Low clouds lie at the heart of climate feedback uncertainties. The representation of clouds in global climate models relies on parameterization of many sub-grid scale processes that are crucial to understanding cloud responses to climate; low clouds in particular exist as a result of tightly coupled microphysical, mesoscale, and synoptic mechanisms. The influence of anthropogenic aerosols on cloud properties could have important ramifications for our understanding of how clouds respond to a changing climate. The VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS REx) sampled the persistent stratocumulus cloud deck located off the coast of Peru and Chile in the southeastern Pacific ocean. Several cloud features found in the stratocumulus deck during VOCALS exhibit signs of interesting aerosol-cloud interactions, including pockets of open cells (POCs). POCs are regions of open-cellular convection surrounded by closed cell stratocumulus, exhibiting not only a marked transition in mesoscale organization and cloud morphology, but also sharp microphysical gradients (especially in droplet concentration) across the boundary between open-cellular and closed cellular convection. In addition, precipitation is often higher at the POC boundaries, hinting at the importance of precipitation in driving their formation. In order to evaluate the microphysical characteristics of POCs prior cloud breakup, we use Lagrangian trajectories coupled with geostationary satellite imagery and cloud retrievals, as well as observational data from VOCALS REx and model data. In three of our case studies, we found regions of anomalously low droplet concentration 18-24 hours prior to POC formation (coupled with liquid water path similar to or higher than surrounding cloud), supporting a precipitation driven mechanism for POC formation. Another group of features with interesting aerosol-cloud interactions observed during VOCALS were mesoscale hook-like features of high droplet concentration which extend far offshore into regions of normally very clean cloud. We use Lagrangian trajectories to investigate the source of the high droplet concentrations of the mesoscale "hooks," and evaluate whether boundary layer transport of coastal pollutants alone can account for their extent. We find that boundary layer trajectories past 85 W do not pass sufficiently close to the coastline to explain high aerosol concentrations offshore.

Marine low clouds are an important feature of the climate system, cooling the planet due to their high albedo and warm temperatures. They display a variety of different mesoscale organizations, which are tied to the varying environmental conditions in which they occur. This dissertation explores the drivers of marine low cloud variability using an observational perspective that draws upon aircraft, satellite, and reanalysis data, and uses the application of a number of machine learning techniques. The focus is largely though not exclusively on mesoscale organization. In the first part of this work, data from the Cloud System Evolution over the Trades (CSET) campaign over the Pacific stratocumulus-to-cumulus transition are organized into 18 Lagrangian cases suitable for study and future modeling, made possible by the use of a track-and-resample flight strategy. Analysis of these cases shows that 2-day Lagrangian coherence of long-lived species (CO and O3) is high (r=0.93 and 0.73, respectively), but that of subcloud aerosol, MBL depth, and cloud properties is limited. Although they span a wide range in meteorological conditions, most sampled air masses show a clear transition when considering 2-day changes in cloudiness (-31%averaged over all cases), MBL depth (1560 m), estimated inversion strength (EIS; 22.2K), and decoupling, agreeing with previous satellite studies and theory. Changes in precipitation and droplet number were less consistent. The aircraft-based analysis is augmented by geostationary satellite retrievals and reanalysis data along Lagrangian trajectories between aircraft sampling times, documenting the evolution of cloud fraction, cloud droplet number concentration, EIS, and MBL depth. An expanded trajectory set spanning the summer of 2015 is used to show that the CSET-sampled air masses were representative of the season, with respect to EIS and cloud fraction. Two Lagrangian case studies attractive for future modeling are presented with aircraft and satellite data. The first features a clear Sc-Cu transition involving MBL deepening and decoupling with decreasing cloud fraction, and the second undergoes a much slower cloud evolution despite a greater initial depth and decoupling state. Potential causes for the differences in evolution are explored, including free-tropospheric humidity, subsidence, surface fluxes, and microphysics. The remaining work focuses on the mesoscale organization of marine low clouds. A convolutional neural network (CNN) model is trained to classify 128 km by 128 km scenes of marine low clouds into six categories: stratus, open-cellular mesoscale cellular convection (MCC), closed-cellular MCC, disorganized MCC, clustered cumulus, and suppressed cumulus. Overall model test accuracy was approximately 90%. This model is applied to three years of data in the southeast Pacific, as well as the 2015 northeast Pacific summer for comparison with the CSET campaign. Meteorological variables related to marine low cloud processes are composited by mesoscale cloud type, allowing for the identification of distinct meteorological regimes. Presentation of MCC is largely consistent with previous literature, both in terms of geographic distribution boundary layer structure, and cloud-controlling factors. The two more novel types, clustered and suppressed cumulus, are examined in more detail. The patterns in precipitation, circulation, column water vapor, and cloudiness are consistent with the presentation of marine shallow mesoscale convective self-aggregation found in previous large eddy simulations of the boundary layer. Although they occur under similar large-scale conditions, the suppressed and clustered low cloud regimes are found to be well-separated by variables associated with a low-level mesoscale circulation, with surface wind divergence being the clearest discriminator between them, whether reanalysis or satellite observations are used. Divergence is consistent with near-surface inflow into clustered regimes and outflow from suppressed regimes. To further understand the dependencies of mesoscale cloud type on environmental factors, a second classification model is built. This uses a random forest of decision trees to predict cloud type, but instead of using an image of a cloud scene, mesoscale averages of meteorological variables are used as inputs. The model uses the three-year dataset output from the CNN model for training, and overall accuracy is approximately 50%. Rotated principal component analysis of the meteorological variables is used to create a set of decorrelated features on which to train the model, allowing for the application of certain statistical analyses which rely on uncorrelated data. Permutation feature importance is used to quantify which variables are most important for correct prediction of cloud mesoscale organization. Overall, temperature and stability are approximately equally important; for correctly distinguishing between open-MCC and closed-MCC, stability is the most important feature, and for correctly distinguishing between suppressed and clustered cumulus, surface divergence is the most important variable. Partial dependence analysis is used to show the relationship between each input variable and the likelihood of observing each cloud type, and 2-dimensional partial dependence analysis shows bimodal distributions of MCC types, consistent with their subtropical and midlatitude incarnations. The random forest model is able to reproduce the geographic distributions of cloud type occurrences.

This dissertation investigates the impacts of meteorological factors and aerosol indirect effects on the costal marine stratocumulus (Sc) variations in the southeast Pacific, a region that has been largely unexplored and is a major challenge of the modeling community, through both observational and numerical studies. This study provides a unique dataset for documenting the characteristics of the marine Sc-topped BL off the coast of Northern Chile. The observational study shows that the boundary layer (BL) over this region was well mixed and topped by a thin and non-drizzling Sc layer on days synoptically-quiescent with little variability between this region and the coast. The surface wind, the surface fluxes and the BL turbulence appeared to be weaker than those over other ocean regions where stratocumulus clouds exist. The weaker turbulence in the BL may contribute to a relatively low entrainment rate calculated from the near cloud top fluxes. This in-situ data set can help us better understand cloud processes within this coastal regime, and also be valuable for the calibration of the satellite retrievals and the evaluation of numerical models operating at a variety of scales. A strong positive correlation between the liquid water path (LWP) and the cloud condensation nuclei (CCN) was observed under similar boundary layer conditions. This correlation cannot be explained by some of the hypotheses based on previous modeling studies. The satellite retrievals obtained upstream one day prior to the flight observations reveal some sign that the clouds under the high CCN concentrations have minimal LWP loss due to precipitation suppression effects. The results from large eddy simulations with a two-momentum bulk microphysics scheme under different idealized environment scenarios based on aircraft observations indicate that 1) the simulated Sc responds more quickly to changes in large-scale subsidence than to those changes in surface fluxes, free-tropospheric humidity, and the BL-top stability; 2) large-scale vertical wind shear clearly induces cloud-top mixing and enhances entrainment rate; 3) the solar radiation could weaken the BL turbulence, reduce the entrainment rate and decouple the BL; and 4) the impact of the reduced cloud sedimentation due to increasing aerosol on the cloud is small.

This book presents descriptions of numerical models for testing cumulus in cloud fields. It is divided into six parts. Part I provides an overview of the problem, including descriptions of cumulus clouds and the effects of ensembles of cumulus clouds on mass, momentum, and vorticity distributions. A review of closure assumptions is also provided. A review of "classical" convection schemes in widespread use is provided in Part II. The special problems associated with the representation of convection in mesoscale models are discussed in Part III, along with descriptions of some of the commonly used mesoscale schemes. Part IV covers some of the problems associated with the representation of convection in climate models, while the parameterization of slantwise convection is the subject of Part V.

This volume presents a series of overview articles arising from a workshop exploring the links among shallow clouds, water vapor, circulation, and climate sensitivity. It provides a state-of-the art synthesis of understanding about the coupling of clouds and water vapor to the large-scale circulation. The emphasis is on two phenomena, namely the self-aggregation of deep convection and interactions between low clouds and the large-scale environment, with direct links to the sensitivity of climate to radiative perturbations. Each subject is approached using simulations, observations, and synthesizing theory; particular attention is paid to opportunities offered by new remote-sensing technologies, some still prospective. The collection provides a thorough grounding in topics representing one of the World Climate Research Program’s Grand Challenges. Previously published in Surveys in Geophysics, Volume 38, Issue 6, 2017 The aritcles “Observing Convective Aggregation”, “An Observational View of Relationships Between Moisture Aggregation, Cloud, and Radiative Heating Profiles”, “Implications of Warm Rain in Shallow Cumulus and Congestus Clouds for Large-Scale Circulations”, “A Survey of Precipitation-Induced Atmospheric Cold Pools over Oceans and Their Interactions with the Larger-Scale Environment”, “Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review”, “Mechanisms and Model Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review”, “Structure and Dynamical Influence of Water Vapor in the Lower Tropical Troposphere”, “Emerging Technologies and Synergies for Airborne and Space-Based Measurements of Water Vapor Profiles”, “Observational Constraints on Cloud Feedbacks: The Role of Active Satellite Sensors”, and “EUREC4A: A Field Campaign to Elucidate the Couplings Between Clouds, Convection and Circulation” are available as open access articles under a CC BY 4.0 license at link.springer.com.