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All IPCC definitions taken from Climate Change 2007: The Physical Science Basis. Working Group I Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Annex I, Glossary, pp. 941-954. Cambridge University Press.

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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)

At a glance

So, what are computer models? Computer modelling is the simulation and study of complex physical systems using mathematics and computer science. Models can be used to explore the effects of changes to any or all of the system components. Such techniques have a wide range of applications. For example, engineering makes a lot of use of computer models, from aircraft design to dam construction and everything in between. Many aspects of our modern lives depend, one way and another, on computer modelling. If you don't trust computer models but like flying, you might want to think about that.

Computer models can be as simple or as complicated as required. It depends on what part of a system you're looking at and its complexity. A simple model might consist of a few equations on a spreadsheet. Complex models, on the other hand, can run to millions of lines of code. Designing them involves intensive collaboration between multiple specialist scientists, mathematicians and top-end coders working as a team.

Modelling of the planet's climate system dates back to the late 1960s. Climate modelling involves incorporating all the equations that describe the interactions between all the components of our climate system. Climate modelling is especially maths-heavy, requiring phenomenal computer power to run vast numbers of equations at the same time.

Climate models are designed to estimate trends rather than events. For example, a fairly simple climate model can readily tell you it will be colder in winter. However, it can’t tell you what the temperature will be on a specific day – that’s weather forecasting. Weather forecast-models rarely extend to even a fortnight ahead. Big difference. Climate trends deal with things such as temperature or sea-level changes, over multiple decades. Trends are important because they eliminate or 'smooth out' single events that may be extreme but uncommon. In other words, trends tell you which way the system's heading.

All climate models must be tested to find out if they work before they are deployed. That can be done by using the past. We know what happened back then either because we made observations or since evidence is preserved in the geological record. If a model can correctly simulate trends from a starting point somewhere in the past through to the present day, it has passed that test. We can therefore expect it to simulate what might happen in the future. And that's exactly what has happened. From early on, climate models predicted future global warming. Multiple lines of hard physical evidence now confirm the prediction was correct.

Finally, all models, weather or climate, have uncertainties associated with them. This doesn't mean scientists don't know anything - far from it. If you work in science, uncertainty is an everyday word and is to be expected. Sources of uncertainty can be identified, isolated and worked upon. As a consequence, a model's performance improves. In this way, science is a self-correcting process over time. This is quite different from climate science denial, whose practitioners speak confidently and with certainty about something they do not work on day in and day out. They don't need to fully understand the topic, since spreading confusion and doubt is their task.

Climate models are not perfect. Nothing is. But they are phenomenally useful.

Please use this form to provide feedback about this new "At a glance" section. Read a more technical version below or dig deeper via the tabs above!

Further details

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 shown 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. Sea level rise is a good example (fig. 1).

Fig. 1: 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. A 2019 study led by Zeke Hausfather (Hausfather et al. 2019) 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."

Talking of empirical evidence, you may be surprised to know that huge fossil fuels corporation Exxon's own scientists knew all about climate change, all along. A recent study of their own modelling (Supran et al. 2023 - open access) found it to be just as skillful as that developed within academia (fig. 2). We had a blog-post about this important study around the time of its publication. However, the way the corporate world's PR machine subsequently handled this information left a great deal to be desired, to put it mildly. The paper's damning final paragraph is worthy of part-quotation:

"Here, it has enabled us to conclude with precision that, decades ago, ExxonMobil understood as much about climate change as did academic and government scientists. Our analysis shows that, in private and academic circles since the late 1970s and early 1980s, ExxonMobil scientists:

(i) accurately projected and skillfully modelled global warming due to fossil fuel burning;

(ii) correctly dismissed the possibility of a coming ice age;

(iii) accurately predicted when human-caused global warming would first be detected;

(iv) reasonably estimated how much CO2 would lead to dangerous warming.

Yet, whereas academic and government scientists worked to communicate what they knew to the public, ExxonMobil worked to deny it."

Exxon climate graphics from Supran et al 2023

Fig. 2: Historically observed temperature change (red) and atmospheric carbon dioxide concentration (blue) over time, compared against global warming projections reported by ExxonMobil scientists. (A) “Proprietary” 1982 Exxon-modeled projections. (B) Summary of projections in seven internal company memos and five peer-reviewed publications between 1977 and 2003 (gray lines). (C) A 1977 internally reported graph of the global warming “effect of CO2 on an interglacial scale.” (A) and (B) display averaged historical temperature observations, whereas the historical temperature record in (C) is a smoothed Earth system model simulation of the last 150,000 years. From Supran et al. 2023.

 Updated 30th May 2024 to include Supran et al extract.

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

Last updated on 30 May 2024 by John Mason. View Archives

Printable Version  |  Offline PDF Version  |  Link to this page

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.

Denial101x videos

Here are related lecture-videos from Denial101x - Making Sense of Climate Science Denial

Additional video from the MOOC

Dana Nuccitelli: Principles that models are built on.

Myth Deconstruction

Related resource: Myth Deconstruction as animated GIF

MD Model

Please check the related blog post for background information about this graphics resource.

Fact brief

Click the thumbnail for the concise fact brief version created in collaboration with Gigafact:

fact brief


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Comments 1 to 25 out of 1081:

  1. Look at plate 1 in Hansen's 88 paper, the model includes the oceans. Hansen's Scenario C is the one that most closely matches the "Land – Ocean" temperature. John Cook wrote: "A way to test the accuracy of models is through hindcasting - see whether they successfully predict what has been observed over the past century." Not true for any model. All that shows is they can fit the model to the history. That is beside the point as the IPCC does not claim that the models can predict anything. John Cook wrote: "The key point is that all the models fail to predict recent warming without taking rising CO2 levels into account." Given enough "tunable parameters" that should come as no surprise. The modelers also assume that there is some positive feedback, there is no proof that this is the case. Here is one for you straight from the IPCC, Chapter 8, page 596: "The number of degrees of freedom in the tuneable parameters is less than the number of degrees of freedom in the observational constraints used in model evaluation." IOW, the models are nothing more then sophisticated curve fits. Calling the models "predictions" does not instill confidence that you have done your homework. Kevin E. Trenberth "In fact there are no predictions by IPCC at all. And there never have been. The IPCC instead proffers “what if” projections of future climate that correspond to certain emissions scenarios." And from the same letter: "Even if there were, the projections are based on model results that provide differences of the future climate relative to that today. None of the models used by IPCC are initialized to the observed state and none of the climate states in the models correspond even remotely to the current observed climate. In particular, the state of the oceans, sea ice, and soil moisture has no relationship to the observed state at any recent time in any of the IPCC models." John Cook wrote: "Satellite measurements show that the troposphere is warming" The models predict that the troposphere should warm faster then the surface, it isn't.
    Response: Re tropospheric warming, I recommend reading Satellite show little to no warming in the troposphere. The argument over "prediction" vs "projection" is semantics. Kevin Trenberth is merely saying we don't know with certainty what future emissions will be so we make predictions based on various emission scenarios. However, lest it be a stumbling block, I'll update the text. Thanks for the feedback!
  2. Models are the biggest gun in the arsenal for AGW. What people like Dyson are telling us is that the models use assumptions that are not validated by observation and that cannot account for many known effects. The models might be right but they haven't got a good track record except in hind sight. (After they've been fudged to fit the past) Someday they will probably be good they are better than 20 years ago.
  3. I recommend this paper and it references for this section as well
  4. Leaving aside the silly notion that you can 'prove' a model's accuracy by checking it's fitting to the historical record--I mean honestly, you are aware that these models are tweaked *until* they fit the historical record, aren't you? The past is not the problem. The Hansen forecast sounded impressive, so I looked over the paper and did some googling. There is definitely a different spin on the accuracy of the forecast. Discussed here: which demonstrates that scenario B is nowhere near the perfect fit implied by your article or Hansen. Hansen could be right, but he doesn't seem to explain where he is getting his data from. I can only find vague references to 'Station Data' and 'Land-Ocean'. What data is it he is using? How has it been adjusted? At least the sceptical article above is up front on where the data is coming from. This doesn't prove that Hansen is wrong. But it doesn't leave one with a high degree of confidence either.
  5. Well, here is NASA telling us there is no meaningful comparison of models to observed global temp change "The analysis by Hansen et al. (2005), as well as other recent studies (see, e.g., the reviews by Ramaswamy et al. 2001; Kopp et al. 2005b; Lean et al. 2005; Loeb and Manalo-Smith 2005; Lohmann and Feichter 2005; Pilewskie et al. 2005; Bates et al. 2006; Penner et al. 2006), indicates that the current uncertainties in the TSI and aerosol forcings are so large that they preclude meaningful climate model evaluation by comparison with observed global temperature change. These uncertainties must be reduced significantly for uncertainty in climate sensitivity to be adequately con- strained (Schwartz 2004). Helping to address this chal- lenging objective is the main purpose of the National Aeronautics and Space Administration (NASA) Glory mission, a remote sensing Earth-orbiting observatory"
  6. Here is an interesting quote from IPPC's AR4 found in chapter 1: "The strong emphasis placed on the realism of the simulated base state provided a rationale for introducing ‘flux adjustments’ or ‘flux corrections’ (Manabe and Stouffer, 1988; Sausen et al., 1988) in early simulations. These were essentially empirical corrections that could not be justified on physical principles, and that consisted of arbitrary additions of surface fluxes of heat and salinity in order to prevent the drift of the simulated climate away from a realistic state. The National Center for Atmospheric Research model may have been the first to realise non-flux-corrected coupled simulations systematically, and it was able to achieve simulations of climate change into the 21st century, in spite of a persistent drift that still affected many of its early simulations. Both the FAR and the SAR pointed out the apparent need for flux adjustments as a problematic feature of climate modelling (Cubasch et al., 1990; Gates et al., 1996). By the time of the TAR, however, the situation had evolved, and about half the coupled GCMs assessed in the TAR did not employ flux adjustments. That report noted that ‘some non-flux adjusted models are now able to maintain stable climatologies of comparable quality to flux-adjusted models’ (McAvaney et al., 2001). Since that time, evolution away from flux correction (or flux adjustment) has continued at some modelling centres, although a number of state-of-the-art models continue to rely on it." A 'flux adjustment' is where you discover that the model's predictions start to vary so much from the historical record that you have to go in and change the values inside the software to re-fit the model to what's actually happening. Very confidence inspiring. And what does 'a number of' mean? 50%? 20%? 80%? How many of these models are manually fiddled with to get them to continue to work...?
  7. Here is another posting assessing Hansen's model work in a not very favourable way: Whether these alternate assessments of Hansen's work stand up is a separate issue. I would point out we should not accept them blindly any more than we should blindly accept Hansen's paper on how brilliant Hansen's previous work was, as this naive article does...
  8. "The models might be right but they haven't got a good track record except in hind sight. (After they've been fudged to fit the past)" "Leaving aside the silly notion that you can 'prove' a model's accuracy by checking it's fitting to the historical record--I mean honestly, you are aware that these models are tweaked *until* they fit the historical record, aren't you?" Nonsense. Are you saying that Hansen, way back in 1988, was able to travel in a time machine to 2006 and back, so that he could make the adjustments to his 1988 models to make them agree all the way to the present? The denialists have nothing but nonsense.
  9. Oh, and ClimateAudit is a barrel of laughs:
  10. And besides, if models can be "fudged" to fit anything -- as our `skeptics' claim -- why are the _same_ `skeptics' saying that they can't get Hansen's model to fit the data? Can it be because our `skeptics' are simply full of junk?
  11. No, we are saying that Hanson's model from 1988 does not fit the present, even his conservative projections are significantly high of actual observation at this point. (High relative to the ground based measurements and wildly high compared to satellite and balloon measurements to be more specific) If a model can't take past conditions and produce results that fit current reality it would be obviously useless. However since modelers are not simpletons that isn't the problem that was being discussed! The problem is just because current models have been changed so they can somewhat be used to fit past observations that doesn't mean those changes were the correct changes, therefore it doesn't mean that they are making correct predictions. The models still contain assumptions for various parameters that have not or perhaps can not presently be varified. Freeman Dyson is correct here, Models are improving but they have a long way to go before they are better than educated guesses. You should read Dyson's entire statement this is a bit out of context.
  12. Wondering Aloud: "because current models have been changed" You're clearly off spouting rubbish you don't know a thing about. Look at the temperature predictions in Hansen et al. (2006) and Hansen et al. (1998). They are _exactly_ _the_ _same_. The 1998 model has _not_ been changed at all, and it still agrees all the way to 2006. All your talk about "fudge factors" can't explain that.
  13. And you say I'm the one who clearly doesn't know what he's talking about!
  14. Models are as reliable as the data put into them.
  15. I thought this comment was interesting and relevant. It is taken from the US Senate Committee on the Environment and Public Works - Physicist Dr. Freeman Dyson, Professor Emeritus of Physics at the Institute for Advanced Study, in Princeton, is a fellow of the American Physical Society, a member of the US National Academy of Sciences, and a fellow of the Royal Society of London. Dyson called himself a "heretic" on global warming. "Concerning the climate models, I know enough of the details to be sure that they are unreliable. They 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 behavior in a world with different chemistry, for example in a world with increased CO2 in the atmosphere.," Dyson said in an April 10, 2007 interview. Dyson is also a fellow of the American Physical Society, a member of the US National Academy of Sciences, and a fellow of the Royal Society of London.
  16. I'll also raise the question whether anyone really believes this extract (from above) which appears to be a basic premise for the page: "This betrays a misunderstanding of the difference between weather, which is chaotic and unpredictable and climate which is weather averaged out over time. While you can't predict with certainty whether a coin will land heads or tails, you can predict the statistical results of a large number of coin tosses. Or expressing that in weather terms, you can't predict the exact route a storm will take but the average temperature and precipitation will result the same for the region over a period of time." It's a false analogy. Random *independent" events provide statistical certainty over a period of time. The climate does not fit this description. Can anyone provide some evidence -peer reviewed citations - that long range climate forecasting is more accurate than weather forecasting? I know the IPCC claimed it in their report but they didn't backup the claim.
    Response: That's the problem with using analogies - the comparison always breaks down at some point when you compare it directly. The main point is the contrast between short term, random unpredictability and long term, statistical summations. While weather is chaotic and non-linear, long term climate trends are discernable and predictable. As is seen in these peer reviewed studies analysing the success of climate forecasts.
  17. A common comment regarding GCMs is that they do not account for clouds very well. This is a substantial weakness. There have been assertions that warming increases atmospheric water vapor which, through a feedback mechanism, increases warming. Certainly increased atmospheric water vapor would produce more, lower-level clouds. How do the GCMs account for this? A simple cloudy-planet point model where standard atmosphere tables are used to get average cloud temperature vs altitude shows that a change of average cloud altitude of 305 meters would result in an eventual average earth temperature change of 0.75C. Many other factors known to influence cloud formation are not accounted for in the GCMs.
  18. Frankbi said: "And besides, if models can be "fudged" to fit anything -- as our `skeptics' claim -- why are the _same_ `skeptics' saying that they can't get Hansen's model to fit the data? Can it be because our `skeptics' are simply full of junk?" I am surprised that John did not reply to this, but I assume he does not have the time to respond to every nonsensical claim that appears on his board. Frank, if you do not know that GCMs (and many other models) are "tweaked" to fit past data, then you have no place attacking others. It is common practice, and there is little that is nefarious about it, though it may appear as such. You probably know that there are many uncertainties and complexities in the climate system, and in attempting to model such a system, you must used what has already been observed to better your understanding and accuracy. If models were based purely upon theory for such a complex system, they would appear wildly inaccurate and worthy of no utilization. -Robert
  19. I'm repeating here what I've said in another place on your blog: The IPCC summary of computer simulations you link above only go back to 1850 and blurs out problems with individual models by replacing the spaghetti curve with a grayed out region. (Errors in the simulations are highly correlated from year to year, the figure makes it seem they are not, which is false and misleading.) Also did you notice the huge 0.3°C anomaly around 1940-1950 that the models, even with the fuzzing provided by IPCC, are unable to explain? Where did that warming come from? I would conclude from that, that we aren't at the place yet, even for a 150-year period with a lot of fudge factors thrown in, where we can accurately describe past climate, let alone accurately predict future climate. Secondly did you notice that there was very little anthropogenic forcing before 1970, according to the models? Have you ever considered how disingenuous it is, given this fact, to compare glaciers from e.g. 100 years ago to current, when the models say that almost all warming prior to 1970 was natural?
  20. Robert S: Yes, I do know that model parameters are usually adjusted according to some past data, _and_ the resulting model has to be validated with data that are _not_ used to configure the models in the first place. If I didn't make this clear enough, my apologies. From my understanding, this approach of tweaking and holdout validation is what climate scientists have been doing. And it's perfectly good science, of course.
  21. Poptech When I was in college we were taguht Fortan IV, even though it had already been supplanted by Fortan 77. I did not realize that anyone was still using it. My own last experience was in SAS and that was in the 90s. Are you saying that these climate models are being coded in Fortran?
  22. Wow Poptech, what a rousing, impassioned, statesman-like speech. Unfortunately, it contains no verifiable concrete facts.
  23. Poptech, nice job of trying to help people understand what computer modeling is and what it can do. Folks, if a climate model doesn't predict past data 100% perfectly then it's useless. You can create an infinite number of different mathematical models that will predict any data series 100% perfectly. To deserve any respect these climate models must predict the previous data perfectly as a start, none should even be thought about unless it does that, and then it has to predict the future better than a simple polynomial fit that also perfectly predicts past data. Frankbi, all the facts in Poptech's post are verifiable. I learned them in school. His analysis is spot on.
  24. "all the facts in Poptech's post are verifiable. I learned them in school." I don't think that's what "verifiable" means. As always, the "criticisms" of climate models are devoid of any concrete, testable facts. -- bi, International Journal of Inactivism,
  25. Here is a new study that evaluates the accuracy of climate models: D.Kutsoyiannis,N.Mamassis,A.Christofides,A.Efstratiadis,􀈱S.M.􀈱Papalexiou Department of Water Resources and Environmental Engineering National Technical University of Athens ( Or to sum up the study: "Climatic models generally fail to reproduce the long term changes on temperature and precipitation."

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