Improvement and Sensitivity Analysis of Thermal Thin-Ice Thickness Retrievals

• Published in: IEEE Transactions on Geoscience and Remote Sensing
• Main Authors: Adams, Susann, Schroeder, David, Heinemann, Günther, Krumpen, Thomas
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: IEEE 2012
• Subjects: Arctic; Laptev Sea; laptev; Sea ice
• Online Access: https://epic.awi.de/id/eprint/31971/; https://epic.awi.de/id/eprint/31971/1/06357238.pdf; http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6357238; https://hdl.handle.net/10013/epic.40665; https://hdl.handle.net/10013/epic.40665.d001

Considering the sea ice decline in the Arctic during the last decades, polynyas are of high research interest since these features are core areas of new ice formation. The determination of ice formation requires accurate retrieval of polynya area and thin-ice thickness (TIT) distribution within the polynya. We use an established energy balance model to derive TITs with MODIS ice surface temperatures $(T_{s})$ and NCEP/DOE Reanalysis II in the Laptev Sea for two winter seasons. Improvements of the algorithm mainly concern the implementation of an iterative approach to calculate the atmospheric flux components taking the atmospheric stratification into account. Furthermore, a sensitivity study is performed to analyze the errors of the ice thickness. The results are the following: 1) 2-m air temperatures $(T_{a})$ and $T_{s}$ have the highest impact on the retrieved ice thickness; 2) an overestimation of $T_{a}$ yields smaller ice thickness errors as an underestimation of $T_{a}$; 3) NCEP $T_{a}$ shows often a warm bias; and 4) the mean absolute error for ice thicknesses up to 20 cm is $pm$4.7 cm. Based on these results, we conclude that, despite the shortcomings of the NCEP data (coarse spatial resolution and no polynyas), this data set is appropriate in combination with MODIS $T_{s}$ for the retrieval of TITs up to 20 cm in the Laptev Sea region. The TIT algorithm can be applied to other polynya regions and to past and future time periods. Our TIT product is a valuable data set for verification of other model and remote sensing ice thickness data.