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Evaluation of cloudiness in LMDZ GCM using CALIPSO and PARASOL

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.

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Evaluation of cloudiness in LMDZ GCM using CALIPSO and PARASOL

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  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 • Error’s compensation between : the Cloud Optical thickness and the Total Cloud Cover • - vertically integrated values within a lat x lon grid box - Instantaneaous .vs. Monthly mean (b) Error’s compensation in time averaging : Same TOA fluxes (c) Error’s compensation in vertical distribution : All errors (a) (b), (c) will impact the cloud feedback predicted by climate models

  4. Objectives : 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 » • COSP Simulator : • COSP/CALIPSO • COSP/PARASOL Observational datasets consistent with simulator outputs: CALIPSO-GOCCP PARASOL-1dir Climate Simulations with two different models: - LMDz 4 - LMDz «New Physic » A-train

  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 1 PARASOL image PARASOL Reflectance Obs. (JFM) The Parasol Reflectance in a specific direction is mainly dependent on the cloud optical thickness

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

  8. (3) Reflectance .vs. Cloud Cover Monthly mean values 0.7 LMDZ4 + Simulators LMDZ new physic + Simulators Observations Reflectance Valeurs Instantanées 0.8 0 1 0 1 0 1 Instantaneous values 0.8 LMDZ new physic + Simulators LMDZ4 + Simulators Observations Reflectance 0 1 0 1 0 1 Cloud cover Cloud cover Cloud cover

  9. (4) Cloud Vertical Distribution LMDZ New Physic + Simulator LMDZ4 + Simulator CALIPSO-GOCCP HIGH MID LOW CLOUD FRACTION – Seasonal mean Chepfer et al. 2008, GRL

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

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

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

  13. In the Tropics : Cloud types LMDZ4 + Simulator LMDZ New Physic + Simulator CALIPSO-GOCCP Tropical warm pool Pressure 0 5 Clouds 80 0 5 80 0 5 80 Hawai trade cumulus Pressure Lidar signal intensity (SR) Lidar signal intensity (SR) Lidar signal intensity (SR)

  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 CLOUD FRACTION High Low Mid w500 (hPa/day) w500 (hPa/day) ___ Obs ----- LMDz4 _._. LMDz new physic _._. Obs ----- LMDz4 ___ LMDz new physic

  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) 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 Histograms of Scattering Ratio (Lidar signal intensity) CALIPSO-GOCCP GCM + Lidar SIMULATOR (COSP) HIGH Nb of occurrence (log scale) PRESSURE MID LOW Lidar signal intensity (SR) Lidar signal intensity (SR) SR>3 : cloud SR>3 : cloud Along the GPCI – Gewex Pacific Cross section Intercomparison Transect

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

  21. Evaluer les nuages dans les modèles de climat (2) Flux égaux Motivation: Compensations d’erreurs 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) Relation Réflectance (Parasol) - Couverture nuageuse (Calipso) au dessus des océans : Valeurs Instantanées 0.8 GCM + Simulateurs Observations Valeurs Moyennes Mensuelles 0.6 0.6 Observations GCM + Simulateurs Réflectance (Epaisseur Optique) 1 0 1 0 1 0 0 1 Couverture nuageuse Couverture nuageuse Thèse D. Konsta (en cours) La bonne reproduction des flux au sommet de l’atmosphère résulte d’une compensation d’erreurs entre couverture nuageuse et épaisseur optique

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

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

  24. Low clouds CALIOP 330M SR3 CALIOP 330M SR5 CALIOP L2 V01 GLAS L2 Low Clouds P>680 hPa (night time - October) ISCCP L2

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

  26. (2) The Cloud Vertical Distribution types Histograms of Scattering Ratio (Lidar signal intensity) CALIPSO-GOCCP GCM + Lidar SIMULATOR (COSP) HIGH Nb of occurrence (log scale) PRESSURE MID LOW Lidar signal intensity (SR) Lidar signal intensity (SR) SR>3 : cloud SR>3 : cloud Along the GPCI – Gewex Pacific Cross section Intercomparison Transect

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