UNET-vs-Analogues_data

Data for Estimation of the atmospheric circulation contribution to the European temperature variability with convolutional neural network.

Abstract

In Europe, temperature variations are mainly driven by the North Atlantic atmospheric circulation. Here we investigate a convolutional neural network (a UNET) for reconstructing daily temperature anomalies in Europe from sea level pressure as a proxy of the atmospheric circulation, and we compare the results with the traditional analogues approach. We show an excellent ability of the UNET to estimate temperature variations given information from sea level pressure only. This novel method outperforms the analogue 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 work opens up promising prospects for estimating the contribution of atmospheric variability to temperature variations