The cloud droplet number concentration (Nd) is of central interest to improve the understanding of cloud physics and for quantifying the effective radiative forcing by aerosol‐cloud interactions. ...Current standard satellite retrievals do not operationally provide Nd, but it can be inferred from retrievals of cloud optical depth (τc) cloud droplet effective radius (re) and cloud top temperature. This review summarizes issues with this approach and quantifies uncertainties. A total relative uncertainty of 78% is inferred for pixel‐level retrievals for relatively homogeneous, optically thick and unobscured stratiform clouds with favorable viewing geometry. The uncertainty is even greater if these conditions are not met. For averages over 1° ×1° regions the uncertainty is reduced to 54% assuming random errors for instrument uncertainties. In contrast, the few evaluation studies against reference in situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. Nd uncertainty is dominated by errors in re, and therefore, improvements in re retrievals would greatly improve the quality of the Nd retrievals. Recommendations are made for how this might be achieved. Some existing Nd data sets are compared and discussed, and best practices for the use of Nd data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative Nd estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high‐quality ground‐based observations are examined.
Plain Language Summary
Clouds have a very large influence on weather and climate. It is thus a prime task for satellite‐ and ground‐based observations to measure clouds. For satellites and many other instruments this is done by remote sensing—radiation is measured, and knowledge about clouds is inferred. Liquid water clouds consist of numerous droplets of order of 10 μm in size. A key quantity that describes clouds is the number of droplets in a given volume or the droplet number concentration. However, satellite observations of droplet number concentration are only emerging, and the quality of these observations is poorly known. This review fulfills two tasks, namely, (1) to quantify how uncertain the current way to observe droplet number concentrations from satellite is and (2) to propose ways toward better approaches. It is concluded that the current way to obtain cloud droplet number concentration works for homogeneous stratus and stratocumulus clouds, with, however, a substantial error of around 50%. For cumulus clouds the observations are substantially worse. New avenues that are proposed for a better estimate of cloud droplet concentration exploit instruments that emit light (lidar) or microwaves (radar), and measure the reflected signal, or explore the polarization of light induced by clouds.
Key Points
Satellite cloud droplet concentration uncertainties of 78% for pixel‐level retrievals and 54% for 1 by 1 degree retrievals are estimated
The effective radius retrieval is the most important aspect for improvement, and more in situ evaluation is needed
Potential improvements using passive and active satellite, and ground‐based instruments are discussed
Postfrontal clouds (PFC) are ubiquitous in the marine boundary layer, and their morphology is essential to estimating the radiation budget in weather and climate models. Here we examine the roles of ...sea surface temperature (SST) and meteorological factors in controlling the mesoscale morphology and evolution of shallow clouds associated with a cold-air outbreak that occurred on 1 March 2020 during phase I of the Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE). Our results show that the simulated PFC structure and ambient conditions by the Weather Research and Forecasting (WRF) Model are generally consistent with observations from GOES-16 and dropsonde measurements. We also examine the thermodynamical and dynamical influences in the cloud mesoscale morphology using WRF sensitivity experiments driven by two meteorological forcing datasets with different domain-mean SST and spatial gradients, which lead to dissimilar values of hydrometeor water path and cloud core fraction. The SST from ERA5 leads to weaker stability and higher inversion height than the SST from FNL does. In addition, the use of large-scale meteorological forcings from ERA5 yields a distinctive time evolution of wind direction shear in the inner domain, which favors the formation and persistence of longer cloud rolls. Both factors contribute to a change in the time evolution of domain-mean water path and cloud core fraction of cloud streets. Our study takes advantage of the simulation driven by the differences between two large-scale forcing datasets to illustrate the importance of SST and wind direction shear in the cloud street morphology in a realistic scenario
Fifteen years of Aqua Clouds and the Earth's Radiant Energy Systems (CERES) and MOderate resolution Imaging Spectroradiometer (MODIS) observations are combined to derive nearly global maps of ...shortwave albedo (A) and flux (F) response to changes in cloud droplet number concentration (Nd). Absolute (
Sa=∂A∂Nd) and relative (
Sr=∂A∂lnNd) albedo susceptibilities are computed by exploiting the linear relationship between A and ln (Nd) for shallow liquid clouds. Subtropical stratiform clouds over the eastern Pacific, eastern Atlantic, and off the coast of eastern Asia yield the highest Sr, followed by the extratropical oceans during their hemispheric summer. When Sr is cast in terms of F, the eastern Pacific clouds dominate Sr. Sa is mainly governed by Nd, with offshore clouds producing high Sa. While both Sa and Sr are advantageous for understanding radiative aspects of the aerosol indirect effect, Sr is more suitable for calculating changes in A and F due to the linearity of the A‐ln (Nd) relationship.
Plain Language Summary
The amount of solar radiation reflected back to space is strongly controlled by liquid clouds. These clouds are highly sensitive to changes in the environmental conditions due to both natural and anthropogenic factors. Atmospheric aerosols can regulate the cloud microstructure by modifying the number of droplets in the cloud. These changes can lead to an enhancement of the solar energy reflected by clouds. Here satellite data from two sensors are combined to quantify the amount of reflected solar radiation associated with changes in the cloud droplet number concentration. Results are shown globally, with an emphasis in the nonpolar oceanic regions, where liquids clouds tend to be more frequent. The results presented in this study can help shed light into the processes that modulate the cloud radiative response due to aerosol‐cloud interactions.
Key Points
CERES‐MODIS satellite data allow for the estimation of the albedo (A) response to changes in cloud droplet number concentration (Nd)
Pristine environments are more susceptible to change their albedo due to absolute changes in Nd
A linearly changes with fractional changes in Nd; this relationship is the strongest over the eastern Pacific, Atlantic, and coastal regions
Biomass burning smoke is advected over the southeastern
Atlantic Ocean between July and October of each year. This smoke plume
overlies and mixes into a region of persistent low marine clouds. Model
...calculations of climate forcing by this plume vary significantly in both
magnitude and sign. NASA EVS-2 (Earth Venture Suborbital-2) ORACLES
(ObseRvations of Aerosols above CLouds and their intEractionS) had deployments for field campaigns off the west coast of Africa in 3 consecutive years (September 2016, August 2017, and October 2018) with the goal of better characterizing this plume as a function of the monthly evolution by measuring the parameters necessary to calculate the direct aerosol radiative effect. Here, this dataset and satellite retrievals of cloud properties are used to test the representation of the smoke plume and the underlying cloud layer in two regional models (WRF-CAM5 and CNRM-ALADIN) and two global models (GEOS and UM-UKCA). The focus is on the comparisons of those aerosol and cloud properties that are the primary determinants of the direct aerosol radiative effect and on the vertical distribution of the plume and its properties. The representativeness of the observations to monthly averages are tested for each field campaign, with the sampled mean aerosol light extinction generally found to be within 20 % of the monthly mean at plume altitudes. When compared to the observations, in all models, the simulated plume is too vertically diffuse and has smaller vertical gradients, and in two of the models (GEOS and UM-UKCA), the plume core is displaced lower than in the observations. Plume carbon monoxide, black carbon, and organic aerosol masses indicate underestimates in modeled plume concentrations, leading, in general, to underestimates in mid-visible aerosol extinction and optical
depth. Biases in mid-visible single scatter albedo are both positive and
negative across the models. Observed vertical gradients in single scatter
albedo are not captured by the models, but the models do capture the coarse
temporal evolution, correctly simulating higher values in October (2018)
than in August (2017) and September (2016). Uncertainties in the measured
absorption Ångstrom exponent were large but propagate into a negligible
(<4 %) uncertainty in integrated solar absorption by the aerosol and, therefore, in the aerosol direct radiative effect. Model biases in cloud fraction, and, therefore, the scene albedo below the plume, vary significantly across the four models. The optical thickness of clouds is, on average, well simulated in the WRF-CAM5 and ALADIN models in the stratocumulus region and is underestimated in the GEOS model; UM-UKCA simulates cloud optical thickness that is significantly too high. Overall, the study demonstrates the utility of repeated, semi-random sampling across multiple years that can give insights into model biases and how these biases affect modeled climate forcing. The combined impact of these aerosol and cloud biases on the direct aerosol radiative effect (DARE) is estimated using a first-order approximation for a subset of five comparison grid boxes. A significant finding is that the observed grid box average aerosol and cloud properties yield a positive (warming) aerosol direct radiative effect for all five grid boxes, whereas DARE using the grid-box-averaged modeled properties ranges from much larger positive values to small, negative values. It is shown quantitatively how model biases can offset each other, so that model improvements that reduce biases in only one property (e.g., single scatter albedo but not cloud fraction) would lead to even greater biases in DARE. Across the models, biases in aerosol extinction and in cloud fraction and optical depth contribute the largest biases in DARE, with aerosol single scatter albedo also making a significant contribution.
Cloud microphysical observations collected in situ during the VAMOS Ocean‐Cloud‐Atmosphere‐Land Study Regional Experiment within the Chile‐Peru stratocumulus cloud deck during October–November 2008 ...were used to assess MODIS Level 2 cloud property retrievals. The in situ aircraft‐derived cloud property values were constructed from the drop size distributions measured by the Cloud Droplet Probe (drop diameter <52 micron) and Two‐Dimensional Cloud Probe (drop diameters up to 1600 micron) during 20 vertical profiles. Almost all of the MODIS cloud scenes were highly homogeneous. MODIS cloud optical thickness correlated well with the aircraft‐derived value with a slight offset within instrumental/retrieval uncertainties. In contrast, the standard 2.1 micron‐derived MODIS effective radius (re) systematically exceeded the in situ cloud top reby 15%–20%, for an absolute error that increased with droplet size. The individual effective radius retrievals at 1.6, 2.1, and 3.7 micron did not provide additional information on cloud vertical structure for our data sample. The secondarily derived MODIS liquid water path also exceeded the in situ value. A MODIS‐derived cloud droplet number concentration (Nd) estimate agreed the best of the four MODIS variables with the aircraft observations. The analysis also highlighted a lack of agreement in published satellite‐derivedNd values, despite drawing on the same sources. A best a priori formula choice for Nd is likely to vary regionally. Four sources of errors within the MODIS reretrieval were investigated further: the cloud mode droplet size distribution breadth, the presence of a drizzle mode, above‐cloud water vapor absorption, and sensor viewing angles. These processes combined conspired to explain most of the observed bias. The above‐cloud water vapor paths were poorly specified, primarily because the cloud top heights are placed too high, and secondarily because the water vapor paths are unrealistic. Improvement of the above‐cloud water vapor path specification can most easily and systematically improve the MODIS effective radius and liquid water path retrievals.
Key Points
MODIS cloud effective radius overestimates the in situ observations
MODIS bias cannot be explained by the vertical structure or droplet spectra
MODIS‐derived number of droplets show the best agreement with the observations
The mean structure and diurnal cycle of southeast (SE) Atlantic boundary layer clouds are described with satellite observations and multiscale modeling framework (MMF) simulations during austral ...spring (September–November). Hourly resolution cloud fraction (CF) and cloud-top height (HT
) are retrieved fromMeteosat-9radiances using modified Clouds and the Earth’s Radiant Energy System (CERES) Moderate Resolution Imaging Spectroradiometer (MODIS) algorithms, whereas liquid water path (LWP) is from the University of Wisconsin microwave satellite climatology. The MMF simulations use a 2D cloud-resolving model (CRM) that contains an advanced third-order turbulence closure to explicitly simulate cloud physical processes in every grid column of a general circulation model. The model accurately reproduces themarine stratocumulus spatial extent and cloud cover. The mean cloud cover spatial variability in the model is primarily explained by the boundary layer decoupling strength, whereas a boundary layer shoaling accounts for a coastal decrease in CF. Moreover, the core of the stratocumulus cloud deck is concomitant with the location of the strongest temperature inversion. Although the model reproduces the observed westward boundary layer deepening and the spatial variability of LWP, it overestimates LWP by 50%. Diurnal cycles ofHT
, CF, and LWP from satellites and the model have the same phase, with maxima during the early morning and minima near 1500 local solar time, which suggests that the diurnal cycle is driven primarily by solar heating. Comparisons with the SE Pacific cloud deck indicate that the observed amplitude of the diurnal cycle is modest over the SE Atlantic, with a shallower boundary layer as well. The model qualitatively reproduces these interregime differences.
The representation of aerosols in climate–chemistry models is important for air quality and climate change research, but it can require significant computational resources. The objective of this ...study was to improve the representation of aerosols in climate–chemistry models, specifically in the carbon bond mechanism, version Z (CBMZ), and modal aerosol modules with three lognormal modes (MAM3) in the WRF-CAM5 model. The study aimed to enhance the model’s chemistry capabilities by incorporating biomass burning emissions, establishing a conversion mechanism between volatile organic compounds (VOCs) and secondary organic carbons (SOCs), and evaluating its performance against observational benchmarks. The results of the study demonstrated the effectiveness of the enhanced chemistry capabilities in the WRF-CAM5 model. Six simulations were conducted over the western U.S. and northeastern Pacific region, comparing the model’s performance with observational benchmarks such as reanalysis, ground-based, and satellite data. The findings revealed a significant reduction in root-mean-square errors (RMSE) for surface concentrations of black carbon (BC) and organic carbon (OC). Specifically, the model exhibited a 31% reduction in RMSE for BC concentrations and a 58% reduction in RMSE for OC concentrations. These outcomes underscored the importance of accurate aerosol representation in climate–chemistry models and emphasized the potential for improving simulation accuracy and reducing errors through the incorporation of enhanced chemistry modules in such models.
The energy balance equation of an atmospheric column indicates that two approaches are possible to compute regional net surface energy flux. The first approach is to use the sum of surface energy ...flux components
F
net,c
and the second approach is to use net top-of-atmosphere (TOA) irradiance and horizontal energy transport by the atmosphere
F
net,t
. When regional net energy flux is averaged over the global ocean,
F
net,c
and
F
net,t
are, respectively, 16 and 2 Wm
–2
, both larger than the ocean heating rate derived from ocean temperature measurements. The difference is larger than the estimated uncertainty of
F
net,t
of 11 Wm
–2
. Larger regional differences between
F
net,c
and
F
net,t
exist over tropical ocean. The seasonal variability of energy flux components averaged between 45°N and 45°S ocean reveals that the surface provides net energy to the atmosphere from May to July. These two examples demonstrates that the energy balance can be used to assess the quality of energy flux data products.
Solar radiation absorption by biomass burning aerosols has a strong warming effect over the southeast Atlantic. Interactions between the overlying smoke aerosols and low‐level cloud microphysics and ...the subsequent albedo perturbation are, however, generally ignored in biomass burning radiative assessments. In this study, Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are combined with Aqua satellite observations from Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer–EOS (AMSR‐E), and Clouds and the Earth's Radiant Energy System (CERES) to assess the effect of variations in the boundary layer height and the separation distance between the cloud and aerosol layers on the cloud microphysics. The merged data analyzed at a daily temporal resolution suggest that overlying smoke aerosols modify cloud properties by decreasing cloud droplet size despite an increase in the cloud liquid water as boundary layer deepens, north of 5°S. These changes are controlled by the proximity of the aerosol layer to the cloud top rather than increases in the column aerosol load. The correlations are unlikely driven by meteorological factors, as three predictors of cloud variability, lower tropospheric stability, surface winds, and mixing ratio suggest that cloud effective radius, cloud top height, and liquid water path should correlate positively. Because cloud effective radius anticorrelates with cloud liquid water over the region with large microphysical changes—north of 5°S—the overall radiative consequence at the top of the atmosphere is a strong albedo susceptibility, equivalent to a 3% albedo increase due to a 10% decrease in cloud effective radius. This albedo enhancement partially offsets the aerosol solar absorption. Our analysis emphasizes the importance of accounting for the indirect effect of smoke aerosols in the cloud microphysics when estimating the radiative impact of the biomass burning at the top of the atmosphere.
Key Points
Overlying smoke aerosols modify clouds by decreasing cloud droplet sizeChanges are controlled by the proximity of the aerosol layer to the cloud topAnticorrelation between droplet size and water path not driven by meteorology
The dependence of the top‐of‐the‐atmosphere (TOA) albedo A on cloud microphysical properties was investigated for the three largest maritime stratocumulus clouds regimes: off California, Southeast ...Pacific (Chile‐Peru), and southwest Africa (Namibia‐Angola). Absolute S and relative SR albedo susceptibilities to perturbations in cloud droplet number concentrations Nd, defined as dA/dNd and dA/dln(Nd) respectively, were calculated for the season having maximum cloud cover during the period 2006–2010. Satellite‐based susceptibilities were computed by combining an adiabatically based Nd estimate and liquid water path (LWP) derived from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals matched with TOA A from the Clouds and the Earth's Radiant Energy System. Empirical susceptibility maps were calculated for three constant LWP intervals at 25, 50, and 90 g−2. It was found that S increases with LWP, with small and spatially homogeneous values for low LWP, and a contrasting increase far offshore for larger LWP values. An overall increase of SR with LWP was also observed, with larger values near the coast for LWP = 25 and 50 g−2. A relatively homogeneous spatial pattern of maximum SR values covered most of each regime's domain for a LWP of 90 g−2. These results highlight the importance of LWP in modulating the albedo susceptibility. The dependencies of S and SR on LWP are mostly explained by variations in the mean Nd and cloud optical thickness (τ), with an increase of S with LWP linked to a decrease in Nd, whereas SR increased with τ and A, until reaching a maximum for A and τ near 0.36–0.4 and 12–14 respectively, and decreasing thereafter, consistent with expectations based on two‐stream estimates. Larger SR values in the Southeast Pacific are thought to be the consequence of a drier and more pristine atmosphere. Radiative transfer simulations with realistic values of above‐cloud water vapor path and aerosol optical thickness showed that differing atmospheric compositions could explain why the Chile‐Peru regime was the marine stratocumulus cloud deck most susceptible to change its TOA albedo due to fractional changes in Nd.
Key Points
Albedo susceptibility increases with liquid water path
Susceptibilities mainly controlled by number of droplets and optical thickness
Differences among regimes are also explained by the atmospheric composition
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