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\begin{document}

\title{Exploring a data-driven approach to identify regions of change
associated with future climate scenarios}



\author[1]{Zachary M. Labe}%
\author[2]{Thomas L. Delworth}%
\author[3]{Nathaniel Johnson}%
\author[4]{William F. Cooke}%
\affil[1]{Princeton University}%
\affil[2]{GFDL}%
\affil[3]{Cooperative Institute for Modeling the Earth System}%
\affil[4]{Geophysical Fluid Dynamics Laboratory}%


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  \date{May 15, 2024}


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\begin{abstract}
A key consideration for evaluating climate projections is uncertainty in
radiative forcing scenarios. Although it is straightforward to monitor
greenhouse gas concentrations and compare those observations with
specified climate scenarios, it remains less obvious on how to connect
regional climate patterns with these scenarios in real time. Here we
introduce a machine learning approach for linking patterns of climate
change with radiative forcing scenarios and use an attribution method to
understand how these linkages are made. We train a neural network using
output from the SPEAR Large Ensemble to classify whether temperature or
precipitation maps are most likely to originate from one of several
potential radiative forcing scenarios. The neural network learns to
identify ``fingerprint'' patterns that associate signals of climate
change with the scenarios. We illustrate this using output from
additional mitigation experiments and highlight regions that are
critical for associating the new simulations with likely radiative
forcing scenarios.%
\end{abstract}%



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