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When the data inconveniently refuses to conform to your theory, just how should a True Believer in that theory respond?
Why, add an epicycle, of course!
Ocean Cooling to Briefly Halt Global Warming, Researchers Say
Parts of North America and Europe may cool naturally over the next decade, as shifting ocean currents temporarily blunt the global-warming effect caused by mankind, Germany's Leibniz Institute of Marine Sciences said.
Average temperatures in areas such as California and France may drop over the next 10 years, influenced by colder flows in the North Atlantic, said a report today by the institution based in Kiel, Germany. Temperatures worldwide may stabilize in the period.
Let me get this straight. Lower and/or stable temperatures are now a feature and/or evidence of ongoing global warming. Hmmm, better read farther.
"Natural variations over the next 10 years might be heading in the cold direction," Wood said. "If you run the model long enough, eventually global warming will win."
Well, I'd really hate to misunderstand this, so let's sum up. If there is a slight rise in global temperatures over a few decades that falls entirely within natural variation consistent with historical world climate, we must all panic and spend trillions and trillions of dollars to change our ways, even starve some folks pursuing alternate fuels, because someone's theoretical and unverifiable model says we might raise the temperature of the planet a bit. But if temperatures fail to actually accomodate the models and theory, as they have for the last several years, it's just natural variation interfering with the upward temperature trend signal. And if that lack of predicted temperature trend persists, then it must actually be due to an unanticipated effect of global warming and natural variation.
Because it can't possibly be that the theory underlying the model simply does not correspond all that well to the real world in the first place. When the data and the model do not match, obviously there's something wrong with reality, not with the model! At worst, it just means we need to add another epicycle to the model to get all those orbits temperature trends smoothed out. Because, you know, sooner or later the model will win!
How could we ever doubt such a powerful consensus?
UPDATE: Roger Pielke, Jr. of the CU Center for Science and Technology Policy Research weighs in:
For a while now I've been asking climate scientists to tell me what could be observed in the real world that would be inconsistent with forecasts (predictions, projections, etc.) of climate models, such as those that are used by the IPCC. I've long suspected that the answer is "nothing" and the public silence from those in the outspoken climate science community would seem to back this up. Now a paper in Nature today (PDF) suggests that the world may cool over the next 20 years, and this would not be inconsistent with predictions of longer-term global warming.
I am sure that this is an excellent paper by world class scientists. But when I look at the broader significance of the paper what I see is that there is in fact nothing that can be observed in the climate system that would be inconsistent with climate model predictions. If global cooling over the next few decades is consistent with model predictions, then so too is pretty much anything and everything under the sun.
More commentary on the NATURE paper at The Reference Frame: Nature: AMO will stop warming until 2020 blog. A taster:
Their choice of words is a testimony of the political correctness. For example, in Richard Black's article in the BBC, Keenlyside says:
"One message from our study is that in the short term, you can see changes in the global mean temperature that you might not expect given the reports of the Intergovernmental Panel on Climate Change (IPCC)."
If you wonder what this sentence means, it means "Our models, if correct, imply that the IPCC projections for the next 15-20 years are incorrect."
Gee, it all sounds so familiar somehow.
Previous, with links to MUCH more previous
Ongoing compilation of climate blog posts at Tom Nelson blog.
Still more from our friend Rich Horton over at Blue Crab Boulevard, in the aptly-titled “Global Warming” Goes Ptolemaic.
And still more from American Thinker blog: NASA Confirms Natural Climate Shift
UPDATE: Gaius at Blue Crab Boulevard weighs in with Up To Two Feet of Epicycles.
Wood tells us that "[i]f you
Wood tells us that "[i]f you run the model long enough, eventually global warming will win." What does "eventually" mean? Does it mean by 2020? 2050? 2100? The latter seems a reasonable candidate for what Wood had in mind given his colleague's comment. We're told (I assume accurately) that "Global temperatures can't rise by more than 2 degrees Celsius ... without risking the worst effects of climate change," and Rajendra Pachauri is paraphrased as saying that "[t]he world will become at least 2.5 degrees Celsius warmer by 2100, compared with the pre-industrial period." I'm left confused: can't rise more than two degrees from present averages without catastrophe, or pre-industrial averages? It matters insofar as Pachauri's claim boils down to "the world will become at least 1.5 degrees warmer by 2100 as compared with today." So: assuming all other premises and constant trends arguendo, are we talking the apocalypse by 2100, or just closer thereto than we are today? When do we get to Wood's "eventually"?
"When someone says their heart needs lifting, don't ask how come, ask how high."
Look twice at that
Look twice at that statement, Simon, note my bolding, and think about it for a minute.
"[i]f you run the model long enough, eventually global warming will win."
Wood's giving us the classic line that when a treasured theoretical model fails to correspond with reality, that the model and theory are still correct, it's the reality that's wrong. Maybe the model just needs tweaking, but the underlying assumptions and theory are still correct, so theory in the form of the model projections will eventually triumph over reality!
If the model already fails to correspond accurately to reality, of course, it does not matter at all what "eventually...wins" within the model in the course of X iterations. If the model does not correspond to the reality, then the specifics of the model assumptions are--blatantly, patently, obviously--not correct. The theory might be correct in general, but "in general" is not remotely sufficient for the detailed assumptions required in building extremely complicated multi-dimensional organic models and having them be of any use whatsoever in real-world application.
I've touched on this before, the pitfalls of how even extremely simple and highly accurate models consistently fail in real-world application once one begins to step beyond a few cycle iterations. A model that is 90% accurate in single-iteration projection has less than three chances in ten million of returning an accurate projection for the hundredth iteration. Indeed, by the twentieth iteration it's already down to a barely 1 in 8 chance of accuracy. Past the first dozen iterations or so, it has better odds of returning a correct answer by sheer chance than by any predictive skill. And that's a simple model.
To be assured that "eventually global warming will win" requires that the model contain detailed assumptions of "guiding" feedback mechanisms that return the baseline back to the (theoretical, inherent, assumed) trend. But it's pretty apparent at this point that the assumptions of the model itself are wrong in great enough degree that it's worthless as a predictive model, and there's no reason at all to believe that the feedback assumptions are any better than the primary assumptions that are already failing to replicate reality. So rather than going back to square one and rebuilding the model, the True Believers tack on an after-the-fact epicycle--an explanatory Band-Aid, a "new" assumed primary feedback mechanism.
Falsum in uno, falsum in omnibus--tweaking the "orbits" with additional epicycles may provide an improved retrofit to explain past/present real-world data that fails to conform to the original predictions of the model, but is highly unlikely to improve the future-predictive capability of it. Quite the opposite. Multiplying assumptions is nearly certain to reduce the model's predictive utility, make it less accurate than it aleady is.
I've got a new model myself...
...it predicts that I will die tomorrow.
If that doesn't happen I wont worry. Eventually the model will win.
LMAO. Well, yeah, in the
LMAO. Well, yeah, in the long run....
And in the long run, global warming will strike us with a REAL vengeance...but CO2 won't be the problem. ;-)