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The document discusses utilizing neural networks to analyze climate forcing in large ensemble simulations, emphasizing the annual mean temperature of Earth and its variations due to factors like greenhouse gases and aerosols. It introduces explainable AI methods, specifically layer-wise relevance propagation, to identify significant climate pattern indicators and assess model predictions. The research indicates that artificial neural networks can more accurately predict real-world data when trained on ensembles that do not evolve time-dependent aerosols.











































