Forecasting climate?
The average value of a meteorological element over 30 years is defined as a climatological normal. Source: NOAA/NWS
It’s been done:
From the University of California – Los Angeles
Can scientists look at next year’s climate?
Is it possible to make valid climate predictions that go beyond weeks, months, even a year? UCLA atmospheric scientists report they have now made long-term climate forecasts that are among the best ever — predicting climate up to 16 months in advance, nearly twice the length of time previously achieved by climate scientists.
Forecasts of climate are much more general than short-term weather forecasts; they do not predict precise temperatures in specific cities, but they still may have major implications for agriculture, industry and the economy, said Michael Ghil, a distinguished professor of climate dynamics in the UCLA Department of Atmospheric and Oceanic Sciences and senior author of the research.
The study is currently available online in the journal Proceedings of the National Academy of Sciences (PNAS) and will be published in an upcoming print edition of the journal.
“Certain climate features might be predictable, although not in such detail as the temperature and whether it will rain in Los Angeles on such a day two years from now,” said Ghil, who is also a member of UCLA’s Institute of Geophysics and Planetary Physics. “These are averages over larger areas and longer time spans.”
Long-term climate forecasts could help predict El Niño events more than a year in advance. El Niño is a climate pattern characterized by the warming of equatorial surface waters, which dramatically disrupts weather patterns over much of the globe and strikes as often as every second year, as seldom as every seventh year or somewhere in between.
A major issue addressed by Ghil and his colleagues in the PNAS research is the difficulty of separating natural climate variability from human-induced climate change and how to take natural variability into account when making climate models.
For the study, Ghil and his UCLA colleagues Michael Chekroun and Dmitri Kondrashov of the department of atmospheric and oceanic sciences analyzed sea-surface temperatures globally. To improve their forecasts, they devised a new algorithm based on novel insights about the mathematics of how short-term weather interacts with long-term climate. Weather covers a period of days, while climate covers months and longer.
As is customary in this field, Ghil and his colleagues used five decades of climate data and test predictions retrospectively. For example, they used climate data from 1950 to 1970 to make “forecasts” for January 1971, February 1971 and beyond and see how accurate the predictions were. They reported achieving higher accuracy in their predictions 16 months out than other scientists achieved in half that time.
The research was federally funded by the U.S. Department of Energy and the National Science Foundation.
Extreme climate, extreme events
Ghil also led a separate, three-year European Commission–funded project called “Extreme Events: Causes and Consequences” involving 17 institutions in nine countries. In a recent paper on extreme events, published this summer in the journal Nonlinear Processes in Geophysics, Ghil and colleagues addressed not only extreme weather and climate but extreme events such as earthquakes and other natural catastrophes, and even extreme economic events. Their study included an analysis of the macro-economic impact of extreme events.
“It turns out, surprisingly, that it is worse when catastrophes occur during an economic expansion, and better during a recession,” Ghil said. “If your roof blows off in a hurricane, it’s easier to get somebody to fix your roof when many people are out of work and wages are depressed. This finding is consistent with, and helps explain, reports of the World Bank on the impact of natural catastrophes.”
Ghil spoke this past July about a mathematical theory of climate sensitivity at the International Congress on Industrial and Applied Mathematics, in Vancouver, a quadrennial event that showcases the most important contributions to the field over the preceding four years.
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Putting a good face on bad times.
It seems to me if there are bad economic times ahead it will be because government caused it and not because of weather or climate.
There is nothing wrong with research on the economic impact of weather or climate and while they admit their limitations they are dealing with extremes that do not exist and that is wrong because there can be only one cause of extremes and that is the claim of global warming.
A major issue addressed by Ghil and his colleagues in the PNAS research is the difficulty of separating natural climate variability from human-induced climate change and how to take natural variability into account when making climate models..
how to take natural variability into account ??
If I had the Goracle’s vocabulary I could properly respond to this but my mother would return from her grave to wash my mouth.
“If your roof blows off in a hurricane, it’s easier to get somebody to fix your roof when many people are out of work and wages are depressed. ”
Perhaps true if you are a tenured professor who still has a job, but that makes you a sort of exceptional case. For most people, it is NOT easier to have your roof fixed “when many people are out of work and wages are depressed” because YOU are more likely to be one of the people who are out of work or receiving depressed wages.
I do not normally call names, but I am sorry, these people are credentialed idiots.
But did they predict the second La Nina?
ahh c’mon, finding the normal is easy, just take the cross product of the vectors and divide by the magnitude…oh maybe that isn’t the point…
Climate forecasting helps predict El Nino? Isn’t this putting the cart before the horse, or at least strapping the horse along side of the cart?
Isn’t it also funny how the climate scientists have become economists? As the news item describes their thinking, all people have perfectly substitutable skills. Having lots of people out of work makes finding roofers easier and cheaper–as if the out-of-work bank executive can just climb a ladder and start with the flashing. This is the shovel-ready concept run amok.
Much of the time I read comments on this site, I have to look back at the article and re-read it. I don’t understand much of the negativity about this. Some rightfully ask questions about tuning the models express doubt on future forecasting… but it still remains that this is really cool! They just doubled the forecasting that was available previously! That is no small feat. They put forward a theoretical model that is TESTABLE! Let’s see what happens in the next 16 months and see if they are right. If not, we know their model is wrong. If it works, we can look at the 16 months after that and begin to improve upon it… you know… use science?
Looking at the photo, how many of these guys would you ask to change a light bulb?
Well, disparity in comments by Bill Illis and Spinifers capture it all: “Paper is here. Pretty complex math involved.” and “How is it that the more “educated” people are the stupider they seem to be?” What can we glean form these statements? Clearly, Bill recognizes that climate modeling requires some math. Some complex math. But, the math described in the paper is not much beyond what a college course in differential equations would teach (after you’ve taken integral and differential calculus). You don’t need a course in statistics per se to understand what the authors’ are attempting to do when applying their perturbation approach. It’s pretty simple, but the equations will get in the way for someone like Bill. To his credit, Bill recognizes that he does not have the background to understand the math and therefore he is unlikely to be able to make a judgement on its veracity or usefulness of the model and its predictions. Spinifers, on the other hand, clearly does not comprehend either the complexity of the modeling and maths required nor that he (she) does not have the wherewithal to comment sensibly on either the veracity or usefulness of the predictions. To Bill I suggest you contact someone who understands the maths (does not need to be a climate scientist! and perhaps should not be!). Try the physics dept at a local college where a graduate student can help you out). To Spinifers? His (her) words speak for themselves. Note: I was amused in the paper to see dx/dt referred to (in the vector sense as bold) as X(dot). Classic!
O/T
I’ve just noticed the ENSO meter has gone down, nice to see them catching up with the “climate”.
REPLY: Part of that has to do with my correspondence with NOAA last week where I pointed out one division was issuing a press release saying we are in a La Nina, and we have a meter run by another division that says we aren’t. The initial response was basically that we aren’t connected to that, and when I said we were doing our own, I think the point finally hit home for them. – Anthony
Steve Schaper says:
“Hmm. Are they doing better than the Farmer’s Almanac, yet?”
Thank you, Steve. Nail on the head and all that.
No mention of absolute accuracy, just “Better than before” – which I interpret as “still lousy”.
“It turns out, surprisingly, that it is worse when catastrophes occur during an economic expansion, and better during a recession,” Ghil said. “If your roof blows off in a hurricane, it’s easier to get somebody to fix your roof when many people are out of work and wages are depressed. This finding is consistent with, and helps explain, reports of the World Bank on the impact of natural catastrophes.”
I live in Spain and have just had a quote for a small structure identical to one that my neighbour had two years ago. Bearing in mind that unemployment in Spain is 20% plus. My neighbour told me that he paid 1500.Euros for the work.
I have been quoted 3000.Euros for the identical work.
Neil Jones says:
“Looking at the photo, how many of these guys would you ask to change a light bulb?”
My take: How many of these guys would you trust to change a light bulb?
So they predicted January and February 1971? Great. How about January 2012?
JCG, I would like to rebut your claim that Spinifer doesn’t understand the math. I do and I understand wholeheartedly what a boneheaded claim this paper is. Seasonal weather 16 months from now is affected by so many variables that we do not know (including what I am certain are a great many unknown unknowns, variables that we do not know and don’t know that it’s even important) that prediction is ludicrous. True error bars (in situations where they can even be calculated) are several times that of the predicted change.
That’s why less than two years ago, the Met office stopped its seasonal forecasts due to laughable accuracy 3 months out that caused mismanagement and insufficient preparation for one of the worst winters of the past century. These people are claiming 16 months? That is a complete disconnection from reality
But if the climate is changing and only history is used to predict the future no matter how much stochastic analytical skill is used the change will not be predicted, because climate is chaotically deterministic and the rules and drivers are not fully understood. I wonder if they are at this very moment working on the hindcast prediction that ENSO is returning to the negative.
Easy to predict La Nina, or El Nino. However the accuracy of the prediction increases dramatically after six years without one. This article is a bit lame. Caught myself saying ‘huh? a few times, especially the economy connection. BTW, I’ll fix my own roof. and/or repair the house.
You report that .
“A major issue addressed by Ghil and his colleagues in the PNAS research is the difficulty of separating natural climate variability from human-induced climate change and how to take natural variability into account when making climate models.”
How is this addressed?
I thought that the major issue in climate change, is the amount of man made global warming induced.
If we ignore man made induced warming , do the models perform better?
JGC,
It is obvious you took the time to read the study. As others have noted above, why don’t they make public their predictions of the next 16 months so we can all judge the skill of their model. Did they in fact include predictions in the paper? Could you point us to them?
pesadia – I think when we look back at this decade 10 years from now, we will see the inflationary policies of the central banks will account for the majority of that price increase, with the rest of it being caused by government intervention in the labor market particularly in the “living wage” laws. The socialist policies of many governments (with Greece just being the canary in the coal mine) are not sustainable and economic corrections occur whether the governments plan for them or not. In a free market system, the hypothesis is correct, but the world hasn’t had a free market system in at least 80 years.
One wonders if they’ve asked their colleagues at the Met Office here in the UK.
They’re pretty good a complicated maths, I’d think, seeing that they have the bestest super-computer to help them out.
Pity they don’t do long-range forecasts any more. The cases of the Barbecue Summer that was a total wash-out, and the ‘normal’ winter last year that started with heavy snowfall in November and brought Heathrow to a standstill, might help those ‘new’ forecasters to decide that what they forecast better not be published until after the event … people have this inconvenient idea of checking a forecast against what really happens.
Let’s wait until they’ve actually predicted, rather than hindcasted, anything for 16 months before attaching the superlatives, eh?
Jason Calley says:
“I do not normally call names, but I am sorry, these people are credentialed idiots.”
Like my dear old granddad used to say, these people are educated beyond their capacity to think.
Oh Piers and Joe we have some people in dire need of help from you 2 gents.
About twelve years ago I was asked to inspect a stochastic investment model which had been successfully back-tested over several previous five year cycles. For the next eighteen months or so it proved an excellent forecasting tool . Then the dot.com bubble grew until it burst. But banks never learn and the same bank that had placed so much faith in the original model created and adopted a new and better one just in time for that model also to collapse under the weight of the Lehman banking crisis.
Climate cycles are much longer than economic cycles. Fifty years of back-testing may not be enough but I see no reason why this new climate forecasting model should not produce good (very short term)results until it, too, crashes.
All modellers should remember the words of Albert Einstein “Insofar as the propositions of mathematics give an account of reality they are not certain; insofar as they are certain they do not describe reality.”
Hold it folks…I just read the abstract to the paper, and it turns out this “new” method is applied to a simplified ENSO model, not a general climate model!
“The method is placed in the framework of pathwise linear-response theory and is then applied to an El
Nino Southern Oscillation (ENSO) model derived by the empirical model reduction (EMR) methodology; this nonlinear model has 40 coupled, slow, and fast variables.”
The press release is VERY misleading (as usual)…
“UCLA atmospheric scientists report they have now made long-term climate forecasts that are among the best ever predicting climate up to 16 months in advance, nearly twice the length of time previously achieved by climate scientists.”
What bilge…