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« Reactions to low climate sensitivity | Main | Cox and Ince on the scientific method »

Why doesn't the AR5 SOD's climate sensitivity range reflect its new aerosol estimates?

This is a guest post by Nic Lewis. Please note that comments will be tightly moderated for tone and relevance.

There has been much discussion on climate blogs of the leaked IPCC AR5 Working Group 1 Second Order Draft (SOD). Now that the SOD is freely available, I can refer to the contents of the leaked documents without breaching confidentiality restrictions.

I consider the most significant – but largely overlooked – revelation to be the substantial reduction since AR4 in estimates of aerosol forcing and uncertainty therein. This reduction has major implications for equilibrium climate sensitivity (ECS). ECS can be estimated using a heat balance approach – comparing the change in global temperature between two periods with the corresponding change in forcing, net of the change in global radiative imbalance. That imbalance is very largely represented by ocean heat uptake (OHU).

Since the time of AR4, neither global mean temperature nor OHU have increased, while the IPCC's own estimate of the post-1750 change in forcing net of OHU has increased by over 60%. In these circumstances, it is extraordinary that the IPCC can leave its central estimate and 'likely' range for ECS unchanged.

I focussed on this point in my review comments on the SOD. I showed that using the best observational estimates of forcing given in the SOD, and the most recent observational OHU estimates, a heat balance approach estimates ECS to be 1.6–1.7°C – well below the 'likely' range of 2–4.5°C that the SOD claims (in Section is supported by the observational evidence, and little more than half the best estimate of circa 3°C it gives.

The fact that ECS, as derived using the new aerosol forcing estimates and a heat balance approach, appears to be far lower than claimed in the SOD is highlighted in an article by Matt Ridley in the Wall Street Journal, which uses my calculations. There was not space in that article to go into the details – including the key point that the derived ECS estimate is very well constrained – so I am doing so here.

How does the IPCC arrive at its estimated range for climate sensitivity?

Methods used to estimate ECS range from:

(i) those based wholly on simulations by complex climate models (GCMs), the characteristics of which are only very loosely constrained by climate observations, through

(ii) those using simpler climate models whose key parameters are intended to be constrained as tightly as possible by observations, to

(iii) those that rely wholly or largely on direct observational data.

The IPCC has placed a huge emphasis on GCM simulations, and the ECS range exhibited by GCMs has played a major role in arriving at the IPCC's 2–4.5°C 'likely' range for ECS. I consider that little credence should be given to estimates for ECS derived from GCM simulations, for various reasons, not least because there can be no assurance that any of the GCMs adequately reflect all key climate processes. Indeed, since in general GCMs significantly overestimate aerosol forcing compared with observations, they need to embody a high climate sensitivity or they would underestimate historical warming and be consigned to the scrapheap. Observations, not highly complex and unverifiable models, should be used to estimate the key properties of the climate system.

Most observationally-constrained studies use instrumental data, for good reason. Reliance cannot be placed on paleoclimate proxy-based estimates of ECS – the AR4 WG1 report concluded (Box 10.2) that uncertainties in Last Glacial Maximum studies are just too great, and the only probability density function (PDF) for ECS it gave from a last millennium proxy-based study contained little information.

Because it has historically been difficult to estimate ECS purely from instrumental observations, a standard estimation method is to compare observations of key observable climate variables, such as zonal temperatures and OHU, with simulations of their evolution by a relatively simple climate model with adjustable parameters that represent, or are calibrated to, ECS and other key climate system properties. A number of such ‘inverse’ studies, of varying quality, have been performed; I refer later to various of these. But here I estimate ECS using a simple heat balance approach, which avoids dependence on models and also has the advantage of not involving choices about niceties such as truncation parameters and Bayesian priors, which can have a major impact on ECS estimation.

Aerosol forcing in the SOD – a composite estimate is used, not the best observational estimate

Before going on to estimating ECS using a heat balance approach, I should explain how the SOD treats forcing estimates, in particular those for aerosol forcing. 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. It is more appropriate to base ECS estimates on AF than on RF.

The main difference between the AF and RF measures relates to aerosols. In addition, the AF uncertainty for well-mixed greenhouse gases (WMGG) is double that for RF. 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.

It cannot be right, when providing an observationally-based estimate of ECS, to let it be influenced by including GCM-derived estimates for aerosol forcing – a key variable for which there is now substantial observational evidence. 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. Table 8.7 only gives estimated AFs for 2011, but Figure 8.18 gives their evolution from 1750 to 2010, so it is possible to derive historical figures using the recent observational AFari+aci estimate as follows.

The values in Figure 8.18 labelled 'Aer-Rad Int.' are actually for RFari, but that equals the purely observational estimate for AFari (−0.4 W/m² in 2011), so they can stand. Only the values labelled 'Aer-Cld Int.', which are in fact the excess of AFari+aci over RFari, need adjusting (scaling down by (0.73−0.4)/(0.9−0.4), all years) to obtain a forcing dataset based on a purely observational estimate of aerosol AF rather than the IPCC's composite estimate. It is difficult to digitise the Figure 8.18 values for years affected by volcanic eruptions, so I have also adjusted the widely-used RCP4.5 forcings dataset to reflect the Section 7.5.3 observational estimate of current aerosol forcing, using Figure 8.18 and Table 8.7 data to update the projected RCP4.5 forcings for 2007–2011 where appropriate. The result is shown below.

The adjustment I have made merely brings estimated forcing into line with the IPCC's best observationally-based estimate for AFari+aci. 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. If so, ECS estimates should be lowered further.

Reworking the Gregory et al. (2002) heat balance change derived estimate of ECS

The best known study estimating ECS by comparing the change in global mean temperature with the corresponding change in forcing, net of that in OHU, is Gregory et al. (2002). This was one of the studies for which an estimated PDF for ECS was given in AR4. Unfortunately, ten years ago observational estimates of aerosol forcing were poor, so Gregory used a GCM-derived estimate. In July 2011 I wrote an open letter to Gabi Hegerl, joint coordinating lead author of the AR4 chapter in which Gregory 02 was featured, pointing out that its PDF was not computed on the basis stated in AR4 (a point since conceded by the IPCC), and also querying the GCM-derived aerosol forcing estimate used in Gregory 02. Some readers may recall my blog post at Climate Etc. featuring that letter, here. Using the GISS forcings dataset, and corrected Levitus et al. (2005) OHU data, the 1861–1900 to 1957–1994 increase in QF (total forcing - OHU) changed from 0.20 to 0.68 W/m². Dividing 0.68 W/m² into ΔT', the change in global surface temperature, being 0.335°C, and multiplying by 3.71 W/m² (the estimated forcing from a doubling of CO2 concentration) gives a central estimate (median) for ECS of 1.83°C.

I can now rework my Gregory 02 calculations using the best observational forcing estimates, as reflected in Figure 8.18 with aerosol forcing rescaled as described above. The change in Q - F becomes 0.85 W/m². That gives a central estimate for ECS of 1.5°C.

An improved ECS estimate from the change in heat balance over 130 years

The 1957–1994 period used in Gregory 02 is now rather dated. Moreover, using long time periods for averaging makes it impossible to avoid major volcanic eruptions, which involve uncertainty as to the large forcing excursions involved and their effects. I will therefore produce an estimate based on decadal mean data, for the decade to 1880 and for the most recent decade, to 2011. Although doing so involves an increased influence of internal climate variability on mean surface temperature, it has several advantages:

a) neither decade was significantly affected by volcanic activity;

b) neither decade encompassed exceptionally large ENSO events, such as the 1997/98 El Nino, and average ENSO conditions were broadly neutral in both decades (arguably with a greater tendency towards warm El Nino conditions in the recent decade); and

c) the two decades are some 130 years apart, and therefore correspond to similar positions in the 60–70 year quasi-periodic AMO cycle (which appears to have a peak-to-peak influence on global mean temperature of the order of 0.1°C).

Since estimates of OHU have become much more accurate during the latest decade, as the ARGO network of diving buoys has come into action, the loss of accuracy by measuring OHU only over the latest decade is modest.

I summarise here my estimates of the changes in decadal mean forcing, heat uptake and global temperature between 1871–1880 and 2002–2011, and related uncertainties. Details of their derivations are given in Appendix 2.

Change in global decadal mean between 1871–1880 and 2002–2011

Mean estimate

Standard deviation


Adjusted forcing: CO2 and other well-mixed greenhouse gases




Adjusted forcing: all other sources (balancing error standard dev.)




Adjusted forcing: total








Earth's heat uptake








Surface temperature





Now comes the fun bit, putting all the figures together. The best estimate of the change from 1871–1880 to 2002–2011 in decadal mean adjusted forcing, net of the Earth's heat uptake, is 2.09 − 0.43 = 1.66 W/m². Dividing that into the estimated temperature change of 0.727°C and multiplying by 3.71 W/m² gives an estimated climate sensitivity of 1.62°C, close to that from reworking Gregory 02.

Based on the estimated uncertainties, I compute a 5–95% confidence interval for ECS of 1.03‑2.83°C – see Appendix 3. That implies a >95% probability that ECS is below the IPCC's central estimate of 3°C.

ECS estimates from recent studies – good ones…

As well as this simple estimate from heat balance implying a best estimate for ECS of approximately 1.6°C, and the reworking of the Gregory 02 results suggesting a slightly lower figure, two good quality recent observationally-constrained studies using relatively simple hemispheric-resolving models also point to climate sensitivity being about 1.6°C: 

  • Aldrin et al. (2012), an impressively thorough study, gives a most likely estimate for ECS of 1.6°C and a 5–95% range of 1.2–3.5°C.        
  • Ring et al. (2012) also estimates ECS as 1.6°C, using the HadCRUT4 temperature record (1.45°C to 2.01°C using other records).

And the only purely observational study featured in AR4, Forster & Gregory (2006), which used satellite observations of radiation entering and leaving the atmosphere, also gave a best estimate of 1.6°C, with a 95% upper bound of 4.1°C.

and poor ones…

Most of the instrumental-observation constrained studies featured in IPCC reports that give PDFs for ECS peaking at significantly over 2°C have some identifiable deficiency. Two such studies were featured in Figure 9.21 of AR4 WG1: Forest 06 and Knutti 02. Forest 06 has several statistical errors (see here) and other problems. Knutti 02 used a weak statistical procedure and an arbitrary combination of five ocean models, and there is little information content in its probabilistic ECS estimate.

Five of the PDFs for ECS from 20th century studies featured in Figure 10.19 of the AR5 SOD peak significantly above 2°C:

  • one is Knutti 02;
  • three are various cases from Libardoni and Forest (2011), a study that suffers the same deficiencies as Forest 06;
  • one is from Olson et al. (2012); the Olson PDF, like Knutti 02's, is extremely wide and contains almost no information.


In the light of the current observational evidence, in my view 1.75°C would be a more reasonable central estimate for ECS than 3°C, perhaps with a 'likely' range of around 1.25–2.75°C.

Nic Lewis

Appendix 1: Inverse estimates of aerosol forcing – the expert range largely reflects the poor studies

The AR5 WG1 SOD composite AFari+aci estimate of −0.9 W/m² is derived from mean estimates from satellite observations (−0.73 W/m²), GCMs (−1.45 W/m² from AR4+AR5 models including secondary processes, −1.08 W/m² from CMIP5/ACCMIP models) and an "expert" range of −0.68 to −1.52 W/m² from combined inverse estimates. These figures correspond to box-plots in the lower panel of Figure 7.19. One of the inverse studies cited hasn't yet been published and I haven't been able to obtain it, but I have examined the other twelve studies.

Because of its strong asymmetry between the northern and southern hemispheres, in order to estimate aerosol forcing with any accuracy using inverse methods it is essential to use a model that, at a minimum, resolves the two hemispheres separately. Only seven of the twelve studies do so. Of the other five:

  • one is just a survey and derives no estimate itself;
  • one (Gregory 02) merely uses an AOGCM-derived estimate of a circa 100-year change in aerosol forcing, without itself deriving any estimate;
  • three are based on global-mean only data (with two of them assuming an ECS of 3°C when estimating aerosol forcing).

One of the seven potentially useful studies is based on GCM simulations, which I consider to be untrustworthy. A second does not estimate aerosol forcing over 90S–28S, and concludes that over 1976–2007 it has been large and negative over 28S–28N and large and positive over 28N–60N, the opposite of what is generally believed. A third study is Andronova and Schlesinger (2001), which it turns out had a serious code error. Its estimate of −0.54 to −1.30 W/m² falls to −0.42 to −0.99 W/m² when using the corrected model (Ring et al., 2012). Three of the other studies, all using four latitude zones, estimate aerosol forcing to be even lower: in the ranges −0.14 to −0.74, −0.3 to −0.95 and −0.19 to −0.83 W/m². The final study estimates it to be around or slightly above −1 W/m², but certainly below −1.5 W/m². One recent inverse estimate that the SOD omits is −0.7 W/m² (mean – no uncertainty range given) from Aldrin et al. (2012).

In conclusion, I wouldn't hire the IPCC's experts if I wanted a fair appraisal of the inverse studies. A range of −0.2 to −1.3 W/m² looks more reasonable – and as it happens, is centred almost exactly on the mean of the estimates derived from satellite observations.

Appendix 2: Derivation of the changes in decadal mean global temperature, forcing and heat uptake

Since it extends back before 1880 and includes a correction to sea surface temperatures in the mid-20th century, I use HadCRUT4 global mean temperature data, available as annual data here. The difference between the mean for the decade 2002–2011 and that for 1871–1880 is 0.727°C. The uncertainty in that temperature change is tricky to work out because the various error sources are differently correlated in time. Adding the relevant years' total uncertainty estimates for the HadCRUT4 21-year smoothed decadal data (estimated 5–95% ranges 0.17°C and 0.126°C), and very generously assuming the variance uncertainty scales inversely with the number of years averaged, gives an error standard deviation for the change in decadal temperature of 0.08°C (all uncertainty errors are assumed to be normally distributed, and independent except where otherwise stated). There is also uncertainty arising from random fluctuations in the internal state of the climate. Surface temperature simulations from a GCM control run suggest that error source could add a further error standard deviation of 0.08°C for both decades. However, the matching of their characteristics as set out in the main text, points a) to c), and the fact that some fluctuations will be reflected in OHU, suggests a reduction from the 0.11°C error standard deviation implied by adding two 0.08°C standard deviations in quadrature, say increasing halfway, to 0.095°C. Adding that to the observational error standard deviation of 0.08°C gives a total standard deviation of 0.124°C.

The change between 1871‑1880 and 2002–2011 in decadal mean AF, with aerosol forcing scaled to reflect the best recent observational estimates, is 2.09 W/m², taking the average of the Figure 8.18 and RCP4.5 derived estimates (which are both within about 1% of this figure). The total AF uncertainty estimate of ± 0.87 W/m² in Table 8.7 equates to an error standard deviation of 0.44 W/m², which is taken as applying for 2002–2011. Using the observational aerosol forcing error estimate given in Section 7.5.3 instead of the corresponding Table 8.7 uncertainty range gives the same result. Although there would be some uncertainty in the small 1871–1880 mean forcing estimate, the error therein will be strongly correlated with that for 2002–2011. That is because much of the uncertainty relates to the relationships between:

  • concentrations of WMGG and the resulting forcing
  • emissions of aerosols and the resulting forcing,

the respective fractional errors in which are common to both periods. Therefore, the error standard deviation for the change in forcing between 1871–1880 and 2002–2011 could well be smaller than that for the forcing in 2002–2011. However, for simplicity, I assume that it is the same. Finally, I add an error standard deviation of 0.05 W/m² for uncertainty in volcanic forcing in 1871–1880 and a further 0.05 W/m² for uncertainty therein in 2002–2011, small though volcanic forcing was in both decades. Solar forcing uncertainty is included in Table 8.7. Summing the uncertainties, the total AF change error standard deviation is 0.45 W/m².

I estimate 2002–2011 OHU from a regression over 2002–2011 of 0–2000 m pentadal ocean heat content estimates per Levitus et al. (2012), inversely weighting observations by their variance. OHU in the 2000–3000 m layer is estimated to be negligible. After conversion from zeta Joules/year, the trend equates to 0.433 W/m², averaged over the Earth's surface. The standard deviation of the OHU estimate as computed from the regression residuals is 0.031 W/m², but because of the autocorrelation implicit in using overlapping pentadal averages the true figure will be much higher. Multiplying the standard deviation by sqrt(5) provides a crude adjustment for the autocorrelation, bringing the standard deviation to 0.068 W/m². There is no alternative to using GCM-derived estimates of OHU for the 1871–1880 period, since there were no measurements then. I adopt the OHU estimate given in Gregory 02 for the bracketing 1861–1900 period of 0.16 W/m², but deduct only 50% of it to compensate for the Levitus et al. (2012) regression trend implying a somewhat lower 2002-2011 OHU than is given in the SOD. Further, to be conservative, I treble Gregory 02's optimistic-looking standard deviation, to 0.03 W/m². That implies a change in OHU of 0.353 W/m², with a standard deviation of 0.075 W/m², adding the uncertainty variances. Although Gregory 02 ignored non-ocean heat uptake, some allowance should be made for that and also for any increase in ocean heat content below 3000 m. The (slightly garbled) information in Section 3.2.5 of the SOD implies that 0–3000 m ocean warming accounts for 80–85% of the Earth's total heat uptake, with the error standard deviation for the remainder of the order of 0.03 W/m². Allowing for all components of the Earth's heat uptake implies an estimated change in total heat uptake of 0.43 W/m² with an error standard deviation of 0.08 W/ m². Natural variability in decadal OHU should be the counterpart of natural variability in decadal global surface temperature, so is not accounted for separately.

Appendix 3: Derivation of the 5–95% confidence interval

In the table of changes in the variables between 1871–1880 and 2002–2011, I split the AF error standard deviation between that for CO2 and other greenhouse gases (0.291 W/m²), and for all other items (0.343 W/m²). The reason for doing so is this. Almost all the SOD's 10.2% error standard deviation for greenhouse gas AF relates to the AF magnitude that a given change in the greenhouse gas concentration produces, not to uncertainty as to the change in concentration. When estimating ECS, whatever that error is, it will affect equally the 3.71 W/m² estimated forcing from a doubling of equivalent CO2 concentration used to compute the ECS estimate. Most of the uncertainty in the ratio of AF to concentration is probably common to all greenhouse gases. Insofar as it is not, and the relationship between changes in greenhouse gases is different in the future to in the past, then the two AF estimation fractional errors will differ. I ignore that here. As most of the past greenhouse gas forcing is due to CO2 and that is expected to be the case in future, any inaccuracy from doing so should be minor.

So, I estimate a 5–95% confidence interval for ECS as follows. Randomly draw a million realisations from each of the following independent Normal(mean, standard deviation) distributions:

a: AF WMGG uncertainty – before scaling    – from N(0,1) 

b: Total AF without WMGG uncertainty       – from N(2.09,0.343)

c: Earth's heat uptake                                      – from N(0.43,0.08)

d: Surface temperature                                    – from N(0.727,0.124)

and for each quartet of random numbers compute ECS as: 3.71 * (1 + 0.102*a) * d / (0.291*a + b − c).

One then computes a histogram for the million ECS estimates and finds the points below which 5% and 95% of the total estimates lie. The resulting 5–95% range comes out at 1.03 to 2.83°C.

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Reader Comments (64)

The climate system is too complex to conclude that sensitivity is low, but it is not too complex to be certain that it is high. Hmmmmm.

Dec 20, 2012 at 10:18 AM | Unregistered CommenterBuck

O. Bothe
" We would expect the Gregory-ECS to be closer to the Transient Climate Response estimates since it is not calculated from equilibrium assumptions, and indeed it is closer to TCR."
" One more note. I wrote yesterday on twitter that Aldrin does not give the most likely as 1.6C."

Many thanks for your thoughtful comments. But I would like to take you up on the above two statements, which I think are both incorrect.

First, the simple issue. The most likely value of ECS lies where the probability density for the estimated value is highest – that is the peak of the PDF curve (the mode). Aldrin (2012) gave a PDF for its climate sensitivity estimate; it is Figure 1(a). If you enlarge that and measure where the peak is, as I did, you will find it is at about 1.55°C. So what I wrote is correct. Maybe you could issue a corrective tweet to your previous one?

It is common for papers that estimate climate sensitivity to quote the most likely value in their results: for a strongly asymmetrical distribution the mean is not a good central estimate. However, Aldrin quoted the mean estimated sensitivity, which is of course higher, at 2.0°C. Perhaps doing so made for an easier peer review process! Note that had Aldrin used a noninformative prior rather than a uniform prior for ECS, so as to achieve an objective probability distribution, the tail of his PDF would have been much shorter and the mean closer to the mode.

On the ECS vs TCR issue, it is simply not correct in principle that the Gregory 02 measure of ECS is closer to TCR than ECS. Gregory 02 says " Although it is defined in terms of a steady-state climate, the [equilibrium] climate sensitivity can be estimated from any climate state" and then goes on to just that. What it measures is the strengths of climate feedbacks evaluated for evolving non-equilibrium conditions, and is equivalent to the forcing associated with a doubling of equivalent CO2 concentration divided by the climate feedback parameter. Gregory 02 states, correctly, that its measure of equilibrium climate sensitivity is called the "effective climate sensitivity". I stuck with the term equilibrium climate sensitivity because it is better known.

The IPCC has tended to treat "equilibrium climate sensitivity" and "effective climate sensitivity" rather interchangeably: Figure 9.21 in AR4 WG1 labelled the x-axis Equilibrium Climate Sensitivity even though all the PDF curves shown corresponded to effective climate sensitivity. That seems reasonable to me where studies measure changes (including in ocean heat uptake) over multi-decadal periods – it is unclear that effective climate sensitivity measured by such studies would be much different from that measured when the ocean comes to full equilibrium. A new term, Earth System sensitivity, has been introduced for when the equilibrium involved extends to ice sheets, the carbon cycle, and other climate components with multi-millennial timescales.

As I commented earlier, Transient climate response (TCR), the change in global temperature after a 70 year period in which CO2 concentration doubles, with forcing increasing linearly, is quite different. TCR makes no adjustment for ocean imbalance, and hence it conflates equilibrium/effective climate sensitivity with ocean heat uptake parameters (mixed layer depth, effective vertical diffusivity, upwelling strength). Not a very good parameter to use, IMO. FWIW, with ECS at 1.6 C and reasonably mainstream ocean heat uptake parameters, TCR would likely be in the range 1.25 -1.4C. When ECS is low, ocean heat uptake is a small proportion of the heat balance adjustment process, and there is not much difference between TCR and ECS. When ECS is high, TCR will be much smaller than ECS.

Dec 20, 2012 at 10:30 AM | Unregistered CommenterNic Lewis

Richard Betts

Thanks for your continued input here. It is much appreciated.

I would think we would all agree that the overriding proof here is time, dear boy. Is it not? And that observational evidence for a defined ECS is not there yet. We have paid little attention to it until the the last couple of decades, and rely on historical instrumental or paleo evidence to look back.

Would you agree that the CS we can best discern from observations we can most trust is below previous IPPC boundaries?

Dec 20, 2012 at 10:37 AM | Unregistered CommenterGixxerboy

Good one there Buck. The mode of thinking you describe has been formalized with the term 'tail catastrophism', i.e., one worries about the tail of low-probability catastrophic possibilities that belongs to the huge dog of high-probability that is sitting in the corner of more mundane outcomes.

Dec 20, 2012 at 11:00 AM | Registered Commentershub

"Having re-read you article, you appear to relate one element to another quite tightly, along with stating your assumptions. I would be surprised if any critical response would be to this standard."


Dec 20, 2012 at 11:32 AM | Unregistered CommenterNic Lewis

Keith Jackson
"The GCMs appear to be wrong not only because they assume too much aerosol cooling, but also because, a) most of them arrive at a too-high temperature change between the 1880-1890 and 2000-2010 time frames, and b) because they generally predict too much ocean heat uptake, particularly in the Southern Hemisphere. The high Southern Hemisphere ocean heat uptake compensates for the too-high aerosol cooling in the Northern Hemisphere to allow the GCMs to get relative hemispheric temperature changes that are more-or-less in the right ballpark, but for the wrong reason. Because of the too-high sensitivity they also over-predict volcanic cooling effects, but the AR5 assumed forcings minimize that problem by halving volcanic aerosol forcing over previously used (and in the case of Pinatubo, observed) values."

Thank you for your detailed comment, Keith. You make excellent points. I hadn't really focussed on how GCMs manage to get the geographical historical warming approximately correct when their climate sensitivity and aerosol forcing are both excessive, although I was well aware that they had too high ocean heat uptake generally, but what you say about Southern Hemisphere heat uptake makes good sense.

When you say that AR5 assumed halved volcanic forcings compared with previous estimates (and Pinatubo observations), are you referring to the RCP forcings? I had noticed that the RCP4.5 forcing dataset (final, Nov. 2009 version) has far lower volcanic forcings than those in the GISS dataset, which uses Sato's volcanic forcing estimates. RCP4.5 forcings should correspond to the IPCC's CMIP5 project forcings, although those are meant to be worked out in each participating GCM from concentrations.

The Fig. 8.18 graph in the SOD for the evolution of forcings over time has volcanic forcings that appear to be between the RCP4.5 and the GISS values. And Fig. 8.14 shows big differences between the Sato estimates and those from two other reconstructions. The Ammann dataset ( is different again. Moreover, when carrying out a crude study fitting the post 1850 evolution of global temperature to forcings using global energy balance models (EBMs), I found the optimised fit was better if the sum of the response from two EBMs was used, with one forced just by volcanic forcing, and that the optimised climate sensitivity parameter was much lower for the volcanism-forced EBM than for the EBM using the remaining forcings.

These volcanic uncertainties were a major reason for my energy balance estimation of climate sensitivity being based on two single decades, as both the most recent decade and 1871-1880 had little volcanic activity.

Dec 20, 2012 at 12:08 PM | Unregistered CommenterNic Lewis

So, Your Grace - am I allowed to say: The SOD got it wrong..?

Dec 20, 2012 at 1:45 PM | Unregistered CommenterDavid

Dec 20, 2012 at 10:08 AM | Don Keiller

Luckily I do not rely on CAGW for my pay cheque :-)

Responses to others (including ZedsDeadBed) to come when I get a minute later...

Dec 20, 2012 at 2:54 PM | Registered CommenterRichard Betts

@Anthony Watts,
"Someday, I'll get to tell the backstory of all this."

Gee., Anthony, why don't you tease us some more? <G>

Dec 20, 2012 at 3:38 PM | Unregistered CommenterPeter D. Tillman

@Richard, glad to hear it:-)

However there are many who do- and what is more lack your knowledge and expertise.

Moreover these zeolots are driving a economical and political agenda that is crippling Western Societies.

Dec 20, 2012 at 4:31 PM | Unregistered CommenterDon Keiller

Dec 20, 2012 at 2:54 PM | Richard Betts>>>>>


Up till now mean global temperature has been derived from the sets of surface temperature statistics available on the Met Office blog and published as factual.

Is this still the situation or are these statistics now unreliable because of hypothesized heat sinking by the oceans?

I ask because many AGW activists try to argue that, after 16 years of flatlining, surface temperature statistics, which they previously pointed to as proof of the effects of CO2, are wrong because of this 'hidden heat' which seems to be unquantified.

You, yourself, described the past 15 years as "flatlining" with the caveat that this was just within the model predictions, although I feel the error bars for those models appear to be so generous as to allow almost any outcome as valid.

Your response, as a professional, would be most welcome as activists now seem to be changing the goalposts.

Dec 20, 2012 at 5:30 PM | Registered CommenterRKS

Nic: If I had signed up to be a reviewer of the FOD and had been brilliant enough to present the IPCC authors with an analysis of climate sensitivity somewhat like yours, would I have been told why the authors rejected my suggestions? Are reviewers expected to keep their comments and the IPCC's responses confidential? Or would the reasons for ignoring my comments remain a secret until AR5 had been published and the comments revealed?

If I were discussing estimates of climate sensitivity, I might divide the subject up by the methods used, so that their general strengths/weaknesses can be discussed. Part 1 might cover estimates from the IPCC's official models (with little new). Part 2 might cover the estimates from ensembles of models, which allow the parameters used by models to vary within a range consistent with current knowledge. The width of the pdfs from large ensembles of possible models is much wider and probably more realistic than for the small subset of models the IPCC uses. Part 3 might cover estimates from paleoclimate data. Part 4 might cover estimates derived principally from observations with limited help from GCMs. Part 5 might cover estimates made without any help from climate models.

Dec 21, 2012 at 6:04 AM | Unregistered CommenterFrank

yes, only after publication of the full report will all the comments and the reactions on the comments by the IPCC authors be released. This is a disadvantage for the reviewers. You don't know what they did with your comments when the SOD comes.

Dec 21, 2012 at 9:39 AM | Unregistered CommenterMarcel Crok

Nic Lewis,

"When you say that AR5 assumed halved volcanic forcings compared with previous estimates (and Pinatubo observations), are you referring to the RCP forcings? I had noticed that the RCP4.5 forcing dataset (final, Nov. 2009 version) has far lower volcanic forcings than those in the GISS dataset, which uses Sato's volcanic forcing estimates. RCP4.5 forcings should correspond to the IPCC's CMIP5 project forcings, although those are meant to be worked out in each participating GCM from concentrations."

I was referring to the RCP forcings. You are correct that the CMIP5 project forcings are ment to be worked out in each participationg GCM: I have, however, downloaded radiation data for the "average CMIP5 model", and the average net TOA forcing in 1992 (the peak year for Pinatubo) seems to be about -1.3 to -1.4 W/m^2, which is approximately equal to the quoted RCP4.5 forcing. In comparison, in the AR4 report (Chapter 2, page 194) they quote peak forcing values for the Pinatubo eruption of about -3 W/m^2, which is close to the Hansen value. Also, in AR4 chapter 9, page 676 Figure 9.3 shows outgoing SW radiation measured by satellites peaking between -3 and -4 W/m^2. So far, I have been unable to find any rationale for the change.

I might note, though, that in an EBM I developed, I get a good fit to the Pinatubo eruption with climate sensitivities in the range 1.1 to 1.6 degrees C, though the response for the Krakatau eruption is still a little too high. With a sensitivity of 3 degrees C the response to all volcanic eruptions is significantly too high, and matches typical GCM values with the same sensitivity.

One other minor point: the most recent sensitivity result I quoted in my comment was taken from a simple closed-form analytical two-hemisphere energy-balance model which is made possible if measured ocean heat fluxes are available. I also use decadal averages for the analysis, starting with the 11-year period between 1872 and 1882 (since that is the period with the least volcanic forcing of that era) and ending with the years 2000-2010. HadCRUT4 gives a global temperature increment between those two periods of about 0.67 deg. C - slightly lower than the value you used, I believe. My best estimate of "heat into the climate system" was 0.5 W/m^2 - very close to yours. In gereral, the GCMs do a very poor job of matching both these numbers on a gobal level, and an even worse job of matching them on a hemispheric level. The analytic model can show how much each GCM "error" contributes to the pushing the sensitivity up. Again, thanks for your excellent work.

Dec 21, 2012 at 9:29 PM | Unregistered CommenterKeith Jackson

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