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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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Climate Hustle

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.

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 31 December 2016 by pattimer. View Archives

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Further reading

Update

On 21 January 2012, 'the skeptic argument' was revised to correct for some small formatting errors.

Comments

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Comments 801 to 850 out of 1028:

  1. I'm still not sure what "increasing importance of precipitation data" means in the context of general circulation modeling.  Is it not important enough right now?  Why should it become more important?  

  2. Well, to the extent that certain specific models can be improved I think you'll see a decrease in public misunderstanding and "denialism", and an increased acceptance of the science.

  3. protagorias @802:

    First, your claim is false.  There has been a remarkable and continuous improvement of the models over time.  Going back to Hansen 88, models did not even include aerosols.  By the TAR, they universally did, but did not show ENSO like fluctuations.  By the AR4, some did, but others did not.  Now, with AR5 they all do.  None of this has had any noticable effect on public non-acceptance of climate science - which is not driven by the science, but by political ideology.

    Second, no scientist anywhere should alter how they do science to "increase acceptance of the science".  Doing so is just modifying science to suit political ends by a different name.  Indeed, that is exactly what you are after, a fact given a way by your desire "that certain specific models" be "improved", rather than that all of them be improved by standard methods.  By "improved" you mean no more than adjusted to give the results that you want.

  4. Tom Curtis,


    So what is the purpose of the Coupled Model Intercomparison Project as mentioned in #800, if all the models are homogenized as you appear to suggest? According to CIMP5, one of the goals is "determining why similarly forced models produce a range of responses". Clearly, not all models are equally valid in their predictions. I'm not suggesting that they don't all improve over time, but merely that certain improvements may more accurately reflect appropriate environmental factors. For example, heat in the oceans may be an appropriate factor.

    Furthermore, what is the purpose of discussing consensus itself as a specific factor if not to encourage acceptance of the facts, if non-acceptance (which I agree is motivated by ideology) - leads to unhelpful inaction on the part of the public?

  5. protagorias

    "Well, to the extent that certain specific models can be improved I think you'll see a decrease in public misunderstanding and "denialism", and an increased acceptance of the science."

    With respect I think that is utterly naive. Most misunderstanding and denialism has diddly-squat to do with the minutiae of the accuracy of specific models. 99.999% of the earths population have no idea of how 'the models' differ.

  6. Glenn Tamblyn

    You may be right.

    Be that as it may, I will continue to maintain that increased accuracy in measurement is one of the primary drivers of model improvement.

    Response:

    [PS] Then maybe you would like to provide evidence to support that assertion.

  7. Only one quick example

    International Journal of Climatology Vol 25, Issue 15 High Resolution Climate Surfaces for Global Land Areas


     For many applications, data at a fine spatial resolution are necessary to capture environmental variability that can be partly lost at lower resolutions, particularly in mountainous or other areas with steep climate gradients. However, such high-resolution data are only available for certain parts of the world...


     

    Response:

    [PS] Except that it is not. What is discussed is not input into any GCM.

  8. Yet that is precisely what is relevant. Why else would climate modellers be asking for better software from which to better model climate change, if they didn't have, or at least recognize, the need for better input?


    Please note this recent article in the Insurance Journal. Climate Change Modelling on Cusp of Paradigm Shift

     

    Silicon Valley-based modeler Risk Management Solutions last year partnered on the Risky Business initiative, a year-long effort co-chaired by former New York Mayor Michael Bloomberg, former Treasury Secretary Henry Paulson, and Farallon Capital founder Tom Steyer, to quantify and publicize the economic risks the U.S. faces from the impacts of a changing climate.

    For the initiative RMS provided an analysis of the impacts that climate change will likely have on coastal infrastructure and related assets. Risky Business issued a report late last year focused on the clearest and most economically significant risks: “Damage to coastal property and infrastructure from rising sea levels and increased storm surge, climate-driven changes in agricultural production and energy demand, and the impact of higher temperatures on labor productivity and public health.”

    Paul Wilson, vice president of model development for RMS and leader of the firm’s North Atlantic hurricane modeling team, said clients are often asking the same question: “How much variability can we expect?”

    “That’s a conversation RMS has very regularly with our clients,” Wilson said. “We need to think about to what degree climate change is impacting that variability, to what degree is climate change impacting that baseline around which we build our models.”

    In response to these conversations, RMS will be incorporating more variability into more models in future, although it’s the overall concern over variability, and not necessarily climate change, that may be driving some of that interest, he added.

  9. Protagorias, I suggest you actually read the articles you link to.  That insurance one is about insurance models.  It has nothing to do with GCMs.

  10. DSL,

    You're right. I hadn't even noticed that! I tend to make wild leaps sometimes, i apologize for that.

  11. protagorias @various, of course climate modellers want to make their models more accurate.  The problem is that you and they have different conceptions as to what is involved.  There are several key issues on this.

    First, short term variations in climate are chaotic.  This is best illustrated by the essentially random pattern of ENSO fluctuations - one that means that though a climate model may model such fluctuations, the probability that it models the timeing and strengths of particular El Ninos and La Ninas is minimal.  Consequently, accuracy in a climate model does not mean exactly mimicing the year to year variation in temperature.  Strictly it means that the statistics of multiple runs of the model match the statistics of multiple runs of the Earth climate system.  Unfortunately, the universe has not been generous enough to give us multiple runs of the Earth climate system.  We have to settle with just one, which may be statisticaly unusual relative to a hypothetical multi-system mean.  That means in turn that altering a model to better fit a trend, particularly a short term trend may in fact make it less accurate.  The problem is accentuated in that for most models we only have a very few runs (and for none do we have sufficient runs to properly quantify ensemble means for that model).  Therefore the model run you are altering may also be statistically unusual.  Indeed, raw statistics suggests that the Earth's realized climate history must be statistically unusual in some way relative to a hypothetical system ensemble mean (but hopefully not too much), and the same for any realized run for a given model relative to its hypothetical model mean.

    Given this situation, they way you make models better is to compare the Earth's realized climate history to the multi-model ensemble mean; but assume only that that realized history is close to statistically normal.  You do not sweat small differences, because small differences are as likely to be statistical aberrations as model errors.  Instead you progressively improve the match between model physics and real world physics; and map which features of models lead to which differences with reality so as you get more data you get a better idea of what needs changing.

    Ideally we would have research programs in which this was done independently for each model.  That, however, would require research budgets sufficient to allow each model to be run multiple times (around 100) per year, ie, it would require a ten fold increase in funding (or thereabouts).  It would also require persuading the modellers that their best gain in accuracy would be in getting better model statistics rather than using that extra computer power to get better resolution.  At the moment they think otherwise, and they are far better informed on the topic than you or I, so I would not try to dissuade them.  As computer time rises with the fourth power of resolution, however, eventually the greater gain will be found with better ensemble statistics.

    Finally, in this I have glossed over the other big problem climate modellers face - the climate is very complex.  Most criticisms of models focus entirely on temperature, often just GMST.  However, an alteration that improves predictions of temperature may make predictions of precipitation, or windspeeds, or any of a large number of other variables, worse.  It then becomes unclear what is, or is not an improvement.  The solution is the same as the solution for the chaotic nature of wheather.  However, these too factors combined mean that one sure way to end the progressive improvement of climate models is to start chasing a close match to GMST trends in the interests of "accuracy".  Such improvements will happen as a result of the current program, and are desirable - but chasing it directly means either tracking spurious short term trends, or introducing fudges that will worsten performance in other areas.

  12. Tom Curtis,

    That's a lot to think about. We'll see what happens with improved computers, better instruments and measurements.

  13. Responding to ryland's complaint (from an inappropriate thread) that models are wrong due to an error in computing insolation:  Richard Telford in his blog Musings on Quantitative Paleoecology explained that it is a non-issue for global insolation (instead it is only a local issue), a non-issue for some models, a trivial issue for other models, and a small issue for a few models.  In other words, what scaddenp and Kevin C already explained to ryland--but Richard has added a couple graphs.

  14. ask yourself what is the purpose of climate models. As I see it the purpose is to predict the future of climate. However, the future of climate can not be predicted because it is a non linear chaotic system. The idea of increased co2 in the atmosphere causing increased temperature is a linear concept. Increasing the temperature causing sea level rise is a linear concept. In fact increasing temperate may set off a series of events that leads you into an ice age. Can the models predict these occurances, no they can not. in fact the use of models already have proven to be ineffective for prediction of global temperatures. they are unable to deal with chaotic events such as el ninos, volcanic eruptions, etc.

    in fact we don't really know what the future will bring and therefore it is easier to adapt to the change than to prepare for and event that is not likely to occur,

    Response:

    [TD] See the response to the myth "Climate is Chaotic and Cannot Be Predicted."  After you read the Basic tabbed pane there, read the Intermediate tabbed pane.  If you have questions or comments about chaos, please make them on that thread, not this one.  Everybody who wants to respond to that particular aspect of Rhwool's comment please do so over there, not here.  Other aspects of Rhwool's comment are legitimately responded to on this thread.

    [TD] Rhwool, please read the original post at the top of this thread, which presents empirical evidence of the success of climate models.  After you read the Basic tabbed pane, read the Intermediate one.  Please restate your claims that climate models cannot predict, in specific, concrete ways that confront that empirical evidence.  Vague, evidence-free, and especially evidence-contradictory, claims have no place in a scientific discussion and therefore no place on this Skeptical Science site.

  15. I would have say say that I am quite an expert when it comes to modeling real world phenomema. I used computers to model hydrology and finite element analysis regular at work.  I've been doing this for 40 years. The two applications represent  linear mathematics. The most important aspect of modeling is confidence in the model. Confidence is gained by checking the results against the real world applications. I get the luxury of testing this as soon as a month but normally with 6 months. 

    I know now the mathematics for the finite element analysis is rock solid. It's based on Hardy Cross's Method of Virtual Work. Basically just one equation. Hydrology is based on many equations. If someone else analyses the model it's normal to get results within 15 percent. This has been tested 1000's of times. Getting these results gives you high confidence.

     Different results are due to initial conditions of the model, calculation interpretation, etc. 

    but alas it's not so simple. As model complexity increases the confidence level goes down. You get unexpected results. Small changes in the model  seem to produce large changes in the results. It starts to behave non linear.

    The he modeller has a preconceived notion as what to expect from the result. When the results are not what you think they should be you will test this by altering the conditions. This is performed in all modeling. When they ran the climate models to test against the piñatuba volcano I guarantee you they massaged the model quite a few iterations to achieve this result. In reality this give you a better understanding of how the model works. 

    One argument you hear is Gigo. It's my belief this is not accurate And a bad argument. Believe me...the modeller spends a large amont of time to get all the parameters as precise as he can to ensure the best result. 

    This is my believe that the climate models can not be trusted

    1 the results of all the models indicate a wide spread. The ipcc show predicted temperature errors is in the +/- 75 percent range.  This would yield a low confidence. If I got results in my work for that spread I would trash the result and use another method.  In fact it would be nearly impossible to design anything based on that result.

    2. The model has not been tested against enough real world events to judge the reliability of the model. It takes 1000s of tests to ensure model reliability.  

    3. The non linearity signficantly complicates the model performance. 

     4 model complexity increases errors through unexpected results. 

  16. Rhoowl...  Have you actually tried engaging with researchers who are actively working on climate models? 

  17. Hydrology is basically a micro climate Model....you go through very similar steps to model the system....you have to use storm data to calculate rainfall. Break them into isoheytals. Quantify drainage areas and land parameters. Calculate stage storage discharge relationships. Understand how the fluid mechanics affects your models. 

    In in reality the steps you go through in the analysis isnt any different than doing finite element analysis.  Even though hydrology isnt anything like finite element analysis in theory....

    ive have have worked mostly with other engineers..... I have a degree in civil engineering  environmental emphasis. 

  18. Rhowl - I think you should read up on actually how climate models work and particularly make sure you understand the difference between a weather forecast models and climate models.

    "When they ran the climate models to test against the piñatuba volcano I guarantee you they massaged the model quite a few iterations to achieve this result."

    I am lost to understand how you can conclude that. When Pinatuba erupted, the model prediction was made at the time (published as Hansen et al 1992). The evaluation of model prediction was done with Hansen et al 1996 and Soden 2002. I also notice that the incredibly primitive Manabe model used by Broecker 1975 is doing pretty well.

    I am not quite sure what you understand what the predictions of a climate model to mean. As the modellers would happily tell you, models have no skill at sub-decadal or even decadal prediction of surface temperature. That is basically weather not climate. In the short term, large scale, unpredictable internal variability like ENSO dominate. They do have skill at climate prediction - ie 30 year trends. That said, climate sensitivity is difficult to pin down. It is most likely in the range 2-3.5. We would desparately like to be pinned down better than that but perhaps you should look at the recent Ringberg workshop presentations to understand why this is so difficult. Nonetheless, the 2-3.5 is certainly good enough to drive policy. Whatever the shortcomings of climate models, their skill is far better than reading chicken entrails etc.

  19. Rhoowl @815, you claim that with respect to temperature, the AR5 models show an error spread of plus or minus 75%.  That is completely false.  The models is in AR5 show a range of predicted absolute global mean surface temperature (1961-1990) from 285.7 to 288.4 K, with a mean of 286.9 K and a standard deviation of 0.6 K.  The observed values are given as 287.1 K, for an error range (minimum to maximum) of -0.49 to +0.45%.  You think there is a larger percentage error range, but that is only because values are stated as anomalies of the 1961-1990 mean, ie, they eliminate most of the denominator for convenience.  That is approriate for their studies, but if you are going to run the argument that the models are so inaccurate as to be useless, you better compare the models actual ability to reproduce the Earth's climate, not merely the exact measure of its reproduction of minor divergences in that climate.

  20. http://www.ipcc.ch/publications_and_data/ar4/wg1/en/spmsspm-projections-of.html

    this give a predicted future temperature...and plus or minus therefrom...errors are in the range +/- 100% for constant to somewhat less as you move down the chart....so 75% is a reasonable figure

    these estimates are based on their models

  21. scaddenp....i don't know where you see in my posts where i am comparing weather forecast models to climate models....although those two models are very similiar...

     as far as pinatubo...

    http://earthobservatory.nasa.gov/Features/Volcano/

    this explains how they used this eruption to model aerosols and test it against real world effects..it also went on to explain they ran several simulations..this is actually critical in determining the accuracy of the model...without real world test the models mean nothing..but you need many tests to ensure your model is properly working. trouble is the events that they can test are few and far between...it will take a very long time before they can refine the models to get accurate results..

    not sure what your other comments are about...never mentioned any of those either.

    Response:

    [JH] Link activated.

  22. Rhoowl @820, so you are going to stick dogmatically to the belief that the possible range of Earth temperatures is restricted to 287 K plus or minus a couple of degrees not matter how conditions at the surface, or astronomically vary?  Because the only way a comparison for accuracy matters if you are determining whether the models are any good is by comparing their predictions relative to the possible range.  They are skillful if they narrow that range, and not otherwise.  Given that the range of possible plantetary surface temperatures is known from observation to be from around 2 to around 600 K, that shows a remarkable level of dogmatism on your part. 

  23. Rhoowl - you are comparing your hydrology models to climate models. Understanding the differences between weather and climate (initial value versus boundary value) would give you some insight into the difference. While using the pinatuba data to improve aerosols is certainly a way to test and improve models, I am noting that modellers published an essentially correct prediction of what would happen with pinatuba in advance.

    The other comments were explaining what are the known issue with limits on temperature prediction (the problem of climate sensitivitiy) which explains some of the spread in model prediction. You claim models cant be trusted but I am trying to point out that

    a/ they can be trusted to predict various climate variables within useful limits. You can get your "1000s of tests" by looking at model versus observation on a whooping range of climate variables over various time intervals. AR4 has lengthy chapter on model validation.

    b/ they are the best tool we have estimate future climate change. You dont need a model to tell you that if you add extra radiation to a surface is going to warm it up but you do need one to tell you by how much.

  24. Rhoowl

    "weather forecast models to climate models....although those two models are very similiar...".

    That is the nub of it. They aren't.

    Here is a simple analogy.
    I have a swimming pool in my backyard. Summer is approaching and the water level is low. So I throw the garden hose in and turn on the tap. Big pool, small hose - it will take quite a while to fill. While it is filing, my family are using the pool, getting in and out, adjusting the water level due to the displacement of their bodiea. Lots of splashing, waves, the dog jumping in after a frisbee.

    I could build two models. One model attempts to predict the detailed water level across the pool, all those waves and stuff. Pretty complex and it can only be done for short timescales. The other model attempts to predict the slower variation of the average height of the water. Much simpler; pool, hose, tap, flow rate, that's about it. Can't predict short term small scale variations but pretty good at predicting long term changes in averages.

    The first model is an initial value problem. It takes the current state of the surface of the pool, in all its messy complexity, and attempts to project it forward for seconds, minutes at best. Because over that timescale th change in total volume of water in the pool is a minor component.

    The second model is a boundary value problem. It is looking at those factors that determine the boundaries within which the smaller scale phenomena play out. Essentially in this case, how much water is in the pool.

    Although the two models are based on similar basic principals, the goal and methods of the models are very different. At its simplest, weather models are attempting to model the detailed distribution of energy within the climate system to determine local effects, but essentially assuming that the total pool of energy within the entire system is largely constant. Esentially modelling intra-system energy flows.

    Climate models are firstly modelling how the total pool of energy for the entire system changes in size over time. Then secondly they attempt broad estimations of general intra-system distributions of energy. But they can't attempt detailed estimations of intra-system distributions, only broad characteristics.

    In a simple sense, weather models model the waves, climate models model the water volume. Weather models ignore the change in water volume, climate models ignore the details of each individual wave.

  25. Rhoowl

    "Hydrology is basically a micro climate Model....you go through very similar steps to model the system....you have to use storm data to calculate rainfall"

    Nope. It's a micro weather model!

    Your hydro model is trying to model how a system responds to a set of external inputs. The best analogy with climate models would be if you were trying to predict what the storm data will be. Modelling the micro detail of behaviour from given inputs is different from modelling what the inputs will be AND broad general behaviour in response to that.

  26. Tom Curtis......please run a calculation based on the table for the error in the model and present it here

     

    Glenn tamblyn similar is a relative term.....ie those two are a lot more similar than say a models that attempts to predict the behavior of atoms...please direct arguments to the 4 items listed in the original posts. Disprove the statements directly

     

    hydrology has nothing to do with weather. It's purpose is to calculate quantities of water and the ability of a system to convey the water

     

    i was watching a video of how the climate models work By a scientist who uses them. He described the math of the model and basic parameters that are used for the model. He discussed how he arrived at values for the Parameters. His method was no different than anything I do to arrive at parameters for the models I operate.

  27. Rhoowl, instead of watching some video, try reading the detail. What you are doing is projecting what you know from hydrology model into a supposed knowledge of how climate models work from simplistic information. It seems to me that what your hydrology models have in common with weather models, is that they are both initial value problems. Climate is not. That is the point both I and Glenn are trying to make. Your 4 points are based around a misunderstanding of the models essentially. The IPCC chapters on modelling are a far better starting point then some video. We are doing our best to point you to useful information which is rather more than can stuffed into a comment reply. Now if you are going to stand by your original suppositions rather than learn new information, I dont think there is much point in continuing the discussion.

  28. Rhoowl, you might also like to consider why there isnt a weather forecast that is going to predict the temperature on say June 25th 2015, let alone Dec 22nd 2015. However, several methods will give you quite accurate predictions for the June average monthly temperature and the December average monthy temperature. Ie. no amount of chaos in weather systems is going to change summer into winter. Summer is different from winter because the energy balance in temperate regions is so different. Adding CO2 is same effect on a global scale.

  29. Scaddenp

    please site some data backing your claim.....the program I use are not predicting anything that can not be predicted..... 

    i will ill give you a real world example of the climate Model 

    climate models lead to a  prediction that sea level will rise from 10 inches to Ive heard some estimates to 20 ft. This is based on the temperature increase. That's a huge error....l 

    i have to design a sea wall around NYC to prevent losS of the city. Which value to you choose. The 10 inch sea wall will cost about 100million.

    20 ft will cost 100 billion....probably not too far off with those figures Loss of the city... 2 trillion.  

    We we know sea level was about 250 ft higher than now in the past. So the 20 ft is not unreasonable.  

    Which model do do you choose

    Response:

    [JH] A range of estimates for differing scenarios do not equate to "error".

  30. Rhoowl, please cite the actual sea level rise estimates you are referring to. Are they for the same timeframes? Do they assume the same future emissions paths?

    Your claim that there is "huge error" in the modeled range of sea level rise is a provable position... all you need to do is cite the actual source of the estimates ("from 10 inches ... to 20 ft."). You're right... that'd be a huge uncertainty range for a single set of assumptions. So go ahead, cite the source and prove your point.

  31. Rhoowl... I don't want to dogpile here, but I'd like to renew my first question.

    If this is a related area of expertise for you, why would you not attempt to engage with people who are actively working with climate models in order to better understand what they're doing? Why are you engaging on SkS instead of talking with someone who builds climate models?

    The best I can tell from your comments here, you have a misguided expectation of what climate models are intended to accomplish. But instead of attempting to better understand the matter you're tossing out the entire field of research, deeming it an impossible task.

    Not fully understanding something, even for an expert, is no big deal. Most of us are experts in something, and we are all ignorant of the things which we haven't yet learned. But it's not acceptable to be ignorant of something and refuse to learn what other experts already understand. If you actually have the expertise you claim, there is absolutely no excuse for not contacting a professional climate modeler and asking questions.

    [I emphasis "if" here because I have a very hard time fathoming why someone with 40 years experience would not first do exactly what I'm suggesting long before posting on this website.]

  32. Rhoowl,

    Real Climate is a blog written by climate scientists.  The main organizer is Gavin Schmidt who is a climate modeler.  They have a lot of background information and old posts that explain all your questions.  Perhaps if you read some posts there you would begin to understand how climate models work.

  33. Moderation Request

    Please do not respond to future posts by Rhoowl until a Moderator has had a chance to review them. He/she is skating on the thin ice of posting nonsense.  

  34. "please site some data backing your claim". Certainly, but which claim? That monthly temperature for a station can be estimated accurately? Or that climate model is boundary value model not initial value model. Or that difference between summer and winter is due to differing energy input? Or which claim? As to sealevel - well in your model you should be able to use it to give response to given storm event. Ie the rainfall for a storm event is an input. I would expect that you could use it to give a range of response to different sized storm events. The sealevel estimates you are looking at are just that - range of estimates for different emission scenarios. Each scenario has its own error estimate but dont confuse the range for different scenarios with error. Climate models certainly do not predict what humans will emit in the future.

  35. JH yes the range of estimates are different scenarios....each scenario had an error. The error equates to a diferrent level of sea level rise. 

    RH someday an engineer is going to ba asked to design these systems based on the models. when that occurs they they will ask some of the questions I am posing here. I'm not sure that an climate would want to speak me to about these questions. I would respect that their time is valuable. 

    CBD iposted the graph one of the more extreme scenarios showed 4c warming with 2.4 to 6.4 I believe error. With the way China and India are building coal plants is this implausible scenario? The one post I had which is no longer visible shoe Sea level rise with the worst case bing 11.5m....this probably corresponds to the 6.4 scenario. The 2.3 scenariio should be in the 5m range based on the 2m/degree C value given. That's a pretty big error 

    Scaddenp I never made any claims about inputs except that I tbought the modellers were entering the best figures. Although the scenarios listed deal with the inputs.....but that's another matter which complicates the design process. 

  36. I'm typing this on an Ipad please forgive some of the English it's the spell checker messing some of it up

  37. Rhool... Modelers' time is most certainly valuable, but I think you'd find that many of them would consider explaining their work to be a valuable use of their time. It just requires an open mind and a willingness to learn.

  38. I need to explain something about design. Engineers use an acceptable risk of failure about 1 in 10,000. That's a high standard. Failures can be catastrophic. We absolutely need to know the design parameters within 20 percent. We have safety factors. they range from 1.5 to 4. Something like a sea wall would have overturning sf of 1.5. there would also be free board distance from top of wall to water. Not more than two feet. If the wall gets breached there's a high risk of failure. Water washes out the toe resulting in loss of stability. This is why getting the parameters correct is so important.  So having a range if sea level rise would not be usefull. You need to know what it is.

    Response:

    [JH] Is English your first language?

  39. Rhoowl, you are incorrect that "engineers" in the sense of all engineers, use an acceptable risk of failure of "about 1 in 10,000."  Perhaps that is true in the very narrow particular engineering field in which you have spent your career, but that absolutely is not a universal rule; it's not even a general guideline.  Risk tolerance depends entirely on the particular situation.  In my field of spacecraft design, for example, the risk tolerance differs from one spacecraft to another, from one type of risk to another, on the timeframe and other circumstances, and always depends on costs (money, time, labor) for lowering the risk and for dealing with consequences if the bad thing happens.  For example, a nanosatellite usually needs to be cheap and fast to develop and launch, so usually the risk of total failure is higher than for big spacecraft, because the funders are not williing to spend enough resources to lower the risk further.  For any spacecraft, tolerance for spacecraft failure is lower before the primary mission is accomplished, and higher after that.  Much lower risk is tolerated for spacecraft that put human health and life at risk than for mere property risk.

    When engineers chose how high to make a seawall, you are correct that they must design to a particular, target, level of sea level.  But that particular level is dictated to them by people who take into account the full range of all I've written about, including the range of probabilities of various levels of sea level rise.

  40. " So having a range if sea level rise would not be usefull. You need to know what it is."

    There are needs and then there are wants...

    Let's compare and contrast this with wind loading: uncertainty abounds and engineers deal with it all the time unlike you suggest!

  41. BOzza wind is a part of the design....Theier a very specific design standards in that regards.....Including standard design to wind tunnel testing. This area has been heavily researched. But again....the only time I hear about failures are due to extreme force events..ie hurricanes, tornados etc. the wind code didn't get very stringent until after hurricane Andrew. Buildings built before then didn't all have the necessary shear elements and hold downs. The design process evolved over centuries of construction. Like climate modeling is still in its infancy. Over time you will see the models refine to produce more accurate results. 

    TD what type of standards do you design to and what type of safety factors

  42. Rhoowl: The Dutch, who know something about protecting their land from the sea, do not seem to be waiting for futher refinement of Global Climate Models to take action. For example, see the City of Rotterdam's  Climate Proof: Adaptation Programme adopted in 2009. 

  43. If you are in a low lying city and wondering about building sea walls, then you have two level of uncertainty. One is range of error in a model scenario but far harder is guessing what humans will do about reducing emissions. If you are forced to make the assumption that no political will to tackle the problem exists, then you must use the range of sea level rise values for the upper RCPs. ie 0.81-1.65 in latest papers. A cautious engineer would be going for the larger number at least because sealevel rise doesnt magically stop in 2100.

    Unfortunately. sealevel rise has yet another level of uncertainty. The GCMs tell you climate (with a range of uncertainty), but another set of models have to come into play to convert that climate into a rate of ice sheet collapse. This is not a well understood problem and is the major source of the wide range in the numbers.

    In my opinion, uncertainty is not your friend. The prudent response is to reduce emissions as fast as it can possibly be done.

  44. Rhoowl, climate modeling is fairly tightly bounded over climate-scale periods. There is no physical reason--barring extremely unusual heavy, persistent volcanic activity or a massive drop in insolation--why the long-term trend should not continue as it has done for the last fifty years and, in fact, increase. On the decadal scale and at medium or high spatial resolution, climate (or, rather, weather) is very complicated, noisy. On the multidecadal scale and at low resolution, the internal variation can be accounted for, and the primary forcings and feedbacks dominate the trend.

    Baseball is a good analogy. Given that a team's talent stays generally consistent, projecting the team's chances at the playoffs is pretty easy. However, take any two-week period in the season, and the team's success might not be evident whatsoever--short-term injuries, bad calls, distractions, slumps, etc. It's that "given" that's key. We understand the major "givens" for climate. Indeed, an overwhelming majority of the uncertainty lies in the human response.

    We understand the physics well enough. We don't understand the human response. Yet we don't have time to wait for a better understanding of the human emissions pathway. Even if we go to zero emissions tomorrow, we're still looking at a major response from the ice sheets (among many other sub-systems) as they come to equilibrium with the elevated level of forcing. Atmospheric CO2 doesn't just return to pre-industrial once we zero our emissions, and it is extremely unlikely that we'll zero emissions anytime soon.

  45. Rhoowl asked me "TD what type of standards do you design to and what type of safety factors."  The answers are NASA standards for mission operations software for JSC to monitor the ISS, Orion, and other vehicles; for mission ops software for JPL to monitor their uncrewed large and small spacecraft including Mars rovers; for ARC nanosat spacecraft flight software and hardware; for rocket guidance, navigation, and control software-hardware packages; for an assortment of hardware-software payloads for an assortment of spacecraft being produced and launched by an assortment of international organizations; and for autonomous aerial drones ground-plus-flight hardware and software.

    But I suspect you asked because you believe there are a few books of acceptable risks probabilities to which engineers turn.  There is a smattering of such numbers, mostly for small subsystems, and mostly for hardware, but even for those, fundamentally it all comes down to subjective human judgment of acceptable risks for each situation by not just the engineers but the other stakeholders in the project, as I described earlier.  The most important design standards for risk are standards for process--for how those design judgments are made, and then how the implementations and testings of those designs and implementations are done.

  46. Rhoowl wrote: "The one post I had which is no longer visible..."

    Actually, it's still visible. You just posted it to a different thread.

    Yes, the 'gap' between 11.5 meters near maximum (upper 5% uncertainty band) sea level rise assuming the RCP8.5 emissions scenario and 0.13 meters near minimum (lower 5% uncertainty band) on the RCP3PD emissions scenario is big. However, it is not any kind of "error"... because you are looking at two different things. You might as well argue that weather models are useless for predicting the next day's temperature because they show a maximum daily temp of 90 F in Atlanta vs a minimum daily temp of 10 F in Nome... what a "big error"!

  47. Rhoowl claimed "So having a range if sea level rise would not be useful. You need to know what it is."

    Suppose you are an engineer hired by the town of NearlySubmerged to design a new seawall to protect it in the year 2100 to the same degree the old seawall protected the town when it was built in 1900.  (The town is not in Florida, where for most shores the porous land allows the water to seep under the seawalls.)  Think of Superstorm Sandy and New York City.

    The town already has made some of the decisions that I described in my previous comment.  The town has decided that they want the risk that they had in the year 1900.  The town did not look up that risk level in an engineering book.  That risk level is not "standard."  It is not objectively calculated.  It is a choice by the town.  They could have decided differently, for example to preserve the risk they have today, in 2015, which is considerably higher than the risk they had in 1900.

    Even with that information, you still need to know the projection of sea level rise by 2100.  You cannot make that decision by yourself.  You must ask the town for their choice of which IPCC emissions scenario they prefer to assume will come to pass.  Then given that chosen emissions scenario, you must ask the town whether they want to use that sea level projection's mean value, or its range's 95% upper bound, or its range's 95% lower bound, or some other value.  (To simplify this example, let's skip you asking the town to choose a projection of storm surge changes by 2100.)

    Only with all that information can you then design the seawall.

    But the town will balk at making any of those decisions, because those decisions are subjective.  They will ask you, as all savvy shoppers do, to present them with the cost of constructing each design to meet each of those projected sea levels.  To shorten your task, probably you will first design for the sea level at the top of the 95% range of the most emissive of the emissions scenarios, and for the one at the bottom of the 95% range of the least emissive of the emissions scenarios.  You might discover that the difference in cost of those two extreme designs is so small that the town feels it is well worth the cost to design for the highest projected sea level.  But probably that cost difference will be large enough that the town wants you to give them costs for intermediate projections, until the town (not you) decides which sea level projection to use.  Now you have enough information to finish designing.  That information is not "1 in 10,000"; it is several pieces of information.

    In that process, imagine that the minimum projected sea level rise by 2100 was .13 meter, and the maximum was, say, .14 meter.  The town might decide they will not build a new seawall but will live with the risk increase, because they think they will spend less money to cope with anything in that range than they would spend on a new seawall.  That is the town's decision, not yours.  You don't simply look up that decision in your notes from engineering class. 

    But suppose the town decides that that the least-emissive emissions scenario is impossible--that it will not come to pass.  So they tell you to ignore all of the sea level projections from that optimistic emissions scenario.  Suppose that the emissions scenarios they tell you to use have 1 meter sea level rise as the lowest end of the 95% range of the least-emissive of those allowed scenarios.  Suppose the town decides that they most definitely want to be protected from a 1 meter rise, but they are unwilling or unable to spend the money to protect against anything higher.  It does not matter that the upper bound of sea level rise in those within-scope scenarios is 11 meters, because the town has decided not to protect against that much rise, even though it would be catastrophic. 

    Back to your claim that a range of projected values is useless:  You are wrong.  The large span of that range does not make the projection useless, if even the minimum value is large enough to demand action.  A range is useful for decision makers (requirements deciders) to choose from in picking out the value to hand to you, the engineer, to design to meet.

  48. Rhoowl - "So having a range if sea level rise would not be useful. You need to know what it is."

    Knowing a range of risks is sufficient to evaluate risk avoidance - fire insurance is an excellent example. You don't know if your house will or won't catch fire, if it catches fire you don't know how much damage that might cause, yet you buy fire insurance to cover the likely range of damages. That span of damages is not an error, but rather the bounds on risk. The same holds for sea walls, and in fact for every other consideration of risks vs. benefits. 

    Back on topic - the models are quite good within their stated limits: 30 year or so projections of the average climate response due to stated forcing changes, with bounds determined by climate variability. Your insistence on 1/10,000 risk levels, and in fact your treatment of climate models (boundary problems) as weather models (initial condition problems) are IMO demands of impossible expectations.

  49. Wow so many posts...I see most of you guys are scientists....

    JH the Dutch are more susceptible to sea level rise than any other country. something like 1/2 of the country is below sea level...a lot of their dykes, dams and polders have been there for 500 years or more. So they design for those types of timeframes.  A 1m sea level would stress all of the dykes and dewatering systems. 

    [TD:  Please keep the conversation going, by directly answering questions and by responding in ways that are directly relevant to the topic of this original post ("Models are Unreliable").  In the above paragraph you have agreed with John Hartz, but have tried to not admit it.  Please directly admit you agree with him, but specifically on the topic of the utility of climate models.  If you actually disagree with his specific point about the utility of climate models, say so explicitly and explain, directly, why.]

    Scaddenp 

    DSL why do you have to reduce co2 emissions. Co2 emissions have been regularly increasing. You have to figure that the govt of the world will not come to an agreement to reduce co2. Why not focus on other areas like bioengineering plants(specifically algae) to use the increased levels of co2. They'd have such an advantage theyd quickly overtake plants that have evolved to survive a max 300 ppm atmosphere. How much money is spent on this?   Why not push for nuclear ( I'm not a huge fan of this But I can live with it 

    Why not push for technologies to replacing the internal combustion engine. Hydrogen fuel cels are looking promising. Fusion is starting to look promising with ITER Why not push the govt to develop our own tokamak. 

    it not inconceivable by 2050 having fusion reactors produce hydrogen to power vehicles. 

    Instead ad the goft want to tax carbon with no specific plan to actually reduce co2. The govt wants to promote solar and wind......solar and wind should be considered dead end.....reliability, storage, ecological problems. I think solar might actually cause more global heating than eqivalent co2. Think about it.....a solar is placed over the ground....the energy is sent into my home. Heats my house. Heat goes into the ground beaneath the house for slow release. Where the panel is it kills the plant life beneath and co2 reduction is reduced.

    [TD] The paragraphs I've struck through are very much off topic.  You are giving the appearance of deliberately attempting to avoid admitting you are wrong.  It's called the Chewbaca defense.  There are posts here on Skeptical Science that are relevant to those comments.  Use the Search field at the top left of this page.]

    Td td the guidelines for the design would never be left at the town level. you'd have an organization ASCE (american society of civil engineers) working working with scientists making the decision.  The ASCE is responsible for all structural design building guidelinEs in the coUntry.

    [TD:  You have avoided addressing the actual point that is relevant to this "Models are Unreliable" post, by skipping off into a different topic.]

    Response:

    [TD] You have veered way off the topic "Models are Unreliable."  Please comment on appropriate threads.  At the left side of this page, click the "View All Arguments..." link to find relevant arguments, and lower on the left side of this page look at the list of Latest Posts, and then click the Archives link at the bottom of that list.  Off-topic comments on this thread will be deleted.

    Everybody else:  Please support your local moderator by putting any of your replies to the off-topic comments on appropriate threads, and posting a short comment on this thread linking him/her to you appropriately-placed comment.  (Right-click on the date/time stamp of your appropriate placed comment to get a link to it.)  Thank you for your support.

  50. Rhoowl:

    Read my lips...

    The Dutch, who know something about protecting their land from the sea, do not seem to be waiting for futher refinement of Global Climate Models to take action. [My bold]

    Your propensity to move the goalposts sideways when responding to someone is not an acceptable behaviour on this site.

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