Sunday, June 3, 2012


date: Sun, 7 Dec 2003 07:49:37 -0800 (PST)
from: Stephen H Schneider <>
subject: Dessai-Hume review (fwd)
to: Mike Hulme <>

Hi Mike, hope all is well. Haven't heard back about the APril review and
my very close schedule--any decisions?
I forward to you--spoke to Suraje already about it--my review of your
excellent paper on uncertainties, but of course a few mostly
narcissistic nit-picks. hOpe it is useful. Cheers, Steve
PS pls forward to Suraje, I'v misplaced my address book

Stephen H. Schneider, Professor
Dept. of Biological Sciences
Stanford University
Stanford, CA 94305-5020 U.S.A.

Tel: (650)725-9978
Fax: (650)725-4387

---------- Forwarded message ----------
Date: Sun, 7 Dec 2003 07:40:24 -0800 (PST)
From: Stephen H Schneider <>
To: Climate Policy <>
Subject: Dessai-Hume review

HI Ray, sorry to take so long with this, but I finally read it on the
plane to COP9-just discussed my minor complaints with Suraje, so he knows
who I am-I nearly always self-confess, as I encourage most Climatic Change
reviewers to do, but of course I do not insist.

In short, this is an excellent review, brings lots of literature in-some
of which even I who am at the center of this uncertainties battle-didn't
know, so it will be a clearly valuable entry for the Climate Policy
readership and beyond. It lays out the paradigmatic differences among
groups fairly, and tries to be neutral in laying out pros and cons. Some
in certain schools will think that wimpy, but it is the best summary I've
seen of the state of the art, so my hat off to Suraje and Mike for a fair
and balanced piece. It could be shorter and still make it's main points,
but then some of the excellent scholarship would be lost so I vote to
publish it about how it is now. Of course, I have a few nit-picks, mostly
narcissistic, which I'll list below. Other than that I think it should be
provisionally accepted right now subject to a final version that deals
with my minor comments and other reviewers comment--presuming you get some
of those too.

P10-analogs discussion. While literature is cited about analogs to past
adaptation, the authors need to warn the readers that global change
forcing may be unique and no-analog impacts seem likely, so analogs,
either to paleoclimatic states or adaptations are just the backdrop
against which we calibrate our understanding of how the system works, but
not necessarily analogs to the unique and transient changes now evolving.
Also on this page, in the middle, the Pielke and Sarewitz little polemical
sentence is quoted suggesting irrelevancy of probabilities for "climate
adaptation policy". This is a speciesist prejudice-only humans count. For
plants and animals, for which adaptation is much less likely, but systems
would be damaged, for humans to decide how much they worry about this
possibility, relative to other calls on our scarce resources,
probabilities are essential, not irrelevant. The likelihood of 2 versus 5
degrees is the difference between some species lost and a mass extinction
event. Also, in a sentence below the meaningless word "accurate" is given.
As Moss and I complained two thousand times in the TAR, words like
accurate, definitive, certain etc are meaningless rhetoric if not defined
versus a quantitative scale of subjective probabilities, since one
analyst's "accuracy" might be a 95% chance of something being true, where
another's is a 5% chance because they adhere to precaution rather than
proof. Just unpack this a bit with caveats along the lines I call for

P14; The worry that uncertainty may increase with more research is a
certainty, in fact lots of literature--including later in this paper--show
how climate sensitivity has grown with research. Of course it will narrow
as nature continues to perform the warming experiment, but no need to be
tentative-some things will grow less certain, others more as research
progresses, depending on the maturity of the field at the point of the
research increase and to some extent on luck. More complex systems more
likely to have uncertainty grow at first with more research than simple
well-constrained systems.

The point that neither I nor Naki/Arnulf explicitly mentioned reflexivity"
is a bit unfair for two reasons. (1) We were debating in a narrow
column-SRES scenarios/storylines which were self-constructed to be "policy
independent". Now they can criticize rightly SRES for thinking such a
thing is meaningful, but we kept our debate in those citations to those
issues mainly for the one-point-at-a-time principle. (2) The second reason
is in my rebuttal to Naki/Arnulf a year later in Climatic Change (2002)
that Suraje/Mike do cite, I explicitly address this as in the quoted
section below (see especially the caps), though I don't use the word
"reflexive" but rather feedback, but it means the same (quote on page 445
of my Editorial):

Moreover, Gr�bler and Nakicenovic (2001) also argue that probabilities in
science are different from those in social science, since we can perform
experiments in the former, whereas in the latter we must make judgments.
and Nakicenovic say that
in an interdisciplinary scientific assessment, the concept of
probabilities as used in natural sciences should not be imposed on the
social sciences. Probability in the natural sciences is a statistical
approach relying on repeated experiments and frequencies of measured
outcomes, in which the system to be
analysed can be viewed as a 'black box'. Scenarios describing possible
future developments in society, economy, technology, policy and so on, are
radically different. First, there are no independent observations and no
repeated experiments:
the future is unknown, and each future is 'path-dependent': that is, it
results from a large series of conditionalities ('what if. . . then'
assumptions) that need to be followed through in constructing internally
consistent scenarios. Socio-economic variables and their alternative
future development paths cannot be combined at will and are not freely
interchangeable because of their inter-dependencies.

However, natural scientific projections for the future still require
as no frequency experiments can be made before the fact. We must still
that our assumptions which govern the structural design of our systems
will hold in the future, often for values of dependent variables that are
of the range of past experience. Moreover, there are conditionalities in
science as well, and the solutions are, like Gr�bler and Nakicenovic
rightly assert
for social systems, 'path dependent' for natural systems as well as social
Therefore, I believe there is no in principle difference between natural
and social
sciences in this regard, since both require feedback mechanisms and
contain path
dependent systems. However, I agree there is one aspect in which social
are harder to predict than natural systems. Although in both social and
systems interactions among subsystems can cause alterations over time, IN
While the latter property of social systems is different in kind from
system predictions, to me both natural and social systems models involve
necessity to model feedback processes, and thus are very similar. In
essence, we
need a systems model that explicitly deals with the many subcomponents
that we
believe will influence the evolving emergent properties of a complex
system, and that when social sciences are included, the system becomes
complex in detail, but not necessarily in principle. For us simply to
redefine the
classical definition of risk to consequences alone, because subjective
analysis is fraught with deep uncertainties, is in essence to offer no
advice to the
policy community as to how it should order its investments in alternative
for without probabilities it is very difficult to engage in risk
management. And if
we in the scientific assessment business do not offer some explicit
notions of the
likelihood of projected events, then the users of our products - policy
analysts and
policy makers - must guess what we think these likelihood estimates are.
That is
hardly preferable in my view to a carefully worded set of subjective
estimates in which our (often low) confidence in such estimates
accompanies any
likelihood statements.
p16-be careful about nobody does reflexive modeling assertions. What about
the whole integrated assessment cabal with agent-based decision making
responding to evolving climate and mitigation costs. Nordhaus' DICE is the
most famous example. I have been personally critical of the assumptions he
and other neo-classical economists use in their current models, but in
principle they are modeling human reactions to evolving climate and
imposing policy changes that feed back on the climate and society. In fact
I've said one gets emergent properties of coupled socio-natural systems in
the pages of Climate Policy-particularly when abrupt changes are included.
Mastrandrea, M. and S.H. Schneider, 2001: Integrated Assessment of Abrupt
Climatic Changes. Climate Policy, 1, 433-449.

So, to be sure feedback-reflexivity-is a major obstacle as asserted, but
because it is hard doesn't mean there haven't been some heroic-even if
weak-attempts and that many more will and should be forthcoming. Just tell
the story straight.

P18-Myles Allen has already started, not about to as said at bottom. Might
also note that part of the model-data inter-comparison test will reveal
model errors, part will reveal errors in the forcings used to drive the
model simulations and some error will be in the instrumental data
themselves. Thus independent tests-like looking for climate signals in
plants and animals--also needed. See, e.g.:

Root, T.L., J.T. Price, K.R. Hall, S.H. Schneider, C. Rosenzweig, and A.
Pounds, 2003: Fingerprints of Global Warming on Wild Animals and Plants.
Nature, 421, 57-60.

P24. I think the Clark /Pulwarty quote is itself misleading, since it is
missing an essential requirement (in the Moss/Schneider guidance paper to
IPCC on uncertainties), which is all probabilistic info-via pdfs,
presumably, should also contain a measure of subjective confidence in the
pdf itself. So I fully agree we should not wait for perfect information
via a single pdf, but we can offer pdfs AND confidence assessments of them
in the meanwhile, as better than offering no pdfs at all. Just because
some who do not understand probabilities will also not understand
probabilistic formulations for problems other than that of climate
policy-how about medical or military policy. We cannot refuse to do
probabilistic information because of ignorance outside of us, when that is
the most honest assessment of the state of the art. What is called for in
my view is expert popularization using gambling, health and insurance
metaphors to make probabilistic formulations clearer to non-specialists,
not abandonment of the most honest descriptors of the state of the art.
Most scientists are obscure and lousy popularizers I admit, but correct
the problem right, not by suppressing pdfs and subjective confidence
estimation-that is my view and I don't expect the authors to necessarily
agree with it but I do expect they will raise these issues explicitly in
their text and give their views.

P25, 1st paragraph-anthropocentrism again.

P 26
Statement "Human reflexive uncertainty is unquantifiable in probabilistic
terms" is certainly wrong-it has been done in the economics/integrated
assessment literature for a dozen years already. Now, is it very
credible?--that is another thing. Some predictions-like production will
respond to price signals--probably pretty robust, whereas others-how will
future generations see the intrinsic value of a songbird-much tougher to
have even medium confidence in. But ALL are quantifiable via various
techniques: modeling, CV or decisional analytic elicitations. That is
where the confidence assessment part comes in, for some such predictions
will carry very low confidence and that must be said explicitly-but not
all will and thus don't over generalize or miss the distinction between
the possibility of quantification per se and its relative credibility-two
different things that should be explicitly separated in the text.

OK That's my nit-pick list. I look forward to seeing this in Climate
Policy soon.


Stephen H. Schneider, Professor
Dept. of Biological Sciences
Stanford University
Stanford, CA 94305-5020 U.S.A.

Tel: (650)725-9978
Fax: (650)725-4387

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