GCM-Oriented CALIPSO Cloud Product
The CALIPSO-GOCCP (GCM Oriented Cloud Calipso Product) is designed to evaluate GCM cloudiness.
CALIPSO-GOCCP contains observational cloud diagnostics fully consistent with the ones simulated by the ensemble
CALIPSO-GOCCP is used for the CFMIP pilot model intercomparison study, it has been processed at LMD/IPSL with the support of NASA/CNES, ICARE and ClimServ.
CALIPSO-GOCCP is derived from CALIPSO L1/NASA products (NASA Langley ASDC - CALIPSO Data Sets, data availability and quicklooks can be found here) and contains the following types of files:
References relative to GOCCP developments:
- T. Vaillant de Guélis, H. Chepfer, V. Noel, R. Guzman, D.M. Winker and R. Plougonven, 2017b: "Using space lidar observations to decompose longwave cloud radiative effect variations over the last decade", Geophys. Res. Lett., doi:10.1002/2017GL074628, http://onlinelibrary.wiley.com/doi/10.1002/2017GL074628/abstract (uses GOCCP v3.1.1)
- T. Vaillant de Guélis, H. Chepfer, V. Noel, R. Guzman, P. Dubuisson, D.M. Winker and S. Kato, 2017a: "The link between outgoing longwave radiation and the altitude at which a spaceborne lidar beam is fully attenuated", Atmos. Meas. Tech., 10, 4659-4685, doi:10.5194/amt-10-4659-2017, https://www.atmos-meas-tech.net/10/4659/2017/amt-10-4659-2017.html (uses GOCCP v3.1.1)
- R. Guzman, H. Chepfer, V. Noel, T. Vaillant de Guélis, J.E. Kay, P. Raberanto, G. Cesana, M.A. Vaughan, and D.M. Winker, 2017: "Direct atmosphere opacity observations from CALIPSO provide new constraints on cloud-radiation interactions", J. Geophys. Res. Atmos., 122, 1066-1085, doi:10.1002/2016JD025946, http://onlinelibrary.wiley.com/doi/10.1002/2016JD025946/full (uses GOCCP v3.1.1)
- G. Cesana, H. Chepfer, D. Winker, B. Getzewich, X. Cai, O. Jourdan, G. Mioche, H. Okamoto, Y. Hagihara, V. Noel, and M. Reverdy, 2016: "Using in situ airborne measurements to evaluate three cloud phase products derived from CALIPSO", J. Geophys. Res. Atmos., 121, 5788-5808, doi:10.1002/2015JD024334, http://onlinelibrary.wiley.com/doi/10.1002/2015JD024334/full
- G. Cesana and H. Chepfer, 2013: "Evaluation of the cloud thermodynamic phase in a climate model using CALIPSO-GOCCP", J. Geophys. Res. Atmos., 118, 7922-7937, doi:10.1002/jgrd.50376, http://onlinelibrary.wiley.com/doi/10.1002/jgrd.50376/full
- H. Chepfer, G. Cesana, D. Winker, B. Getzewich, M. Vaughan, and Z. Liu, 2013: "Comparison of two different cloud climatologies derived from CALIOP-attenuated backscattered measurements (Level 1): The CALIPSO-ST and the CALIPSO-GOCCP", J. Atmos. Oce. Tech., 30.4, 725-744, doi:10.1175/JTECH-D-12-00057.1, [PDF]http://journals.ametsoc.org/doi/full/10.1175/JTECH-D-12-00057.1
- G. Cesana, J. E. Kay, H. Chepfer, J. M. English, and G. deBoer (2012), Ubiquitous low-level liquid-containing Arctic clouds: New observations and climate model constraints from CALIPSO-GOCCP, Geophys. Res. Lett., 39, L20804, doi:10.1029/2012GL053385 http://onlinelibrary.wiley.com/doi/10.1029/2012GL053385/full
- G. Cesana and H. Chepfer, 2012: "How well do climate models simulate cloud vertical structure? A comparison between CALIPSO-GOCCP satellite observations and CMIP5 models", Geophys. Res. Lett., 39.20, doi:10.1029/2012GL053153, http://onlinelibrary.wiley.com/doi/10.1029/2012GL053153/full
- H. Chepfer, S. Bony, D. M. Winker, G. Cesana, J.-L. Dufresne, P. Minnis, C.J. Stubenrauch, and S. Zeng, 2010: "The GCM Oriented CALIPSO Cloud Product (CALIPSO-GOCCP)", J. Geophys. Res., 115, D00H16, doi: 10.1029/2009JD012251, [PDF]http://onlinelibrary.wiley.com/doi/10.1029/2009JD012251/full
- H. Chepfer, S. Bony, D. M. Winker, M. Chiriaco, J.-L. Dufresne, and G. Seze, 2008: "Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model", Geophys. Res. Lett., vol. 35, L15704, doi: 10.1029/2008GL034207, [PDF]http://onlinelibrary.wiley.com/doi/10.1029/2008GL034207/full
Informations and updates:
Please send us an e-mail if you download data, so we will keep you informed of CALIPSO-GOCCP updates.
Mail to: goccp@lmd.polytechnique.fr
1a) 3D_CloudFraction
- 3D_CloudFraction file contains the following monthly (or seasonal) fractions in longitude/latitude/altitude boxes: cloud fraction, clearfraction, and uncertain fraction. The sum of this fraction in a longitude/latitude/altitude box is equal to 1 (The file contains also an information on the fully attenuated point. The way to calculate this fraction is not fixed yet)
- The following figure is an example of what you can do with the CALIPSO-GOCCP 3D_CloudFraction DATA for the period JJA 2006-2009 (a) (c) (d) and JFM 2007-2009 (b).
File contents:
Size: 6.4Mo
Dimensions:
Variables :
1b) 3D_CloudFraction_phase
- The name of the 3D cloud fraction phase files begin with 3D_CloudFraction_Phase330m_* and contain liquid/ice/undefined-Phase cloud fractions and the relative percentage of ice in cloud with respect to the total condensate.
- CF3D Phase: Cloud phase vertical distribution observed by CALIPSO-GOCCP in JFM. Ice cloud fraction (left column), liquid (center) cloud fraction, and ice fraction with respect to the total condensate (right).
New Variables :
1c) 3D_CloudFraction_phase_temp
- There are also 3D_CloudFraction_Temp330m_* files that contain cloud fractions for all clouds and for liquid/ice clouds as a function of the temperature instead of height and for each longitude/latitude grid box. The temperature bins and ranged every 3°C. The temperature is taken from GMAO data (Global Modeling and Assimilation Office, Bey et al., 2001), which is part of CALIPSO level 1 ancillary data. For each CALIOP level 1 profile, we interpolate the GMAO temperature over the 480m-vertical levels of CALIPSO-GOCCP, and use it as the cloudy pixel temperature.
- Cloud temperature distribution annual mean observed by CALIPSO-GOCCP for 2012: a) Total cloud fraction, b) Ice cloud fraction, c) liquid cloud fraction and d) Relative percentage of ice with respect to the total condensate.
New Variables :
1d) 3D_CloudFraction_OPAQ (new in v3.X)
- The name of the 3D cloud fraction OPAQ files begin with 3D_CloudFraction_OPAQ330m_*. They contain:
The relations between these 3D fraction variables are the following:
clcalipso + clrcalipso + uncalipso = 1 (as in GOCCP v2.X)
clcalipso = clcalipso_opaque + clcalipso_notopaque (new cloud fraction partition in GOCCP v3.X)
clrcalipso = clrcalipso_opaque + clrcalipso_notopaque (new clear fraction partition in GOCCP v3.X)
uncalipso = uncalipso_opaque + uncalipso_notopaque (new uncertain fraction partition in GOCCP v3.X)
Zonal cloud fraction distributions and opacity distribution observed by CALIPSO-GOCCP from GOCCP v3.1.2 nighttime data for June 2010: (a) "clcalipso", (b) "clcalipso_opaque", (c) "clcalipso_notopaque" and (d) "calipsoopacity".
Python routine: CloudFraction_3D_month.py
New Variables :
Ftp data access:
Matlab routine: 3D_CloudFraction
2a) MapLowMidHigh
- MapLowMidHigh file contains maps of Low-Mid-High cloud fractions, total cloud fraction and clear fraction:
- The total cloud/clear fraction (called cltcalipso for cloud and clccalipso for clear) enable to know the cloud/clear fraction present in a longitude/latitude column.
- The following figure is an example of what you can do with the CALIPSO-GOCCP MapLowMidHigh DATA for the period JJA 2006-2009.
File contents:
Size: 140ko
Dimensions:
Variables :
2b) MapLowMidHighphase
- The name of the cloud phase map files begin with MapLowMidHigh_Phase330m_*. They contain:
- Map Phase: Ice and Liquid cloud maps observed by CALIPSO-GOCCP in JFM: Liquid cloud covers observed (a-c) and Ice cloud covers observed (g-i) at high, mid, low altitudes.
New Variables :
2c) Map_OPAQ (new in v3.X)
- The name of the OPAQ product map files begin with Map_OPAQ330m_*. They contain:
Version 3.0 (nighttime data only)
New variables from v3.X are all derived from the OPAQ algorithm which relies on a simple paradigm applied to each single profile: if a surface echo is detected near the ground, the profile is either declared as clear-sky or as a thin cloud profile if a cloud is detected within the profile. If the surface echo is not detected, the profile is declared as an opaque cloud profile. This section briefly describes the new variables introduced in GOCCP v3.0 but all the variables that existed in version 2.X are still available and are unchanged.
This new partition of clouds into opaque and thin clouds allows us to describe the mean atmospheric states in three distinct covers: (a) the opaque cloud cover, (b) the thin/broken cloud cover and (c) the clear-sky.
Variables of the example figure below: (a) "cltcalipso_opaque", (b) "cltcalipso_thin" and (c) "clccalipso" from 2D Maps files.
Python routine: Covers.py
Z_opaque maps
The altitude of opacity z_opaque, as defined by the OPAQ algorithm, can only be defined for opaque cloud profiles. Hence, a mean z_opaque altitude is computed for each 2° x 2° lat-lon gridpoint from all opaque cloud profiles (and only opaque cloud profiles) sounded in a 2° x 2° lat-lon gridpoint. The significance of this mean z_opaque value is strongly dependent on the opaque cloud cover, and it is particularly meaningful where opaque cloud covers are large.
Variables of the example figure below: (a) "zopaque" for all opaque cloud covers and (b) "zopaque" only where "cltcalipso_opaque" > 0.3, from 2D Maps files.
Python routine for z_opaque: Z_opaque_maps.py
Python routine for climatologies: climato_Maps_OPAQ_variables_v3.1.2.py
Version 3.1.1 (nighttime data only)
GOCCP v3.1.1 has new map variables for the Opaque and Thin clouds as defined in the Vaillant de Guélis et al., 2017a reference above. These new 2D fields include the temperature and the altitude of Opaque and Thin clouds, and the emissivity of Thin clouds.
Variables of the example figure below: (a) "cltcalipso_opaque", (b) "cltcalipso_thin", (c) "cltcalipso_opaque_z", (d) "cltcalipso_thin_z", and (f) "cltcalipso_thin_emis" from Map_OPAQ v3.1.1 files.
Python routine: Maps_OPAQ_altitude_v3.1.1.py
Version 3.1.2 (consistent daytime and nighttime data)
The GOCCP v3.1.2 dataset is significantly different from v3.1.1. In order to have the OPAQ variables defined in the previous GOCCP v3.X for daytime, the surface detection threshold has been adapted to the noisier daytime data. The surface detection threshold is now higher for nighttime data because we choose to have a unique and consistent detection threshold for daytime and nighttime opaque clouds, which is hence constrained by the noisier daytime data. The major difference with GOCCP v3.1.1 is that more opaque clouds are detected during nighttime because of the higher surface detection threshold (see figure below).
Variables of the example figure below: (a) and (b) "cltcalipso_opaque", (c) and (d) "cltcalipso_thin", (e) and (f) "cltcalipso_thin_emis" from Map_OPAQ files for v3.1.1 and v3.1.2, respectively.
Python routine: Comp_OPAQ_v3.1.1_v3.1.2.py
Besides having all the OPAQ variables for the daytime data, which are consistent with the new surface detection threshold used for the nighttime data in GOCCP v3.1.2, all altitude maps exist now with respect to the Surface Elevation (variables having a "_se" at the end of the variable name). The figure below shows some of the OPAQ maps for the daytime multi-year mean where we can see the new z_opaque computed with respect to the Surface Elevation plotted in subplot (e).
Variables of the example figure below: (a) "cltcalipso_opaque", (b) "cltcalipso_thin", (c) "cltcalipso_opaque_z", (d) "cltcalipso_thin_z", (e) "zopaque_se", and (f) "cltcalipso_thin_emis" from Map_OPAQ v3.1.2 files.
Python routine: Maps_OPAQ_altitude_v3.1.2.py
Finally, as developped in Vaillant de Guélis et al. 2017a and Vaillant de Guélis et al. 2017b, the TOA LongWave Cloud Radiative Effect (LWCRE) can be estimated by using the 5 following GOCCP cloud variables only: opaque cloud cover ("cltcalipso_opaque"), opaque cloud altitude ("cltcalipso_opaque_z"), thin cloud cover ("cltcalipso_thin"), thin cloud altitude ("cltcalipso_thin_z") and thin cloud emissivity ("cltcalipso_thin_emis"). The relation between these cloud variables and the LWCRE is the following:
LWCRE = LWCRE_opaque_clouds + LWCRE_thin_clouds
= 11×cltcalipso_opaque×cltcalipso_opaque_z + 11×cltcalipso_thin×cltcalipso_thin_z×cltcalipso_thin_emis
The figure below shows the 2008-2015 multi-year mean of the day+night LWCRE from (a) the CERES EBAF Ed.4.0 product, (b) the CALIPSO-derived estimate from the 5 GOCCP v3.1.2 cloud variables and (c) their difference.
We computed the day+night LWCRE from GOCCP v3.1.2 by doing the average between the daytime and the nighttime estimates. The uncertainty for the CERES EBAF total 24-h average global mean LW TOA flux is 3.7 W/m2 [Loeb et al., 2009, Journal of Climate].
Python routines: Diff_EBAF-CRELW_v3.1.2.py plot_Diff_EBAF-CRELW_day+night_v3.1.2.py
New Variables (ASL=Above Sea Level; SE=Surface Elevation):
Ftp data access:
Matlab routine: MapLowMidHigh
3a) SR_histograms
- SR_histo file contains the number of points encontered in each SR bin (= cfad_lidarsr532_Occ & cfad_lidarsr532_Occ2).
- The 19 SR interval boundaries are : -888,-777,-776,0, 0.01, 1.2, 3, 5, 7, 10, 15, 20, 25, 30, 40, 50, 60, 80, 10000.
- In the simulator outputs, the three first interval boxes (-888,-777,-776 to 0) do not have their counterpart in the simulator outputs, they are associated to the following situation:
- SR_histo is defined for each longitude/latitude/altitude box.
- The following figures are examples of what you can do with the CALIPSO-GOCCP SR_histo DATA for the period JJA 2006-2009:
File contents:
Size: 33Mo
Dimensions:
Variables :
3b) SR_histograms phase
- The name of the SR_histo phase files begin with SR_histo_Phase330m_* and contain liquid/ice/undefined-Phase SR histograms of clouds.
- SR histograms Phase : Seasonal two-dimensional histograms of scattering ratio and height from CALIPSO-GOCCP Arctic (70-82 N, ocean-only) observations. a) SON liquid-containing cloud, b) DJF liquid-containing cloud, c) MAM liquid-containing cloud, d) JJA liquid-containing cloud, e-h) as in a-d) but for ice-dominated cloud, i-l) as in a-d) but for unclassified cloud:
New Variables :
3c) SR_histograms OPAQ (new in v3.X)
- The name of the SR_histo OPAQ files begin with SR_histo_OPAQ330m_* and contain opaque/non-opaque profile SR histograms.
Two-dimensional histograms of scattering ratio (SR) and height from GOCCP v3.1.2 nighttime data for January 2010: (a) "cfad_lidarsr532_Occ", (b) "cfad_lidarsr532_Occ_opaque" and (c) "cfad_lidarsr532_Occ_notopaque".
Python routine: SR_histo330m_012010_night_GOCCP_v3.1.2.py
New Variables :
Ftp data access:
Matlab routine: SR_histo
4a) Instant_SR_CR_DR
- Instant_SR_CR_DR file contains the scattering ratio (SR), Color Ratio (CR) and Depolarization Ratio (DR) average over the 40 vertical levels of the CFMIP grid. The horizontal day track resolution is 330m (about 60000 points).
- The longitude, latitude, hour and SE are saved in the instantaneous files too.
File contents:
Size: about 10Mo
Dimensions:
Variables :
4b) Instant_SR_CR_DR phase
-The cloud phase mask has been added to the existing instant_SR_CR_DR files (instant_Phase variable).
- Instant Cloud Phase: Cloud phase mask CALIPSO-GOCCP for a CALIPSO orbit, January 1st 2007 during night time (latitude between 72°13 and -58°93; longitude between 173°85 and -4°86).
New Variables :
4c) Instant_OPAQ (new in v3.X)
At the highest horizontal resolution of the CALIOP lidar, the Instant files provide (a) an updated Cloud mask (instant_Cloud_OPAQ) and (b) a new OPAQ mask (instant_OPAQ). As shown in the figure below, opaque cloud profiles are clearly identified in the OPAQ mask by their full opacity level (green), termed z_opaque, corresponding to the altitude where the lidar beam is fully attenuated and no further information on the atmospheric state can be confidently retrieved below that altitude for that particular profile. Unlike the opaque cloud profiles, clear-sky and thin cloud profiles are completely vertically documented from the lower stratosphere to the surface.
Variables of the figure below: (a) "Instant_Cloud_OPAQ" and (b) "Instant_OPAQ" masks from an Instant file (15 June 2007, orbit file "T20-42-00ZN", profiles 15454 to 15560).
Python routine: Convection_case_study.py
New Variables :
Ftp data access: instant_SR_CR_DR
Matlab routine: instant_SRCRDR
Update of the 29/04/2013 ==> cloud phase diagnosis
The list of changes is summarized here after:
Main changes:
New Cloud Phase files:
Update of the 01/2012 ==> CMOR data available
The list of changes is summarized here after:
Update of the 23/03/2011 ==> CALIPSO-GOCCP version 2.1
The list of changes is summarized here after: