CLIMATE MODEL BASED ON SOLAR ACTIVITY
The Houw Liong
F. H. Widodo
The galactic cosmic rays collide with air molecules in the upper atmosphere and produce secondary particles. Generally the charged particles so produced cannot penetrate to lower layers of the atmosphere, except gamma ray, neutrons and the muons. When gamma ray, neutrons and muons interact with the air molecules or water molecules, they become charged and together with aerosols particles 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 galactic cosmic ray that penetrates earth atmosphere becomes maximum which in turn increases the coverage of clouds. This implies that solar irradiation reaching the earth will be minimized. Conversely, during solar activity maximum or sunspot maximum, the intensity of galactic cosmic ray reaching lower levels of the atmosphere decreases, less cloud condensation nuclei are produced, hence the cloud cover decreases, furthermore extra energy received from flares during prominent eruptions, maximizes the amount of solar energy reaches the earth.
Although global cloud cover produces a warming effect or the greenhouse effect, but a cooling effect due to reflections against direct solar irradiation is more dominant factor [Svensmark,2007].
Furthermore during solar activity maximum, the intensity of ultraviolet that penetrates the earth increases. Solar activity maximum usually is followed by increasing coronal mass ejection. Both effects caused greater amount of energy penetrates the earth and this will influence the climate through the dynamics of the atmosphere and oceans.
Using rainfall data in Indonesia from NCEP Reanalysis at
and sunspot numbers time series, we can get relations between sunspot numbers and rainfall in various Indonesian regions. The determination of sunspot numbers on yearly basis against the yearly rainfall for various regions in Indonesia based on time series data are shown in Figures 3, 4 and 5 .
From Figure 3 we can conclude that eastern Indonesia (Jayapura region) which represented Eastern Indonesian Maritime Continent is strongly influenced by ENSO.
After 1976 sunspot numbers maximum SMax and sunspot numbers minimum SMin correspond to precipitations above normal also to La Nina and maximum eruptions CME corresponding to precipitations below normal and also to El Nino.[4, 5] In Pontianak region which represent middle Indonesian Maritime Continent, the yearly precipitation is mainly determined by sunspot cycles (Figure 4). Precipitations above normal occur at sunspot maximum SMax, and precipitations below normal at sunspot minimum SMin. Precipitations in east Indonesia which represent North Australia Indonesian Monsoon are influenced by ENSO similar to those observed in Jayapura region. (Figure 3) Precipitations in Jakarta region or Jabodetabek are weakly influenced by ENSO. The peaks of yearly precipitations correspond to the peaks of sunspot numbers, but at the sunspot numbers minimum which correspond to galactic comic ray maximum, the yearly precipitations also maximum.(Figure 5).
The west Indonesian region is mainly influenced by IOD that also correlated to solar cycle. 
By analyzing monthly rainfall time series in Jakarta region, we can predict 6 months ahead of time (Figure 8.) and for short range predictions we can use pentad rainfall time series and WRF model to anticipate extreme rainfall in Jakarta region
The accuracies of very long range predictions (more than ten years ahead) of climate models are very poor due to the chaotic nature of the atmosphere-ocean system.
A climate model using ANFIS and fuzzy clustering can be used to predict extreme rainfall in Indonesian regions.
The fuzzy c-means clustering shows that the western Indonesian region is influenced mainly by IOD, the eastern Indonesian region is influenced mainly by ENSO and the middle region is mainly influenced by solar activity.
So, by knowing sunspot number time series as predicted by ANFIS and fuzzy clustering of climate regions we can predict the coming extreme weather for each regions in Indonesia.
By analyzing monthly rainfall time series in Jakarta region, we can predict 6 months ahead of time and for short range predictions we can use pentad rainfall time series and WRF model to anticipate extreme rainfall in Jakarta region.