From NC State Press Releases
(Note: the actual paper was not included with this press release)
It’s hot out right now, but new research from North Carolina State University will help us know what to expect when the weather turns cold. Researchers have developed a new methodology that improves the accuracy of winter precipitation and temperature forecasts. The tool should be valuable for government and utility officials, since it provides key information for use in predicting energy consumption and water availability.
“Predicting winter precipitation is extremely useful, because winter is the most important season in terms of re-charging water supplies in the United States, ensuring water will be available in the summer,” says Dr. Sankar Arumugam, author of the study and an assistant professor of civil, construction and environmental engineering at NC State. The study was co-authored by Naresh Devineni, a Ph.D. student at NC State.
Researchers were able to reduce uncertainty in winter climate predictions by developing a methodology that incorporates multiple climate forecast models and also accounts for the activity of El Nino conditions in the Pacific.
“Predicting temperature is also important, because temperature determines energy consumption,” Arumugam says. “When it is very cold, people use more energy to heat their homes.”
The researchers were able to reduce uncertainty in winter climate predictions over the United States by developing a methodology that incorporates multiple general climate forecast models (GCMs) and also accounts for the activity – or inactivity – of El Nino conditions in the Pacific.
Winter precipitation and temperature over many regions of the continental United States are predominantly determined by the El Nino Southern Oscillation (ENSO), which denote hot (El Nino) or cold (La Nina) sea surface temperature conditions in the tropical Pacific.
Most GCMs are better at predicting the winter climate when ENSO is quite active, and are less accurate under neutral ENSO conditions. The methodology developed by the researchers accounts for the skill of the models under active and neutral ENSO conditions in combining multiple GCMs, resulting in reduced uncertainty in predicting the winter climate.
“Improving precipitation and temperature predictions should help government, water and energy utility officials plan more effectively,” Arumugam says, “because they will have a better idea of what conditions to expect.”
The study, “Improving the Prediction of Winter Precipitation and Temperature over the continental United States: Role of ENSO State in Developing Multimodel Combinations,” was published online this month by Monthly Weather Review. The research was funded by the North Carolina Water Resources Research Institute.
NC State’s Department of Civil, Construction and Environmental Engineering is part of the university’s College of Engineering.
-shipman-
Note to editors: The study abstract follows.
“Improving the Prediction of Winter Precipitation and Temperature over the continental United States: Role of ENSO State in Developing Multimodel Combinations”
Authors: Naresh Devineni, A. Sankarasubramanian, North Carolina State University
Published: June 2010 (made available in July), Monthly Weather Review
Abstract: Recent research in seasonal climate prediction has focused on combining multiple atmospheric General Circulation Models (GCMs) to develop multimodel ensembles. A new approach to combine multiple GCMs is proposed by analyzing the skill of candidate models contingent on the relevant predictor(s) state. To demonstrate this approach, we combine historical simulations of winter (December-February, DJF) precipitation and temperature from seven GCMs by evaluating their skill – represented by Mean Square Error (MSE) – over similar predictor (DJF Nino3.4) conditions. The MSE estimates are converted into weights for each GCM for developing multimodel tercile probabilities. A total of six multimodel schemes are considered that includes combinations based on pooling of ensembles as well as based on the long-term skill of the models. To ensure the improved skill exhibited by the multimodel scheme is statistically significant, we perform rigorous hypothesis tests comparing the skill of multimodels with individual models’ skill. The multimodel combination contingent on Nino3.4 show improved skill particularly for regions whose winter precipitation and temperature exhibit significant correlation with Nino3.4. Analyses of weights also show that the proposed multimodel combination methodology assigns higher weights for GCMs and lesser weights for climatology during El Nino and La Nina conditions. On the other hand, due to the limited skill of GCMs during neutral conditions over the tropical Pacific, the methodology assigns higher weights for climatology resulting in improved skill from the multimodel combinations. Thus, analyzing GCMs’ skill contingent on the relevant predictor state provide an alternate approach for multimodel combination such that years with limited skill could be replaced with climatology.
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Sounds like broken watch science.
I’ve got a broken watch stuck at 12. To determine whether the time is closer to noon or midnight I installed a solar powered indicator light on my broken watch.
Statistical analysis shows that if the solar powered indicator light is lit, then the time is closer to noon rather then midnight.
I can now more accurately predict whether it is day or night using my broken watch.
Well, well, well. A model that increases weighting on ENSO events. Hansen, are you reading this? You might want to tinker with your model code by adding a bit of weight to the oceans (GCM’s), and subtracting a bit of weight off your AGW climatological code. If you won’t listen to us, maybe you will listen to colleagues. I won’t hold my breath.
Does it beat Joe Bastardi?
Very soon, maby within two decades they understand what Bastardi and Corbyn allready know?
Improves the accuracy from what? zero to 1?
This is real cutting edge science, they just figured out that ENSO might have something to do with it, and wrote it into a computer game.
They are still hind-casting. Trying to predict the future by what’s happened, when no one knows enough about it to know what happened.
and screaming because no one believes them about global warming
Uhmm, anyone else notice they state “New Methodology Improves Winter Climate Forecasting”, but we see no quantification of how much it is improved or if it actually improves forecasting at all. Giving weight to already known climate related phenomena is something I thought was already being done. (Anthony, isn’t this already being done in meteorology?) Using multiple GCM’s isn’t new either. What am I missing?
Department of Civil, Construction and Environmental Engineering
“Engineers” ? Not Scientists … Heretics !! … LOL
Deadly cold
http://en.trend.az/regions/world/ocountries/1723309.html
At least 175 people have died in the coldest winter in South America in recent years, officials in six affected countries said, dpa reported.
The cold was worst in southern Peru, where temperatures in higher altitudes of the Andes dropped to minus 23 degrees Celsius. Officials said Monday that since the beginning of last week 112 people died of hypothermia and flu.
The Met Office has been wrong 10 of 10 years. They have forecasted too hot for 9 of 10 years.
Last winter Joe Bastardi forecasted a huge storm around New Years Day hitting England. His forecast was over a week in advance. It was noted in The Met Forecast at the time it hit.
Hey, lighten up guys! This is the work of a student learning to be a research scientist. Throttle back on the ‘tude. At least the student will go out into the world with an appreciation for ENSO and the ocean effect on climate.
Cheers,
BillN
Improves the accuracy of forecasting . . . . .? Doesn’t say how many years it’s been tested over – that I can see . . surely the abstract or summary would have referred to historical facts if this claim could be substantiated . . . or is this retrospective model comparisons . . . Why don’t they come back when they’ve got five or more years of real data that supports their improved accuracy claims . . . .Is this what science has come to?
I know for some of us this seems like such a “duh” moment. But Hansen style climate models place little weight on changing short and long term ENSO parameters and instead rely quite heavily on the mathematically calculated “assumed” effects of increased greenhouse gas forcing as the primary long term pattern of concern.
Here is my position. In essence, if GCM’s predict long term climate better than either a mixed, or an AGW climatological model, those of us who understand the oscillating heat holding and heat letting capacity of oceans will be vindicated. That is not to say that greenhouse gasses do not cause warming (they do), or that increased amounts will not cause more warming (they probably do). But the oscillating power of the oceans just buries this tiny change in temperature.
Golly! Another model. [snip]
Kids still insisting on working for computer games, in the realm of the twilight zone,dark black holes and devilish antimatter, not really researching for real causes, though there are a lot of researchers, all over the world, applying real science not IPCC-like witchcraft.
Researchers were able to reduce uncertainty in winter climate predictions by developing a methodology that incorporates multiple climate forecast models</B. and also accounts for the activity of El Nino conditions in the Pacific
This is politicized and convenient meteorology.
BillN says:
July 20, 2010 at 9:47 am
Hey, lighten up guys!
==========================================================
Nope, the field of main stream climate science has become a business of preying on victims. The more victims they create, the more money they make, and the better their job security.
This paper was peer reviewed by people that have been in this business long enough to know the game and work the game.
They have not been able to predict squat, and this paper does not improve that one bit. It’s just another smoke and mirror to make it seem like they can.
robust, new, improved,
No slack is coming from me at all.
I’m all confused. Is this weather or climate? Seems to me (as mentioned above) Joe Bastardi and other good weather forecasters already do this.
Here is a link to the paper: http://www.ce.ncsu.edu/research/hydroclimatology/people/sankar/pubs/mwr_in_press.pdf
Excuse me, but shouldn’t the title read “New Methodology Improves Winter Weather Forecasting”? I get so mixed up these days. Personally, I’ll stick with Joe. He’s also got a sense of humour – which is more than you can say about most of the new puritans.
Cyclic peaks and troughs by definition are predictable in terms of a phenomenon’s amplitude, frequency, and wavelength. As activity bends high and low about a central mode, or trend, regression-to-the-mean is mathematically a given, regardless of internal dynamics driving the effect.
Climate studies are not an empirical, experimental discipline, but rather classification schemes akin to botany. Just as evolutionary biology is absolutely incapable of forecasting mutant variations, so climatologists (sic) deal only in hindsight, their circular “models” necessarily built on more or less unrealistic preconceptions.
For mathematical and physical reasons ranging from Chaos Theory to Fractal Geometry and Ilya Prigogine’s “catastrophes”, linear extrapolations of complex dynamic system such as Earth’s atmosphere are impossible in principle. This leaves climate hysterics either to admit pervasive ignorance or sneakily fabricate conclusions in the guise or objective, rational scientific inquiry.
Since no “peer review” can replicate non-empirical results, corrupted panels merely accede to knowing fraud for purposes of agenda-driven propaganda, refusing access to base-data or manipulative computations while mounting vicious ad hominem attacks on all competing theses. From Pachauri’s grubby little REDD-based IPCC to once-respected institutions such as GISS/NASA, Penn State, CRU/UEA, the rot is far advanced. Next up: EU debate-suppression statutes akin to Canada’s astoundingly maleficent “hate speech” panels, Star Chamber exercises in (as Sr. AW has said) “CYA BS” on every level.
What is to be done?
I tried eating one insect and it does not taste good at all. So, I will improve the flavor by mixing 12 different insects and eating that instead. Well, it’s a plan at least.
It cannot be known whether this model improves predictions until it has made a fair number of predictions and then the actual weather is compared to the predictions.
I am regularly astonished by the aggressively self-confident claims of “modern” scientists. I cannot remember whether it is NOAA or NASA that uses the language “_____ understands the oceans and atmosphere and makes accurate predictions, etc” (paraphrasing), when scientists of proper humility would add in the words “seeks to understand……”.
This also reminds me of the language of educationist bureaucrats in their curricula stating “The student will learn and comprehend X”, when we all know darn well that most of our students will neither learn nor comprehend much of the curriculum presented to them. Perhaps this is where recent scientists have learned their verbal grandiosities.
Orwell taught us about the power of debauched language to produce debauched thinking. The apparently oxymoronic “Climate Scientist” seems to be a case in point.
“New Methodology Improves Winter Climate Forecasting”
1. Never call it a prediction, ever.
2. Never use absolute words like ‘will’ and ‘mus’t, stick to ‘could’ and ‘may’.
3. Only make projections at least 50 years out.
4.7 Get a larger dart board (or look out the window)
6. Collect some old copies copy of The Farmers Almanac.
7. Learn to cut-and-paste.
5.31 Get a good press agent.
Most GCMs are better at predicting the winter climate when ENSO is quite active,
Mostly because they were designed to handle ever-increasing heat content and the attendant warming that resulted.
One of the problems with short term gain for long term pain.
“It’s hot out right now, but new research from North Carolina State University will help us know what to expect when the weather turns cold.”
~~~~~
How about ‘may’. Seems you rarely see correct key words being applied in science literature and journalism today.
Anthony you might take a detailed look at what my lunar declination cyclic forecast produces, to see how the 6 year long forecast (2008 through end of 2013) on my site has scored compared to climatology over the first 30 months since it was posted.
Peru is not the only country suffering from cold
http://www.guardian.co.uk/world/2010/jul/20/mongolia-nomads-livestock-winter-poverty
Somehow the Guardian couldn’t get AGW is to blame into the story but bet they tried hard.