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DOE OSTI2022

Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network

Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-based remote-sensing instruments still poses observational challenges, yet improvements are crucial since the existence of multi-layer liquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud lifetime, and precipitation formation processes. Hydrometeor target classifications such as from Cloudnet that require a lidar signal for the classification of liquid are limited to the maximum height of lidar signal penetration and thus often lead to underestimations of liquid-containing cloud layers. Here we evaluate the Cloudnet liquid detection against the approach of Luke et al. (2010) which extracts morphological features in cloud-penetrating cloud radar Doppler spectra measurements in an artificial neural network (ANN) approach to classify liquid beyond full lidar signal attenuation based on the simulation of the two lidar parameters particle backscatter coefficient and particle depolarization ratio. We show that the ANN of Luke et al. (2010) which was trained under Arctic conditions can successfully be applied to observations at the mid-latitudes obtained during the 7-week-long ACCEPT field experiment in Cabauw, the Netherlands, in 2014. In a sensitivity study covering the whole duration of the ACCEPT campaign, different liquid-detection thresholds for ANN-predicted lidar variables are applied and evaluated against the Cloudnet target classification. Independent validation of the liquid mask from the standard Cloudnet target classification against the ANN-based technique is realized by comparisons to observations of microwave radiometer liquid-water path, ceilometer liquid-layer base altitude, and radiosonde relative humidity. In addition, a case-study comparison against the cloud feature mask detected by the space-borne lidar aboard the CALIPSO satellite is presented. Three conclusions were drawn from the investigation. First, it was found that the threshold selection criteria of liquid-related lidar backscatter and depolarization alone control the liquid detection considerably. Second, all threshold values used in the ANN framework were found to outperform the Cloudnet target classification for deep or multi-layer cloud situations where the lidar signal is fully attenuated within low liquid layers and the cloud radar is able to detect the microphysical fingerprint of liquid in higher cloud layers. Third, if lidar data are available, Cloudnet is at least as good as the ANN. The times when Cloudnet outperforms the ANN in liquid detections are often associated with situations where cloud dynamics smear the imprint of cloud microphysics on the radar Doppler spectra.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2006

Improved Thin Cirrus and Terminator Cloud Detection in CERES Cloud Mask

Thin cirrus clouds account for about 20-30% of the total cloud coverage and affect the global radiation budget by increasing the Earth's albedo and reducing infrared emissions. Thin cirrus, however, are often underestimated by traditional satellite cloud detection algorithms. This difficulty is caused by the lack of spectral contrast between optically thin cirrus and the surface in techniques that use visible (0.65 micron ) and infrared (11 micron ) channels. In the Clouds and the Earth s Radiant Energy System (CERES) Aqua Edition 1 (AEd1) and Terra Edition 3 (TEd3) Cloud Masks, thin cirrus detection is significantly improved over both land and ocean using a technique that combines MODIS high-resolution measurements from the 1.38 and 11 micron channels and brightness temperature differences (BTDs) of 11-12, 8.5-11, and 3.7-11 micron channels. To account for humidity and view angle dependencies, empirical relationships were derived with observations from the 1.38 micron reflectance and the 11-12 and 8.5-11 micron BTDs using 70 granules of MODIS data in 2002 and 2003. Another challenge in global cloud detection algorithms occurs near the day/night terminator where information from the visible 0.65 micron channel and the estimated solar component of 3.7 micron channel becomes less reliable. As a result, clouds are often underestimated or misidentified near the terminator over land and ocean. Comparisons between the CLAVR-x (Clouds from Advanced Very High Resolution Radiometer [AVHRR]) cloud coverage and Geoscience Laser Altimeter System (GLAS) measurements north of 60 N indicate significant amounts of missing clouds from CLAVR-x because this part of the world was near the day/night terminator viewed by AVHRR. Comparisons between MODIS cloud products (MOD06) and GLAS in the same region also show similar difficulties with MODIS cloud retrievals. The consistent detection of clouds through out the day is needed to provide reliable cloud and radiation products for CERES and other research efforts involving the modeling of clouds and their interaction with the radiation budget.

Trepte, Qing↗

NASA NTRS2005

Multidecadal Changes in Near-Global Cloud Cover and Estimated Cloud Cover Radiative Forcing

The first paper was Multidecadal changes in near-global cloud cover and estimated cloud cover radiative forcing, by J. R. Norris (2005, J. Geophys. Res. - Atmos., 110, D08206, doi: lO.l029/2004JD005600). This study examined variability in zonal mean surface-observed upper-level (combined midlevel and high-level) and low-level cloud cover over land during 1971-1 996 and over ocean during 1952-1997. These data were averaged from individual synoptic reports in the Extended Edited Cloud Report Archive (EECRA). Although substantial interdecadal variability is present in the time series, long-term decreases in upper-level cloud cover occur over land and ocean at low and middle latitudes in both hemispheres. Near-global upper-level cloud cover declined by 1.5%-sky-cover over land between 1971 and 1996 and by 1.3%-sky-cover over ocean between 1952 and 1997. Consistency between EECRA upper-level cloud cover anomalies and those from the International Satellite Cloud Climatology Project (ISCCP) during 1984-1 997 suggests the surface-observed trends are real. The reduction in surface-observed upper-level cloud cover between the 1980s and 1990s is also consistent with the decadal increase in all-sky outgoing longwave radiation reported by the Earth Radiation Budget Satellite (EMS). Discrepancies occur between time series of EECRA and ISCCP low-level cloud cover due to identified and probable artifacts in satellite and surface cloud data. Radiative effects of surface-observed cloud cover anomalies, called "cloud cover radiative forcing (CCRF) anomalies," are estimated based on a linear relationship to climatological cloud radiative forcing per unit cloud cover. Zonal mean estimated longwave CCRF has decreased over most of the globe. Estimated shortwave CCRF has become slightly stronger over northern midlatitude oceans and slightly weaker over northern midlatitude land areas. A long-term decline in the magnitude of estimated shortwave CCRF occurs over low-latitude land and ocean, but comparison with EMS all-sky reflected shortwave radiation during 1985-1997 suggests this decrease may be underestimated.

Norris, Joel↗

NASA NTRS2006

A Climatology of Midlatitude Continental Clouds from the ARM SGP Central Facility: Cloud Fraction and Radiative Forcing - Part II

Data collected at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) central facility are analyzed for determining the variability of cloud fraction and radiative forcing at several temporal scales between January 1997 and December 2002. Cloud fractions are estimated for total cloud cover and for single-layer low (0-3 km), middle (3-6 km), and high clouds (greater than 6 km) using ARM SGP ground-based paired lidar-radar measurements. Shortwave (SW), longwave (LW), and net cloud radiative forcings (CRF) are derived from up- and down-looking standard precision spectral pyranometers and precision infrared radiometer measurements. The annual averages of total, and single-layer, nonoverlapped low, middle and high cloud fractions are 0.49, 0.11, 0.03, and 0.17, respectively. Total and low cloud amounts were greatest from December through March and least during July and August. The monthly variation of high cloud amount is relatively small with a broad maximum from May to August. During winter, total cloud cover varies diurnally with a small amplitude, mid-morning maximum and early evening minimum, and during summer it changes by more than 0.14 over the daily cycle with a pronounced early evening minimum. The diurnal variations of mean single-layer cloud cover change with season and cloud height. Annual averages of all-sky, total, and single-layer high, middle, and low LW CRFs are 21.4, 40.2, 16.7, 27.2, and 55.0 Wm(sup -2), respectively; and their SW CRFs are -41.5, -77.2, -37.0, -47.0, and -90.5 Wm(sup -2). Their net CRFs range from -20 to -37 Wm(sup -2). For all-sky, total, and low clouds, the maximum negative net CRFs of -40.1, -70, and -69.5 Wm(sup -2), occur during April; while the respective minimum values of -3.9, -5.7, and -4.6 Wm(sup -2), are found during December. July is the month having maximum negative net CRF of -46.2 Wm(sup -2) for middle clouds, and May has the maximum value of -45.9 Wm(sup -2) for high clouds. An uncertainty analysis demonstrates that the calculated CRFs are not significantly affected by the difference between clear-sky and cloudy conditions. A more comprehensive cloud fraction study from both surface and satellite observations will follow.

Dong, Xiquan↗

NASA NTRS2009

Relation of Cloud Occurrence Frequency, Overlap, and Effective Thickness Derived from CALIPSO and CloudSat Merged Cloud Vertical Profiles

A cloud frequency of occurrence matrix is generated using merged cloud vertical profile derived from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR). The matrix contains vertical profiles of cloud occurrence frequency as a function of the uppermost cloud top. It is shown that the cloud fraction and uppermost cloud top vertical pro les can be related by a set of equations when the correlation distance of cloud occurrence, which is interpreted as an effective cloud thickness, is introduced. The underlying assumption in establishing the above relation is that cloud overlap approaches the random overlap with increasing distance separating cloud layers and that the probability of deviating from the random overlap decreases exponentially with distance. One month of CALIPSO and CloudSat data support these assumptions. However, the correlation distance sometimes becomes large, which might be an indication of precipitation. The cloud correlation distance is equivalent to the de-correlation distance introduced by Hogan and Illingworth [2000] when cloud fractions of both layers in a two-cloud layer system are the same.

Kato, Seiji↗

NASA NTRS2011

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↗

DOE OSTI2026

Haze–cloud correlations mediated by supersaturation fluctuations

Atmospheric aerosol particles that contain water-soluble components can absorb water vapor in humid environments and form either haze particles or cloud droplets, depending on supersaturation conditions. Laboratory and in situ measurements have shown that haze particles and cloud droplets often coexist and compete for available water vapor in shallow clouds and fogs, especially under polluted conditions. It is expected that more aerosol particles can form more haze particles and cloud droplets, so that the haze and cloud number concentrations are positively correlated. However, recent large-eddy simulations show that haze and cloud number concentration can be negatively correlated under extremely polluted conditions. Haze–cloud interactions across different environmental settings remain poorly understood. In this study, experiments in a convection cloud chamber with the same aerosol injection rate show that, as supersaturation forcing increases (i.e., changing from polluted to clean conditions), the covariance between haze and cloud droplet number concentration changes from negative to positive and finally to zero. Large-eddy simulations (LES) of the cloud chamber with a fixed supersaturation forcing but varying aerosol injection rates show a similar result for the haze–cloud correlation: near zero in clean, mean-supersaturation-dominated activation conditions; positive in moderate, supersaturation-fluctuation-influenced activation conditions; and negative in polluted, supersaturation-fluctuation-dominated activation conditions. A theoretical covariance framework was developed to interpret this behavior based on the relative magnitude and signs of the correlations between supersaturation and the populations of haze and cloud droplets. Significantly, experiments, LES, and theory all yield the same three-regime behavior for the sign of the haze–cloud covariance. Furthermore, our results show that the haze–cloud covariance remains robust and easily measurable, thereby providing a useful metric for regime identification in the atmosphere, improving regime-aware parameterizations, and informing aerosol interventions such as fog dispersion, rainfall enhancement, and albedo modification.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Quantifying Diurnal Cloud Radiative Effects by Cloud Type in the Tropical Western Pacific

Cloud radiative effects are examined using long-term datasets collected at the three Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facilities in the tropical western Pacific. We quantify the surface radiation budget, cloud populations, and cloud radiative effects by partitioning the data by cloud type, time of day, and as a function of large scale modes of variability such as El Niño Southern Oscillation (ENSO) phase and wet/dry seasons at Darwin. The novel facet of our analysis is that we break aggregate cloud radiative effects down by cloud type across the diurnal cycle. The Nauru cloud populations and subsequently the surface radiation budget are strongly impacted by ENSO variability whereas the cloud populations over Manus only shift slightly in response to changes in ENSO phase. The Darwin site exhibits large seasonal monsoon related variations. We show that while deeper convective clouds have a strong conditional influence on the radiation reaching the surface, their limited frequency reduces their aggregate radiative impact. The largest source of shortwave cloud radiative effects at all three sites comes from low clouds. We use the observations to demonstrate that potential model biases in the amplitude of the diurnal cycle and mean cloud frequency would lead to larger errors in the surface energy budget compared to biases in the timing of the diurnal cycle of cloud frequency. Our results provide solid benchmarks to evaluate model simulations of cloud radiative effects in the tropics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Characteristic Vertical Profiles of Cloud Water Composition in Marine Stratocumulus Clouds and Relationships With Precipitation

This study uses airborne cloud water composition measurements to characterize the vertical structure of air-equivalent mass concentrations of water-soluble species in marine stratocumulus clouds off the California coast. In this work, a total of 385 cloud water samples were collected in the months of July and August between 2011 and 2016 and analyzed for water-soluble ionic and elemental composition. Three characteristic profiles emerge: (i) a reduction of concentration with in-cloud altitude for particulate species directly emitted from sources below cloud without in-cloud sources (e.g., Cl - , Na + ); (ii) an increase of concentration with in-cloud altitude (e.g., NO 2 - , formate); and (iii) species exhibiting a peak in concentration in the middle of cloud (e.g., non-sea salt SO 4 2- , NO 3 - , organic acids). Vertical profiles of rainout parameters such as loss frequency, lifetime, and change in concentration with respect to time show that the scavenging efficiency throughout the cloud depth depends strongly on the thickness of the cloud. Thin clouds exhibit a greater scavenging loss frequency at cloud top, while thick clouds have a greater scavenging loss frequency at cloud base. The implications of these results for treatment of wet scavenging in models are discussed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A Consistent Representation of Cloud Overlap and Cloud Subgrid Vertical Heterogeneity

Many global climate models underestimate the cloud cover and overestimate the cloud albedo, especially for low-level clouds. We determine how a correct representation of the vertical structure of clouds can fix part of this bias. We use the 1D McICA framework and focus on low-level clouds. Using Large Eddy Simulations results as reference, we propose a method based on exponential-random overlap that represents the cloud overlap between layers and the subgrid cloud properties over several vertical scales, with a single value of the overlap parameter. Starting from a coarse vertical grid, representative of atmospheric models, this algorithm is used to generate the vertical profile of the cloud fraction with a finer vertical resolution, or to generate it on the coarse grid but with subgrid heterogeneity and cloud overlap that ensures a correct cloud cover. Doing so we find decorrelation lengths are dependent on the vertical resolution, except if the vertical subgrid heterogeneity and interlayer overlap are taken into account coherently. We confirm that the frequently used maximum-random overlap leads to a significant error by underestimating the low-level cloud cover with a relative error of about 50%, that can lead to an error of SW cloud albedo as big as 70%. Not taking into account the subgrid vertical heterogeneity of clouds can cause a relative error of 20% in brightness, assuming the cloud cover is correct.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2022

Assessment of Two Stochastic Cloud Subcolumn Generators Using Observed Fields of Vertically Resolved Cloud Extinction

We evaluate two stochastic subcolumn generators used in GCMs to emulate subgrid cloud variability enabling comparisons with satellite observations and simulations of certain physical processes. Our evaluation necessitated the creation of a reference observational dataset that resolves horizontal and vertical cloud variability. The dataset combines two CloudSat cloud products that resolve two-dimensional cloud optical depth variability of liquid, ice, and mixed phase clouds when blended at ~200 m vertical and ~ 2 km horizontal scales. Upon segmenting the dataset to individual “scenes”, mean profiles of the cloud fields are passed as input to generators that produce scene-level cloud subgrid variability. The assessment of generator performance at the scale of individual scenes and in a mean sense is largely based on inferred joint histograms that partition cloud fraction within predetermined combinations of cloud top pressure –cloud optical thickness ranges. Our main finding is that both generators tend to underestimate optically thin clouds, while one of them also tends to overestimate some cloud types of moderate and high optical thickness. Associated radiative flux errors are also calculated by applying a simple transformation to the cloud fraction histogram errors, and are found to approach values almost as high as 3 Wm-2 for the cloud radiative effect in the shortwave part of the spectrum.

cloud variability↗

NASA NTRS2023

Tropical Three-dimensional Cloud Climatology during El Niño Events with CALIPSO Level-3 Cloud Products

The El Niño–Southern Oscillation (ENSO) is a large-scale climatic phenomenon that originates in the tropical Pacific Ocean but affects global climate patterns. Its warm phase El Niño occurs irregularly every two to seven years and peaks in winter. A previous case study showed opposite cloud anomalies at high, middle and low altitude regions during a weak 2006/2007 El Niño and a moderate 2009/2010 El Niño, suggesting a non-linear response of tropical clouds to El Niño sea surface temperature (SST) forcing. To further understand the cloud response to SST, we examine the three-dimensional (3-D) cloud anomalies during all recent five El Niño events since 2006 using level-3 (L3) cloud products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission. During its 17+ years operation, CALIPSO delivered near-global measurements of cloud and aerosol profiles at unprecedented high resolution and sensitivity. In this work, we provide a comprehensive picture of cloud anomalies during El Niño, with a particular focus on thin cirrus clouds at high altitudes, which are challenging for passive sensor detection algorithms. Specifically, the vertical cloud anomalies are calculated with the L3 3-D Cloud Occurrence Product, the horizontal cloud anomalies are from the L3 Global Energy and Water Exchanges (GEWEX) product, and the ice cloud extinction profiles and ice water content are obtained from the L3 Ice Cloud product. These findings will improve current knowledge of ENSO from a new perspective.

El Nino↗

DOE OSTI2020

Toward Understanding the Simulated Phase Partitioning of Arctic Single-Layer Mixed-Phase Clouds in E3SM

21 Mixed-phase clouds are widespread in the Arctic and have substantial impacts on the 22 surface energy budget, sea ice extent, and regional climate. Significant changes are found in the 23 modeled phase partitioning of Arctic mixed-phase clouds in the newly developed U.S. 24 Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model 25 version 1 (EAMv1) compared to its predecessor (CAM5/EAMv0). The mixed-phase clouds 26 simulated by EAMv1 is overly dominated by super-cool liquid which is often underestimated by 27 CAM5. Compared to CAM5, EAMv1 has made several important changes in its cloud physical 28 parameterizations, including using CLUBB to unify shallow convection, PBL turbulence, and 29 cloud macrophysics, as well as updating the MG two-moment cloud microphysics scheme and 30 using the CNT heterogeneous ice nucleation scheme to replace the Meyers scheme for mixed- 31 phase clouds. To understand which of the newly-implemented physical parameterizations is 32 responsible for the change of modeled mixed-phase cloud phase partitioning in EAMv1, 33 sensitivity tests are conducted by utilizing the short-term hindcast approach and results are 34 compared to the observations from the ARM Mixed-Phase Arctic Cloud Experiment (M-PACE) 35 field campaign. 36 It is found that although each scheme contributes to the decrease of cloud ice water 37 content (IWC) and increase of cloud liquid water content (LWC), only the combined effect of 38 CLUBB and CNT can explain the significant underestimation of IWC. By examining the budgets 39 of cloud microphysical processes, CLUBB is found to play a dominant role due to the fact that 40 CLUBB does not diagnoses cloud ice phase water in its assumed joint PDF method and thus may 41 not provide sufficient cloud ice to initialize ice phase microphysical processes such as the growth 42 of ice crystals through vapor deposition. Although a more effective heterogeneous ice nucleation 43 may alleviate the underestimation of IWC, the ice phase process in CLUBB requires urgent 44 attention in the future model development.

Zhang, Meng↗

DOE OSTI2014

First observations of tracking clouds using scanning ARM cloud radars

Tracking clouds using scanning cloud radars can help to document the temporal evolution of cloud properties well before large drop formation (‘‘first echo’’). These measurements complement cloud and precipitation tracking using geostationary satellites and weather radars. Here, two-dimensional (2-D) Along-Wind Range Height Indicator (AW-RHI) observations of a population of shallow cumuli (with and without precipitation) from the 35-GHz scanning ARM cloud radar (SACR) at the DOE Atmospheric Radiation Measurements (ARM) program Southern Great Plains (SGP) site are presented. Observations from the ARM SGP network of scanning precipitation radars are used to provide the larger scale context of the cloud field and to highlight the advantages of the SACR to detect the numerous, small, non-precipitating cloud elements. A new Cloud Identification and Tracking Algorithm (CITA) is developed to track cloud elements. In CITA, a cloud element is identified as a region having a contiguous set of pixels exceeding a preset reflectivity and size threshold. The high temporal resolution of the SACR 2-D observations (30 sec) allows for an area superposition criteria algorithm to match cloud elements at consecutive times. Following CITA, the temporal evolution of cloud element properties (number, size, and maximum reflectivity) is presented. The vast majority of the designated elements during this cumulus event were short-lived non-precipitating clouds having an apparent life cycle shorter than 15 minutes. The advantages and disadvantages of cloud tracking using an SACR are discussed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Assessing CESM2 Clouds and Their Response to Climate Change Using Cloud Regimes

Abstract The Community Earth System Model, version 2 (CESM2), has a very high climate sensitivity driven by strong positive cloud feedbacks. To evaluate the simulated clouds in the present climate and characterize their response with climate warming, a clustering approach is applied to three independent satellite cloud products and a set of coupled climate simulations. Using k -means clustering with a Wasserstein distance cost function, a set of typical cloud configurations is derived for the satellite cloud products. Using satellite simulator output, the model clouds are classified into the observed cloud regimes in both current and future climates. The model qualitatively reproduces the observed cloud configurations in the historical simulation using the same time period as the satellite observations, but it struggles to capture the observed heterogeneity of clouds which leads to an overestimation of the frequency of a few preferred cloud regimes. This problem is especially apparent for boundary layer clouds. Those low-level cloud regimes also account for much of the climate response in the late twenty-first century in four shared socioeconomic pathway simulations. The model reduces the frequency of occurrence of these low-cloud regimes, especially in tropical regions under large-scale subsidence, in favor of regimes that have weaker cloud radiative effects.

58 GEOSCIENCES↗

DOE OSTI2017

Thin ice clouds in the Arctic: cloud optical depth and particle size retrieved from ground-based thermal infrared radiometry

Multiband downwelling thermal measurements of zenith sky radiance, along with cloud boundary heights, were used in a retrieval algorithm to estimate cloud optical depth and effective particle diameter of thin ice clouds in the Canadian High Arctic. Ground-based thermal infrared (IR) radiances for 150 semitransparent ice clouds cases were acquired at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada (80° N, 86° W). We analyzed and quantified the sensitivity of downwelling thermal radiance to several cloud parameters including optical depth, effective particle diameter and shape, water vapor content, cloud geometric thickness and cloud base altitude. A lookup table retrieval method was used to successfully extract, through an optimal estimation method, cloud optical depth up to a maximum value of 2.6 and to separate thin ice clouds into two classes: (1) TIC1 clouds characterized by small crystals (effective particle diameter ≤ 30 µm), and (2) TIC2 clouds characterized by large ice crystals (effective particle diameter > 30 µm). The retrieval technique was validated using data from the Arctic High Spectral Resolution Lidar (AHSRL) and Millimeter Wave Cloud Radar (MMCR). Inversions were performed over three polar winters and results showed a significant correlation ( R 2 = 0.95) for cloud optical depth retrievals and an overall accuracy of 83 % for the classification of TIC1 and TIC2 clouds. A partial validation relative to an algorithm based on high spectral resolution downwelling IR radiance measurements between 8 and 21µm was also performed. It confirms the robustness of the optical depth retrieval and the fact that the broadband thermal radiometer retrieval was sensitive to small particle (TIC1) sizes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

The impact of neglecting ice phase on cloud optical depth retrievals from AERONET cloud mode observations

Clouds present many challenges to climate modelling. To develop and verify the parameterisations needed to allow climate models to represent cloud structure and processes, there is a need for high-quality observations of cloud optical depth from locations around the world.Retrievals of cloud optical depth are obtainable from radiances measured by Aerosol Robotic Network (AERONET) radiometers in “cloud mode” using a two-wavelength retrieval method. However, the method is unable to detect cloud phase, and hence assumes that all of the cloud in a profile is liquid.This assumption has the potential to introduce errors into long-term statistics of retrieved optical depth for clouds that also contain ice.Using a set of idealised cloud profiles we find that, for optical depths above 20, the fractional error in retrieved optical depth is a linear function of the fraction of the optical depth that is due to the presence ofice cloud (“ice fraction”). Clouds that are entirely ice have positive errors with magnitudes of the order of 55 % to 70 %. We derive a simple linear equation that can be used as a correction at AERONET sites where ice fraction can be independently estimated. Using this linear equation, we estimate the magnitude of the error for a set of cloud profiles from five sites of the Atmospheric Radiation Measurement programme. The dataset contains separate retrievals of ice and liquid retrievals; hence ice fraction can be estimated. The magnitude of the error at each location was related to the relative frequencies of occurrence in thick frontal cloud at the mid-latitude sites and of deep convection at the tropical sites – that is, of deep cloud containing both ice and liquid particles. The long-term mean optical depth error at the five locations spans the range 2–4, which we show to be small enough to allow calculation of top-of-atmosphere flux to within 10 % and surface flux to about 15 %.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1989

Operational implications of a cloud model simulation of space shuttle exhaust clouds in different atmospheric conditions

A three-dimensional cloud model was used to characterize the dominant influence of the environment on the Space Shuttle exhaust cloud. The model was modified to accept the actual heat and moisture from rocket exhausts and deluge water as initial conditions. An upper-air sounding determined the ambient atmosphere in which the cloud would grow. The model was validated by comparing simulated clouds with observed clouds from four actual Shuttle launches. Results are discussed with operational weather forecasters in mind. The model successfully produced clouds with dimensions, rise, decay, liquid water contents, and vertical motion fields very similar to observed clouds whose dimensions were calculated from 16 mm film frames. Once validated, the model was used in a number of different atmospheric conditions ranging from very unstable to very stable. Wind shear strongly affected the appearance of both the ground cloud and vertical column cloud. The ambient low-level atmospheric moisture governed the amount of cloud water in model clouds. Some dry atmospheres produced little or no cloud water. An empirical forecast technique for Shuttle cloud rise is presented and differences between natural atmospheric convection and exhaust clouds are discussed.

Zak, J. A.↗