Arguments
How reliable are climate models?
What the science says...
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Basic
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Intermediate
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Models successfully reproduce temperatures since 1900 globally, by land, in the air and the ocean. |
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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.
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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 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 (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."
There's one chart often used to argue to the contrary, but it's got some serious problems, and ignores most of the data.
Last updated on 26 May 2023 by John Mason. View Archives
Winston2014@,
Non only. You are ignoring my previous comment asserting that ESC is determined by multiple lines, namely various paleo studies. Check for example here. In your reference (some "skeptic" blog) to the method of ECS estimation we read:
I stopped reading after that. If the author is acknowledging ocean heat capacity is having large impact on surface temps but then ignores OHC in his calculation of ECS, then he simply contradicts himself and undermines the validity of his calculations. And as we know, the multi-decadal ocean oscillations (ENSO, AMO) can and do influence the short-term surface temp records (such as since 1998) so that the surface temp data (just 4% of total heat content) is highly irrelevant to the total radiative balance.
It's time for you to ditch such sources and move on to more liable ones, if you want to discuss your point. Unless of course, you don't want to be taken seriously.
Modertor's Comment:
Both Razo and Winston2014 are playing a game I call "Trvia Prusuit" They posit trivial observations about climate models and expect other readers to pursue that trivia. They also gloss over or ignore the learned responses to their trivia provided to them by other readers. All thing cosnsidered, they are both engaging in a form of concern trolling. They both are on the cusp of relinquishing their repective privilege of posting on the SkS comments threads.
John Hartz:
Further to your inquiry appended to my previous comment, as far as I can see I opened the comment up immediately by addressing it to Winston2014.
[JH] My bad. My comment was meant for another commenter, not you. I will make appropriate corrections. I apologize for the mistake.
@KR Nice words, but that is all they are.
And nice article on chaos, but I already knew all that.
Where is the mathematical proof that averages can be predicted in the climate model? Words are cool and I am sure they make you feel better but they prove nothing.
Show me the proof. An engineer would have to prove his building will stand. Climate scientists wave their hands point to an article on chaos and predict all kinds of nonesense.
Again, if you want anyone to believe the climate predictions, a mathematical proof that your models can accurately predict averages should be the topic of every PhD thesis in the business.
Oh, and by the way, where is the proof?
@KR
Don't get me wrong, Im not dissing climate models. They represent our best guess at future climate. I would argue that they are -not- unbiased, but still a decent guess.
But please quit presenting your playing around with models (which is what you are doing from an engineering perspective) with hard, accurate science. You would get run out of engineering in a minute claiming that you think a building will stand because you ran a few models and everything looked good.
Rigor and experimentation are not the same thing.
@KR
And by the way, the topic of this thread is 'Are climate models accurate.' My comments apply. So please do not point me back to dated and sophmoristic articles and say I should comment there. My comments here are completely relevant.
It is exacly this type of scholastic bullying that climate gate exposed and a big part of the reason that climate science lost a huge amount of its credibility a few years back...
"Where is the mathematical proof that averages can be predicted in the climate model?"
This comment shows a lack of understanging of science. You can't prove lots of empirical truths in science, but that doesn't mean they are not true. Similar arguments could be made of much statistical physics, and they would be equally poor arguments.
See my post on the other article for an example of a system that is obviously chaotic, but where common sense ought to be enough to show that its statistical properties are not chaotic.
nickels - I have responded on the appropriate thread.
@ Dikran Marsupial
I have a PhD in mathatics. examples and common sense are very often wrong. I don't feel like having huge amounts of my tax dollars chasing other peoples 'common sense'.
And nobody else does, which is why climate science is not more important.
I challenge your science to study the Verification and Validation methods used commonly in engineering. Climate owes the public and the scientific community intense focus on this issue.
A proof even for a very simple model is unlikely. But I (am many many people, scientists and not) have a philosophical difference with your community over the comment sense argument. In math and engineering we call this 'hand waving'.
Climate models are important, but not rigorous.
Validation:
Your model is actually correctly solving the equations it claims. (I have worked with the CESM and there has never been a focus on this. Honestly, the fortran codes are so huge and spagetti'fied that this is a serious issue. Numerical error in integration, adaptive integration, aposteriori error analysis would be apropo. At Sandia there was intense focus on this for the engineering codes).
Verification:
Does the code and the model actually model the physics. This is intensely difficult for climate since there are a myriad of physical processes that are parameterised in these codes. No first principles. It is not clear whether these parameterizations are relevant in future states.
Its a hard problem. Climate science owes the world some major V&V investment if they want the answer to this forum's topic to be 'quite reliable'.
@KR
And by the way, I am making honest, serious arguments about V&V.
You are engaging in non-arguments and strict scholastic bullying. Just for the record, you will convince no one with that approach, only distance them further from engaging in your cause. Good luck. I will not respond to you after this.
nickels, you have reversed the definitions of verification and validation. That is suprising given your self-claimed expertise.
[JH] Nickels is already skating on the thin ice of sloganeering and his haughty attitude is duly noted. His future posts will be closely monitored for compliance with the SkS Comments Policy.
nickels, climate models have been validated (using the proper definition of validation, after reversing your definitions) empirically, as is explained thoroughly in the original post at the top of this comment thread. (Be sure to read Intermediate tabbed pane, too, and the cited peer-reviewed publications.) Your challenge to the "science" (sic) "to study the Verification and Validation methods used commonly in engineering" is odd, because climate modelers in fact do use V&V methods commonly used in engineering. In contrast, you seem to believe erroneously that V&V in engineering relies heavily or even exclusively on mathematical proof. Your statement "You would get run out of engineering in a minute claiming that you think a building will stand because you ran a few models and everything looked good" is correct, but your implication that bridge designers instead use only mathematical proof to convince themselves that it will stand, is wildly wrong.
My job largely is V&V of spacecraft software and some hardware and certainly their interaction, of both software used on the ground to monitor and control spacecraft, and software that runs on the spacecraft itself. Mathematical proof is only a tiny portion of that V&V.
A good place to start learning about V&V of climate models is at Steve Easterbrook's blog Serendipity. He has a good recent video of a TED talk (you should read the text surrounding that video on his blog), a short but good description of V&V, a short description of massive and thorough comparisons of the outputs of 24 climate models, an explanation of why some formal methods cannot be applied to climate models but Agile-like methods can, and especially relevant for you is his post Do Climate Models Need Independent Verification and Validation? You would benefit from reading other posts of his that you can find by using his blog's Search field to look for "verification" or "validation."
Also useful for you to read is Tamsin Edwards's series of four short blog posts the links to which are near the top of her post Possible Futures.
nickels wrote "I don't feel like having huge amounts of my tax dollars chasing other peoples 'common sense'."
sorry, whether science is correct is not dependent on your views of taxation (the causal relation should lie in the other direction).
"I have a PhD in mathatics."and "I challenge your science to study the Verification and Validation methods used commonly in engineering."
I have a PhD in engineering; the methods used in climate models are used in computational fluid dynamics in a wide variety of engineering industries, for example aviation, motor racing, ship design. All without mathematical proof of the nature that you are asking for.
I spent my career building models of financial markets. The notion that a model is 'good' if it correctly predicts unseen data from the historical record is laughable (i.e. the model is tested on a rolling window of data to see if it accurately predicts the subsequent unseen period).
There are two problems, one well understood and one almost universally ignored. The first is that as new explanatory variables are added to the model to improve the forecast accuracy, the unreliability of the model increases. This can be calculated - and almost always means that in complex systems, simple models outperform as predictors even though they are less accurate when back tested. Any discussion of the models that does not discuss this trade off is nonsense. In markets this means that the 'best' models are only slightly better than random, but are reliably better - the key then is risk management. I believe that the same should apply to a complex system like climate. The uncertainty in a 'good' model will make it useless for predicting the future and only useful for risk management.
The less common problem ignored by scientists in many many disciplines, is that knowing what models do not work is a hidden 'look ahead' that is the bane of quant reseachers in financial markets. For example, when building a model of the stock market, it is very very difficult to forget that it crashed in 1987. This knowledge influences the choices that model builders make - they just cannot help themselves. That is why so few people make money in systemaic trading - it is not just a scientific, mathematical, statistical and computational challenge - it is philosophically and psychologically challenging. In markets it doesn't really matter - long live the deluded models with their artificial certainty! They represent profit opportunity for other participants. In building climate models we do not have this comfort.
For the record, I believe that the world is warming and that this will have consequences. I also believe that the models are laughably wrong and that there only reliable attribute is that they will continue to fail to predict the outcome at any useful level of accuracy once unleashed on truly unknown data (otherwise known as the future).
The sooner the debate moves on to how we manage the risk of a warming planet, the better.
Oh, by the way, it is also obvious that we cannot stop it warming by flying less or driving a Prius. This is is not just an economic observation (though economics alone mean it will not happen) but also an obvious consequence of the prisoner's dilemma. Why should I stop flying if the Chinese are building a new coal fired power station every week? I repeat risk management - if it warms by more than X, what could/should we do? That is where the money and time should be spent.
[JH] You assert:
Please document the sources of your expressed "beliefs."
nearlyman, just out of curiosity, and to get a baseline understanding, what counts--for you--as "laughably wrong" where climate modeling is concerned?
[JH] I addressed nearlyman's assertion in a Moderator's comment to his post.
nearlyman, there is a *big* difference between models used for financial prediction and climate models, which is that climate models are based on physics, rather than being statistical models that have been fit to the data. With statistical models, the more parameters you have in the model, the (exponentially) more data you need to estimate their parameters correctly (the "curse of dimensionality"). This is not the case with physics based models, where most of the parameters of the models are constrained by physics (i.e. we can perform separate experiments to characterise what different components of the model do).
However, if you really do believe the models are "laughably wrong", that suggests to me that perhaps you have been getting your information on the performance of models from the blogsphere, rather than from the journal papers (or even blog articles written by those who have read and understood the journal papers). If you would like to give a specific example of a model projection that is "laughably wrong" (as JH suggests), I am sure that there will be plenty of people here willing to discuss it with you. If you are unwilling to provide specifics, I suspect your posts will be viewed as trolling; this is intended as well meant advice.
It's interesting. I see people who do other forms of modeling coming from two different sides to diss climate modeling. One side comes from financial modeling where the modeling is purely statistical. The other side is from engineering modelers, who say that the physics can't be sufficiently constrained to return reliable data.
These are two completely contradictory positions, with both sides claiming to have a deep understanding of modeling.
All modeling is wrong. That's just a fact. The point of modeling is that it is instructive. It teaches you things that you otherwise could not understand in the absence of the models.
I have responded to nearlyman's complaint of "its too hard" in a more suitable thread.
There is a further fundamental difference between financial models and models of physical systems.
Financial models are far more tightly coupled to the system they model. Indeed, the financial model is itself part of the system it models. That is, a model of some form (and probably many models of many forms) will have been developed and adopted as a guide to decision-making by those involved in the financial trading that is being modelled. Likewise any learning from the modelling about the workings of the financial system will also feed back into the workings of the system. Such coupling between model and system is probably seen as a problem by the financial modellers.
There is potential feedback from climate models into the climate system but here it is the difficulty in achieving that feedback which is seen as a problem (eg CO2 emissions have bad consequences => stop CO2 emissions).
Responding on this appropriate thread, to Donny's comment on an inappropriate thread:
Spencer followed up his claim that you linked, with another claim this time about "90 models" but likewise severely flawed. Hotwhopper clearly explained Spencer's biggest...um, "mistake"...of playing loose and fast with baselines. There is also the issue of Spencer falsely giving the impression that the RSS and UAH satellite trends are consistent.
On another thread, Donny asks:
"Let me ask one more question of the accurate models. ... when will the surface temperatures begin to significantly rise again? What do they predict? Also there are so many of them. ... which one should we believe? "
Since no one else has, I will attempt a response.
The question implies a considerable misunderstanding of GCMs and their output. Let's start with some basics. Firstly, models are evaluated in terms of their skill. A skillful model gives more information than a naive heuristic. (eg climate will be stay the same). For all the faults of models, (and modellers can quickly point to their deficiencies), they remain the best tools we have predicting future climate. Even the incredibly simple Manabe model from 1975 managed to nail 2010 temperatures pretty well. Secondly, GCMs for all their usefulness are not the basis for AGW and nor are the only way to estimate climate sensitivity to an increase in CO2. That can be done "bottom up" from pure physical consideration of feedbacks, or from empirical means. Whatever way, you end up with climate sensitivity likely in the range from 2-4.5.
In terms of Donny's question, the next thing to understand is that models have no skill at decadal level prediction of surface temperature (and many other associated parameters). Over short times intervals, the surface temperature variability is dominated by ENSO. This is a chaotic ocean-atmosphere phenomena which is extremely difficult to predict even a few months out. In the El Nino phase, the atmosphere (and thus the surface temperature) gets a huge boost from heat stored in the ocean. Over last 15 years, La Nina or neutral conditions have predominated however. Climate however is about 30-year averages and the effects cancel out. Climate models are skillful estimating future 30-year average.
So what do they predict? Well over a 30 year period, they predict the climate will be close to the ensemble mean. They predict that actual temperatures will follow a trace as variable as one of the grey lines on the graph at the bottom of the article. They do not predict a exact path. Rerun the same model with slightly different initialization and you get a different grey line. Do many runs on many models and you get that nest of grey lines which make up the model mean. I am not aware that there is evidence that would suggest that any one of the 10 or so modelling groups is significantly more skillful than the others. The ensemble mean is the average of them all.
When will you get significant more warming? When the next El Nino cycle happens. If the climate response is more muted than expected, then that will cause some examination of the models. The strength of the aerosol forcing remains an uncertainty as do precise strength of cloud feedbacks.
What is much easier to predict than surface temperature is total ocean heat content. However, we have only had detailed, accurate measurements since 2004. While OHC continues to climb (unlike the decline in mid-20th C or after Mt Punatoba), then it can be expected that surface temperatures will also rapidly climb in an El Nino.
aren't there concerns with climate models as well? what are they?
You tell us, it's your contention.
Shadow Dragon,
It is good that you brought this up. Many people thing that the models do not accurately include many positive feedbacks like arctic carbon or sea bed methane. This means they systematicly underestimate the expected warming and things are worse than we think.
One only has to look at sea level rise, one of the worst long term problems of AGW. Sea level rise runs at the very top of the model results. It is clear that future sea level rise is much more likely to run over the IPCCC projections than under them.
Thank you for pointing out that the models are often too conservative.