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Claudia Stubenrauch Atmospheric Radiation Analysis (ARA) group

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,

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Claudia Stubenrauch Atmospheric Radiation Analysis (ARA) group

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  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. any assessments of global records … (for climate studies & model evaluation)… should include • 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) it takes time & effort & communication

  3. Cloud Assessment Timeline 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

  4. GEWEX Cloud assessment data base monthly statistics per year & obs time, 1° x 1° ●average, ●variability, ●(joint) histograms, ●# orbit passages 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

  5. participating sensor teams most complete data sets: ISCCPGEWEX cloud dataset 1984-2007 (Rossow et al. 1983, 1999) TOVS Path-B7h30/19h30 1987-1995 (Stubenrauch et al. 1999, 2006) AIRS-LMD2003-2009(Stubenrauch et al. 2008, 2010) MODIS-ST2001/3-2009(Ackerman et al.; Platnick et al.)MODIS-CE2001/3-2006(Minnis et al.) relatively new retrieval versions: PATMOS-x (AVHRR) 1982-2009 (Heidingeret al.)(histo 03-09) ATSR-GRAPE (ERS) 1999-2002 (Poulsen et al.) (ENVISAT) 2003-2009 (Poulsen et al.) POLDER(O2 & Rayleigh)2006-2008(Riedi et al.) only CA or CAE & CT: HIRS-NOAA only av 1982-2008 (Wylie et al. 2005) CALIPSO-ST av & histos2007-2008(Winker et al. 2007, 2009) CALIPSO-GOCCP 2007-2008(Chepfer et al. 2009) only averages of CA’s: MISR2001-2007 (DiGirolamo et al.)

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

  9. difference ocean-land 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

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

  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. HCA July ISCCP AIRS-LMD CALIPSO geographical distributions • high cloud cover depends depends on sensitivity of the instrument • order for sensitivity • CALIPSO • TOVS/AIRS • MODIS/PATMOS • ISCCP • POLDER/MISR high cloud cover

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

  14. bulk and microphysics • derived quantities (also based on subsample) display significant diversity … work ahead ! optical depth water content drop/cry size

  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. 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. extras

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

  20. Specific regions Rossow et al. J. Clim. 2002 10° x 10° regions of typical climate regimes with increasing small scale variations: (1 – <COD(rad)>/<COD(lin)>) 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 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

  21. Cloud optical depth, water path, effective particle radius 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 15mm and 25mm

  22. Seasonal cycles of specific regions agree very well

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