Speaker
Description
Regional Climate Models (RCMs) bring significant added value compared to using global earth system models directly for modelling the Surface Mass Balance of the Antarctic Ice Sheet. However the high computational cost of RCMs limits their application across both the CMIP ensemble and in running different pathways. An alternative is the use of deep learning based climate emulators to expand the ensemble of SMB products available. In this study we first demonstrate the added-value of a multi-model ensemble of RCMs in estimating Antarctic SMB using the SUMUP database of in-situ and radar observations. We then use the output of one member (Harmonie-Climate, HCLIM) of the ensemble to produce downscaled SMB at high temporal and spatial resolution with a U-Net-based neural network. ERA5, a reanalysis nudged by observations, represents the actual observed weather and improves fidelity in the historical period, unlike the Earth System Models (ESMs) which have their own internal variability. The foundational training of the emulator is therefore based on ERA5-driven HCLIM model output, such that it learns the most realistic climate and SMB patterns. We then fine-tune the emulator on a variable number of years of HCLIM driven by the ESMs CESM2 and MPI-ESM. We evaluate the transferability of the emulator by estimating its performance as a function of the number of years used for fine-tuning. We also investigate the transferability to emissions pathways not available in the training dataset. Our emulation approach allows us to produce a wider ensemble of daily SMB values than currently available by RCM downscaling alone. We apply this technique to a range of several CMIP6 ESM members for the period 2015-2100 and quantify the representativity of using only one of the ensemble members to produce future projections of SMB.