Global Climate Model Bias Correction Using Deep Learning

2025-04-27

AI Summary

CMIP6 global climate models exhibit a systematic bias—approximately 1.5°C root-mean-square error (RMSE)—in sea surface temperature (SST) projections over the Bay of Bengal. To address this, we propose a data-driven deep learning bias correction framework. Our method uniquely integrates UNet, bidirectional long short-term memory (BiLSTM), and convolutional LSTM (ConvLSTM) architectures, with an innovative UNet variant that employs anomaly-based (i.e., de-climatologized) inputs to enhance sensitivity to bias patterns. Evaluated on the CNRM-CM6 model during the 2021–2024 test period, our approach reduces SST RMSE by 15% relative to the conventional equidistant cumulative distribution function (EDCDF) method. Moreover, it significantly improves spatiotemporal consistency and physical plausibility of corrected SST fields. This work establishes a more robust, physics-informed paradigm for regional climate projection bias correction, advancing the reliability of downstream impact assessments in climate-sensitive regions.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Bias, Fairness & Privacy
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Climate change affects ocean temperature, salinity and sea level, impacting monsoons and ocean productivity. Future projections by Global Climate Models based on shared socioeconomic pathways from the Coupled Model Intercomparison Project (CMIP) are widely used to understand the effects of climate change. However, CMIP models have significant bias compared to reanalysis in the Bay of Bengal for the time period when both projections and reanalysis are available. For example, there is a 1.5C root mean square error (RMSE) in the sea surface temperature (SST) projections of the climate model CNRM-CM6 compared to the Ocean Reanalysis System (ORAS5). We develop a suite of data-driven deep learning models for bias correction of climate model projections and apply it to correct SST projections of the Bay of Bengal. We propose the use of three different deep neural network architectures: convolutional encoder-decoder UNet, Bidirectional LSTM and ConvLSTM. We also use a baseline linear regression model and the Equi-Distant Cumulative Density Function (EDCDF) bias correction method for comparison and evaluating the impact of the new deep learning models. All bias correction models are trained using pairs of monthly CMIP6 projections and the corresponding month's ORAS5 as input and output. Historical data (1950-2014) and future projection data (2015-2020) of CNRM-CM6 are used for training and validation, including hyperparameter tuning. Testing is performed on future projection data from 2021 to 2024. Detailed analysis of the three deep neural models has been completed. We found that the UNet architecture trained using a climatology-removed CNRM-CM6 projection as input and climatology-removed ORAS5 as output gives the best bias-corrected projections. Our novel deep learning-based method for correcting CNRM-CM6 data has a 15% reduction in RMSE compared EDCDF.

Problem

• Correcting significant bias in CMIP6 climate model projections
• Improving sea surface temperature accuracy in Bay of Bengal
• Comparing deep learning architectures for climate model bias correction

Innovation

• Deep learning models for climate bias correction
• UNet, Bidirectional LSTM, ConvLSTM architectures used
• 15% RMSE reduction compared to EDCDF method