QUERY Climate change

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Mitchel Kling <mklingmd@comca
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Post by Mitchel Kling <mklingmd@comca »

Posted by Mitchel Kling <mklingmd@comcast.net>

To the SD community,

The UN report released yesterday on global warming and climate change inspired me to wonder whether anyone has tried to apply SD methods and models to test and compare potential strategies for combatting climate change. Although I am a relative newcomer to the SD community, this field increasingly appealing to me (perhaps given my MIT undergraduate education, among other things) as a means of modeling complex problems and identifying potential solutions that would not have been derived from intuitive processes (and may often, as Professor Forrester and others have repeatedly shown, be counterintuitive).

In this regard, it seems to me that there has been a good deal of work on modeling the current and future impact of industrialization on global CO2 production and climate. I am thinking, though, that more work needs to be done to incorporate human behavior factors and to explore what steps we as society can and should take to reduce the impact of these changes, which the UN report concludes are ""90% likely"" to have been caused by human activities.

If there are those who have attempted to apply SD methods to test potential societal responses to climate change, and if such models have yielded potential solutions (or preferential approaches), it seems to me that they should be more widely publicized in the media and among policymakers so they can receive more attention and interest. If not, I would think this is a fruitful area for those with the requisite background and skills to explore further.

The timing seems right for SD to have a ""global"" impact, which could only strengthen this field, provide it with more credibility, and hopefully stimulate more talented people to join the community.

- Mitch
-----------------------------
Mitchel A. Kling, M.D.
Staff Clinician
Mood and Anxiety Disorders Program
Intramural Research Programs
National Institute of Mental Health
NIH 10/2D-46, MSC 1284
10 Center Drive
Bethesda, MD 20892-1284
Posted by Mitchel Kling <mklingmd@comcast.net> posting date Sat, 3 Feb 2007 11:41:43 -0500 _______________________________________________
John Sterman <jsterman@MIT.ED
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Post by John Sterman <jsterman@MIT.ED »

Posted by John Sterman <jsterman@MIT.EDU>

For SD approaches to climate change, see the work of Tom Fiddaman, including

Fiddaman, T. Exploring policy options with a behavioral climate- economy model System Dynamics Review Volume 18, Issue 2, Date: Summer 2002, Pages: 243-267

Tom won the 2006 Forrester award for this work. See also his website, http://www.metasd.com/index.html.

Linda Booth Sweeney and I have also done work on understanding the source of pubic complacency about climate change. See http:// web.mit.edu/jsterman/www/Understanding_public.html where you can download our most recent paper on the topic (Sterman, J. and L. Booth Sweeney (2007). ""Understanding Public Complacency About Climate
Change: Adults' Mental Models of Climate Change Violate Conservation of Matter."" Climatic Change 80(3-4): 213-238.).

At that same site you can play with an online simulator to learn how greenhouse gas emissions would have to change to stabilize atmospheric GHG concentrations.

See also Sterman, J. D. and L. Booth Sweeney (2002). ""Cloudy Skies:
Assessing Public Understanding of Global Warming."" System Dynamics Review 18(2): 207-240, which was recently republished (in edited
form) in Reflections, the journal of the Society for Organizational Learning.

John Sterman
Posted by John Sterman <jsterman@MIT.EDU> posting date Sun, 4 Feb 2007 12:04:33 -0500 _______________________________________________
""David Corben"" <david@dsc-c
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Post by ""David Corben"" <david@dsc-c »

Posted by ""David Corben"" <david@dsc-consulting.co.uk>

I must confess, I haven never understood the affection that the SD community seems to have for climate models, models which do not pass some of the key tests of what we believe as system dynamicists constitutes a good model of a complex dynamic system.


1) Endogenously Replicate the Observed Reference Mode

Climate model cannot replicate an ice age followed by an interglacial; they get stuck at a stable equilibrium point of a frozen earth. Showing that these models are structurally deficient and are lacking at least one major feedback loop. They will also fail the extreme parameter test for the same reason. This fact alone should set alarm bells ringing for any system dynamicist worth their salt.


2) Adopt an appropriate time frame for the model

Ice ages can last for up to a few million years and interglacials are measured in 10s of thousand of years. Milankovitch identified time constants in the climate system in the range 41,000 to 23,000 years. This suggests to me that any model of climate should be working on a time frame of at least a few 100,000s of years with a time step in the range of a hundred to a thousand years. Climate models seem to be trying to forecast (again not something system dynamicists generally approve of) climate for a few decades ahead. Can we trust a model whose timeframe is so much shorter than the time constants of the system, isn't there a danger that all that is being modelled is noise?

An analogy here would be to try and understand business cycles by building a model that forecasted the economy for a couple of months.


3) Looking at a System at a Point in Time Can be Misleading (event based
thinking)

System dynamics cautions against this, but global warming is a classic example of this mindset. People look at the melting ice caps and see this as some kind of abnormal event that requires an abnormal explanation. But if you look at the dynamic history of the system, then the geological record clearly shows the earth has undergone periods of glaciation, some lasting several million years, that are separated by very much longer periods of mild climate. So the normal state of the earth is not to be glaciated, so what is actually happening is the earth is moving from an abnormal state (ice caps) to its normal unglaciated state.

The SD community has a history of strongly attacking economic forecasting models (a system about which we have far better data and far greater understanding than the climate) yet climate forecasting models go unchallenged, I wonder why this is?


In my view what is required is a climate model that can endogenously replicate the observed reference mode of behaviour over the last 2 million years, only when such a model has been built can a system dynamicist be confident that we really understand the dynamics of climate change.


David Corben
Posted by ""David Corben"" <david@dsc-consulting.co.uk> posting date Mon, 5 Feb 2007 12:11:26 -0000 _______________________________________________
""Simonovic, Slobodan"" <ssim
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Post by ""Simonovic, Slobodan"" <ssim »

Posted by ""Simonovic, Slobodan"" <ssimonovic@eng.uwo.ca>

Mitch

One of my graduate students, Mr. Evan Davies, is working on the problem you expressed interest in. Work is still in progress. However, some publications documenting the progress and the main idea are available:

Simonovic, S.P., and G.R. Davies (2006), ""Are we modeling impacts of climate change properly?"", invited commentary, Hydrological Processes Journal, 20, pp.431-433

Davies, E.G.R., and S.P. Simonovic (2006) ""Modelling Social-Economic- Climatic Feedbacks for Policy Development"", Proceedings of the Climate
Change: Engineering Challenges and Solutions in the 21st Century, The Engineering Institute of Canada, Ottawa, May 10-12, CD-ROM paper 1568981407.

If interested, you can download them from my web site http://www.slobodansimonovic.com - go to publications and after selecting t he one you are interested in click on download).

Cheers
Slobodan P. Simonovic, Ph.D., P.Eng.
Professor - Civil and Environmental Engineering The University of Western Ontario
3115 SEB, London, Ontario N6A 5B9, Canada Posted by ""Simonovic, Slobodan"" <ssimonovic@eng.uwo.ca> posting date Mon, 5 Feb 2007 08:02:57 -0500 _______________________________________________
""James Winebrake"" <jjwgpt@r
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Post by ""James Winebrake"" <jjwgpt@r »

Posted by ""James Winebrake"" <jjwgpt@rit.edu>

Please see an initial inquiry into this in the book: Dynamic Modeling of Environmental Systems by M. Deaton and J. Winebrake, Springer-Verlag, 2000. One of the chapters in that book builds a STELLA model on carbon buildup in the atmosphere and temperature. Students/readers of the book are invited and asked to create interventions and model the impact of those interventions on the climate.

JW
Posted by ""James Winebrake"" <jjwgpt@rit.edu> posting date Sun, 4 Feb 2007 12:26:51 -0500 _______________________________________________
""Andrew Jones"" <apjones1@be
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Post by ""Andrew Jones"" <apjones1@be »

Posted by ""Andrew Jones"" <apjones1@bellsouth.net>

Dear System Dynamicists:

Mitchel Kling asked about efforts to use system dynamics to address climate change.

Here are several current activities, primarily from a US perspective.

1. Tom Fiddaman has built a suite of insightful climate-economy models, captured in his MIT PHD thesis, a flight simulator with Bert de Vries, and the SD Review paper for which he won the most recent Forrester award (18(2) Summer 2002). Resources at: http://www.metasd.com/index.html

2. We at Sustainability Institute have been working with Rocky Mountain Institute to help electrical utility leaders and their investors explore the dynamics of reducing their CO2 output and strategies for possible future carbon taxes, using a system dynamics model and flight simulator. Article
at:
http://www.sustainabilityinstitute.org/ ... lities.pdf

3. John Sterman and Linda Booth Sweeney's experimental work aimed at understanding public complacency on climate change action from a system dynamics perspective was recently published in Climatic Change. Their article is here:
http://web.mit.edu/jsterman/www/Underst ... ublic.html

4. Aiming to correct the problems identified in the previous paper, there are two efforts:

a. a team including Sterman, Sweeney, Sustainability Institute, Peter Senge at SoL, and Michael Tempel at SEED are working to create an online educational simulation that will help learners understand climate dynamics using a stock-flow-bathtub perspective. The work is in progress and will eventually reside at:
http://www.seed.slb.com/en/scictr/watch ... /index.htm

b. Beth Sawin and Phil Rice at Sustainability Institute are developing approaches that employ systems thinking concepts to educate the public on the need to act on climate change -- ""Our Climate Ourselves.""
http://www.sustainabilityinstitute.org/oco/index.html

5. Chris Soderquist and team have built an online system dynamics game, CO2FX, that explores addressing climate change from a national perspective.
Play it at www.globalwarminginteractive.com .

6. Two sessions at the Society for Organizational Learning's ""Sustainability Consortium"" Business Forum March 27-29 in Atlanta USA will focus on using models and learning tools to help business address climate change. The event is open to non-SoL members. Info at:
http://www.solsustainability.org/forum2007.htm

AND, this long list is paltry relative to the potential impact our field could have. We have SO much to contribute. Bring it on!

Drew Jones
Sustainability Institute
Posted by ""Andrew Jones"" <apjones1@bellsouth.net> posting date Sun, 4 Feb 2007 10:16:40 -0500 _______________________________________________
""Jim Thompson"" <james.thomp
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Post by ""Jim Thompson"" <james.thomp »

Posted by ""Jim Thompson"" <james.thompson@strath.ac.uk>

David Corben wrote: ""...I have never understood the affection that the SD community seems to have for climate models, models which do not pass some of the key tests of what we believe as system dynamicists constitutes a good model of a complex dynamic system.""

The observed affection may be an extension of an old saying about weather:
Everybody likes to talk about the climate, but nobody ever does anything about it.

The Population and Climate Change Program of the International Institute of Applied Systems Analysis <http://www.iiasa.ac.at/Research/PCC/index.html>
has published prolifically on the climate and implications for populations.
If not an expression of 'system dynamics methodology', IIASA's models simulate dynamical systems with tools familiar to SD. Perhaps there are modelling techniques disclosed in IIASA's publications that would help overcome David Corben's concerns.
Jim Thompson
Posted by ""Jim Thompson"" <james.thompson@strath.ac.uk> posting date Mon, 5 Feb 2007 17:41:55 -0500 _______________________________________________
""Mitchel A. Kling, M.D."" <k
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Post by ""Mitchel A. Kling, M.D."" <k »

Posted by ""Mitchel A. Kling, M.D."" <klingm@mail.nih.gov>

I appreciate and am gratified by the responses so far on the use of SD models to address climate change issues. As several of you have indicated, it does seem that considerable work has been done, but more work is urgently needed to direct the kinds of actions that will have a tangible impact on mitigating the effects of global industrialization. I think this becomes even more necessary with the rapidly-progressing development of economic activity in populous countries such as China, India, and others. We are truly a global society now; hopefully we can continue the dialogue towards solutions that will benefit all of us in the long run.

- Mitch
-----------------------------------
Mitchel A. Kling, M.D.
Staff Clinician
Mood and Anxiety Disorders Program
National Institute of Mental Health
NIH Bldg. 10 Room 2D-46
10 Center Drive, MSC 1284
Bethesda, MD 20892-1284
Posted by ""Mitchel A. Kling, M.D."" <klingm@mail.nih.gov> posting date Mon, 05 Feb 2007 10:44:53 -0500 _______________________________________________
Richard Stevenson <richard@co
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Post by Richard Stevenson <richard@co »

Posted by Richard Stevenson <richard@cognitus.co.uk>

I couldn't agree more with David Corben.

The human race implicitly thinks on a very short geological time agenda, and sadly we completely misunderstand our ability to change the natural long-term cycle of events. Model what you may - climate models may ultimately help us to understand this system, but we are ultimately quite unable to change it.

This has nothing to do with whether humans are accelerating global warming or not - we are, but probably only accelerating it. The long term solution is not to fight the system but rather to adapt to it.

Read Richard Dawkins ""The Ancestors Tale"" for some insight as to how life on earth relates to the geological time frame. Oh - and maybe also Dawkins ""The God Delusion"" to get a reference on how every human must take responsibility for his own future. Governments can't - and won't - take actions that can significantly alter climate change.

That means some pretty ghastly changes for our children and their ancestors - but better to be prepared, I think. System Dynamics models, appropriately framed in time, may well provide a useful contribution to help people take useful decisions for themselves.

Richard Stevenson
Valculus Ltd
High Mill Farm
Markington
Harrogate
HG3 3NR
UK
Posted by Richard Stevenson <richard@cognitus.co.uk> posting date Mon, 5 Feb 2007 14:56:54 +0000 _______________________________________________
Tom Forest <tforest@promethea
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Post by Tom Forest <tforest@promethea »

Posted by Tom Forest <tforest@prometheal.com>

With respect to David Corbin's advocacy of a MUCH longer timeframe for the model, and notwithstanding my affection for such models, I think that the dynamics of interest here are indeed captured in the geologically minuscule intervals of human lifetimes: punctuated equilibria. In particular, there is a loop dominance shift that takes places in ten years or less, according to the ice core samples from Greenland among other sources. David says ""This fact alone should set alarm bells ringing for any system dynamicist worth their salt."" Speaking of salt, in ""The Two-Mile Time Machine: Ice Cores, Abrupt Climate Change, and Our Future,""
(Princeton University Press, 2000 ISBN 0-691-10296-1) Richard B. Alley discusses the global thermohaline circulation as a keystone to our current balmy interglacial. It is possible that human-caused global warming will turn off the thermohaline circulation and plunge us into the next full glacial--the most counterintuitive of outcomes for global warming but the sort at which SD excels in illuminating. On p. 110-1, describing the ""Younger Dryas, the last cold gasp of the Ice Age between about 12,800 and 11,500 years ago,"" Allen says of changes in the ice cores at the end of that period that they are ""most directly interpreted as a twofold change in three years, with most of that change in one year, and with a 'flicker'
when the climate bounced up and down."" By that he means that the Greenland snow levels typical of cooler climate shifted to those typical of warm climate in three years or less.

I also think that mass extinctions (Permian/Triassic, Cretaceous/Tertiary, and the human-influenced Pleistocene/Recent) are driven by finely-grained transition dynamics, and am working on a model to articulate that point.

David, if you want to build a long timeframe model, Alley's book is a good starting point. I'd like to see it, and will help if I can!

Tom Lum Forest
Forest Grove, Oregon
Posted by Tom Forest <tforest@prometheal.com> posting date Mon, 5 Feb 2007 13:58:25 -0500 _______________________________________________
Tom Fiddaman <tom@ventanasyst
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Post by Tom Fiddaman <tom@ventanasyst »

Posted by Tom Fiddaman <tom@ventanasystems.com>


David Corben is puzzled at the SD community's apparent affection for climate models. As far as I know the SD community has little direct involvement with physical models of climate, and is mainly concerned with the policy implications of the physics given uncertainty.
Nevertheless the community should be concerned with the content and quality of the physical models because the implications are important.
Unfortunately the suggested model tests are misapplied in this case.
The very long term dynamics of glacial cycles are not the primary yardstick for climate models, because they are nearly constant on time scales important to our current climate and economy. Once this straw-man test is set aside, it's evident that climate models do pass a lot of relevant tests, and rely not at all on event-based mentality.
Waiting for unattainably perfect models to manage a system with long delays is not a robust strategy.

Let's look at the suggested criteria point by point:

> 2) Adopt an appropriate time frame for the model
>
> Ice ages can last for up to a few million years and interglacials are
> measured in 10s of thousand of years. Milankovitch identified time
> constants
>
> An analogy here would be to try and understand business cycles by
> building a model that forecasted the economy for a couple of months.


Another analogy is closer to hand: I just flipped through my copy of Industrial Dynamics and found graphs showing 3 to 5 years of behavior. Many companies have been in business for centuries ( http://en.wikipedia.org/wiki/List_of_oldest_companies ).
Does this imply that ID is invalid or ""modeling the noise""? Not at all. Choosing a time frame equal to the longest time constant evident in a real system is a poor rule of thumb. Certainly there are plenty of situations where decision makers are thinking on too short a time scale to solve their problem, and modelers should push for a broader boundary and longer horizon (one could argue that this is the case for climate - politicians are thinking in years and need to be pushed towards centuries). However, that does not mean every model needs to worry about the expansion of the sun to a red giant.

Choosing an overly long time horizon can be dangerous because the usual difficulty in representation of a system is the short time constants, which can result in a ""stiff system"" that must be simulated with short time steps. Climate is such a system, because there are important processes requiring short time scales and small spatial scales for adequate resolution. They can't be spatially aggregated or assumed to be in equilibrium due to nonlinearity, chaotic behavior, and limited understanding. Fortunately, the processes governing climate on a human time scale are reasonably separable from those governing Milankovitch cycles.

Nevertheless there are models that simulate climate in exactly the manner suggested, collapsing short term weather (and sometimes even multicentury ocean circulation) to equilibrium and focusing on the long-term dynamics of ice sheet advance and retreat with million-year horizons. No model with a 100-year time step can tell you much about the dynamics of water vapor in the atmosphere (time constant=weeks), which is a crucial determinant of the response of temperature to CO2, but these deep-time paleoclimate models agree with the short-term models on the plausible range of sensitivity of climate to external forcings.

> 1) Endogenously Replicate the Observed Reference Mode
>
> Climate model cannot replicate an ice age followed by an interglacial;
> they

As above, ""climate model"" refers to a wide variety of models ranging from low-order globally aggregated energy balance models that fit on a napkin to physically explicit 3D spatial models (GCMs) requiring supercomputers. The latter generate most of the headlines and say the most about the sensitivity of climate to greenhouse gases, but they don't operate on the kind of time scales needed to make the above reference mode relevant - they are typically run for a few centuries. They do replicate current climate with considerable fidelity, and can also replicate conditions in the far past (where the major challenge is deciding what the forcings were) and on other planets.
The assertion that models drift to a stable frozen equilibrium is not generally true.

If models are missing an important feedback loop with a time constant of 50k years, that is interesting, frustrating, limiting, etc. but doesn't imply that the shorter-term dynamics are wrong. For that you need to confront the models with other reference modes:

- Can low-order energy balance models replicate observed surface temperatures
over the last ~1000 years? (Yes, but only if greenhouse gases are included)
- Same for GCMs? (same answer)
- Can GCMs replicate tropical temperature-water vapor interactions? (Yes, but
only if water vapor behavior is consistent with the standard range of
temperature sensitivity)
- Are weather forecasts better when greenhouse signals are included in model
boundary conditions? (Yes)
- Can GCMs replicate the cooling following a large volcanic eruption like
Pinatubo? (Yes)
- Can GCMs replicate the temperature trends in upper layers of the atmosphere?
(Yes, and they predicted rising tropospheric temperatures in the face of
data showing cooling - the data was wrong) etc.

What should be of greater concern than replicating glacial-interglacial cycles is the fact that models omit many faster feedback loops as well. Carbon cycle-temperature feedbacks, ice sheet collapse dynamics, methane hydrates, and a variety of other phenomena are absent in most models. There's good reason to think that not all these omissions will turn out to be benign.

> 3) Looking at a System at a Point in Time Can be Misleading (event
> based
> thinking)
>
> System dynamics cautions against this, but global warming is a classic
> example of this mindset. People look at the melting ice caps and see
> this as

There is no doubt that in politics and media, human influence on climate does get simplified to the event level, which is detrimental to good judgment. But that has nothing to do with the scientific understanding of climate.
Anthropogenic global warming was a theory more than 100 years ago - long before it could be observed. The data used to verify models is far richer than a few events like Katrina. It includes both long aggregate time series and shorter, spatially detailed series from from satellites delivering terabytes daily.

The geological record is comparatively thin (in part because paleoclimate research has been underfunded) but there's no doubt that there have been large fluctuations in the past. Some of the changes involve time constants so long that it's hard to imagine that we should worry about them (the rock weathering cycle, continental drift, orbital changes, emergence of plants).

Past variability should not be regarded as comforting. It suggests instabilities that could be triggered by comparatively small interference on our part. Even if a warmer, ice-free world is somehow better, the transient to get there could be rough for coastal inhabitants, agriculture, forests, etc.
The last rapid excursion in temperature and carbon (the PETM, 55m years ago) was a major extinction event. The timing of the next ice age (if there is one) is not our concern so much as what will happen within the lifetime of our cars, power plants, cities, trees, etc - a few decades to a thousand years, perhaps.

Nor does historic variability say much about attribution of current changes.
Any change observed is a function of external forcings and internal dynamics.
Any important features of the system should be observable by monitoring the forcings (e.g. the sun) and the internal states (ocean heat content, sea ice extent, etc.). Large changes in the past might prompt us to look for mechanisms operating now, but when we don't find them in the present, we should look for things that do explain global temperature trends (GHGs) rather than blaming ""natural variability"" that somehow remains unmeasurable.

> The SD community has a history of strongly attacking economic
> forecasting models (a system about which we have far better data and
> far greater

The economy is actually far more complex than climate - there are more diverse actors and products than there are climate-relevant physical quantities, and we know far less about decision making than we do about the physics and chemistry of the atmosphere (except in a few areas like cloud nucleation). The economic data we do have is ambiguous about the most fundamental concepts, like GDP and prices. It's not even possible to reliably decide whether capital-output ratios are rising or falling, for example.

SD's aversion of forecasting is not due to some fundamental flaw in the very idea of forecasting. It's due to the poor practices that prevail in the creation and use of forecasts (a review of Industrial Dynamics, Appendix I is instructive). Economic forecasts are typically bad because too much is exogenous, modelers feel free to ""add factor"" (fudge) without documentation or rigor, and models are not subject to even the most basic tests of quality and robustness. Forecasts are used with the mindset, ""if I know what will happen, I'll know what to do."" As Jay has often pointed out, the interval over which it is possible to reliably forecast (due to momentum) seldom overlaps the interval over which it is possible to influence the system.
Actions taken on the basis of forecasts often become self-fulfilling or self-defeating.

SD has generally adopted a more sensible attitude toward forecasting, which we could probably argue at length but I'll assert boils down to an emphasis on behavior modes and response to policy levers rather than point forecasts.
However, it's still about prediction - only the content of the prediction has been made more reasonable. The things that make a bad forecast (wrong model, wrong inputs) can just as easily lead to a bad prediction of the qualitative response to a policy. However, we are less likely to have those bad components, because we (hopefully) spend more time getting key dynamics into the model, testing a wide range of alternative hypotheses and extreme conditions, and searching for policies that are robust to the uncertainties in inputs.

Climate modelers by and large share our attitude. They explicitly reject point prediction of weather as a reasonable critieria for model evaluation.
They clearly state that forecasts are contingent on uncertain inputs (emissions, volcanoes). They test extremes (removing all water vapor from the atmosphere, for example). I wish they would adopt some of our other habits (transparent model documentation with units of measure, for example).
If there were a significant history of failed climate forecasts - as there is for economic forecasts - I would worry more about climate model validity, but there isn't. In fact, the few early forecasts of temperature contingent on emissions (James Hansen's 1988 congressional testimony, the scenarios in the 1990 IPCC Scientific Assessment) have proven to be right on the money with respect to global temperatures.

Over the same period, critics argued that climate is insensitive to greenhouse gases and have attributed changes to measurement error, natural variablility and a variety of other forcings (solar irradiance, cosmic ray flux, etc.). Because a correct forecast doesn't prove a model - it could be just luck, modelers have examined those alternative hypotheses exhaustively.
The basic result - that current global temperatures and other features of climate can't be explained without GHGs - still stands. Some skeptic assertions - e.g. that temperatures would soon revert to a downward trend - are looking rather silly.

> In my view what is required is a climate model that can endogenously
> replicate the observed reference mode of behaviour over the last 2
> million

As described above, there are already models that can endogenously replicate ice age advance and retreat, though there is still debate about the mechanisms.
The particular trajectory over the Quaternary period will never be replicated because there is no way to recover the trajectory of inputs like solar irradiance with any accuracy.

It's a basic SD insight that systems with long delays require anticipatory action for effective control. We can't just sit on our hands until we are fully satisfied with climate models. So, what are we to do in the interim?
Seems like we should be doing what SD does best: assimilate the knowledge of domain experts (like climatologists) into a framework that makes it relevant to policy on time scales we can influence. Where there is uncertainty, that goes into the model as subjective probability distributions, along with a lot of reality checks, and we look for policies that are robust. Wait-and-see, or wait-for-perfect-models, is not a robust policy.

Tom



****************************************************
Tom Fiddaman
Ventana Systems, Inc.
Posted by Tom Fiddaman <tom@ventanasystems.com> posting date Mon, 05 Feb 2007 14:19:20 -0700 _______________________________________________
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