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\begin{document}

\title{Microphysical Sensitivity of Superparameterized Precipitation Extremes
in the Continental US Due to Feedbacks on Large-scale Circulation}



\author[1]{Alexander Charn}%
\author[2]{William Collins}%
\author[3]{Hossein Parishani}%
\author[4]{Mark Risser}%
\author[5]{Travis O'Brien}%
\affil[1]{University of California Berkeley}%
\affil[2]{Berkeley Lab and UC Berkeley}%
\affil[3]{University of California Irvine}%
\affil[4]{Lawrence Berkeley National Laboratory}%
\affil[5]{Lawrence Berkeley National Lab}%


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  \date{May 26, 2024}


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\begin{abstract}
Superparameterized (SP) global climate models have been shown to better
simulate---as compared to conventional models---various features of
precipitation, including diurnal timing as well as extreme events. While
various studies have looked at the effect of differing microphysics
parameterizations on precipitation within limited-area cloud-resolving
models, we examine here the effect on continental-US extremes in a
global SP model. We vary the number of predicted moments for hydrometeor
distributions, the character of the rimed ice species, and the
representation of raindrop self-collection and breakup. Using a
likelihood ratio test and accounting for the effects of
multiple-hypothesis testing, we find that there are some regional
differences, both in the current climate and in a warmer climate with
uniformly increased sea-surface temperatures. These differences are most
statistically significant and widespread when the number of moments is
changed. To determine whether these results are due to (fast) local
effects of the different microphysics or the (slower) ensuing feedback
on the large-scale atmospheric circulation, we run a series of short,
5-day simulations initialized from reanalysis data. We find that the
differences largely disappear in these runs and therefore infer that the
different parameterizations impact precipitation extremes indirectly via
the large-scale circulation. Finally, we compare the present-day results
with hourly rain-gauge data and find that, for the model configuration
and resolution used, SP underestimates extremes relative to observations
regardless of which microphysics scheme is used.%
\end{abstract}%



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