous atmospheric conditions during ARs. Warm, moist and cloudy weather causes considerable melting by enhancing sensible heat flux in cold seasons but has restrictive influences in summer due to blocked solar radiation. Heavy precipitation during ARs is also nonnegligible, especially during the summer melt.

Near-surface air temperature at the Argentinian research base Esperanza on the northern tip of the Antarctic Peninsula reached 18.3 °C on 6 February 2020, which is the highest temperature ever recorded on the entire Antarctic continent. Here we use weather observations since 1973 together with the ERA5 reanalysis to investigate the circulation that shaped the 2020 event, and its context over the past decades. We find that, during the 2020 event, a high-pressure ridge over the 40°-100°W sector and a blocking high on the Drake Passage led to an anticyclonic circulation that brought warm and moist air from the Pacific Ocean to the Antarctic Peninsula. Vertical air flows in a foehn warming event dominated by sensible heat and radiation made the largest contribution to the abrupt warming. A further analysis with 196 extreme warm events in austral summer between 1973 and 2020 suggests that the mechanisms behind the 2020 event form one of the two most common clusters of the events, exhibiting that most of the extreme warm events at Esperanza station are linked to air masses originating over the Pacific Ocean.