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Cloud Feedback Katinka Nov 7,2011 *Photo taken by Paquita Zuidema in Maldives

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Feedbacks How to estimate feedbacks On cloud changes: Thermodynamics & Dynamics Outline:

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Forcing vs. Feedback: Forcing = process external to the system Feedback = process internal to the system e.g.: CO 2 is an external forcing of climate change, but CO 2 internal variations have occurred naturally in past. In climate models:

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RADIATIVE BALANCE AT TOA G = ext. forcing (e.g. CO 2, change in solar constant) G = G(R(T)) Transient radiative imbalance at TOA Radiative damping (i.e. feedbacks) Feedbacks:

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When the system returns to equilibrium: Climate sensitivity parameter, i.e. FEEDBACKS(regulate radiative damping) Climate Sensitivity: Transient radiative imbalance at TOA

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Climate Sensitivity: equilibrium change in global mean surface temperature ( T) that results from a specified change in radiative forcing ( G) +3.6 (T+lapse rate) W m -2 K W m -2 λ > 0 -> NEGATIVE feedback λ POSITIVE feedback

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Computing Climate Sensitivity (i.e. ΔT) from models: Inverse method Forward method In: ΔT (+2K/-2K) AGCM (prescribed SST) Out: ΔR In: ΔR (2xCO 2 ) AOGCM Out: ΔT (Cess 88, Soden 04)

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1.CRF (cloud forcing analysis) 2.PRP (partial radiative perturbation) 1.Radiative Kernels How to estimate feedbacks:

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1. CRF (cloud forcing analysis) *Refs: Cess JGR90, Cess JGR96, Bony JC06 Water vapor+sfc albedo+Temp. (i.e. doesnt separate feedbacks) Change in radiative impact of clouds Major criticism: CRF can be negative, but cloud feedback positive, best e.g: *Bony JC06 Big upside: can be directly compared against observations (e.g. Bony GRL05)

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2. PRP (partial radiative perturbation) *Refs: Soden et al JC08, Soden et al JC04, Wetherald and Manabe JAS 88 F=OLR Q=SW μ=geographical position, time of the day, time of the year Net TOA FLUX The total perturbation can be written in terms of the PRP (partial radiative perturbations): Feedback Parameter (for each variable X: w,T,c,a) offline Radiative Transfer Climate Model output

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offline calculations, i.e. radiative response (only radiation code): The FB of each variable is estimated by changing only that variable in the radiation model and computing the resulting net perturbation at TOA -> all R(..) involve an offline radiative transfer simulation. Feedback Parameter (for each variable X: w,T,c,a) offline Radiative Transfer Climate Model output EXAMPLE (Soden JC04): Use inverse method, i.e. +/- 2 K exp. C B -> from B = + 2K, all others are from A = – 2K note: need 2 GCM simulations

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Cloud FeedBack is calculated as a residual *Ref: Soden JC08 w B -> from B = + 2K, all others from A = – 2K 1.need GCM simulations 2.R has to be run for each time step PRP: with decorrelation (primes) Radiative Kernel: 3. PRP evolves in Radiative Kernels: -> perturbation at each level: doesnt perturb correlations. Small compared to w B (t)-w A (t).

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Water Vapor Feedback using Kernels Water Vapor Kernel (from RT code)Water Vapor Response to 2xCO2 (from GCM) x Water Vapor Feedback = Kernel x Response = *B.Soden

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Cloud FeedBack is calculated as a residual *Ref: Soden JC08 Issue: Uncertainty in experiments with change in radiative forcing (e.g. CO 2 )… why not use CRF?

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R.K. CRF Clouds mask other FB

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W/m 2 /K/100 mb Total sky Clear sky Water vapor Kernel (zonal mean annual mean) What are the masking effect of clouds we need to correct for? *Soden JC08

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Alternative to CF: Adjusted CRF *Ref: Soden JC08 dR at TOA can be written in two ways: CLOUD FEEDBACK: To be included in exp in which there are forcing changes Corrections to masking effects of clouds on other FB

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*Soden JC08

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References: Cess R.D. and G.L. Potter, 1988: A Methodology for Understanding and Intercomparing Atmospheric Climate Feedback Processes in General Circulation Models. J.Gehopys.Res. Cess R.D. et al., 1990: Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. J. Geophys. Res. Cess R.D. et al., 1996: Cloud feedback in atmospheric general circulation models: An update. J.Geophys.Res. Soden B. et al. 2004: On the Use of Cloud Forcing to Estimate Cloud Feedback. Letters. J.Clim. Soden B. and I. Held, 2006: An Assessment of Climate Feedbacks in Coupled Ocean– Atmosphere Models. J.Clim. Soden B. et al. 2008: Quantifying Climate Feedbacks Using Radiative Kernels. J.Clim. Bony S. et al.,2006: How Well Do We Understand and Evaluate Climate Change Feedback Processes? Review article. J.Clim.

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