Bias correction with a deep neural model to improve Global Climate Model salinity projections in the Bay of Bengal

Global Climate Models (GCMs) simulate the Earth’s climate dynamics and forecast the future climate under different shared socioeconomic pathways. However, GCM salinity projections in the Bay of Bengal have high errors as evaluated for the years in which both projections and reanalysis are available. We develop a convolutional encoder-decoder deep neural operator model (UNet) for bias correction of GCM salinity projections in the Bay of Bengal. We analyze the corrected projections quantitatively and qualitatively to examine the mean and variability of surface salinity and the top 200m depth-averaged salinity projections. The root mean square error (RMSE) and Pattern Correlation Coefficient (PCC) analysis quantitatively demonstrate UNet’s superior performance compared to the baseline Equi-Distant Cumulative Distribution Function (EDCDF) method for bias correction. The UNet-corrected projections demonstrate significant improvements with up to 45% lower RMSE and 5% higher PCC for sea surface salinity, and up to 35% lower RMSE and 2% higher PCC for 200m depth-averaged salinity during the test period. Crucially, the modified salinity patterns in the UNet-corrected projections suggest changes to major features such as the barrier layer, East India Coastal Current, and Southwest Monsoon Current, which in turn affect the health of mangroves and the primary productivity of the region.