Articles | Volume 29, issue 3
Nonlin. Processes Geophys., 29, 255–264, 2022
https://doi.org/10.5194/npg-29-255-2022
Nonlin. Processes Geophys., 29, 255–264, 2022
https://doi.org/10.5194/npg-29-255-2022
Research article
05 Jul 2022
Research article | 05 Jul 2022

Predicting sea surface temperatures with coupled reservoir computers

Benjamin Walleshauser and Erik Bollt

Data sets

GHRSST Level 4 MUR 0.25 deg Global Foundation Sea Surface Temperature Analysis (v.4.2) PO.DAAC https://doi.org/10.5067/GHM25-4FJ42

Model code and software

BenWalleshauser/Predicting-SST-w-.-Coupled-RCs: Predicting SST w Coupled RCs (SST_Archive) Ben Walleshauser https://doi.org/10.5281/zenodo.6647777

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Short summary
As sea surface temperature (SST) is vital for understanding the greater climate of the Earth and is also an important variable in weather prediction, we propose a model that effectively capitalizes on the reduced complexity of machine learning models while still being able to efficiently predict over a large spatial domain. We find that it is proficient at predicting the SST at specific locations as well as over the greater domain of the Earth’s oceans.