Partial Radiative Perturbation Cloud Radiative Forcing Method

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Partial Radiative Perturbation Cloud
Radiative Forcing Method

Advantages Explicitly measures differential
behavior of radiative fluxes in response to
imposed climate change scenarios Isolates each
feedback effect Disadvantages Computationally
expensive Results cannot be compared with
observations
Uses SST perturbations to induce TOA fluxes, and
clear-sky and total-sky fluxes are used to
determine the sensitivity Advantages Straightfor
ward to implement Requires little computational
overhead Definition is consistent with
observable quantities Disadvantage Does not
account for potential differences in T and H2O
vapor distributions between clear and cloudy
atmospheres
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Model and Experimental Design Geophysical Fluid
Dynamics Laboratory (GFDL) Atmospheric Model 2
(AM2) Several different model versions are used
in this study (such that models with different
climate sensitivities can be compared) To induce
climate changes, ?2K SST perturbations are
used Given radiative forcing G, climate system
restores equilibrium by inducing a change in
surface temperature G ?(F-Q) (FOLR,
Qabsorbed SW radiation, both at TOA) Climate
Sensitivity, ? ?TS / ?(F-Q) Cloud
Radiative Forcing, CRF CRF (Fclear F )
(Qclear Q )
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Partial Radiative Perturbations To compute cloud
feedback, input values for profiles of
temperature, water vapor, and surface albedo are
used from the 2K simulation, while cloud amount
and cloud water paths are taken from the 2K
simulation RF-Q T, C, r profiles of
temperature, clouds and H2O vapor ?S surface
albedo Also, feedback parameter for each variable
can be written ?Ts-G/?, Where ??T?C?r?as

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How is this different from the CRF approach?
The prime quantities represent the altered
properties in the perturbed climate, i.e.
TT?T
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Important Radiative Feedbacks
                                                
                                                  
                                                  
              
Total Sky Feedback Clear Sky Feedback
Temperature Water Vapor Surface a Total
-0.04 -0.2 -0.05 -0.29
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Lets now assess the change in net cloud forcing
(due to cloud and non-cloud feedbacks)
Now, assume a scenario with no cloud feedback
(?C0)
This represents the effects of noncloud
feedbacks, and is inherently incorporated into
calculations of cloud feedbacks using the CRF
method
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And, if we further partition ?CRF into LW and
SW components, then ?CRF-LW
(?T-?T(CLR))(?r-?r(CLR)) -0.24
Wm-2K-1 ?CRF-SW (?aS-?aS(CLR)) -0.05
Wm-2K-1 Thus, we could expect the CRF method to
yield LW and SW cloud feedbacks that are about
0.24 Wm-2K-1 and 0.05 Wm-2K-1 smaller
respectively than those calculated by the PRP
method
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We see the right trend at least for the LW
feedback (as the CRF LW feedback are all smaller
than the PRP LW feedback), but not for the SW
feedback The net cloud feedback somehow seems
reasonable, as the CRF method yields cloud
feedbacks that are smaller by 0.3-0.4Wm-2K-1 th
an the PRP method
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Final Thoughts ?Though there is a possible
problem with the SW figure, the main point of the
paper was to highlight subtle, yet important
differences between the PRP and CRF methods for
calculating cloud feedbacks ?Reductions in cloud
forcing can be associated with a positive cloud
feedback (as noncloud effects are potentially
very important) ?It is argued that most of the
models used in the Cess. et al. (1996) would
actually have positive cloud feedback if the PRP
method were utilized (even though ?CRFlt0 in
nearly half of the models)