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\lhead{\footnotesize\sc ASHPC25 -- Austrian-Slovenian HPC Meeting 2025}
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\rhead{\footnotesize\sc Rimske Toplice, 19--22 May, 2025} 

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\section{High clouds, high grid spacing to the rescue?}
\subsection{Blaž Gasparini$^{\mathrm{a}}$, Rachel Atlas$^{\mathrm{b}}$, Aiko Voigt$^{\mathrm{a}}$, Martina Krämer$^{\mathrm{c,d}}$, and Peter Blossey$^{\mathrm{e}}$}

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\affiliation{$^a$Department of Meteorology and Geophysics, University of Vienna, Austria}
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\affiliation{$^b$CNRS-Laboratoire de Météorologie Dynamique, LMD, France} \par
\affiliation{$^c$Institute for Atmospheric Physics, University of Mainz, Germany} \par
\affiliation{$^d$IEK-7, Forschungszentrum Jülich, Jülich, Germany} \par
\affiliation{$^e$Department of Atmospheric and Climate Science, University of Washington, USA}
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Climate models used for projections typically have a horizontal grid spacing of about 100 km and a vertical spacing of 0.5 to 1 km in the upper troposphere. They have been the backbone of climate science for decades, but they have intrinsic limitations due to their coarse grid spacing. Most notably, thunderstorms, the fundamental building block of tropical climate, occur at subgrid scales and cannot be resolved by a climate model's dynamical core. Instead, imperfect and often empirical parameterizations are used, leading to significant uncertainty in climate projections due to varying representations of thunderstorms (Fig. 1a).

Thunderstorms are the major source of tropical precipitation and inject large amounts of ice crystals high in the atmosphere, forming persistent anvil clouds, the most frequent and radiatively  important cloud type in the tropics. Increasing the model grid spacing to O(1 km) is thought to be sufficient to directly resolve deep convective updrafts, which are the main source of high clouds in the tropics. This allows for a more physical simulation of the anvil cloud lifecycle, as shown by km-scale model output that is often barely distinguishable from satellite observations (e.g., Fig. 1c).

However, removing the need to parameterize one process exposes biases in micro-scale processes that remain parameterized, such as cloud formation and dissipation (known as cloud microphysics). While advanced microphysical schemes are considered expensive and are rarely implemented in global km-scale models or for weather forecasting, we show that most microphysical model bias can be removed by inexpensive modifications to the code. Our work reveals a simple, numerically inexpensive recipe that substantially improves simulations of tropical cirrus in the System for Atmospheric Modeling (SAM) model and can be applied to other atmospheric models.\vspace{-0.3cm}


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{\small{\bf{Fig. 1:}} Snapshots of cross-sections from the tropical Pacific simulated by (a) the ICON-NWP climate model, (b) a standard version of the SAM climate model, and (c) SAM with an improved microphysical scheme. %Panel (a) represents a cross-section through the Walker cell, panels (b) and (c) represent cross-sections through a thunderstorm. 
The grey line indicates the tropopause. Blue indicates cloud condensate, purple indicates rainfall.}
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%  \item [{[1]}] LastName, X.Y., ...., and ...., Journal {\bf{volume}}, firstPage (year).
  %\item [{[2]}] Author1, B.C., Author2, D., and Author3, F.X., Phys. Rev. Lett. {\bf{92}}, 10134 (2015).
  %\item [{[3]}] Author1, B.C., and Author2, F.X., Nature {\bf{104}}, 424 (2015).
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