Plato M. Siregar 1) Deni Septiadi 2) The Houw Liong 3)
1) Science Atmosphere Division, Faculty of Earth Science and Mineral Technology, ITB
2) Climatology Station of Siantan Pontianak, Meteorological and Geophysical Agency, BMG
3) Physics of Complex System Division, Faculty of Mathematics and Natural Sciences, ITB
BMKG Symposium, Jakarta 2008
Weather/Climate in Indonesia is influenced by four main quasi periodic cycles: Solar Activity Cycle (Sunspot Numbers Cycle), Galactic Cosmic Ray Cycle, El Nino Southern Oscillation (ENSO) Cycle, and Indian Ocean Dipole Mode (IOD) Cycle. It can be shown that solar activity cycle can be considered as primary cycle that influence other cycles. In practice eastern Indonesian region is dominantly influenced by ENSO. When the heat pools moves to eastern Indonesian region, then rainfall in this region will be above normal. On the other hand when the heat pool leaves eastern Indonesian region and moves to Pacific Ocean then the rainfall in this region will be below normal.
During a typical Indian Ocean Dipole Mode (IOD) event the weakening and reversal of winds in the central equatorial Indian Ocean lead to the development of unusually warm sea surface temperatures in the western Indian Ocean. IOD negative means wet condition or the rainfall will be above normal along the western Indonesian region.
Precipitation in Pontianak region which represent middle Indonesian region correlated strongly with sunspot numbers cycle (solar activity cycle) has been proven using observation of rainfall data in west Kalimantan.
Using ANFIS (Adaptive Neuro Fuzzy Inference System) we are able to predict sunspot numbers cycles so that extreme weather in Indonesian regions can be predicted.
Fuzzy c-means is used to classify regions that are influenced strongly by sunspot numbers (solar activity), IOD, and ENSO cycles. This method is based on fuzzy set as fuzzy c-partition of three cycles above and as cluster center. Fuzzy c-partition matrix for grouping a collection of n data set into c classes.
This study explores the physical of climate predictions and classifications of Indonesian regions and its physical interpretations.
Keywords : ANFIS, fuzzy clustering, climate, solar activity
Weather/climate model always being interest topic to explored, even no one can give high accuracy and stable that applicable for different of time and space. This model especially for predicting necessary, needs involving complex parameters of weather/climate. Although that condition, weather/climate prediction technique indicated significant progress. The pattern not only limit by statistics approach but dispersion as mathematics through computation technique.
Quantitative Forecast of Precipitation (QPF) could make by subjective prediction technique, statistics prediction technique, and dynamic prediction technique. Subjective prediction technique making by experience, expertise and forecaster comprehension. Statistics prediction technique making by statistics prosedure, mean while, dynamic prediction technique make based on equations solution of simplified atmosphere processes [Rainbird, 1970].
Weather/Climate model can be constructed by using the law of physics for the atmosphere i.e.: The Navier-Stokes equation, the conservation of mass, the conservation of energy, the equations of states, including schemes for cloud formations, carbon and sulfur cycle, interactions between atmosphere and land surface, oceans, cryosphere, and biosphere, furthermore we have to include forcing by volcanic eruptions, the solar activity and galactic cosmic rays.
Researchers from LAPAN using GCM and DARLAM have reported some results of climate prediction for Indonesian regions [Ratag, 2002]. Under a scenario that CO2 concentration doubled in 100 years then the temperature in Indonesian regions will increase on the average about 0.03 degrees Celsius per year. This research showed that the result of prediction of rainfall in these regions is still poor (the correlations on the average are below 0.5) and need some modification on cloud formation scheme.
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 and ultraviolet intensity increases with higher solar activity. This in turn will be followed by coronal mass ejection (CME) that increases the charged particles emitted by the sun which could alter the interplanetary magnetic field, and hence the intensity of galactic cosmic rays reaching the earth. The galactic cosmic ray intensity reaching the earth decreases with higher solar activity. Thus the solar activity is often considered as the dominant factor that determines the dynamics of climate [Svensmark, 2007; Landscheidt, 1988]. 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 galactic cosmic ray reaching the earth [Carlslaw, 2002].
During 1645 – 1715 exceptionally low solar activity (also known as the Maunder minimum) which means high intensity of galactic cosmic ray reached the earth increased cloud cover that 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 middle Indonesian region and solar activity and the relation of solar activity and rainfall of other regions. Using this fact we can predict the climate in Indonesian regions by predicting the sunspot numbers (solar activity). It can be shown that to get a good accuracy of predicting a quasi periodic time series as sunspot numbers is possible.
The possibility of reducing the negative effect of climate using weather modification methods is also considered.
2. Data Used
This research using monthly rainfall data (mm) collected by 5 raingauges in West Kalimantan (BMKG) with 46 years length of data (1961-2006). Additionally data are monthly sunspot and cosmic rays data (1961-2006) of Royal Observatory of Belgium and Sunspot Index Data Center at http://www.astro.oma.be/SIDC.