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NASA NTRS2000

Diurnal Variability of the Hydrologic Cycle and Radiative Fluxes: Comparisons Between Observation and a GCM

The simulated diurnal cycle is in many ways an ideal test bed for new physical parameterizations. The purpose of this paper is to compare observations from the Tropical Rainfall Measurement Mission, the Earth Radiation Budget Experiment, the International Satellite Cloud Climatology Project, the Clouds and the Earth's Radiant Energy System Experiment, and the Anglo-Brazilian Amazonian Climate Observation Study with the diurnal variability of the Amazonian hydrologic cycle and radiative energy budget as simulated by the Colorado State University general circulation model, and to evaluate improvements and deficiencies of the model physics. The model uses a prognostic cumulus kinetic energy (CKE) to relax the quasi-equilibrium closure of the Arakawa-Schubert cumulus parameterization. A parameter, alpha, is used to relate the CKE to the cumulus mass flux. This parameter is expected to vary with cloud depth, mean shear, and the level of convective activity, but up to now a single constant value for all cloud types has been used. The results of the present study show clearly that this approach cannot yield realistic simulations of both the diurnal cycle and the monthly mean climate state. Improved results are obtained using a version of the model in which alpha is permitted to vary with cloud depth.

Lin, Xin↗

NASA NTRS2012

Comparison of Gravity Wave Temperature Variances from Ray-Based Spectral Parameterization of Convective Gravity Wave Drag with AIRS Observations

The realism of ray-based spectral parameterization of convective gravity wave drag, which considers the updated moving speed of the convective source and multiple wave propagation directions, is tested against the Atmospheric Infrared Sounder (AIRS) onboard the Aqua satellite. Offline parameterization calculations are performed using the global reanalysis data for January and July 2005, and gravity wave temperature variances (GWTVs) are calculated at z = 2.5 hPa (unfiltered GWTV). AIRS-filtered GWTV, which is directly compared with AIRS, is calculated by applying the AIRS visibility function to the unfiltered GWTV. A comparison between the parameterization calculations and AIRS observations shows that the spatial distribution of the AIRS-filtered GWTV agrees well with that of the AIRS GWTV. However, the magnitude of the AIRS-filtered GWTV is smaller than that of the AIRS GWTV. When an additional cloud top gravity wave momentum flux spectrum with longer horizontal wavelength components that were obtained from the mesoscale simulations is included in the parameterization, both the magnitude and spatial distribution of the AIRS-filtered GWTVs from the parameterization are in good agreement with those of the AIRS GWTVs. The AIRS GWTV can be reproduced reasonably well by the parameterization not only with multiple wave propagation directions but also with two wave propagation directions of 45 degrees (northeast-southwest) and 135 degrees (northwest-southeast), which are optimally chosen for computational efficiency.

convective gravity wave drag↗

DOE OSTI2021

Time Evolution and Diurnal Variability of the Parametric Sensitivity of Turbine-Height Winds in the MYNN-EDMF Parameterization

The Mellor-Yamada-Nakanishi-Niino (MYNN) parameterization applied in the Weather Research and Forecasting (WRF) model has been augmented to include the Eddy-Diffusion Mass-Flux (EDMF) approach to better represent transport by boundary-layer eddies. This change includes the addition of new parameters associated with convective updrafts and boundary-layer clouds. These additions lead to new parametric sensitivities in the hub-height wind speed compared to simulations using the standard MYNN parameterization that were explored previously. This work builds on efforts focused on WRF’s MYNN parameterization by examining the sensitivity of hub-height wind speed to parameters in the MYNN-EDMF parameterization as a function of simulation lead time. Summer and winter periods were selected from the second Wind Forecast Improvement Project (WFIP2). Five sets of simulations were completed for each season, with lead times ranging from 2 to 6 days. The results show that the sensitivity to the new parameters associated with the EDMF scheme is generally small compared to other parameters, with the largest impact in convective boundary layers. The spread in the perturbed parameter ensembles was found to grow quickly over the first 8 to 19 hours in the summer simulations and 17 to 24 hours in the winter simulations with little change after that, regardless of the simulation length. A strong diurnal cycle in the parameter sensitivity was also found associated with changes in the atmospheric stability, as well as an increase in the sensitivity to the entrainment parameter used in the EDMF parameterization that is associated with increasing fractional areas covered by plumes.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1985

On the mean particle size and water content of polar mesospheric clouds

Results from the ultraviolet spectrometer experiment (UVS) aboard the Solar Mesospheric Explorer, which measures ultraviolet sunlight scattered from air molecules in the mesosphere, are reported and discussed. The UVS experiment, its observing modes, and details of the observing sequence are described. The data are compared with predictions of Mie scattering theory, assuming the size distribution is parameterized by a mean radius and a width parameter. The water ice content, mean particle radius, and column number are calculated as a function of cloud brightness, and in terms of the unknown width parameter. It is concluded that polar mesospheric cloud particles are quite small, having average volume-effective radii less than 70 mm, and that clouds differ in their optical properties mainly as a result of their mean particle size.

Thomas, G. E.↗

NASA NTRS2020

Clouds and Convective Self-Aggregation in a Multi-Model Ensemble of Radiative-Convective Equilibrium Simulations

The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective56equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climatecsensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique amongst intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than un-aggregated simulations. Plain Language Summary This study investigates tropical clouds and climate using results from more than 30 different numerical models set up in a simplified framework. The dataset of model simulations is unique in that it includes a wide range of model types configured in a consistent manner. We address some of the biggest open questions in climate science, including how cloud properties change with warming and the role that the tendency of clouds to form clusters plays in determining the average climate and how climate changes. While there are large differences in how the different models simulate average temperature, humidity, and cloudiness, in a majority of models, the amount of high clouds decreases as climate warms. Nearly all models simulate a tendency for clouds to cluster together. There is agreement that when the clouds are clustered, the atmosphere is drier with fewer clouds overall. We don’t find a conclusive result for how cloud clustering changes as the climate warms.

Allison A. Wing↗

DOE OSTI2022

Small field campaign: aerosol – ice formation closure pilot study. Final Report

Prediction of atmospheric ice formation from aerosol particles by heterogeneous nucleation represents one of the grand challenges in atmospheric science. Our insufficient predictive understanding of primary ice formation is the reason that climate models typically do not include heterogeneous ice nucleation with subsequent effects on climate uncertainty. Mixed-phase clouds, where supercooled water droplets and ice crystals coexist play globally an important role regulating climate. This is especially the case for the Arctic region that experiences the greatest warming due to climate change compared to other regions in the world. Immersion freezing initiated by ice-nucleating particles (INPs) in supercooled water droplets is recognized as the dominant primary ice formation pathway in mixed-phase cloud regimes. For this reason, it is crucial to evaluate our capability to predict immersion freezing for a given ambient aerosol population. The goal of this project is conducting a field-based pilot study at the U.S. DOE Atmospheric Radiation Measurement (ARM) user facility at Southern Great Plains (SGP) to evaluate our capability to predict the number concentration of aerosol particles that serve as INPs in the immersion freezing mode. Successful prediction of INP number concentrations is also termed “closure”. This field-observational approach represents a first-of-its kind attempt of an aerosol–ice formation closure study (AEROICESTUDY). Very few closure studies related to INPs have been conducted, and to our knowledge, none using robust size-resolved ambient aerosol composition measurements as a starting point. Achievement of aerosol–ice formation closure relies on our ability to characterize the ambient aerosol population with respect to particles size and composition and to determine INP number concentrations for specified freezing temperatures. This requires numerous online and offline instrumentation resulting in this pilot field campaign being a multi-institutional and community-collaborative effort. We chose the ARM SGP megasite for this first aerosol-ice formation closure pilot study due to its significant measurement capabilities available to obtain detailed physical characterization of the local aerosol population including size distribution, mass loading, and chemical composition of non-refractory aerosol particles. The overall objective of this project is to identify ice nucleation parameterizations that produce the most robust predictions of INP numbers and thus are best suited to be included in cloud and climate models. This objective includes the following goals for this field and laboratory-based project: i) What are the crucial aerosol physicochemical property measurements needed to accurately guide ice nucleation representations in models and long-term INP measurements? ii) What level of parameter details needs to be known to achieve aerosol–ice formation closure? iii) What are the leading causes for climate model bias in INP predictions? We found that the advances in our understanding of immersion freezing garnered over the last 20 years allowed us to yield partial and full closures of atmospheric immersion freezing from ambient aerosol particles. When the aerosol population is physicochemically complex and parameterizations for representative INP types are not yet available, we still struggle to accurately predict INP number concentrations. This project clearly demonstrates that with more laboratory and field measurements that are accompanied by particle composition analysis, the necessary datasets to achieve aerosol–ice formation closure for various locations will emerge, thus providing a robust foundation for guiding the representation of INPs in cloud and climate models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Evaluating Subgrid-Scale Properties of Low Clouds over the Beaufort Sea in Arctic System Reanalysis using ARISE Airborne In Situ Observations

Arctic low clouds influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Unfortunately, atmospheric models and retrospective analysis (reanalysis) products struggle to accurately simulate the occurrence and properties of low clouds in the Arctic. To better understand the magnitude and possible causes of this problem, we analyze the disagreements between the Arctic System Reanalysis version 2 (ASR) and data taken during the September 2014 Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne campaign conducted over the Beaufort Sea. Previous analysis of grid scale-averaged meteorological variables revealed a lack of cloud water produced in ASR, related to warm/dry biases, a failure to produce sufficient cloud water in conditions that are observed to be favorable for large cloud water values, and sampling issues with the ARISE flight paths. However, ARISE collected meteorological observations at one second intervals, offering spatial data resolution comparable to the subgrid scale of ASR. Subgrid scale properties and processes must be parameterized in conventional atmospheric models and reanalyses such as ASR, and often rely on assumed distributions of meteorological variables about the grid box mean state. To enable an examination of the realism of ASR’s subgrid scale properties, we use high resolution data from ARISE determine the “subgrid scale” distributions of cloud water and thermodynamic variables in the real atmosphere. Preliminary results show that the subgrid scale distribution of thermodynamic variables about the grid scale mean resembles the triangular distribution assumed in many bulk microphysical parameterization schemes, but also with tails resembling Gaussian distributions. The main goal is to understand the partitioning of the total available atmospheric water into water vapor and cloud water. Models typically covert some percentage of total water in a grid box to cloud water based on the estimated amount of supersaturation from the assumed vapor distribution. The ARISE measurements can reveal the relationship between subgrid scale supersaturation and the partition between cloud water and water vapor.

Jason Brant Dodson↗

DOE OSTI2026

Tendencies of Soil Microbial NO Emissions During HI‐SCALE as Predicted by a Nitrification/Denitrification Scheme

Many atmospheric chemical processes, including the formation of secondary organic aerosol (SOA), are strongly modulated by the reactions of NO and NO 2 (NO x ). Though NO x is controlled by anthropogenic emissions near urban areas, in rural areas soil microbes can be a significant contribution to NO emission globally. The relative rates of emissions of different nitrogen-containing species (e.g., NO, N 2 O, HONO, and N 2 ) are strong functions of soil properties such as temperature, moisture content, pH, and soil carbon and nitrogen pools. However, typical large-scale biogeochemical models either express these emissions simplistically, or not at all. Here we investigate the potential impact of soil NO emissions on atmospheric chemistry and SOA formation over regional and monthly time scales, specifically the 2016 spring and summer Intensive Observational Periods (IOPs) of the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) field campaign. We implement the soil NO nitrification/denitrification parameterization of Rasool et al. (2019) into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), supplemented by a 1-km soil moisture analysis. We then simulate both IOPs over the U.S. Great Plains, evaluating against ground stations and flight data. We show that soil NO emissions can account for a large fraction of total NO x and locally increase O 3 concentrations by up to 25%, while alleviating negative biases of gases and aerosols toward observations. Soil moisture and temperature changes between IOP1 and IOP2 lead to overall differences in emissions, but with large regional variability due to heterogeneous surface characteristics.

Atmoshpheric Chemistry↗

DOE OSTI2025

Dark magnetohydrodynamics: Black hole accretion in superradiant dark photon clouds

Black holes threaded by massive vector fields can be subject to a superradiant instability, growing a cloud of massive vector particles around it. In this work, we consider what happens if such a dark matter candidate field mimicking a dark photon interacts with an accretion flow onto the black hole. By including a kinetic mixing term with the standard model photon, we extend the commonly used equations of general-relativistic magnetohydrodynamics to a dark photon constituent. The coupling to the dark photon then appears as an effective dynamo term together with a dark Lorentz force acting on the accreting matter. We numerically study the interactions between the superradiant dark photon cloud and the inner accretion flow by solving the coupled system in full numerical relativity. By parameterically varying the mixing parameter between the dark and standard model sector, we provide a first investigation of how the accretion flow could be modified. In conclusion, depending on the coupling strength, our solutions exhibit increased wind launching, as well as oscillation modes in the disk.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Can general circulation models (GCMs) represent cloud liquid water path adjustments to aerosol–cloud interactions?

Abstract. General circulation models (GCMs), unlike other lines of evidence, indicate that anthropogenic aerosols cause a global-mean increase in cloud liquid water path (ℒ) and thus a negative adjustment to radiative forcing of the climate by aerosol–cloud interactions. In part 1 of this series of papers, we showed that this is true even in models that reproduce the negative correlation observed in present-day internal variability in ℒ and cloud droplet number concentration (Nd). We studied several possible confounding mechanisms that could explain the noncausal cloud–aerosol correlations in GCMs and that possibly contaminate observational estimates of radiative adjustments. Here, we perform single-column and full-atmosphere GCM experiments to investigate the causal model-physics mechanisms underlying the model radiative adjustment estimate. We find that both aerosol–cloud interaction mechanisms thought to be operating in real clouds – precipitation suppression and entrainment evaporation enhancement – are active in GCMs and behave qualitatively in agreement with physical process understanding. However, the modeled entrainment enhancement has a negligible global-mean effect. This raises the question of whether the GCM estimate is incorrect due to parametric or base-state representation errors or whether the process understanding gleaned from a limited set of canonical cloud cases is insufficiently representative of the diversity of clouds in the real climate. Regardless, even at limited resolution, the GCM physics appears able to parameterize the small-scale microphysics–turbulence interplay responsible for the entrainment enhancement mechanism. We suggest ways to resolve tension between current and future (storm-resolving) global modeling systems and other lines of evidence in synthesis climate projections.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1992

The effects of small ice crystals on cirrus infrared radiative properties

Using a model that combines single-scattering properties for spheroidal and hexagonal ice crystals, the thermal infrared radiative properties of cirrus clouds have been investigated. Infrared scattering and absorption properties for randomly oriented spheroids and hexagons are parameterized based on the anomalous diffraction theory and a geometric ray-tracing method, respectively. Using observed ice crystal size distributions, upwelling radiances at the top of cirrus cloudy atmospheres have been computed. Results show that the presence of small ice crystals can produce significant brightness temperature differences between two infrared wavelengths in the 10-micron window. Theoretical results have been compared with observed brightness temperature differences between 8.35 and 11.16 microns and between 11.16 and 12 microns. The observed values were obtained from the high-spectral resolution interferometer sounder. It is shown that the use of the present nonspherical model for ice crystals in radiative transfer calculations leads to a significantly better interpretation of the observed data than does the use of the spherical model.

Takano, Y.↗

NASA NTRS1993

Scientific investigations planned for the Lidar in-Space Technology Experiment (LITE)

The Lidar In-Space Technology Experiment (LITE) is being developed by NASA/Langley Research Center for a series of flights on the space shuttle beginning in 1994. Employing a three-wavelength Nd:YAG laser and a 1-m-diameter telescope, the system is a test-bed for the development of technology required for future operational spaceborne lidars. The system has been designed to observe clouds, tropospheric and stratospheric aerosols, characteristics of the planetary boundary layer, and stratospheric density and temperature perturbations with much greater resolution than is available from current orbiting sensors. In addition to providing unique datasets on these phenomena, the data obtained will be useful in improving retrieval algorithms currently in use. Observations of clouds and the planetary boundary layer will aid in the development of global climate model (GCM) parameterizations. This article briefly describes the LITE program and discusses the types of scientific investigations planned for the first flight.

Mccormick, M. P.↗

NASA NTRS1990

Global analyses of water vapor, cloud and precipitation derived from a diagnostic assimilation of SSM/I geophysical retrievals

An analytical approach is described for diagnostically assimilating moisture data from Special Sensor Microwave Imager (SSM/I) into a global analysis of water vapor, cloud content, and precipitation. In this method, 3D fields of wind and temperature values taken from ECMWF gridded analysis are used to drive moisture conservation equations with parameterized microphysical treatment of vapor, liquid, and ice; the evolving field of water vapor is periodically updated or constrained by SSM/I retrievals of precipitable water. Initial results indicate that this diagnostic model can produce realistic large-scale fields of cloud and precipitation. The resulting water vapor analyses agree well with SSM/I and have an additional advantage of being synoptic.

Robertson, Franklin R.↗

NASA NTRS2019

Utilization of Airborne Observations to Assess Model Parametrizations of Critical RH Profiles in the Arctic Ocean

Observational paucity is a reality in the Arctic Ocean. This is especially true for near-surface variables such as temperature, moisture, heat fluxes and BL clouds. As a result, modeling has become one of the major avenues for understanding current and future Arctic trends. Reanalyses are frequently used to force global ocean circulation and sea-ice models. But in northern high latitudes, model integrations and reanalyses are known to have large uncertainties in temperature and humidity profiles, and in boundary layer cloudiness. These are common sources of error in the surface radiative budget terms.An important way to diagnose these biases spatially and temporally is by using satellite remote sensing data. However, remotely-sensed observations also have large uncertainties, especially in near-surface temperature and relative humidity profiles.In situ observational studies are important in bridging our knowledge gap in regions such as the Arctic Ocean. Here, we utilize airborne and ship observations from the ARISE, ACME-IV, and ASCOS campaigns to construct critical relative humidity (RH) profiles over the Beaufort Sea. Such profiles are used as parameterization inputs in the NASA GOES global model to derive the total water condensate in a model grid-box, which determines the cloud fraction. Currently, the critical RH profiles are derived by global AIRS data, relaying mostly on mid-latitude regions, which are not necessarily relevant to the Arctic.We derive campaign-wide mean, standard deviation, and critical RH values, for grid size of 50x50 km and altitude bins between 50 to 400 m, covering both open-ocean and sea-ice covered regions. We compare profiles over open ocean and sea-ice, and look at correlations between the observed critical RH values and water condensate (by cloud number concentration) from observations versus the modeled ones. We then input our calculated values of minimal critical RH values into a set of GEOS single column model (SCM) simulations over the ARISE and ASCOS regions and compare the differences between the predicted values of cloud liquid water path (LWP), ice water path (IWP) and surface fluxes with the observed ones under the range of input parameterizations. Finally, we discuss the implications on surface radiative budget predictions in this region.

Segal-Rozenhaimer, Michal↗

DOE OSTI2023

Cloud microphysical response to entrainment and mixing is locally inhomogeneous and globally homogeneous: Evidence from the lab

Entrainment of dry air into clouds strongly influences cloud optical and precipitation properties and the response of clouds to aerosol perturbations. The response of cloud droplet size distributions to entrainment–mixing is examined in the Pi convection-cloud chamber that creates a turbulent, steady-state cloud. The experiments are conducted by injecting dry air with temperature (T e ) and flow rate (Q e ) through a flange in the top boundary, into the otherwise well-mixed cloud, to mimic the entrainment–mixing process. Due to the large-scale circulation, the downwind region is directly affected by entrained dry air, whereas the upwind region is representative of the background conditions. Droplet concentration (Cn) and liquid water content (L) decrease in the downwind region, but the difference in the mean diameter of droplets (D m ) is small. The shape of cloud droplet size distributions relative to the injection point is unchanged, to within statistical uncertainty, resulting in a signature of inhomogeneous mixing, as expected for droplet evaporation times small compared to mixing time scales. As T e and Q e of entrained air increase, however, Cn, L, and D m of the whole cloud system decrease, resulting in a signature of homogeneous mixing. The apparent contradiction is understood as the cloud microphysical responses to entrainment and mixing differing on local and global scales: locally inhomogeneous and globally homogeneous. This implies that global versus local sampling of clouds can lead to seemingly contradictory results for mixing, which informs the long-standing debate about the microphysical response to entrainment and the parameterization of this process for coarse-resolution models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2002

Cloud-Scale Numerical Modeling of the Arctic Boundary Layer

The research objective of this NASA grant-funded project was to determine in detail how large-scale processes. in combination with cloud-scale radiative, microphysical, and dynamical processes, govern the formation and multi-layered structure of Arctic stratus clouds. This information will be useful for developing and improving 1D (one dimensional) boundary layer models for the Arctic. Also, to quantitatively determine the effects of leads on the large-scale budgets of sensible heat, water vapor, and condensate in a variety of Arctic winter conditions. This information will be used to identify the most important lead-flux processes that require parameterization in climate models. Our approach was to use a high-resolution numerical model, the 2D (two dimensional) University of Utah Cloud Resolving Model (UU CRM), and its 1D version, the University of Utah Turbulence Closure Model (UU TCM), a boundary layer model based on third-moment turbulence closure, as well as a large-eddy simulation (LES) model originally developed by C.H. Moeng.

Kruegen, Steven K.↗

NASA NTRSDate not supplied

Comparison of INP Parameterizations for Dust Minerals in Climatological Simulations With a Global Model

The effect of aerosol particles on ice nucleation and, in turn, the formation of ice and mixed phase clouds is recognized as one of the largest sources of uncertainty in weather and climate prediction. We utilize an improved sectional dust module in NASA GISS Earth System ModelE2.1, which distinguishes eight different mineral species and accretions between iron oxides and the other minerals. Simulations over a period of 20 years have been carried out with this model, and the mineral fields and other model variables (temperature, relative humidity) are used to calculate the ice nucleating particle (INP) number concentration, applying time-independent and time-dependent INP parameterizations, such as active site parameterization and water activity based immersion freezing model (ABIFM). We study how the dependence of the parameterizations on different model variables affects the mean INP number concentration. The sensitivity of the INP number concentration to fundamental dust properties such as emitted mineral size distributions and mixing state between minerals is also investigated. Results show that the sensitivity of the total INP number concentration to the emitted dust size distribution is rather small, but the sensitivity over the whole size range obscures offsetting differences in the magnitude and the sign of the sensitivity between smaller and larger particles.

INP Parameterizations↗

NASA NTRS2015

TPSAS-NF1676L-22800-DND

This lecture will provide an overview of an advanced turbulence closure scheme used in cloud-resolving models (CRMs) and climate modeling effort with the superparameterization approach. The CRM is embedded into each atmospheric column of a climate model as a super parameterization. It replaces the traditional subgrid-scale physical parameterizations in a conventional climate model. Two sensitivity simulations with doubling of CO2 and an increase of sea surface temperature, respectively, are performed and compared to similar simulations without this advanced turbulence closure in CRM to better understand the role of low-level clouds in climate sensitivity.

Kuan-Man Xu↗