Using AI-driven weather prediction models for attribution of extreme events to climate change

Anthropogenic climate change (ACC) is intensifying the frequency and severity of extreme events globally, such as extreme heatwaves and heavy precipitation. Attributing individual extreme events (EEs) to ACC is critical for assessing the risks of climate change. A common method for addressing this challenge is the pseudo-global-warming (PGW) approach, which involves removing the thermodynamic ACC signal from the initial and boundary conditions in a weather or climate model to simulate the event under preindustrial conditions. However, traditional numerical, physics-based models require substantial computational resources and expertise, often delaying attribution results. This study introduces a novel attribution method that integrates the PGW approach with cutting-edge artificial intelligence weather prediction (AIWP) models. By leveraging AIWP models, which offer rapid and efficient computations, this method significantly accelerates the process of extreme event attribution, thus copying with the demand of information on due time. The ACC signal is estimated using CMIP6 historical simulations and subtracted from the initial conditions to enable AIWP model forecasts of the event without ACC influence.

Using this hybrid approach, we quantify the impact of ACC on several recent heatwave events, including the 2018 Iberian heatwave the 2022 Pacific Northwest heatwave, and the 2023 Brazilian heatwave. Our results reveal clear ACC fingerprints in the forecasted temperature fields, showing an overall increase in the severity of these events due to climate change, but with regional differences. We further validate these findings by applying the method to a hybrid-AI atmospheric model, which quantifies the role of sea surface temperature anomalies in intensifying these extreme events.

Beyond heatwaves, this approach demonstrates its versatility by detecting ACC fingerprints in extratropical cyclones. For example, the method indicates that ACC contributed to the enhanced winds associated with the extratropical bomb cyclone Ciarán that impacted Western Europe in 2023. While the method has some limitations, such as sensitivity to initial conditions and uncertainties in CMIP6 projections, it represents a significant step forward in the rapid and accessible attribution of extreme events.