d. konsta, h. chepfer, j-l. dufresne, s.bony, g. cesana
DESCRIPTION
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, - PowerPoint PPT PresentationTRANSCRIPT
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Une description statistique multi-variable Une description statistique multi-variable des nuages au dessus de l’océan tropical à des nuages au dessus de l’océan tropical à partir des observations de jour de l’A-train partir des observations de jour de l’A-train en haute résolution spatiale pour évaluer la en haute résolution spatiale pour évaluer la paramétrisation des processus nuageuses paramétrisation des processus nuageuses
dans les modèles climatiques. dans les modèles climatiques.
D. Konsta, H. Chepfer, J-L. Dufresne,
S.Bony, G. CesanaLaboratoire de Météorologie Dynamique LMD
Institut Pierre Simon Laplace IPSL, Paris
Seminaire INTRO, 30 September 2010
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Climate ModelsObservations
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 – GOCCPPARASOL- reflectance in 1constant
direction (θv=30°, φv=320°)
COSP Simulator: - Subgrid cloud simulator-SCOPS- Lidar simulator- PARASOL simulator
Simulated DatasetsCALIPSO-likePARASOL-like
consistency
LMDZ5LMDZ-New Physics
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Zonal Mean Cloud Fraction – monthly mean
Latitude
Pre
ssur
e (h
Pa)
Latitude
CALIPSO-GOCCP OBS LMDZ New Physics +SIM
LMDZ5 LMDZ New Physics
0
0.3
LMDZ5Overestimate:- High cloudsUnderestimate:- Tropical low/mid clouds- Congestus- Mid level mid lat
LMDZ New PhysicsBetter representation of clouds
0
0.3
LMDZ5+SIM
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Cloud Cover and Cloud Vertical Distribution in circulation regimes- Monthly mean
ω500 (hPa/day) ω500 (hPa/day)ω500 (hPa/day)
Pre
ssur
e
CF CALIPSO-GOCCP CF LMDZ5+SIM CF 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
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Clouds Optical depth (or Reflectance)
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
(for θs=30°)
Optical ThicknessR
efle
ctan
ce
50 0
0.9
0PARASOLReflectance 1constant direction
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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-GOCCPOBS
LMDZ New Physics+lidar simulator
LMDZ5+parasol simulator
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Cloud Cover and Cloud Optical Depth in circulation regimes- Monthly mean
CLOUDY REFLECTANCE CLOUD FRACTION
ω500 (hPa/day) 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
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A case study: low tropical boundary layer clouds- high resolution obs -
Impact of the spatial resolution of the sensorsNeed a clean separation clear/cloudyNeed 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
Alti
tude
(km
)
Latitude
Latitude
Long
itude
Clo
ud F
ract
ion
0
1 Reflectance
0
0.2MODIS
CALIPSO
PARASOL
A methodology: from the case study to global statistics using high spatial resolution data
Reflectance MODIS 250m
CDF
1-CF
1°
1°
All Sky Refl=0.04
Cloudy Refl=0.07
Clear Refl=0.02
=0.4
=0.6
Same methodology for simulator’s outputsIn each grid box (obs/mod): Cloud Fraction and Cloudy Refl
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Cloud Optical DepthEvaluation 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 Reflectance0 0.80.2 0.4 0.6
All SKy Reflectance0 0.80.2 0.4 0.6
LMDZ5+SIM
LMDZ new+SIM
PDFOptical thickness (spherical particles and θs=30°)
0 40.53.4 8.1 16.5
Corresponding CDF: 50% of the cloud: Obs optical depth = 2.6 Models cloud optical depth = 4.8
Tropical ocean
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Relationship between Cloud Cover and Cloud Optical Depth
OBSERVATIONS Tropical ocean
Cloud Fraction0 1
All
Sky
Ref
lect
ance
0
0.6
Clo
udy
Ref
lect
ance
0
0.6
Obs monthly
Obsdaily
Obsdaily
→ 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)
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Relationship between Cloud Cover and Cloud Optical Depth
Tropical ocean
Cloud Fraction0 1
All
Sky
Ref
lect
ance
0
0.6
Clo
udy
Ref
lect
ance
0
0.6
Obs monthly
Obsdaily
Obsdaily
Cloud Fraction0 1
Cloud Fraction0 1
LMDZ5daily
LMDZ newdaily
LMDZ5daily
LMDZ newdaily
LMDZ5monthly
LMDZ newmonthly
→ Model has difficulties to reproduce the ‘instantaneous’ relationship
=> Here after, we use « High Resolution » : Cloudy Refl, Daily
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Relationship between Cloud Cover and Cloud Optical Depth for high and low tropical oceanic clouds
Clo
udy
Ref
lect
ance
Clo
udy
Ref
lect
ance
1
0
1
0
Cloud Fraction0 1
Cloud Fraction0 1
Cloud Fraction0 1
Highclouds
Low clouds
OBS
OBS
LMDZ5
LMDZ5
LMDZ- new
LMDZ- new
• Error compensation between optically thin high clouds and very thick boundary layer clouds• Underestimation of the Cloud Fraction
Tropical ocean
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Cloud Cover versus Vertical distribution versus Cloud ReflectanceP
ress
ure
Cloud Fraction (CF(p)/CFtot) Cloud Fraction (CF(p)/CFtot)
OPTICALLY THIN CLOUDS OPTICALLY THICK CLOUDS
OBS- PARASOL
LMDZ5+SIM
LMDZ new+SIM
0 0.6 0 0.6
Tropical ocean
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Focus on low level boundary layer clouds:Relationship between the Cloud Top Pressure and the Cloudy Reflectance
Tropical ocean
Cloudy ReflectanceOr Optical depth
Cloudy Reflectance Cloudy Reflectance0.05 0.3
Pto
p
OBSERVATIONS LMDZ5+SIM LMDZ new+SIM
0.2 0.9 0.2 0.9
• 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
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ConclusionsA 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)
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Thank you!