Search NASA⌕ Search

SEARCH · Search NASA

Results for “CLOUDS. RADIATION. MODELING.”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Science support for the Earth radiation budget experiment

The work undertaken as part of the Earth Radiation Budget Experiment (ERBE) included the following major components: The development and application of a new cloud retrieval scheme to assess errors in the radiative fluxes arising from errors in the ERBE identification of cloud conditions. The comparison of the anisotropy of reflected sunlight and emitted thermal radiation with the anisotropy predicted by the Angular Dependence Models (ADM's) used to obtain the radiative fluxes. Additional studies included the comparison of calculated longwave cloud-free radiances with those observed by the ERBE scanner and the use of ERBE scanner data to track the calibration of the shortwave channels of the Advanced Very High Resolution Radiometer (AVHRR). Major findings included: the misidentification of cloud conditions by the ERBE scene identification algorithm could cause 15 percent errors in the shortwave flux reflected by certain scene types. For regions containing mixtures of scene types, the errors were typically less than 5 percent, and the anisotropies of the shortwave and longwave radiances exhibited a spatial scale dependence which, because of the growth of the scanner field of view from nadir to limb, gave rise to a view zenith angle dependent bias in the radiative fluxes.

Coakley, James A., Jr.↗
NASA NTRS2000

Raman Lidar Measurements of Water Vapor and Cirrus Clouds During the Passage of Hurricane Bonnie

The NASA/GSFC Scanning Raman Lidar (SRL) was stationed on Andros Island in the Bahamas during August - September, 1998 as a part of the third Convection and Moisture Experiment (CAMEX-3) which focussed on hurricane development and tracking. During the period August 21 - 24, hurricane Bonnie passed near Andros Island and influenced the water vapor and cirrus cloud measurements acquired by the SRL. Two drying signatures related to the hurricane were recorded by the SRL (Scanning Raman Lidar) and other sensors. Cirrus cloud optical depths (at 351 nm) were also measured during this period. Optical depth values ranged from approximately 0.01 to 1.4. The influence of multiple scattering on these optical depth measurements was studied with the conclusion that the measured values of optical depth are less than the actual value by up to 20% . The UV/IR cirrus cloud optical depth ratio was estimated based on a comparison of lidar and GOES measurements. Simple radiative transfer model calculations compared with GOES satellite brightness temperatures indicate that satellite radiances are significantly affected by the presence of cirrus clouds if IR optical depths are approximately 0.02 or greater. This has implications for satellite cirrus detection requirements.

Whiteman, D. N.↗
NASA NTRS2000

Raman Lidar Measurements of Water Vapor and Cirrus Clouds During the Passage of Hurricane Bonnie

The NASA/GSFC Scanning Raman Lidar (SRL) was stationed on Andros Island in the Bahamas during August - September, 1998 as a part of the third Convection and Moisture Experiment (CAMEX-3) which focussed on hurricane development and tracking. During the period August 21 - 24, hurricane Bonnie passed near Andros Island and influenced the water vapor and cirrus cloud measurements acquired by the SRL. Two drying signatures related to the hurricane were recorded by the SRL and other sensors. Cirrus cloud optical depths (at 351 nm) were also measured during this period. Optical depth values ranged from approximately 0.01 to 1.4. The influence of multiple scattering on these optical depth measurements was studied with the conclusion that the measured values of optical depth are less than the actual value by up to 20%. The UV/lR cirrus cloud optical depth ratio was estimated based on a comparison of lidar and GOES measurements. Simple radiative transfer model calculations compared with GOES satellite brightness temperatures indicate that satellite radiances are significantly affected by the presence of cirrus clouds if IR optical depths are approximately 0.02 or greater. This has implications for satellite cirrus detection requirements.

Whiteman, D. N.↗
NASA NTRS2000

Raman Lidar Measurements of Water Vapor and Cirrus Clouds During The Passage of Hurricane Bonnie

The NASA/GSFC Scanning Raman Lidar (SRL) was stationed on Andros Island in the Bahamas during August - September, 1998 as a part of the third Convection and Moisture Experiment (CAMEX-3) which focussed on hurricane development and tracking. During the period August 21 - 24, hurricane Bonnie passed near Andros Island and influenced the water vapor and cirrus cloud measurements acquired by the SRL. Two drying signatures related to the hurricane were recorded by the SRL and other sensors. Cirrus cloud optical depths (at 351 nm) were also measured during this period. Optical depth values ranged from less than 0.01 to 1.5. The influence of multiple scattering on these optical depth measurements was studied. A correction technique is presented which minimizes the influences of multiple scattering and derives information about cirrus cloud optical and physical properties. The UV/IR cirrus cloud optical depth ratio was estimated based on a comparison of lidar and GOES measurements. Simple radiative transfer model calculations compared with GOES satellite brightness temperatures indicate that satellite radiances are significantly affected by the presence of cirrus clouds if IR optical depths are approximately 0.005 or greater. Using the ISCCP detection threshold for cirrus clouds on the GOES data presented here, a high bias of up to 40% in the GOES precipitable water retrieval was found.

Whiteman, D. N.↗
NASA NTRS2007

Coincident Occurrences of Tropical Individual Cirrus Clouds and Deep Convective Systems Derived from TRMM Observations

Measurements of cloud properties and atmospheric radiation taken between January and August 1998 by the Tropical Rainfall Measuring Mission (TRMM) satellite were used to investigate the effect of spatial and temporal scales on the coincident occurrences of tropical individual cirrus clouds (ICCs) and deep convective systems (DCSs). It is found that there is little or even negative correlation between instantaneous occurrences of ICC and DCS in small areas, in which both types of clouds cannot grow and expand simultaneously. When spatial and temporal domains are increased, ICCs become more dependent on DCSs due to the origination of many ICCs from DCSs and moisture supply from the DCS in the upper troposphere for the ICCs to grow, resulting in significant positive correlation between the two types of tropical high clouds in large spatial and long temporal scales. This result may suggest that the decrease of tropical high clouds with SST from model simulations is likely caused by restricted spatial domains and limited temporal periods. Finally, the radiative feedback due to the change in tropical high cloud area coverage with sea surface temperature appears small and about -0.14 W/sq m per degree Kelvin.

Lin, Bing↗
NASA NTRS2020

Cloud Detection over Snow and Ice with Oxygen A- and B-band Observations from the Earth Polychromatic Imaging Camera (EPIC)

Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and cold/bright surfaces; cloud mask algorithms often heavily rely on shortwave infrared (IR) channels over such surfaces. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) does not have infrared channels, which makes cloud detection over snow and ice surfaces even more challenging. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios in the A band (764, 780 nm) and B band (688, 680 nm) for cloud detection over the snow and ice surfaces. We develop a novel elevation and zenith-angle-dependent threshold scheme based on radiative transfer model simulations that achieves significant improvements over the existing algorithm. When compared against a composite cloud mask based on geosynchronous Earth orbit (GEO) and low Earth orbit (LEO) sensors, the positive detection rate over snow and ice surfaces increased from around 36 % to 65 % while the false detection rate dropped from 50 % to 10 % for observations of January 2016 and 2017. The improvement in July is less substantial due to relatively better performance in the current algorithm. The new algorithm is applicable for all snow and ice surfaces including Antarctic, sea ice, high-latitude snow, and high-altitude glacier regions. This method is less reliable when clouds are optically thin or below 3 km because the sensitivity is low in oxygen band ratios for these cases.

EPIC↗
DOE OSTI2017

Instantaneous Linkages between Clouds and Large-Scale Meteorology over the Southern Ocean in Observations and a Climate Model

Instantaneous, coincident, footprint-level satellite observations of cloud properties and radiation taken during austral summer over the Southern Ocean are used to study relationships between clouds and large-scale meteorology. Cloud properties are very sensitive to the strength of vertical motion in the middle-troposphere, and low-cloud properties are sensitive to estimated inversion strength, low-level temperature advection, and sea surface temperature. These relationships are quantified. An index for the meteorological anomalies associated with midlatitude cyclones is presented, and it is used to reveal the sensitivity of clouds to the meteorology within the warm- and cold-sector of cyclones. The observed relationships between clouds and meteorology are compared to those in the Community Atmosphere Model version 5 (CAM5) using satellite simulators. Low-clouds simulated by CAM5 are too few, too bright, and contain too much ice, and low-clouds located in the cold-sector of cyclones are too sensitive to variations in the meteorology. The latter two biases are dramatically reduced when CAM5 is coupled with an updated boundary layer parameterization know as Cloud Layers Unified by Binormals (CLUBB). More generally, this study demonstrates that examining the instantaneous timescale is a powerful approach to understanding the physical processes that control clouds and how they are represented in climate models. Such an evaluation goes beyond the cloud climatology and exposes model bias under various meteorological conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Building an AI-enhanced modeling framework to address multiscale predictability challenges

Focal Area(s): Build an AI-enhanced modeling framework that integrates the three focus areas in the solicitation. Science Challenge: The 4M-2N complexities (Multiscale, Multiphysics, Multibody, Multidimension, Non-linearity, and Non-Gaussianality) of atmospheric aerosol-cloud-precipitation-turbulence-radiation system poses physical and computational challenges to further advance predictive models; We plan to address the challenges by developing an AI-enhanced modeling framework that facilitates automated calibration and improvement of subgrid parameterizations, enhance data assimilation of measurements to improve initial and boundary conditions used to drive the physical model, and optimally blends data-driven and physics-based forecasting models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Response to marine cloud brightening in a multi-model ensemble

In this paper we show results from Earth system model simulations from the marine cloud brightening experiment G4cdnc of the Geoengineering Model Intercomparison Project (GeoMIP). The nine contributing models prescribe a 50 % increase in the cloud droplet number concentration (CDNC) of low clouds over the global oceans in an experiment dubbed G4cdnc, with the purpose of counteracting the radiative forcing due to anthropogenic greenhouse gases under the RCP4.5 scenario. The model ensemble median effective radiative forcing (ERF) amounts to –1.9 Wm –2 , with a substantial inter-model spread of –0.6 to –2.5 Wm –2 . The large spread is partly related to the considerable differences in clouds and their representation between the models, with an underestimation of low clouds in several of the models. All models predict a statistically significant temperature decrease with a median of (for years 2020–2069) –0.96 [–0.17 to –1.21] K relative to the RCP4.5 scenario, with particularly strong cooling over low-latitude continents. Globally averaged there is a weak but significant precipitation decrease of –2.35 [–0.57 to –2.96]% due to a colder climate, but at low latitudes there is a 1.19 % increase over land. This increase is part of a circulation change where a strong negative top-of-atmosphere (TOA) shortwave forcing over subtropical oceans, caused by increased albedo associated with the increasing CDNC, is compensated for by rising motion and positive TOA longwave signals over adjacent land regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Using High-Resolution NSRDB Data to Evaluate Cloud Mask Forecast from WRF-Solar EPS

Validating spatiotemporal distributions of cloud forecasts using numerical weather prediction (NWP) models is difficult as this requires high-quality cloud-property at significantly high spatial and temporal resolution over extended periods of time. Observations of cloud properties, such as cloud mask, cloud optical thickness, and cloud type, are vital for assessing the capability of NWP models to forecast various types of clouds. Using the National Solar Radiation Database (NSRDB), this research evaluates ensemble cloud-mask predictions from the WRF-Solar ensemble prediction system (WRF-Solar EPS). From the WRF-Solar EPS, day-ahead solar forecasts for the contiguous United States (CONUS) for 2018 are simulated. Given the NSRDB data is accessible at a resolution of 2 km, we can calculate the cloud fraction across the 9-km grid of WRF-Solar EPS. This allows us to spatially assess the cloud-mask forecasts using two methods against the high-resolution NSRDB: (a) considering all 2-km NSRDB clouds in the forecast domain (EMAll), and (b) using a minimum cloud fraction threshold of 50% to designate a pixel as cloudy (EMP50). The low-resolution cloud masks from WRF-Solar EPS are evaluated directly against the cloud-resolving scale gridded observations from NSRDB using EMAll. With EMP50, we presume that scenes with less than 50% cloud cover from the 2-km NSRDB are clear. Thus, this assessment approach allows for a fair comparison with WRF-Solar EPS resolved to a 9-km grid. A method of point-by-point verification is used to evaluate dichotomous (yes/no) cloud mask predictions against the NSRDB. For each pixel of model extent, cloud frequency and traditional metrics (e.g., probability of detection, false alarm rate, and hit rate, etc.) are computed and compared with satellite-derived data sets. Mismatched cloud frequency (MCF) is computed to measure the present capability of WRF-Solar EPS in representing various types of clouds, which are categorized using three levels of cloud top height (CTH) and cloud optical depth (COD) across entire CONUS. Preliminary results show that the WRF-Solar EPS provides MCF values ranging from 9% to 46%, 16% to 33%, and 8% to 27% for low-level, middle-level, and high-level clouds, respectively, for three CTHs. The model produces MCFs ranging from 27% to 46%, 13% to 34%, and 8% to 19% for thin, medium-thickness, and thick clouds, respectively, for three CODs. The presentation will include a detailed description of the current outcomes as well as potential future extensions. The evaluation approach established in this study is readily extensible to the evaluation of cloud predictions from different ensemble NWP models. In addition, the findings of the suggested evaluation technique aid in identifying model weaknesses and will ultimately lead to advances in WRF-Solar EPS's skill in predicting clouds and solar irradiance.

cloud mask forecast↗
DOE OSTI2022

Physics-guided machine learning for improved accuracy of the National Solar Radiation Database

The National Solar Radiation Database (NSRDB) provides high-resolution spatiotemporal solar irradiance data for the entire globe. The NSRDB uses a two-step Physical Solar Model (PSM) to compute the effects of clouds and other atmospheric variables on the solar radiation reaching the surface of the Earth. Physical and optical cloud properties are fundamental inputs to the PSM and are derived from the National Oceanic and Atmospheric Administration's Geostationary Operational Environmental Satellites. This paper describes recent improvements to the NSRDB driven by physics-guided machine learning methods for cloud property retrieval. The impacts of these new methods on the NSRDB irradiance data are validated using an extensive set of ground measurement sites, showing significant improvement for all sites. We report on average, the mean absolute percentage error for global horizontal irradiance and direct normal irradiance show reductions of 2.16 and 3.95 percentage points respectively for all daylight conditions, 5.92 and 17.39 percentage points respectively for cloudy conditions, and 9.00 and 22.59 percentage points respectively for gap-filled cloudy conditions. These new methods will help improve the quality and accuracy of the irradiance and cloud data in the NSRDB.

14 SOLAR ENERGY↗
NASA NTRS1990

Calculations of atmospheric radiative flux profiles

Results are presented of experiments with a diagnostic approach to the radiation budget by using all available data sets to describe the properties of the atmosphere, clouds, and surface at each time and place, together with a radiative transfer model that calculates the radiative fluxes using this information. Each parameter in the calculations is represented explicitly, so that improved data sets can be easily substituted. The model and data are combined to calculate profiles of radiative fluxes, including individual flux components, heating/cooling rates, as well as top-of-atmosphere and surface radiation budget components. The advantage of this explicit approach is that all contributions to the radiation budget can be diagnosed separately; in particular, cloud-radiative feedbacks can be isolated from the effects of other atmospheric and surface changes.

Rossow, William B.↗
NASA NTRS2007

The Plane-parallel Albedo Bias of Liquid Clouds from MODIS Observations

In our most advanced modeling tools for climate change prediction, namely General Circulation Models (GCMs), the schemes used to calculate the budget of solar and thermal radiation commonly assume that clouds are horizontally homogeneous at scales as large as a few hundred kilometers. However, this assumption, used for convenience, computational speed, and lack of knowledge on cloud small scale variability, leads to erroneous estimates of the radiation budget. This paper provides a global picture of the solar radiation errors at scales of approximately 100 km due to warm (liquid phase) clouds only. To achieve this, we use cloud retrievals from the instrument MODIS on the Terra and Aqua satellites, along with atmospheric and surface information, as input into a GCM-style radiative transfer algorithm. Since the MODIS product contains information on cloud variability below 100 km we can run the radiation algorithm both for the variable and the (assumed) homogeneous clouds. The difference between these calculations for reflected or transmitted solar radiation constitutes the bias that GCMs would commit if they were able to perfectly predict the properties of warm clouds, but then assumed they were homogeneous for radiation calculations. We find that the global average of this bias is approx.2-3 times larger in terms of energy than the additional amount of thermal energy that would be trapped if we were to double carbon dioxide from current concentrations. We should therefore make a greater effort to predict horizontal cloud variability in GCMs and account for its effects in radiation calculations.

Oreopoulos, Lazaros↗
DOE OSTI2022

Polar Cloud Microphysics and Surface Energy Budget from AWARE (Final Technical Report)

This was a collaborative project between the Scripps Institution of Oceanography at the University of California San Diego (SIO), Byrd Polar and Climate Research Center at The Ohio State University (BPCRC), The Department of Meteorology and Atmospheric Science at The Pennsylvania State University (Penn State), and Brookhaven National Laboratory (BNL). The project’s over-arching objectives involved maximizing the scientific potential of the ARM West Antarctic Radiation Experiment (AWARE) data set, in two respects: (1) application of polar-optimized climate models to test the most current and comprehensive cloud microphysical parameterizations; and (2) analysis of the most advanced ARM Mobile Facility sensors (e.g., cloud radar, spectroradiometer, and aerosol observing system data) to advance understanding of Antarctic cloud microphysical and radiative properties, particularly on a climatological basis with emphasis on contrasts with the high Arctic. The team’s efforts have resulted in 16 peer-review publications, in addition to an overview of the AWARE campaign in the Bulletin of the American Meteorological Society. Our emphasis throughout this program was to go beyond the traditional “case study” approach to model evaluation, in which a “classical” or otherwise well-characterized surface-atmosphere-cloud system of short duration is simulated by several climate models. Instead, we attempted analysis of longer time series in the AWARE data, both climatologically and with models. We have numerous successful results from both AWARE locations: The main AMF-2 deployment at McMurdo Station on Ross Island (13 months duration), and the extended facility at the West Antarctic Ice Sheet (WAIS) Divide Ice Camp (40 days during austral summer 2015-16).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Interactions Between Asian Air Pollution and Monsoon System: South Asia (ROSES-2014 ACMAP)

Asia's rapid economic growth over the past several decades has brought a remarkable increase in air pollution levels in that region. High concentrations of aerosols (also known as particulate matter or PM) from pollution sources pose major health hazards to half of the world population in Asia including South Asia. How do pollution and dust aerosols regulate the monsoon circulation and rainfall via scattering and absorbing solar radiation, changing the atmospheric heating rates, and modifying the cloud properties? We conducted a series of regional model experiments with NASA-Unified Weather Research and Forecast (NUWRF) regional model with coupled aerosol-chemistry-radiation-microphysics processes over South Asia for winter, pre-monsoon, and monsoon seasons to address this question. This study investigates the worsening air quality problem in South Asia by focusing on the interactions between pollution and South Asian monsoon, not merely focusing on the increase of pollutant emissions.

regional model experiments with NASA-Unified Weath↗
NASA NTRS1977

The atmosphere of Saturn - An infrared perspective

A model of the radiative portion of the equatorial atmosphere of Saturn, constrained by the infrared data of various observers, has been constructed using a technique which includes the variation of thermal flux with depth. The model has a high-altitude temperature inversion due to the absorption of ultraviolet sunlight. The inversion causes the observed infrared emission peaks at 8 microns (methane) and 12 microns (ethane). Mixing ratios of these gases to hydrogen are computed from these emission features. The bottom of the modeled region occurs at the radiative convective boundary. At this level, an opaque cloud consisting of solid ammonia condensation particles is postulated. Above the cloud is a thin haze, also composed of ammonia particles. The haze is required to match infrared observations near 9.5 microns and hydrogen quadrupole equivalent widths near 0.64 micron. Predictions of the model are given for further observational tests.

Caldwell, J.↗
DOE OSTI2022

Connecting the Radiative Influences of Aerosol upon the Mass Flux Profiles of Shallow Cumuli across the Southeast Atlantic Ocean Basin and its Boundaries (Final Report)

The Atlantic Ocean covers approximately 25% of Earth’s surface and the atmosphere above it is home to a complex array of clouds and aerosols that have important influences on regional and global weather and climate. These influences must be accurately depicted in short range, medium range, seasonal, and climate forecast models. Conditions over the tropical Atlantic are particularly complex due to continental scale plumes of dust from the Sahara Desert and smoke from agricultural burning in Africa that drift across the Atlantic Ocean basin toward the Americas. These plumes are often found meandering in the lower atmosphere above shallow tropical clouds that form above the ocean surface, presumably mingling with these clouds on occasion due to convective mixing processes. Elevated dust and smoke particles absorb incoming sunlight and substantially warm the marine atmosphere in the layer in which they are present. This warming may alter the thermal stability of the marine atmosphere and may throttle or enhance the development of clouds, change their internal structure and the rate at which they precipitate. Alternatively, it may isolate the lower atmosphere from drier layers above enabling water vapor to accumulate near the ocean surface potentially leading to the development of deeper convection. Our study was organized around the principal concept of determining the impact of African smoke and dust plumes upon cloud development at ASI and understanding how a popular shallow convection parameterization used in models responds to the presence of this aerosol. We analyzed observations collected during the US Department of Energy (DOE) Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign using the Atmospheric Radiation Measurement Program’s Mobile Facility #1 (AMF-1), which was deployed to Ascension Island (ASI) in Southeastern Atlantic for a one-year period. Ascension Island is immediately downwind from an African source of these plumes, but far enough removed to enable the lower atmosphere to have reacted to their presence. A main goal of our study was to compute radiative heating rate profiles over Ascension Island and along the trajectory from the biomass burning regions along coastal Africa to Ascension Island. To compute the radiative heating rate due to aerosols and clouds, we employed observed profiles of temperature, humidity, and clouds from LASIC alongside aerosol optical properties from the Modern-era Retrospective analysis for Research and Applications Version 2 (MERRA-2), as input for the Rapid Radiation Transfer Model (RRTM). Radiative heating was also assessed across the southeast Atlantic Ocean using an ensemble of back trajectories from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We were successful in this effort and the resulting publication is already being cited despite its relatively short lifetime in the literature. The second part of our study involved the process-level representation of the clouds observed over the Southeastern Atlantic in models. Our initial task, upon which the balance of this portion of the study depended, was to evaluate the representativeness of the clouds observed by AMF-1. What orographic influence did Ascension Island have on the measured cloud structure? We set out to answer this question by trying to separate observations that were clearly indicative of orographic forcing from those that were representative of open ocean. The siting of AMF-1, which was 340-m above sea-level on the slope of a steep escarpment, proved demonstrably problematic for cloud process measurements. Despite considerable effort using multiple approaches including artificial intelligence (AI), we were unable to successfully compensate for the orographic forcing present at the AMF-1 site on ASI and, hence, produce process-level summaries of the convective mass flux representative of open ocean over the Southeastern Atlantic. Even though the project has officially ended, we are making a final attempt using two new types of clustering algorithm (i.e., AI) on the recommendation of a former student, who is an expert in this area. Since we have only recently begun to test these new AI schemes, the recommendations from the second part of our project outlined below are based on our experience at the time of this report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Baselining the Indirect Effect by Improving Quantification of Sea Spray and Marine Sources at Ascension Island (Final Report)

Oceans cover two-thirds of the Earth and understanding the interactions of aerosols with clouds in these large marine regions requires quantifying the man-made contributions to the budget of cloud-drop forming particles (known as cloud condensation nuclei, or CCN) relative to the non-manmade “baseline” conditions and understanding the meteorology of boundary layer clouds. Modeling studies have shown substantial uncertainties and sensitivities to natural marine CCN sources, meaning that to reduce uncertainties in indirect effects we must be able to better quantify the CCN budget in ocean regions. While models provide important constraints on these uncertainties, actually reducing uncertainties requires substantial observations in open-ocean and coastal regions in order to establish the baseline on which manmade emissions are added. The tropical South Atlantic Ocean is one of the least-sampled regions of the planet, making the comprehensive measurements of the Department of Energy Atmospheric Radiation Measurement Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign provides the longest record of aerosol size distribution measurements from a differential mobility analyzer in a cloud-influenced marine location in the ARM database, with 17 months of ground-based aerosol size distribution measurements. This project addressed the research topic of Aerosol-Cloud Interactions (ACI) by supporting three publications from the LASIC measurements: 1. The ARM measurements were combined with a new value-added technique that was pioneered by the Russell group for quantifying sea salt. This quantification of sea salt uses supermicron scattering measurements for retrieving reasonable sea spray mass concentrations, providing the best-available, observationally-constrained estimate of the sea spray mode properties when supermicron size distribution measurements are not available. 2. The fitted modes of the distribution were used to investigate the signatures of cloud processing for very clean to very smoky aerosol conditions, revealing not only differences in the particles that activate in clouds but also in the mechanisms that control that droplet formation process. In clean air, the size required to form a cloud droplet is influenced by the number of particles, as well as how quickly particles take up water during growth in cloud. 3. Building on this aerosol characterization, cloud and meteorological conditions were used to evaluate aerosol-related changes in cloud albedo and optical depth. We introduced a new method of decomposing the impact of aerosols on clouds known as the Twomey effect by incorporating retrieved supersaturation from two independent sets of observations to constrain the feedback of aerosol particles on cloud properties. The method quantifies the reduction of the Twomey effect at high aerosol concentrations, which has never been explained quantitatively by observations. In addition, the results provide the first direct validation for the conditions observed in the tropical South Atlantic of a parcel-based approach that is embedded in many climate models. Together these findings illustrate how ARM extended field campaigns in stratocumulus-covered regions can be used to constrain ACI processes with direct observations, providing specific radiative effects without models. By making such process-specific constraints available to improve ACI in global climate models, ARM observations play a key role in supporting model development.

58 GEOSCIENCES↗