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\begin{document}

\title{Improve Climate Predictions by Reducing Initial Prediction Errors: A
Benefit Estimate Using Multi-model ENSO Predictions}



\author[1]{Gan Zhang}%
\affil[1]{University of Illinois at Urbana Champaign}%


\vspace{-1em}



  
  \date{June 15, 2024}


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\selectlanguage{english}
\begin{abstract}
Climate risk management relies on accurate predictions of key climate
variations such as El Ni\selectlanguage{ngerman}ño-Southern Oscillation (ENSO), but the skill of
ENSO predictions has recently plateaued or even degraded. Here we
analyze the North American Multi-Model Ensemble (NMME) and estimate how
the seasonal prediction of ENSO may benefit from reducing initial
prediction errors. An analysis of predictable signals and system noises
identifies a high-predictability regime and a low-predictability regime.
The latter corresponds to the spring predictability barrier and is
related to a rapid drop in the signal-to-noise ratio, which is caused by
the comparably strong dampening of predictable signals. Reducing
first-month prediction errors (FPEs) will likely reduce root-mean-square
errors of the ENSO prediction. As a conservative estimate, halving the
FPEs may extend the NMME's skill by one to two months. Importantly, this
study identifies the regions where reducing FPE is the most effective.
Unlike the predictions initialized after the boreal spring, the
March-initialized predictions of the wintertime ENSO will likely benefit
the most from FPE reductions in the tropical Northwest Pacific. An
opportunistic thought experiment suggests the buoy observation changes
during 1995--2020 may have contributed to FPEs associated with large
cold biases (\textgreater{}1K) in some El Niño-year predictions. While
data availability prevented in-depth analyses of physical processes, the
findings suggest that prioritizing modeling and observation in certain
regions can improve climate predictions cost-effectively. The analytical
framework here is applicable to other climate processes, thus holding
wide potential for benefiting climate predictions.%
\end{abstract}\selectlanguage{ngerman}%



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\textbf{Hosted file}

\verb`973609_0_art_file_11367316_s0qt3m.docx`  available at \url{https://authorea.com/users/664189/articles/666017-improve-climate-predictions-by-reducing-initial-prediction-errors-a-benefit-estimate-using-multi-model-enso-predictions}

\textbf{Hosted file}

\verb`973609_0_supp_11367317_s0qt3m.docx`  available at \url{https://authorea.com/users/664189/articles/666017-improve-climate-predictions-by-reducing-initial-prediction-errors-a-benefit-estimate-using-multi-model-enso-predictions}\selectlanguage{english}
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