Evaluating Skill and Stability of ArchesWeather and ArchesWeatherGen under Multi-Decadal Climate Simulations
arXiv:2605.29976v2 Announce Type: replace-cross Abstract: We evaluate the climate simulation capabilities of ArchesWeather and ArchesWeatherGen, two machine learning models originally trained for weather forecasting and evaluated up to a 10-day lead time. ArchesWeather is a deterministic model, while ArchesWeatherGen is a probabilistic flow-matching model leveraging ArchesWeather's forecasts, enabling ensemble-based uncertainty quantification. In this work, we adapt these models to act as forced atmospheric models by using additional conditioning on the monthly mean sea surface temperature (SS
The accelerating pace of AI development is enabling its application to complex scientific simulations previously constrained by computational limitations, making climate modeling a prime candidate.
Advanced AI models like ArchesWeather can significantly improve the accuracy and efficiency of multi-decadal climate simulations, providing better long-term forecasts for critical strategic planning across various sectors.
The ability to use machine learning models, originally for weather, for long-term climate simulations changes the paradigm of geophysical modeling, offering faster and potentially more robust predictions than traditional numerical methods.
- · Climate science research
- · Insurance and reinsurance industry
- · Governments and policymakers
- · Energy sector
- · Traditional climate modeling software
- · Sectors unprepared for climate shifts
Improved climate model accuracy leads to more reliable long-term environmental projections.
Better projections enable more effective adaptation and mitigation strategies for climate change.
Enhanced climate foresight could influence geopolitical stability and resource allocation for nations facing significant climate-related challenges.
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Read at arXiv cs.AI