Joint Satellite SST and Dynamic SSS as Key Constraints on the Thermodynamics of Tropical Instability Wave Variability in the Eastern Equatorial Pacific

Abstract

Tropical instability waves (TIWs) generate mesoscale sea surface temperature (SST) fluctuations in the eastern equatorial Pacific and influence the evolution of the El Niño‐Southern Oscillation (ENSO). Yet satellite‐based prediction of TIW‐related SST anomalies remains largely SST‐centric and makes limited use of sea surface salinity (SSS) observations. Here we show that satellite SSS provides a practically useful constraint on TIW‐related SST forecasts. We develop a dual‐branch deep learning framework trained on Copernicus Marine Service SST and SSS products, in which a ConvLSTM‐UNet3D trend branch represents slowly varying evolution and a frame‐difference branch highlights transient TIW disturbances. A Mean Deviation Loss strengthens learning in regions of strong anomalies, including the TIW triangular zone and the cold tongue. SST is the sole prognostic output, while SSS is prescribed at each rollout step under three protocols with distinct operational meanings: a deployable, leakage‐free SST‐recursive forecast using training‐derived monthly SSS climatology; a diagnostic upper bound using observed SSS at the corresponding forecast dates; and a constant‐field Zero‐map stress test. Compared with the SST‐only configuration, incorporating time‐varying SSS reduces the one‐step 5‐day SST RMSE from 0.30 to 0.27°C, while the deployable climatological‐SSS setting attains 0.28°C, within 0.01°C of the observed‐SSS upper bound at the single‐step horizon and closely tracking it under multi‐step rollouts. Cross‐spectral and time‐lagged regression analyses further reveal that coherent leading SSSA modes can lead SSTA by 5–12 days on TIW timescales, indicating that seasonal‐background and event‐scale SSS carry physically meaningful, complementary predictive information for TIW‐related SST evolution.