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A supervised neural network algorithm is used to categorize near‐global satellite retrievals into three mesoscale cellular convective (MCC) cloud morphology patterns. At constant cloud amount, morphology patterns differ in brightness associated with the amount of optically thin cloud features. Environmentally driven transitions from closed MCC to other morphology patterns, typically accompanied by more optically thin cloud features, are used as a framework to quantify the morphology contribution to the optical depth component of the shortwave cloud feedback. A marine heat wave is used as an out‐of‐sample test of closed MCC occurrence predictions. Morphology shifts in optical depth between 65°S and 65°N under projected environmental changes (i.e., from an abrupt quadrupling of CO2) assuming constant cloud cover contributes between 0.04 and 0.07 W m−2 K−1 (aggregate of 0.06) to the global mean cloud feedback. Plain Language Summary Marine boundary layer clouds are essential to the energy balance of Earth, reflecting sunlight back to space and covering a large percentage of the globe. These clouds can organize into open, closed, and disorganized cellular structures. Cloud morphology patterns differ in their ability to reflect sunlight back to space. Closed cellular clouds transition to open and disorganized clouds associated with changes in environmental factors (i.e., sea surface temperature and the stability of the lower atmosphere). This study examines how a shift in cloud morphology with climate change will change the amount of sunlight reflected back to space: a shortwave cloud feedback. We predict the frequency of occurrence of closed cellular clouds based on changes in environmental factors estimated from global climate model simulations under climate change scenarios. An observed marine heat wave is used to test occurrence predictions. The change in reflected sunlight due to the shift between morphology types at fixed fractional cloud cover produces a global feedback that ranges between 0.04 and 0.07 W m−2 K−1. Key Points Mesoscale cloud morphology albedo varies with fraction of optically thin cloud features Closed mesoscale cellular convection occurrence changes are predictable from environmental controls Environmentally driven cloud morphology changes in optical depth produce a shortwave feedback of 0.04–0.07 W m−2 K−1

The response of marine low‐cloud mesoscale morphologies to climate change and emission reductions remains poorly understood. Here, we link long‐term trends in six cloud morphologies to variations in large‐scale meteorology and aerosols. The trends show strong spatial heterogeneity, with closed and disorganized mesoscale cellular convection decreasing in the Northeast Pacific and Southeast Atlantic. We develop a deep learning model (UMorNet) to predict instantaneous cloud morphologies from meteorology and cloud droplet number concentration (Nd), a proxy for aerosols. UMorNet achieves an average test accuracy of 0.55 and captures spatial patterns of climatology and long‐term trends. Out‐of‐sample test with a marine heatwave event further demonstrates the model's performance. Sensitivity experiments identify Nd, marine cold‐air outbreak index, sea surface temperature, and inversion strength as key drivers. Different responses of clustered Cu and suppressed Cu to Nd was identified. These findings highlight the potential role of aerosols in shaping cloud morphological changes.

Mesoscale convective systems (MCSs) are important to the water and energy budget of the tropical climate and are essential ingredients of the tropical circulation. MCSs are readily observed in satellite infrared geostationary imagery as cloud clusters that evolve in time from small structures to well-organized large patches of cloud shield before dissipating. The MCS cloud shield is the result of a large ensemble of mesoscale dynamical, thermodynamical, and microphysical processes. This study shows that a simple parametric model can summarize the time evolution of the morphological characteristics of the cloud shield during the life cycle of the MCS. It consists of a growth–decay linear model of the cloud shield and is based on three parameters: the time of maximum extent, the maximum extent, and the duration of the MCS. It is shown that the time of maximum is frequently close to the middle of the life cycle and that the correlation between maximum extent and duration is strong all over the tropics. This suggests that 1 degree of freedom is left to summarize the life cycle of the MCS cloud shield. Such a model fits the observed MCS equally well, independent of the duration, size, location, and propagation characteristics, and its relevance is assessed for a large number of MCSs over three boreal summer periods over the whole tropical belt. The scaling of this simple model exhibits weak (strong) regional variability for the short- (long-) lived systems indicative of the primary importance of the internal dynamics of the systems to the large-scale environment for MCS sustainability.

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.

Based on the extent and eccentricity characteristics, mesoscale convective systems (MCSs) formed in the middle reaches of the Yangtze River Basin were classified into six subtypes: large circular (LC), large elongated (LE), medium circular (MC), medium elongated (ME), small circular (SC), and small elongated (SE). The lifespans of all L‐scale and most M‐scale MCSs exceed 6 h, whereas the majority of S‐scale MCSs last less than 6 h. The cold cloud coverage frequency for E‐type MCSs exhibits relative uniformity, with high‐frequency regions located south of the Yangtze River. In contrast, C‐type MCSs display a more scattered high‐frequency distribution with higher maxima. E‐type MCSs predominantly retain an elongated shape throughout their life cycles. Additionally, as the area of LE and ME MCSs expands, their eccentricity progressively decreases, leading to a greater inclination towards the east–west direction. For C‐type MCSs, they maintain a circular shape for less than half of their duration but tend to adopt an elongated shape during the development or dissipation stages. These findings provide a foundation for further investigation into the formation mechanisms and associated mesoscale systems of MCSs, which could enhance the prediction accuracy of the location and intensity of severe weather events linked to different types of MCSs. Multivariate distribution of MCS extents, eccentricities, and orientations across various morphological types. Each scatter represents a time step of MCSs. The scatter locations indicate extents (horizontal axis) and eccentricities (vertical axis), while orientations are shown by the color scale, as indicated in the color bar. Blue lines represent the kernel densities of scatters.

Mesoscale organization of boundary layer clouds modulates their radiative properties and contributes to the tropical hydrologic cycle. Trade wind cumuli (Cu) have varying organization and are a source of uncertainty in global climate models (GCMs). The linkage between Cu development and dynamics is difficult to capture, impacting low cloud feedback estimates. We investigate the relationship between mesoscale organization and Cu updraft dynamics in their early development stages using wintertime shipborne observations. We contrast two periods with similar cloud sizes but more (MO) and less (LO) organized states. MO clouds are dynamically more efficient than LO clouds: for a given core size, MO clouds have stronger sub-cloud and cloud-base updrafts, implying greater vertical moisture transport. Despite similar background plume behaviors, cloud-topped plumes are wider and more frequently successful for MO than LO. Updraft strength is persistent despite diurnal environmental variations. MO turbulence is enhanced by early-morning surface flux maximization and LO updrafts may be assisted by daytime environmental conditions. MO cloud amount persists, while LO clouds suffer daytime depredations. We hypothesize that, once established, MO clouds are maintained through the assistance of cloud-layer-driven mesoscale circulations that increase dynamic efficiency through reinforcing plumes and their updrafts. Dynamic efficiency is likely a key contributor to the moisture–convection feedback critical to mesoscale organization. Organizational modulation of cloud dynamics through enhancing updrafts is another unresolved factor in GCM parameterizations. Understanding this efficiency, and the potential environmental resilience of MO clouds, will be informative for simulating Cu behaviors under current and future climates.

Marine low cloud mesoscale morphology in the southeastern Pacific Ocean is analyzed using a large dataset of machine-learning generated classifications spanning three years. Meteorological variables and cloud properties are composited 10by mesoscale cloud type, showing distinct meteorological regimes of marine low cloud organization from the tropics to the midlatitudes. The presentation of mesoscale cellular convection, with respect to geographic distribution, boundary layer structure, and large-scale environmental conditions, agrees with prior knowledge. Two tropical and subtropical cumuliform boundary layer regimes, suppressed cumulus and clustered cumulus, are studied in detail. The patterns in precipitation, circulation, column water vapor, and cloudiness are consistent with the representation of marine shallow mesoscale convective 15 self-aggregation by large eddy simulations of the boundary layer. Although they occur under similar large-scale conditions, the suppressed and clustered low cloud types are found to be well-separated by variables associated with low-level mesoscale circulation, with surface wind divergence being the clearest discriminator between them, whether reanalysis or satellite observations are used. Clustered regimes are associated with surface convergence and suppressed regimes are associated with surface divergence.

This study documents atmospheric conditions, development, and evolution of a severe weather outbreak that occurred on 11 August 2017 in Poland. The emphasis is on analyzing system morphology and highlighting the importance of a mesovortex in producing the most significant wind damages. A derecho-producing mesoscale convective system (MCS) had a remarkable intensity and was one of the most impactful convective storms in the history of Poland. It destroyed and partially damaged 79 700 ha of forest (9.8 million m 3 of wood), 6 people lost their lives, and 58 were injured. The MCS developed in an environment of high 0–3-km wind shear (20–25 m s −1 ), strong 0–3-km storm relative helicity (200–600 m 2 s −2 ), moderate most-unstable convective available potential energy (1000–2500 J kg −1 ), and high precipitable water (40–46 mm). Within the support of a midtropospheric jet, the MCS moved northeast with a simultaneous northeastward inflow of warm and very moist air, which contributed to strong downdrafts. A mesocyclone embedded in the convective line induced the rear inflow jet (RIJ) to descend and develop a bow echo. In the mature stage, a supercell evolved into a bookend vortex and later into a mesoscale convective vortex. Swaths of the most significant wind damage followed the aforementioned vortex features. A high-resolution simulation performed with initial conditions derived from GFS and ECMWF global models predicted the possibility of a linear MCS with widespread damaging wind gusts and embedded supercells. Simulations highlighted the importance of cloud cover in the preconvective environment, which influenced the placement and propagation of the resulting MCS.

This study evaluates the impact of dust–radiation–cloud interactions on the development of a mesoscale convective system (MCS) by comparing numerical experiments run with and without dust–radiation and/or dust–cloud interactions. An MCS that developed over North Africa on 4–6 July 2010 is used as a case study. The CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellites passed over the center of the MCS after it reached maturity, providing valuable profiles of aerosol backscatter and cloud information for model verification. The model best reproduces the MCS’s observed cloud structure and morphology when both dust–radiation and dust–cloud interactions are included. Our results indicate that the dust–radiation effect has a far greater influence on the MCS’s development than the dust-cloud effect. Results show that the dust-radiative effect, both with and without the dust–cloud interaction, briefly delays the MCS’s formation but ultimately produces a stronger storm with a more extensive anvil cloud. This is caused by dust–radiation-induced changes to the MCS’s environment. The impact of the dust–cloud effect on the MCS, on the other hand, is greatly affected by the presence of the dust–radiation interaction. The dust–cloud effect alone slows initial cloud development but enhances heterogeneous ice nucleation and extends cloud lifetime. When the dust–radiation interaction is added, increased transport of dust into the upper portions of the storm—due to a dust–radiation-driven increase in convective intensity—allows dust–cloud processes to more significantly enhance heterogeneous freezing activity earlier in the storm’s development, increasing updraft strength, hydrometeor growth (particularly for ice particles), and rainfall.

Postfrontal clouds (PFC) are ubiquitous in the marine boundary layer, and their morphology is essential to estimating the radiation budget in weather and climate models. Here we examine the roles of sea surface temperature (SST) and meteorological factors in controlling the mesoscale morphology and evolution of shallow clouds associated with a cold-air outbreak that occurred on 1 March 2020 during phase I of the Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE). Our results show that the simulated PFC structure and ambient conditions by the Weather Research and Forecasting (WRF) Model are generally consistent with observations from GOES-16 and dropsonde measurements. We also examine the thermodynamical and dynamical influences in the cloud mesoscale morphology using WRF sensitivity experiments driven by two meteorological forcing datasets with different domain-mean SST and spatial gradients, which lead to dissimilar values of hydrometeor water path and cloud core fraction. The SST from ERA5 leads to weaker stability and higher inversion height than the SST from FNL does. In addition, the use of large-scale meteorological forcings from ERA5 yields a distinctive time evolution of wind direction shear in the inner domain, which favors the formation and persistence of longer cloud rolls. Both factors contribute to a change in the time evolution of domain-mean water path and cloud core fraction of cloud streets. Our study takes advantage of the simulation driven by the differences between two large-scale forcing datasets to illustrate the importance of SST and wind direction shear in the cloud street morphology in a realistic scenario