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• W4225321588 abstract "Abstract The global mean surface temperature (GMST) record exhibits both interannual to multidecadal variability and a long‐term warming trend due to external climate forcing. To explore the predictability of temporary slowdowns in decadal warming, we apply an artificial neural network (ANN) to climate model data from the Community Earth System Model Version 2 Large Ensemble. Here, an ANN is tasked with whether or not there will be a slowdown in the rate of the GMST trend by using maps of ocean heat content (OHC) at the onset. Through a machine learning explainability method, we find the ANN is learning off‐equatorial patterns of anomalous OHC that resemble transitions in the phase of the Interdecadal Pacific Oscillation in order to make slowdown predictions. Finally, we test our ANN on observed historical data, which further reveals how explainable neural networks are useful tools for understanding decadal variability in both climate models and observations." @default.
• W4225321588 created "2022-05-05" @default.
• W4225321588 creator A5038101556 @default.
• W4225321588 creator A5054390435 @default.
• W4225321588 date "2022-05-09" @default.
• W4225321588 modified "2023-10-14" @default.
• W4225321588 title "Predicting Slowdowns in Decadal Climate Warming Trends With Explainable Neural Networks" @default.
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