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\begin{document}

\title{Assessing Memory in Convection Schemes Using Idealized Tests}



\author[1]{Yi-Ling Hwong}%
\author[2]{Maxime Colin}%
\author[1]{Philipp Aglas-Leitner}%
\author[3]{Caroline Muller}%
\author[4]{Steven Sherwood}%
\affil[1]{Institute of Science and Technology Austria}%
\affil[2]{Leibniz Centre for Tropical Marine Research}%
\affil[3]{ISTA}%
\affil[4]{University of New South Wales}%


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  \date{May 25, 2024}


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\begin{abstract}
Two assumptions commonly applied in convection schemes---the diagnostic
and quasi-equilibrium assumptions---imply that convective activity
(e.g., convective precipitation) is controlled only by the large-scale
(macrostate) environment at the time. In contrast, numerical experiments
indicate a ``memory'' or dependence of convection also on its own
previous activity whereby subgrid-scale (microstate) structures boost
but are also boosted by convection. In this study we investigated this
memory by comparing single-column model behavior in two idealized tests
previously executed by a cloud-resolving model (CRM). Conventional
convection schemes that employ the diagnostic assumption fail to
reproduce the CRM behavior. The memory-capable org and LMDZ cold pool
schemes partially capture the behavior, but fail to fully exhibit the
strong reinforcing feedbacks implied by the CRM. Analysis of this
failure suggests that it is because the CRM supports a linear (or
superlinear) dependence of the subgrid structure growth rate on the
precipitation rate, while the org scheme assumes a sublinear dependence.
Among varying versions of the org scheme, the growth rate of the org
variable representing subgrid structure is strongly associated with
memory strength. These results demonstrate the importance of
parameterizing convective memory, and the ability of idealized tests to
reveal shortcomings of convection schemes and constrain model structural
assumptions.%
\end{abstract}%



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