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\begin{document}

\title{Reduced Complexity Model Intercomparison Project Phase 2: Synthesising
Earth system knowledge for probabilistic climate projections}



\author[1,1]{Zebedee R.J. Nicholls}%
\author[1,1]{Malte Alexander Meinshausen}%
\author[2,2]{Jared Lewis}%
\author[3,3]{Maisa Rojas Corradi}%
\author[4,4]{Kalyn Dorheim}%
\author[5,5]{Thomas Gasser}%
\author[6,6]{Robert Gieseke}%
\author[7,7]{Austin Patrick Hope}%
\author[8,8]{Nicholas Leach}%
\author[7,7]{Laura Anne McBride}%
\author[9,9]{Yann Quilcaille}%
\author[10,10]{Joeri Rogelj}%
\author[7,7]{Ross J. Salawitch}%
\author[11,11]{Bjørn Hallvard Samset}%
\author[12,12]{Marit Sandstad}%
\author[4,4]{Alexey N Shiklomanov}%
\author[13,13]{Ragnhild Bieltvedt Skeie}%
\author[14,14]{Christopher J Smith}%
\author[15,15]{Steven Smith}%
\author[16,16]{Xuanming SU}%
\author[17,17]{Junichi Tsutsui}%
\author[18,18]{Benjamin Aaron Vega-Westhoff}%
\author[4,4]{Dawn Woodard}%
\affil[1]{University of Melbourne}%
\affil[2]{Climate \& Energy College, School of Earth Sciences, University of Melbourne}%
\affil[3]{University of Chile}%
\affil[4]{Pacific Northwest National Laboratory}%
\affil[5]{International Institute for Applied Systems Analysis (IIASA)}%
\affil[6]{Independent Researcher}%
\affil[7]{University of Maryland, College Park}%
\affil[8]{University of Oxford}%
\affil[9]{International Institute for Applied Systems Analysis}%
\affil[10]{Imperial College London}%
\affil[11]{CICERO Center for International Climate and Environmental Research - Oslo}%
\affil[12]{CICERO}%
\affil[13]{Center for International Climate and Environmental Research - Oslo (CICERO)}%
\affil[14]{University of Leeds}%
\affil[15]{Pacific Northwest National Laboratory (DOE)}%
\affil[16]{National Institute for Environmental Studies}%
\affil[17]{Central Research Institute of Electric Power Industry}%
\affil[18]{University of Illinois at Urbana-Champaign}%


\vspace{-1em}



  
  \date{May 19, 2024}


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\begin{abstract}
Over the last decades, climate science has evolved rapidly across
multiple expert domains. Our best tools to capture state-of-the-art
knowledge in an internally self-consistent modelling framework are the
increasingly complex fully coupled Earth System Models (ESMs). However,
computational limitations and the structural rigidity of ESMs mean that
the full range of uncertainties across multiple domains are difficult to
capture with ESMs alone. The tools of choice are instead more
computationally efficient reduced complexity models (RCMs), which are
structurally flexible and can span the response dynamics across a range
of domain-specific models and ESM experiments. Here we present Phase 2
of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2),
the first comprehensive intercomparison of RCMs that are
probabilistically calibrated with key benchmark ranges from specialised
research communities. Unsurprisingly, but crucially, we find that models
which have been constrained to reflect the key benchmarks better reflect
the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across
the RCMs, median peak warming projections range from 1.3 to 1.7\{degree
sign\}C (relative to 1850-1900, using an observationally-based
historical warming estimate of 0.8\{degree sign\}C between 1850-1900 and
1995-2014). Further developing methodologies to constrain these
projection uncertainties seems paramount given the international
community's goal to contain warming to below 1.5\{degree sign\}C above
pre-industrial in the long-term. Our findings suggest that users of RCMs
should carefully evaluate their RCM, specifically its skill against key
benchmarks and consider the need to include projections benchmarks
either from ESM results or other assessments to reduce divergence in
future projections.%
\end{abstract}%



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