\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[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{Detection and attribution of climate change using a neural network}



\author[1]{Constantin Bône}%
\author[2]{Guillaume Gastineau}%
\author[2]{Sylvie Thiria}%
\author[3]{Patrick Gallinari}%
\author[2]{Carlos Mejia}%
\affil[1]{UMR LOCEAN, ISIR, IPSL, Sorbonne-Université, IRD, CNRS, MNHN}%
\affil[2]{UMR LOCEAN, IPSL, Université, IRD, CNRS}%
\affil[3]{UMR ISIR, Sorbonne-Université, CNRS, INSERM}%


\vspace{-1em}



  
  \date{June 14, 2024}


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





\selectlanguage{english}
\begin{abstract}
A new detection and attribution method is presented and applied to the
global mean surface air temperature (GSAT) from 1900 to 2014. The method
aims at attributing the climate changes to the variations of greenhouse
gases, anthropogenic aerosols, and natural forcings. A convolutional
neural network (CNN) is trained using the simulated GSAT from historical
and single-forcing simulations of twelve climate models. Then, we
perform a backward optimization with the CNN to estimate the
attributable GSAT changes. Such a method does not assume additivity in
the effects of the forcings. The uncertainty in the attributable GSAT is
estimated by sampling different starting points from single-forcing
simulations and repeating the backward optimization. To evaluate this
new method, the attributable GSAT changes are also calculated using the
regularized optimal fingerprinting (ROF) method. Using synthetic
non-additive data, we first find that the neural network-based method
estimates attribuable changes better than ROF. When using GSAT data from
climate model, the attribuable anomalies are similar for both methods,
which might reflect that the influence of forcing is mainy additive for
the GSAT. However, we found that the uncertainties given both methods
are different. The new method presented here can be adapted and extended
in future work, to investigate the non-additive changes found at the
local scale or on other physical variables.%
\end{abstract}%



\sloppy




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

