[1] New space-borne active sensors make it possible to observe the three-dimensional structure of clouds. Here we use CALIPSO lidar observations together with a lidar simulator to evaluate the cloudiness simulated by a climate model: modeled atmospheric profiles are converted to an ensemble of subgrid-scale attenuated backscatter lidar signals from which a cloud fraction is derived. Except in regions of persistent thick upper-level clouds, the cloud fraction diagnosed through this procedure is close to that actually predicted by the model. A fractional cloudiness is diagnosed consistently from CALIPSO data at a spatiotemporal resolution comparable to that of the model. The comparison of the model's cloudiness with CALIPSO data reveals discrepancies more pronounced than in previous model evaluations based on passive observations. This suggests that space-borne lidar observations constitute a powerful tool for the evaluation of clouds in large-scale models, including marine boundary-layer clouds.

Abstract. 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 20 km 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. CloudSatCorrespondence to: M. Haeffelin (martial.haeffelin@lmd.polytechnique.fr) 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.

Abstract. We document, for the first time, how detailed vertical profiles of cloud fraction (CF) change diurnally between 51∘ S and 51∘ N, by taking advantage of 15 months of measurements from the Cloud-Aerosol Transport System (CATS) lidar on the non-sun-synchronous International Space Station (ISS). Over the tropical ocean in summer, we find few high clouds during daytime. At night they become frequent over a large altitude range (11–16 km between 22:00 and 04:00 LT). Over the summer tropical continents, but not over ocean, CATS observations reveal mid-level clouds (4–8 km above sea level or a.s.l.) 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 15:00) and high-altitude clouds (minimum between 08:00 and 14:00). 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 upwards and reach a maximum occurrence at about 2.5 km a.s.l. in the early afternoon (around 14:00). Our work also shows that (1) the diurnal cycles of vertical profiles derived from CATS are consistent with those from ground-based active sensors on a local scale, (2) the cloud profiles derived from CATS measurements at local times of 01:30 and 13:30 are consistent with those observed from CALIPSO at similar times, and (3) the diurnal cycles of low and high cloud amounts (CAs) 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 01:30 and 13:30 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 applicable to other instruments with local overpass times similar to CALIPSO's, such as all the other A-Train instruments and the future EarthCARE mission.

Climate models predict that the geographic distribution of clouds will change in response to anthropogenic warming, though uncertainties in the existing satellite record are larger than the magnitude of the predicted effects. Here we argue that cloud vertical distribution, observable by active spaceborne sensors, is a more robust signature of climate change. Comparison of Atmospheric Model Intercomparison Project present day and +4 K runs from Coupled Model Intercomparison Project Phase 5 shows that cloud radiative effect and total cloud cover do not represent robust signatures of climate change, as predicted changes fall within the range of variability in the current observational record. However, the predicted forced changes in cloud vertical distribution (directly measurable by spaceborne active sensors) are much larger than the currently observed variability and are expected to first appear at a statistically significant level in the upper troposphere, at all latitudes.

International audienceThe identification of the land-atmosphere interactions as one of the key source of uncertainty in climate models calls for process-level assessment of the coupled atmosphere/land continental surface system in numerical climate models. To this end, we propose a novel approach and apply it to evaluate the standard and new parametrizations of boundary layer/convection/clouds in the Earth System Model (ESM) of Institut Pierre Simon Laplace (IPSL), which differentiate the IPSL-CM5A and IPSL-CM5B climate change simulations produced for the Coupled Model Inter-comparison Project phase 5 exercise. Two different land surface hydrology parametrizations are also considered to analyze different land-atmosphere interactions. Ten-year simulations of the coupled land surface/atmospheric ESM modules are confronted to observations collected at the SIRTA (Site Instrumental de Recherche par Télédection Atmosphérique), located near Paris (France). For sounder evaluation of the physical parametrizations, the grid of the model is stretched and refined in the vicinity of the SIRTA, and the large scale component of the modeled circulation is adjusted toward ERA-Interim reanalysis outside of the zoomed area. This allows us to detect situations where the parametrizations do not perform satisfactorily and can affect climate simulations at the regional/continental scale, including in full 3D coupled runs. In particular, we show how the biases in near surface state variables simulated by the ESM are explained by (1) the sensible/latent heat partitionning at the surface, (2) the low level cloudiness and its radiative impact at the surface, (3) the parametrization of turbulent transport in the surface layer, (4) the complex interplay between these processes. We also show how the new set of parametrizations can improve these biases

July 2006 was particularly warm in Europe. The consistency of this kind of anomaly with large-scale circulation conditions or local processes is a key issue for regional climate evolution. Using observations from space and ground-based observatory, together with simulations from regional model, shows that two concomitant but disconnected drivers explain this heatwave. The first driver corresponds to large-scale conditions (specific atmospheric condition with advection of continental air favoring clear sky). The second condition relates to local processes (dry soil, amplifying surface temperature in heatwave for first 5 days, and making this event warm enough to induce a monthly mean anomaly). This large-scale event is studied at a site in northern France, where comprehensive observation data carefully reanalyzed are available. A regional model is able to produce the amplitude of the event, for both temperature and cloud large-scale anomalies. Coupling model and observations allow discriminating the surface contribution to the temperature anomaly.

From Lagrangian back trajectories it was found that the dust was mobilized from sources in Mauritania six days earlier, while the dry layer subsided over the north Atlantic. Off the Moroccan coasts, the dry layer folded down beneath the dusty air mass and the two-layer structure was advected to the Rhine valley in about two days. By heating the atmosphere, the dust layer changed the static stability of the atmosphere and thus the occurrence of convection. A study of sensitivity to the radiative effect of dust showed a better prediction of precipitation when a dust prognostic scheme was used rather than climatology or when dust effects were ignored. This result suggests that dust episodes that occur prior to convective events might be important for quantitative precipitation forecasts.

Abstract.A scientific approach is presented to aggregate and harmonize a set of 60 geophysical variables at hourly timescale over a decade, and to allow multiannual and multi-variable studies combining atmospheric dynamics and thermodynamics, radiation, clouds and aerosols from ground-based observations. Many datasets from ground-based observations are currently in use worldwide. They are very valuable because they contain complete and precise information due to their spatio-temporal co-localization over more than a decade. These datasets, in particular the synergy between different type of observations, are under-used because of their complexity and diversity due to calibration, quality control, treatment, format, temporal averaging, metadata, etc. Two main results are presented in this article: (1) a set of methods available for the community to robustly and reliably process ground-based data at an hourly timescale over a decade is described and (2) a single netCDF file is provided based on the SIRTA supersite observations. This file contains approximately 60 geophysical variables (atmospheric and in ground) hourly averaged over a decade for the longest variables. The netCDF file is available and easy to use for the community. In this article, observations are "re-analyzed". The prefix "re" refers to six main steps: calibration, quality control, treatment, hourly averaging, homogenization of the formats and associated metadata, as well as expertise on more than a decade of observations. In contrast, previous studies (i) took only some of these six steps into account for each variable, (ii) did not aggregate all variables together in a single file and (iii) did not offer an hourly resolution for about 60 variables over a decade (for the longest variables). The approach described in this article can be applied to different supersites and to additional variables. The main implication of this work is that complex atmospheric observations are made readily available for scientists who are non-experts in measurements. The dataset from SIRTA observations can be downloaded at http://sirta.ipsl.fr/reobs.html (last access: April 2017) (Downloads tab, no password required) under https://doi