Tropical Water Vapor and Cloud Feedbacks in CCSM3.5: A Preliminary Evaluation D.-Z. Sun and T. Zhang University of Colorado & National Oceanic & Atmospheric Administration In collaboration with R. Neale and P. Rasch National Center for Atmospheric Research

Outline Findings from Sun et al. (2009) Corresponding Results from CCSM3.5 (Zhang et al. 2009) Further analysis/experiments Sun, D.-Z., Y. Yu, and T. Zhang, 2009: Tropical Water Vapor and Cloud Feedbacks in Climate Models: A Further Assessment Using Coupled Simulations. J. Climate, 22, T. Zhang, D.-Z. Sun, R. Neale, and R. Rasch, 2009: An Evaluation of Feedbacks from Deep Convection in CCSM3.5. J. Climate, in preparation.

Methodology Use El Nino as the forcing signal and obtain the feedbacks by examining the response of various energy fluxes to El Nino Warming

The Physical Processes

Results for Models Assessed in Sun et al. (2009)

SST-Precipitation Relationship in Models and Obs.

SST-Cloud albedo Relationship in Models and Observations

Feedbacks in CCSM3.5

SST-Precipitation Relationship in CCSM3.5 and Obs.

SST-Cloud Albedo Relationship in CCSM3.5 and Observations

Summary An overestimate of the positive feedback from water vapor is a common bias An underestimate of the negative feedback from cloud albedo is a common bias A weaker regulatory effect from deep convection is a common bias But NCAR CCSM3.5 stands out in its strong negative cloud albedo feedback.

Some planned work Better understand what are behind the improvements in the feedbacks in CCSM 3.5 Assess whether these improvements are responsible for the improvements in MJO, ENSO, and mean climate in CCSM3.5 Extend the feedback analysis to CCSM4 Sensitivity experiments with CCSM to better understand the physical processes that determines the feedbacks from deep convection and its net regulatory effect

Feedbacks in Observations and Models

The Water Vapor Response

Feedbacks in Climate Models

Regulation of Tropical SST in Observations and Models

Some Other Questions We Are Addressing What are the impacts of these biases in the cloud and water vapor feedbacks on the large-scale tropical ocean- atmosphere interaction? Will these biases in the cloud and water vapor feedbacks affect the model’s projection of the response of the coupled tropical climate system to global warming? What are the implications of the reduced sensitivity of precipitation to SST forcing in the models for the teleconnection between the tropics and the extratropics?

Cold phase for other models IPCC ID∂ Ga ∂T ∂ Cl ∂T ∂Cs ∂T ∂ Da ∂T ∂Fa ∂T ∂ Fs ∂T IAP GAMIL 6.46± ± ± ± ± ±1.38 GFDL AM2p ± ± ± ± ± ±1.70 GFDL AM2p ± ± ± ± ± ±2.32 CCSR MIROC_H 6.54± ± ± ± ± ±1.66 CCSR MIROC_M 6.40± ± ± ± ± ±2.11 MRI CGCM 7.89± ± ± ± ± ±2.79 NASA GISS_ER 5.29± ± ± ± ± ±1.55 NASA NSIPP1 6.69± ± ± ± ± ±1.58 UKMO HadGAM1 6.57± ± ± ± ± ±2.38 MPI ECHAM5 8.05± ± ± ± ± ±2.97 IPSL LMDZ4 7.08± ± ± ± ± ±1.67 CNRM ARPEGE3 7.47± ± ± ± ± ±2.20 INM CM3 8.60± ± ± ± ± ±1.98

Feedbacks in the NCAR models assessed from their AMIP runs

Feedbacks in other models (from AMIP runs)

A Feedback Analysis Using Surface Fluxes: Results from NCAR Models

A Feedback Analysis Using Surface Fluxes: Results from other Models

Why Feedbacks? Feedbacks determine the sensitivity of the climate system to anthropogenic forcing Feedbacks determine the amplitude of the natural variability in the climate system Feedbacks determine the equilibrium state of the climate system--the time-mean climate