1. GEWEX Cloud Assessment a review & guidance for other international coordinated assessment activities Claudia Stubenrauch Atmospheric Radiation Analysis (ARA) group Atmosphere Biosphere Climate (per remote sensing) [ABC(t)]– team C.N.R.S./IPSL - Laboratoire de Météorologie Dynamique, Ecole Polytechnique, France + GEWEX Cloud Assessment Team

2. 2 any assessments of global records …  Description of retrieval algorithms and references documentation, documentation  Averages and distributions of different variables maps, latitude bands, specific regions  Time variability inter-annual, seasonal and diurnal variability  Uncertainties and biases due to: calibration, sampling (time & space), instr. sensitivity, retrieval method  Common data base providing monthly statistics (for 1x1 lat/lon gridded, in netCDF) (for climate studies & model evaluation) … should include it takes time & effort & communication

3. Cloud Assessment initiated by GEWEX Radiation panel 2005/06: 2 meetings at Madison (Campbell, Baum, Stuben.) focus on cloud amount 2007:preparation of data for intercomparisons (via http://climserv.ipsl.polytechnique.fr/gewexca) 2008:meeting at New York (Stubenrauch, Kinne) intercompare of all reported cloud variables 1.WCRP report draft (75 pages; on CA, CAHR, CAMR, CALR) 2009:preparation of common format data base data checking (GEWEX news article) 2010:meeting at Berlin (Stubenrauch, Kinne) 2011:finish WRCP report, make data-sets public Timeline

4. 4 GEWEX Cloud assessment data base properties cloud amount CA (total, H, M, L, W, I) rel. cloud amount CAR (H, M, L, W, I) VIS optical depth COD (total, H, M, L, W, I) IR emissivity CEM (total, H, M, L, W, I) eff cloud amount CAE (total, H, M, L, W, I) pressure/ height CP/CZ (total) temperature CT (total, H, M, L, W, I) water path CLWP/CIWP (W, I, IH) eff. radius CRE (W, I, IH) joint histograms COD – CP CEM – CP COD – CRE monthly statistics per year & obs time, 1° x 1° ● average, ● variability, ● (joint) histograms, ● # orbit passages

5. 5 most complete data sets: ISCCP ISCCP GEWEX cloud dataset 1984-2007 (Rossow et al. 1983, 1999) TOVS Path-B TOVS Path-B 7h30/19h301987-1995 (Stubenrauch et al. 1999, 2006) AIRS-LMD AIRS-LMD 2003-2009 (Stubenrauch et al. 2008, 2010) MODIS-ST MODIS-CE MODIS-ST 2001/3-2009 (Ackerman et al.; Platnick et al.) MODIS-CE 2001/3-2006 (Minnis et al.) only averages of CA’s: MISR MISR 2001-2007 (DiGirolamo et al.) relatively new retrieval versions: PATMOS-x PATMOS-x ( AVHRR)1982-2009 (Heidingeret al.) (histo 03-09) ATSR-GRAPE ATSR-GRAPE ( ERS) 1999-2002 (Poulsen et al.) (ENVISAT) 2003-2009 (Poulsen et al.) POLDER POLDER (O2 & Rayleigh)2006-2008 (Riedi et al.) only CA or CAE & CT: HIRS-NOAA HIRS-NOAA only av1982-2008 (Wylie et al. 2005) CALIPSO-ST av & histos CALIPSO-ST av & histos2007-2008 (Winker et al. 2007, 2009) CALIPSO-GOCCP CALIPSO-GOCCP 2007-2008 (Chepfer et al. 2009) participating sensor teams

6. 6 Assessment Report Outline homogenized documentation –on sensor, calibration, method, ancillary data, sampling, own evaluation state strength, limitations and suitable applications by exploring –global averages, spatial patterns, regional-, inter-annual-, seasonal-, daily- variability, (joint) histograms, long-term anomalies

7. 7 data example: cloud cover globally 60-70% –plus+5% thin Ci –40% high –40% single low high depends on sensor sensitivity –high->mid-level by ISCCP, ATSR and POLDER

8. 8 ‘effective’ cloud cover ? … would lower discrepancy eff.cloud cover is defined as: cloud cover * IR-emissvity

9. 9 difference ocean-land 15% more cloud cover over oceans –for total cover –for low cover 10% less cloud cover over oceans –For high cover –For mid cover but higher opt.depth  similar eff.cover ocean | land

10. 10 cloud monitoring Earth coverage be aware of sampling differences MODIS 120% ISCCP 100% CALIPSO 5% YEAR Earth coverage

11. 11 strengths and weaknesses less consistency due to diff instrument sensitivities and data samples more consistency for geographical distributions and seasonality bulk and microphyical data diversity needs ‘research’ attention

12. 12 geographical distributions high cloud cover depends depends on sensitivity of the instrumenthigh cloud cover depends depends on sensitivity of the instrument order for sensitivityorder for sensitivity – – CALIPSO –TOVS/AIRS –MODIS/PATMOS –ISCCP –POLDER/MISR July ISCCP AIRS-LMD HCAHCA CALIPSO high cloud cover

13. 13 seasonal cycles HCA/CA LCA/CA example for SH tropics land (left), ocean (right)

14. 14 bulk and microphysics derived quantities (also based on subsample) display significant diversity … work ahead ! optical depthdrop/cry sizewater content

15. 15 conclusions (1) slow progress: all groups particiapted in spare time, delay by data-errors required netcdf format posed a challenge (fortran sample seemed not to help) establishing a common data base (many variables) was/is a challenge once inconsistencies and errors have/will be fixed … data base is rather useful

16. 16 conclusions (2) data-sets are a reference for climate studies / model evaluation especially via –geographical distribution and –latitudinal & seasonal variations cloud cover, without being understood in the context of sensor sensitivity, will always differ  thus a limited reference continous monitoring of cloud properties has improved, but still remains a challenge

17. 17 extras

18. 18 reasons for a cloud assessment cooperation of (12 cloud) teams insights on how clouds are perceived by different sensors assessment of individual retrievals production of L3 cloud products common data base of cloud property establish usefulness (error character) as evaluation tool for climate models reference (also for new data sets)

19. 19 CALIPSO: T(cld top) & including subvis Ci pass remote sensing: T(rad. cld height), => PATMOSX should not be like CALIPSO for high clouds T cld distributions reflect increase of vert extent of troposphere from poles to tropics SHtrp SHmid SHpol K Cloud temperature: latitudinal variation & distributions latitudinal variation & distributions

20. 20 10° x 10° regions of typical climate regimes with increasing small scale variations: (1 – / ) Specific regions Rossow et al. J. Clim. 2002 1: SH Str Africa2: SH Str America 3: SH midlat 4: NH EPacific 5: NAtlantic storms 6: SH Ci off America7: SH Ci Amazon 8: SH Cb Africa9: NH Cb Indonesia 10: ARM Southern Great Plain Compared to global means: ITCZ (8,9) has largest CAHR (linked to Ci) & monthly CT variability Storm regions (3,4,5) have largest CA NAtlantic (5) has less high clouds (but thicker) & monthly CT variability Stratocumulus regions (1,2) have average CAHR, but optically thin

21. 21 Whereas CA, CEM,CT, CP of the data base are well understood, differences in COD, WP, RE have still to be further explored, especially the outliers like ATSR-GRAPE or MODIS-ST There are also less data sets providing bulk microphysical properties Global averages of CREW and CREI(H) agree quite well with 15  m and 25  m Cloud optical depth, water path, effective particle radius

22. 22 Seasonal cycles of specific regions agree very well