\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{Quantifying ``climate distinguishability'' after stratospheric aerosol
injection using explainable artificial intelligence}



\author[1]{Antonios Mamalakis}%
\author[1]{Elizabeth A. Barnes}%
\author[1]{James Wilson Hurrell}%
\affil[1]{Colorado State University}%


\vspace{-1em}



  
  \date{May 30, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Stratospheric aerosol injection (SAI) has been proposed as a possible
complementary solution to limit global warming and its societal
consequences. However, the climate impacts of such intervention remain
unclear. Here, we introduce an explainable artificial intelligence (XAI)
framework to quantify how distinguishable an SAI climate might be from a
pre-deployment climate. A suite of neural networks is trained on Earth
system model data to learn to distinguish between pre- and
post-deployment periods across a variety of climate variables. The
network accuracy is analogous to the ``climate distinguishability''
between the periods, and the corresponding distinctive patterns are
identified using XAI methods to gain insights into the emerging signals
from SAI. For many variables, the two periods are less distinguishable
under SAI than under a no-SAI scenario, suggesting that the specific
intervention modeled decelerates future climatic changes. Other climate
variables for which the intervention has negligible effect are also
highlighted.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

