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Cloud Feedback Katinka Nov 7,2011

Cloud Feedback Katinka Nov 7,2011. *Photo taken by Paquita Zuidema in Maldives. Outline:. Feedbacks How to estimate feedbacks On cloud changes: T hermodynamics & Dynamics. Forcing vs. Feedback:.

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Cloud Feedback Katinka Nov 7,2011

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  1. Cloud Feedback Katinka Nov 7,2011 *Photo taken by PaquitaZuidema in Maldives

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

  3. Forcing vs. Feedback: e.g.: CO2is an “external” forcing of climate change, but CO2“internal” variations have occurred naturally in past. In climate models: • Forcing =process external to the system • Feedback = process internal to the system

  4. Feedbacks: G = ext. forcing (e.g. CO2, change in solar constant) G = G(R(T)) RADIATIVE BALANCE AT TOA Transient radiative imbalance at TOA Radiative damping (i.e. feedbacks)

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

  6. equilibrium change in global mean surface temperature (DT) that results from a specified change in radiative forcing (DG) Climate Sensitivity: λ > 0 -> NEGATIVE feedback λ < 0 -> POSITIVE feedback + 4 W m-2 -0.4 W m-2 K-1 -0.3 +3.6 (T+lapse rate) -1.6

  7. Computing Climate Sensitivity (i.e. ΔT) from models: Forward method Inverse method In: ΔR (2xCO2) In: ΔT (+2K/-2K) AGCM (prescribed SST) AOGCM Out: ΔR Out: ΔT (Cess88, Soden 04)

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

  9. 1. CRF (cloud forcing analysis) *Refs: Cess JGR90, Cess JGR96, Bony JC06 Change in radiative impact of clouds Water vapor+sfcalbedo+Temp. (i.e. doesn’t separate feedbacks) 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)

  10. 2. PRP (partial radiative perturbation) *Refs: Soden et al JC08, Soden et al JC04, Wetherald and ManabeJAS 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): Climate Model output Feedback Parameter (for each variable X: w,T,c,a) “offline” Radiative Transfer

  11. Climate Model output Feedback Parameter (for each variable X: w,T,c,a) “offline” Radiative Transfer EXAMPLE (Soden JC04): Use “inverse method”, i.e. +/- 2 K exp. “offline calculations” , i.e. radiative response (only radiation code): CB -> from B = + 2K, all others are from A = – 2K note: need 2 GCM simulations 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.

  12. 3. PRP evolves in Radiative Kernels: Cloud FeedBack is calculated as a residual *Ref: Soden JC08 PRP: with decorrelation (primes) wB -> from B = + 2K, all others from A = – 2K need 2 + 1 GCM simulations R has to be run for each time step Radiative Kernel: -> perturbation at each level: doesn’t perturb correlations. Small compared to wB(t)-wA(t).

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

  14. Cloud FeedBack is calculated as a residual *Ref: Soden JC08 Issue: Uncertainty in experiments with change in radiative forcing (e.g. CO2)… why not use CRF?

  15. R.K. CRF Clouds mask other FB

  16. What are the “masking” effect of clouds we need to correct for? Water vapor Kernel (zonal mean annual mean) Total sky Clear sky W/m2/K/100 mb *Soden JC08

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

  18. *Soden JC08

  19. 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. • CessR.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 RadiativeKernels. 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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