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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 651 to 700 out of 1297:

  1. dvaytw It sounds to me as if your firnd may be thinking of "flux corrections" but modern climate models have improved to the point where they are no longer necessary.  Sadly unless he can give a concrete example, it is difficult to address his concerns more directly.  The modellers do however perform experiments to determine the sensitivity to the parameters, which I understand are called "perturbed physics experiments".  IIRC there are on-line repositories that contain the results of such experiments resulting from a sort of SETI@home type project where people can use their spare processor time to run climate models (I'll see if I can find it again).

  2. This gets raised at RC from time to time. You might like to look at the response to this comment. Sounds like an objection based on dated information and overstating the problem.

  3. Also note that the "flux corrections" discussed by Dikran are specific to models that incorporate simulations of both atmospheric and oceanic circulations (note the AOGCM acronym in the wikipedia page Dikran references - AO means Atmosphere/Ocean General Circulatation Model). Such flux corrections do not exist in any other categories of climate models, and they are not in all AOGCMs.

    Simulating both atmospheric and oceanic circulation at the same time is a complex problem: you need small time steps to deal with the rapid atmospheric changes, but you need long simulation times to deal with the slower motions and changes in the ocean. Various tricks (oh, there is that awful word again) are used to deal with the mathematical complexities.

  4. Thanks guys.  My friend now makes the following objection in defense of his refusal to actually simply appear in the comments section and ask his questions himself:

    What I am saying is that your belief in climate models is based on accounts written by science journalist, none of whom actually do the research themselves. In fact, looking through Skeptical Science, I am left wondering if the writers of the blogs actually can understand the original studies they are reporting on. I did a search through the blog for hindcasting and looked at some of the studies they comment on. It's clear that their representation of hindcasting in your link is not at all what the original researchers thought they were doing. Even in Skeptical Science account of the study, it's clear the aim of the research is to isolate relevant variables, not to 'test models'. But none of this surprises me when I look at the background of the people posting on the blog. Almost all of them are computer scientists or involved in Earth science of some sort. None of them have formal backgrounds in experimental research. They are like talking to economists about society and culture. They would have had little instruction or research experience in issues related to causality and experimental control. And as I said above, none of them have even published in this field. I don't know if you read through the comments to your link, but many of the points Tom and I have been stating here have been brought up, and if you watch closely, they get brushed aside, as if paying too much attention to them would spoil the game. Which, indeed, it would. They're bloggers, after all, not climate scientists. 

    It strikes me that I've never paid too much attention to credentials here, but would y'all respond to this?


  5. dvaytw @654.
    Your physicist friend-of-a-friend asks an exceedingly narrow question (presented @650) given he is describing his reasons for entirely dismissing climate modelling. And now he impunes SkS blog-writers & blogs and accuses commentors of wielding Okhams broom.
    All in all, folk who speak with such 'authority' do not do so in such a dismissive way if they are genuine. (The apparent incoherence is not untypical of a 'technophile' but not entirely a good sign.)  So I fear that that broom is in the hands of you physicist
    You could ask him to (or may be you could yourself) describe the "many of the points Tom and (he) have been stating (t)here (that) have been brought up, and if you watch closely, ... get brushed aside, as if paying too much attention to them would spoil the game." It would allow all to get the measure of each other without his personal appearance here at SkS which he is so afeared to do.

  6. dvaytw's friend wrote "What I am saying is that your belief in climate models is based on accounts written by science journalist, none of whom actually do the research themselves. In fact, looking through Skeptical Science, I am left wondering if the writers of the blogs actually can understand the original studies they are reporting on."

    Actually, I have worked on statistical downscaling of climate models, which is a task that requires a reasonable working knowledge of the operation, mathematical and statistical principles involved.  The paper was published in the international journal of climatology here:

    M. R. Haylock, G. C. Cawley, C. Harpham, R. Wilby and C. M. Goodess, Downscaling heavy precipitation over the United Kingdom: A comparison of dynamical and statistical methods and their future scenarios, International Journal of Climatology, volume 26, issue 10, pp. 1397-1415, August 2006. (doi)

    However, given he starts his question with an ad-hominem and just continues it, and adds little detail of the scientific question he wanted to ask, I rather doubt that any answer will change his opinion.

    Credentials are irrelevant in science, what matters is the strength of the evidence and the internal consistency of the argument.  Now if your friend is really concerned about credentials, ask him why he doesn't accept the views of the IPCC on models, as the authors of the IPCC report have as good a set of credentials as you could possibly want to see.  I suspect the reply will be another ad-hominem, but do ask him.

  7. MA Rodger & Dikran Marsupial:

    It is possible that dvaytw's "friend" is a ruse by dvaytw to get around the SkS Comments Policy. You should keep this possibility in mind when responding to dvaytw's "friend."  I and other Moderators need to keep this possibility in mind when reviewing dvaytw's posts.

  8. JH, surely nobody would stoop to that sort of behaviour on climate blogs?  Perish the thought! ;o)

  9. John Hartz - I would strongly disagree based on dvaytw's presentation of SkS information on other forums. He indeed is arguing the science with support from discussions here. 

    dvaytw - I would strongly suggest including such links (if possible) to these various discussions whenever referring questions from deniers? As in, every time? Not only does that make your position clearer, but provides context that might greatly aid answering such questions. In fact, that might cut short a pattern I've seen with your questions from several back-and-forths to a briefer conversation that more directly addresses the denier myths involved.

    As it is, your questions feel like a game of Telephone...


    [JH] Thanks for the feedback re dvaytw's credentials. Your advice to him is also appreciated.

  10. Moderators - My apologies, the link in my previous post should be "dvaytw's presentation of SkS information on other forums".

  11. Echoing KR@659, I too have occasioned upon dvaytw engaging the denialista. My only criticism would be his (once) suggesting that I was an "actual climate scientist" which I am not. Then as @565 "Credentials are irrelevant in science, what matters is the strength of the evidence and the internal consistency of the argument. "

  12. Could anyone explain why Antarctic climate models would be harder to get right than full global climate models?


    [DB] Please support your assertion with a link to a credible source establishing that claim.

  13. Climate models predict more snowfall than ice melting during the next 50 years, but models are not good enough for them to be confident about the prediction.


    The British Antarctic survey team confirm this but their site is down at the moment


    [TD] Part of the answer is that the smaller the geographic region, the more difficult it is to project its climate.  The whole globe is easier than any subregion of the globe.  That's because difficult-to-predict factors regionally, tend to cancel out those same factors in other regions. This difficulty of projecting at short geographic scales is similar to the difficulty of projecting at short time scales--even when the "region" is the whole globe.  See the post The Difference Between Weather and Climate.  See also the National Academy of Sciences' excellent series of short videos, Climate Modeling 101.

    For a given size of region, naturally some regions are more difficult to project than others are, but I don't know about the difficulty of Antarctica versus other regions of similar size.

  14. First of all, copying the work of others (as you do) without the usage of quotes is considered plagiarism, FYI.  The only remaining question is whether you copied it from Wiki or one of the many denier sites parroting that phrase.

    Further, I see no mention in the BAS site of the phrase you use.  Feel free to look yourself.

  15. Daniel, for what it is worth, wikipedia's (and hence Vonnegut's) claim is based on the following statement by the BAS:

    "Global climate model predictions of how the Antarctic climate may change over the next 100 years differ in detail from model to model. Most models, however, indicate relatively modest temperature rises around Antarctica over the next 50 years and, over this time period, increased snowfall over the continent should more than compensate for increased melting of Antarctic ice and will thus partially offset the rise in sea level resulting from thermal expansion of the oceans and melting of icecaps and glaciers elsewhere in the world. However, many processes occurring in the polar regions are not well represented in climate models at present and further research is needed to improve our confidence in these predictions. This is particularly true for predictions beyond 50 years, when Antarctica may start to warm enough to have a significant impact on the ice sheets."

    The only problem is that the statement comes from a page that was taken down by the BAS sometime between Feb 7th, 2006 and June 7th, 2007.  Ergo the statement precedes the IPCC AR4, let alone AR5.

    The nearest recent equivalent (from the page you are currently redirected to) reads:

    "Antarctica is a vast ice sheet, around the size of the USA, and it is not surprising that different areas are behaving differently. On the Antarctic Peninsula, where climate is warming rapidly, 87% of glaciers are retreating but the area is small and the contribution to sea-level rise, a few centimetres per century, is comparable to that from Alaskan glaciers. The East Antarctic ice sheet appears close to balance, although increased snowfall may cause this area to thicken slowly in future. In West Antarctica, there is an area roughly the size of Texas where the ice sheet is thinning rapidly — the Amundsen Sea Embayment (ASE). Close to the coast in ASE, thinning rates are more than 1 metre per year."

    (Current to at least Jan 17, 2014)

    Of course, that does not support Vonnegut's claim.

  16. Thank you @665 Tom

    Global climate computer model predictions of how the Antarctic climate may change over the next 100 years differ in detail from model to model. Most models, however, indicate relatively modest temperature increases around Antarctica over the next 50 years. Over this time period, the models predict increased snowfall over Antarctica, which should more than compensate for increased melting of Antarctic ice. However, many natural processes occurring in the Antarctic are not well represented in present climate models and further research is needed to improve our confidence in these predictions

    This link

    I just find it odd that being a simple climate in relative terms that its not easier to model.

  17. Vonnegut @666:

    1)  Antarctica does not have a simple climate, not even in relative terms.

    2)  Quoting a 2004 interview and assuming comments made regarding models in 2004 are relevant now represents a specious argument.  If you cannot find a relevant modern quote, your Find a relevant modern quote or your questions are without basis.

  18. A problem I have with this entry supporting climate models is that it seems to skirt what is most critical of them.  Most current climate models completely missed the current warming pause.  This is an issue that has spawned a host of investigation.  A recent article about stronger trade winds in the media for example.  Also, it also does not address that ALL models of complex systems mispredict to some extent.  Given climate models are long term projections of where we are going, it is reasonable to expect that they will miss shorter term climate movements.  From my point of view, it is more indicative that climate models seem to still say we should be warming even after the past decade or so of flat atmospheric temperatures is taken into account.  Those who would deny that CO2 causes ANY warming or that its effect is small and not significant may point to the past decade as proof of future results.  I'd love to sell them a bridge in NY for that risky logic.

  19. knaugle "Most current climate models completely missed the current warming pause."

    This is actually completely unsurprising, however to see why this is, you need to understand how GCMs operate, which in trun will show what they can reasonably expect to be able to predict and what they can't.

    Imagine we had a quantum mirror which we could use to visit Earths in parallel universes.  Say we could choose only those where the climate forcings (e.g. solar, volcanic, aerosols, CO2 etc.) are exactly the same as they are on our Earth, but the initial conditions are slightly different (e.g. a butterfly flapped its wings on one, but decided not to bother in another).  In this case, the response to the forcings will be exactly the same on the parallel Earths, but each will have a different set of variations in climate due to sources of "internal variability" such as ENSO.

    Now you could have no better climate model than this, even in theory, as the parallel Earths have exactly the same laws of physics and infinite temporal and spatial resolution.  Would they have predicted the "warming pause"?  Well yes and no.  The warming pause is likely the effect of sources of internal variability, in fact such sources of variability are completely sufficient as an explanation of the lack of warming (e.g. Foster and Rahmstorf).  These are chaotic, and while these periods of little or no warming will be observed occasionally on the parallel Earths, they won't generally ocurr at the same time as the pause on this Earth.

    So what can we predict from the model?  Well if you take the average of the temperatures of the Parallel Earths, you will get an estimate of the forced response (i.e. the reaction of the climate to the change in the forcings, e.g. CO2).  This is what is relevant to climate policy, not the effects of internal variability which are quasi-cyclical and have little long term effect on the climate.  The forced response does not show pauses, but that doesn't mean the models are not predicting that pauses will happen everynow and again (but without being able to specify when)

    Now, lets return to real climate models, Easterling and Wehner show that these pauses are also found in the output of individual model runs as well, but again the timing of the pauses is unpredictable.  So it is unfair to say that the modells have missed the pause.  They have said we should expect them to happen, but can't predict when they will happen.

    Why should we expect real climate models to be able to predict something that a theoretically perfect model could not?

  20. A recent article in the WSJ by John Christy and Richard McNider (I'm sure many of you know about it) claims that the models predicted more warming than was actually observed. I'll leave it here so that other contributors can explain why it is either misleading or flat-out wrong. The image I am referring to is entitled "Warming Predictions vs. the Real World". I would just add the image, but I can't figure out how, so here's the article.


    [RH] Hotlinked url.

  21. jsmith - Christy and McNider are using the same "on-year baseline" trick that they have been using for quite a while.  This is a method used to make the difference between the models and the observations look bigger than it actually is, for details see dana's recent blog post at the Guardian (don't be put off by the title - the stuff about Christy and McNider is the second half of the post after the stuff anout Roy Spencer's unfortunate meltdown).  Of course this won't fool anybody that understands how the models work and what the ensemble method does, but it does make great fodder for the media and "skeptic" blogs.

  22. jsmith, they are essentially using the very same trick I already explained to you here.

  23. jsmith, I think it's the same tricks as described on HotWhopper i.e. they picked a nice big spike in the observation data to "align" the models starting point with.  My understanding is that models should be started at a multi-year average temperature - not a particular cherry picked start year e.g. 1979 in this case.

  24. sapient fridge - the start date isn't cherry picked in this case as 1979 is the start of the satelite record, ut the use of a single year baseline is still incorrect for the reasons I demonstrated to jsmith on the previous thread.

  25. I would be shocked if I was the first to bring this up, but a recent article in Nature Climate Change contends that the models predicted more global warming (as defined by global surface temperatures only) than actually happened. To wit: "Global mean surface temperature over the past 20 years (1993–2012) rose at a rate of 0.14 ± 0.06 °C per decade (95% confidence interval)1. This rate of warming is significantly slower than that simulated by the climate models participating in Phase 5 of the Coupled Model Intercomparison Project (CMIP5)." Does this in any way validate those who state that the models can't be trusted?

  26. Not if they bothered to read the whole paper instead just quote mining. How about this quote then:

    "For example, the forced trends in models are modulated up and down by simulated sequences of ENSO events, which are not expected to coincide with the observed sequence of such events."

    What the modellers firmly state is that the models have no skill at decadal level prediction. They do not predict the ENSO events and trends are modified by this long period La Nina/neutral.

    A better to question is to ask what skill do models have. If you dont trust models, then you must instead rely on simpler means to guide your policy. The verification of AGW do not depend on models so by what means would you guess the effect?  The models despite their flaws remain the very best means we have predicting long term changes to the climate.

  27. This comment is a continuation of a conversation that started on the Falsifiability thread; this continuation is more appropriate on this Models Are Unreliable thread.

    PanicBusiness: I can say that evaluating GCMs' temperature projections requires evaluating the GCMs' hindcasts rather than forecasts, when the hindcast execution differs from forecast execution only in the hindcast having the actual values of forcings--at least solar forcing, greenhouse gases (natural and artificial), and aerosols (natural and artificial). That is because GCMs' value in "predicting" temperature does not include predicting those forcings. Instead, GCMs are tools for predicting temperature given specific trajectories of forcings. Modelers run GCMs separate times for separate scenarios of forcings. GCMs are valuable if they "sufficiently" accurately predict temperature for a given scenario, when "sufficient" means that scenario is useful for some purpose such as one input in policy decisions.

    My other requirement for evaluating GCMs is that even within an accurate scenario of forcings, that the short-term noise be ignored. Perhaps the most important known source of that noise is ENSO. ENSO causes short term increases in warming and short term decreases in warming, but overall balances out to a net zero change, meaning it is noise on top of the long term temperature trend signal. You can do that by comparing the observed temperature trend to the range of the model run result trends rather than to the trend that is the mean of the individual runs. In GCM trend charts sometimes those individual model runs are shown as skinny lines, as in the "AR4 Models" graph in the "Further Reading" green box below the original post. (Unfortunately, in many graphs those skinny lines are replaced by a block of gray, which easily can be misinterpreted to mean a genuine probabilistic confidence interval around the mean trend.) Actual temperature is expected to not follow the mean trend line! Actual temperature is expected instead to be jagged like any one of those skinny model run lines. The GCMs do a good job of predicting that ENSO events occur and that they average out to zero, but a poor job at predicting when they occur. The mismatches in timing across model runs get averaged out by the model run ensemble mean, leading easily to the misinterpretation that the models project a trend without that jaggedness.

    Another way to see past ENSO and to match observed forcings is to statistically adjust the GCMs' projections for those factors. That approach has been taken for observations rather than models by Foster and Rahmstorf. That approach just now has been taken for model projections by Gavin Schmidt, Drew Shindell, and Kostas Tsigaridis--paywalled, but one of their figures has been posted by HotWhopper. Doing so shows that observations are well within the range of model runs.

  28. PanicBusiness, you wrote on another thread:

    I personally find it very likely that in the coming five years there will be no significant warming or there will even be significant cooling. If that happens I want the CAGW community to not come up with additional excuses, and hand-waving like it was totally expected.

    "The CAGW community" specifically disclaims the ability to predict temperatures for five year and even ten year spans.  That's weather, not climate.  So you've set up a strawman if you mean you want predictions from now for the next five years.

    If instead you mean that in five years the trend over the previous 30 years (25 years ago from now, plus 5 years into the future) will be below the GCMs' projections, then first you will need to verify that result after having used the models to hindcast using the actual forcings during that period (Sun, aerosols, and greenhouse gases), or at least will have to statistically adjust the model projections to accommodate the actual forcings, as was done by Schmidt, Shindell, and Tsigaridis.  To be thorough you should remove ENSO as well, though over a 30-year period it should average out to about zero.  When I say 30 years, I mean really 30 years.  Focusing on the last five years of a 30-year period is just looking at weather, so if the projections were within range for 29 years and in the 30th year dipped below the 90% range, you can't yell about that 30th year as if that is climate. 

    If after doing all that, five years from now the 30-year trend is below the 90% range of the model projections, then I would say that the projections were too high and that policies depending on those projections need to be modified.  But the modifications of policy would not be to assume the models are "falsified" in the sense that they are useless.  Instead, policies would need to be reworked to suit projections that are lower than those original projections, but lower only as much as indicated by the difference from the observations.

  29. PanicBusiness, you wrote:

    I want the AGWers (who are on supposed consensus) to state their predictions now and clearly as to how much surface temperature warming will happen in function of CO2 emissions with confidence intervals in the future. This is how you make predictions.

    It seems you have not done much research.  The IPCC has produced its AR5 report, containing those projections.  One thing you absolutely must take into account is that the projections depend on assumptions about forcings such as greenhouse gas emissions, aerosols from volcanoes and humans, and solar intensity.  Those are uncertain.  We can't model each one of the infinite scenarios of forcings.  So the IPCC defined a few scenarios that span the range of reasonable expectations of scenarios.  GP Wayne has written an excellent explanation of the greenhouse gas emission aspects of those scenarios.  How well the models project temperature depends in large part on how well the real world forcings match each of the scenarios.  Even the best-matching scenario will not assume exactly the same forcings as happened in the real world.  That is not an excuse, it is as unavoidable a problem as is the traffic condition that will actually happen during your drive to work, versus your beforehand scenarios of possible traffic conditions--the scenarios you use to decide when to leave for work.

    The projections themselves are in the AR5.  Projections from 2016 to 2050 are in the Near Term chapter; see figure 11.9.  Projections beyond that are in the Long Term chapter.

  30. Obviously not in the format I wanted, and It is very hard to infer actual confidence intervals as highlighted in 12.2.3:

    These ensembles are therefore not designed to explore uncertainty in a coordinated manner, and the range of their results cannot be straightforwardly interpreted as an exhaustive range of plausible outcomes

    But it does provide some predictions. The problem remains that it will be still very hard to publicly demonstrate the strengths and weaknesses of this report. The reason for that is it is nearly impossible to recreate the predictions for a quantiatively defined scenario. Later it says explicitly that I will not get what I wanted. (But it is great that they acknowledge the need to have it)

    In summary, there does not exist at present a single agreed on and robust formal methodology to deliver uncertainty quantification estimates of future changes in all climate variables.

    But I may not need it after all If there will be no significant warming in the next 10 years it will cast serious doubt on CAGW scenarios.

  31. Since the science never refers to "CAGW", perhaps you had better define it for us? And of course you are going to go on record as changing your mind if there is seriously significant warming in next 5 years? (I think there is a high likelihood of El Nino in that period) - or are you firmly of the opinion that surface temperature record has nothing to do with ENSO modes?

  32. But this picture of the models compared to actual temperatures appears to support that the AGW threat is less imminent than AGWists used to think. If there is no significant warming in the next few years, It suggests that High sensitivity AGW supporters are either extremely unlucky(in a sense that an implausible scenario happens) or wrong.near-term projections vs temperatures

    As seen in IPCC AR5


    [RH] Changed image width to preserve page formatting.

  33. "If" is a lovely word, isn't it? 

    PB: "But I may not need it after all If there will be no significant warming in the next 10 years it will cast serious doubt on CAGW scenarios."

    Yes, and if we find out that aliens have been manipulating our instruments, that will make a big difference as well.  Perhaps you'll agree that such a scenario is unlikely.  Upon what basis do you imply that "no significant warming" is likely?  What model are you using, and is it "falsifiable" as you define it? 

  34. PanicBusinss...  Curious if there was a reason you omitted the lower panel of the figure.

    Also wondering why you would link to a tinypic without citing the actual location of the source material. This is located in AR5, Chapter 11, Fig 11.25.

    What you fail to grasp is that there are a number of things that could be wrong with this. Models could be running hot. Surface temp readings could be reading low (poor polar coverage). More heat may be going into the deep oceans than anticipated. There may be an under counting of volcanic activity. There may be an under counting of industrial aerosols.

    What we're likely to find is that it is some combination of these things. Problem for you is that, none of these would invalidate models since models are just a function of the inputs. 

    Ultimately what doesn't change is the fact that we have a high level of scientific understanding regarding man-made greenhouse gases. The changes in radiative forcing from GHG's relative to natural radiative forcing is large. That gives scientists a high level of confidence that we are warming the planet in a very serious way, regardless of how the models may perform on a short term basis.

  35. Interesting. Here's the passage right next to Fig 11.25.

    The assessment here provides only a likely range for GMST. Possible reasons why the real world might depart from this range include: RF departs significantly from the RCP scenarios, due to either natural (e.g., major volcanic eruptions, changes in solar irradiance) or anthropogenic (e.g., aerosol or GHG emissions) causes; processes that are poorly simulated in the CMIP5 models exert a significant influence on GMST. The latter class includes: a possible strong ‘recovery’ from the recent hiatus in GMST; the possibility that models might underestimate decadal variability (but see Section; the possibility that model sensitivity to anthropogenic forcing may differ from that of the real world (see point 5); and the possibility of abrupt changes in climate (see introduction to Sections 11.3.6 and 12.5.5).


  36. PanicBusiness @682.

    To truly get a handle on what you are on about, what would you consider defines "High sensitivity AGW supporters "?

    And regarding the part of AR5 Figure 11.25 that you pasted in the thread above. Is this not what you have requested? A projection based on current climate science that you can compare to you own particular view that it is "very likely that in the coming five years there will be no significant warming or there will even be significant cooling."? Indeed if you examine Figure 11.25 you will find it is projection a global temperature rise of 0.13ºC to 0.5ºC/decade averaged over the next two decades.

    And regarding your comment that "High sensitivity AGW supporters are either extremely unlucky(in a sense that an implausible scenario happens) or wrong." What you describe as an "implausable scenario" is presently explainable by the recent run of negative ENSO conditions. The underlying global temperature rise remains ~0.2ºC/decade which does not as of today indicate any "unlucky" 'hiatus' unless it is an accelerating rise in temperature that is being projected.


    Of course, climate science is expecting such an acceleration, that being evident in AR5 Figure 11.25. However talk of acceleraton may not be very helpful for somebody still grappling with the concept of average global surface temperatures getting higher with time. Where many have difficulty when they reflect on global climate is the vast size of the system under examination. It functions on a different timescale to that we humans are used to. So it will not give definitive answers on the basis of  5 or 10 years data.

  37. All: PanicBusiness has been banned from further posting on the SkS website. The person behind the PanicBusiness screen is the same person the was behind the Elephant In The Room screen.  Sock puppetry is strictly prohibited by the SkS Comments Policy. Persons engaging in sock puppetry automatically lose their posting privileges,  

  38. Can someone give me some information concerning John C. Fyfe; & Nathan P. Gillett which  that claims global warming over the past 20 years is significantly less than that calculated from 117 simulations of the climate?

  39. I asked this question because I posted on our newspapaer the following.


    The letter writer wrote:  " Patrick Moore, co-founder of Greenpeace, the international n February, he spoke before Congress about the futility of relying on computer models to predict the future."

    I wrote, "But computer models predicted our current state of warming more than 30 years ago when winter temperatures were consistently sub zero around the nation. They predicted sea level rise. They identify trends not year to year predictions."


    This is a reply I recieved.

    "But computer models predicted our current state of warming more than 30 years ago..."

    No, they haven't. Please read:

    “Recent observed and simulated warming” by John C. Fyfe & Nathan P. Gillett published in Nature Climate Change 4, 150–151 (2014) doi:10.1038/nclimate2111 Published online 26 February 2014"

    I tried to find some disscusion here, REal Climate and Tamino and didn't have any success. 

    (Rob P) - The climate model simulations in CMIP5 do indeed show greater warming than is observed. But the simulations use projections from either 2000 onwards, or 2005 onwards, rather than actual observations. This animation below shows what happens when the models are based on what actually happened to the climate system - well our best estimate so far anyway.

    See this post: Climate Models Show Remarkable Agreement with Recent Surface Warming on Schmidt et al (2014).

  40. Re Stranger at 02:28 AM on 30 April, 2014

    Try this. Not very conclusive, but it has some preliminary hints.

  41. Thanks Alexandre but I was hoping for someting more that I could sink my teeth into. 

  42. Just a different scientific perspective on modeling from another arena...

    Semicondcutor device physics is used to model behavior of transistors that is key to driving the whole of small scale electronics that runs today's devices.  These are complicated models that are relied on by designers (like myself) to create working circuits.  If the models are wrong, the some or all of the millions of transistors and other devices on the chips don't work.  This is modeling.  The reason it works is that not only does it predict past behavior and predict future behavior, but the underlying science for why the models are accurate is understood to extreme detail.

    As a scientist, what is missing for me in climate modeling is the actual scientific understanding of how climate works.  Understanding this is a tall order, you would think that some humility would exist in the climate modeling community due to their understandable ignorance about something as complicated as the climate of a planet over long periods.
    (-snip-) Climatology is a relatively young endeavor and a worthwhile one but it has a long way to go to be considered settled and understood.


    [Dikran Marsupial] Inflammatory tone snipped, suggesting that the climate modelling community lacks humility is also sailing close to the wind.  In my experience, climatologists are only too happy to discuss the shortcomings of the models, for example, see the last paragraph of this RealClimate article.  Please read the comments policy and abide by it, as you are new to SkS I have snipped your post, rather than simply deleting it, however moderation is an onerous task, and this will not generally be the case in future.

  43. marisman @692...  You're making several common of mistakes regarding climate modeling and climate science.

    First, climate science is not young. It dates back nearly 150 years. It's a well developed field with well in excess of 100,000 published research papers, and 30,000+ active climate researchers today. The fundamental physics is very much settled science. There are uncertainties but those are generally constrained through other areas of research. For instance, we have high uncertainty on cloud responses to changes in surface temperature, but we also have paleodata that shows us how the planet has responded in the past to changes in forcing, thus we can assume the planet will behave at least fairly similarly today.

    Second, it sounds to me like you're making huge overreaching generalizations about climate modeling when you don't even have a basic understanding of how climate modeling is different from your own field. Climate models is a boundary conditions problem rather than an initial conditions problem. Climate modelers do not pretend they are creating a perfect model of how weather will progress over time, leading to climate. Rather, climate models test responses to changes in forcing and project (rather than predict) what might occur run out over many decades. They do not pretend to predict climatic changes from year to year or even within decadal scales.

    Third, you make an error in stating that modelers don't have a scientific understanding of how climate works. The link below is to Dr Gavin Schmidt discussing how models are created. Maybe instead of making such sweeping claims about a field of science where you have little understand, you can take a moment to try to start to understand what climate modelers actually do.

    Dr Gavin Schmidt, TED Talk

  44. Listening to Dr. Gavin Schmidt speak, he spends a fair amount of time talking about how complex the problem is.  I agree.  That is my point entirely.  While I don't study climatology, I do understand what you do.  I also understand computer modeling.  It matters little whether your are modeling semiconductor physics, planetary motion, human intelligence, or the Earth's climate.  Many of the same principles and limitations apply.

    I believe a better solution for the modeling problem is to cease making it an all encompassing model as Dr. Schmidt argues in favor.  He says the problem cannot be broken down to smaller scales  - "it's the whole or its nothing".  I could not disagree more.  The hard work of proper modeling is to exactly break the problem down in to small increments that can be modeled on a small scale, proven to work, and the incorporated into a larger working model.

    Scientists didn't succeed in semiconductor physics by first trying to model artificial intelligence using individual transistors.  They began by modeling one transistor very well and understanding it thoroughly.  Thus, assumptions and simplifications that were of necessity made moving forward through increasing complexity were made with a thorough understanding of the limitations.

    Let me suggest then as an outsider that you exactly do what Dr. Schmidt says can't be done.  Create a model of weather with proper boundary conditions on a small geographical scale. 

    It seems like a good place to start is a 100 km^2 slice.  That would give similar scale up factors (7-8 orders of magnitude) to the largest semiconductor devices today.  Create a basic model for weather patterns of this small square. 

    Hone that model. Make it work. Show that it does. Understand the order of effects so that you then have the opportunity to use any number of mathematical techniques to attach those models with their boundary conditions side by side with increasing complexity and growing area but necessarily greater simplification yet losing little accuracy.

    That is exactly the way that successful complex models have been built in other fields.  I think the current approach tries to short cut the process by trying to jump to the big problem of modeling over decades too soon.  If you have links to those that might be attacking this small scale modeling project, I'd like to have that resource.  It would interest me greatly.

  45. marisman - So, you would prefer a complete ab initio approach from the ground up, extrapolating from basic physics (do your semconductor models go from the level of quarks, since those affect electrical charge and mass at the molecular level?). This rapidly scales to the ridiculous. 

    At each scale level you still need to validate the behavior of that model scale against observations - meaning that when looking at global models you will still be dealing with effectively black box parameterizations at scales below what you can computationally afford. 

    Which is exactly what gets done in any computational fluid dynamics (CFD) or finite element analysis (FEA) - once you get below computational scale you use parameterizations. Techniques that have a proven track record despite sub-element black boxes. 

    Granted, GCMs don't do as well at the regional level, let alone the microscale of local weather. But as pointed out before, they are boundary value models, not initial value models, and energy bounded chaotic behavior from ENSO to detailed cloud formation mean that there will be variability around the boundary conditions. Variability that, while it cannot be exactly predicted in trajectory, is not the output goal of GCMs. Rather, they are intended to explore mean climate under forcing changes (~30 year running behavior).

    Note that coupled GCMs do use sub-models such as atmosphere, ocean, ice, land use, etc., individually validated and exchanging fluxes to form the model as a whole. 

  46.  Saying that models are mathematically representative of interactions in the climate, is far to simple a statement. As I understand, they basically numerically integrate Navier Stokes Equations (NSE). While NSE are quite complete, integrating them is no simple minded task. Grid size, time steps, and most importatnly boundary and starting condidtions have a big effect on the model's results. Also there are many constants, linear and non  linear, that may only be described in limited or aproximate way, or omitted.

    Hindcasting is of course a practicle method of checking all the assumption, but it does not guarantee results. It is entirely possible that thousands of papers can be written all using limited and poor models for constants and make bad assumptions, only refering to the work of another reseacher. For example, people numerically intgrate equations that describe reinforced concrete. They  describe cracks as a softening and ignore aggregate interlock. Engineers forget when the limits of these assumtions are reached; normally they just add more steel to be safe.

    It also been all over the news now that temperatures have not risen in the last 15 years.I realize the oceans are storing heat, and their are trade winds, and that this post started years ago, neverthess average tempratures are not rising. Also early models could not predict trade winds! What, they don`t have oceans either? We have waited now 20 years and the models are wrong. So it seems that although tested with hindcasting on data that showed increasing temperatures, the models could not predict the temperatures staying constant.


    (Rob P) - The news, in general, is hardly a reliable source of information. Some news media organizations seem little concerned with things such as facts.

    Yes, the rate of surface warming has been slower in the last 16-17 years, but it has warmed in all datasets apart from the RSS satellite data - see the SkS Trend calculator on the left-hand side of the page.

    As for hindcasts see: Climate Models Show Remarkable Agreement with Recent Surface Warming.


  47. Reduced integration techniques are a good example of how researchers can BS themselves for years, just to get papers published (which I will eplain). So the analytical solution is a series of sines and cosines. One tries to approximate the solution using a third order polynimial, obviously in small segments to reduce the error. They use three gausss points (? i dont remeber) for each axis to integrate their equations. Their results are crap. So they just remove a Gauss point and say it has ceratin advantages. Another resaercher removes another Gauss points and says it has some advantages and some problems. They create word like 'shear locking' or 'zero energy mode'--how about 'wrong solution'? This goes on for 20 years and thousands of papers. Meanwhile the pile of paper of what you have to learn gets higher and these methods are put in commercial programs. The next generation of people have fanciful intellectual musings on what a 'zero-energy' mode is, because the pile of paers is so full of crap and the fact that its the 'wrong solution' is lost.

  48. Although, to be complete, theses solutions ^, do get tested and conditioned to work reasonably well under normal circomstances, ie hindcasting. However this is the mathematically equivalent of rigging it with duct tape. Certainly for highly non linear equations, like NSE, this is not good for extrapolation.

  49. Razo, if you find the models failing, what practical conclusions do you draw from it?  In other words, so what?  Let's imagine that the sign of the alleged failure was in the other direction.  Would you still make the comment?

  50. So a question: if we had the GCM of today 30 years ago, would we be having the same conversation? how would it be different?

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