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\begin{document}

\title{A Prototype for Remote Monitoring of Ocean Heat Content}



\author[1]{D S Trossman}%
\author[2]{R H Tyler}%
\affil[1]{University of Texas}%
\affil[2]{NASA Goddard Space Flight Center}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\begin{abstract}
A new approach to monitor ocean heat content (OHC) is proposed to
overcome challenges with observing OHC over the entire ocean. The output
of an ocean state estimate (ECCO) is sampled along historical
hydrographic transects, a machine learning algorithm (GAM) is trained on
these samples, and OHC is estimated everywhere using information
inferable from various global satellite coverage. Assuming the ECCO
output is perfect observational data, a GAM can estimate OHC within
0.15\% spatial root-mean-square error (RMSE). This RMSE is sensitive to
the spatial variance in OHC that gets sampled by hydrographic transects,
the variables included in the GAM, and their measurement errors when
inferred from satellite data. OHC could be remotely monitored over
sufficiently long time scales when enough spatial variance in OHC is
explained in the training data over those time scales.%
\end{abstract}%



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