Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach
Abstract. Heterogeneous radiative forcing in mid-latitudes, such as that exerted by aerosols, has been found to affect the Arctic climate, though the mechanisms remain debated. In this study, we leverage Deep Learning (DL) techniques to explore the complex response of the Arctic climate system to local radiative forcing over Europe. We conducted sensitivity experiments using the Max Planck Institute Earth System Model (MPI-ESM1.2) coupled with atmosphere-ocean–land surface components. Utilizing a DL-based clustering method, we classify atmospheric circulation patterns in a lower-dimensional space, focusing on Poleward Moist Static Energy Transport (PMSET) as our primary parameter. We developed a novel method to analyze the circulation patterns' contributions to various climatic parameter anomalies. Our findings indicate that the negative forcing over Europe alters existing circulation patterns and their occurrence frequency without introducing new ones. Specifically, we identify changes in a circulation pattern with a high-pressure system over Scandinavia as a key driver for reduced Sea Ice Concentration (SIC) in the Barents-Kara Sea during autumn. This circulation pattern also influences middle atmospheric dynamics, although its contribution is relatively minor compared to other circulation patterns that resemble the phases of the North Atlantic Oscillation (NAO). Our multidimensional approach combines DL algorithms and human expertise to offer a novel analytical tool that could have broader applications in climate science.
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