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\begin{document}

\title{Combining Neural Networks and CMIP6 Simulations to Learn Windows of
Opportunity for Skillful Prediction of Multiyear Sea Surface Temperature
Variability~~~~}



\author[1]{Frances V. Davenport}%
\author[1]{Elizabeth A. Barnes}%
\author[1]{Emily M Gordon}%
\affil[1]{Colorado State University}%


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  \date{May 25, 2024}


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\begin{abstract}
We use neural networks and large climate model ensembles to explore
predictability of internal variability in sea surface temperature
anomalies on interannual (1-3 year) and decadal (1-5 and 3-7 year)
timescales. We find that neural networks can skillfully predict SST
anomalies at these lead times, especially in the North Atlantic, North
Pacific, Tropical Pacific, Tropical Atlantic and Southern Ocean. The
spatial patterns of SST predictability vary across the nine climate
models studied. The neural networks identify ``windows of opportunity''
where future SST anomalies can be predicted with more certainty. Neural
networks trained on climate models also make skillful SST predictions in
reconstructed observations, although the skill varies depending on which
climate model the network was trained. Our results highlight that neural
networks can identify predictable internal variability within existing
climate datasets and show important differences in how well patterns of
SST predictability in climate models translate to the real world.%
\end{abstract}%



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