This is an official NCAR News Release (National Center for Atmospheric Research) Apparently, they have solar forecasting techniques down to a “science”, as boldly demonstrated in this press release. – Anthony
Scientists Issue Unprecedented Forecast of Next Sunspot Cycle
BOULDER—The next sunspot cycle will be 30-50% stronger than the last one and begin as much as a year late, according to a breakthrough forecast using a computer model of solar dynamics developed by scientists at the National Center for Atmospheric Research (NCAR). Predicting the Sun’s cycles accurately, years in advance, will help societies plan for active bouts of solar storms, which can slow satellite orbits, disrupt communications, and bring down power systems.
The scientists have confidence in the forecast because, in a series of test runs, the newly developed model simulated the strength of the past eight solar cycles with more than 98% accuracy. The forecasts are generated, in part, by tracking the subsurface movements of the sunspot remnants of the previous two solar cycles. The team is publishing its forecast in the current issue of Geophysical Research Letters.
“Our model has demonstrated the necessary skill to be used as a forecasting tool,” says NCAR scientist Mausumi Dikpati, the leader of the forecast team at NCAR’s High Altitude Observatory that also includes Peter Gilman and Giuliana de Toma.
Understanding the cycles
The Sun goes through approximately 11-year cycles, from peak storm activity to quiet and back again. Solar scientists have tracked them for some time without being able to predict their relative intensity or timing.
NCAR scientists Mausumi Dikpati (left), Peter Gilman, and Giuliana de Toma examine results from a new computer model of solar dynamics. (Photo by Carlye Calvin, UCAR) |
Forecasting the cycle may help society anticipate solar storms, which can disrupt communications and power systems and affect the orbits of satellites. The storms are linked to twisted magnetic fields in the Sun that suddenly snap and release tremendous amounts of energy. They tend to occur near dark regions of concentrated magnetic fields, known as sunspots.
The NCAR team’s computer model, known as the Predictive Flux-transport Dynamo Model, draws on research by NCAR scientists indicating that the evolution of sunspots is caused by a current of plasma, or electrified gas, that circulates between the Sun’s equator and its poles over a period of 17 to 22 years. This current acts like a conveyor belt of sunspots.
The sunspot process begins with tightly concentrated magnetic field lines in the solar convection zone (the outermost layer of the Sun’s interior). The field lines rise to the surface at low latitudes and form bipolar sunspots, which are regions of concentrated magnetic fields. When these sunspots decay, they imprint the moving plasma with a type of magnetic signature. As the plasma nears the poles, it sinks about 200,000 kilometers (124,000 miles) back into the convection zone and starts returning toward the equator at a speed of about one meter (three feet) per second or slower. The increasingly concentrated fields become stretched and twisted by the internal rotation of the Sun as they near the equator, gradually becoming less stable than the surrounding plasma. This eventually causes coiled-up magnetic field lines to rise up, tear through the Sun’s surface, and create new sunspots.
The subsurface plasma flow used in the model has been verified with the relatively new technique of helioseismology, based on observations from both NSF– and NASA–supported instruments. This technique tracks sound waves reverberating inside the Sun to reveal details about the interior, much as a doctor might use an ultrasound to see inside a patient.
NCAR scientists have succeeded in simulating the intensity of the sunspot cycle by developing a new computer model of solar processes. This figure compares observations of the past 12 cycles (above) with model results that closely match the sunspot peaks (below). The intensity level is based on the amount of the Sun’s visible hemisphere with sunspot activity. The NCAR team predicts the next cycle will be 30-50% more intense than the current cycle. (Figure by Mausumi Dikpati, Peter Gilman, and Giuliana de Toma, NCAR.) |
Predicting Cycles 24 and 25
The Predictive Flux-transport Dynamo Model is enabling NCAR scientists to predict that the next solar cycle, known as Cycle 24, will produce sunspots across an area slightly larger than 2.5% of the visible surface of the Sun. The scientists expect the cycle to begin in late 2007 or early 2008, which is about 6 to 12 months later than a cycle would normally start. Cycle 24 is likely to reach its peak about 2012.
By analyzing recent solar cycles, the scientists also hope to forecast sunspot activity two solar cycles, or 22 years, into the future. The NCAR team is planning in the next year to issue a forecast of Cycle 25, which will peak in the early 2020s.
“This is a significant breakthrough with important applications, especially for satellite-dependent sectors of society,” explains NCAR scientist Peter Gilman.
The NCAR team received funding from the National Science Foundation and NASA’s Living with a Star program.
IMPORTANT NOTE:
The date of this NCAR News Release is March 6, 2006
Source: http://www.ucar.edu/news/releases/2006/sunspot.shtml
(hat tip to WUWT reader Paul Bleicher)
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It seems to be in our nature to think that we know everything. I wonder if this is part of our psychological mindset. If it has not been done already, sociologists or perhaps psychologists could have a field day analyzing people’s responses to global events such as swine flu and global warming. Assuming that they can maintain professional detachment from the events happening around them.
Perhaps it’s the times we live in but we seem to have lost a sense of proportion over such events. It is claimed by Kofi Anan that 300,000 die because of an unsubstantiated consequence of “global warming”. How many die of wars, polluted water, disease, malnutrition, smoking, traffic accidents etc.?
From http://saintseminole.awardspace.com/stats.htm & others
47,000 die in the US every year because of the flu & respiratory diseases (USA Today)
112,000 die in the US every year because of obesity
418,000 die in the US every year because of smoking
39,000 die in the US every year because of traffic accidents
1.2 million die in the world, annually, in traffic accidents
and so on…
Rick Sharp (18:18:29) :
“FOR SALE: One 2006 Predictive Flux-transport Dynamo Model. Make offer…..”
LMAO!! The funniest thing I’ve read all week…
Really, the sunspot cycle prognosticator boys desperately need to just STFU until they’ve got some hard observable facts to base a new forecast on. They’re making Jeanne Dixon look good at this point. Shameful.
A great example of developing a computer model based on a very limited dataset (the past 12 cycles) of a highly complex phenomenon with no “out-of-sample” testing to validate the model. The vaunted AGW models spewing their predictions of runaway warming all suffer from the same deficiencies. How can such educated people make such basic and obvious errors?
So, we see that they (the team) have NOT changed their press release – despite two out of 7 years of their FIRST prediction being completely wrong: 2007 – 2009 being completely flat, rather than rapidly increasing to apeak of 2013.
What changed – so their predictions for 2007 and 2008 can be updated? What did NOT change – to indicate that their program was right, but off in its initial conditions? What did they correct in their program? Why are they being paid – if they REFUSE to update and revise their program’s predictions when proven so abjectly wrong?
How can they tolerate their hypocrisy and stubbornness?
Yup – you had me going there too.
But fairl play to them for coming up with a predictive model that could be tested in the near term. Pity it was way wrong though…
Jeremy (18:47:05) :
The photo is priceless. This to me summarizes the complete lack of scientific approach to everything these days. Everyone sits round a PC and blindly believes whatever nonsense it spews out.
Guess this means my iphone climate modeling app isn’t going to do that well. . . ,
The good news is that the forecast made back in 2006 might have verified purely by by accident. Consider what the subsequent effects of this would have been. We are indeed fortunate that the prediction was falsified on the first shot out of the box.
I’ve spent a lifetime involved with weather/climate observations and making operational forecasts and along the way looking at every medium to long-range forecast scheme that’s ever been publicized. I’ve even tried to make a few of them myself. After a while it starts to sink in. None of these schemes has stood the test of time. Most have failed shortly after their publication.
Computers can fit curves to an amazing variety of prior observations, but using the same algorithm to make predictions is almost never successful.
“Researchers” who continue to pursue these phantasms can have many motivations. They often believe in their own ideas. More recently there seems to be a horde of investigators that have no particular loyalty to their precepts but find them to be convenient vehicles for advancement in their chosen profession.
Experience has proven, at least to me, tht the only appropriate position to take on unverified predictions is skepticism, with or without prejudice, depending on the track record of the proponent(s).
The field of meteorology is one in which failure to acheive verifiable results is not a handicap to success.
“The CAGW disinformation machine often employs boring, rhetorically-challenged advocates who deliver their messages of fear and confusion in inaccurately measured and untested absurdities.” M. Bryant
Robert Wood (18:30:15) :
Perhaps Leif can come to the aid of my confused mind.
As we don’;t udnerstand the Sun, how can we have an accurate model of it? Or am I being pedantic?
Secondly, tweaking an arbitary model tof the Sun to match the previous solar cycles does not invest the model with “predictive skill”. It simply means one has tweaked a computer program to produce the desired result … a posteriori.
When the solar cycle prediction panel started its deliberations I produced this http://www.leif.org/research/Grow-N-Crash%20Prediction%20Model.pdf computer model to show how easy it is to match previous cycles with a reasonable physical model. I called it the Grow-N-Crash model and it simply says that cycles grow and grow until they are too big, then crash and the process repeats…My model does a very good job, too, and even predicted a large [140] SC24 as theirs… The exercise was to show how easy this was and how their model was not unique in its predictive ‘power’.
BTW, I was a reviewer of their prediction paper. My review is here: http://www.leif.org/research/Dikpati%20Referee%20Report.pdf
They in their paper “thank two anonymous referees for a thorough and critical review of an earlier version of this paper”
I believe that all reviews should be published [as an electronic supplement to the paper] and I have never insisted on anonymity in reviewing. IMHO, the review is as important to the public as the paper itself.
[snip]
I am afraid that I cannot go along with the crowd in poking fun at the funny failed computer model prediction. If, as advertised, the failed prediction is based on a model that incorprated physical dynamics related to sunspot formation in the past, then the failure might well siggest that something important has changed in the sunspot department. Maybe the slowing of the slowing of the conveyor belt that was reported a while back, for example. While I am certainly not a CO2 denouncer, my not so humble opinion is that this failed prediction is worth serious consideration
“The next sunspot cycle will be 30-50% stronger than the last one . . .The scientists expect the cycle to begin in late 2007 or early 2008, . . .”
Contrast:
It will be interesting to see whose prediction is closer to reality.
Scientific modeling is useful for the continuing progress of science, but using un-validated models to build “consensus” or “settled science” and guide political policy is crazy, and not “based on the facts”. I wouldn’t trust a climate model further than I could throw it.
When I was in business school I spent a lot of time studying models of economic and financial forecasting. The first thing you learn in this process is that it is a fundamental mistake to assume that the future is going to resemble the past. You can construct a model to “predict” the past with incredible precision, but when you apply it to the future it doesn’t work very well. If that were not the case, there would be models that predict the future behavior of financial markets with tremendous precision.
The problem with models is, when it’s actually your own money at stake, you usually lose your money. That’s the case here as well.
NCAR has(had) some heavy duty solar dynamo researchers like Miesch. Never have seen dynamo researchers making sunspot predictions however.
I’d say the ‘solar conveyor recycling flux tubes’ is going to be lightly regarded if not considered falsified by 2015 or so. Why they don’t figure the baroclinic forces at the tachocline create a Coriolis effect and start there I can’t figure.
Some folks just have to be the smartest man in the room.
Really, someone create a Facebook Quiz on Cycle 24 at this point –it will have at least as much validity and more entertainment value.
And you all thought the MIT Climate Wheel was a bad idea!
That’s the great thing about these solar cycle predictions – we don’t have to wait long to know if they were accurate. Hopefully this lull is actually providing an incredible learning opportunity for solar scientists and is also reminding them of how much they DON’T know about the sun just yet.
This prediction was correct(ish) in one regard – this next cycle is starting late!
On the topic of forecasting; I’m sure alot of you have seen these videos on youtube.
Share them with your contacts. Help inform others with, IMHO, the view of an expert on weather and climate.
Aww … Now that’s just cruel! 8^D
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You have to give them credit for standing up with several firm predictions. They had several bold numbers which would tend to confirm their model if they had been achieved. The start date had a broad spread, but predicting a late start would have made their high peak all the more interesting. At the moment all we know is that their start date prediction was wrong.
I don’t have time to look in the thesaurus for proper descriptions for how late the start has actually become.
Often wonder why they are telling me this instead of making their own fortunes.
Most unfortunate, dear sir, they are having invested vast winnings to diamond concern being based in Nigeria. That we see you are one most worthy of trust, we asking of your kind assistance, which will being mutual profitable . . .
When I was in business school I spent a lot of time studying models of economic and financial forecasting. The first thing you learn in this process is that it is a fundamental mistake to assume that the future is going to resemble the past. You can construct a model to “predict” the past with incredible precision, but when you apply it to the future it doesn’t work very well. If that were not the case, there would be models that predict the future behavior of financial markets with tremendous precision.
Back in the mid-1970s a friend of mine had a letter published in Nature in which he forecast Cycle 21 based on a power series analysis; every other cycle had a negative sunspot count, making the 22-year cycle look sort of like a sine wave. But he admitted it was “pure numerology” with no attempt to take any underlying physics into account. This can work in some circumstances — you can get very good ephemerides of planetary positions this way, and you don’t need to know anything about gravitation, or Kepler’s Laws, or any of that stuff. As I recall, the prediction for Cycle 21 turned out to be pretty accurate, but cycles 22, 23, and 24 just fell to pieces — the curve didn’t even look particularly sinusoidal.
As they say, “past performance is not necessarily indicative of future results.”
e
No first principles. No cause and effect. No understanding of physics. mathematics or even statistics.
Since we’d lose our grant were we to contradict it,
We’ll predict it.