Estimating the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and ice mass balance buoy data

• Published in: The Cryosphere
• Main Authors: Kilic, Lise, Tonboe, Rasmus Tage, Prigent, Catherine, Heygster, Georg
• Format: Text
• Language: English
• Published: 2019
• Subjects: Arctic; Sea ice
• Online Access: https://doi.org/10.5194/tc-13-1283-2019; https://tc.copernicus.org/articles/13/1283/2019/

Mapping sea ice concentration (SIC) and understanding sea ice properties and variability is important, especially today with the recent Arctic sea ice decline. Moreover, accurate estimation of the sea ice effective temperature ( T eff ) at 50 GHz is needed for atmospheric sounding applications over sea ice and for noise reduction in SIC estimates. At low microwave frequencies, the sensitivity to the atmosphere is low, and it is possible to derive sea ice parameters due to the penetration of microwaves in the snow and ice layers. In this study, we propose simple algorithms to derive the snow depth, the snow–ice interface temperature ( T Snow−Ice ) and the T eff of Arctic sea ice from microwave brightness temperatures (TBs). This is achieved using the Round Robin Data Package of the ESA sea ice CCI project, which contains TBs from the Advanced Microwave Scanning Radiometer 2 (AMSR2) collocated with measurements from ice mass balance buoys (IMBs) and the NASA Operation Ice Bridge (OIB) airborne campaigns over the Arctic sea ice. The snow depth over sea ice is estimated with an error of 5.1 cm, using a multilinear regression with the TBs at 6, 18, and 36 V. The T Snow−Ice is retrieved using a linear regression as a function of the snow depth and the TBs at 10 or 6 V. The root mean square errors (RMSEs) obtained are 2.87 and 2.90 K respectively, with 10 and 6 V TBs. The T eff at microwave frequencies between 6 and 89 GHz is expressed as a function of T Snow−Ice using data from a thermodynamical model combined with the Microwave Emission Model of Layered Snowpacks. T eff is estimated from the T Snow−Ice with a RMSE of less than 1 K.