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\begin{document}

\title{Using A Phase Space of Environmental Variables to Drive an Ensemble of
Cloud-resolving Simulations of Low Marine Clouds}



\author[1,1]{Ehsan Erfani}%
\author[2]{Robert Wood}%
\author[2]{Peter Blossey}%
\author[2]{Sarah Doherty}%
\author[2]{Ryan Eastman}%
\affil[1]{Desert Research Institute}%
\affil[2]{University of Washington}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\begin{abstract}
Low marine clouds are a major source of uncertainty in cloud feedbacks
across climate models and in forcing by aerosol-cloud interactions. The
evolution of these clouds and their response to aerosol are sensitive to
the ambient environmental conditions, so it is important to be able to
determine different responses over a representative set of conditions.
Here, we propose a novel approach to encompassing the broad range of
conditions present in low marine cloud regions, by building a library of
observed environmental conditions. This approach can be used, for
example, to more systematically test the fidelity of Large Eddy
Simulations (LES) in representing these clouds. ERA5 reanalysis and
various satellite observations are used to extract and derive
macrophysical and microphysical cloud-controlling variables (CCVs) such
as SST, estimated inversion strength (EIS), subsidence, and cloud
droplet number concentrations. A few locations in the stratocumulus (Sc)
deck region of the Northeast Pacific during summer are selected to fill
out a phase space of CCVs. Thereafter, Principal Component Analysis
(PCA) is applied to reduce the dimensionality and to select a reduced
set of components that explain most of the variability among CCVs in
order to efficiently select cases for LES simulations that encompass the
observed CCV phase space. From this phase space, 75-100 cases with
distinct environmental conditions will be selected and used to
initialize 2-day LES modeling to provide a spectrum of aerosol-cloud
interactions and Sc-to-Cumulus transition under observed ambient
conditions. Such a large number of simulations will help create
statistics to assess how well the LES can simulate the cloud lifecycle
when constrained by the `best estimate' of the environmental conditions,
and how sensitive the modeled clouds are to changes in these driving
fields.%
\end{abstract}%



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