Sea Ice-thickness product iNter-comparison eXerciSe – The ESA SIN’XS project

• Main Authors: Da Silva, E., Haas, C., Fleury, S., Hendricks, S., Paul, S., Tsamados, M., Munesa, E., Pastor, J.
• Format: Conference Object
• Language: English
• Published: 2023
• Subjects: Arctic; Antarctic; Antarc*; Sea ice
• Online Access: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019859

The SIN’XS project is a three-year activity (May 2022 – May 2025) funded by ESA in the frame of the Polar Science Cluster. In light of rapid changes of the Arctic and Antarctic sea ice cover, continued and improved observations, understanding, and predictions of its thickness are particularly important for a range of fields from climate studies to offshore operations in ice. Systematic and accurate ice thickness observations are now available from several satellite missions. However, they differ in used processing algorithms and assumptions, temporal and spatial coverage and resolution, and applicability to stakeholder needs like modelling and assimilation, numerical weather prediction, and ship routing. These differences between products have so far complicated the consistent use of the various data products and there is little consensus about Arctic and Antarctic Sea ice volume variability and change. The Sea Ice-thickness product iNter-comparison eXerciSe (SIN’XS) will identify some of these gaps by carrying out in-depth intercomparisons of a wide range of satellite ice thickness products from altimetry and other methods, in close collaboration with an international community of scientific and operational sea ice experts, and in partnership with the WMO Global Cryosphere Watch (GCW). It will develop joint protocols for the intercomparison of ice thickness products and their validation, based on established approaches from the QA4EO project and by further developing a framework for Fiducial Reference Measurements (FRMs). SIN’XS will develop an online system to engage the community with data submission and to support scientific analysis of the data sets and intercomparisons.