Using AI-based numerical weather prediction models for climate applications


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nikolay.koldunov [ at ] awi.de

Abstract

State-of-the-art AI-based numerical weather prediction models (AI-NWP) produce forecasts that are comparable or even outperform conventional forecasting systems while being orders of magnitude faster. Since climate projections are obtained by simulating the long-term evolution of weather states with appropriate forcing, the use of AI-NWP models for climate modeling is a promising avenue that has received little attention so far. We present two applications of AI-NWP models for climate modeling: (i) downscaling and (ii) weather forecasting initialised from climate projection data. Both applications use ERA5-pre-trained AI-NWP models without fine-tuning for the tasks or for the input data. For downscaling, we use low-resolution CMIP6 simulation data as initial condition and obtain high-resolution, bias corrected output fields by performing short-term forecasting with the existing model; see Fig. 1 for an example. Our results show a remarkable robustness of AI-NWP to unseen states from historical and climate simulations of different resolutions. For AI-based weather forecasting in future climates, we obtain almost unchanged RMSE scores in a 2o warmer climate although a more detailed analysis shows a cold bias in the forecasts. We believe that differences between climate model results and AI-NWP forecasts have the potential to provide insights into the physics and deficiencies of both climate models (e.g. for short time scales) and AI-NWP models (on long time scales). Based on our results, we discuss how existing AI-NWP models can be extended for climate projections, e.g. to sample extreme weather events, and hence help with adaptation to climate change.



Item Type
Conference (Lecture)
Authors
Divisions
Primary Division
Programs
Primary Topic
Helmholtz Cross Cutting Activity (2021-2027)
Publication Status
Published
Eprint ID
59156
Cite as
Koldunov, N. , Rackow, T. and Lessig, C. (2024): Using AI-based numerical weather prediction models for climate applications


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