Effects of Sea Salt Aerosol Emissions for Marine Cloud Brightening on Atmospheric Chemistry: Implications for Radiative Forcing
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Hannah M. Horowitz↗
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On the Importance of Sea Surface Temperature for Aerosol-Induced Brightening of Marine Clouds and Implications for Cloud Feedback in a Future Warmer Climate
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54 ENVIRONMENTAL SCIENCES↗
A modeling study of marine boundary layer clouds
Marine boundary layer (MBL) clouds are important components of the earth's climate system. These clouds drastically reduce the amount of solar radiation absorbed by the earth, but have little effect on the emitted infrared radiation on top of the atmosphere. In addition, these clouds are intimately involved in regulating boundary layer turbulent fluxes. For these reasons, it is important that general circulation models used for climate studies must realistically simulate the global distribution of the MBL. While the importance of these cloud systems is well recognized, many physical processes involved in these clouds are poorly understood and their representation in large-scale models remains an unresolved problem. The present research aims at the development and improvement of the parameterization of these cloud systems and an understanding of physical processes involved. This goal is addressed in two ways. One is to use regional modeling approach to validate and evaluate two-layer marine boundary layer models using satellite and ground-truth observations; the other is to combine this simple model with a high-order turbulence closure model to study the transition processes from stratocumulus to shallow cumulus clouds. Progress made in this effort is presented.
Wang, Shouping↗
Influence of Entrainment on Centimeter-Scale Cloud Microphysics in Marine Stratocumulus Clouds Observed during CSET
Abstract Cloud microphysical relationships observed during the Cloud System Evolution in the Trades (CSET) campaign held between Northern California and Hawaii were analyzed to study the effects of entrainment and subsequent mixing of free-tropospheric and cloudy air on cloud microphysical properties of marine stratocumulus clouds. The data measured by Holographic Detector for Clouds (HOLODEC) were extensively used because they could provide the 3D positions and sizes of droplets within sample volume on the centimeter scale, making it possible to explore the 3D spatial distribution of droplets, which has not been possible for conventional cloud probes. This study focused on analyzing the 3D spatial distribution of droplets and visual traits of inhomogeneous mixing and on quantifying the relationship between 3D spatial distributions and traits of inhomogeneous mixing. Two types of spatial distributions are compared. The first is measured droplet spatial distribution and the second type is generated randomly distributed droplets using the Monte Carlo approach, that is, to analyze whether or not clustering is strong enough to classify as a clustered distribution for a hologram. The difference between the two types of spatial distributions depends on whether they are affected by entrainment and mixing. The holograms observed near the cloud top, where the effects of entrainment and mixing would be immediate, showed relatively high confidence in the significance test for spatially clustered populations of droplets. Moreover, spatially clustered holograms appeared to exhibit stronger visual traits of inhomogeneous mixing than perfectly randomly distributed holograms only when observed near the cloud top. On the other hand, these characteristics did not appear for holograms observed deeper into the cloud where the effects of entrainment and mixing would be reduced. Such 3D structural characteristics of droplet distributions seem to be consistent with vertical circulation mixing.
Meteorology & Atmospheric Sciences↗
First EarthCARE CPR Observations of Dynamics–Microphysics Coupling in Marine Cold‐Air Outbreak Clouds
Marine cold-air outbreak (MCAO) clouds begin as shallow cloud streets near the ice edge and evolve into broken open-cell convection downstream. Their rapid transitions in cloud structure and microphysics are closely linked to vertical motions, but this interplay has not previously been observed from space. The EarthCARE satellite, carrying the first spaceborne Doppler radar (Cloud Profiling Radar, CPR), now measures hydrometeor sedimentation velocity and vertical air motion. Using CPR observations over the Norwegian and Barents Seas (December 2024–May 2025), we analyze MCAO and non-MCAO clouds. We find that strong updrafts in MCAO clouds coincide with larger supercooled liquid water (SLW) path (LWP). These conditions are accompanied by increases in sedimentation velocity and its vertical gradient, reflecting rapid riming-driven ice growth. In contrast, non-MCAO clouds show lower riming efficiency at similar LWP due to relatively weaker dynamical support. This emphasizes the importance of dynamics in shaping polar cloud structure and microphysics.
Kim, Jiseob [McGill Univ., Montreal, QC (Canada)] ↗
Influence of Entrainment on Centimeter-Scale Cloud Microphysics in Marine Stratocumulus Clouds Observed during CSET
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54 ENVIRONMENTAL SCIENCES↗
Vertical Variations of Cloud Microphysical Relationships in Marine Stratocumulus Clouds Observed During the ACE‐ENA Campaign
Abstract This study examines the vertical variations of cloud microphysical relationships and their implications to cloud microphysical processes in marine stratocumulus clouds using in‐situ aircraft observations during the Aerosol and Cloud Experiments in Eastern North Atlantic (ACE‐ENA) field campaign. A new diagram with a coordinate system based on cloud droplet liquid water content (L c ) and phase relaxation time scale is proposed to investigate mixing mechanisms. This new diagram analysis shows that the inhomogeneous mixing trait is dominant near the cloud top, but homogeneous mixing trait is stronger at lower altitudes. The relevant scale parameters (i.e., transition length scale and transition scale number) also indicate a high likelihood of inhomogeneous mixing. The relationship between L c and standard deviation of droplet radius (σ R ) clearly shows the vertical transition: the correlation between L c and σ R is positive at lower cloud altitudes, but it becomes negative as altitude increases. Such a vertical transition is consistent with the vertical circulation mixing, modulating the cloud microphysical relationships to suggest homogeneous mixing at a significant depth from the cloud top.
54 ENVIRONMENTAL SCIENCES↗
Vertical variation of turbulent entrainment mixing processes in marine stratocumulus clouds using high-resolution digital holography
Marine stratocumulus clouds contribute signi?cantly to the Earth’s radiation budget due to their extensive coverage and high albedo. Yet, subgrid variability in cloud properties such as aerosol concentration, droplet number and precipitation rates lead to considerable errors in global climate models. While these clouds usually have small vertical ex-tent, turbulent entrainment-mixing and precipitation can generate signi?cant variations in droplet number, size and relative dispersion with altitude. In this paper, we analyze turbulent entrainment-mixing processes and the variability in cloud microphysical properties as a function of height within a warm marine stratocumulus cloud layer over the Eastern North Atlantic. We use high resolution airborne holographic measurements and compare them with local turbulence measurements. We ?nd that entrainment-mixing is primarily inhomogeneous near cloud top leading to larger droplet sizes and homogeneous near cloud base leading to smaller droplet sizes. Further analysis of Damk¨ohler number measurements are able to explain the mixing mechanisms at di?erent cloud heights, reinforcing the importance of turbulent mixing and microphysical time scales in deter-mining cloud microphysics.
Desai, Neel↗
Locally narrow droplet size distributions are ubiquitous in stratocumulus clouds
Marine stratocumulus clouds are the “global reflectors,” sharply contrasting with the underlying dark ocean surface and exerting a net cooling on Earth’s climate. The magnitude of this cooling remains uncertain in part owing to the averaged representation of microphysical processes, such as the droplet-to-drizzle transition in global climate models (GCMs). Current GCMs parameterize cloud droplet size distributions as broad, cloud-averaged gammas. Here, using digital holographic measurements of discrete stratocumulus cloud volumes, we found cloud droplet size distributions to be narrower at the centimeter scale, never resembling the cloud average. These local distributions tended to form pockets of similar-looking cloud regions, each characterized by a size distribution shape that is diluted to varying degrees. These observations open the way for new modeling representations of microphysical processes.
54 ENVIRONMENTAL SCIENCES↗
Effects of Wind Shear and Aerosol Conditions on the Organization of Precipitating Marine Stratocumulus Clouds
Here this study examines how wind shear affects precipitating marine stratocumulus clouds under different cloud droplet number concentrations (N d ). We performed a series of large eddy simulations (LES) of nocturnal marine stratocumulus clouds using Cloud Model 1 (CM1). The simulations show that N d is the dominant factor for cloud cellular organization in this cloud regime rather than wind shear. Low N d characterizes the open cellular structure with a high in-cloud liquid water path (LWP). When wind shear is increased, the cloud fraction tends to decrease along with LWP, suggesting the cloud top region is significantly influenced by the entrainment and mixing of dry air from the free troposphere. We also examine cold pools in open and closed cellular clouds. Open-cell clouds produce larger and deeper cold pools compared to closed-cell clouds. Interestingly, cold pools can exist without surface precipitation and are produced by evaporation of light precipitation (drizzle) below the cloud base with weak downdrafts. The evaporation of raindrops and drizzle play an important role in initiating new convection, particularly where colliding outflows occur downstream of the cloud. This secondary convection contributes to the development and maintenance of the cloud cellular organization and formation.
54 ENVIRONMENTAL SCIENCES↗
Aerosol-Cloud-Radiation Interactions in Marine Boundary Clouds Derived From Vertically-Resolved Aerosol Properties From CALIPSO
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David Painemal↗
Climatology of Cloud-Top Radiative Cooling in Marine Shallow Clouds
A one-year’s worth of near-global marine shallow single-layer cloud top radiative cooling (CTRC) is derived from a radiative transfer model with inputs from the satellite cloud retrievals and reanalysis sounding. The mean cloud top radiative flux divergence is 61 Wm -2 , decomposed into the longwave and shortwave components of 73 and -11 W m -2 , respectively. Equatorward of 30°N/S, the CTRC is largely a reflection of free-atmospheric specific humidity distribution: a dry atmosphere enhances CTRC by reducing downward thermal radiation. Consequently, the cooling minimizes in the “wet” tropics and maximizes in the “dry” eastern subtropics. Poleward of 30°N/S, the CTRC decreases slightly due to the colder clouds that emit less effectively. Furthermore, the CTRC exhibits distinctive seasonal cycles with stronger cooling in the winter and has amplitudes of order 10–20 Wm -2 in stratocumulus-rich regions. The datasets were used to train a machine-learning model that substantially speeds up the retrieval.
54 ENVIRONMENTAL SCIENCES↗
On the Nature and Extent of Optically Thin Marine low Clouds
Macrophysical properties of optically thin marine low clouds over the nonpolar oceans (60 deg S-60 deg N) are measured using 2 years of full-resolution nighttime data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Optically thin clouds, defined as the subset of marine low clouds that do not fully attenuate the lidar signal, comprise almost half of the low clouds over the marine domain. Regionally, the fraction of low clouds that are optically thin (f(sub thin,cld)) exhibits a strong inverse relationship with the low-cloud cover, with maxima in the tropical trades (f(sub thin,cld) greater than 0.8) and minima in regions of persistent marine stratocumulus and in midlatitudes (f(sub thin,cld) less than 0.3). Domain-wide, a power law fit describes the cloud length distribution, with exponent beta = 2.03 +/- 0.06 (+/-95% confidence interval). On average, the fraction of a cloud that is optically thin decreases from approximately 1 for clouds smaller than 2 km to less than 0.3 for clouds larger than 30 km. This relationship is found to be independent of region, so that geographical variations in the cloud length distribution explain three quarters of the variance in f(sub thin,cld). Comparing collocated trade cumulus observations from CALIOP and the airborne High Spectral Resolution Lidar reveals that clouds with lengths smaller than are resolvable with CALIOP contribute approximately half of the low clouds in the region sampled. A bounded cascade model is constructed to match the observations from the trades. The model shows that the observed optically thin cloud behavior is consistent with a power law scaling of cloud optical depth and suggests that most optically thin clouds only partially fill the CALIOP footprint.
Leahy, L. V.↗
Outlook for research on subtropical marine stratiform clouds
A detailed description of the goals and methodology of the First International Satellite Cloud Cover Project Regional Experiment (FIRE) is presented. The purpose of the experiment is to develop physical models and parameterizations of fractional cloud cover over the Pacific Basin. In order to determine fractional cloud cover parameters, satellite observations by radar and lidar instruments will be combined with in situ measurements of the cloud-capped marine boundary layer. A description of a candidate experiment for the program is presented, and some general problems connected with the statistical characterization of satellite imagery are discussed.
Randall, D. A.↗
Self-Supervised Cloud Classification
Abstract Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets. Significance Statement Marine clouds heavily influence Earth’s weather and climate, and improved understanding of marine clouds is required to improve our atmospheric modeling capabilities and physical understanding of the atmosphere. Recently, deep learning has emerged as a powerful research tool that can be used to identify and study specific marine cloud types in the vast number of images collected by Earth-observing satellites. While powerful, these approaches require hand-labeling of training data, which is prohibitively time intensive. This study evaluates a recently developed self-supervised deep learning method that does not require human-labeled training data for processing images of clouds. We show that the trained algorithm performs competitively with algorithms trained on hand-labeled data for image classification tasks. We also discuss potential downstream uses and demonstrate some exciting features of the approach including application to multiple satellite instruments, resilience against changing image brightness, and its learned internal representations of cloud types. The self-supervised technique removes one of the major hurdles for applying deep learning to very large atmospheric datasets.
54 ENVIRONMENTAL SCIENCES↗
The diurnal variability of marine stratocumulus clouds
Observations of the diurnal variation of marine stratocumulus clouds obtained during July 1987 as part of the First ISCCP Regional Experiment are described. The observations show a clear diurnal signature in the structure of the cloud-topped marine boundary layer, with a large variation in the height of the cloud base, with lower heights observed at night. The observational results are numerically simulated using a 1D second-moment turbulence-closure model of Koracin and Rogers (1990) for a hydrostatically incompressible fluid. The model results indicate that the differential heating of the cloud layer promotes mixing in the upper part of the cloud and stabilizes the lower part of the layer.
Rogers, David P.↗
Identifying Meteorological Influences on Marine Low Cloud Mesoscale Morphology Using Satellite Classifications
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.
Johannes Mohrmann↗
An Assessment of Differences Between Cloud Effective Particle Radius Retrievals for Marine Water Clouds from Three MODIS Spectral Bands
The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud product provides three separate 1 km resolution retrievals of cloud particle effective radii (r (sub e)), derived from 1.6, 2.1 and 3.7 micron band observations. In this study, differences among the three size retrievals for maritime water clouds (designated as r (sub e), 1.6 r (sub e), 2.1 and r (sub e),3.7) were systematically investigated through a series of case studies and global analyses. Substantial differences are found between r (sub e),3.7 and r (sub e),2.1 retrievals (delta r (sub e),3.7-2.l), with a strong dependence on cloud regime. The differences are typically small, within +/- 2 micron, over relatively spatially homogeneous coastal stratocumulus cloud regions. However, for trade wind cumulus regimes, r (sub e),3.7 was found to be substantially smaller than r (sub e),2.1, sometimes by more than 10 micron. The correlation of delta r(sub e),3.7-2.1 with key cloud parameters, including the cloud optical thickness (tau), r (sub e) and a cloud horizontal heterogeneity index (H-sigma) derived from 250 m resolution MODIS 0.86 micron band observations, were investigated using one month of MODIS Terra data. It was found that differences among the three r (sub e) retrievals for optically thin clouds (tau <5) are highly variable, ranging from - 15 micron to 10 micron, likely due to the large MODIS retrieval uncertainties when the cloud is thin. The delta r (sub e),3.7-2.1 exhibited a threshold-like dependence on both r (sub e),2.l and H-sigma. The re,3.7 is found to agree reasonably well with re,2.! when re,2.l is smaller than about 15J-Lm, but becomes increasingly smaller than re,2.1 once re,2.! exceeds this size. All three re retrievals showed little dependence when H-sigma < 0.3 (defined as standard deviation divided by the mean for the 250 m pixels within a 1 km pixel retrieval). However, for H-=sigma >0.3, both r (sub e),1.6 and r (sub e),2.1 were seen to increase quickly with H-sigma. On the other hand, r (sub e),3.7 statistics showed little dependence on H-sigma and remained relatively stable over the whole range of H-sigma values. Potential contributing causes to the substantial r (sub e),3.7 and r (sub e),2.1 differences are discussed. In particular, based on both 1-D and 3-D radiative transfer simulations, we have elucidated mechanisms by which cloud heterogeneity and 3-D radiative effects can cause large differences between r (sub e),3.7 and r (sub e),2.l retrievals for highly inhomogeneous clouds. Our results suggest that the contrast in observed delta r (sub e)3.7-2.1 between cloud regimes is correlated with increases in both cloud r (sub e) and H-sigma. We also speculate that in some highly inhomogeneous drizzling clouds, vertical structure induced by drizzle and 3-D radiative effects might operate together to cause dramatic differences between r (sub e),3.7 and r (sub e),2.1 retrievals.
Platnick, Steven↗