Articles | Volume 29, issue 3
Nonlin. Processes Geophys., 29, 255–264, 2022
Nonlin. Processes Geophys., 29, 255–264, 2022
Research article
05 Jul 2022
Research article | 05 Jul 2022

Predicting sea surface temperatures with coupled reservoir computers

Benjamin Walleshauser and Erik Bollt

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Cited articles

Bollt, E.: On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrast to VAR and DMD, Chaos, 31, 013108,, 2021. a, b
Case, J. L., Santos, P., Lazarus, S. M., Splitt, M. E., Haines, S. L., Dembek, S. R., and Lapenta, W. M.: A Multi-Season Study of the Effects of MODIS Sea-Surface Temperatures on Operational WRF Forecasts at NWS Miami, FL, New Orleans, LA, (last access: 29 June 2022), 2008. a
Collins, D. C., Reason, C. J. C., and Tangang, F.: Predictability of Indian Ocean sea surface temperature using canonical correlation analysis, Clim. Dynam., 22, 481–497,, 2004.  a
Dado, J. M. B. and Takahashi, H. G.: Potential impact of sea surface temperature on rainfall over the western Philippines, Prog. Earth Planet. Sci., 4, 23,, 2017. a
Gauthier, D. J., Bollt, E., Griffith, A., and Barbosa, W. A. S.: Next generation reservoir computing, Nat. Commun., 12, 5564,, 2021. a
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.