Arctic sea ice radar freeboard retrieval from ERS-2 using altimetry: Toward sea ice thickness observation from 1995 to 2021

• Main Authors: Bocquet, Marion, Fleury, Sara, Piras, Fanny, Rinne, Eero, Sallila, Heidi, Garnier, Florent, Rémy, Frédérique
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: article; Verlagsveröffentlichung; Arctic; Sea ice
• Online Access: https://doi.org/10.5194/egusphere-2022-214; https://noa.gwlb.de/receive/cop_mods_00061718; https://egusphere.copernicus.org/preprints/egusphere-2022-214/egusphere-2022-214.pdf

Sea ice volume significant interannual variability requires long-term series of observations to identify trends in its evolution. Despite improvements in sea ice thickness estimations from altimetry during the past few years thanks to CryoSat-2 and ICESat-2, former ESA radar altimetry missions such as Envisat and especially ERS-1 and ERS-2 have remained under-exploited so far. Although solutions have already been proposed to ensure continuity of measurements between CryoSat-2 and Envisat, there is no time series integrating ERS. The purpose of this study is to extend the Arctic freeboard time series back to 1995. The difficulty to handle ERS measurements comes from a technical issue known as the pulse-blurring effect, altering the radar echos over sea ice and the resulting surface height estimates. Here we present and apply a correction for this pulse-blurring effect. To ensure consistency of the CryoSat-2/Envisat/ERS-2 time series, a multi-parameters neural network-based method to calibrate Envisat against CryoSat-2 and ERS-2 against Envisat is presented. The calibration is trained on the discrepancies observed between the altimeter measurements during the missions-overlap periods and a set of parameters characterizing the sea ice state. Monthly radar freeboards are provided with uncertainty estimations based on a Monte Carlo approach to propagate the uncertainties all along the processing chain, including the neural network. Comparisons of corrected radar freeboards during overlap periods reveal good consistencies between missions, with a mean bias of 3 mm for Envisat/CryoSat-2 and 2 mm for ERS-2/Envisat. The monthly maps obtained from Envisat and ERS-2 are then validated by comparison with several independent data such as airborne, moorings, direct measurements and other altimeter products. Except for two data sets, comparisons lead to correlation ranging from 0.42 to 0.94 for Envisat, and 0.6 to 0.76 for ERS-2. The study finally provides radar freeboard estimation for winters from 1995 to 2021 (from ERS-2 ...