Global High-Resolution Random Forest Regression Maps of Ocean Heat Content Anomalies Using In Situ and Satellite Data

Abstract

The ocean, with its low albedo and vast thermal inertia, plays key roles in the climate system, including ab-sorbing massive amounts of heat as atmospheric greenhouse gas concentrations rise. While the Argo array of profiling floats has vastly improved sampling of ocean temperature in the upper half of the global ocean volume since the mid-2000s, they are not sufficient in number to resolve eddy scales in the oceans. However, satellite sea surface temperature (SST) and sea surface height (SSH) measurements do resolve these scales. Here we use random forest regressions to map ocean heat content anomalies (OHCA) using in situ training data from Argo and other sources on a 7