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.

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Presentation transcript:

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 Climate Models Observations Evaluation of clouds in climate models Data processing (starting from level1) : Lidar CALIPSO (Cloud cover: 330m,Vertical structure: 30m) Radiometer PARASOL (reflectance: 6km) Radiometer MODIS (reflectance: 250m- 500m-1km) CFMIP-OBS: observational datasets consistent with the simulator CALIPSO – GOCCP PARASOL- reflectance in 1constant direction (θv=30°, φv=320°) COSP Simulator: - Subgrid cloud simulator-SCOPS - Lidar simulator - PARASOL simulator Simulated Datasets CALIPSO-like PARASOL-like consistency LMDZ5 LMDZ-New Physics

3 Zonal Mean Cloud Fraction – monthly mean Latitude Pressure (hPa) Latitude CALIPSO-GOCCP OBSLMDZ New Physics +SIM LMDZ5LMDZ New Physics LMDZ5 Overestimate: - High clouds Underestimate: - Tropical low/mid clouds - Congestus - Mid level mid lat LMDZ New Physics Better representation of clouds LMDZ5+SIM

4 Cloud Cover and Cloud Vertical Distribution in circulation regimes - Monthly mean ω500 (hPa/day) Pressure CF CALIPSO-GOCCPCF LMDZ5+SIMCF LMDZ new+SIM OBSERVATIONS: - Subsidence regimes → Strong presence of low stratiform clouds - Convective regimes → clouds at high troposphere + mid level clouds Tropical ocean 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) Radiometer PARASOL: directional reflectances, selection of one constant single direction (θ v =30°, φ s - φ v =320°)  Reflectance is a proxy of optical thickness Spherical Particles Non Spherical Particles (for θ s =30°) Optical Thickness Reflectance PARASOL Reflectance 1constant direction

6 Cloud Cover and All Sky Reflectance – monthly mean 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 ALL SKY REFLECTANCE CLOUD FRACTION LMDZ New Physics +parasol simulator LMDZ5 +lidar simulator PARASOL 1con.dir. OBS CALIPSO-GOCCP OBS LMDZ New Physics +lidar simulator LMDZ5 +parasol simulator

7 Cloud Cover and Cloud Optical Depth in circulation regimes - Monthly mean CLOUDY REFLECTANCECLOUD FRACTION ω500 (hPa/day) OBS LMDZ5+SIM LMDZ new+SIM Tropical ocean -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

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

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

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

11 Cloud Optical Depth Evaluation of the model at high resolution → 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. OBS- PARASOL Cloudy Reflectance All SKy Reflectance LMDZ5+SIM LMDZ new+SIM PDF Optical thickness (spherical particles and θ s =30°) Corresponding CDF: 50% of the cloud:Obs optical depth = 2.6 Models cloud optical depth = 4.8 Tropical ocean

12 Relationship between Cloud Cover and Cloud Optical Depth OBSERVATIONS Tropical ocean Cloud Fraction 01 All Sky Reflectance Cloudy Reflectance Obs monthly Obs daily Obs daily → 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)

13 Relationship between Cloud Cover and Cloud Optical Depth Tropical ocean Cloud Fraction 01 All Sky Reflectance Cloudy Reflectance Obs monthly Obs daily Obs daily Cloud Fraction LMDZ5 daily LMDZ new daily LMDZ5 daily LMDZ new daily LMDZ5 monthly LMDZ new monthly → Model has difficulties to reproduce the ‘instantaneous’ relationship => Here after, we use « High Resolution » : Cloudy Refl, Daily

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

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

16 Focus on low level boundary layer clouds: Relationship between the Cloud Top Pressure and the Cloudy Reflectance Tropical ocean Cloudy Reflectance Or Optical depth Cloudy Reflectance Ptop OBSERVATIONS LMDZ5+SIM LMDZ new+SIM 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!