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\begin{document}

\title{Detecting climate signals using explainable AI with single-forcing large
ensembles}



\author[1,1]{Zachary M. Labe}%
\author[1,1]{Elizabeth A. Barnes}%
\affil[1]{Colorado State University}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\begin{abstract}
It remains difficult to disentangle the relative influences of aerosols
and greenhouse gases on regional surface temperature trends in the
context of global climate change. To address this issue, we use a new
collection of initial-condition large ensembles from the Community Earth
System Model version 1 that are prescribed with different combinations
of industrial aerosol and greenhouse gas forcing. To compare the climate
response to these external forcings, we adopt an artificial neural
network (ANN) architecture from previous work that predicts the year by
training on maps of near-surface temperature. We then utilize layer-wise
relevance propagation (LRP) to visualize the regional temperature
signals that are important for the ANN's prediction in each climate
model experiment. To mask noise when extracting only the most robust
climate patterns from LRP, we introduce a simple uncertainty metric that
can be adopted to other explainable artificial intelligence (AI)
problems. We find that the North Atlantic, Southern Ocean, and Southeast
Asia are key regions of importance for the neural network to make its
prediction, especially prior to the early-21st century. Notably, we also
find that the ANN predictions based on maps of observations correlate
higher to the actual year after training on the large ensemble
experiment with industrial aerosols held fixed to 1920 levels. This work
illustrates the sensitivity of regional temperature signals to changes
in aerosol forcing in historical simulations. By using explainable AI
methods, we have the opportunity to improve our understanding of
(non)linear combinations of anthropogenic forcings in state-of-the-art
global climate models.%
\end{abstract}%



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