Data Driven Deep Learning for Correcting Global Climate Model Projections of SST and DSL in the Bay of Bengal

• URL: http://arxiv.org/abs/2504.20620v1
• Date: Tue, 29 Apr 2025 10:40:37 GMT
• Title: Data Driven Deep Learning for Correcting Global Climate Model Projections of SST and DSL in the Bay of Bengal
• Authors: Abhishek Pasula, Deepak N. Subramani,
• Abstract summary: We introduce a new data-driven deep learning model to correct for this bias.<n>The deep neural model for each variable is trained using pairs of climatology-removed monthly climate projections as input and the corresponding month's ORAS5 as output.<n>Compared to the conventional EquiDistant Cumulative Distribution Function (EDCDF) statistical method for bias correction, our approach decreases RMSE by 0.15C for SST and 0.3 m for DSL.
• Score: 0.4972323953932129
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Climate change alters ocean conditions, notably temperature and sea level. In the Bay of Bengal, these changes influence monsoon precipitation and marine productivity, critical to the Indian economy. In Phase 6 of the Coupled Model Intercomparison Project (CMIP6), Global Climate Models (GCMs) use different shared socioeconomic pathways (SSPs) to obtain future climate projections. However, significant discrepancies are observed between these models and the reanalysis data in the Bay of Bengal for 2015-2024. Specifically, the root mean square error (RMSE) between the climate model output and the Ocean Reanalysis System (ORAS5) is 1.2C for the sea surface temperature (SST) and 1.1 m for the dynamic sea level (DSL). We introduce a new data-driven deep learning model to correct for this bias. The deep neural model for each variable is trained using pairs of climatology-removed monthly climate projections as input and the corresponding month's ORAS5 as output. This model is trained with historical data (1950 to 2014), validated with future projection data from 2015 to 2020, and tested with future projections from 2021 to 2023. Compared to the conventional EquiDistant Cumulative Distribution Function (EDCDF) statistical method for bias correction in climate models, our approach decreases RMSE by 0.15C for SST and 0.3 m for DSL. The trained model subsequently corrects the projections for 2024-2100. A detailed analysis of the monthly, seasonal, and decadal means and variability is performed to underscore the implications of the novel dynamics uncovered in our corrected projections.

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