Representing Cloud Mesoscale Variability in Superparameterized Climate Models

Global reanalyzes like ERA5 accurately capture atmospheric processes at spatial scales of km or larger. By downscaling ERA5 with large‐eddy simulation (LES), LES can provide details about processes at spatio‐temporal scales down to meters and seconds. Here, we present an open‐source Python package named the “Large‐eddy simulation and Single‐column model—Large‐Scale Dynamics,” or (LS)2D in short, designed to simplify the downscaling of ERA5 with doubly‐periodic LES. A validation with observations, for several sensitivity experiments consisting of month‐long LESs over Cabauw (the Netherlands), demonstrates both its usefulness and limitations. The day‐to‐day variability in the weather is well captured by (LS)2D and LES, but the setup under‐performs in conditions with broken or near overcast clouds. As a novel application of this modeling system, we used (LS)2D to study surface solar irradiance variability, as this quantity directly links land‐surface processes, turbulent transport, and clouds, to radiation. At a horizontal resolution of 25 m, the setup reproduces satisfactorily the solar irradiance variability down to a timescale of seconds. This demonstrates that the coupled LES‐ERA5 setup is a useful tool that can provide details on the physics of turbulence and clouds, but can only improve on its host reanalysis when applied to meteorological suitable conditions.

Section: Discussionsupporting

confidence: 87%

Section: Discussionsupporting

confidence: 86%

The Benefits and Challenges of Downscaling a Global Reanalysis With Doubly‐Periodic Large‐Eddy Simulations

van Stratum,

van Heerwaarden,

Vilà‐Guerau de Arellano

2023

“…For over 5 years since the first experiments with HR MMF, it has been unclear whether a chronic over‐entrainment bias preventing realistic amounts of Sc liquid water was surmountable. It has been natural to wonder if the inherent idealizations of MMFs that make them computationally attractive—that is, the limited domain size, dimensionality, moderate (200‐m) interior horizontal resolution, lateral periodicity, and associated inability to laterally advect liquid water conservatively—(Jansson et al., 2022; Muller & Held, 2012)—might impose fundamental limitations.…”

Section: Discussionmentioning

confidence: 99%

“…The overall impact is to under‐predict daytime cloud liquid water resulting in too little time mean shortwave reflectivity. Meanwhile, the assumptions inherent in an MMF that can limit its ability to laterally advect condensed water between adjacent CRMs have caused some to question its capacity to maintain low clouds (Jansson et al., 2022). While the scale separation inherent in the MMF also introduces distortions (such as the neglect of the mesoscale), it does allow a global model to simulate these fine scales, making it possible to represent physical processes (e.g., cloud top entrainment, aerosol activation in updrafts) that drive critical sensitivities of low clouds to anthropogenic influence.…”

Section: Introductionmentioning

confidence: 99%

Improving Stratocumulus Cloud Amounts in a 200‐m Resolution Multi‐Scale Modeling Framework Through Tuning of Its Interior Physics

Peng,

Blossey,

Hannah

et al. 2024

High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due to reduced parameterization and ability for multidecadal throughput on modern computing hardware. However low clouds in past HR have suffered a stubborn problem of over‐entrainment due to an uncontrolled source of mixing across the marine subtropical inversion manifesting as stratocumulus dim biases in present‐day climate, limiting their scientific utility. We report new results showing that this over‐entrainment can be partly offset by using hyperviscosity and cloud droplet sedimentation. Hyperviscosity damps small‐scale momentum fluctuations associated with the formulation of the momentum solver of the embedded large eddy simulation. By considering the sedimentation process adjacent to default one‐moment microphysics in HR, condensed phase particles can be removed from the entrainment zone, which further reduces entrainment efficiency. The result is an HR that can produce more low clouds with a higher liquid water path and a reduced stratocumulus dim bias. Associated improvements in the explicitly simulated sub‐cloud eddy spectrum are observed. We report these sensitivities in multi‐week tests and then explore their operational potential alongside microphysical retuning in decadal simulations at operational 1.5° exterior resolution. The result is a new HR having desired improvements in the baseline present‐day low cloud climatology, and a reduced global mean bias and root mean squared error of absorbed shortwave radiation. We suggest it should be promising for examining low cloud feedbacks with minimal approximation.

“…As an aid to navigating the ensemble, we have prepared a web page with a set of plots and animations for each member. This page and the images and animations can be downloaded and used offline (Jansson, Janssens, Grönqvist, Siebesma, et al., 2023).…”

Section: Data Set Descriptionmentioning

confidence: 99%

Cloud Botany: Shallow Cumulus Clouds in an Ensemble of Idealized Large‐Domain Large‐Eddy Simulations of the Trades

Jansson,

Janssens,

Grönqvist

et al. 2023

Small shallow cumulus clouds (<1 km) over the tropical oceans appear to possess the ability to self‐organize into mesoscale (10–100 km) patterns. To better understand the processes leading to such self‐organized convection, we present Cloud Botany, an ensemble of 103 large‐eddy simulations on domains of 150 km, produced by the Dutch Atmospheric Large Eddy Simulation model on supercomputer Fugaku. Each simulation is run in an idealized, fixed, larger‐scale environment, controlled by six free parameters. We vary these over characteristic ranges for the winter trades, including parameter combinations observed during the EUREC4A (Elucidating the role of clouds–circulation coupling in climate) field campaign. In contrast to simulation setups striving for maximum realism, Cloud Botany provides a platform for studying idealized, and therefore more clearly interpretable causal relationships between conditions in the larger‐scale environment and patterns in mesoscale, self‐organized shallow convection. We find that any simulation that supports cumulus clouds eventually develops mesoscale patterns in their cloud fields. We also find a rich variety in these patterns as our control parameters change, including cold pools lined by cloudy arcs, bands of cross‐wind clouds and aggregated patches, sometimes topped by thin anvils. Many of these features are similar to cloud patterns found in nature. The published data set consists of raw simulation output on full 3D grids and 2D cross‐sections, as well as post‐processed quantities aggregated over the vertical (2D), horizontal (1D) and all spatial dimensions (time‐series). The data set is directly accessible from Python through the use of the EUREC4A intake catalog.