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\begin{document}

\title{Cloud-Radiation Interactions and their Contributions to Convective
Self-Aggregation}



\author[1]{Kieran Nicholas Pope}%
\author[2]{Christopher E Holloway}%
\author[1]{Thorwald Hendrik Matthias Stein}%
\author[1]{Todd Russell Jones}%
\affil[1]{University of Reading}%
\affil[2]{University of Reading, UK}%


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  \date{June 17, 2024}


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\begin{abstract}
This study investigates the direct radiative-convective processes that
drive and maintain aggregation within convection permitting elongated
channel (and smaller square) simulations of the UK Met Office Unified
Model (UM). Our simulations are configured using three fixed sea surface
temperatures (SSTs) following the radiative-convective equilibrium model
intercomparison project (RCEMIP) protocol. By defining cloud types based
on the vertical distribution of condensed water, we study the importance
of radiative interactions with each cloud type to aggregation. We
eliminate the dependence of the vertically-integrated frozen moist
static energy (FMSE) variance budget framework on SST by normalizing
FMSE between theoretical upper and lower limits based on SST. The
elongated channel simulations reach similar degrees of aggregation
across SSTs, despite the contributions of normalized shortwave and
longwave interactions decreasing with SST. High-cloud longwave
interactions are the main drivers and maintainers of aggregation. Their
influence decreases with SST as high clouds become less abundant. This
SST-dependence is consistent with changes in grid spacing and RHcrit,
however the magnitude of high-cloud longwave interactions is likely
reduced as grid spacing and RHcrit are reduced. Both factors tend to
decrease condensed water path and cloud top height, decreasing the
anomalous longwave heating rates of these clouds. Shortwave interactions
with water vapor are key maintainers of aggregation and are dependent on
SST and the degree of aggregation itself. The analysis method used
provides a new framework to compare the effects of radiative-convective
processes on self-aggregation across different SSTs and model
configurations in order to improve our understanding of
self-aggregation.%
\end{abstract}%



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