1. 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.

2. Clouds and climate feedbacks Dufresne and Bony, 2008

3. 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

4. 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-GOCCP PARASOL-1dir

5. (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

6. (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

7. (3) Reflectance and Cloud Cover LMDZ New Physic + PARASOL Simulator LMDZ 4 + PARASOL Simulator PARASOL Obs. LMDZ 4 + CALIPSO Simulator LMDZ « New Physic » + CALIPSO Simulator CALIPSO – GOCCP Obs. Reflectance JFM Cloud Cover JFM

8. (3) Reflectance.vs. Cloud Cover Valeurs Instantanées 0.8 Cloud cover 000111 Reflectance 0.8 0.7 0 00111 Monthly mean values Instantaneous values Observations LMDZ4 + Simulators LMDZ4 + Simulators LMDZ new physic + Simulators LMDZ new physic + Simulators

9. (4) Cloud Vertical Distribution CLOUD FRACTION – Seasonal mean LO W MI D HIG H Chepfer et al. 2008, GRL LMDZ4 + Simulator CALIPSO-GOCCP LMDZ New Physic + Simulator

10. JFM LMDZ4 + Simulator CALIPSO-GOCCP (4) Low, Mid, High Cloud Covers LMDZ New Physic + Simulator HIGH MID LOW

11. In the Tropics : Reflectance and Total Cloud cover REFLECTANCE w500 (hPa/day) ----- LMDz4 ___ LMDz New Physic w500 (hPa/day) CLOUD COVER

12. In the Tropics : Cloud Vertical Distribution w500 (hPa/day) CLOUD FRACTION – Seasonal mean LMDZ4 + Simulator CALIPSO-GOCCP LMDZ New Physic + Simulator w500 (hPa/day) log

13. In the Tropics : Cloud types Tropical warm pool Hawai trade cumulus LMDZ4 + Simulator CALIPSO-GOCCP LMDZ New Physic + Simulator Pressure Lidar signal intensity (SR ) Clouds08050 50 5

14. Conclusion CALIPSO 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

15. 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.

16. Fin

17. In the Tropics : Reflectance and Cloud fraction remplacer par tot CC ? REFLECTANCE w500 (hPa/day) CLOUD FRACTION Low Mid High _._. Obs ----- LMDz4 ___ LMDz new physic ___ Obs ----- LMDz4 _._. LMDz new physic w500 (hPa/day)

18. Conclusion An tool to compare cloudiness in GCM and CALIOP data : Lidar simulator is now part of COSP (CFMIP Observation Simulator Package: http://www.cfmip.net)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

19. 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

20. Evaluation of the Cloud vertical distribution GCM + Lidar SIMULATOR (COSP)CALIPSO-GOCCP Californian Strato Cumulus JJA PRESSURE SR VALUE Mid-latitudes North Pacific JJA PRESSURE Lidar signal intensity

21. 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 égaux Motivation: Compensations d’erreurs Thèse D. Konsta (en cours) Relation Réflectance (Parasol) - Couverture nuageuse (Calipso) au dessus des océans : Valeurs Moyennes Mensuelles Valeurs Instantanées Réflectance (Epaisseur Optique) Couverture nuageuse Observations GCM + Simulateurs GCM + Simulateurs 0 0.6 0.8 000111 1 0.6

22. Tropical regions (dynamical regimes) JFM CALIOPGCM + SIMULATOR W500 (hPa/day) 0 50 -70

23. Observations : Cloud detection criteria (1 orbite) ATB (583 levels) ATB (19 levels) SR (19 levels) CLOUDS UNCLASSIFIED CLEAR FULLY ATTENUATED 80 S80 N0 Altitude 35 0 GOCCP

24. Low clouds Low Clouds P>680 hPa (night time - October) CALIOP 330M SR3CALIOP 330M SR5 CALIOP L2 V01 GLAS L2 ISCCP L2

25. Observations : Cloud detection criteria > 3 : CLOUDY SR

26. (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