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.