AI reconstruction of European weather from the Euro-Atlantic regimes

TL;DR

This paper introduces a non-linear AI model that reconstructs European temperature and precipitation anomalies from Euro-Atlantic Weather regimes, improving seasonal forecasts by capturing complex atmospheric-land interactions.

Contribution

It presents a novel AI-based approach to estimate ground-level climate variables from Weather regimes, surpassing traditional linear methods and demonstrating practical forecasting improvements.

Findings

AI model outperforms linear methods in reconstructing temperature and precipitation anomalies.

Reconstruction accuracy improves with more Weather regimes used.

Model achieves comparable or better skill than ECMWF SEAS5 forecasts.

Abstract

We present a non-linear AI-model designed to reconstruct monthly mean anomalies of the European temperature and precipitation based on the Euro-Atlantic Weather regimes (WR) indices. WR represent recurrent, quasi-stationary, and persistent states of the atmospheric circulation that exert considerable influence over the European weather, therefore offering an opportunity for sub-seasonal to seasonal forecasting. While much research has focused on studying the correlation and impacts of the WR on European weather, the estimation of ground-level climate variables, such as temperature and precipitation, from Euro-Atlantic WR remains largely unexplored and is currently limited to linear methods. The presented AI model can capture and introduce complex non-linearities in the relation between the WR indices, describing the state of the Euro-Atlantic atmospheric circulation and the corresponding surface temperature and precipitation anomalies in Europe. We discuss the AI-model performance in reconstructing the monthly mean two-meter temperature and total precipitation anomalies in the European winter and summer, also varying the number of WR used to describe the monthly atmospheric circulation. We assess the impact of errors on the WR indices in the reconstruction and show that a mean absolute relative error below 80% yields improved seasonal reconstruction compared to the ECMWF operational seasonal forecast system, SEAS5. As a demonstration of practical applicability, we evaluate the model using WR indices predicted by SEAS5, finding slightly better or comparable skill relative to the SEAS5 forecast itself. Our findings demonstrate that WR-based anomaly reconstruction, powered by AI tools, offers a promising pathway for sub-seasonal and seasonal forecasting.