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\begin{document}

\title{Semi-automatic tuning of coupled climate models with multiple intrinsic
timescales: lessons learned from the Lorenz96 model}



\author[1]{Redouane Lguensat}%
\author[2]{Julie Deshayes}%
\author[3]{Homer Durand}%
\author[4]{Venkatramani Balaji}%
\affil[1]{Institut Pierre-Simon Laplace}%
\affil[2]{LOCEAN-IPSL, CNRS}%
\affil[3]{LOCEAN-IPSL, Sorbonne Université}%
\affil[4]{NOAA/Geophysical Fluid Dynamics Laboratory}%


\vspace{-1em}



  
  \date{June 15, 2024}


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\begin{abstract}
The objective of this study is to evaluate the potential for History
Matching (HM) to tune a climate system with multi-scale dynamics. By
considering a toy climate model, namely, the two-scale Lorenz96 model
and producing experiments in perfect-model setting, we explore in detail
how several built-in choices need to be carefully tested. We also
demonstrate the importance of introducing physical expertise in the
range of parameters, a priori to running HM. Finally we revisit a
classical procedure in climate model tuning, that consists of tuning the
slow and fast components separately. By doing so in the Lorenz96 model,
we illustrate the non-uniqueness of plausible parameters and highlight
the specificity of metrics emerging from the coupling. This paper
contributes also to bridging the communities of uncertainty
quantification, machine learning and climate modeling, by making
connections between the terms used by each community for the same
concept and presenting promising collaboration avenues that would
benefit climate modeling research.%
\end{abstract}%



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