Data driven deep learning for correcting global climate model projections of sea surface temperature and dynamic sea level in the Bay of Bengal

Key Points

• Correcting climate model projections leads to improved predictions of sea surface temperature and dynamic sea level.
• The study shows a significant reduction in forecasting errors, enhancing model accuracy relative to standard approaches.
• This analysis utilizes deep learning techniques to refine climate model outputs for better reliability in predictions.
• The findings suggest a need for further integration of advanced algorithms in climate modeling for regional assessments.