CMIP5 Model Temperature Results in Excel

And why do you think he is not? Look, Mosher believes that Carbon Dioxide concentration drives the mean temperature in a monotonic way outside of all other sources of variation. So do I. So does Monckton. So does Anthony, AFAICT since he only rarely personally injects his own perceptions of things into the discussion (which is more a blessing than a curse, given the plethora of sites dominated by the views of the blog owner/manager). So does Nick Stokes. So, do many of the science-educated site participants because there are some really excellent reasons to think that it is so. Reasons that include direct measurements and observations, a fairly straightforward argument (that can and does become a lot more complex as one considers the system as a whole, so it is not certain, merely probable), good agreement with the simplest physically founded computation of its average effect and global observations. It isn’t a matter of “I want to believe” or “I have a dog in the race” it is a comparatively simple matter of physics and observation that makes the “global average temperature all things being equal should be a saturable monotonic function (most likely a natural log) of carbon dioxide concentration in the atmosphere” a probably true statement, better to believe than disbelieve given our sound knowledge of physics and the evidence. This isn’t a religious belief, however often it is argued on WUWT from little more than a religious basis — both ways.
That is a completely distinct issue from whether or not any particular General Circulation Model is an accurate predictor of future temperatures. Here, look, I’ll ask him! Since I personally think his a reasonable human being and not a troll, and since I ask politely, maybe he’ll give me a reasonable answer!
Steve, do you think any or all of the GCMs are accurate predictors of future temperature, beyond all discussion or need to compare to observation?
Who knows, maybe he will surprise you and say that the answer is: of course not! Because that is, in fact, the correct answer for any reasonable scientist or statistician. Models are useful to the precise degree that they a) correspond to past events being modelled and b) predict future events. Steve might disagree with you, or with me, as to whether or not any given model or all of the models collectively have or have not been falsified yet, but until we establish a meaningful statistical basis for a claim for falsification and agree that it is a reasonable if not correct one, we are all just hand-waving. That’s why Willis’ kindness at fighting the KNMI demons for us is so greatly appreciated. Steven McKintyre also has similar directories, but to be honest all of these sites are absolutely miserably designed and make it a royal pain to find and download the data in a usable, well-documented form. I have a directory of my own filled with files named things like “FIO-ESM_rcp45_ave.tab” which turns out to be compressed tabular data that one can, with some effort, unpack and read into R. But a single CSV files is human readable and vastly easier to parse and understand.
There is still the problem of connecting results with time. Global temperature doesn’t vary with time in a greenhouse model, it varies with greenhouse gas concentration (plus some comparatively short relaxation times) and hence one has to have a model for CO_2 as a function of time in order to model future climate or fit past climate. RCP4.5 is already systematically underestimating Mauna Loa (IMO the only reliable data we have on CO_2 concentration) by 2013 (about 1 ppm too low), 395.6 vs 396.5 but that is still within the noise, so the models should have been using CO_2 levels that closely corresponded to measured values across the modern era. I’m about to look at what it claims for 1850 to the present compared to e.g. Siple or Laws Dome data and my own interpolating model. Just eyeballing the data itself it looks to be in pretty good agreement, but then, given a concentration ballpark 285 ppm in 1850 and Mauna Loa starting in 1959 and a requirement of believable smoothness in between, it is difficult to be otherwise.
I should note well that RCP4.5 claims that the total greenhouse CO-equivalent forcing using the standard 5.35*ln(cCO_2/cCO2) formula is already around 403 ppm, and the Kyoto-equivalent forcings neglecting presumed aerosol cancellations is more like 450 ppm. This is very worrisome IMO, because one has to wonder just what is being input into the GCMs — the raw extrapolated concentrations or the now trebly modelled GHG forcing equivalents? Are GCMs the model of a model of a model, or worse? The problem here is straightforward — obviously the authors of RCP4.5 already built a model — really a whole stack of models — that made assumptions about not just CO_2 but about methane, aerosols, nitrous oxide — and extrapolated not just the concentrations indefinitely into the future but the CO_2 concentration equivalent using a presumed model for the CO_2-specific forcing. This is odd beyond compare, presuming knowledge that a) nobody actually has; and b) begs the question, when one uses this presumption in putatively quantitative computations as the basis for future forcing.
And it is all really pretty silly. Anybody can draw a line from 2014 to 2100 and say “behold”, I assume that CO_2 will do this between now and then” and then make a guess as to global average temperature (anomaly) in 2100. There is really only one reliable way to make such an estimate, and it is not building a GCM, or at least not building a GCM until the simple, reliable way to make the estimate fails. That reliable way suggests that the TCS of 3.7 is just about exactly a factor of 2 too high — it does not explain the past data without monkeying far, far too much with other stuff about which we are cosmically ignorant.
But I digress. The point is this. Steven is making a very reasonable statement — Willis has made it very easy to compare CMIP5 models to observed surface temperature. RCP4.5 is adequate as far as CO_2 in particular is concerned between 1850 and the present, hence it should permit models to do a reasonable job of modelling HadCRUT4 if one assumes that HadCRUT4 itself is a reasonable model for the global average surface temperature in between. The game is then fair, with those stipulations. If you want to play, as Willis generally asks — be specific. Which model are you addressing? Why does it succeed or fail? How are you determining success or failure (and what is its quantitative basis and statistical support)?
These are questions I’m all getting ready to ask myself because they are absolutely key to doing science instead of voicing opinions. Sure, we can look at the spaghetti and conclude that something is seriously collectively amiss in that things are not in good agreemen, but is it really amiss or just within the acceptable noise and uncertainty? We cannot answer this “collectively” for exactly the same reason that the MME mean and variance are meaningless. It has to be answered one model at a time, and one has to answer it quantitatively and using an open criterion that is subject to criticism and debate. In the end, all reasonable souls should be able to agree that the proper application of statistics to the models and data either does or does not support the assertion “this model is working to predict the data”. In the end, it all comes down to a p-value of a hypothesis test — what is the probability that model X is correct and and that the real world observation occurred? If p is low, then the null hypothesis “Model X is correct” can correspondingly be rejected with some confidence. If p is anything but low, one cannot assert that Model X is probably incorrect, but (the way hypothesis testing works) neither can one assert this as positive evidence that it is correct, because there can be a near-infinity of models that fit the data over some interval but are utterly false. All we can say is that the data does not falsify it yet, and incrementally increase our degree of belief in it compared to the large number of models that fail the test with low p-values.
So next: How do we compute the p-value of the null hypothesis for just one model curve given the data? I know a fair bit of statistics pretty darn well, but I’m going to have to think about that one. I can think of several ways to do a computation that would lead to a p-value, but they all make certain assumptions, and those assumptions are Bayesian priors for the computation and one has to have some way of defending those assumptions that isn’t just asserting that they “must” be true. Most of them will rely on using the variance of the data itself to determine when it is resolvably separated, but then one has to ask — the variance over what time interval?
This is the really, really difficult problem. There is no good reason to think that the climate is stationary neglecting CO_2. Indeed, we are pretty certain that it isn’t. That implies many time scales and many associated ranges of variation. Again, the usual thing is going to be to assume our ignorance of this dynamic outside of maybe (if we are wise) factoring it into a humble lack of certainty in our final conclusions. This is exactly what the defenders of the GCMs do when they assert that deviation hasn’t lasted long enough to reject the null hypothesis for (fill in the blank — usually for the collective MME mean but also by assumption for each model contributing to the MME mean). There is a clear 67 year harmonic signal with amplitude around 0.1 C, for example, around the general smooth rise in HadCRUT4 — this suggests that we might well be misled about the climate sensitivity if we look at the wrong part of the data and try to fit it. Hence the moving goalposts of 12 years, 15 years, 17 years, whatever, of deviation before we reject the GCMs at least collectively. Sadly, the people that argue in this way fail to recognize that that same clearly observable oscillation caused them to initialize and normalize the models themselves in the worst possible reference period, a stretch in the 1980s where the harmonic contribution produced a transient maximum slope so that now they are strongly deviating now that we are in a transient minimum slope around some sort of mean warming behavior — if one assumes that the observed oscillation is indeed a deviation from a mean warming behavior and not the result of transient phenomena that are depressing the climate from a much warmer trajectory that it “should” be on and will eventually return to!
How can one assign a probability to either one? Clearly there are many reasons to prefer the former, but one can hardly exclude the possibility of the latter. And so they hold on by their fingernails and refuse to let go, hoping that the climate will actually “suddenly” warm up and return to the predicted curve. They know this is increasingly unlikely, but it is — maybe — not yet impossibly unlikely.
Or is it? Again, the only way to tell is one model at a time. But the IPCC seems unwilling to take that step, as it would inevitably lead to throwing out models and further to strongly reducing estimates of climate sensitivity just because, well, the observed temperature is well below the models from 2000 on and is apparently diverging from them. And what about the past? We’ve been told that the models hindcast well. But is this true? Only direct comparison, one model at a time, can tell us.
rgb

Thank you rgb.
I am not amused at the vitriol that some folks throw about. I have used some very useful models in engineering and finance that did nothing like “model” or “forecast” what was really going on but simply used empirical, testable, verifiable results to predict an outcome. Fluid dynamics can not model every little vortex, nor every bit of turbulent or laminar flow, but they give adequate information to design a pipeline. Financial models do not have to take every little nuance into account but they do a useful job at predicting income and profit simply using previous years results, backlog, fixed and variable costs.
Mosher, Eisenbach, Tisdale and a host of others provide lots of great input. It is up to the reader to apply the appropriate weighting. Vitriolic comments add little. But sometimes the humour isn’t bad.
Well, just put my skis in the car so have a very MERRY CHRISTMAS everyone and may 2015 be good for everyone – or at least as good as it can be given the climate (pun intended). 😉
Thanks for an entertaining 2014.