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.
“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.”
There I was thinking that catastrophic weather events were a local thing. I’d say no matter whether you’re in a recession or boom it will be hard to find someone in the vicinity to fix your roof after your whole neighborhood has been visited by a hurricane. South Carolina home owners will be unlikely to hire contractors from Washington state for the job.
All of this is based on what is considered “normal”….
….normal extreme events, normal sea ice, normal glaciers, normal CO2 levels, normal temperatures……..normal weather…..etc
When the very people claiming it is not normal, are allowed to define what is normal……
Take that away, and the whole thing falls apart……..
Were any Skeptics or Climate Realists asked to review this paper?
Here’s the test: How many of the scientists featured here will correct the announcement?
Thank you Latitude, *a normal* reply.
Add economics as another topic these guys don’t know squat about:
It’s easier to get somebody to fix anything when demand for that service is lower than the supply. It’s also easier when you will pay above the average market rate for that service. Here’s another thing – in a widespread event like a hurricane, the demand for building materials will outstrip supply. Allowing retailers to charge more for materials will allow the market to respond by moving materials in from other locations.
“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.”
Hey! That explains the super-exponential rebuilding of Haiti after a hurricane impact or Earthquake, or the super-exponential rebuilding of Jamaica after a hurricane impact. And, they accomplish it all while emitting tiny fractions of the CO2 emitted by economically robust countries.
Fortunately, in an act of selfless, laser-focused foresight, the current administration has been busy adjusting the economy to prepare us for the dead certain increased onslaught of hurricanes, tornadoes, floods, tsunamis, earthquakes, and snowmageddons that are already being caused by the coal trains of death.
Oh, and Guam capsizing. Again.
16 months? Childsplay. Try 100 years.
Yours,
IPCC
Paper is here. Pretty complex math involved.
http://www.environnement.ens.fr/IMG/file/MichaelPDF/CKG-Predict_stoch-PNAS'11-Main+SI.pdf
How is it that the more “educated” people are the stupider they seem to be?
I learned that climate changes all the time thanks to countless natural variables when I was in third grade. I fail to see what has changed. Other than who is writing the textbooks, that is.
Perhaps I should just count my blessings to have been born into such an enlightened age of climate certainty.
It seems to me that what we have here is a miss labeling of the kind of forecasting being done by these researchers. Whoever labeled it “climate prediction” obviously was thinking, it isn’t quite weather forecasting. Well, yes. But we have a perfectly good name for it: seasonal weather forecasting. In general, models for this tend to do very poorly. These guys are claiming to have improved skill significantly several more time steps out. If true, that’s good. But I have my doubts. Let’s wait and see if this actually produces better seasonal forecasts.
another fund raising paper meant to attract grants and funding …
Their [other] study included an analysis of the macro-economic impact of extreme events.
How many windmills, solar arrays, and Climate Scientists survived to still do nothing but waste money and thereby “save the world before it’s too late”?
“As is customary in this field, Ghil and his colleagues used five decades of climate data and test predictions retrospectively.”
AKA HINDCASTING
And I’m sure they didn’t tune their models…nope…never.
PS Maybe they could predict the “climate” for this winter. This would be an easy test of their “system”.
This should be required reading for anyone contemplating playing with models…
http://lorenzo-thinkingoutaloud.blogspot.com/2009/03/computer-models-and-cognitive-failure.html
Hmm. Are they doing better than the Farmer’s Almanac, yet?
“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,”
If they cannot tell the temperature and whether it will rain in LA on such a day then what exactly have all the dire predictions been about. Telling folks it will be hotter 100 years from today is now not possible?
I can predict right now that in 25, 50 or 75 years it will be above 60 F for 15 days in the month of July in the UP of Michigan. I will also be below 20 F for 20 days in January.
It will rain and it will snow and possibly in the same month normally November. Some times December.
I can make all kinds of true climate predictions.
So they did a good job of predicting what has already happened. Good first step. So now they can provide us with a forecast for – what? – January 2013 and we’ll see how it pans out. Where can I find that forecast?
I don’t believe the part about it being better to have a hurricane hit in a recession. I do think, however, that if you have less, losing it doesn’t seem as bad as if you have more to start with. People living in Haiti are not as shocked when living in tents as people from New Orleans are. In a recession people don’t have as much to lose, so losing everything doesn’t seem as bad as it would in good times.
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.
Strange, 1950 to 1970 inclusive looks like only 21 years of data to me, not five decades, nor the customary three decades. Is the statement in summary, 21 years of climate data plus 29 years of predictions? And for only 1.3 years out of those 29 the predictions looked good?
Maybe the numbers will look better if I install that new browser plug-in, the Post-Normal Climate Science Calculator. Reportedly it does a great job making these numbers make sense, for example how 100% of Anthropogenic CO2 emissions = 100% of Global Temperature Anomaly Rise. Too bad it’s only available for IE, last I heard, which was a while ago. Perhaps I better check if other versions are available on the GISS site yet. Heck, by now they could have released the iPhone/iPad/iPay app!
It would be nice if they put their projections on line for the whole world to evaluate. It actually would be meaningful to have this sort of shorter term lead time to weather events as preparation is key to reducing weather impacts and saving lives. This actually has merit if it is validated, whereas scenarios of climate in 2100 do nothing to prepare us for more immediate decadal weather threats.
“they used climate data from 1950 to 1970 to make “forecasts” for January 1971, February 1971″… These remind me those playing stocks. They also “test” their algorithms on the past. No one of them predicted crash 2008…
I have my doubts about them being able to forecast ENSO, it quite looks like a flip-flop butterfly effect thingy. Who’d have thought we’ll have another La Nina this year? Perhaps producing two forecasts, one with El Nino and one with La Nina will do the trick? Or they can just declare 50% certainity…
If El Niño occurs every two to seven years it sounds normal to me. How can something that happens as oftern as every other year “dramitcally disrupt weather patterns? And what about La Niña? How oftern does it occur and does it “dramitcally disrupt weather patterns” also? Normal/average is useless without standaerd deviation and range.
Ice Ages don’t exist …
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…
amazing stuff…..the term climate addresses the PAST weather, it is simply the average of the weather over the previous 30 years…..climate is NOT in any way a predictor of the future, IF the earth had a very stable climate then indeed it could be a very good idea of what is coming, BUT in our chaotic system of constant CHANGE the climate says NOTHING about the future weather tomorrow, next week, next month or 10 years from now.
I haven’t read the comments but just wanted to say that I love the photo at the top of the post. I am so glad that WUWT has immortalised this image – now as previously – in the minds of so many people worldwide.
You have the young guy holding the wheel making a mysterious inverted ‘V’ sign on the desk while looking serious, the older guy touching his own thigh with one hand while the other hand tries to restrain or remove it. The guy with the arms folded looking quite pissed off and the amiable looking guy who looks like somebody you’d be happy to meet at a nice barbeque!
I think it’s a classic image.
Truly it is said: “a picture paints a thousand words”. In this case about state of climate ‘science’.
The forecasts on my pages have been posted there for 44 months now and there are still 28 months of forecast to go. It is a compilation of three analog periods of the harmonic oscillation of the inner planets orbital periods and the Moons orbital parameters that repeat every 6558 days.
http://research.aerology.com/aerology-analog-weather-forecasting-method/
The original raw data was pulled and averaged together (in December of 2007) to see if there were any patterns that repeated, does better than the NWS 5 day forecast on average, with detailed maps for daily Highs, Lows, Precipitation with range to expect, Snow fall and Snow on ground.
It has been drier in Texas than the past patterns show, but they still shows a drouth, and since the solar minimum, the Gulf states have been dryer than usual (from the past patterns). The fronts still arrive on time just no rain, did a good job on the Tornadoes this last spring.
Major revisions are under way to upgrade the detail of the maps, and add Canada, Alaska, and Australia. With the completion of outside forecast verification testing I will be able to give you accuracy numbers relative to Clim stats, NWS, and other service providers. All funds have come out of my own pocket. Ideas from around the net can be found in the research Blog format on the current state of the knowledge of the “Natural Variability”. Who’s doing what, and when they discovered it, can be followed in the original blog articles I copied comments from.
I see now how they get the temperature forcasts …
They spin this wheel, and whatever temperature is at the apex is the IPCC forecasted temperture due to AGW … Brilliantly simple.
“They reported achieving higher accuracy in their predictions 16 months out than other scientists achieved in half that time.”
After much practice, they can now fire an arrow with higher accuracy than anyone before, though they still can’t hit the target…
At least that’s how it sounds to me.
The propaganda war is ugly, but the eco-freaks are starting to sound like a broken record.
Anyways, Dr. Qamar from India had this to say on more proof of global warming:
Here is the link
If anyone is interested in seeing me bash this to shreds. you know where to go.
Anybody got $50? I’ll sell you a model that is guaranteed to “forecast” the weather near you for 1971 & 1972. Or for just another $10 you can have the premium model which will give you 1981 & 1982 as well. You can readily check for yourself how incredibly accurate this model is in predicting backwards and anything that good must work in the other direction. No? Ask me about my generous discounts for bulk user licences.
If you can’t extend the science, then truncate the definition. That’s about all they’ve done and the latter is far easier than the former.
Two of the authors, Kondrashov and Ghil, run one of the ENSO forecasting models – UCLA TCD.
It doesn’t look to be that accurate going back into the Archives (from March predictions, missed the 2007 La Nina, accurate on the double-dip 2008 La Nina, missed the 2009 El Nino, missed the 2010 La Nina), but some of the more recent predictions are closer.
http://iri.columbia.edu/climate/ENSO/currentinfo/modelviews/ensofcst_UCLA.gif
If thirty years is the norm then we dont need a lot of statistical obfuscation to predict the future -just continue the trend.Lets do a little calculation from the SST data for the simple minded using the Hadley Center SST’s
1981 SST .027
2011 thru July 0.284
Thirty year increase = 0.257
90 years brings us to 2101
Increase to 2101 = 3x 0.257 = 0.771
We can live with that. With higher CO2 and a slightly warmer wetter world and fewer droughts agricultural productivity should jump nicely. Cancel the end of civilisation
Well I believe; I’ve seen the light; Halelujah !
I mean if climate science can now postdict an extra 700,000 years of Greenland “ice core climate data” previous to the mere 100,000 years of Greenland “ice core climate data” for which we actually have real H2O brand ice cores, using a mathematical formula prestidigitation; then preguessing the next 16months of “climate weather” must be duck soup.
If you are lucky enough to have a paper copy of that now defunct NOAA global pole to pole CO2 abundance record for about a dozen years or so, then you aare aware, that the north polar annual CO2 peak to peak cycle, is 18 ppm; about 7 months going up, and 5 months coming back down; and that 18 ppm range is three times the mere 6 ppm amplitude at Mauna Loa.
In contrast, the south polar CO2 cycle is about 1 ppm p-p tops; well actually -1ppm p-p since it is 6 months out of phase with the north pole.
So with such a gross pole to pole assymetry in CO2 abundance; who in their right mind, would believe that Antarctic ice cores, can be used to predict what non-existent Greenland ice cores would show; but for the slight inconvenience of their non-existence.
So yeah, I believe with my whole being, that they already know the next 16 months of climate weather.
How many of them would it take to change a light bulb?
Easy!
It takes ALL OF THEM!
One of them has to apply for a grant to study the problem of how to change a light bulb, one of them has to administer the grant, while another one has to apply for a grant to study light bulbs themselves, and someone has to administer that grant … …
ad infinitum ad nauseum … … …
Most of us have had doubts about long term climate forecasting, but to have actual scientists claim that 16 months is “nearly twice the length of time previously achieved by climate scientists” is quite amazing!
“The method has two steps: (i) select noise samples—or “snippets”—from the past noise,
which have forced the system during short-time intervals that resemble the LFV phase just preceding the currently observed state; and (ii) use these snippets to drive the system from the current state into the future.”
I just started looking at this and maybe it’s just me but doesn’t the above suggest that who is doing the selecting and which “snippets” are selected provide a fairly obvious window for bias to enter into the system?
“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.”
If “human-induced climate change” is such a big factor, why separate it from natural climate variability? Won’t it negatively effect any predictions? I mean, if “human-induced climate change” exacerbates droughts, snowfall, rainfall and so on, how can any model that doesn’t take this into account be accurate? Or am I missing something?
Lets hope they are as good as the MET Office UK at seasonal to long term prediction. Last time I checked the MET stopped issuing them to the public as they were wrong 5 years in a row. “Barbeque Summers” and “snow being a rare and exciting event” They still issue their wildly innacurate forecasts to the government but then when did politicians care about getting things right or even in the ball park?
At the risk of being hanged…
May I please drop the documentations from Dr. Theodor Landscheidt into discussion, who made El Nino/La Nina predictions with a high prediction quality up to 7 years?
This documantation was written in 1998.
Please notice the predictions in Chapter 11, that were completely correct.
http://www.john-daly.com/sun-enso/sun-enso.htm
So predictions are obviously possible, but on other methods than the above.
Again, “It’s the sun, stupid”.
I think most people were reacting to the news article, and not to the research paper per se. However, Bill Illis is right that this work contains some difficult mathematics–it is beyond a first course in differential equations because, among other things, we never present stochastic driving in such a course, and usually not in vector form. Moreover, one needs some statistics, and probably beyond introductory statistics, in order to be comfortable with such things as PDFs, stochasticity, and so forth. Finally, one does not encounter Lyapunov exponents short of graduate school, nor spectral components, nor low-frequency variability unless one has had a lot of education, or is educated in specific areas. None of this is as elementary as you suggest–I know I’ve taught a lot of this material. Maybe at Cal Tech things are different. I think the paper is difficult to read and comprehend without a lot of effort, and without looking at the papers in the bibliography.
I notice the pic uses a Wheel ranging all the way from a +3.5° C anomaly to >7°. The mindset of the proponents/perpetrators is thereby immediately revealed.
It would be interesting to rate the accuracy of this AGW biased forecast against other long range forecasts such as Farmers Almanack, Piers Corbyn and others. The only other AGW biased forecast was from the UK Met Office which was so abysmal it had to be stopped as it was so embarrasing to AGW supporters.
I think that 3 month forecasts would be better as it is soon enough for a quick check on accuracy. Six months from now there should be sufficient feedback to see if their claims are at all credible.
PS. They may need to start adding negative numbers to their wheel!
They’ve taken the picture link down so you’ll need to re-host it yourself
>>
JCG says:
September 12, 2011 at 9:13 am
<<
Sometimes individuals are so anxious to demonstrate their limited knowledge and to belittle others (such as Bill Illis) that their attempts are obviously naive.
One has to be careful when reading science papers, because the authors may be redefining various standard notations. For example, your comment:
>>
Note: I was amused in the paper to see dx/dt referred to (in the vector sense as bold) as X(dot). Classic!
<<
is really silly. If you read the” supporting information,” shortly after discussing two predator-prey models, they redefine dt as:
dt = (a + x₁)d[tau]; (where [tau] represents the lower case Greek letter tau.)
The authors further define x dot as the derivative of x with respect to [tau]. They aren’t really using Newton’s dot notation (at least, not in the supporting section).
Earlier in the section they mention “convolution product.” I doubt that convolution algebra, the Convolution Theorem, and Fourier and Laplace transforms are taught in elementary calculus.
I agree with Kevin Kilty. You’re not going to understand this paper with a cursory reading. It may take several days to several weeks, investigation of the references, and running of models to really get a handle on the paper.
Jim
As always the test of the model is in the accuracy of the predictions it makes, not whether the maths is a bit difficult. It would be great to have an accurate long range weather forecast, so I look forward to seeing how it all turns out.
Goldie says:
September 12, 2011 at 6:16 pm
And the benefit of this approach is that you shan’t wait long!
Jim Masterson says:
September 12, 2011 at 6:09 pm
I agree with Kevin Kilty. You’re not going to understand this paper with a cursory reading. It may take several days to several weeks, investigation of the references, and running of models to really get a handle on the paper.
Jim
In the present world we are inundated with stories about one or another of the key commodities necessary for human well being which are at or approaching Peak. Various fossil fuels, food crops, potable water are but a few of those factors I’ve seen suggested to be on the path to inadequacy. All these discussions never even mention the commodity that is and always has been the scarcest and least sustainable. At the point where we each emerge in the delivery room we are all at Peak Life. We will never have more potential lifetime available to us than we do at that moment.
I make this point in this context, because you’ve suggested, and I would agree quite rightly, that a thorough dissection of this opus would require from several days to several weeks to accomplish. My admittedly rather superficial scan of this paper suggests to me that, even if I should be able to get back up to speed on mathematics that I knew reasonably well 40 years ago but which I have had almost no occasion to utilize in the intervening decades, In the end I’d still be left with the same question I have after my brief scan i.e. is something that is merely worthless a significantly large enough advance on something that is completely worthless to make it worth the investment of my rapidly dwindling lifetime to make the assessment. The authors may be correct in asserting their method’s superiority, but judging from the graphics included neither method is likely to be the kind of thing I will have much confidence in and, as I’ve said , Life’s too short.
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Dave Wendt says:
September 12, 2011 at 8:26 pm
. . . something that is completely worthless . . . . Life’s too short.
<<
I agree. Life’s too short to waste on worthless nonsense.
Jim
Looks like the guys have invented a new roulette wheel.
So basically, as I’m getting from the comments, this is a veritable jungle of incredibly complex math, so dense and nigh-impenetrable it will takes weeks to months to hack through it and discover whether what’s on the other side is gold or guano.
This immediately brings to mind numerous examples, presented here and elsewhere, where much complex climate-related math, including whole models, could be replaced with simple one-line equations that reproduce the results with highly-acceptable accuracy. (Example, example, also read this.) The truckloads of tweaks and adjustments add up to self-canceling noise, as should be expected (one nudges up, next nudges down, etc). The incredibly complex math takes in a number, the result is always that number times 2, but the model is actually using 10,000 calculations involving advanced trigonometry, exponents and logarithms, and many 1st- and 2nd-order derivative functions. As is very easy to do in math (and computer programming btw), one can lose track of what is being done and how the different parts interact, resulting in further additions without figuring out the possible simplifications of what has done before.
Indeed, as mentioned here, simple models have been used to simulate the output of vastly more complex Global Climate Models, as was used for the IPCC TAR.
Unless it can be demonstrated the math has to be this complex, I’d consider this an interesting “first report” of something that may be highly promising, but much simplification is still indicated to make it truly usable for real science. “Black boxes” tend to be most profitable only for black box sellers.
I’m sorry but….have you ever seen anything so really truly completely and totally naff as that ‘Wheel of Climate’? There Are Not The Words to describe it, apart from, you know- naff.
Even David Appell couldn’t come up with anything so……. naff
And they’re stood there, (apart from the seated, proud and glowing mother of this circular stupidity) all smug and self satisfied with this monumental piece of naffness.
Lord help us.
There again, on a totally different level, I see a group of Mafiosi introducing some bizarre TV game show, probably quite close to the actuality when you think about.
Wheel looks like a Chuck-A-Luck in a 19th century Kansas cathouse. And them boys look gullible.
My complaint is not with the research itself, but with the press release. Do people here agree that the following claim is bogus?
“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.”
There is no “long term climate” forecast being made here! They have not yet demonstrated that they have definitely improved ENSO forecasts until they, in fact, make a forecast and later compare it with reality. And in any case, they can not say from their work whether next year’s growing season will be hot/cooler or wetter/dryer than this years. I would just as soon consult the farmer’s almanac!
The fastest way to turn people off to climate science (and science in general) is to make unsupportable claims like this. And it is up to the scientists involved to correct the spin in the press release, lest it damage their reputations in the long run.
From their pasty appearance, it looks like the Warming Models don’t even let their Wheel of Climate guys go outside! Probably so they won’t be ‘biased’ by real data…or real women?
Would it be wrong to suggest that the lack of rebuttal from The Team means this isnt real science?
“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.”
Well then… since we have 1) no direct control over climate, but now supposedly have 2) a way to predict impending natural disasters, the only usefulness of such an analysis is to encourage us to 3) ensure that we have a serious economic downturn at just the right time to coincide with the predicted disasters!
Brilliant!
To quote Burke Breathed, “No matter how thin you slice it, it’s still baloney.”