H. Chepfer

Improving Retrievals of Cirrus Cloud Particle Size Coupling Lidar and Three-Channel Radiometric Techniques

This study is intended to illustrate the potential advantage of combining lidar measurements and the split-window technique based on the infrared spectral information contained at the 8.65-, 11.15-, and 12.05-μm bands for inferring the microphysical properties of cirrus clouds. The lidar returns are employed to detect cirrus clouds. The optical properties of nonspherical ice crystals computed from the state-of-the-art scattering computational methods are used for the present forward radiative transfer simulation that fully accounts for both gaseous absorption and multiple scattering processes in the atmosphere. A combination of the radiances at the three infrared (IR) bands with lidar backscatter returns cannot uniquely specify the effective size of ice crystals because of its dependence on the particle aspect ratios. To avoid the shortcoming associated with a potential multivalued retrieval, lidar depolarization observation is used to constrain the specification of the particle aspect ratio in the retrieval implementation based on a precalculated lookup library. The present methodology for inferring the microphysical properties of cirrus clouds is implemented for nine cases by using the measurements from a 532-nm lidar located at the Palaiseau, France, ground-based site and the infrared spectral bands from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra platform. It is shown that the three IR wavelengths are quite complementary in constraining the retrieval of the particle size, leading to a significant advance in comparison with two-channel techniques, whereas the aspect ratio constraint due to lidar depolarization reduces the uncertainty of retrieved particle size by more than 20% for 70% of the cases and more than 65% for 40% of the cloud cases.

Comparison of CALIPSO-Like, LaRC, and MODIS Retrievals of Ice-Cloud Properties over SIRTA in France and Florida during CRYSTAL-FACE

This study compares cirrus-cloud properties and, in particular, particle effective radius retrieved by a Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)-like method with two similar methods using Moderate-Resolution Imaging Spectroradiometer (MODIS), MODIS Airborne Simulator (MAS), and Geostationary Operational Environmental Satellite imagery. The CALIPSO-like method uses lidar measurements coupled with the split-window technique that uses the infrared spectral information contained at the 8.65-, 11.15-, and 12.05-μm bands to infer the microphysical properties of cirrus clouds. The two other methods, using passive remote sensing at visible and infrared wavelengths, are the operational MODIS cloud products (using 20 spectral bands from visible to infrared, referred to by its archival product identifier MOD06 for MODIS Terra) and MODIS retrievals performed by the Clouds and the Earths Radiant Energy System (CERES) team at Langley Research Center (LaRC) in support of CERES algorithms (using 0.65-, 3.75-, 10.8-, and 12.05-μm bands); the two algorithms will be referred to as the MOD06 and LaRC methods, respectively. The three techniques are compared at two different latitudes. The midlatitude ice-clouds study uses 16 days of observations at the Palaiseau ground-based site in France [Site Instrumental de Recherche par Teledetection Atmospherique (SIRTA)], including a ground-based 532-nm lidar and the MODIS overpasses on the Terra platform. The tropical ice-clouds study uses 14 different flight legs of observations collected in Florida during the intensive field experiment known as the Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment (CRYSTAL-FACE), including the airborne cloud-physics lidar and the MAS. The comparison of the three methods gives consistent results for the particle effective radius and the optical thickness but discrepancies in cloud detection and altitudes. The study confirms the value of an active remote sensing method (CALIPSO like) for the study of subvisible ice clouds, in both the midlatitudes and Tropics. Nevertheless, this method is not reliable in optically very thick tropical ice clouds, because of their particular microphysical properties.

Particle habit in tropical ice clouds during CRYSTAL-FACE: Comparison of two remote sensing techniques with in situ observations

[1]xa0Ice crystal shapes in tropical ice clouds are estimated with two different remote sensing methods and compared with measurements from an in situ cloud aerosol spectrometer (CAS) during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida Area Cirrus Experiment (CRYSTAL-FACE) campaign conducted in Florida during July 2002. The remote sensing techniques use dual-satellite reflectances and lidar linear depolarization rates. The ice crystal shape is derived from CAS measurements of forward and backscattered light from individual particles in the size range from 1 to 45 μm. The remote sensing and in situ retrievals are based on ice crystal optical models, which incorporate the scattering phase functions integrated over the collection angles used by the CAS, the view angles from dual-satellites retrievals, and the complete scattering matrix for lidar. Owing to the space and time collocation constraint between in situ and remote sensing techniques, data from only 1 day are used to evaluate the dual-satellite technique (11 July) and from 3 days for the lidar (23, 26, and 29 July). Data from 23 and 29 July are also used to compare the two remote sensing techniques. In total, 40 shape retrievals were obtained for 20 different cloud areas, allowing paired comparisons of the methods. The results show consistent particle shapes for half of the cloud areas studied. The discrepancies for the other cases can be explained by insufficient spatial-temporal collocations of the data or limitations of the CAS that constrain its range to particles <45 μm, whereas the remote sensing techniques are influenced by particles outside the size range of the CAS.

Disagreement among global cloud distributions from CALIOP, passive satellite sensors and general circulation models

Cloud detection is the first step of any complex satellite-based cloud retrieval. No instrument detects all clouds, and analyses that use a given satellite climatology can only discuss a specific subset of clouds. We attempt to clarify which subsets of clouds are detected in a robust way by passive sensors, and which require active sensors. To do so, we identify where retrievals of Cloud Amounts (CAs), based on numerous sensors and algorithms, differ the most. We investigate large uncertainties, and confront retrievals from the CALIOP lidar, which detects semitransparent clouds and directly measures their vertical distribution, whatever the surface below. We document the cloud vertical distribution, opacity and seasonal variability where CAs from passive sensors disagree most. nCALIOP CAs are larger than the passive average by +0.05 (AM) and +0.07 (PM). Over land, the +0.1 average difference rises to +0.2 over the African desert, Antarctica and Greenland, where large passive disagreements are traced to unfavorable surface conditions. Over oceans, CALIOP retrievals are closer to the average of passive retrievals except over the ITCZ (+0.1). Passive CAs disagree more in tropical areas associated with large-scale subsidence, where CALIOP observes a specific multi-layer cloud population: optically thin, high-level clouds and opaque (z>7km), shallow boundary layer clouds (z<2km). nWe evaluate the CA and cloud vertical distribution from 8 General Circulation Models where passive retrievals disagree and CALIOP provides new information. We find that modeled clouds are not more realistic where cloud detections from passive observations have long been robust, than where active sensors provide more reliable information.

The Potential of a Multidecade Spaceborne Lidar Record to Constrain Cloud Feedback

Synthetic multi‐decadal space‐borne lidar records are used to examine when a cloud response to anthropogenic forcing would be detectable from space‐borne lidar observations. The synthetic records are generated using long‐term cloud changes predicted by two CMIP5 (Coupled Model Intercomparison Program 5) models seen through the COSP/lidar (CFMIP –Cloud Feedback Model Intercomparison Project, Observation Simulators Package), and cloud inter‐annual variability observed by the CALIPSO (Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations) spaceborne lidar during the past decade. nCALIPSO observations do not show any significant trend yet. Our analysis of the synthetic time series suggests, the Tropical cloud longwave feedback and the Southern ocean cloud shortwave feedback might be constrained with 70% confidence with, respectively, a 20‐year and 29‐year uninterrupted lidar‐in‐space record. A 27‐year record might be needed to separate the two different models predictions in the tropical subsidence clouds. nAssuming that combining the CALIPSO and Earth‐CARE (Earth Clouds, Aerosols and Radiation Explorer) missions will lead to a space‐borne lidar record of at least 16 years, we examine the impact of gaps and calibration offsets between successive missions. A 2‐year gap between EarthCARE and the following space‐borne lidar would have no significant impact on the capability to constrain the cloud feedback if all the space lidars were perfectly inter‐calibrated. Any intercalibration shift between successive lidar missions would delay the capability to constrain the cloud feedback mechanisms, larger shifts leading to longer delays.