tropical water vapor and cloud feedbacks in ccsm3.5: a preliminary evaluation
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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Tropical Water Vapor and Cloud Feedbacks in CCSM3.5: A Preliminary Evaluation
D.-Z. Sun and T. ZhangUniversity of Colorado &
National Oceanic & Atmospheric AdministrationIn collaboration with
R. Neale and P. RaschNational Center for Atmospheric Research
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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, 1287-1304.
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.
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Methodology
• Use El Nino as the forcing signal and obtain the feedbacks by examining the response of various energy fluxes to El Nino Warming
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The Physical Processes
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Results for Models Assessed in Sun et al. (2009)
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Results for Models Assessed in Sun et al. (2009)
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SST-Precipitation Relationship in Models and Obs.
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SST-Cloud albedo Relationship in Models and Observations
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Feedbacks in CCSM3.5
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SST-Precipitation Relationship in CCSM3.5 and Obs.
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SST-Cloud Albedo Relationship in CCSM3.5 and Observations
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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.
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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
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Feedbacks in Observations and Models
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Feedbacks in Observations and Models
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The Water Vapor Response
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Feedbacks in Climate Models
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Regulation of Tropical SST in Observations and Models
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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?
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Cold phase for other modelsIPCC ID ∂ Ga
∂T∂ Cl∂T
∂Cs∂T
∂ Da∂T
∂Fa∂T
∂ Fs∂T
IAPGAMIL 6.46±0.35 14.50±1.16 -1.68±0.73 -15.75±1.65 -2.94±1.37 -8.98±1.38
GFDLAM2p10 6.80±0.53 16.31±1.38 -6.51±1.01 -17.51±1.60 -7.71±1.70 -13.68±1.70
GFDLAM2p12 7.65±0.47 18.13±1.68 -6.44±1.43 -17.36±2.20 -5.68±2.32 -11.68±2.32
CCSRMIROC_H 6.54±0.41 13.35±1.32 0.42±0.97 -14.72±1.85 -0.95±1.66 -6.79±1.66
CCSRMIROC_M 6.40±0.35 16.43±1.13 -2.51±0.98 -24.08±2.20 -10.16±2.11 -15.95±2.11
MRICGCM 7.89±0.61 18.01±1.77 0.58±2.17 -28.07±3.00 -9.47±2.73 -15.51±2.79
NASAGISS_ER 5.29±0.56 11.99±1.18 -4.12±0.81 -12.98±1.55 -5.14±1.60 -9.14±1.55
NASANSIPP1 6.69±0.29 13.39±0.84 -3.74±0.83 -14.95±1.51 -5.30±1.57 -11.32±1.58
UKMOHadGAM1 6.57±0.69 14.33±1.74 -5.70±1.33 -18.72±2.10 -10.09±2.37 -15.84±2.38
MPIECHAM5 8.05±0.62 19.67±1.85 -11.82±1.81 -20.53±2.68 -12.68±2.96 -18.44±2.97
IPSLLMDZ4 7.08±0.42 18.71±1.35 -4.72±1.10 -19.70±2.00 -5.70±1.67 -11.71±1.67
CNRMARPEGE3 7.47±0.51 17.30±1.87 -7.88±1.39 -11.27±2.60 -1.85±2.18 -7.05±2.20
INMCM3 8.60±0.66 17.37±1.90 -6.17±1.91 -11.39±1.96 -0.19±1.96 -6.15±1.98
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Feedbacks in the NCAR models assessed from their AMIP runs
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Feedbacks in other models (from AMIP runs)
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A Feedback Analysis Using Surface Fluxes: Results from NCAR Models
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A Feedback Analysis Using Surface Fluxes: Results from other Models
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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