Modélisation climatique globale et observations des nuages et du rayonnement : Quelles interactions ? Sandrine Bony, LMD/IPSL, Paris Quels enjeux dans les prochaines années ? Processus nuageux & études climatiques Implications pour les observations

Modélisation climatique globale et observations des nuages et du rayonnement : Quelles interactions ? Sandrine Bony, LMD/IPSL, Paris Quels enjeux dans les prochaines années ? Processus nuageux & études climatiques Implications pour les observations

A changing paradigm for climate change modeling since the IPCC AR4 Further global warming inevitable over the next few decades from alarm to action Need to inform decisions about climate adaptation and mitigation focus on two different time scales (near-term, long-term) importance of regional climate changes and extreme events The need to improve the reliability of climate models and to assess the robustness of climate projections has never been so high Projections of anthropogenic climate change :

Promotes a standard set of model simulations in order to : evaluate how realistic the models are in simulating the recent past provide projections of future climate change on two time scales understand some of the factors responsible for model differences Two timescales and two sets of science problems Will be assessed by the IPCC AR5 Near-Term : (next 3-4 decades) decadal climate predictability regional climate changes (high resol) climate extremes air quality changes (aerosols, chemistry) Long-Term : (past to 2100 & beyond) evaluation of climate models (recent past, A-Train, paleo) climate sensitivity and physical feedbacks (e.g. clouds) biogeochemical feedbacks (e.g. carbon) ice sheets and sea level CMIP5 : a framework for climate change modeling over the next 5+ years

Clouds & Precipitation Earth's energy balance & Hydrological Cycle General Circulation Processus nuageux : Une composante clé de la modélisation du climat Rôle critique de ces interactions dans : Sensibilité climatique (rétroactions) Réponse de la précipitation en changement climatique Evénements extrêmes Modes de variabilité (MJO, ENSO..) Structure de l'ITCZ Biais du climat moyen (affecte la prévisibilité)

Climate change cloud feedbacks & climate sensitivity : (Dufresne & Bony 2008) multi-model mean T : inter-model differences : cloud feedbacks Low-sensitivity OAGCMs (Bony & Dufresne 2005 ) High-sensitivity OAGCMs boundary-layer clouds IPCC AR4, Bony et al. (2006)'s review : 1. Inter-model differences in cloud radiative feedbacks constitute the primary source of uncertainty in climate sensitivity estimates. 2. The response of marine boundary-layer clouds is the primary contributor to inter-model differences in global cloud feedbacks

Tropospheric cloud-radiative effects and the large-scale atmospheric circulation Cloud-radiative effects strengthen the Hadley-Walker circulation, make the ITCZ more narrow, and affect its large-scale structure Precipitation with/without ACRF 1 st order effect in the simulation of climate

Rôle des processus nuageux dans la variabilité tropicale (e.g. intra- saisonnière) (Zurovac-Jevtic, Bony & Emanuel, 2006) Simulations 2D aqua-planète Importance des interactions nuages - rayonnement et vapeur d'eau – convection dans l'organisation à grande échelle et la variabilité de l'atmosphère tropicale

Cloud Feedback Model Inter-comparison Project Phase-2 CFMIP-2 ( Understanding Evaluation GCM process & sensitivity studies CRMs/LES/SCMs via GCSS A-Train/ISCCP & simulators Assessment of cloud-climate feedbacks + Upcoming european project (FP7) : EUCLIPSE (EU CLoud Intercomparison, Process Study & Evaluation project)

Bridging models and observations to better evaluate cloud and moist processes in models At the large-scale : CFMIP Satellite Simulator (COSP) to facilitate the comparison of model outputs with satellite observations (CALIPSO, CloudSat, PARASOL, ISCCP, MISR) Used in some CMIP5 experiments

COSP oriented satellite data Link available from

Bridging models and observations to better evaluate cloud and moist processes in models At the large-scale : CFMIP Satellite Simulator (COSP) to facilitate the comparison of model outputs with satellite observations (CALIPSO, CloudSat, PARASOL, ISCCP, MISR) Used in some CMIP5 experiments At the process scale : Detailed model outputs at selected locations where field experiments or instrumented sites are available (e.g. ARM, VOCALS, AMMA) Included in CMIP5 outputs

CFMIP/CMIP5 model outputs at selected locations (118 locations, high-frequency, detailed cloud diagnostics) ARM, CEOP, CloudNet instrumented sites GPCI / Tropical West & South East Pacific / AMMA transects Field experiments / GCSS case studies Locations of large inter-model spread of cloud feedbacks (CMIP3) GPCI AMMA VOCALS Oklahoma Barro w TOGA-COARE ASTEX GATE SHEBA SIRTA Chibolton Tibet RICO Darwi n

Simulation of water stable isotopes (Oxygen 18, Deuterium) in LMDZ GCM Observations LMDZiso On-going model evaluation using isotopic data from : in -situ observations (GNIP) + satellite measurements (TES, SCIAMACHY, etc) + ice cores Water isotopes: great tools to evaluate the model representation of : past climate changes (temperature at high latitudes, precip at low latitudes) troposphere-stratosphere exchanges convective processes (e.g. rain reevaporation, precip efficiency) land-surface processes (e.g. partition between evaporation and transpiration) (Thèse de C. Risi, LMD/IPSL)

Vertical distribution of D simulated by the LMDZ4-iso GCM p max = p max = 0.99 tropical mean D -650% o -500% o Besoin d'observations de la composition isotopique de l'atmosphère pour contraindre : - dans la haute troposphère : le transport convectif de vapeur d'eau et la microphysique de la précipitation - dans la moyenne troposphère et dans la couche limite : les processus convectifs (e.g. downdrafts), la réévaporation de la pluie et les échanges sol-atmosphère (e.g. evapotranspiration) (C. Risi & S. Bony, LMD)

Conclusion L'étude du rôle des processus nuageux dans le climat, et l'évaluation de ces processus dans les modèles sont primordiales pour la modélisation du climat à grande échelle. Une révolution est en marche avec l'arrivée des nouvelles observations (e.g. A-Train). Dans l'avenir, il importe : - de maintenir la surveillance sur des échelles décennales d'un grand nombre de variables climatiques (e.g. bilan radiatif, nuages, vapeur d'eau) - de faciliter l'accès des modélisateurs aux différents jeux de données : (grillage, format, programmes de lecture, documentation, assessments, etc) - d'interagir autour des besoins spécifiques des modélisateurs (e.g. simulateurs d'observations, besoin d'observations particulières)