Why Reconstructions Matter Part II
Posted by Jeff Condon on October 25, 2009
When both of my computers went down, I received several offers for guest posts which are immensely appreciated. This one is from John Pittman, a unique person who like myself doesn’t fear disagreement. This time he looks at a question often asked most often in blogland by non-skeptic AGW believers. Why do reconstructions matter:
The following quotes are from IPCC AR4 Chapter 9 Section 6 and are indented for clarity and bolded, underlined for emphasis. This is to establish why the problems with MH9x, Briffa, stripbark, JeffID’s deamplification, do matter. The last post was about attributing climate change. In that post, climate sensitivity was introduced but the relationship was stipulated as correct. In this post, an examination of climate sensitivity and reconstructions is conducted to examine the impact of reconstructions that differ from the “spaghetti graph.” We start with definitions.
‘Equilibrium climate sensitivity’ (ECS) is
the equilibrium annual global mean temperature response to a
doubling of equivalent atmospheric CO2 from pre-industrial
levels and is thus a measure of the strength of the climate
system’s eventual response to greenhouse gas forcing.
We will come back to this definition. But for now, we use this as the working definition.
‘Transient climate response’ (TCR) is the annual global mean temperature
change at the time of CO2 doubling in a climate simulation
with a 1% yr–1 compounded increase in CO2 concentration (see
Glossary and Section 188.8.131.52 for detailed definitions). TCR is a
measure of the strength and rapidity of the climate response to
greenhouse gas forcing, and depends in part on the rate at which
the ocean takes up heat.
Later, we will explore the relation of ECS and TCR, but will first look at ECS. The next quote outlines the problem for the IPCC.
While the direct temperature change
that results from greenhouse gas forcing can be calculated in
a relatively straightforward manner, uncertain atmospheric
feedbacks (Section 8.6) lead to uncertainties in estimates of
future climate change.
This is about climate change; in fact future climate change. Thus, GHG must be forceful and the uncertainties reduced if a ROBUST, VERY LIKELY claim is to be maintained. For if not, the human species has no need to worry about climate change. This is NOT an accusation of a conspiracy or wrong-doing. It is simply a fact that if predictions of climate change in the future were a non-issue much of the past scientific efforts would have been unnecessary and directed elsewhere. Stating the constraints necessary for one to claim that climate change needs to be addressed should cause no one to claim someone else is reaching for their tin foil cap with antennae. So let’s bring in the proxies.
The objective here is to assess estimates
of ECS and TCR that are based on observed climate changes,
while Chapter 8 assesses feedbacks individually. Inferences
about climate sensitivity from observed climate changes
complement approaches in which uncertain parameters in
climate models are varied and assessed by evaluating the
resulting skill in reproducing observed mean climate (Section
A couple of points need to be remembered. The most important is the last quote that the Inferences about climate sensitivity from observed climate changes are used (complement) by climate model approaches to uncertainty. The second point is in answer to why do they need to complement these models. They have to evaluate the resulting skill in reproducing the observed mean climate. By http://masterresource.org/?p=5240at Lucia’s http://rankexploits.com/musings/2009/adding-apples-and-oranges-to-cherry-picking/ one can find an evaluation of the model’s recent skill. At http://danhughes.auditblogs.com/2008/11/04/averaging-planet-earths-or-averaging-planet-xs/#more-58 one can get an understanding of the necessity of getting the mean temperature correct with this nice post by Dan Hughes. Without skill, claims of robust and very likely are unsupportable. And of course, if not robust and very likely, demands of large capital expenditures would be unreasonable.
While observed climate changes have the advantage
of being most clearly related to future climate change, the
constraints they provide on climate sensitivity are not yet very
strong, in part because of uncertainties in both climate forcing
and the estimated response (Section 9.2). An overall summary
assessment of ECS and TCR, based on the ability of models
to simulate climate change and mean climate and on other
approaches, is given in Box 10.2. Note also that this section
does not assess regional climate sensitivity or sensitivity to
forcings other than CO2.
The constraints on climate sensitivity that observed climate changes provide are not very strong. Wait a minute…not very strong? How do we get to that robust and very likely with “not very strong?” Isn’t not very strong the same as “weak?” The reasoning for “robust and very likely” was in the above quote. The inferences about climate sensitivity from observed climate changes are used to complement the climate model approaches. OK. They are weak on their own, but together, they are robust. That’s nice to know.
9.6.1 Methods to Estimate Climate Sensitivity
The most straightforward approach to estimating climate
sensitivity would be to relate an observed climate change to a
known change in radiative forcing. Such an approach is strictly
correct only for changes between equilibrium climate states.
Climatic states that were reasonably close to equilibrium in the
past are often associated with substantially different climates
than the pre-industrial or present climate, which is probably
not in equilibrium (Hansen et al., 2005). An example is the
climate of the LGM (Chapter 6 and Section 9.3). However,
the climate’s sensitivity to external forcing will depend on the
mean climate state and the nature of the forcing, both of which
affect feedback mechanisms (Chapter 8). Thus, an estimate of
the sensitivity directly derived from the ratio of response to
forcing cannot be readily compared to the sensitivity of climate
to a doubling of CO2 under idealised conditions.
Two problems, one is that the simple method of estimation is correct only for changes between equilibrium climate states. The other problem is that the CS (climate sensitivity) to external forcing (typically a doubling of CO2) will depend on the mean climate state, and the nature of the forcing because both effect feedback mechanisms.
An alternative approach, which has been pursued in most work reported here,
is based on varying parameters in climate models that influence
the ECS in those models, and then attaching probabilities to the
different ECS values based on the realism of the corresponding
climate change simulations. This ameliorates the problem of
feedbacks being dependent on the climatic state, but depends
on theassumption that feedbacks are realistically represented
in models and that uncertainties in all parameters relevant
for feedbacks are varied. Despite uncertainties, results from
simulations of climates of the past and recent climate change
(Sections 9.3 to 9.5) increase confidence in this assumption.
Now to the heart of why Yamal matters. The goal is determine probabilities based on the realism derived from results of simulations of climates of the past and recent climate change. This goal is necessary because one HAS to assume that feedbacks are realistically represented in models and that uncertainties in all parameters relevant for feedbacks are varied.
That is a TALL order. The feedbacks have to be realistically represented in the models. Well, the first problem is that aerosols that have been used to suggest the cooling period around 1970 has estimates that range from positive to negative feedback. Pretty had to justify that assumption of realistic if one does not know whether it is positive or negative. Next,that uncertainties in all parameters relevant for feedbacks are varied,is problematic. I would say that a feedback that you do not know whether it is positive or negative would be hard to justify that all because the relevant point is the uncertainties. Don’t miss that point. In order to vary all relevant for feedbacks, one has to be sure of the uncertainties for each and every, they did say all, parameter relevant to a feedback, and that to a mean climate state, since it was stated that the climate’s sensitivity to external forcing will depend on the mean climate state and the nature of the forcing, both of which affect feedback mechanisms (Chapter 8),
However, Yamal and chorus to the rescue.Despite uncertainties, results from simulations of climates of the past and recent climate change (Sections 9.3 to 9.5) increase confidence in this assumption. Of all the quotes, we have seen so far, this is the largest understatement. In fact, it deserves special attention.
The first item to be considered is the phrase “increase confidence in this assumption.”The assumption is thatuncertainties in all parameters relevant for feedbacks are varied. Remember that the uncertainties for all parameters is not varying them from plus to minus infinity, it is to vary them within a constraint of realistic in order to attach probabilities to the simulations. And of course that means that they have a handle on the uncertainties of each parameter. The goal is realistic skillful probabilities. However, the problem is with the word all.The models have to vary, in a realistic manner, all parameters to which their uncertainties must be known. Wonder if the IPCC ever heard of the problem with unknown unknowns?
At this point, we are going to send all to the penal colony Rura Penthe where there is no escape. Compliments to the creators and artists of Star Trek for their creativity and art. For those who have not had legal or regulatory training, all is one of those words one avoids UNLESS you are ready to back it up. All, like everything, is an absolute. No squirming or handwaving allowed. It is really honest of the authors of this section to state such. It recognizes that unknown unknowns can make the best efforts fall short, even way short. So in order to avoid a circular argument (their assumption) or the argument of Wrong Method plus Right Answer Equals Bad Science, the authors of this section grab the bull by the horns and make … an understatement.
Ignoring the weight of the understatement, we are now ready to state why Yamal matters. In the previous post http://noconsensus.wordpress.com/2009/10/04/how-important-is-yamal/ we found why Yamal and chorus were important for attributing climate change to anthropogenic activities (CO2). It should be apparent from the quotes of this and the previous post that attribution and climate sensitivity are joined at the hip. Literally in the sense that attribution would not be possible without a handle on sensitivity and feedbacks, and also by methodology. Both use the reconstructions of the past to reduce the uncertainties (for the claim of very likely), and to support the assumptions (for the claim of robust or skillful). Stating that the reconstructions do not matter is incorrect reasoning if one wants to support climate change activism. Obviously, if the reconstructions change the parameters of the models will have to be varied to match the reconstructions in order to maintain the IPCC’s claim. Or we can simply ignore those clamoring for us to do something, such as spend trillions of dollars.