On the origin of subvisible cirrus clouds in the tropical upper troposphere

Spaceborne lidar observations have recently revealed a previously undetected significant population of Subvisible Cirrus (SVC). We show them to be colder than −74 °, with an optical depth below 0.0015 on average. The formation and persistence over time of this new cloud population could be related to several atmospheric phenomena. In this paper, we investigate if these clouds follow the same formation mechanisms as the general tropical cirrus population (including convection and in-situ ice nucleation), or if specific nucleation sites and trace species play a role in their formation. The importance of three scenarios in the formation of the global SVC population is investigated through different approaches that include comparisons with data imaging from several spaceborne instruments and back-trajectories that document the history and behavior of air masses leading to the point in time and space where subvisible cirrus were detected. In order to simplify the study of their formation, we singled out SVC with coherent temperature histories (mean variance lower than 4 K) according to back-trajectories along 5, 10 or 15 days (respectively 58, 25 and 11% of SVC). Our results suggest that external processes, including local increases in liquid and hygroscopic aerosol concentration (either through biomass burning or volcanic injection forming sulfate-based aerosols in the troposphere or the stratosphere) have very limited short-term or mid-term impact on the SVC population. On the other hand, we find that ~20% of air masses leading to SVC formation interacted with convective activity 5 days before they led to cloud formation and detection, a number that climbs to 60% over 15 days. SVC formation appears especially linked to convection over Africa and Central America, more so during JJA than DJF. These results support the view that the SVC population observed by CALIOP is an extension of the general upper tropospheric ice clouds population with its extreme thinness as its only differentiating factor

Effects of solar activity on noise in CALIOP profiles above the South Atlantic Anomaly

We show that nighttime dark noise measurements from the spaceborne lidar CALIOP contain valuable information about the evolution of upwelling high-energy radiation levels. Above the South Atlantic Anomaly (SAA), CALIOP dark noise levels fluctuate by ±6% between 2006 and 2013, and follow the known anticorrelation of local particle flux with the 11-year cycle of solar activity (with a 1-year lag). By analyzing the geographic distribution of noisy profiles, we are able to reproduce known findings about the SAA region. Over the considered period, it shifts westward by 0.3° year⁻¹, and changes in size by 6° meridionally and 2° zonally, becoming larger with weaker solar activity. All results are in strong agreement with previous works. We predict SAA noise levels will increase anew after 2014, and will affect future spaceborne lidar missions most near 2020

Nitric Acid Particles in Cold Thick Ice Clouds Observed at Global Scale: Link with Lightning, Temperature, and Upper Tropospheric Water Vapor

Signatures of nitric acid particles (NAP) in cold thick ice clouds have been derived from satellite observations. Most NAP are detected in the Tropics (9 to 20% of clouds with T less than 202.5 K). Higher occurrences were found in the rare mid-latitudes very cold clouds. NAP occurrence increases as cloud temperature decreases and NAP are more numerous in January than July. Comparisons of NAP and lightning distributions show that lightning is the main source of the NOx, which forms NAP in cold clouds. Qualitative comparisons of NAP with upper tropospheric humidity distributions suggest that NAP play a role in the dehydration of the upper troposphere when the tropopause is colder than 195K

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 particle effective radius retrieved by a CALIPSO-like method with two similar methods using MODIS, MODI Airborne Simulator (MAS), and GOES 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-micrometer, 11.15-micrometer and 12.05-micrometer bands to infer the microphysical properties of cirrus clouds. The two other methods, sing passive remote sensing at visible and infrared wavelengths, are the operational MODIS cloud products (referred to by its archival product identifier MOD06 for MODIS Terra) and MODIS retrievals performed by the CERES team at LaRC (Langley Research Center) in support of CERES algorithms; the two algorithms will be referred to as MOD06- and LaRC-method, respectively. The three techniques are compared at two different latitudes: (i) the mid-latitude ice clouds study uses 18 days of observations at the Palaiseau ground-based site in France (SIRTA: Site Instrumental de Recherche par Teledetection Atmospherique) including a ground-based 532 nm lidar and the Moderate Resolution Imaging Spectrometer (MODIS) overpasses on the Terra Platform, (ii) the tropical ice clouds study uses 14 different flight legs of observations collected in Florida, during the intensive field experiment CRYSTAL-FACE (Cirrus Regional Study of Tropical Anvils and cirrus Layers-Florida Area Cirrus Experiment), including the airborne Cloud Physics Lidar (CPL) 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 sub-visible ice clouds, in both mid-latitudes and tropics. Nevertheless, this method is not reliable in optically very thick tropical ice clouds

CALIPSO observations of wave-induced PSCs with near-unity optical depth over Antarctica in 2006-2007

International audienceGround-based and satellite observations have hinted at the existence of polar stratospheric clouds (PSCs) with relatively high optical depths, even if optical depth values are hard to come by. This study documents a type II PSC observed from spaceborne lidar, with visible optical depths up to 0.8. Comparisons with multiple temperature fields, including reanalyses and results from mesoscale simulations, suggest that intense small-scale temperature fluctuations due to gravity waves play an important role in its formation, while nearby observations show the presence of a potentially related type Ia PSC farther downstream inside the polar vortex. Following this first case, the geographic distribution and microphysical properties of PSCs with optical depths above 0.3 are explored over Antarctica during the 2006 and 2007 austral winters. These clouds are rare (less than 1% of profiles) and concentrated over areas where strong winds hit steep ground slopes in the Western Hemisphere, especially over the peninsula. Such PSCs are colder than the general PSC population, and their detection is correlated with daily temperature minima across Antarctica. Lidar and depolarization ratios within these clouds suggest they are most likely ice-based (type II). Similarities between the case study and other PSCs suggest they might share the same formation mechanisms

The EarthCARE satellite: the next step forward in global measurements of clouds, aerosols, precipitation, and radiation

The collective representation within global models of aerosol, cloud, precipitation, and their radiative properties remains unsatisfactory. They constitute the largest source of uncertainty in predictions of climatic change and hamper the ability of numerical weather prediction models to forecast high-impact weather events. The joint European Space Agency (ESA)–Japan Aerospace Exploration Agency (JAXA) Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) satellite mission, scheduled for launch in 2018, will help to resolve these weaknesses by providing global profiles of cloud, aerosol, precipitation, and associated radiative properties inferred from a combination of measurements made by its collocated active and passive sensors. EarthCARE will improve our understanding of cloud and aerosol processes by extending the invaluable dataset acquired by the A-Train satellites CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Aqua. Specifically, EarthCARE’s cloud profiling radar, with 7 dB more sensitivity than CloudSat, will detect more thin clouds and its Doppler capability will provide novel information on convection, precipitating ice particle, and raindrop fall speeds. EarthCARE’s 355-nm high-spectral-resolution lidar will measure directly and accurately cloud and aerosol extinction and optical depth. Combining this with backscatter and polarization information should lead to an unprecedented ability to identify aerosol type. The multispectral imager will provide a context for, and the ability to construct, the cloud and aerosol distribution in 3D domains around the narrow 2D retrieved cross section. The consistency of the retrievals will be assessed to within a target of ±10 W m–2 on the (10 km)2 scale by comparing the multiview broadband radiometer observations to the top-of-atmosphere fluxes estimated by 3D radiative transfer models acting on retrieved 3D domains

SIRTA, a ground-based atmospheric observatory for cloud and aerosol research

Ground-based remote sensing observatories have a crucial role to play in providing data to improve our understanding of atmospheric processes, to test the performance of atmospheric models, and to develop new methods for future space-borne observations. Institut Pierre Simon Laplace, a French research institute in environmental sciences, created the Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA), an atmospheric observatory with these goals in mind. Today SIRTA, located 20km south of Paris, operates a suite a state-of-the-art active and passive remote sensing instruments dedicated to routine monitoring of cloud and aerosol properties, and key atmospheric parameters. Detailed description of the state of the atmospheric column is progressively archived and made accessible to the scientific community. This paper describes the SIRTA infrastructure and database, and provides an overview of the scientific research associated with the observatory. Researchers using SIRTA data conduct research on atmospheric processes involving complex interactions between clouds, aerosols and radiative and dynamic processes in the atmospheric column. Atmospheric modellers working with SIRTA observations develop new methods to test their models and innovative analyses to improve parametric representations of sub-grid processes that must be accounted for in the model. SIRTA provides the means to develop data interpretation tools for future active remote sensing missions in space (e.g. CloudSat and CALIPSO). SIRTA observation and research activities take place in networks of atmospheric observatories that allow scientists to access consistent data sets from diverse regions on the globe

Incorporating EarthCARE observations into a multi-lidar cloud climate record: the ATLID (Atmospheric Lidar) cloud climate product

Despite significant advances in atmospheric measurements and modeling, clouds' response to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. The launch of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite in 2006 started the era of long-term spaceborne optical active sounding of Earth's atmosphere, which continued with the CATS (Cloud-Aerosol Transport System) lidar on board the International Space Station (ISS) in 2015 and the Atmospheric Laser Doppler Instrument (ALADIN) lidar on board Aeolus in 2018. The next important step is the Atmospheric Lidar (ATLID) instrument from the EarthCARE (Earth Clouds, Aerosols and Radiation Explorer) mission, expected to launch in 2024. In this article, we define the ATLID Climate Product, Short-Term (CLIMP-ST) and ATLID Climate Product, Long-Term (CLIMP-LT). The purpose of CLIMP-ST is to help evaluate the description of cloud processes in climate models, beyond what is already done with existing space lidar observations, thanks to ATLID's new capabilities. The CLIMP-LT product will merge the ATLID cloud observations with previous space lidar observations to build a long-term cloud lidar record useful to evaluate the cloud climate variability predicted by climate models. We start with comparing the cloud detection capabilities of ATLID and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) in day- and nighttime, on a profile-to-profile basis in analyzing virtual ATLID (355 nm) and CALIOP (532 nm) measurements over synthetic cirrus and stratocumulus cloud scenes. We show that solar background noise affects the cloud detectability in daytime conditions differently for ATLID and CALIPSO. We found that the simulated daytime ATLID measurements have lower noise than the simulated daytime CALIOP measurements. This allows for lowering the cloud detection thresholds for ATLID compared to CALIOP and enables ATLID to better detect optically thinner clouds than CALIOP in daytime at high horizontal resolution without false cloud detection. These lower threshold values will be used to build the CLIMP-ST (Short-Term, related only to the ATLID observational period) product. This product should provide the ability to evaluate optically thin clouds like cirrus in climate models compared to the current existing capability. We also found that ATLID and CALIPSO may detect similar clouds if we convert ATLID 355 nm profiles to 532 nm profiles and apply the same cloud detection thresholds as the ones used in GOCCP (GCM-Oriented CALIPSO Cloud Product; general circulation model). Therefore, this approach will be used to build the CLIMP-LT product. The CLIMP-LT data will be merged with the GOCCP data to get a long-term (2006–2030s) cloud climate record. Finally, we investigate the detectability of cloud changes induced by human-caused climate warming within a virtual long-term cloud monthly gridded lidar dataset over the 2008–2034 period that we obtained from two ocean–atmosphere coupled climate models coupled with a lidar simulator. We found that a long-term trend of opaque cloud cover should emerge from short-term natural climate variability after 4 years (possible lifetime) to 7 years (best-case scenario) for ATLID merged with CALIPSO measurements according to predictions from the considered climate models. We conclude that a long-term lidar cloud record built from the merging of the actual ATLID-LT data with CALIPSO-GOCCP data will be a useful tool for monitoring cloud changes and evaluating the realism of the cloud changes predicted by climate models.</p