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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Retrievals of Cloud Fraction and Cloud Albedo from Surface-based Shortwave Radiation Measurements: A Comparison of 16 Year Measurements

Ground-based radiation measurements have been widely conducted to gain information on clouds and the surface radiation budget; here several different techniques for retrieving cloud fraction (Long2006, Min2008 and XL2013) and cloud albedo (Min2008, Liu2011 and XL2013) from ground-based shortwave broadband and spectral radiation measurements are examined, and sixteen years of retrievals collected at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are compared. The comparison shows overall good agreement between the retrievals of both cloud fraction and cloud albedo, with noted differences however. The Long2006 and Min2008 cloud fractions are greater on average than the XL2013 values. Compared to Min2008 and Liu2011, the XL2013 retrieval of cloud albedo tends to be greater for thin clouds but smaller for thick clouds, with the differences decreasing with increasing cloud fraction. Further analysis reveals that the approaches that retrieve cloud fraction and cloud albedo separately may suffer from mutual contamination of errors in retrieved cloud fraction and cloud albedo. Potential influences of cloud absorption, land-surface albedo, cloud structure, and measurement instruments are explored.

albedo↗
DOE OSTI2018

A New Approach for Short-Term Solar Radiation Forecasting Using the Estimation of Cloud Fraction and Cloud Albedo

Solar generation is an increasing part of the energy portfolio in the United States. An accurate forecast of the available solar resource and power is essential to managing the electric grid, market operations, and reducing the cost of solar energy. High-frequency forecasts of solar radiation in intra-hour horizons is important for real-time electric power system energy management, especially at the distribution level. Conventional Numerical Weather Prediction models perform poorly in intra-hour, high-frequency forecasts because of the limits on real-time computing, spatial resolution, and infrequent availability of observations. Although a number of alternative technologies, e.g., time-series analysis and machine learning, have been used to fill this gap, the smart persistence model is among the top-performing models in short-term forecasting and therefore often serves as the baseline to evaluate other forecasting models. Although the smart persistence model often serves as the baseline model in these intra-hour forecasts, obvious uncertainties exist in the current smart persistence model: (1) clear-sky index does not respond to the variation of the solar incident angle when cloud conditions are persistent within the forecast horizon, and (2) cloud coverage is inherently persistent though it is constrained by cloud advection. In this study, we developed a Physics-Based Smart Persistence Model for Intra-Hour Solar Forecasting (PSPI) that integrates cloudy property estimation, a radiative transfer model, and cloud fraction forecasts to improve the performance of the smart persistence model. Compared to the smart persistence model, PSPI does not require additional observations of various atmospheric parameters past global horizontal irradiance, but it is customizable because additional observations, if available, can be ingested to further improve the forecast. Our results show that the PSPI outperforms the persistence and smart persistence model on 5-minute, 15-minute, and 30-minute forecast horizons. The software package of PSPI is flexible to users' needs and provides low computational time to run at site-specific locations across the continental United States.

14 SOLAR ENERGY↗
DOE OSTI2017

Analysis of albedo versus cloud fraction relationships in liquid water clouds using heuristic models and large eddy simulation: ALBEDO VERSUS CLOUD FRACTION RELATIONSHIPS

The relationship between the albedo of a cloudy scene $A$ and cloud fraction f c is studied with the aid of heuristic models of stratocumulus and cumulus clouds. Existing work has shown that scene albedo increases monotonically with increasing cloud fraction but that the relationship varies from linear to superlinear. The reasons for these differences in functional dependence are traced to the relationship between cloud deepening and cloud widening. When clouds deepen with no significant increase in f c (e.g., in solid stratocumulus), the relationship between$A$ and f c is linear.When clouds widen as they deepen, as in cumulus cloud fields, the relationship is superlinear. A simple heuristic model of a cumulus cloud field with a power law size distribution shows that the superlinear $A$-f c behavior is traced out either through random variation in cloud size distribution parameters or as the cloud field oscillates between a relative abundance of small clouds (steep slopes on a log-log plot) and a relative abundance of large clouds (flat slopes). Oscillations of this kind manifest in large eddy simulation of trade wind cumulus where the slope and intercept of the power law fit to the cloud size distribution are highly correlated. Further analysis of the large eddy model-generated cloud fields suggests that cumulus clouds grow larger and deeper as their underlying plumes aggregate; this is followed by breakup of large plumes and a tendency to smaller clouds. The cloud and thermal size distributions oscillate back and forth approximately in unison.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Patterns of Diurnal Marine Stratocumulus Cloud Fraction Variability

The spatial patterns of subtropical marine stratocumulus cloud fraction variability on diurnal time scales are examined using high-temporal-resolution cloud masks that are based on 30-min, 4 km × 4 km geosynchronous infrared data for 2003–10. This dataset permits comparison of the characteristics of variability in low cloud fraction among the three subtropical marine stratocumulus regions in the northeastern (NE) Pacific, southeastern (SE) Pacific, and SE Atlantic Oceans. In all three regions, the largest diurnal cycles and earliest time of cloud breakup occur on the edges of the cloud field where cloud fractions are generally lower. The rate at which the cloud breaks up during the day is tied to the starting cloud fraction at dawn, which determines the amount of longwave cooling that is initially available to offset shortwave radiative fluxes during the day. The maximum rate of cloud breakup occurs near 1200 LT. Cloud fraction begins to increase by 1600 LT (before the sun sets) and reaches its maximum value just before dawn. Here, the diurnal-cycle characteristics of the SE Pacific and SE Atlantic marine stratocumulus cloud decks are more similar to each other than to those in the NE Pacific. The NE Pacific cloud deck has a smaller-amplitude diurnal cycle, slower rates of cloud breakup during the day for a given cloud fraction at dawn, and a higher probability of cloud breakup overnight.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Subdiurnal Stratocumulus Cloud Fraction Variability and Sensitivity to Precipitation

This paper presents an analysis of subtropical marine stratocumulus cloud fraction variability using a 30-min and 3° × 3° cloud fraction dataset from 2003 to 2010. Each of the three subtropical marine stratocumulus regions has distinct diurnal characteristics, but the southeast (SE) Pacific and SE Atlantic are more similar to each other than to the northeast (NE) Pacific. Here, the amplitude and season-to-season diurnal cycle variations are larger in the Southern Hemisphere regions than in the NE Pacific. Net overnight changes in cloud fraction on 3° × 3° scales are either positive or neutral >77% of the time in the NE Pacific and >88% of the time in the SE Pacific and SE Atlantic. Cloud fraction often increases to 100% by dawn when cloud fraction at dusk is >30%. In the SE Pacific and SE Atlantic, a typical decrease in cloud area (median ≤ –5.7 × 10 5 km 2 ) during the day is equivalent to 25% or more of the annual-mean cloud deck area. Time series for 3° × 3° areas where cloud fraction was ≥90% sometime overnight and <60% at dawn, such as would result from nocturnal formation of pockets of open cells (POCs), only occur 1.5%, 1.6%, and 3.3% of the time in the SE Pacific, SE Atlantic, and NE Pacific, respectively. Comparison of cloud fraction changes to ship-based radar and satellite-derived precipitation intensity and area measurements shows a lack of sensitivity of cloud fraction to drizzle on time scales of 1–3 h and spatial scales of 100–300 km.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Bimodal Diagnostic Cloud Fraction Parameterization. Part II: Evaluation and Resolution Sensitivity

A wide range of approaches exists to account for subgrid cloud variability in regional simulations of the atmosphere. This paper addresses the following questions: 1) Is there still benefit in representing subgrid variability of cloud in convection-permitting simulations? 2) What is the sensitivity to the cloud fraction parameterization complexity? 3) Are current cloud fraction parameterizations scale-aware across convection-permitting resolutions? These questions are addressed for regional simulations of a 6-week observation campaign in the U.S. southern Great Plains. Particular attention is given to a new diagnostic cloud fraction scheme with a bimodal subgrid saturation-departure PDF, described in Part I. The model evaluation is performed using ground-based remote sensing synergies, satellite-based retrievals, and surface observations. It is shown that not using a cloud fraction parameterization results in underestimated cloud frequency and water content, even for stratocumulus. The use of a cloud fraction parameterization does not guarantee improved cloud property simulations, however. Diagnostic and prognostic cloud schemes with a symmetric subgrid saturation-departure PDF underestimate cloud fraction and cloud optical thickness, and hence overestimate surface shortwave radiation. These schemes require empirical bias-correction techniques to improve the cloud cover. The new cloud fraction parameterization, introduced in Part I, improves cloud cover, liquid water content, cloud-base height, optical thickness, and surface radiation compared to schemes reliant on a symmetric PDF. Furthermore, cloud parameterizations using turbulence-based, rather than prescribed constant subgrid variances, are shown to be more scale-aware across convection-permitting resolutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Bimodal Diagnostic Cloud Fraction Parameterization. Part I: Motivating Analysis and Scheme Description

Cloud fraction parameterizations are beneficial to regional, convection-permitting numerical weather prediction. For its operational regional midlatitude forecasts, the Met Office uses a diagnostic cloud fraction scheme that relies on a unimodal, symmetric subgrid saturation-departure distribution. This scheme has been shown before to underestimate cloud cover and hence an empirically based bias correction is used operationally to improve performance. This first of a series of two papers proposes a new diagnostic cloud scheme as a more physically based alternative to the operational bias correction. The new cloud scheme identifies entrainment zones associated with strong temperature inversions. Additionally, for model grid boxes located in this entrainment zone, collocated moist and dry Gaussian modes are used to represent the subgrid conditions. The mean and width of the Gaussian modes, inferred from the turbulent characteristics, are then used to diagnose cloud water content and cloud fraction. It is shown that the new scheme diagnoses enhanced cloud cover for a given gridbox mean humidity, similar to the current operational approach. It does so, however, in a physically meaningful way. Using observed aircraft data and ground-based retrievals over the southern Great Plains in the United States, it is shown that the new scheme improves the relation between cloud fraction, relative humidity, and liquid water content. An emergent property of the scheme is its ability to infer skewed and bimodal distributions from the large-scale state that qualitatively compare well against observations. A detailed evaluation and resolution sensitivity study will follow in Part II.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Estimation of Cloud Fraction Profile in Shallow Convection Using a Scanning Cloud Radar

Large spatial heterogeneities in shallow convection result in uncertainties in estimations of domain-averaged cloud fraction profiles (CFP). This issue is addressed using large eddy simulations of shallow convection over land coupled with a radar simulator. Results indicate that zenith profiling observations are inadequate to provide reliable CFP estimates. Use of Scanning Cloud Radar (SCR), performing a sequence of cross-wind horizon-to-horizon scans, is not straightforward due to the strong dependence of radar sensitivity to target distance. An objective method for estimating domain-averaged CFP is proposed that uses observed statistics of SCR hydrometeor detection with height to estimate optimum sampling regions. This method shows good agreement with the model CFP. Results indicate that CFP estimates require more than 35 min of SCR scans to converge on the model domain average. Lastly, the proposed technique is expected to improve our ability to compare model output with cloud radar observations in shallow cumulus cloud conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Impacts of ENSO events on cloud radiative effects in preindustrial conditions: Changes in cloud fraction and their dependence on interactive aerosol emissions and concentrations

The impacts of the El Niño–Southern Oscillation (ENSO) events on shortwave and longwave cloud radiative effects (CRE SW and CRE LW ) and the underlying changes in cloud fraction as well as aerosol emissions, wet scavenging and transport are quantified using three 150-year simulations in preindustrial conditions by the CESM model. Compared to recent observations from Clouds and the Earth’s Radiant Energy System (CERES), the model simulation successfully reproduced larger variations of CRE SW over the tropical western and central Pacific, Indonesian regions, and the eastern Pacific Ocean, as well as large variations of CRE LW located mainly within the tropics. The ENSO cycle is found to dominate interannual variations of cloud radiative effects, especially over the tropics. Relative to those during La Niña events, simulated cooling (warming) effects from CRE SW (CRE LW ) during El Niño events are stronger over the tropical western and central Pacific Ocean, with the largest difference exceeding 40 Wm –2 (30 Wm –2 ), with weaker effects of 10–30 Wm –2 over Indonesian regions and the subtropical Pacific Ocean. Here, sensitivity tests show that variations of cloud radiative effects are mainly driven by ENSO-related changes in cloud fraction. The variations in medium and high cloud fractions each account for about 20–50% of the interannual variations of CRE SW over the tropics and almost all of the variations of CRE LW between 60°S and 60°N. The variation of low cloud fraction contributes most interannual variations of CRESW over the mid-latitude oceans. Variations in natural aerosol concentrations considering emissions, wet scavenging and transport explained 10–30% of the interannual variations of both CRE SW and CRE LW over the tropical Pacific, Indonesian regions and the tropical Indian Ocean. Changes in wet scavenging of natural aerosol modulate the variations of cloud radiative effects. Because of increased (decreased) precipitation over the tropical western Pacific Ocean in El Niño (La Niña) events, increased (decreased) wet scavenging of natural aerosols dampens more than 4–6% of variations of cloud radiative effects averaged over the tropics. In contrast, increased surface winds cause feedbacks that increase sea spray emissions that enhance the variations by 3–4% averaged over the tropics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Model Evaluation and Intercomparison of Marine Warm Low Cloud Fractions With Neural Network Ensembles

Abstract Low cloud fractions (LCFs) and meteorological factors (MFs) over an oceanic region containing multiple cloud regimes are examined for three data sets: one Energy Exascale Earth System Model (E3SM) simulation with the default 72‐layer vertical grid (E3SM72), another one with 8‐times vertical resolution via the Framework for Improvement by Vertical Enhancement (E3SM 8), and one with MFs from ERA5 reanalysis and LCFs from the CERES SSF product (ERA5‐SSF). Neural networks (NNs) are trained to capture the relationship between MFs and LCF and to select the best‐performing MF subsets for predicting LCF. NN ensembles are used to (a) confirm the performance of selected MF subsets, (b) to serve as proxy models for each data set to predict LCFs for MFs from all data sets, and (c) to classify MFs into those in shared and uniquely occupied MF subspaces. Overall, E3SM72 and E3SM 8 have large fractions of MFs in shared MF subspace, but less so near the Californian and Peruvian stratocumulus decks. E3SM 8 and ERA5 have small fractions of MFs in shared MF subspace but greater than E3SM72 and ERA5, especially in the Southeast Pacific. The differences in LCFs between three pairs of data sets are decomposed into those associated with the differences in the LCF‐MF relationship and those involving different MFs. Given the same MFs, LCFs produced by E3SM 8 are greater than those produced by E3SM72 but are still different from those in ERA5‐SSF. In general, the shift in MFs dominates the difference in the LCFs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Comparison of scattering ratio profiles retrieved from ALADIN/Aeolus and CALIOP/CALIPSO observations and preliminary estimates of cloud fraction profiles

The space-borne active sounders have been contributing invaluable vertically resolved information of atmospheric optical properties since the launch of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) in 2006. To build long-term records from space-borne lidars useful for climate studies, one has to understand the differences between successive space lidars operating at different wavelengths, flying on different orbits, and using different viewing geometries, receiving paths, and detectors. In this article, we compare the results of Atmospheric Laser Doppler INstrument (ALADIN) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidars for the period from 28 June to 31 December 2019. First, we build a dataset of ALADIN–CALIOP collocated profiles (Δdist<1°; Δtime<6 h). Then we convert ALADIN's 355 nm particulate backscatter and extinction profiles into the scattering ratio vertical profiles SR(z) at 532 nm using molecular density profiles from Goddard Earth Observing System Data Assimilation System, version 5 (GEOS-5 DAS). And finally, we build the CALIOP and ALADIN globally gridded cloud fraction profiles CF(z) by applying the same cloud detection threshold to the SR(z) profiles of both lidars at the same spatial resolution. Before comparing the SR(z) and CF(z) profiles retrieved from the two analyzed lidar missions, we performed a numerical experiment to estimate the best achievable cloud detection agreement CDA norm (z) considering the differences between the instruments. We define CDA norm (z) in each latitude–altitude bin as the occurrence frequency of cloud layers detected by both lidars, divided by a cloud fraction value for the same latitude–altitude bin. We simulated the SR(z) and CF(z) profiles that would be observed by these two lidars if they were flying over the same atmosphere predicted by a global model. By analyzing these simulations, we show that the theoretical limit for CDA$_{norm}^{theor}$(z) for a combination of ALADIN and CALIOP instruments is equal to 0.81±0.07 at all altitudes. In other words, 19 % of the clouds cannot be detected simultaneously by two instruments due to said differences. The analyses of the actual observed CALIOP–ALADIN collocated dataset containing ~78 000 pairs of nighttime SR(z) profiles revealed the following points: (a) the values of SR(z) agree well up to ~3 km height. (b) The CF(z) profiles show agreement below ~3 km, where ~80 % of the clouds detected by CALIOP are detected by ALADIN as expected from the numerical experiment. (c) Above this height, the CDA$_{norm}^{obs}$(z) reduces to ~50 %. (d) On average, better sensitivity to lower clouds skews ALADIN's cloud peak height in pairs of ALADIN–CALIOP profiles by ~0.5±0.6 km downwards, but this effect does not alter the heights of polar stratospheric clouds and high tropical clouds thanks to their strong backscatter signals. (e) The temporal evolution of the observed CDA$_{norm}^{obs}$(z) does not reveal any statistically significant change during the considered period. This indicates that the instrument-related issues in ALADIN L0/L1 have been mitigated, at least down to the uncertainties of the following CDA$_{norm}^{obs}$(z) values: 68±12 %, 55±14 %, 34±14 %, 39±13 %, and 42±14 % estimated at 0.75, 2.25, 6.75, 8.75, and 10.25 km, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Sensitivity of Cloud-Radiative Effects to Cloud Fraction Parametrizations in Tropical, Mid-Latitude and Arctic Kilometre-Scale Simulations

The Regional Atmosphere (RA) configuration of the Met Office Unified Model currently requires different cloud fraction parametrizations (CFP) for tropical and midlatitude simulations. To explore the scope for unification of these two RA configurations, this paper presents a detailed evaluation of simulations over tropical, midlatitude and arctic domains, with two different diagnostic CFPs, a prognostic CFP, and no CFP at all. Furthermore, a novel, hybrid approach was used that treats liquid cloud diagnostically and ice cloud prognostically. Using observations from three U.S. Department of Energy Atmospheric Radiation Measurement supersites, it is shown that none of these CFPs stands out as superior over all domains. Over the frequently overcast Arctic, the all-or-nothing approach best captures the cloud radiative properties. Conversely, CFPs are of benefit in regions with frequent partial cloudiness, such as the midlatitudes and the tropics. However, their improved cloud radiative properties often hide an error compensation. All models underestimate overcast, low-base cloud with small water paths in convective environments. In addition, mid-latitude overcast, low-base, optically thick clouds in the morning, possibly associated with overnight convection, are frequently too broken. Diagnostic schemes compensate for these errors by producing spurious, scattered afternoon cloud, which could be due to a correct cloud response to too eager convective initiation. Winter clouds over the midlatitudes are improved when liquid cloud is represented diagnostically with a bimodal saturation-departure PDF, without error compensation. While it is difficult to unify the RA across the globe around a single CFP scheme, the newly proposed Hybrid scheme performs reasonably well for cloud cover across all regions. It also exhibits SW biases that are smaller than most other configurations and is less affected by excessive liquid water paths and compensating errors than fully diagnostic schemes. Surface precipitation is fairly insensitive to the CFP in the simulations shown here.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Cloud fraction at the ARM SGP site: Reducing uncertainty with self-organizing maps

Instrument downtime leads to uncertainty in the monthly and annual record of cloud fraction (CF), making it difficult to perform time series analyses of cloud properties and perform detailed evaluations of model simulations. As cloud occurrence is partially controlled by the large-scale atmospheric environment, this knowledge is used to reduce uncertainties in the instrument record. Synoptic patterns diagnosed from the North American Regional Reanalysis (NARR) during the period 1997–2010 are classified using a competitive neural network known as the self-organizing map (SOM). The classified synoptic states are then compared to the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) instrument record to determine the expected CF. A number of SOMs are tested to understand how the number of classes and the period of classifications impact the relationship between classified states and CFs. Bootstrapping is utilized to quantify the uncertainty of the instrument record when statistical information from the SOM is included. Although all SOMs significantly reduce the uncertainty of the CF record calculated in Kennedy et al. (Theor Appl Climatol 115:91–105, 2014), SOMs with a large number of classes and separated by month are required to produce the lowest uncertainty and best agreement with the annual cycle of CF. Lastly, this result may be due to a manifestation of seasonally dependent biases in NARR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Improving Low‐Cloud Fraction Prediction Through Machine Learning

Abstract In this study, we evaluated the performance of machine learning (ML) models (XGBoost) in predicting low‐cloud fraction (LCF), compared to two generations of the community atmospheric model (CAM5 and CAM6) and ERA5 reanalysis data, each having a different cloud scheme. ML models show a substantial enhancement in predicting LCF regarding root mean squared errors and correlation coefficients. The good performance is consistent across the full spectrums of atmospheric stability and large‐scale vertical velocity. Employing an explainable ML approach, we revealed the importance of including the amount of available moisture in ML models for representing spatiotemporal variations in LCF in the midlatitudes. Also, ML models demonstrated marked improvement in capturing the LCF variations during the stratocumulus‐to‐cumulus transition (SCT). This study suggests ML models' great potential to address the longstanding issues of “too few” low clouds and “too rapid” SCT in global climate models.

Geology↗
DOE OSTI2021

Sunlight-absorbing aerosol amplifies the seasonal cycle in low-cloud fraction over the southeast Atlantic

Abstract. The mean altitude of the smoke loading over the southeast Atlantic moves from the boundary layer in July to the free troposphere by October. This study details the month-by-month changes in cloud properties and the large-scale environment as a function of the biomass burning aerosol loading at Ascension Island (8∘ S, 14.5∘ W) from July to October, based on island measurements, satellite retrievals, and reanalysis. In July and August, the smoke loading predominantly varies within the boundary layer. During both months, the low-cloud fraction is less and is increasingly cumuliform when more smoke is present, with the exception of a late morning boundary layer deepening that encourages a short-lived cloud development. The meteorology varies little, suggesting aerosol–cloud interactions explain the cloudiness changes. September marks a transition month during which midlatitude disturbances can intrude into the Atlantic subtropics, constraining the free tropospheric aerosol closer to the African coast. Stronger boundary layer winds on cleaner days help deepen, dry, and cool much of the marine boundary layer compared to that on days with high smoke loadings, with stratocumulus reducing everywhere but at the northern deck edge. The September free troposphere is better mixed on smoky days compared to October. Longwave cooling rates, generated by a sharp water vapor gradient at the aerosol layer top, encourage a small-scale vertical mixing that could help maintain the well-mixed smoky September free troposphere. The October meteorology primarily varies as a function of the strength of the free tropospheric winds advecting aerosol offshore. The free tropospheric aerosol loading is less than in September, and the moisture variability is greater. Low-level clouds increase and are more stratiform in October when the smoke loadings are higher. The increased free tropospheric moisture can help sustain the clouds through a reduction in evaporative drying during cloud-top entrainment. Enhanced subsidence above the coastal upwelling region, increasing cloud droplet number concentrations, may further prolong cloud lifetime through microphysical interactions. Reduced subsidence underneath stronger free tropospheric winds at Ascension Island supports slightly higher cloud tops during smokier conditions. Overall, the monthly changes in the large-scale aerosol and moisture vertical structure act to amplify the seasonal cycle in low-cloud amount and morphology. This is climatically important, as cloudiness changes dominate changes in the top-of-atmosphere radiation budget.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Projecting Stratocumulus Transitions on the Albedo—Cloud Fraction Relationship Reveals Linearity of Albedo to Droplet Concentrations

Satellite images show solid marine stratocumulus cloud decks (Sc) that break up over the remote oceans. The Sc breakup is initiated by precipitation and is accompanied by a strong reduction in the cloud radiative effect. Aerosol has been shown to delay the Sc breakup by postponing the onset of precipitation, however its climatic effect is uncertain. Here we introduce a new approach that allows us to re-cast currently observed cloud cover and albedo to their counterfactual cleaner world, enabling the first estimate of the radiative effect due to delayed cloud breakup. Using simple radiative approximation, the radiative forcing with respect to pre-industrial times due to delayed Sc breakup is –0.39 W m –2 . The radiative effect changes nearly linearly with aerosol due to the droplet concentration control on the cloud cover, suggesting a potentially accelerated warming if the current trend of reduction in aerosol emissions continues.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Cloud vertical distribution from combined surface and space radar–lidar observations at two Arctic atmospheric observatories

Abstract. Detailed and accurate vertical distributions of cloud properties (such as cloud fraction, cloud phase, and cloud water content) and their changes are essential to accurately calculate the surface radiative flux and to depict the mean climate state. Surface and space-based active sensors including radar and lidar are ideal to provide this information because of their superior capability to detect clouds and retrieve cloud microphysical properties. In this study, we compare the annual cycles of cloud property vertical distributions from space-based active sensors and surface-based active sensors at two Arctic atmospheric observatories, Barrow and Eureka. Based on the comparisons, we identify the sensors' respective strengths and limitations, and develop a blended cloud property vertical distribution by combining both sets of observations. Results show that surface-based observations offer a more complete cloud property vertical distribution from the surface up to 11 km above mean sea level (a.m.s.l.) with limitations in the middle and high altitudes; the annual mean total cloud fraction from space-based observations shows 25–40 % fewer clouds below 0.5 km than from surface-based observations, and space-based observations also show much fewer ice clouds and mixed-phase clouds, and slightly more liquid clouds, from the surface to 1 km. In general, space-based observations show comparable cloud fractions between 1 and 2 km a.m.s.l., and larger cloud fractions above 2 km a.m.s.l. than from surface-based observations. A blended product combines the strengths of both products to provide a more reliable annual cycle of cloud property vertical distributions from the surface to 11 km a.m.s.l. This information can be valuable for deriving an accurate surface radiative budget in the Arctic and for cloud parameterization evaluation in weather and climate models. Cloud annual cycles show similar evolutions in total cloud fraction and ice cloud fraction, and lower liquid-containing cloud fraction at Eureka than at Barrow; the differences can be attributed to the generally colder and drier conditions at Eureka relative to Barrow.

58 GEOSCIENCES↗