A Regimes-Based Approach to Identifying Seasonal State-Dependent Prediction Skill

Abstract

Subseasonal-to-decadal atmospheric prediction skill attained from initial conditions is typically limited by the chaotic nature of the atmosphere. However, for some atmospheric phenomena, prediction skill on subseasonal-to-decadal timescales is increased when the initial conditions are in a particular state. In this study, we employ machine learning to identify sea surface temperature (SST) regimes that enhance prediction skill of North Atlantic atmospheric circulation. An ensemble of artificial neural networks is trained to predict anomalous, low-pass filtered 500 mb height at 7–8 weeks lead using SST. We then use self-organizing maps (SOMs) constructed from 9 regions within the SST domain to detect state-dependent prediction skill. SOMs are built using the entire SST time series, and we assess which SOM units feature confident neural network predictions. Four regimes are identified that provide skillful seasonal predictions of 500 mb height. Our findings demonstrate the importance of extratropical decadal SST variability in modulating downstream ENSO teleconnections to the North Atlantic. The methodology presented could aid future forecasting on subseasonal-to-decadal timescales.