CHAPTER 6. “GLOBAL ENERGY SPIKE”
6.1. Interrelation between dynamics of drift rate of Earth’s magnetic poles and natural disaster statistics.
It is known that Earth’s endogenous activity in the form of earthquakes and volcanic eruptions is merely an external manifestation of our planet’s internal energy, the bulk of which comes from its core and adjacent layers. Today, science has yet to obtain accurate and definitive information on release mechanisms of the Earth’s internal energy that causes convection in the mantle and, consequently, movement of lithospheric plates. Meanwhile, it is known for certain that Earth’s magnetic field has been formed by the processes occurring in the inner and outer core of our planet. The Earth’s magnetic field formation model generally recognized to date was reviewed in previous sections.
One of the most distinct indicators of energetic processes in Earth’s core is the speed of movement of its geomagnetic poles. There are different theoretical models that explain the drift of the geomagnetic poles; however, regardless of the model considered, it is obvious that a significant “leap” in the velocity of the North geomagnetic pole points to an energy increase at the level of Earth’s core and surrounding layers. The leap in the velocity of the North Magnetic Pole by more than 500% might be related to significant changes in the energy processes in its inner and outer core. In that case, the release of Earth’s internal energy must lead to increased planetary endogenous activity in the form of large earthquakes and volcanic eruptions.
On the other hand, a sharp change in the speed of Earth’s North Magnetic Pole movement must also have an impact on global climate change. It is known that Earth’s magnetic field influences plasma motion, electric currents, and general electrical properties of the upper region of the ionosphere. In addition, Earth’s geomagnetic field captures highenergy charged particles and has a significant effect on magnetospheric processes.
The fivefold acceleration of the North Magnetic Pole’s drift and opening of cusp angles alters the energy potential of the ionosphere and upper atmosphere, with a possible impact on the redistribution of cyclones and anticyclones. This idea requires further thorough study and is put forward in order to show a probable physical mechanism of Earth’s geomagnetic field redistribution influencing global climatic processes.
Numerous studies by various authors (Campbell, 2003; Newitt, et al., 2002; Barton, 2002; Alldridge 1987; Kuznetsov, 1990, 1997) were dedicated to the development of a mathematical model describing the formation of Earth’s magnetic field.
As V. V. Kuznetsov points out in his works, the magnetic poles’ drift (its direction and speed) is one of the most important characteristics of geomagnetism.
Meanwhile, many questions can be answered by studying the possible correlation between changes in the velocity of Earth’s North Magnetic Pole and the dynamics of numbers of large earthquakes, volcanic eruptions, and tsunamis.
Fig. 52 shows a comparison of graphs for the North Magnetic Pole’s drift rate variations, numbers of large earthquakes, tsunamis, and volcanic eruptions between 1900 and 2010.
A comparative analysis makes it possible to identify two characteristic cycles, designated A and B, of increased statistical values for each graph’s parameters. Cycle A covers the period from 1970 to 1983 and cycle B from 1998 to the present. Within cycle A, an acceleration of the North Magnetic Pole’s drift is observed, from approximately 8 to 18 km per year.
In the same period of time, there was a surge in the number of people killed during large earthquakes, along with an increase in the numbers of large earthquakes, catastrophic tsunamis, and volcanic eruptions. Although the most pronounced increase is in the numbers of large earthquakes, of earthquake victims, and of volcanic eruptions, the presence of an increased activity cycle for large tsunamis is also clearly visible for that period.
Let us take the second and most pronounced cycle of a sharp rise in all the statistical indicators, which is cycle B. This cycle covers the period from 1998 to the present. During this period, there was a surge in all the statistical indicators of the reviewed disasters. For instance, the increase in the drift rate of the North Magnetic Pole by 1998 had approached its maximum, that is, about 50 km per year.
The graphs clearly show that 1998, a turning point for all the reviewed disasters, saw a sharp growth in the numbers of large earthquakes and earthquake fatalities, as well as in the numbers of major tsunamis and volcanic eruptions. It is noteworthy that the statistical parameters for this period had been rising at an exponential rate and now all the statistical indicators are at a stage of steadily continuing growth, as evidenced by the deeper investigation of the nature of these processes’ dynamics using trend analysis in Appendix 1.
Fig. 52. Comparison of graphs for change of North Magnetic Pole’s drift rate and parameters reflecting dynamics of natural disasters between 1900 and 2010 (by E. N. Khalilov, 2010) (1) – graph for drift rate of Earth’s North Magnetic Pole; (2) – graph for number of dead during large earthquakes; (3) – graph for dynamics of large (M>8) earthquake numbers; (4) – graph for dynamics of catastrophic tsunami numbers; (5) – graph for dynamics of volcanic eruption numbers.
6.2. Dynamics and interrelation between the J2 coefficient and natural disaster statistics
Traditional natural disaster research does not include study of some geophysical parameters one of which is the J_{2 }coefficient. This coefficient is determined by measuring with the help of the satellite laser ranging systems.
In satellite laser ranging (SLR), a global network of stations gauges the instantaneous time of propagation of ultrashort pulses of light going from ground stations to satellites equipped with special reflectors, and reflected back. It ensures millimeter accuracy during instant measurement of distance. This data is stored for precise determination of the satellites’ orbits as well as for various researches. SLR is the most accurate method available today for dealing with the geocentric satelliteEarth system, making it possible to carry out precise calibration of radar measurements and distinguish longterm equipment bias from secular changes in ocean topography. The capability to measure temporal variations in Earth’s gravitational field and monitor the movement of a network of stations with due regard for the geocenter, along with its ability to control the vertical motion in an absolute system, makes SLR unique for modeling and estimating longterm climate change by providing a reference system for the postglacial surge, changes in sea level, and volume of ice. SLR makes it possible to identify temporal redistributions of masses of the solid Earth, the ocean, and the atmospheric system. 25 years of obtaining data using SLR have helped create a reference model for Earth’s standard, highprecision, longwave gravitational field and for studying its temporal variations due to the redistribution of mass (http://ilrs.gsfc.nasa.gov).
To measure temporal changes in the gravitational field, SLR gauges mass redistribution effects within Earth’s overall system. Decades of monthly values determined by satellite laser ranging of the second zonal harmonic of Earth’s gravity provide an independent verification of the mass redistribution implied by the global atmospheric circulation models used to predict global climate change.
1998 witnessed the beginning of abnormal changes in some of Earth’s geophysical parameters – a leap in J_{2 }coefficient values in particular. An article by Christopher Cox and Benjamin Chao published in the Science magazine has reported on new and completely unexpected findings about Earth’s gravitational field variations. The authors used satellite laser ranging data over the last 25 years to determine longterm variations in the zonal coefficient of Earth’s spherical harmonic of the second degree, the socalled J_{2 }coefficient. The J_{2 }coefficient reflects the dynamics of the ratio between Earth’s equatorial and polar radii. It was decreasing for many years, supposedly due to the release of meltwater from the mantle since the ice age. Meanwhile, the latest data show that since 1998, J_{2}, has started to grow (B. Chao and C. Cox, 2002).
The satellite laser ranging (SLR) data shown in Fig. 53 indicate shifts in Earth's oblateness variations along the timescale. However, while the J_{2 }coefficient remained roughly constant at –2.8 • 10^{–11} per year from 1980 until 1997, the opposite J_{2}(t) change has accelerated since 1998 in line with some unknown mechanism.
Fig. 53. Variations of J_{2} coefficient values according to C. Cox and B. F. Chao, 2002
According to NASA, this process reflects Earth’s expansion along the equator and its flattening at the poles, as shown in Fig. 53. NASA experts link the SLR data based deviations of the orbits of Earth’s artificial satellites to global changes of Earth’s gravitational field. Thus, as can be seen from the graph, a certain globalscale event occurred in 1998, causing a dramatic change of Earth’s shape.
B. F. Chao (B. F. Chao, 2003) points out that, according to the model generally accepted today, the straightline trend of the J_{2} coefficient shown in Fig. 53 may indicate an increase in Earth’s radius at the poles and, consequently, its reduction along the equator resulting from deceleration of Earth’s rotation, which leads to the approximation of Earth’s shape to spherical. At the same time, the leap in J_{2} values observed in 1998 may signify a reverse trend in changing of Earth’s shape, that is, a reduction of its radius at the poles and expansion in the equatorial areas. B. F. Chao’s study (B. F. Chao, 2003) also contains a graph for earthquakes that occurred within the same period. He points out that earthquakes have a cumulative effect on Earth. During the past 25 years, earthquakecaused changes of J_{2} may be a factor of 100 less than the observed anomalous value.
There have been a number of subsequent works attempting to attribute the soaring J_{2} coefficient to melting of Antarctic ice and redistribution of water in the oceans.
As suggested in a study by Frank G. Lemoine and others (Frank G. Lemoine, et al., 2009), the J_{2} leap may be a deviation belonging to a category periodically recurring in certain years. According to the authors, to observe these changes in the J_{2} coefficient, an extra 2 mm difference between the equatorial and polar radii is needed.
Meanwhile, B. F. Chao in his work states that those factors are insufficient for such deviations of the J_{2} coefficient to happen. Some studies examine the possibility of influence by some ultralongperiod gravitationalwave pulse which, having passed through Earth, quadrupolely altered its shape and the spacetime continuum of nearearth space (E. N. Khalilov, 2004).
6.3. When did the global “energy spike” begin?
Following a research by F. Deleflie, et al., 2003, it was concluded that the 1998 leap in the J_{2} coefficient values could not be explained by the postglacial rebound or the known cyclicity with a period of 18.6 years as the scale of those changes is much less than the effects observed. The authors believe that studying the relationship between the J_{2} coefficient and geodynamic processes may shed some light on this problem.
Fig. 54 demonstrates a comparison of graphs for sea level fluctuations of the Indian Ocean and Western & Central Pacific Ocean with those of the Eastern Pacific and Atlantic oceans, as well as the overall graph for global sea level fluctuations.
Fig. 54. Comparison of graphs for sea level fluctuations of Indian Ocean and Western & Central Pacific Ocean with those of Eastern Pacific and Atlantic oceans, as well as overall graph for global sea level fluctuations http://i29.tinypic.com/71oa6q.png
The comparison result obtained by the Climate Observations (Notes From Bob Tisdale on Climate Change and Global Warming http://bobtisdale.blogspot.com/2009/08/ensoismajorcomponentofsealevel.html) showed that between 1997 and 1999, sea level fluctuations of the Indian Ocean and Western & Central Pacific Ocean were out of phase with fluctuations of the Eastern Pacific and Atlantic oceans. While the level of the Eastern Pacific and Atlantic oceans began to rise sharply in 1997 with a peak in 1998 (about 3 cm), the level of the Indian Ocean and Western and Central Pacific Ocean was falling with a minimum in 1998 (about 3 cm).
This very surprising tendency requires a special study. It is the specifics of El Niño that explain those unusual variations in the levels of different oceans.
El Niño is a global oceanicatmospheric phenomenon. As characteristic features of the Pacific, El Niño and La Niña are temperature fluctuations in surface waters of the tropical eastern Pacific Ocean. The circulation named thus by Gilbert Thomas Walker in 1923 is an essential aspect of the ENSO (El Niño Southern Oscillation) phenomenon of the Pacific.
ENSO is a set of interacting parts of a global system of oceanatmospheric climate fluctuations occurring as a sequence of oceanic and atmospheric circulations. ENSO is the world’s most famous source of interannual weather and climate variations (from 3 to 8 years). When there is a significant temperature rise in the Pacific, El Niño heats up and expands into the most of the tropical Pacific, as it is directly related to the intensity of the SOI (Southern Oscillation Index). While the majority of ENSO events occur between the Pacific and Indian Oceans, ENSO events in the Atlantic lag behind them by 1218 months.
Fig. 55 shows a comparison of J_{2} coefficient variations (top) with ocean level evolution graphs (bottom). As can be seen from the image, the timing of maximum values of ocean level variations coincides (1998) with the beginning of a sharp leap in the J_{2} coefficient. So, a natural question arises: to what extent can the observed ocean level changes and El Niño processes cause the registered J_{2} variations?
The “Climate Observations” study directly links the 1998 J_{2} coefficient anomaly to El Niño processes. Meanwhile, as B. F. Chao and others (B. F. Chao, et al., 2003) point out in their article, studies of the J_{2} coefficient have revealed correlations with northern and southern Pacific basin sea level changes.
However, even taking into account the pattern of the possible impact of water mass redistribution in the World Ocean, the actually observed effect of the J_{2} coefficient is 3 times greater than that impact. Therefore, El Niño and other processes in the atmosphere and hydrosphere cannot explain the 1998 variations of the J_{2} coefficient.
Fig. 55. Comparison of J_{2} coefficient variations (top) with sea level evolution graphs for Indian Ocean, Western & Central Pacific Ocean, Eastern Pacific, and Atlantic Ocean, and with overall graph for global sea level fluctuations (bottom)
Comparing the J_{2} coefficient variations with global temperature changes in the troposphere has also helped discover some correlation with the 1998 J_{2} anomaly, Fig. 56. It is remarkable that in 1998, abnormally high changes of the troposphere’s global temperature were observed as well. Thus, we are finding a correlation between the 1998 anomalous J_{2} leap and processes in the hydrosphere and atmosphere.
Fig.56. Comparison of J_{2} coefficient variations (top) with global temperature changes in troposphere (The source of the global tropospheric temperature variations graph: http://wattsupwiththat.files.wordpress.com/2009/05/uah_april_2009.png )
It is of interest to compare the J_{2} coefficient variations with the evolution of geodynamic processes, the variations of large М>8 earthquake numbers between 1980 and May 2010 in particular. As can be seen from the comparison in Fig. 57, there has been a sharp increase in the numbers of large earthquakes and their victims according to an exponential law since 19971999.
Fig. 57. Comparison of graphs for J_{2} coefficient variations (1),dynamics of numbers of large earthquakes (2) and numbers of earthquake fatalities (3) from 1980 to May 2010 Exponential trends are marked in blue.
The time period between 1998 and 2003 encompassing the J_{2} coefficient anomaly is actually a turning point and marks the beginning of the “leap” in the statistics for large earthquakes and earthquake victims from 1980 to May 2010.
Fig. 58. Comparison of graphs for J_{2} coefficient variations (top) and numbers of volcanic eruptions from 1980 to 2010. Graph for volcanic eruption numbers from 1980 to May 2010 is marked in yellow; Volcanic eruption numbers trend smoothed with 11year running averages is marked in blue.
Comparing the volcanic eruptions graph with the J_{2} coefficient variations graph also demonstrates that the years of 19971998 mark the deep minimum in volcanic activity and are a watershed followed by a sharp increase in volcanic activity still observed today, Fig. 58.
Fig. 59 (A) contains graphs for the dynamics of tsunami numbers between 1965 and May 2010. It clearly shows that there has beena dramatic change in the trend for the statistical distribution of annual rates for catastrophic, mediumsized, and weak tsunamis since 1998. The “leap” in the statistics for annual tsunami numbers observed since 1998 is depicted by the exponential trends shown in Fig. 59 (B).
Fig. 59. Graphs showing evolution of tsunami numbers between 1965 and 2010. Yaxis: on the left  the number of mediumsized and weak tsunamis, on the right – the number of catastrophic tsunamis. (A) graphs show evolution of annual tsunami numbers; Catastrophic tsunamis graph is marked in yellow;weak and mediumsized tsunamis graph is marked in blue; (B) graphs show exponential trends of evolution of annual tsunami numbers. Catastrophic tsunamis trend is marked in yellow; mediumsized and weak tsunamis trend is marked in blue.
Analysis of U.S. flood statistics from 1980 to 2008 also indicates that since 1998 there has been a dramatic increase in the number of floods that is still present today (May 2010), Fig. 60.
Fig. 61. Graph for U.S. flood fatality figures between 1910 and 2010 Based on data from the website: http://www.weather.gov/oh/hic/flood_stats/flood_trends.JPG with additions by E. N. Khalilov (2010) Annual figures are marked in white; 5year average figures are marked in blue; straightline trend is marked in orange; exponential trend is marked in yellow.
The graph for the fatality rate during U.S. floods also highlights the aforementioned tendency. The growing number of floodcaused deaths in the United States between 1910 and May 2010 is most effectively depicted by an exponential trend, Fig. 61.
However, the observed “leap” in the statistical indicators within the specified time period is not limited only to catastrophic processes encompassing the lithosphere and hydrosphere. Let us have a look at the distribution of tornado dynamics in different regions of the world. Fig. 62 contains a graph for Germany’s tornado dynamics by decades.
Fig. 62. Graph for tornado dynamics in Germany between 1800 and 2000. Diagram showing tornado numbers for tenyear time intervals (last period covering 5 years) is marked in red; Exponential trend is marked in blue.
In Fig. 62, one can observe a distinct tendency for the number of German tornadoes to grow. In order to avoid significant loss of information over some historical time while examining the tendency, let us take the period from 1900 as a basis for our study. A sharp increase in the number of tornadoes since the late 1990's is seen quite clearly.
The observed “leap” cannot be regarded as accidental since the five year (20002005) number of tornadoes in Germany is 2.5 times higher than the number of tornadoes for the preceding 10 years.
Studying the dynamics of North Atlantic tropical storms from 1925 to 2005 reveals its consistency with the tendencies found in the dynamics of other natural disasters, and a surge in the number of storms has also been observed since 1998. The exponential trend reflects the general tendency of evolution of North Atlantic tropical storm statistics, Fig. 63.
Fig. 63. Graph for numbers of North Atlantic tropical storms between 1925 and 2007 North Atlantic storms graph is marked in blue; Exponential trend is marked in red.
A no less significant indicator of climate change dynamics are forest fires causing enormous environmental damage and leading to huge economic losses and human casualties.
Fig. 64. Graph for annual numbers of U.S. forest fires between 1960 and 2007 Polynomial trend of fifth degree is marked in red.
The graph for evolution of the annual numbers of U.S. forest fires from 1960 to 2007 (Fig. 64) demonstrates a growing tendency for forest fires, with the beginning of the “leap” in 1998 as well. This is reflected well in the polynomial trend shown on the graph.
Fig. 65. Graph for frequency of Kazakhstan forest fires between 1950 and 2000. Registered number of forest fires is marked in red; Areas affected by forest fires are marked light in violet.
A similar evolution pattern in forest fire statistics is observed for other regions of Earth as well. For instance, Kazakhstan in 1997 witnessed a “leap” in the form of a sharp increase in the number of forest fires and fireaffected areas, Fig. 65.
The tendency for a sharp increase in the annual numbers of forest fires is observed for the territory of Eastern and Western Europe and the CIS as well. The general nature of forest fire dynamics in this region can also be described by an exponential trend marked in red in Fig. 66. As one can see from the graph, there is a 1998 “leap” in the number of forest fires.
