Long-Term Arctic Snow/Ice Interface Temperature from Special Sensor for Microwave Imager Measurements

The Arctic sea ice region is the most visible area experiencing global warming-induced climate change. However, long-term measurements of climate-related variables have been limited to a small number of variables such as the sea ice concentration, extent, and area. In this study, we attempt to produ...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Sang-Moo Lee, Byung-Ju Sohn, Christian D. Kummerow
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2018
Subjects:
snow/ice interface temperature
SSM/I
microwave measurement
fundamental climate data record
apparent emissivity
Arctic sea ice
Science
Q
Arctic
Arctic Ocean
Climate change
Cold Regions Research and Engineering Laboratory
Global warming
Sea ice
Online Access:https://doi.org/10.3390/rs10111795
https://doaj.org/article/56df12061e1f4254bd96e71f8efbb62f
Description
Summary:The Arctic sea ice region is the most visible area experiencing global warming-induced climate change. However, long-term measurements of climate-related variables have been limited to a small number of variables such as the sea ice concentration, extent, and area. In this study, we attempt to produce a long-term temperature record for the Arctic sea ice region using Special Sensor for Microwave Imager (SSM/I) Fundamental Climate Data Record (FCDR) data. For that, we developed an algorithm to retrieve the wintertime snow/ice interface temperature (SIIT) over the Arctic Ocean by counting the effect of the snow/ice volume scattering and ice surface roughness on the apparent emissivity (the total effect is referred to as the correction factor). A regression equation was devised to predict the correction factor from SSM/I brightness temperatures (TBs) only and then applied to SSM/I 19.4 GHz TB to estimate the SIIT. The obtained temperatures were validated against collocated Cold Regions Research and Engineering Laboratory (CRREL) ice mass balance (IMB) drifting buoy-measured temperatures at zero ice depth. It is shown that the SSM/I retrievals are in good agreement with the drifting buoy measurements, with a correlation coefficient of 0.95, bias of 0.1 K, and root-mean-square error of 1.48 K on a daily time scale. By applying the algorithm to 24-year (1988⁻2011) SSM/I FCDR data, we were able to produce the winter-time temperature at the sea ice surface for the 24-year period.

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