I published an op-ed in the Wall Street Journal on the

subject of climate sensitivity.

Here are:

1. The article

2. An essay by Nic Lewis expanding on many of the points in the

article.

3. My response to one of the critiques of the article

Forget the Doha climate jamboree that ended earlier this month.

The theological discussions in Qatar of the arcana of climate

treaties are irrelevant. 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? The Intergovernmental

Panel on Climate Change has to pronounce its answer to this

question in its Fifth Assessment Report next year.

The general public is not privy to the IPCC debate. But I have

been speaking to somebody who understands the issues: Nic Lewis. A

semiretired successful financier from Bath, England, with a strong

mathematics and physics background, Mr. Lewis has made significant

contributions to the subject of climate change.

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).

Mr. Lewis is an expert reviewer of the recently leaked draft of

the IPCC’s WG1 Scientific Report. The IPCC forbids him to quote

from it, but he is privy to all the observational best estimates

and uncertainty ranges the draft report gives. What he has told me

is dynamite.

Given what we know now, there is almost no way that the feared

large temperature rise is going to happen. Mr. Lewis comments:

“Taking the IPCC scenario that assumes a doubling of CO2, plus the

equivalent of another 30% rise from other greenhouse gases by 2100,

we are likely to experience a further rise of no more than

1°C.”

A cumulative change of less than 2°C by the end of this century

will do no net harm. It will actually do net good-that much the

IPCC scientists have already agreed upon in the last IPCC report.

Rainfall will increase slightly, growing seasons will lengthen,

Greenland’s ice cap will melt only very slowly, and so on.

Some of the best recent observationally based research also

points to climate sensitivity being about 1.6°C for a doubling of

CO2. An impressive study published this year by Magne Aldrin of the

Norwegian Computing Center and colleagues gives a most-likely

estimate of 1.6°C. Michael Ring and Michael Schlesinger of the

University of Illinois, using the most trustworthy temperature

record, also estimate 1.6°C.

The big question is this: Will the lead authors of the relevant

chapter of the forthcoming IPCC scientific report acknowledge that

the best observational evidence no longer supports the IPCC’s

existing 2°-4.5°C “likely” range for climate sensitivity?

Unfortunately, this seems unlikely-given the organization’s record

of replacing evidence-based policy-making with policy-based

evidence-making, as well as the reluctance of academic scientists

to accept that what they have been maintaining for many years is

wrong.

#### ***

How can there be such disagreement about climate sensitivity if

the greenhouse properties of CO2 are well established? Most people

assume that the theory of dangerous global warming is built

entirely on carbon dioxide. It is not.

There is little dispute among scientists about how much warming

CO2 alone can produce, all other things being equal: about

1.1°-1.2°C for a doubling from preindustrial levels. The way

warming from CO2 becomes really dangerous is through amplification

by positive feedbacks-principally from water vapor and the clouds

this vapor produces.

It goes like this: A little warming (from whatever cause) heats

up the sea, which makes the air more humid-and water vapor itself

is a greenhouse gas. The resulting model-simulated changes in

clouds generally increase warming further, so the warming is

doubled, trebled or more.

That assumption lies at the heart of every model used by the

IPCC, but not even the most zealous climate scientist would claim

that this trebling is an established fact. For a start, water vapor

may not be increasing. A recent paper from Colorado State

University concluded that “we can neither prove nor disprove a

robust trend in the global water vapor data.” And then, as one

Nobel Prize-winning physicist with a senior role in combating

climate change admitted to me the other day: “We don’t even know

the sign” of water vapor’s effect-in other words, whether it speeds

up or slows down a warming of the atmosphere.

Climate models are known to poorly simulate clouds, and given

clouds’ very strong effect on the climate system-some types cooling

the Earth either by shading it or by transporting heat up and cold

down in thunderstorms, and others warming the Earth by blocking

outgoing radiation-it remains highly plausible that there is no net

positive feedback from water vapor.

If this is indeed the case, then we would have seen about 0.6°C

of warming so far, and our observational data would be pointing at

about 1.2°C of warming for the end of the century. And this is, to

repeat, roughly where we are.

The scientists at the IPCC next year have to choose whether they

will admit-contrary to what complex, unverifiable computer models

indicate-that the observational evidence now points toward lukewarm

temperature change with no net harm. On behalf of all those poor

people whose lives are being ruined by high food and energy prices

caused by the diversion of corn to biofuel and the subsidizing of

renewable energy driven by carboncrats and their crony-capitalist

friends, one can only hope the scientists will do so.

Addendum:

Here is Nic Lewis’s detailed account of his

reasoning:

Guest post by Nic Lewis

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 focused 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 10.8.2.5)

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 Q − F (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 | Units |

Adjusted forcing: CO2 and other well-mixed greenhouse gases |
0.29 | W/m² | |

Adjusted forcing: all other sources (balancing error standard dev.) |
0.34 | W/m² | |

Adjusted forcing: total | 2.09 | 0.45 | W/m² |

Earth’s heat uptake | 0.43 | 0.08 | W/m² |

Surface temperature | 0.73 | 0.12 | °C |

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.

Conclusions

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.

UPDATE: Dr. Judith Curry provides her take on the

issue, and endorses the leak:

http://judithcurry.com/2012/12/19/climate-sensitivity-in-the-ar5-sod/

And here is my response to one of the critiques of the Wall

Street Journal article:

Joe Romm of ThinkProgress described my Wall Street Journal op-ed

http://online.wsj.com/article/SB10001424127887323981504578179291222227104.html

as:

riddled with basic math and science errors

Yet he fails to find a single basic math or science error in my

piece.

He says I :

can’t do simple math

and then fail to produce a single example of my failing to do

simple math.

He says I apparently don’t know the difference between water

vapor and clouds. He produces no evidence for this absurd claim,

which is wrong. Water vapor is a gas; clouds are droplets of liquid

water that condense from water vapor. I do know the difference.

He quotes a scientist as saying

it is very clear water vapor … is an amplifying

effect. It is a very strong warmer for the

climate.

I agree. My piece states:

water vapor itself is a greenhouse gas.

So there is no confusion there. At least not on my part.

However, I do discuss the possibility that clouds, formed from

water vapor, either amplify or damp warming – and nobody at this

stage knows which. This is the point that my physicist informant

was making: the consequence of increased temperatures and water

vapor in the atmosphere may be changes in clouds that have a

cooling effect. You will find few who disagree with this. As the

IPCC AR4 said:

Cloud feedbacks remain the largest source of

uncertainty.

Joe Romm disagrees with this consensus, saying

The net radiative feedback due to all cloud types

is likely positive.

He gives no backing for this dogmatic conclusion. By contrast,

Professor Judith Curry of Georgia Tech says

(http://judithcurry.com/2012/12/19/climate-sensitivity-in-the-ar5-sod/#more-10669):

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.

Joe Romm quotes Robert Kaufman as saying

“I know of no evidence that would suggest that the temperature

effect of sulfur emissions are small.”

My piece never claimed that aerosols arising from sulfur

emissions had a small effect, however as Nic Lewis points out

(http://bishophill.squarespace.com/blog/2012/12/19/why-doesnt-the-ar5-sods-climate-sensitivity-range-reflect-it.html),

in the draft AR5 report,

“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.”

With regard to the rate of ocean heat absorption, which I

wrote was fairly modest, Joe Romm quotes Kevin

Trenberth as writing:

“On the contrary there is now very good evidence that a

lot of heat is going into the deep ocean in unprecedented

ways…”

and then provides a link to an article citing a study

estimating the Earth’s current heat absorption

as 0.5 W/ m². So what “fairly modest” figure

does Nic Lewis use? Actually slightly higher: 0.52 W/m²!

Romm then says:

Ridley apparently doesn’t have the first clue what the climate

sensitivity means

This is not true. I define sensitivity clearly as the

temperature change for a doubling of CO2. I am not talking about

the Transient Climate Response, which relates to temperature change

only over a 70 year period. There is no confusion at my end.

Romm then says that

Schlesinger notes that an aggressive program of carbon

mitigation can limit warming to 2°C and avoid the worst

impacts

and that

“It is worth pointing out that there is a healthy debate about

Schlesinger’s low estimate”.

So maybe there is some confusion at Romm’s end about what

Schlesinger concludes. This is what his paper says (in “Causes of

the Global Warming Observed since the 19th Century” in Atmospheric

and Climate Science 2012) –

“Additionally, our estimates of climate sensitivity using our

SCM and the four instrumental temperature records range from about

1.5 ̊C to 2.0 ̊C. These are on the low end of the estimates in the

IPCC’s Fourth Assessment Report. So, while we find that most of the

observed warming is due to human emissions of LLGHGs, future

warming based on these estimations will grow more slowly compared

to that under the IPCC’s “likely” range of climate sensitivity,

from 2.0 ̊C to 4.5 ̊C.”

Many other recent papers have come to similar conclusions: For

example, Schmittner et al. in Science Dec. 11, 2011 URL http://www.sciencemag.org/content/334/6061/1385.short:

Combining extensive sea and land surface temperature

reconstructions from the Last Glacial Maximum with climate model

simulations, we estimate a lower median (2.3 K) and reduced

uncertainty (1.7 to 2.6 K as the 66% probability range, which can

be widened using alternate assumptions or data subsets). Assuming

that paleoclimatic constraints apply to the future, as predicted by

our model, these results imply a lower probability of imminent

extreme climatic change than previously thought.

Meanwhile for transient climate response, similar low estimates

are also now being made. See for example Gillett et al.’s 2012

article “Improved constraints on 21st-century warming derived using

160 years of temperature observations” in Geophysical Research

Letters at http://www.agu.org/pubs/crossref/2012/2011GL050226.shtml:

Our analysis also leads to a relatively low and

tightly-constrained estimate of Transient Climate Response of

1.3-1.8°C, and relatively low projections of 21st- century warming

under the Representative Concentration Pathways.

Or Padilla et al.’s 2011 article “Probabilistic estimated

of transient climate sensitivity subject to uncertainty in forcing

and natural variability” in the Bulletin of the American

Meteorological Association at URL: http://journals.ametsoc.org/doi/abs/10.1175/2011JCLI3989.1:

For uncertainty assumptions best supported by global surface

temperature data up to the present time, this paper finds a most

likely present-day estimate of the transient climate sensitivity to

be 1.6 K, with 90% confidence the response will fall between 1.3

and 2.6 K, and it is estimated that this interval may be 45%

smaller by the year 2030. The authors calculate that emissions

levels equivalent to forcing of less than 475 ppmv CO2

concentration are needed to ensure that the transient temperature

response will not exceed 2 K with 95% confidence.

Mr Romm seems confused about methane outgassing feedbacks,

arguing that even if climate sensitivity is low, these may

dominate. Suffice to say that in this he has drifted a long way

from the consensus.

Mr Romm seems determined to rule out even the possibility of low

climate sensitivity in the teeth of strong evidence. I can see why

he wishes to do so, his job depending on there being a dangerous

future. I do not understand where he gets his certainty.

Finally, Mr Romm throws the term “anti-science” at me, again

with no evidence. I cited peer reviewed papers and made the

scientific argument that the latest data be considered in

estimating sensitivity. That is pro science. What is anti-science

is to make false accusations and try to shut down legitimate

debate. Hard working people all over the world are now risking

their lives as well as their wallets for the consequences of

current climate policy (see Indur Goklany’s paper “Could biofuel

policies increase death and disease in developing countries?”

http://www.jpands.org/vol16no1/goklany.pdf). They have a right to

ask that those who determine the science behind such policies are

open-minded. On the evidence of MrRomm’s astonishing outburst, my

doubts about this are growing.