Monsoons
Monsoons are seasonal changes in prevailing wind direction. The Monsoon is essentially the Mother of All Sea Breezes (and Land Breezes). Water warms and cools more slowly than land. It is common for coasts to experience a sea breeze (wind blowing from the sea toward the land) during the morning, the air over the land warming first. Then, in the evening, there is a land breeze in the reverse direction, the air over the sea cooling last.
In a monsoon, this happens on a giant scale, and the "breeze" persists for several months. The summer monsoon, being a giant sea breeze, results in high humidity. And the winter monsoon, being a giant land breeze, brings dry conditions. (Unless the winds sweep over intervening water, such as the Bay of Bengal, before they reach you.)
In the Indian Ocean, there is a summer monsoon, with winds from the southwest, and a winter monsoon, with winds from the northeast. The exact dates of arrival and departure of the monsoons varies depending on exactly where you are located.
There is absolutely no doubt that there were monsoons in the Indian Ocean and the western Pacific Ocean before the Ring of Fire; in fact, trade and agriculture depended upon them.
The failure of the monsoon can result in a megadrought, with substantial loss of life.
A network of tree ring chronologies has been used to reconstruction monsoon conditions for Sino-Indian Asia; Figures 5A-5D show the "Palmer Drought Severity Index" (red dry, blue wet) for the region for the years 1630-1641 (NOAA/MADAgrid). Don't fret about the particular years, since the RoF will scramble that, but note the degree of variation from year to year.
Figures 5A-5D
El Nino/Southern Oscillation (ENSO)
In the Pacific, there is a alternation between two climate patterns, "El Nino" and "La Nina" over a period of three to seven years. These opposite states may persist for just a few months or for as much as two years.
In an "El Nino" state, there is (by definition) a warming of at least 0.5oC of the surface temperature in the east-central tropical Pacific Ocean. This weakens the trade winds of the South Pacific, and causes drought in the western Pacific and rainfall in the eastern Pacific. Off the coast of Peru, the upwelling of cold water is inhibited, and this results in fish kills. Winters in western South America tend to be much warmer and wetter than usual. The same is true, to a lesser degree, on the Pacific Coast of Central America, Mexico, and southern and central California. However, in the northwestern United States and western Canada, winters are warmer and drier.
We know from documentary evidence (mostly from Peru) that there was a very strong event in 1578 (comparable in strength to the 1982-3 event), and since then, strong ones in 1607, 1614, 1618, 1619, and 1624 (comparable to the 1972-3 event). If climate follows the old time line path, there will be strong ones in 1634-35 and 1640-41, and a moderately strong one in 1647 (Arteaga; Quinn).
Interdecadal Pacific Oscillation (IPO)
There's also the IPO, which flips every 15-30 years. In a positive IPO, India experiences a weak monsoon and associated drought, and the American West Coast is wet.
India
A chronology prepared by Sindh historian M.H. Panwhar has some interesting climate-related entries for our period of interest:
"Famine in Gujarat and Deccan due to failure of monsoons. This famine was due to failure or rains in 1630 and excessive rain in 1631. People sold their children so that they may live. . . . Hides of catle [sic] and flesh of dogs were eaten, cremated bones of dead were sold with flour and cannibalism became common. . . . Three million people died between 1630-1633 in Gujarat.
"1636-1637. Punjab had famine. . . .
"1640. Heavy rain caused floods and destroyed crops in the Punjab and Kashmir, causing famine. . . .
"1640-44. Rains failed continuously in many parts of Northern India and famines occurred in Agra province. . . .
"1642. Famine occurred due to heavy rain and floods in the Punjab. . . .
"1646. Drought in Agra and Ahmedabad. . . .
"1647. Rains failed in Marwar. Famines, high mortality. . . .
"1648. Failure of rains in Agra area. . . ."
Southeast Asia
Newson (35) writes, "there is some evidence, notably from tree-ring data from Java, that during the seventeenth century some countries in Southeast Asia experienced unstable climatic conditions, perhaps linked to the 'Little Ice Age' in Europe, that included frequent dry periods that resulted in food shortages and famines. However, Peter Boomgaard suggests that climatic conditions were probably not so anomalous . . ."
China
China experienced severe cold in 1629-43, as well as severe drought in 1637-43 (Brooks 269). Not coincidentally, the Ming dynasty fell in 1644. Climate extremes led to famine (the first big one was in 1630), which led to peasant revolts.
Famine also meant greater vulnerability to disease, and migrations to flee affected areas aided disease transmission. There were significant epidemics in the northwest beginning in 1633, and in the Yangtze valley in 1639. (Brooks 250ff).
In the Yangtze Delta, historical records show that 1635-1644 was marred by four flood events (years?) and six drought events. This was part of a larger trend; there were many flood and drought events in the period 1540-1670. In contrast, in 1495-1504, there was one flood event and no droughts (Jiang). Qiang says in 1550-1850, calamities "occurred by turns and sometimes, both drought and flooding occurred in the same year." There was snowfall and frost on low ground in south China in 1635 and 1636, and the River Huai froze over in 1640 (LambCPFF 612).
Perhaps the most unique aspect of the Little Ice Age in China was the increase in reports of dragon sightings (Brooks 6ff); for example, "two dragons were spotted in autumn of 1643. . . ." (14). Dragons were associated with water, and thus with storms and, more generally, bad weather. But they were both symbols of the emperor and celestial messengers. If people were seeing dragons, then what they were really perceiving was climatic evidence that the emperor had lost the Mandate of Heaven. And of course these reports in turn made it more likely that the emperor would lose support.
Japan
In Japan, 17th-century (and earlier) temperatures have been reconstructed on the basis of
– the dates of cherry blossom viewing parties in Kyoto
Overall, the average full-flowering date was day 105; the average for the 17th century was 106 (April 16), and for the 20-21c, 101 (April 11). The estimated March mean temperature for the 1630s was around 7oC, and the LIA low was around 6oC in late-17th century and early 18th century. A deeper low of ~5oC was inferred for the early-14th century)(Aono). In 1633, the cherry trees blossomed on April 8 (LambCPFF 607).
– the dates of freezing (December-January), buckling (Omiwatari, supposedly the footprints of a kami), and thawing of Lake Suwa in Nagano
Overall, the average freezing date was January 15, and the mean for the 1630s was about 12 days early, and that was actually one of the coldest decades recorded)(Lamb 256). By way of example, the dates were 1634: Jan. 9, 1635: Dec. 28, 1636: Jan. 2, 1637: Jan. 11, 1638: Dec. 31, 1639: Jan. 21 (LambCPFF 609).
There is a correlation between the mean winter temperature at Tokyo (Edo) and the Lake Suwa freezing date; the estimated temperature is 4.1oC for the 1630s and 1640s (LambCPPF 610).
– the first snow cover in Tokyo
This was January 6 (1633), December 16 (1638), February 2 (1640), November 28 (1642), and January 10 (1648). (611).
– the proportion of a cold-adapted species in pine pollen from Ozegahara, a raised bog 150 km north of Tokyo
Most of the 17th century appears to have been a bit on the warm side (Batten 18).
– tree ring data
That from Yaku Island in the south shows two sharp temperature drops in the 17th century, and from central Honshu shows slow decline in temperature during 17th century)(Batten 19, 21).
Generally speaking, the winters in central Japan were most severe in 1500-1520, 1700-10, and 1850-80, not in the period of interest to us now (LambCHMW 227).
While the Genroku (1695-6), Tenmei (1782-7) and Tempo (1833-39) famines all occurred during particularly cold periods, Japan's population still doubled from 1600 to 1721 (Batten, 57, 59).
Pacific Ocean
The Spanish take advantage of wind (midlatitude westerlies, subtropical northeast trades) and currents (the North Pacific gyre) in the Manila-Acapulco galleon trade. Spanish archives show that the average duration of the Acapulco-Manila passage (westing made mostly around 12oN latitude) increased steadily from 80 days in 1600 to 100 in 1640 and a peak of a little over 120 days in 1655, then descended to gradually to a plateau of 90-100 days in 1690-1750. "Virtual voyage" calculations indicated that the slowing was most likely the result of a northeastward shift in the position of the "southwest monsoon trough" (the ICTZ) in June (Garcia-Herrera).
PART III: THE EFFECT OF THE RING OF FIRE
The fictional cosmic event we call the Ring of Fire replaced a six mile diameter of 1631 Thuringian air with one from 2000 West Virginia. What we will speculate about in this part is just how profound an effect this event would have had on weather (short-term) and climate (long-term), both locally and remotely.
Okay, folks, hold your hats. It's time for our intellectual roller coaster to plunge into the abyss of chaos theory. Just be thankful that I am sparing you the mathematics that I studied, and that I am concentrating on the implications.
Let's begin the descent gently by talking about the origin of the term "butterfly effect." It comes from the title of a presentation by the mathematical meteorologist Edward Lorenz: "Does the flap of a butterfly's wings in Brazil set off a tornado in Texas?"
This was a reference to his observation of chaotic behavior in his atmospheric convection model. Chaotic behavior means that a small perturbation in the initial conditions results, eventually, in a large and to some degree unpredictable divergence ("bifurcation") in the "final" state. This chaos was not the result of randomness; Lorenz' model was completely deterministic. However, the chaos had the appearance of randomness.
Chaotic behavior can be inherent in the actual physical system-and the existence of nonlinear relationships is necessary for chaotic behavior to emerge. It can also be an artifact of numeric evaluation of a mathematical model of a physical system. (The input data is for grid points, not spatially continuous; the evolution of the model is calculated in discrete time steps, not continuously, which means that we are working with finite differences not true derivatives; and there will be rounding errors inherent in how computers handle numbers.) Of course, it isn't necessarily easy to separate the two!
The nonlinear dynamics of the climate system include both positive and negative feedback loops. As an example of positive feedback, increasing the surface temperature of land or water just below the freezing point results in conversion of snow and ice (high albedo) to bare earth or liquid water (low albedo), which increases absorption of solar radiation, which increases global temperature. Also, warming the air allows it to hold more moisture, and since water is a greenhouse gas that results in more absorption of solar radiation and its partial re-radiation back into space. And warming water causes it to hold less carbon dioxide, so it releases carbon dioxide (another greenhouse gas) into the atmosphere. On the other hand, an increase in the temperature will cause an increase in black body emission of infrared radiation, i.e., a loss of heat energy. An increase in atmospheric carbon dioxide will result in an increase in dissolved carbon dioxide.
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The Ring of Fire is essentially what a chaos theorist would call an "initial condition perturbation." Nonlinear dynamics has been intensely studied the last few decades, and there are several things you should know about perturbations.
First, when you perturb a nonlinear dynamic system, it initially evolves toward what the mathematicians call an "attractor." This is the particular subset of all the possible values of all the possible variables that the system, by virtue of its dynamics, prefers to be in, i.e., gravitates toward. There are point attractors (stable states), limit cycle attractors (periodic states), and "strange" or "chaotic" attractors (which obviously are the ones that exhibit chaotic behavior). (These terms are defined by reference to something called "phase space," but we don't need to talk about that. . . .)
Second, depending on what part of the "attractor" the system gravitates to after the perturbation, the perturbation may grow exponentially, grow slowly, remain static or even dissipate. This can be seen with the Lorenz 1963 climate model that led Lorenz to his discovery of chaos (Kalnay).
Third, in complex nonlinear dynamic systems, it is typical for the exponential growth to reach a saturation point and level out. That doesn't mean that the system becomes static, just that the "swings" stop getting larger and larger. One set of nonlinearities creates the exponential growth, then another set kicks in and curbs it (Toth 3298). Bear in mind, the system remains perturbed; the "weather" is still different.
Fourth, the mere size of the perturbation isn't necessarily important. It appears that small perturbations may have the same exponential growth rate and same saturation levels as large ones; being smaller they just take a little longer to reach that level (Lopez Fig. 1). However, there is some scholarship suggesting that small amplitude perturbations are more likely to grow at a constant, not exponential, rate (Noone 8).
Fifth, it does matter whether the perturbation is a random one. A random perturbation is more likely to be inconsistent with the flow regimes established by the underlying physics, and dampen out rapidly: "purely random perturbations yield unbalanced flow structures and lead to the perturbation energy being dissipated as gravity waves during the initial time steps." (Magnusson2002). Yes, I'll take his word for it.
That's relevant for the Ring of Fire, because let's face it, the RoF rather haphazardly dumped matter and energy into one small segment of the world. While the masses may be roughly equal, they aren't identical in toto, and certainly not in chemical composition. And the heat and pressure-based energies of the old and new hemispheres are certainly different. There is no real life physical process that could have caused the sudden change in temperature, pressure and atmospheric composition from what had been there an instant earlier.
But even "balanced" random perturbations will decay initially, until they reach the attractor (Toth 3300; Magnusson2008).
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Annann and Connolley ran two runs of the 64 bit version of the HADAM3 model (this is the Hadley atmosphere model, with a horizontal resolution of 3.75x2.5o in longitude x latitude, a vertical resolution of 19 altitudes, and a standard timestep of 30 minutes) (Wikipedia/HadCM3). The two runs differed in that the pressure in a single grid point (the authors think it was somewhere in the Arctic) was changed by just one part in 1015. They then calculated the root-mean-square of the differences in pressure between the two runs across the entire grid, i.e., globally.
The difference (in pascals) was below 50 up to day 10, then started climbing rapidly, flattening in the 800-1000 range at day 25. Standard weather charts only show differences of 400 pascals, so the difference was practically insignificant, on a global scale, up to around day 15.
They also plotted the location of the differences as of days 4, 15, and 31. On day 4, the differences were mostly in the tropics. By day 15, they were mostly outside the tropics, in both hemispheres. Come day 31, and the positive differences over the USA and Europe had become negative ones.
Note that these runs demonstrate both how a small perturbation can grow rapidly and how it can then reach a saturation point. The pressure perturbation in question is much smaller than what was likely caused by the RoF, but it's also worth noting that the spatial extent of the RoF is much smaller than than a single HADAM3 "grid box," and that pressure is more susceptible than temperature to chaotic fluctuation.
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The way that weather and climate forecasters have coped with chaos is through "ensemble" forecasting. That means that they created a set (small enough to be computationally practical
) of perturbed initial conditions and ran the same weather or climate model on each of them. They then based predictions on what the entire ensemble said would happen in the future, with the reliability of the prediction being considered an inverse function of the degree of divergence of the ensemble members.
It used to be that the ensemble members were simply random perturbations of the "observed" initial conditions, with the magnitude of the perturbation related to the assumed observational error. The meteorologists ran into that damping problem I mentioned, and therefore imposed dynamical constraints on the perturbations (a fancy way of saying, selecting perturbations that were more likely to have existed in the real world).
Also, in order to maximize the bang for the computational buck, they developed tricks for selecting just the perturbations that were likely to grow the fastest, and were therefore the best test of the reliability of the prediction.
The perturbations typically used by forecasters are much larger than the RoF perturbation.
Toth (3309) talks about global-scale perturbations in the range of 10-20% of the natural climate seasonal variability (rms variance). Zorita conducted two different runs (ERIK1, ERIK2) of the ECHO-G global climate model, each starting at 1000 A.D., with ERIK2 postulating colder conditions. I have not been able to ascertain the global difference in the initial condition, but in the Baltic area, at least, ERIK2 annual mean temperature was colder by 0.5oC (Hunicke 21). If difference on the global scale is the same, that's a huge perturbation. In fact, that difference by itself adds up to a bigger perturbation energy than what could possibly be represented by the RoF. For that reason, I can't infer from the spread of ensemble member behavior how much the RoF will disrupt the OTL climate.
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Also, as perturbations go, the RoF is small potatoes, both in size and extent. While it certainly has the potential to make the climate, not just the weather, quite different from that of the old time line, it has to compete with other external influences of much greater magnitude.
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