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The fundamental features of one kind of rarely known stratocumulus, which was termed as "Millipede Cloud," occurred over the Eastern Pacific Ocean in 2017 were first documented by using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. These clouds had long and meandering "central axes" extending from several hundreds to thousands kilometers, and a number of "radical cloud arms" extending several tens of kilometers in its two sides. Total 59 "Millipede Clouds," 4 and 55 of them, were formed over the Northern and the Southern Hemispheres, respectively. Their environmental backgrounds were analyzed by using ERA5 reanalysis data and MODIS sensor Level‐2 data. The cloud top pressures of these "Millipede Clouds" were between 850 and 800 hPa, and their top heights were about 1–2 km. There existed "inversion layer" of air temperature near the cloud tops at 800 hPa, which strongly suggested that these clouds were lower stratocumulus in essence. Plain Language Summary: "Millipede Cloud," one kind of rarely known stratocumulus which looks like "Millipede" shape, is termed for the first time in this paper. It has an obvious "central axis" and a number of well‐organized "radial cloud arms" in two sides of the "central axis" extending in several tens of kilometers length. This paper introduces the fundamental features of "Millipede Clouds" occurred over the Eastern Pacific Ocean in 2017 from the perspective of satellite image. Totally, 59 "Millipede Clouds" were found to occur over the Eastern Pacific Ocean. Their geographic distribution, cloud top features and vertical structure of one typical case on 16 July 2017 were documented. Key Points: The fundamental features of "Millipede Clouds" over the Eastern Pacific Ocean in 2017 were documented by using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imageryThe environmental backgrounds of these "Millipede Clouds" were analyzed by using ERA5 reanalysis data and MODIS dataThe cloud top pressures of these "Millipede Clouds" are between 850 and 800 hPa, and their top heights are about 1–2 km [ABSTRACT FROM AUTHOR]

In this paper, we examine differences in cloud adjustments (often called rapid adjustments) that occur as a direct result of abruptly increasing the solar constant by 4% or abruptly quadrupling of atmospheric CO2. In doing so, we devise a novel method for calculating the cloud adjustments for the abrupt solar forcing simulations that uses differences between coupled model simulations with abrupt solar and CO2 forcing, in combination with uncoupled, atmosphere‐only, abrupt CO2 forced experiments that have prescribed sea‐surface temperature. Our main findings are that (a) there are substantial differences in the responses of stratocumulus and cumulus clouds to solar and CO2 forcing, which follow the differences in the direct radiative effect that solar and CO2 forcing have at cloud top, and (b) there are differences in the adjustment of the average optical depth of high clouds to solar and CO2 forcing that we speculate are driven by the differences in the vertical profile of radiative heating and differences in the pattern of sea‐surface temperature change (for a fixed global mean temperature). These cloud adjustments contribute significantly to the total net cloud radiative effect, even after 150 years of simulation. Plain Language Summary: In climate change, clouds change due to a variety of mechanisms including surface temperature, dynamical circulations, and radiative forcing. In this paper, we examine the latter: how clouds respond to radiative forcing. We study this topic using climate model simulations where the brightness of the sun is abruptly increased by 4% and compare those with simulations where CO2 concentration is abruptly quadrupled. In doing so we find that, there are differences in the cloud response to changes in solar and CO2 forcing which include the occurrence of thick and thin high cloud, as well as the amount and height of low and mid‐level clouds. Key Points: Increasing CO2 causes a reduction and lowering of mid‐level and low‐level clouds which does not occur from solar forcingThere is large reduction in optically thin high clouds from solar forcing, especially as compared with CO2Even after 150 years adjustments make a significant contribution to the total net cloud radiative effect [ABSTRACT FROM AUTHOR]

There are many uncertainties in future climate, including how the Earth may react to different types of radiative forcing, such as CO2, aerosols, and even geoengineered changes in the amount of sunlight absorbed by Earth's surface. Here, we analyze model simulations where the climate system is subjected to an abrupt change of the solar constant by ±4%, and where the atmospheric CO2 concentration is abruptly changed to quadruple and half its preindustrial value. Using these experiments, we examine how clouds respond to changes in solar forcing, compared to CO2, and feedback on global surface temperature. The total cloud response can be decomposed into those responses driven by changes in global surface temperature, called the temperature mediated cloud feedbacks, and responses driven directly by the forcing that are independent of the global surface temperature. In this paper, we study the temperature mediated cloud changes to answer two primary questions: (a) How do temperature mediated cloud feedbacks differ in response to abrupt changes in CO2 and solar forcing? And (b) Are there symmetrical (equal and opposite) temperature mediated cloud feedbacks during global warming and global cooling? We find that temperature mediated cloud feedbacks are similar in response to increasing solar and increasing CO2 forcing, and we provide a short review of recent literature regarding the physical mechanisms responsible for these feedbacks. We also find that cloud responses to warming and cooling are not symmetric, due largely to non‐linearity introduced by phase changes in mid‐to‐high latitude low clouds and sea ice loss/formation. Plain Language Summary: As the global mean temperature changes, there are changes in cloud amount, location, and thickness, which can all impact the radiative balance of the Earth. Cloud changes driven directly by global temperature change are called temperature mediated cloud feedbacks. In this paper, we study the temperature mediated cloud feedbacks that occur in model simulations where the amount of sunlight incident upon the Earth is increased or decreased abruptly, and then held constant for 150 years. We compare the cloud changes in these experiments with experiments where the CO2 concentration is similarly increased or decreased abruptly and held constant for 150 years. In doing so we find that the temperature mediated cloud feedbacks following abrupt changes in solar radiation are characteristically similar to those occurring following CO2 increase. There are however substantial differences in the temperature mediated cloud feedbacks that occur while the climate is warming versus cooling. Key Points: The temperature mediated cloud changes and feedbacks incurred by changes in solar and CO2 forcing are similarOptical depth changes at high latitudes produce substantial differences in cloud feedbacks in cooling and warming experimentsLikewise, tropical circulations respond differently in models to cooling and warming, with a stronger change in the Walker circulation in warming experiments [ABSTRACT FROM AUTHOR]

Fast atmospheric processes such as deep convection and severe storms are challenging to observe and understand without adequate spatiotemporal sampling. Geostationary (GEO) imaging has the advantage of tracking these fast processes continuously at a cadence of the 10 min global and 1 min mesoscale from thermal infrared (TIR) channels. More importantly, the newly-available GEO-GEO stereo observations from our 3D-Wind algorithm provide more accurate height assignment for atmospheric motion vectors (AMVs) than those from conventional TIR methods. Unlike the radiometric methods, the stereo height is insensitive to radiometric TIR calibration of satellite sensors and can assign the feature height correctly under complex situation (e.g., multi-layer clouds and atmospheric inversion). This paper shows a case study from continuous GEO-GEO stereo observations over the Eastern Pacific during 1–5 February 2023, to highlight diurnal variations of clouds and dynamics in the planetary boundary layer (PBL), altocumulus/congestus, convective outflow and tropical tropopause layer (TTL). Because of their good vertical resolution, the stereo observations often show a wind shear in these cloud layers. As an example, the stereo winds reveal the classic Ekman spiral in marine PBL dynamics with a clockwise (counterclockwise) wind direction change with height in the Northern (Southern) Hemisphere subtropics. Over the Southeastern Pacific, the stereo cloud observations show a clear diurnal variation in the closed-to-open cell transition in the PBL and evidence of precipitation at a lower level from broken stratocumulus clouds. [ABSTRACT FROM AUTHOR]

Time is critical in the resource provisioning process in the Cloud Computing paradigm when serving cloud resources to cloud users. It's difficult for a cloud provider to serve a large number of users while also reducing long wait times after they've submitted a request. It is possible to improve the time factor by using a systematic resource provisioning process. This paper examines several time-based resource provisioning frameworks in greater detail. Many researchers focused on various time parameters that assist cloud service providers in providing the best resource-serving services to their customers. The primary goal of this paper is to assist future researchers, as well as cloud providers in observing and selecting the best time-based resource provisioning technique also they can emphasize building a new dynamic resource provisioning paradigm in the future with this paper work's observations. To validate these observations, a novel Particle Swarm Optimization (PSO) based model is designed in this work, which uses the selected time-based resource provisioning technique, and applies it to real-time cloud scenarios. It was observed that the proposed model was able to showcase better efficiency of scheduling, and optimum cloud utilization when compared with other time-based resource provisioning models for different cloud deployments. [ABSTRACT FROM AUTHOR]

Cloud droplet number concentration (Nd) is crucial for understanding aerosol–cloud interactions (ACI) and associated radiative effects. We present evaluations of four ground-based Nd retrievals based on comprehensive datasets from the Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The Nd retrieval methods use ARM ENA observatory ground-based remote sensing observations from a micropulse lidar, Raman lidar, cloud radar, and the ARM NDROP (Droplet Number Concentration) value-added product (VAP), all of which also retrieve cloud effective radius (re). The retrievals are compared against aircraft measurements from the fast cloud droplet probe (FCDP) and the cloud and aerosol spectrometer (CAS) obtained from low-level marine boundary layer clouds on 12 flight days during summer and winter seasons. Additionally, the in situ measurements are used to validate the assumptions and characterizations used in the retrieval algorithms. Statistical comparisons of the probability distribution function (PDF) of the Nd and cloud re retrievals with aircraft measurements demonstrate that these retrievals align well with in situ measurements for overcast clouds, but they may substantially differ for broken clouds or clouds with low liquid water path (LWP). The retrievals are applied to 4 years of ground-based remote sensing measurements of overcast marine boundary layer clouds at the ARM ENA observatory to find that Nd (re) values exhibit seasonal variations, with higher (lower) values during the summer season and lower (higher) values during the winter season. The ensemble of various retrievals using different measurements and retrieval algorithms such as those in this paper can help to quantify Nd retrieval uncertainties and identify reliable Nd retrieval scenarios. Of the retrieval methods, we recommend using the micropulse lidar-based method. This method has good agreement with in situ measurements, less sensitivity to issues arising from precipitation and low cloud LWP and/or optical depth, and broad applicability by functioning for both daytime and nighttime conditions. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a testbed for evaluating and comparing climate modeling systems at cloud resolving scales using hindcasts of the June 2012 North American derecho. To demonstrate its utility for model intercomparison, the testbed is applied to two models: the regionally‐refined Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) at 6.5, 3.25 and 1.625 km grid spacing and the Weather Research and Forecasting (WRF) model with 3.2 and 1.6 km grid spacing. We find the simulation results to be highly sensitive to the initial conditions (ICs), initialization time, and model configurations, with ICs from the Rapid Refresh producing the best simulation. Significant improvement is identified in both models as horizontal grid spacing is refined. While a propagation delay of approximately 2 hr is found in both models, SCREAM at 1.625 km simulates the observed bow echo structure of the derecho well and predicts strong surface gusts that exceed 30 m/s. In comparison, WRF has difficulty producing surface wind over 25 m/s, with wind gusts in WRF 42%–46% lower than in SCREAM. However, WRF has a lower bias in simulating cloud top temperature and extent, but overestimates precipitation intensity. Both models reproduce the observed outgoing longwave radiation spatial patterns well (Pearson correlation >0.88), but, compared with NEXRAD observations, simulate generally larger areas of composite radar reflectivity >40 dBZ and underestimate the precipitating area by ∼47%. Plain Language Summary: This paper describes a testbed for evaluating model performance on a particular high‐impact weather event—the June 2012 North American derecho, a storm event associated with extreme winds and precipitation. The testbed is applied to evaluate the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) and the Weather Research and Forecasting (WRF) model at resolutions that resolve cloud systems. The performance of both models is shown to be sensitive to the data set used for model initialization. Finer grid resolution generally leads to better model performance. All simulations show a 2‐hr delay in predicting the evolution of the derecho and produce more intense rainfall. SCREAM generates a more realistic convective front than WRF and produces stronger surface winds. The evaluation protocol can be used to better understand the credibility of model simulations of extreme events and guide model development. Key Points: A testbed of observational products, diagnostics, and metrics is constructed to evaluate hindcasts of the June 2012 North American derechoHindcast results are sensitive to initial conditions, initialization time, horizontal resolutions, and convective and microphysics schemesThe Simple Cloud‐Resolving E3SM Atmosphere Model at 1.625 km successfully reproduces severe surface gusts with wind speeds above 30 m/s [ABSTRACT FROM AUTHOR]

In this paper we develop a novel framework aimed to significantly reduce biases related to marine stratocumulus clouds in general circulation models (GCMs) while circumventing excessive computational cost requirements. Our strategy is to increase the horizontal resolution using a regionally refined mesh (RRM) over our region of interest in addition to using the Framework for Improvement by Vertical Enhancement (FIVE) to increase the vertical resolution only for specific physical processes that are important for stratocumulus. We apply the RRM off the coast of Peru in the southeastern Pacific, a region that climatologically contains the most marine stratocumulus in the subtropics. We find that our new modeling framework is able to replicate the results of our high-resolution benchmark simulation with much fidelity, while reducing the computational cost by several orders of magnitude. In addition, this framework is able to greatly reduce the long-standing biases associated with marine stratocumulus in GCMs when compared to the standard-resolution control simulation. [ABSTRACT FROM AUTHOR]

This paper presents the third edition of The Satellite Application Facility on Climate Monitoring's (CM SAF) cloud, albedo, and surface radiation dataset from advanced very-high-resolution radiometer (AVHRR) data, CLARA-A3. The content of earlier CLARA editions, namely cloud, surface albedo, and surface radiation products, has been extended with two additional surface albedo products (blue- and white-sky albedo), three additional surface radiation products (net shortwave and longwave radiation, and surface radiation budget), and two top of atmosphere radiation budget products (reflected solar flux and outgoing longwave radiation). The record length is extended to 42 years (1979–2020) by also incorporating results from the first version of the advanced very high resolution radiometer imager (AVHRR/1). A continuous extension of the climate data record (CDR) has also been implemented by processing an interim climate data record (ICDR) based on the same set of algorithms but with slightly changed ancillary input data. All products are briefly described together with validation results and intercomparisons with currently existing similar CDRs. The extension of the product portfolio and the temporal coverage of the data record, together with product improvements, is expected to enlarge the potential of using CLARA-A3 for climate change studies and, in particular, studies of potential feedback effects between clouds, surface albedo, and radiation. The CLARA-A3 data record is hosted by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) CM SAF and is freely available at 10.5676/EUM_SAF_CM/CLARA_AVHRR/V003 (Karlsson et al., 2023b). [ABSTRACT FROM AUTHOR]

Clouds, especially low clouds, are crucial for regulating Earth's energy balance and mediating the response of the climate system to changes in greenhouse gas concentrations. Despite their importance for climate, they remain relatively poorly understood and are inaccurately represented in climate models. A principal reason is that the high computational expense of simulating them with large‐eddy simulations (LES) has inhibited broad and systematic numerical experimentation and the generation of large data sets for training parametrization schemes for climate models. Here we demonstrate LES of low clouds on tensor processing units (TPUs), application‐specific integrated circuits that were originally developed for machine learning applications. We show that TPUs in conjunction with tailored software implementations can be used to simulate computationally challenging stratocumulus clouds in conditions observed during the Dynamics and Chemistry of Marine Stratocumulus (DYCOMS) field study. The TPU‐based LES code successfully reproduces clouds during DYCOMS and opens up the large computational resources available on TPUs to cloud simulations. The code enables unprecedented weak and strong scaling of LES, making it possible, for example, to simulate stratocumulus with 10× speedup over real‐time evolution in domains with a 34.7 km × 53.8 km horizontal cross section. The results open up new avenues for computational experiments and for substantially enlarging the sample of LES available to train parameterizations of low clouds. Plain Language Summary: The study of clouds has been impeded by, among other factors, limitations in our ability to simulate them rapidly and on sufficiently large domains. In particular, computational limitations in simulating low clouds are among the reasons for the difficulties of representing them accurately in climate models; this is one of the dominant uncertainties in climate predictions. This paper demonstrates how the large computing power available on tensor processing units (TPUs) (integrated circuits originally designed for machine learning applications) can be harnessed for simulating low clouds. We demonstrate the largest simulations of low clouds to date, with hundreds of billions of variables, and we document their fidelity to aircraft observations. The results open up the large computational resources available on TPUs, hitherto primarily used for machine learning, to the study of clouds in the climate system. Key Points: We introduce a large‐eddy simulation (LES) framework that runs on tensor processing units (TPUs, accelerators designed for machine learning)The fidelity of the LES is established by reproducing aircraft observations of nocturnal stratocumulus clouds over the PacificThe LES exhibit unprecedented scalability on TPUs, enabling the large‐scale generation of training data for cloud parameterizations [ABSTRACT FROM AUTHOR]

The estimation of cloud radiative forcing due to aerosol‐cloud interactions, RFaci (also known as the first indirect effect), relies on approximating the cloud albedo susceptibility to changes in droplet concentration, β. β depends on the cloud albedo and droplet concentration, both of which can be observed by satellites. Satellite observations are often spatially aggregated to coarser resolutions, typically 1 × 1° scenes. However, on such spatial scales, the cloud albedo tends to be heterogeneous, whereas the β approximation assumes homogeneity. Here, we demonstrate that the common practice of aggregating satellite data and neglecting cloud albedo heterogeneity results in an average overestimation of 10% in previous estimates of the RFaci. Additionally, we establish a relationship between the magnitude of the bias in β and Stratocumulus morphologies, providing a physical context for cloud heterogeneity and the associated bias. Lastly, we propose a correction method that can be applied to cloud albedo gridded data. Plain Language Summary: This paper explores the effect of cloud albedo morphology, which is a reflection of cloud heterogeneity, on radiative forcing due to aerosol‐cloud interactions (RFaci). The RFaci is estimated from satellite observations based on the assumption that clouds are homogeneous within a given scene. However, when satellite data is spatially aggregated to reduce the amount of data to a user‐friendly gridded format—a common practice—this assumption is no longer valid. Consequently, an overestimation of the RFaci occurs, particularly in heterogeneous scenes, where the overestimation can reach up to 50%. This means that the RFaci is lower than previously estimated. Our results also suggest that cloud albedo enhancement due to an increase in droplet concentrations would be most effective in homogeneous scenes. Therefore, marine cloud brightening strategies should take cloud albedo homogeneity into account to achieve the most effective albedo enhancement. Key Points: The common practice of spatial aggregation of satellite data into 1 × 1° scenes leads to an average 10% overestimation of the RFaciThe overestimation is due to neglecting cloud albedo heterogeneity, and is associated to different types of Stratocumulus morphologiesA correction is proposed, which calls for the incorporation of cloud reflectance statistics in Level 3 data [ABSTRACT FROM AUTHOR]

The cloud properties and governing processes in Southern Ocean marine boundary layer clouds have emerged as a central issue in understanding the Earth's climate sensitivity. While our understanding of Southern Ocean cloud feedbacks have evolved in the most recent climate model intercomparison, the background properties of simulated summertime clouds in the Southern Ocean are not consistent with measurements due to known biases in simulating cloud condensation nuclei concentrations. This paper presents several case studies collected during the Capricorn 2 and Marcus campaigns held aboard Australian research vessels in the Austral Summer of 2018. Combining the surface‐observed cases with MODIS data along forward and backward air mass trajectories, we demonstrate the evolution of cloud properties with time. These cases are consistent with multi‐year statistics showing that long trajectories of air masses over the Antarctic ice sheet are critical to creating high droplet number clouds in the high latitude summer Southern Ocean. We speculate that secondary aerosol production via the oxidation of biogenically derived aerosol precursor gasses over the high actinic flux region of the high latitude ice sheets is fundamental to maintaining relatively high droplet numbers in Southern Ocean clouds during Summer. Plain Language Summary: The amount of warming the Earth will experience because of increasing carbon dioxide levels in the atmosphere is sensitive to the properties of clouds that occur over the Southern Ocean. The atmosphere over the circumpolar Southern Ocean is poorly understood and presents significant challenges to climate models. Here we document the properties of the ubiquitous Southern Ocean low‐level clouds that exert a strong influence on the albedo of this region. We find that high cloud droplet number concentrations are associated with air masses that have taken paths over the high‐altitude ice sheets. The chemistry of the aerosol on which the cloud droplets form suggests that aerosols that have recently condensed from gasses emitted by phytoplankton in the highly productive waters near Antarctica are an important component of the cloud properties that must be correctly simulated in models. Key Points: High cloud droplet number concentrations are associated with air masses that have recently passed over continental AntarcticaCloud droplet number concentrations decrease with time as clouds evolve in over‐water trajectories due to scavenging by precipitationKatabatic flows bring high concentrations of cloud condensation nuclei to the marine boundary layer where they influence cloud properties [ABSTRACT FROM AUTHOR]

The semi-permanent stratocumulus clouds over the south-eastern Atlantic Ocean (SEA) can act as an "air conditioner" to the regional and global climate system. The interaction of aerosols and clouds becomes important in this region and can lead to negative radiative effects, partially offsetting the positive radiative forcing of greenhouse gases. A key pathway by which aerosols affect cloud properties is by acting as cloud condensation nuclei (CCN). In this paper, we use the United Kingdom Earth System Model (UKESM1) to investigate the sources of CCN (from emissions and atmospheric processes) in the SEA as well as the response of the cloud droplet number concentration (CDNC), the cloud liquid water path (LWP), and radiative forcing to these sources during 2016 and 2017. Overall, free and upper troposphere nucleated aerosols are the dominant source of the boundary layer CCN concentration at 0.2 % supersaturation (CCN 0.2%), contributing an annual average of ∼ 41 % as they subside and entrain into the marine boundary layer, which is consistent with observations highlighting the important role of nucleation in the boundary layer CCN concentration. In terms of emission sources, anthropogenic emissions (from energy, industry, agriculture, etc.) contribute the most to the annual average CCN 0.2% in the marine boundary layer (∼ 26 %), followed by biomass burning (BB, ∼ 17 %). In the cloud layer, BB contributes about 34 % of the annual CCN 0.2% , midway between the contributions from aerosol nucleation (36 %) and anthropogenic sources (31 %). The contribution of aerosols from different sources to the CDNC is consistent with their contribution to CCN 0.2% within the marine boundary layer, with free and upper troposphere aerosol nucleation being the most important source of the CDNC overall. In terms of emission sources, anthropogenic sources are also the largest contributors to the annual average CDNC, closely followed by BB. However, during the BB season, BB and free and upper troposphere aerosol nucleation are equally the most important sources of the CDNC. The contribution of BB to the CDNC is more significant than its increase to CCN 0.2% , mainly because BB aerosols are mostly located directly above the inversion layer in the model; thus, they can increase the in-cloud CDNC by enhancing the supersaturation through the dynamical feedback due to short-wave absorption. An aerosol source that shows an increase in the CDNC also shows an increase in the LWP resulting from a reduction in autoconversion. Due to the absorption effect, BB aerosol can enhance existing temperature inversions and reduce the entrainment of sub-saturated air, leading to a further increase in the LWP. As a result, the contribution of BB to the LWP is second only to aerosol nucleation on annual averages. These findings demonstrate that BB is not the dominant source of CCN within the marine boundary layer from an emission source perspective. However, as most BB aerosols are located directly above the inversion layer, their effect on clouds increases due to their absorption effect (about the same as anthropogenic sources for the CDNC and more than anthropogenic sources for the LWP), highlighting the crucial role of their radiative effect on clouds. The results on the radiative effects of aerosols show that BB aerosol exhibits an overall positive RF ari (radiative forcing associated with aerosol–radiation interactions), but its net effective radiative forcing remains negative due to its effect on clouds (mainly due to its absorbing effect). By quantifying aerosol and cloud properties affected by different sources, this paper provides a framework for understanding the effects of aerosol sources on marine stratocumulus clouds and radiation in the SEA. [ABSTRACT FROM AUTHOR]

A new version of the stochastic multiplume Jet Propulsion Laboratory Eddy‐Diffusivity/Mass‐Flux (JPL‐EDMF) parameterization which consistently couples the simplified Khairoutdinov and Kogan (2000), https://doi.org/10.1175/1520-0493(2000)128<0229:ANCPPI>2.0.CO;2, warm phase cloud microphysical parameterization with the parameterization of cloud macrophysical and subgrid scale dynamical processes is described. The new parameterization combines the EDMF approach with an assumed shape of a joint probability density function of thermodynamic and kinematic variables which provide the basis for the computation of all parameterized processes. As far as we are aware this is the first attempt to consistently couple all of these parameterized processes in the EDMF framework. This paper is part one of a two paper series. Here, the JPL‐EDMF parameterization is described and benchmark simulations of precipitating stratocumulus and cumulus convection are performed in a single‐column‐model framework. The parameterization results compare favorably to the reference large‐eddy‐simulation results. In the second part (Smalley et al., 2022, https://doi.org/10.1029/2021MS002729) the JPL‐EDMF parameterization is validated for a wide range of observation‐based scenarios covering the continuous transition from subtropical stratocumulus to cumulus convection derived from global reanalysis, and parameterization uncertainties are studied in detail. Plain Language Summary: Parameterizations are components of atmospheric models that represent the impact of unresolved processes on the resolved scale flow and are crucial for shaping simulated dynamics and thermodynamics. In many atmospheric models, multiple parameterizations are used in parallel and each of them represents subgrid processes only in part, or only for a certain atmospheric regime. Recent parameterization developments focus on unified approaches which represent key unresolved processes in a mathematically consistent framework that allows to capture important interactions among unresolved processes, which is hard to achieve with modularized approaches. We describe a new unified parameterization which represents subgrid‐scale dynamical, cloud and rain processes, thus removing key shortcomings of traditional parameterizations. In this new parameterization, the subgrid‐scale dynamical processes are represented by the stochastic multiplume Eddy‐Diffusivity/Mass‐Flux framework from our previous works. Cloud and rain processes are represented by well‐known parameterizations developed for high‐resolution models. The key features of the new parameterization are a set of consistent assumptions about subgrid variability which is used to evaluate grid mean rates of these three processes, and representation of interactions between them. Our new parameterization is tested against benchmark simulations of marine stratocumulus and cumulus convection and the results compare favorably to the reference large‐eddy‐simulation results. Key Points: We describe a novel unified parameterization that represents interactive subgrid scale dynamical and warm cloud and rain processesThe new parameterization is based on a stochastic multiplume Eddy‐Diffusivity/Mass‐Flux approachThe parameterization successfully reproduces benchmark cases of precipitating marine cumulus and stratocumulus convection [ABSTRACT FROM AUTHOR]

This paper provides an overview of the United States (US) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2 (E3SMv2) fully coupled regionally refined model (RRM) and documents the overall atmosphere, land, and river results from the Coupled Model Intercomparison Project 6 (CMIP6) DECK (Diagnosis, Evaluation, and Characterization of Klima) and historical simulations – a first-of-its-kind set of climate production simulations using RRM. The North American (NA) RRM (NARRM) is developed as the high-resolution configuration of E3SMv2 with the primary goal of more explicitly addressing DOE's mission needs regarding impacts to the US energy sector facing Earth system changes. The NARRM features finer horizontal resolution grids centered over NA, consisting of 25 → 100 km atmosphere and land, a 0.125 ∘ river-routing model, and 14 → 60 km ocean and sea ice. By design, the computational cost of NARRM is ∼3× of the uniform low-resolution (LR) model at 100 km but only ∼ 10 %–20 % of a globally uniform high-resolution model at 25 km. A novel hybrid time step strategy for the atmosphere is key for NARRM to achieve improved climate simulation fidelity within the high-resolution patch without sacrificing the overall global performance. The global climate, including climatology, time series, sensitivity, and feedback, is confirmed to be largely identical between NARRM and LR as quantified with typical climate metrics. Over the refined NA area, NARRM is generally superior to LR, including for precipitation and clouds over the contiguous US (CONUS), summertime marine stratocumulus clouds off the coast of California, liquid and ice phase clouds near the North Pole region, extratropical cyclones, and spatial variability in land hydrological processes. The improvements over land are related to the better-resolved topography in NARRM, whereas those over ocean are attributable to the improved air–sea interactions with finer grids for both atmosphere and ocean and sea ice. Some features appear insensitive to the resolution change analyzed here, for instance the diurnal propagation of organized mesoscale convective systems over CONUS and the warm-season land–atmosphere coupling at the southern Great Plains. In summary, our study presents a realistically efficient approach to leverage the fully coupled RRM framework for a standard Earth system model release and high-resolution climate production simulations. [ABSTRACT FROM AUTHOR]

Measurements of the global cloud vertical structure (CVS) are critical to better understanding the effects of the CVS on climate. Current CVS algorithms based on OCO-2 have to be combined with cloud top height products from CALIPSO and CloudSat, which are no longer available after these two satellites left A-Train in 2018. In this paper, we derive a machine learning-based algorithm using only OCO-2 oxygen A-band hyperspectral measurements to simultaneously predict the cloud optical depth (COD), cloud top pressure (p_top), and cloud pressure thickness (CPT) of single-layer liquid clouds. For validation of real observations, the root mean square errors (RMSEs) of the COD, p_top, and CPT are 7.31 (versus the MYD06_L2), 35.06 hPa, and 26.66 hPa (versus the 2B-CLDCLASS-LIDAR). The new algorithm can also predict CVS parameters trained with p_tops from CALIPSO/CloudSat or CODs from MODIS. Controlled experiments show that known p_tops are more conducive to CPT prediction than known CODs, and experiments with both known CODs and p_tops obtain the best accuracy of RMSE = 20.82 hPa. Moreover, a comparison with OCO2CLD-LIDAR-AUX products that rely on CALIPSO shows that our CVS predictions only using OCO-2 measurements have better CODs for all clouds, better p_tops for clouds with a p_top < 900 hPa, and better CPTs for clouds with a CPT > 30 hPa. [ABSTRACT FROM AUTHOR]

This paper investigates the quality of global cloud fraction and cloud-top height products provided by the third edition of the CM SAF cLoud, Albedo and surface RAdiation dataset from the AVHRR data (CLARA-A3) climate data record (CDR) produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). Compared with with CALIPSO–CALIOP cloud lidar data and six other cloud CDRs, including the predecessor CLARA-A2, CLARA-A3 has improved cloud detection, especially over ocean surfaces, and improved geographical variation and cloud detection efficiency. In addition, CLARA-A3 exhibits remarkable improvements in the accuracy of its global cloud-top height measurements. For example, in tropical regions, previous underestimations for high-level clouds are reduced by more than 2 km. By taking advantage of more realistic descriptions of global cloudiness, this study attempted to estimate trends in the observable fraction of low-level clouds, acknowledging their importance in producing a net climate cooling effect. The results were generally inconclusive in the tropics, mainly due to the interference of El Nino modes during the period under study. However, the analysis found small negative trends over oceanic surfaces outside the core tropical region. Further studies are needed to verify the significance of these results. [ABSTRACT FROM AUTHOR]

The underestimation of cloud fraction, especially the low stratus cloud (LSC) fraction over the eastern oceans, remains a problem in most atmospheric global climate models. This study investigated potential improvements through perturbing nine moist physical parameters, using uniform sampling and Latin hypercube sampling methods, and quantified the parametric uncertainty and effects of non‐linear interaction between parameters on the tropical cloud fraction in the Grid‐Point Atmospheric Model of the Institute of Atmospheric Physics/State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, version 2. Results showed that the uncertainty ranges of the tropical total cloud fraction and LSC associated with multiple‐parameter perturbation were larger than those from any single‐parameter perturbation. The total cloud fraction was significantly improved with multiple‐parameter perturbation and the LSC also increased notably when using parameter values optimized for the total cloud fraction because of the indirect parametric effect on lower‐tropospheric stability. Non‐linear effects between parameters on simulating LSC were much stronger than those on simulating the total cloud fraction. Furthermore, the non‐linear interaction reduced large values of total cloud fraction but had a strong incremental effect on large values of LSC over the southeast Pacific. The findings demonstrate the feasibility of improving the simulation of LSC over the eastern oceans by tuning moist parameters, and provide further insight into the non‐linear effects between parameters on the simulation of cloud fraction. Plain Language Summary: Clouds play a crucial role in Earth's climate system by modulating radiation budgets. This paper reports the results of a study aimed at better understanding the moist parametric uncertainty and non‐linear effects on the tropical cloud fraction in climate model simulations. This is important because underestimation of the tropical cloud fraction—particularly over marine stratus regions—remains a problem for climate models. Determining whether the biases can be reduced by tuning parameters may provide a solution. Our results demonstrate the feasibility of improving the simulation of total cloud fraction, and even marine stratus, through parameter tuning. Furthermore, our results highlight the different non‐linear effects on cloud over different areas, which should motivate further studies on these non‐linear effects within other important physical processes or phenomena in climate simulations. Key Points: Two methods are used to quantify the parametric uncertainty and non‐linear effects between parameters on tropical cloud fractionThe total cloud fraction is improved and the marine stratus also increases because of the indirect parametric effect on lower‐tropospheric stabilityThe effects of non‐linear interaction between parameters on the marine stratus are stronger than those on the total cloud fraction [ABSTRACT FROM AUTHOR]

Cloud microphysics parametrizations control the transfer of water between phases and hydrometeor species in numerical weather prediction and climate models. As a fundamental component of weather modelling systems cloud microphysics can determine the intensity and timing of precipitation, the extent and longevity of cloud cover and its impact on radiative balance, and directly influence near surface weather metrics such as temperature and wind. In this paper we introduce and demonstrate the performance of a double moment cloud microphysical scheme (CASIM: Cloud AeroSol Interacting Microphysics) in both midlatitude and tropical settings using the same model configuration. Comparisons are made against a control configuration using the current operational single moment cloud microphysics, and CASIM configurations that use fixed in‐cloud droplet number or compute cloud droplet number concentration from the aerosol environment. We demonstrate that configuring CASIM as a single moment scheme results in precipitation rate histograms that match the operational single moment microphysics. In the midlatitude setting, results indicate that CASIM performs as well as the single moment microphysics configuration, but improves certain aspects of the surface precipitation field such as greater extent of light (<$$ < $$1 mm ·$$ \cdotp $$ hr−1$$ {}^{-1} $$) rain around frontal precipitation features. In the tropical setting, CASIM outperforms the single moment cloud microphysics as evident from improved comparison with radar derived precipitation rates. [ABSTRACT FROM AUTHOR]

This paper investigates the use of model cloud information in the assimilation of low-level atmospheric motion vectors (AMVs) in the ECMWF global data assimilation system, with the aim to characterize and address issues encountered in the assimilation of these observations. An analysis of background departure statistics (comparison of observations with the model background) shows that AMVs placed above the model cloud show larger deviations from the model background relative to those placed unrealistically close to the surface. Reassigning the pressure of AMVs diagnosed above the model cloud layer to either the model cloud top, cloud base, or average pressure leads to improvements in rootmean- square vector difference (RMSVD) and speed bias against the background wind fields. In assimilation experiments, reassigning AMVs placed above the model cloud to the model cloud top, cloud base, or average pressure results overall in a positive impact on subsequent forecasts. The reassignment to an average model cloud pressure performs best in this respect, and this approach has been implemented in the operational ECMWF system in October 2021. [ABSTRACT FROM AUTHOR]

This paper presents the Backscattering Lidar Signal Simulator (BLISS), an end-to-end lidar simulator developed by the Centre National d'Etudes Spatiales (CNES). We computed the constant multiple-scattering (MS) coefficient of BLISS with a Monte Carlo (MC) code in the framework of CALIOP/CALIPSO observations for different homogeneous and plane-parallel stratocumulus and cirrus cloud geophysical scenes. The MS coefficient varies from 0.46 to 0.63. Then we evaluated the Level 1 products of BLISS. Above and in-cloud relative difference between the attenuated backscattering coefficient vertical profile simulated by BLISS and by the MC code is smaller than 0.5% under single-scattering regime and smaller than 10% (30% if optical depth of cirrus is large) under multiple-scattering regime, thus confirming the robustness of BLISS. [ABSTRACT FROM AUTHOR]

For many pumping stations that draw water from sediment-laden rivers, the flow patterns in their intake structure are disordered due to sediment deposition, which seriously threatens the safe operation of projects. In order to accurately construct the complex and refined three-dimensional (3D) geometric model of the intake structure, and further explore the hydraulic characteristics of the water–sediment two-phase flow inside, this paper proposes a reverse modeling method and carries out numerical simulation. A typical forward intake structure in the arid region of Northwest China is selected as the research object. According to the point cloud data of the intake structure obtained by the 3D laser scanner, the curved surface models are rebuilt and imported into ICEM-CFD for improvement, thereby obtaining the 3D computational domain model of the actual intake structure; after lossless docking with FLUENT, the hydraulic characteristics are analyzed by applying the Realizable k-ε turbulence model coupled with the Mixture multiphase flow model considering interphase slip; finally, the reliability of the simulation is verified by in-situ flow measurements. Results demonstrate that the method can precisely reflect the actual geometric boundary and siltation of intake structure, and the numerical model can accurately identify the two-phase flow; the siltation improves the local flow field structure of the forebay to some extent, but cause the loss of the overall hydraulic performance of the intake structure. This study can provide a scientific reference for the geometric model construction and hydraulic analysis of intake structures with complex boundary and sediment deposition. [ABSTRACT FROM AUTHOR]

A method to derive the 3D cloud envelope and the cloud development velocity from high spatial and temporal resolution satellite imagery is presented. The CLOUD instrument of the recently proposed C3IEL mission lends itself well to observing at high spatial and temporal resolutions the development of convective cells. Space-borne visible cameras simultaneously image, under multiple view angles, the same surface domain every 20 s over a time interval of 200 s. In this paper, we present a method for retrieving cloud development velocity from simulated multi-angular, high-resolution top of the atmosphere (TOA) radiance cloud fields. The latter are obtained via the image renderer Mitsuba for a cumulus case generated via the atmospheric research model SAM and via the radiative transfer model 3DMCPOL, coupled with the outputs of an orbit, attitude, and camera simulator for a deep convective cloud case generated via the atmospheric research model Meso-NH. Matching cloud features are found between simulations via block matching. Image coordinates of tie points are mapped to spatial coordinates via 3D stereo reconstruction of the external cloud envelope for each acquisition. The accuracy of the retrieval of cloud topography is quantified in terms of RMSE and bias that are, respectively, less than 25 and 5 m for the horizontal components and less than 40 and 25 m for the vertical components. The inter-acquisition 3D velocity is then derived for each pair of tie points separated by 20 s. An independent method based on minimising the RMSE for a continuous horizontal shift of the cloud top, issued from the atmospheric research model, allows for the obtainment of a ground estimate of the velocity from two consecutive acquisitions. The mean values of the distributions of the stereo and ground velocities exhibit small biases. The width of the distributions is significantly different, with higher a distribution width for the stereo-retrieved velocity. An alternative way to derive an average velocity over 200 s, which relies on tracking clusters of points via image feature matching over several acquisitions, was also implemented and tested. For each cluster of points, mean stereo and ground positions were derived every 20 s over 200 s. The mean stereo and ground velocities, obtained as the slope of the line of best fit to the mean positions, are in good agreement. [ABSTRACT FROM AUTHOR]

Tropical convection is expected to decrease with warming, in a variety of ways. Specific incarnations of this idea include the "stability‐iris" hypothesis of decreasing anvil cloud coverage, as well as the decrease of both tropospheric and cloud‐base mass fluxes with warming. This paper seeks to encapsulate these phenomena into three "rules," and to explore their interrelationships and robustness, using both analytical reasoning as well as cloud‐resolving and global climate simulations. We find that each of these rules can be derived analytically from the usual expression for clear‐sky subsidence, so they all embody the same essential physics. But, these rules do not all provide the same degree of constraint: the stability‐iris effect is not entirely robust due to unconstrained microphysical degrees of freedom, and the decrease in cloud‐base mass flux is not entirely robust due to unconstrained effects of entrainment and detrainment. Tropospheric mass fluxes on the other hand are shown to be well‐constrained theoretically, and when evaluated in temperature coordinates they exhibit a monotonic decrease with warming at all vertical levels and across a hierarchy of models. Plain Language Summary: Tropical convection and high cloudiness are expected to decrease with global warming, in a variety of ways. This phenomenon has a few different manifestations in the literature, whose relationships are unclear. We show analytically that these explanations all embody the same essential physics, but are not equally robust and thus do not all have the same predictive power. Key Points: Three constraints on tropical convection can all be derived analytically from clear‐sky subsidenceTropospheric mass fluxes at a fixed isotherm should robustly decrease under global warmingDecreases in anvil cloud area and cloud‐base mass flux are not as robust [ABSTRACT FROM AUTHOR]

This paper examines the physical controls of extratropical humidity and clouds by isolating the effects of cloud physics factors in an idealized model. The Held–Suarez dynamical core is used with the addition of passive water vapor and cloud tracers, allowing cloud processes to be explored cleanly. Separate saturation adjustment and full cloud scheme controls are used to consider the strength of advection–condensation theory. Three sets of perturbations to the cloud scheme are designed to test the model’s sensitivity to the physics of condensation, sedimentation, and precipitation formation. The condensation and sedimentation perturbations isolate two key differences between the control cases. First, the sub-grid-scale relative humidity distribution assumed for the cloud macrophysics influences the location and magnitude of the extratropical cloud maxima, which interrupt the isentropic transport of moisture to the polar troposphere. Second, within the model’s explicit treatment of cloud microphysics, re-evaporation of hydrometeors moistens and increases clouds in the lower troposphere. In contrast, microphysical processes of precipitation formation (specifically, the ratio of accretion to autoconversion) have negligible effects on humidity, cloudiness, and precipitation apart from the strength of the largescale condensation and formation cycle. In addition, counterintuitive relationships}such as cloud condensate and cloud fraction responding in opposing directions}emphasize the need for careful dissection of physical mechanisms. In keeping with advection–condensation theory, circulation sets the patterns of humidity, clouds, and precipitation to first order, with factors explored herein providing secondary controls. The results substantiate the utility of such idealized modeling and highlight key cloud processes to constrain. [ABSTRACT FROM AUTHOR]

For quantitative precipitation estimation (QPE) based on polarimetric radar (PR) and rain gauges (RGs), the quality of the radar data is crucial for estimation accuracy. This paper proposes a combined radar quality index (CRQI) to represent the quality of the radar data used for QPE and an algorithm that uses CRQI to improve the QPE performance. Nine heavy rainfall events that occurred in Guangdong Province, China, were used to evaluate the QPE performance in five contrast tests. The QPE performance was evaluated in terms of the overall statistics, spatial distribution, near real-time statistics, and microphysics. CRQI was used to identify good-quality data pairs (i.e., PR-based QPE and RG observation) for correcting estimators (i.e., relationships between the rainfall rate and the PR parameters) in real-time. The PR-based QPE performance was improved because estimators were corrected according to variations in the drop size distribution, especially for data corresponding to 1.1 mm < average Dm < 1.4 mm, and 4 < average log10Nw < 4.5. Some underestimations caused by the beam broadening effect, excessive beam height, and partial beam blockages, which could not be mitigated by traditional algorithms, were significantly mitigated by the proposed algorithm using CRQI. The proposed algorithm reduced the root mean square error by 17.5% for all heavy rainfall events, which included three precipitation types: convective precipitation (very heavy rainfall), squall line (huge raindrops), and stratocumulus precipitation (small but dense raindrops). Although the best QPE performance was observed for stratocumulus precipitation, the biggest improvement in performance with the proposed algorithm was observed for the squall line. [ABSTRACT FROM AUTHOR]

The Cassini Grand Finale provided a unique opportunity to study Saturn's deep zonal flow. In this paper, we present a comprehensive deep zonal flow model for Saturn using a joint inversion of observed gravity and zonal flow-induced Ohmic dissipation in the semi-conducting region, under the assumption that the planet's cloud-level wind is limited to a shallow weather layer. Our model unveils a strong equatorially symmetric zonal flow (O (100) m s−1) and a weaker antisymmetric zonal flow (O (1) m s−1) beneath the cloud-level winds. Furthermore, we show that the maximum depth of the deep zonal flow is around 7800 km, surpassing previous results derived from gravity alone and with the assumption that the rapid cloud-level winds extend deep into the planet's interior. The meridional profile of the deep zonal flow differs significantly from the cloud-level zonal winds and predicts a strong westward zonal flow in the region with latitude around ±23°, where the observed cloud-top winds remain eastward. We also demonstrate that the zonal flow inside and outside the tangent cylinder exhibits significant differences in speed and scale. Moreover, our findings suggest that the coupling between the deep zonal flow and cloud-level winds varies across latitudes, with the shallow-wind model applicable to polar regions within the tangent cylinder and the deep-wind model more relevant to equatorial regions outside the tangent cylinder. Our findings highlight the importance of accounting for the planet's deep zonal flow in future studies of Saturn's atmospheric dynamics. [ABSTRACT FROM AUTHOR]

Advances in high‐performance computing have enabled large‐eddy simulations (LES) of turbulence, convection, and clouds. However, their potential to improve parameterizations in global climate models (GCMs) is only beginning to be harnessed, with relatively few canonical LES available so far. The purpose of this paper is to begin creating a public LES library that expands the training data available for calibrating and evaluating GCM parameterizations. To do so, we use an experimental setup in which LES are driven by large‐scale forcings from GCMs, which in principle can be used at any location, any time of year, and in any climate state. We use this setup to create a library of LES of clouds across the tropics and subtropics, in the present and in a warmer climate, with a focus on the transition from stratocumulus to shallow cumulus over the East Pacific. The LES results are relatively insensitive to the choice of host GCM driving the LES. Driven with large‐scale forcing under global warming, the LES simulate a positive but weak shortwave cloud feedback, adding to the accumulating evidence that low clouds amplify global warming. Plain Language Summary: Clouds remain one of the largest uncertainties in our understanding and in predictions of climate change because it is challenging to represent their small‐scale dynamics in climate models. However, high‐resolution simulations can provide faithful simulations of clouds and turbulence in limited areas, and these can be used to calibrate climate models. So far, only a limited set of simulations has been used for calibration of climate models, with focus on a few specific locations. This study presents an experimental setup that allows the high‐resolution simulations to be run anywhere on the globe, in any climate state, driven by output from different climate models. The setup is used to create a library of high‐resolution simulations of clouds across the tropics and subtropics in both the current and a warmer climate. The library substantially expands the data set available for the calibration and evaluation of climate models. Key Points: A library of high‐resolution simulations of clouds is created with large‐eddy simulations (LES) driven by a few global climate models (GCMs)Clouds simulated by LES driven by different GCMs are more similar to one another than the corresponding GCM‐simulated cloudsUnder global warming, LES‐simulated low clouds exhibit a weak but positive feedback on warming [ABSTRACT FROM AUTHOR]

Kelvin–Helmholtz (KH) waves are a frequent source of turbulence in stratiform precipitation systems over mountainous terrain. KH waves introduce large eddies into otherwise laminar flow, with updrafts and downdrafts generating small-scale turbulence. When they occur in cloud, such dynamics influence microphysical processes that impact precipitation growth and fallout. Part I of this paper used dual-Doppler, 2D wind and reflectivity measurements from an airborne cloud radar to demonstrate the occurrence of KH waves in stratiform orographic precipitation systems and identified four mechanisms for triggering KH waves. In Part II, we use similar observations to explore the effects of KH wave updrafts and turbulence on cloud microphysics. Measurements within KH wave updrafts reveal the production of liquid water in otherwise ice-dominated clouds, which can contribute to snow generation or enhancement via depositional and accretional growth. Fallstreaks beneath KH waves contain higher ice water content, composed of larger and more numerous ice particles, suggesting that KH waves and associated turbulence may also increase ice nucleation. A large-eddy simulation (LES), designed to model the microphysical response to the KH wave eddies in mixed-phase cloud, shows that depositional and accretional growth can be enhanced in KH waves, resulting in more precipitation when compared to a baseline simulation. While sublimation and evaporation occur in KH downdrafts, persistent supersaturation with respect to ice allows for a net increase in ice mass. These modeling results and observations suggest that KH waves embedded in mixed-phase stratiform clouds may increase precipitation, although the quantitative impact remains uncertain. Significance Statement: This study investigates how the turbulence caused by Kelvin–Helmholtz (KH) waves embedded in deep clouds affect precipitation growth. To answer this question, we used a Doppler radar on board a research aircraft to locate KH waves inside of clouds. These waves often break, and produce fallstreaks, which may descend down to the surface. Aircraft measurements from within these fallstreaks confirmed that they contain larger, more numerous ice particles. This evidence of enhanced precipitation coincided with turbulence and supercooled liquid water produced by the KH waves. Modeled KH waves show that some of the precipitation enhancement is caused by accretion and deposition within updrafts, but further research is needed to understand the role of turbulence and ice initiation in KH waves. [ABSTRACT FROM AUTHOR]

Aerosol and cloud microphysical measurements were collected by a research aircraft during August 2019 over the United Arab Emirates (UAE). The majority of scientific flights targeted summertime convection along the eastern Al Hajar Mountains bordering Oman, while one flight sampled non-orographic clouds over the western UAE near the Saudi Arabian border. In this work, we study the evolution of growing cloud turrets from cloud base (9 ∘ C) up to the capping inversion level (-12 ∘ C) using coincident cloud particle imagery and particle size distributions from cloud cores under different forcing. Results demonstrate the active role of background dust and pollution as cloud condensation nuclei (CCN) with the onset of their deliquescence in the subcloud region. Subcloud aerosol sizes are shown to extend from submicron to 100 µ m sizes, with higher concentrations of ultra-giant CCN (d>10 µ m) from local sources closer to the Saudi border, compared with the eastern orographic region where smaller CCN are observed. Despite the presence of ultra-giant CCN from dust and pollution in both regions, an active collision–coalescence (C–C) process is not observed within the limited depths of warm cloud (<1000 m). The state-of-the-art observations presented in this paper can be used to initialize modeling case studies to examine the influence of aerosols on cloud and precipitation processes in the region and to better understand the impacts of hygroscopic cloud seeding on these clouds. [ABSTRACT FROM AUTHOR]

The goal of this study is to challenge a large-eddy simulation model with a range of observations from a modern field campaign and to develop case studies useful to other modelers. The 2015 Cloud System Evolution in the Trades (CSET) field campaign provided a wealth of in situ and remote sensing observations of subtropical cloud transitions in the summertime northeast Pacific. Two Lagrangian case studies based on these observations are used to validate the thermodynamic, radiative, and microphysical properties of large-eddy simulations (LES) of the stratocumulus to cumulus transition. The two cases contrast a relatively fast cloud transition in a clean, initially well-mixed boundary layer versus a slower transition in an initially decoupled boundary layer with higher aerosol concentrations and stronger mean subsidence. For each case, simulations of two neighboring trajectories sample mesoscale variability and the coherence of the transition in adjacent air masses. In both cases, LES broadly reproduce satellite and aircraft observations of the transition. Simulations of the first case match observations more closely than for the second case, where simulations underestimate cloud cover early in the simulations and overestimate cloud top height later. For the first case, simulated cloud fraction and liquid water path increase if a larger cloud droplet number concentration is prescribed. In the second case, precipitation onset and inversion cloud breakup occur earlier when the LES domain is chosen to be large enough to support strong mesoscale organization. Significance Statement: Low-lying clouds over the ocean are difficult to represent in global climate models and contribute to uncertainty in climate predictions. To improve understanding and simulation of these clouds, an intensive airborne measurement campaign in 2015 over the northeast Pacific Ocean sampled these clouds and the surrounding air mass as the trade winds carried them toward Hawaii. In this paper, we simulate two contrasting case studies from this campaign with a high-resolution model that captures cloud-scale motions and processes. The observations test the model's fidelity in representing the transition from widespread to broken cloud cover, while the model suggests that this transition is accelerated by weather conditions promoting unusually weak subsidence and by the onset of drizzle. [ABSTRACT FROM AUTHOR]

Spectral width of the cloud droplet spectrum is important for radiative properties and drizzle/rain development in warm ice‐free clouds. We use an adiabatic rising parcel model to study activation and diffusional growth of cloud droplets, focusing on the spectral width evolution, and contrasting clean and polluted environments. A comprehensive droplet growth equation is used that includes kinetic, solute, and surface tension effects. We show that those effects have an appreciable impact on the spectral width evolution above the cloud base. Without those effects, the droplet area standard deviation should not change once activation is completed. In contrast, simulation results show that the area standard deviation does increase with height, especially for weak updrafts and polluted environments. Implications of those results for cloud modeling, especially applying conventional bin microphysics, are discussed. Plain Language Summary: Shape of the droplet spectrum is an important parameter affecting cloud radiative properties as well as drizzle and rain development in warm ice‐free clouds. We consider formation and growth of cloud droplets in an air parcel rising across the subcloud layer, reaching the cloud base, and continuing its rise carrying cloud droplets growing by condensation of water vapor. We focus on the evolution of the spectrum shape. We show that the simplified droplet growth equation often applied in cloud simulations is not accurate enough to faithfully predict shape of the droplet spectrum aloft, especially in polluted weak‐updraft shallow clouds such as a continental stratocumulus. Key Points: Evolutions of cloud droplet spectral width in an adiabatic parcel rising through cloud base differ between clean and polluted environmentsSmaller droplet sizes make the solute and surface tension effects more influential for spectral width evolutions in polluted cloudsConventional bin microphysics are not suitable for studying weak‐updraft shallow polluted clouds such as continental stratocumulus [ABSTRACT FROM AUTHOR]

This paper provides an overview of the United States (US) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2 (E3SMv2) fully coupled Regionally Refined Model (RRM) and documents the overall atmosphere, land, and river results from the Coupled Model Intercomparison Project 6 (CMIP6) DECK (Diagnosis, Evaluation, and Characterization of Klima) and historical simulations - a first-of-kind set of climate production simulations using RRM. The North American 5 (NA) RRM (NARRM) is developed as the high-resolution configuration of E3SMv2 with the primary goal of more explicitly addressing DOE's mission needs regarding impacts to the US energy sector facing Earth system changes. The NARRM features finer horizontal resolution grids centered over NA, consisting of 25→100 km atmosphere and land, 0.125° river routing model, and 14→60 km ocean and sea ice. By design, the computational cost of NARRM is ~3x of the uniform low-resolution (LR) model at 100 km but only ~10-20% of a globally uniform high-resolution model at 25 km. A novel hybrid timestep strategy for the atmosphere is key for NARRM to achieve improved climate simulation fidelity within the high-resolution patch without sacrificing the overall global performance. The global climate, including climatology, time series, sensitivity, and feedback, is confirmed to be largely identical between NARRM and LR as quantified with typical climate metrics. Over the refined NA area, NARRM is generally superior to LR, including for precipitation and clouds over the contiguous US (CONUS), summertime marine stratocumulus clouds off the coast of California, liquid and ice phase clouds near the North 15 polar region, extratropical cyclones, and spatial variability in land hydrological processes. The improvements over land are related to the better resolved topography in NARRM, whereas those over ocean are attributable to the improved air-sea interactions with finer grids for both atmosphere and ocean/sea ice. Some features appear insensitive to the resolution change analyzed here, for instance the diurnal propagation of organized mesoscale convective systems over CONUS, and the warm-season land-atmosphere coupling at the Southern Great Plains. In summary, our study presents a realistically efficient approach to leverage the RRM framework for a standard Earth system model release and high-resolution climate production simulations. [ABSTRACT FROM AUTHOR]

To enhance our understanding of cloud simulations over land, this study provides the first assessment of coupling between cloud and land surface in the Large‐Eddy Simulation (LES) Atmospheric Radiation Measurement Symbiotic Simulation and Observation (LASSO) activity for the shallow convection scenario. The analysis of observation data reveals a diurnal cycle of cloud‐land coupling, which co‐varies with surface fluxes. However, coupled (or decoupled) cumulus clouds are inadequately simulated, manifesting as a too‐high (or low) occurrence frequency during the afternoon. This discrepancy is mirrored by the overestimated cloud liquid water path and cloud‐top height. These overestimations are linked to the overpredicted boundary‐layer development and the easier trigger of shallow convection misrepresented in LES runs. Our study underscores the need to improve the representations of boundary‐layer processes and cloud‐land interactions within LES to better simulate shallow clouds in the future. Plain Language Summary: Cloud‐surface coupling is crucial in weather and climate predictions, yet its processes, particularly over the land surface, remain poorly understood. This study examined the evolution of coupled cloud occurrence frequency and cloud macro‐physical properties [e.g., cloud fraction, liquid water path (LWP), and cloud‐top height (CTH)] in LASSO Large‐Eddy Simulations (LESs) for 77 shallow convection case days against observations. We found that LES runs erroneously simulate a too‐high occurrence frequency of coupled cumulus clouds during the afternoon. In accord with that, shallow convection development is also markedly overestimated during the same period, manifested by overestimated cloud LWP and CTH. These errors are possibly linked to the misrepresentation of a key physical process, namely, the shallow convective trigger, which needs to be improved to achieve more accurate cloud simulations. Key Points: Observed cloud‐land coupling has notable diurnal variations but is inadequately simulated by Large‐Eddy Simulations (LESs)LESs overestimate the frequency and vertical extent of coupled cumuli in the afternoonThese overestimations by LESs are related to the misrepresentation of triggering shallow convection [ABSTRACT FROM AUTHOR]

The impact of clouds on the mixed layer (ML) is critical for understanding the evolution of boundary layer humidity and temperature over the course of a day. We found that accounting for moistening of the cloud layer (CL) by humidity originating in the ML dramatically alters the interaction between the ML and the CL in a one‐dimensional cloud‐topped boundary layer model: Chemistry Land‐surface Atmosphere Soil Slab (CLASS) (Vilà‐Guerau de Arellano et al., 2015, https://doi.org/10.1017/CBO9781316117422). We demonstrate that enabling CLASS to moisten the lower CL improves the prediction of humidity (and the flux of humidity) both above and below the ML top (h). To account for this moistening, we propose a length scale, L, above h, over which mixing of mass fluxes into the environment occurs. The mass fluxes are assumed to decrease linearly from h to a height L meters above h, analogous to a convective plume detraining into the environment at a height‐independent rate. Accounting for this process is accomplished by modifying the differential equations representing the growth of the jumps (sharp changes in humidity and temperature) at h. From analysis of a large number of diurnal Large Eddy simulations (covering approximately 11,000 different early morning initial conditions), we provide a regression model for parameterizing L from early morning weather variables. With the regression‐based estimate of L, the modified model (CLASS‐L) accounts for moistening the lower CL, and as a result, yields improved humidity dynamics, humidity flux profiles, and cloud growth under a broad range of conditions. Plain Language Summary: This study improved the representation of clouds above the mixed layer (ML) top in a simplified cloud‐topped atmospheric boundary layer model, Chemistry Land‐surface Atmosphere Soil Slab (CLASS) (Vilà‐Guerau de Arellano et al., 2015, https://doi.org/10.1017/CBO9781316117422). The CLASS model does not moisten the lower cloud layer (CL) when moisture originating from the ML reaches the CL. To address this, we propose a mixing length scale, L, above the ML top over which moisture originating from the ML is mixed into the environment, and name this model CLASS‐L. A power‐law regression is used to estimate L with regression coefficients based on a large set of daytime diurnal simulations (covering approximately 11,000 different initial conditions) using CLASS‐L and state‐of‐the‐art, three‐dimensional Large Eddy Simulation. With this simple enhancement to CLASS, CLASS‐L dramatically improved humidity dynamics, cloud growth, and humidity flux profiles both above and within the ML under a very broad range of initial conditions. Key Points: We enhance land‐atmosphere‐cloud interactions in a simplified model (Vilà‐Guerau de Arellano et al., 2015, https://doi.org/10.1017/CBO9781316117422)We propose an empirically determined length scale above the mixed layer over which mixing of humidity mass flux with the environment occursThe modifications (Chemistry Land‐surface Atmosphere Soil Slab‐L) dramatically alter the state and flux profiles in cloudy conditions, in agreement with Large Eddy Simulations [ABSTRACT FROM AUTHOR]

The super-droplet method (SDM) is a Lagrangian particle-based numerical scheme for cloud microphysics. In this work, a series of simulations based on the DYCOMS-II (RF02) setup with different horizontal and vertical resolutions are conducted to explore the grid convergence of the SDM simulations of marine stratocumulus. The results are compared with the double-moment bulk scheme (SN14) and model intercomparison project (MIP) results. In general, all SDM and SN14 variables show a good agreement with the MIP results and have similar grid size dependencies. The stratocumulus simulation is more sensitive to the vertical resolution than to the horizontal resolution. The vertical grid length DZ ≪ 2.5 m is necessary for both SDM and SN14. The horizontal grid length DX < 12.5 m is necessary for the SDM simulations. DX ≤ 25 m is sufficient for SN14. We also assess the numerical convergence with respect to the super-droplet numbers. The simulations are well converged when the super-droplet number concentration (SDNC) is larger than 16 super-droplets per cell. Our results indicate that the super-droplet number per grid cell is more critical than that per unit volume at least for the stratocumulus case investigated here. Our comprehensive analysis not only offers guidance on numerical settings essential for accurate stratocumulus cloud simulation but also underscores significant differences in liquid water content and cloud macrostructure between SDM and SN14. These differences are attributed to the inherent modeling strategies of the two schemes. SDM's dynamic representation of aerosol size distribution through wet deposition markedly contrasts with SN14's static approach, influencing cloud structure and behavior over a 6 h simulation. Findings reveal sedimentation's crucial role in altering aerosol distributions near cloud tops, affecting the vertical profile of cloud fraction (CF). Additionally, the study briefly addresses numerical diffusion's potential effects, suggesting further investigation is needed. The results underscore the importance of accurate aerosol modeling and its interactions with cloud processes in marine stratocumulus simulations, pointing to future research directions for enhancing stratocumulus modeling accuracy and predictive capabilities. [ABSTRACT FROM AUTHOR]

This study proposed an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. The matching characteristics between aerosol and cloud particle size distributions and gamma distributions were studied using aircraft observation data. The feasibility of the retrieval of the particle effective radius from lidar ratios and backscatter ratios was simulated and studied. A method for inverting the effective radius and number concentration of atmospheric aerosols or small cloud droplets using the backscatter ratio was proposed, and the error sources and applicability of the algorithm were analyzed. This algorithm was suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. Compared with the previous study, this algorithm could quickly obtain the microphysical parameters of atmospheric particles and has good robustness. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 µm. For cloud droplets, it is 1.0–10 µm. An atmospheric-observation experiment was conducted using the multi-wavelength lidar developed by Xi'an University of Technology, and a thin cloud layer was captured. The microphysical parameters of aerosol and clouds during this process were retrieved. The results clearly demonstrate the growth of the effective radius and number concentration. [ABSTRACT FROM AUTHOR]

The supercooled liquid fraction (SLF) in mixed-phase clouds (MPCs) is an essential variable of cloud microphysical processes and climate sensitivity. However, the SLF is currently calculated in spaceborne remote sensing only as the cloud phase–frequency ratio of adjacent pixels, which results in a loss of the original resolution in observations of cloud liquid or ice content within MPCs. Here, we present a novel method for retrieving the SLF in MPCs based on the differences in radiative properties of supercooled liquid droplets and ice particles at visible (VIS) and shortwave infrared (SWI) channels of the geostationary Himawari-8. Liquid and ice water paths are inferred by assuming that clouds are composed of only liquid or ice, with the real cloud water path (CWP) expressed as a combination of these two water paths (SLF and 1-SLF as coefficients), and the SLF is determined by referring to the CWP from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The statistically relatively small cloud phase spatial inhomogeneity at a Himawari-8 pixel level indicates an optimal scene for cloud retrieval. The SLF results are comparable to global SLF distributions observed by active instruments, particularly for single-layered cloud systems. While accessing the method's feasibility, SLF averages are estimated between 74 % and 78 % in Southern Ocean (SO) stratocumulus across seasons, contrasting with a range of 29 % to 32 % in northeastern Asia. The former exhibits a minimum SLF around midday in summer and a maximum in winter, while the latter trend differs. This novel algorithm will be valuable for research to track the evolution of MPCs and constrain the related climate impact. [ABSTRACT FROM AUTHOR]

Given the importance of constraining cloud droplet number concentrations (Nd) in low-level clouds, we explore two methods for retrieving Nd from surface-based remote sensing that emphasize the information content in lidar measurements. Because Nd is the zeroth moment of the droplet size distribution (DSD), and all remote sensing approaches respond to DSD moments that are at least 2 orders of magnitude greater than the zeroth moment, deriving Nd from remote sensing measurements has significant uncertainty. At minimum, such algorithms require the extrapolation of information from two other measurements that respond to different moments of the DSD. Lidar, for instance, is sensitive to the second moment (cross-sectional area) of the DSD, while other measures from microwave sensors respond to higher-order moments. We develop methods using a simple lidar forward model that demonstrates that the depth to the maximum in lidar-attenuated backscatter (Rmax⁡) is strongly sensitive to Nd when some measure of the liquid water content vertical profile is given or assumed. Knowledge of Rmax⁡ to within 5 m can constrain Nd to within several tens of percent. However, operational lidar networks provide vertical resolutions of > 15 m, making a direct calculation of Nd from Rmax⁡ very uncertain. Therefore, we develop a Bayesian optimal estimation algorithm that brings additional information to the inversion such as lidar-derived extinction and radar reflectivity near the cloud top. This statistical approach provides reasonable characterizations of Nd and effective radius (re) to within approximately a factor of 2 and 30 %, respectively. By comparing surface-derived cloud properties with MODIS satellite and aircraft data collected during the MARCUS and CAPRICORN II campaigns, we demonstrate the utility of the methodology. [ABSTRACT FROM AUTHOR]

Cloud albedo susceptibility to droplet number perturbation remains a source of uncertainty in understanding aerosol–cloud interactions and thus both past and present climate states. Through the Energy Exascale Earth System Model (E3SM) v2 experiments, we probe the effects of competing processes on cloud albedo susceptibility of low-lying marine stratocumulus in the northeast Pacific. In present-day conditions, we find that increasing precipitation suppression by aerosols increases cloud albedo susceptibility, whereas increasing cloud sedimentation decreases it. By constructing a hypothetical model configuration exhibiting negative susceptibility under all conditions, we conclude that cloud albedo change due to aerosol perturbation cannot be predicted by present-day co-variabilities in E3SM v2. As such, our null result herein challenges the assumption that present-day climate observations are sufficient to constrain past states, at least in the context of cloud albedo changes to aerosol perturbation. [ABSTRACT FROM AUTHOR]

Three supervised neural network cloud classification routines are applied to daytime MODIS Aqua imagery and compared for the year 2018 over the North Atlantic Ocean. Routines surveyed here include the Morphology Identification Data Aggregated over the Satellite-era (MIDAS), which specializes in subtropical stratocumulus (Sc) clouds; sugar, gravel, flowers, and fish (SGFF), which is focused on shallow cloud systems in the tropical trade winds; and the community record of marine low-cloud mesoscale morphology supported by the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) dataset, which is focused on shallow clouds globally. Comparisons of co-occurrence and vertical and geographic distribution show that morphologies are classified in geographically distinct regions; shallow suppressed and deeper aggregated and disorganized cumulus are seen in the tropical trade winds. Shallow Sc types are frequent in subtropical subsidence regions. More vertically developed solid stratus and open- and closed-cell Sc are frequent in the mid-latitude storm track. Differing classifier routines favor noticeably different distributions of equivalent types. Average scene albedo is more strongly correlated with cloud albedo than cloud amount for each morphology. Cloud albedo is strongly correlated with the fraction of optically thin cloud cover. The albedo of each morphology is dependent on latitude and location in the mean anticyclonic wind flow over the North Atlantic. Strong rain rates are associated with middling values of albedo for many cumuliform types, hinting at a complex relationship between the presence of heavily precipitating cores and cloud albedo. The presence of ice at cloud top is associated with higher albedos. For a constant albedo, each morphology displays a distinct set of physical characteristics. [ABSTRACT FROM AUTHOR]

In this paper we develop a novel framework aimed to significantly reduce biases related to marine stratocumulus clouds in general circulation models (GCMs) while circumventing excessive computational cost requirements. Our strategy is to increase the horizontal resolution using a regionally refined mesh (RRM) over our region of interest in addition to using the Framework for Improvement of Vertical Enhancement (FIVE) to increase the vertical resolution only for specific physical processes that are important for stratocumulus. We apply the RRM off the coast of Peru in the Southeast Pacific, a region that climatologically contains the most marine stratocumulus in the subtropics. We find that our new modeling framework is able to replicate the results of our high resolution benchmark simulation with much fidelity, while reducing the computational cost by several orders of magnitude. In addition, this framework is able to greatly reduce the longstanding biases associated with marine stratocumulus in GCMs when compared to the standard resolution control simulation. [ABSTRACT FROM AUTHOR]

The higher‐order turbulence scheme, Cloud Layers Unified by Binormals (CLUBB), is known for effectively simulating the transition from cumulus to stratocumulus clouds within leading atmospheric climate models. This study investigates an underexplored aspect of CLUBB: its capacity to simulate near‐surface winds and the Planetary Boundary Layer (PBL), with a particular focus on its coupling with surface momentum flux. Using the GFDL atmospheric climate model (AM4), we examine two distinct coupling strategies, distinguished by their handling of surface momentum flux during the CLUBB's stability‐driven substepping performed at each atmospheric time step. The static coupling maintains a constant surface momentum flux, while the dynamic coupling adjusts the surface momentum flux at each CLUBB substep based on the CLUBB‐computed zonal and meridional wind speed tendencies. Our 30‐year present‐day climate simulations (1980–2010) show that static coupling overestimates 10‐m wind speeds compared to both control AM4 simulations and reanalysis, particularly over the Southern Ocean (SO) and other midlatitude ocean regions. Conversely, dynamic coupling corrects the static coupling 10‐m winds biases in the midlatitude regions, resulting in CLUBB simulations achieving there an excellent agreement with AM4 simulations. Furthermore, analysis of PBL vertical profiles over the SO reveals that dynamic coupling reduces downward momentum transport, consistent with the found wind‐speed reductions. Instead, near the tropics, dynamic coupling results in minimal changes in near‐surface wind speeds and associated turbulent momentum transport structure. Notably, the wind turning angle serves as a valuable qualitative metric for assessing the impact of changes in surface momentum flux representation on global circulation patterns. Plain Language Summary: The Cloud Layers Unified by Binormals (CLUBB) scheme offers a promising way to model the complexities of cloud behavior, but its impact on winds and global circulation has been less explored. In our study, we investigate how different ways of representing the complex coupling between surface drag and the lowest kilometer of the Earth's atmosphere affect global wind speeds and circulation. We specifically examine two distinct approaches: a static approach, which feeds a constant surface drag to CLUBB, and a dynamic approach, which adjusts the surface drag based on the winds updates computed by CLUBB. Over a present‐day climate, we find that static coupling tends to produce excessively large wind speeds in certain regions, like the Southern Ocean and parts of the North Atlantic and North Pacific. Instead, dynamic coupling produces excellent near‐surface wind speeds in these regions, and also over the rest of the globe. Moreover, we discover that dynamic coupling reduces the downward turbulent transport of momentum, highlighting the enhancements in near‐surface wind speeds found with this approach are physically consistent. Lastly, we use the change in wind direction with height to qualitatively evaluate how the two coupling methods affect global circulation patterns. Key Points: Dynamic coupling between Cloud Layers Unified by Binormals (CLUBB) and surface momentum flux enhances global winds climate simulations bringing CLUBB in line with control atmospheric climate modelIn midlatitude regions, the dynamic coupling enhances the boundary‐layer momentum transport compared to the static couplingThe wind turning angle turns out a useful qualitative metric, linking changes in surface momentum flux to the changes in global circulation [ABSTRACT FROM AUTHOR]

The spatial heterogeneity related to complex topography in California demands high-resolution (< 5 km) modeling, but global convection-permitting climate models are computationally too expensive to run multi-decadal simulations. We developed a 3.25 km California climate modeling framework by leveraging regional mesh refinement (CARRM) using the U.S. Department of Energy (DOE)'s global Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) version 0. Four 5-year time periods (2015–2020, 2029–2034, 2044–2049, and 2094–2099) were simulated by nudging CARRM outside California to 1° coupled simulation of E3SMv1 under the Shared Socioeconomic Pathways (SSP)5-8.5 future scenario. The 3.25 km grid spacing adds considerable value to the prediction of the California climate changes, including more realistic high temperatures in the Central Valley and much improved spatial distributions of precipitation and snowpack in the Sierra Nevada and coastal stratocumulus. Under the SSP5-8.5 scenario, CARRM simulation predicts widespread warming of 6–10 °C over most of California, a 38 % increase in statewide average 30 d winter–spring precipitation, a near-complete loss of the alpine snowpack, and a sharp reduction in shortwave cloud radiative forcing associated with marine stratocumulus by the end of the 21st century. We note a climatological wet precipitation bias for the CARRM and discuss possible reasons. We conclude that SCREAM RRM is a technically feasible and scientifically valid tool for climate simulations in regions of interest, providing an excellent bridge to global convection-permitting simulations. [ABSTRACT FROM AUTHOR]

This study examines marine boundary layer cloud regime transition during a cold air outbreak (CAO) over the Norwegian Sea, simulated by a global storm‐resolving model (GSRM) known as the Simple Cloud‐Resolving Energy Exascale Earth System Model Atmosphere Model (SCREAM). By selecting observational references based on a combination of large‐scale conditions rather than strict time‐matched comparisons, this study finds that SCREAM qualitatively captures the CAO cloud transition, including boundary layer growth, cloud mesoscale structure, and phase partitioning. SCREAM also accurately locates the greatest ice and liquid in the mesoscale updrafts, however, underestimates supercooled liquid water in cumulus clouds. The model evaluation approach adopted by this study takes advantages of the existing computational‐expensive global simulations of GSRM and the available observations to understand model performance and can be applied to assessments of other cloud regimes in different regions. Such practice provides valuable guidance on the future effort to correct and improve biased model behaviors. Plain Language Summary: Cold air outbreaks occur when cold, dry air moves over warmer ocean regions, forming extensive boundary layer clouds. However, current climate models struggle to accurately represent these clouds due to their complex nature. This study examines the performance of the global storm‐resolving model, the Simple Cloud‐Resolving Energy Exascale Earth System Model Atmosphere Model (SCREAM), in simulating marine boundary layer clouds during cold air outbreaks over the Norwegian Sea. This study compares the SCREAM simulated clouds during a cold air outbreak event to observations under similar large‐scale conditions from satellites and ground‐based measurements collected during a field campaign of the Atmospheric Radiation Measurement program. The results indicate that SCREAM successfully simulates three distinct cloud patterns during cold air outbreaks with credible mesoscale structures. Yet, it tends to underestimate supercooled liquid water and consequently, the cloud liquid water fraction, especially in cumulus clouds. The study suggests that using high‐resolution observations under similar large‐scale conditions can effectively evaluate global storm‐resolving models. This approach helps identify areas for improvement without requiring expensive global storm‐resolving model simulation designed for specific cases. Key Points: The Simple Cloud‐Resolving Energy Exascale Earth System Model Atmosphere Model (SCREAMv0), at a resolution of 3 km, simulated three distinctive cloud regimes in cold air outbreaks with credible mesoscale structuresSCREAMv0 qualitatively captures the transition of the cloud phase partitioning based on high‐resolution observationsObservations selected based on similar large‐scale conditions can be important references for global storm‐resolving model evaluation [ABSTRACT FROM AUTHOR]

Biomass burning plumes are frequently transported over the southeast Atlantic (SEA) stratocumulus deck during the southern African fire season (June–October). The plumes bring large amounts of absorbing aerosols and enhanced moisture, which can trigger a rich set of aerosol–cloud–radiation interactions with climatic consequences that are still poorly understood. We use large-eddy simulation (LES) to explore and disentangle the individual impacts of aerosols and moisture on the underlying stratocumulus clouds, the marine boundary layer (MBL) evolution, and the stratocumulus-to-cumulus transition (SCT) for three different meteorological situations over the southeast Atlantic during August 2017. For all three cases, our LES shows that the SCT is driven by increased sea surface temperatures and cloud-top entrainment as the air is advected towards the Equator. In the LES model, aerosol indirect effects, including impacts on drizzle production, have a small influence on the modeled cloud evolution and SCT, even when aerosol concentrations are lowered to background concentrations. In contrast, local semi-direct effects, i.e., aerosol absorption of solar radiation in the MBL, cause a reduction in cloud cover that can lead to a speed-up of the SCT, in particular during the daytime and during broken cloud conditions, especially in highly polluted situations. The largest impact on the radiative budget comes from aerosol impacts on cloud albedo: the plume with absorbing aerosols produces a total average 3 d of simulations. We find that the moisture accompanying the aerosol plume produces an additional cooling effect that is about as large as the total aerosol radiative effect. Overall, there is still a large uncertainty associated with the radiative and cloud evolution effects of biomass burning aerosols. A comparison between different models in a common framework, combined with constraints from in situ observations, could help to reduce the uncertainty. [ABSTRACT FROM AUTHOR]

Average properties of the cloud field, such as cloud size distribution and cloud fraction, have previously been observed to show periodic, oscillatory changes. Identifying this behaviour, however, remains difficult due to the intrinsic variability of the boundary-layer cloud distribution. We use the Gaussian Process (GP) regression to identify this oscillatory behaviour in the statistical distributions of individual cloud properties. Individual cloud samples are retrieved from high-resolution LES model results, and the distribution of cloud sizes is modelled as a power-law distribution. We construct the time-series for the slope of the cloud size distribution b , a slope that is consistent with satellite observations of marine boundary-layer clouds, by observing the changes in the slope of the modelled cloud size distribution. Then, we build a GP model based on prior assumptions about the cloud field following observational studies: a boundary-layer cloud field goes through a phase of relatively strong convection where large clouds dominate, followed by a phase of relatively weak convection where precipitation causes formation of cold pools and suppression of convective growth. The GP model successfully identifies oscillatory motions from the noisy time-series, with a period of 95 ± 3.2 minutes. Furthermore, we examine the time-series of cloud fraction fc and average vertical mass flux M , whose periods were 93 ± 2.5 and 93 ± 3.7 minutes, respectively. The oscillations reveal the role of precipitation in governing convective activities through recharge-discharge cycles. [ABSTRACT FROM AUTHOR]

Recent field campaigns and advances in observational techniques have yielded a wealth of observations of stable water isotopes in the atmosphere, but the heirarchy of isotope-enabled models is not well-placed to leverage these observation for improving constraints on parameterized physics in global models. Here, we introduce the isotope-enabled Python Cloud Large-Eddy Simulation model (iPyCLES) for mixed-phase clouds. Isotopic tracers are implemented in a parallel passive water cycle and experience all processes and phase changes that affect the model's prognostic total water variable. Isotopic fractionation occurs during cloud and precipitation processes as well as surface evaporation, with facilities for applying external forcing. In addition to isotopic tracers, we extend the two-moment warm cloud microphysics scheme to enable prognostic simulation of cloud liquid water and ice while eliminating dependence on saturation adjustment. Relative to a one-moment mixed-phase scheme with saturation adjustment, the new microphysical scheme yields substantial benefits in simulating phase partitioning and isotopic exchange in mixed-phase regions. The LES model is based on an energetically-consistent implementation of the anelastic equations and employs high-order weighted, essentially non-oscillatory numerics, and is therefore theoretically suitable for simulations spanning the gray zone of the convective spectrum. In this initial evaluation, we present the results of test cases for non-precipitating subtropical shallow cumulus, precipitating subtropical shallow cumulus, and precipitating Arctic mixed-phase stratocumulus clouds. The iPyCLES simulations agree well with available observations and previous model simulations in all three cases, with distinct signatures among the cases that highlight the added potential of isotopic tracers. The benefits of the revised microphysics scheme are especially evident in the Arctic mixed-phase cloud test case, with vapor-liquid-ice exchange within the cloud producing a conspicuous peak in deuterium excess near the top of the cloud. As an idealized testbed, the iPyCLES model can bridge gaps between cloud chamber experiments, real-world observations, and global and regional models, allowing information provided by water isotopes to be translated more effectively into observational constraints for cloud and boundary layer parameterizations. [ABSTRACT FROM AUTHOR]

Marine cold air outbreaks (MCAOs) strongly affect the Arctic water cycle and, thus, climate through large-scale air mass transformations. The description of air mass transformations is still challenging partly because previous observations do not resolve fine scales, particularly for the initial development of a MCAO, and lack information about cloud microphysical properties. Therefore, we focus on the crucial initial development within the first 200 km over open water of two MCAO events with different strengths observed during the HALO-(AC)3 campaign. Based on unique sampling of high-resolution airborne remote sensing and in-situ measurements, the development of the boundary layer, formation of clouds, onset of precipitation, and riming are studied. For this purpose, we establish a novel approach, solely based on radar reflectivity measurements, to detect roll circulation that forms cloud streets. The two MCAO events observed in April 2022 just three days apart occurred under relatively similar thermodynamic conditions. However, for the first event, colder airmasses from the central Arctic led to a marine cold air outbreak index twice that high as for the second event. Thus, the two cases exhibit different properties of clouds, riming, and roll circulations though the width of the roll circulation is similar. For the stronger MCAO, cloud tops are higher, more liquid-topped clouds exist, the liquid layer at cloud top is wider, and the liquid water path, mean radar reflectivity, precipitation rate, and occurrence are increased. These parameters evolve with distance over open water, as seen by, e.g., boundary layer deepening and cloud top height rising. Generally, cloud streets form after traveling 15 km over open water. After 20 km, this formation enhances cloud cover to just below 100 %. After around 30 km, precipitation forms, though for the weaker event, the development of precipitation is shifted to larger distances. For the stronger event, we detect riming for cloud temperatures below -20 °C. The variability of rime mass has the same horizontal scales as the roll circulation implying the importance of roll circulation on precipitation. The detailed observations of the two MCAO events could serve as a valuable reference for future model intercomparison studies. [ABSTRACT FROM AUTHOR]

To improve cloud simulation in a general circulation model, we develop a new cloud macrophysics scheme that treats detrained cumulus generated by convective detrainment process separately from pure stratus; prognoses two symmetric triangular probability density functions of total specific humidity for each detrained cumulus and pure stratus; and diagnoses both cloud fraction and cloud condensate in all liquid, ice, and mixed‐phases in a consistent and unified way without any adjustment to remove empty or very dense cloud. Supersaturation is allowed within ice cloud. The new scheme ("NEW") is compared with the previous model ("OLD") using single‐column simulations for subtropical marine stratocumulus (DYCOMS2) and continental deep convection (ARM97) cases. In DYCOMS2, both NEW and OLD produce vertical profiles of grid‐mean cloud condensate similar to large‐eddy simulation. In ARM97, compared with OLD, NEW simulates less sporadic vertical profiles of in‐cloud condensates, due to consistent diagnosis of cloud fraction and cloud condensate; more continuously‐varying detrained cumulus with time, due to prognostic treatment of detrained cumulus; and ice cloud fraction and ice condensate similar to those of OLD, in spite of completely different treatment of ice cloud processes. The global performance of NEW is similar to OLD with improved relative humidity. Compared to OLD, NEW simulates more and improved cloud condensate, but less and degraded cloud fraction, particularly, in the lower troposphere. Detrained cumulus is moister and colder and has a larger moisture variance than pure stratus. Overall, NEW simulates stronger condensation‐deposition rates than OLD, due in part to the separate treatment of detrained cumulus and pure stratus. Plain Language Summary: This study suggests a new cloud parameterization scheme in a general circulation model that can represent clouds in nature in a realistic way. Our new cloud scheme separates two types of stratus, called detrained cumulus and pure stratus, by using a set of prognostic equations. Moreover, both liquid, ice, and mixed‐phase clouds are treated in a consistent way. We found that the new scheme successfully operates for two single‐column simulations for subtropical marine stratocumulus and continental deep convection cases. In addition, we performed the global simulation and found that the new scheme simulated more cloud condensate but less cloud fraction than the old scheme with some improvements. Key Points: We develop a new cloud macrophysics scheme by prognosing two triangular probability density functions for each detrained cumulus and pure stratusThe new scheme computes both cloud fraction and cloud condensate in all liquid, ice, and mixed‐phases in a consistent and unified wayThe new scheme simulates more cloud condensate but less cloud fraction than the old scheme with improved relative humidity and global performance [ABSTRACT FROM AUTHOR]