Statistical improvement of TAG Index Prediction Skill in DCPP-A Hindcast Experiments Using Deep Learning

Reliable climate information at multi-year lead times is essential for informed decision-making and long-term planning. Such information helps policymakers and stakeholders prepare for climate-related risks and build resilience to ongoing climate variability and change.

Decadal climate variability (DCV) affects regional climate patterns all over the world on timescales of several years to decades. Skillful prediction of these modes and their impacts can support planning several years in advance. The Tropical Atlantic SST Gradient (TAG) index is one such DCV mode, characterized by differences in sea surface temperature across the tropical Atlantic Ocean. Variations in TAG strongly affect rainfall patterns, circulation, and climate extremes in surrounding regions, including parts of Africa and South America, with important socio-economic consequences. The Decadal Climate Prediction Project (DCPP), conducted under CMIP6, provides coordinated decadal hindcast and forecast experiments to study and predict such variability.

However, traditional statistical approaches often struggle to represent the complex, non-linear, and non-stationary nature of DCV modes like TAG. Deep learning (DL) methods offer a promising alternative, as they are well suited to capturing both long-term trends and shorter-term fluctuations, as well as changes in the phase of variability.

In this study, we aim to strengthen the prediction skill of the CMIP6 multi-model ensemble (MME) TAG index for lead years 1–10 using DL-based post-processing. We apply a recurrent neural network (LSTM) to correct the raw CMIP6 MME TAG forecasts. Our results indicate that DL methods have strong potential to enhance the prediction of TAG variability, particularly in terms of its trend and phase. These findings suggest that DL can serve as a valuable complementary tool to existing dynamical models, improving real-time decadal predictions and increasing confidence in operational climate forecasting systems.