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Evaluation of cloudiness in LMDZ GCM using CALIPSO and PARASOL. H. Chepfer , S. Bony, G. Cesana, JL Dufresne, D. Konsta, LMD/IPSL D. Winker, NASA/LaRC D. Tanré, LOA. Clouds and climate feedbacks. Dufresne and Bony, 2008. Evaluation of clouds in climate models. - PowerPoint PPT Presentation
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Evaluation of cloudiness in LMDZ GCM using CALIPSO and PARASOL
H. Chepfer, S. Bony, G. Cesana, JL Dufresne, D. Konsta, LMD/IPSL
D. Winker, NASA/LaRC
D. Tanré, LOA.
Clouds and climate feedbacks
Dufresne and Bony, 2008
Evaluation of clouds in climate models
- mostly based on Monthly mean TOA fluxes ERBE, ScaRaB, CERES, and ISCCP
(eg. Zhang et al. 2005, Webb et al. 2001, Bony et al. 2004, ….)
- but a good reproduction of monthly mean TOA fluxes can be due to error’s compensations
(c) Error’s compensation in vertical distribution :Same TOA fluxes
All errors (a) (b), (c) will impact the cloud feedback predicted by climate models
(a) Error’s compensation between : the Cloud Optical thickness and the Total Cloud Cover
- vertically integrated values within a lat x lon grid box -
(b) Error’s compensation in time averaging : Instantaneaous .vs. Monthly mean
Use A-Train observations to track errors in climate models on :(1) the Cloud Cover (2) the Cloud Optical Thickness (3) the relationship between the Cloud Cover and the Cloud Optical Thickness(4) the Cloud Vertical distribution (5) the « Cloud Type »
Objectives :
Climate Simulations with two different models:- LMDz 4- LMDz «New Physic »
COSP Simulator :- COSP/CALIPSO- COSP/PARASOL
A-train
Observational datasets consistent with simulator outputs:
CALIPSO-GOCCPPARASOL-1dir
(1) Total Cloud Cover - observations
« GCM Oriented Calipso Cloud Product » (CALIPSO – GOCCP):
an observationnal dataset fully consistent with COSP outputs
CALIPSO – GOCCP compared with others cloud climatologies
Chepfer et al., 2009, JGR
(2) Cloud Optical Thickness : Reflectance -observations
PARASOL Reflectance Obs. (JFM) 1 PARASOL image
The Parasol Reflectance in a specific direction is mainly dependent on the cloud optical thickness
(3) Reflectance and Cloud Cover
LMDZ New Physic + PARASOL Simulator
LMDZ 4 + PARASOL Simulator
PARASOL Obs.
LMDZ 4 + CALIPSO Simulator
LMDZ « New Physic » + CALIPSO SimulatorCALIPSO – GOCCP Obs.
Reflectance JFM
Cloud Cover JFM
(3) Reflectance .vs. Cloud Cover
Valeurs Instantanées0.8
Cloud cover Cloud cover Cloud cover0 0 01 1 1
Reflectance
Reflectance
0.8
0.7
0 0 01 1 1
Monthly mean values
Instantaneous values
Observations
Observations LMDZ4 + Simulators
LMDZ4 + Simulators
LMDZ new physic+ Simulators
LMDZ new physic+ Simulators
(4) Cloud Vertical Distribution
CLOUD FRACTION – Seasonal mean
LOW
MID
HIGH
Chepfer et al. 2008, GRL
LMDZ4 + SimulatorCALIPSO-GOCCP LMDZ New Physic + Simulator
JFM
LMDZ4 + SimulatorCALIPSO-GOCCP
(4) Low, Mid, High Cloud Covers
LMDZ New Physic + Simulator
HIGH
MID
LOW
In the Tropics : Reflectance and Total Cloud cover
REFLECTANCE
w500 (hPa/day)----- LMDz4 ___ LMDz New Physic
w500 (hPa/day)
CLOUD COVER
In the Tropics : Cloud Vertical Distribution
w500 (hPa/day) w500 (hPa/day)
CLOUD FRACTION – Seasonal mean
LMDZ4 + SimulatorCALIPSO-GOCCP LMDZ New Physic + Simulator
w500 (hPa/day)
log
In the Tropics : Cloud types
Tropical warm pool
Hawai trade cumulus
LMDZ4 + SimulatorCALIPSO-GOCCP LMDZ New Physic + Simulator
Pressure
Pressure
Lidar signal intensity (SR) Lidar signal intensity (SR) Lidar signal intensity (SR)
Clouds0 805 0 8050 805
ConclusionCALIPSO and PARASOL provide powerful observations to identify error’s
compensation: (1) between vertically integrated Cloud Cover and Optical Thickness, (2) in the time averaging : instantaneous .vs. monthly mean (3) in the cloud vertical distribution
Both versions of the model, LMDz4 and « LMDz new physic » exhibit error’s compensations
« LMDz new physic » is more consistent with observations than LMDZ4 for :- total cloud cover- low level oceanic tropical clouds (cloud cover and reflectance)- instantaneous relationship between cloud cover and cloud fraction *
« LMDz new physic » main defaults :- overestimation of the high cloud amount- underestimate of the total cloud amount and the tropical low level oceanic cloud amont in subsidence regions
None of the two versions of the model is able to reproduce the different « Cloud Types » characterized by the « histograms of lidar intensity », e.g. the 2 modes of low level clouds and the congestus clouds
CALIPSO and PARASOL simulators are included in COSP:http://www.cfmip.net
Chepfer H., S. Bony, D. Winker, M. Chiriaco, J-L. Dufresne, G. Sèze, 2008: Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model, Geophys. Res. Let., 35, L15704, doi:10.1029/2008GL034207.
CALIPSO- GOCCP « GCM oriented CALIPSO Cloud Product » and PARASOL Reflectances Observations :http://climserv.ipsl.polytechnique.fr/cfmip-atrain.html
Chepfer H., S. Bony, D. Winker, G. Cesana, JL. Dufresne, P. Minnis, C. J. Stubenrauch, S. Zeng, 2009: The GCM Oriented Calipso Cloud Product (CALIPSO-GOCCP), J. Geophys. Res., under revision.
Fin
In the Tropics : Reflectance and Cloud fraction remplacer par
tot CC ?REFLECTANCE
w500 (hPa/day)
CLOUD FRACTION
LowMid
High
_._. Obs----- LMDz4 ___ LMDz new physic
___ Obs----- LMDz4 _._. LMDz new physic
w500 (hPa/day)
ConclusionAn tool to compare cloudiness in GCM and CALIOP data :
Lidar simulator is now part of COSP
(CFMIP Observation Simulator Package: http://www.cfmip.net)
An GCM oriented CALIPSO cloud Product GOCCP :
An observational dataset fully consistent with the « GCM + Simulator output »
• Tests done but not shown here, to check robustness of the results:
Sensitivity to multiple scattering factor (in simulator)
Add color ratio threshold to avoid misinterpretation cloud/aerosols (CALIOP/GOCCP)
Sensitivity to the frequency simulator called (3hrs, 6hrs, …) (GCM)
Sensitivity to the diurnal cycle (GCM)
• LMDz cloudiness:– High clouds … not to bad : slightly underestimated– Mid clouds in mid latitudes : seem to be shifted to low level clouds – Low clouds : underestimated in the tropics– Tropical low clouds : inhomogeneous fractionnated clouds and subsidence regions clouds are strongly
underestimated in the model …
those are likely playing a key role in the uncertainty on cloud climate feedback predicted by different GCM
Evaluation of the Cloud vertical distribution
GCM + Lidar SIMULATOR (COSP)CALIPSO-GOCCP
Histograms of Scattering Ratio (Lidar signal intensity)
LOW
MID
HIGH
PRESSURE
Nb of occurrence(log scale)
Lidar signal intensity (SR)
Along the GPCI – Gewex Pacific Cross section Intercomparison Transect
Lidar signal intensity (SR)SR>3 : cloud SR>3 : cloud
Evaluation of the Cloud vertical distributionGCM + Lidar SIMULATOR (COSP)CALIPSO-GOCCP
Californian Strato Cumulus
JJA
PRESSURE
SR VALUE
Mid-latitudesNorth Pacific
JJA
PRESSURE
Lidar signal intensity Lidar signal intensity
Evaluer les nuages dans les modèles de climat (2)
La bonne reproduction des flux au sommet de l’atmosphère résulte d’une compensation d’erreurs entre couverture nuageuse et épaisseur optique
Les flux moyens mensuels au sommet de l’atmosphère sont bien reproduits par les modèles de climat
(ie. Zhang et al. 2005 – intercomparaison modèles de climat)
Epaisseur optique
Distribution verticale des nuages
(Slingo et al. 1988)
Flux égauxMotivation:Compensations d’erreurs
Thèse D. Konsta (en cours)
Relation Réflectance (Parasol) - Couverture nuageuse (Calipso) au dessus des océans :
Valeurs Moyennes Mensuelles
Valeurs Instantanées
Réflectance(EpaisseurOptique)
Couverture nuageuse Couverture nuageuse
Observations
Observations
GCM +Simulateurs
GCM +Simulateurs
0
0.6
0.8
0 00 111 1
0.6
Tropical regions (dynamical regimes) JFM
CALIOPGCM + SIMULATOR
W500 (hPa/day)W500 (hPa/day) 0 50 50-70-70
Observations : Cloud detection criteria (1 orbite)
ATB (583 levels)
ATB (19 levels)
SR (19 levels)
CLOUDS
UNCLASSIFIEDCLEAR
FULLY ATTENUATED
80 S 80 N0
Altitude
35
0
GOCCP
Low clouds
Low Clouds P>680 hPa(night time - October)
CALIOP 330M SR3 CALIOP 330M SR5
CALIOP L2 V01 GLAS L2
ISCCP L2
Observations : Cloud detection criteria
> 3 : CLOUDYSR
(2) The Cloud Vertical Distribution types
GCM + Lidar SIMULATOR (COSP)CALIPSO-GOCCP
Histograms of Scattering Ratio (Lidar signal intensity)
LOW
MID
HIGH
PRESSURE
Nb of occurrence(log scale)
Lidar signal intensity (SR)
Along the GPCI – Gewex Pacific Cross section Intercomparison Transect
Lidar signal intensity (SR)SR>3 : cloud SR>3 : cloud