December 22, 2012

Climate Sensitivity, the Wall Street Journal, and Media Matters

Filed under: Uncategorized — troyca @ 8:48 am

I wanted to comment a bit on the recent Wall Street Journal article by Matt Ridley ("The scientists at the IPCC next year have to choose whether they will admit…that the observational evidence now points toward lukewarm temperature change with no net harm.") and the response from Media Matters ("Ridley is just plain wrong to claim that future warming will be mild or even helpful.") (HT BH) . The primary reason is that it deals with climate sensitivity and the energy balance equation we’ve now seen over and over again:

ΔN = ΔF – λ (ΔT)

Where F is the top of atmosphere (TOA) radiative forcing, N is the TOA flux imbalance (which can be derived from Ocean Heat Content, as I did in my own recent estimate), T is the surface temperature, and λ is the strength of radiative restoration.  We can thus solve for λ, and (assuming it is constant) determine the Equilibrium Climate Sensitivity (ECS) using 3.7 W/m^2 / λ.  (3.7 W/m^2 being the forcing associated with a doubling of CO2).

1. What’s to like about the WSJ article?

Essentially, the sensitivity estimate is straight-forward and should be seriously considered (I’ve used a similar approach and got similar results).  Nic Lewis goes into more detail here.  It is his rework of the Gregory et al (2002) result, using more recent OHC estimates and aerosol forcings from AR5, that Ridley relies on to note that the 1.6-1.7 K sensitivity is below the likely value in the AR5 report.  So, let’s walk through how these numbers shifted to give the lower sensitivity between AR4 and the 2nd draft of AR5, and see to what extent the Media Matters vs. WSJ pieces have merit.

These numbers are rough, as I’m reading them off the IPCC figures, but essentially for AR4-era estimates you have (in changes from 1880):

Temperature: 0.75 K
TOA Imbalance: 0.85 W/m^2 (from Hansen et al., 2005 modelling effort)
Anthropegenic Aerosol Forcing: -1.2 W/m^2
Other Forcings: 2.9 W/m^2

To solve for λ, we would have used Net forcing (2.9-1.2 W/m^2) minus TOA imbalance (0.85 W/m^2) divided by T (0.75 K) to yield a λ of 1.13 W/m^2/K.  The estimated ECS from AR4 is thus 3.7 W/m^2 / 1.13 W/m^2/K = 3.3 K…so at least this approach would have yielded an estimate in line with IPCC AR4 estimates using the older numbers (one might have pointed out at the time that the strong aerosol forcing was not supported by the NH/SH warming ratio, or that using the energy balance models on volcanic eruptions yielded lower sensitivities, or that the OHC data did not support an 0.85 W/m^2 estimate for TOA imbalance, however).

Flash forward to the AR5 2nd draft, and we have updated best estimates:

Temperature: 0.75 K (no change here)
TOA Imbalance: 0.5 W/m^2 (from Loeb et al., 2012, down 0.35 W/m^2)
Anthropogenic Aerosol Forcing: -0.7 W/m^2 (Difference of ~ 0.5 W/m^2 from before!)
Other Forcings: 3.2 W/m^2 (increase in CO2 forcing)

So, essentially, not only have we decreased our estimate for current TOA imbalance, but we have also greatly increased our net forcing, primarily as a result of a lessening of the cooling effect by aerosols.  If we only updated our TOA imbalance, the resulting estimate would be an ECS of ~ 2.3 K.  Updating both that AND the net forcing yields an ECS of around 1.4 K (the difference from Nic’s value comes because of slight differences in the adjusted forcing and because he is using a more robust method which accounts for uncertainties in the individual variables).

I want to stress that Nic Lewis’s approach is nothing new here (as he acknowledges), nor is it out of the mainstream.  I think the method is conceptually sound and this estimate in this WSJ should be taken seriously.  While there are reasons why this may not be the end-all be-all estimate (I get into that below), I do think it is indeed somewhat awkward for it to fall outside the range of "likely" estimates (2-4.5 K) from the IPCC report (although this may just be a consequence of how it is put together).

Moreover, the fact that the AOGCMs generally do a poor job of simulating the magnitude of λ (which is directly tied to the sensitivity), as I go over in a slightly different period, should raise serious questions about their ability to produce accurate sensitivity estimates on longer timescales.  This is because while temperature may be greatly affected by things like the rate of ocean diffusion and ENSO variations, making the link between temperature on shorter scales and climate sensitivity less obvious, the understatement of λ typically indicates an overstatement of ECS (with caveats that I will note in the bottom section).

2. What’s to like about the Media Matters article?

To me, not a lot.  There are reasons to be skeptical of the sensitivity estimate in Ridley article, but MM fails to focus on these, instead quoting scientists and making points that do not actually engage the issues:

First, Ridley wrongly argues that three variables factored into current climate models are overstated (and thus that climate models are "unproven"). In fact, experts agree that the impacts of each variable that Ridley cites — the cooling effect of aerosols (or particles in the air); the rate of heat absorption by the world’s oceans; and the role of water vapor in amplifying climate change — are unambiguous.

First, given the large uncertainty in the aerosol forcing, I’m not sure I would consider their impact "unambiguous".  MM quotes John Abraham, who notes that "it is very clear [aerosols] have a cooling impact", and Kaufman, who notes that "I know of no evidence that would suggest that the temperature effect of sulfur emissions are small".  However, Lewis (and Ridley) is not claiming that aerosols do not have a substantial cooling effect…in fact they are noting that recent best estimates indicate that the cooling effect is likely closer to -0.7 (-0.9 adjusted) W/m^2 rather than the -1.2 (-1.4 adjusted) W/m^2 estimate of before.  Neither Kaufman nor Abraham’s statement would seem to contradict the value used.

Second, MM notes, "As this chart from Skeptical Science shows, the rate of ocean heat absorption remains high".  Unfortunately, it again fails to quantify this.  In fact, the chart referenced is using the same Ocean Heat Content data used by Lewis!  If you take the slope of the recent decade on the chart to convert from OHC to TOA imbalance, and convert the units appropriately, you get the same TOA imbalance used by Lewis…well down from the previously modeled assumption of 0.85 W/m^2.  So again, nobody is claiming here that the ocean isn’t heating up, but rather that this rate is ~0.5 W/m^2 from observations rather than the 0.85 W/m^2 from modeling efforts.

Neither of these objections are of any value in assessing the sensitivity estimate.  The MM articles does, I think, correctly point out that Ridley makes some claims about water vapor that are contradicted by most observational evidence.  I get into that below.

3. What’s to be skeptical of in the WSJ article?

For one, I find the claim that "’We don’t even know the sign’ of water vapor’s effect" highly dubious.  Unlike cloud feedbacks, which may behave very differently in response to ENSO variations versus longer timescales, the water vapor feedback appears to be certainly positive across all timescales, as one would expect from the physics.  Even if the "water vapor effect" is extended to include cloud feedbacks, the net effect of these two is almost certainly positive.  Consider even those lower estimates of 1.2K to 1.7K.  This ranges from a net neutral feedback to a slightly net positive feedback relative to the Planck response…since the magnitude of the negative lapse rate feedback is likely greater than the magnitude of the positive albedo feedback, the combined water vapor + cloud must still be slightly positive to get to that 1.2K level.

Now, one may argue that the water vapor feedback hasn’t been as positive lately as in models, largely due to the warming distribution, as I’ve done previously on this blog.  However, such a distribution is almost completely counter-acted by a lessening of the negative lapse rate feedback with the same distribution, as I also went over, due to the close relationship between the two.  In fact, the combined water vapor + lapse rate feedback is very similar across all AOGCMs (in stark contrast to the cloud feedback).

Second, the claim that < 2 C ECS means that future warming won’t be dangerous, or the lack of discussion of paleo estimates (seemingly implying they are unimportant), both made me raise my eyebrows.  I am not particularly familiar with the literature in these areas, but I would definitely classify those more as opinions rather than well-established science.

Third, the real reason to be wary of the sensitivity estimate is the possibility that λ varies across timescales.  This is related to why I think we need to consider paleo as well, and is the typical reason one should be wary of making certain estimates for ECS while in transient states.  This is NOT to be confused with saying that the Gregory et al. (2002) method used by Lewis is calculating the transient sensitivity (it is indeed calculating ECS), or that the method does not take into account heating "in the pipeline" (it does indeed via the ocean uptake term).  Rather, it is possible that the strength of feedbacks themselves change between (for example) 100-year timescales and the hundreds to thousands of years it takes to equilibriate.

This has been discussed several times on this blog and elsewhere (I’ll just refer to here for now for some background).  However, suffice to say, it is not clear whether the "effective sensitivity" response (λ_eff) calculated from transient states departs significantly from the "equilibrium sensitivity" response (λ_eq), or even whether this effect means using transient states would lead to underestimates or overestimates of climate sensitivity.

The following table highlights the relationship between T_eff and T_eq in the CMIP3 models, calculated with numbers taken from Winton et al (2010) and Soden and Held (2006):

 Model T_eff/T_eq GFDL CM2.0 1.02 GFDL CM2.1 0.75 GISS ER 0.92 IPSL 1.29 MIROC MEDRES 0.85 MRI 0.72 MPI ECHAM5 1.34 NCAR CCSM3 0.90 NCAR PCM1 1.12 UKMO HADCM3 1.03 Mean 1.00 SD .21

As you can see, the models average out to about 1, meaning that T_eff may very well be a good estimate for T_eq.  On the other hands, you see several models where they two values diverge, so on top of all the other uncertainty in this method, we are also uncertain about this factor.  I don’t know of any source for the CMIP5 model relationships since running them to equilibrium is not part of the experiment, and in fact Andrews et al. (2012) uses the "effective sensitivity"/transient state method to diagnose ECS for the CMIP5 models (albeit using an instantaneous quadrupling of CO2).  Of course, if this T_eff is much smaller in observations than in AOGCMs, it is worth questioning whether these AOGCMs actually provide any solid insight into the T_eq/T_eff factor at all.