Sea Surface Temperature Predictability at the interface between oceanographic modelling and machine learning

Ensemble models, statistical analysis and machine learning (ML) can be used to predict novel conditions in a rapidly changing ocean. Traditionally, ML has been understood as a purely data-driven approach. Recently, success has been reported in training ML on both observational and model data to forecast Sea Surface Temperature (SST) anomalies. Here we use ML trained only on climate model simulations to predict regional SST variations, thereby suggesting a novel role for ML as an ensemble model interpolator. We propose a measure of the predictability provided by different ML implementations as well as by standard time series analysis methods. Weighting each forecast by this predictability measure computed on model data only, provides a significant improvement in forecast skill. We demonstrate the performance of this approach for regions around Australia, the Nino3.4 region (central-eastern equatorial Pacific) and in the eastern equatorial Pacific and discuss the implications for SST predictability as a function of geographical location, area size, seasonality, proximity to the coast and model data quality.