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How reliable are climate models?

What the science says...

Select a level... Basic Intermediate

Models successfully reproduce temperatures since 1900 globally, by land, in the air and the ocean.

Climate Myth...

Models are unreliable

"[Models] are full of fudge factors that are fitted to the existing climate, so the models more or less agree with the observed data. But there is no reason to believe that the same fudge factors would give the right behaviour in a world with different chemistry, for example in a world with increased CO2 in the atmosphere."  (Freeman Dyson)

Climate models are mathematical representations of the interactions between the atmosphere, oceans, land surface, ice – and the sun. This is clearly a very complex task, so models are built to estimate trends rather than events. For example, a climate model can tell you it will be cold in winter, but it can’t tell you what the temperature will be on a specific day – that’s weather forecasting. Climate trends are weather, averaged out over time - usually 30 years. Trends are important because they eliminate - or "smooth out" - single events that may be extreme, but quite rare.

Climate models have to be tested to find out if they work. We can’t wait for 30 years to see if a model is any good or not; models are tested against the past, against what we know happened. If a model can correctly predict trends from a starting point somewhere in the past, we could expect it to predict with reasonable certainty what might happen in the future.

So all models are first tested in a process called Hindcasting. The models used to predict future global warming can accurately map past climate changes. If they get the past right, there is no reason to think their predictions would be wrong. Testing models against the existing instrumental record suggested CO2 must cause global warming, because the models could not simulate what had already happened unless the extra CO2 was added to the model. All other known forcings are adequate in explaining temperature variations prior to the rise in temperature over the last thirty years, while none of them are capable of explaining the rise in the past thirty years.  CO2 does explain that rise, and explains it completely without any need for additional, as yet unknown forcings.

Where models have been running for sufficient time, they have also been proved to make accurate predictions. For example, the eruption of Mt. Pinatubo allowed modellers to test the accuracy of models by feeding in the data about the eruption. The models successfully predicted the climatic response after the eruption. Models also correctly predicted other effects subsequently confirmed by observation, including greater warming in the Arctic and over land, greater warming at night, and stratospheric cooling.

The climate models, far from being melodramatic, may be conservative in the predictions they produce. For example, here’s a graph of sea level rise:

Observed sea level rise since 1970 from tide gauge data (red) and satellite measurements (blue) compared to model projections for 1990-2010 from the IPCC Third Assessment Report (grey band).  (Source: The Copenhagen Diagnosis, 2009)

Here, the models have understated the problem. In reality, observed sea level is tracking at the upper range of the model projections. There are other examples of models being too conservative, rather than alarmist as some portray them. All models have limits - uncertainties - for they are modelling complex systems. However, all models improve over time, and with increasing sources of real-world information such as satellites, the output of climate models can be constantly refined to increase their power and usefulness.

Climate models have already predicted many of the phenomena for which we now have empirical evidence. Climate models form a reliable guide to potential climate change.

Mainstream climate models have also accurately projected global surface temperature changes.  Climate contrarians have not.

Various global temperature projections by mainstream climate scientists and models, and by climate contrarians, compared to observations by NASA GISS. Created by Dana Nuccitelli.

A 2019 study led by Zeke Hausfather evaluated 17 global surface temperature projections from climate models in studies published between 1970 and 2007.  The authors found "14 out of the 17 model projections indistinguishable from what actually occurred."

There's one chart often used to argue to the contrary, but it's got some serious problems, and ignores most of the data.

Christy Chart

Basic rebuttal written by GPWayne

Update July 2015:

Here is a related lecture-video from Denial101x - Making Sense of Climate Science Denial

Additional video from the MOOC

Dana Nuccitelli: Principles that models are built on.

Last updated on 9 September 2019 by pattimer. View Archives

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Argument Feedback

Please use this form to let us know about suggested updates to this rebuttal.

Further reading

Carbon Brief on Models

In January 2018, CarbonBrief published a series about climate models which includes the following articles:

Q&A: How do climate models work?
This indepth article explains in detail how scientists use computers to understand our changing climate.

Timeline: The history of climate modelling
Scroll through 50 key moments in the development of climate models over the last almost 100 years.

In-depth: Scientists discuss how to improve climate models
Carbon Brief asked a range of climate scientists what they think the main priorities are for improving climate models over the coming decade.

Guest post: Why clouds hold the key to better climate models
The never-ending and continuous changing nature of clouds has given rise to beautiful poetry, hours of cloud-spotting fun and decades of challenges to climate modellers as Prof Ellie Highwood explains in this article.

Explainer: What climate models tell us about future rainfall
Much of the public discussion around climate change has focused on how much the Earth will warm over the coming century. But climate change is not limited just to temperature; how precipitation – both rain and snow – changes will also have an impact on the global population.


On 21 January 2012, 'the skeptic argument' was revised to correct for some small formatting errors.


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Comments 601 to 650 out of 1297:

  1. JackO'Fall - " a 2004 scenario (for example), yet adding in 'known future' levels of things like CO2 and volcano emissions. If that exists, please let me know." See Comparing climate projections to observations up to 2011, Rahmstorf et al 2011, which does exactly that. "...they (models) are primarily tuned by trial&error, not scientific principles [please note the word 'tuned']" I would have to call this an incorrect assertion based upon not knowing how these models are developed - ie, from the physics. "What I was referring to is a lack of source-code with documentation for the GCMs. If it exists, I am clearly wrong and fully retract that statement." See the RealClimate links for GCM model codes, also radiative transfer models and others. Again, these are widely available, and have always been. "The range of uncertainty for those models is so large it doesn't really tell us anything. A result so broad that it would be difficult for it to ever be wrong is also not very right." On the contrary - predictions of climate change due to anthropogenic influence data back 120 years, and have been demonstrated to be correct over and over again. --- To be blunt, Jack, you have made a long list of assertions that are (a) wholly incorrect, and (b) you would have found to be so with only a little effort in looking things up. I strongly suggest you do a bit more reading.
  2. No, Jack, you don't get a pass just because your "time is limited". You have plenty of time to type pompous assertions, so certainly you have time to at least click the links we provide you before you type more. There is no "if" about it--source code and documentation of GCMs easily are available, if you just click. We have given you links to only a subset of them. Another one is the Monash Simple Climate Model (based on GREB)--so simple that you should easily be able to confirm the quality of its code, the sensibility of the factors it includes, and the deviation from horizontal of its projections. (Hat tip to the Rabbett.) You responded "The range of uncertainty for those models is so large it doesn't really tell us anything. A result so broad that it would be difficult for it to ever be wrong is also not very right." Your statement is obviously wrong, unless you can't draw a horizontal band with your imagination, in the graphs we provided you. Well, maybe you can't, so this first figure's authors have done it for you. It would be really easy for the models to be wrong, but they aren't. Being wrong means the globe will not warm, so there's no need to do anything. In contrast, being right even at the low edge of the predictive envelope most definitely means needing to act substantively right now. When you do bother to click the links we provide, you need to then read carefully. Your interpretation of RealClimate's FAQs on GCMs as "primarily tuned by trial&error, not scientific principles [please note the word 'tuned']" is a result of your skimming when you lack the most basic prior knowledge. If you had bothered to watch the short Climate Modeling 101 videos I pointed you to, you might have had the knowledge to understand that those tunings (of which there are relatively few) are done not to make global temperature evolution match the observed evolution, but instead to make a few very narrow, specific, component, physical processes in the model match the observations about those very narrow, specific, component, physical processes. As KR responded to you, these are physical models, not statistical ones.
  3. You boys completely miss the point The Earth's weather system is a chaotic dynamical system which is part of the Solar System which was proven to be a chaotic dynamical system by H.Poincare. A feature of any chaotic dynamical system is that even if it appears to exhibit quasi- periodicity its future behaviour cannot be predicted.
    Response: [TD] For additional information beyond Dikran's response, see the Skeptical Science Argument "Climate is chaotic and cannot be predicted." Please make further comments on that topic there, not here. Even if you wish to respond to Dikran's comment, do so there. He will see it anyway, because he like most regular readers monitor the Comments page that shows the union of all comment streams.
  4. JohnApplied Maths While weather is indeed chaortic, that does not mean that climate (long term statistical properties of the weather) is chaotic. It is straightforward to find examples of this, for instance consider a double pendulum made of iron placed near an electromagnet. We are unable to predict the exact trajectory of the pendulum, but we can predict the effects of the electromagnet, which will bias the mean position of the pendulum. Similarly, while we can't expect to predict the internal variability, we can predict the effects of the forcings (which correspond to the force applied by the electromagnet), and that is exactly what the modellers attempt to do. The thing about quasi-periodic behaviour is that in the long run, it averages out to zero, but the effect of the forcing do not, so there is good reason to think the GCMs will have skill over a timespan long enough for the quasi-periodic chaotic features suce as ENSO to average out.
  5. JackO'Fall, setting aside the scientific issues (which others have dealt with extensively) I find your economic argument bizarre on a few different levels. First, you argue that 'fixing a broken window does not improve the economy'. Let's take the situation on a grand scale... hurricanes break lots of windows. You argue that cannot possibly lead to economic improvement... yet reality shows that the reconstruction boom after major hurricanes has greatly benefited local economies time and time again. The same thing has been seen with wars. When the US entered WWII and built all those planes and ships the economy took off. Planes and ships obviously don't inherently improve the economy... indeed, many of them were built to replace others that had been blown up (more 'broken windows'). You argue that giving the people who built the planes and ships (or replaced the broken windows) money cannot possibly result in economic growth... and yet here in reality it has done so time and time again. It's all very well to subscribe to anti-Keynesian / freshwater / Chicago style economics, but you do still need to make an attempt at reconciling these beliefs with reality if you want to be taken seriously. The second big problem with your argument is that it seems to reverse the situation... global warming is the thing which is going to 'break a lot of windows'. Spending money now to prevent that would be the equivalent of putting up shutters to protect the windows. Basically, you're arguing against disaster prevention on the grounds that disasters are bad for the economy... which makes absolutely no sense. Finally, your idea that having money tied up in investments generates greater economic efficiency than having that money circulating through the economy is simplistic and wrong more often than not. Think about it. If 100% of the currency were in investments no one would be able to buy food, gas, housing, et cetera and the economy would collapse over night. If 0% of the currency were in investments then people would go on buying things and the economy would be fine... just with less money devoted to speculation. Between those two extremes the economy suffers proportionally as the percentage of investment goes up. This can be offset if the investment leads to new technologies which grow the economy, but given that the vast majority of investment these days is in financial flim-flammery rather than production of new technology it is really just a heavily rigged form of gambling and a further drain on economic efficiency.
    Response: [TD] Please take the economic discussion elsewhere. JackO'Fall already apologized for being off topic.
  6. I have a question on model reliability in predicting arctic sea ice. I just read (parts of) "Constraining projections of summer Arctic sea ice" by Massonnet et al. Looking at their figure 1, it is clear that most CMIP5 models do not accurately model
    past september sea ice extent (SSIE). This is even more visible in their figure 2, which shows that 90% of the models either simulate an average SSIE in
    1979-2010 outside of 2 sigma of the actual average, or a trend in SSIE outside of 2 sigma of the actual trend. If the physics of the models were perfect, only 10% of model runs would be expected to fall outside this window.

    To my mind, this shows that the models still miss essential physics, and it would be unwise to attribute too much predictive power to them. A linear trend may be a better predictor. Yet, I never hear a scientist say 'The models aren't good enough at the moment, so it may be wiser to look at the linear trend.'

    Why is that?

    Is there a fundamental discussion somewhere on the relative merits of a linear trend versus a model as a predictor of the future?

  7. @bouke The fact that the models are obviously missing some physics that is important for regional (i.e. Arctic) climate does not imply that they are not useful for global climate projection, for the simple reason that the missing physics is only relevant to a particular region, and hence this doesn't substantially affect the global climate.

    Scientists are perfectly happy to discuss the flaws in the models, however the reason they don't often explicitly say that "the models aren't good enough" is because it isn't an all-or-nothing issue.  There are some things the models predict well, and others that they don't.

    The main reason they use models rather than linear trends on the other hand has nothing to do with predictive power.  A physical model allows you to test the consequences of a set of assumptions of how the physics of climate works.  A linear trend is just a statistical model based on correlations and does not allow you to draw any causal conclusions.

    I doubt there is a fundamental discussion on the relative merits of the two approaches for the simple reason that they are two tools with different jobs.  Secondly a linear model is only appropriate for a situation where the (rate of change) of the forcings are approximately constant, which is unlikely to be true of centennial scale projections for which physical models are more appropriate.

  8. The 'intermediate' article for this topic notes that Arctic Sea ice decline and global sea level rise models have proven to be very conservative in comparison to actual results.

    The reasons for this are also generally known... scientists have only recently begun to get a handle on the rate of ice loss from Greenland and Antarctica and thus these were excluded from sea level rise models. Now that we know that there is significant ice loss going on that will be factored in to the models and they will move closer to the observed values. Similarly, recent findings have shown that mechanical effects (i.e. ice breaking up under wind), bottom melt due to unexpected circulation patterns, ice export, and other significant factors were not included in the Arctic ice loss models.

    This does not mean that all climate models are similarly lacking important factors. The atmospheric temperature models have been largely on target. The PIOMAS Arctic ice volume model has been confirmed by recent sattelite measurements. Et cetera.

    No model includes the wing flaps of every butterfly on the planet, but we have a pretty good idea of which models include major uncertainties and which do not. Also, contrary to what you say, it is very common for linear (and other) trends to be used when looking at various climate factors. Indeed, a linear trend is a model... just a very simple one. Search on Maslowski or Neven for examples of this pertaining specifically to Arctic sea ice. Their trend projections show sea ice extent dropping to near zero in the next few years.

  9. bouke,

    First, for reference, readers can look at the two figures that you reference. In each figure, the black line/square represents the NSIDC observed value, while the colored lines/squares represent the values resulting from various model ensembles. The first graph is of 5-year running mean September sea ice extent, while the second cross references the1979-2010 mean and trend (the observed value is in the center of the 2-sigma black box, and the average of all of the models is shown as an orange cross).

    Note that these figures focus on the September mean. They show nothing concerning the September minimum or the overall flux of ice of the course of the year.  They also show extent, not area or volume.  As such, the main question one must ask is "how does this short lived model discrepency affect overall global mean temperature within the model."  In particular, it should be noted that most models overestimate the ice extent, especially in relation to the recent plummet, and so most models are most likely underestimating the expected feedback (with respect to Arctic ice extent) in recent years.

    Figure 1:

    Figure 2:

    Your question conflates any number of false assumptions.

    First, let me point out that the existence of this flaw in the models is first and foremost excellent evidence against the silly denier's misunderstanding that models are "tweaked" and "parameterized" to produce a particular result. This is not the case. The models are based on physics, and the only valid way (in most cases) to adjust the model is to refine the physics to bring the final result more in line with observations.

    With that said, one of the first issues with comparing models to observations is the quality of the observations. In the case of sea ice extent, there is a lot of wiggle room for how one computes the extent... how much of a "pixel" of sea area do you count as ice or open water? You can use this page to view some of the various methods all at once. There is a fair spread in the values of the various methods within observations. Why then would you not expect a spread in the model estimates?

    Secondly, and this is the more important point, the number of factors in the models is huge. You've picked one and said it doesn't work, so throw out the models and substitute the most unphysical thing you can imagine, a linear trend -- which in turn is the ultimate in parameterization. Such a false dichotomy is absurd. This is rather like complaining that some people die in car crashes, so until no one dies in a car crash, cars are too unsafe to drive and everyone should walk everywhere.

    Thirdly, you are missing the point of the climate models, and you present a strawman before your question ("it may be wiser to look at the linear trend"). Look at the linear trend for what? Sea ice prediction? Global mean temperature? For what reason? To evaluate the climate system, or to address policy issues?

    The climate models exist first and foremost to study the climate. Scientists will know they have Arctic sea ice modeled very well when they start to fall within that 10% range you discuss, but honestly, I don't think that will ever happen, because at the current rate the ice will be completely gone before they've figured out what is innaccurate within the models. Then they can just cut sea ice out of the picture.

    Climate models have another value in being one predictor of climate sensitivity. The linear trend that you suggest would need to be based on something. Observations? The flaw there is that we only have a very short time span to use as a basis. Paleo-studies? Very valuable, but also very constrained by the inference of "observations" through indirect means.

    In the end, however, what we do see is that the estimates for climate sensitivity from a very wide variety of models, observations, paleostudies and other methods all converge in the same 2˚C to 4.5˚C range. This tells us that, despite individual flaws within the models, the overall answers are pretty darn good.

    Lastly, I would point out that people have long been pointing to the plunge in Arctic ice, and the discrepency in the models, in that the Arctic ice is failing far faster than anyone ever imagined possible. I do wish that the modelers would redirect their attentions to that sphere, and figure out whe the models are so far off in that respect.

    But, as I've already said, there may be little value in that. It might be that heat transport under the ice is far greater than expected. It may be that additional factors such as black soot and altered weather patterns have a greater influence than expected. It may be that summer water runoff from North America and Siberia has a far greater influence on sea surface temperatures and salinity. It may be that the polar amplification of warming is even worse than expected (something that is difficult to perfectly measure, since most global mean temperature analyses are sparse in the polar regions).

    But whatever the reason... I'm pretty sure that the Arctic is going to be ice free for large parts of the summer and fall long before the modelers get around to understanding all of the physics behind the particular details of Arctic ocean currents and ice, and the Arctic will have become such an alien environment that that entire part of the models will more easily be replaced with a very, very simple linear trend... a flat line centered on zero.


  10. @Sphearica, Dikran, CBDunkerson: You misunderstand me. I don't have a problem with climate models, I just thought this was the most appropriate place to ask my question. It is narrowly about SSIE, and I understand that it does not generalize to other output parameters of models. I guess I should have qualified my "it would be unwise to attribute too much predictive power to them" with "regarding SSIE".

    The issue I have is that I see scientists making predictions on the basis of their models. Those predictions will be used as the basis for policy, so they should be as accurate as possible. At the moment, a case can be made that simple statistics (or very simple models, if you like) are more accurate than these models regarding SSIE. I am missing that fact in the scientific discourse.

    I just read the leaked AR5 on the matter (paragraph, and it does not touch linear trends at all. The probable reason for that is that there are no papers on it, which in turn is because it is hard to present a new scientific result on just a simple linear trend. The result is that the AR5 is missing a piece of analysis that should have been present, IMHO.

    Btw, I read Neven's blog, so I know what these trends look like: ice free around 2020. A little earlier if you take PIOMAS volume, or an exponential fit, a little later if you take sea ice extent, or a linear fit. According to AR5, the best estimate for RCP4.5 is 2035–2065. There's an unexplained discrepancy there, IMHO.

    @Sphearica specifically: I am a bit surprised at your tone. I understand that you thought that I claimed that model problems regarding SSIE disqualified models altogether, which would indeed be a grandiose claim. The logical fallacy in this case would be Hasty Generalization. However, the things you mention, "false assumptions", "false dichotomy", "strawman", I really don't see in my post. I don't really think that using such strong words is helpful.

  11. bouke: Maslowski, et al 2012

  12. Bouke wrote: "The issue I have is that I see scientists making predictions on the basis of their models. Those predictions will be used as the basis for policy,"

    Exactly what are the policy decisions that are being made based on predictions of SSIE?

  13. bouke,

    I'll give you a half-apology, if in fact your question was innocent and genuine, but I've reread it several times, and it strikes me as part concern-troll, part denier-mythology.  You make a lot of broad-brush statements, dripping with doubt, like "I never hear a scientist say..." and "Why is that?" and "The issue I have is that I see scientists..." — as if your role as armchair-jurist is to judge how well scientists are doing their jobs, and your neighbor told you they aren't.

    You read one paper, and then drew a bunch of unjustified conclusions.  I suspect that much of your interpretation is tainted by the uneducated comments you may have seen on other sites, and if that is the case, I'd suggest that you follow up such comments with solid research to confirm their validity.

    In particular I would suggest that you follow the following Google Scholar search and see what you can infer from the actual work that is being done by scientists, rather than hobby-site comments and a single paper:

    Arctic Sea Ice Studies, 2009-present

  14. @CBDunkerson
    Interesting, I didn't know about that.

    The current policy decision is to do nothing, more or less. If, when AR5 comes out, it contains an earlier possible date for an ice free summer, initiatives like this (met-office-investigating-arctic-link-in-record-low-temperatures)
    would gain in urgency. I think that would be a good thing.

    It is very hard to convey irony and things like that through text alone. My personal strategy to combat miscommunications is to remove as much rhetorics as I can: If I ask a question, it is because I want to know something. If I consciously make an assumption, I state that. If I make a statement of fact, I am very careful that I'm sure its true. Unfortunately, this sometimes makes posts difficult to distinguish from concern trolls, but I don't really know a way to avoid that.

    You wrote 'You read one paper, and then drew a bunch of unjustified conclusions'. I read more than one paper, and haven't drawn many conclusions yet. I could go on a rant that it is actually you who is drawing unjustified conclusions, but such a thing would be a rhetorical trick, and as such unproductive. I don't want to go there.

    Perhaps I should tell something about were I'm coming from. I get my information from skeptical science,, occasionally papers (it takes a lot of energy to read them), youtube recordings of scientists presenting their work, 'the discovery of global warming' ( When I am tempted to tell myself I know something for a fact, I seek out information from sources that say the opposite. When I detect that those sources contain lies of omission, or just plain lies, my confidence goes up. When those sources contain interesting new insights, I learn something.

    Currently, I am tempted to conclude that the scientific consensus, as represented in AR5, is too conservative regarding SSIE. There's been research that shows that the IPCC is too conservative more often than too alarmist, so that increases my confidence that this may indeed be the case here. I ask myself how this could happen, and one hypothesis is that scientists are in the business of understanding things, not in the business of predicting things. Current understanding (i.e. the current models) isn't good enough to make good predictions. Nevertheless, generally, only those predictions are publicized, and so only those predictions end up in AR5.

    Something like an expert elicitation study for sea ice could shed some light on my question, but I don't know if they exist for sea ice.

  15. @Bouke, it is a pity that you dodged my question.  You suggested that the lack of skill in the models is an issue as they are used to decide policy.  You also clarified that the lack of skill you mentioned was specific to sea ice extent (your post 610).  However there are no policy decisions that rest on prediction of sea ice extent, which reveals that you concerns are baseless.

    You claim that your aim is to remove as muich rhetoric as possible.  I would suggest you start by avoiding the sort of evasion in which you have just engaged and in future give direct answers to direct questions. 

    Please try again: "Exactly what are the policy decisions that are being made based on predictions of SSIE?"  if the answer is "none", then please say so explicitly.

  16. @Dikran

    You suggested that the lack of skill in the models is an issue as they are used to decide policy. You also clarified that the lack of skill you mentioned was specific to sea ice extent (your post 610)

    Agreed. Well, sea ice extent minimum to be more precise.

    However there are no policy decisions that rest on prediction of sea ice extent, which reveals that you concerns are baseless.

    Disagreed. Firstly, I think 'rest on' is too strong a wording for the conclusion you are trying to make. It is enough for a policy decision to be influenced by the prediction, to make my concerns not baseless.

    Secondly, perhaps we disagree on what constitutes a policy decision. I think doing nothing about a (perceived) problem is an actual policy decision, equally valid as doing something. It may be the best policy decision, actually. But I guess you want specifics of policy decisions that are not taken.

    "To fund research to study changes in weather patterns as a consequence of a seasonally ice free arctic" is a policy decision. The current policy is not to spend the money. I think this decision is influenced by the prediction of sea ice extent minimum.

    "To limit CO2 emissions to a specific target" is a policy decision. One of the political parties in my country (the VVD) say they have a CO2 emissions target, but they don't spend funds or propose regulation to achieve this goal. In effect, they are doing nothing. I think this decision is influenced by the prediction of sea ice extent minimum. I think the influence is small, but real. If a consequence of climate change may be right around the corner, it becomes less defensible to do nothing.

  17. @bouke O.K. substitute "are substantially influenced by" for "rest on":  "What policy decisions are substantially influenced by predictions of sea ice extent?"

    I agree that "do nothing" is a policy decision, nothing I wrote suggests otherwise.  Funding projects on changes in weather patterns due to changes in Arctic sea ice extent on the other hand is not a policy decision (grants are awarded competitively and are effectively selected by peer review, although there are also calls for proposals on particular topics).  However, this is still not a decision where deficiencies in the models has any real impact on the decision as the observations are perfectly clear, so again it doesn't support your stated concern.

    "To limit CO2 emissions to a specific target" IS a policy decision, but is not one where model projections of Arctic sea ice extent have big impact for the simple reason that the reason for the limit on CO2 is to limit the effects of GLOBAL climate change.  Arctic sea ice at this point is essentially history more or less no matter what we do to limit CO2, so anybody arguing that CO2 should be limited to save Arctic sea ice extent has pretty much missed the boat.

    Essentially the point is that for Arctic sea ice extent, the observations tell you what is happening, the models predict that the Arctic sea ice will last longer than the observations suggest to the extent that we know that their projections are wrong, and hence nobody should be taking them sufficiently seriously as a basis for policy.

    Now you think that they do have an effect on policy, but do you have any evidence to support that?

  18. @Dikran

    1. You asked "Exactly what are the policy decisions that are being made based on predictions of SSIE?".
    2. I answered "The current policy decision is to do nothing".
    3. You stated "it is a pity that you dodged my question".
    4. I stated "perhaps we disagree on what constitutes a policy decision"
    5. You stated "I agree that "do nothing" is a policy decision, nothing I wrote suggests otherwise."

    The only way I could reconcile (3) with (2) was if you didn't consider doing nothing a policy decision. That's where (4) came from.

    Essentially the point is that for Arctic sea ice extent, the observations tell you what is happening, the models predict that the Arctic sea ice will last longer than the observations suggest to the extent that we know that their projections are wrong, and hence nobody should be taking them sufficiently seriously as a basis for policy.

    Now you think that they do have an effect on policy, but do you have any evidence to support that?

    The entire purpose of the IPCC is to inform policymakers. The IPCC's AR5 draft mostly discusses models and their projections. If you think that the projections of those models are wrong, why are these projections in AR5?

    Anyway, this is the last post by me on this matter. I will read answers but won't post more. Except for CBDunkersons link, the discussion here hasn't been very useful for me and I doubt that will change.

  19. @bouke The current policy decision to do nothing [and it is a policy decision] is not based on the projections of sea ice extent, so you did dodge the question. 

    I'm sorry, I have better things to do with my time than engage in evasive rhetorical discussions. If you want to demonstrate that is not what you are trying to do, then give a cogent explanation of how sea ice extent projections support the policy decision to do nothing.   The models clearly indicate that sea ice extent is diminishing and that we will see effectively ice free summers on a timescale relevant to human lives, so even though they underestimate the rate of ice loss, the clearly argue that we should do something, not do nothing.

  20. Stealth (on another thread) wrote "I would love to examine the source code of some GCMs to see if Dr. Freeman Dyson’s claim that GCMs are full of fudge factors is true. I suspect that it is true because software modelers always have to make assumptions and design trades in order to get software to run in a reasonable amount of time."

    Stealth, Freeman Dyson has no idea what is in climate models, as he himself has publicly admitted.  For some reason he is comfortable admitting he knows nothing about climatology but in the same conversation stating his admittedly baseless speculations as if he is absolutely confident they are facts.

    Probably what Dyson meant by "fudge factors" were parameterizations, which are not merely fudge factors.  RealClimate has short explanations of parameterizations in its FAQ on Climate Models Part II.

    Regarding the overall quality of the software engineering aspects of models:  No software is perfect (not even software for controlling spacecraft), but good software is good enough for the purposes it is put to. Determining whether software is sufficiently good is a process called verification and validation (V&V). Climate models do go through V&V. The type of V&V must be appropriate to the type of software and the contexts of its construction and use. The same is true of V&V of climate models, as Steve Easterbrook explained; Steve used to do V&V for NASA software.

    But the real test of GCMs' adequacy is in their ability to predict, which is quite good at the time scales they are expected to predict well.  The forcing and feedbacks interact to produce temperature changes that bounce up and down. Those changes in periods of less then 30 years are called "weather," but those short-term weather changes average out across periods of 30 years or more, so 30 years is approximately the definition of "climate."  Weather is not climate, so predicting weather is not the same as predicting climate. Weather, especially in small geographic areas, is nearly impossible to predict more than a week in advance, but climate, especially for large geographic areas such as the entire globe, is and has been successfully predicted decades in advance--notably by Arrhenhuis in the 1890s, with more fine tuning in each generation of model, for example by Hansen in 1988, and with increasing accuracy with each improvement in the models. The accuracy is most evident in graphics if you properly position the observed temperature line at its mean trend at the start of the graph, as explained with the last three figures in a post by Tamino.


  21. Stealth, on another thread:

    KR @ 248: I get the impression that you do not understand physics; fudge factors are not an accusation of fraud in any way. The comment of “fudge factors” is from Dr. Freeman Dyson, a world renowned physicist -- I am certain he is qualified to speak to both physics and fudge factors.

    But he is not qualified to speak on models, as Tom Dayton showed, and your original use of the phrase "Dyson's claim that GCMs are full of fudge factors" didn't sound like an innocuous use of physics jargon.

    As an example, in the CO2 forcing equation, ΔF = 5.35*ln(C/C0) W/m2, the 5.35 value is a “fudge factor”, and so is the natural logarithm function. Over a much larger sample of data, for example, a logarithm with a base of pi may fit better than one with a base of e. [Emphasis mine.]

    Erm... You may want to rethink that.

    The “laws of physics” are not something given to us from on high. They are simply a mathematical representation of what we think is happening, or a way to describe how something behaves.

    They embody our understanding of physical reality. If you are going to base your arguments on ignoring the laws of physics on the basis that they may not be correct, it's only fair to say so. It's important to be able to give "models" that are based on what we don't think is happening the weight they deserve.

    Your statement of “They are full of physics” as a way to assert truth or correctness of the models, is both meaningless and ignorant.

    What he means is that they embody our understanding of physical reality. That's hardly meaningless and ignorant.

    Some of the other folks here are a little combative or condescending.

    Sorry about that. I get a bit testy when someone expresses doubt that they can get hold of the source code of the models when they obviously haven't attempted to do so, then when pointed to the correct location to download the source code proclaim that they've wasted enough time trying and question whether those who gave links have actually attempted it themselves, while finding time to twist statements that they found "interesting" to mean other than what was intended. As the steps I gave showed, it was hardly an onerous exercise.

    All the while not admitting that the accuracy of the coefficient 5.35 is actually far higher than suggested earlier.

    This is what I mean when I say “all models are wrong.” This doesn’t mean they are not useful

    You're hardly the first to make that observation.

    but it means they are limited in their effectiveness because of the underlying assumptions. How GCMs handle this effect is critical across every equation they use. My primary concern is that almost every equation in GCMs have various limitations or assumptions because they are calibrated to measurements made recently.

    Firstly, GCMs aren't parameter-fitting statistical models that are simply fitted to recent observations. Where parameterisation is performed, it is at a low level and it is based on observation of the particular physical process in question. The fact that, when combined, the model as a whole actually reproduces the observed record extremely well (and is unable to when anthropogenic influences are removed) is a testament to the model's accuracy.

    Secondly, no matter how complicated the model, and no matter how many "fudge factors" are involved, at the end of the day the model still has to satisfy some pretty basic physics. This is why a simple energy balance model can reproduce the temperature record remarkably well, even though it cannot tell you what the regional effects will be, or changes to ocean circulation, or Arctic sea ice. These limit its accuracy but there are still some pretty strong bounds on the possible behaviour of the more sophisticated models.

    JasonB @258: I followed your instructions and still failed. Trying to extract the ModelE1 tarball using Windows and WinZIP fails.

    My instructions were to use 7-zip, not WinZIP. I'm surprised that someone with your experience developing software was stumped by a .tar.gz file.

    So now I have actual Fortran code for a 10 year old model.

    Well, what did you expect? That's the source code for one of the models that was used to create the forecasts for the still-current IPCC report. You want to check those forecasts, you look at the source code for those models. It's also an ancestor of one of the models used for the next IPCC report.


    [TD] Thank you for replying on an appropriate thread!

  22. Many of the issues being dicussed on this thread are addressed in the article,

    Seeking Clues to Climate Change: computer models provide insights to Earth's climate future, Science & Technology Review, June 2012, Lawrence Livermore National Laboratory 

  23. I have had some conversations with Richard Moore, who says,

    "The warming we have experienced is real, but it is not unusual, it is not alarming, and it bas nothing to do with CO2. What I'm looking for is an intelligent response to my analysis:"

    I find it a bit too detailed to get the gist of what he is saying.  Does anyone have the time to comment on it?



    ...Alan James

  24. acjames76 - I have replied on the more appropriate It's not us thread. 


    [TD] Thank you for thread-herding!

  25. The thing about the models that is screwing it up now is the divergence with solar and CO2, which had always been closely linked. No the solar is backing off, and the mistaken notion that a high solar level isn't enough (it is), an ever incresing one being needed (false), is showing the pundits they hooked their cart to the wrong horse. The solar has been and still is the driver. All those folks who put CO2 in charge are just waiting for the trend to go up again. Good luck folks. When the solar activity returns to warming levels it will go up again. Til then you are out of luck. The math of the models is flawed and the flaw is showing. It did great when solar and CO2 were in lockstep (the past), but picked the wrong driver and is now showing the error big time.

  26. cruzn246...  What you say might be interesting if it were based on any facts.  

    Do you think for even two seconds that the many thousands of scientists who work on climate issues have not considered the relative forcing of solar vs GHG's?  

    Global Mean Radiative Forcings

  27. The image from the link above:

  28. cruzn...  Please look again at the chart I linked to before.  It clearly shows that there is a low LOSU (level of scientific understanding) related to solar.  But that doesn't mean it could be absolutely anything.  Still the likely range solar forcing is a fraction of GHG forcing, which has a high LOSU.  

    You do realize, of course, we have satellites which are measuring solar variation, and you were even quoting radiative forcing figures, data that comes from those satellites.

  29. cruzn246:

    "Tell me why it has essentially stopped warming just when the solar torch let up?"

    See the SkS climate myth #2 "it's the sun".  The first graph does not support your statement above.  Also, what do you mean, specifically in @628 when you say "it has stopped warming". Specifically what do you mean when you say "it".

    Finally, I think it is a strech to imply that the modelers and those that utilize them to aid in understanding climate change "think they have it all figured out". However, you sound like that. And in my opinon have much less of a leg to stand on in your assertions.


    [DB] Please note that cruzn246 has an extensive history of sloganeering and trolling in this venue.

  30. (-snip-).


    [DB] Please comport yourself with this site's Comments Policy.  In it you will note under the Sloganeering section that if you wish to differ with established science, you will need to bring reputable evidence to support that chosen differing.  Mere assertion, as has been your wont, fails to rise to that burden of proof.

    Sloganeering snipped.

  31. cruzn...  I think you'll actually find that people here are willing to entertain contrarian ideas, but you have to be able to substantiate your position far better than you're currently doing.

    Just because you think something is obvious does not make you automatically correct.  As the moderator is saying, you must back your claims with actual science and research.

  32. Kishoreragi, to add to Michael's comment: that is the way general circulation modeling is done. The Earth itself is one big experiment, and it has been well-demonstrated that the minor variables vary within certain ranges and rarely, if ever, end up leading global or even regional climate by the nose long enough to significantly alter major elements of general circulation. There is a strong tendency to regress to the mean, and the mean is driven by the major elements of the climate: continental drift, orbital variation, vulcanism, solar output, collision with significant extra-terrestrial objects, and, now, artificial enhancement of the greenhouse effect. Everything else is a feedback: something that reacts but is unlikely to change on its own -- biosphere (with rare exceptions), snow/ice albedo, ocean carbon cycle, clouds, water vapor, natural greenhouse effect, etc. Feedbacks vary and are integrated in different ways, but they regress to the mean of net forcing, with minor temporal variations driven primarily by ocean thermal capacity.

    Thus it's a little inaccurate to describe the climate system as comprised of components that vary but are all of equal or near-equal power in shaping the future of the system. And, in addition to the major forcings, modeling does take into account many of the major and minor feedbacks. Of course, in the short-term (months to years), the interplay of major and minor feedbacks can produce significant but temporary anomalies, but the resonance of those anomalies across the long-term trend is ultimately insignificant. Arctic sea ice (ASI) is a great example. ASI is never, with all forcings stabilized, going to vary strongly and consistently to the extent that a glacial cycle is initiated. Only orbital or solar variation (or a one-timer) can do that (partially through the mechanism of snow/ice albedo feedback).

  33. Given that climate models can closely approximate past climate, I'd think the onus is on 'skeptics' to show some reason why we should assume that they will not be equally accurate for future climate.

    Just saying, 'there are too many factors involved to ever model climate' doesn't cut it given the established reality of models which already do successfully match past climate. Heck, we even know most of the causes of short term variation... such that if you plug major volcanic eruptions, variations in solar output, ocean cycles, and other such 'unpredictable' factors into model runs of past climate they then match not only the long term trends, but even the short term fluctuations around the trends. That's shockingly accurate for something which is supposedly 'impossible'.

  34. kishoreragi at 17:00 PM on 18 September 2013 9 (found in What's causing global warming? Look for the fingerprints)

    Your “a forest is like a climate model” analogy would benefit if you could identify the critical variables omitted from the models or issues with how climate models utilize them.

    Yes, there are a lot of different animals moving around in a forest. Yes, modeling everything going on in a forest would require understanding how sensitive each thing is to changes in each other thing. But it’s not a valid analogy to just list some of the different things going on in a forest, and then jumping straight to the insinuation that climate models omit critical features. In short, you don’t provide any solid rational that climate models are not useful with respect to predicting changes in climate.


    [JH] Thank you for responding to kishoreragi on this thread. 

  35. Roger D

    I don't want to go predict big things right away with incomplete models, but I want to eloborate what I wanted to inform here.

    I have given this analogy because there is a need to understand each and every animal (here it is physical process) is related (friendly/hostile, here for climate, how processes are intricately mixed- diminishing/strengthening) on forest variables (We can choose any variable here) on specific part of forest(Any region of the world). So that little by little, we can understand about the comple forest and their inter-relation.

    My main point is that the science has progressed much further, but in wrong path. The simple fix for this is to make the system simple and see the intricacies among the processes, leaving the comparision with observations, on regional climate variables so that in the FAR future, we may be in a position to see the BIG picture like AGW without hesitation from anybody (skeptics/supporters) and with clear understanding. As far as I understand(ofcourse I am still reseach student), there is no other way as climate system super complex !!!

  36. kishoreragi, you only need to model every conceivable input if you need to know every conceivable output... which isn't the case. To take your forest example, if the goal of the model is to determine how the forest will grow then the actions of deer and bears are largely irrelevant... they might impact a tree here and there, but they are not going to change the overall growth pattern of the forest. Instead, you are going to look at weather, human logging, beavers, and other factors which can actually have a significant impact.

    Ditto climate models. No, they cannot possibly model every individual cloud and gust of wind... but there is absolutely no reason they would need to, because those things are not going to impact the overall climate trends.

    Again, you do understand that climate models already work, right? They can successfully model the past and even the relatively primitive climate models in use 30 years ago produced results consistent with the past 30 years. You are arguing that something which has been done, cannot be done. You're wrong before you even get started.

  37. Thank you all for reading my views and their valuable comments/suggestions.

    CBDunkerson, thank you for your valuable comment on my analogy, and insights into climate modeling. I understand that climate models work. I have been trying to study precipitions but, I felt, with supervisor's advise, like I was cheating science because model resolution is coarse and cloud physics is not yet well understood. Hence, I have changed my thought to study other variables. May be climate models worked for few variables even before 30 years but, climate is not of those few and rest of them won't work even 21st century. May be you are looking at those few variables(I agree with you here) but, I always see other side with completely different view to progress further.

    I will try to correct myself (that is why I have been focusing on literature from all corners) before get started my research as well begun is half done !!! I am off on this thread. Thank you all !!!

  38. kishoreragi - I think what you are overlooking is that there is an approriate level of detail for studying anything. Details below the scale of GCM's are parameterized, treated as blocks that have known (as in, tied to observations) responses to inputs, and that physically based parameterization works just fine for global and regional level modeling. 

    More detail would be needed if you wanted to look at microclimates and the chances of a particular bush getting wet during a closely timed rain shower. But that's not the level of study for GCM's, and if subscale responses are reproduced well a GCM will give a fine answer at the scale it is actually studying. 

    These models aren't looking at the level of individual trees and gusts - hence they just don't need to simulate at that level to get a good answer for the regional/global scales studied. 

  39. I just saw "Overestimated global warming over the past 20 years", by Fyfe, Gillett and Zwiers in Nature Climate Change, Vol 3, Sept 2013, page 767. They contend that the divergence of models and observation is statistically significant. In trying to rationalize the discrepancy it seems to me that they don't consider the possibility that atmosphere-ocean heat transfer coupling methodology (particularly below several hundred meters depth) in the models may not yet be up to par. 

    What is the SkS take on this article? 

  40. tcflood's source in pdf, in case SkS can't access it by other means.

  41. @tcflood it looks like there was indeed less warming over the period 1997-2012 than the models predicted, however, what "skeptics" often fail to mention is that "the surface warming trend from 1993 to 2007 was significantly higher than projections." See here for more information:

  42. @jsmith.  Thanks for the response. I read somewhere that many of the GCMs actually do produce ENSO episodes but that the timing of their appearance (as in nature) is chaotic. If modeling results are reported as an average of many models, wouldn't the effect be to remove the ENSO effects thereby insuring some departures of observation from the models when real ENSOs would occur? So, hypothetically, if there were some way to trigger an episode at the same time in all the models, the match between models and observation might be much improved. I don't pose this as a realistic modeling strategy, but rather as an accurate and effective counterargument to this common cavil in the denier echo chamber?  

    Also, the denier rant that called my attention to the above-cited paper said Zwiers is a vice chair of the IPCC. I infer from your comment that he is nonetheless a skeptic. The paper certainly reads like he is.   


  43. tcflood "wouldn't the effect be to remove the ENSO effects thereby insuring some departures of observation from the models when real ENSOs would occur? "

    Essentially true. See for instance the graph here. Forcing ENSO in model to match what happens is somewhat like Kosaka and Xie did. See discussion here.

    Comment from Mike at RC on Zwiers.

    "[Response: Francis is a top-rate scientist of the highest integrity. I strongly suspect that he has been misquoted and mischaracterized quite a bit lately. -mike]

  44. Further to Zwiers. Any good scientist is a skeptic - a real one. Fake skeptics are only skeptical about things they disagree with and will swallow any kind of junk uncritically if it supports their notions.

  45. "Climate trends are weather, averaged out over time - usually 30 years. Trends are important because they eliminate - or "smooth out" - single events that may be extreme, but quite rare."

    So if we look at the past 30 years we should get the climate trend. According to skepticalscience's trend calculator from 1983 to 2013 the earth's temp has increased by 0.17 C per decade (GISTEMP moving average 12 months). Since CO2 has risen at about a linear rate since 1980 (according to:, is it reasonable to predict a best guess rise of 1.7C from decades (1990 - 2000) to (2090 - 2100)? This would point to the low scenario from IPCC 2007, which has a best guess of 1.8C.

  46. Engineer, the issue is estimating climate sensitivity is non-linear feedbacks. Decreasing ice and increasing methane emissions would be two examples. Models attempt to build these influences in whereas linear extrapolation does not. 

  47. @ Scaddenp,

    Of course, but I thought climate sensitivity can be considered constant for small changes in temp. ∆T = k * 5.35*ln (C/C0), where k is climate sensitivity. I graphed 5.35*ln(C/C0) and it looks approximately linear in the range for C: (275 ppm, 550 ppm) and C0=275ppm. Is climate sensitivity not supposed to be considered constant even for small changes in temp?Thanks.

  48. But you would only expect temperature rise to be linear if transient climate sensitivity was same as equilibrium climate sensitivity. Emperical determinations of ECS suggest it is reasonably robust over quite a temperature range. However this is an observation, not an assumption.

  49. You would also need to separate out the other forcings at play if trying to establish k by simple fits. I would recommend Chpter 10 of the newly out AR5 WG1 which has a section on estimation of TCS and ECS, along with references to the papers which attempt this from various observational sets. The section of estimation from the instrumental record looks like it would interest you most.

  50. I have a friend of a friend who is a physicist.  He is ~not~ an AGW denier; he does however have objections to models as he claims they are used in climate science.  He fears that, to quote him:

    Where are the studies of model sensitivity to variations in the way any given model fixes up non-conservation of mass and/or energy? Every model I have looked at in detail has non-conservative processes, and they are always fixed up by hand at the end of each time step. This is a very bad thing. and I have yet to find any study on the sensitivity to the various choices one might make as to how to do this.

    I suggested he post this question himself, but since he didn't feel it worth his time (which, I pointed out, is maybe why, though ignorant myself, I shouldn't take his objection too seriously), I told him I'd post it and see what people had to say.  If this question doesn't explain his position clearly enough, I have some longer expositions of it I can post as well.

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