Application and Evaluation of Data-Driven Weather Prediction (DWP) Model for Climate Modeling

Recent advancements in AI/ML weather prediction models have attracted significant attention for their innovative approaches to forecasting. These models, leveraging deep learning techniques applied to the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis data, predict future states of meteorological variables iteratively over specific time steps to generate forecasts. Known as Data-Driven Weather Prediction (DWP), this methodology has demonstrated comparable accuracy to Numerical Weather Prediction (NWP) models for certain variables while requiring substantially less computational effort. Despite its advantages, DWP’s reliance on historical data patterns limits its ability to predict extreme or evolving weather phenomena influenced by global warming and climate change. These limitations present challenges for its application in climate simulations and projections.

To address these limitations, this study explored the application of the GraphCast DWP model in climate research, focusing on global climate downscaling and bias correction. Preliminary experiments with 24-hour GraphCast integrations spanning 36 years (1979–2014) demonstrated that GraphCast’s climate integrations closely align with the mean state and trends of the HiRAM climate simulation. Additionally, the model demonstrates variance in precipitation and surface temperature comparable to ERA5. The primary objective of this study is to demonstrate that this innovative approach to global climate modeling provides both computational efficiency and robust performance, effectively capturing climate phenomena while preserving critical information from climate simulations. Furthermore, the proposed methodology underscores the potential of GraphCast to advance global climate modeling, indicating its suitability for future projections conducted by low-resolution climate models.