Daily Archives: May 3rd, 2012

NASA/Hathaway’s updated solar cycle prediction – smallest in 100 years | Watts Up With That?

…the predicted size makes this the smallest sunspot cycle in about 100 years

(Updated 2012/05/01)

From: http://solarscience.msfc.nasa.gov/predict.shtml

The current prediction for Sunspot Cycle 24 gives a smoothed sunspot number maximum of about 60 in the Spring of 2013. We are currently over three years into Cycle 24. The current predicted size makes this the smallest sunspot cycle in about 100 years.

The prediction method has been slightly revised. The previous method found a fit for both the amplitude and the starting time of the cycle along with a weighted estimate of the amplitude from precursor predictions (polar fields and geomagnetic activity near cycle minimum). Recent work [see Hathaway Solar Physics; 273, 221 (2011)] indicates that the equatorward drift of the sunspot latitudes as seen in the Butterfly Diagram follows a standard path for all cycles provided the dates are taken relative to a starting time determined by fitting the full cycle. Using data for the current sunspot cycle indicates a starting date of May of 2008. Fixing this date and then finding the cycle amplitude that best fits the sunspot number data yields the current (revised) prediction.

ssn_predict.gif (2208 bytes)

Click on image for larger version.

Predicting the behavior of a sunspot cycle is fairly reliable once the cycle is well underway (about 3 years after the minimum in sunspot number occurs [see Hathaway, Wilson, and Reichmann Solar Physics; 151, 177 (1994)]). Prior to that time the predictions are less reliable but nonetheless equally as important. Planning for satellite orbits and space missions often require knowledge of solar activity levels years in advance.

A number of techniques are used to predict the amplitude of a cycle during the time near and before sunspot minimum. Relationships have been found between the size of the next cycle maximum and the length of the previous cycle, the level of activity at sunspot minimum, and the size of the previous cycle.

Among the most reliable techniques are those that use the measurements of changes in the Earth’s magnetic field at, and before, sunspot minimum. These changes in the Earth’s magnetic field are known to be caused by solar storms but the precise connections between them and future solar activity levels is still uncertain.

Of these “geomagnetic precursor” techniques three stand out. The earliest is from Ohl and Ohl [Solar-Terrestrial Predictions Proceedings, Vol. II. 258 (1979)] They found that the value of the geomagnetic aa index at its minimum was related to the sunspot number during the ensuing maximum. The primary disadvantage of this technique is that the minimum in the geomagnetic aa index often occurs slightly after sunspot minimum so the prediction isn’t available until the sunspot cycle has started.

An alternative method is due to a process suggested by Joan Feynman. She separates the geomagnetic aa index into two components: one in phase with and proportional to the sunspot number, the other component is then the remaining signal. This remaining signal has, in the past, given good estimates of the sunspot numbers several years in advance. The maximum in this signal occurs near sunspot minimum and is proportional to the sunspot number during the following maximum. This method does allow for a prediction of the next sunspot maximum at the time of sunspot minimum.

A third method is due to Richard Thompson [Solar Physics 148, 383 (1993)]. He found a relationship between the number of days during a sunspot cycle in which the geomagnetic field was “disturbed” and the amplitude of the next sunspot maximum. His method has the advantage of giving a prediction for the size of the next sunspot maximum well before sunspot minimum.

We have suggested using the average of the predictions given by the Feynman-based method and by Thompson’s method. [See Hathaway, Wilson, and Reichmann J. Geophys. Res. 104, 22,375 (1999)] However, both of these methods were impacted by the “Halloween Events” of October/November 2003 which were not reflected in the sunspot numbers. Both methods give larger than average amplitude to Cycle 24 while its delayed start and low minimum strongly suggest a much smaller cycle.

The smoothed aa index reached its minimum (a record low) of 8.4 in September of 2009. Using Ohl’s method now indicates a maximum sunspot number of 70 ± 18 for cycle 24. We then use the shape of the sunspot cycle as described by Hathaway, Wilson, and Reichmann [Solar Physics 151, 177 (1994)] and determine a starting time for the cycle by fitting the latitude drift data to produce a prediction of the monthly sunspot numbers through the next cycle. We find a maximum of about 60 in the Spring of 2013. The predicted numbers are available in a text file, as a GIF image, and as a pdf-file. As the cycle progresses, the prediction process switches over to giving more weight to the fitting of the monthly values to the cycle shape function. At this phase of cycle 24 we now give 66% weight to the amplitude from curve-fitting technique of Hathaway, Wilson, and Reichmann Solar Physics 151, 177 (1994). That technique currently gives similar values to those of Ohl’s method.

Note: These predictions are for “smoothed” International Sunspot Numbers. The smoothing is usually over time periods of about a year or more so both the daily and the monthly values for the International Sunspot Number should fluctuate about our predicted numbers. The dotted lines on the prediction plots indicate the expected range of the monthly sunspot numbers. Also note that the “Boulder” numbers reported daily at www.spaceweather.com are typically about 35% higher than the International sunspot number.

Another indicator of the level of solar activity is the flux of radio emission from the Sun at a wavelength of 10.7 cm (2.8 GHz frequency). This flux has been measured daily since 1947. It is an important indicator of solar activity because it tends to follow the changes in the solar ultraviolet that influence the Earth’s upper atmosphere and ionosphere. Many models of the upper atmosphere use the 10.7 cm flux (F10.7) as input to determine atmospheric densities and satellite drag. F10.7 has been shown to follow the sunspot number quite closely and similar prediction techniques can be used. Our predictions for F10.7 are available in a text file, as a GIF image, and as a pdf-file. Current values for F10.7 can be found at: http://www.spaceweather.ca/sx-4-eng.php.

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Climate Change Boosts Then Quickly Stunts Plants, Decade-long Study Shows

skepticalscience.com

Posted on 23 April 2012 by John Hartz

This is a reprint of a press release posted by the National Science Foundation (NSF) on April 10, 2012.

Global warming may initially make the grass greener, but not for long

Photos of ecosystems studied

Composite of the ecosystems studied, arranged left to right in order of increasing elevation.

Global warming may initially make the grass greener, but not for long, according to new research results.

The findings, published this week in the journal Nature Climate Change, show that plants may thrive in the early stages of a warming environment but then begin to deteriorate quickly.

“We were really surprised by the pattern, where the initial boost in growth just went away,” said scientist Zhuoting Wu of Northern Arizona University (NAU), a lead author of the study. “As ecosystems adjusted, the responses changed.”

Ecologists subjected four grassland ecosystems to simulated climate change during a decade-long study.

Plants grew more the first year in the global warming treatment, but this effect progressively diminished over the next nine years and finally disappeared.

The research shows the long-term effects of global warming on plant growth, on the plant species that make up a community, and on changes in how plants use or retain essential resources like nitrogen.

“The plants and animals around us repeatedly serve up surprises,” said Saran Twombly, program director in the National Science Foundation (NSF)’s Division of Environmental Biology, which funded the research.

“These results show that we miss these surprises because we don’t study natural communities over the right time scales. For plant communities in Arizona, it took researchers 10 years to find that responses of native plant communities to warmer temperatures were the opposite of those predicted.”

The team transplanted four grassland ecosystems from a higher to lower elevation to simulate a future warmer environment, and coupled the warming with the range of predicted changes in precipitation–more, the same, or less.

The grasslands studied were typical of those found in northern Arizona along elevation gradients from the San Francisco Peaks down to the Great Basin Desert.

The researchers found that long-term warming resulted in loss of native species and encroachment of species typical of warmer environments, ultimately pushing the plant community toward less productive species.

The warmed grasslands also cycled nitrogen more rapidly. This should make more nitrogen available to plants, scientists believed, helping plants grow more. But instead much of the nitrogen was lost, converted to nitrogen gases in the atmosphere or leached out by rainfall washing through the soil.

Bruce Hungate, senior author of the paper and an ecologist at NAU, said the study challenges the expectation that warming will increase nitrogen availability and cause a sustained increase in plant productivity.

“Faster nitrogen turnover stimulated nitrogen losses, likely reducing the effect of warming on plant growth,” Hungate said. “More generally, changes in species, changes in element cycles–these really make a difference. It’s classic systems ecology: the initial responses elicit knock-on effects, which here came back to bite the plants. These ecosystem feedbacks are critical–you can’t figure this out with plants grown in a greenhouse.”

The findings caution against extrapolating from short-term results, or from experiments with plants grown under artificial conditions, where researchers can’t measure the feedbacks from changes in the plant community and from nutrient cycles.

“The long-term perspective is key,” said Hungate. “We were surprised, and I’m guessing there are more such surprises in store.”

Co-authors of the paper include George Koch and Paul Dijkstra, both at NAU.

Source Paper:

Zhuoting Wu, Paul Dijkstra,George W. Koch,and Bruce A. Hungate, Biogeochemical and ecological feedbacks in grassland responses to warming, Nature Climate Change(2012) doi:10.1038/nclimate1486

Received 01 June 2011 Accepted 08 March 2012 Published online 08 April 2012

Click here to access the Abstract.

 

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