Assimilation sensitivity of satellite-derived surface melt into the Regional Climate Model MAR: case study over the Antarctic Peninsula

• Main Authors: Dethinne, Thomas, Glaude, Quentin, Picard, Ghislain, Kittel, Christoph, Orban, Anne, Fettweis, Xavier
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: article; Verlagsveröffentlichung; Antarctic; The Antarctic; Antarctic Peninsula; Antarc*
• Online Access: https://doi.org/10.5194/egusphere-2022-1371; https://noa.gwlb.de/receive/cop_mods_00064009; https://egusphere.copernicus.org/preprints/egusphere-2022-1371/egusphere-2022-1371.pdf

The study of the recent variability and the future projections of the poles’ climate currently relies on polar-oriented Regional Climate Models (RCMs). However, RCMs are subject to biases and systematic errors that impact the results of their simulations. Remote Sensing (RS) data can help to reduce these ambiguities by providing indirect observations to the modeled estimates. Using the behavior of radiofrequency signals with regard to the presence of water in a snowpack, passive and active microwave instruments such as AMSR2, ASCAT, and Sentinel-1 are used to detect melt at the surface of the snowpack. In this paper, we investigate the sensitivity of the RCM “Modèle Atmosphérique Régional” (MAR) to the assimilation of surface melt occurrence estimated by RS datasets. The assimilation is performed by nudging the MAR snowpack temperature to match the observed melt state by satellite. The sensitivity is tested by modifying parameters of the assimilation: (i) the depth to which MAR snowpack is warmed up or cooled down (corresponding to the penetration depth of the satellites) to match with satellite, and (ii) the quantity of water required into the snowpack to qualify a MAR pixel as melting or not, and (iii) by assimilating multiple RS datasets. The data assimilation is performed over the Antarctic Peninsula for the 2019-2021 period. The results show an increase in the melt production (+66.7 % on average, or +95 Gt) going along with a small decrease in surface mass balance (SMB) (-4.5 % on average, or -20 Gt) for the 2019–2020 melt season. The model is sensitive to the three parameters tested but with different orders of magnitude. The sensitivity to the assimilated dataset is reduced by using multiple datasets during the assimilation and discarding the remote observations that are not coherent. For the other two parameters, the penetration depth has more impact on the assimilation than the quantity of liquid water used as melt threshold. The first one is especially sensitive for the sensors with a shorter ...