Assessing Tropical Pacific-induced Predictability of Southern California
Precipitation Using a Novel Multi-input Multi-output Autoencoder
Abstract
We construct a novel Multi-Input Multi-Output Autoencoder-decoder
(MIMO-AE) to capture the non-linear relationship of Southern California
precipitation (SC-PRECIP) and tropical Pacific Ocean sea surface
temperature (TP-SST). The MIMO-AE is trained on both monthly TP-SST and
SC-PRECIP anomalies simultaneously. The co-variability 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 years of output from a
historical simulation with the Energy Exascale Earth Systems Model
version 1 (E3SMv1) and a segment of observational data. We further use
Long Short-Term Memory (LSTM) networks to assess sub-seasonal
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 four months as compared to Nino 3.4 index and the El
Nino Southern Oscillation Longitudinal Index.