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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 151 to 175 out of 244:

  1. cloneof, well, it's hard to make any comment on a paper if you do not give a reference. Assuming it's J. Clim. 2008, 21, 5624, it has nothing to do with models. It's on the empirical (mainly from satellite data) determination of feedback operation.
  2. cloneof, i'd like to add that, as a rule of thumb, when a paper is ignored by the other scientists you can safely assume that it is considered of no value, not even worth of a reply or a quote.
  3. Riccardo As I have just a few minutes let me first just apologize for not giving the refrence. It was almost ill responsible to brag about a paper that I didn't even give the refrence to. I will give you an response later today.
  4. Riccardo I was going to write about the paper, but I actually found a good video where Spencer explains hi's paper. And at least he gives the impression that it is supposed to do something about climate models.
  5. cloneof, the video is a sort of short version of the paper. He calculated a possible cause of error in empirical sensitivity estimates based on random variability of some internal forcing factor. Tuning the variability with just satellite tropical data he found a lower climate sensitivity than the global sensitivities obtained by various GCMs. This is all it has to do with models, Spencer compares his sort of "tropical sensitivity" with GCMs. While the effect of random variability on short term empirical estimates of the sensitivity might in principle make sense, it does not make any sense when he compares his tropical sensitivity with GCMs. This paper is on the very same track of the more recent (and wrong) Lindzen and Choi 2009 paper, similar wrong reasoning that they sell as the definitive proof.
  6. Riccardo Alright, thank you Riccardo. That totally cleared the things for me.
  7. cloneof, you might be interested in this paper in press which specifically addresses Spencer and Braswell 2009 and to this discussion. Thanks to Ari Jokimäki at AGWObserver for informing us on this not yet published paper.
  8. Riccardo: Oh really? Sweet, got to see that when it get's published. Thanks for the link, even though I'm a few weeks late.
  9. transferred over from "CO2 is not the only driver of climate" re yocta at 09:53 AM on 4 June, 2010 You say: ...and as can be seen with the IPCC tracked models there is quite a divergence... Please quantify this statement These projections from the IPCC Fourth Assessment Report: must surely be the most recognisable of any and quantify the divergence sought. Figure 10.5. Time series of globally averaged (left) surface warming (surface air temperature change, °C) and (right) precipitation change (%) from the various global coupled models for the scenarios A2 (top), A1B (middle) and B1 (bottom). Numbers in parentheses following the scenario name represent the number of simulations shown. Values are annual means, relative to the 1980 to 1999 average from the corresponding 20th-century simulations, with any linear trends in the corresponding control run simulations removed. A three-point smoothing was applied. Multi-model (ensemble) mean series are marked with black dots. just the opinion of the forecasters as to which one was most likely to eventuate. Can you provide evidence of the forecaster's opinion? If weather was relevant to your livelihood rather than merely a subject of academic interest or topic of conversation, then you would surely follow professional forecasters rather than those who present it as part of the evening entertainment. By following the professional services, the processes by which forecasts are developed will over time become clearer as forecasts are continually updated as situations develop and the forecast period shortens. all the models should begin converging until about 24 hours out they all should be fairly well aligned. why 24 hours? What physical basis do you have for this? See above. However there is another scenario that can and does occur, they are all proved wrong. It is obviously impossible for them all to be proved right. This statement is too vague. See above.
  10. johnd - we (if I recall correctly) got into this topic over on CO2 is not the only driver of climate, where we were discussing weather. In weather a 24 hour outlook is part of forecasting, with declining accuracy over longer periods due to the non-linear chaotic system of atmospheric physics - deterministic (not stochastic, sorry about that in an earlier post) progression but complex and with extreme sensitivity to starting conditions, which we don't absolutely know. The IPCC models and predictions you give above are for climate changes; the temporal progression of averages over the period of years. 24 hour time periods make absolutely no sense whatsoever with regard to climate predictions. As to the differences between the models - these are related to different estimates on feedback (active research to establish amounts and time constants), and different human future actions (how much CO2 do we continue to put into the air, how many aerosols?). They are multiple year "what if" scenarios. Since they're dependent on feedback refinement and how we respond to the issue, they of course are different in predictions. Weather and climate are not the same thing, not in time scale, variability, or predictive range. It's important not to confuse them.
  11. A clarification - the differences shown between the means in the three scenarios is due to different what-if postulations, while the spread of predictions under different assumptions is the difference in multiple year predictions of different models, with different estimations of feedback and sensitivity. Again, the exact values for feedback and time constants are under refinement, which is where climate scientists get to write papers. You'll note, however, that all of the models go "up"... regardless of assumptions.
  12. KR at 23:29 PM on 4 June, 2010, the point I started out to make was that models, be they forecasting the weather or the climate, should be within themselves 100% valid. That is, the combination of assumptions cannot be shown to be incorrect. If they could be then that particular model would be flawed and should not be used. Because each individual model is based on valid assumptions then it has as much chance of being correct as any other individual model. With the IPCC they take the mean as being the most likely outcome. With weather forecasters the process is similar with a number of different models all being run simultaneously with a range of different outcomes. When the forecasters are required to give a forecast for an extended outlook the use their best JUDGEMENT to select the output of whatever model they think at that time to be the most likely to eventuate. As I had mentioned earlier, this at times has resulted in different agencies simultaneously issuing forecasts totally 100% opposing each other. Obviously someones best judgement is different to someone else. They both can't be right, just as all models, be they weather or climate models, cannot all be right. Only one can hope to be right. HOWEVER as does happen with weather forecast models, at times ALL can be wrong. There is no fundemental reason also why all the climate models tracked by IPCC cannot be all wrong. After all, weather forecasting provides much of the data that that is plugged into GCM's that end up being plugged into all the climate models.
  13. After all, weather forecasting provides much of the data that that is plugged into GCM's that end up being plugged into all the climate models. No.
  14. Johnd, forecasting is not what climate models do. You're probably on top of that but it's an important distinction for folks less up on the topic, frequently the source of confusion.
  15. doug_bostrom at 07:57 AM, doug, which part do you disagree with, that weather data is plugged into GCM's, or that GCM's are plugged into climate models?
  16. johnd - when dealing with models, it's really not a black-and-white issue of right/wrong. What's important is the predictive capability of the model, which is a sliding scale; how close is the prediction to the actual outcome? Newtonian physics is "wrong" according to General Relativity - but accurate enough to compute most orbital paths outside of Mercury. Each of these models is 'valid' for the assumptions used - the relationships, the feedbacks, time scales, input values, etc. These assumptions can be shown to be incorrect - if a feedback value is incorrect, or an important relationship neglected, discovering the more accurate value or relationship can lead to abandoning or modifying a model. And if your assumptions are wildly off, your model is as well. These different models all disagree where questions about actual values (current and future research questions!) are still open. If the climate sensitivity is somewhere between 2-4.5 degrees C to a CO2 doubling, then any assumption in that range is in itself valid, and the models predictions will vary. This doesn't mean that the climate sensitivity is therefore 0.1 or 15! The models are close. None of the models are perfect - they are not exactly right on the input assumptions, input conditions, relationships, etc. The only complete model would be a copy of the Earth! But after sufficient testing (multiple runs with historic data compared to present, future predictions checked after a couple of years, etc.), they are close, or they are abandoned. And if they are close, they are useful for decision making. In my opinion (for whatever that's worth) weather predictions are far more likely to be wrong than climate models, given equal accuracy on assumptions - weather is a short term non-linear chaotic system, and the smallest bit of error in starting conditions, or insufficient granularity of the model, will result in the weather departing from the model after a time. Climate, on the other hand, is far less chaotic - long term averaging overrides any short term non-linear variance. And as doug_bostrom said, detailed weather forcasting models have nothing to do with the GCM's - only the long term average measurements are inputs to GCM's.
  17. Johnd, I suspect you made a simple typographical error. Weather forecast outputs are not a part of general circulation model inputs. Meanwhile, GCM's -are- climate models; the full term is "general circulation model of climate." Finally, just to be extra clear for bystanders, climate models do not produce forecasts nor is that the purpose of such models. For the curious, see background information on general circulation models here.
  18. doug - thanks for the link! Fascinating reading... I hadn't realized the complexity of the models used. My apologies for inaccuracies, johnd - looks like GCM models have some similarities to short term weather forcasts, but are far more extensive and detailed. I'll repeat, though, that each refinement brings the GCM's closer to matching the actual world behaviors, and makes them more and more useful for looking at the "what-if" scenarios.
  19. doug_bostrom at 08:17 AM, the link below may help illustrate the overlap of weather data and climate modelling. Note that the various forecasts used are grouped as Coupled GCM's, Ensembles, and Statistical, and are identified with each agency that produces each. I personally favour the Japanese Sintex model as being one of the most accurate, often identifying any change in trends well ahead of any of the others. Until May last year they were extremely accurate, correctly forecasting conditions completely opposed to the more recognised agencies that generally had rather more dismal success. They then updated their computer system which, without absolutely any changes to the models or the data being inputted, began throwing up forecasts more in line with other agencies. Even when they ran old data through, the results turned up different to the forecasts produced on the old system, even though the original forecasts were extremely accurate. I think they are still trying to identify as to why this has occurred, but it does then make one wonder if all agencies use similar computing systems, is there some inbuilt logic in the computer itself that will influence how the data is processed. The Fast Break Newsletter
  20. doug_bostrom at 08:17 AM, thanks for an interesting article. Especially interesting that the article should mention the following:- "It was now evident, in particular, where clouds brought warming and where they made for cooling. Overall, it turned out that clouds tended to cool the planet — strongly enough so that small changes in cloudiness would have a serious feedback on climate.(89)"
  21. Johnd, let me reiterate that general circulation models are not used to produce forecasts in the sense that we use the word to describe predicting weather. GCM utility lies in predicting tendencies. There's a huge difference between the two objectives. With regard to clouds, from all that I've read any real skeptic would do well to zero in on those as the single largest possible weakness of GCM's. But don't get your hopes up.
  22. doug_bostrom at 10:28 AM, with regards to your last comment. They do, and we have.
  23. This review article is a little long in the tooth but is pleasingly boggy in terms of showing the difficulty wading through the complexity of cloud treatments. It's also a nice illustration why so few skeptics are capable of emerging from the other side of the cloud swamp bearing useful contributions to the problem; one might say the "Cloud Swamp" is a test capable of identifying what real skepticism looks like. Cloud feedbacks in the climate system: A critical review
  24. johnd, with regard to the Sintex model and changes based on computer platform - it might be worthwhile for them to look at any differences in floating point calculations: IEEE compliant or not, single versus double precision, compiler/math library updates, etc. That kind of change is enough to make a difference on these scales. The original work on chaos and the Lorenz attractor came out of a very simplified weather model (3 variables, planar planet, etc.) that exhibited chaotic behavior - extreme dependence on starting conditions. Lorenz found that restarting his simulation with values rounded by 1/1000 (from a printout) was sufficient to get entirely different results! That result in the early 70's was sufficient to jump start non-linear system analysis and chaos theory.
  25. Most relevant to this thread about climate models, is this snippet from the Spencer Weart site that Doug linked to:
    That was a fundamentally different type of problem from forecasting. Weather prediction is what physicists and mathematicians call an "initial value" problem, where you start with the particular set of conditions found at one moment and compute how the system evolves, getting less and less accurate results as you push forward in time. Calculating the climate is a "boundary value" problem, where you define a set of unchanging conditions, the physics of air and sunlight and the geography of mountains and oceans, and compute the unchanging average of the weather that these conditions determine.

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