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\begin{document}

\title{Disentangling forced trends in the North Atlantic jet from natural
variability using deep learning}



\author[1]{Alejandro Hermoso}%
\author[1]{Sebastian Schemm}%
\affil[1]{ETH Zurich}%


\vspace{-1em}



  
  \date{June 14, 2024}


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\begin{abstract}
Regional weather variability and extremes over Europe are strongly
linked to variations in the North Atlantic jet stream, especially during
the winter season. Projections of the evolution of the North Atlantic
jet are essential for estimating the regional impacts of climate change.
Therefore, separating forced trends in the North Atlantic jet from its
natural variability is an extremely relevant task. Here, a deep learning
based method, the Latent Linear Adjustment Autoencoder (LLAE), is used
for this purpose on an ensemble of fully-coupled climate simulations.
The LLAE is based on an autoencoder and an additional linear component.
The model predicts the wind component affected by natural variability by
using detrended temperature and geopotential as inputs. The residual
between this prediction and the original wind field is interpreted as
the forced component of the jet. The method is first tested for the
geostrophic wind for which the forced trend can be obtained analytically
from the difference between geostrophic wind computed from detrended and
full geopotential. Despite the large variability of the original trends,
the LLAE is shown to be effective in extracting the forced component of
the trend for each individual ensemble member in both geostrophic and
full wind fields. The LLAE-derived forced trend shows an increase in the
upper-level zonal wind speed along a southwest-northeast oriented band
over the ocean and a jet extension towards Europe. These are common
characteristics over different periods and show some similarities to the
upper-level zonal wind speed trend obtained from the ERA5 reanalysis.%
\end{abstract}%



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