Harnessing Data-Driven Weather Prediction (DWP) Model for Climate Modeling

Recent advancements in AI/ML-based Data-Driven Weather Prediction (DWP) have revolutionized meteorological forecasting. By leveraging deep learning architectures trained on the ECMWF ERA5 reanalysis, DWP models can iteratively predict atmospheric states with accuracy comparable to traditional Numerical Weather Prediction (NWP) while requiring orders of magnitude less computational power. However, DWP’s reliance on historical training data poses challenges for climate-scale simulations, particularly in representing evolving phenomena influenced by non-stationary climate change. This study investigates the applicability of the GraphCast DWP model for climate research, specifically focusing on its potential for global climate downscaling and bias correction.

To evaluate performance across varying initial conditions, we conducted three distinct 72-hour GraphCast integration experiments. The first experiment utilized high-resolution (0.25°) ERA5 data from 2000–2010 to assess model reproducibility (H-ERA5), while the second experiment employed low-resolution (1.0°) ERA5 data to quantify sensitivity to initial horizontal grid spacing (L-ERA5). In the third experiment, we utilized 36 years (1979–2014) of HiRAM climate simulations as initial conditions to evaluate a novel DWP-based climate modeling framework (GC-HiRAM).

Results from the H-ERA5 and L-ERA5 experiments demonstrate that GraphCast effectively reproduces the climate mean state and variance of the ERA5 dataset. However, both experiments exhibited an underestimation of tropical cyclone (TC) frequency and intensity, consistent with known TC climatology biases in ERA5. Notably, the GC-HiRAM experiment closely aligned with the mean states and long-term trends of the original HiRAM simulations while yielding precipitation and surface temperature variances comparable to ERA5. Interestingly, the inherent TC underestimation in GraphCast served as a functional bias correction for HiRAM, which traditionally overestimates TC frequency, thereby improving overall simulation skill. Our findings suggest that this innovative DWP-driven approach provides a computationally efficient and robust framework for global climate modeling, effectively capturing essential climate phenomena while introducing a viable pathway for high-resolution climate downscaling and ensemble simulations.