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