Fractal and Variability in Sunspot Cycles

Kontribusi Fisika Indonesia
Vol. 13 No.2, April 2002

Fraktal dan Variabilitas dalam Siklus Bintik Matahari
Dhani Herdiwijaya dan Baju Indradjaja
Departemen Astronomi,
Fakultas Matematika dan Ilmu Pengetahuan Alam, Institut Teknologi Bandung
Jl. Ganesha 10 Bandung 40132
E-mail: dhani@as.itb.ac.id

Abstrak
Berdasarkan data jumlah bintik matahari rata-rata bulanan dalam periode tahun 1749-2000 dilakukan pengujian terhadap variabilitas dan kompleksitas aktivitas matahari. Periodogram ditentukan berdasarkan algoritma Lomb dan wavelet Morlet. Metoda Detrended Fluctuations Analysis (DFA) dipergunakan untuk menguji fenomena kompleksitas. Periodisitas siklus bintik matahari rentang 11 tahun terlihat dominan, dibandingkan perioda siklus magnetik, maupun perioda Gleissberg. Amplitudo wavelet periodisitas tersebut berubah terhadap waktu. Dalam rentang 200 tahun terakhir, amplitudo perioda 11 tahun menguat dua kali lipat. Metode DFA menghasilkan komponen α yang berfluktuasi antara 0,5 dan 1,5. Hal ini menunjukkan adanya korelasi dalam perubahan nilai data sebagai fungsi waktu. Sedangkan dimensi fraktal, berada dalam rentang nilai yang sama. Disimpulkan bahwa variasi waktu dan amplitudo jumlah bintik matahari merupakan proses yang non stasioner, tetapi memperlihatkan adanya korelasi untuk perioda kurang dari 132 tahun. Terjadinya cross-over dengan α=0,7 (D=1,3) berasosiasi dengan skala temporal 11 tahun dan skala luas granulasi matahari.
Kata kunci: matahari, aktivitas matahari, siklus matahari, bintik matahari, kompleksitas

Abstract
Monthly sunspot numbers during period 1749 to 2000 were used to determine their fractal properties. Lomb’s algoritm and Morlet wavelet were applied to detect the distinct period of 11 year. By using Detrended Fluctuations Analysis (DFA), fractal dimension was measured. It is shown that during the last two centiries, 11-year wavelet amplitude showed doubled increment, in contrast with Gleissberg period. The evolution of fractal dimension varied in between 0.5 to 1.5, which imply that sunspot number time series are nonstationary and correlated to each other within 132-year period. The detected cross-over feature at D=1.3 corresponds to 11-year period and granulation size.
Keywords: Sun, solar activity, solar cycle, sunspot, complexity

The Effect of Solar Activity and Cosmic Ray Flux to Weather and Climate

The Effect of Solar Activity and Cosmic Ray Flux to Weather and Climate

The Houw Liong* and Plato Martuani Siregar**



*Department of Physics,FMIPA, ITB
**Department of Geophysics and Meteorology,FITB, ITB

The cosmic rays interact in the upper atmosphere and produce secondary particles. Generally the charged particles so produced cannot penetrate to lower layers of the atmosphere, except the neutrons and the muons (below 6 km heights). When the neutrons or the muons interact with the air molecules or water molecules, they become charged and act as condensation nuclei for the formation of clouds. The cosmic ray becomes the source of ions in the air besides radiation coming from earth originated by the radio isotope radon.
During the sunspot minimum, the intensity of the cosmic ray becomes maximum which in turn increase the coverage of clouds. This implies that solar radiation reaching the earth will be minimized. Conversely, during sunspot maximum, the intensity of cosmic ray reaching lower levels of the atmosphere reduces, the cloud cover decreases, furthermore extra energy received from flares during prominent eruptions, maximizes the amount of solar energy received on earth.
The global cloud cover produces global warming (the greenhouse effect) which amounts to 13%, but it also causes a cooling effect as much as 20% due to reflections against direct solar radiation(1). The total energy derived from the sun is thus the solar constant averaging to 6.3 X1020 joules/hour which is equal to the energy of 40 tropical cyclones or 60 times the energy released by a major earthquake in Indonesia.
From the 21st solar cycle the irradiance received on earth shifted between 1367.0 W/m2 and 1368.5 W/m2 - it varies by 0.15 % only5). However, the large quantity of energy derived from the sun together with the forcing of atmosphere and oceans and the variation of the irradiance contribute considerably to the weather and climate.
Landscheidt(4) has shown that between years 1950 to 1975 very strong correlations existed between the events of El Nino and sunspot minimum SMin and its harmonics to SMin/2 or sunspot maximum SMax. The occurrences of La Nina correspond to maximum eruption ME and its harmonics ME/2. Then around the year 1975, a phase reversal occurred, and this continued from year 1976 up to the present, there the ME and its harmonics correlated well to El Nino, while SMax and its harmonics correspond to La Nina. Therefore, in this way, one can predict that the year 2006 will be the year of La Nina.
Starting from year 1950 to 1976, during the occurrences of El Nino, the sea temperature in the eastern region of the archipelago was low, and conversely during La Nina, the sea temperature was high, which means that low sea temperature in the archipelago correlates positively to sunspot minimum SMin and its harmonics, while the high sea temperature correlates positively to maximum eruptions ME and its harmonics. In 1976 phase reversal occurred, SM and SM/2 or sunspot maximum SMax correlate positively with high sea surface temperature in eastern Indonesia and as a result precipitations increased.
The Correlation of Sunspot to Rainfall in the Indonesian Archipelago
With the equator crossing Indonesia, the sensible heat flux plays an important role in global circulations. The latent heat which originates mainly from the release of latent heat when water vapour condenses into clouds droplets(a number of large clouds form through convections in the Inter Tropical Convergence Zone (ITCZ) which is above Indonesia). The cold monsoon season in northern hemisphere (Asian monsoon) and in the southern hemisphere (Australian monsoon) are influenced by the heat source distribution or the release of latent heat above Asia and in the neighbourhood regions(13). At present it seems that the Indonesian zone holds the key to southern oscillation system which determines the forcing of El Nino-Southern Oscillation (ENSO)14). Therefore, Indonesia, through which the equator crosses has the maximum sensible heat flux, high rainfall, and monsoon circulations. Consequently, it is one of the most primal zones for convection processes, an equatorial-tropical zone where Coriolis effects are practically nullified, where atmospheric circulations are very different compared to the extra-tropical zones15).
The observations and studies on Indonesian climate are limited, and the mathematical formulations of tropical dynamics are far more complex relative to those in the extra-tropical zones. For decades the awareness of the importance of climates in Indonesia have been neglected by international research community(16). The distinct daily convection variability induced by land-sea wind circulations over some islands in Indonesia characterizes the aspect of rainfall throughout the Indonesian Archipelago which are very different from other regions on the earth(17). The studies mentioned above, show that rainfall is an important quantity in the Indonesian Archipelago and sunspot is believed to be the major predictor.
Although there is an indirect physical link between sunspot and rainfall, the correlations which existed in general are weak. In other words, these signify that the dynamics of the atmosphere is being viewed as the cause of the small correlations. However, in the case of static model atmosphere, determination of correlations based on data-averaging of anomalies of sunspot on a monthly basis against the average anomaly of rainfall for various stations in Indonesia, one comes to time series as shown in Figures 1a, 1b, and 1c at various regions for the period 1948-2003.
From Figure 1 and Figure 2 we can conclude that eastern Indonesia (Jayapura region) which represent Eastern Indonesian Maritime Continent is strongly influenced by ENSO. After 1976 sunspot maximum SMax and sunspot minimum SMin correspond to precipitations above normal also to La Nina and maximum eruptions ME corresponding to precipitations below normal and also to El Nino. In Pontianak region which represent western Indonesian Maritime Continent, the yearly precipitation is mainly determined by sunspot cycles. Precipitations above normal occur at sunspot maximum SMax, and precipitations below normal at sunspot minimum SMin. Precipitations in middle and east Java which represent North Australia Indonesian Monsoon are influenced by ENSO similar to those observed in Jayapura region. Precipitations in Jakarta region are weakly influenced by ENSO.
The fuzzy c-means clustering shows that the west Indonesian regions are influenced by IOD, the east Indonesian regions are influenced by ENSO and the middle region is mainly influenced by sunspot numbers.

PREDICTION OF EXTREME WEATHER AND CLIMATE IN THE INDONESIAN MARITIME CONTINENT BASED ON SUNSPOT NUMBERS

PREDICTION OF EXTREME WEATHER AND CLIMATE IN THE INDONESIAN MARITIME CONTINENT BASED ON SUNSPOT NUMBERS


The Houw Liong* and Plato Martuani Siregar**
*Department of Physics,FMIPA, ITB
**Department of Geophysics and Meteorology,FITB,ITB, Bandung, INDONESIA

Abstract
From various geographical stations in the Indonesian Archipelago, anomalies of yearly rainfall were collected and plotted against the anomalies of yearly sunspot numbers between 1948 and 2003. It is seen that there is a strong correlation between sunspot numbers and the various geophysical variables, such as the mean temperature of Earth, the cloud cover, the sea surface temperature and the rainfall throughout the regions. The ability of cosmic ray particles to penetrate the earth’s atmosphere is limited by the earth's magnetic field. In addition, during sunspot maximum the magnetic field of the solar wind increases and this in turn strongly reduces the flux of cosmic rays that reach the earth.
A correlation exists between cosmic rays, formation of clouds and climate as some researchers suggest. This paper shows that the knowledge of sunspot numbers can be used to predict extreme climate and weather in Indonesia.

Introduction
The relative positions of the sun in the sky during the seasons, as well as the cycles of solar activity influence the weather and climate throughout the Indonesian archipelago. Solar irradiance increases with higher solar activity. This in turn increases the solar wind which consists of charged particles emitted by the sun which could alter the interplanetary magnetic field, and hence the intensity of cosmic rays reaching the earth. The cosmic ray intensity increases with higher solar activity. Thus the solar activity is often considered as the dominant factor that determines the dynamics of climate(1,2). The dynamics of earth's atmosphere and oceans, evaporation, clouds formation and rainfall, are influenced by the solar energy entering the earth. Several studies indicate that strong correlations exist between the cloud cover and the intensity of cosmic rays.3)
Both phenomena may affect the climate, for example during 1645 – 1715 exceptionally low solar activity (also known as the Maunder minimum) led to low temperatures causing what is known as the little ice age.
The present study shows that there is a strong correlation between rainfall in the Archipelago and sunspot numbers.

Reference
1. E. Bryant, Climate Process and Change, Cambridge University Press, 1977.
2. T. Landscheidt, Solar Activity: A Dominant Factor in Climate Dynamics, Schroeter Institute for Research in Cycles of Solar Activity, http://www.johndaly.com/solar/solar.htm, 1988.
3. K.S. Carlslaw, R.G. Harrison, J. Kirkby, Cosmic Rays, Clouds, and Climate, Science’s Compass, Vol. 298, 2002.
4. T. Landscheidt, New ENSO Forecast Based on Solar Model, Schroeter Institute for Research in Cycles of Solar Activity, 2003.
5. P. Foucal and J. Lean, An Empirical Model of Total Solar Irradiance Variation between 1874 and 1988, Science, 247,556-558, 1990.
6. Friis-Cristensen,E. and Lassen,.K.,(1991), Length of The Solar Cycle :an Indicator of Solar Activity Closely Associated with Climate,J.sience, 254,698.
7. Baliunas,S. and W.Soon,(1996).The Sun-Climate Connection. Sky & Telescope, Dec.,38-41.
8. Reid.G.C,(1987),Nature Vol.329,hal.142.
9. Ratag,M.A.,(1999), Dampak Variabilitas Matahari terhadap Vegetasi:Cincin-cincin Kayu, Prosiding lokakarya program Iklim Nasional,126-132,Jakarta.
10. Ratag,M.A.,(1999), Fraktal Variabilitas Matahari dan Kaitannya dengan Dinamika Variabilitas iklim,Prosiding lokakarya program Iklim Nasional,133-144,Jakarta.
11. Ratag,M.A.,(1999), Dinamika Sistem Matahari-Bumi dan Perubahan Iklim Global, Prosiding lokakarya program Iklim Nasional,150-160,Jakarta.
12. Ratag,M.A.,(1994),Perubahan iklim global dan hubungan matahari-bumi, Proc.Media dirgantara LAPAN,101-115.
13. Arief,S.,(1999), Analisis aktivitas konveksi di benua maritim Indonesia dan sekitarnya pada perioda monsun Asia 1990-1997,Lokakarya Program Iklim Nasional Terpadu.174-187.
14. Trenberth,K.E.,and T.J.Hoar,(1996),The 1990-1995 El-Nino/Southern-Oscillation Event: Longest on Record,Geophys.Res. Lett.,23,57-60.
15. McBride,J., (1992),The Meteorology of Indonesia and The Maritime Continet. The Fourth International Symposium on Equatorial Atmosphere Observation over Indonesia,Nov,10-11,Jakarta.
16. Salby,M.L.,and D.J Sheaq,(1991), Correlation Between Solar Activity and the Atmosphere:An Unphysical Explanation.,J.Geophys.Res.,96,22,579-22,595.
17. Johnson.R.A.,and D.W.Wichem,(1992), Applied Multivariate Statistical Analysis, third edition,Prectice Hall,New Jersey.
18. Ziegler, J. F. (1998), Terrestrial Cosmic Ray Intensities, IBM Journal of Research and Development, Vol. 42, No. 1

Coherent telescope array with self-homodyne interferometric detection for optical communications

Coherent telescope array with self-homodyne interferometric detection for optical communications
Opt. Eng., Vol. 42, 3139 (2003); DOI:10.1117/1.1612512
Online Publication Date: 31 October 2003

ABSTRACT
REFERENCES (28)
CITING ARTICLES
Bernard Eng-Kie Souw

BMS Enterprise, P.O. Box 5524, Herndon, Virginia 20172-5524
E-mail: souw1@juno.com



The performance of a coherent telescope array (CTA) as an optical communications receiving system is analyzed with regards to the benefits and disadvantages in comparison to monolithic large-aperture single telescopes and conventional photon bucket systems, especially in terms of background photons. The bit error rates for differential phase-shift keying (DPSK) and binary pulse position modulation (BPPM) schemes are derived, the former using self-homodyne interferometry as a novel demodulation technique, in which the background photons interact incoherently. The possibility of further dividing the receiver's aperture into N>2 smaller subtelescopes is explored, and its adaptability for implementing the N binary phase-shift keying (N-BPSK) technique with optical code division multiplexing (OCDM) is discussed. An N-CTA receiver system with an N-BPSK/OCDM technique is envisioned not only to achieve multiterabit per second data transfer capability, but also to adapt a novel noise suppression technique based on photon correlations, which would render a need for expensive and complicated adaptive optics obsolete. Further combined with significant reduction in weight and overall costs, an N-CTA system with an N-BPSK/OCDM technique could serve in deep space communications as well as in defense to achieve information dominance in the battlefield.

©2003 Society of Photo-Optical Instrumentation Engineers.
History: Received Feb. 6, 2003; revised May 6, 2003; accepted May 12, 2003
DOI Link: http://dx.doi.org/10.1117/1.1612512

Two Categories of Earthquake Precursors, Physical and Tectonic

PAGEOPH, Vol. 126, Nos. 24 (1988) 0033-4553/88/020687-1451.50+0.20/0
9 1988 Birkhiiuser Verlag, Basel

Two Categories of Earthquake Precursors, Physical and Tectonic, and Their Roles in Intermediate-Term Earthquake Prediction
KATSUHIKO ISHIBASHI

Abstract
I suggest that earthquake precursors can be divided into two major categories, physical and tectonic. I define physical precursor to be a direct or indirect indication of initiation or progression of an irreversible rupture-generating physical process within the preparation zone of a forthcoming earthquake.
Tectonic precursor is defined as a manifestation of tectonic movement which takes place outside the preparation zone of an impending earthquake as a link in a chain of particular local tectonism in each individual area preceding the earthquake.
Most intermediate-term, short-term and immediate precursors of various disciplines within the source regions of main shocks are considered physical ones. Some recursory crustal deformations around the source regions are, however, possibly tectonic precursors, because they may be caused by episodic plate motions or resultant block movements in the neighboring regions of the fault segments that will break. A possible example of this phenomena is the anomalous crustal uplift in the Izu Peninsula, Japan, before the 1978 Izu-Oshima earthquake of M s 6.8. Some precursory changes in seismicity patterns in wide areas surrounding source regions also seem to be tectonic precursors, because they were probably caused by the particular tectonic setting of each region. A typical example is a so-called doughnut patternbefore the 1923 Kanto, Japan, earthquake of Ms 8.2.
Although most studies on earthquake precursors so far seem to regard implicitly all precursory phenomena observed as physical ones, the two categories should be distinguished carefully when statistical analysis or physical modeling is carried out based on reported precursory phenomena. In active plate boundary zones, where a practical strategy for earthquake prediction may well be different from that in intraplate regions, tectonic precursors can be powerful additional tools for intermediate-term earthquake prediction.
Key words: Earthquake prediction, earthquake precursor, physical precursor, tectonic precursor, 1978 Izu-Oshima earthquake, 1923 Kanto earthquake.

Local and global effects of space earthquake precursor anomalies

Copyright © 2000 Published by Elsevier Ltd.

Local and global effects of space earthquake precursor anomalies

Yu. Ya. Ruznin, A. Kh. Depueva and V. I. Larkina
IZMIRAN, Troitsk, Moscow Region, 142092, Russia


Available online 6 June 2000.
Abstract

It is shown that two main groups of the earthquake precursors exist in near-earth space. There are plasma anomalous structures which appear above the epicentral area of future earthquake at full range of ionospheric heights. There are “noise” belts in VLF band registered at both hemispheres by low-orbit satellites. For the both types of space precursors it was claimed that they have their local image which depends strongly on epicenter location and on time and geographical position of precursors observations.

Article Outline

• References

Potential Biases in Feedback Diagnosis from Observational Data

Potential Biases in Feedback Diagnosis from Observational Data: A Simple Model Demonstration
ROY W. SPENCER AND WILLIAM D. BRASWELL
Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama
(Manuscript received 24 September 2007, in final form 28 February 2008)
ABSTRACT
Feedbacks are widely considered to be the largest source of uncertainty in determining the sensitivity of the climate system to increasing anthropogenic greenhouse gas concentrations, yet the ability to diagnose them from observations has remained controversial. Here a simple model is used to demonstrate that any nonfeedback source of top-of-atmosphere radiative flux variations can cause temperature variability, which then results in a positive bias in diagnosed feedbacks. This effect is demonstrated with daily random flux variations, as might be caused by stochastic fluctuations in low cloud cover. The daily noise in radiative flux then causes interannual and decadal temperature variations in the model’s 50-m-deep swamp ocean. The amount of bias in the feedbacks diagnosed from time-averaged model output depends upon the size of the nonfeedback flux variability relative to the surface temperature variability, as well as the sign and magnitude of the specified (true) feedback. For model runs producing monthly shortwave flux anomaly and temperature anomaly statistics similar to those measured by satellites, the diagnosed feedbacks have positive biases generally in the range of
0.3 to
0.8 W m
2 K
1. These results suggest that current observational diagnoses of cloud feedback—and possibly other feedbacks—could be significantly biased in the positive direction.

JOURNAL OF CLIMATE VOLUME 21
http://www.drroyspencer.com/Spencer-and-Braswell-08.pdf

Climate Change due to Cosmic Rays

Henrik Svensmark, a weather scientist at the Danish National Space Center believes that the planet is experiencing a natural period of low cloud cover due to fewer cosmic rays entering the atmosphere, which is responsible for much of the global warming we are experiencing.

Svensmark claims carbon dioxide emissions due to human activity are having a smaller impact on climate change than scientists think. If he is correct, it could mean that mankind has more time to reduce our effect on the climate.

Svensmark published the first experimental evidence from five years' research on the influence that cosmic rays have on cloud production in the Proceedings of the Royal Society Journal A: Mathematical, Physical and Engineering Sciences.

Svensmark claims that the number of cosmic rays hitting the Earth changes with the magnetic activity around the Sun. During high periods of activity, fewer cosmic rays hit the Earth and so there are less clouds formed, resulting in warming. "Evidence from ice cores," he said, "show this happening long into the past. We have the highest solar activity we have had in at least 1,000 years."

Humans are having an effect on climate change, but by not including the cosmic ray effect in models it means the results are inaccurate.The size of man's impact may be much smaller and so the man-made change is happening slower than predicted.

http://www.dailygalaxy.com/my_weblog/2009/05/the-chilling-st.html

Tropical climate influences on drought variability over Java, Indonesia

Tropical climate influences on drought variability over Java, Indonesia

Rosanne D'Arrigo

Lamont-Doherty Earth Observatory, Palisades, New York, USA

Jason E. Smerdon

Lamont-Doherty Earth Observatory, Palisades, New York, USA

We investigate relationships between Indonesian drought, the state of the equatorial Indian Ocean, and ENSO using three instrumental indices spanning 1884–1997 A.D.: 1. EQWIN, a zonal wind index for the equatorial Indian Ocean; 2. the Dipole Mode Index (DMI), an indicator of the Indian Ocean SST gradient; and 3. tropical Pacific Niño-3.4 SSTs. A regression model of the Java Sep–Dec Palmer Drought Severity Index (PDSI) using a combination of these indices provides significant predictive skill (ar2 = 0.50). Both the DMI and EQWIN correlate strongly with Java droughts (r = 0.71 and 0.66, respectively), but weakly with wet events (r = 0.21 and 0.18, respectively), while the Niño SST index correlates moderately with both dry and wet events (r = 0.31 and 0.36, respectively). Our findings indicate that Java droughts are intensified during El Niños that coincide with negative EQWIN conditions, which are also linked to a strengthened Indian monsoon

Received 6 November 2007; accepted 4 February 2008; published 8 March 2008.

Citation: D'Arrigo, R., and J. E. Smerdon (2008), Tropical climate influences on drought variability over Java, Indonesia, Geophys. Res. Lett., 35, L05707, doi:10.1029/2007GL032589.