Climate Models Blur Science And Advocacy
oonbz
July 31 2009
QUOTE: models do contain some well-established science but they also contain
implicit and explicit assumptions, guesses, and gross approximations,
referred to as parameters, mistakes in any of which can invalidate the model
outputs when compared to real world observations
QUOTE: a problem with modeling:
that last sentence implies a subjective judgment on the part of the modeler
regarding the greater likelihood of the model providing a good prediction
than a less closely tuned similar model. In other words there is the
possibility that tuneable parameters can be used as a 'fudge' factor, in
either model prediction or hindcasting making the model fit already observed
data.
QUOTE: Data that challenges the AGW hypothesis are simply changed. In some
instances, data that was thought to support the hypothesis is found not to,
and is then changed . . . Bias can be introduced by simply considering only
those errors that change answers in the desired direction.
QUOTE: Model projections, it should be recalled, are the basis for our
greenhouse concerns
QUOTE: In climate science the most notorious example of obfuscation through
omission is what has become known as Mann's Hockey Stick.
QUOTE: there was a mediaeval warm period that was somewhat warmer than the
present as well as the little ice age that was cooler. The presence of a
period warmer than the present in the absence of any anthropogenic
greenhouse gases was deemed an embarrassment for those holding that present
warming could only be accounted for by the activities of man. Not
surprisingly, efforts were made to get rid of the medieval warm period. The
most infamous effort was that due to Mann et al
QUOTE: It is important that models, in this context, remain a tool of
climate science and not a tool of advocacy.
QUOTE: It is unfortunate, then, that there is a reticence for some climate
scientists and modellers not to share data, especially all the codes or
algorithms used that would allow the models to be fully replicated and, if
necessary, challenge the validity of the models. This reticence goes to the
core of scientific thought and process.
MANY mainstream media science, economic and environmental journalists are
not sufficiently trained to be aware of the limitations of models when they
present climate-modelled output computated projections not only as data but
also advocate this output as supposed proof of the threat posed by
anthropogenic global warming, particularly with regard to runaway or
catastrophic climate change.
This disjunct between the scientific and media presentation when contained
within the paradigm of advocacy represents a threat to the integrity and
falsifiability of science.
Science seeks the truth in knowledge; some media advocacy seeks to
propagandise this knowledge.
The impact is reinforced if a climate scientist/modeller is directly quoted
as an expert, further blurring the line between science and advocacy.
This has societal repercussions as the science of anthropogenic global
warming (AGW) and the perceived impact of runaway or catastrophic climate
change is so model-dependent that the citizenry is not always able to
differentiate between the science and advocacy - the implications of which,
as regards policy development in term of climate change mitigation, are
likely to have a profound effect on society.
Climate models are used, in part, to determine future climate change
scenarios related to anthropogenic global warming (AGW) and are described by
the IPCC as "mathematical representations of the climate system, expressed
as computer codes and run on powerful computers."
Furthermore, the IPCC states that climate models:
"Are derived from fundamental physical laws (such as Newton's laws of
motion), which are then subjected to physical approximations appropriate for
the large-scale climate system, and then further approximated through
mathematical discretization. Computational constraints restrict the
resolution that is possible in the discretized equations, and some
representation of the large-scale impacts of unresolved processes is
required (the parametrization problem)."
In other words a climate model is a numerical model or simplified
mathematical representation of the Earth's climate system, or parts thereof.
It includes data from real world observations and creates parameters or
variables for the unresolved or unknown processes.
The ability of a model to simulate interactions within the climate system
depends on not only the level of understanding of the physical, geophysical,
chemical and biological processes that govern the climate system but on how
accurately these processes are expressed as algorithms within the model, and
how closely they represent real-world data.
These models do contain some well-established science but they also contain
implicit and explicit assumptions, guesses, and gross approximations,
referred to as parameters (the parametrization problem mentioned above),
mistakes in any of which can invalidate the model outputs when compared to
real world observations. In other words computer models are just
concatenations of theoretical calculations; as such they do not constitute
evidence.
Climate models are data and parameters dependent. Data is based on direct or
indirect observations from the environment; parameters (or parametrizations)
are defined by the IPCC as:
"Typically based in part on simplified physical models of the unresolved
processes . . . Some of these parameters can be measured, at least in
principle, while others cannot. It is therefore common to adjust parameter
values (possibly chosen from some prior distribution) in order to OPTIMISE
model simulation of particular variables or to IMPROVE global heat balance.
This process is often known as 'tuning'."
Tuning is considered justifiable if two conditions are met:
that parameter ranges do not exceed observational ranges where applicable
(though this does not necessarily constrain parameter values, which could
lead to model output problems); that adjusted (or tuneable) parameters are
allotted less degrees of freedom than in the observational constraints used
in the model's evaluation.
The IPCC states that,
"If the model has been tuned to give a good representation of a particular
observed quantity, then agreement with that observation cannot be used to
build confidence in that model. However, a model that has been tuned to give
a good representation of certain key observations MAY have a greater
likelihood of giving a good prediction than a similar model . . . that is
less closely tuned."
Herein lies a problem with modeling:
that last sentence implies a subjective judgment on the part of the modeler
regarding the greater likelihood of the model providing a good prediction
than a less closely tuned similar model. In other words there is the
possibility that tuneable parameters can be used as a 'fudge' factor, in
either model prediction or hindcasting making the model fit already observed
data.
Prominent climatologist Richard Lindzen, writing in "Climate Science:
Is it currently designed to answer questions?" a paper he presented at the
"Creativity and Creative Inspiration in Mathematics, Science, and
Engineering: Developing a Vision for the Future" conference held in San
Marino, August 2008, summarises the problem thusly:
"Data that challenges the AGW hypothesis are simply changed. In some
instances, data that was thought to support the hypothesis is found not to,
and is then changed . . . Bias can be introduced by simply considering only
those errors that change answers in the desired direction.
The desired direction in the case of climate is to bring the data into
agreement with models, even though the models have displayed minimal skill
in explaining or predicting climate.
Model projections, it should be recalled, are the basis for our greenhouse
concerns.
That corrections to climate data should be called for is not at all
surprising, but that such corrections should always be in the 'needed'
direction is exceedingly unlikely.
Although the situation suggests overt dishonesty, it is entirely possible,
in today's scientific environment, that many scientists feel that it is the
role of science to vindicate the greenhouse paradigm for climate change as
well as the credibility of models. Comparisons of models with data are, for
example, referred to as MODEL VALIDATION STUDIES rather than model tests."
It needs to be kept in mind that computer climate models do not output data:
their results are simply computations of the input data. Obviously then, the
accuracy or otherwise of the computated output is dependent upon the
accuracy of the input data.
Furthermore, a climate model's output is only reliable to the degree that
the model's performance can be validated, not necessarily by comparisons
with other models but from raw data recorded or observed from the real
world. Of course, tuned parameter corrections may be legitimate but only if
they include both those corrections that bring observations into agreement
with the model, and those that do not - to exclude the latter is to
obfuscate the model's outcome through omission.
In climate science the most notorious example of obfuscation through
omission is what has become known as Mann's Hockey Stick.
Lindzen again:
"In the first IPCC assessment (IPCC, 1990), the traditional picture of the
climate of the past 100 years was presented. In this picture, there was a
mediaeval warm period that was somewhat warmer than the present as well as
the little ice age that was cooler. The presence of a period warmer than the
present in the absence of any anthropogenic greenhouse gases was deemed an
embarrassment for those holding that present warming could only be accounted
for by the activities of man. Not surprisingly, efforts were made to get rid
of the medieval warm period . . .
The most infamous effort was that due to Mann et al . . . which used
primarily a few handfuls of tree ring records to obtain a reconstruction of
Northern Hemisphere temperature going back eventually a thousand years that
no longer showed a medieval warm period. Indeed, it showed a slight cooling
for almost a thousand years culminating in a sharp warming beginning in the
nineteenth century.
"The curve came to be known as the hockey stick, and featured prominently in
the next IPCC report, where it was then suggested that the present warming
was unprecedented in the past 1000 years. The study immediately encountered
severe questions concerning both the proxy data and its statistical
analysis."
The Mann Hockey Stick has since been discredited by two independent
assessments, both statistically and by reference to historical and
archeological records, though his initial claim that the current (late 20th
century) warming is unprecedented remains within the lexicon of adherents to
the AGW hypothesis.
There is a problem here for the reliability of science when models fail,
either through prediction or hindcasting, but are still given the same
validity as observed or model input data. One could suspect that advocacy is
overriding science in this instance. While advocates and politicians might
think that the science of AGW is settled scientists and climate modelers
need to be able to, and be seen to, separate clearly what is science and
what is advocacy otherwise their research may be subjected to political
manipulation.
The computated output of climate models, often used in conjuction with
models from outside the field of climate science, have been used to
construct climate change scenarios, often abbreviated as SRES, an acronym
for Special Report on Emission Scenarios. SRES was developed by the IPCC to
develop scenarios with which to analyze, according to SRES:
"How driving forces may influence future greenhouse gas emission outcomes
and to assess the associated uncertainties. They assist in climate change
analysis, including climate modeling and the assessment of impacts,
adaptation, and mitigation. The possibility that any single emissions path
will occur as described in scenarios is highly uncertain . . . Any scenario
necessarily includes subjective elements and is open to various
interpretations."
The output of SRES models, alternative views of how the future may unfold,
are termed projections. Projections are often stated or implied erroneously,
particularly in the media in connection with runaway climate change, as
forecasts. This creates the impression that the SRES model output is new
data, even proof, as opposed to being simply a projection of computated
input data and parameters from a number of sources within and beyond the
field of climate science.
Multi-model SRES climate change scenarios are said to create an ensemble of
climate change projections. Modellers then consider the spread of these SRES
projections, upon which has been built the notion that if the spread is
close together then they can have confidence in the projections while if the
spread significantly differs then there is uncertainty about the projections
even though they may offer a range of possibilities.
The SRES approach is problematic: it is assumptive; prone to exaggerated
errors; unscientific. Firstly, assumptions are made about unresolved
processes by using tuned parameters while, secondly, errors may be
exaggerated by (i) using the computational outputs as new 'data' by
reintroducing that 'data' as inputs into a new model, upon which new
projections are predicted and (ii) it assumes not only that a close spread
within an ensemble raises the confidence of the prediction but also that a
broad spread, rather than disproving the accuracy or otherwise of the
models, indicates a range of possibilities, though with a lower (assumed)
confidence. Thirdly, multi-model SRES outputs are based on so many
assumptions that its use is inherently unscientific because many of the
model elements are not falsifiable. It is, nonetheless, a good tool for
advocacy though, especially when presented in the guise of science.
This SRES approach has no place in the scientific processes as its outputs
can not be verified with real world data: projections are not records and
models are not data generators. As yet there is no scientific principle that
says that one can derive valid estimates from model outputs until the model
output resembles the observed non-modelled data. The uncertainty of an
ensemble of climate change projections will always depend on the accuracy of
the raw data input irrespective of the spread of projections.
This is not to say that there is no place for models in climate science:
even though they are a tool and not data generators there are there are many
examples of statistical climate forecasting models providing good projection
examples over short time frames.
It is important that models, in this context, remain a tool of climate
science and not a tool of advocacy.
A problem with climate modelling is that of replication and validation.
Because models are tools used in order to calculate, usually complicated,
data inputs it is important that the computated outputs can be tested (the
models are validated, or not) and repeated (the models' results are
replicated, or not) - by doing so helps remove bias (the models' outputs are
easily influenced by data inputs, flux adjustments and parameterisation)
thus increasing the confidence that the models do in some way represent what
they are seeking to show.
Not to do so means that the models' computated outputs could be used for
non-scientific purposes, such as advocating a predetermined position,
without the ability for others, perhaps affected by this advocacy, having
the ability to ascertain that the models' computated outputs actually
represent what they seek to show.
This is especially true for climate science, and the repercussions that the
computated outputs have on public policy as regards AGW, climate change,
tipping points, emissions trading schemes, etc.
Furthermore, as much climate science is publicly funded through government
grants, etc then it is even more imperative that the funders, ie the public,
receive information that can be trusted.
It is unfortunate, then, that there is a reticence for some climate
scientists and modellers not to share data, especially all the codes or
algorithms used that would allow the models to be fully replicated and, if
necessary, challenge the validity of the models. This reticence goes to the
core of scientific thought and process.
Warmest Regards
Bonzo
Surfer
>
>July 31 2009
>
>QUOTE: models do contain some well-established science but they also contain
>implicit and explicit assumptions, guesses, and gross approximations,
>referred to as parameters, mistakes in any of which can invalidate the model
>outputs when compared to real world observations
>
However....
http://www.newscientist.com/article/dn11649
<Start extract>
The validity of models can be tested against climate history. If they
can predict the past (which the best models are pretty good at) they
are probably on the right track for predicting the future - and indeed
have successfully done so.
Climate modellers may occasionally be seduced by the beauty of their
constructions and put too much faith in them. Where the critics of the
models are both wrong and illogical, however, is in assuming that the
models must be biased towards alarmism - that is, greater climate
change. It is just as likely that these models err on the side of
caution.
Most modellers accept that despite constant improvements over more
than half a century, there are problems. They acknowledge, for
instance, that one of the largest uncertainties in their models is how
clouds will respond to climate change. Their predictions, which they
prefer to call scenarios, usually come with generous error bars. In an
effort to be more rigorous, the most recent report of the IPCC has
quantified degrees of doubt, defining terms like "likely" and "very
likely" in terms of percentage probability.
Indeed, one recent study suggests that the feedbacks in climate
systems means climate models will never be able to tell us exactly how
much warming to expect. However, there is no doubt that there will be
warming.
Given the complexity of our climate system, most scientists agree that
models are the best way of making sense of that complexity. For all
their failings, models are the best guide to the future that we have.
<End extract>
I M @ good guy
It would be absolutely awful if that were true.
Bruce Richmond
> On Thu, 6 Aug 2009 15:51:22 +1000, "oonbz" <a...@z.com> wrote:
>
> >July 31 2009
>
> >QUOTE: models do contain some well-established science but they also contain
> >implicit and explicit assumptions, guesses, and gross approximations,
> >referred to as parameters, mistakes in any of which can invalidate the model
> >outputs when compared to real world observations
>
>
> <Start extract>
>
> The validity of models can be tested against climate history. If they
> can predict the past (which the best models are pretty good at) they
> are probably on the right track for predicting the future - and indeed
> have successfully done so.
>
> Climate modellers may occasionally be seduced by the beauty of their
> constructions and put too much faith in them. Where the critics of the
> models are both wrong and illogical, however, is in assuming that the
> models must be biased towards alarmism - that is, greater climate
> change. It is just as likely that these models err on the side of
> caution.
>
> Most modellers accept that despite constant improvements over more
> than half a century, there are problems. They acknowledge, for
> instance, that one of the largest uncertainties in their models is how
> clouds will respond to climate change. Their predictions, which they
> prefer to call scenarios, usually come with generous error bars. In an
> effort to be more rigorous, the most recent report of the IPCC has
> quantified degrees of doubt, defining terms like "likely" and "very
> likely" in terms of percentage probability.
>
> Indeed, one recent study suggests that the feedbacks in climate
> systems means climate models will never be able to tell us exactly how
> much warming to expect. However, there is no doubt that there will be
> warming.
Oh really? There have been ups and downs in the past. Somehow
whatever caused the downs seems to have been left out of the models.
We are currently experiencing at the very least a leveling off. None
of the models predicted it. If the model cannot be trusted to make
accurate prdictions then it should have no place in our decision
making process.
Ouroboros Rex
Cite?
> We are currently experiencing at the very least a leveling off. None
> of the models predicted it.
Yes, the PDO was only discovered in 2002.
If the model cannot be trusted to make
> accurate prdictions then it should have no place in our decision
> making process.
There's no reason to depend on the models. We now have sufficient direct
meaurement. Regardless, your statement is still infantile bunk.
Bruce Richmond
Look it up yourself. Or are you trying to claim the temp has never
gone down? If so please explain the ice ages.
> > We are currently experiencing at the very least a leveling off. None
> > of the models predicted it.
>
> Yes, the PDO was only discovered in 2002.
PDO (Pacific Decadal Oscillation) is just a fancy way of saying the El
Niño to La Niña cycle. They have been aware of El Niño since at least
1969.
http://en.wikipedia.org/wiki/El_Nino
"The El Niño-Southern Oscillation is often is abbreviated in
scientific jargon as ENSO and in popular usage is commonly called
simply El Niño."
"Jacob Bjerknes in 1969 helped toward an understanding of ENSO, by
suggesting that an anomalously warm spot in the eastern Pacific
can ..."
> >If the model cannot be trusted to make
> > accurate prdictions then it should have no place in our decision
> > making process.
>
> There's no reason to depend on the models. We now have sufficient direct
> meaurement.
Measurements tell you what happen in the past. Models are supposed to
tell you what will happen in the future. That's what most of us are
interested in.
> Regardless, your statement is still infantile bunk.
Meaning you have nothing to refute it.