Preprints
https://doi.org/10.5194/egusphere-2022-214
https://doi.org/10.5194/egusphere-2022-214
 
01 Jul 2022
01 Jul 2022

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

Marion Bocquet1,2, Sara Fleury1, Fanny Piras2, Eero Rinne3,4, Heidi Sallila3, Florent Garnier1, and Frédérique Rémy1 Marion Bocquet et al.
  • 1LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS (Toulouse), France
  • 2Collecte Localisation Satellites (CLS), Toulouse, France
  • 3Marine Research, Finnish Meteorological Institute, Helsinki, Finland
  • 4University Centre in Svalbard (UNIS), PO Box 156, N-9171 Longyearbyen, Norway

Abstract. 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 mission to CryoSat-2).

Marion Bocquet et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-214', Jack Landy, 21 Jul 2022
  • CC1: 'Comment on egusphere-2022-214', Robbie Mallett, 29 Aug 2022
  • RC2: 'Comment on egusphere-2022-214', Robbie Mallett, 26 Sep 2022
  • RC3: 'Comment on egusphere-2022-214', Anonymous Referee #3, 10 Nov 2022

Marion Bocquet et al.

Marion Bocquet et al.

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Short summary
Sea ice has a large interannual variability, studying its evolution requires long time series of observation. In this paper, we propose the first method to extend Arctic sea ice thickness measurements time series from ERS-2 altimeter. The developed method is based on a neural network to calibrate past missions on the current one by taking advantage of their differences during the mission-overlap periods. Data are available as monthly maps for each winter between 1995 and 2021.