“…ERBE data appear to demonstrate a climate sensitivity of about 0.5°C which is easily distinguished from sensitivities given by models.”
On the determination of climate feedbacks from ERBE data
Richard S. Lindzen and Yong-Sang Choi
Revised on July 14, 2009 for publication to Geophysical Research Letters
Climate feedbacks are estimated from fluctuations in the outgoing radiation budget from the latest version of Earth Radiation Budget Experiment (ERBE) nonscanner data. It appears, for the entire tropics, the observed outgoing radiation fluxes increase with the increase in sea surface temperatures (SSTs). The observed behavior of radiation fluxes implies negative feedback processes associated with relatively low climate sensitivity. This is the opposite of the behavior of 11 atmospheric models forced by the same SSTs. Therefore, the models display much higher climate sensitivity than is inferred from ERBE, though it is difficult to pin down such high sensitivities with any precision. Results also show, the feedback in ERBE is mostly from shortwave radiation while the feedback in the models is mostly from longwave radiation. Although such a test does not distinguish the mechanisms, this is important since the inconsistency of climate feedbacks constitutes a very fundamental problem in climate prediction.
The purpose of the present note is to inquire whether observations of the earth’s radiation imbalance can be used to infer feedbacks and climate sensitivity. Such an approach has, as we will see, some difficulties, but it appears that they can be overcome. This is important since most current estimates of climate sensitivity are based on global climate model (GCM) results, and these obviously need observational testing.
To see what one particular difficulty is, consider the following conceptual situation:
We instantaneously double CO2. This will cause the characteristic emission level to rise to a colder level with an associated diminution of outgoing longwave radiation (OLR). The resulting radiative imbalance is what is generally referred to as radiative forcing. However, the resulting warming will eventually eliminate the radiative imbalance as the system approaches equilibrium. The actual amount of warming associated with
equilibration as well as the response time will depend on the climate feedbacks in the system. These feedbacks arise from the dependence of radiatively important substances like water vapor (which is a powerful greenhouse gas) and clouds (which are important for both infrared and visible radiation) on the temperature. If the feedbacks are positive, then both the equilibrium warming and the response time will increase; if they are negative, both will decrease. Simple calculations as well as GCM results suggest response times on the order of decades for positive feedbacks and years or less for negative feedbacks [Lindzen and Giannitsis, 1998, and references therein].
The main point of this example is to illustrate that the climate system tends to eliminate radiative imbalances with characteristic response times.
Now, in 2002–2004 several papers noted that there was interdecadal change in the top-of-atmosphere (TOA) radiative balance associated with a warming between the 1980’s and 1990’s [Chen et al., 2002; Wang et al., 2002; Wielicki et al., 2002a, b; Cess and Udelhofen, 2003; Hatzidimitriou et al., 2004; Lin et al., 2004]. Chou and Lindzen  inferred from the interdecadal changes in OLR and temperature that there was a strong negative feedback. However, this result was internally inconsistent since the
persistence of the imbalance over a decade implied a positive feedback. A subsequent correction to the satellite data eliminated much of the decadal variation in the radiative balance [Wong et al., 2006].
However, it also made clear that one could not readily use decadal variability in surface temperature to infer feedbacks from ERBE data. Rather one needs to look at temperature variations that are long compared to the time scales associated with the feedback processes, but short compared to the response time over which the system equilibrates. This is also important so as to unambiguously observe changes in the radiative budget that are responses to fluctuations in SST as opposed to changes in SST resulting from changes in the radiative budget; the latter will occur on the response time of the system. The primary feedbacks involving water vapor and clouds occur on time scales of days [Lindzen et al., 2001; Rodwell and Palmer, 2007], while response times for relatively strong negative feedbacks remain on the order of a year [Lindzen and Giannitsis, 1998, and references therein]. That said, it is evident that, because the system attempts to restore equilibrium, there will be a tendency to underestimate negative feedbacks relative to positive feedbacks that are associated with longer response times.
In Figure 3, we show 3 panels. We see that ERBE and model results differ
substantially. In panels a and b, we evaluate Equation (3) using ΔFlux for only OLR and only SWR. The curves are for the condition assuming no SW feedback and assuming no LW feedback in panels a and b, respectively. In panel a, model results fall on the curve given by Equation (3), because the model average of SW feedbacks is almost zero. In panel b, models with smaller LW feedbacks are closer to the curve for no LW feedback; the model results would lie on the curve assuming positive LW feedback. When in panel c we consider the total flux (i.e., LW + SW), model results do lie on the theoretically expected curve.
Looking at Figure 3, we note several important features:
1) The models display much higher climate sensitivity than is inferred from ERBE.
2) The (negative) feedback in ERBE is mostly from SW while the (positive) feedback in
the models is mostly from OLR.
3) The theoretical relation between ΔF/ΔT and sensitivity is very flat for sensitivities
greater than 2°C. Thus, the data does not readily pin down such sensitivities. This was
the basis for the assertion by Roe and Baker  that determination of climate
sensitivity was almost impossible [Allen and Frame, 2007]. However, this assertion
assumes a large positive feedback.
Indeed, Fig. 3c suggests that models should have a range of sensitivities extending from about 1.5°C to infinite sensitivity (rather than 5°C as commonly asserted), given the presence of spurious positive feedback. However, response time increases with increasing sensitivity [Lindzen and Giannitsis,1998], and models were probably not run sufficiently long to realize their full sensitivity. For sensitivities less than 2°C, the data readily distinguish different sensitivities, and ERBE data appear to demonstrate a climate sensitivity of about 0.5°C which is easily distinguished from sensitivities given by models.
Note that while TOA flux data from ERBE are sufficient to determine feedback factors, this data do not specifically identify mechanisms. Thus, the small OLR feedback from ERBE might represent the absence of any OLR feedback; it might also result from the cancellation of a possible positive water vapor feedback due to increased water vapor
in the upper troposphere [Soden et al., 2005] and a possible negative iris cloud feedback involving reduced upper level cirrus clouds [Lindzen et al., 2001]. With respect to SW feedbacks, it is currently claimed that model SW feedbacks are largely associated with the behavior of low level clouds [Bony et al., 2006, and references therein]. Whether this is the case in nature cannot be determined from ERBE TOA observations.
However,more recent data from CALIOP do offer height resolution, and we are currently studying such data to resolve the issue of what, in fact, is determining SW feedbacks. Finally, it should be noted that our analysis has only considered the tropics. Following Lindzen et al. , allowing for sharing this tropical feedback with neutral higher latitudes could reduce the negative feedback factor by about a factor of two. This would lead to an
equilibrium sensitivity that is 2/3 rather than 1/2 of the non-feedback value. This, of course, is still a small sensitivity.
see the full paper here (PDF)
h/t to Leif Svalgaard