Assessing Tropical Pacific–Induced Predictability of Southern California Precipitation Using a Novel Multi-Input Multioutput Autoencoder

Abstract

Abstract We construct a novel multi-input multioutput autoencoder (MIMO-AE) to capture the nonlinear relationship of Southern California precipitation and tropical Pacific Ocean sea surface temperature. The MIMO-AE is trained on both monthly tropical Pacific sea surface temperature (TP-SST) and Southern California precipitation (SC-PRECIP) anomalies simultaneously. The covariability of the two fields in the MIMO-AE shared nonlinear latent space can be condensed into an index, termed the MIMO-AE index. We use a transfer learning approach to train a MIMO-AE on the combined dataset of 100 yr of output from a historical simulation with the Energy Exascale Earth Systems Model, version 1, and a segment of observational data. We further use long short-term memory networks to assess subseasonal predictability of SC-PRECIP using the MIMO-AE index. We find that the MIMO-AE index provides enhanced predictability of SC-PRECIP for a lead time of up to 4 months as compared with the Niño-3.4 index and the El Niño–Southern Oscillation longitudinal index. Significance Statement Traditional El Niño–Southern Oscillation indices, like the Niño-3.4 index, although well predicted themselves, fail to offer reliable subseasonal-to-seasonal predictions of western U.S. precipitation. Here, we use a machine learning approach called a multi-input, multioutput autoencoder to capture the relationship between tropical Pacific Ocean and Southern California precipitation and project it onto a new index, which we call the MIMO-AE index. Using machine learning–based time series predictions, we find that the MIMO-AE index offers enhanced predictability of Southern California precipitation up to a lead time of 4 months as compared with other ENSO indices.