A process oriented descrip-on of oceanic clouds derived from A- train observa-ons, for climate model evalua-on

Transcription

1 A process oriented descrip-on of oceanic clouds derived from A- train observa-ons, for climate model evalua-on D. Konsta, H. Chepfer, JL Dufresne, G. Cesana, S. Bony LMD/IPSL Konsta D. et al : A process oriented descrip-on of tropical oceanic clouds for climate model evalua-on, based on a sta-s-cal analysis of day-me A- train high spa-al resolu-on observa-ons, submiled to Climate Dynamics Konsta D. et al: What do we learn about clouds in climate models from A- train observa-ons, to be submiled to Climate Dynamics ** all the model / observa-on comparisons presented in this talk use COSP**

2 SW albedo SW albedo depends on the Cloud Reflectance and the Cloud Frac-on Cloud Reflectance a drop for Op-cal Depth Cloud Frac-on

3 Tropics, in regimes SW albedo Cloud Reflectance a drop for Cloud Op-cal Depth W500 (hpa/day) Cloud Frac-on W500 (hpa/day)

4 Obs: Calipso- GOCCP Tropics, in regimes Ver-cal Distribu-on of the Cloud Frac-on LMDZ5 W500 (hpa/day) LMDZ- NP PRESURE PRESURE PRESURE W500 (hpa/day) W500 (hpa/day)

5 Monthly/ Sesonal mean model- obs comparisons: Bias in model SW albedo at high la-tudes and in subsidence tropical regions significant evolu-on in the obs in subsidence tropical regions Correct SW albedo results of error compensa-ons between cloud cover and cloud op-cal depth The cloud ver-cal structure is poorly reproduced by the model too much high thick clouds lack of mid and Low level clouds LMDZ5- NP leads to significant improvement compared to LMDZ5, but s-ll far from observa-ons

6 Monthly/ Sesonal mean model- obs comparisons: Bias in model SW albedo at high la-tudes and in subsidence tropical regions significant evolu-on in the obs in subsidence tropical regions Correct SW albedo results of error compensa-ons between cloud cover and cloud op-cal depth The cloud ver-cal structure is poorly reproduced by the model too much high thick clouds lack of mid and Low level clouds LMDZ5- NP leads to significant improvement compared to LMDZ5, but s-ll far from observa-ons Could the «A- train» learn us more.about clouds in climate models?

7 For climate variability studies, we need to characterize how cloud proper-es (R) vary with environment variables (E) around the cloud R: Cloud property T : Surface Temperature E : Environment This sensi-vity is usually es-mated over inter- annual -me scale (i.e. Webb et al., Bony et al. 2004) => can not be directly linked to parameteriza-on

8 For climate variability studies, we need to characterize how cloud proper-es (R) vary with environment variables (E) around the cloud R: Cloud property T : Temperature E : Variable of Environment is usually es-mated over inter- annual -me scale (i.e. Webb et al., Bony et al. 2004) => can not be directly linked to parameteriza-on R: SW albedo CF : Cloud Frac-on CR : Cloud Reflectance (drop for Cloud Op-cal Depth) is es-mated over inter- annual -me scale (i.e. Klein and Hartmann 1993), but also at nearly instantaneous -me scale (Zhang et al 2010)

9 For climate variability studies, we need to characterize how cloud proper-es (R) vary with environment variables (E) around the cloud R: Cloud property T : Temperature E : Variable of Environment is usually es-mated over inter- annual -me scale (i.e. Webb et al., Bony et al. 2004) => can not be directly linked to parameteriza-on R: SW albedo CF : Cloud Frac-on CR : Cloud Reflectance (drop for Cloud Op-cal Depth) is es-mated over inter- annual -me scale (i.e. Klein and Hartmann 1993), but also at nearly instantaneous -me scale (Zhang et al 2010) How do CF and CR vary «instantaneously» simultaneously for a same change of environment E?

10 CLOUDY REFLECTANCE CLOUDY REFLECTANCE OBSERVATIONS MONTHLY OBSERVATIONS INSTANTANEOUS CLOUD FRACTION OBS A B C LMDZ5 + SIM MONTHLY 1 INSTANTANEOUS CLOUD FRACTION MOD BUT LMDZ5- NP + SIM MONTHLY INSTANTANEOUS CLOUD FRACTION

11 PRESSURE (hpa) CLOUD OPTICAL DEPTH CLOUD FRACTION CLOUDY REFLECTANCE op-cally thin clouds : low level, low CF - op-cally thick clouds : mul-layer clouds - very thick clouds : abundance of high clouds 12% PDF 8% 4% CLOUDY REFLECTANCE 11

12 PRESSURE (hpa) OBSERVATIONS CLOUD FRACTION LMDZ5 + SIM CLOUD FRACTION CLOUDY REFLECTANCE CLOUDY REFLECTANCE LMDZ5- NP + SIM CLOUD FRACTION CLOUDY REFLECTANCE Models biases : - more op-cally thin high clouds - few op-cally thick high clouds - no mid level clouds (or not well simulated) - overes-ma-on of op-cally thick low clouds

13 CLOUD TOP PRESSURE (hpa) CLOUD OPTICAL DEPTH CLOUDY REFLECTANCE τ cloud >3 0.8<τ cloud <3 τ cloud <0.8 P TOP (hpa) Cloud op-cal depth increases with cloud top al-tude 13

14 CLOUD OPTICAL DEPTH CLOUD TOP PRESSURE (hpa) OBS LMDZ5- NP LMDZ5 CLOUDY REFLECTANCE Model needs more Liquid Water to form clouds 14

15 OBS LMDZ5 LMDZ5 NP δcf δτ cloud δτ cloud >> typically shallowcumulus typically stratocumulus CCCMA δcf δτ cloud Assess the rela-on between δcf and δτ cloud at the cloud scale 15

16 Conclusion A- train obs give access to cloud pictures at «instantaneous» -me scale: it shows how the cloud proper-es (here CF and CR) vary together under a same change of environment around the cloud These pictures are sta-s-cally significant (several years of data) These pictures are powerful to evaluate cloud descrip-on in climate model, a step towards a more straightorward evalua-on of cloud descrip-on at the process scale in the model The models evaluated here are not able to reproduce instantaneous cloud pictures: ex; in model cloud op-cal depth decreases when the cloud extends horizontally (CF increases), whereas in the obs the cloud op-cal depth increases with the cloud horizontal extent The LMDZ5- NP is closer to the obs than LMDZ5

17 «News»: current development in the Lidar simulator / COSP the cloud phase CALIPSO-GOCCP water clouds Very preliminary LATITUDE Cloud Phase 3D for March 2008

18 «News»: recent updates of CFMIP- OBS database Observa-ons for model evalua-on: ARM Ground * CALIPSO- GOCCP sat CERES Sat CLOUDNET Ground * CLOUDSAT Sat ISCCP Sat MISR Sat MODIS Sat * MULTI- SENSORS Analysis Sat * MULTI- SENSORS Sat * PARASOL Sat References*** gcesana@lmd.polytechnique.fr chepfer@lmd.polytechnique.fr hlp://climserv.ipsl.polytechnique.fr/fr/cfmip- observa-ons.html Contributors : S. Bony (IPSL/LMD), H. Chepfer (IPSL/LMD), M. Chiriaco (IPSL/LATMOS), J- L. Dufresne (IPSL/LMD), N. Loeb (NASA/LarC), S. Klein (LLNL), R. Marchand (Univ. SeaLle), D. Tanré (LOA), M. Webb (UKMO), D. Winker (NASA/LarC), R. Pincus (University of Colorado), Shaocheng Xie (LLNL), Y. Zhang (LLNL)

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20 LONGITUDE ALTITUDE (km) VERTICAL DISTRIBUTION clouds CALIPSO- GOCCP LATITUDE HORIZONTAL DISTRIBUTION Reflectance MODIS 250m La-tude LATITUDE Need of: a) Instantaneous and colocated obs b) The highest possible resolu-on the full A- Train capabili-es are required CALIPSO trace 20

21 1- CF 1 PDF All Sky Refl=0.04 CF 1 or model grid box 1 CALIPSO trace CDF = CF PDF PDF Cloudy Refl=0.07 Clear Sky Refl=0.02 From case study to sta-s-cal study conserving the informa-on contained in high resolu-on obs REFLECTANCE 21

22 High clouds SCATTERING RATIO OBS LMDZ5 LMDZ- NP HighCloud Frac-on HighCloud Frac-on HighCloud Frac-on