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D. Konsta, H. Chepfer, J-L. Dufresne, S.Bony, G. Cesana

Une description statistique multi-variable des nuages au dessus de l’océan tropical à partir des observations de jour de l’A-train en haute résolution spatiale pour évaluer la paramétrisation des processus nuageuses dans les modèles climatiques. D. Konsta, H. Chepfer, J-L. Dufresne,

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D. Konsta, H. Chepfer, J-L. Dufresne, S.Bony, G. Cesana

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  1. Une description statistique multi-variable des nuages au dessus de l’océan tropical à partir des observations de jour de l’A-train en haute résolution spatiale pour évaluer la paramétrisation des processus nuageuses dans les modèles climatiques. D. Konsta, H. Chepfer, J-L. Dufresne, S.Bony, G. Cesana Laboratoire de Météorologie Dynamique LMD Institut Pierre Simon Laplace IPSL, Paris Seminaire INTRO, 30 September 2010

  2. Evaluation of clouds in climate models Observations Climate Models LMDZ5LMDZ-New Physics Data processing (starting from level1) : Lidar CALIPSO (Cloud cover: 330m,Vertical structure: 30m) • Radiometer PARASOL (reflectance: 6km) • Radiometer MODIS (reflectance: 250m-500m-1km) COSP Simulator: - Subgrid cloud simulator-SCOPS- Lidar simulator- PARASOL simulator CFMIP-OBS: observational datasets consistent with the simulator CALIPSO – GOCCP PARASOL- reflectance in 1constant direction (θv=30°, φv=320°) Simulated Datasets CALIPSO-like PARASOL-like consistency

  3. Zonal Mean Cloud Fraction – monthly mean CALIPSO-GOCCP OBS LMDZ5+SIM LMDZ New Physics +SIM 0.3 0 Latitude LMDZ5 LMDZ New Physics • LMDZ5 • Overestimate: • High clouds Underestimate: • Tropical low/mid clouds • Congestus - Mid level mid lat LMDZ New Physics Better representation of clouds 0.3 Pressure (hPa) 0 Latitude

  4. Cloud Cover and Cloud Vertical Distribution in circulation regimes - Monthly mean Tropical ocean CF CALIPSO-GOCCP CF LMDZ5+SIM CF LMDZ new+SIM Pressure ω500 (hPa/day) ω500 (hPa/day) ω500 (hPa/day) • OBSERVATIONS: • Subsidence regimes → Strong presence of low stratiform clouds • Convective regimes → clouds at high troposphere • + mid level clouds LMDZ5: -underestimation of low level clouds -no mid level clouds -overestimation of high convective clouds • LMDZ New Physics: • representation of boundary layer clouds in all regimes • overestimation of mid level clouds in one single layer • fewer high clouds

  5. Clouds Optical depth (or Reflectance) 0.9 Radiometer PARASOL: directional reflectances, selection of one constant single direction (θv=30°, φs- φv=320°) Reflectance is a proxy of optical thickness Spherical Particles Non Spherical Particles Reflectance (for θs=30°) PARASOL Reflectance 1constant direction 0 0 50 Optical Thickness

  6. Cloud Cover and All Sky Reflectance – monthly mean ALL SKY REFLECTANCE LMDZ New Physics +parasol simulator PARASOL 1con.dir. OBS LMDZ5 +parasol simulator CLOUD FRACTION LMDZ New Physics +lidar simulator LMDZ5 +lidar simulator CALIPSO-GOCCP OBS Error compensations between Total Cloud Cover and Optical depth (vertically integrated value within the lat x lon grid box) → Need to evaluate the cloud parameterizations in climate models

  7. Cloud Cover and Cloud Optical Depth in circulation regimes - Monthly mean Tropical ocean CLOUDY REFLECTANCE CLOUD FRACTION LMDZ5+SIM OBS LMDZ new+SIM ω500 (hPa/day) ω500 (hPa/day) • Subsidence regimes: • strong underestimation of cloud fraction but strong overestimation of cloud optical depth (less from LMDZ New Physics) • Convective regimes: • underestimation of cloud cover and cloud optical depth • → Need to evaluate the cloud parameterizations in climate models

  8. To evaluate the cloud parameterizations in climate models: Monthly mean observations are not sufficient We need to use high resolution (spatial and temporal ) observations

  9. A case study: low tropical boundary layer clouds- high resolution obs - Reflectance MODIS 1km Reflectance MODIS 500m CALIPSO Level 1 Reflectance MODIS 250m Longitude Reflectance CALIPSO 125m CALIPSO-GOCCP CF MODIS 5km Altitude (km) CF PARASOL 18.5km CLOUDSAT Latitude 1 0.2 MODIS Cloud Fraction Latitude CALIPSO Reflectance Impact of the spatial resolution of the sensors Need a clean separation clear/cloudy Need colocated and simultaneous observations PARASOL 0 0

  10. A methodology: from the case study to global statistics using high spatial resolution data PDF All Sky Refl=0.04 =0.4 CDF 1-CF =0.6 1° Cloudy Refl=0.07 1° Clear Refl=0.02 Same methodology for simulator’s outputs • In each grid box (obs/mod): Cloud Fraction and Cloudy Refl Reflectance MODIS 250m

  11. Cloud Optical DepthEvaluation of the model at high resolution Tropical ocean Optical thickness (spherical particles and θs=30°) PDF 0 3.4 8.1 16.5 40.5 OBS- PARASOL LMDZ new+SIM LMDZ5+SIM 0 0.2 0.4 0.6 0.8 0 0.2 0.4 0.6 0.8 All SKy Reflectance Cloudy Reflectance → Overestimation of low values of All-Sky Reflectance and underestimation of high values. BUT for cloudy reflectance (no clear sky contribution): → More optically thick clouds and less optically thin clouds simulated. Corresponding CDF: 50% of the cloud: Obs optical depth = 2.6 Models cloud optical depth = 4.8

  12. OBSERVATIONS Tropical ocean Relationship between Cloud Cover and Cloud Optical Depth Obs daily All Sky Reflectance 0.6 Obs daily 0 Cloudy Reflectance → The relation between optical depth and Cloud Fraction changes with the scale of averaging changes in time (monthly.vs. daily) and in space (all sky .vs. cloudy) 0.6 Obs monthly 0 0 1 Cloud Fraction

  13. Tropical ocean Relationship between Cloud Cover and Cloud Optical Depth Obs daily LMDZ5 daily LMDZ new daily → Model has difficulties to reproduce the ‘instantaneous’ relationship => Here after, we use « High Resolution » : Cloudy Refl, Daily All Sky Reflectance 0.6 Obs daily LMDZ5 daily LMDZ new daily 0 Cloudy Reflectance 0.6 Obs monthly LMDZ5 monthly LMDZ new monthly 0 0 1 0 1 0 1 Cloud Fraction Cloud Fraction Cloud Fraction

  14. Relationship between Cloud Cover and Cloud Optical Depth for high and low tropical oceanic clouds Tropical ocean 1 OBS LMDZ5 LMDZ- new Cloudy Reflectance High clouds 0 1 OBS LMDZ5 LMDZ- new Cloudy Reflectance Low clouds 0 0 1 0 1 0 1 Cloud Fraction Cloud Fraction Cloud Fraction • Error compensation between optically thin high clouds and very thick boundary layer clouds • Underestimation of the Cloud Fraction

  15. Cloud Cover versus Vertical distribution versus Cloud Reflectance Tropical ocean OPTICALLY THIN CLOUDS OPTICALLY THICK CLOUDS LMDZ5+SIM OBS- PARASOL LMDZ new+SIM Pressure 0 0.6 0 0.6 Cloud Fraction (CF(p)/CFtot) Cloud Fraction (CF(p)/CFtot)

  16. Focus on low level boundary layer clouds: Relationship between the Cloud Top Pressure and the Cloudy Reflectance Tropical ocean OBSERVATIONS LMDZ5+SIM LMDZ new+SIM Ptop 0.05 0.3 0.2 0.9 0.2 0.9 Cloudy Reflectance Or Optical depth Cloudy Reflectance Cloudy Reflectance • OBS: The cloud optical depth increases with the cloud top altitude (and with the cloud cover) → the cloud grows vertically (and horizontally) • Difficulties of the model to reproduce the relationship

  17. Conclusions A statistical view of clouds with A-train observations: • simultaneous and independent observations of multiple cloud parameters at high resolution→ assess cloud process parameterization in climate models • the spatial resolution of different sensors and the temporal resolution of the statistical analysis are critical • study of cloud properties only (excluding ‘Clear sky’ contribution) • link between Cloud Cover, Vertical Structure and Cloud Optical Depth • low clouds: cloudy reflectance increase with the cloud top altitude • LMDZ model evaluation: • Error’s compensations between • - underestimation of low tropical clouds/ few medium clouds and overestimation of high clouds • underestimation of the total Cloud Cover and overestimation of the Cloud Optical Depth (mainly in regions of subsidence) • Optically thinner high clouds and optically thicker boundary layer clouds • Better representation of clouds from LMDZ New Physics • Perspectives: • Similar analysis based on “high resolution” A-train observations to evaluate other climate models • Analysis of the subgrid variability (observations and models)

  18. Thank you!

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