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Figure 2.3: Global average sea level rise 1990 to 2100 by several of the SRES scenarios source from: IPCC 2001
A set of scenarios was developed to represent the range of driving forces and emissions in the scenario literature to reflect current understanding and knowledge
about underlying uncertainties. The scenarios are based on an extensive assessment of driving forces and emissions in the scenario literature, alternative modelling
approaches, and an “open process” that solicited wide participation and feedback. Several data models projection with set of IPCC scenarios SRES has been
done by some research institutes namely: BCC-CM 1 model Beijing Climate Center, China, BCM 2.0 model Bjerknes Centre for Climate Research, Norway, CGCM3.1
model Canadian Centre for Climate Modelling and Analysis, MIROC3.2 Model for Interdisciplinary Research on Climate, Japan, Mk3.0 3.5 model CSIRO-
Commonwealth Scientific and Industrial Research Organisation, Australia, HadCM3 model Hadley Centre for Climate Prediction, UK, INMCM3.0 model
Institute for Numerical Mathematics, Russia, CM3 model from Meteo France, MRI model from Meteorogical Research Institute - Japan, GISS model NASA-Goddard
Institute for Space Studies, and CM 2.0 2.1 from NOAA, etc.
2.3 MRI-CGCM in SRES A1B Model
MRI ground hydrology model is Earth system modeling for the carbon cycle and chemical mass transport which develop by Y.Nikaidou from Japan
Meteorological Agency using Fortran 77 computer language program, and firstly
11 used in 1993. Data needed to develop this model is Global data of snow depth and
density with several meteorological variables to drive, namely: precipitation, air temperature, wind speed, wind direction, humidity, down-welling long and short
wave radiation, cloud cover, and surface pressure Noda, 1999. The SRES A1B was taken in this research, because the A1B scenario is being
considered to represent the current trend of emissions IPCC data model projection used as reference and parameter to make projection in 2100 Yamashiki et al., 2010.
Takaya 2009 has reported that MRI-CGCM has high predictability of precipitation and air temperature over the Eastern Asia even without statistical
applications. It is also noted that the seasonal prediction skills are strongly dependent on regions, seasons and the elements to predict as well as ENSO situations.
Compared to AGCM Atmospheric General Circulation Model, CGCM has improvement performance in forecast and high predictability response to the mid-
latitude atmospheric circulation that appears behind an El Niño event Naruse, 2009. Evaluation from Takaya 2008 noted that, this model has good performance in
typhoon seasonal forecast, moreover Stockdale et al., 2009 added, within the fiscal 2009 year coupled model JMAMRI-CGCM will be employed for all the JMA
long-range forecasts.
2.4 Land Subsidence
Land subsidence is the lowering gradual process of the land-surface elevation that take place underground Leake, 2004. These natural phenomena usually occur
in big city within coastal area which standing on top of sediment layer such as Jakarta Abidin et.al, 2009, Surabaya Tobing, 2004, Semarang Marfai, 2003,
Bangkok Phien-wej, et al., 2005, Osaka, Tokyo Yamamoto, 1995, Shanghai Wei, 2006, Taiwan Chu and Sung, 2003, etc. There are some factors which cause land
subsidence namely: excessive water suction, heavy building container surface load force, intrusion, erosion, mud extraction Lapindo case, oil and gas extraction,
underground mining and tectonic movements. When a tidal wave comes from the sea or water overflow from the river, the
lower parts of the ground due to the land subsided will be inundated. Land subsidence in coastal and alluvial floodplain areas causes extensive flood inundation
Smith et, al., 1998.
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2.5 El Niño and La Nina
