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\begin{document}

\title{Sea Surface Salinity Provides Subseasonal Predictability for Forecasts
of Opportunity of U.S. Summertime Precipitation}



\author[1]{Marybeth Arcodia}%
\author[1]{Elizabeth A. Barnes}%
\author[2]{Paul James Durack}%
\author[3]{Patrick Keys}%
\author[4]{Juliette Rocha}%
\affil[1]{Colorado State University}%
\affil[2]{Lawrence Livermore National Laboratory (DOE)}%
\affil[3]{Department of Atmospheric Science, Colorado State University}%
\affil[4]{Texas A\&M University}%


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  \date{May 24, 2024}


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\begin{abstract}
As oceanic moisture evaporates, it leaves a signature on sea surface
salinity. Roughly 10\% of the moisture that evaporates over the ocean is
transported over land, allowing the salinity fields to be a predictor of
terrestrial precipitation. This research is among the first in published
literature to assess the role of sea surface salinity for improved
predictions on low-skill summertime subseasonal timescales for
terrestrial precipitation predictions. Neural networks are trained with
the CESM2 Large Ensemble using North Atlantic salinity anomalies to
quantify predictability of U.S. Midwest summertime heavy rainfall events
at 0 to 56-day leads. Using explainable artificial intelligence,
salinity anomalies in the Caribbean Sea and Gulf of Mexico are found to
provide skill for subseasonal forecasts of opportunity, e.g. confident
and correct predictions. Further, a moisture-tracking algorithm applied
to reanalysis data demonstrates that the regions of evaporation
identified by neural networks directly provide moisture that
precipitates in the Midwest.%
\end{abstract}%



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\verb`984624_0_art_file_11783280_s7d5lp.docx`  available at \url{https://authorea.com/users/541956/articles/701151-sea-surface-salinity-provides-subseasonal-predictability-for-forecasts-of-opportunity-of-u-s-summertime-precipitation}

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