Leaked AR5 SOD Challenges IPCC Scientists to Stop Cherry-Picking Data 1


Climate sensitivity in the AR5 SOD

Original Article Here – Posted on December 19, 2012

by Judith Curry

By far the most important debate about climate change is taking place among scientists, on the issue of climate sensitivity: How much warming will a doubling of atmospheric carbon dioxide actually produce?  – Matt Ridley

Chapter 12 of the SOD includes the discussion on sensitivity (download chapter here).  The main summary conclusion on equilibrium climate sensitivity is stated as follows:

Equilibrium climate sensitivity is likely in the range 2°C–4.5°C, and very likely above 1.5°C. The most likely value is near 3°C. Equilibrium climate sensitivity greater than about 6°C–7°C is very unlikely.

Compare this statement with the corresponding statement in the AR4 (SPM, p 12):

It is likely to be in the range 2°C to 4.5°C with a best estimate of about 3°C, and is very unlikely to be less than 1.5°C. Values substantially higher than 4.5°C cannot be excluded, but agreement of models with observations is not as good for those values.

Essentially no change, other than placing an upper limit.  In the AR5 SOD, refer to Figure 1 Box 12.2 on p 153).  This figure includes substantially more sensitivity estimates than did the AR4.  A quick eyeball of the figure shows substantial density below 2C and even below 1.5C.  What is the rationale for ‘very unlikely’ below 1.5C?  It seems to be tied to the GCM climate model results (second panel from the top on the figure).  Hence it seems that the large coupled global climate model simulations are given the predominant weighting in the assessment. The problems with this strategy were discussed in my Uncertainty Monster paper.

Matt Ridley and Nic Lewis

Matt Ridley has an article in the WSJ entitled Cooling Down the Fears of Climate Change.  Excerpts:

Mr. Lewis tells me that the latest observational estimates of the effect of aerosols (such as sulfurous particles from coal smoke) find that they have much less cooling effect than thought when the last IPCC report was written. The rate at which the ocean is absorbing greenhouse-gas-induced warming is also now known to be fairly modest. In other words, the two excuses used to explain away the slow, mild warming we have actually experienced—culminating in a standstill in which global temperatures are no higher than they were 16 years ago—no longer work.

In short: We can now estimate, based on observations, how sensitive the temperature is to carbon dioxide. We do not need to rely heavily on unproven models. Comparing the trend in global temperature over the past 100-150 years with the change in “radiative forcing” (heating or cooling power) from carbon dioxide, aerosols and other sources, minus ocean heat uptake, can now give a good estimate of climate sensitivity.

The conclusion—taking the best observational estimates of the change in decadal-average global temperature between 1871-80 and 2002-11, and of the corresponding changes in forcing and ocean heat uptake—is this: A doubling of CO2 will lead to a warming of 1.6°-1.7°C (2.9°-3.1°F).

This is much lower than the IPCC’s current best estimate, 3°C (5.4°F).

 Nic Lewis has provided an extensive post at Bishop Hill outlining his arguments, entitled Why doesn’t the AR5 SOD’s climate sensitivity range reflect its new aerosol estimates?   Its an extensive post, here is the key point:

Previous IPCC reports have just given estimates for radiative forcing (RF). Although in a simple world this could be a good measure of the effective warming (or cooling) influence of every type of forcing, some forcings have different efficacies from others. In AR5, this has been formalised into a measure, adjusted forcing (AF), intended better to reflect the total effect of each type of forcing. 

The main difference between the AF and RF measures relates to aerosols.  Table 8.7 of the SOD summarises the AR5 RF and AF best estimates and uncertainty ranges for each forcing agent, along with RF estimates from previous IPCC reports. The terminology has changed, with direct aerosol forcing renamed aerosol-radiation interactions (ari) and the cloud albedo (indirect) effect now known as aerosol-cloud interactions (aci).

Table 8.7 shows that the best estimate for total aerosol RF (RFari+aci) has fallen from −1.2 W/m² to −0.7 W/m² since AR4, largely due to a reduction in RFaci, the uncertainty band for which has also been hugely reduced. It gives a higher figure, −0.9 W/m², for AFari+aci. However, −0.9 W/m² is not what the observations indicate: it is a composite of observational, GCM-simulation/aerosol model derived, and inverse estimates. The inverse estimates – where aerosol forcing is derived from its effects on observables such as surface temperatures and OHU – are a mixed bag, but almost all the good studies give a best estimate for AFari+aci well below −0.9 W/m²: see Appendix 1 for a detailed analysis.

To find the IPCC’s best observational (satellite-based) estimate for AFari+aci, one turns to Section 7.5.3 of the SOD, where it is given as −0.73 W/m² with a standard deviation of 0.30 W/m². That is actually the same as the Table 8.7 estimate for RFari+aci, except for the uncertainty range being higher. 

But one expert on the satellite observations, Prof. Graeme Stephens, has stated that AFaci is at most ‑0.1 W/m², not ‑0.33 W/m² as implied by the IPCC’s best observationally-based estimates: see here and slide 7 of the linked GEWEX presentation. 

The main implication is this.  If only GHG forcing is used, without aerosols, the surface temperature in the last decade or so is about 0.3-0.4C higher than observations; adding in aerosols has a cooling effect of about 0.3-0.4C (and so cancelling out a portion of the GHG warming), providing a fairly good match between the climate model simulations and the observations. If the aerosol effect is too large, then it is inferred that the model response to GHG forcing is also too large.

The climate models have an aerosol forcing that is too large.  The aci effect (associated with clouds) is either specified in the model as forcing, or the model allows the aerosols to interact directly with the cloud microphysical processes.  There are problems with both methods.

Here is a quick summary of the issue:  The effects of aerosols on clouds consist of three linked elements.  Increased numbers of aerosols provide additional locations for droplet nucleation and, all else being equal, result in clouds with more and smaller droplets hence being more reflective to solar radiation (a cooling effect).  The increased number of smaller droplets is hypothesized to hinder the formation of rain because more smaller droplets do not collide and coalesce into precipitation as efficiently.  Suppression of precipitation leads to longer lived clouds that reflect solar radiation back to space.  While this sequence aerosol-cloud effects is easily understood and widely accepted, in many cloud systems the cloud dynamics has a dominant effect over aerosol/microphysical effects, and there is scant observational evidence for a large value of aci in real clouds. Climate models that include these aerosol-cloud interactions fail to include a number of buffering responses, such as rainfall scavenging of the aerosols and compensating dynamical effects (which would reduce the magnitude of the aci cooling effect).

So, recent research is narrowing the range of uncertainty of the aci, and overall reducing the magnitude of the aci effect.  But most climate models still include the inappropriately large values of aci.  It is difficult to then avoid the conclusion that the model-based sensitivity analyses (and observationally based analyses that use large values of aci) produce GHG equilibrium sensitivity values that are too large.

Nic Lewis in the post at BishopHill does a very nice empirically based sensitivity analysis following the general methodology of the Gregory et al (2002) heat balance change derived value of the equilibrium climate sensitivity, determining a value of ECS of 1.6-1.7C.  While there are some necessary assumptions in empirically based sensitivity analyses that I am not entirely comfortable with, the qualitative conclusion made by Nic Lewis seems robust:  lower values of ECS seem justified in view the reduce values of aci.

Over juiced cloud and water vapor feedback (?)

While the direct GHG forcing is well understood (the skydragons haven’t come up with anything convincing),  I suspect that the culprits are the water vapor feedback and/or the cloud feedback.  The atmospheric dynamical core treats water vapor and moist thermodynamics in the manner of numerical weather prediction models, where small approximations don’t make much of a difference.  However at longer timescales these errors can accumulate in a systematic direction.

While I need to get around to actually publishing this, here in a nutshell is what I think is wrong with climate model simulations of water vapor feedback.  There are three simplifications  that concern me (note, if better documentation on these issues existed it would be easier to document these):

1.  Oversimplified moist thermodynamics.  A section from my book Thermodynamics of Atmospheres and Oceans on conserved moist thermodynamic variables is posted [here].   As far as I can tell, climate models use the equivalent potential temperature, rather than the entropy potential temperature.  Read the section from my book to see the approximations that are being made here.  While it is difficult to reason through what any change to the equations would actually result in terms of radiation balance given all the nonlinearities, use of entropy potential temperature would tend to cooler upper troposphere and altered water vapor and clouds.

2.  Saturation vapor pressure over ice  at very cold temperatures.  At the cold temperatures of the tropical upper troposphere and stratosphere, it is not clear that correct values of the saturation vapor pressure are being used.  This would influence both the water vapor and cloud water content in the atmosphere.

3.  Atmospheric continuity equation ignores changes in water vapor content.  Note:  this ties into the argument that Anastassia Makarieva has been making.  I don’t know how to reason through in a simple way the implications of this for water vapor feedback since this would tie into the atmospheric dynamics in a fundamental way (both large scale and convective scale).

A post at WUWT by Forest Mims  points out the SOD failed to cite the von der Haar et al. (2012) paper that finds no global trend in water vapor path from satellite data

Thomas H. Vonder Haar, Janice L. Bytheway and John M. Forsythe. Weather and climate analyses using improved global water vapor observations. GEOPHYSICAL RESEARCH LETTERS, VOL. 39, L15802, 6 PP., 2012.doi:10.1029/2012GL052094.

One of the more interesting chapters in the AR5 is chapter 7 on Clouds and Aerosols.  Their main conclusion re cloud forcing:

The net radiative feedback due to all cloud types is likely (>66% chance) positive, although a negative feedback (damping global climate changes) is still possible. We assign a very likely range of −0.2 to 1.4 W m–2 K–1 for the cloud feedback parameter. This conclusion is reached by considering a plausible range for unknown contributions by processes yet to be accounted for, in addition to those occurring in current climate models. The cloud feedback remains the most uncertain radiative feedback in climate models. Observations alone do not currently provide a robust, direct constraint, but multiple lines of evidence now indicate positive feedback contributions from changes in both the height of high clouds and the horizontal distribution of clouds. Additional feedback from low cloud amount is also positive in most climate models, but that result is not well understood, nor effectively constrained by observations, so confidence in it is low.

The key point is this.  The cloud forcing values are derived from climate models; we have already seen that climate models have some fundamental problems in how clouds are treated (e.g. aerosol-cloud interactions, moist thermodynamics).  So, climate model derived values of cloud forcing should be taken with a grain of salt.  Empirically based determinations of cloud forcing are needed.  At AGU, I spoke with a scientist that has completed such a study, with the paper almost ready for submission.  Punchline:  negative cloud feedback.

JC summary:  The leak of the SOD was a good thing; the IPCC still has the opportunity to do a much better job, and the wider discussion in the blogosphere and even the mainstream media places pressure on the IPCC authors to consider these issues; they can’t sweep them under the rug as in previous reports.

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  1. Pingback: Russians Freezing to Death during Global Warming Period Read more: http://politicalo - ALIPAC

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