Linking European Temperature Variations to Atmospheric Circulation With a Neural Network: A Pilot Study in a Climate Model

Abstract

In Europe, temperature variations are mainly driven by the North Atlantic atmospheric circulation. Here, with data from the MIROC6 large ensemble, we investigate a convolutional neural network (a UNET) for reconstructing daily temperature anomalies in Europe from Sea Level Pressure (SLP) as a proxy of the atmospheric circulation, and we compare the results with a traditional analogs approach. We show an excellent ability of the UNET to estimate temperature variations given information from SLP only. This novel method outperforms the analogs method, at both daily and inter-annual time scales. Our study also shows that during the training, the UNET learns information such as the seasonal cycle of the relationship between sea-level pressure and temperature anomalies, which could explain part of its excellent scores. This exploratory work opens up promising prospects for estimating the contribution of atmospheric variability to observed temperature variations.