Dynamical-adjustment-Europe-data

Data for "Role of the atmospheric circulation in the observed warming over Europe using a neural network" Abstract Quantifying the role of the atmospheric circulation in the temperature increase in Europe over the recent decades remains an open question. Here, we present an innovative dynamical adjustment framework based on a convolutional neural network (UNET) trained on CMIP6 simulations and fine-tuned on reanalysis, to estimate the circulation-induced temperature at the daily timescale and the subsequent trends over 1979-2024. Following this methodology which provides high reconstruction scores at the daily timescale, we find temperature trends induced by the combination of horizontal winds at 850~hPa of 0.05 [-0.03,0.14] °C/decade annually, 0.08 [-0.00,0.17] °C/decade in summer and 0.09 [-0.11,0.29] °C/decade in winter. In addition, we show that using sea-level pressure rather than upper-level winds would result in slightly higher trends, especially in summer.