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\begin{document}

\title{Influence of parameterization changes on Arctic low cloud properties and
cloud radiative effects in two versions of the HadGEM3 Atmospheric
Model: GA7.1 and GA6}



\author[1]{Patrick Charles Taylor}%
\author[2]{Robyn C. Boeke}%
\author[3]{Alejandro Bodas-Salcedo}%
\affil[1]{National Aeronautics and Space Administration (NASA)}%
\affil[2]{Science Systems and Applications, Inc.}%
\affil[3]{Met Office Hadley Centre}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\begin{abstract}
Arctic clouds play a key role in Arctic climate variability and change;
however, contemporary climate models struggle to simulate cloud
properties accurately. Model-simulated cloud properties are determined
by the physical parameterizations and their interactions within the
model configuration. Quantifying effects of individual parameterization
changes on model-simulated clouds informs efforts to improve cloud
properties in models and provides insights on climate system behavior.
This study quantities the influence of individual parameterization
schemes on Arctic low cloud properties within the Hadley Centre Global
Environmental Model 3 atmospheric model using a suite of experiments
where individual parameterization packages are changed one-at-a-time
between two configurations: GA6 and GA7.1. The results indicate that
individual parameterization changes explain most of the cloud property
differences, whereas multiple parameterizations, including non-cloud
schemes, contribute to cloud radiative effect differences. The influence
of a parameterization change on cloud properties is found to vary by
meteorological regime. We employ a three-term decomposition to quantify
contributions from (1) regime independent, (2) regime dependent, and (3)
the regime frequency of occurrence changes. Decomposition results
indicate that each term contributes differently to each cloud property
change and that non-cloud parameterization changes make a substantial
contribution to the LW and SW cloud radiative effects by modifying
clear-sky fluxes differently across regimes. The analysis provides
insights on the role of non-cloud parameterizations for setting cloud
radiative effects, a model pathway for cloud-atmosphere circulation
interactions, and raises questions on the most useful observational
approaches for improving models.%
\end{abstract}%



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