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EECLAT light : Ce document est un extrait de la version soumise au TOSCA. Extraction des taches principalement LEFE : T1.2 / 1.3 /1.7 T2.1 /2.3 /2.5 T3.2/T3.3 T4.1/4.2/4.3 T5.2
RENSEIGNEMENTS GENERAUX
1.1 INTITULÉ DE LA PROPOSITION: EECLAT: "Expecting Earth-‐Care, Learning from A-‐Train"
1.2. SCIENTIFIQUE PROPOSANT
Nom : NOEL Prénom : VINCENT Téléphone : 01 69 33 51 46 e-‐mail : [email protected] Nom du Laboratoire : IPSL/LMD Adresse : Ecole Polytechnique 91128 Palaiseau Organisme de tutelle : CNRS Organisme gestionnaire :
DR5 CNRS
Nom : DELANOË Prénom : JULIEN Téléphone : 01 80 28 52 19 e-‐mail : [email protected] Nom du Laboratoire : IPSL/LATMOS Adresse : 11 Boulevard d’Alembert, 78280 Guyancourt Organisme de tutelle : UVSQ / CNRS Organisme gestionnaire :
DR5 CNRS
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1.3 CO-PROPOSANTSi
Personnel permanent: Par ordre alphabetique
1. Ancellet, G., LATMOS, DR 2. Armante, R., LMD 3. Bastin, S., LATMOS, CR 4. Bazureau, A., LATMOS, IR 5. Bony, S., LMD, DR 6. Bouniol, D., CNRM, CR 7. Chepfer, H., LMD, Pr 8. Chiriaco, M., LATMOS, MC 9. Cornet, C., LOA, MC 10. Couvreux, F., CNRM, IPC 11. Defer, E., LERMA, CR 12. Protat, A., CAWCR, DR 13. Philippe Dubuisson, LOA, Pr 14. Dufresne, J. – L., LMD, DR 15. Dupont, J. – C., IPSL, Phys. Adj. 16. Duroure, C., LaMP 17. El Amraoui, L., CNRM, CR 18. Freville, P., OPGC 19. Gourbeyre, C., LaMP, IR 20. Guichard, F., CNRM, CR 21. Haeffelin, M., IPSL, IR 22. Hertzog, A., LMD, MC 23. Jourdan, O., LaMP, MC 24. Keckhut, P., LATMOS, Phys. 25. Lefevre, F., LATMOS 26. Montoux, N., LaMP, MC 27. Pelon, J., LATMOS, DR 28. Pommereau, J.-‐P., LATMOS, DR 29. Rivière, E., GSMA, MC 30. Sarkissian, A., LATMOS, Phys. 31. Schwarzenboeck, A., LaMP, Prof 32. Scott, N., LMD 33. Seze, G., LMD, CR 34. Stubenrauch, C., LMD, DR 35. Szczap, F., LaMP, MC 36. G. Tournois, OHP 37. S. Turquety, LMD, MC 38. Vidot, J., CMS, CR
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Par Laboratoire CMS CNRM-‐GAME GSMA IPSL LERMA J. Vidot D. Bouniol
F. Couvreux F. Guichard
E. Riviere J-‐C. Dupont M. Haeffelin
E. Defer
LaMP/OPGC LATMOS LMD LOA C. Duroure P. Freville O. Jourdan N. Montoux A. Schwarzenboeck V. Shcherbakov F. Szczap Y. Gour
G. Ancellet S. Bastin A. Buzureau M. Chiriaco J. Delanoe J. Jumelet P. Keckhut A. Garnier F. Lefevre J. Pelon G. Tournois
R. Armante S. Bony V. Capelle H. Chepfer J.-‐L. Dufresne A. Hertzog N. Jacquinet L. Menut V. Noel N. Scott G. Seze C. Stubenrauch S. Turquety
C. Cornet P. Dubuisson
http://eeclat.ipsl.jussieu.fr/
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PROJETS ENGAGESii
CONTEXTE DE LA PROPOSITIONiii
The main objective of this proposal is to optimize coordination of the research activities related to the validation of the CALIPSO mission, the scientific exploitation of the A-‐Train (with emphasis on CloudSat and CALIPSO), and the preparation of the ESA EarthCARE mission to be launched late 2015. We propose two scientific meetings per year, one focused on scientific exchanges (autumn-‐winter meeting) and one dedicated to the preparation of the following year’s proposal (spring meeting). This is the third EECLAT proposal (first submission in April 2011). This document presents our results obtained in 2012-‐2013 and our propositions for 2014 and later. Note that some tasks span several years. The duration of this proposal is in adequation with CloudSat-‐CALIPSO and EarthCare time plans. Extended CloudSat and CALIPSO operations should be confirmed later this year by NASA and CNES, and the time left will be dedicated to the preparation of EarthCARE algorithms. We also address the transition between those two missions. Summary of the proposal
I. Introduction II. Description of the proposed work in the mission context
A) The CALIPSO/IIR mission B) Exploitation of A-‐train observations C) Preparation of the EarthCare mission
III. Scientific projects T0) Follow-‐up of “CALIPSO Validation” Proposal T1) Local and regional cloud studies T2) Large-‐scale and global cloud studies T3) Aerosols T4) Polar stratospheric clouds T5) Radiative transfer for A-‐train and Earth-‐Care T6) Airborne datasets for preparation of EarthCare
IV. List of references mentioned in the text. V. Budget VI. Publications using CALIPSO and A-‐Train data by scientists in EECLAT 2011-‐2013
This proposal will be submitted to both TOSCA (CNES) and LEFE (INSU), as requested by them. In order to help evaluate the different aspects of this proposal, sections II.A and II.C are to be evaluated by TOSCA only, while section II.B is to be evaluated by both TOSCA and LEFE. This proposal has been written in English to be used as a reference document for discussions with ESA. It must be noted that the scientific exploitation of the PARASOL satellite (which is another component of the A-‐train) is detailed in a companion proposal. It is foreseen that some joint scientific animation should be discussed between the proposants.
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DESCRIPTION DETAILLEE DE LA PROPOSITION iv
I. INTRODUCTION The active remote sensors (lidars and radars) in orbit since 2006 have provided an unprecedented view of the troposphere, documenting it along the vertical dimension at a high spatial resolution (up to 30m for CALIPSO and 480m for CloudSat). This simple but key element for describing the structure of the stratified atmosphere was before 2006 documented very indirectly from space. The vertical dimension alone is, by itself, cutting edge information for atmospheric science studies, especially for clouds and aerosols. As the lidar and radar are part of the A-‐train constellation, they collect vertical information simultaneously and in collocation with mature passive remote sensors such as CERES, PARASOL, MODIS that provide complementary information like the Earth radiative budget, the amount and directionality of shortwave reflected light, and infrared emitted radiances. The A-‐train synergy allows documenting simultaneously the macrophysical, radiative, and in part microphysical properties of atmospheric particles: aerosols, clouds and polar stratospheric clouds (PSC). The synergy of A-‐train observations opens new avenues for atmospheric process studies at regional and global scales. The continuous success of these missions for 7 years, has allowed the collection of a large amount of data, which can characterize seasonal and inter-‐annual variabilities, key for climate studies. Building from this success, the forthcoming European Space Agency (ESA) Earth-‐Care mission puts in perspective the following two important next steps: (i) acquiring more than 10 years of active remote sensing observations from space by merging the A-‐train and the Earth-‐Care data, which will let us document the decadal evolution of clouds, aerosols, and PSC, and (ii) improve lidar and radar observation capabilities based on the advanced design of AtLid (EarthCare lidar with High Spectral Resolution, not included in Calipso) and CPR (EarthCare radar with Doppler capability, not included in CloudSat) that will provide new information on atmospheric particles. In this framework, the international community has produced an impressive amount of new scientific results based on the analysis of A-‐train data. The design of the A-‐train as well as its scientific exploitation is mostly supported by NASA and US scientists. The French scientific community has been participating in this effort for many years, through the involvement of CNES in CALIPSO (and PARASOL, described in a companion proposal), its support of CloudSat research activities in France, and its support for instrumental and infrastructure developments for the demonstration and validation of these spaceborne instruments (e. g., RALI, SIRTA). A list of publications using CALIPSO and CloudSat data by French scientists is given in Sect. V. This community of remote sensing experts devises advanced techniques for core analysis of low-‐level A-‐Train measurements (with a special focus on CALIPSO and CloudSat, and using MODIS, CERES and PARASOL). Their expertise on measurement technologies allows them to lead in the research of several well-‐identified scientific issues. Moreover, through the valuable experience gained by this involvement the French community is able to contribute significantly to the preparation of the European Earth-‐Care mission. The current proposition aims at presenting the current and future work of the French scientific community relevant to the A-‐Train. The activities presented in this proposal address key questions on the follow-‐up CALIPSO validation activities and nominal synergistic products (T0), on four scientific research themes (T1-‐T4), and two technical themes (T5-‐T6):
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T0. Follow-‐up of “CALIPSO Validation” proposal and nominal products Products developed in the frame of the CALIPSO mission are now widely used in the
scientific community. They are gaining in quality and in perimeter to better address scientific needs. The main purpose of this task is to describe follow-‐up activities related to the development of the operational CALIOP / IIR algorithms, some science algorithms, the validation and comparison of related products as well as their implications for ICARE and the objectives of the CALIPSO mission. This task now describes, in its second half, the french development of two CALIPSO-‐based products: DARDAR and CALIPSO-‐GOCCP.
T1. Local and Regional Clouds Studies
Clouds properties are macrophysical (altitude, spatial extension, water content), optical (optical depth), microphysical (particle size, shape, orientation), and radiative (shortwave and longwave fluxes). They are strongly linked to each other, and primarily driven by the atmospheric environment (thermodynamic and dynamic). As different regions provide different environments (i.e. dry air in polar regions compared to the Tropics, deep convection at low latitude, storm-‐tracks at mid-‐latitude, interaction between surface and atmosphere above ocean and continent, etc.), the processes leading to the formation, maintenance and dissipation of clouds cannot be simply summed up in one picture that would be valid everywhere. In this theme, we take advantage of new satellite observations to examine specific cloud properties in different regions (Tropics, mid-‐latitude, poles) in relation with their environment to better understand regional specificities of cloud processes.
T2. Large-‐scale and Global Cloud Studies
Here, we consider clouds at global scale (more directly relevant to climate) to explore large patterns, relating cloud occurrence and properties to large-‐scale atmospheric circulation, or to their global radiative impact. Emphasis is put on high-‐level, optically thin ice clouds, which tend to trap infrared outgoing radiation (the greenhouse effect, which produces a net heating) and reflect poorly incoming solar shortwave radiation (the cooling albedo effect) due to their semi-‐transparency (Stephens et al. 1990). The sign of the net radiative forcing of these high cirrus therefore depends on their optical depth and the vertical distribution of ice water content, concentration, effective radius, shape and orientation of their crystals. Another emphasis is given to the evaluation of the description of tropical boundary layer clouds in climate models, as they create significant uncertainty for estimates of future climate (Bony et al. 2004). Since they document simultaneously multiple variables at global scale, during several years, at high spatial resolution, A-‐train observations can yield significant advances on these topics.
T3. Aerosols
Our main purpose here is to analyze the information provided by the A-‐Train and EarthCare observations on aerosol sources (natural and anthropogenic) and on the evolution of aerosol optical properties during transport on a regional scale (Euro-‐Mediterranean region) and at the high latitudes of the Northern Hemisphere. The good coverage and resolution of the data will help understand and quantify the relative impact of long-‐range transport on regional budgets. Emphasis is placed on analyzing the relative contribution of emissions from large fires. Wildfires regularly emit large amounts of aerosols during the spring and summer in Europe and boreal forests. Their impact is not well quantified in chemistry transport models due to uncertainties on the emissions (amount emitted, size of the particles) but also on the injection height (as pyroconvection may lift emissions above the boundary layer for intense fires) and on the evolution during transport. Combined analysis of CALIPSO and in situ observations
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in Russia will help characterize boreal fire plumes and their impact. Using horizontal and vertical distributions of aerosol optical properties from A-‐Train should help improving the modeling of fire plumes and their impact. More generally, we will use these observations to conduct a full evaluation of the aerosol simulation by chemistry transport models (CHIMERE and MOCAGE).
T4. Polar stratospheric clouds (PSC)
Polar Stratospheric Clouds (PSC) are frequent above polar regions during winter. They contribute to the formation of the ozone hole and slow down its recovery. They differ greatly from tropospheric clouds, as they contain ice, nitric acid trihydrate (NAT), and supercooled ternary solution (STS) particles formed through various combinations of water vapour, HNO3 and H2SO4. PSC composition determines its impact on ozone loss. Due to their hard-‐to-‐reach location, and their difficult detection using passive instruments, we have a lot to learn about how atmospheric and microphysical processes drive their formation and composition. Here, we take advantage of CALIOP's high resolution, sensitivity to optically thin atmospheric features and ability to identify particle shape to document the spatial cover and composition of PSC, and their evolution through polar winters. Using the synergy between instruments and the excellent coverage of polar regions by the A-‐Train, complemented by ground-‐based observations and atmospheric modelling, we relate PSC properties with local and large-‐scale atmospheric phenomena, to describe processes that drive their formation and composition.
T5. Development of radiative transfer tools and future products
For measurement calibration, model evaluation, data assimilation, and inversion algorithm design, we need to simulate Level 1 observations collected by the lidar, radar or radiometers part of the A-‐train and EarthCare. We need to develop and adapt forward radiative transfer models to the specificities of each instrument (wavelength, resolution, sensitivity, etc.) for specific scientific applications. Here, we summarize research that aims to improve the tools that are developed for dedicated application using the A-‐train or EarthCare. In most cases, mature tools are later made available to the community.
T6. Datasets for EarthCare preparation As the instrumental design of the Earth-‐Care lidar and radar are different from the A-‐
train counterparts, level 1 datasets will be different for the two missions. We need to examine carefully and understand properly the signal produced by EarthCare instruments in comparison with the A-‐train ones, in order to prepare future analyses of EarthCare data. To do so, we can simulate through code the signal that will be observed by EarthCare, but experience from previous missions suggests that acquiring actual data with actual instruments is much more informative about the signal that will be observed. Here our goal is to build test databases to train our future analysis of EarthCare data, using observations from ground (IPRAL), airborne (RALI) and spaceborne (CATS-‐ISS) instruments that mirror capabilities of EarthCare. Those instruments are either new, in the design stage or yet to launch, and using their data for Earth-‐Care preparation requires extensive prior analysis. This work will (i) stimulate new ideas of geophysical information that can be extracted from EarthCare mission, (ii) help future algorithms development, (iii) prepare the French contribution to validation plan of the EarthCare mission, (iv) bridge the gap between CALIOP and EarthCare observations by providing third-‐party datasets, were EarthCare to launch after the CALIOP mission ends.
The detailed scientific themes, and how we plan to use the CALIPSO, A-‐train and EarthCare data to contribute to address these themes are presented in Sect. III.
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II. DESCRIPTION OF PROPOSED WORK IN MISSION CONTEXT In this section, we describe the work to be done in the context of satellite missions, with the aim to highlight how each mission contributes to the different scientific themes. We also describe the articulation between the A-‐train and Earth-‐Care missions.
II.A. Calipso-‐IIR mission The French community is in charge of the IIR (Infrared Imaging Radiometer) within the CALIPSO mission, and this section summarizes the work to be done to validate IIR observations (Levels 1 and 2), and the ongoing development of IIR algorithms. The CALIPSO/IIR measures infrared radiances in 3 channels (8.7, 10.5, 12 µm) collocated with the CALIOP lidar. The algorithms developed in France and run operationally at Icare and NASA/DAAC provide ice crystal particle size, shape ratio, etc. A1-‐ Validation from airborne campaigns of IIR/CALIOP data (Levels 1 and 2), improvement to nominal algorithms L2 IIR/CNES and L2 CALIOP/NASA. A2-‐ Validation of CALIPSO Level 1, IIR and CALIOP (outside of airborne campaigns, see A1): ground-‐based sites, radiative transfer calculations, satellite sensors intercomparisons A3-‐ Improvement and development of nominal algorithms IIR/CALIPSO Level 2 and 3. The scientific contact for section II.A is the CALIPSO French PI, Jacques Pelon. II.B. A-‐train The A-‐train contains 6 satellites flying in formation over the same point of the globe less than 10 minutes apart. This proposal does not pretend to cover all the scientific topics that can be explored using A-‐train observations, and does not use all A-‐train observations. We focus mostly on the two active remote sensing instruments for which most of us have a specific expertise based on intensive analyses of ground-‐based and airborne active remote sensing observation in pre-‐A-‐train period and analysis of actual A-‐Train data since its launch. CALIOP onboard the CALIPSO platform is a 532nm and 1064nm lidar with polarization capability, well suited for clouds, aerosols and PSC observations. CPR on the CloudSat platform is a 94GHz radar suited to study clouds and precipitations. We complete our scientific analysis as needed with AIRS, CERES, MODIS, PARASOL and MLS data, which are part of the A-‐train. We classify our A-‐train activities in four different categories: B1-‐ Development of research algorithms (except the IIR nominal algorithm mentioned in Sect. A), from single or multi-‐sensor observations, leading to an end product.
B2-‐ Valorisation of A-‐Train observations, using single-‐ or multi-‐sensor datasets: scientific exploitation of A-‐Train data to improve our understanding of clouds, aerosols and PSC through the production of climatologies, climatology intercomparisons, statistics, coupling of observation with reanalyses, case studies, etc.
B3-‐ Valorisation of observation synergies: coupling of ground-‐based, airborne and A-‐Train observations to improve our understanding of clouds, aerosols, PSC (except for validation purposes, see A1)
B4-‐ Valorisation of A-‐Train observations to improve and develop models (forecasting, climate prediction, air quality, etc.), methodological development at the model/observation interface.
Scientific contacts for Section II.B are V. Noel (LMD) and J. Delanoë (LATMOS).
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II.C. EarthCare The ESA/JAXA EarthCare mission to be launched late 2015 contains a lidar named ATLID, which differs from CALIOP by its wavelength (355nm instead of 532nm) and its High Spectral Resolution capability (HSR), a 94GHz radar which differs from CloudSAT by its Doppler capability, and a radiative budget radiometer (MSI) which differs from CERES by its scanning capability. In the framework of EarthCare, our work mostly aims at
(i) documenting Earth with these three instruments (two being active remote sensors) in continuity with what we are doing for the A-‐train, in order to get longer time series that make sense for interannual-‐variation and climate studies
(ii) infering new information on precipitations based on the Doppler radar capability, and new information on aerosols optical properties based on HSR retrievals.
We classify our EarthCare activities in 6 categories: C1 -‐ Validation of EarthCare Level 1 data: update validation methods based on radiative transfer calculations or colocated satellite observations for the EarthCare mission C2 -‐ Algorithmic development, adaptation: update existing A-‐Train algorithms to prepare the analysis of EarthCare observations (different in instrument characteristics: wavelength, view, etc…) C3 -‐ Algorithmic development, new: Define new algorithms EarthCare that break free from A-‐Train constraints and are based on EarthCare specificities (Doppler Radar, High spectral resolution lidar, BBR specific viewing angles, etc.) C4 – Building a pre-‐launch test dataset to help algorithmic development: Coupled HSR Lidar and Doppler Radar observations (ground-‐based and/or airborne) C5 -‐ Valorisation of EarthCare observations in the modeling community: update observation simulators, comparisation framework, etc. C6 -‐ Preparation of EarthCare validation actions: (1) airborne missions and (2) ground-‐based observatories. Scientific contacts for Section II.C are H. Chepfer (LMD) and J. Delanoë (LATMOS), members of the ESA Mission Advisory Group (EMAG) for Earth-‐CARE.
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III. SCIENTIFIC PROJECTS
T1 – Local and Regional Cloud Studies Coordination: D. Bouniol (CNRM) As discussed in the introduction, clouds and aerosols play a leading role in weather and climate change processes. The processes involved in their life cycle are still poorly understood. Radar-‐Lidar observations from space analyzed at regional scale offer new perspectives to better understand cloud formation, maintenance, and dissipation and the variability of dominant processes over different climatic regions.
By working at the regional scale, one may ensure that sampled clouds occur in a given family of meteorological regime characteristic of the sampled region. In addition, a number of field deployments (AMMA, HYMEX, CINDY-‐DYNAMO, POLARCAT...) implementing a large set of airborne and ground-‐based facilities took place in the recent years. These facilities documented local spatial and temporal variability that may not be reachable by satellite sampling and provide climatological references for the satellite retrieved products.
The representation of clouds, aerosols, and their so-‐called direct, semi-‐direct, and indirect effects in cloud-‐resolving models (CRM), numerical weather prediction models (NWP) and global climate models (GCM) is still deficient, despite a clear improvement of the realism of cloud parameterizations. The improvement of this representation of clouds and the cloud-‐aerosol interactions in models, and the better understanding of the impact of cloud and aerosol on radiation and dynamics are therefore the two major objectives for the coming years or decades for the international science community. Such improvement in NWP models and GCMs must arise from a better understanding of the physical, optical, microphysical, and radiative properties of clouds and their interactions at local and regional scales, which totally constrain the feedback of clouds on climate. This extensive work requires studies in very different climatic regions, using unique features offered by ground-‐based (long-‐term datasets, cutting-‐edge instrumentation with more capabilities and possible synergies), airborne (detailed in-‐situ observations at the scale of individual cloud particles, targeted observations with in-‐situ and remote sensing), and spaceborne (full regional coverage) observations.
In the present proposal, studies will be conducted:
1. in the Tropics (T1.1, T1.2, and T1.3), where water, heat, and energy are redistributed by intense deep convective activity, with considerable impact on the Earth radiative budget.
2. at mid-‐latitudes (T1.4 and T1.5), where cutting-‐edge long-‐term ground-‐based data are being collected to better understand cloud properties over observatories (SIRTA, OHP, OPGC) and a field experiment will identify cloud processes associated with extreme flood-‐producing precipitating systems (HYMEX).
3. in the arctic region (T1.6), where the effects of climate change are expected to be largest in terms of surface warming and where we don’t know much about the complex cloud processes.
4. in Antarctica and the southern ocean (T1.7), because clouds there contribute significantly to the top-‐of-‐atmosphere radiation balance and are poorly represented by climate models.
Regional studies such as those will allow for detailed processes to be identified and will contribute to improve our understanding of how these processes differ in response to different local or synoptic atmospheric forcing.
T1.2 – Statistical properties of West-‐African clouds and convection (AMMA) using A-‐Train measurements [B2, B3, B4]
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Coordination: D. Bouniol (CNRM) Main contributors: D. Bouniol, F. Couvreux, F. Guichard, O. Geoffroy, R. Roehrig, I. Beau (CNRM), G. Sèze, C. Rio (LMD), J. Aublanc, R. Roca (LEGOS)
Objectives Most of the studies on tropical clouds have been carried out over the Ocean with very little focus on clouds over land. Therefore, there is a lack in the knowledge of clouds in the continental Tropics in particular over West Africa. Moreover, this region is particular sensitive to cloud feedbacks that impact the surface processes and the surface energetic budget (e.g. Betts and Viterbo 2005; Betts 2007; Garrat 1993). Here, we propose to jointly analyze multiple datasets (in particular A-‐Train, Megha-‐Tropiques, MSG, GERB, ground in-‐situ data) in order to document and better understand cloud properties (notably their type and occurrence) and their radiative impact in this region before and during the monsoon period. The main objective is by the end to use the satellite L2 data set to build a set of climatological properties (from macrophysical properties to heating profile) for the various cloud types encountered over this region. This climatology may be used to evaluate the representation of clouds in models as well as their associated effects. The predominant biases in NWP and climate models will then be identified in this major continental region of the Tropics.
By combining ground and satellite observations (Bouniol et al. 2012), we have shown that the Niamey region was characterized during the pre-‐monsoon and full monsoon by four types of clouds (high-‐level clouds, deep convective clouds, shallow convective clouds and mid-‐level clouds). The diurnal cycle of each cloud category and its seasonal evolution has been investigated. The A-‐Train data were used in order to demonstrate that these four cloud types (in addition to stratocumulus clouds over the ocean) are not a particularity of the Niamey Sahelian area and that mid-‐level clouds are present over the Sahara during most of the Monsoon season. In a second step, the radiative impact of each type of clouds at the surface has been quantified in the shortwave and longwave range at Niamey with ground-‐based datasets.
We now propose to make use of the elaborated products (L2) from the A-‐Train data and other platforms, in order to provide a robust climatology of cloud properties as a function of the cloud type, taking into account various time scales (e.g. Seasonal, diurnal).
Concerning the deep convective clouds, we complement the A-‐Train data with results of a tracking algorithm (TOOCAN, Fiolleau and Roca, 2012) applied to geostationary data to follow a convective system from its birth up to its dissipation. This algorithm gives information about the life stage of the convective system and allows projecting the various cloud properties derived from the A-‐Train in this lagrangian framework (Fiolleau, 2010). The TOOCAN dataset exists for the entire tropical belt and building the same cloud characteristic climatology in other tropical regions allows inferring physical processes at play within various environments (see Cetrone and Houze, 2009 for an illustration). This tracking algorithm let us discriminate, in the A-‐Train dataset, between high-‐level clouds and deep convective clouds. Cirrus cloud properties can then be unambiguously documented. Bouniol et al. (2012) have identified mid-‐level clouds as a major cloud type affecting this continental region. There is a real need to better understand the mechanisms involved in their life cycle and the satellite data will be used for this purpose.
This continental region is particular for remote radiative budget measurements (in particular at the surface) since it is affected by strong aerosol loading (dust in particular, Prospero et al. 2002). It is therefore of crucial importance to have ground-‐based measurements as reference for satellite estimates. Radiative measurements are available at the surface for several years at various sites along the meridional Greenwich transect (AMMA CATCH, ARM and BSRN sites), and, interestingly, have also been selected for climate model evaluation by the CFMIP project, conducted within the frame of the AR5. High-‐frequency process-‐oriented outputs from the AR5 CMIP5 simulations are now available at those points. We will therefore study the radiative budget (at the surface and top of the atmosphere) at those sites in relation to the cloud occurrence in observations and models (in link with the FP-‐7 EUCLIPSE project). Radiative
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transfer calculations will also be performed with an offline radiative transfer code to identify more precisely the contribution of aerosols and clouds in the radiation measurements.
Results since last update The combined CloudSat-‐CALIPSO cloud profiles have been analysed and classified according to the simple morphological criteria established in Bouniol et al. (2012). As stressed by Hourdin et al. (2009) this region is characterised by a good zonal geometry and nearly each North-‐South sampling by the A-‐Train between 10°W and 10°E may be representative of the 0° North-‐South slice. The seasonal evolution of the various cloud categories have been documented and a climatology based on five year of measurements (CloudSat/CALIPSO measurements) has been built. Fig. 1.2.1 shows how the total cloud cover is geographically structured according to the various cloud categories.
Fig. 1.2.1: Hovmuller diagram of total, high, mid, low cloud cover (from left to right, rain and drizzle being excluded) over West-‐Africa between April and September. A 1.5° latitudinal resolution is used and a 5 days
smoothing is applied.
This climatology was used to evaluate the ability of the climate models participating to the CFMIP project in simulating the present and future climate over this region. This analysis has been jointly performed for the cloud radiative properties and the main results are presented in Roehrig et al. (2013). Fig. 1.2.2a highlights the difficulty of climate models in simulating the observed cloud cover even if the wide majority of these models present a reasonable seasonal cycle of the West Africa Monsoon. All models capture to some extent the observed cloud structure with a maximum in cloud fraction related to the deep convective systems collocated with the mean ITCZ position, although some models do not reproduce the observed vertical extent of cloud fraction.
No model reproduced the observed high amount of mid-‐level clouds between 15°N and 30°N, even if some of them partly capture their occurrence. Stratocumulus over the Gulf of Guinea are also challenging for most models, as they are often not deep enough when they occur and some models completely miss them.
Fig. 1.2.2b shows how the cloud structure in the model impacts the surface radiative budget. Over the Guinea coast, more than half of models underestimate the incoming shortwave flux in response to a too thin and reflective cloud layer. Within the ITCZ, some models strongly overestimate this radiative flux and over the Sahara, most models overestimate the incoming shortwave flux. Fig. 1.2.2b pointed a clear deficit of mid-‐level cloudiness, which has a strong impact in the shortwave (Bouniol et al. 2012). The description of aerosols may also explain a
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large part of the spread. The bias in the incoming longwave flux is also strong (same order of magnitude as the incoming shortwave) in the Sahara region. The lack of mid-‐level clouds may partly explain the underestimation. Finally, one may also note the relatively large spread of the various observational datasets (black lines) displayed in these figures.
Fig. 1.2.2: a) Latitude-‐height diagrams of cloud fraction averaged between 10°W-‐10°E, for JAS 2006-‐2010 for the CloudSat-‐CALIPSO dataset and 1979-‐2008 for the models. b) Downward shortwave and longwave radiative flux at the surface (in W m−2), averaged over 10°W-‐10°E, for JAS 1979-‐2008. Mean fluxes for the
ground-‐based sites along the transect with their yearly minima and maxima are indicated.
Deep convection is an important source of cloudiness over this region and A-‐Train products are used to characterize cloud properties depending of the stage of the life cycle. The convective systems are tracked using the TOOCAN algorithm (Fiolleau and Roca, 2012) and their life cycle may be divided in ten steps using linear growth and Decay model (Fiolleau, 2010). Depending on the region considered in the model (convective core, stratiform or cirriform regions) and the life step, one may examine cloud properties and the microphysical processes involved may be inferred, for instance from the evolution of the distribution of reflectivity as Fig. 1.2.3 shows for the stratiform region of the convective system.
Note that, as the systems mature, the reflectivity tends to decrease with altitude corresponding to the occurrence of larger particles at cloud base (above the freezing level), suggesting aggregation is involved. However, due to the proximity of the convective core (and thus large vertical velocity), some growth through vapor deposition may be expected. The distribution of reflectivity is also broader (measured by the interquartile distance) at the beginning of the life cycle, suggesting a great variety of processes involved in producing ice particles.
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Fig. 1.2.3: Distribution of reflectivity in dBZ of the stratiform region as a function of altitude for the ten steps of the convective system life cycle. Red line shows the cloud frequency of occurrence (upper x-‐axis), green is the mean reflectivity profile, blue is the median and dashed blue lines give the interquartile difference.
Revised work plan Most of the work described above is ongoing and will be continued in the coming years, in particular:
§ We will use the five-‐year cloud climatology already developed to evaluate the cloud cover simulated by the CNRM climate model. A systematic evaluation process is under definition (in the framework of a M1 project) to make an optimum use of the “satellite to model” or “model to satellite” approaches (connection with T2.5). It appears from our work that the CloudSat/CALIPSO climatology is a good tool to identify the major drawbacks of the model and the various A-‐Train simulators are powerful to diagnose deficiencies of the models in region of common occurrence. This joint approach (still under definition) will be used as a reference to evaluate and improve the new physical package of the CNRM model.
§ The documentation and analysis of cloud processes involved in the different regions of the convective system as a function of the step of the life cycle will be continued. In particular, elaborated products (heating profiles, radiative effect) will be analysed to understand how the evolution of the microphysical processes modified the radiative properties at the surface and at the top of the atmosphere, and hence the radiative impact of the clouds. This analysis will be applied to the whole tropical belt, to determine how the environment (continent/ocean, different surface type) impacts the convective system life cycle.
§ A particular attention will be dedicated to mid-‐level clouds that occur a large part of the year over the Sahara region. We expect that they originate from different processes (convective origin, stable layer...) as found by e.g. Riihimaki et al. 2012. It is necessary to document the thermodynamical environments associated with their occurrence. The FENNEC data will be particularly interesting for this purpose since dropsondes have been launched within such clouds, simultaneously observed by airborne radar. A cross validation of their occurrence from ground-‐based observation and MSG cloud classification will be performed, and once validated the cloud classification product will
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be used to study the diurnal and intraseasonal variability of these mid-‐level clouds. At these time scales, the CM SAF Cloud Albedo and Radiation dataset based on AVHRR GAC data will be used in conjunction with GERB data to document the radiative effects of such clouds at the surface and at the top of the atmosphere.
§ Finally, we will analyze the radiative budget at different locations along the Greenwich meridional transect. For this purpose, a radiative transfer model will be used and complementary calculations will be performed: clear and clean sky (to quantify the aerosol impact in clear sky profile), clear sky, cloudy sky (to better quantify the respective importance of microphysical and macrophysical properties). This work will be done with additional financial support from FP-‐7.
2013 2014 2015 2016 Evaluate CNRM model cloud cover Analyze radiative budget along Greenwich
transect
T1.3 – Cloud cover diurnal cycle in the tropical regions: combined use of geostationary satellite, A-‐Train and lidar/radar ground station measurements [B2] Coordination : G. Sèze (LMD) Main contributors : G. Sèze (LMD), D. Bouniol, F. Couvreux (CNRM), J. Delanoë (LATMOS)
Objectives In this work package, we use active measurements of the A-‐train, geostationary data and LMD AIRS cloud datasets together to characterize at first the cloud cover diurnal cycle for two regions over land in the tropics, West Africa/Sahel/Sahara region and Northern Australia. When available, the LMD IASI cloud and aerosol data sets will be introduced in the analysis. Lidar and radar observations from ARM ground-‐based stations will also be introduced to further the interpretation of the geostationary dataset to describe the diurnal cycle of the cloud cover in the tropics.
This work, complementary to those proposed in T1.2, will be useful for the evaluation of the representation of the diurnal cycle of the cloud cover in the LMDZ global circulation model over these two regions and over some of the 119 instrumented sites retained in the frame of CMIP5 (phase five of the Cloud Feedback Intercomparison Project).
Results since last update The comparison of the geostationary (GEO) cloud type classification and cloud top pressure field from the SAFNWC (Satellite Application Facility for NowCasting) algorithm (Derrien and Legleau, 2005, 2009) and the CALIOP lidar cloud layer product, performed in collaboration with J. Pelon (LATMOS), M. Derrien and H. Legleau (Météo-‐France), in the frame of the MEGHA-‐TROPIQUES mission, has led to a paper submitted to the QJRMS special issue on MEGHA-‐TROPIQUES (see the MEGHA-‐TROPIQUES proposal).
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Fig. 1.3.1: (left column) SEVIRI cloud top pressure frequency distribution for july 2009 (top row) and july 2011 (bottom row) and for January 2010 (center column, bottom row), (center column and top row) when the sky is
cloud covered, frequency of midlevel cloud top at 2100GMT, (right column) CALIOP and SEVIRI cloud top frequency distribution for all sky cases and the June to September 2009 period over the North-‐Sahel-‐Sahara region (top row)
and the North-‐Africa/Atlas region (bottom row).
In collaboration with Nada Sellami, Enseignant-‐chercheur of the university of Mascara, the analysis of the diurnal cycle observed with SEVIRI/MSG over the North-‐Africa/Atlas region and the North Sahel/Sahara region (Fig. 1.3.1) in Algeria was continued. The vertical distribution of cloud cover, its diurnal and seasonal cycles observed with the SAFNWC cloud classification and cloud top pressure are currently evaluated for the 2009-‐2012 period. Over the North-‐Sahel/Sahara region the mid-‐level is the more frequent cloud type during the West-‐Africa monsoon season. The top is close to 450hPa as observed from CALIOP (Fig. 1.3.1). During winter these mid-‐level clouds are rare and high-‐level clouds are more frequent. The cloud top distributions observed from simultaneous and coincident data in summer 2009 for CALIOP and SEVIRI point out that SEVIRI retrievals underestimate the high cloud top altitude but more surprisingly overestimate the mid-‐level cloud top altitude (Fig. 1.3.1-‐ right column).
Revised work plan For the North Sahel/Sahara and the Atlas regions, we will use the SAFNWC SEVIRI/MSG cloud dataset to finalize the analysis of the diurnal cycle, the phases of the seasonal cycle, and its inter-‐annual change. To better interpret/evaluate the results, in addition to CALIOP data, CloudSat data (and if available LMD AIRS/IASI cloud data) will be introduced in the analysis. A similar study will be engaged for North-‐Australia, using the SAFNWC MTSAT cloud data. It will benefit from the evaluation of the MTSAT cloud cover diurnal cycle described in the MEGHA-‐TROPIQUES proposal.
The cloud types and their diurnal cycle observed with the SAFNWC SEVIRI/MSG cloud data set and the lidar and radar observations from NIAMEY ARM ground-‐based station will be compared. Following previous work (see 2012 EECLAT proposal), we will analyze the SEVIRI radiance dataset associated with the ARM station cloud types, to improve the SEVIRI/MSG cloud classification. A special focus will be given to the characterization of the mid-‐level cloud type properties as a function of the environment (convective systems, AEJ, boundary layer). The satellite cloud data set prepared at the CGTD ICARE over the 119 CMIP5 stations (project 51 in http://www.icare.univ-‐lille1.fr/drupal/projects/status/) will be added in the analysis.
2013 2014 2015 2016 Analyse cloud cycles Improve SEVIRI/MSG cloud classification
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T1.7 – Clouds in Antarctica and in the Southern Ocean Coordination: G. Cesana (LMD) Main contributors: G. Cesana, H. Chepfer (LMD)
Scientific context and objectives Climate models are the major tool for understanding and identifying future cloud processes that will appear within the next century. Yet, cloud feedbacks still constitute the major source of uncertainties in climate estimates. Our confidence in the future climate change projections by general circulation models is linked to the ability of these models to simulate the present climate. Trenberth and Fasullo (2010) have shown that climate models have large regional cloud-‐related radiation biases including excessive shortwave absorption in the extra-‐tropics, but insufficient shortwave radiation in the tropics. These regional shortwave radiation biases have global implications because they affect poleward heat transport and hemispheric scale atmospheric circulation patterns. Indeed, a recent study (Hwang and Frierson, 2013) found excessive shortwave absorption over the Southern Ocean explains a ubiquitous climate model tropical precipitation bias: the double Inter-‐tropical Convergence Zone (ITCZ).
In this work package, we want to evaluate and understand the processes and the key parameters controlling 21st century Southern Ocean cloud-‐climate feedbacks. Our goal is to improve this research using climate simulations during the 21th century together with active sensor data (CALIPSO, Winker et al. [2009]; CloudSat, Stephens et al. [2002]) and ground-‐based as well as ship-‐board observations. Thanks to CALIPSO-‐GOCCP cloud observations (Chepfer et al., 2010), it is now possible to observe cloud vertical structure over reflective surface during a substantial period of 7 years at a high resolution and with additional information on cloud phase and cloud optical depth. Combining with CERES (Wielicki, 1996) data on TOA radiative fluxes will allow us to determine the statistical relationships between cloud amount, cloud height and impacts on the radiation budget. This will help to augment our understanding of Southern Ocean climatology.
The use of active sensor to improve our understanding of model representation of cloudiness and radiation has been already used in previous studies (Kay et al 2012; Cesana et al. 2012). In the arctic region, most reliable observations of clouds were collected by ground-‐based sites (Shupe et al., 2006; de Boer et al., 2009) that do not provide a complete view of the region. The new cloud product CALIPSO-‐GOCCP derived from the CALIOP lidar onboard CALIPSO satellite flying in the A-‐Train constellation can observe directly some of the key missing cloud properties like the cloud vertical distribution at high spatial resolution (480m), and detect clouds over reflective surfaces and over the polar region equatorward of 82°. In addition with the lidar simulator (Chepfer et al., 2008) it appears to be a useful dataset to reveal systematic cloud model biases at regional and global scale (e.g. Barton et al., 2012; Bodas-‐Salcedo et al., 2012; Cesana and Chepfer, 2012; Dufresne et al., 2012; Hourdin et al., 2012; Kay et al., 2012; Stephens et al., 2013; Wang and Su, 2013) and better suited to model evaluation than other CALIPSO products (Chepfer et al., 2012). Using the new cloud phase diagnosis of CALIPSO-‐GOCCP used along with a lidar simulator (Cesana and Chepfer, 2013), Cesana et al. (2012) have demonstrated: 1) the inability of a climate model to accurately recreate the amount of liquid-‐containing Arctic clouds and 2) liquid phase biases in this climate model limit its ability to reproduce observed distributions of net surface radiative fluxes.
Work plan Here after a list of the main tasks that will be performed within the next 3 years listed as a function of their priority level, the year is given for indication:
1) Begin running and analyzing LMDZ simulations (2013) 2) Identifying key parameters controlling 21st century Southern Ocean cloud-‐climate
feedbacks through simulations and observations (2014)
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3) Analyze CALIPSO/CloudSat/CERES datasets over the Southern Ocean and compare to simulations on actual/past climate (2014)
4) Finish running and analyzing simulations using these satellite observations (2014) 5) Publish results from the simulation analysis and evaluation against observations (2015) 6) Extend the analysis to other models and other observations such as ground-‐based, in situ
and other passive/active sensors available at this time (2015-‐2016)
2013 2014 2015 2016 LMDZ runs
Analysis and evaluation of simulations using CALIPSO/CloudSat/CERES datasets
Extension of the analysis to other models and observations
T2 -‐ Clouds at Large and Global Scale Coordinator: V. Noel (LMD) This theme groups projects that aim at improving our knowledge of clouds and cloud processes on large and global scales using A-‐Train observations. Here several years of observations are used statistically to highlight patterns in clouds behavior that make climatic sense: how do clouds at global scale contribute to regulate the global climate? Our main scientific objectives are summarized in the figure below. Our work is to (i) build new, or improving existing large and global-‐scale cloud climatologies from A-‐train observations, taking advantage of synergies between instruments, and (ii) analyze these climatologies. Since building advanced algorithms cannot be separated from the scientific statistical analysis, a constant back-‐and-‐forth iterative process between level 1 data and a scientific analysis of climatologies is required to extract the best scientific information out of A-‐train observations.
As a consequence, work packages in this theme contain heavy algorithm development, applied to several years of A-‐train observations, and pluri-‐annual, global-‐scale statistical scientific analysis related to key questions on the role of clouds in climate and large-‐scale atmospheric dynamics. Most WPs prepare analyses of Earth-‐Care observations with the objectives (i) to merge A-‐Train and Earth-‐Care data for long-‐term cloud monitoring (the practical goal being to adapt our current algorithms to EarthCare) and (ii) to explore advances offered by Earth-‐care specificities compared to the A-‐train (i.e. what retrievals are possible from EarthCare that were not from the A-‐Train).
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Recent observations of ice clouds from the A-‐Train, thanks in part to the improved sensitivity of the CALIOP lidar, have deeply modified our understanding of their space and time distributions. For instance, they revealed a considerable amount of subvisible cirrus clouds (SVC) in areas that were previously thought devoid of clouds (SVC are invisible to most passive remote sensors). The multi-‐instrument dataset from the A-‐Train let us document in a coherent way the properties of ice clouds at a global scale (T2.1, 2.2, 2.4) and evaluate the importance of high tropospheric ice clouds in the global radiative budget (T2.4). Moreover, in recent years flaws have been progressively recognized in our understanding of ice cloud formation. In the traditional view, clouds form when water vapor levels rise above saturation, with a decrease below saturation after cloud formation. This is challenged by lots of evidence from in-‐situ measurements (e.g. from the MOZAIC experiment) that document stable supersaturation in clear-‐sky areas and persisting supersaturation during and after cloud formation (Peter et al. 2006), especially at temperatures below 200K. While today no consensus explains these observations, they suggest that atmospheric models based on the traditional view overestimate upper tropospheric ice clouds formation. Since these clouds are the main dehydration pathway for air masses in the main area of troposphere-‐to-‐stratosphere transport (Fueglistaler et al. 2009), wrongly predicting their formation creates major uncertainty when investigating the unexplained variations in stratospheric water vapor (Fueglistaler et al., 2005), with potentially dire effects for climate prediction. In parallel, recent studies showed that requiring high supersaturation (>140%) before cloud formation in models led to improved water vapor representation compared to observations (James et al. 2008), in line with the in-‐situ observed supersaturation. Here we investigate the relevancy of several scenarios of ice cloud formation in regard with water vapor levels (T2.2, 2.3).
T2.1 – Ice clouds: Statistical properties and forecast model evaluation at global scale [B1, B2, B3, C2, C3] Coordination: J. Delanoë (LATMOS) Main contributors: M. Ceccaldi, J. Delanoë (LATMOS), R. Hogan (U. Reading), A. Protat (CAWCR), R. Forbes (ECMWF)
Objectives Large-‐scale models have reached a high level of complexity, leading to obvious improvements in the realism of the model physics through the introduction of complex processes, which however makes the evaluation and improvement of models increasingly difficult (Jakob 2003). These models have also large grid-‐boxes (from 15 km for the latest global ECMWF (European Centre for Medium Range Weather Forecasts) model to 300 km for some climate models), which is incompatible with a detailed description of cloud processes happening at a much smaller scale. We need to understand if, despite their limitations, these models can accurately represent ice cloud properties, especially the vertical distribution of ice. Further evaluations and improvements of model performances and development of new cloud parameterizations must now rely not only on a better understanding of cloud processes (using detailed in-‐situ microphysical observations of the nucleation and growth processes), but also on a better understanding of the statistical properties of clouds and deep convection and their variability (regional, temporal, vertical, or as a function of large-‐scale atmospheric or cloud regimes, e.g., Su et al. 2008; Marchand et al. 2006; Mace et al. 2006b; Sassen et al. 2008; Jakob and Tselioudis, 2003).
The main objective of this work is to characterize the statistical properties of clouds using unique radar-‐lidar products developed at global scale by LATMOS. Delanoë et al. (2011) evaluated the statistical distributions of ice water content and ice cloud fraction from the ECMWF and UK Met Office models, exploiting the synergy between the CloudSat radar and CALIPSO lidar. They compared the global ice cloud occurrence as a function of temperature and latitude and a global statistical comparison of the occurrence of grid-‐box mean (IWC) at different temperatures derived from models and observations. Unfortunately, they have been using only
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the last three weeks of July 2006, which is rather short to analyze annual and seasonal variations. However they have already highlighted model weaknesses.
Our main objectives are 1) to characterize these statistical properties using unique radar-‐lidar products developed at global scale by LATMOS, 2) to evaluate ice cloud representation in GCMs using these properties (focusing on ECMWF and UK Met Office models).
Description of work The current ice cloud products available from the CloudSat and CALIPSO missions are either not not accurate enough (CloudSat 2B-‐CWC-‐RO, see Mioche et al. 2010; Protat et al. 2010) or limited in the amount of clouds included (no thick ice clouds for CALIPSO, no thin cirrus and not much low-‐level clouds for CloudSat). The strength of the CloudSat-‐CALIPSO synergy has clearly not been exploited yet to characterize the statistical properties of ice clouds at global scale. The DARDAR product has been developed by LATMOS and University of Reading (Delanoë et al. 2010), made operational by the CGTD ICARE (Lille), and is now available to registered users. A major objective in this WP is to develop the first global-‐scale radar-‐lidar climatology of ice cloud properties using DARDAR.
To reach this objective, spaceborne microphysical retrievals will be first refined using the knowledge gained in WP1.1 and 1.7 about the statistical properties of the particle size distribution, and the relationships between crystal density, fall speed, and projected area as a function of maximum crystal dimension. Second, global-‐scale microphysics retrievals will be obtained by modifying the current version of the Delanoë and Hogan (2010) method used at CGTD ICARE (and possibly other simpler retrieval methods). This part will be addressed in WP0.B.
Our second activity will be the evaluation of forecast models at global scale using CloudSat/ CALIPSO. We propose to evaluate the ice water content and ice cloud fraction statistical distributions from the ECMWF and UK Met Office models, exploiting the synergy between the CloudSat radar and CALIPSO lidar. We will cooperate with R. Forbes from ECMWF and extend the study from Delanoë et al. (2011) who were using only the last 3 weeks of July 2006 to analyze the global ice cloud occurrence as a function of temperature and latitude in other seasons. We will look at inter-‐annual variability in data already collected from CloudSat and CALIPSO (over 6 years). We will address aspects of the comparison not investigated in detail in Delanoë et al. (2011), including:
1. Assessing the representativeness of observations. The satellite track represents only a 2D slice through a 3D model grid-‐box and there is uncertainty as to the representativeness of these observations for a model 3D grid-‐box. A statistical approach could be used to evaluate the variability of the cloud properties and estimate an error due to non-‐ uniform filling (Stiller 2010). To do so we will use the IIR and WFC (onboard CALIPSO) information to describe the horizontal filling of the model grid-‐box. Note that radar-‐lidar measurements already perfectly describe the vertical properties of clouds.
2. Extending the time period for seasonal and inter-‐annual variations. In Delanoe et al. 2011 the focus was on three weeks of data from the observations and re-‐runs of the global model simulations for July 2006, sufficient to identify the main features of the model-‐observation comparison in terms of global statistics and a break down into latitude bands. We plan to extend this study to different seasons and different years to assess seasonal and inter-‐annual variations.
These tasks have not been addressed yet. 2013 2014 2015 2016 DARDAR evolutions Model evaluation
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Ice cloud statistics
T2.3 – Ice clouds and interaction with water vapor [B2, B3] Coordination: C. Hoareau (LMD/Post-‐doc CNES) Contributors: V. Noel, C. Hoareau, H. Chepfer (LMD)
Objectives In Martins et al. (2011), we showed Subvisible Cirrus (SVC) are ubiquitous in CALIOP observations over the Tropics, in conflict with our current understanding of ice cloud formation that posits their crystals should immediately convert back to water vapor. Coupled with accumulating evidence from in-‐situ measurements for sustained high supersaturation inside cirrus clouds and in clear-‐sky conditions (Peter et al. 2006), this suggests that unidentified constituents and mechanisms interfere with the formation and growth of ice crystals (e.g. Murray, 2008). This limits improvements in the representation of ice clouds in atmospheric models, and our capacity to realistically predict their role as dehydrators of air before it enters the stratosphere, where water vapor is of foremost climatic importance. Our purpose here is thus to better understand the mechanisms driving the balance between water vapor supersaturation and ice cloud formation in the Tropics.
Last year, our objectives were to identify the formation mechanisms of SVC, and to study how they interact with water vapor, the idea being that SVC form at the absolute minimum supersaturation required for nucleation. Our results since (cf. below) suggested that SVC share formation mechanisms with generic cirrus clouds. Based on this conclusion, we stopped our investigations of SVC as a specific cloud family and refocused this WP. Our current objective is now to study how cirrus clouds in general interact with water vapor, by documenting how supersaturation fluctuate along their lifetime, and identify the mechanisms responsible for supersaturation persistence.
Results since last update We extended and finalized the SVC study presented in the last proposal, by investigating the relationship between all SVC observed by CALIOP and convective activity derived from the global Brightness Temperature dataset MERG, which combines observations from 5 geostationary imagers at 4km resolution between ±60° since February 2000. These results were published in Reverdy et al. (2012). According to the revised objectives of this WP (cf. above), this line of research is dropped, and work to relate SVC formation with atmospheric waves abandoned.
The second line of research described last year, now our main focus, proposed the study of simultaneous high-‐resolution profiles of ice clouds and water vapor concentration retrieved from the analysis of ground-‐based Raman lidar observations in the Tropics (La Reunion Island) to inform and force simulations of supersaturation. Successfully conducting this simultaneous retrieval requires a deep understanding of the new challenges posed by the analysis of Raman signal. Water vapor profiles must be carefully calibrated using stable external sources (e.g. Cryogenic Frost point Hygrometer sondes), while the simultaneous retrieval of water vapor and cloud cover in the upper troposphere requires innovative methodology that involves the averaging of Raman signals over variable time periods, long enough to reach the best possible signal-‐to-‐noise ratio but short enough to still document the temporal variability of atmospheric layers (Hoareau et al. 2009). Over the past year, we thusly analyzed and documented several cirrus cases over La Reunion.
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In parallel to data analysis, we ran the Weather Research and Forecasting model (WRF) in a domain centered at La Reunion to reproduce the prior observations. We used two-‐way nesting (15, 5 and 1.6km resolutions) and forced the model with ERA-‐Interim reanalysis every 6h. We experimented with several nudging/nesting configurations and microphysical schemes, converging on choices that let the model best reproduce our observations (Fig. 2.3.1).
Fig. 2.3.1: Cirrus observations from Raman lidar as a function of time and altitude (left) and simulated cloud cover from WRF in three configurations among those considered (rows 2 to 4). Configuration 2 (row 3) was
selected as the most appropriate.
From there, we were able to simultaneously document the evolution of water vapor supersaturation and cloud cover from Raman observations and compare it to simulations. This is, to our knowledge, the first time this is attempted. Although the model correctly predicts ice at the altitude and period of the observed cloud, it fails to reproduce the measured high supersaturation (~140% vs. a maximum of 95% in the model). We attribute this to how ice cloud formation in the model is based on a subgrid-‐scale redistribution of water vapor based on standardized PDFs that fail to take into account the existence of high supersaturation and do not reflect reality.
Revised work plan Ongoing activities (2013) include: simulating lidar observations from the WRF output (ACTSIM tool) to provide a consistent basis for comparisons with measurements, finalizing our choices of model configuration and microphysical scheme. Future work (2014) involves extrapolating our results to the entire tropical belt, by identifying in the CALIOP 6-‐years dataset cirrus observations that take place in similar thermodynamical contexts and synoptic conditions. We also intend to port this study from La Reunion to the midlatitudes, using the new Raman lidar IPRAL, whose installation at the SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique) is planned in 2013 and which will mirror the one from La Reunion. Such measurements that allow the parallel retrieval of high-‐resolution cloud properties and water vapor in the UTLS do not yet exist in France.
2013 2014 2015 2016 Simulate lidar obs from WRF with ACTSIM
Extrapolate results to global
Port study to midlatitudes
T2.5 -‐ Evaluation of clouds in climate models using A-‐train/EarthCare observations [B1, B2, B4, C2, C5] Coordinator: H. Chepfer (LMD) Contributors: H. Chepfer, S. Bony, J.-‐L. Dufresne, G. Cesana, M. Reverdy (LMD), D. Winker (NASA/LaRC, PI CALIOP), D. Tanré (LOA, PI Parasol)
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Objectives The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) reaffirms that cloud-‐climate feedbacks remain one of the largest sources of uncertainty in global climate model projections for the 21st century response to anthropogenic radiative forcing. Climate models still predict a wide range of cloud radiative feedbacks (Soden and Held 2006, Webb et al. 2006, Ringer et al. 2006). Differences in the strength and even sign of these feedbacks are the dominant contributor (by a factor of three or more) to the uncertainty in model-‐based estimates of climate sensitivity, both for equilibrium and transient climate changes (Dufresne and Bony 2008).
Our objective is to reduce uncertainties in the representation of cloud processes and feedbacks in climate models. We will achieve this by first developing systematic evaluations of the representation of clouds by models in the CMIP-‐5 intercomparison project in support of the next IPCC assessment report. These process-‐based evaluations make innovative use of space-‐based active remote sensing. They clarify which cloud types and processes are responsible for the climate sensitivity spread in current climate models, help target process-‐level understanding to improve the representation of these key cloud types and processes, and guide the development of new climate model experiments to test our understanding.
Evaluating model output in a process-‐oriented manner is complicated, particularly when satellite retrievals are the primary evaluation dataset. There is no unique definition of clouds or cloud types in models or in observations. To compare models with observations, and even to compare models with each other, we must use a consistent definition of clouds. By using model outputs to define quantities that are actually observed (rather than inferred) from satellites (e.g. visible/infrared radiances, radar reflectivities or lidar backscattered signals), software packages known as simulators let models and observations speak the same language and be compared quantitatively. The ISCCP simulator, now routinely used by many modelling groups, has been very valuable to compare models with each other and with observations from passive remote sensing instruments, to point out systematic biases of climate models, and to analyse cloud feedbacks (e.g. Webb et al. 2001, Zhang et al. 2005, Webb et al. 2006, Williams and Tselioudis 2007, Williams and Webb 2008). Comparing GCM outputs with CALIPSO observations (Chepfer et al. 2008) and CloudSat radar reflectivities (Haynes et al. 2007, Bodas-‐Salcedo et al. 2008) has shown the great potential of these measurements for revealing systematic biases in the simulated clouds. To take advantage of these new measurements, new simulators are required. For this purpose, CFMIP has been developing the CFMIP Observational Simulator Package (COSP), a package that currently consists of three simulators: ISCCP, CloudSat and CALIPSO-‐PARASOL. The CALIPSO-‐PARASOL simulator (CAPSIM) is developed at LMD (see T5.4 in this proposal). http://www.cfmip.net/
LMD also produces and distributes to the model community “GCM-‐oriented products” based on observations from CALIPSO (Chepfer et al. 2010; see T0B) and PARASOL, that are fully consistent with diagnostics derived from COSP CALIPSO and PARASOL simulators (Chepfer et al. 2008, http://climserv.ipsl.polytechnique.fr/cfmip-‐atrain.html), e.g. 3D cloud fraction on 40 vertical levels from CALIPSO or mono-‐directional reflectance associated with several solar zenith angles from PARASOL.
Results since last update Numerous results were obtained based on COSP and CALIPSO-‐GOCCP. Most significant are:
i) We evaluated the LMDZ5 tropical boundary layer clouds against A-‐train observations (Konsta et al. 2012, and Konsta et al. in revision)
ii) We evaluated the transition between liquid and ice clouds in LMDZ5 against CALIPSO-‐GOCCP observations (Cesana and Chepfer 2013). We showed that phase transition is about 10° too warm in LMDZ5. Moreover, CALIPSO-‐GOCCP has pointed out that many low-‐level liquid-‐containing Arctic clouds above continental surfaces are not reproduced by climate models (Cesana et al. 2012) and bias the radiative fluxes reaching the surface.
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iii) We evaluated the cloud cover and the 3D distribution of clouds simulated by climate models (Cesana and Chepfer, 2012). Fig. 2.5.1 shows an example.
iv) We identified systematic errors in the simulation of clouds and radiation, and compensating errors (e.g. between predicted cloud fraction and optical thickness) in the simulation of top-‐of-‐atmosphere radiative fluxes (Nam et al. 2012).
Fig. 2.5.1: Arctic low level cloud cover (a) observed by CALIPSO-‐GOCCP (b-‐f) simulated by five climate models+COSP simulator. From Cesana and Chepfer, GRL, 2012.
Revised Work plan Process-‐oriented evaluation
Process-‐oriented evaluations of the description of clouds in GCMs using A-‐train observations are under way. Some focus on the representation of the boundary layer clouds in the tropics, others are related to the description of the ice-‐liquid transition in polar tropospheric clouds. In these studies, we use statistical relationships between different clouds variables at high spatio-‐temporal resolution to mimic the behavior supposed to be produced by the model’s parameterization. These observational relationships are built by accumulating years of instantaneous data (no averaging in time) at a ~hundreds meters spatial resolution (no averaging in space). By doing so, observations not only evaluate the cloud description in the model, but also improve the clouds scheme in GCMs, and propose inputs for model developments. Thus observations contribute to improve the realism of GCM cloud schemes.
Inter-‐annual variability
The 10-‐years CALIPSO data that will be collected allow studying the inter-‐annual variability of cloud properties (detailed vertical structure, clouds above continents and snow/ice surfaces, etc.) that were not observed with passive remote sensing. We will analyze these observations i)
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to understand cloud natural variability and ii) to explore which observed cloud variables on what minimum period would be required to identify a signature of atmospheric change due to anthropogenic forcing.
2013 2014 2015 2016 Process-‐oriented evaluation Interannual variability studies T3. Aerosols Coordination: S. Turquety (LMD), G. Ancellet (LATMOS) The main purpose of this task is to analyze the information provided by the A-‐Train and EarthCare observations on aerosol sources (natural and anthropogenic) and on the evolution of aerosol optical properties during transport on a regional scale (Euro-‐Mediterranean region) and at the high latitudes of the Northern Hemisphere. The good coverage and resolution of the data will help better understand and quantify the relative impact of long-‐range transport on regional budgets. A particular emphasis will be placed on the analysis of the relative contribution of emissions from large fires. Wildfires regularly emit large amounts of aerosols during the spring and summer in Europe and in boreal forests. However, their impact is still not well quantified in chemistry transport models due to uncertainties on the emissions (amount emitted, size of the particles) but also on the injection height (as pyroconvection may lift emissions above the boundary layer for intense fires) and on the evolution during transport. Combined analysis of CALIPSO and in situ observations in Russia will help characterizing boreal fire plumes and their impact. Using both horizontal and vertical distributions of aerosol optical properties from the A-‐Train observations should then help improving the modeling of fire plumes and their impact. More generally, these observations will be used to conduct a full evaluation of the aerosol simulation by chemistry transport models (CHIMERE and MOCAGE).
This task proposes three work packages to:
1. Analyze transport from different sources (wildfires, anthropogenic and dust) towards the high latitudes of the Northern Hemisphere using a combination of satellite and in situ observations, and with transport model simulations to attribute sources (3.1)
2. Analyze the capabilities of a state-‐of-‐the-‐art air quality model (CHIMERE) to simulate the distributions observed from space, and quantifying the relative contributions from the different sources (anthropogenic, biogenic, dust, fires) (3.2)
3. Validate the aerosol version of the MOCAGE global chemistry-‐transport model and quantify the impact of the long range transport towards the Euro-‐Mediterranean region (3.3)
The analysis of satellite observations with models requires the development of comparison tools (observation simulation). Databases of in-‐situ aerosol measurements will be obtained from existing and forthcoming airborne campaigns. Complementary with other missions (IASI/METOP for instance) will be also be used.
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T3.2 Evaluation of aerosol description in a regional chemistry-‐transport model (CHIMERE) using remote-‐sensing observations [B1, B2, B3, B4, C2, C5] Coordinators: S. Turquety (LMD) Main Contributors: S. Turquety, L. Menut (LMD), Y. Kim (CDD CNES), G. Rea (PhD KIC/UPMC)
Objectives Our scientific objective is to take advantage of the complementary observations from passive and active remote sensors to better understand and quantify the relative impact of the main aerosol sources on regional air quality in the Euro-‐Mediterranean region. An emphasis is placed on the large emissions from fires and dust, which are particularly difficult to simulate due to their sporadic nature and large uncertainties on the driving processes.
For this purpose, regional model simulations will be compared with the aerosol distributions retrieved from complementary instruments, taking advantage of both the good horizontal coverage and resolution of passive instrumentation (MODIS, PARASOL in the A-‐Train, MSI on EarthCare), and the vertical profiling capabilities of the lidars (CALIOP and ATLID). In addition, ground-‐based observations (AERONET network sunphotometers and EARLINET network lidars) will be used. This will allow the evaluation and improvement of current parameterizations of aerosol transport (advection, convection), but also of their evolution during the transport. We will study the link between meteorology, aerosol transport and properties, in order to infer the impact of different sources (both local sources and from long range transport) on regional particulate matter pollution budgets (PM2.5, PM10). The main objectives are to evaluate:
• Regional emissions; • The transport and evolution of aerosol plumes in the model (size distributions in particular); • The temporal variability of the impact of the main sources (especially natural sources like
mineral dust and wildfires). The comparison using A-‐Train observations provides insights on current capabilities of satellite-‐based passive and active remote sensing to help constrain chemistry-‐transport models. Based on these analyses, the expected additional scientific contribution of the EarthCare mission will be analyzed, and the methods developed adapted for the preparation of systematic analysis of the data as soon as they are available. This study also allows a complete intercomparison of aerosol properties derived from complementary instrumentation.
This study is based on the CHIMERE chemistry transport model (Menut et al., 2013). This model allows a full simulation of the aerosol impacts on pollution budgets and radiative forcing for
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regions combining several contributions (anthropogenic, biogenic, mineral dust and biomass burning) for primary emissions and secondary aerosol formation, extending from the surface to 200hPa (~ top of the troposphere). In addition to the improvement of the model simulations, our objective is to work on methods for accurate comparisons between model simulations and satellite observations, fully accounting for the instrumental characteristics.
We will also work on the constraint provided by observations alone on surface PM concentrations in a context of air quality monitoring. This work will be done in the framework of the PhD thesis of Geraldine Rea (ED129, UPMC, Climate-‐KIC fellowship in collaboration with Hygeos).
Results since last update
OPTSIM Software development
We have developed software to compare model aerosol simulation to the available satellite observations that can be adapted to several instruments and models. Our approach aims at directly comparing to the level 1 lidar observations, hence rather simulating observations from the model outputs than using level 2 retrievals (which may rely on conflicting assumptions on aerosol properties). This tool builds on work previously done in the group (Hodzic et al., 2007; Vuolo et al., 2009) fully described in Stromatas et al. (2012). It is easily adaptable to the EarthCare mission, once the corresponding characteristics are known precisely. A user’s guide is currently being written by Y. Kim (CDD CNES on this proposal) and the software will be distributed via a dedicated web page (www.lmd.polytechnique.fr/optsim).
The development of OPTSIM and most of the analyses described below have been undertaken in the framework of the thesis of Stavros Stromatas (CNES/ADEME fellowship), to be defended in May 2013.
Analysis of aerosol variability in the Euro-‐Mediterranean region
The spatial and temporal variability of aerosol observations in the Euro-‐Mediterranean region has been analyzed using both surface measurements from the AIRBASE network, ground-‐based remote sensing from the AERONET network, and satellite measurements from MODIS. Fig. 3.2.1 shows the seasonal AOD from MODIS. This analysis has highlighted the general seasonal cycle of the AOD, with values between 0.1 and 0.5, reaching a maximum during summer (while surface concentrations reach a maximum in winter). Higher values are observed North of 40°N with a dominance of the fine mode fraction (more than 50% of the total AOD), while AOD South of 40°N are mainly in the coarse mode. The coarse mode is also predominant in the Western part of the domain, and smaller particles are observed in Eastern Europe. About half of the highest values are observed in the Mediterranean area and are associated to the long-‐range transport of dense plumes (dust and fires). Finally, while AIRBASE observations have shown an apparent decreasing trend in surface concentrations, due to decreasing anthropogenic emissions, no trend is observed for the AOD from either AERONET or MODIS, again suggesting a significant impact of natural extreme events.
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Fig. 3.2.1 – Seasonal average of the MODIS total AOD at 550nm based on the 2004-‐2010 observations.
Work is currently ongoing to analyze aerosol layers properties in the Euro-‐Mediterranean region based on the lidar observations from the EARLINET network and the CALIPSO mission.
Evaluating fire plume modeling in the CHIMERE model
A particular focus during the past year was the evaluation and understanding of aerosol properties in fire plumes, using the case study of the summer 2007 during which intense fires burned in Southern and Eastern Europe. Comparisons to surface and satellite observations show that the fire plumes general transport pathways are well captured but that the emissions tend to be overestimated in Eastern Europe, and underestimated in the Mediterranean area during the most intense events. A slight temporal shift is the fire activity can also have consequences on the conclusions since the core of the plume may be displaced. Comparisons to lidar observations have shown that, although plumes are simulated at correct altitudes, too large diffusivity (both horizontally and vertically) in the model may explain part of the underestimate.
Fig. 3.2.2 – Comparisons between CHIMERE and CALIOP attenuated backscatter during the long-‐range
transport from Greek fires in August 2007.
In complement, a complete evaluation of the importance of pyroconvection in the Euro-‐Mediterranean region has been started (part of the thesis of Geraldine Rea), combining the information on aerosol plume height observations from MISR and CALIOP with fire detection from MODIS. This database will be used to evaluate parameterizations of thermal plume lofting in the CHIMERE CTM.
Revised work plan
Analysis of aerosol budget in the Euro-‐Mediterranean region using CHIMERE
We will conduct a full sensitivity analysis with the CHIMERE CTM to evaluate the driving contributions to surface concentrations and to total and fine mode AOD. This analysis will be undertaken based on simulations for one winter, one spring and one summer month, and turning off one source at a time (anthropogenic, biogenic, fire emissions, secondary production,
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etc.) Intra-‐model comparisons and comparisons with the observations for both surface concentrations, AOD and lidar attenuated backscattered signal will allow us to fully evaluate the capabilities of CHIMERE in representing the main driving processes. This work will be focused on the Mediterranean region and done in the framework of the thesis of Geraldine Réa.
Constrain surface PM from satellite observations
Another underlying objective of the CHIMERE sensitivity study described above is to analyze the link between surface concentrations and total AOD in order to evaluate the possibilities to use satellite observations to constrain surface PM, that are monitored for air quality. As a first step, the PM/AOD relationships in the model simulations will be confronted to the satellite observations. The complementary constrain from lidar observations will also be explored.
EarthCare
The observation to model comparison tool will be adapted to the new instrumental characteristics as they become available. The additional scientific return will then be evaluated using an observation system simulation experiment (OSSE) approach, i.e. simulating synthetic observations.
2013 2014 2015 2016
Aerosol budget using CHIMERE
Earth-‐Care adaptation
Constrain surface PM from satellite
T3.3 Improvement of aerosol representation in the chemistry and transport model MOCAGE [B3, B4] Coordinators: L. El Amraoui (CNRM-‐GAME) Contributors: L. El Amraoui, B. Sic (PhD), CDD-‐IR (to be financed within this project)
Objectives MOCAGE is the chemistry-‐transport model (CTM) of Météo-‐France, able to represent the distribution of chemical species and aerosols simultaneously in the troposphere and stratosphere.
The current version of MOCAGE-‐aerosol contains several types of primary aerosols: black carbon, dust and sea salt, plus one secondary type: sulfate. In addition to turbulent diffusion and convective and large-‐scale transport, aerosols are usually subject to specific phenomena, which take into account the size and nature of the considered particles. Gravity, precipitations and erosion induce the removal of particles from the atmosphere and are considered in MOCAGE. On the other hand, some phenomena cause the physical and chemical processing of aerosols. In this regard, only the hygroscopicity of marine aerosols is parameterized in MOCAGE. Finally, emissions are taken into account by cadastres or fixed models to calculate the particle flux entering the atmosphere based on meteorological conditions and the state of the vegetation (Martet, 2009). MOCAGE assumes aerosols are externally mixed. The individual species are assumed to coexist in the considered air volume as a first approximation.
The overall goal of this project is to improve the representation of different aerosol components in MOCAGE at regional and global scales. The approach will consist on 2 steps: i) Compare MOCAGE outputs to A-‐train observations in terms of Aerosol Optical Depth (AOD) in order to evaluate the model with respect to the aerosol load. This will give an idea about the behaviour of MOCAGE depending on different aerosol types. ii) Compare MOCAGE fields to CALIOP measurements in terms of backscatter profile. This will evaluate how the model simulates the
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aerosol vertical concentrations during a dust outbreak event or a volcanic eruption. These results will be useful to authorities that take decisions regarding the air quality standards, and to civil aviation safety.
Results since last update Insertion of volcanic aerosol into MOCAGE
Volcanic emissions are an important source of aerosol in the troposphere, and have important implications for the Earth’s radiation budget at various temporal and spatial scales. Volcanogenic aerosol can also transport trace metals and other pollutants, with impacts on terrestrial ecosystems and human health on local to regional scales (e.g., Delmelle, et al., 2001). Besides, airborne volcanic ash from eruptions is a major threat to aviation (e.g., Miller and Casadevall 1999; Simpson et al. 2000). It can provide direct aerosol transport into the middle and upper troposphere in larger concentrations, which may be harmful to aircraft. It is thus very important to understand the evolution and the long-‐range transport of these pollutants in order to assess their impact on the tropospheric chemistry and on aviation safety. In-‐situ and satellite observations can be assimilated into chemistry and transport models. This leads, on the one hand, to a feedback that is expected to improve forecasting, and, on the other hand, provides more consistent datasets. Météo-‐France is the ninth VAAC (Volcanic Ash Advisory Centre), designated by the International Civil Aviation Organization to provide their expertise to civil aviation in case of significant volcanic eruptions. This project well fit into efforts deployed by Météo-‐France to accurately predict the evolution of volcanic aerosol in case of huge volcanic eruptions.
We have worked on the insertion of the volcanic aerosol into the MOCAGE-‐aerosol model. The first step consisted of comparing the outputs from MOCAGE-‐aerosol to those of MOCAGE-‐Accident, which is used operationally for predicting atmospheric dispersion of accidental atmospheric releases (plumes and retro-‐plumes) or volcanic effluents. Fig. 3.3.1 compares the volcanic aerosol from both models. The difference between the two concerning the volcanic aerosol does not exceed 1.5% which very satisfactory and validates the aerosol volcanic in MOCAGE as a primary aerosol.
Fig. 3.3.1 – Volcanic aerosol within MOCAGE (top-‐left) compared to the operational forecast of volcanic aerosol (top-‐right). The absolute difference is bottom-‐left. The relative difference (bottom-‐right) doesn’t
exceed 1.5%.
Improvement of aerosol parameterisations
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We have also improved aerosol parameterisations: wet deposition, sedimentation and emissions of sea salt. Here we focus only on the wet deposition since its impact is very important on the aerosol concentrations. The new wet deposition scheme in MOCAGE takes into account the following processes: Collection efficiency, Diffusion, Interception, Impaction, Thermo-‐phoresis, Diffusion-‐phoresis, Electric charges, and snow/ice melting. The new wet deposition gives very good results compared to the old one. Fig. 3.3.2 shows these improvements validated by MODIS data.
Fig. 3.3.2 – AOD of MOCAGE with the old scheme of wet deposition (left), and with the new scheme (right). MODIS AOD is at bottom. The improvement is clear for the sea-‐salt signature over UK and also for the desert
dust signature over The Arabian Peninsula.
Revised work plan Validation during TRAQA campaign
In the framework of the TRAQA campaign (June-‐July 2012) over the Mediterranean basin, many measurements of the optical properties and distributions of aerosols have been conducted. During this campaign an important aerosol dust transport over the Mediterranean basin has been identified on late June 2012. In this context, we will take part on the study of this event using MOCAGE. We will use the aerosol measurements recorded in-‐situ during this event, in conjunction with other datasets, to validate aerosol outputs given by MOCAGE. The validation will consists on the AOD, the concentrations and also the aerosol distributions.
Assimilation of AOD satellite measurements in MOCAGE
This task has the objective to improve the aerosol representation in MOCAGE by assimilating satellite measurements. This is very important for air quality forecasts and aviation safety. During this year, the first tests of AOD assimilation have been successful in MOCAGE, considering AOD as the control variable. The next step is to consider aerosol concentrations as the control variable. In this sense, we are working on the modification and the implementation of the observation operator within MOCAGE. Such an implementation will give us the possibility to improve further the aerosol concentrations in the model.
2013 2014 2015 2016
MOCAGE validation Assimilation of satellite data
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T4 – Polar Stratospheric Clouds (PSC) Coordination: V. Noel (LMD) PSC form during polar winter night, when the stratosphere becomes cold enough for nucleation, even at its extremely low concentrations of chemical species. Their impact on stratospheric chemistry is important, as 1) sun-‐activated chemical reactions on the surface of their particles when winter ends activate passive reservoir species (e.g. Chlorine, Bromine), leading to ozone loss, and 2) sedimentation of PSC particles scavenge nitric acid compounds, this denitrification slows down the reconversion of active species into passive (Mann et al. 2005). PSC also influence stratospheric heating rates. These poorly understood effects limit our ability to represent accurately the influence of PSCs in chemical transport and general circulation models that predict trends in the future evolution of polar ozone and climate. This is especially true in the Arctic stratosphere, where temperatures are near the nucleation threshold and even a slight cooling can trigger PSC formation and ozone loss, but is also relevant in the Antarctic. A major scientific project is thus to accurately portray the evolution of PSC cover during polar winters, and how it reacts to temperature fluctuations e.g. during Sudden Stratospheric Warming events.
Further complication is that PSC composition is heterogeneous, generally dominated by liquid droplets of Sulfate Ternary Solution (STS, HNO3, H2O and H2SO4) mixed with solid crystals of ice or Nitric Acid Trihydrate (NAT, HNO3 and H2O). Chemical compounds follow various pathways leading to particle formation, depending on their individual mixing ratios, ambient temperature and pressure, and the availability of formation nuclei. These pathways and their relative importance are still poorly understood, especially the nucleation processes responsible for the formation of NAT crystals, which are critical for stratospheric denitrification. As each particle type influences differently ozone loss through nitric acid captation, another endeavour is therefore to 1) evaluate the ability of different types of observations to identify PSC types; 2) document how their cloud cover relates to and influences available mixing ratios of stratospheric species to better understand the formation processes; and 3) relate PSC occurrence with documented ozone loss above the polar regions. These major questions are summarized in Fig. T4.
Since the last proposal, most of the objectives of T4.1, investigating how PSC interact with atmospheric waves, were reached. Its results were published as three articles (Noel et al. 2008, 2009 and recently Noel and Pitts, 2012). Some of the questions that remained in its work plan were integrated in the new T4.1, mostly based on the previous T4.2.
WP4.1 -‐ Microphysical modelling of PSC case studies [B1, B2, B4] Coordination: E. Rivière (GSMA), V. Noel (LMD) Contributors: E. Rivière (GSMA), F. Lefebvre (LATMOS), H. Chepfer, A. Hertzog, V. Noel (LMD)
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Activity on this WP was delayed due to the coordination by E. Rivière of the TRO-‐pico project, including a field campaign in Brazil in 2012 and 2013, and the absence of manpower funding in previous evaluations. However, the main tool for this WP, the MiPLaSMO model, has been installed on Climserv at LMD/Polytechnique and successfully applied on mesoscale trajectories during a well-‐understood case study during a kickstart week in summer 2012.
Objectives Mesoscale processes such as waves play an important role in both polar hemispheres (Noël et al., 2009; Hopfner et al., 2006; Rivière et al., 2000), and their impact in the Arctic may be crucial for the global PSC coverage since temperatures there are often above or at the PSC nucleation threshold and cannot alone explain the PSC statistics of the whole winter. Our aim is to simulate specific PSC case studies considering several nucleation processes and compare them with observations, to learn more about the conditions that activate PSC formation mechanisms, especially for NAT nucleation. Our approach follows the Match-‐satellite technique (Terao et al., 2002), which consists in analyzing at least a pair of satellite measurements that belong to the same airmass as shown by trajectory analysis. At least one of the measurements shall document a PSC, so we can follow its formation and dissipation.
Work plan From a climatology of 2009-‐2010 Arctic winter PSC provided by CALIPSO, we will select specific cases. We will give precedence to PSC cases sampled during the RECONCILE campaign (see CALIPSO PSC statistics in Pitts et al., 2011) that took place the same season, so that more than CALIOP information can be used in our analysis. If possible, we will select cases that have been sampled several times from satellite, to obtain precious information about the PSC lifetime and evolution. We will select several cases of synoptic PSCs, and several cases of gravity wave PSCs, which may not form in the same manner. Following Hopfner et al. (2006) conclusions based on MIPAS observations, our recent work in the Antarctic confirms the possibility of NAT formation on ice nuclei in mountain waves (Noël et al., 2009, 2012). During the EUPLEX campaign, Voigt et al. (2005) suggested another explanation of fast NAT formation at low saturation with respect to HNO3: heterogeneous nucleation on meteoritic particles. We hope here to check the importance of these mechanisms in the Arctic.
We will perform microphysical modeling of the selected case studies with MiPLaSMO (Rivière et al., 2000; Brogniez et al., 2003; Rivière et al., 2003). This PSC model is coupled with a stratospheric chemistry model to be run along trajectories. Its spectral microphysical model is adapted from the Danish meteorological institute model (Larsen et al., 2000), and uses 50 particle size bins from 0.001 μm to 100 μm. It simulates size-‐dependent processes such as the homogeneous nucleation of NAT and sedimentation, and includes the description of liquid particles (binary aerosols and PSC 1b), solid aerosols such as SAT (sulfuric acid tetrahydrate) and ice PSC. The chemistry model includes heterogeneous reactions on PSCs and binary solution aerosols involving chlorine and bromine reservoirs as well as N2O5. Formation mechanisms can be activated to test microphysical processes. The heterogeneous nucleation of NAT on ice is not included yet, but we plan to test its importance in gravity wave PSCs. Initializing MIPLASMO will require finding the best source able to document during the 2009-‐2010 winter the stratospheric concentrations of species: H2O and HNO3 for the microphysics, other compounds for chemical simulations. Considered sources include the REPROBUS CTM model simulation performed at LATMOS, or retrievals based on spaceborne observations from the Microwave Limb Sounder in the A-‐Train. Depending on the chemical data available for the selected cases, we will run MiPLaSMO in its fully coupled microphysics/chemistry mode or only in its microphysical mode.
We will run MiPLaSMO along trajectories from the FLEXTRA model (Stohl et al., 1998) using ECMWF analyses, and from vortex-‐scale mesoscale simulations from the WRF model, in order to check the importance of gravity wave processes in triggering PSC nucleation. Typical horizontal grid spacing of 10 km and a vertical resolution of 500 m will be needed to correctly describe the
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propagation of the gravity waves. We have run successfully MiPLaSMO on such trajectories to model the PSC case study from Noël et al. (2009).
As mentioned, we will give preference to PSC sampled several times by satellite. We will check that samples belong to the same air mass through the match technique, which compares the concentrations of trace gases measured at least twice at different times and locations but belonging to the same air parcel (Rex et al., 1998). We will identify “Match satellite trajectories”, by first computing a cluster of forward trajectories from the first sample and check if they pass through the second sample at the correct time. Reciprocally, we will compute backward trajectories from the second sample and check if they pass through the location of the first sample at the correct time. We successfully used this technique, adapted to satellite by Terao et al. (2002), in Rivière et al., (2003) to study the denitrification observed by ILAS onboard ADEOS with MiPLaSMO.
2013 2014 2015 2016
Run WRF simulations Run MiPLaSMO
Identify best initialization dataset for each species
Test several microphysical schemes
Identify promising case studies in CALIPSO dataset
Compare simulated PSC cover and particle types with observations
T4.2 -‐ Improving heterogeneous ozone in chemistry climate models using PSC lidar observations from ground and space [B1, B2, B4] Coordinator: J. Jumelet (LATMOS) Contributors: P. Keckhut, J. Jumelet, A. Hauchecorne, S. Bekki, M. Marchand, A. Sarkissian (LATMOS), F.-‐J. Lubken, G. Baumgarten, J. Fiedler (IAP), R. Neuber, C. Ritter, M. Maturilli (AWI)
Objectives To refine model parameterization for polar stratospheric chemistry and cloud microphysics, we will investigate the accuracy of case study comparisons between observations (and associated inverse modeling) and direct model results. This can be made at different levels: Ground-‐based and satellite lidar measurements provide reference optical datasets for direct models to be compared to. This optical comparison can be complemented with a lower level comparison of microphysical quantities derived from multiwavelength lidar measurements.
Stratospheric aerosols provide surfaces for the well-‐known heterogeneous chemistry leading to polar ozone destruction. Besides, the radiative effect linked to changes in global stratospheric aerosol load is still uncertain. For this reason, and also because they show a lower optical response than clouds, aerosols, as PSC precursors, present a high scientific interest. Studying the coincidences between small aerosol particle filaments and lidar measurements will provide a valuable insight in gathering both accurate modeling and detection capabilities.
Updated work plan Our efforts will be in two parts. First we will finalize a case study of a smoke aerosol filament originating from the southern tropics and transported southwards down to Antarctica after the 2009 Australian bushfires. The lidar backscatter signal associated to aerosol plumes and particles in the upper troposphere and lower stratosphere is weaker than the one produced by
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ice clouds and (most of the time, especially in Antarctica) by PSCs. In our study, we coupled the DDU aerosol lidar and the spaceborne CALIPSO lidar measurements, assimilated in the high-‐resolution microphysical transport model MIMOSA-‐µφ, to characterize the particle plume, the optical properties observed by lidar and directly modelled from the size distribution being highly consistent. After nearly a month of transport the plume has been observed on the ground-‐based measurements and successfully characterized by the model despite the very small scale (around 100km). Aerosol plumes, if repeatedly observed above the polar region, may have a strong impact on the background load and PSC occurrences as their microphysics is closely related to the one of the latter clouds.
Second we plan to keep on classifying PSC observations acquired at the Dumont d’Urville station to strengthen the classification against the one using CALIPSO observations. The Backscatter/ depolarization classification (Fig. 4.2.1 and 4.2.2) allows for PSC type discrimination and comparing the ground-‐based and satellite classifications may lead to a sensitivity threshold, something critical when studying PSCs as these clouds have temperature formation thresholds with rapid composition changes (cf. T4.3). Small temperature variations lead to very different particle properties and a comprehensive dataset is really valuable.
Fig. 4.2.1. 2006/2010 CALIOP data averaged in a ~1° lat/lon box. Black lines separate different PSC types. Low 1-‐1/BR means only aerosol presence, stronger values are associated to geometrically or optically larger
particles.
Fig. 4.2.2. Same as Fig. 4.2.1 for 2008/2010 DDU data.
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We will also further investigate the modeling of volcanic aerosol transport and microphysics, using recent eruptions, especially the 2011 eruption from the Nabro volcano in Eritrea. Successful modeling of the plume at the global scale has been performed last year and comparisons of the simulated optical properties against ground-‐based lidar measurements are still in progress following the results of last year. The goal is twofold: assess the global sulphuric load within the plume from the aerosol geometrical properties and concentrations; characterize any influence of these low latitude eruptions on the high latitude and polar aerosol load through the transport.
2013 2014 2015 2016 Finalize smoke filament study
Develop PSC classification from DDU lidar
Model volcanic aerosol transport T4.3 – Arctic Polar stratospheric clouds [B2, B3] Coordination: J.-‐P. Pommereau (LATMOS) Contributors: A. Sarkissian, J. Pelon (LATMOS), P. Rannou, D. Toledo (GSMA), R. Kivi (Finish Meteorological Institute)
Objectives Polar Stratospheric Clouds forming during the cold winter stratosphere are responsible for chlorine activation leading to ozone depletion inside the vortex which amplitude varies between 5 and 38 % from one year to another depending on the meteorology of the stratosphere. Since PSCs are forming below a given temperature threshold, the key issue for predicting the further evolution of ozone in the Arctic during the next decades before ozone depleting substances (chlorine and bromine) will disappear around 2060, is the understanding of the possible trend of PSC formation.
According to ECMWF reanalysis the temperature of the Arctic cold stratospheric winter is getting colder, a suspected impact of climate change (WMO 2011). In addition, although the mechanism responsible is not understood, an increase of water vapour concentration is also observed which, if continued, will favour the formation of PSCs. A long-‐term observation of PSCs along with the temperature at which they form (highly dependent on the water vapour concentration) is thus mandatory to better understand the relationship between climate and ozone depletion.
Currently, the best PSC measurements available are those of the CALIPSO lidar launched in 2006. But although its operation will be likely extended for two more years, it will stop in the near future without replacement.
The proposal is to perform long-‐term twice daily automatic PSC altitude and optical thickness observations using an ODS (Optical Depth Sensor) of Latmos at the Finish Meteorological Institute (FMI) station of Sodankyla in Northern Finland, where two daily radiosonde and lidar PSC profiles are available, and a SAOZ spectrometer deployed there since 1991 for measuring total ozone and NO2 indicative of state of denitrification of the stratosphere (Pommereau et al. 2013), as well as PSCs, but until 93° SZA only because of its lesser sensitivity and thus missing altitude clouds above 25 km.
Instrumentation A PSC above the station results in a reddening of the sky at zenith at twilight (Sarkissian et al. 1991). Its altitude and optical thickness are derived from the solar zenith angle (SZA) of the maximum reddening and the amplitude of the signal. Since low clouds are already in the darkness, the measurements can be performed on all weather conditions.
Our Optical Depth Sensor (ODS) was originally developed by LATMOS for detecting high altitude clouds from a meteorological station on the Mars planet. It was further selected for several
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missions on Earth for the detection of cirrus clouds and aerosols optical thickness in the tropics and recently for equipping a series of buoys drifting in the Arctic in the frame of the Equipex project IAOOS (Ice Atmosphere Ocean Observing System). The main advantage of this two-‐wavelengths instrument is its logarithmic amplifier allowing solar zenith sky observations up to 96° SZA and even moon zenith measurements at night (Maria et al., 2005).
The retrieval of PSC altitude and optical thickness is performed with radiative transfer models in spherical geometry. A Monte-‐Carlo model was specifically developed for ODS (Tran et al., 2006) and now operated in GSMA in Reims along with other standard models (single scattering models in spherical shell, SPSDISORT, SHDOMPP).
We propose to install the available ODS scientific model permanently in Sodankyla in Northern Finland whose data will be transmitted automatically daily to GSMA and LATMOS.
Work plan We plan to deploy the instrument at the meteorological station of Sodankyla, as soon as possible and preferably before the coming winter. We plan to include a real time display of PSC information allowing the local staff to decide when performing COBALD diode laser / FLASH water vapour radiosonde flights providing size and nature of the PSC particles and operate the lidar. Our immediate objective in 2014 is to finalize and validate the retrieval procedure using these sondes and CALIPSO observations These comparisons will be continued as long as CALIPSO will be operating to ensure the robustness of the ODS data on the long term. Radiative transfer modeling and data interpretation will be performed at GSMA by a PhD student, Daniel Toledo, under the supervision of P. Rannou in cooperation with LATMOS.
2013 2014 2015 2016 Deploy ODS at Sodankyla
Finalize and validate retrieval procedure vs. CALIPSO
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T5 – Radiative transfer tools for A-‐train and EarthCare Coordination: H. Chepfer (LMD) This theme groups work to further improve or develop existing radiative transfer tools used to analyse and validate the Level 1 A-‐train and EarthCARE satellite missions in the visible (CALIOP), infrared (MSI, MODIS, IIR), and microwave (CloudSat, AMSR-‐E). For each science objective, dedicated versions of the radiative transfer tools are already under validation or must be developed. In most cases these tools or outputs from these tools are made available to our scientific community when reaching a mature stage.
A number of these community radiative transfer tools exist in the community. This proposal only mentions those specifically developed in the A-‐train and EarthCare frameworks (as defined in the introduction to this proposal) and for well-‐identified scientific objectives.
The contributions to this theme are summarized below.
T5.2: Validation of the RTTOV cloud and aerosol parameterizations in the visible, near infrared and infrared with CALIPSO and EarthCare [B2, B4] Coordination and main contributor: J. Vidot (CMS, Lannion)
Objectives To date, the assimilation of satellite measurements into numerical weather prediction (NWP) models has mainly focused on the clear atmosphere. In order to improve NWP, the assimilation of cloud-‐affected radiances is now recognized as a major issue. The new generation of visible, infrared and microwaves meteorological satellites will provide a great deal of information about clouds and aerosols. The fast radiative transfer model RTTOV has been developed since 1991 in order to assimilate satellite observations into various European and International NWP models. RTTOV is able to simulate top of atmosphere radiances in infrared and microwaves spectral ranges but it is planned to extend RTTOV to visible and near infrared in close future. To assimilate cloud and aerosol affected radiances into NWP models, RTTOV make use of cloud and aerosol parameterizations to convert the cloud and aerosol properties predicted in NWP models into optical properties mandatory for radiative transfer calculations [Saunders et al., 2010]. Nevertheless, these clouds and aerosols parameterizations have not been validated.
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The objective of this WP is to use CALIPSO and EarthCare products to validate the cloud and aerosol parameterizations of RTTOV. CALIPSO and EarthCare products are very interesting because of their high accuracy compared with clouds or aerosols products from classical meteorological satellites. The validation will be done by comparing CALIPSO Level 2 products with optical properties calculated by RTTOV as well as by comparing top of atmosphere radiances in presence of various types of cloud and aerosol in both thermal infrared and solar spectral domains.
Results since last update Following the previous proposal, the first year of the project has been devoted to the validation of the RTTOV ice cloud parameterizations. To simulate radiances at the top of atmosphere in presence of ice clouds, RTTOV needs the cloud temperature TC and the ice water content IWC of each cloud layer (see Saunders et al. (2010) for details). The next version of RTTOV (version 11) will feature eleven options to parameterize ice cloud optical properties:
§ Eight parameterizations of ice cloud optical properties as functions of IWC and ice crystal effective diameter (Deff). Deff is estimated through four empirical relationships of IWC and/or TC (from the works of Ou and Liou, 1995; Wyser, 1998; Boudala et al., 2002 and McFarqhuar et al., 2003). Furthermore, the parameterizations of ice cloud optical properties were calculated for two ice crystal shapes (hexagonal or aggregates).
§ Two options to input Deff in RTTOV instead of using empirical relationships. Here again, hexagonal or aggregates ice crystals are possible.
§ One new parameterization of the ice cloud optical properties in the thermal infrared from the most recent ensemble database from A. Baran (Baran et al., 2009; Baran et al., 2011) have been implemented into RTTOV-‐11 (Vidot et al., 2013). This new database solves the problem of choosing between the eight parameterizations described above because (1) it allows a direct parameterization of the optical properties from the RTTOV inputs for ice cloud (TC and IWC) without the need of estimating the effective size of ice cloud particle through the four different empirical relationships, and (2) the optical properties of the Baran database where simulated for an ensemble of different ice cloud particle shapes which is expected to be more realistic than the two RTTOV ice particles shapes (hexagonal or aggregates).
We used one week (25-‐31/08/2010) of combined CloudSat-‐CALIOP ice cloud products (from operational CloudSat data center and from DARDAR). We compared IIR observations with RTTOV simulations. As RTTOV inputs, we used the ECMWF atmospheric profiles and surface temperature information contained in the ECMWF-‐AUX file. For clouds, we tested two products: the DARDAR product and the 2C-‐ICE product.
We firstly selected ice cloud profiles by considering only situations where:
§ the visible cloud optical depth is between 0.5 and 5 (from DARDAR), § there are only ice cloud layers in the profile (from DARDAR mask), § the CloudSat spot is considered homogeneous (according to the MODIS cloud mask in the
vicinity of the CloudSat Field-‐Of-‐View), § the cloud is monolayer.
Within these assumptions, we get 3393 ice cloud profiles to use for simulating IIR-‐like top of atmosphere brightness temperature (BT). Fig. 5.2.1 maps (left) the IIR BT at 12 μm of the selected profiles, and (right) the DARDAR visible cloud optical depth. BTs range between 223 and 292 K and most profiles have visible cloud optical depths below 2. Note that profiles are well distributed all over the globe.
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Fig. 5.2.1. Left: IIR TOA BT at 12μm for selected ice cloud profiles. Right: Visible cloud optical depth for
selected ice cloud profiles.
Fig. 5.2.2 displays histograms of difference between IIR and simulated BT at 12 μm for the selected profiles. We compare only possible parameterizations for hexagonal ice crystal shapes because the parameterizations based on aggregates produced non physical thermal IR spectra.
Fig. 5.2.2. Histograms of IIR-‐RTTOV BT at 12 μm of selected profiles for ice cloud parameterizations of (a) Ou and Liou (1995), (b) Wyser (1998), (c) Boudala et al. (2002), (d) McFarquhar et al., (2003) and (e) when Deff is used as input from combined CloudSat-‐CALIOP ice cloud products. For (a) to (e), the hexagonal ice crystal shape was used. (f) new ice cloud parameterization from Baran database. IWC and/or Deff products from DARDAR (in red) and from 2C-‐ICE (in blue) were used as RTTOV input. Biases and RMSE are provided.
Overall, the 2C-‐ICE ice clouds product provides better statistics in terms of both bias and RMSE for any parameterizations compared with DARDAR product. This might be explained by the fact that the DARDAR products are provided with a much higher vertical resolution than the 2C-‐ICE product (419 vs. 105 layers, respectively). Then the DARDAR ice cloud is described with much layer than the 2C-‐ICE. If we overestimate the cloud optical properties with parameterizations (especially the extinction in each layer), the total optical depth will be much over estimated from DARDAR IWC than from 2C-‐ICE IWC. A possible way to see this effect will be to interpolate the DARDAR profile at the 2C-‐ICE vertical resolution. Lowest biases are with the parameterization of
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Wyser (1998) with a mean overestimation from RTTOV of 1.54 K. The lowest RMSE are obtained with the input Deff (9.19 K). The use of the new parameterization from Baran database overestimate the BT by 5.69 K with an RMSE of 11.9 K, that makes this parameterizations better than 2 of older parameterizations (OL95 and B02) and in the same order of magnitude as the MF03. Interestingly, the distribution of BT differences with 2C-‐ICE product and the B02 parameterization exhibits two modes. At 8.7 and 10.2 μm, results are similar.
This validation exercise shows that the best parameterization of RTTOV to simulate IIR BT is when the effective diameter profile is input together with the IWC profile. However, the effective diameter is not a prognosticated parameter from NWP model. We found that the Wyser (1998) parameterization with hexagonal ice crystal shows the best agreement. We will soon test an improved database from A. Baran.
2013 2014 2015 Validation of RTTOV ice cloud parameterization by using ice cloud profiles from combined CloudSat-‐Caliop product.
Improvements of the Baran database parameterization. Validation of RTTOV aerosol parameterization.
Simulation of MSI-‐like TOA radiances (especially in VIS, NIR and SWIR bands) and comparison with SEVIRI.
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simulation for the evaluation of aerosols in chemistry transport models, Geosci. Model Dev., 5, 1543-‐1564, 2012.
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• Stubenrauch. C. J.. S. Cros. A. Guignard, and N. Lamquin, A six-‐year global cloud climatology from the Atmospheric InfraRed Sounder aboard the Aqua Satellite: statistical analysis in synergy with CALIPSO and CloudSat. Atmos. Chem. Phys., 10, 7197−7214 (2010).
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V. BUDGET 2014 Références financières: 1 PC = 2.5kE 1 mission hors Europe = 3kE 1 mission Europe = 2.5kE 1 gratification = 2.274kE
Petit Matériel, consommables, publications, informatique 2014 Labo Tache Responsable Description kE LATMOS 0.A J. Pelon 1 Publication 2.5
Hardware 5 License 2.5
LATMOS 1.1 J. Delanoe 2 Publications 5 CNRM 1.2 D. Bouniol Publication 2.5 LAMP 1.4 N. Montoux Publication 2.5
License IDL 1 LMD 1.5 V. Noel Publication 2.5 LATMOS 1.5 J. Delanoe 1 PC 2.5 LAMP 1.6 O. Jourdan 2 licenses 1.9
2 publis 5 LMD 1.7 G. Cesana 1 publi 2.5
Stockage 5 LMD 2.2 C. Stubenrauch 1 publi 2.5 LMD 2.3 C. Hoareau 1 PC 2.5
2 publications 5 LERMA 2.4 E. Defer 1 publi 2.5 LMD 2.5 H. Chepfer 2 publis 5 LATMOS 3.1 G. Ancellet Entretien materiel lidar 2 LMD 3.2 S. Turquety Stockage 4 GSMA 4.1 E. Riviere License 0.5 LATMOS 4.2 J. Jumelet Entretien (jeton Alomar) 11.5
1 PC 2.5 1 publi 2.5
LATMOS 4.3 J.-‐P. Pommereau 1 PC 2.5 LAMP 5.2 F. Szczap 1 jeton matlab 0.7
1 publi 2.5 1 PC 2.5
LMD 5.3 H. Chepfer 1 publi 2.5 LMD 5.4 M. Reverdy 1 publi 2.5 LATMOS 6.1 J. Delanoe Entretien materiel 10 LMD 6.3 V. Noel 1 publi 2.5 Stockage 3
Total 109.6
Missions 2014 Labo Tache Responsable Description kE IPSL V. Noel Workshop 15
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LATMOS 0A J. Pelon 4 missions US 2 missions France
14.4
LMD 0B H. Chepfer 1 mission US 3
CNRM 1.2 D. Bouniol 3 missions France 2 missions US
9.6
LAMP 1.4 N. Montoux 1 mission France 1.2 LATMOS 1.5 J. Delanoe 2 missions US 6 LMD 1.5 V. Noel 2 missions France 2.4
1 mission Europe 2.5 LAMP 1.6 O. Jourdan 5 missions France 6
1 mission US 3 LMD 1.7 G. Cesana 2 missions US 6 LMD LMD
2.2 2.2
C. Stubenrauch C. Stubenrauch
1 mission Europe 2.5 1 mission US 3
LMD 2.3 C. Hoareau 1 mission US 3 LMD 2.5 H. Chepfer 2 missions Europe 5
1 mission US 3 LATMOS 3.1 G. Ancellet 1 mission Europe 2.5 LMD 3.2 S. Turquety 2 missions US 6 CNRM 3.3 L. El Amraoui 2 missions US 6 GSMA 4.1 E. Riviere 3 missions France 3.6
1 mission Europe 2.5 LATMOS 4.2 J. Jumelet 2 missions Europe 5 LATMOS 4.3 J.-‐P. Pommereau 1 mission US 3
2 missions Europe 5 CMS 5.1 J. Vidot 1 mission Europe 2.5 LAMP 5.2 F. Szczap 1 mission France 1.2
1 mission Europe 2.5 LMD 5.3 H. Chepfer 1 mission US 3
1 mission Europe 2.5 LMD 5.4 M. Reverdy 1 mission Japon 3
1 mission Europe 2.5 IPSL 6.2 M. Haeffelin 1 mission France 1.2 LMD 6.3 V. Noel 2 missions US 6
Total 143.6
Personnel 2014 Labo Tache Responsable Description kE LATMOS 0A J. Pelon CDD 54
Stage etudiant 2.274 LMD 0B H. Chepfer CDD 54 CNRM 1.2 D. Bouniol Stage etudiant 2.274 LMD 1.3 G. Ceze Stage etudiant 2.274 LAMP 1.4 N. Montoux Stage etudiant 2.274 LAMP 1.6 O. Jourdan CDD 54 LERMA 2.4 E. Defer Stage etudiant (2) 4.548 LMD 2.5 H. Chepfer Stage etudiant 2.274 LMD 3.2 S. Turquety Stage etudiant 2.274 CNRM 3.3 L. El Amraoui CDD 54 GSMA 4.1 E. Riviere Stage etudiant 2.274
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LMD 5.4 M. Reverdy Stage etudiant 2.274 LATMOS 6.1 J. Delanoe CDD 54
Total 319.74
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VI. LIST OF PUBLICATIONS USING CALIPSO AND CLOUDSAT DATA BY FRENCH SCIENTISTS
Cette liste n’est probablement pas exhaustive mais regroupe au moins toutes les publications des proposants inclus dans la présente demande. Elle montre la forte implication de la communauté française dans la préparation, la validation, et l’exploitation scientifique de l’A-‐Train, à travers 49 publications pendant les 2 dernieres annees (2011-‐2013).
Submitted (10)
1. Ceccaldi M., J. Delanoë, R. J. Hogan , N. Pounder , A. Protat , J. Pelon: From CloudSat-‐CALIPSO to Earth-‐Care : Evolution of the DARDAR cloud classication and its validation using airborne radar-‐lidar observations. J. Geophys. Res., in revision
2. Chiriaco M., H. Chepfer : Inter-‐annual variability of tropical cloud cover in a subsidence area, using 23 years of ISCCP observations: a dynamical regime analysis. J. of Geophys. Res., in revision.
3. Couvreux F., F Guichard, A Gounou, D Bouniol, P Peyrillé and M Kohler: Modelling of the thermodynamical diurnal cycle in the lower atmosphere; a joint evaluation of four contrasted regimes in the Tropics over land. Boundary Layer Meteorology, in revision
4. Defer, E., and H.-‐D. Betz: Properties of convective cloud and associated lightning activity over Western Europe as sensed by A-‐Train and LINET, submitted to JGR.
5. Garnier, A., J. Pelon, P. Dubuisson,P. Yang, M. Faivre, O. Chomette, P. Lucker, 2013 : Retrieval of cloud properties using CALIPSO Imaging Infrared Radiometer. Part II: effective diameter and ice water path, J. Appl. Meteor. Climatol., submitted
6. Konsta D., JL. Dufresne, H. Chepfer, A. Idelkali, G. Cesana, 2012: Evaluation of clouds simulated by the LMDZ5 GCM using A-‐train satellite observations (CALIPSO-‐PARASOL-‐CERES), Climate Dynamics, in review
7. Hoareau, C., P. Keckhut, V. Noel, H. Chepfer and J.-‐L. Baray: A decadal cirrus clouds climatology from ground-‐based and spaceborne lidars above south of France (43.9°N-‐5.7°E). submitted to Atmos. Chem. Phys.
8. Protat, A., S. A. Young, S. McFarlane, T. L'Ecuyer, G. G. Mace, J. Comstock, C. Long, E. Berry, and J. Delanoë, 2013: Reconciling Ground-‐Based and Space-‐Based Estimates of the Frequency of Occurrence and Radiative Effect of Clouds around Darwin, Australia, J. Appl. Meteor. Clim., submitted, March 2013
9. Sèze, G., J. Pelon, H. Legleau, M. Derrien, B. Six: Evaluation of the Global Cloud Cover Distribution obtained from Multi-‐geostationary data in the frame of the MEGHA-‐TROPIQUES mission using CALIPSO lidar observations. submitted to QJRMS
10. Vernier J.P., T. D. Fairlie, J. J. Murray, A. Tupper, C. Trepte, D. Winker, J. Pelon, A. Garnier, J. Jumelet, M. Pavolonis, A.H. Omar and K.A. Powell, 2013 :An advanced system to monitor the 3D structure of diffuse volcanic ash clouds, J. Appl. Meteor. Climatol., submitted.
2013 (12)
1. Bardosa J., M. Haeffelin, H. Chepfer, 2013: Scales of spatial and temporal variation of solar irradiance on Reunion Tropical Island, Solar Energy 88, 42-‐56, doi:10.1016/j.solener.2012.11.007.
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2. Battaglia A. and J. Delanoë, 2013 : Synergies and complementarities of CloudSat-‐CALIPSO snow observations. J. Geophys. Res. 118 721-‐731, doi:10.1029/2012JD018092
3. Cesana G. and H. Chepfer, 2013: Evaluation of the cloud water phase in a climate model using CALIPSO-‐GOCCP, J. Geophys. Res., doi: 10.1002/jgrd.50376.
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7. Roehrig, R., Bouniol, D., Guichard, F., Hourdin, F., Redelsperger, J.-‐L. 2013: The present and future of the West African monsoon: a process-‐oriented assessment of CMIP5 simulations along the AMMA transect. Journal of Climate, in press.
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7. Guignard, A., C. J. Stubenrauch, A. J. Baran, and R. Armante, 2012: Bulk microphysical properties of semi-‐transparent cirrus from AIRS: a six year global climatology and statistical analysis in synergy with geometrical profiling data from CloudSat-‐CALIPSO, Atmos. Chem. Phys., 12(1), 503–525, doi:10.5194/acp-‐12-‐503-‐2012.
8. Hoareau, C., Keckhut, P., Baray, J.-‐L., Robert, L., Courcoux, Y., Porteneuve, J., Vömel, H., and Morel, B.: A Raman lidar at La Reunion (20.8° S, 55.5° E) for monitoring water vapour and cirrus distributions in the subtropical upper troposphere: preliminary analyses and description of a future system, Atmos. Meas. Tech., 5, 1333-‐1348, doi:10.5194/amt-‐5-‐1333-‐2012, 2012.
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2. Chaboureau JP, Richard E, Pinty JP, Cyrille Flamant, Paolo Di Girolamo, Christoph Kiemle, Andreas Behrendt, Hélène Chepfer, Marjolaine Chiriaco and Volker Wulfmeyert, 2011: Long-‐range transport of Saharan dust and its radiative impact on precipitation forecast: a case study during the Convective and Orographically-‐induced Precipitation Study (COPS), Q. J. Roy. Met. Soc., Vol. 137, DOI: 10.1002/qj.719
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