Clouds cover about 70% of Earth's surface and play a dominant role in the energy and water cycle of our planet. Only satellite observations provide a continuous survey of the state of the atmosphere ...over the entire globe and across the wide range of spatial and temporal scales that compose weather and climate variability. Satellite cloud data records now exceed more than 25 years; however, climate data records must be compiled from different satellite datasets and can exhibit systematic biases. Questions therefore arise as to the accuracy and limitations of the various sensors and retrieval methods. The Global Energy and Water Cycle Experiment (GEWEX) Cloud Assessment, initiated in 2005 by the GEWEX Radiation Panel (GEWEX Data and Assessment Panel since 2011), provides the first coordinated intercomparison of publicly available, standard global cloud products (gridded monthly statistics) retrieved from measurements of multispectral imagers (some with multiangle view and polarization capabilities), IR sounders, and lidar. Cloud properties under study include cloud amount, cloud height (in terms of pressure, temperature, or altitude), cloud thermodynamic phase, and cloud radiative and bulk microphysical properties (optical depth or emissivity, effective particle radius, and water path). Differences in average cloud properties, especially in the amount of high-level clouds, are mostly explained by the inherent instrument measurement capability for detecting and/or identifying optically thin cirrus, especially when overlying low-level clouds. The study of long-term variations with these datasets requires consideration of many factors. The monthly gridded database presented here facilitates further assessments, climate studies, and the evaluation of climate models.
Phase transitions leading to cloud glaciation occur at temperatures that vary between
−38°C and 0°C depending on aerosol types and concentrations, the meteorology, and cloud microphysical and ...macrophysical parameters, although the relationships remain poorly understood. Here, we statistically retrieve a cloud glaciation temperature from two passive space‐based instruments that are part of the NASA/CNES A‐Train, the POLarization and Directionality of the Earth's Reflectances (POLDER) and the MODerate resolution Imaging Spectroradiometer (MODIS). We compare the glaciation temperature for varying bins of cloud droplet effective radius, latitude, and large‐scale vertical pressure velocity and specific humidity at 700 hPa. Cloud droplet size has the strongest influence on glaciation temperature: For cloud droplets larger than 21
μm, the glaciation temperature is 6°C higher than for cloud droplets smaller than 9
μm. Stronger updrafts are also associated with lower glaciation temperatures.
Key Points
The temperature at which liquid clouds glaciate is inferred using passive space‐based instruments
Glaciation temperature is classified by droplet radius, latitude, updraft speed, and specific humidity
Substantially less supercooling is required for glaciation of liquid clouds composed of large droplets
The rate of warming in the Arctic depends upon the response of low‐level microphysical and radiative cloud properties to aerosols advected from distant anthropogenic and biomass‐burning sources. ...Cloud droplet cross‐section density increases with higher concentrations of cloud condensation nuclei, leading to an increase of cloud droplet absorption and scattering radiative cross sections. The challenge of assessing the magnitude of the effect has been decoupling the aerosol impacts on clouds from how clouds change solely due to natural meteorological variability. Here we address this issue with large, multi‐year satellite, meteorological, and tracer transport model data sets to show that the response of low‐level clouds in the Arctic to anthropogenic aerosols lies close to a theoretical maximum and is between 2 and 8 times higher than has been observed elsewhere. However, a previously described response of arctic clouds to biomass‐burning plumes appears to be overstated because the interactions are rare and modification of cloud radiative properties appears better explained by coincident changes in temperature, humidity, and atmospheric stability.
Key Points
Arctic liquid clouds are particularly sensitive to anthropogenic pollution from lower latitudes
Biomass burning has a weaker impact compared to aerosol arriving from anthropogenic sources
Analyses of interactions between pollution and clouds must account for local meteorological variability
Reduced precipitation rates allow pollution within air parcels from midlatitudes to reach the Arctic without being scavenged. We use satellite and tracer transport model data sets to evaluate the ...degree of supercooling required for 50% of a chosen ensemble of low‐level clouds to be in the ice phase for a given meteorological regime. Our results suggest that smaller cloud droplet effective radii are related to higher required amounts of supercooling but that, overall, pollution plumes from fossil fuel combustion lower the degree of supercooling that is required for freezing by approximately 4 °C. The relationship between anthropogenic plumes and the freezing transition temperature from liquid to ice remains to be explained.
Plain Language Summary
Anthropogenic pollution plumes from midlatitudes can be transported long distances to the Arctic. In this study, we analyze the impact of these plumes on how easily liquid clouds over the Arctic Ocean freeze by using a novel combination of satellite measurements and a pollution transport model. We find that liquid clouds in polluted air switch phase to become ice clouds at temperatures that are 4 °C higher they would otherwise in pristine air. Because ice clouds in the Arctic precipitate more easily than liquid clouds, the potential is that distant industrial pollution sources are acting to reduce arctic cloud life time.
Key Points
MODIS and POLDER‐3 can be used to provide space‐based measurements of phase transitions in liquid arctic clouds
A smaller droplet effective radius is associated with an increase in the amount of supercooling required for freezing
Anthropogenic pollution plumes are associated with a decrease in the amount of supercooling required for freezing
This study investigates the magnitude of the error
introduced by the co-registration and interpolation in computing Stokes
vector elements from observations by the Multi-viewing, Multi-channel,
...Multi-polarisation Imager (3MI). The Stokes parameter derivation from the
3MI measurements requires the syntheses of three wide-field-of-view images
taken by the instrument at 0.25 s interval with polarizers at different
angles. Even though the synthesis of spatially or temporally inhomogeneous
data is inevitable for a number of polarimetric instruments, it is
particularly challenging for 3MI because of the instrument design, which prioritizes
the stability during a long life cycle and enables the
wide-field-of-view and multiwavelength capabilities. This study therefore
focuses on 3MI's motion-induced error brought in by the co-registration
and interpolation that are necessary for the synthesis of three images. The
2-D polarimetric measurements from the Second-generation Global Imager
(SGLI) are weighted and averaged to produce two proxy datasets of the 3MI
measurements, with and without considering the effect of the satellite
motion along the orbit. The comparison of these two datasets shows that the
motion-induced error is not symmetric about zero and not negligible when the
intensity variability of the observed scene is large. The results are
analyzed in five categories of pixels: (1) cloud over water, (2) clear sky over water, (3) coastlines, (4) cloud over
land, and (5) clear sky over land. The
most spread distribution of normalized polarized radiance (Lp)
difference is in the cloud-over-water class, and the most spread
distribution of degree of linear polarization (DOLP) difference is in the
clear-sky-over-water class. The 5th to 95th percentile ranges of Lp difference for
each class are (1) -0.0051,0.012, (2) -0.0040,0.0088, (3)
-0.0033,0.012, (4) -0.0033,0.0062, and (5) -0.0023,0.0032. The same
percentile range of DOLP difference for each class are (1) -0.023,0.060,
(2) -0.043,0.093, (3) -0.019,0.082, (4) -0.0075,0.014, and (5)
-0.011,0.016. The medians of the Lp difference are (1) 0.00035, (2)
0.000049, (3) 0.00031, (4), 0.000089, and (5) 0.000037, whereas the medians
of the DOLP difference are (1) 0.0014, (2) 0.0015, (3) 0.0025, (4) 0.00027,
and (5) 0.00014. A model using Monte Carlo simulation confirms that the
magnitude of these errors over clouds are closely related to the spatial
correlation in the horizontal cloud structure. For the cloud-over-water
category, it is shown that the error model developed in this study can
statistically simulate the magnitude and trends of the 3MI's motion-induced
error estimated from SGLI data. The obtained statistics and the simulation
technique can be utilized to provide pixel-level quality information for 3MI
Level 1B products. In addition, the simulation method can be applied to the
past, current, and future spaceborne instruments with a similar design.
Ice clouds are an important element in the radiative balance of the earth's climate system, but their microphysical and optical properties still are not well constrained, especially ice particle ...habit and the degree of particle surface roughness. In situ observations have revealed common ice particle habits and evidence for surface roughness, but these observations are limited. An alternative is to infer the ice particle shape and surface roughness from satellite observations of polarized reflectivity since they are sensitive to both particle shape and degree of surface roughness. In this study an adding–doubling radiative transfer code is used to simulate polarized reflectivity for nine different ice habits and one habit mixture, along with 17 distinct levels of the surface roughness. A lookup table (LUT) is constructed from the simulation results and used to infer shape and surface roughness from PARASOL satellite polarized reflectivity data over the ocean. Globally, the retrievals yield a compact aggregate of columns as the most commonly retrieved ice habit. Analysis of PARASOL data from the tropics results in slightly more aggregates than in midlatitude or polar regions. Some level of surface roughness is inferred in nearly 70% of PARASOL data, with mean and median roughness near σ = 0.2 and 0.15, respectively. Tropical region analyses have 20% more pixels retrieved with particle surface roughness than in midlatitude or polar regions. The global asymmetry parameter inferred at a wavelength of 0.865 μm has a mean value of 0.77 and a median value of 0.75.
Cloud droplet number concentration (CDNC) is an important microphysical property of liquid clouds that impacts radiative forcing, precipitation and is pivotal for understanding cloud–aerosol ...interactions. Current studies of this parameter at global scales with satellite observations are still challenging, especially because retrieval algorithms developed for passive sensors (i.e., MODerate Resolution Imaging Spectroradiometer (MODIS)/Aqua) have to rely on the assumption of cloud adiabatic growth. The active sensor component of the A-Train constellation (i.e., Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)/CALIPSO) allows retrievals of CDNC from depolarization measurements at 532 nm. For such a case, the retrieval does not rely on the adiabatic assumption but instead must use a priori information on effective radius (re), which can be obtained from other passive sensors. In this paper, re values obtained from MODIS/Aqua and Polarization and Directionality of the Earth Reflectance (POLDER)/PARASOL (two passive sensors, components of the A-Train) are used to constrain CDNC retrievals from CALIOP. Intercomparison of CDNC products retrieved from MODIS and CALIOP sensors is performed, and the impacts of cloud entrainment, drizzling, horizontal heterogeneity and effective radius are discussed. By analyzing the strengths and weaknesses of different retrieval techniques, this study aims to better understand global CDNC distribution and eventually determine cloud structure and atmospheric conditions in which they develop. The improved understanding of CDNC can contribute to future studies of global cloud–aerosol–precipitation interaction and parameterization of clouds in global climate models (GCMs).
The detection of aerosol above clouds is critical for the estimate of both the aerosol and cloud radiative impacts. In this study, the authors present a new method to retrieve the aerosol properties ...over clouds that uses the multiangle polarization measurements of the Polarization and Directionality of Earth Reflectances (POLDER)-Polarization and Anisotropy of Reflectances for Atmospheric Sciences Coupled with Observations from a Lidar (PARASOL) instrument. The method is illustrated and applied to a case study exploiting the coincident observations from other passive and active sensors of the NASA A-Train satellite constellation. The case study is relative to an elevated biomass burning aerosol layer that originates from southern Africa and is then transported over low-level clouds extending over the Atlantic Ocean. It is shown that the comparison between the cloud-top heights retrieved with the different passive techniques developed for the A-Train sensors can be used to detect the presence of aerosols above clouds. The analysis of the PARASOL observations showed that the aerosols significantly affect the polarized light reflected by the clouds over the 80-120 scattering angle range and in the rainbow region. A single scattering model permitted the reproduction of the polarization observations and the retrieval of an estimate of the aerosol layer optical thickness of 0.225 at 0.865 km. The retrieved aerosol optical thicknesses over clouds agree quantitatively with the closest ones retrieved over clear-sky ocean (c0.04 as a maximum departure), demonstrating the value of the method. This innovative technique based solely on passive measurements is expected to provide a better understanding of aerosol properties in regions where significant cloud cover usually prevents the retrieval of aerosol optical thickness. As such, this new retrieval method can provide significant and valuable information about the radiative impact of clouds and aerosols, especially where they can potentially interact strongly with each other.
Most of the current aerosol retrievals from passive sensors are restricted to cloud-free scenes, which strongly reduces our ability to monitor the aerosol properties at a global scale and to estimate ...their radiative forcing. The presence of aerosol above clouds (AAC) affects the polarized light reflected by the cloud layer, as shown by the spaceborne measurements provided by the POlarization and Directionality of Earth Reflectances (POLDER) instrument on the PARASOL satellite. In a previous work, a first retrieval method was developed for AAC scenes and evaluated for biomass-burning aerosols transported over stratocumulus clouds. The method was restricted to the use of observations acquired at forward scattering angles (90–120°) where polarized measurements are highly sensitive to fine-mode particle scattering. Non-spherical particles in the coarse mode, such as mineral dust particles, do not much polarize light and cannot be handled with this method. In this paper, we present new developments that allow retrieving also the properties of mineral dust particles above clouds. These particles do not much polarize light but strongly reduce the polarized cloud bow generated by the liquid cloud layer beneath and observed for scattering angles around 140°. The spectral attenuation can be used to qualitatively identify the nature of the particles (i.e. accumulation mode versus coarse mode, i.e. mineral dust particles versus biomass-burning aerosols), whereas the magnitude of the attenuation is related to the optical thickness of the aerosol layer. We also use the polarized measurements acquired in the cloud bow to improve the retrieval of both the biomass-burning aerosol properties and the cloud microphysical properties. We provide accurate polarized radiance calculations for AAC scenes and evaluate the contribution of the POLDER polarization measurements for the simultaneous retrieval of the aerosol and cloud properties. We investigate various scenes with mineral dust particles and biomass-burning aerosols above clouds. For clouds, our results confirm that the droplet size distribution is narrow in high-latitude ocean regions and that the droplet effective radii retrieved from both polarization measurements and from total radiance measurements are generally close for AAC scenes (departures smaller than 2 μm). We found that the magnitude of the primary cloud bow cannot be accurately estimated with a plane parallel transfer radiative code. The errors for the modeling of the polarized cloud bow are between 4 and 8% for homogenous cloudy scenes, as shown by a 3-D radiative transfer code. These effects only weakly impact the retrieval of the Aerosol Optical Thickness (AOT) performed with a mineral dust particle model for which the microphysical properties are entirely known (relative error smaller than 6%). We show that the POLDER polarization measurements allow retrieving the AOT, the fine-mode particle size, the Ångström exponent and the fraction of spherical particles. However, the complex refractive index and the coarse-mode particle size cannot be accurately retrieved with the present polarization measurements. Our complete and accurate algorithm cannot be applied to process large amounts of data, so a simpler algorithm was developed to retrieve the AOT and the Ångström exponent above clouds in an operational way. Illustrations are provided for July–August 2008 near the African coast. Large mean AOTs above clouds at 0.865 μm (>0.3) are retrieved for oceanic regions near the coasts of South Africa that correspond to biomass-burning aerosols, whereas even larger mean AOTs above clouds for mineral dust particles (>0.6) are also retrieved near the coasts of Senegal. For these regions and time period, the direct AAC radiative forcing is likely to be significant. The final aim of this work is the global monitoring of the AAC properties and the estimation of the direct aerosol radiative forcing in cloudy scenes.
Cloud optical thickness (COT) is one of the most important parameter for the characterization of cloud in the Earth radiative budget. Its retrieval strongly depends on instrument characteristics and ...on many cloud and environment factors. Using coincident observations from POLDER/PARASOL and MODIS/AQUA in the A-Train constellation, geographical distributions and seasonal changes of COT are presented, in good agreement with general cloud climatology characteristics. Retrieval uncertainties mainly associated to sensor spatial resolution, cloud inhomogeneity and microphysical assumptions are discussed. Comparisons of COT derived from POLDER and MODIS illustrate that as the primary factor, the sensor spatial resolution impacts COT retrievals and statistics through both cloud detection and sub-pixel cloud inhomogeneity sensitivity. The uncertainties associated to cloud microphysics assumptions, namely cloud phase, particle size and shape, also impact significantly COT retrievals. For clouds with unambiguous cloud phase, strong correlations exist between the two COTs, with MODIS values comparable to POLDER ones for liquid clouds and MODIS values larger than POLDER ones for ice clouds. The large differences observed in ice phase cases are due to the use of different microphysical models in the two retrieval schemes. In cases when the two sensors disagree on cloud phase decision, COT retrieved assuming liquid phase is systematically larger. The angular biases related to specific observation geometries are also quantified and discussed in particular based on POLDER observations. Those exhibit a clear increase of COT with decreasing sun elevation and a decrease of COT in forward scattering directions due to sub-pixel inhomogeneities and shadowing effects, this especially for lower sun. It also demonstrates unrealistic COT variations in the cloudbow and backward directions due to inappropriate cloud optical properties representation and an important increase of COT in the sun-glint directions in case of broken cloud.
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