Search

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

The surface turbulent fluxes, namely sensible and latent heat fluxes, are keys factors governing the boundary layer processes. Therefore, their correct representation in numerical models is crucial for accurate weather forecasts and climate projections. However, the formulation of these fluxes in such models is the second source of uncertainty, leading to incorrect surface-atmosphere interactions in the simulations. Model evaluation is essential to draw development perspectives. Existing methods mostly consist of direct comparison between observed and modelled fluxes, blending other sources of errors such as incoherent grid-scale representation (soil and vegetation types) and inaccurate environmental forcing (radiative fluxes, temperature, moisture and wind speed). Thus, quantifying errors solely due to fluxes formulation is still challenging. This study, within the framework of the French project MOSAI (Model and Observation for Surface-Atmosphere Interactions), aims at proposing a novel evaluation approach to better identify the weakness of numerical models in surface turbulent fluxes formulation. The concept is to freeze the errors due to other sources by using a machine-learning model, notably a multi-layer perceptron, trained to estimate the fluxes from variables describing the conditions in the surface layer. Hourly data collected over several years at three operational instrumented sites of ACTRIS-France research infrastructure (SIRTA in Paris, Météo-pole in Toulouse and P2OA in Lannemezan), are used. Then, after adaptation to the outputs of numerical models involved in the MOSAI project (RegIPSL, LMDZ, AROME and ARPEGE), the trained-perception will be applied to assess their surface turbulent fluxes.

One of the most well-known and appreciated characteristics of organic food is the lower pesticide residual which reduces the risks for human health. Moreover, the main benefit clearly recognized to the organic farming is the environmental sustainability compared to conventional ones, with a minor pressure of chemicals, reduced negative impacts on biodiversity and water quality and improved agricultural soil and vitality. However, the effect of organic farming on global warming and climate change mitigation is instead a still open debate in the scientific community. Depending on the boundary of the study, the adopted methodology, the soil and climatic characteristic of the agrosystem, the analyzed crops and the availability of primary data, different studies in literature provide diverse and even opposite results: organic farming is considered to perform better in some cases and worse in other cases in terms of contribution to climate change, compared to conventional farming.The carbon footprint (CF) is one of the most used indicators to measures the contribution to climate change in terms of GHG emissions with different metrics (e.g. GHG per unit of product or per unit of land). With the aim to contribute to a more informed debate on the actual contribution to climate change in terms of GHG emissions of organic and conventional agriculture, we carried out a systematic analysis of the existing peer-reviewed studies allowing an unbias comparison of product-based vs land-based CF.The results of the review show that organic food has on average lower impact on climate than conventional ones both when the CF is assessed per area unit (-43% GHG emissions, average) and per product unit (-12% GHG emissions, average), solving the existing scientific debate in favor of the organic food production, being more sustainable both in terms of total climate altering gases released in atmosphere and in terms of GHG emission intensity per product.According to these results and in view of the global climate policies’ targets which foster organic food production and the transition to sustainable diets, a potential full conversion of the actual global croplands into organic lands would nearly halve the emissions from the land sector, from the current 11 GtCO2eq yr–1 to 6 GtCO2eq yr–1.

Local temperature variations at the surface are mainly dominated by small-scale processes coupled through the surface energy budget terms, which depend mostly on radiation availability and thus cloud processes. A method to determine each of these terms based almost exclusively on observations is presented in this paper, with the main objective to estimate their importance in hourly surface temperature variations at the SIRTA observatory, near Paris. Almost all terms are estimated from the multi-year dataset SIRTA-ReOBS, following a few parametrizations. The four main terms acting on temperature variations are radiative forcing (separated into clear-sky and cloud radiation), atmospheric heat exchange, ground heat exchange, and advection. Compared to direct measurements of hourly temperature variations, it is shown that the sum of the four terms gives a good estimate of the hourly temperature variations, allowing a better assessment of the contribution of each term to the variation, with an accurate diurnal and annual cycles representation, especially for the radiative terms. A random forest analysis shows that whatever the season, clouds are the main modulator of the clear sky radiation for 1-hour temperature variations during the day, and mainly drive these 1-hour temperature variations during the night. Then, the specific role of clouds is analyzed exclusively in cloudy conditions considering the behavior of some classical meteorological variables along with lidar profiles. Cloud radiative effect in shortwave and longwave and lidar profiles show a consistent seasonality during the daytime, with a dominance of mid- and high-level clouds detected at the SIRTA observatory, which also affects surface temperatures and upward sensible heat flux. During the nighttime, despite cloudy conditions and having a strong cloud longwave radiative effect, temperatures are the lowest and are therefore mostly controlled by larger-scale processes at this time.

Clouds and aerosols play an important role in the Earth’s energy budget through a complex interaction with solar, atmospheric, and terrestrial radiation, and air humidity. Optically thick clouds efficiently reflect the incoming solar radiation and, globally, clouds are responsible for about two thirds of the planetary albedo. Thin cirrus trap the outgoing longwave radiation and keep the planet warm. Aerosols scatter or absorb sunlight depending on their size and shape and interact with clouds in various ways.Due to the importance of clouds and aerosols for the Earth’s energy budget, global satellite observations of their properties are essential for climate studies, for constraining climate models, and for evaluating cloud parameterizations. Active sounding from space by lidars and radars is advantageous since it provides the vertically resolved information. This has been proven by CALIOP lidar which has been observing the Earth’s atmosphere since 2006. Another instrument of this kind, CATS lidar on-board ISS provided measurements for over 33 months starting from the beginning of 2015. The ALADIN lidar on-board ADM/Aeolus has been measuring horizontal winds and aerosols/clouds since August 2018. More lidars are planned – in 2022, the ATLID/EarthCare lidar will be launched and other space-borne lidars are in the development phase.In this work, we compare the scattering ratio products retrieved from ALADIN and CALIOP observations. The former is aimed at 35 deg from nadir, it measures the atmospheric backscatter at 355nm from nadir, is capable of separating the molecular and particular components (HSRL), and provides the profiles with a vertical resolution of ~1km up to 20km altitude. The latter, operating at 532nm is aimed at 3 deg from nadir and measures the total backscatter up to 40 km. Its natural vertical resolution is higher than that of ALADIN, but the scattering ratio product used in the comparison is provided at ~0.5km vertical grid.We have performed a search of nearly simultaneous common volume observations of atmosphere by these two instruments for the period from 28/06/2019 through 31/12/2019 and analyzed the collocated data. We present the zonal averages of scattering ratios as well as the instantaneous profile comparisons and the statistical analysis of cloud detection, cloud height agreement, and temporal evolution of these characteristics.The preliminary conclusion, which can be drawn from this analysis, is that the general agreement of scattering ratio profiles retrieved from ALADIN and CALIOP observations is good up to 6-7 km height whereas in the higher atmospheric layers ALADIN is less sensitive to clouds than the CALIOP. This lack of sensitivity might be compensated by further averaging of the input signals and/or by an updating of the retrieval algorithms using the collocated observations dataset provided in the present work.

Clouds and aerosols play an important role in the Earth’s energy budget through a complex interaction with solar, atmospheric, and terrestrial radiation, and air humidity. Optically thick clouds efficiently reflect the incoming solar radiation and, globally, clouds are responsible for about two thirds of the planetary albedo. Thin cirrus trap the outgoing longwave radiation and keep the planet warm. Aerosols scatter or absorb sunlight depending on their size and shape and interact with clouds in various ways.Due to the importance of clouds and aerosols for the Earth’s energy budget, global satellite observations of their properties are essential for climate studies, for constraining climate models, and for evaluating cloud parameterizations. Active sounding from space by lidars and radars is advantageous since it provides the vertically resolved information. This has been proven by CALIOP lidar which has been observing the Earth’s atmosphere since 2006. Another instrument of this kind, CATS lidar on-board ISS provided measurements for over 33 months starting from the beginning of 2015. The ALADIN lidar on-board ADM/Aeolus has been measuring horizontal winds and aerosols/clouds since August 2018. More lidars are planned – in 2021, the ATLID/EarthCare lidar will be launched and other space-borne lidars are currently in the development phase.Needless to say that the quality of the retrieved data strongly depends on the quality of the calibration of the lidar system and its components. Besides “classical” calibration methods (laboratory calibration, calibration in space using on-board sources and/or known external sources and calibration through collocation, which involves comparisons with ground-based station-, balloon-, and aircraft measurements), one can also make use of a vicarious calibration, where the sites with known properties are used. In this work, we use the whole atmosphere for quality control of the space-borne lidar system, which includes the laser, the sending optics, the receiver with its telescope, and the detection system.We describe the quality control approach based on a set of several indicators, which characterize the behavior of the lidar system on a day-to-day basis using the L1 (and even L2) data as an input. With the help of this set one can trace:(a) the stability of the detection chain for the lidar channels (Rayleigh, Mie);(b) the drift of crosstalk coefficients;(c) the stability of day- and nighttime stratospheric noise;(d) the stability of the radiation detection for all atmospheric scenarios and over the whole globe using a clustering algorithm applied to the scattering ratio (SR) histograms.We demonstrate the results using the L1B and L2A data flow of Aeolus obtained from the first days of its operation and up to now, compare them with the results obtained for CALIOP, and discuss the applications of the approach.

We take advantage of 15 months of measurements from the Cloud and Aerosol Transport System (CATS) lidar on the non-sun-synchronous International Space Station (ISS) to document, for the first time, the diurnal cycle of detailed vertical profiles of Cloud Fraction between 51° S and 51° N. After processing CATS lidar data, we analyzed the diurnal cycles of the cloud profiles over ocean and over continent in two different seasons. Over the Tropical ocean in summer, the high clouds geometric thickness increases significantly from 1 km near 5 PM to 5 km near 10 PM, resulting in a high clouds maximum at nighttime. Over the summer tropical continents, CATS observations reveal the presence of a mid-level cloud layer (4–8 km ASL) persisting all-day long, with a weak diurnal cycle (minimum at noon). Over the Southern Ocean, diurnal cycles appear for the omnipresent low-level clouds (minimum between noon and 3 PM) and for the high-altitude clouds (minimum between 8 AM and 2 PM). Both cycles are time-shifted, with high-altitude clouds following the changes in low-altitude clouds by several hours. Over all continents at all latitudes during summer, the low-level clouds develop vertically and reach a maximum occurrence at about 2.5 km ASL in the early afternoon (around 2 pm). Our work also show that 1) the diurnal cycles of vertical profiles derived from CATS are consistent with those from ground-based active sensors at local scale, 2) the cloud profiles derived from CATS measurements at local times of 0130 AM and 0130 PM are consistent with those observed from CALIPSO at similar times, 3) the diurnal cycles of low and high cloud amounts derived from CATS are in general in phase with those derived from geostationary imagery but less pronounced. Finally, the diurnal variability of cloud profiles revealed by CATS strongly suggests that CALIPSO measurements at 0130 AM and PM document the daily extremes of the cloud fraction profiles over ocean and are more representative of daily averages over land, except at altitudes above 10 km where they capture part of the diurnal variability. These findings are equally applicable to other instruments with local overpass times similar to CALIPSO's, like all the other A-Train instruments and the future Earth-CARE mission.

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.