For the first time, scientists have used artificial intelligence to not only predict sunspots, but also to correct the incomplete recording of past sunspot activity.
A new paper just published in Advances in Space Research by Dr. Victor Velasco Herrera, a theoretical physicist at the National Autonomous University of Mexico, Dr. Willie Soon, an award-winning solar astrophysicist at the Harvard-Smithsonian Center for Astrophysics, and Professor David Legates, The Climatologist at the University of Delaware and former director of the U.S. Global Change Research Program predicts the new 11-year solar cycle that occurred before has recently begun, will have near record low sunspot activity that will last through the middle of the century.
Both Dr. Soon (2004) and Dr. Velasco Herrera (2008) had previously published articles speculating that the first half of the 21st century would be a period of unusually fewer sunspots, potentially slowing the rate of global warming
Sunspots are important. When there are many sunspots and the sun is active, there is a risk that a strong solar output directed at Earth could damage or even destroy the thousands of satellites on which the world depends for everything from radio, telephone , Television and internet communication to monitoring the climate and observing the remotest areas of the universe.
Worse still, a really strong solar storm could damage the largely unshielded terrestrial power grid. Most power lines and transformers are above ground and therefore acutely vulnerable. Solar collectors could also shorten their lives through intense solar radiation.
The three scientists taught a machine learning algorithm how to identify underlying patterns and cycles in sunspot records over the past 320 years. The algorithm then discovered a previously unnoticed interaction between the 5.5-year solar half-cycles (blue) and the 120-year Gleissberg double cycles (red dotted lines), which enabled it to confirm the earlier predictions of a quiet half-century – predictions, which are now shared by solar physicists.
This interaction between the two periodicities led the algorithm to point out that from the 1730s to 1760s, at the beginning of modern sunspot recording (the gray band below), sunspots were under-recorded: as the 120-year cycle was nearing its maximum Amplitude, sunspots should have been more numerous than reported at the time.
Periods of minimum and maximum solar activity from 1700 to 2020, analyzed by machine learning.
The algorithm then predicted sunspots from 2021 to 2100. He suggests that the current low solar activity is likely to last until 2050:
The sun can be calm for half a century
Dr. Velasco Herrera said: “Not everyone agrees with our expectation that solar activity will continue to be low for another three solar cycles. In an article in solar physics by Dr. Scott McIntosh of the US National Center for Atmospheric Research says the upcoming solar cycle will be unusually active, with a maximum sunspot count of 233, compared to our estimate of less than 100. Place your bets on the battle of solar cycles! “
Dr. Soon said, “The machine learning algorithm, with its interesting interplay between the very short 5.5-year cycle and the long 120-year cycle, confirms our findings from 10 to 15 years ago and suggests that the next three or four solar cycles doing this will be comparatively inactive. This is the first time the two problems of questioning incomplete past records and predicting the future have been combined into a single analysis. “
Dr. Legates said: “President Trump recognized the importance of space weather, and the sun in particular, in influencing the global climate. It was he who incorporated the ProSwift Act of October 2020 into law to aid the study and prediction of space weather. Given the history of previous periods of comparative solar activity, the weather may get a little cooler between now and 2050. By then, if we are right, our power grids and satellites should be safe. “
Interested readers can download the paper free of charge from the magazine before June 6th
https://authors.elsevier.com/a/1cwLK%7E6OilW7X
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