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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 376 to 400 out of 903:

  1. Forecasters at it again. However, I see you asked over at Realclimate as well (good idea), so it's worth noting Gavin's response: "Actually it isn't that terrible. They clearly spent more time trying to understand the science than previous forecasting researchers and they do a reasonably constructive job of trying to see whether you can improve on climate model projections. They slip up a little in mixing up decadal intialized predictions with the wider climate model enterprise but it is a reasonable first effort."
  2. Scott I had a look at it a while back and was not impressed by it. As Gavin's comment at RealClimate suggests the main problem is that they didn't spend enough time learning about the climatology or the way in which models operate and are used. This meant they ended up doing things like evaluating model predictions on station level data. It is well known that models can't be expected to do so (as the climate at station level depends on local geography on a scale much smaller than the typical grid box of a GCM) and in practice modellers use downscaling methods. So they are criticisng the use of models for something models are never actually used. I rather doubt their statistical methodology is robust either, but I can't remember the details off-hand, I'll have a look for my earlier post. The thing that I found irritating though was the constant mention of the IPCC, when none of the issues raised in the paper had any bearing on what the IPCC have already done, just on what they are planning to do for the next WG1 report. However reading the paper you might think it somehow casts doubt on the accuracy of existing reports. There has been at least one comment paper submitted to the journal. I was thinking of submitting one myself on the statistical aspects, but I just don't have the time to run the simulations etc. In essence, it was a rather poor paper, that shouldn't have been published in its final form, IMHO.
  3. This page could be improved a lot by considering at least one global circulation model. Pick one that's referenced in IPCC AR 4, describe the initialization state, describe the data series being fed as inputs, show the output over the time period between when the model was written and the present, then compare its predictions to predictions of less complex models like a time series. I actually think a lot of folks who come to this page will be surprised by the lack of such a comparison.
    Response: [Dikran Marsupial] You can probably find most of this at, although you would have to add the time series models yourself. I am not sure there is much point in comparison with the time series models, for a start would they give coherent spatial predictions? This sort of comparison has been done, and the model runs etc archived (search for CMIP3). However writing a good blog article on this would be a huge amount of work and beyond my capabilities (and I have worked with the models!).
  4. 382, rcglinski, I'm not sure that anyone could do what you ask except for a primary researcher working with the model in question, and then I think that compiling and presenting that information is a fair amount to ask of that person, even with their familiarity. I also think the models are so complex that the result may be many, many pages long, and require a lot of supporting information. All in all... I think (I could be wrong) that your request is way, way out of line with what is reasonable. Anyone who has that level of interest needs to go to the models and look at them themselves. Beyond that, there is a huge, huge wealth of information below these comments, under Notes, including links to papers, pages about modeling, and blog articles. Maybe a list of links to the pages for the GCMs, as well as those that can be downloaded and run by the really ambitious, should be added to that, but again... if you're that involved that you're going to do that much work, then you can use Google to find them.

    [DB] Bob, I don't believe the Notes section is viewable.  If there's a relevant section in it, you may want to post it in a comment.

  5. For Anthony Mills - you might like to consider that you can make useful prediction with even very simple models. eg Broecker's model However, it is also fair to say that climate models have no skill at short period prediction, not even decadal, for reasons that include your concerns.
  6. And yet another thought - if we were getting an extra 2W/m2 of solar radiation (twice the range of the 11 year solar cycle), would you say that models were incapable of predicting that this would warm the planet?
  7. "The Conversation" has a sceptic asking the following. Anyone got an answer? Doug Cotton starts with this... < quote > Speaking of error bars, Michael, in this paper Zhang et al claim to "reduce the overall uncertainties from 10–15 to 5–10 W/m2 at TOA and from 20–25 to 10–15 W/m2 at SRF." Now, empirical data appears to suggest that the difference between upwelling and downwelling flux at TOA tends to range between about plus and minus 0.5% of total incoming insolation. It seems to me that those error bars are, in most cases, too large to even confirm with certainty whether the net flux is positive or negative, that is, whether we should expect warming or cooling. < end quote > Then claims this: < quote > Doug Cotton Doug Cotton Maths and Physics university tutor Score: +1 insightful + report abuse • unconstructive - I don't think that is really the case Michael. The post below was just deleted within an hour from today's thread about media reporting. Yes, I agree there is a "consensus among scientists" but history is full of examples when "the science" of the day, or "the medicine" of the day has ultimately been proven wrong. Majorities are not always right. So why should the media block out those who put forward legitimate questions about the science? Without such questioning (which should not be equated with scepticism) nothing will ever change in this world and errors will be perpetuated. There are four main areas, as I see it, which need careful consideration in the climate debate and which I, for one, question: (1) The degree of accuracy in the models used by the IPCC and others is simply not sufficient to prove that warming should be happening. In general, they determine the difference between downwelling and upwelling radiation and it is only this difference which (depending on its sign) indicates warming or cooling. But the difference in fact is rarely more than 0.5% (plus or minus) of the total incoming solar insolation. The error in the figures used to determine such a difference is greater than that. Hence there is no valid proof from the models than we have +0.5% rather than, say, -0.5%. (2) An as yet unpublished study of temperature data from hundreds of boreholes (being prepared by myself and colleagues) reveals that there is a very strong correlation between surface temperatures and the temperatures determined by an extrapolation of the underground temperature plot determined only from measurements more than 200 metres underground which are well beyond the influence of solar insolation. The probability of this happening at random is absolutely infinitesimal. Hence we can deduce that underground temperatures supported by heat flowing out from the core are the forcing factors, rather than any processes relating to solar insolation or gases in the atmosphere. (3) Trenberth's trend (shown at the top here ) shows a curved line now past its maximum and starting to decline. Adding data to 31 Aug 2011 shows that downward trend continuing. (Sea surface temperatures (using highly accurate NASA data) are probably the best indicator because about 90% of heat above the crust is stored in the oceans and sea ice.) The gradient of this curved trend is now statistically significantly different from the lowest gradient of IPCC projections. This proves such projections incorrect with 99% probability. (4) The role of greenhouse gases in radiating away heat obtained by collision with non-greenhouse molecules would not appear to be considered in the models. Oxygen in particular is a very stable molecule which radiates very little itself at atmospheric temperatures. Nitrogen is a close second. Quantum mechanics shows why molecules can only radiate the frequencies (wavelengths) which they can also absorb. < end quote > And this: < quote> "Yes, we know IR radiation is captured by GH gas molecules, and further photons are then re-emitted. The emission of even more photons takes place as the GH gas molecules (including CO2 of course) cool off. Some of the radiation goes back to Earth, then heats the Earth and more conduction and radiation occurs as a result. Heat is carried upwards partly by radiation and partly by physical movement of molecules - ie convection. Eventually, between 99.5% and 100.5% of all incoming solar insolation is radiated to space. So yes, GH gas can delay the process by a few minutes, maybe an hour or two, but it can also speed up the process of cooling 98% of air molecules. Who knows which dominates? By night nearly all heat will escape, except in local summer when the oceans will warm, but lose their extra heat again by winter. The models appear to "overlook" the above potential cooling effect. But, even if I'm wrong on that (and someone else show me where) the models are still not accurate enough to be able to determine whether it is 99.5% or 100.5% and that makes all the difference between warming and cooling. The world has been misled by bad statistical accounting for margins of error. You simply can't take a difference of two numbers each over 300 (with errors about plus or minus 5) and prove that the difference is +1 rather than -1 for example. " < end quote > Anyone got any papers on this?

    [DB] Eclipse, Mr. Cotton feels his own special pet hypothesis, using as-yet-undiscovered physics, proves that the Earth is warm due to heat escaping from it's core. 

    Thus, he (Mr. Cotton) is right despite a lack of any published studies to support his position.  And therefore hundreds of years of research by hundreds of thousands of scientists is wrong, despite an overwhelming amount of physical evidence to the contrary.

    Dialogue with Mr. Cotton is thus impossible, as science says that 2+2=4.  Mr. Cotton says it equals 16 and also that on Tuesdays water flows uphill, the sky is green and the Moon is actually made of cheese (Edam, I we could smell it from here if it was Limburger).

  8. This was my (rather embarrassing and ill informed) tentative reply. *** I'm no scientist Doug, but isn't it the case that thermodynamics states heat energy can't move from a hot source to a hotter source? Doesn't heat always travel 'down' temperature grades? If greenhouse gases are emitting IR heat that they've prevented from radiating out into space, then surely the GHG's are the ones warming the other gases, not the other way around?

    [DB] "isn't it the case that thermodynamics states heat energy can't move from a hot source to a hotter source?"

    The 2nd Law refers to Net heat exchange...

    "Doesn't heat always travel 'down' temperature grades?"

    For convection, yes.  Back radiation is 360 degrees...

  9. DB inline @386, if only Doug Cotton where so succinct, and so sane in his theories.
  10. Eclipse @386: 1) Doug Cotton is certainly not in a position to judge this (see point 4), and indeed is completely wrong. Specifically, climate models very accurately predict the change in radiative forcing due to changes in greenhouse gas levels. To give you an idea how accurate line by line models are in those predictions, here is a comparison of model data (dotted line) and observed data (solid line) over the Gulf of Mexico: Global circulation models are not quite that exact, but more than exact enough to narrow the expected temperature rise per doubling of CO2 to the range of 1.5 to 4.5 degrees C. Copious physical evidence from diverse sources narrows that still further to 2 to 4.5 degrees C, with a most probable value of 3 degrees C. 2) An as yet unpublished paper that refutes a well established theory is worth no more than the paper it is currently printed on. In fact,from clues about the contents of that paper Cotton has left on this website, the paper is not only unpublished but unpublishable as it shows no knowledge of basic physical laws, including those relating to thermal conduction (the theory on which the paper is supposedly grounded), but also directly contradicts well determined measures of heat flux from the core to the surface. (I have linked you to my posts, but other posters have equally effectively rebutted Cotton's nonsense.) 3) Cotton just made that 99% figure up. In fact, the reduced rate of global warming is not unexpected given high aerosol emissions by China, a recent solar minimum lower than anything since 1910, and three strong La Nina's in just four years. But China is curtailing its emissions, La Nina's come and go, and the Sun is now well into its next solar cycle so expectations of anything but renewed warming are just wishful thinking. 4) Curiously I am responsible for this belief by Cotton. He came on this site saying the majority of thermal emissions from the Earth's atmosphere were from oxygen and nitrogen. This is an absurd falsity. When I demonstrated to him that he was wrong, he without pause or consideration switched to this new theory. He had just made a massive change in his theory but it made no difference at all to his conclusion, ie, that global warming is false. It is safe to conclude that Cotton want's to retain that conclusion, and no near detail of fact or logic will be allowed to prevent him from doing so. He is one of those unfortunate people of whom it can be said that he is always in error, but never in doubt.
  11. Thank you both. I've copied your replies back to the "The Conversation" and have bookmarked this thread for further reading. Great job!
  12. Say I wanted to play around with the data, to look for relationships in a pretty amateur way. There's: Sea Level Surface/Air temp C02 ppm Sunspot number SOI What am I missing and where will such a simple model fail?
  13. #391: watch out for cause-effect relationships between different factors, but you'll also want volcanic forcing in there, and perhaps aerosol forcing too. With a reasonably simple model you can reconstruct surface temperature changes using CO2, SOI, volcanic - see Tamino's Open mind for some examples, one here and a better (superb) one here. I'm not sure about sea level, though a recent post on this site speaks of the impact of ENSO on short-term variations. Simple models don't capture the complexities of the interactions in each system, but they have their value in identifying some of the key elements to a system.
  14. Thanks a bunch Sky :)
  15. Tristan if you want to play with simple phenomenological model with say multiple regression, then have a look at Benestad and Schmidt 2009 for an example. Sunspot number is very crude - you should use one of latest TSI proxy constructions (eg look at here. Very importantly though, you need an aerosol term, both industrial and volcano. For internal variability, you should include an ENSO index.
  16. This is an interesting abstract. Does anyone here have a journal handy to read it and know if it is worth the subscription rental? Climate models don't show the warming in the early 20th century
  17. Camburn @395, I cannot comment on the paper, but I can comment on your misrepresentation of the abstract. The relevant sentence, just one item out of many discussed is:
    "Few models reproduce the strong observed warming trend from 1918 to 1940. The simulated trend is too low, particularly in the tropics, even allowing for internal variability, suggesting there is too little positive forcing or too much negative forcing in the models at this time."
    There is a very large difference between the claim that "Climate models don't show the warming in the early 20th Century" and the actual claim in the abstract that the warming shown by most models is not as great as that observed. There is also a difference between your blanket "Climate models" (indicating all Climate models) and the abstracts concession that a few models do in fact show the correct trend. It is difficult to not believe that your misrepresentation of the contents of the abstract is deliberate. Further, your choice of just one sentence to highlight out of the abstract also shows bias. Why not, for example, discuss this sentence:
    "Over the whole of the 20th century, the feedback strength is likely to be underestimated by the multimodel mean."
    The answer, I am sure, is that you do not want people thinking about the possibility that climate sensitivity is more than that which the models indicate.
  18. Tom: The hindsight of the models do a poor job replicating the temp pattern in the early 20th century. That is obvious from the abstract. As to my question, is the paper worth paying the rental fee for? Someone may be able to access this and give an opinion. Abstracts are a hint, but the meat of an issue is in the paper itself.
  19. Tom: As far as feedback, that is in question. The statement of the abstract that the models do not do a good job of hindcast is a fact.
  20. 397, Camburn, Lame response. You've been caught red-handed misrepresenting a paper abstract to try to imply doubt about climate science. Then you compound the error by acting as if your misrepresentation is still a reasonable interpretation. This is typical denialism, laid bare for anyone with half a brain to look at and recognize. Thank you for the demonstration.
  21. Camburn#398: "The statement of the abstract that the models do not do a good job of hindcast is a fact. " Of what use is a selectively chosen, isolated fact, without context or mechanism? This paper calls for higher positive feedback; Camburn is on record siding with Spencer on the side of low feedback and therefore low sensitivity. I would think you'd be running away from this paper as fast as possible - if you agree with it, you are contradicting your support of Spencer and tacitly siding with Dessler. Surely that's not your intent?
  22. Camburn @397, the performance of the models against the early 20th century has been known for a long time: As can be seen, the trend of the observed temperature changes in the early twentieth century is very close to the modeled temperature changes. Exceptions can be seen in 1909-10 and 1915-17 when the observed temperatures are significantly below the modeled temperatures, but both of those periods coincide with < ahref="">strong La Nina years (exceptionally so in 1917). A further exception can be found in the period 1938-1945 in which the observed temperatures lie well above the modeled line. This is partially explained by a strong El Nino in 1940-41. The unexplained increase represents approximately 10% of the increase in temperature between the 1910's and the 1940's. It may well be explained by a dip in anthropogenic sulfates at the time, or indeed by a sudden influx of black carbon aerosols. Regardless, trying to interpret an approx 10% at one point as "Climate models don't show the warming in the early 20th Century" (my emphasis) is bizzare. Your statement was both unequivocal and wrong. Your follow up that "models do a poor job replicating the temp pattern" seems to come down to this - Climate models do a poor job at retrodicting the exact year of ENSO fluctuations (as opposed to their frequency), and the onset of wars and depressions. Well, your probably right on that, but I don't think a failure to predict WWII (or the exact amount of black carbon released by the blitz) constitutes a serious problem for climate modelers.
  23. muoncounter: So you have read the paper? Does it provide anything new that is worthy of paying the rental fee?
  24. I have been corresponding with an ex-engineer with regards to skepticism regarding anthropogenic climate change. Here is one of his remarks. "We certainly do not have the computing power to perform the ANOVA for the effect of orbit/tilt, solar output, geological processes, etc. on GTA. That makes attempts to correlate recent measurements of GTA with its inputs otiose." Basically, he believes that we are applying way too much certainty with regards to CO2, and understating our uncertainty with regards to other variables. I can debunk this at a qualitative level, but not quantitatively because I truly have no expertise in computer science. Can anybody help me out here, or is he right?
  25. chuidburg The answer is yes and no. We do have enough computer power to run the simulations that demonstrate quite unequivocally that anthropogenic climate change is real and that natural forcings are unable to explain much of the observed warming. However, our characterisation of the uncertainties involved will continue to improve the more experiments we perform. So whether you think there has been enough depends on where you put the goalposts (which your friends has been extremly vague about). I suggest you challenge your friend to read chapters 8 and 9 of the most recent IPCC WG1 report and make a specific suggestion of an experiment that they haven't pursued, that relates to a scenario or theory that is plausible and has some support from observational evidence. Ask him to specify exactly what simulations would need to be run to perform the ANOVA to his satisfaction (an ANOVA is not the right tool anyway, as correlation is not causation, but that is another matter).

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