Cloud Feedbacks: their Role in Climate Sensitivity and How to Assess them

Transcription

1 Cloud Feedbacks: their Role in Climate Sensitivity and How to Assess them Sandrine Bony, Jean Louis Dufresne Hélène Chepfer, Marjolaine Chiriaco, Ionela Musat, Geneviève Sèze LMD/IPSL et SA/IPSL, Paris, France ICARE IPSL, Paris, 17 Sept. 27

2 Climate sensitivity estimates from CMIP3 GCMs participating in the IPCC AR4 : Equilibrium Climate Sensitivity : (warming for sustained 2xCO2) Transient Climate Response : (1% CO2/yr, transient warming at 2xCO2) Climate sensitivity and TCR estimates depend on : radiative forcing climate feedbacks ocean heat uptake (transient only)

3 How do these different components contribute to inter model differences in climate sensitivity? multi model mean inter model differences (standard deviation) (Dufresne & Bony 27, submitted)

4 How do these different components contribute to inter model differences in climate sensitivity? multi model mean inter model differences (standard deviation) (Dufresne & Bony 27, submitted)

5 What explains the spread of GCMs' cloud feedbacks? deep convective activity baroclinic activity & frontal clouds boundary-layer turbulence and clouds

6 Sensitivity of the Tropical Cloud Radiative Forcing to Global Warming [ CRF SW SST ] ω High sensitivity OAGCMs Low sensitivity OAGCMs (Bony and Dufresne, GRL, 25)

7 Cloud feedbacks have been confirmed as the primary source of climate sensitivity uncertainty. The SW response of clouds is the most uncertain. Recent studies point to low level clouds as a primary culprit. (Bony and Dufresne, GRL, 25; Webb et al., Clim. Dynamics, 26; Williams and Tselioudis, Clim. Dynamics 27) Which of the model cloud feedbacks are the more credible?... What cloud properties should we evaluate to address this issue?

8 Cloud Feedback Model Intercomparison Project Phase 2 (CFMIP-2) WGCM/WCRP initiative to foster coordinated research in the area of cloud feedbacks in GCMs Coordination committee: Mark Webb, Sandrine Bony, Chris Bretherton, George Tselioudis GCM process diagnostics CRMs/LES/SCMs via GCSS Understanding of cloud feedbacks A Train/ISCCP & simulators Evaluation of cloud fields Assessment of climate change cloud feedbacks Full CFMIP 2 proposal available on

9 CFMIP activities for an improved evaluation of modelled clouds using observations : GCM process CRMs/LES/SCMs A Train/ISCCP & diagnostics via GCSS simulators Understanding of cloud feedbacks Evaluation of cloud fields Assessment of climate change cloud feedbacks Current developments : development of a CloudSat radar simulator (CSU, PNNL/UW, LLNL) development of a CALIPSO lidar simulator (LMD/IPSL) improved version of the ISCCP simulator (Hadley Centre) assemblage of these different modules into a combined CFMIP ISCCP CloudSat CALIPSO Simulator named CICCS Plans : release of a beta version of CICCS : end 27, pilot model intercomparison studies using this simulator : early 28 production version for use in CMIP4/CFMIP2 and in CAPT: mid 28. Endorsed and strongly supported by WGCM, CLIVAR and GEWEX panels (WCRP) (sept. 27)

10 CALIPSO lidar simulator Development of the CFMIP ISCCP-CloudSat-CALIPSO simulator (CICCS) to be used in future GCM inter-comparison projects (CFMIP, CMIP/IPCC simulations). Why a simulator? => meaningful comparisons between models and observations require consistency of observed and model variables => ISCCP data widely used by modelers only since development and distribution of an ISCCP simulator (model to satellite approach) Focus on model comparison => degrade the CALIPSO observations at the coarse model resolution.

11 Approach Observations GCMs CALIPSO level 1 data Model: 3D output fields Horizontal average of 3 profiles (1km) Vertical average on a coarse grid Subgrid cloud simulator (SCOPS) Lidar simulator (ACTSIM) Cloud detection Cloud detection Statistics on model grid Statistics on model grid Comparison

12 CALIPSO lidar measurements : Towards a near global view of the 3D structure of clouds from space low level cloud fraction derived from CALIPSO (Chepfer et al. 27, submitted)

13 Low Level cloud fraction (Ptop > 68hPa) Sept Oct Nov GCM + CALIPSO simulator GCM GCM + ISCCP simulator (Chepfer et al. 27, submitted)

14 Low Level clouds fraction (Ptop > 68hPa) Sept Oct Nov GCM + CALIPSO simulator ISCCP data GCM + ISCCP simulator

15 Low Level clouds fraction (Ptop > 68hPa) Sept Oct Nov CALIPSO data GCM + CALIPSO simulator ISCCP data GCM + ISCCP simulator

16 GCM / CloudSat comparison of radar reflectivities Mid latitude system in the North Atlantic (UK Met Office global forecast model, Jul 7th 26) Alejandro Bodas Salcedo

17 CONCLUSIONS Much progress since the TAR in our interpretation of inter model differences in climate sensitivity estimates. Cloud feedbacks remain the largest source of spread of climate sensitivity and TCR, and low level clouds appear to be the largest contributors. It is not a pious wish to expect some substantial progress in the area of cloud climate feedbacks in the next few years (A Train, GCSS CFMIP collaborations, enhanced scrutiny of models' clouds). The wide participation of modeling groups in CFMIP 2 will help : to improve the assessment of cloud feedbacks and climate sensitivity to correct systematic biases in GCMs such as tropical SST biases, precipitation patterns, tropical waves, etc.

18 CONCLUSIONS Dans le cadre de CFMIP: Réalisation, évaluation et distribution d'un simulateur CALIPSO Réalisation d'un jeu de données d'observations cohérent avec le simulateur A court terme (fin 27-début 28), premières études pilotes avec quelques ( 5) modèles Analyse des observations, évaluation de GCMs (LMDZ+...) (thèse Dimitra Konsta) Combinaison CERES/CALIPSO pour caractériser l'impact radiatif des nuages/distribution verticale Quels sont les caractéristiques des nuages bas, si nombreux? Pour un (ou quelques) type de nuage: comment leurs caractéristiques dépendent-elles des variables d'environnement à grande échelle (subsidence, inversion de temp...)? Analyse en parallèle dans les observations et dans le(s) modèle(s)

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20 Mean cloud properties simulated by low sensitivity and high sensitivity GCMs in the current climate (2th century run) High sensitivity GCMs (8 OAGCMs) Low sensitivity GCMs (7 OAGCMs) Observations

21 But cloud feedbacks are associated with the RESPONSE of clouds to changing climate conditions. Therefore, it is NOT SUFFICIENT to evaluate mean cloud properties. We ALSO have to evaluate the SENSITIVITY of cloud properties to changing environmental conditions.

22 Sensitivity of the SW CRF to interannual SST changes (an example, not an analogue of climate change) 15 AR4 OAGCMs (2th Century simulations) vs Observations subsidence convective regimes High Sensitivity models Low Sensitivity models Observational range [ CRF SW SST ] ω data : ISCCP FD / ERBE rad fluxes Reynolds SST ERA4 / NCEP2 reanalyses (Bony and Dufresne, GRL, 25)

23 CALIPSO observations One Calipso track One vertical «slice» average over 3 profiles of level 1 (1 km) altitude (km) 3 altitude (km) S 4 S 4 N 8 N vertical average on GCM grid S 4 N 8 N 4 Signal Ratio (SR) SR > 3 : cloud 5 8 S altitude (km) 35 SR< 1.2 : clear sky 5