Preprints
https://doi.org/10.5194/egusphere-2022-1371
https://doi.org/10.5194/egusphere-2022-1371
 
15 Dec 2022
15 Dec 2022
Status: this preprint is open for discussion.

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

Thomas Dethinne1,2, Quentin Glaude1,2, Ghislain Picard3, Christoph Kittel4, Anne Orban2, and Xavier Fettweis1 Thomas Dethinne et al.
  • 1University of Liège, Laboratory of Climatology, Liège, Belgium
  • 2University of Liège, Centre Spatial de Liège, Laboratory of Signal Processing, Liège, Belgium
  • 3Institut des Géosciences de l’Environnement (IGE), Université Grenoble Alpes/CNRS/UMR 5001, Grenoble, France
  • 4Institut des Géosciences de l’Environnement (IGE), Université Grenoble Alpes/CNRS/IRD/G-INP, Grenoble, France

Abstract. 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 penetration depth. In the first centimeters, a densification due to a refreeze can impact the melt production and cause an overestimation of the melt production. For the second threshold, the impact is more important on the number of melt days rather than the melt production itself. The values tested for the quantity of liquid water required into the snowpack to qualify a MAR pixel as melting or not (0.1 or 0.2 % of the snowpack mass being water) are lower than during typical melt days (~1.2 %) and impact results mainly at the beginning and end of the melt period when lower values are reached. Such an assimilation will allow an uncertainty estimation of MAR’s melt production, as well as identifying potential issues at the snowpack surface processes.

Thomas Dethinne et al.

Status: open (until 02 Mar 2023)

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Thomas Dethinne et al.

Thomas Dethinne et al.

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Short summary
We investigate the sensitivity of the regional climate model “Modèle Atmosphérique Régional” (MAR) to the assimilation of surface melt occurrence estimated by remote sensing datasets. The assimilation is performed by nudging the MAR snowpack temperature. 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 %) and a decrease in surface mass balance (-4.5 %) of the model for the 2019–2020 melt season.