Kiehl (2007) on Tuning GCMs

Eduardo Zorita sent me an interesting paper today from Kiehl, a prominent climate modeler, analyzes the paradox of how GCMs with very different climate sensitivities nonetheless all more or less agree in their simulations of 20th century climate. Kiehl found that the high sensitivity models had low aerosol forcing history and vice versa. Kiehl observed:

These results explain to a large degree why models with such diverse climate sensitivities can all simulate the global anomaly in surface temperature. The magnitude of applied anthropogenic total forcing compensates for the model sensitivity.

Eduardo’s take was as follows:

surprisingly the attached paper, from a main stream climate scientist, seems to admit that the anthropogenic forcings in the 20th century used to drive the IPCC simulations were chosen to fit the observed temperature trend. It seems to me a quite important admission.

Here are some excerpts from Kiehl 2007 together with his key graphics.

Kiehl introduced the problem as follows:

Climate model simulations of the 20th century can be compared in terms of their ability to reproduce this temperature record. This is now an established necessary test for global climate models. Of course this is not a sufficient test of these models and other metrics should be used to test models, for example the ability to simulate the evolution of ocean heat uptake over the later part of the 20th century [Levitus et al., 2001; Barnett et al., 2001], or a models ability to simulate trends in various modes of variability. All of these can be viewed as necessary test for climate models. For example all models simulate a global warming of 0.5 to 0.7 deg C over this time period to within 25% accuracy.

This seems at odds with Schmidt’s statement that station data is not used in climate models, but let’s proceed. Kiehl poses the conundrum of climate models with differing sensitivities all agreeing on their 20th century histories:

One curious aspect of this result is that it is also well known [Houghton et al., 2001] that the same models that agree in simulating the anomaly in surface air temperature differ significantly in their predicted climate sensitivity. The cited range in climate sensitivity from a wide collection of models is usually 1.5 to 4.5 deg C for a doubling of CO2, where most global climate models used for climate change studies vary by at least a factor of two in equilibrium sensitivity.

The question is: if climate models differ by a factor of 2 to 3 in their climate sensitivity, how can they all simulate the global temperature record with a reasonable degree of accuracy. Kerr [2007] and S. E. Schwartz et al. (Quantifying climate change–too rosy a picture?, available at http://www.nature.com/reports/climatechange, 2007) recently pointed out the importance of understanding the answer to this question. Indeed, Kerr [2007] referred to the present work and the current paper provides the ‘‘widely circulated analysis’’ referred to by Kerr [2007]. This report investigates the most probable explanation for such an agreement. It uses published results from a wide variety of model simulations to understand this apparent paradox between model climate responses for the 20th century, but diverse climate model sensitivity.

Kiehl observes that there are no standard datasets for ozone, aerosols or natural forcing factors (all prominent features of moden models):

It is believed that much of the range in model climate sensitivity is due to uncertainties in cloud feedback processes [Cess et al., 1996]. Although there are established data for the time evolution of well-mixed greenhouse gases, there are no established standard datasets for ozone, aerosols or natural forcing factors. Results from nine fully coupled climate models [Dai et al., 2001; Boer et al., 2000; Roeckner et al., 1999; Haywood et al., 1997; Mitchell et al., 1995; Tett et al., 2002; Meehl et al., 2004; Meehl et al., 2000] and two energy balance models [Crowley and Kim, 1999; Andronova and Schlesinger, 2000] have been used to consider the relationship between total anthropogenic climate forcing and climate sensitivity.

Kiehl’s Figure 1 shows a distinct inverse relationship between total 20th century anthropogenic forcing and climate sensitivity:
kiehl35.gif
Figure 1. Total Anthropogenic Forcing (Wm2) versus equilibrium climate sensitivity (deg C) from nine coupled climate models and two energy balance models that were used to simulate the climate of the 20th century. Solid line is theoretical relationship from equation (4). Dashed lines arise from assuming a ±0.2 Wm2 uncertainty in ocean energy storage in equation (4).

Kiehl commented:

Note that the range in total anthropogenic forcing is slightly over a factor of 2, which is the same order as the uncertainty in climate sensitivity. These results explain to a large degree why models with such diverse climate sensitivities can all simulate the global anomaly in surface temperature. The magnitude of applied anthropogenic total forcing compensates for the model sensitivity.

This strongly suggests that the scatter among the models is mostly due to the range in modeled change in ocean heat storage.

These results indicate that the range of uncertainty in anthropogenic forcing of the past century is as large as the uncertainty in climate sensitivity and that much of forcing uncertainty is due to aerosols. In many models aerosol forcing is not applied as an external forcing, but is calculated as an integral component of the system. Many current models predict aerosol concentrations interactively within the climate model and this concentration is then used to predict the direct and indirect forcing effects on the climate system.

Kiehl’s Figure 2 shows the forcing from the 9 models:
kiehl36.gif

Figure 2. Total anthropogenic forcing (Wm2) versus aerosol forcing (Wm2) from nine fully coupled climate models and two energy balance models used to simulate the 20th century.

Kiehl noted that his review is not of the latest iteration of GCMs, but expresses his judgment that the results would apply to them as well.

It could also be argued that these results do not invalidate the application of climate models to projecting future climate it is hard to imagine that these results do not apply to the latest coupled models.

An editorial comment. The apparent conclusion that GCMs are tuned to 20th century history does not imply that doubled CO2 is not an issue; merely that the GCMs may do little more than embody certain key assumptions. GCMs are not “truth machines”. But articles like this definitely point the way towards a more detailed and more nuanced examination of 20th century aerosol forcing histories.

Reference:
Jeffrey T. Kiehl, 2007. Twentieth century climate model response and climate sensitivity. GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L22710, doi:10.1029/2007GL031383, 2007


253 Comments

  1. tetris
    Posted Dec 1, 2007 at 5:36 PM | Permalink | Reply

    SteveM
    Are we to understand that Kiehl is arguing that the various GCMs are akin to a zero sum game in which, no matter how the key variables are changed, the trade-off end result is the same? Zorita seems to be left with that impression. Would much appreciate your comments.

  2. Pat Keating
    Posted Dec 1, 2007 at 5:42 PM | Permalink | Reply

    We already knew that the models were tuned by adjusting the many parameters to get the right temperature behavior, of course.

  3. Larry
    Posted Dec 1, 2007 at 5:51 PM | Permalink | Reply

    I’m not sure whether I’m surprised or not.

  4. bender
    Posted Dec 1, 2007 at 5:54 PM | Permalink | Reply

    Thank you for providing the full reference. And thanks for the thread. I predict this is going to be a very long one. Let’s try our best to keep it clean. The GCMs are the one wild card holding up the whole house of cards, so let’s do a careful job reading the paper and understanding it before launching off. RC is going to be watching CA on this one.

  5. Tim
    Posted Dec 1, 2007 at 6:03 PM | Permalink | Reply

    “Although there are established data for the time evolution of well-mixed greenhouse gases, there are no established standard datasets for ozone, aerosols or natural forcing factors.”

    Wow. While I’m just a layperson – that statement seems to me to be a huge admission of the weakness of current models.

    My takeaway from this whole thing is that this pretty much opens the door for the modelers to tweak the inputs to get the output they are looking for. Is this what “science” has become?

  6. DocMartyn
    Posted Dec 1, 2007 at 6:05 PM | Permalink | Reply

    The end result is always the same because the data is fitted to the “temperature record”, what ever that is. You can fit damned near any line shape as long as you have five exponentials and a few constants.
    I have never once believed that the fits they get were due to how good their models were, as designed from first principles.

    I myself have written along the the lines of
    “Figure 5 shows a typical trace recorded at ……”
    You can be sure I show the best trace, without the spike at 4 minutes, with the least baseline, e.t.c. That is taken as read.

  7. Larry
    Posted Dec 1, 2007 at 6:13 PM | Permalink | Reply

    But can they predict the future?

  8. Wansbeck
    Posted Dec 1, 2007 at 6:20 PM | Permalink | Reply

    Isn’t the only surprise the ‘admission’!
    Surely a model should be based on known physical relationships or its ability to predict.
    Since predictions in this field take too long to evaluate then models not based on known, or at least likely, physical relationships are just curve fitting exercises.

  9. Joel McDade
    Posted Dec 1, 2007 at 6:23 PM | Permalink | Reply

    I just paid the $9 for the article. That’s a first, but I think it is going to be worth it.

    No I won’t be crass and share the PDF. To save you 2 seconds of google time, if not 9 USD:

    http://www.agu.org/pubs/crossref/2007/2007GL031383.shtml

  10. Edward
    Posted Dec 1, 2007 at 6:28 PM | Permalink | Reply

    They cannot predict the future. It has been said here many times that many ecomonic models tuned to a period of past (some sort of stat issues) history fall apart when predicting the future.

  11. Wansbeck
    Posted Dec 1, 2007 at 6:29 PM | Permalink | Reply

    Re #9 Joel

    I had seen it on offer for $9 which is no big deal but will I paying that to hear someone state, IMHO, the obvious?

  12. Posted Dec 1, 2007 at 6:30 PM | Permalink | Reply

    Steve,

    A similar graph about the relationship aerosols – climate sensitivity can be found at the RC discussion (2005) about aerosols. I have reacted several times on the different discussions about the influence of aerosols, including aerosol specialists on the RC pages, with no response (see e.g. here). I have the impression that the climate modelers are very aware of the problem, but that they need the influence of aerosols to match the cooler 1945-1975 period.

    Anyway, I have experimented with the GHG/aerosol forcings tandem in a simple EBM (energy balance model), obtained from a one-day course in Oxford about climate modelling. The influence of aerosols on the endresult is huge, within wide margins: if the influence is low, then the influence of GHGs need to be reduced to match the temperature trend, and any future projection is reduced too, no matter the future scenario. If I reduce the tropospheric cooling aerosol to one quarter (why not? even the sign of the forcing of all combined aerosols is not sure), I need to halve the influence of CO2, and the model even has a better match with the temperature record of the past century…
    The graphs for the “standard” forcings and the “reduced” forcings are here.

    Btw, that simple EBM program (a spreadsheet in Excel) indeed performs better than the great GCM’s in retrofitting the past century’s temperature trend, see the Kaufmann – Stern paper, already discussed at CA

  13. steve mosher
    Posted Dec 1, 2007 at 6:50 PM | Permalink | Reply

    Well,

    It has always bothered me that the guys in charge of the data, both historical and current,
    are also in charge of the models. That means you can match the model to the “data” via several methods

    1. goose the paleo record
    2. goose the instrument record
    3. goose the model and make her laugh.

    Old school, we would never let a model runner near the data he had to calibrate against.

  14. Doug
    Posted Dec 1, 2007 at 7:02 PM | Permalink | Reply

    Anyone who has worked with a sophisticated earth science model knows that by tweaking the variables, any desired result can be obtained. The frightening part, is that one’s preferred result can usually be obtained tweaking all variables within a perfectly reasonable and defendable range. It is hard to convince people who have not done some modelling just how little can be concluded by the results. Have at this paper, it is a step in the right direction.

  15. Jim Clarke
    Posted Dec 1, 2007 at 7:05 PM | Permalink | Reply

    I agree with Wansbeck (#9). The conclusions of this paper have been stated countless times by climate crisis skeptics for at least 10 years. If this paper helps the rest of the climate change community come to terms with the obvious, then that is great, but why is it taking so long?

    Steve: I haven’t seen any prior paper specifically showing that model climate sensitivity had an inverse relationship to the version of aerosol forcing history. People may have hypothesized something similar but this is more substantial.

  16. Frank K.
    Posted Dec 1, 2007 at 7:28 PM | Permalink | Reply

    As someone who has been working in computational fluid dynamics for over twenty years, this result does not surprise me. I always suspected that the source terms (i.e. “forcings”) embodied in the transport equations and physical models used in the GCMs were tuned after the fact. Moreover, I found it amusing that climate modelers would demonstrate the accuracy of their models by presenting (among other things) a plot of the ubiquitous “world average temperature” as a function of time, which naturally would be in good agreement with the surface temperature record (which we all know to be highly accurate – especially back to 1880!). Well of course it compares well – tuned models always compare favorably with the data to which they’ve been tuned!

    A better measure of the usefulness of climate models is their ability to predict climate 6 – 12 months out. Or to predict how active the hurricane season will be…

  17. bender
    Posted Dec 1, 2007 at 7:32 PM | Permalink | Reply

    Finished reading the paper. Pardon the earlier comments, but I was worried that maybe this was a really important one. Not. I think I’ll catch the end of the Missouri-Oklahama game. Comments later.

  18. Wansbeck
    Posted Dec 1, 2007 at 7:37 PM | Permalink | Reply

    I have only recently got involved in this blogging business but an early response from Philip_B (unthreaded 25 #682) suggested much the same as this thread: the problem lies in the models and the validity of their predictions.

    Part of my reply was:
    ‘I appreciate the doubts over the models used. I frequently use modeling but unfortunately I then have to produce a device that works in the real world. The models that I use are based on known physical relationships or detailed empirical data and have over many years been tested against their real world equivalents and refined accordingly. They can still give unrealistic results and if you try hard enough they can give almost any result you want. It would be great if I could spend my time making magnificent models without having to prove that they work.’

    People who work with models that have to produce workable results know this, don’t they?

    Oh, and thanks Bender, you’ve saved me $9.

  19. Pat Keating
    Posted Dec 1, 2007 at 7:47 PM | Permalink | Reply

    10 Edward

    They cannot predict the future. It has been said here many times that many economic models tuned to a period of past (some sort of stat issues) history fall apart when predicting the future.

    Have you read Gerald Browning’s posts over at the Exponential thread, where he says even the near-term weather-forecast models blow up trying to predict past 36 hours?

  20. Sylvain
    Posted Dec 1, 2007 at 8:00 PM | Permalink | Reply

    Roger Pielke Sr has a recent post at Climate Science that concern forcings and IPCC estimates.

    href=”http://climatesci.colorado.edu/2007/11/30/climate-metric-reality-check-1-the-sum-of-climate-forcings-and-feedbacks-is-less-than-the-2007-ipcc-best-estimate-of-human-climate-forcings/”> Roger Pielke sr’s blog

  21. Philip_B
    Posted Dec 1, 2007 at 8:10 PM | Permalink | Reply

    My comment referred to above was that the IPCC relies heavily on the models and their accuracy. It happens I know a lot about testing large complex software. Any piece of software is only as accurate/correct as the data used to test it. IMO even the temperature record pre-satellite isn’t accurate or comprehensive enough to adequately test the models. Even if the science was perfectly understood, the lack of good test data means the models and their outputs will contain significant errors. Wansbeck makes essentially the same point.

  22. Christopher
    Posted Dec 1, 2007 at 8:31 PM | Permalink | Reply

    Ferdinand Engelbeen, do you have a link to the Excel EBM?

  23. Tim G
    Posted Dec 1, 2007 at 8:51 PM | Permalink | Reply

    Shouldn’t we doing “climate model report cards”?

    I apologize, I am not a climate scientist and I don’t keep up with the details of the research. But as an engineer, I always wondered why we don’t compare the predictions with the measurements after the fact. We’ve had “accurate” climate models for at least a decade. And each of those models predict out over more than the next decade. Why not have a “report card” each year to compare the measured results with the predictions made by the models in the previous years?

    Is there some reason we don’t do that? Or, do we and I just never heard of it. If the modelers are confident in their models, they should be the ones pushing this idea.

    Anyway, maybe I’m missing something. Sorry for interrupting :)

    –t

  24. Anthony Watts
    Posted Dec 1, 2007 at 9:07 PM | Permalink | Reply

    Just for reference to discussions in this thread, here is an email from Gavin Schmidt admonishing me for saying the surface temperature record is used in climate modeling:

    http://wattsupwiththat.wordpress.com/2007/06/22/a-note-from-a-nasa-climate-researcher/

    He makes some interesting comments, it is worth a read.

    Now to be fair, I have not waded through the recently released GISS code to confirm or falsify Gavin’s claim. But in deference to his greater experience, I’ve put the issue on hold in my online discourse.

    But I do have to wonder this: If the surface temperature record is not useful to the climate modeling effort, either as a tuning, and/or as a target, why does GISS expend so much effort on GISTEMP? Likewise, why does HadCRU maintain their own surface network?

  25. Philip_B
    Posted Dec 1, 2007 at 9:12 PM | Permalink | Reply

    Previous thread on the accuracy (or should I say innaccuracy) of the models from 2006. Tim G, I think it answers your question.

  26. steve mosher
    Posted Dec 1, 2007 at 9:25 PM | Permalink | Reply

    RE 25. Technically he is correct

    1. They dont use GISS they use HADCRU.. at least for IPCC studies HADCRU was used.

    2. The temperatures are not used as inputs. They are used to “check” outputs

    At least that’s the way he explained it to me.. plus if you get the Educational version of GCM
    you will see that temps are not inputs.

    So basically, run the model. compare to hadcru, twist a Knob

    basically I would put the whole process in a loop and overfit the SOB like degrees of freedom came for free.

    Right Bender?

  27. Joel McDade
    Posted Dec 1, 2007 at 9:27 PM | Permalink | Reply

    #24 Tim G

    I’m not sure I agree with all of them, but Warwick Hughes has a running scorecard of GCM predictions v. observations:

    http://www.warwickhughes.com/hoyt/scorecard.htm

    The main one IMHO is tropospheric warming relative to surface. It seems to me that if the Team is inflating the surface measurements they are falsifying their own hypothesis. Weird, but true.

  28. Posted Dec 1, 2007 at 9:33 PM | Permalink | Reply

    RE27 Mosh then from my first post in 25, surface temps are then a “target” for the model output. i.e. the temperature knob.

  29. steve mosher
    Posted Dec 1, 2007 at 9:49 PM | Permalink | Reply

    RE 29. It’s more like this. You run the model and it outputs 42.

    historical says it should be 37.

    So, you “adjust” the model.. more cow bell!

    You run the model again.. 37.3. There you got just the right amount of cowbell

    The temp record is 37. The Cowbell is the secret sauce.

  30. George M
    Posted Dec 1, 2007 at 9:59 PM | Permalink | Reply

    A further question is whether real temperatures are used, or adjusted temperatures? This gridding business can conceal a lot of manipulation also.

  31. bender
    Posted Dec 1, 2007 at 10:00 PM | Permalink | Reply

    basically I would put the whole process in a loop and overfit the SOB like degrees of freedom came for free.
    Right Bender?

    Yes, this is my take on what they do. Gavin Schmidt insists that I am mistaken, however. Or rather, that I am misrepresenting the statistics of it. I do not think so. We’ll go there in a bit; the Mizzou game ain’t over yet. [Meanwhile, read "exponential growth" #1,#2,#3. Will pull out some relevant quotes tomorrow.]

  32. George M
    Posted Dec 1, 2007 at 10:06 PM | Permalink | Reply

    28-Joel:

    Thanks for that reference to model scores. My understanding of models is that they are simply(!) a set of simultaneous equations. Am I wrong?

    Anyone:
    What I would really like to see is a tabular list of inputs (variables) which these models use, with an additional column indicating whether each input is from real physical measurements, or “forced”. i. e. a guess.

  33. Jon
    Posted Dec 1, 2007 at 10:25 PM | Permalink | Reply

    Steve, #27 writes:

    2. The temperatures are not used as inputs. They are used to “check” outputs

    At least that’s the way he explained it to me.. plus if you get the Educational version of GCM
    you will see that temps are not inputs.

    None of which is relevant. ModelE runs on its own of course. The question is how was the model developed, not what happens when you get a code drop and press go.

    The source code is riddled with non-physical fitting factors. I encourage you to look yourself: http://www.giss.nasa.gov/tools/modelE/

    AFAIK, modelE is a very complex program; sadly it lacks complexity precisely in those areas more relevant to the debate and instead dives into what is arguable minutiae. Take this passage from their own summary:

    “The soils are modelled separately for bare and vegetated
    soil. Additionally, there is a canpoy layer over the
    vegatated portion. The soils are modelled using 6 variable
    depth layers. Evapotransiration takes water from the surface
    pools, and from below as a function of rooting depths.
    Runoff is calculated using a TOPmodel approach.”

    (spelling errors preserved)

    Reading stuff like that makes you wonder how the model manages to do anything meaningful at all. Too Many Variables.

  34. Peter Hartley
    Posted Dec 1, 2007 at 10:31 PM | Permalink | Reply

    Hasn’t Lindzen been saying this for some time? Also, I thought this related comment from Nir Shaviv on “The fine art of fitting elephants” had been remarked upon here before.

  35. James Lane
    Posted Dec 1, 2007 at 11:03 PM | Permalink | Reply

    I’ll briefly chime in to echo comments made by others working with models. I also have extensive experience working with models in a commercial environment. It’s obvious that you can tune your model to replicate historical data, and that there are any number of ways to do it. To that extent I think Kiehl’s conclusions are banal. The interesting part is that they are being published by a mainstream climate sciientist.

  36. Steve McIntyre
    Posted Dec 1, 2007 at 11:04 PM | Permalink | Reply

    #35 and others. Kiehl’s article is not generalized venting against overfitting but a specific empirical article linking model climate sensitivity to the selected history of aerosols.

  37. Jon
    Posted Dec 1, 2007 at 11:13 PM | Permalink | Reply

    #35 and others. Kiehl’s article is not generalized venting against overfitting but a specific empirical article linking model climate sensitivity to the selected history of aerosols.

    Yes, he points out that some sort of fitting must be going on. This is what we’re harping about. His assertion that the fitting is a result of different aerosol datasets may well be true, but I doubt it is the aerosol datasets that are being manipulated. Rather, people are designing the model to get the desired match given whatever (honest) data they feed into the front-end.

  38. Frank K.
    Posted Dec 1, 2007 at 11:16 PM | Permalink | Reply

    Re: 34

    I would go further and say that ModelE is effectively undocumented code. All one has is the source code (and that is, in my opinion, poorly commented) and this “reference manual”:

    http://www.giss.nasa.gov/tools/modelE/modelE.html

    Not a differential equation in sight(!) – just terse descriptions of specific subroutines. This is unforgivable for a code which is currently being used to generate various climate disaster scenarios such as contained in this paper:

    http://pubs.giss.nasa.gov/abstracts/2007/Hansen_etal_1.html

    For an example of proper documentation, have a look at NCAR’s CAM3:

    http://www.ccsm.ucar.edu/models/atm-cam/docs/description/

  39. bender
    Posted Dec 1, 2007 at 11:20 PM | Permalink | Reply

    Bob Stoops is genius; this paper is not.

    The paper starts with a premise that “climate forcing” and “climate sensitivity” are two (of three) important factors shown to determine Earth’s global mean surface temperature. It goes on to illustrate that “climate forcing” and “climate sensitivity” estimated in nine GCMs and two EBMs is negatively correlated. Is this a scientific discovery, or is it a tautology – true merely by definition? It seems to me to be an absolutely trivial result of the initial premise. If f=ma or d=vt or temperature=forcing*sensitivity, then any plot of the two factors against one another is going to lead to a hyperbolic curve of the exact shape produced in Fig 1. The result is predetermined by the premise.

    I will let JEG decide whether tautology as proof qualifies as pseudo-science.

    The author refers in the Introduction to the “curious result” that so many GCM parameterizations can lead to such a narrow range of outcomes. This also is trivially obvious. With a large number of free parameters and a number of different model formulations, this MUST be true, if all the models are being tuned to match a single output. Again, a tautology presented in the guise of a “result”.

    The work involved in putting the paper together is not trivial, but the results are. This is pure pedagogy.

    After all the effort putting this paper together, the author misses the most important point of all – which is the point made by Gerald Browning, and one made by several commenters here already – that multiple models all giving the same result using different tunings is not a sign of strength; it’s a sign of weakness. I won’t go further with that line of argumentation because it’s not my argument. (But we can go there tomorrow if you like.)

    I close by asking the experts – like JEG: is this paper more “novel”, more “innovative” and more “publication-worthy” than, say, Loehle (2007) in EE? Who would like to step up and argue that case with me? Finally, where are the critics of EE and the defenders of GRL? Explain to me: who peer-reviewed this paper, and decided it was worth $9? I missed the game winning touchdown by Oklahama for this?

    Save your $$$ folks.

  40. Willis Eschenbach
    Posted Dec 1, 2007 at 11:20 PM | Permalink | Reply

    I think this paper is important because Kiehl is a mainstream researcher. What he says is that the models do well historically speaking because they are tuned to do well. More than that, he tells us exactly how they are tuned, by adjusting the aerosol forcing.

    Now I’ve both known and said that for a long time, see my previous posts on aerosols and forcings, but I’m just a reformed cowboy living in the Solomon Islands, what do I know? But when Kiehl says it, people will have to listen.

    So kudos to Kiehl for stating what is obvious to some, but not to others.

    w.

  41. bender
    Posted Dec 1, 2007 at 11:59 PM | Permalink | Reply

    Kiehl makes a serious mistake in confusing actual climate forcing and climate sensitivity with modeled climate forcing and climate sensitivity. The mistake is made starting in paragraph 6 and carries through paragraph 8, to the conclusion in paragraph 17:

    These results indicate that (i) the range of uncertainty in anthropogenic forcing of the past century is as large as the uncertainty in climate sensitivity and that (ii) much of forcing uncertainty is due to aerosols.

    He is assuming that the true uncertainty is represented by the range of variable parameterizations used in the models (Fig. 1). This assumption is not necessarily true.

    The true uncertainty on these “factors” is determined by the propagation of error through all the parameters (free and fixed, tuned and untuned) and equations that lead up to the computation of the “climate sensitivity” and “climate forcing” factors. Which he ignores. The “uncertainty” represented by the variation in the Fig 1 parameterizations is merely an indicator of random drift in arbitrary and subjective choices made by independently evolving modeling research groups. It is not the true uncertainty. It is human/institutional uncertainty, not physical/statistical uncertainty.

    The argument advanced in the conclusion is somewhat dangerous. He argues that aerosols are the lynchpin, suggesting the community ought to target this aspect of the models for improvement. Sounds innocent enough. But what improvement for what purpose? The purpose IMO is not to improve individual model performance, but to shore up a consensus that has obvious weaknesses (e.g. Fig. 1 variability). The goal IMO is to reduce the human uncertainty evident in Fig. 1, with intent to infer that this is equivalent to a reduction in the physical uncertainty in the models.

    The pea under the thimble.

    Watch out for modelers that mistake their models for reality. See the hidden tautology. See the pattern of groupthink emerge. See the premium that is placed on getting this very mundane paper published. See the house of cards being buttressed.

  42. bender
    Posted Dec 2, 2007 at 12:27 AM | Permalink | Reply

    when Kiehl says [they are tuned by adjusting the aerosol forcing], people will have to listen

    But that’s not the part of Kiehl (2007) that is going to be listened to. That’s not his message. [Besides, this is old news to climatologists. The modelers have never denied the tuning of several parameters. They've simply argued that "several" is not the same thing as "too many".]

    I think this paper is important

    I think it is revealing (in a way that is important for people to understand), but not important (to climate science). [There are undoubtedly far more important GCM papers to come. I'll save my $9 for those.]

  43. Leonard Herchen
    Posted Dec 2, 2007 at 12:34 AM | Permalink | Reply

    I think this paper is merely stating the obvious. Climate modelling is a history match effort similar to the reservoir modeling used by petroleum engineers (no irony there). To match the same data set of history, there will be a natural correlation between the way different modellers tune variables to get a match. It is not unscientific to tune a model to get a result. That is the only way to refine a model.

    The fundamental problem with simulations is, as many readers know, is that there is no unique solution, so, even when we get a good history match, we can’t be sure if it is coincidence or not, and the value of a particular model as a predictive tool is nearly impossible to measure. A model with a perfect history match can go badly wrong as a predictor very quickly and there is no way to know in advance.

    The anthropogenic climate change hypothesis is based on the observation that history matched models do not match if the CO2 forcing is turned off. It looks pretty convincing when you read the IPCC work or even one of Hansen’s first papers (Science 28th August, 1981, Volume 213, Number 4511, “Climate Impact of Increasing Atmospheric Carbon Dioxide”)

    One way to test the hypothesis is to see if a good history match can be done without the CO2 Forcing using parameters that are physically believable. The “mainstream” climate scientists say it can’t be done. I remain unconvinced. I’m not sure how hard they are trying.

    It should be noted that Hansen’s predicts in his 1981 paper that world temperature will increase by a factor outside of natural variabiity (he uses 2 sigma) by the end if “this century”, that is by 2000. If the surface temperature record is correct, that has occurred and his 1981 model has some support from predictive success.

    The reality is that the range of outcomes suggested by Hansen in 1991 hasn’t actually been refined much by 20 years of climate modeling effort.

  44. James Lane
    Posted Dec 2, 2007 at 12:43 AM | Permalink | Reply

    Well I stand by my conclusion that the result is banal, in the sense that, like Willis, I’d assumed all along that this is what was going on. OTOH, Bender, who has actually read the paper, considers it a step backwards. That gives me cause for pause.

    I’d be interested in Gavin’s take on Kiehl (2007), assuming that RC deigns to discuss it all.

  45. trevor
    Posted Dec 2, 2007 at 1:15 AM | Permalink | Reply

    I have spent much of my career developing financial models that are expected to provide a “value” for a mining project. The standard methodology requires that analysts estimate the most likely operating costs, capital costs, and revenue streams, derived from the underlying tonnage throughput, grades, recoveries etc and then use the Capital Asset Pricing Model (CAPM) to derive the NPV of forecast revenues and so arrive at a “value” for the project.

    I soon discerned that by judicious manipulation of key inputs, ie, just shifting most of the inputs 1-2% in one direction or the other, one could ‘push’ the resultant number wherever one wanted, and over quite a wide range.

    In an effort to address the problem properly, I investigated Monte Carlo simulation and applied it to these models. This involved applying a probability distribution to key input variables and then running, say, 1000 iterations using a marvellous off the shelf program called @risk – an add-in to Excel.

    The results are illuminating. To illustrate, 25 years ago, I was required to develop a value for a particular resource asset. I applied the CAPM approach (as is still the standard approach) and obtained a result of A$1100 million. At about the same time, I was asked to value another, much smaller, resource project. I applied CAPM, and achieved a result of $33m.

    The essence of CAPM is that you establish the “Beta” (the volatility of the share-price or a proxy for the share price) and that helps you to calculate the appropriate discount rate. Since the discount rate for the first project came out (using CAPM) at around 9%, and the second project around 14%, the CAPM theory says that it is safe to pay $1100m for the first asset, and $33m for the second.

    However, I then applied Monte Carlo, running 1000 iterations for each model. The outcome in the case of the first asset was that the Median NPV was $1100m, as expressed earlier, but 1 Standard Deviation was plus/minus $55m, or around 5%. This is very tight for a resource asset, and clearly shows that (subject to other due diligence) an investor can afford to pay $1100m for the asset, since it is readily financeable with that low SD, and of course, the nature of NPV calculations is that they don’t consider the “option” elements, of which there were many, most importantly the possibility of resource additions.

    The outcome in the second case was wildly different. The Median NPV was still $33m, but 1 Standard Deviation was plus/minus $75m million, giving a range of “value” from -$58 million to $108 million. I learned that the term for this distribution is “flat kurtosis”. Of course, any of us faced with that additional information would be very careful about paying the CAPM valuation of $33m, since it is demonstrably not financeable. An astute investor might pay around $5m for the option value, but not much more. My question is, if this is the outcome, then how valuable is CAPM is valuing such assets?

    To this day, so far as I know, Monte Carlo simulation is held in low regard in corporate finance circles, but, as I hope my example shows, it can be extraordinarily valuable in providing additional key information that can dramatically change the outcome.

    Further analysis illustrated why the outcomes were as they were. The first project had a very stable revenue stream (for reasons that I won’t go into) and very strong operating margins. The outcome is that varying key input factors in the likely rangese did not have much effect on the margin, with the result outlined above.

    By contrast, the second project was a high cost producer in its industry with narrow operating margins, and was very vulnerable to fluctuations in key input factors, particularly exchange rates and price assumptions.

    In the event, the second project went belly up, while the first project thrived, and delivered much more than the $1100m value to its investors.

    The point about going on at some length here is to point out that Monte Carlo simulation, properly applied, gives a direct measure of the merit of the valuation. I suggest that if similar approaches were used in climate forecasting models, that the results would show very flat kurtosis, ie pretty much equal probability of any outcome.

  46. bender
    Posted Dec 2, 2007 at 2:58 AM | Permalink | Reply

    #24
    Anthony Watts, I read your link, and encourage others to do so as well. I see you’ve been victimized, as have I, by the honest word-smithing obfuscationist. I understood at once the nature of his complaint in his first reply. I spotted immediately that his problem with your text was your use of the word “in”. That’s why he challenged you to scan the model code and locate the data. My hunch was confirmed when later, in his subsequent communication, he writes:

    “The station data are not used *in* climate models, and they are not used to predict future climate.”

    The Clintonesque cheek. The data, you see, are not used *in* the model code. They reside “outside” the model code. Silly you. Never mind that the data are part and parcel of the modeling process.
    They are used IN the tuning process.

    That is precisely where there is a thread at CA devoted to the topic of honesty in how these models are characterized by their gatekeepers.

  47. bender
    Posted Dec 2, 2007 at 3:20 AM | Permalink | Reply

    #28
    Yes, there you go. You have expressed it exactly but without resorting to the offending word: “in”. Much better.

  48. Chris Harrison
    Posted Dec 2, 2007 at 3:30 AM | Permalink | Reply

    The data, you see, are not used *in* the model code. They reside “outside” the model code. Silly you. Never mind that the data are part and parcel of the modeling process.
    They are used IN the tuning process.

    So the station data is used in a feedback loop during tuning and we can say that this data is a GCM feedback rather than a forcing!

  49. Philip_B
    Posted Dec 2, 2007 at 4:15 AM | Permalink | Reply

    “The station data are not used *in* climate models, and they are not used to predict future climate.”

    This is a patently ludicrous claim. Data is used all models. That’s pretty much the definition of a computer program. Data comes in, process occurs, data comes out (ie whether there is any data or reference to the data in the code is irrelevant). Climate models must use initial values in their processing. So is he saying they start the models from random values. Of course they don’t. They use station data however processed pre-satellite and it appears post-satellite as well).

    What he means is current station data is not used. Well, duh. The point of a model is to predict. And to think these people get paid for this kind of analysis.

  50. Ivan
    Posted Dec 2, 2007 at 4:27 AM | Permalink | Reply

    If I correctly understood, Kiehl only repeats what many already knew: Aerosol forcing is basically unknown, even the sign is in question, and in order to fine-tune the models so as to reproduce temperature anomaly of XX century you can (among other things) either to lower CO2 sensitivity, or to increase aerosol cooling effect. Why is this so important “discovery”, apart from being published by mainstream journal and by mainstream climate modeler? Even IPCC admits that the level of scientific understanding of aerosol forcing effects is low, as well as solar and volcanoes, which are completely omitted from the famous chart in AR4, due to their “episodic nature”, nothing to say about cloud cover and precipitation, that are also omitted and whom models miserably failed to reproduce. All those factors are basically adjustable parameters you can use as you see fit to make models agree with observations, and to “attribute” warming or cooling to any arbitrarily chosen factor. But, every one, including every person who had read any IPCC report already know they know nothing about magnitude (and sometimes even about sign) of forcing of the most climate factors. IPCC itself admits this. Why to be so surprised when someone form “inner circle” admits obvious? It was scandalous for them to not admit that much earlier.

  51. Posted Dec 2, 2007 at 5:21 AM | Permalink | Reply

    Re #22,

    Christopher, the next course can be found at: http://cpd.conted.ox.ac.uk/env/courses/modelling.asp. The individual fee is quite high, but if you are a member of any NGO (no matter what, doesn’t need to be climate related), it is bearable. We combined it with a nice round trip in Ireland and spent a few days in Oxford…
    The Excel spreadsheet is propietary of the Oxford Environment Department and may be used up to 1 month after the course. I obtained a longer (life-time?) allowance, but may not share it with others.

    But in fact, you can build it yourself, as the data series are available (besides that you can use different series for aerosols and solar forcing), and the effect of a change in any forcing on the ocean heat content (the main short to long-term influence on temperature) is known too…

    The nice part is that you can change each individual effect factor for any individual forcing. That means that one can change the effect of e.g. CO2 vs. solar changes in forcing. In current climate models, they have similar effectiviness for 1 W/m2 solar as for 1 W/m2 GHGs (+/- 10%), while that is certainly not true (solar and volcanic have their highest influence in the stratosphere, GHGs and aerosols in the troposphere).

    Willis has done some multivariable atttribution experiments in that sense, but I have seen that he differentiated a little too much between many variables (like black aerosols and sulfate aerosols). If he may repeat that with only four variables (all GHGs lumped together, all types of aerosols lumped together, solar and volcanic), the “best fit” might be more clear. Even that doesn’t mean that the “best fit” is what really happens in nature, as most data series themselves have rather high error margins and as you can see in the one can change the GHG/aerosol tandem within wide margins with little influence on the correlation of the model result with temperature.

  52. JM
    Posted Dec 2, 2007 at 6:48 AM | Permalink | Reply

    As I understand, temperature change along the XX century doesn’t have enough degrees of freedom to distinguish between models with very different assumptions.

    Even more important, attribution of temperature rise to CO2 emissions is only possible if one is able to quantify with great accuracy all other factors. If at least one of the factors (e.g. solar radiation effects, aerosols, water vapor feedback effects) is not known with great accuracy, it is impossible to attribute temperature rise to CO2 emissions. This work seems to be a recgnitions that attributions is, for the moment, impossible.

  53. Posted Dec 2, 2007 at 8:38 AM | Permalink | Reply

    Steve McI,

    I noticed a discrepancy between graph 2 of Kiehl and the graph used as reference at RealClimate:

    The graph at Realclimate is right: if the influence of aerosols (forcing or sensitivity or both) is low, one need to lower the sensitivity for GHGs too, or the 1945-1975 temperature dip can not be reproduced and/or the increase after 1975 is too fast.

    I have not purchased the Kiehl article yet (and didn’t go to the library either), thus I don’t know why they show opposite graphs…

    And of course, graph 1 of Kiehl is only true because the models use similar sensitivities for all kind of forcings…

  54. Pat Keating
    Posted Dec 2, 2007 at 9:43 AM | Permalink | Reply

    43 Leonard

    I suspect that even better model agreement with history would be obtained by (a) turning down (or off) the ‘climate sensitivity’ of CO2 (i.e., the parameterized strength of CO2 as a GHG), and (b) introducing insolation effects, with increased ‘climate sensitivity’ to fit in the same way the models treat CO2. However, the AGW modelers will not do that…..

  55. Tony Edwards
    Posted Dec 2, 2007 at 9:53 AM | Permalink | Reply

    Pat Keating #19 says “Have you read Gerald Browning’s posts over at the Exponential thread, where he says even the near-term weather-forecast models blow up trying to predict past 36 hours?”
    Shouldn’t that be postdict?

  56. bender
    Posted Dec 2, 2007 at 9:57 AM | Permalink | Reply

    #50 asks “Why is this so important ‘discovery’”
    It’s not so important, but he does quantify (1) the tradeoff between adjusting sensitiy vs forcing (Fig 1), and (2) the non-independence relationship between total and aerosol forcing. It’s one thing to intuit that a relationship exists, quite another to quantify it.

    Analogy: Steve M knew the HS was junk. But he didn’t stop there; he proved it. See the diff?

    #53: Look carefully at the axes labels. y-axis in both Kiehl figures is “total forcing”.

  57. bender
    Posted Dec 2, 2007 at 10:00 AM | Permalink | Reply

    Do we know which models are associated with which points on the two graphs?

  58. Posted Dec 2, 2007 at 10:38 AM | Permalink | Reply

    Re #56,

    Thanks Bender, that makes a big difference, and resolves the discrepancy.

    The RealClimate graph gives a much better idea of the endresult: lower aerosol influence results in a lower sensitivity for GHGs in order to match the temperature trend of the past century…

    And of course, the “total” forcing in the two graphs and their effect largely depends of the non-anthro forcings and their effect. If the latter effects are higher than currently implemented in the GCM’s, then the anthro forcings/effects need to be reduced further…

  59. Anthony Watts
    Posted Dec 2, 2007 at 11:13 AM | Permalink | Reply

    RE46, Thanks Bender. I always suspected as much, but scanning old Fortran code is about as pleasant as having a tooth pulled, so I never got around to checking Gavin’s challenge, having a project to run.

    Henceforth I shall say then, that “surface data is used to tune the models that predict our climate future” where such a description may be appropriate. I now beleive that statement to be fully accurate, unless evidence is presented otherwise.

  60. Michael Hansen
    Posted Dec 2, 2007 at 11:16 AM | Permalink | Reply

    Stephen Schwartz has an interesting paper [1], elaborating on some of the thoughts expressed in Kiehl 2007. Key-quote:

    “Uncertainty in aerosol forcing must be reduced at least three-fold for uncertainty in climate sensitivity to be meaningfully reduced and bounded.”

    [1] http://www.ecd.bnl.gov/steve/pubs/UncertaintyReqmentsAWMA.pdf.

  61. bender
    Posted Dec 2, 2007 at 12:00 PM | Permalink | Reply

    Re #59 Where’s the code? I’ll audit it.

  62. hswiseman
    Posted Dec 2, 2007 at 12:06 PM | Permalink | Reply

    You don’t need a weatherman to know which way the wind blows, but you might need a lawyer to help you wordsmith the forecast. Gavin Schmidt must have an enormous blindspot if he believes that his talmudic objections to Anthony’s language actual serve to validate the underlying construction of the GCMs. His fine linguistic distinctions are disingenuous at best and diminish his stature and scientific credibility.

  63. bender
    Posted Dec 2, 2007 at 12:24 PM | Permalink | Reply

    #60
    More of the same. See the objective here?: Get the models to converge on one another (“reduce uncertainty”) to make it appear that there is consensus on the various forcings and sensitivity parameters. Re-read #41. That ain’t prediction uncertainty, folks. That’s institutional uncertainty about how to model climate. Ask yourself: what is the purpose of “reduced uncertainty” on aerosol forcing? Better forecasts? Increased physicality of model structure & realism of model behavior? No. Lower variation in institutional opinion. Why? New clothes for the emperor. The policy makers are embarrassed by Kiehl Fig 1. Why? Because they don’t understand what it means. They don’t understand that it’s inevitable. That:

    The parameterizations are extremely crude in the climate models.

    Has anyone asked why there are so many climate models and NWP models if the
    numerical errors are small? The obvious answer is that there are so many different eddy viscosity types (that are too large) and so many different parameterizations that are used to overcome the inappropriate
    viscosity in order to obtain a spectrum that kind of looks reasonable.

    It has been known for some time that the highest resolution global model (ECMWF) has not converged to a better forecast exactly for this reason.

    This is science?

    Let’s ask JEG what kind of science he thinks this is.

    [DISCLAIMER: Do not take my word on these issues. Investigate them for yourself. I am not a climatologist. My hunches are purely speculative.]

  64. bender
    Posted Dec 2, 2007 at 12:27 PM | Permalink | Reply

    The source of the quote in #63 is provided in the embedded link.

  65. Jon
    Posted Dec 2, 2007 at 12:29 PM | Permalink | Reply

    Bender, #61:

    I posted the link to modelE earlier.

  66. bender
    Posted Dec 2, 2007 at 12:33 PM | Permalink | Reply

    #65 Thanks, I missed that. I see it’s in #33.

  67. Anthony Watts
    Posted Dec 2, 2007 at 12:38 PM | Permalink | Reply

    RE66,

    Bender there’s also “model E light” aka EdGCM at: http://edgcm.columbia.edu/

    Which unlike Model E, does not require you to install a FORTRAN compiler on your machine to run.

  68. Peter D. Tillman
    Posted Dec 2, 2007 at 1:00 PM | Permalink | Reply

    Sylvain says, #20, Pielke Sr. blog

    This suggests that, at least up to the present, the effect of human climate forcings on global warming has been more muted than predicted by the global climate models. [boldface added]

    This issue was inadequately discussed by the 2007 IPCC report. Climate Science has weblogged on this in the past [link], but so far this rather obvious issue has been ignored.

    Thanks for the link. Interesting post.

    Cheers — Pete Tillman

  69. Posted Dec 2, 2007 at 1:28 PM | Permalink | Reply

    @Bender #31.
    With regard to how the fitting is done, you may or may not be correct. Gavin describe the way tuning is supposed to be done in principle. In practice, there are several difficulties:

    a) individual laboratory experiments may give a range for a parameter. The range may be large or small, depending on what you are trying to parameterize.

    b) various different forms are suggested for parameterization. That is, entirely different sets of equations might be proposed. All, in principle, may looks ok compared to a laboratory experiment. (Generally, more complicated forms can give the illusion of capturing more physical processes, but they generally also create more tunable-constants, take more computational power, and have been tested in fewer experiments.)

    c) laboratory experiments are more or less “clean”. One might do one experiment with only forced convection. Another with pure free convection and so forth. One might try mixed convection over some particular range, but with a very simple geometry. Sometimes, there really aren’t experimental data that fully test a parameterization outside a particular code.

    Mind you, some parameterizations suffer from few of these difficulties and are bullet proof. (But, I’d guess the ones in GCM’s are NOT bullet proof. That’s likely why JimD mentions that tuning is an art when discussing tuning on the exponential thread. )

    But, some parameterization is required to create a code, so the person assembling the full model picks a code and a parameter.

    In principle, that would be that. You picked the ‘best’ values based on lab experiments.

    But in practice, model are run by humans who do want their results to look good. Any modeler remembers they had to make a choice about the magnitudes of “constants” and also about the specific closures to select. If the model or tunable-constants results in poor predictions for the global model, people do start adjusting the constants to see which best match the test data (in some sense.)

    This isn’t necessarily a bad thing to do, but you have identified the general problem. The knobs do get adjusted someone to fit the existing data. In most fields the modelers would list the magnitude of all values of “tunable constants” one might have selected, and the papers describing what the magnitudes are in lab experiments should be mentioned.

    In the thread on exponential growth, you’ll notice I’m asking Jerry B. to explain precisely why he thinks the closure is unphysical. That’s because I’m trying to figure out how far people are turning knobs.

    From my point of view, getting the answer for on particular parameterization would be enlightening, even if it’s not the most important one.

    The answers to the questions I’m trying to pose would tell me whether the truth of what modelers are doing is closer to what Gavin says (which is possible) or what you suggest (which is also possible.)

    For better or worse, both methods of getting parameters have been known to occur. It should be possible to tell which is happening if someone who runs GCM (possible Gavin) will answer with sufficient detail. (As in: for example, our boundary layer model uses a blah, blah blah type closure proposed by joe, fred and holly — give reference. Model constants were obtained from experiments conducted by Marvin- give reference. )

  70. bender
    Posted Dec 2, 2007 at 1:35 PM | Permalink | Reply

    you may or may not be correct

    Yes, I realize this and fully agree. Hence the disclaimer. Hence my efforts to clarify at RC exactly how the heck they work the tuning process. I know how I would do it. I want to know EXACTLY how THEY do it.

  71. Boris
    Posted Dec 2, 2007 at 1:36 PM | Permalink | Reply

    Why? Because they don’t understand what it means.

    Oh, come on now. This thread is getting ridiculous.

  72. bender
    Posted Dec 2, 2007 at 1:45 PM | Permalink | Reply

    That’s likely why JimD mentions that tuning is an art

    Hey, I love art. What I don’t love is art dressed as science. If there are uncertainties in the models, account for them. If the models are overfit, account for it. Whatever you do, don’t hide it. Again: the divorcing of model fitting from formal statistical analysis. Have these guys not learned from the paleoclimatology experience? I suppose we need another Wegman to analyze what these guys are doing?

  73. bender
    Posted Dec 2, 2007 at 1:55 PM | Permalink | Reply

    #71 It doesn’t bother me that you don’t agree with my hypothesis as to how Kiehl’s Fig 1 might be interpreted by policymakers. It’s a side point that is fairly irrelevant to the overall argument, which is that there is a move afoot to shore up the “consensus” surrounding the models.

    Which leads to the fairly robust, testable prediction that the literature is about to get silly with efforts to band-aid that which is not easily fixed.

  74. RomanM
    Posted Dec 2, 2007 at 1:55 PM | Permalink | Reply

    #39. bender says:

    The author refers in the Introduction to the “curious result” that so many GCM parameterizations can lead to such a narrow range of outcomes. This also is trivially obvious. With a large number of free parameters and a number of different model formulations, this MUST be true, if all the models are being tuned to match a single output. Again, a tautology presented in the guise of a “result”.

    Naw, you’ve just got the wrong spin on it. These folks will tell you that the “actual” reason is that these models are so good that they all give you the CORRECT answer. ;)

    On a quick read of the paper, I didn’t find it particularly incisive. However, I did notice as in many other papers dealing with AGW (or as I have come to think of it – the Y2.1K problem), there is a gratuitous admission that any faults found in the AGW evidence structure aren’t really damaging because … (substitute some external reason which was never specifically addressed within the body of that particular paper). Despite the admission that because of the uncertainties involved, the models are not capable of doing a reasonable job, Kiehl states in the conclusions section:

    It could also be argued that these results do not invalidate the application of climate models to projecting future climate for, at least, two reasons. First, within the range of uncertainty in aerosol forcing models have been benchmarked against the 20th century as a way of establishing a reasonable initial state for future predictions. The analogy would be to weather forecasting where models assimilate information to constrain the present state for improved prediction purposes. Climate models are forced within a range of uncertainty and yield a reasonable present state, which improves the models predictive capabilities. Second, many of the emission scenarios for the next 50 to 100 years indicate a substantial increase in greenhouse gases with associated large increase in greenhouse forcing. Given that the lifetime of these gases is orders of magnitude larger than that of aerosols, future anthropogenic forcing is dominated by greenhouse gases. Thus, the relative uncertainty in aerosol forcing may be less important for projecting future climate change.

    I am not sure if I even understand the exact point of the first reason, but I will try to interpret it. “We will overfit these models to match what’s happened so far and because of that, the models will be able to predict what happens next” (ignoring the fact that no predictive ability has been demonstrated in the “benchmarking” process). The second reason seems a bit more straightforward. “We are predicting that so many GHGs will be pumped into the atmosphere during the next half of the century that it won’t matter that the models aren’t reliable – the temperature will shoot up as high as the models predict”. You see, bender, they will give you the correct answer regardless.

  75. bender
    Posted Dec 2, 2007 at 2:00 PM | Permalink | Reply

    The genuflecting is nauseating.

  76. Kenneth Fritsch
    Posted Dec 2, 2007 at 2:16 PM | Permalink | Reply

    Bender, I share your anger and pain: (1) paying for a paper with great expectations that on reading are dashed, (2) missing part of a game where the outcome may affect the bowl bid of a favorite university (Florida for you and Illinois for me) and (3) reading a paper that intends to provide a band aid as a fix for the climate modeling uncertainty problem. Have you decided yet which one made you the most angry?

  77. Ivan
    Posted Dec 2, 2007 at 2:28 PM | Permalink | Reply

    Second, many of the emission scenarios for the next 50 to 100 years indicate a substantial increase in greenhouse gases with associated large increase in greenhouse forcing. Given that the lifetime of these gases is orders of magnitude larger than that of aerosols, future anthropogenic forcing is dominated by greenhouse gases. Thus, the relative uncertainty in aerosol forcing may be less important for projecting future climate change.

    But, problem is high sensitivity for “projecting future climate change”. Aerosols may or may not become less important as a forcing in the future, but none can establish “desired” high GHG sensitivity without attributing very high cooling effect to aerosols in XX century. So, it’s not clear how Kiehl can eat the cake and to have it in the same time: if you didn’t establish properly large cooling effect of XX century aerosols (as you didn’t), you also didn’t establish high CO2 sensitivity – from here to eternity, whatever happens in the future. Period. Whether aerosols will became less important factor in future is therefore completely irrelevant.

  78. Joe Black
    Posted Dec 2, 2007 at 2:28 PM | Permalink | Reply

    Climate Science 101 for dummies?:

    http://www-das.uwyo.edu/~geerts/cwx/notes/chap12/nwp_gcm.html

  79. bender
    Posted Dec 2, 2007 at 2:33 PM | Permalink | Reply

    From RC:

    bender Says:
    18 May 2007 at 12:42 AM
    Parameterized models typically fail soon after they are used to simulate phenomena in domains outside those in which the model was parameterized. As climate continues to warm, the probability of model failure thus increases. The linear trend fit of the Hansen model to observations during the last few decades thus does not imply that we can expect more of the same. This is why I am interested in the uncertainty associated with the parameters that make up the current suite of parameterizations. How likely is it that those parameters are off, and what would be the consequences, in terms of overall prediction uncertainty? Is moist convection the wild card? Why or why not? Tough question, I know. Possibly even overly presumptive. Maybe the best way to answer is through a new post?

    [Response: You need to remember that the models are not parameterised in order to reproduce these time series. All the parameterisations are at the level of individual climate processes (evaporation, cloud formation, gravity wave drag etc.). They are tested against climatology generally, not trends. Additionally, the models are frequently tested against ‘out of sample’ data and don’t ‘typically fail’ - vis. the last glacial maximum, the 8.2 kyr event, the mid-Holocene etc. The sensitivity of any of these results to the parameterisations is clearly interesting, but you get a good idea of that from looking at the multi-model meta-ensembles. One can never know that you have spanned the full phase space and so you are always aware that all the models might agree and yet still be wrong (cf. polar ozone depletion forecasts), but that irreducible uncertainty cuts both ways. So far, there is no reason to think that we are missing something fundamental. -gavin]

    Bold mine.

    I wasn’t suggesting they are “missing something fundamental” in their models. I was asking what was their confidence was in their parameters. i.e. What’s the probability they’ve inadvertently built in a warm bias? I asked for a post on the topic of model tuning. e.g. What are these “out-of-sample” scenarios? How many knobs? How do you handle the potential non-ergodicity of oceanic flows (i.e. non interchangeability of time-series and ensemble-series and ensemble-”ensemble” series)? etc. I did not get the post that I politely asked for. Instead, I got snowed.

    There were two other times when the same thing happened. On equally important questions of how GCMs are tuned.

    Why don’t they answer questions, if they are the high priests of climate science? I don’t get it. Steve M answers questions asked of him. Moreover, the tougher they are, the more likely he is to answer.

  80. Michael Hansen
    Posted Dec 2, 2007 at 2:37 PM | Permalink | Reply

    bender #63;

    I think Schwartz’s paper is quite an honest attempt at establishing at least necessary conditions for making reliably forecasts; I see no evidence that Schwartz also think that these conditions are sufficient. Is Schwartz and Kiehl just a part of a bigger gang, trying to institutionalize uncertainty? Wow, maybe. But that’s pure speculation, and since we have more than enough of that in climate science, I’ll pas on that one.

    Ultimately, as with all other theories, this will come down to the capability to predict. Do surface temperature and other parameters evolve as predicted in 1988, 1996, 2001 and 2007, and does the predictions match better than simple linearization, or some wavelet / Fourier-analysis? If yes-yes, then, fine, the models have skills and is fair game; if else – as I think is the case right now – the politicians should, for the moment, abstain from making rash decisions based on analysis comparable to throwing a dice or flipping a coin.

  81. bender
    Posted Dec 2, 2007 at 2:43 PM | Permalink | Reply

    Ken, it’s #79 that gets my goat. Kiehl (2007) appears to be functioning as a whip to the modeling community. A call for even more groupthink.

  82. bender
    Posted Dec 2, 2007 at 2:50 PM | Permalink | Reply

    Schwartz and Kiehl just a part of a bigger gang, trying to institutionalize uncertainty?

    No. Re-read. They’re trying to do pretty much the opposite. They are trying to “make” the models more precise. “Make” being the operative word. They are trying to reduce the apparent uncertainty; and I allege that they confuse actual uncertainty in individual model predictions with variability among model formulations.

    Fairly simple, really.

  83. bender
    Posted Dec 2, 2007 at 3:08 PM | Permalink | Reply

    Appeal to authority #78 is fine with me, although it’s usually more helpful if you can be specific about what it is there that would help to advance the discussion.

    I’ve had my say for the day, and I’m sure Steve will agree: that’s quite enough.

  84. Wansbeck
    Posted Dec 2, 2007 at 3:29 PM | Permalink | Reply

    I have seen several posts on the good or bad of tuning.
    It is perfectly acceptable, and common, to tweak models to make their predictions more accurate. If a model gives reliable accurate predictions it is a good model no matter how it was tweaked. This is completely different to tuning a model to fit historic data. If a model does not match historic data then it is wrong. The offending article or articles need to be found, re-examined and sound reasons given for why and how they were wrong and why different parameters should be used. To simply change parameters to get a better fit without being able to give a physical explanation for this is ,shall we say, dubious.

  85. Frank H. Scammell
    Posted Dec 2, 2007 at 3:56 PM | Permalink | Reply

    I find it interesting that a lot of the shouting and hollering by the AGW/RC crowd is about catastrophes, tipping points etc., because these events are clearly nonlinear and probably chaotic. When you are talking about climate sensitivity (to a doubling of CO2, for example), that is a linearized estimate. The models do not permit chaotic solutions because they are highly damped (atmospheric viscosity, boundary conditions, etc.). Otherwise, there would be exponentially divergent solutions (some of which would show -horrors- lower temperature histories -predictions – projections – estimates for the future.) The models are seeking an equilibrium solution some time in the future when it is obvious that the climate is a non equilibrium entity (non-stationary). It may be that most of the equation set are real physics, but non physical elements are required to deliberately constrain the equation set, in order to produce a stable solution. Monte Carlo runs assume that equations have been linearized. It would be quite unsettling if a Gaussian distribution among the variables produced a bifurcated, clearly nonGaussian output. Hard to estimate a climate sensitivity !

  86. George M
    Posted Dec 2, 2007 at 4:00 PM | Permalink | Reply

    Well, to state the “problem” in simple terms. The earlier models predicted x but y actually happened. The climateers respond: “Ah, but we’ve fixed that.” “The current model will be shown to be correct. Now, hurry and pony up for thse carbon credits (before this model fails just like the previous ones did).” They continue to obsfucate long enough for people to forget how badly previous models performed, thus being a moving target as it were, meanwhile forcing the political agenda.

    Now, somewhat OT: Which thread had the discussion on who pays Hansen(not Michael)? Thanks.

  87. Frank K.
    Posted Dec 2, 2007 at 4:07 PM | Permalink | Reply

    “I wasn’t suggesting they are “missing something fundamental” in their models. I was asking what was their confidence was in their parameters.”

    As I mentioned in an earlier response, there is no way, for modelE specifically, that you can know if they’ve “missed something fundamental” because they have NOT adequately documented their code. I REALLY is that bad – and I don’t think Dr. Schmidt will be doing anything soon to rememdy the situation.

    I encourage everyone to go have a look at the FORTRAN. It’s nearly impossible as it stands to determine if they have everything implemented correctly. These kinds of research codes frequently break when you go outside some established range of parameters because the authors in many cases cannot anticipate every possible permutation and combination of inputs. Add to this the fact that the various modules interact with each other in a myriad of complex ways, and you have even more opportunities for problems. And yet it is the output from these codes that is being used to “predict” global climate distaster. Accordingly, it is NOT too much to ask for some due dilligence on the part of NASA/GISS with repsect to code documentation!

  88. George M
    Posted Dec 2, 2007 at 4:50 PM | Permalink | Reply

    86, my Hansen request, found it, thanks.

  89. Follow the Money
    Posted Dec 2, 2007 at 5:04 PM | Permalink | Reply

    #85, Frank,

    I find it interesting that a lot of the shouting and hollering by the AGW/RC crowd is about catastrophes, tipping points etc., because these events are clearly nonlinear and probably chaotic.

    Tipping points etc. are for political/public consumption to scare nations recalictrant to enact Kyoto financial market carbon offset schemes by creating a sense of crisis. Whether Hansen invented the “tipping point” talking point, or it was put into his hands by the wordsmiths at Oglivy Mathers is still unknown…

    Australia has now elected Kyoto profiteers, the USA is holding out, hence the recent emanation from Bali about how USA joing Kyoto is “essential.”

  90. bender
    Posted Dec 2, 2007 at 5:18 PM | Permalink | Reply

    I want to clarify my #4. The phrase “house of cards” should have quotes around it. This is a figure of speech referring to a conditional probabilistic assertion that is contingent upon a chain of prior assertions, each of those in turn probabilistic and to some degree uncertain. I don’t necessarily disagree with the AGW assertion – it’s just that I would really like to have some idea of the uncertainty around it.

  91. henry
    Posted Dec 2, 2007 at 5:35 PM | Permalink | Reply

    One way to test the hypothesis is to see if a good history match can be done without the CO2 Forcing using parameters that are physically believable. The “mainstream” climate scientists say it can’t be done. I remain unconvinced. I’m not sure how hard they are trying.

    It sounds like the CO2 forcing could be TOTALLY REMOVED, and still the tuning could give a good match within the models.

    Or am I seeing it wrong?

  92. Al
    Posted Dec 2, 2007 at 5:37 PM | Permalink | Reply

    How do current GCM’s stack up against a flat-out “empirical fit” model?

    The way I’ve always performed any time-series statistics involves taking a third of your data out in advance, fitting and tuning to the remaining data… then evaluating against the sequestered data. You haven’t predicted anything or tested your model unless there is a significant swath of data that was sequestered during the entire ‘tuning’ step!

    I can understand the desire to use the last twenty years of data – that’s the part that is going to be tricky to model if it was excluded during the tuning step. But it sure seems like there isn’t any sequestered data. Perhaps this is the true reason the hockey stick was so beloved by the modelers – doing a reasonable model of the flat shaft would obviously be much simpler than developing a model that correctly followed the MWP/LIA – even if you sequestered some of the earlier data. You’re just ‘growing the stick’ – and you’ve got a model that will essentially output a ‘shaft’ regardless of whether you sequestered 1800-1900 or 1700-1800.

    So, without trying to model individual gridcells, and just focusing on the ‘global average temperature’, how close of a fit can be managed by completely ignoring the “science” of the model – and aiming instead for the purely empirical “engineering” model? Because with the way the error bars and methodologies are on the current models, it sure looks like we’re truly a long way from a purely first-principle scientific understanding of the climate. But there seems to be enough data for the brute force statistics approach to work: throw the global averages of the presumed relevant parameters in there along with polynomial expansions of the same, then use pure statistics to narrow or widen the pool.

  93. Wansbeck
    Posted Dec 2, 2007 at 5:48 PM | Permalink | Reply

    re Al #92

    In my view this is a sound point.
    However, with the presently disputed models won’t people simply check for a match with the sequestered data, ‘tune’ accordingly, and then remodel?

  94. Harry Eagar
    Posted Dec 2, 2007 at 5:49 PM | Permalink | Reply

    ‘it is obvious that the climate is a non equilibrium entity (non-stationary).

    That isn’t obvious to me. I’d say that climate seems to be antichaotic — you can change the inputs by huge amounts (of, say, CO2) and get niggling amounts of temperature change outputs.

    The unknown is, what keeps climate within such a narrow band? Why, if weather can vary by 80 degrees in a day, is climate constrained to vary by no more than 10 degrees per eon?

    And, no, I won’t accept Gaia as an answer.

  95. JS
    Posted Dec 2, 2007 at 6:10 PM | Permalink | Reply

    Responses about good ‘out of sample’ performance deny the reality of model making. The only good ‘out of sample’ data are data that were unavailable to the researcher when they chose the model. Thus, you find many models can break down 5 years after they are completed because that is when data truly unavailable to the researcher becomes available.

    In most cases, ‘out of sample’ testing is no more than parameter stability testing. But robustness to out of sample data sounds sounds so much more impressive than ‘parameters are stable within subperiods’.

    So, in #92, I must applaud your effort. Most researchers probably can’t resist the urge to peek at the data. But even then, you will only publish a result that demonstrates parameter stability – not one that can be guaranteed to have good forecasting performance. For that, one must wait a number of years. And in that light, so very few models of complex systems survive.

    I only wonder whether or not Gavin/GCM modellers have enough introspection to realise the frailty of their process and enough balls to confront it head on.

  96. Jon
    Posted Dec 2, 2007 at 7:13 PM | Permalink | Reply

    #95, please be careful with the brush you are using.
    Statistical learning theory and machine learning are both major intellectual fields–rigorous and well developed. I assure you, they are populated by people doing the right thing.

    People involved in modeling of complex systems should consult “the experts” just like people wanting to do new and novel reconstructions of temperature (Mann) should team up with a statistician or two.

    Models fail even when their creators lock-away the verification and validation sets. Models fail because the underlying structure of what they were built upon changed. I have in mind ‘computational finance’ here. e.g., the crash of Long Term Capital Management.

    I assure you that there are reasons for models to fail other than outright incompetence.

  97. JS
    Posted Dec 2, 2007 at 7:24 PM | Permalink | Reply

    #96
    I’m not suggesting incompetence. Just that the process of building models it very complicated and it is easy to present the model as having more robustness than it does. The models can be better (although obviously not perfect) if the practitioners acknowledge and respond to the inherent weakness of the process.

    Interesting that you should mention LTCM. The underlying structure didn’t change – it has always been what it was. Rather, an assumption that they relied upon turned out not to be true. It was true enough for a while, but after a while it wasn’t. Keynes got it best “The market can remain irrational longer than you can remain solvent.” (Many models assume a liquid market to dispose of bad investments – markets usually turn illiquid right when you want to dispose of bad investments. Just look at the sub-prime meltdown; people don’t want to buy at any price.)

  98. Pat Keating
    Posted Dec 2, 2007 at 7:37 PM | Permalink | Reply

    94 Harry

    you can change the inputs by huge amounts (of, say, CO2) and get niggling amounts of temperature change outputs

    I assume your comment is in regard to the real world. I don’t know whether that’s true, or not.

    It certainly isn’t true of the models. I ran a well-known climate model with only 1ppm CO2 and an Ice Age resulted.

  99. hswiseman
    Posted Dec 2, 2007 at 8:24 PM | Permalink | Reply

    Could someone provide feedback on my thought process set forth below? I got an A- in differential calc. (my last math class) as a freshman 30 years ago. I may be completely out of my depth here using layman rationales and what seems like common sense instead of the actual mathematics and stats. Feel free to say so. Thanks in advance (strapping on bulletproof gear)…..

    If the modeler is entitled to tune the forcings or feedback value of the model parameters, it can only be because the potential range of errors for the adjusted parameter supports the alternative value. This is really the best case scenario scientifically, as the modeler chooses an adjusted value within support range of the research error bars in order to produce a better fit. A less acceptable alternative is parameter adjustment outside the error bar range, supported by the proposition that “I know all my other values are pretty darn close, so the science must be wrong on this parameter.” Using the GCM to solve for a parameter value is AKA “just makin’ the s**t up” and the error range in this technique would be huge. I think this is essentially what Kiehl 2007 tells us. The error bars for the GCM itself should be some derivative of the number of parameters that are fair game for tuning and the latitude allowed within error ranges for such parameters (as well as the base level of certainty/error range in the science for the underlying error range). Any way you slice it, if you are taking liberties with the parameters, it must be because the underlying science of the particular parameter is uncertain or inadequate. The error bars for the GCM once overfitting stopped (i.e. for predictive future results) would be huge as you have layers of compounded uncertainty baked into the cake, and probably render the model useless as a predictive tool. A massive ensemble run might remove some of the outliers, but the model would still have error ranges far greater than the average individual parameter error ranges. As the model propagates over time, the initialization error range should follow some compounding function (the rule of 72′s comes to mind). A theoretical 10 percent initialization error range from GCM ensembles might approach 100 percent after about 30 years.

  100. Kristen Byrnes
    Posted Dec 2, 2007 at 8:31 PM | Permalink | Reply

    I’ll say it again:

    The modelers know very little about the sun, clouds, water vapor and etc. They can’t predict volcanoes, ENSO, PDO or Kelvin waves.
    They tune these programs to death, run 2389470934578 simulations and give themselves large ranges for “natural variation.” Under these circumstances I can program my Nintendo 007 game to say that all the dead spies were killed by GHG’s.

  101. Mhaze
    Posted Dec 2, 2007 at 8:31 PM | Permalink | Reply

    #79 bender says:
    December 2nd, 2007 at 2:33 pm

    Part of quote from RC:
    [Response: You need to remember that the models are not parameterised in order to reproduce these time series. All the parameterisations are at the level of individual climate processes (evaporation, cloud formation, gravity wave drag etc.). They are tested against climatology generally, not trends. Additionally, the models are frequently tested against ‘out of sample’ data and don’t ‘typically fail’ - vis. the last glacial maximum, the 8.2 kyr event, the mid-Holocene etc....... -gavin]

    Such assertions are rampart regarding climate models. Where is the verification, the proof of such assertions? Where is a history of parameters for just one of the models?

  102. Pat Keating
    Posted Dec 2, 2007 at 8:40 PM | Permalink | Reply

    hswiseman

    You are more or less correct. You are perhpas a little over-harsh re “making sh* up”. There are occasions when it is appropriate to use a model to estimate a parameter that is new and highly uncertain, because then someone can go out and measure it, thereby checking your theory. The “climate sensitivity” parameter does not seem to be independently measurable, however. I certainly don’t see any GW folks trying to do that.

    As far as the error compounding goes, the AGW trick is to ‘reset’ the calculations by finding some new factor (with a parameter) to blame the divergences on, such as aerosols.

  103. bender
    Posted Dec 2, 2007 at 8:42 PM | Permalink | Reply

    #98
    Read what Harry said. He said you can change inputs by X amount and have no effect on ouput. He didn’t say that was always going to be the case.

  104. Pat Keating
    Posted Dec 2, 2007 at 9:02 PM | Permalink | Reply

    103
    I’m not sure what your point is.
    If it helps, here is the beginning of his sentence: “I’d say that climate seems to be antichaotic — you can change the inputs……”
    He doesn’t seem to be using the word ‘can’ in the tentative way you seem to suggest….. and I didn’t say or imply ‘always’ either. So, what am I missing?

  105. bender
    Posted Dec 2, 2007 at 9:18 PM | Permalink | Reply

    #104 You’re right; the context of his quote changes its meaning significantly. My mistake. I’d read #94 but not connected it with your quote.

    I’m no climate modeling authority. I have no comments on anybody’s perceptions or misperceptions of what the climate modelers do or don’t do. I don’t know what they do. That’s what I’m trying to find out! My advice is to go talk to them. And report back to us what you hear.

  106. Posted Dec 2, 2007 at 9:53 PM | Permalink | Reply

    @Mhaze– 101:

    >>All the parameterisations are at the level of individual climate processes (evaporation, cloud formation, gravity wave drag etc.). They are tested against climatology generally, not trends.

    This is at least partly true. Parameterizations are on the individual processes. Also for some processes, testing can be done in field or laboratory experiments and it can be very thorough.

    For example, people can set up tanks, blow air over the surface, measure temperature and evaporation rates. They can also do some more theoretical analysis of the systems.

    Engineers do this to develop correlations all the time. In some cases parameterizations are akin to correlations, but can be more complicated depending on the process.

    That said, there’s a bit of slight of hand here, because it is possible to pick and chose among parameterizations. Also, sometimes, data to test models may be sparse or recent so parameterizations might not have the amount of testing that would let you consider them fully verified.

    Also, experimentalists tend to test parameterizations against simpler data first , when possible. So, one might collect data to test boundary layer model over one type of homogeneous terrain, then another then another. You find the type of plants make a difference. Surface heat flux is different over black plowed earth than prairie– so the choice of parameterizations matters. And worse you find that a checker board of 1/2 plowed earth and 1/2 prairie doesn’t add up the the linear combination of the two types of terrain. So, then what?

    And believe it or not, sometimes getting the data to test parameterizations really can involve traveling to the “back of beyond”. (My husband spent 8 weeks on an icebreaker collecting data for Sheba. He was supposed to be gone 6 weeks, but the “landing pad” melted unexpectedly. There was no Starbucks on the ship.)

    The government doesn’t fund expeditions on icebreakers too often. So, how much true polar data do you think exists?

    So, yes, parameterizations are tested against climatology generally. But that doesn’t entirely exclude tweaking of full models. The way this is done is you create a model, do a sensitivity study varying the magnitude of parameters within accepted ranges, and learn which ones give the best results over all.

    Then after you run the full models, you tweak the parameters you used in the model hoping to get better over all results. :)

    So, anyway, it’s not either test against climatology or tweak. It’s sort of both.

  107. Posted Dec 2, 2007 at 9:54 PM | Permalink | Reply

    #92: I think Bender has addressed this when referring to having seperate people control the data and the modeling process, respectively. I build models on training data and test them on validation data that’s been completely untouched (the exception being removal of bad records from both data sets) up until that point. The problem is that you have to trust that the modelers haven’t peeked at the validation data beforehand…

    I think I asked this here once before, but is there anybody who has compiled many/most of the GCMs from the 90s and 00s and compared actual vs. predicted? It shouldn’t take long for the model to fail if it is incorrectly specified. Do any of them predict well for 5-10 years (a link to an ’06 McIntyre post in this thread shows that at least one did pretty poorly).

  108. Philip_B
    Posted Dec 2, 2007 at 9:58 PM | Permalink | Reply

    The models may well have improved over the last x years. It doesn’t follow their predictive accuracy has improved. That can only be established by empirical evaluation of the model’s predictions. The IPCC case is based on the predictive accuracy of the models. Why don’t we have studies of the predictive capability of the models? (Team hand waving doesn’t count).

  109. bender
    Posted Dec 2, 2007 at 9:59 PM | Permalink | Reply

    Re #106

    This is at least partly true.

    Yes.

    That said, there’s a bit of slight of hand here

    Yes.

  110. bender
    Posted Dec 2, 2007 at 10:02 PM | Permalink | Reply

    #108

    Why don’t we have studies of the predictive capability of the models?

    Good question. Maybe there are some that I haven’t seen? Boris?

  111. Jaye
    Posted Dec 2, 2007 at 10:12 PM | Permalink | Reply

    A few years ago I was hired by an old boss to help with some problems he was having with some of this current staff members. The problems were related to a specific incident. These guys had developed what they thought was a very good prediction tool, it was a maximum likelihood estimator with a grab bag of techniques, some fuzzy logic, some neural nets, some of everything. They were trying to predict the oil futures market. So they got some data from a group of investors and SME’s in the oil future business. Of course, they split the data for a training and a validation set.

    Ok so time for the presentation of the results to the potential investors, who had their experts in attendance. They all walked out after the first graph was presented. Seemed that our guys had “predicted” a spike in the prices, except that this spike was due to a refinery blowing up. The only way to do this was to cheat and use the training data to do the predictions. We shut down that project and went on to more useful things. Too bad the IPCC and others aren’t as decisive as these potential investors.

  112. Jaye
    Posted Dec 2, 2007 at 10:20 PM | Permalink | Reply

    BTW, I would love to see an IV&V report for each one of these models with updates for each major release. Not to mention permanent archiving of parameter values with corresponding code versions, for each output data set on “runs for record”.

  113. bender
    Posted Dec 2, 2007 at 10:32 PM | Permalink | Reply

    Re #112 Who wouldn’t?! Sure, that level of documentation would increase the expense of the work, but I think it would be worth it.

  114. Onar Åm
    Posted Dec 2, 2007 at 11:06 PM | Permalink | Reply

    Re #94

    To me it appears that the stationary/non-stationary model is not sufficient to deal with climate change. It’s quite possible that the climate has internal dynamics that is strongly dominated by negative feedbacks, and at the same time climate is externally modulated in a highly non-stationary manner. Or that some internal parameters are strongly negative and others are wildly fluctuating. Let me give an example from economics, namely the price mechanism of demand and supply. Due to delays in the system this is not a stationary process, but it most certainly is dominated by negative feedbacks. That is, the market will tend to compensate for gluts or queues. However, despite these negative feedbacks the federal reserves can cause the prices to inflate (highly non-stationary) by continually printing money. Here we have negative feedbacks (the price mechanism) superimposed on a highly non-stationary signal.

    I think of the climate in similar terms. E.g. solar variation could cause the climate to trend significantly, whereas the microclimate is dominated by negative feedbacks.

  115. DharmaHunter
    Posted Dec 2, 2007 at 11:17 PM | Permalink | Reply

    On a philosophical note, I think the underlying question driving this discussion is: what is the utility of modeling?

    On a short term scale, the global models are very important in predicting the short and medium term weather for specific locations. Due to their different parameterizations each of the most popular weather models have different biases. It is the skill of the individual meteorologists to digest the model outputs and current weather conditions to make specific forecasts, be it a snow storm next week or a hurricane in the Gulf. The utility of these models are tested on a daily basis, and are assessed and graded on their skill of prediction. The National Hurricane Center, especially under the direction of Max Mayfield, has instituted a fairly rigorous set of criteria to their models and predictions. One model under development is the very tricky prediction of hurricane strength, which was alluded to in a few of their discussions. The goal is to extend their prediction time and increase accuracy. The former is important for communities to prepare and the latter is especially important to avoid “chicken little” forecasts. The NHC discussions are especially informative with regard to the certainty of the forecast. They also refer to the models with the phrase “model guidance.”

    The intermediate term scale, say the 3-6 month window, is important for agriculture, hurricane frequency, and water resource management. These of course are much less accurate, for good reason: it is difficult to predict storm paths and frequency when they have not formed yet. The best that you can do is to forecast general conditions which influence specific, stochastically occurring events. Although there is certainly room for improvement, the ENSO prediction models have done a decent job.

    In the long term, the climate models attempt to take given a certain set of conditions (e.g., CO2, aerosols, etc.) and simulate the general climate, specifically temperature. However, the public has latched onto “predictions” of drought, floods, hurricanes, rising sea levels, heat waves, etc. All of these depend upon the influences of the general climate conditions on regional weather. From what I have read, the GCMs can capture the general features of climate, such as the ITCZ and Hadley cell, but perform poorly on specific features such as the ENSO. Since the ENSO is such an important driver of regional weather and temperature, there is certainly room for improvement beyond the projection of future global temperatures.

    With respect to model predictions, have the current suite of GCMs performed better than a prediction from, say, 1971?

    A Nature editorial (The great greenhouse scare, Nature, 229:514, 1971) reported on a publication by a study group, “1970 Study of Critical Environmental Problems” (MIT Press). Their predictions for year 2000 were for an 18 % increase in CO2 and a corresponding increase in temperature of 0.5 ºC – the actual numbers ~14 % and ~0.5 ºC. Now, I figure (can’t find the report online) these estimates were based on clear air effects of CO2 on temperature and estimation of CO2 increase from the existing trend and projected fossil fuel consumption. While these estimates verified as well as the as the GCMs despite their difference in sophistication, have the GCMs significantly increased our understanding of the workings of the atmosphere?

    With regard to this last question, Isaac Held of GFDL has written an interesting essay entitled, “The gap between simulation and understanding in climate modeling.”
    http://www.gfdl.gov/~ih/papers/21.pdf
    In it he calls for development of a hierarchy of models, from large, comprehensive global models to more specific models of different climate processes. He asks,

    What does it mean, after all, to understand a system as complex as the climate, when we cannot fully understand idealized nonlinear systems with only a few degrees of freedom?

    When the comprehensive models give an output of a specific temperature, do we actually know why? The inverse correlation between anthropogenic forcing and sensitivity as well as the direct correlation between total and aerosol forcing suggest that the key underlying processes are not completely understood (clouds, precipitation) or there are processes that are missing. Held goes further to emphasize building a hierarchy of models to better understand the workings of the atmosphere. The need for such a hierarchy derives from his view:

    When a fully satisfactory systematic bottom-up approach to model building is unavailable, the development process can be described, without any pejorative connotations intended whatsoever, as engineering, or even tinkering. (Our most famous inventors are often described as tinkerers!) Various ideas are put forward by the team building the model, based on their wisdom and experience, as well as their idiosyncratic interests and prejudices. To the extent that a modification to the model based on these ideas helps ameliorate a significant model deficiency, even if it is, serendipitously, a different deficiency than the one providing the original motivation, it is accepted into the model. Generated by these informed random walks, and being evaluated with different criteria of merit, the comprehensive climate models developed by various groups around the world evolve along distinct paths.

    The value of a holistic understanding of climate dynamics for model development is in making this process more informed and less random, and thereby more efficient.

    He points out as an example of hierarchal model building, the success of biological research (with which I am familiar). He rightly points out that nature has provided biologists with a natural pre-made hierarchy of systems, from simple to complex. The success of biological research has been in defining the circuits and interactions. Indeed, structural research on biological nuts and bolts informs larger complex systems such as cell cycle control. It has only been recently that numerical simulations have been applied to biological systems, with some very interesting results. It should be said that many biologists are distrustful of these models and question their utility. This is likely because the emphasis in biology has been on experiment with very little theoretical development of the workings of biological systems, though this is changing.

    Conversely, climate science has emphasized theory over experiment, which is not surprising since its tough to isolate the ENSO and study it in the laboratory. Held’s view is that smaller processes have to be studied and understood before we will be able to judge the strength and validity of the models. For example he asks,

    …if stratosphere-troposphere interactions in one comprehensive model result in a trend in the North Atlantic Oscillation as a result of increasing carbon dioxide, but not in other models, how does one judge which is correct? One can try to judge which model has the most realistic stratospheric-tropospheric interactions by comparing against observations, with the understanding that theoretical guidance is required to design these comparisons. One can also analyze more idealized models designed to capture the essence of the interaction in simpler contexts, within which the climate dynamics community can focus directly on the central issues. These idealized studies can then suggest optimal ways of categorizing or analyzing more comprehensive models. If we are to claim some understanding of this issue, our modeling hierarchy, from idealized to realistic, should tell a consistent story.

    He goes on to suggest several systems that would be of value to study. I think that this is a worthy goal. In biological research (specifically molecular biology), the gains in understanding different systems have been through the development of techniques that reduced the number of degrees of freedom. I think this is what he is saying with regard to climate research. By concentrating on specific systems, such as moist convection, both experiments and theory will be better defined and understandable. In his concluding remarks he states,

    ..without the solid foundation provided by careful study of an appropriate model hierarchy, there is a danger that we will be faced with a Babel of modeling results that we cannot in any satisfying way relate to one another.

  116. Jaye
    Posted Dec 2, 2007 at 11:57 PM | Permalink | Reply

    RE:113

    IMO, its too costly not to do that sort of thing. However, most academics are amateurs when it comes to handling large software systems.

  117. Scott
    Posted Dec 3, 2007 at 12:16 AM | Permalink | Reply

    I want to reiterate, as a guy somewhat familiar with modeling…it’s all about parametrization.

  118. Hans Erren
    Posted Dec 3, 2007 at 1:56 AM | Permalink | Reply

    I want to reiterate, as a guy somewhat familiar with modeling…it’s all about fiddle factors.

  119. Willis Eschenbach
    Posted Dec 3, 2007 at 4:02 AM | Permalink | Reply

    Dharmahunter, thank you for your most interesting post.

    I had to laugh, though, at the last sentence you quoted from Held …

    ..without the solid foundation provided by careful study of an appropriate model hierarchy, there is a danger that we will be faced with a Babel of modeling results that we cannot in any satisfying way relate to one another.

    Will be faced? Will be? We’ve faced it for years …

    Figure 1. Notched boxplot of the Santer data and model hindcasts. Gray horizontal lines across the figure are the 95% confidence intervals on the medians and quartiles of the data. Notches in each boxplot show the 95% CI of the median of that data. If notches the notches of two datasets do not overlap, they are outside the 95%CI, and are statistically distinct.

    These are boxplots of the observational data and the models from the Santer study, I could find the URL, hang on … OK, Science mag, subscription required …

    Now, there’s some things of note here. First, the pattern of the two observational datasets is very similar. They are both compact, ocean temperatures don’t move around much, it takes a lot of power to warm or cool the ocean. They are both asymmetrical with regard to the median, being longer on the top. They both have numerous outliers, with many more outliers above than below the median. It is a curious and distinctive pattern.

    Now, compare that to the model hindcasts. There is one, and only one, model that produces what I call “lifelike” data. The rest are all quite unlike the real stuff. And clearly this, the simplest of comparisons, has already produced what, in Isaac Held’s lovely turn of phrase, is assuredly “a Babel of modeling results that we cannot in any satisfying way relate to one another.” What can we make of model CM2.1, which has the temperature flailing all over the place? Or CCSM3, which periodically hindcasts an oceanic cold swing twice as deep as anything in the record?

    If it were me, I’d likely toss out all but one of those models, the one that is lifelike, and go find another batch to test. And that’s on the first and simplest of tests, that the modeled and observed datasets have to be similar. The tropical ocean has no mountains, no deserts, no ice or snow … man, if you can’t get the temperature of the tropical ocean right, good luck with the rest.

    And yes, I know that being “lifelike” is not a statistical test of any kind … but it is the test that comes before all the fancy statistical measurements. You don’t need statistics when a supposedly “lifelike model” of a princess actually looks like a frog … you go “green skin … check … webbed feet … check … NEXT!” …

    Here’s another look. This one compares skew, kurtosis, and power. Skew and kurtosis measure departures from normality. Skew refers to the asymmetry I spoke of before, more data on one side of the mean than the other. Kurtosis is how clumped the data is around the mean compared to a normal distribution. Power is the amount of energy moved to warm or cool the ocean. Here’s a chart that compares the models and the observational data:

    Figure 2. Skew, kurtosis, and power for the models (white) and the observational data, shown as probability clouds. The center of the cloud is the data point. The width, height, and depth of the cloud represents one standard error in the respective direction.

    As you can see, the observational data are distinctly non-normal, with both large skew and kurtosis. The natural system also expended significantly less power than any of the models. The ocean is simply not doing, and has never been recorded to do, what the models say it is doing. Natural systems, in accordance with the constructal law, are not profligate of power. The models haven’t figured out that part of the climate system yet. The ocean doesn’t get real hot one month and real cool next month, never happened. Nor does it hardly vary from month to month, as some models have it doing. And whatever it is doing … the models are a long ways away.

    So, while I commend Isaac Hurd on his ideas and his suggestions, I would respectfully suggest that things are far, far worse than he thinks. Babel is upon us.

    w.

  120. Willis Eschenbach
    Posted Dec 3, 2007 at 4:19 AM | Permalink | Reply

    Hmmm …. no images … hmmm

    w.

  121. Hans Erren
    Posted Dec 3, 2007 at 4:22 AM | Permalink | Reply

    aerosols are the ultimate fiddle factor.

  122. Don Keiller
    Posted Dec 3, 2007 at 4:48 AM | Permalink | Reply

    “Climate models are forced within a range of uncertainty and yield a reasonable present state, which improves the models predictive capabilities.”

    Another blasphemy against IPCC-speak. Wasn’t the World informed that GCMs produce “projections” rather than “predictions”?

  123. Geoff Sherrington
    Posted Dec 3, 2007 at 5:03 AM | Permalink | Reply

    Re # 12 Ferdinand Englebeen

    The influence of aerosols in the 1970s alleged cooling period is more than a discomfort in trying to explain the cooling. What worries me is that the cooling component became greater and greater to the present, so although the temperature graph rises recently, it throws out of balance the attributed causes of the rises and their magnitude. It’s like motion on a waterbed. Put your foot down in one place and it bulges in another. Now try to set up a comprehensive model of waterbed motion during a romantic interlude.

    I find it hard to ascribe quantitative effects to qualitative processes. For the same reason I am VERY suspicious about oxygen isotopes in ice cores as temperature proxies.

  124. Tim Ball
    Posted Dec 3, 2007 at 6:24 AM | Permalink | Reply

    A long time ago Tolstoi said, “I know that most men, including those at ease with problems of the greatest complexity, can seldom accept even the simplest and most obvious truth if it be such as would oblige them to admit the falsity of conclusions which they delighted in explaining to colleagues, which they have proudly taught to others, and which they have woven, thread by thread,into the fabric of their lives.”

  125. Tom Vonk
    Posted Dec 3, 2007 at 8:27 AM | Permalink | Reply

    I wasn’t suggesting they are “missing something fundamental” in their models. I was asking what was their confidence was in their parameters. i.e. What’s the probability they’ve inadvertently built in a warm bias? I asked for a post on the topic of model tuning. e.g. What are these “out-of-sample” scenarios? How many knobs? How do you handle the potential non-ergodicity of oceanic flows (i.e. non interchangeability of time-series and ensemble-series and ensemble-”ensemble” series)?

    Bender you should have affirmed (not suggested) that they missed something fundamental .
    Actually 2 HUGE somethings fundamental .

    1) The proof of convergence

    Of course if you model , even very wrongly , a system that you submit to strong constraints , the numerical run will not blow up because of teh constraints .
    For instance energy , impulsion and mass conservation are extremely strong constraints that will make sure that your model will not go wild and unphysical .
    However that is not convergence .
    Convergence is that you prove that the computed dynamical states of the system are uniformly converging to a certain trajectory in the phase space when the size of the step goes against 0 .
    For the sake of the argument imagine that you take a spatial step of 1000 km and a time step 1 month .
    A very crude model indeed that could run on your PC and behave very stably .
    Yet even this model will get some things “right” – Arctics will melt in summer and freeze in winter and with the hypothesis of well mixed gases you can even implement an extremely sophisticated radiation transfer .
    Now the problem is that everything below this size is unresolved and that means that even if you know the laws of physics on the sub grid scale , you have to substitute in them monthly averages .
    And of course that is a big sin against physics because the laws of nature are local and instantaneous and being mostly
    non linear as they are , they never work for averages .
    So now you take 100 km and 4 hours . Many of the previously subgrid phenomenons must now be explicit and you obtain a new dynamical state that will be rather far from the previous – even if Arctics stil melt in summer and freeze in winter .
    What would happen with 1 km and 30 minutes ?
    Nobody knows because the smaller gets the scale , the greater the sensibility and the risk of wild divergence at longer times .
    So not only the convergence is not proven but it is not clear how to tackle the problem .

    2) The definition of metrics and stochastical behaviour

    The problem above is a problem of a trade off – with crude resolution you get stability and are far from reality , with fine resolution you are (supposed to be) near reality but get unstable .
    Unfortunately the problem cannot be treated mathematically because the GCMs do NOT solve differential equations in which case it would be possible to speculate about the existence , regularity and features of the solution and the ability to approach it with numerical simulations .
    What the GCMs basically do instead is to simulate the system by having N equations tying together N variables .
    The computer solves for t and moves to t + 1 .
    That’s why it has no problem with the initial and boundary conditions that are crucial for every physical problem .
    Now what could mean this series of computed numbers ?
    Certainly NOT a solution to the uderlying differential equations because they are partly unknown and if they were known , their number would be astronomically huge .
    The modellers then make the following assumptions : unresolved size is random fluctuations and even if one simulation is not equivalent to a prediction , the difference between 2 simulations with different values of a parameter represents the sensibility of the system to this parameter .
    Such bold assumptions need proofs .
    First there is no necessity than anything subgrid be random and indeed it mostly isn’t – the reason is the already mentioned fact that the law of nature are local and are not valid for averages .
    So everything subgrid (with a 100 km grid !) dismissed by hand waving and saying that it’s random and that it “averages out” must be proven what is again not possible because the exact solution of the system is unknown – the ergodicity you mentioned is also part of this kind of problems .
    Secondly to interpret the difference between 2 runs as a sensibility , you need metrics .
    What is the metrics for the validity of GCMs and/or for comparisons between 2 GCMs ?
    Temperature not and “global” temperature surely not .
    R.Pielke has written n times that the only relevant metrics for the climate is the energy content of the oceans and I think that he is right .
    It doesn’t seem that the “climatologues” have ever cared to benchmark their models against reality and among themselves in this metrics .
    A rather big fundamental something in my book .

  126. Richard S Courtney
    Posted Dec 3, 2007 at 9:29 AM | Permalink | Reply

    Dear Steve:

    At point#15 you say;

    “I haven’t seen any prior paper specifically showing that model climate sensitivity had an inverse relationship to the version of aerosol forcing history. People may have hypothesized something similar but this is more substantial.”

    I published a paper that showed this is true some years ago, but it was only demonstrated for one model.
    Ref. Courtney RS, “An Assessment of Validation Experiments Conducted on Computer Models of Global Climate (GCM) Using the General Circulation Model of the UK Hadley Centre’, Energy & Environment v.10 no.5 (1999)

    All the best

    Richard

  127. Richard S Courtney
    Posted Dec 3, 2007 at 9:32 AM | Permalink | Reply

    Dear Steve:

    At point#15 you say;

    “I haven’t seen any prior paper specifically showing that model climate sensitivity had an inverse relationship to the version of aerosol forcing history. People may have hypothesized something similar but this is more substantial.”

    I published a paper that showed this is true some years ago, but it was only demonstrated for one model.
    Ref. Courtney RS, “An Assessment of Validation Experiments Conducted on Computer Models of Global Climate (GCM) Using the General Circulation Model of the UK Hadley Centre’, Energy & Environment v.10 no.5 (1999)

    All the best

    Richard

  128. Christopher
    Posted Dec 3, 2007 at 9:39 AM | Permalink | Reply

    Rapidshare has this paper for those not willing to spend 9$
    http://rapidshare.com/files/74023818/Kiehl_2007_GRL_Twentieth_century_climate_model_response_and_climate_sensitivity.pdf.html

  129. bender
    Posted Dec 3, 2007 at 9:47 AM | Permalink | Reply

    #124 I suspect Dr Schmidt would have then quibbled over the meaning of “fundamental”. But I think the thread is still open if you would like to follow up, Tom. Actually – I did bring up the ergodicity issue at a later date. But then I was assured that the internal variability in terawatt heat engine Earth’s climate is miniscule compared to the external forcings. I believe it was “mike” who told me that. Unfortuantely, he did not supply a proof of that assertion. I have no way of determining how true or false that statement might be.

    Steve has mentioned Dr. Demetris Koutsoyiannis work in the past, and I thought I would provide a link to his website. In particular, there is a paper there, titled Climate change, Hurst phenomenon, and hydrologic statistics, from which I would like to quote:

    The intensive research of the recent years on climate change has led to the strong conclusion that climate has ever, through the planet history, changed irregularly on all time scales. Climate changes are closely related to the Hurst phenomenon, which has been detected in many long hydroclimatic time series and is stochastically equivalent with a simple scaling behaviour of climate variability over timescale. The climate variability, anthropogenic or natural, increases the uncertainty of the hydrologic processes. It is shown that hydrologic statistics, the branch of hydrology that deals with uncertainty, in its current state is not consistent with the varying character of climate.

    In his view, statistical hydrology is in its infancy. This is an important statement because it suggests we have only begun to describe ocean hydrological behavior in statistically robust ways. If that is true, it means that GCM tuning is far more art than science.

    I have not read all of Koutsoyiannis’s work, but I like what I have seen so far. It supports the hypothesis that an ASA Journal of Statistical Climatology is badly needed.

  130. bender
    Posted Dec 3, 2007 at 9:52 AM | Permalink | Reply

    Another comment at RC on GCMs where I thought a reasonable and important question was blown off:

    bender Says:
    27 May 2007 at 9:26 AM
    Thanks for this post (Why global climate models do not give a realistic description of the local climate).

    You say:
    “Most GCMs are able to provide a reasonable representation of regional climatic features such as ENSO, the NAO, the Hadley cell, the Trade winds and jets in the atmosphere. They also provide a realistic description of so-called teleconnection patterns, such as wave propagation in the atmosphere and the ocean.”

    I would like to know, given that these (and other) regional features have not been studied all that long, how stable they are, in the mathematical sense of the term.

    A second question is: what do you mean by “*reasonable* representation”? Any links to the primary literature on either question would be appreciated.

    Thanks again. I look forward to more on the topic of how GCMs are constructed and parameterised.

    [Response:The best link is probably to the IPCC AR4 chapter 8. -rasmus]

    Um, thanks for the linkie.

  131. Harry Eagar
    Posted Dec 3, 2007 at 10:09 AM | Permalink | Reply

    Yes, Pat, when I said ‘can’ I meant, ‘the real world already has tried it.’

    1 ppm CO2 would mean no life on Earth any more, with resultant effects on, among other things, aerosol production, so I expect the models would be far outside their parameters by then.

  132. bender
    Posted Dec 3, 2007 at 10:11 AM | Permalink | Reply

    This one at RC elicited the sound of crickets:

    bender Says:
    23 May 2007 at 4:36 PM

    Forgive me for having to ask a third time. Hopefully my question gets more precise and understandable with each iteration.

    I want to understand the issue of structural instabilities.

    Given that there is some irreducible imprecision in model formulation and parameterization, what is the impact of this imprecision? I’m glad to hear that the models aren’t tuned to fit any trends, that they are tuned to fit climate scenarios. (Do you have proof of this?) But it is the stability of these scenarios that concerns me. If they are taken as reliable (i.e. persistent) features of the climate system then I can see how there might exist “a certain probability distribution of behaviors” (as conjectured by #160). My concern is that these probability distributions in fact do not exist, or, rather, are unstable in the long-run. If that is true, then the parameterizations could be far off the mark.

    What do you think the chances are of that? Please consider an entire post devoted to the issue of structural instability in GCMs. Thanks, as always.

    Actually, that’s not quite true. There was a troll heard in the background:

    Steve Bloom Says:
    24 May 2007 at 5:45 PM
    Re #232: Along with the rest of the ClimateAstrology crowd, what you *want* is to prove your belief that the models are invalid along with the rest of climate science. It’s an interesting hobby for Objectivists with time on their hands, but as has been proved again and again by the few climate scientists who have braved the CA gauntlet, in the end it’s a complete waste of their time.

    Which is not quite the soothing evidence I was looking for.

    Anyways, if anyone can help clarify the wording in my question, or the question itself, I would be prepared to resubmit it to the “high priests” of the “house of cards”.

    One more comment to come – if I can dig it out – on internal vs external variability in Earth’s climate – an issue at the core of what they are tuning the GCMs to produce.

  133. kim
    Posted Dec 3, 2007 at 10:19 AM | Permalink | Reply

    I laughed at another blog when someone dismissed my authority as a ‘little known water hydraulics guy’.
    ===============================================================

  134. kim
    Posted Dec 3, 2007 at 10:20 AM | Permalink | Reply

    Wexman Who?
    ========

  135. kim
    Posted Dec 3, 2007 at 10:21 AM | Permalink | Reply

    Better:

    The Wexmen Cometh Whence?
    ===================

  136. Bob Koss
    Posted Dec 3, 2007 at 10:56 AM | Permalink | Reply

    The BBC was touting a public participation GCM a year or two ago. If anyone is interested, here are the tunable parameters for that model. I counted 34 of them. With that many, the number of ways to tune the model must be an 18 digit number or more. Why should anyone have confidence in output from something like that?

    I have no idea what model they are using. Didn’t bother to search around.

  137. bender
    Posted Dec 3, 2007 at 11:17 AM | Permalink | Reply

    Suppose each “knob” is a “card” …

  138. bender
    Posted Dec 3, 2007 at 11:20 AM | Permalink | Reply

    … then consider the “neighborhood” “mapped” out in Fig. 1, where each dot represents a “house” …

  139. bender
    Posted Dec 3, 2007 at 11:22 AM | Permalink | Reply

    If I were to “huff” and “puff” on those “dots” …

  140. SteveSadlov
    Posted Dec 3, 2007 at 11:24 AM | Permalink | Reply

    Fascinating. Over on the CO2 thread, where I’ve been both building on and challenging Erwin vis a vis causals of the late Permian Extinction, I had noted that Kiehl is a major proponent of the view that a spike in CO2 was the primary root cause of that extinction. I question that. I believe that the extinction caused the CO2 spike.

  141. bender
    Posted Dec 3, 2007 at 11:28 AM | Permalink | Reply

    Discerning cause-and-effect is very hard when you have a delayed feedback loop A<->B that is being forced by other agents, C.

  142. Frank K.
    Posted Dec 3, 2007 at 11:32 AM | Permalink | Reply

    Re: 136

    I poked around the climateprediction.net site. Not a differential equation in site – in fact very few equations at all!! Are there links to actual documentation of their code? Maybe I was overlooking something…

  143. Frank K.
    Posted Dec 3, 2007 at 11:32 AM | Permalink | Reply

    Re: 136

    I poked around the climateprediction.net site. Not a differential equation in sight – in fact very few equations at all!! Are there links to actual documentation of their code? Maybe I was overlooking something…

  144. steve mosher
    Posted Dec 3, 2007 at 11:34 AM | Permalink | Reply

    re 129. Hurst. thanks for the linky. Oke also used the Hurst component to identify Microsite
    changes..in the Oke Runnals paper

  145. Craig Loehle
    Posted Dec 3, 2007 at 11:41 AM | Permalink | Reply

    It is disengenuous to say that the models are not “tuned” to trends. If they were purely from basic climatology, then you would fix the parameters from studies of clouds, el nino, PDO etc and then let it rip—but in fact early versions of the models gave an ice covered earth or other crazy behavior. The obsession with aerosols in the models is because without them the models won’t replicate the cooling from 1945 to 1980 (or whatever). This part of the model was NOT calibrated from the ice age model comparisons or other such paleo-tests. To prove that the models are NOT “tuned” to 20th century trends, an audit trail would be required: where, when, why and by whom each model change and parameter was derived. Guess what: no audit trail. Sorry Bender—you will never get your answer because no one wrote it down.

  146. bender
    Posted Dec 3, 2007 at 11:54 AM | Permalink | Reply

    Sorry Bender—you will never get your answer because no one wrote it down.

    Surely they have lab notebooks similar to the one I keep? Surely they can more or less duplicate the parameterization process in the absence of a notebook full of notes?

    It is disengenuous to say that the models are not “tuned” to trends.

    I thought so too, but whuddoiknow; I’m a “plebe” not a “high priest”*.

    (*Can I switch to caps, Bloom? Much easier than quotes.)

  147. Bob Koss
    Posted Dec 3, 2007 at 2:09 PM | Permalink | Reply

    RE: 142

    Frank K.

    I doubt there is any documentation there. I think they have a forum of some sort though. Don’t know if it’s public posting. You might try asking there.

    Went there quite a while back. Knew I wasn’t going to be too impressed when on their home page I saw they post the number of model years run to three decimals. Had to look twice to read the figure correctly. I was off by a factor of 1000 with my first read. An 8.76 hour resolution. That’s nutty. Probably did it intentionally. ;)

  148. Wansbeck
    Posted Dec 3, 2007 at 2:21 PM | Permalink | Reply

    re BBC model

    From what I can remember the BBC model was run in conjunction with a major university; I think it was Cambridge. Each participant got a number of parameter permutations to run since as mentioned by Bob Koss there were too many to run easily on one computer. There was an explanation of how the model worked but I don’t know if it got as far as the code. I am probably totally wrong here but I only got as far as the ‘calibration’ part that seemed to say that only permutations that gave steady temperatures with constant CO2 went through to the next round. I had meant to go back to get a proper understanding of this but sadly never did.
    Maybe something is still available on the university website?

  149. Willis Eschenbach
    Posted Dec 3, 2007 at 2:22 PM | Permalink | Reply

    The Koutsoyannis paper bender mentioned above is a very important one. The models do not give reasonable Hurst coefficient results. Here’s the range, standard deviation, and Hurst coefficient of the models used in the Santer paper

    Note the models either have Hurst coefficients of about 0.5 (no long-term structure), while the data is quite a bit larger. Some of the models even have a Hurst coefficient below 0.5 (the mid-line on the graph), which I have never seen in a surface temperature dataset. A veritable Babel, indeed.

    w.

  150. Pat Keating
    Posted Dec 3, 2007 at 2:35 PM | Permalink | Reply

    148 Wansbeck

    There’s an educational version of the GISS Model II at http://edgcm.columbia.edu/
    You can play with some of the parameters, but not many of them.

  151. Wansbeck
    Posted Dec 3, 2007 at 2:42 PM | Permalink | Reply

    Re #150, Thanks.

    I have just found some information on the BBC website. It says “The BBC Climate Change Experiment is a climate model, designed and produced by climateprediction.net researchers based at Oxford University” not Cambridge.

  152. SteveSadlov
    Posted Dec 3, 2007 at 2:59 PM | Permalink | Reply

    RE: #131 – Which raises an important question, how low is it safe to go? Are we already in the danger zone on the low side? If not, what is our current margin of safety?

  153. Frank K.
    Posted Dec 3, 2007 at 3:09 PM | Permalink | Reply

    Re: 150
    “There’s an educational version of the GISS Model II at http://edgcm.columbia.edu/
    You can play with some of the parameters, but not many of them.”

    I went to this site and, while you do have access the FORTRAN source code, alas, there again is NO formal documentation for this code outside of its use by third parties – that is, no equations, no mathematics, no numerical analysis, no theory – nothing. I detect a pattern here…

    Well, there was this…

    http://edgcm.columbia.edu/downloads/documentation/Hansen1983.pdf

    The terse description of the mathematical formulation contained therein does NOT substitute for code documentation since it is unlikely that all of the models provided by the source code are completely covered in the paper.

    And of course, the purpose of the edgcm software is to give users that great GCM experience!!

    “The software allows users to experience the full scientific process including: designing experiments, setting up and running computer simulations, post-processing output, using scientific visualization to display results, and creating scientific manuscripts ready for publishing to the web.”

    Software for cranking out scientific manuscripts – what a concept!

  154. Pat Keating
    Posted Dec 3, 2007 at 3:37 PM | Permalink | Reply

    152
    Dare I say that we are close to the tipping point?

  155. Gerald Browning
    Posted Dec 3, 2007 at 3:39 PM | Permalink | Reply

    Frank K. (#153),

    The NCAR documentation on the continuum equations and numerical approximations for their CAM3 model is fairly complete and well done.

    However, the mathematical properties of the system are not discussed and for that info I refer you the the Exponential Growth threads.

    Jerry

  156. Chris R
    Posted Dec 3, 2007 at 4:22 PM | Permalink | Reply

    Interestingly, the same Stephen Schwartz referred to above (post #60)
    has also recently published a paper based on a simple energy-balance
    argument which seems to state that the time constant of the Earth’s
    climate system is very short (circa 5 years), that very little further
    warming from direct CO2 forcing is to be expected, and that over the 20th century, the sum total of all non-greenhouse gases is slightly negative.

    Here is a link to the paper:

    http://www.ecd.bnl.gov/steve/pubs/HeatCapacity.pdf

    Make of this what you will.

  157. Bill F
    Posted Dec 3, 2007 at 4:39 PM | Permalink | Reply

    If the models are not tuned to trends and are tested against out of sample data, where are the documented results of the tests? Where are the published papers reporting on “lessons learned” from failed model runs where valuable information about flawed process modules was gained by unrealistic model output? Surely somebody’s model failed badly enough that it was newsworthy to study “why” and report on it? Whats that? All the runs are successful? Nobody ever had to go back and adjust the modules to make the outcome realistic? Why do I have trouble believing that?

    Did I just write an entire post composed of questions?

  158. SteveSadlov
    Posted Dec 3, 2007 at 4:39 PM | Permalink | Reply

    RE: #155 – I have a new idea for a film, sort of a namephreak off of an old sci fi classic – “Stromatolite Alley!” … :lol:

  159. Frank K.
    Posted Dec 3, 2007 at 5:46 PM | Permalink | Reply

    Re: #156

    Yes I agree – I provided a link to NCAR’s CAM3 documentation in comment #38 in this thread…That is the kind of documentation I think is acceptable at a minimum.

    However, researchers at NASA/GISS, for some reason, appear to believe that proper code documentation is not worth their time…and that is unacceptable!

  160. Wansbeck
    Posted Dec 3, 2007 at 5:51 PM | Permalink | Reply

    re #158 Bill F

    With the BBC/climateprediction model mentioned earlier part of the explanation says:

    “Calculating the climate for the 20th century might sound like an odd thing to do. It’s a check of the validity of the model’s parameters. If the model’s prediction for 2007 is very inaccurate (for example, if the whole world turned to ice), then the model is rejected. But if not, the model continues into the 21st century.”

    Strictly speaking they do not ‘tune’ models. They just run thousands then pick the best.

  161. erikG
    Posted Dec 3, 2007 at 6:28 PM | Permalink | Reply

    re # 161

    That sounds like a fallacy. I mean, they can do that, but they have not proved anything about the accuracy of the model. what is url?

  162. Wansbeck
    Posted Dec 3, 2007 at 7:00 PM | Permalink | Reply

    Re #163 ericG

    To clarify my earlier post, #161, “thousands of models” were their words not mine.

    I think what they mean, as I’m sure most folks here have guessed, is that they run the same model thousands of times with different parameters. (Do different parameters make it a different model or is it the same model with different parameters?)

    Also, if a model fails nobody tries to find out what went wrong; it is simply rejected. It seems to me that the main reason for this is that you can’t know what is wrong if nobody knows what is right. Not knowing what is right is not a criticism; it is an extremely complex subject but, IMHO, giving an impression of near certainty is another matter.

    The url is: (sorry for long url, haven’t got the hang of the link yet)
    http://www.bbc.co.uk/sn/climateexperiment/theexperiment/distributedcomputing.shtml

  163. Yorick
    Posted Dec 3, 2007 at 7:15 PM | Permalink | Reply

    They just run thousands then pick the best.

    Kind of like looking for temp proxy BCPs…

    OT but Steve M mentioned looking for the Law Dome borehole data, here is some.

    http://www.gfy.ku.dk/~www-glac/data/ddjtemp.txt

  164. bender
    Posted Dec 3, 2007 at 7:49 PM | Permalink | Reply

    Actually, “genetic algorithms” are a formal way of overfitting parameters to models, through a fully accountable and transparent tuning process. [snip]

  165. DocMartyn
    Posted Dec 3, 2007 at 7:53 PM | Permalink | Reply

    The cut off for photosynthesis is about 90 ppm [CO2]
    Whiteman and Koller, 1967…This file is very slow to load
    http://www.blackwell-synergy.com/doi/pdf/10.1111/j.1469-8137.1967.tb06025.x

    Low levels of CO2 at mid-day can get to about 190ppm, but that is rally low (no ref to hand, I can post one when I am at work).

  166. bender
    Posted Dec 3, 2007 at 8:12 PM | Permalink | Reply

    The topic here is not CO2. It was mentioned in #131 only because of its link to aerosols, which is on-topic.

  167. Posted Dec 3, 2007 at 10:05 PM | Permalink | Reply

    >>You continue to assert that you have read our manuscripts, but continue to ask questions that seem to indicate that you have not read them very carefully.

    No. I did not assert I had read all your manuscripts.

    I said I read the comments in these three threads. I said I read “Silvie’s Manuscript” (once I was able to identify which manuscript you meant by this.) I told you Kress and Browning 1984 is money walled (see above). I don’t have it. I have not read it. (I read the abstract)

    I said I have not read the book chapter you advised I read after I asked you for a reference that might list the full set of the equations you call “the hydrostatic system” and “the non-hydrostatic system”. I wanted a reference so I can be certain I know precisely what set of equations you mean when you use these terms. (I can guess, but wouldn’t it be better if you told me?

    That chapter is also money walled. I plan to get it from the library, as there appears to be no other way to get you to specify the precise specific set of equations you use these terms to describe. (Why you can’t scan the page with the equations, and ask Steve to upload the page is beyond me. It would fall well within fair use of copyright. But, for now, until I get the chapter, I am somehow left to guess. )

    As to any other “manuscripts”, you will need to be more specific about their titles authors dates etc. Otherwise, neither I, nor anyone reading this thread, is likely to read the manuscripts.

    >>It is time that you start to answer some questions so I can see what you have learned from the manuscripts.
    >>Before we continue, have you or have you not read the multiscale manuscript and if so please describe the system of equations cited in (2.1) so we can both agree on the system described in the earlier comment (inviscid, compressible NS with Coriolis and gravitational forces).

    1) I have no clue which manuscript you have given the nickname “the multiscale manuscript”. I know this is a blog, and we don’t have formal citations rules, but, since I don’t know which paper that is, I can’t answer your question.

    2) When you say “the earlier comment” which specific earlier comment do you mean?

    >>What are the horizontal, vertical, and time scales and dependent variable scales.
    >>Why is dissipation left off of these equations?
    >> What does the S_1 scaling parameter mean. Where is the heating term?

    Are you asking me where the heating term is in equation 2.1 or “the multiscale manuscript”? Beats me! Even if I have read the manuscript, I still wouldn’t know which one you call “the multiscale manuscript”. So, clearly, I can’t know which is equation 2.1.

    If you mean to ask if there is dissipation in inviscid compressible NS with Coriolis and gravitational forces: No. Viscosity is neglected. Consequently this set of approximate equation has no viscous dissipation. ( That said, I’m not sure I would ever use the term “inviscid NS”. Why not say Euler equations? )

    Did I answer the question you meant to ask?

    If not, ask your question more clearly, and I’ll try to answer it. If showing the equation is necessary, then get a scanner, scan the equations, and upload the pdf. Doing that wouldn’t violate copyright, and it’s easy. I could answer more quickly.

    Now, that I tried to answer the question I think you meant to ask, will you answer the ones I asked?

    Are you saying that that one can or one cannot get smooth bounded numerical solutions to:
    a) The hydrostatic system (full NS) when molecular viscosity is included?
    b) The non-hydrostatic system when molecular viscosity is included?

    These are simple questions I am asking because I am you are claiming in your things you say here in comments. The answers are either “yes one can”, “no one can’t”, or “I don’t know.”

    I already asked these questions, and I know you can answer them without saying “Please read my manuscripts”. These questions can be answered without regard to your manuscripts because you are not the only person in the world who has done DNS!

  168. Posted Dec 3, 2007 at 10:10 PM | Permalink | Reply

    Shoot bender– I was writing in a text editor and pasted into the wrong window!!! Delete that– I’ll put it in the correct thread.

  169. Demesure
    Posted Dec 4, 2007 at 7:46 AM | Permalink | Reply

    #162: Wansbeck: ALL the results of their climate experiments are at climateprediction.net
    Go have a look before they sweep the inconvenient results under the carpet.

    Besides the how to reconcile different models, there is the question of how to reconcile different results of a SAME model by other things than the pseudo-scientific probability density functions.

  170. steve mosher
    Posted Dec 4, 2007 at 9:34 AM | Permalink | Reply

    I found this comforting:

    “Chris Knight, together with various members of the cpdn team, have published a paper in the Proceedings
    of the National Academy of Sciences which looks at what factors most effect the result that a climate model
    gives, using data from the first, 3 phase, slab ocean experiments
    (see http://www.climateprediction.net/science/strategy.php).
    Reassuringly, the paper finds that model parameters matter much more than what computer you happen to run the model on.”

  171. Tom Vonk
    Posted Dec 4, 2007 at 9:40 AM | Permalink | Reply

    Steve has mentioned Dr. Demetris Koutsoyiannis work in the past, and I thought I would provide a link to his website. In particular, there is a paper there, titled Climate change, Hurst phenomenon, and hydrologic statistics, from which I would like to quote:

    Thanks Bender , I got it and read it .
    Excellent !
    I didn’t know this work but it corresponds exactly to what I have been saying for some 10 years – the climatic system is self similar at all time scales .
    Follows that you can’t use standard statistics hence I mentioned in the post above the second fundamental missed point – the stochastics of the climate system .
    And that means that it suffices to take a meteorological time series (f.ex for the humidity) with a scale unit of 1 hour ,
    unzoom to a scale unit of 10 years and obtain the same kind of variability .
    Self similarity implies of course much more than only different statistic and needs completely different tools than what the modellers use today .
    Of course as the modellers are deterministic and standard statisticians , they are 2 times wrong .
    As the Hurst variability is “wilder” than the standard variability , using it would make several hundreds (thousands ?) modellers jobless :)
    You wouldn’t want that , would you ?

    On a particular note I seem to remember this individual S.Bloom from the R.Pielke website .
    I have never met on the net something more slimy and obnoxious than this thing (robot ? lunatic computer programm ?)
    As he has no clue about science and posesses a skull that has been so perfectly brainwashed that the vacuum within contains only a kind of pulsion to bite and snarl , his “posts” have always been unpleasant , insulting and irrelevant .
    Your quote shows well the Bloom thing at its finest .

  172. Posted Dec 4, 2007 at 9:46 AM | Permalink | Reply

    #169 Demesure:
    Without defending the climate models in general, I don’t understand your concerns with the climateprediction.net results in your graph. Those results were obtained during the sensitivity analysis part of the experiment. The purpose of the experiment was to run many sets of parameters, most of which would be known to be wrong. From the page linked by steven mosher in #170 (emphasis added):

    “This experiment is more about learning how the model reacts to changes in initial conditions and parameters than about actually trying to replicate the Earth’s climate. For this reason, the model we use has a sophisticated atmosphere, but a simplified ocean (a single layer, ‘slab’ ocean). This means that some aspects of the climate system (such as oceanic currents, and the El Nino oscillation) are not replicated, but the model runs a lot faster and a lot more calculations can be completed.

    The knowledge we gain from this experiment about the way the model reacts to changes to the parameters will be used to design the next phases of the climateprediction.net experiment – combinations of parameters that obviously do not work can be avoided.”

    One interesting aspect of the results is that they seem to indicate a relatively short time constant for warming (~5 years?). I wonder if this is a side-effect of the slab ocean used in this simple model, since more complex models generally have a longer time constant.

  173. Jan Pompe
    Posted Dec 4, 2007 at 9:48 AM | Permalink | Reply

    Tom

    didn’t know this work but it corresponds exactly to what I have been saying for some 10 years – the climatic system is self similar at all time scales .

    Is this, you think, why we see things like the same thirty degree lag in diurnal warming (hottest part of day is 2hrs after noon) and a thirty degree seasonal lag (the hottest day usually a month after the longest day) and so on, or is that something else?

  174. Pat Keating
    Posted Dec 4, 2007 at 10:03 AM | Permalink | Reply

    173 Jan

    Interesting. The CO2 lag during de-glaciation is roughly 8% (30 degrees in your terminology) of the rise-time of the temperature.
    What does it mean physically, though?

  175. Michael Jankowski
    Posted Dec 4, 2007 at 10:04 AM | Permalink | Reply

    A somewhat out-dated but still humorous application of GCMs – the 2000 US Nat’l Assessment http://www.usgcrp.gov/usgcrp/Library/nationalassessment/overviewlooking.htm.

    Both the Hadley and Canadian models of the day project warming, mostly in the midwest/west, but to varying degrees. They do differ on the direction of precipitation for much of the US, however. And when combined to determine soil moisture temp, forget it…they have different directions for about half of the nation.

    Gavin will tell you that GCMs aren’t supposed to work on a resolution below global/continental scale, but obviously policymakers do use them as such.

  176. Harry Eagar
    Posted Dec 4, 2007 at 10:16 AM | Permalink | Reply

    Sadlov, I reached way back to Pat Michaels’ ‘Satanic Gases’ for the crack about too little CO2. As I recall, he then wrote that during the depth of the last cold period, CO2 got to ‘within a few parts per million’ of being too low to support life.

    Off-topic or not,that’s the sort of thing that makes me think an extra few parts per million of CO2 in the air is not entirely a bad idea.

    It’s possible to get overfine about these things. I live in Hawaii, where we are celebrating the 50th anniversary of CO2 monitoring atop Mauna Loa.

    Well, fine. But there is another monitor now at Hilo (about 50 miles east and 2 miles down). If the tablets brought down from Mauna Loa are holy writ, makes you wonder why the Scribes are writing a different Scripture in Hilo.

    But I’m just a provincial business reporter, what do I know? (Other than to become suspicious when I learn there are two sets of books.)

  177. steve mosher
    Posted Dec 4, 2007 at 10:26 AM | Permalink | Reply

    RE 172. Guys I suggest you read the whole experimental approach first to the BBC thing.
    It might focus the discussion. No judgements. Actually I was a participant in the experiment
    early on ( researching distributed stuff) but they had some issues and had to reset.

    Anyway, http://www.climateprediction.net/science/scientific_papers.php

  178. Boris
    Posted Dec 4, 2007 at 10:33 AM | Permalink | Reply

    One interesting aspect of the results is that they seem to indicate a relatively short time constant for warming (~5 years?). I wonder if this is a side-effect of the slab ocean used in this simple model, since more complex models generally have a longer time constant.

    It is.

  179. steve mosher
    Posted Dec 4, 2007 at 10:35 AM | Permalink | Reply

    RE 172. Thanks JohnV. Back inthe day we used the same approach in our modelling. We would
    use a low resolution model ( effects based) to map out the boundaries of the parameter space
    and to judge the sensitivity of given parameters so that when we ran the full blown physics
    models we could devote more runs to the most sensitive paramaters. So there was a hierachy of models.

    One thing I would like to hear bender Opine on is the stats behind this hierachical approach.

    I’m sure it depends on the exact system of equations. Also, Much to our suprise we would often find
    that the highest level models had a mind of their own.

  180. bender
    Posted Dec 4, 2007 at 10:40 AM | Permalink | Reply

    Re #171 Tom,
    Glad you enjoyed the paper. Yes – the climate modelers are “doubly wrong” – that is my hunch as well. But it seems they’re in so deep now, committed to a paradigm, they can’t back out without losing face, and worse, upsetting institutions. Into denial they will go.

    Funny you should comment on that particular character; I just did so myself in a crosspost. buffoonery

  181. Jan Pompe
    Posted Dec 4, 2007 at 10:53 AM | Permalink | Reply

    Pat Keating says:
    December 4th, 2007 at 10:03 am

    Interesting. The CO2 lag during de-glaciation is roughly 8% (30 degrees in your terminology) of the rise-time of the temperature.
    What does it mean physically, though?

    Yes I tend to think of these matters in terms of control systems and while the tradesmen I used to work with understand circles and degrees their eyes glaze over when starting to talk in radians and to them ‘lag’ is the insulation on hot water/gas pipes. Old habits die hard.

    I really haven’t a clue but I did notice the similarity relative to each of the cycles but in terms of actual time constant they are wildly different from a few hours to millennia. This of course points different systems with different time constants and it will be interesting to try and sort out the interplay. The only thing I can’t think of presently is that it’s a coincidence and I’m reading too much into it, but on the other hand it’s tempting to think there is a connection. I’m wondering what lag there is in the response to the sunspot cycles something for another time it’s 4 am and I’m off to bed.

  182. SteveSadlov
    Posted Dec 4, 2007 at 10:57 AM | Permalink | Reply

    RE: #169 – Classic overshoot. The arc continues, and eventually leads downward. To understand the eventual shape of any arc, look at the teal colored “minority” solutions. There is actaually physicality to explain this. Eventually, if you can force average temps to (temporarily) rise a few degrees, precip at high latitudes increases. That alone can bring on the ice. When the ice starts to build, the warming gets overcome.

  183. SteveSadlov
    Posted Dec 4, 2007 at 10:59 AM | Permalink | Reply

    Notice also the obvious cooling bias, expressed during the control phase. That is not at all surprising – we are after all in an interglacial, and not in an overall time frame such as 100 M years ago (Greenhouse World). We are in Ice World. Experiencing a temporary thaw.

  184. bender
    Posted Dec 4, 2007 at 11:00 AM | Permalink | Reply

    #179 mosh
    “Stats” would be too fine a term for the big issue here, which is the epistemological consequence of fishing for relationships. You are trying to derive a working hypothesis of how climate functions. As long as you are not intending inference from said model, all methods are in play. (It’s the difference bewteen descriptive statistics and inferential statistics.) When you are building a hypothesis there is no penalty for burning up degrees of freedom that you do not have. Think of it as borrowing degrees of freedom from the future: pay me now, or pay me later. The problem is testing the hypothesis against data to check whether it is safe to make inferences based on it. AFAICT climatologists are not ready to go there yet. They are stuck in the first stage. Consequently policy-makers are forced to develop policy based on models that are mere working hypotheses. And this decision is justified on the basis of the precautionary principle. [High Priests: Shall I quote that term?]

    This is only my non-climatological POV. I would love to have an open discussion with Dr Gavin Schmidt about the statistics and epistemology of climate modeling. Not to show what I know, but to work towards getting the real experts to finally face each other.

  185. Anthony Watts
    Posted Dec 4, 2007 at 11:58 AM | Permalink | Reply

    RE132 Bender,

    You may find this tidbit interesting. Here is a paper: Falsifcation Of The Atmospheric CO2 Greenhouse Effects Within The Frame Of Physics
    Gerlich and Tscheuschner, July 24, 2007 available here: http://arxiv.org/PS_cache/arxiv/pdf/0707/0707.1161v2.pdf

    In this paper they make a very interesting observation about GCM’s:

    Computer models of higher dimensional chaotic systems, best described by non-linear partial differential equations (i.e. Navier-Stokes equations), fundamental differ from calculations where perturbation theory is applicable and successive improvements of the predictions – by raising the computing power – are possible. At best, these computer models may be regarded as a heuristic game.

    and also this:

    The temperature rises in the climate model computations are made plausible by a perpetuum mobile of the second kind. This is possible by setting the thermal conductivity in the atmospheric models to zero, an unphysical assumption. It would be no longer a perpetuum mobile of the second kind, if the “average” fictitious radiation balance, which has no physical justifcation anyway, was given up.

    but this one made me laugh:

    The choice of an appropriate discretization method and the definition of appropriate dynamical constraints (flux control) having become a part of computer modelling is nothing but another form of data curve fitting. The mathematical physicist v. Neumann once said to his young collaborators: “If you allow me four free parameters I can build a mathematical model that describes exactly everything that an elephant can do. If you allow me a fifth free parameter, the model I build will forecast that the elephant will fly.” (cf. Ref. [185].)

  186. Posted Dec 4, 2007 at 12:13 PM | Permalink | Reply

    #185 Anthony Watts:
    Oh no, not that paper. Seriously?

  187. bender
    Posted Dec 4, 2007 at 12:19 PM | Permalink | Reply

    John V, do you care to address these specific points raised in the paper? Failing that, can you link us to a rebuttal? Or are you just going to wave your hands?

  188. Anthony Watts
    Posted Dec 4, 2007 at 12:22 PM | Permalink | Reply

    John V RE186, I was was just pointing out how the paper made me laugh. Love it or hate it, the flying elephant part is funny. If we lose the ability to laugh, then all is lost.

    Though, I do think there is some merit to the “heuristic game” point. Modeling, like gaming, is a learning experience forged from trial and error. Much like chess. I see the same sort of learning went on with your own development of “opentemp”.

  189. bender
    Posted Dec 4, 2007 at 12:27 PM | Permalink | Reply

    I caution all (myself included) to stay on topic, or else take it to the rubber room.

  190. Anthony Watts
    Posted Dec 4, 2007 at 12:34 PM | Permalink | Reply

    RE189, Well I thought the post I made was relevant to GCM tuning. Tuning and heuristic gaming seem to be much alike, IMHO. I once spent a weekend tuning some SU carburetors on a British Leyland vehicle. I find chess easier, but they both require a lot of trial and error to get right. I don’t see GCM’s as any different.

  191. Tom Vonk
    Posted Dec 4, 2007 at 12:36 PM | Permalink | Reply

    Jan

    Is this, you think, why we see things like the same thirty degree lag in diurnal warming (hottest part of day is 2hrs after noon) and a thirty degree seasonal lag (the hottest day usually a month after the longest day) and so on, or is that something else?

    It is similar but not exactly as quantitative .
    What I mean is that if I show you a chart with a climatic parameter X drawn on a scale of 1 hour and then the runing average of the same parameter over a certain period T but with a scale of 10 years (or 50 or whatever) , you would be unable to make the difference and specifically if the time scale is not on the chart , you would be unable to distinguish which one is the short term and which one is the long term .
    Of course the values of X and the running average of X (aka scale of the vertical axis) are not necessarily the same on both charts .
    That’s also called scale invariance because self similarity leads to scale invariance and fractional dimensions .
    In other words if you have problems to correctly quantify/estimate the short term (weather) , any amount of averaging won’t help you and you will find the same problems possibly with different/additional causes on the long term (climate) too .
    That’s exactly the feature displayed f.ex by the Mandelbrot set – only an example because this set doesn’t correspond to a physical system but it gives the idea .

    At least as long as the climate is defined as a temporal average of the short term dynamics what is the widely accepted definition up to now .

  192. bender
    Posted Dec 4, 2007 at 12:44 PM | Permalink | Reply

    Re #190: #185 is on topic – the topic of parameterization, which is central to Fig 1. The warning was in response to sweeping dismissals of a whole paper, when what were offered in #190 were carefully extracted assertions. It is not logical to insist we accept or reject the paper as a whole. Those kinds of arguments belong elsewhere.

  193. Posted Dec 4, 2007 at 12:45 PM | Permalink | Reply

    #188 Anthony Watts:
    The flying elephant quote is funny because it’s true.

    #187 bender:
    I’m not going to bother getting into an argument on the Gerlich paper. Even if I did, it would get snipped for being off-topic.

  194. bender
    Posted Dec 4, 2007 at 12:46 PM | Permalink | Reply

    #193 thank you.

  195. bender
    Posted Dec 4, 2007 at 12:51 PM | Permalink | Reply

    #191 Self-similarity is one of Spence_UK’s favorite subjects. (I say that only because he’s not here, but may wish to visit at some point.)

  196. bender
    Posted Dec 4, 2007 at 1:22 PM | Permalink | Reply

    AW (and JV),
    There is a quote in that paper that is relevant to my #132, where I said to RC:

    I can see how there might exist “a certain probability distribution of behaviors”. My concern is that these probability distributions in fact do not exist, or, rather, are unstable in the long-run. If that is true, then the parameterizations could be far off the mark.

    The quote is:

    While it is always possible to construct statistics for any given set of local temperature data, an infnite range of such statistics is mathematically permissible if physical principles provide no explicit basis for choosing among them.

    I must admit: I am somewhat taken aback to realize that this is the fundamental idea behind one of the arguments that Steve does not want discussed on the blog. (John A is likely laughing. I had quietly assumed that their argument was based on a superficial understanding of the spatial temperature field. I was wrong.) For the record: I do not read the skeptical literature. I have arrived at my skepticism completely independently of this article.

    I will not discuss the idea any further at CA (there are better places). But I must note that it underlies one of the major dangers being pointed to by Tom Vonk, Demetris Koutsiyiannis, Spence_UK, and now … myself. Variability occurring at all time scales is a major reason why you might get a good tuning during a calibration interval that fails miserably in an honest out of sample forecasting test.

    Speculative assertion: chaotic weather is continuously folded into the climate trajectory, yielding a long-term product whose time-series realizations over short time-scales appear to exhibit well-defined statisical probability distributions, but whose ensembles do not. Climate is non-ergodic, and the climate modelers are blind to the problem because they are divorced from mainstream statistics.

  197. Cliff Huston
    Posted Dec 4, 2007 at 1:41 PM | Permalink | Reply

    #190 Anthony Watts,

    You may be on to something here. Only the British would design carburetors that could only be tuned by a Zen master with a good ear and soda straw. Since the ICCP is largely a British invention, I am wondering how the Zen master needs to be applied in this case. I’m not sure a soda straw would work with the climate models. Do you think an A.M. radio would help? (ref. Daisy et al)

    Cliff

  198. bender
    Posted Dec 4, 2007 at 1:46 PM | Permalink | Reply

    Anyone wishing to pose question #196 to Dr. Edward Wegman has my blessing to do so. I feel the question may be about as well-posed as I can make it.

  199. SteveSadlov
    Posted Dec 4, 2007 at 2:05 PM | Permalink | Reply

    RE: #196 – slightly reworded:

    Problem Statement: chaotic weather is continuously folded into the climate trajectory, yielding a long-term product whose time-series realizations over short time-scales appear to exhibit well-defined statisical probability distributions, but whose ensembles do not. Climate is non-ergodic, and the climate modelers are blind to the problem because they are divorced from mainstream statistics.

  200. bender
    Posted Dec 4, 2007 at 2:22 PM | Permalink | Reply

    Thanks for #199.

    I think it is the failure to understand #199 that causes climatological modelers (and their disciples) to falsely assert that “weather is unpredictable, but climate is not”. I’ve puzzled over that statement for quite a while. So, believe me, I understand: it’s alluring. Yet … it is wrong. But how can it be so alluring if it’s wrong? How does chaotic weather lead to non-chaotic climate? The answer lies in the secretive ergodicity assumption – the dark monster in the closet. The answer is that the face of chaos changes as you change the temporal scale of perception & analysis.

    But again, the topic is not chaos or weather vs. climate. It’s Kiehl (2007), and what to make of the parameterizations in his Fig. 1. If these parameterizations were to converge via groupthink on aerosols, would the model performance, in terms of fit to Gavin’s scenarios, degrade? If the parameterizations are junk, then, yes, they would. So junk parameterization is a core issue.

  201. SteveSadlov
    Posted Dec 4, 2007 at 4:10 PM | Permalink | Reply

    Junk parameterization is a key issue. This is how clouds are tap danced around, to name but one example.

    RE: Kiehl – check out his full bibliography, it says a lot about his overall frame of reference and his attitude about GCMs. You mentioned pea and thimble, whether conscious or subconscious. And that, to me, is also a microcosm of how this whole GCM / climate prediction mess ever got so ugly. Final note about Kiehl’s misplaced faith in parameterization / GCMs (and again, this is a microcosm of “The community”) – it is that same faith in parameterization / GCMs as they are presently implemented that led him to a highly questionable cause – effect depiction of GHG concentration and the late Permian “die back.” These models tend to assume GHG as a lead rather than lag in nearly all cases. I’ve seen enough examples that seem to fly in the face of that, that I no longer take this as a given.

  202. SteveSadlov
    Posted Dec 4, 2007 at 4:16 PM | Permalink | Reply

    Bender: “Watch out for modelers that mistake their models for reality. See the hidden tautology. See the pattern of groupthink emerge. See the premium that is placed on getting this very mundane paper published. See the house of cards being buttressed.”

    ++++++++++

    Climate Model Links Higher Temperatures to Prehistoric Extinction

    August 24, 2005

    BOULDER—Scientists at the National Center for Atmospheric Research (NCAR) have created a computer simulation showing Earth’s climate in unprecedented detail at the time of the greatest mass extinction in the planet’s history. The work gives support to a theory that an abrupt and dramatic rise in atmospheric levels of carbon dioxide triggered the massive die-off 251 million years ago. The research appears in the September issue of Geology.

    “The results demonstrate how rapidly rising temperatures in the atmosphere can affect ocean circulation, cutting off oxygen to lower depths and extinguishing most life,” says NCAR scientist Jeffrey Kiehl, the lead author.

  203. SteveSadlov
    Posted Dec 4, 2007 at 4:31 PM | Permalink | Reply

    Unbelievable:

    http://www.proquestk12.com/curr/snow/snow1095/snow1095.htm

    I know this is, as Bloom would quip, “old,” but it reveals much. Here’s the money … quote:

    Jeff Kiehl is a scientist who doesn’t believe in the scientific method. “If science proceeded the way the textbooks say it does, every scientific problem would have been solved long ago,” he quips. Instead, he points to creativity and intuition as critical elements in solving the complex puzzles the natural world poses.

  204. SteveSadlov
    Posted Dec 4, 2007 at 4:59 PM | Permalink | Reply

    http://www.cgjungpage.org/index.php?option=com_content&task=view&id=664&Itemid=40

    Hey, we’ve all got our extracurricular activities and hobbies. That said, this is quite interesting.

  205. SteveSadlov
    Posted Dec 4, 2007 at 5:05 PM | Permalink | Reply

    http://oregonstate.edu/~shellk/radiative_kernel.pdf

  206. SteveSadlov
    Posted Dec 4, 2007 at 5:16 PM | Permalink | Reply

    http://www.nsf.gov/od/lpa/news/02/tip020722.htm

    Jeffrey Kiehl, a key developer of the model at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado, expects the CCSM-2 to play an integral role in the next climate assessment by the Intergovernmental Panel on Climate Change, the international organization that issues periodic assessments of global climate change.

  207. SteveSadlov
    Posted Dec 4, 2007 at 5:19 PM | Permalink | Reply

    http://www.ccsm.ucar.edu/models/

    Anyone up for some serious auditing?

  208. Anthony Watts
    Posted Dec 4, 2007 at 6:29 PM | Permalink | Reply

    RE207, just some cursory browsing tells me this project is far more open source and open data oriented than GISS could ever hope to be under current management. The downside is that you need a Cray or SGI origingin to run these models.

    On the plus side, I found an SGI Origin on Ebay for $799, SGI stuff is cheap these days since the company is DOA. A lot of this stuff will run on the IRIX OS, but IRIX is fast disappearing.

  209. E&M
    Posted Dec 4, 2007 at 8:03 PM | Permalink | Reply

    bender: #132 “One more comment to come – if I can dig it out – on internal vs external variability in Earth’s climate – an issue at the core of what they are tuning the GCMs to produce.”

    I would very much like to see this comment. I have wondered for some time if climate variation must be
    closely tied to driver variation or if the climate system has internals modes that can play out for years
    after some external perturbation.

  210. bender
    Posted Dec 4, 2007 at 9:21 PM | Permalink | Reply

    E&M #209 I looked and couldn’t find it. Since you asked, though, I’ll look again.

  211. bender
    Posted Dec 4, 2007 at 9:44 PM | Permalink | Reply

    William Connolley may be “movin on” from RC, but his legacy lives on. Check out this rebuttal to Roger Pielke Sr., in a thread on chaos and climate at RC:

    Roger A. Pielke Sr. Says:
    4 November 2005 at 11:11 AM

    James and William- your post, unfortunately, perpetuates the use of climate to refer to long term weather statistics. You state that

    “The chaotic nature of atmospheric solutions of the Navier-Stokes equations for fluid flow has great impact on weather forecasting (which we discuss first), but the evidence suggests that it has much less importance for climate prediction.” and that

    “Fortunately, the calculation of climatic variables (i.e., long-term averages) is much easier than weather forecasting, since weather is ruled by the vagaries of stochastic fluctuations, while climate is not. ”

    This is incorrect.

    First, the more appropriate scientific definition of climate is that it is a system involving the oceans, land, atmosphere and continental ice sheets with interfacial fluxes between these components, as we concluded in the 2005 National Research Council report. Observations show chaotic behavior of the climate system on all time scales, including sudden regime transitions, as we documented in Rial, J., R.A. Pielke Sr., M. Beniston, M. Claussen, J. Canadell, P. Cox, H. Held, N. de Noblet-Ducoudre, R. Prinn, J. Reynolds, and J.D. Salas, 2004: Nonlinearities, feedbacks and critical thresholds within the Earth’s climate system. Climatic Change, 65, 11-38.

    [Response: Roger - you appear to have your own, curious definition. You’re welcome to use your own definitions, for your own purposes; but if you redefine words in that way you can expect to have confusing conversations. We’re using the standard definition of climate. See, for example, the IPCC glossary - William]

    That the model simulations that you discuss in your weblog do not simulate rapid climate transitions such as we document in our paper illustrates that the models do not skillfully create chaotic behavior over long time periods as clearly occurs in the real world.

    [Response: This is wrong. The current climate does not display rapid transitions, which is why good GCMs don’t simulate them. Past rapid climate change appears to be associated with the Laurentide ice sheet, which is why we don’t see it now. See this post for more - William]

    That climate is an integrated system and is sensitive to initial conditions is overviewed in Pielke, R.A., 1998: Long-term variability of climate. J. Atmos. Sci., 51, 155-159). We show in this study that even short-periodic natural variations of climate forcing can lead to significant long-term variability in the climate system.

    We need to move the discussion to studying climate as a complex, nonlinear system which displays chaotic behavior if we are going to provide scientifically robust understanding to policymakers. Readers of your weblog are invited to read my postings at http://climatesci.atmos.colostate.edu if they would like to read a different perspective on climate science.

    [Response: Everyone studies climate as a complex non-linear system. Few however expect chaotic behaviour from it - William]

    A pity that James Annan wasn’t the one doing the inline responses. The article he wrote had a much more realistic conclusion:

    The climate of a model can be easily defined in terms of the limit of the statistics of the model output as the integration time tends to infinity, under prescribed boundary conditions. This limit is well-defined for all climate models. However, the real world is slightly messier to deal with. The real climate system varies on all time scales, from daily weather, through annual, multi-year and decadal (ENSO), Milankovitch, glacial-interglacial cycles, plate tectonics and continental configurations, right up to the ultimate death of the Sun. The average temperature, and all other details of the climate system, will vary substantially depending on the time scale used. So how can we talk meaningfully about “the climate” and “climate change”? Well, although there are interesting scientific questions to ask across all the different time scales, the directly policy-relevant portion is on the multi-decadal and centennial time scale. It is quite clear that the pertubation that we are currently imposing is already large, and will be substantially larger, by up to an order of magnitude, than any plausible natural variability over this time scale. So for the policy-relevant issues, we generally focus on the physical atmosphere-ocean system, sometimes with coupled carbon-vegetation system, and treat the major ice sheets, orbital parameters and planetary topography as fixed boundary conditions. It’s an approximation, but a pretty good one.

    Annan suggests that chaotic internal climate variability is not a problem for simulating responses to external forcings. In principle, this makes sense to me. In practice, I wonder how well those parameterization schemes will work when they are being tuned to non-persistent features of the cliamte system – a system which we all agreet “varies on all time scales”.

  212. bender
    Posted Dec 4, 2007 at 10:06 PM | Permalink | Reply

    And Annan’s reply to Roger Pielke Sr.:

    James Annan Says:
    4 November 2005 at 6:04 PM
    I’m really not sure what nits Roger (#2) is trying to pick here. We explicitly agree (final paragraph of main article) that the climate has historically shown significant variability on all time scales. People have looked hard for possible “exciting” transitions from the current system, and so far have pretty much drawn a blank. With the anthropogenic perturbation likely to be around 2C and maybe more in the next 100 years (that’s a global average, it will be much more over northern hemisphere land where we actually live), there are simply no comparable sources of natural variability, and the historical record shows that such temperatures have not been approached in the last 2000 years. I don’t by any means think that current climate models are the whole answer, but it seems clear that their outputs are directly relevant to anyone who is interested in the future of the climate system.

    Ah, but where did that 2 degree increase estimate come from, James? It came from the very models whose parameterizations
    we are questioning. Presupposition is a serious logical fallacy. THOSE are the nits being picked here, James.

    So where did the GHG sensitivity coefficients come from? Kiehl (2007) shows us they are linked to aerosol sensitivity coefficients. And where do those come from? From the temperature variation (“trends”) that could not be explained by the climate models internal dynamics. But I thought we agreed climate varies on all time scales? Er, well, we did.

    You can’t say you are assuming something (e.g. climate varies on all time scales) and then relax that assumption when it becomes convenient. Well, actually, you can. But it’s called special pleading.

    Must have been a bad day for James, two logical errors in one paragraph. Lucky for him that Roger tired of the argument. Lucky for us that RC has archived these exchanges. For now.

  213. bender
    Posted Dec 4, 2007 at 10:37 PM | Permalink | Reply

    #209 Alas, I cannot find my comment with the inline reply by “mike”.

    But this will do. It is Gavin Schmidt on the thread Planetary Energy Imbalance, May 3 2005, discussing a graph of his simulation model output matched to the ocean’s heat content over time (10y):

    Firstly, as surface temperatures and the ocean heat content are rising together, it almost certainly rules out intrinsic variability of the climate system as a major cause for the recent warming (since internal climate changes (ENSO, thermohaline variability, etc.) are related to transfers of heat around the system, atmospheric warming would only occur with energy from somewhere else (i.e. the ocean) which would then need to be cooling).

    Secondly, since the ocean warming is shown to be consistent with the land surface changes, this helps validate the surface temperature record, which is then unlikely to be purely an artifact of urban biases etc.

    Thirdly, since the current unrealised warming “in the pipeline” is related to the net imbalance, 0.85+/-0.15 W/m2 implies an further warming of around 0.5-0.7 C, regardless of future emission increases.

    This exposition is much clearer than the one “mike” provided.

  214. bender
    Posted Dec 4, 2007 at 10:40 PM | Permalink | Reply

    I believe the “ocean heat content” calculation used by Schmidt is under audit but I will check.

  215. Evan Jones
    Posted Dec 5, 2007 at 12:02 AM | Permalink | Reply

    “seems to admit that the anthropogenic forcings in the 20th century used to drive the IPCC simulations were chosen to fit the observed temperature trend.”

    Woof! That sounds about as bad as my historical BvG storyboard model for the Civil War. (Of course I was openly unashamed about it–with no pretentions.)

    How well do I know (by analogy) what those so-and-sos were up to. The accuracy of these-here “models” seems about as “realistic” as your average wargame. (That’s a base insult, BTW.) Well, this crass game designer can only doff his hat to his fellow “modelers”. (We rough out a plasticine Pygmalion and then have the face to claim we’ve created mankind.)

    I’d be honored to loan them my shoehorn!

  216. Posted Dec 5, 2007 at 2:10 AM | Permalink | Reply

    RE bender says:
    December 4th, 2007 at 10:37 pm
    quote the thread Planetary Energy Imbalance, May 3 2005, unquote

    Interesting discussion there and fascinating graphs. My immediate take was that there are no cloud feedbacks on the first graph — I see that GS (?) said he would be reporting on cloud results later after further study. My second thought was that cloud forcings were there but they were off the top of the scale. Has anyone seen that further study reported? The measurement of SSTs to thousandths of a degree mentioned brings a wry smile — I thought of the bucket ‘correction’.

    A few questions. Did the tuning involve parametising clouds? What about Palle’s albedo results? Of course, if the albedo changes were added the graph would look very different — the vertical scale would have to be compressed by a factor of ten to get the forcing into the box.

    Is this the model for which the bucket ‘correction’ was discovered?

    Has anyone run one of the big models with different assumptions about the primary forcing? What, for example, could one output if one assumed albedo change as a forcing, calcualted and matched the SST warming and parameterised around that? Could one produce a realistic climate?

    Do any modellers use major what-ifs, or are they all tinkering on the edges of an already determined scenario?

    quote “seems to admit that the anthropogenic forcings in the 20th century used to drive the IPCC simulations were chosen to fit the observed temperature trend.” unquote

    Isn’t the situation even worse than that? Look at the SSTs with and without the bucket correction and wonder which looks more ‘real’. So it’s ‘chosen to fit the manufactured temperature trend.’

    I’m suffering a crisis of belief here. Surely it’s not possible that this weird science is all there is behind the great global warming scare? Tinkering with half a watt here and there when there’s a measured 20 watts staring us in the face, is that sensible?

    JF

  217. Tom Vonk
    Posted Dec 5, 2007 at 3:52 AM | Permalink | Reply

    #202

    BOULDER—Scientists at the National Center for Atmospheric Research (NCAR) have created a computer simulation showing Earth’s climate in unprecedented detail at the time of the greatest mass extinction in the planet’s history. The work gives support to a theory that an abrupt and dramatic rise in atmospheric levels of carbon dioxide triggered the massive die-off 251 million years ago. The research appears in the September issue of Geology.

    “The results demonstrate how rapidly rising temperatures in the atmosphere can affect ocean circulation, cutting off oxygen to lower depths and extinguishing most life,” says NCAR scientist Jeffrey Kiehl, the lead author.

    This one made me explode laughing !
    It is known that the Earth’s orbit , as well as any 3 (or more) body orbit is chaotic (see f.ex Malhotra,Holman, Ito) .
    That of course doesn’t mean that the orbits are random or that they can do anything crazy in short times .
    However it means that the orbits are never the same and that the distance between 2 orbits with as close initial conditions as one wishes will increase exponentially with time .
    This exponential parameter is called Lyapunov coefficient and is for the Earth orbit between 5 – 10 millions years .
    That means that any calculation , be it forward or backward in time for more than let’s say 100 millions year gives an orbit whose parameters have no significance and could be completely random for all that matters .
    Therefore considering that one can’t know the orbit parameters with any reasonable accuracy for 200 millions years ago , one wonders if that “paper” is more relevant than throwing a die . Or could it be something so mundane as choosing the orbital parameters in a manner that the to be proven thesis holds ?

    # 211

    That climate is an integrated system and is sensitive to initial conditions is overviewed in Pielke, R.A., 1998: Long-term variability of climate. J. Atmos. Sci., 51, 155-159). We show in this study that even short-periodic natural variations of climate forcing can lead to significant long-term variability in the climate system.

    We need to move the discussion to studying climate as a complex, nonlinear system which displays chaotic behavior if we are going to provide scientifically robust understanding to policymakers. Readers of your weblog are invited to read my postings at http://climatesci.atmos.colostate.edu if they would like to read a different perspective on climate science.

    [Response: Everyone studies climate as a complex non-linear system. Few however expect chaotic behaviour from it - William]

    To make this understandable , I will rephrase what Connolley wrote in order to see what he thinks .

    [Response : Everyone studies climate as a complex non-linear system because it is unfortunately so . However every time we think that we can get away with it , we'll linearize as much as possible . Never mind the validity . We are scared s...less that the system might be chaotic at all time scales . Unfortunately none of us has a clue about those fancy theories of the young people . They really talk crazy things - fractional dimensions , exponential divergences , bifurcations , state transitions - can you imagine ? Even without understanding we believe that all that talk means that computer models are not worth the silicium they are running on and that is a clear no no . So everybody who opens his mouth implying that there could be chaotic behaviour we make him shut up . As for us , we won't certainly shoot ourselves in the foot and we certainly don't expect any chaotic behaviour . It goes without saying that we don't need any proof as our priority is to save mankind and it is in your interest to not expect anything chaotic too . And now go away please .]

    “The chaotic nature of atmospheric solutions of the Navier-Stokes equations for fluid flow has great impact on weather forecasting (which we discuss first), but the evidence suggests that it has much less importance for climate prediction.” and that

    “Fortunately, the calculation of climatic variables (i.e., long-term averages) is much easier than weather forecasting, since weather is ruled by the vagaries of stochastic fluctuations, while climate is not. ”

    And here we are again in the heart of the matter . Stochasticity , ergodicity – the single most important questions that have to be asked to make any progress .
    On a particular note Connolley and Anan show one more time very explicitely that they understand nothing about deterministic chaos .
    They seem to assimilate it both to a kind of randomness and to fluid dynamics (Navier Stokes) .
    When they say that ” … but the evidence suggests that it has much less importance for climate prediction .” then that MIGHT be true for N-S and specifically small scale turbulence and I say might because even that would need a rather sophisticated mathematical proof .
    But they didn’t understand that it is not even the point .
    If the climate is chaotic , then it is because of the multiple strong non linear interactions between the subsystems each with a different characteristical time scale (f.ex annual ice cover variations , ENSO , NAO , cloudiness , use of land etc etc) .
    That has nothing or extremely little to do with turbulence and N-S .
    Chaos theory applies to any dynamical non linear complex system – fluid dynamics and N-S within fluid dynamics is only a tiny part of it .
    It seems they don’t even understand the difference between weather and climate or that they have a very restrictive definition of “climate” that would be only the time average of atmospheric parameters what would of course be dramatically inadequate to formulate anything relevant about the whole system (= Lithosphere&Biosphere&Hydrosphere&Biosphere&Atmosphere) .

  218. KevinUK
    Posted Dec 5, 2007 at 4:05 AM | Permalink | Reply

    #216 JF

    No you’re not suffering from a crisis of belief. There would have to be something to believe in in the first place for that to be the case and with ‘unprecented’ AGW there isn’t anything substantive to believe in. Like many who visit here, thanks to Steve’s sterling auditing efforts, you’ve worked out that the so called instrumented temperature record has been ‘rigged’ to show an ‘unprecented’ modern warm period, the proxies have been ‘rigged’ to remove any signals that they might show for the indispute historically documented Medieval Warm Period and the Little Ice Age so that the contrived modern warm period looks to be ‘unprecented in the last millyun years’.

    This has been done by a small group of ‘true believers’ with the backing of their political supporters in an attempt to provide some kind of credibility to the pre-determined alarmist future predictions of the GCMs so that we can be forced to suffer increased centralised control over our lives.

    Its high time now that the control of the adjustments to the temperature records is removed from the very same people who are in control of the models. In particular, the whole climate change issue must be removed from the ‘gravy train’ UN under its current auspices of the IPCC.

    Regards

    KevinUK

  219. Max P.
    Posted Dec 5, 2007 at 10:28 AM | Permalink | Reply

    Alright, here is a really dubious idea from a lay person…

    In the social science of 30 years ago we used to have fun with step-wise regression as a “initial” analysis. Of course, that requires a data set of the suspected independent variables that might contribute to the dependent variable (in this case temperature). So has anyone taken only the measured data (greenhouse gases, rainfall, etc.) and run a simple multiple regression analysis?

    Naturally, given the number of unmeasured unknowns, I would expect it to find little correlation to greenhouse gases (or any measured variable). Still, it would be interesting to see the results, as well as the verification scores.

    Us dummies in S.Science would have simply “moved on” after finding our hypothesis false. We didn’t know how to construct ‘naturalistic’ models of society with invented data for the unknown parts of the nations social environment. If we had, we could have fit federal election results to the historical record and “predicted” elections for the next 100 years…

    Sigh. If only I had been born later…

  220. Posted Dec 5, 2007 at 12:04 PM | Permalink | Reply

    @TomV 217

    However every time we think that we can get away with it , we’ll linearize as much as possible .

    I remember a discussion at an ASME meeting. One assistant professor at MIT was talking to a former assistant professor at MIT. (The first was occupying the former office of the second.) The first commenting on his work with some irony. Evidently, as he put it, his research consisted of identifying moderately interesting proceses involving nonlinear dynamics . He would identify a small parameter, expand about a small term and find a solution that might be interesting.

    When the problem addressed is industrially relevant, these sorts of things can be really useful. Otherwise, you publish a journal article, a few people read it and say “That’s moderately interesting.”

    And of course, the advantage to doing these sorts of linearizations instead of running code is you can test when the solution becomes self inconsistent. For example, if your small parameter contained a velocity ‘u’ which was assumed small when you linearized, then you check it’s value when you finish. If the solution shows ‘u’ exhibits exponential growth with time, you admit your solution only applies for small time. Then you seek a different scaling that applies in the limit of larger times. If you are lucky, you will even find you can do a matched assymptotic expansion that spans the mid-range.

    Oh… but I digress. :)

    With regard to this thread when a model (like that in a climate code) rely on linearization of some any kind one must at least test whether or not neglecting the non-linear terms is appropriate after one obtains a solution.

    Parameterizations for any non-linear phenomena nearly always involve some degree of linearization. Depending on the model and the parameterization, it can be impossible to do a consistency check because the the information just isn’t contained in the computational model.

    Some checks can be done against data, some against experimental data, and some against other more detailed codes. The Climate Modelers say they do this. I don’t know enough to say they do; I don’t know enough to say they don’t. However, it’s not easy, and can be overlooked.

  221. bender
    Posted Dec 5, 2007 at 12:29 PM | Permalink | Reply

    Digressions not so bad if you can label them as such, stop it there, and bank it for discussion elsewhere. (exponential growth in informational systems)

  222. SteveSadlov
    Posted Dec 5, 2007 at 12:56 PM | Permalink | Reply

    RE: #216 – this is what happens when would be alchemists dabbling in entertainment reviews, and other “creative” folks who hate the “confinement” of the scientific method, create climate models.

  223. bender
    Posted Dec 5, 2007 at 1:36 PM | Permalink | Reply

    #223 Sadlov
    If it is the objective, independently verifiable truth one seeks, nothing is more powerful, or painstakingly slow, than the scientific method. Those who choose the fast track … the consequences are yours to live with.

  224. SteveSadlov
    Posted Dec 5, 2007 at 2:22 PM | Permalink | Reply

    RE: #224 – that is so true. Even in business, in “high tech” / “engineering” – it is soooo true … (he said, as he contemplated the rich accumulation of scar tissue in various places …)

  225. Chas
    Posted Dec 5, 2007 at 2:56 PM | Permalink | Reply

    Willis, Re 119 & 149 [Very OT, so apologies to everyone]: Those are very nice, clear, 3d graphics : Are they yours -if so, what did you do them with ?

  226. bender
    Posted Dec 6, 2007 at 7:57 AM | Permalink | Reply

    #149 Willis

    The models do not give reasonable Hurst coefficient results.

    This is an interesting point. Has anyone ever mentioned this before to G. Schmidt? I wonder what the Hurst coefficients are for the GCM runs that they weed out for lack of convergence.

  227. Tom Vonk
    Posted Dec 6, 2007 at 8:23 AM | Permalink | Reply

    This is an interesting point. Has anyone ever mentioned this before to G. Schmidt? I wonder what the Hurst coefficients are for the GCM runs that they weed out for lack of convergence.

    Some little bird is whispering to me that chances are that they are significantly above 0.5 .
    Not that they’d bother to calculate them .
    But of course we can’t know unless Gavin publishes a peer reviewed paper explaining that the variability estimators had to be revised because the modellers and IPCC have been using wrong statistics for 25 years .
    Somehow I think that once the paper comes , it will not come from that general area .
    Now why should I think that … :)

  228. bender
    Posted Dec 6, 2007 at 9:14 AM | Permalink | Reply

    The models do not give reasonable Hurst coefficient results.

    OTOH, we only have one relatively short run of the real climate system. So it’s not like we know the actual Hurst coefficients for the real system. (Do we?) What spatial scales are these Hurst coefficients measured at, anyways? All?

  229. steve mosher
    Posted Dec 6, 2007 at 9:59 AM | Permalink | Reply

    I mentioned Hurst components on RC once.

    I heard this:

    http://new.wavlist.com/soundfx/014/cricket-1.wav

  230. Willis Eschenbach
    Posted Dec 6, 2007 at 2:30 PM | Permalink | Reply

    bender, as always, your comments are interesting

    “The models do not give reasonable Hurst coefficient results.”

    OTOH, we only have one relatively short run of the real climate system. So it’s not like we know the actual Hurst coefficients for the real system. (Do we?) What spatial scales are these Hurst coefficients measured at, anyways? All?

    While we only have one climate system, we have lots of measurements of different parts of it. The part that I was looking at in the graphs above was the tropical sea surface temperature. But many, perhaps most, other temperature series have Hurst coefficients greater than 0.5. This is true both of global measurements and individual station measurements. Hurst(CET) = 0.972, Hurst(CRUTEM)=0.83, Hurst(HadSST) =0.77, Hurst(Zurich temp) = 0.84, Hurst(Uccle temp) = 0.61, etc.

    Which should not be particularly surprising. We know that today’s temperature depends in some sense on yesterday’s temperature, and that this month’s temperature depends on last month’s. In addition, there are a variety of longer-term cycles that give longer-term persistence to a wide variety of climate variables.

    w.

  231. bender
    Posted Dec 6, 2007 at 4:13 PM | Permalink | Reply

    As a fundamentally thermohaline process, what is the chance that ocean currents express BZ wave-like behavior as the ocean strives for thermal equilibrium? Could this be a source of long-term persistence? Would additional forcing by wind, solar, and other quasiperiodic agents not make the resulting LTP chaotic? How would a GCM parameterization process cope with these complexities? Is this why the GCMs fail to exhibit Hurst-like all-scale variability?

    Wunsch dynamics :: Koutsiyiannis statistics?

    [Steve M: snip if you wish. (Ocean thermodynamics)]

  232. Larry
    Posted Dec 6, 2007 at 4:27 PM | Permalink | Reply

    230, could be because when you google “hurst components”, you get this:

    http://en.wikipedia.org/wiki/Oldsmobile_Hurst/Olds

    Probably wondering why you were babbling about Oldsmobiles, and thought you were making a reference to Teddy Kennedy.

  233. bender
    Posted Dec 6, 2007 at 4:32 PM | Permalink | Reply

    heh heh :)
    but wikipedia’s an authority!

  234. steve mosher
    Posted Dec 6, 2007 at 6:22 PM | Permalink | Reply

    RE 233. I’ve been struggling all day with trying to figure out a hurst shifter joke. Damn you to bloody
    hell and back Larry.

    I think they avoid the hurst stuff because of the dreaded F word. fractal
    and the C word. Chaos.

  235. steve mosher
    Posted Dec 6, 2007 at 6:46 PM | Permalink | Reply

    RE 225. Best way to cure bad engineeritis is to make them eat their own damn dogs breakfast for a year.
    You built that? use it.

  236. steve mosher
    Posted Dec 6, 2007 at 6:46 PM | Permalink | Reply

    RE 225. Best way to cure bad engineeritis is to make them eat their own damn dogs breakfast for a year.
    You built that? use it.

  237. Posted Dec 6, 2007 at 8:16 PM | Permalink | Reply

    @steve mosher in 236 (and 237)
    You don’t even need to force engineers to eat their own damn dogs breakfast. Serve it to the dog. If he won’t eat it then, “Okay, Houston, we’ve had a problem here.”

  238. SteveSadlov
    Posted Dec 6, 2007 at 9:43 PM | Permalink | Reply

    I forgot my umbrella and rain jacket today. I went into town on errands and ended up parking in a place that was very expensive. I walked from that spot to do all my errands. Toward the end, the light rain got very heavy. My body ended up burning more energy for an equivalent set of tasks, than it would have had it not been raining. In order to become the coolant that it ended up being, that rain had started out as water evaporating off of the coast of SE Asia, then traveled up to the tropopause, where it condensed, emitting energy into space. From there, it was transported across the Pacific. It then collided with supercooled air from the McKenzie Delta. It then fell on me, taking me in the hypothermic direction. How do the GCMs handle the integral over time and space, of many similar situations?

  239. Pat Keating
    Posted Dec 6, 2007 at 9:52 PM | Permalink | Reply

    How do the GCMs handle the integral over time and space, of many similar situations?

    Gunnar, I think that’s your cue.

  240. Willis Eschenbach
    Posted Dec 7, 2007 at 12:07 AM | Permalink | Reply

    OT!

    Chas, you ask about the 3-d graphics. The graphics are mine. They were done with a combination of Excel and Vectorworks (VW), which is a 3-d architectural/mechanical/lighting graphics program.

    One of the best parts of VW is that it contains a programming language, which is a superset of pascal with lots of graphic primitives. So I wrote a program to make each probability cloud as a 3-d solid ellipsoid with a length of 1 standard deviation for each of the three semi-diameters, at the appropriate location in 3-space.

    The walls and floor were hand-built, they don’t change. The text is added by the program and extruded a bit.

    Put in three lights, to cast the three shadowplots.

    Oh, and in what I thought was a nice touch, I laid in the grid on the walls as shadows. I just put a grid between the light source and the scene, you can see the shadows on the ellipsoids.

    Then you get to pick your point of view, render it, and take a screenshot. Change the names and the data, run the program again for a different trio.

    Here, I tell you what, Chas. I’m not supposed to do this, but you seem like a nice guy. Come in here, and step behind this curtain, I’ll show you how it’s done. You understand, you can’t say a word about this, not everyone gets to see the actual stage where the magic goes on, where we build the model, but just this once, it’ll be ok. It’s right around this corner here, step over those power cables for the lighting, and watch out, it’s kinda bright because we’re looking towards the lights …

    You can see the grille of lines that casts the shadows to make the grids on the walls. And as you no doubt noticed, one GCM’s results were so off that they actually pushed through the wall. And towards the back you can see …

    … oh-oh, I hear someone coming. You gotta go, you’re not supposed to see this. Follow me, right this way, there’s a back exit to the street …

    Why did we have to leave so quickly, you say? Well, I shouldn’t be telling you this either, but we computer model makers are required to hide our secrets. It’s part of the blood oath that we have to swear when we’re initiated into the Guild … what Guild? Why, the Guild of Computer Mythmakers, of course, what do you think “GCM” actually … but no, I’ve said too much already, pleasure meeting you, Chas, I gotta run …

    w.

  241. Chas
    Posted Dec 7, 2007 at 12:55 AM | Permalink | Reply

    Thanks Willis, I wont tell a soul, I promise. :-)

  242. Willis Eschenbach
    Posted Dec 7, 2007 at 12:56 AM | Permalink | Reply

    SteveSadlov, you ask a very good question:

    In order to become the coolant that it ended up being, that rain had started out as water evaporating off of the coast of SE Asia, then traveled up to the tropopause, where it condensed, emitting energy into space. From there, it was transported across the Pacific. It then collided with supercooled air from the McKenzie Delta. It then fell on me, taking me in the hypothermic direction. How do the GCMs handle the integral over time and space, of many similar situations?

    The one word answer is “poorly”.

    The more detailed answer is, they parameterize it. There’s a reasonable description of the GISSE model here (pdf). From that document:

    Relative to SI2000, ModelE includes several additional significant changes [described more fully in Del Genio et al. (2005a)]. Grid boxes are now divided into subgrid convective (updraft and subsidence, assumed to have equal area) and nonconvective (cloudy and clear sky) parts.

    Stratiform cloud formation below the cumulus detrainment level is restricted to the nonconvective portion of the gridbox. This has the beneficial effect of suppressing some of the excessive low cloud in tropical convective regions, and it thus permits the stratiform cloud scheme’s threshold relative humidity to be lower than would otherwise be the case, thereby increasing cloud somewhat in the eastern ocean marine stratocumulus regions while maintaining global radiative balance.

    An improved atmospheric turbulence scheme is used (see below), which leads to water vapor being effectively vented from the lowest model layer, eliminating the need (as in SI2000) for a separate threshold humidity calculation to avoid excessive first-layer cloudiness.

    Separate equations relating stratiform cloud cover to clear-sky relative humidity, and clear-sky humidity to a threshold relative humidity, at different points in the code were combined into a single equation, reducing high-frequency noise in cloud cover.

    Threshold liquid water contents for efficient precipitation were halved for liquid phase stratiform clouds.

    Cloud morphology was originally specified to allow stratiform clouds to fill the grid box horizontally but not vertically under stable conditions, but in the current version the maximum horizontal cloud fraction is less than 100% unless the grid box is saturated.

    Note the various “thresholds” used. The modelmakers say there’s not a lot of parameters. Perhaps they are not counting thresholds. One of the main ones is the Threshold Relative Humidity, which I’ll call TRH. The TRH is the threshold relative humidity at which an atmospheric process (e.g. cloud fomation) starts to occur. Note above they say that their new “restriction” (a turbulence threshold) changes the cloud distribution so that

    it thus permits the stratiform cloud scheme’s threshold relative humidity to be lower than would otherwise be the case

    This is a revealing quote. The modelers are always saying that they are setting the parameters based on the fundamental physics. This quote reveals that:

    1. The previous TRH for the clouds was known to be set artificially high.

    2. They knew that, they changed things (with a new threshold), and their change puts them nearer to (but apparently not at) the observational TRH value.

    But the story doesn’t stop there. From the same text:

    The model is tuned (using the threshold relative humidity
    Uoo for the initiation of ice and water clouds) to
    be in global radiative balance (i.e., net radiation at
    TOA within ±0.5 W m 2 of zero) and a reasonable
    planetary albedo (between 29% and 31%) for the control
    run simulations. In these experiments we use Uoo =
    0.59, 0.57, and 0.59 for ice clouds and 0.82, 0.82, and
    0.83 for water clouds in M20, F20, and M23, respectively.

    In other words, the TRH is messed with until a) the albedo matches the known value and b) the system is in radiative balance … I guess you could call that setting the parameters based on fundamental physical principles, but I would call it forcing a fit.

    Of course, when you force a fit like that, something’s gotta give. What gives in the model is cloud cover, which occupies only 58% of the modeled globe, whereas observations have it at 69%.

    So the model is in radiative balance, but it has way less cloud cover than the real world … and yet it manages (as do all of the GCMs) to reproduce the historical record.

    Go figure …

    w.
    … rainy evening in Honiara, tropical thunderstorm, but how do the clouds know what the TRH is? …

  243. Willis Eschenbach
    Posted Dec 7, 2007 at 4:42 AM | Permalink | Reply

    PS – Seeing the lightning outside my window reminds me that there is a large body of observational evidence that the processes that the modelers are controlling with various applications of the TRH (e.g. cloud formation, precipitation) are greatly influenced by the electromagnetic properties of the clouds. A storm cell is not just a giant solar driven engine for removing heat from the surface and adding cold to the surface. It is also a huge van de Graff generator, producing unbelievable voltages and amperages. These monster electric fields modify all of the parts of the condensation – evaporation – coalition – precipitation cycle in poorly understood ways.

    There is also evidence that the electromagnetic properties of the clouds are influenced by the electromagnetic properties of the ionosphere … and that the electromagnetic properties of the ionosphere are driven in part by a combination of the solar heliomagnetic field, the coronal solar wind, the solar flare proton storms, and the coronal mass ejections (CMEs, also called “solar storms”) of the sun.

    I can tell you how the modelers deal with that obviously critical group of phenomena … they don’t include them. Simple and clean, wouldn’t you say?

    w.

  244. Pat Keating
    Posted Dec 7, 2007 at 8:57 AM | Permalink | Reply

    243

    What gives in the model is cloud cover, which occupies only 58% of the modeled globe, whereas observations have it at 69%.

    So the model is in radiative balance, but it has way less cloud cover than the real world … and yet it manages (as do all of the GCMs) to reproduce the historical record.

    That’s odd, because they seem to have matched the planetary albedo, which is largely determined by clouds, I think — or have they?

  245. Jaye
    Posted Dec 7, 2007 at 9:21 AM | Permalink | Reply

    RE: Hurst coefficients

    In the late 90′s I was involved in some ATR algorithm development for targeting FLIRS. One technique we tried was computing hurst coeff’s, using a convolution like kernel approach, on each image frame. The result was an image of hurst values. Worked fairly well in high clutter but was extremely slow. Anyway, we found that the ability to accurately measure hurst values was limited to about the middle half, give or take, of the available scales in the image. Low res scales had too little data and high res stuff suffered from aliasing, etc. We tested on 1 and 2d fbm’s generated with know hurst coeff’s.

  246. Ian Castles
    Posted Dec 18, 2007 at 12:15 AM | Permalink | Reply

    CA readers may be interested in a just-published book on “Science and Public Policy” by Aynsley Kellow, Professor and Head of the School of Government at the University of Tasmania. Chapter 3 (“Climate science as ‘post-normal’ science”) includes sections headed “Statistics, models and scenarios”, “The hockey stick controversy: a tree ring circus”, “Problems with climate science” and “Political science”. Climate science debates also loom large in some of the other chapters. Here’s an extract from the conclusion of Chapter 3:

    “In 2005 Margaret Beckett, UK Secretary of State for the Environment, Food and Rural Affairs, announced funding … to change public attitudes to climate change. She stated that ‘We need people to understand that climate change is happening … It is essential to deepen popular understanding and support for action on climate change’… Leaving aside the point that there is something quite bizarre (and slightly undemocratic) about a government spending taxpayers’ money to change their minds, such efforts could well be undermined by the extensive reliance upon models in climate science…

    “Reliance on modelling gives rise to problems: assumptions must be made at every stage of model construction; their complexity limits the possibility that they can be audited; they must omit factors in order to simplify reality and these omissions can prove crucial in unforeseen ways; even simple measurements can be affected by the desires and expectations of researchers; models are often tested against essentially the same observations they were built from; they do not account for chaotic changes such as the 1976 Pacific Climate Shift … If models are to be used, it is essential that this occurs within a climate of open scepticism and contestation. Lahsen (2005) found that climate modellers are often least able to see the problems with their models (p. 87).

  247. bender
    Posted Feb 5, 2008 at 10:17 PM | Permalink | Reply

    This bump is for Eric McFarland who wants to illustrate his deep knowledge of the hard physics underlying the GCMs. I will ask him some questions shortly. Study up, Eric.

  248. bender
    Posted Feb 5, 2008 at 10:19 PM | Permalink | Reply

    Eric’s confident assertions can be found here: http://www.climateaudit.org/?p=2600#comment-208283

  249. bender
    Posted Feb 6, 2008 at 9:09 AM | Permalink | Reply

    1. If GCMS are ruled by hard physics, why the different parameterizations above? (Why many dots on a line? Why not a cluster?)
    2. Why THESE parameterizations, and not some other intermediate ones? (Why are the dots located where they are?)
    3. Why this RANGE of parameterization and not something wider? (Why not more dots on either end of the curve?)

    [Hint: These are fudgings, err, tunings. They are the free parameters whose governance by hard physics is uncertain.]

    The floor is yours, Eric. Maybe you have a nice YouTube dodge for us?

  250. bender
    Posted Feb 6, 2008 at 9:25 PM | Permalink | Reply

    No reply from Eric yet. Will check back periodically.

  251. conard
    Posted Feb 6, 2008 at 10:28 PM | Permalink | Reply

    Bender,

    Give him a little time. He is currently debugging the last few lines of LaPlace’s demon ;-)

  252. Posted Apr 23, 2008 at 9:57 AM | Permalink | Reply

    Tom ,

    It is known that the Earth’s orbit , as well as any 3 (or more) body orbit is chaotic (see f.ex Malhotra,Holman, Ito) .That of course doesn’t mean that the orbits are random or that they can do anything crazy in short times . However it means that the orbits are never the same and that the distance between 2 orbits with as close initial conditions as one wishes will increase exponentially with time . This exponential parameter is called Lyapunov coefficient and is for the Earth orbit between 5 – 10 millions years . That means that any calculation , be it forward or backward in time for more than let’s say 100 millions year gives an orbit whose parameters have no significance and could be completely random for all that matters .

    Your post came to my mind when reading this on the news

    Jacques Laskar of the Observatoire de Paris in France authored the other study. He ran 1001 computer simulations of the solar system over time, each with slightly different starting conditions for the planets based on the range of uncertainties in the observations. In 1 to 2% of the cases, Mercury’s orbit became very elongated over time due to gravitational tugs by Jupiter. In these cases, its orbit reached an “eccentricity” of 0.6 or more (an eccentricity of 0 means the orbit is a perfect circle, while 1 is the maximum possible elongation). ..

    In one of Batygin and Laughlin’s simulations, Mercury was thrown into the Sun 1.3 billion years from now. In another, Mars was flung out of the solar system after 820 million years, then 40 million years later Mercury and Venus collided.

    http://space.newscientist.com/article/dn13757-solar-system-could-go-haywire-before-the-sun-dies.html

  253. pottereaton
    Posted Apr 16, 2013 at 1:21 PM | Permalink | Reply

    More model trends and tuning at RealClimate.

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