[POLICY BRIEF] Opportunities & Risks of AI-applications in Climate Sciences
This policy brief summarizes key learnings from the H2020 XAIDA and CLINT projects and formulates recommendations about the use of AI-applications in Climate Sciences.Description : Artificial Intelligence (AI) is unlocking new frontiers in climate science and climate services—from improving impact-based seasonal forecasts to longer-horizon climate risk projections.
However, the rapid growth of AI also brings challenges related to reliability, interpretability and trustworthiness. To fully harness AI’s potential while maintaining public trust, we need to invest in data-driven and hybrid models, transparent and open data practices, and robust validation. Funders, academics, and climate-tech startups must work together to develop scientifically credible AI-based climate services – at a time when informed responses to climate risks are more critical than ever.
In recent years, AI has made a remarkable entrance into climate research, revolutionizing weather forecasting. A major advancement has been the development of so-called ‘Foundation Models,’ which train Deep Learning (DL) architectures on large climate datasets to learn the complex underlying dynamics. Big tech companies like Google, Huawei, Nvidia, but also the European Centre for Medium-Range Weather Forecasts (ECMWF) have developed purely data-driven weather forecasting models that learn patterns directly from historical data.
These AI models outperform traditional Numerical Weather Prediction (NWP) models in standard forecast verification scores for some meteorological variables and timescales.This joint Policy Brief adresses a Science message and a Climate Services message to help the decision-making process.
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Pascal Yiou, vice-coordinator of the XAIDA project •
