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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Sensitivity of Anthropogenic Cloud Droplet Number Change to Preindustrial Emission Inventories and Physics Parameterizations

Understanding the preindustrial (PI) to present-day (PD) change in cloud droplet number concentration (ΔNd) is important to constrain the anthropogenic influence on clouds and aid future model projections. Perturbed parameter ensemble simulations of PI and PD conditions in the Energy Exascale Earth System Model (E3SMv3) reveal two locations with consistent negative ΔNd signal across parameter space–the Southeastern US and the United Kingdom–despite a trend of positive ΔNd globally from increased fossil fuel consumption. These negative ΔNd signals are driven by higher anthropogenic biofuel emissions in the PI. By varying the PI aerosol mass and/or number emissions across these data sets' uncertainty ranges, we can change the magnitude and sign of ΔNd and the effective radiative forcing from aerosol-cloud interactions (ERFaci) regionally and globally, with near-zero ΔNd leading to near-zero ERFaci. These results highlight the need to constrain PI emission uncertainty to better understand Earth system responses to aerosol-cloud interactions.

Brown, Hunter Y. [University of Wyoming, Laramie, ↗
NASA NTRSDate not supplied

A Neural Network Parametrization of Volumetric Cloud Fraction Profiles Using Satellite Observations and MERRA-2 Reanalysis Meteorological Data

Clouds play a crucial role in regulating the hydrologic cycle and Earth's radiative energy budget, yet they are often poorly represented in global climate models (GCMs). This study applies deep machine learning techniques to develop a physical parameterization of volumetric cloud fraction (VCF), the fraction of a 3-D grid volume occupied by clouds using satellite lidar-radar measurements. The neural network (NN) captures the complicated relationships between observed VCF profiles and collocated meteorological variables from MERRA-2 reanalysis data. Our results show that the NN model, particularly a sequence-to-sequence long short-term memory (LSTM) network with a sixfactor loss function, effectively learns the underlying cloud physical processes. The NN model outperforms MERRA-2 reanalysis in representing low-level clouds in tropical and subtropical regions and low- and middle-level clouds over midlatitude storm-track regions, and also improves VCF histograms. These improvements are reflected in the vertical distributions of zonally, meridionally, and globally averaged VCFs, geographic distributions of low-, middle-, and high-level clouds, and seasonal variations in monthly-mean VCF. Furthermore, the NN predictions effectively capture the El Niño-Southern Oscillation (ENSO) effects and other interannual variations. The NN parameterization is further evaluated through a sensitivity analysis, in which a single predictor is perturbed at a time. This reveals that relative humidity (RH) is the dominant factor influencing variations in globally averaged VCF at low and middle altitudes, followed by temperature. At higher altitudes, temperature becomes the primary driver of VCF through its effect on RH. Changes in wind components had minimal impact on globally averaged VCF.

Shan Zeng↗
DOE OSTI2019

Surface‐Atmosphere Coupling Scale, the Fate of Water, and Ecophysiological Function in a Brazilian Forest

Abstract Tropical South America plays a central role in global climate. Bowen ratio teleconnects to circulation and precipitation processes far afield, and the global CO 2 growth rate is strongly influenced by carbon cycle processes in South America. However, quantification of basin‐wide seasonality of flux partitioning between latent and sensible heat, the response to anomalies around climatic norms, and understanding of the processes and mechanisms that control the carbon cycle remains elusive. Here, we investigate simulated surface‐atmosphere interaction at a single site in Brazil, using models with different representations of precipitation and cloud processes, as well as differences in scale of coupling between the surface and atmosphere. We find that the model with parameterized clouds/precipitation has a tendency toward unrealistic perpetual light precipitation, while models with explicit treatment of clouds produce more intense and less frequent rain. Models that couple the surface to the atmosphere on the scale of kilometers, as opposed to tens or hundreds of kilometers, produce even more realistic distributions of rainfall. Rainfall intensity has direct consequences for the “fate of water,” or the pathway that a hydrometeor follows once it interacts with the surface. We find that the model with explicit treatment of cloud processes, coupled to the surface at small scales, is the most realistic when compared to observations. These results have implications for simulations of global climate, as the use of models with explicit (as opposed to parameterized) cloud representations becomes more widespread.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Differences in Ice Cloud Optical Depth From CALIPSO and Ground-Based Raman Lidar at the ARM SGP and TWP Sites

Ice cloud column optical depths from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite and ground-based Raman lidars (RLs) at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) and Tropical Western Pacific (TWP) sites are compared for 8 and 4 years, respectively. The cloudy mean ice cloud column optical depths from CALIPSO Version 4 data product with a horizontal resolution of 5 km are 78% (63%) larger than those from the RLs with a resolution of 10 min/30 m at SGP (TWP) for collocated transparent profiles. The main difference at SGP is caused by the lidar ratio that for CALIPSO is related to its treatment of the multiple scattering factor. The main difference at TWP is caused by the averaging resolution. Large differences in the optical depth distribution between the CALIPSO and RL are found for small optical depths at both sites, which are due to optically very thin clouds only detectable by the RLs. The differences in ice cloud column optical depth distributions and their mean values between the CALIPSO and RL can largely be reconciled over both sites after accounting for the averaging resolutions, lidar ratios along with the CALIPSO multiple scattering factor treatment, sensitivity to optically very thin ice clouds, and definition of transparent profiles. This work also examines in detail the lidar ratios that are directly observed by the RLs, which does not support the temperature-dependent parameterizations of ice cloud lidar ratio and multiple scattering factor used in CALIPSO's Version 4 data product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1983

Effects of high altitude clouds on the earth's infrared radiation flux

Attention is given to the results of a study of cirrus cloud properties which employed the Goddard Laboratory for Atmospheric Sciences' general circulation model and concentrated on the effects of the nonblackness of high clouds on the IR radiation flux. Although the thermal radiation flux is very sensitive to the treatment of cirrus optical properties in the IR, a more realistic assessment will depend on better parameterizations for cirrus cloud formation, persistence, and dissipation.

Wang, W.-C.↗
NASA NTRS1995

Preliminary analysis of University of North Dakota aircraft data from the FIRE Cirrus IFO-2

The stated goals of the First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment (FIRE) are 'to promote the development of improved cloud and radiation parameterization for use in climate models, and to provide for assessment and improvement of ISCCP projects'. FIRE Phase 2 has focused on the formation, maintenance and dissipation of cirrus and marine stratocumulus cloud systems. These objectives have been approached through a combination of modeling, extended-time observations and intensive field observation (IFO) periods. The work under this grant was associated with the FIRE Cirrus IFO 2. This field measurement program was conducted to obtain observations of cirrus cloud systems on a range of scales from the synoptic to the microscale, utilizing simultaneous measurements from a variety of ground-based, satellite and airborne platforms. By combining these remote and in situ measurements a more complete picture of cirrus systems can be obtained. The role of the University of North Dakota in Phase 2 was three-fold: to collect in situ microphysical data during the Cirrus IFO 2; to process and archive these data; and to collaborate in analyses of IFO data. This report will summarize the activities and findings of the work performed under this grant; detailed description of the data sets available and of the analyses are contained in the Semi-annual Status Reports submitted to NASA.

Poellot, Michael R.↗
NASA NTRS2009

Distribution and Radiative Forcing of Tropical Thin Cirrus Clouds

To understand the radiative impact of tropical thin cirrus clouds, the frequency of occurrence and optical depths of these clouds have been derived. Thin cirrus clouds are defined here as being those that are not detected by the operational Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask, corresponding to an optical depth value of approximately 0.3 or smaller, but that are detectable in terms of the cirrus reflectance product based on the MODIS 1.375-micron channel. With such a definition, thin cirrus clouds were present in more than 40% of the pixels flagged as clear sky by the operational MODIS cloud mask algorithm. It is shown that these thin cirrus clouds are frequently observed in deep convective regions in the western Pacific. Thin cirrus optical depths were derived from the cirrus reflectance product. Regions of significant cloud fraction and large optical depths were observed in the Northern Hemisphere during the boreal spring and summer and moved southward during the boreal autumn and winter. The radiative effects of tropical thin cirrus clouds were studied on the basis of the retrieved cirrus optical depths, the atmospheric profiles derived from the Atmospheric Infrared Sounder (AIRS) observations, and a radiative transfer model in conjunction with a parameterization of ice cloud spectral optical properties. To understand how these clouds regulate the radiation field in the atmosphere, the instantaneous net fluxes at the top of the atmosphere (TOA) and at the surface were calculated. The present study shows positive and negative net forcings at the TOA and at the surface, respectively. The positive (negative) net forcing at the TOA (surface) is due to the dominance of longwave (shortwave) forcing. Both the TOA and surface forcings are in a range of 0-20 W/sq m, depending on the optical depths of thin cirrus clouds.

THIN CIRRUS CLOUDS↗
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↗
NASA NTRS1990

An algorithm to estimate the heating budget from vertical hydrometeor profiles

A simple algorithm to estimate the latent heating of cloud systems from their vertical hydrometeor profiles is proposed. The derivation as well as the validation of the algorithm is based on output generated by a nonhydrostatic cloud model with parameterized microphysical processes. Mature and decaying stages of a GATE squall-type convective system have been tested. The algorithm-derived heating budget is in reasonable agreement with the budget predicted by the cloud model. The input to the proposed algoritm can be obtained from either a rain retrieval technique based on information from multichannel passive microwave signals or a kinematic cloud model based on information from Doppler radar wind fields and radar reflectivity patterns. Such an application would have significant implications for spaceborne remote sensing and the large-scale weather prediction data assimilation problem.

Tao, Wei-Kuo↗
NASA NTRS2005

Parameterizations of the Vertical Variability of Tropical Cirrus Cloud Microphysical and Optical Properties

Our multi-investigator effort was targeted at the following areas of interest to CRYSTAL-FACE: (1) the water budgets of anvils, (2) parameterizations of the particle size distributions and related microphysical and optical properties (3) characterizations of the primary ice particle habits, (4) the relationship of the optical properties to the microphysics and particle habits, and (5) investigation of the ice-nuclei types and mechanisms in anvil cirrus. Dr. Twohy's effort focused on (l), (2), and (5), with the measurement and analysis of ice water content and cirrus residual nuclei using the counterflow virtual impactor (CVI).

Twohy, Cynthia↗
NASA NTRS2008

Summertime Coincident Observations of Ice Water Path in the Visible/Near-IR, Radar, and Microwave Frequencies

Accurate representation of the physical and radiative properties of clouds in climate models continues to be a challenge. At present, both remote sensing observations and modeling of microphysical properties of clouds rely heavily on parameterizations or assumptions on particle size distribution (PSD) and cloud phase. In this study, we compare Ice Water Path (IWP), an important physical and radiative property that provides the amount of ice present in a cloud column, using measurements obtained via three different retrieval strategies. The datasets we use in this study include Visible/Near-IR IWP from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument flying aboard the Aqua satellite, Radar-only IWP from the CloudSat instrument operating at 94 GHz, and NOAA/NESDIS operational IWP from the 89 and 157 GHz channels of the Microwave Humidity Sounder (MHS) instrument flying aboard the NOAA-18 satellite. In the Visible/Near-IR, IWP is derived from observations of optical thickness and effective radius. CloudSat IWP is determined from measurements of cloud backscatter and assumed PSD. MHS IWP retrievals depend on scattering measurements at two different, non-water absorbing channels, 89 and 157 GHz. In order to compare IWP obtained from these different techniques and collected at different vertical and horizontal resolutions, we examine summertime cases in the tropics (30S - 30N) when all 3 satellites are within 4 minutes of each other (approximately 1500 km). All measurements are then gridded to a common 15 km x 15 km box determined by MHS. In a grid box comparison, we find CloudSat to report the highest IWP followed by MODIS, followed by MHS. In a statistical comparison, probability density distributions show MHS with the highest frequencies at IWP of 100-1000 g/m(exp 2) and CloudSat with the longest tail reporting IWP of several thousands g/m(exp 2). For IWP greater than 30 g/m(exp 2), MODIS is consistently higher than CloudSat, and it is higher at the lower IWPs but lower at the higher IWPs that overlap with MHS. Some of these differences can be attributed to the limitations of the measuring techniques themselves, but some can result from the assumptions made in the algorithms that generate the IWP product. We investigate this issue by creating categories based on various conditions such as cloud type, precipitation presence, underlying liquid water content, and surface type (land vs. ocean) and by comparing the performance of the IWP products under each condition.

Pittman, Jasna V.↗
DOE OSTI2018

Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method

Traditional trial-and-error tuning of uncertain parameters in global atmospheric general circulation models (GCMs) is time consuming and subjective. This study explores the feasibility of automatic optimization of GCM parameters for fast physics by using short-term hindcasts. An automatic workflow is described and applied to the Community Atmospheric Model (CAM5) to optimize several parameters in its cloud and convective parameterizations. We show that the auto-optimization leads to 10 % reduction of the overall bias in CAM5, which is already a well-calibrated model, based on a predefined metric that includes precipitation, temperature, humidity, and longwave/shortwave cloud forcing. The computational cost of the entire optimization procedure is about equivalent to a single 12-year atmospheric model simulation. The tuning reduces the large underestimation in the CAM5 longwave cloud forcing by decreasing the threshold relative humidity and the sedimentation velocity of ice crystals in the cloud schemes; it reduces the overestimation of precipitation by increasing the adjustment time in the convection scheme. The physical processes behind the tuned model performance for each targeted field are discussed. Limitations of the automatic tuning are described, including the slight deterioration in some targeted fields that reflect the structural errors of the model. It is pointed out that automatic tuning can be a viable supplement to process-oriented model evaluations and improvement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Automatic tuning of the Community Atmospheric Model CAM5.3 by using short-term hindcasts with an improved downhill simplex optimization method

Traditional trial-and-error tuning of uncertain parameters in global atmospheric General Circulation Models (GCM) is time consuming and subjective. This study explores the feasibility of automatic optimization of GCM parameters for fast physics by using short-term hindcasts. An automatic workflow is described and applied to the Community Atmospheric Model (CAM5) to optimize several parameters in its cloud and convective parameterizations. We show that the auto-optimization leads to 10 % reduction of the overall bias in CAM5, which is already a well calibrated model, based on a pre-defined metric that includes precipitation, temperature, humidity, and longwave/shortwave cloud forcing. The computational cost of the entire optimization procedure is about equivalent to about a single 12-year atmospheric model simulation. The tuning reduces the large underestimation in the CAM5 longwave cloud forcing by decreasing the threshold relative humidity and the sedimentation velocity of ice crystals in the cloud schemes; it reduces the overestimation of precipitation by increasing the adjustment time in the convection scheme. The physical processes behind the tuned model performance for each targeted field are discussed. Limitations of the automatic tuning are described, including the slight deterioration in some targeted fields that reflect the structural errors of the model. It is pointed out that automatic tuning can be a viable supplement to process-oriented model evaluations and improvement.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

The MJO Transition from Shallow to Deep Convection in CloudSat/CALIPSO Data and GISS GCM Simulations

The relationship between convective penetration depth and tropospheric humidity is central to recent theories of the Madden-Julian oscillation (MJO). It has been suggested that general circulation models (GCMs) poorly simulate the MJO because they fail to gradually moisten the troposphere by shallow convection and simulate a slow transition to deep convection. CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data are analyzed to document the variability of convection depth and its relation to water vapor during the MJO transition from shallow to deep convection and to constrain GCM cumulus parameterizations. Composites of cloud occurrence for 10MJO events show the following anticipatedMJO cloud structure: shallow and congestus clouds in advance of the peak, deep clouds near the peak, and upper-level anvils after the peak. Cirrus clouds are also frequent in advance of the peak. The Advanced Microwave Scanning Radiometer for EarthObserving System (EOS) (AMSR-E) columnwater vapor (CWV) increases by;5 mmduring the shallow- deep transition phase, consistent with the idea of moisture preconditioning. Echo-top height of clouds rooted in the boundary layer increases sharply with CWV, with large variability in depth when CWV is between;46 and 68 mm. International Satellite Cloud Climatology Project cloud classifications reproduce these climatological relationships but correctly identify congestus-dominated scenes only about half the time. A version of the Goddard Institute for Space Studies Model E2 (GISS-E2) GCM with strengthened entrainment and rain evaporation that produces MJO-like variability also reproduces the shallow-deep convection transition, including the large variability of cloud-top height at intermediate CWV values. The variability is due to small grid-scale relative humidity and lapse rate anomalies for similar values of CWV. 1.

DelGenio, Anthony G.↗
NASA NTRS1990

Comparison of 30 day integrations with and without interactive clouds

A cloud radiation interaction parameterization package has recently been incorporated into a global spectral General Circulation Model (GCM) used for extended range prediction studies at GFDL. The elements of this package are summarized. Analysis of the time mean radiative and dynamical responses suggests that cloud radiation interaction has a favorable impact, overall, on systematic errors. The possible relevance of this sensitivity study to FIRE is mentioned.

Gordon, C. T.↗
DOE OSTI2022

Automated Identification of Characteristic Droplet Size Distributions in Stratocumulus Clouds Utilizing a Data Clustering Algorithm

Abstract Droplet-level interactions in clouds are often parameterized by a modified gamma fitted to a “global” droplet size distribution. Do “local” droplet size distributions of relevance to microphysical processes look like these average distributions? This paper describes an algorithm to search and classify characteristic size distributions within a cloud. The approach combines hypothesis testing, specifically, the Kolmogorov–Smirnov (KS) test, and a widely used class of machine learning algorithms for identifying clusters of samples with similar properties: density-based spatial clustering of applications with noise (DBSCAN) is used as the specific example for illustration. The two-sample KS test does not presume any specific distribution, is parameter free, and avoids biases from binning. Importantly, the number of clusters is not an input parameter of the DBSCAN-type algorithms but is independently determined in an unsupervised fashion. As implemented, it works on an abstract space from the KS test results, and hence spatial correlation is not required for a cluster. The method is explored using data obtained from the Holographic Detector for Clouds (HOLODEC) deployed during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The algorithm identifies evidence of the existence of clusters of nearly identical local size distributions. It is found that cloud segments have as few as one and as many as seven characteristic size distributions. To validate the algorithm’s robustness, it is tested on a synthetic dataset and successfully identifies the predefined distributions at plausible noise levels. The algorithm is general and is expected to be useful in other applications, such as remote sensing of cloud and rain properties. Significance Statement A typical cloud can have billions of drops spread over tens or hundreds of kilometers in space. Keeping track of the sizes, positions, and interactions of all of these droplets is impractical, and, as such, information about the relative abundance of large and small drops is typically quantified with a “size distribution.” Droplets in a cloud interact locally, however, so this work is motivated by the question of whether the cloud droplet size distribution is different in different parts of a cloud. A new method, based on hypothesis testing and machine learning, determines how many different size distributions are contained in a given cloud. This is important because the size distribution describes processes such as cloud droplet growth and light transmission through clouds.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2006

A Mission to Observe Ice in Clouds from Space

To date there have been multiple satellite missions to observe and retrieve cloud top properties and the liquid in, and precipitation from, clouds. There are currently a few missions that attempt to measure cloud ice properties as a byproduct of other observations. However, we do not yet quantitatively understand the processes that control the water budget of the upper troposphere where ice is the predominant phase, and how these processes are linked to precipitation processes and the radiative energy budget. The ice in clouds either melts into rain or is detrained, and persists, as cirrus clouds affecting the hydrological and energy cycle, respectively. Fully modeling the Earth's climate and improving weather and climate forecasts requires accurate satellite measurements of various cloud properties at the temporal and spatial scales of cloud processes. The uncertainty in knowledge of these ice characteristics is reflected in the large discrepancies in model simulations of the upper tropospheric water budget. Model simulations are sensitive to the partition of ice between precipitation and outflow processes, i.e., to the parameterization of ice clouds and ice processes. This presentation will describe the Submillimeter-wave InfraRed Ice Cloud Experiment (SIRICE) concept, a satellite mission designed to acquire global Earth radiance measurements in the infrared and submillimeter-wave region (183-874 GHz). If successful, this mission will bridge the measurement gap between microwave sounders and shorter-wavelength infrared and visible sensors. The brightness temperatures at submillimeter-wave frequencies are especially sensitive to cirrus ice particle sizes (because they are comparable to the wavelength). This allows for more accurate ice water path estimates when multiple channels are used to probe into the cloud layers. Further, submillimeter wavelengths offer simplicity in the retrieval algorithms because they do not probe into the liquid and near surface portions of clouds, thus requiring only one term of the radiative transfer equation (ice scattering) to relate brightness temperatures to ice. Scientific justification and the SIRICE approach to measuring ice water path and particle size that span a range encompassing both the hydrologically active and radiatively active components of cloud systems will be presented.

Ackerman, S.↗