JamesSavik

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A Kilonova Detected --"A Cosmic Phenomenon Long Theorized But Never Conclusively Observed — Until Now" Daily Galaxy October 16, 2017 In the world of astrophysics, Aug. 17, 2017, was a red-letter day. “This is a game-changer for astrophysics,” said UC Santa Barbara faculty member Andy Howell, who leads the supernova group at the Las Cumbres Observatory (LCO). “A hundred years after Einstein theorized gravitational waves, we’ve seen them and traced them back to their source to find an explosion with new physics of the kind we’ve only dreamed about.” First, NASA’s orbiting Fermi satellite identified a burst of high-energy gamma rays. Then, in the minute leading up to the Fermi burst, scientists noticed microscopic distortions in space caused by gravitational waves passing through the Earth. When they combined the data from the two Laser Interferometer Gravitational-Wave Observatory (LIGO) facilities in Hanford, Washington, and Livingston, Louisiana, with the data from the Virgo detector in Italy, they realized they could localize the disturbance to a relatively small region of the sky — only about 150 times the size of the full moon — near the constellation Hydra. Astronomers at Las Cumbres Observatory (LCO) in Santa Barbara activated their robotic network of 20 telescopes around the world and were one of six teams to co-discover a new source of light in that region and localize it to the galaxy NGC 4993, only about 130 million light years away. “Such a gravitational wave signal had never been seen before but was unmistakably generated by two neutron stars spiraling together,” explained Iair Arcavi, a NASA Einstein postdoctoral fellow in UC Santa Barbara’s Department of Physics and leader of the LCO follow-up effort. The resultant study appears in the journal Nature. The outburst that occurs right after two neutron stars merge is called a kilonova, a phenomenon that had long been theorized though never conclusively observed — until now. Unlike traditional ground-based facilities with single telescopes, the LCO network could observe the phenomenon every few hours for five consecutive days. During that time, the light from the explosion dimmed by a factor of 20, fading at an unprecedented rate for something so luminous. “This marks the first time in history that an astronomical phenomenon has been first sensed through gravitational waves and then seen with telescopes,” Arcavi said. “For years, we’ve heard theorists predict how a kilonova should look. I couldn’t believe we were finally seeing one for the first time.” Kilonovae are thought to be the primary source of all the elements heavier than iron in the universe. For example, most of the gold on Earth may have been created in a kilonova. The name originates from the prediction that a kilonova would be a thousand times brighter than a nova, though dimmer than a supernova. “We know now that one reason they had been so elusive is that they fade too quickly for conventional astronomical facilities to detect,” Arcavi said. “Thanks to knowing where to look and then having telescopes networked together all around the world, we were able to watch this new type of cosmic explosion rise and fade in real time,” said co-author Curtis McCully, a postdoctoral researcher at LCO and in the UCSB Department of Physics. “This is a remarkable story of the advent of gravitational wave astronomy combined with robotic internet-based optical astronomy.” LCO astronomers also used their and other facilities around the world, including the 8-meter Gemini telescope in Chile, to split the light of the kilonova into its chromatic components: a rainbow. McCully led this study, which appears in The Astrophysical Journal Letters. “We found that only a tiny amount of material was ejected in the explosion —only about 1 percent of the total matter in the system,” he noted. “The material was also flung out at an extraordinary speed, as much as 30 percent of the speed of light.” The LCO group also contributed to a third study measuring the Hubble constant, which characterizes the expansion rate of the universe. That research used the inspiraling neutron stars as “standard sirens” to determine their distance from Earth and compared that distance to the redshift, or how much light has been stretched by the expansion of the universe. That study appears in the journal Nature. The Daily Galaxy via UC Santa Barbara

"Everybody Knew It Had To Be There" --Missing Half of Normal Matter in the Universe Has Been Detected October 09, 2017 Daily Galaxy “Everybody sort of knows that it has to be there, but this is the first time that somebody – two different groups, no less – has come up with a definitive detection,” says Ralph Kraft at the Harvard-Smithsonian Center for Astrophysics in Massachusetts. Observations of galaxies and galaxy clusters in the local universe can account for only 10% of the baryon content -made of particles called baryons rather than dark matter- inferred from measurements of the cosmic microwave background and from nuclear reactions in the early Universe. Locating the remaining 90% of baryons has been one of the major challenges in modern cosmology. The missing links between galaxies have finally been found. This is the first detection of the roughly half of the normal matter in our universe – protons, neutrons and electrons – unaccounted for by previous observations of stars, galaxies and other bright objects in space. Models of the universe say there should be about twice as much ordinary matter out there, compared with what we have observed so far. Now, two separate teams found the missing matter – made of particles called baryons rather than dark matter – linking galaxies together through filaments of hot, diffuse gas. “The missing baryon problem is solved,” says Hideki Tanimura at the Institute of Space Astrophysics in Orsay, France, leader of one of the groups. The other team was led by Anna de Graaff at the University of Edinburgh, UK. “There’s no sweet spot – no sweet instrument that we’ve invented yet that can directly observe this gas,” says Richard Ellis at University College London. “It’s been purely speculation until now.” Because it's not quite hot enough for X-ray telescopes to observe. Both teams took advantage of a phenomenon called the Sunyaev-Zel’dovich effect that occurs when light left over from the big bang passes through hot gas to find another way to definitively show that these threads of gas are really there. As photons of light travel, some of them scatters off the electrons in the gas, leaving a dim patch in the cosmic microwave background from the birth of the cosmos that were to faint to be mapped by the Planck satellite in 2015. Both teams selected pairs of galaxies from the Sloan Digital Sky Survey that were expected to be connected by a strand of baryons. They stacked the Planck signals for the areas between the galaxies, making the individually faint strands detectable en masse. Tanimura’s team stacked data on 260,000 pairs of galaxies, and de Graaff’s group used over a million pairs revealing firm evidence of gas filaments between the galaxies. Tanimura’s group found they were almost three times denser than the mean for normal matter in the universe, and de Graaf’s group found they were six times denser confirming that the gas in these areas is dense enough to form filaments. “We expect some differences because we are looking at filaments at different distances,” says Tanimura. “If this factor is included, our findings are very consistent with the other group.” “This goes a long way toward showing that many of our ideas of how galaxies form and how structures form over the history of the universe are pretty much correct,” says Ralph Kraft. Journal references: arXiv, 1709.05024 and 1709.10378v1 The Daily Galaxy via New Scientist and NextBig Future and ARXIV.org

Thank you for setting this up! History Channel: did marie antoinette really say let them eat cake

Some people might think that the 1st memes invented were Keyboard cat, Philosoraptor and the numa-numa guy. That would be very, very wrong. Imagine you lived in a Kingdom that was one of the most powerful and influential in the world but, it had fallen on hard times. The king was, by all accounts, a corrupt incompetent dolt. The people were hungry and the spoiled, foolish Queen, when told there was no bread for the peasants said let them eat cake. While this might sound uncomfortably familiar to modern Americans, this was the climate that gave rise to the French Revolution. It was also the fertile ground for the French satirists who I submit were early generation memers. What people fail to understand about memes is they are much more about ideas than humor but the combination of the two makes them that much more powerful. What we call memes is an evolution of a very old idea called satire more succinctly defined as the illustration of the absurd with absurdity. Every middle schooler naturally knows about satire because that is when they inevitable meet an incompetent and bungling bureaucracy. One must take care with satire as to be a proper smart ass, you must first be smart. Otherwise you are just an ass. There were many French satirists. I would like to focus on a gentleman named Voltaire. Voltaire and the French satirists used their wit to point out the myriad hypocrisies and inequities of their political and social situations. Like Charlie Hebdo, many of the French Revolutionary era satirists faced rather deadly blow-back from those that failed to see the humor in their work. This was an amazing era and one that the modern democracies owe much to because many of the things we see as foundations of democracy were first discussed by the thinkers during this era. They were seeing the end of the monarchy as a practical form of government and looked forward to imagine what the next step would look like. See if you recognize any of Voltaire's ideas: I disapprove of what you say, but I will defend to the death your right to say it. Judge a person by their questions, rather than their answers. To learn who rules over you, simply find out who you are not allowed to criticize. It is dangerous to be right when the government is wrong. Common sense is not so common. It is hard to free fools from the chains they revere. Prejudices are what fools use for reason. As long as people believe in absurdities they will continue to commit atrocities. When it is a question of money, everybody is of the same religion. Superstition sets the whole world in flames; reason quenches them. Voltaire would have been hell on wheels if he had internet access. Be suspicious when you hear people in power discount the ideas of others. Those silly memers that poke them right in the hypocrisies have quite the history. So when you see memers banned from facebook or twitter, what you are really seeing is da man swatting the Socratic gad-fly that stung him in a sore spot. Only a fearful tyrant censors speech. He is afraid that you will tell the people just what a putz he really is.

Friday night I picked up my 14 year clean & sober medallion. The official date is Oct. 1 but that was the first Friday after. Shit happens. I feel like disappearing with a bottle sometimes but once you get in the habit of choosing something different, it gets a little easier with time. That doesn't make it easy. Just easier. Like that shit in Las Vegas this week. That seriously pissed me off. OK- I'm angry but, fuck it. Getting hammered won't help. So I focus my white hot anger elsewhere. What I would do to anyone I caught shooting at kids (at my age that's anybody under 30) doesn't bear close consideration but it would hurt real bad, last a long time and a chainsaw would figure prominently in it. In other matters there is something I've been asked- why didn't you join the new Pit club? No mas. Talking politics is just too toxic.

Proclamation of Extreme Wretchedness When in the course of human events, you come across a day like Monday Oct. 2nd, 2017, drinking heavily is authorized and encouraged by this office. Las Vegas Violence Death of Tom Petty Plumbing problems The only thing that could make it better would be a root canal. All that adds up to holy shit, nuke me now! Shutup Little Kim. Quit playing with your tiny missile or you'll go blind.

The Alien Observatory --“Extinction is the Cosmic Default for Most Life That Has Emerged in the Universe" (WATCH Video) September 26, 2017 Daily Galaxy Something Enrico Fermi overlooked: scientists from the Australian National University’s Research School of Earth Sciences think the reason we haven’t found signs of advanced technological life might be because all the aliens went extinct. “Extinction is the cosmic default for most life that has ever emerged,” the authors of the study write. In a 2016 study published in the journal Astrobiology, Aditya Chopra and his colleagues do a good job detailing something already well established but not necessarily articulated enough among the scientific community at large: the window is too short to allow that life to evolve fast enough where they can survive in the long run. "The universe is probably filled with habitable planets, so many scientists think it should be teeming with aliens," said Dr Aditya Chopra from the ANU Research School of Earth Sciences and lead author on the paper, which is published in Astrobiology. "Early life is fragile, so we believe it rarely evolves quickly enough to survive. Most early planetary environments are unstable. To produce a habitable planet, life forms need to regulate greenhouse gases such as water and carbon dioxide to keep surface temperatures stable." About four billion years ago Earth, Venus and Mars may have all been habitable. However, a billion years or so after formation, Venus turned into a hothouse and Mars froze into an icebox. Early microbial life on Venus and Mars, if there was any, failed to stabilize the rapidly changing environment, said co-author Associate Professor Charley Lineweaver from the ANU Planetary Science Institute. "Life on Earth probably played a leading role in stabilizing the planet's climate," he said. The ESO image above is an artist's impression of Mars some 4 billion years ago with it's northern ocean. "The mystery of why we haven't yet found signs of aliens may have less to do with the likelihood of the origin of life or intelligence and have more to do with the rarity of the rapid emergence of biological regulation of feedback cycles on planetary surfaces," Chopra said. Wet, rocky planets, with the ingredients and energy sources required for life seem to be ubiquitous, however, as physicist Enrico Fermi pointed out in 1950, no signs of surviving extra-terrestrial life have been found. A plausible solution to Fermi's paradox, say the researchers, is near universal early extinction, which they have named the Gaian Bottleneck. "One intriguing prediction of the Gaian Bottleneck model is that the vast majority of fossils in the universe will be from extinct microbial life, not from multicellular species such as dinosaurs or humanoids that take billions of years to evolve," said Associate Professor Lineweaver. A copy of the paper can be downloaded at http://bit.ly/gaianbottleneck. The Daily Galaxy via Australian National University

Scientists Closing in on the Dawn of Plate Tectonics The massive slabs of Earth’s crust might have started their journey more than 3.5 billion years ago Shannon Hall on September 22, 2017 Scientific American Scientists now think early Earth contained light-colored rocks, like the granite within Yosemite’s Half Dome. Such rocks likely formed via plate tectonics. Credit: David Iliff Wikimedia (CC-BY-SA 3.0) Geologists think early Earth may have looked much like Iceland—where jet-black lava fields extend as far as the eye can see, inky mountainsides rise steeply above the clouds and stark black-sand beaches outline the land. But over time the world gradually became less bleak. Today Earth also harbors light-colored rocks, like the granite that composes Half Dome in Yosemite National Park. But scientists remain uncertain as to when the world started to transition from the one that looked like Iceland to that which we know today. A new study published Thursday in Science suggests the shift transpired more than 3.5 billion years ago. Not only does the finding tell scientists the color of the world’s early beaches, it might help them understand when tectonic plates—the interlocking slabs of crust that fit together like puzzle pieces far beneath our feet—started to wake up and shuffle around. That is because the lighter-colored rocks, known as felsic rocks, are actually dark, or mafic, rocks “reincarnated.” In short, felsic rocks form when mafic ones are pushed deep inside Earth—possibly when one tectonic plate slips under another in a process called subduction. Given that light-colored felsic rocks were abundant billions of years ago, plate tectonics had likely already kicked into action. In order to reach that conclusion, Nicolas Greber, a geologist at the University of Chicago, and his colleagues analyzed 78 different layers of sediment to pin down the ratio of felsic to mafic rocks. This was not as simple as counting light versus dark stones (both had long ago eroded into tiny particles). Instead, Greber’s team looked at titanium. Although the metallic element is present in both types of rock, the proportion of its isotopes (chemically identical atoms with the same number of protons but a different number of neutrons) shifts as the rock changes from mafic to felsic. Suppose you mix something that turns out both salty and sweet, Greber says. An analysis like this gives you “an idea of how much salt you added and how much sugar you added.” He had expected the earliest sediments in his sample, which date back 3.5 billion years, would be composed mostly of mafic particles. But to his surprise, roughly half of the particles locked within were felsic. Assuming those rocks formed within subduction zones, that means tectonic plates were already on the go by that time—a conclusion that just might help solve an age-old mystery: the birth date of plate tectonics. Scientists have long argued over the precise date these crustal plates started to rouse from their slumber, with estimates ranging from one billion to 4.2 billion years ago. That range is far too large if scientists want to understand the evolution of early Earth. Shifting plates have the ability to dramatically reshape the planet by sculpting ocean basins and thrusting up mountain ranges. They also alter the composition of the atmosphere and oceans. This would have affected the supply of nutrients available to the fledgling life on our young planet. With such a vast time range involved, it is easy to see why scientists cannot agree on a firm date. Paul Tackley, a geophysicist at the Swiss Federal Institute of Technology, disagrees with the latest interpretation. He contends felsic rocks can form anytime mafic rocks sink deep within Earth—and not only along subduction zones. In fact, he argues this process can occur on a motionless plate. Should a volcano erupt, for example, the newly released lava will push down on mafic rocks until they become so deeply buried that they melt under the high subterranean pressures and temperatures, transforming into felsic rocks. Although Greber agrees felsic rocks can certainly form like this, he argues such a high felsic ratio cannot be explained by Tackley’s rock-sinking explanation alone. Take Iceland, for example—because the island is far from any subduction zones high numbers of light-colored rocks simply do not form—hence the island’s endless black lava fields and black-sand beaches. So Greber argues the high ratios of light-colored rocks discovered in his old sediments can only mean plate tectonics began early in our planet’s history. But 3.5 billion years is just a lower limit. In the future he hopes to find even older rocks, allowing him to pinpoint an exact birth date.

No I won't do my stripper routine for the anniversary! I'm retired. I'll probably break a hip or something. Oh, all right but I get to keep all the tips...

Why the Giant Mexican Earthquake Happened Thursday night's quake, near an undersea crust collision zone, was the strongest to hit Mexico in a century By Josh Fischman 2017 Sep 08 Scientific American The 8.1 magnitude quake happened between the line of the Middle America Trench and Mexico's southern coast. Credit: Image from U.S.G.S. Late Thursday night the biggest earthquake to hit Mexico in 100 years shook the country—and a large part of the globe. The magnitude 8.1 temblor was centered just off the southern end of Mexico’s Pacific coast. It was stronger than the 1985 quake that killed thousands of people in Mexico City. Last night’s quake took 32 lives, according to news reports, and the toll may rise. The quake happened about 54 miles offshore of the southern state of Chiapas, just to the east of an undersea geological feature called the Middle America Trench. Here, several parts of the planet’s crust are colliding. This quake, however, probably occurred within one of those crust slabs, not at the junction, according to the U.S. Geological Survey’s Earthquake Hazards Program. The slab fractured about 43 miles below the surface, which is deeper than the trench zone. The shaking could be detected by seismic instruments across North America and in Asia. It was profound in Mexico City, more than 450 miles away, where warning sirens split the night, buildings shivered, and streetlights and stone monuments swayed back and forth. The noticeable motion in the city is because the earthquake waves were amplified by the loose ground beneath the metropolis, noted Jascha Polet, a seismologist at California Polytechnic State University in Pomona. The city is built on sediments from an ancient lake bed.

New type variable star: Blue Large-Amplitude Pulsator BLAP- wikipedia Arxiv pre-published paper (pdf) appears in Nature June 26, 2017 doi:10.1038/s41550-017-0166 Variable stars are very interesting because they give us some clues how stars tick in general and, what happens when one factor or another is a tad out of balance. The ways stars operate is a complex of six inter-related differential equations that define their, energy states, hydrostatic stability, energy diffusion and a number of other factors. When some stars go a bit wonky, we can infer some of these factors are a bit off. Variable stars of different types provide us with laboratory examples for long term study.

Hurricane Harvey, a cat 4 storm, makes a direct hit on the sixth largest city in the country. In addition to storm surge and wind damage, there is historic levels of flooding damage that add to the misery. It is expected that Harvey might be the most expensive natural disaster in American history. Here we go again. We've been here before with Katrina and New Orleans. Almost right on the tick of the solar cycle(1,2). Only this time we've got another hurricane from the Cape Verde Islands bearing down on Florida. Here's my worry. The US Insurance industry is structured to deal with one of these huge mega-disasters. How is it going to handle two within weeks of each other? Right now Irma is poised to strike some of the most expensive real estate in the country and she's loaded to pack a wallop. Irma has the potential to be even more destructive and costly when it strikes Florida-man's abode. What happens when the huge institutional funds that the insurance industry uses to back their policies have to dump billions of dollars in securities to raise the cash for these disasters? If you are sitting on stock, you might want to ditch it before the institutionals flood the markets with the blue chips. I'm worried that we are about to see a market correction that will make Black Friday look like a Sunday picnic. If we get hit again for another multi-billion dollar disaster, it'll be a giant shit sandwich and everyone will get more than their share. __________________ 1) Nature: Sunspot-hurricane Link Proposed 2) International Journal of Climatology: Evidence Linking Solar Variability and US Hurricanes

In the current story that I'm posting, my latest chapter was about a spring break trip to Galveston, TX. Yeah... that Galveston. The one that got hit by Harvey. Now... I'm not sure what to do... rewrite the chapter? post in anyway? wait till people have un shriveled from being in the water? After the 911 attacks there were all sorts of reactions. A Schwarzenegger action film was delayed for months (Collateral Damage) Some network shows were pulled and redone. Football games were rescheduled. What are your thoughts on this predicament?

Voyager's 40 year trip No space probe has gone as far. It's like the energizer bunny on plutonium- it just keeps going and going... National Geographic NPR

This little problem didn't just crop up. It's been going on for a long time. Trump got famous for a book he wrote years ago called The Art of The Deal. The game the Kim dynasty has been playing is quite different. If they wrote a book it would best be titled The Art of The Shakedown. Now after decades of work on their part its a thousand times worse because they have nukes. The warnings about this go back as far as George Bush, the elder. Clinton told us he had made a deal, read that paid them off, to stop it. Only it didn't and the North Koreans popped their first test nuke in 2006. North Korea wants a pallet of cash and goods and the pressure will be to give them what they want. Like I said, it's a very old game. Dane-Geld -Kipling It is always a temptation to an armed and agile nation To call upon a neighbour and to say: -- "We invaded you last night--we are quite prepared to fight, Unless you pay us cash to go away." And that is called asking for Dane-geld, And the people who ask it explain That you've only to pay 'em the Dane-geld And then you'll get rid of the Dane! It is always a temptation for a rich and lazy nation, To puff and look important and to say: -- "Though we know we should defeat you, we have not the time to meet you. We will therefore pay you cash to go away." And that is called paying the Dane-geld; But we've proved it again and again, That if once you have paid him the Dane-geld You never get rid of the Dane. It is wrong to put temptation in the path of any nation, For fear they should succumb and go astray; So when you are requested to pay up or be molested, You will find it better policy to say: -- "We never pay any-one Dane-geld, No matter how trifling the cost; For the end of that game is oppression and shame, And the nation that pays it is lost!"

Evidence linking solar variability with US hurricanes Authors Robert E Hodges & James B. Elsner First published: 14 July 2010 International Journal of Climatology Abstract The relationship between US hurricanes and solar activity is investigated empirically. First, a relationship between the probability of a US hurricane and the solar cycle is shown conditional on sea surface temperatures (SST). For years of above normal SST, the probability of three or more US hurricanes decreases from 40 to 20% as sunspot numbers (SSN) increase from lower to upper quartile amounts. Second, since SST is in phase with the 11-year total solar irradiance cycle but upper-air temperature is in phase with ultraviolet radiation changes on the monthly time scale, an anomaly index of SSN is constructed. The index is significantly correlated with US hurricanes and major US hurricanes over the period 1866-2008. The chances of at least one hurricane affecting the United States in the lowest and highest SSN anomaly seasons are 68 and 91%, respectively. A similar relationship is noted using hurricane records spanning the period 1749-1850, providing independent corroborating evidence linking solar variability to the probability of a US hurricane. Copyright © 2010 Royal Meteorological Society 1. Introduction Hurricanes, capable of filling the entire vertical extent of the troposphere, transfer large amounts of energy from the ocean to the atmosphere via latent heat released from the condensation of rising moist air. But their development and intensification are highly dependent on ambient influences. Changes in atmospheric water vapour composition (Emanuel, 1991), steering currents (Dong and Neumann, 1986), and sea surface temperatures (SST) (Holland, 1997) affect tropical cyclone intensity (for an overview, see Wang and Wu, 2004). Recently, it has been found that variations in upper-air temperature resulting from fluctuations in ultraviolet (UV) radiation from the sun can also affect tropical cyclone intensity (Elsner and Jagger, 2008 (hereafter, EJ08)). Actually, the idea of a solar connection to tropical cyclones is almost a century and a half old (Meldrum, 1872; Poey, 1873), but the relationship has recently been re-examined. EJ08 show that the annual US hurricane counts are significantly related to solar activity. The relationship results from fewer intense tropical cyclones over the Caribbean and Gulf of Mexico when there are many sunspots. The finding is in accordance with the heat-engine theory of hurricanes, which predicts a reduction in the maximum potential intensity in response to warming in the atmospheric layer near the top of the hurricane. An active sun warms the lower stratosphere and upper troposphere through ozone absorption of additional UV radiation. In short, increased solar activity—associated with more sunspots—means more UV radiation reaching the Earth's upper atmosphere. The extra radiation warms the air aloft and decreases the temperature differential between high and low elevations resulting in less available potential energy for hurricane intensification (Elsner et al., 2010). But increased solar activity also contributes to warming of the ocean and altering of the atmospheric circulations, thus complicating the role solar variability plays in modulating hurricane activity. The purpose of this paper is to (1) further examine the evidence for a sun–US hurricane relationship on the interannual time scale, and (2) show that the relationship is detectable in data that pre-dates the modern record. In doing so, the study provides additional evidence that the relationship is likely the result of changes in upper-level temperature caused by variation in UV radiation. This treatment is statistical, and no attempt is made to directly address the hypothesized causal mechanisms, as is done in Elsner et al. (2010). The paper begins in Section 2 with background information about solar variability and its possible relationship with hurricane activity. In Section 3, the data used in the study are described. In Section 4, we examine the sun–hurricane relationship using data since 1851. The intra-seasonal variability in solar activity is proposed as a better way to describe the sun–hurricane relationship. This is shown using a table of ranked years and, more comprehensively, with a Poisson regression model. In Section 5, the technique of ranking years according to the magnitude of the change in intra-seasonal solar activity is used on a set of US hurricane counts dating back to 1749. The results are consistent in showing an increase in US hurricane probability for years with large sunspot number (SSN) anomalies, featuring relatively low numbers of sunspots during the hurricane season and high numbers immediately before and after the season. 2. Background Sunspots are visible disturbances on the surface of the sun resulting from intense magnetic fields associated with the geographic switching of magnetic poles which occurs, on average, every 11 years (Schwabe cycle) as part of the solar magnetic cycle. Higher outgoing total solar irradiance (TSI) occurs during periods of increased SSN. While TSI in a given cycle varies by only 0.1% in magnitude for all radiation wavelengths, UV and extreme UV (EUV) have been shown to vary by more than 10% (Hoyt and Schatten, 1997). Increases in upper-tropospheric (above 200 hPa) temperature have been shown to occur during solar activity maxima (Labitzke, 2002; Pap and Fox, 2004), with tropical tropopause temperatures also increasing with increased UV radiation (Hood, 2003). Higher solar activity has also been linked with increased global SST. Upper ocean temperatures were shown to increase during periods of increased solar activity over most of the tropical oceans (White et al., 1997). In fact, Elsner et al. (2008) develop a model for North Atlantic SST that includes a term for the 11-year cycle. The maximum potential intensity (MPI) theory postulates that, in an idealized thermodynamic environment, a hurricane's intensity is proportional to the thermodynamic efficiency between input energy (entropy gain from ocean/atmosphere interaction) and outflow venting (mechanical dissipation) (Miller, 1958; Emanuel, 1991; Holland, 1997; Bister and Emanuel, 1998). An increase (decrease) in surface (upper-tropospheric) temperature would increase the thermodynamic potential energy available for convection, all else being equal. The effect of upper-tropospheric and oceanic warming from increased UV exposure presents a bicameral climate effect upon hurricane intensity: warming at the surface increases a hurricane's potential intensity, whereas warming aloft decreases the potential mechanical dissipation and therefore hurricane intensity. An active sun (many sunspots) might lead to warmer SST over regions where hurricanes form (due to additional shortwave energy absorbed by the surface). But it will more immediately lead to a warmer upper troposphere over these same regions (due to additional UV radiation absorbed by ozone). Since, according to the heat-engine theory, warming at the surface acts to enhance cyclone intensification while warming aloft acts to inhibit intensification, the influence of the sun on hurricanes is masked when using simple analyses. A way around this problem is to control for one or the other of the variables. EJ08 do this geographically by examining the sun–hurricane relationship only for hurricanes over the western part of the tropical North Atlantic basin. Over this region, ocean heat content is nearly always high enough for tropical cyclogenesis, so the limiting thermodynamic factor is upper tropospheric temperatures (UTT). Indeed, they find a significant negative relationship between daily SSN and hurricane intensity over this part of the North Atlantic. Because of the region's already sufficiently warm ocean, the extra energy from an active sun limits intensification via a warmer upper troposphere. Thus EJ08 are able to detect a solar signal on the hurricane occurrence by geographically controlling for SST. Over the western Atlantic including the Caribbean Sea and Gulf of Mexico where optimal oceanic heat content for cyclogenesis is spatially and intra-seasonally expansive, the limiting thermodynamic factor for a tropical cyclone to reach maximum intensity is the temperature of the atmosphere near the tropopause. Since this covariate is inversely related to MPI, we note that an active sun warms the lower stratosphere, thereby decreasing potential intensity. Indeed, the correlation between seasonally averaged SSN and temperatures near the tropopause over the domain is consistently positive based on atmospheric data over the period 1948–2006 (EJ08). In contrast, the daily SSN is positively correlated with daily averaged tropical cyclone intensity for cyclones over the eastern Atlantic. The present work examines the sun–hurricane relationship by constructing an index that accounts for the low- and high-frequency variability inherent in the solar cycle. Though findings here corroborate those of EJ08, a potential bicameral role of the sun is examined through the construction of an SSN anomaly index of in-season and out-of-season values. The inclusion of almost 100 years of additional North Atlantic hurricane records back to 1749 (Chenoweth, 2006) confirms the robustness of the interannual sun–hurricane relationship. 3. Data The National Hurricane Center (NHC) houses the North Atlantic-basin hurricane database (HURDAT, or Best Track), containing dates, tracks, wind speeds, and minimum central pressure values as available, providing the best available modern hurricane information dating back to 1851 (Landsea et al., 2004). With refining work begun by Jose Fernandez-Partagás (Partagás and Diaz, 1996), over 5000 additions and alterations have been approved by the NHC Best Track Change Committee in an effort to improve the accuracy of storm data between 1851 and 1910 (AOML-NOAA, 2008). Through the 2008 hurricane season, HURDAT identifies 1362 unique North Atlantic storms (>18 m s−1) and 819 individual hurricanes (>33 m s−1), 283 of which struck the US mainland. Monthly mean SSN for this study are the International Sunspot Number as made available by the National Geographic Data Center, which was originally constructed by Solar Influences Data Analysis Center, World Data Center, at the Royal Observatory of Belgium (Van der Linden, 2009). Reliable monthly observations extend back to 1749. The Swiss astronomer Johann Rudolph Wolf introduced a daily measurement technique that observes both total spots and the quantity of their clusterings. The dataset addresses the observed error by incorporating a weighted average of cooperating observations. Other covariate data include the North Atlantic oscillation (NAO), North Atlantic SST, and the Southern Oscillation index (SOI). The NAO May to June monthly index values (mean of − 0.35 and standard deviation of 1.02) are courtesy of the Climatic Research Unit, and are calculated from sea level pressures at Gibraltar and at a station over southwest Iceland (Jones et al., 1997). More information about the NAO as a covariate for US hurricane activity is provided in Elsner and Jagger (2006). Monthly SST anomalies averaged from June to November (mean of 0.01 °C and standard deviation of 0.20 °C) are departures from the long-term average for 0° to 70°N, and are linearly detrended. The SST data are provided and maintained by the Physical Sciences Division of the Earth System Research Laboratory, NOAA, and are derived from the Kaplan SST dataset (Kaplan et al., 1998). The SOI averaged from June to November (mean of − 0.09 and standard deviation of 0.92) is defined as the normalized sea-level pressure difference between Tahiti and Darwin, and is obtained also from the Climatic Research Unit. A record of US hurricanes pre-dating those listed in HURDAT are available in Chenoweth (2006). This cross-referenced compendium of North Atlantic hurricanes improves upon previous compilations (Tannehill, 1938; Poey, 1855; Ludlum, 1963; Redfield, 1863), providing individual storm positions and dates through cross-referencing and validating individual entries using newspaper accounts, weather diaries, and ships logbooks for the period of 1700–1850. Chenoweth (2006) identifies 383 unique storms and 289 hurricanes, 127 of which affected the US mainland. The archive was digitized for use as a database of individual storm locations in Scheitlin et al. (2010) and we use this digitization here. In summary, the present study considers the evidence for a sun–hurricane relationship by dividing the data into pre- and post-1851. The post-1851 data use the HURDAT records for US hurricane counts and the covariate information as described above. However, the monthly SST data begin in 1856 and the monthly SOI data begin in 1866, so adjustments are made accordingly. The pre-1851 analysis uses the Chenoweth (2006) archive for US hurricane counts and the covariate information is limited to monthly SSN. 4. The sun–hurricane relationship since 1851 4.1. Seasonal variability As previously mentioned, one of the purposes of the present work is to re-examine the evidence shown in EJ08 for a sun–hurricane relationship. EJ08 assume a Poisson model for US hurricane counts and the model includes SST and SSN as covariates. Here, we make the same Poisson assumption and look at the bivariate relationship between US hurricanes and the SSN conditional on above- and below-normal SST. Figure 1 shows the probability distribution of US hurricanes conditioned on values of August to October SSN for seasons of above-normal and below-normal SST, based on upper and lower tercile SST values averaged from June to November, respectively. For each hurricane count, the percent bar represents the probability of that many US hurricanes given the data and the Poisson distribution. For above-normal SST values among lowest terciled August to October SSN, the probability of no US hurricanes is 9%. This probability increases to 20% for highest tercile SSN. Conversely, the probability for exactly four US hurricanes is 13 and 6% for lowest and highest tercile SSN, respectively. Likewise, the probability of three or more US hurricanes occurring dwindles from 40 to 22%. Figure 1. Open in figure viewer Download Powerpoint slide Probability distribution of US hurricanes conditional on terciled August to October SSN values for years of (a) upper- and (b) lower-tercile August to October Atlantic SST anomalies. Sunspots are displayed as the averages of their respective terciles. Data span the period 1856 to 2008 In years of lowest tercile SST values among the lowest tercile August to October SSN, the probability of no US hurricanes is 17%. This probability increases to 39% for the highest tercile SSN. Note that these probabilities are greater than those for warm years, but also demonstrate the same inverse relationship for increasing SSN. Conversely, the probability for exactly four US hurricanes is 7 and 1% for the lowest and highest tercile SSN, respectively. Likewise, the probability of three or more US hurricanes dwindles from 26 to 6%. Note that the higher storm count probabilities are less than for those of the warm years. With maximum hurricane intensity dependent on SST (e.g. Emanuel, 1991; Holland, 1997; Henderson-Sellers et al., 1998), the decrease in overall US hurricane probabilities from warm SST to cold SST is expected. Another way to consider the relationship between SSN and US hurricanes as modulated by SST is through the conditional correlation. Figure 2 shows the correlation between SSN and US hurricane counts at different percentiles of SST. The correlation (Pearson product moment) over all years is − 0.13, as indicated by the left-most point. The correlation is based on a sample size of N = 153 years. Each storm season is considered independent, and the standard error on the overall correlation estimate is 0.139, providing a 90% confidence interval on the correlation value of (−0.264, + 0.01). The confidence interval is shown by the width of the gray band. Figure 2. Open in figure viewer Download Powerpoint slide Correlation between August and October SSN and US hurricane counts. The correlations are computed at increasing percentiles of August through October averaged SST for the period 1856 to 2008. The shaded region represents the 90% confidence band at each computed value. The number of years over which the correlation is based is shown above the abscissa The next point on the graph to the immediate right is the correlation between hurricane counts and SSN after removing the coldest 20% (20th percentile) seasons. The 20th percentile SST value constitutes an anomaly of − 0.17 °C. The correlation changes negligibly, but with reduced sample size the confidence band widens. The plot shows that the correlation does not change much for the coldest 50% of seasons. However, when 60% of the coldest seasons are removed, the correlation decreases to − 0.24 with a 90% confidence interval of (−0.429, − 0.027), based on a sample size of 61 years. The correlation continues to decrease as only the warmest years remain. With sample size decreasing accordingly, the confidence bands expand, but the relationship between the US hurricane counts and August to October SSN remains statistically significant. The strengthening negative correlation indicates that the relationship between hurricane activity and SSN is tightest when ocean temperatures are warmest. Warmer core-season SSTs provide a fertile developing ground for tropical development. As such, suppressed tropical development from high August to October SSN is most evident at this time. 4.2. Intraseasonal variability As mentioned, the proposed sun–hurricane relationship is a result from changes in upper-level temperatures due to changes in UV radiation, thermodynamically decreasing the amount of energy for wind generation. Hood (2003) shows that the tropopause temperature response to changes in UV radiation is maximum at the 100 hPa layer around 16 km, which is commensurate with the tropical thermal tropopause and hurricane cloud height extent. Hurricane cloud heights can extend upward to the tropical tropopause layer. A warming of 0.5 K at the 100 hPa level (Hood, 2003) results in a relative temperature difference (between inflow and outflow) of only about 1%, but a relative decrease in CAPE of about 10%, according to the theory of Emanuel (1994). These thermodynamic changes decrease the relative wind speed by about 5%, which translates to a 3 m s−1 weakening of a 50 m s−1hurricane (Elsner et al., 2010). In short, increased solar activity—associated with sunspots—means more UV radiation reaching the Earth's upper atmosphere. This is corroborated by UV/upper-level temperature studies (Hood, 2003) and solar activity/ozone production increases (Angell, 1989; Calisesi and Matthes, 2007). The extra radiation contributes to warming the air aloft (via the exothermic response from ozone photodissociation), thus decreasing the temperature differential between high and low elevations that would otherwise foster vertical motion and cloud height growth. This thermodynamic explanation is in line theoretically with the observed changes in hurricane intensity concurrent with the diurnal cycle and net radiation changes (Hobgood, 1986; Gray, 1998). As mentioned, however, increased solar activity also contributes to the warming of the oceans (White et al., 1997, 1998), thus complicating the role solar variability plays in modulating hurricane activity. Solar UV radiation at wavelengths near 200 nm is important for ozone production in the Earth's upper stratosphere. Variation in the amount of this radiation is best measured by the Mg II core-to-wing ratio (Mg II index) (Heath and Schlesinger, 1986; Lean et al., 1997; Viereck and Puga, 1999; Viereck et al., 2001) and is shown in Figure 3(a). The time series reveals the well-known 11-year Schwabe cycle and short-term fluctuations (near 27 days) caused by the asymmetric distribution of active rotating solar regions (plages and sunspots). The short-term fluctuations are especially pronounced near solar maxima (Mg II index values exceeding 0.27). Although accurate estimates of UV radiation are available only since the advent of satellite measurements, the record of sunspots dates back to the 18th century. The relationship between daily sunspot number and Mg II index is quite strong (the correlation between the two variables is 0.9), allowing us to use sunspot numbers as a reasonable proxy for UV radiation (see Figure 3(b)). Figure 3. Open in figure viewer Download Powerpoint slide Solar Mg II index values. (a) Time series of the daily solar Mg II index. The series begins on 7 November 1997 and ends on 14 October 2007 (10 578 days). Values are plotted as points. Values below the 11-year peaks indicate the solar cycle number. Horizontal axis is labelled on 1 July of the year. The inset graph shows the Mg II index values as a time series over the hurricane season of 1989. Values are connected as a curve. Breaks in the curve indicate missing values. There are 1331 missing values (12.6% of the days). (b) Scatter plot of daily sunspot number and MgII core-to-wing ratio. The correlation between the two variables is 0.9 The impact on UTT from changes in UV radiation is a fast process. Hood (2003) notes that tropical tropopause (∼15 km) temperatures vary in phase with incoming UV radiation at a zero-day phase lag, or immediately. Given such, UV radiation/UTT fluctuations are better represented on a shorter time scale than the solar influence on warming the oceans, which occurs at a monthly to multi-annual timescale. Thus, if EJ08 are correct, we should be able to detect an immediate solar influence on hurricanes by using changes in UV radiation caused by the 27-day solar rotational period. We do this by defining solar activity during the hurricane season as anomalous if the August to October SSN is substantially different from the SSN during the months of May to July and November. Formally, let SSNanom = SSNMJJN − SSNASO, where SSNMJJN is the sunspot number averaged over May, June, July, and November, and SSNASO is the sunspot number averaged over August through October. Negative (positive) anomalies indicate that solar activity during the hurricane season is greater (less) than the solar activity during the months prior to and after the season. In general, positive anomalies arise during hurricane seasons when the Schwabe cycle is near a peak but sunspot numbers are relatively low during August, September, or October in response to the phase and intensity of the 27-day solar rotation. Negative anomalies arise during hurricane seasons when the Schwabe cycle is near a trough but sunspot numbers are relatively high during August, September, or October (Figure 4b). Thus there is a inverse relationship between total SSN and the SSN anomaly as defined here. The SSN anomaly index has a mean of − 0.80 and standard deviation of 11.37. A time series of the SSN anomaly since 1851 has been provided (Figure 4a). Figure 4. Open in figure viewer Download Powerpoint slide Seasonal sunspot number (SSN) anomalies as defined by the difference in sunspot numbers during May, June, July, November and sunspot numbers during August, September, October. (a) Time series of the anomaly and (b) scatterplot of August to October SSN versus SSN anomalies. Negative anomalies are largest for larger values of SSN. The trend has a value of − 0.08 ± 0.02 (s.e.) with dimensionless units While the hurricane's violent vertical transport of moisture and energy overrides the ambient atmospheric temperatures, changes in temperatures at outflow levels (upper troposphere) alter the available potential energy for given vertical motion. EJ08 found a significant relationship between 150 hPa temperatures and SSN. As SSN increases, so does 150 hPa temperature. The SSN anomaly as described here offers a potential extra-terrestrial proxy for inflow and outflow temperature modification: thus, a seasonal thermodynamic profile. This is because the SSN averaged over the peripheral months of the hurricane season (May, June, July, November) captures the position of the 11-year Schwabe cycle, which is related to SST variability (Dima et al., 2005), and the SSN averaged over the core hurricane season months (August, September, October) captures the position of the 27-day cycle, which is related to upper-air variability from changes in UV radiation. By taking the difference of these two averages, we combine the effects into a single covariate, which we hypothesize is related to the probability of US hurricanes. Hurricane seasons characterized by a positive SSN anomaly (warmer inflow temperature and colder outflow temperatures) should correspond to greater hurricane activity. Table I lists the top and bottom 10 hurricane seasons according to the value of the SSN anomaly using the years 1851–2008. The list includes the observed number of US and major US hurricanes by year along with anomaly values of other covariates which are known to be related to hurricane activity. These covariates include the NAO averaged over May and June prior to the hurricane season, the SOI averaged over the hurricane season months of June through November, and the SST averaged over June through November. The NAO is a precursor signal for hurricane steering (Elsner and Jagger, 2006) and the SOI is an indicator of the El Niño-Southern Oscillation and, therefore, wind shear and subsidence over the tropical Atlantic. Data for the SOI is available only back to 1866. Table I. | SSN anomalies and US hurricanes: 1851–2008 SSN Anomaly US MUS Year NAO SOI SST +28.7 3 1 1999 +1.21 +0.43 +0.19 +27.9 1 0 1984 − 1.01 − 0.22 − 0.26 +26.6 2 1 1938 +0.31 +1.19 +0.29 +25.3 4 1 1906 − 1.72 +0.99 − 0.05 +21.7 4 2 1916 − 1.88 +1.06 − 0.06 +20.8 1 1 1885 +0.96 − 1.23 +0.01 +20.1 2 1 1894 +0.7 − 0.29 − 0.27 +19.0 2 1 1929 +0.31 +0.3 − 0.12 +18.3 3 1 1871 − 1.94 +0.23 +0.01 +17.9 3 2 1950 − 0.24 +1.59 +0.01 +22.6 2.5 1.1 1927 −0.33 +0.4 −0.03 SSN Anomaly US MUS Year NAO SOI SST Years are listed according to the value of the ten most negative SSN anomalies (top) and ten most positive (bottom). Corresponding columns include the number of US hurricanes (US), major US hurricanes (MUS), the NAO as a May to June average anomaly, the SOI as a June to November average anomaly, and SST as a June to November average anomaly. Averages over the 10 years are given in bold. −37.9 0 0 1981 −0.76 0.41 −0.07 −27.4 0 0 1990 −0.39 −0.31 +0.07 −26.9 6 0 1858 +0.15 — −0.17 −26.4 1 0 1909 +1.38 +0.58 −0.06 −25.0 1 1 1957 −0.82 −0.6 +0.14 −24.8 4 1 1880 −0.67 +0.75 +0.13 −22.8 0 0 2001 −0.71 −0.18 +0.25 −21.7 1 1 1958 −0.17 −0.14 +0.21 −20.6 0 0 1978 +0.26 +0.01 −0.19 −25.4 1.4 0.3 1947 +0.12 +0.16 +0.02 Of the top ten positive anomaly years, 2 years featured four US hurricanes and 3 years featured three. There was at least one US hurricane in each of the top ten anomalous years. In contrast, four of the bottom 10 years had no US hurricanes and 3 years had only one. The mean US hurricane rate for positive SSN anomaly years is 37% higher than the overall mean US rate (1.82 hurricanes per year). For negative differences, mean US counts were 56% lower. Mean major US (MUS) hurricanes demonstrated a 72% increase (positive) and a 53% decrease (negative), respectively, in departure from mean seasonal MUS hurricanes (0.64 hurricanes per year). Mean values of the SST and SOI anomalies are clearly not able to explain the difference in hurricane rates; however, the NAO might be a confounding variable. This is examined next in the context of a multivariate regression model. 4.3. A seasonal model incorporating the SSN anomaly To examine the intra-seasonal SSN anomaly as a predictor for US hurricanes, we need a multivariate setting. This is achieved through the development of a regression model. Separate models are developed for hurricanes and major hurricanes that make landfall in the United States. The Poisson regression is a form of statistical regression used to model count data, such as the annual number of US hurricanes. The Poisson regression assumes that the response variable H (annual number of US hurricanes) has a Poisson distribution with a single parameter λ, which is the annual rate of US hurricanes. The logarithm of the annual rate is modelled using a linear combination of covariates. The model coefficients are determined using the method of maximum likelihood. The model covariates include May to June NAO index as an indicator of hurricane steering, the June to November SOI as an indicator of Atlantic basin wind shear and subsidence, June to November SST as an indicator of ocean heat content, and the SSN anomaly as defined above. With the exception of the SSN anomaly, the covariates are identical to those used in Elsner and Jagger (2006) for predicting US hurricane activity. The NAO index is the sea-level pressure difference between the permanent Icelandic Low and semi-permanent Azores High. An inverse relationship between the NAO index and US hurricanes was found in Elsner and Kocher (2000). When the Azores High is located farther to the north and east and is stronger (i.e. high NAO index values), hurricanes that form over the central North Atlantic tend to get steered away from the United States. The SOI is another sea-level pressure difference (Tahiti minus Darwin, Australia) that has been shown to vary in phase with North Atlantic hurricane activity (Gray, 1984). Low values of the SOI correspond with warm eastern equatorial Pacific SST (i.e. El Niño conditions). This warm water tends to shift the focus of Pacific deep tropical convection eastward, leading to sheering winds and subsidence over the North Atlantic basin. North Atlantic hurricane activity also varies in phase with SST. Warm waters provide a breeding ground for genesis and intensification of tropical cyclones, with temperatures below 26 °C generally too cool for supporting organized tropical deep convection (Gray, 1968). Table II lists model parameters and analysis of deviance for a model with the number of US hurricanes as the response variable from 1866 to 2008. The coefficient on the SSN anomaly term indicates a positive relationship with hurricane frequency, consistent with the relationship proposed from the analysis of Table I. Model covariates are added sequentially to the model. Model parameters and analysis of deviance for the first three covariates were originally shown to be statistically significant in EJ08. The SSN anomaly is determined to be the second most important variable (based on the magnitude of deviance) toward explaining the US hurricanes. The model for major US hurricanes is similar, but here the SSN anomaly term is the most important variable. Table II. | Poisson regression of US hurricanes Term Estimate Dev. Res. df Res. Dev. p-Value US hurricanes Regression parameters using all US hurricanes as the response (top) and all major US hurricanes as the response (bottom) for 1866–2008. The columns include the parameter estimate, the deviance (Dev.), the residual degrees of freedom (Res. df), the residual deviance (Res. Dev.) and the p-value. The p-values are based on F-tests for differences in group means. All p-values are less than 0.1, indicating the significance of the term to the model after accounting for the terms already in the model. NULL 0.498 — 142 180.756 NAO −0.176 12.166 141 168.589 0.005 SOI +0.157 6.254 140 162.336 0.022 SST +0.665 4.186 139 158.150 0.034 SSNanom +0.028 6.447 138 151.703 0.011 Major US hurricanes NULL -0.604 142 152.929 NAO −0.190 5.654 141 147.275 0.073 SOI +0.256 5.936 140 141.338 0.022 SST +1.144 4.424 139 136.914 0.030 SSNanom +0.047 6.624 138 130.290 0.010 Note that the SSN anomaly covariate is significant after accounting for the NAO. In a general circulation model, Shindell et al. (2001) find a shift to low index values of the NAO as a response to reduced solar irradiance. This would contribute to an increase in US hurricane probability, as the paths they take are directed through the Caribbean Sea. In fact, the correlation between June values of SSN and the NAO (1866–2008) is + 0.14 (p-value = 0.1), suggesting that some of the solar influence on hurricanes might be through solar-forced changes to the NAO. More work on this area is needed. Model adequacy is checked by examining the residual deviance after all terms are included. For the hurricane model, the residual deviance is 151.7 with 138 degrees of freedom (df). The p-value as evidence in favour of model adequacy from a χ2 distribution with this quantile value and this df is 0.2, indicating no strong evidence against model adequacy. The p-value on the residual deviance of the major hurricane model is 0.7, again providing no evidence against model adequacy. Figure 5 illustrates the model-predicted changes in US hurricane frequency with respect to the extremes of SSN anomalies while assuming neutral conditions for the NAO, SST, and SOI. A zero-count US hurricane season is more than 3 times as likely to occur in a low SSN anomaly season (32%) than high (9%). The chances of at least one major hurricane affecting the United States in the lowest and highest SSN anomaly season are 10 and 39%, respectively. Figure 5. Open in figure viewer Download Powerpoint slide Predicted probability distribution of US hurricanes. Predictions are made using a Poisson regression for values of SSN anomalies corresponding to the 1st and 99th percentiles while keeping the other three covariates at neutral values. The model is based on data from the period 1866–2008 5. The sun–hurricane relationship prior to 1851 Above, we showed that an index that quantifies a portion of the intra-hurricane season sunspot variability is quantitatively related to US hurricane and major hurricane frequency controlling for the other climate variables known to affect hurricanes. The SSN anomaly index captures, to some extent, the combination of low frequency variation associated with the 11-year Schwabe cycle and the 27-day solar rotation. This is important as it allows us to examine the relationship farther back in time with the availability of long records of sunspot numbers and old records of hurricanes over the western part of the North Atlantic basin. In this section, we consider the sun–hurricane relationship back to 1749, which is the start of accurate monthly sunspot counts with the International Sunspot Number, and make use of a portion of the Chenoweth (2006) Atlantic hurricane archive. Figure 6 shows the time series of US counts since 1749 where the counts from the Chenoweth (2006) archive are appended to the HURDAT counts used in the previous section. Hurricane intensity entries in the archive listing a US mainland state or region are qualified as ‘USA hurricane’. Figure 6. Open in figure viewer Download Powerpoint slide Annual US hurricane counts for the periods (a) 1749–1850 and (b) 1851–2008. Data from 1851 through 2008 are from HURDAT and the data prior to 1851 are from the Chenoweth (2006) historical hurricane archive Historical archives are less comprehensive than modern records. So differences in hurricane rates between the modern (HURDAT) and historical (Chenoweth archive) storm records can largely be explained by differences in completeness of the records. Given the nature of the collection of pre-1851 observations, knowledge of cyclone location and occurrence was a function of shipping traffic path frequency and meteorologically attuned populations. As such, for each year there is probability of at least one hurricane making landfall which is not in the record and this probability is almost certainly larger at the beginning of the record than towards the end accounting for, at least partially, the upward trend over this period. Thus we assume that the collection of hurricane counts prior to 1851 is an under-representation of the actual counts. This under-count notwithstanding, here we are interested in the variability in available counts from one year to the next. We can assume that this variability is largely unaffected by trends caused by missing cyclones. In fact, the autocorrelation function computed using the counts from 1749 to 1850 is similar to that computed using the counts from 1851 to 2008. Moreover, it is certainly not likely that observational limitations correlate with the solar cycle. Overlapping portions of the Chenoweth archive and SSN data provide a 102-year segment from which we can further examine the sun–hurricane relationship. The two busiest years in the series are the 1835 and 1837 seasons, with each reporting four hurricanes impacting the United States. There are 73 seasons that record at least one US hurricane, and the mean number of recorded hurricanes is 1.04 per year, giving an annual probability of 28% of at least one hurricane occurrence. This compares with the modern record mean (1.79 hurricanes per year) and annual probability of at least one hurricane occurrence (53%). Table III lists the top and bottom ten hurricane seasons according to the value of the SSN anomaly using the years 1749–1850. The list includes the estimated number of US hurricanes by year. No direct observations for Atlantic SSTs, the ENSO, or the NAO are available for these early years. Of the top ten positive anomaly years, one year featured four recorded US hurricanes and five years featured two US hurricanes. With the exception of 1774, there was at least one US hurricane in each of the top ten anomalous years. In contrast, eight of the bottom ten years had fewer than two US hurricanes, although 1835 featured four. Table III. | SSN anomalies and US hurricanes: 1749–1850 SSN anomaly USA Year +37.6 2 1781 +32.9 1 1749 +26.6 1 1771 +24.9 2 1794 +23.4 2 1838 +22.5 2 1778 +22.1 1 1789 +19.5 2 1752 +19.3 0 1774 +16.9 4 1837 +24.5 1.7 1786 SSN Anomaly USA Year Years are listed according to the value of the ten most negative SSN anomalies (top) and ten most positive SSN anomalies (bottom). Corresponding columns include the number of US hurricanes (USA). Averages over the 10 years are given in bold. −72.7 0 1847 −52.8 1 1839 −33.8 1 1769 −25.9 4 1835 −24.6 2 1787 −20.3 1 1759 −16.9 0 1786 −16.6 1 1850 −15.0 1 1824 −14.6 1 1846 −29.3 1.2 1814 The mean US hurricane rate is 42% higher in the ten positive SSN anomaly years compared to the ten negative anomaly years (the 1851–2008 mean difference is 79%, respectively). However, note that the mean year for the positive anomalies is 1786, 28 years earlier than the mean year for the negative anomalies (1814). Since the idea is for fewer missed hurricanes as time goes by, the actual difference in hurricane rates is probably greater than shown here and closer to what is found over the period 1851–2008. Thus, despite the inherent limitations in the older hurricane data, there is a detectable solar signal that mirrors the signal found in the more recent records, featuring a higher probability of US hurricanes when the SSN anomaly is positive and large. 6. Summary and conclusions Here we examined the evidence for a sun–hurricane relationship for hurricanes affecting the United States, which was identified recently by EJ08. We first looked at seasonal activity and noted a consistent inverse relationship between the probability of a US hurricane and SSN. The relationship occurs for both warm and cold years as defined by Atlantic SSTs. Since it has been argued that the sun–hurricane relationship arises from changes in upper tropospheric temperature associated with variations in the UV radiation (Elsner et al., 2010), next we showed the strong correlation between SSN and a core-to-wing ratio as a satellite-derived measure of the UV flux. Since the SST responds on a slower timescale to changes in total solar irradiance, we derived a SSN anomaly that captures a portion of the intra-hurricane season variability in solar activity by subtracting the SSN averaged over August through October from the SSN averaged over May, June, July, and November. The SSN anomaly is used to divide the hurricane record from 1851 to 2008 into top and bottom years with respect to the 20 most anomalous values. As expected, years with positive SSN anomalies featuring high peripheral month sunspot numbers but low in-season numbers have, on average, significantly more (79%) US hurricanes. This relationship was checked over all years by controlling for SST, the ENSO cycle, and the NAO in a multivariate Poisson regression model. The SSN anomaly was shown to be statistically significant in models for US hurricanes and US major hurricanes after accounting for the other climate variables. The evidence for a sun–hurricane relationship was further bolstered by showing that a similar relationship between the SSN anomaly and US hurricanes (years of high SSN anomaly have more US hurricanes) is detectable in an archive of Atlantic hurricanes dating back to 1749. This lends additional support to the contention that solar variability influences hurricane activity and provides support for the utility of older, less reliable, hurricane records for examining hurricane–climate relationships. Consequently, we feel that this work makes an important new contribution by showing that the sun–hurricane relationship, discussed in EJ08 using mostly 20th century data, does not disappear when examined by another 100 years of data. The work will be extended by examining whether the relationship can be seen in other tropical cyclone basins. Our thermodynamic hypothesis explaining the observed correlation between hurricanes and the solar activity is likely incomplete. As mentioned, increased solar irradiance tends to trend the NAO towards positive anomalies, which reduces the chance that a hurricane will reach the Unites States. Also, under an active sun the stratosphere warms unevenly, with the most pronounced warming occurring at lower latitudes. As a result, stratospheric winds are altered, which could end up changing the strength of tropical cyclones (van Loon et al., 2004; Meehl et al., 2009; van Loon et al., 2007; van Loon and Shea, 1999). Our hunch is that circulation changes could influence weaker tropical cyclones but the intensity of the stronger hurricanes is influenced more by the thermodynamic changes above and below the hurricane. More work in this area is needed. At the time of writing, the 2010 hurricane season is set to occur during the onset stage of Solar Cycle 24, with a running average estimate of about 30 sunspots over the duration of the hurricane season. Using monthly sunspot predictions for from NOAA/Space Weather Prediction Center, the 2010 hurricane season could feature an SSN anomaly of approximately − 5, or around the 32nd percentile of SSN anomaly activity. Utilizing a model similar to that used in Figure 5 and assuming the other three climate values at neutral values (they are clearly not), an SSN anomaly of − 5 corresponds to a 46% chance of two or more US hurricanes, 8% lower than the climatological average (54%) for two or more US hurricanes. Acknowledgements We thank Thomas H. Jagger for help with some of the statistics. Thanks are extended to the SIDC for the sunspot data and to the NHC for HURDAT. All statistical modelling was done using R (R Development Core Team, 2009). The work is supported by the US National Science Foundation (ATM-0738172) and the Risk Prediction Initiative (RPI-08-02-002) of the Bermuda Institute for Ocean Sciences. Interpretations and opinions expressed here are those of the authors and do not necessarily reflect those of the funding agencies.

Sunspot-hurricane link proposed Controversial research hints that solar cycle affects cyclone intensity. Jeff Tollefson 2008 Sep 28 Nature A new study suggests that more sunspots mean less intense hurricanes on Earth. But many hurricane experts are cool on the idea. James Elsner, a climatologist at Florida State University in Tallahassee, has analyzed hurricane data going back more than a century. He says he has identified a 10- to 12-year cycle in hurricane records that corresponds to the solar cycle, in which the Sun's magnetic activity rises and falls. Solar activity varies on a roughly 11-year cycle, in which its magnetic activity waxes and wanes.NASA/TRACE The idea is that increased solar activity - associated with sunspots - means more ultraviolet radiation reaching the Earth's upper atmosphere. That warms the airs aloft and decreases the temperature differential between high and low elevations that otherwise would fuel hurricanes. "Our results indicate that there is an effect in the intensity of storms due to the higher temperatures aloft," says Elsner, who published the results on 19 September in Geophysical Research Letters1. He says the statistical analysis suggests a 10% decrease in hurricane intensity for every 100 sunspots. At the peak of its cycle, the Sun might exhibit around 250 sunspots. Hot debate Establishing such a relationship would be enormously valuable, providing researchers, meteorologists and insurance companies with another tool for predicting storms and assessing financial liabilities. Several hurricane experts called the study provocative but raised questions about statistical analysis, as well as the physical processes at play. Ka-Kit Tung, a climate researcher at the University of Washington in Seattle who has studied the solar cycle, says ultraviolet rays do most of their heating when they are absorbed by ozone higher in the stratosphere. But hurricanes don't make it past the tropopause, the boundary layer between the troposphere and the stratosphere that is about 16 kilometres up at storm latitudes. "We have not established that there is heating at the tropopause due to the solar cycle," Tung says. For the new study, Elsner and a post-doctoral student, Thomas Jagger, used more than a century of records on hurricanes that reached land in the United States as a proxy for records on hurricane intensity. The assumption is that the effect on overall hurricane intensity would make itself evident as more or fewer hurricane-strength storms made land. But not everybody buys the idea that frequency can be reliably translated into intensity. "This is something worth investigating, but they made too many assumptions for me to just accept their conclusion at this point," says Judy Curry, a hurricane researcher at the Georgia Institute of Technology in Atlanta. She and others questioned whether the influence of solar cycles would be strong enough to appear in such data sets. In search of a mechanism Earlier this month Elsner published a paper in Nature2 providing more evidence that rising ocean temperatures will increase the intensity of major storms around the globe. He says the solar-cycle pattern emerged after his team had already taken into account major factors such as sea-surface temperatures. The team also found a correlation when comparing sunspot records with daily intensity values for storms dating back to 1944, using "best-track" records from the National Hurricane Center. "It gives us some faith that we are probably on to something here," Elsner says. The effect would be felt in areas where sea-surface temperatures are high enough to produce big storms, he says. In marginal areas, where waters are cooler, solar activity could promote storms by providing the initial heat to seed a hurricane. "It's an interesting statistical finding, but I'm unclear on the mechanisms here," says Thomas Knutson, a hurricane researcher with the US National Oceanic and Atmospheric Administration in Princeton, New Jersey. "There's more work needed to see if there really is a physical effect and to flesh out the mechanism." References Elsner, J. B. & Jagger, T. J. Geophys. Res. Lett. 35, L18705, doi:10.1029/2008GL034431 (2008). Elsner, J., Kossin, J. P. & Jagger, T. H. Nature 445, 92–95 (2008).

Geologists Find Largest Exposed Fault on Earth Nov 29, 2016 Science News An international team of geologists from the Australian National University and Royal Holloway University of London has for the first time documented the Banda Detachment fault in eastern Indonesia and worked out how it formed. The research is published in the journal Geology. The Banda Detachment fault beneath the Weber Deep basin. A – cross section through eastern Banda arc, cut parallel to grooves on fault surfaces and proposed direction of rollback; geometry of proto-Banda Sea slab is inferred from earthquake hypocenter locations catalogued by International Seismological Centre Online Bulletin; KSZ – Kawa shear zone. B – enlargement of Banda detachment showing schematically the configuration of over-riding continental allochthons (dark red); red triangles represent volcanoes. Image credit: Jonathan M. Pownall et al, doi: 10.1130/G38051.1. “The find will help researchers assess dangers of future tsunamis in the area, which is part of the Ring of Fire – an area around the Pacific Ocean basin known for earthquakes and volcanic eruptions,” said lead author Dr. Jonathan Pownall, from the Australian National University. “The abyss has been known for 90 years but until now no one has been able to explain how it got so deep.” “Our research found that a 4.3-mile (7 km) deep abyss beneath the Banda Sea off eastern Indonesia was formed by extension along what might be Earth’s largest-identified exposed fault plane.” By analyzing high-resolution maps of the Banda Sea floor, Dr. Pownall and co-authors found the rocks flooring the seas are cut by hundreds of straight parallel scars. These wounds show that a piece of crust bigger than Belgium or Tasmania must have been ripped apart by 74.5 miles (120 km) of extension along a low-angle crack, or detachment fault, to form the present-day ocean-floor depression. “This fault, the Banda Detachment, represents a rip in the ocean floor exposed over 14.8 million acres (60,000 sq. km),” Dr. Pownall said. “The discovery will help explain how one of the Earth’s deepest sea areas became so deep.” “This was the first time the fault has been seen and documented by researchers,” said co-author Prof. Gordon Lister, also from the Australian National University. “We had made a good argument for the existence of this fault we named the Banda Detachment based on the bathymetry data and on knowledge of the regional geology.” “I was stunned to see the hypothesized fault plane, this time not on a computer screen, but poking above the waves,” Dr. Pownall said. “Rocks immediately below the fault include those brought up from the mantle. This demonstrates the extreme amount of extension that must have taken place as the oceanic crust was thinned, in some places to zero.” According to the team, the discovery of the Banda Detachment fault would help assesses dangers of future tsunamis and earthquakes. “In a region of extreme tsunami risk, knowledge of major faults such as the Banda Detachment, which could make big earthquakes when they slip, is fundamental to being able to properly assess tectonic hazards,” Dr. Pownall said. _____ Jonathan M. Pownall et al. 2016. Rolling open Earth’s deepest forearc basin. Geology 44 (11): 947-950; doi: 10.1130/G38051.1 _________________________ I find this to be really interesting because Indonesia is home to some of the worlds most volatile and dangerous volcanoes including Toba, Tambora and Krakatoa. -JS

My Take on Climate Change

How Did Hurricane Harvey get so Strong? the Verge

Hurricane Harvey provides lab for U.S. forecast experiments By Paul Voosen Aug. 28, 2017 , 4:24 PM Science Mag For years, U.S. forecasters have envied their colleagues at the European Centre for Medium-Range Weather Forecasts (ECMWF) in Reading, U.K., whose hurricane prediction models remain the gold standard. Infamously, the National Weather Service (NWS) in 2012 failed to predict Hurricane Sandy’s turn into New Jersey, whereas ECMWF was spot on. But two innovations tested during Hurricane Harvey, one from NASA and another from the National Oceanic and Atmospheric Administration (NOAA), could help level the playing field. NOAA’s offering is a brand-new forecasting model. Two years ago, NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL) in Princeton, New Jersey, won a competition to provide the computer code for the next-generation weather model of NWS. Current NWS models must wait for results from a time-consuming global simulation before they can zoom in on a smaller area and run a high-resolution model for hurricanes. With GFDL’s new code, the next-generation model will be able to simulate storms at the same time as it runs globally, in theory, improving forecasts for hurricane paths because its fine-scaled predictions feed immediately into the model’s next run, rather than lagging behind. Last week, GFDL anxiously watched the developing storm to see how it compared with a test run of the next-generation model. On Thursday, a day prior to landfall, the experiment agreed with the European model that Harvey would plow inland, stall, then head back out over the Gulf of Mexico before making a second landfall near Houston, Texas. That progression, close to what’s happening, helps explain the sustained, catastrophic rainfall that has battered the Texas coast. The GFDL model, called FV3, also correctly forecasted that Harvey would develop a double eyewall—a second circular band of storms around the band enclosing the eye. The model’s zoomed-in view also predicted the extreme rainfall totals seen by Houston some 5 days in advance, says Shian-Jiann Lin, the GFDL scientist who led the development of the code powering FV3. Intensity can be even harder to predict than storm paths, and here NASA may be able to help. Many models missed that Harvey would grow to a category-4 storm just prior to landfall, in part because data on wind speeds are spotty and difficult to collect. Last December, NASA launched a constellation of eight identical microsatellites, called the Cyclone Global Navigation Satellite System (CYGNSS), to fill the gap. CYGNSS works by detecting the surface roughness of the ocean—a proxy for wind speeds—from the reflected radio signals of GPS satellites. These long-wavelength signals can pass through the veil of rain that cloaks hurricanes and blocks the microwaves that traditional weather satellites detect. Harvey was the first test for CYGNSS in severe winds. On 25 August, before Harvey made landfall, Christopher Ruf, an atmospheric scientist and engineer at the University of Michigan in Ann Arbor, strapped himself into a P-3 turboprop, an NOAA hurricane hunter, bound for the storm’s eye. His seat fell out beneath him again and again as the aircraft repeatedly plunged into the eyewall. Each time the wind grew more severe: The storm was rapidly intensifying. It will take weeks to know whether CYGNSS captured this sharp intensification, Ruf says. The weather service will be following his results closely. The constellation is technically only a 2-year experiment, but it’s possible the satellites could be pressed into operational service for NOAA, Ruf says. “Our simulations have shown that the forecast skill is improved. Now we need to demonstrate it for real.”