Shallow Clouds Research Articles

Sensitivity of warm clouds to large particles in measured marine aerosol size distributions – a theoretical study

Abstract. Aerosol size distribution has major effects on warm cloud processes. Here, we use newly acquired marine aerosol size distributions (MSDs), measured in situ over the open ocean during the Tara Pacific expedition (2016–2018), to examine how the total aerosol concentration (Ntot) and the shape of the MSDs change warm clouds' properties. For this, we used a toy model with detailed bin microphysics initialized using three different atmospheric profiles, supporting the formation of shallow to intermediate and deeper warm clouds. The changes in the MSDs affected the clouds' total mass and surface precipitation. In general, the clouds showed higher sensitivity to changes in Ntot than to changes in the MSD's shape, except for the case where the MSD contained giant and ultragiant cloud condensation nuclei (GCCN, UGCCN). For increased Ntot (for the deep and intermediate profiles), most of the MSDs drove an expected non-monotonic trend of mass and precipitation (the shallow clouds showed only the decreasing part of the curves with mass and precipitation monotonically decreasing). The addition of GCCN and UGCCN drastically changed the non-monotonic trend, such that surface rain saturated and the mass monotonically increased with Ntot. GCCN and UGCCN changed the interplay between the microphysical processes by triggering an early initiation of collision–coalescence. The early fallout of drizzle in those cases enhanced the evaporation below the cloud base. Testing the sensitivity of rain yield to GCCN and UGCCN revealed an enhancement of surface rain upon the addition of larger particles to the MSD, up to a certain particle size, when the addition of larger particles resulted in rain suppression. This finding suggests a physical lower bound can be defined for the size ranges of GCCN and UGCCN.

Pressure Drag for Shallow Cumulus Clouds: From Thermals to the Cloud Ensemble

AbstractThis study takes the first step to bridge the gap between the pressure drag of a shallow cloud ensemble and that of an individual cloud composed of rising thermals. It is found that the pressure drag for a cloud ensemble is primarily controlled by the dynamical component. The dominance of dynamical pressure drag and its increased magnitude with height are independent of cloud lifetime and are common features of individual clouds except that the total drag of a single cloud over life cycle presents vertical oscillations. These oscillations are associated with successive rising thermals but are further complicated by the evaporation‐driven downdrafts outside the cloud. The horizontal vorticity associated with the vortical structure is amplified as the thermals rise to higher altitudes due to continuous baroclinic vorticity generation. This leads to the increased magnitude of local minima of dynamical pressure perturbation with height and consequently to increased dynamical pressure drag.

Characterization of Surface Heterogeneity‐Induced Convection Using Cluster Analysis

AbstractLand‐atmosphere interactions and boundary layer processes often control the formation of shallow clouds and subsequently deep convective precipitation over the U.S. Southern Great Plains. In this study, we examine the impacts of large‐scale advection on the cloud populations and land‐atmospheric coupling observed during the Holistic Interactions of Shallow Clouds, Aerosols, and Land‐Ecosystems field campaign in 2016. We performed two large eddy simulations using the Weather Research and Forecasting Model for a day in which the transition from clear‐sky to shallow precipitating clouds and isolated deep convection was observed. The control simulation reproduced the overall distribution of cloud populations by using realistic soil conditions with an interactive land model. In the sensitivity simulation where large‐scale advection is removed, a strong relationship between the land and boundary layer is found. To study the timing, location, and intensity of convective initiation and the relationship of clouds with land surface properties, a cluster analysis of equivalent potential temperature is performed for the simulation without large‐scale advection. That analysis shows that convective clouds first form over regions with higher surface sensible heat flux. Precipitation from those convective clouds likely triggers new updrafts nearby about 2 hr later through the lifting associated with cold pools. The cluster analysis also shows that in addition to the spatial pattern of soil moisture, land use and soil texture in western Oklahoma also influence the location of convective initiation.

Increasing Resolution and Resolving Convection Improve the Simulation of Cloud‐Radiative Effects Over the North Atlantic

AbstractClouds interact with atmospheric radiation and substantially modify the Earth's energy budget. Cloud formation processes occur over a vast range of spatial and temporal scales, which make their thorough numerical representation challenging. Therefore, the impact of parameter choices for simulations of cloud‐radiative effects is assessed in the current study. Numerical experiments are carried out using the ICOsahedral Nonhydrostatic (ICON) model with varying grid spacings between 2.5 and 80 km and with different subgrid‐scale parameterization approaches. Simulations are performed over the North Atlantic with either one‐moment or two‐moment microphysics and with convection being parameterized or explicitly resolved by grid‐scale dynamics. Simulated cloud‐radiative effects are compared to products derived from Meteosat measurements. Furthermore, a sophisticated cloud classification algorithm is applied to understand the differences and dependencies of simulated and observed cloud‐radiative effects. The cloud classification algorithm developed for the satellite observations is also applied to the simulation output based on synthetic infrared brightness temperatures, a novel approach that is not impacted by changing insolation and guarantees a consistent and fair comparison. It is found that flux biases originate equally from clear‐sky and cloudy parts of the radiation field. Simulated cloud amounts and cloud‐radiative effects are dominated by marine, shallow clouds, and their behavior is highly resolution dependent. Bias compensation between shortwave and longwave flux biases, seen in the coarser simulations, is significantly diminished for higher resolutions. Based on the analysis results, it is argued that cloud‐microphysical and cloud‐radiative properties have to be adjusted to further improve agreement with observed cloud‐radiative effects.

Understanding the Extreme Spread in Climate Sensitivity within the Radiative‐Convective Equilibrium Model Intercomparison Project

AbstractThe Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) consists of simulations at three fixed sea‐surface temperatures (SSTs: 295, 300, and 305 K) and thus allows for a calculation of the climate feedback parameter based on the change of the top‐of‐atmosphere radiation imbalance. Climate feedback parameters range widely across RCEMIP, roughly from −6 to 3 W m−2 K−1, particularly across general‐circulation models (GCMs) as well as global and large‐domain cloud‐resolving models (CRMs). Small‐domain CRMs and large‐eddy simulations have a smaller range of climate feedback parameters due to the absence of convective self‐aggregation. More than 70–80% of the intermodel spread in the climate feedback parameter can be explained by the combined temperature dependencies of convective aggregation and shallow cloud fraction. Low climate sensitivities are associated with an increase of shallow cloud fraction (increasing the planetary albedo) and/or an increase in convective aggregation with warming. An increase in aggregation is associated with an increase in outgoing longwave radiation, caused primarily by mid‐tropospheric drying, and secondarily by an expansion of subsidence regions. Climate sensitivity is neither dependent on the average amount of aggregation nor on changes in deep/anvil cloud fraction. GCMs have a lower overall climate sensitivity than CRMs because in most GCMs convective aggregation increases with warming, whereas in CRMs, convective aggregation shows no consistent temperature trend.

Assessing CLUBB PDF Closure Assumptions for a Continental Shallow‐to‐Deep Convective Transition Case Over Multiple Spatial Scales

AbstractAssumed‐PDF (probability density function) higher‐order turbulence closures (APHOCs) are now widely used for parameterizing boundary layer turbulence and shallow convection in Earth system models (ESMs). A better understanding of the resolution‐dependent behavior of APHOCs is essential for improving the performance of next‐generation ESMs with intended horizontal resolutions finer than 10 km. In this study, we evaluate the PDF family of Analytic double‐Gaussian 1 implemented in Cloud Layers Unified By Binormals (CLUBB) over a range of spatial scales (Dx) from 2 to 100 km. A 120‐km‐wide large eddy simulation (LES) for a continental convection case during 2016 Holistic Interactions of Shallow Clouds, Aerosols, and Land‐Ecosystems (HI‐SCALE) field campaign serves as benchmark to evaluate the PDF closure using an off‐line approach. We find during the shallow convection period, the CLUBB PDF closure tends to produce positive biases of cloud properties and liquid water flux near cloud base for all scales of analysis. It produces negative biases for these variables near cloud top that are more severe for Dx larger than 25 km. Results show that replacing the CLUBB‐parameterized moisture and temperature skewnesses with LES‐derived ones can fix most of the biases if clipping of input moments is allowed to prevent the occurrence of unrealizable solutions. Overall, the performance of the PDF closure is better for smaller Dx = 2–5 km than for larger Dx = 50–100 km; for a given grid spacing, it is better when the convective clouds become deeper in the late afternoon. Likely causes for the resolution dependence and implications for improving the PDF closure are discussed.

On the Abundance and Common Properties of Continental, Organized Shallow (Green) Clouds

Warm convective clouds play a significant role in the earth's energy and water budgets. However, they still pose a challenge in climate research as their feedback to predicted thermodynamic changes is highly uncertain and considered critical to the overall climate system's response. The focus of this study is continental, organized shallow convective clouds that, although they are spread globally and form in a variety of environments, seem to have common properties. One of these properties seems to be their preferred formation over vegetated areas, thus referred hereafter as green Cu. In this article, we present new observations of emerging universality and explore them using a method that combines fine- and coarse-resolution remote-sensing data sets. First, we use Moderate Resolution Imaging Spectroradiometer (MODIS) true-color images to visually classify cloud fields into different classes and identify green Cu fields. We show that the level and type of organization and the properties of these fields (e.g., cloud size distribution and cloud fraction) are similar throughout the world, regardless of their location. Second, we match the corresponding MODIS level-3 cloud properties to the identified cloud classes, and based on this data sets statistics, we develop a detection method for green Cu along ten years of measurements (2003-2012). We examine the geographical distribution and seasonality of this class and show that these fields are highly abundant over many continental areas and indeed mostly in the vicinity of vegetated regions.

Spatio‐temporal relationships between rainfall and convective clouds during Indian monsoon through a discrete lens

AbstractThe Indian monsoon, a multi‐variable process causing heavy rains during June–September every year, is very heterogeneous in space and time. We study the relationship between rainfall and outgoing longwave radiation (OLR) – a proxy for convective cloud cover – for monsoon between 2004 and 2010. To identify, classify and visualize spatial patterns of rainfall and OLR we use a discrete and spatio‐temporally coherent representation of the data, created using a statistical model based on Markov Random Field. Our approach clusters the days with similar spatial distributions of rainfall and OLR into a small number of spatial patterns. We find that eight daily spatial patterns each in rainfall and OLR, and seven joint patterns of rainfall and OLR, describe over 90% of all days. Through these patterns, we find that OLR generally has a strong negative correlation with precipitation, but with significant spatial variations. In particular, peninsular India (except for the west coast) is under significant convective cloud cover over a majority of days but remains rainless. We also find that much of the monsoon rainfall co‐occurs with low OLR, but some amount of rainfall in Eastern and North‐western India in June occurs on OLR days, presumably from shallow clouds. To study day‐to‐day variations of both quantities, we identify spatial patterns in the temporal gradients computed from the observations. We find that changes in convective cloud activity across India most commonly occur due to the establishment of a north–south OLR gradient which persists for 1–2 days and shifts the convective cloud cover from light to deep or vice versa. Such changes are also accompanied by changes in the spatial distribution of precipitation. The present work thus provides a highly reduced description of the complex spatial patterns and their day‐to‐day variations, and could form a useful tool for future simplified descriptions of this process.

Testing a Physical Hypothesis for the Relationship Between Climate Sensitivity and Double‐ITCZ Bias in Climate Models

AbstractTian (2015, https://doi.org/10.1002/2015GL064119) found that Coupled Model Intercomparison Project Phases 3 and 5 (CMIP3 and CMIP5) climate models with too much precipitation in a region of the Southeast Pacific (due to a double‐Intertropical Convergence Zone [ITCZ] bias) tend to have lower climate sensitivities and suggested that this might form the basis of an “emergent constraint,” which could rule out lower values of climate sensitivity. However, no physical mechanism has been proposed to explain this relationship. Here we advance the hypothesis that deep convection encroaching into regions that should be dominated by shallow clouds hampers the formation of shallow clouds in the present climate and reduces the magnitude of positive low‐level cloud feedbacks, resulting in smaller values of climate sensitivity. We test this hypothesis first by performing sensitivity tests with the HadGEM2‐A aquaplanet model subject to a uniform +4 K sea surface temperature (SST) perturbation, in which we vary the degree to which deep convection associated with the single/double ITCZ extends toward subtropical low‐cloud regions. Experiments with more precipitation encroaching into the subtropics have weaker subtropical cloud radiative effects in the present‐day simulations and less positive subtropical cloud feedbacks, consistent with our hypothesis. We test this hypothesis further by looking for the predicted relationships across multimodel ensembles of SST forced Atmospheric Model Intercomparison Project (AMIP) experiments subject to a uniform +4 K SST increase. Relationships of the expected sign are found in the CMIP5 AMIP+4K experiments, but not all are statistically significant at the 5% level. We find no statistically significant support for our hypothesis in the currently available CMIP6 AMIP+4K experiments.

Simulation of Continental Shallow Cumulus Populations Using an Observation‐Constrained Cloud‐System Resolving Model

AbstractContinental shallow cumulus (ShCu) clouds observed on 30 August 2016 during the Holistic Interactions of Shallow Clouds, Aerosols, and Land‐Ecosystems (HI‐SCALE) field campaign are simulated by using an observation‐constrained cloud‐system resolving model. On this day, ShCu forms over Oklahoma and southern Kansas and some of these clouds transition to deeper, precipitating convection during the afternoon. We apply a four‐dimensional ensemble‐variational (4DEnVar) hybrid technique in the Community Gridpoint Statistical Interpolation (GSI) system to assimilate operational data sets and unique boundary layer measurements including a Raman lidar, radar wind profilers, radiosondes, and surface stations collected by the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) atmospheric observatory into the Weather Research and Forecasting (WRF) model to ascertain how improved environmental conditions can influence forecasts of ShCu populations and the transition to deeper convection. Independent observations from aircraft, satellite, as well as ARM's remote sensors are used to evaluate model performance in different aspects. Several model experiments are conducted to identify the impact of data assimilation (DA) on the prediction of clouds evolution. The analyses indicate that ShCu populations are more accurately reproduced after DA in terms of cloud initiation time and cloud base height, which can be attributed to an improved representation of the ambient meteorological conditions and the convective boundary layer. Extending the assimilation to 18 UTC (local noon) also improved the simulation of shallow‐to‐deep transitions of convective clouds.

Effects of shipping emissions on cloud physical properties over coastal areas near Shanghai

To investigate the effects of shipping aerosols on radiation, cloud physical properties, and near-surface PM2.5, four sensitive experiments with the WRF-Chem model were performed over coastal areas near Shanghai for July 2014. In general, the direct effect of shipping aerosols resulted in negative shortwave (SW) radiation forcing at the land surface. However, when considering the indirect effect, the downward SW radiation at the sea surface declined significantly. By the direct effect, shipping aerosols could modify cloud structure, resulting in a higher cloud base, lower cloud top, and shallower cloud depth. With the indirect effect included, both the cloud base and cloud top showed a declining trend over sea areas. The indirect effect of shipping aerosols was relatively more significant in influencing clouds. For example, the results revealed a 1.2% change of low cloud coverage from the indirect effect but only a 0.1% change due to the direct effect. Through their direct and indirect effects, shipping aerosols cause non-negligible impacts on precipitation, which are concentrated within light precipitation (<0.1 mm h−1). Finally, we concluded that after considering the shipping aerosols, the peak of the cloud droplet spectrum increases by about 50 cm−3/μm. It can be found that when the average volume radius of the cloud droplet is less than 2 μm, the number concentration of cloud droplets increases sharply, and when the average radius of the cloud drop is greater than 2 μm and less than 5 μm, the cloud droplet number concentration drops sharply.

Seasonal Modulation of Trapped Gravity Waves and Their Imprints on Trade Wind Clouds

Abstract Shallow convection over the oceans is responsible for the largest uncertainties in climate projections. Idealized simulations have shown decades ago that shallow clouds generate internal gravity waves, which under certain atmospheric background conditions become trapped inside the troposphere and influence the development of clouds. These feedbacks, which occur at horizontal scales of up to several tens of kilometers. are neither resolved nor parameterized in traditional global climate models (GCMs), while the newest generation of GCMs (grid spacings &amp;lt; 5 km) is starting to resolve them. The interactions between the convective boundary layer and trapped waves have almost exclusively been studied in highly idealized frameworks and it remains unclear to what degree this coupling affects the organization of clouds in the real atmosphere or in the new generation of GCMs. Here, the coupling between clouds and trapped waves is examined in 2.5-km simulations that span the entirety of the tropical Atlantic and are initialized and forced with meteorological analyses. The coupling between clouds and trapped waves is sufficiently strong to be detected in these simulations of full complexity. Stronger upper-tropospheric westerly winds are associated with a stronger cloud–wave coupling. In the simulations this results in a highly organized scattered cloud field with cloud spacings of about 19 km, matching the dominant trapped wavelength. Based on the large-scale atmospheric state, wave theory can reliably predict the regions and times where cloud–wave feedbacks become relevant to convective organization. Theory, the simulations, and satellite imagery imply a seasonal cycle in the trapping of gravity waves.

Precipitation Diurnal Cycle Assessment of Satellite-Based Estimates over Brazil

The main objective of this study is to assess the ability of several high-resolution satellite-based precipitation estimates to represent the Precipitation Diurnal Cycle (PDC) over Brazil during the 2014–2018 period, after the launch of the Global Precipitation Measurement satellite (GPM). The selected algorithms are the Global Satellite Mapping of Precipitation (GSMaP), The Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Prediction Center (CPC) MORPHing technique (CMORPH). Hourly rain gauge data from different national and regional networks were used as the reference dataset after going through rigid quality control tests. All datasets were interpolated to a common 0.1° × 0.1° grid every 3 h for comparison. After a hierarchical cluster analysis, seven regions with different PDC characteristics (amplitude and phase) were selected for this study. The main results of this research could be summarized as follow: (i) Those regions where thermal heating produce deep convective clouds, the PDC is better represented by all algorithms (in term of amplitude and phase) than those regions driven by shallow convection or low-level circulation; (ii) the GSMaP suite (GSMaP-Gauge (G) and GSMaP-Motion Vector Kalman (MVK)), in general terms, outperforms the rest of the algorithms with lower bias and less dispersion. In this case, the gauge-adjusted version improves the satellite-only retrievals of the same algorithm suggesting that daily gauge-analysis is useful to reduce the bias in a sub-daily scale; (iii) IMERG suite (IMERG-Late (L) and IMERG-Final (F)) overestimates rainfall for almost all times and all the regions, while the satellite-only version provide better results than the final version; (iv) CMORPH has the better performance for a transitional regime between a coastal land-sea breeze and a continental amazonian regime. Further research should be performed to understand how shallow clouds processes and convective/stratiform classification is performed in each algorithm to improve the representativity of diurnal cycle.

Combining Crowdsourcing and Deep Learning to Explore the Mesoscale Organization of Shallow Convection

Abstract Humans excel at detecting interesting patterns in images, for example, those taken from satellites. This kind of anecdotal evidence can lead to the discovery of new phenomena. However, it is often difficult to gather enough data of subjective features for significant analysis. This paper presents an example of how two tools that have recently become accessible to a wide range of researchers, crowdsourcing and deep learning, can be combined to explore satellite imagery at scale. In particular, the focus is on the organization of shallow cumulus convection in the trade wind regions. Shallow clouds play a large role in the Earth’s radiation balance yet are poorly represented in climate models. For this project four subjective patterns of organization were defined: Sugar, Flower, Fish, and Gravel. On cloud-labeling days at two institutes, 67 scientists screened 10,000 satellite images on a crowdsourcing platform and classified almost 50,000 mesoscale cloud clusters. This dataset is then used as a training dataset for deep learning algorithms that make it possible to automate the pattern detection and create global climatologies of the four patterns. Analysis of the geographical distribution and large-scale environmental conditions indicates that the four patterns have some overlap with established modes of organization, such as open and closed cellular convection, but also differ in important ways. The results and dataset from this project suggest promising research questions. Further, this study illustrates that crowdsourcing and deep learning complement each other well for the exploration of image datasets.

SEGMENTATION OF CLOUD ORGANIZATION PATTERNS FROM SATELLITE IMAGES USING DEEP NEURAL NETWORKS

Climate change is one of the most important challenges that humanity faces now. The essential part of climate models is the movement of clouds, which affects climate parameters dramatically. Shallow clouds play a huge role in determining the Earth's climate. They'realso difficult to understand and to represent in climate models. Unfortunately, the exact modeling of clouds movement is notoriously tricky and requires perfect knowledge of underling physical processes and initial states. Boundaries between different types of clouds are usually blurry and difficult to define with rule-based decision systems. Simplification of the segmentation step is crucial and can help researchers to develop better climate models. Convolutional neural networks have been successfully applied in many similar areas, and for cloud segmentation itself, too. However, there is a high cost of good, pixel-level labeled datasets, so the industry often uses coarse-labeled datasets with the either region or image-level labels.In this paper, we propose an end-to-end deep-learning-based method for classification and segmentation of different types of clouds from a single colored satellite image. Here, we propose the multi-task learning approach to cloud segmentation. Additionally to the segmentation model, we introduce a separate classifier thatuses features from the middle layer of the segmentation model. The presented method can use coarse, uneven and overlapping masks for clouds. From the experimental results, the proposed method demonstrates stable results and learns good general features from noisy data. As we observed during the experiments, our model finds types of clouds, which are not annotated on the images but seem to be correctly defined. It is ranked in top three percentcompeting methods on Understanding Clouds from Satellite Images Dataset.

Fine‐Scale Variability of Observed and Simulated Surface Albedo Over the Southern Great Plains

AbstractSurface albedo can be highly variable in both space and time. The Department of Energy's Holistic Interactions of Shallow Clouds, Aerosols, and Land‐Ecosystems field study provides a unique opportunity to characterize the variability of surface albedo over the Southern Great Plains of the United States using integrated tower, aircraft, and satellite observations. The primary advantage of the aircraft and satellite observations is the ability to examine the spatial distribution of surface albedo over a large area, while the tower measurements have the ability to examine both diurnal and day‐to‐day variability at a single location. In general, consistency was found between the broadband (BB) albedo measured from the surface, air, and space. There was a small increase from 0.186 to 0.194 in the aircraft BB surface albedo between May and September (about 4% change), while the Moderate Resolution Imaging Spectroradiometer black‐sky BB surface albedo increased from 0.151 to 0.166 over the same period (about 10% change), while the standard deviations in the aircraft and Moderate Resolution Imaging Spectroradiometer BB albedo were similar. The largest seasonal differences in the aircraft BB albedo were found for areas with winter wheat or forest, while areas with pasture or grasses showed a smaller seasonal diversity. The Weather Research and Forecasting model was used to simulate the BB surface albedo. In comparison with the aircraft and satellite observations, the Weather Research and Forecasting‐simulated BB surface albedo had no seasonal change and a much narrower distribution.

Relationships Between Precipitation Properties and Large-Scale Conditions During Subsidence at the Eastern North Atlantic Observatory.

Three years of reanalysis and ground-based observations collected at the Eastern North Atlantic (ENA) observatory are analyzed to document the properties of rain and boundary layer clouds and their relationship with the large-scale environment during general subsidence conditions and following cold front passages. Clouds in the wake of cold fronts exhibit on average a 10% higher propensity to precipitate and higher rain-to-cloud fraction than cloud found in general subsidence conditions. Similarities in the seasonal cycle of rain and of large-scale properties suggest that the large-scale conditions created by the cold front passage are responsible for the unique properties of the rain forming in its wake. The identification of monotonic relationships between rain-to-cloud fraction and rain rate with surface forcing and boundary layer stability parameters as well as between virga base height with stability and humidity measures further supports that large-scale conditions impact precipitation variability. That being said, these relationships between the large-scale and rain properties are less clear than those established between cloud and rain properties, suggesting that cloud macrophysics have a more direct impact on the properties of rain than the large-scale environment. The applicability of previously documented relationships between cloud thickness and rain properties is tested and the relationships adjusted to accommodate the complex shallow clouds and melting precipitation observed to occur in the ENA region. Establishing these relationships opens up opportunities for parametrization development and suggests that a realistic representation of precipitation properties in models relies on the accurate representation of both clouds and the large-scale environment.

Sugar, Gravel, Fish, and Flowers: Dependence of Mesoscale Patterns of Trade-Wind Clouds on Environmental Conditions.

Trade‐wind clouds exhibit a large diversity of spatial organizations at the mesoscale. Over the tropical western Atlantic, a recent study has visually identified four prominent mesoscale patterns of shallow convection, referred to as flowers, fish, gravel, and sugar. We show that these four patterns can be identified objectively from satellite observations by analyzing the spatial distribution of infrared brightness temperatures. By applying this analysis to 19 years of data, we examine relationships between cloud patterns and large‐scale environmental conditions. This investigation reveals that on daily and interannual timescales, the near‐surface wind speed and the strength of the lower‐tropospheric stability discriminate the occurrence of the different organization patterns. These results, combined with the tight relationship between cloud patterns, low‐level cloud amount, and cloud‐radiative effects, suggest that the mesoscale organization of shallow clouds might change under global warming. The role of shallow convective organization in determining low‐cloud feedback should thus be investigated.

Climate Sensitivity and the Direct Effect of Carbon Dioxide in a Limited-Area Cloud-Resolving Model

AbstractEven in a small domain, it can be prohibitively expensive to run cloud-resolving greenhouse gas warming experiments due to the long equilibration time. Here, a technique is introduced that reduces the computational cost of these experiments by an order of magnitude: instead of fixing the carbon dioxide concentration and equilibrating the sea surface temperature (SST), this technique fixes the SST and equilibrates the carbon dioxide concentration. Using this approach in a cloud-resolving model of radiative–convective equilibrium (RCE), the equilibrated SST is obtained as a continuous function of carbon dioxide concentrations spanning 1 ppmv to nearly 10 000 ppmv, revealing a dramatic increase in equilibrium climate sensitivity (ECS) at higher temperatures. This increase in ECS is due to both an increase in forcing and a decrease in the feedback parameter. In addition, the technique is used to obtain the direct effects of carbon dioxide (i.e., the rapid adjustments) over a wide range of SSTs. Overall, the direct effect of carbon dioxide offsets a quarter of the increase in precipitation from warming, reduces the shallow cloud fraction by a small amount, and has no impact on convective available potential energy (CAPE).

The Role of Cloud Size and Environmental Moisture in Shallow Cumulus Precipitation

AbstractCloud models show that precipitation is more likely to occur in larger shallow clouds and/or in an environment with more moisture, in part as a result of decreasing the impacts of entrainment mixing on the updrafts. However, the role of cloud size in shallow cloud precipitation onset from global satellite observations has mostly been examined with precipitation proxies from imagers and has not been systematically examined in active sensors, primarily because of sensitivity limitations of previous spaceborne active instruments. Here we use the more sensitive CloudSat/CALIPSO observations to identify and characterize the properties of individual contiguous shallow cumulus cloud objects. The objects are conditionally sampled by cloud-top height to determine the changes in precipitation likelihood with increasing cloud size and column water vapor. On average, raining shallow cumulus clouds are typically taller by a factor of 2 and have a greater horizontal extent than their nonraining counterparts. Results show that for a fixed cloud-top height the likelihood of precipitation increases with increasing cloud size and generally follows a double power-law distribution. This suggests that the smallest cloud objects are able to grow freely within the boundary layer but the largest cloud objects are limited by environmental moisture. This is supported by our results showing that, for a fixed cloud-top height and cloud size, the precipitation likelihood also increases as environmental moisture increases. These results are consistent with the hypothesis that larger clouds occurring in a wetter environment may be better able to protect their updrafts from entrainment effects, increasing their chances of raining.