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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“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 …