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\begin{document}

\title{Separation of internal and forced variability of climate using a U-Net}



\author[1]{Constantin Bône}%
\author[2]{Guillaume Gastineau}%
\author[3]{Sylvie Thiria}%
\author[4]{Patrick Gallinari}%
\author[5]{Carlos E Mejia}%
\affil[1]{LOCEAN, ISIR,Sorbonne Université/CNRS/IRD/MNHN}%
\affil[2]{LOCEAN, Sorbonne Université/CNRS/IRD/MNHN}%
\affil[3]{Sorbonne Universités , France}%
\affil[4]{ISIR, Sorbonne-Université}%
\affil[5]{LOCEAN, Sorbonne Université}%


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  \date{May 18, 2024}


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\begin{abstract}
The internal variability pertains to fluctuations originating from
processes inherent to the climate component and their mutual
interactions. On the other hand, forced variability delineates the
influence of external boundary conditions on the physical climate
system. A methodology is formulated to distinguish between internal and
forced variability within the surface air temperature. The
noise-to-noise approach is employed for training a neural network,
drawing an analogy between internal variability and image noise. A large
training dataset is compiled using surface air temperature data spanning
from 1901 to 2020, obtained from an ensemble of Atmosphere-Ocean General
Circulation Model (AOGCM) simulations. The neural network utilized for
training is a U-Net, a widely adopted convolutional network primarily
designed for image segmentation. To assess performance, comparisons are
made between outputs from two single-model initial-condition large
ensembles (SMILEs), the ensemble mean, and the U-Net's predictions. The
U-Net reduces internal variability by a factor of four, although notable
discrepancies are observed at the regional scale. While demonstrating
effective filtering of the El Ni\selectlanguage{ngerman}ño Southern Oscillation, the U-Net
encounters challenges in areas dominated by forced variability, such as
the Arctic sea ice retreat region. This methodology holds potential for
extension to other physical variables, facilitating insights into the
enduring changes triggered by external forcings over the long term.%
\end{abstract}\selectlanguage{ngerman}%



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