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\begin{document}

\title{Strong El Ni\selectlanguage{ngerman}ño events lead to robust multi-year ENSO predictability\selectlanguage{english}}



\author[1]{Nathan Lenssen}%
\author[2]{Pedro DiNezio}%
\author[3]{Lisa Goddard}%
\author[4]{Clara Deser}%
\author[3]{Yochanan Kushnir}%
\author[5]{Simon Mason}%
\author[6]{Matthew Newman}%
\author[7]{Yuko M Okumura}%
\affil[1]{Colorado School of Mines}%
\affil[2]{University of Colorado Boulder}%
\affil[3]{Columbia University}%
\affil[4]{National Center for Atmospheric Research}%
\affil[5]{IRI Columbia University}%
\affil[6]{NOAA/PSL}%
\affil[7]{University of Texas at Austin}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\selectlanguage{english}
\begin{abstract}
The El Ni\selectlanguage{ngerman}ño-Southern Oscillation (ENSO) phenomenon -- the dominant
source of climate variability on seasonal to multi-year timescales -- is
predictable a few seasons in advance. Forecast skill at longer
multi-year timescales has been found in a few models and forecast
systems, but the robustness of this predictability across models has not
been firmly established owing to the cost of running dynamical model
predictions at longer lead times. In this study, we use a massive
collection of multi-model hindcasts performed using model analogs to
show that multi-year ENSO predictability is robust across models and
arises predominantly due to skillful prediction of multi-year La Niña
events following strong El Niño events.%
\end{abstract}\selectlanguage{ngerman}%



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\end{figure}\selectlanguage{english}
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\end{figure}\selectlanguage{english}
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\end{figure}

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