Abstract
Stratocumulus occur in closed- or open-cell states, which tend to be associated with high or low cloud cover and the absence or presence of precipitation, respectively. Thus, the transition ...between these states has substantial implications for the role of this cloud type in Earth’s radiation budget. In this study, we analyze transitions between these states using an ensemble of 127 large-eddy simulations, covering a wide range of conditions. Our analysis is focused on the behavior of these clouds in a cloud fraction (
f
c
) scene albedo (
A
) phase space, which has been shown in previous studies to be a useful framework for interpreting system behavior. For the transition from closed to open cells, we find that precipitation creates narrower clouds and scavenges cloud droplets for all
f
c
. However, precipitation decreases the cloud depth for
f
c
> 0.8 only, causing a rapid decrease in
A
. For
f
c
< 0.8, the cloud depth actually increases due to mesoscale organization of the cloud field. As the cloud deepening balances the effects of cloud droplet scavenging in terms of influence on
A
, changes in
A
are determined by the decreasing
f
c
only, causing a linear decrease in
A
for
f
c
< 0.8. For the transition from open to closed cells, we find that longwave radiative cooling drives the cloud development, with cloud widening dominating for
f
c
< 0.5. For
f
c
> 0.5, clouds begin to deepen gradually due to the decreasing efficiency of lateral expansion. The smooth switch between cloud widening and deepening leads to a more gentle change in
A
compared to the transitions under precipitating conditions.
Significance Statement
By reflecting a substantial fraction of solar shortwave radiation back to space, shallow clouds constitute a major cooling agent in Earth’s radiation budget. To constrain this effect, a profound understanding of cloud cover and cloud albedo is necessary. In this study, we analyze the processes that drive the variability in these cloud properties in stratocumulus clouds, a very common cloud type covering approximately 20% of the globe. For these clouds, we show that changes from low to high or high to low cloud cover are different due to the underlying cloud micro- and macrophysics, elucidating this crucial aspect of aerosol–cloud–climate interactions.
Diagnosing the root causes of cloud feedback in climate models and reasons for inter-model disagreement is a necessary first step in understanding their wide variation in climate sensitivities. Here ...we bring together two analysis techniques that illuminate complementary aspects of cloud feedback. The first quantifies feedbacks from changes in cloud amount, altitude, and optical depth, while the second separates feedbacks due to cloud property changes within specific cloud regimes from those due to regime occurrence frequency changes. We find that in the global mean, shortwave cloud feedback averaged across ten models comes solely from a positive within-regime cloud amount feedback countered slightly by a negative within-regime optical depth feedback. These within-regime feedbacks are highly uniform: In nearly all regimes, locations, and models, cloud amount decreases and cloud albedo increases with warming. In contrast, global-mean across-regime components vary widely across models but are very small on average. This component, however, is dominant in setting the geographic structure of the shortwave cloud feedback: Thicker, more extensive cloud types increase at the expense of thinner, less extensive cloud types in the extratropics, and vice versa at low latitudes. The prominent negative extratropical optical depth feedback has contributions from both within- and across-regime components, suggesting that thermodynamic processes affecting cloud properties as well as dynamical processes that favor thicker cloud regimes are important. The feedback breakdown presented herein may provide additional targets for observational constraints by isolating cloud property feedbacks within specific regimes without the obfuscating effects of changing dynamics that may differ across timescales.
Turbulence in clouds enhances collision between cloud droplets, which is critical to drizzle formation in shallow cumulus clouds. In this study, we develop an autoconversion parametrization that ...considers the turbulence‐induced collision enhancement (TICE) obtained from a particle trajectory model. We implement the developed parametrization to a large‐eddy simulation model and simulate the Rain In Cumulus over the Ocean (RICO) case to investigate the effects of TICE on shallow cumulus clouds and their sensitivities to the cloud droplet number concentration and horizontal grid resolution. TICE significantly increases the autoconversion rate, leading to large increases in the accretion rate, rainwater path and surface rain rate. The simulations of shallow cumulus clouds are highly sensitive to the cloud droplet number concentration and horizontal grid resolution, and the inclusion of TICE overall reduces the sensitivities. For example, the large rainwater path and surface rain rate that appear when cloud droplet number concentration is relatively low are reduced by TICE, whereas the very small rainwater path and surface rain rate that appear when cloud droplet number concentration is relatively high are increased by TICE. Comparisons with two other TICE‐aware parametrizations based on direct numerical simulation results of droplet collisions in turbulence show that there are substantial differences in TICE effects for different parametrizations, which implies a large uncertainty in simulating turbulent clouds. Among the three parametrizations, the parametrization developed in this study shows intermediate TICE‐induced increases in rainwater path and surface rain rate.
In this study, we develop an autoconversion parametrization that considers the turbulence‐induced collision enhancement (TICE) and investigate the effects of TICE on drizzling shallow cumuli through large‐eddy simulations. TICE significantly increases the autoconversion rate, leading to large increases in rainwater path and surface rain rate. The simulations of shallow cumuli are highly sensitive to the cloud droplet number concentration and horizontal grid resolution, and the inclusion of TICE overall reduces the sensitivities.
The complex coupling between the large‐scale atmospheric circulation, which is explicitly resolved in modern numerical weather and climate models, and cloud‐related diabatic processes, which are ...parameterized, is an important source of error in weather predictions and climate projections. To quantify the interactions between clouds and the large‐scale circulation, a method is employed that attributes a far‐ and near‐field circulation to the cloud system. The method reconstructs the cloud‐induced flow based on estimates of vorticity and divergence over a limited domain and does not require the definition of a background flow. It is subsequently applied to 12‐ and 2‐km simulations of convective clouds, which form within the large‐scale cloud band ahead of the upper‐level jet associated with an extratropical cyclone over the North Atlantic. The cloud‐induced circulation is directed against the jet, reaches up to 10 m·s−1, and compares well between both simulations. The flow direction is in agreement with what can be expected from a vorticity dipole that forms in the vicinity of the clouds. Hence, in the presence of embedded convection, the wind speed does not steadily decrease away from the jet, as it does in cloud‐free regions, but exhibits a pronounced negative anomaly, which can now be explained by the cloud‐induced circulation. Furthermore, the direction of the reconstructed circulation suggests that the cloud induces a flow that counteracts its advection by the jet. Convective clouds therefore propagate more slowly than their surroundings, which may affect the distribution of precipitation. The method could be used to compare cloud‐induced flow at different resolutions and between different parameterizations.
Clouds are not passively moved in the atmosphere by wind. Instead, clouds actively influence the flow field in their immediate vicinity. In this study, the flow field generated by clouds is quantified. It is shown that clouds create a flow that is directed against the direction of the wind in which they grow. Hence, clouds propagate more slowly than a passive object, and they are more stationary. This can influence the regional cloud and precipitation distribution.
Representing subgrid variability of cloud properties has always been a challenge in global climate models (GCMs). In many cloud microphysics schemes, the warm rain non‐linear process rates calculated ...based on grid‐mean cloud properties are usually scaled by an enhancement factor (EF) to account for the effects of subgrid cloud variability. In our study, we find that the EF derived from Cloud Layers Unified by Binormals in Community Atmosphere Model version 6 (CAM6) is severely overestimated in most of the cloudy oceanic areas, which leads to strong overestimation of the autoconversion rate. We improve the EF in warm rain simulation by developing a new formula for in‐cloud subgrid cloud water variance. With the updated subgrid cloud water variance and EF treatment, the liquid cloud fraction (LCF) and cloud optical thickness (COT) increases noticeably for marine stratocumulus, and the shortwave cloud forcing (SWCF) matches better with observations. The updated formula improves the relationship between autoconversion rate and cloud droplet number concentration, and it decreases the sensitivity of autoconversion rate to aerosols. The sensitivity of liquid water path to aerosols decreases noticeably and is in better agreement with that in MODIS. Although the sensitivity of COT is similar to that in MODIS, CAM6 underestimates the sensitivity of grid‐mean SWCF to aerosols because of the underestimation in the sensitivities of LCF and in‐cloud SWCF. Our results indicate the importance of representing reasonable subgrid cloud variability in the simulation of cloud properties and aerosol‐cloud interaction in GCMs.
Plain Language Summary
Restricted by the coarse horizontal resolution, the global climate model needs to scale the warm rain non‐linear process rates with an enhancement factor to account for the effects of subgrid variability of cloud water. However, this variability is severely overestimated in most of the cloudy oceanic areas in Community Atmosphere Model version 6, which leads to a strong overestimation of the precipitation. We develop a new formula for calculating the subgrid variability of cloud water, which greatly improves the magnitude of the variability and contributes to better simulation of cloud properties and shortwave cloud forcing (SWCF) using satellite observation as a benchmark. The sensitivity of cloud liquid water path to aerosols decreases noticeably with the new formula. The model underestimates the sensitivity of SWCF to aerosols due to the underestimation in the sensitivities of liquid cloud fraction and cloud albedo. Our results indicate the importance of representing reasonable subgrid cloud variability in the simulation of cloud properties and aerosol‐cloud interaction in the climate model.
Key Points
A new formula for the in‐cloud subgrid cloud water variance is developed to improve the enhancement factor of autoconversion rate
The simulated sensitivity of cloud liquid water path to aerosols decreases obviously with the updated formula
The simulated sensitivity of grid‐mean shortwave cloud forcing to aerosols is underestimated compared to MODIS observation
Cloud computing provides computing platforms and facilitates to optimize utilization of infrastructure resources, reduces deployment time and increases flexibility. The popularity of cloud computing ...led to development of interconnected cloud computing environments (ICCE) such as hybrid cloud, inter-cloud, multi-cloud, and federated cloud, enabling the possibilities to share resources among individual clouds. However, individual proprietary technologies and access interfaces employed by cloud service providers made it difficult to share resources. Interoperability and portability are two of the major challenges to be addressed to ensure seamless access and sharing of resources and services.
Many cloud service providers have similar service offerings but different access patterns. It is difficult and time consuming for a cloud user to select an appropriate cloud service as per the application’s requirement. Cloud user has to gather information from various cloud service providers and analyze them. Cloud broker has been proposed to address the challenge of cloud users to get best out of cloud provider. Cloud broker is an entity which works as an independent third party between cloud users and cloud providers. Cloud broker negotiates with several cloud providers as per user’s requirements and tries to select the best services. Cloud broker coordinates the sharing of resources and provides interoperability and portability with other cloud providers.
In this paper, a comprehensive survey of cloud brokering in interconnected cloud computing environments has been provided. The need and importance of cloud broker has been discussed. The existing architectures and frameworks of Cloud Brokering are reviewed. A comprehensive literature survey of various Cloud Brokering techniques is presented. A taxonomy of Cloud Brokering techniques has been presented and analyzed on the basis of their strengths and weaknesses/limitations. The taxonomy includes pricing, multi-criteria, quality of services, optimization and trust techniques. The techniques are analyzed on various performance metrics. Research challenges and open problems are identified from reviewed techniques. A model for cloud broker is proposed to address identified challenges. We hope that our work will enable researchers to launch and dive deep into Cloud Brokering challenges in interconnected cloud computing environments.
•Cloud broker and its need in interconnected cloud environment is discussed.•Cloud brokering frameworks of interconnected cloud environments are reviewed.•Taxonomy of cloud brokering techniques are presented.•Comparative analysis of cloud brokering techniques are presented.•Future research challenges in cloud brokering are identified and discussed.
Cloud detection in optical remote sensing images is a crucial problem because undetected clouds can produce misleading results in the analyses of surface and atmospheric parameters. Sentinel-2 ...provides high spatial resolution satellite data distributed with associated cloud masks. In this paper, we evaluate the ability of Sentinel-2 Level-1C cloud mask products to discriminate clouds over a variety of biogeographic scenarios and in different cloudiness conditions. Reference cloud masks for the identification of misdetection were generated by applying a local thresholding method that analyses Sentinel-2 Band 2 (0.490 μm) and Band 10 (1.375 μm) separately; histogram-based thresholds were locally tuned by checking the single bands and the natural color composite (B4B3B2); in doubtful cases, NDVI and DEM were also analyzed to refine the masks; the B2B11B12 composite was used to separate snow.
The analysis of the cloud classification errors obtained for our test sites allowed us to get important inferences of general value. The L1C cloud mask generally underestimated the presence of clouds (average Omission Error, OE, 37.4%); this error increased (OE > 50%) for imagery containing opaque clouds with a large transitional zone (between the cloud core and clear areas) and cirrus clouds, fragmentation emerged as a major source of omission errors (R2 0.73). Overestimation was prevalently found in the presence of holes inside the main cloud bodies. Two extreme environments were particularly critical for the L1C cloud mask product. Detection over Amazonian rainforests was highly inefficient (OE > 70%) due to the presence of complex cloudiness and high water vapor content. On the other hand, Alpine orography under dry atmosphere created false cirrus clouds. Altogether, cirrus detection was the most inefficient. According to our results, Sentinel-2 L1C users should take some simple precautions while waiting for ESA improved cloud detection products.
•First assessment of the Sentinel-2 L1C cloud mask product in different ecoregions•Cloud configuration and complexity determine most misclassification errors.•The performance of the L1C cloud mask is low in critical environmental conditions.•Cirrus clouds are confirmed to be the most difficult to be detected.•Practical precautions are suggested to minimize effects on surface analyses.
The regional atmospheric model Consortium for Small-scale Modeling (COSMO) coupled to the Multi-Scale Chemistry Aerosol Transport model (MUSCAT) is extended in this work to represent aerosol–cloud ...interactions. Previously, only one-way interactions (scavenging of aerosol and in-cloud chemistry) and aerosol–radiation interactions were included in this model. The new version allows for a microphysical aerosol effect on clouds. For this, we use the optional two-moment cloud microphysical scheme in COSMO and the online-computed aerosol information for cloud condensation nuclei concentrations (Cccn), replacing the constant Cccn profile. In the radiation scheme, we have implemented a droplet-size-dependent cloud optical depth, allowing now for aerosol–cloud–radiation interactions. To evaluate the models with satellite data, the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) has been implemented. A case study has been carried out to understand the effects of the modifications, where the modified modeling system is applied over the European domain with a horizontal resolution of 0.25° × 0.25°. To reduce the complexity in aerosol–cloud interactions, only warm-phase clouds are considered. We found that the online-coupled aerosol introduces significant changes for some cloud microphysical properties. The cloud effective radius shows an increase of 9.5 %, and the cloud droplet number concentration is reduced by 21.5 %.
Both mean and extreme rainfall decreased over India and Northern China during 1979–2005 at a rate of 0.2%/decade. The aerosol dampening effects on rainfall has also been suggested as a main driver of ...mean rainfall shift in India and China. Conflicting views, however, exist on whether aerosols enhance or suppress hazardous extreme heavy rainfall. Using Coupled Model Intercomparison Project phase 5 (CMIP5) multimodel ensemble, here we show that only a subset of models realistically reproduces the late‐20th‐century trend of extreme rainfall for the three major regions in Asia: drying in India and Northern China and wetting in Southern China, all consistent with mean rainfall change. As a common feature, this subset of models includes an explicit treatment of the complex physical processes of aerosol‐cloud interaction (i.e., both cloud‐albedo and cloud‐lifetime effects), while simulation performance deteriorates in models that include only aerosol direct effect or cloud‐albedo effect. The enhanced aerosol pollution during this rapid industrialization era is the leading cause of the spatially heterogeneous extreme rainfall change by dimming surface solar radiation, cooling adjacent ocean water, and weakening moisture transport into the continental region, while GHG warming or natural variability alone cannot explain the observed changes. Our results indicate that the projected intensification of regional extreme rainfall during the early‐to‐mid 21st‐century, in response to the anticipated aerosol reduction, may be underestimated in global climate models without detailed treatment of complex aerosol‐cloud interaction.
Plain Language Summary
Over Asia, a robust pattern of drying‐wetting‐drying trend over three most populated regions (India, South China, and North China, respectively) have been observed in the past few decades. Yet the cause of the 30‐year trend is rather unclear, with conflicting arguments on the importance of natural variability, the greenhouse gas, land cover, and aerosols. Most of the previous studies, however, fail to provide a holistic explanation for all three major regions simultaneously. The aerosol‐cloud interaction‐induced oceanic cooling, as we show here, provides a critical piece in reproducing the past trend. Only a fraction of climate models with complex treatment of aerosol‐cloud interaction capture the observed pattern; thus, unconstrained model data set provides biased outlook of extreme rainfall in this region.
Key Points
Drying‐wetting‐drying trend over India, Southern China, and Northern China is observed in the last few decades of the 20th century
Previous work failed to give a holistic explanation for three regions simultaneously, while this study attributes observed trend to aerosol
The CMIP5 models with more complex treatment of aerosol‐cloud interaction capture the observed pattern better
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