Figure 3.9: Trends o 110.25E-110.51
Figure 3.10: Trends of s 110.51E, 7
3.3 Climate Chan
Projection of climate t version 3 RegCM3 mo
ii cccma_cgcm3_1, iii c vii inmcm3_0, viii ipsl_
xii mri_cgcm2_3_2a, x outputs were provided b
Masutomi, 2009 . The res temperature with 2021 -203
s of seasonal daily maximum temperature in Sema .51E, 7.12S-6.95S extracted from CRU TS2.0 dat
f seasonal daily temperature range in Semarang c , 7.12S-6.95S extracted from CRU TS2.0 dataset.
ange Projections
to future was developed using REGional Cl model and 14 GCMs. The 14 GCMs include i
i cnrm_cm3, iv gfdl_cm2_0, v gfdl_cm2_1, vi gi sl_cm4, ix miroc3_2_medres, x miub_echo_g, xi
xiii ukmo_hadcm3, and xiv
ukmo_hadgem1.
by NIES National Institute for Environmental S resolution is 1 degree and the climate variables are pre
2030, 2051-2060, and 2081-2085.
42 marang city
dataset.
g city 110.25E- .
Climate Model i
bccr_bcm2_0, giss_model_e_r,
xi mpi_echam5,
1.
These GCM l Studies Japan;
precipitation and
43 The RegCM3 was used to generate high resolution of historical rainfall data from
1958-2001. Since the RegCM3 model output shows systematic error compared to observations, we corrected the historical data from RegCM3 using rescaling factor
developed using 752 rainfall stations in Java. The rescaled RegCM3 for grid-i, year-t and month-b rRegCM3i,t,b is defined as
, ,
, ,
3 Re
, ,
3 Re
b t
i R
b t
i gCM
b t
i gCM
r =
Where the scaling factor was determined using the following formula
, ,
3 Re
, ,
, ,
b t
i gCM
m b
t i
O b
t i
R =
Where Oi,t,b is observation data of station-i near the four Grid of RegCM3 at year-t and month-b, while mRegCM3i,t,b is the mean of rainfall of the four grids of
RegCM3 near the station. The current baseline climate in grid-i for month-n is represented rRegCM3i,b by calculating the mean of the rRegCM3 from 1958-
2001:
{ }
2001 1958
, ,
3 Re
, 3
Re
=
=
t
b i
t gCM
r mean
b i
gCM r
The future climate under different GCM is predicted using the following formula:
−
+ =
, ,
, ,
, ,
, ,
, ,
1 ,
3 Re
, ,
, ,
b i
m s
B b
i m
s B
b t
i m
s F
b i
gCM r
b t
i m
s pF
Where ,
, ,
, b
t i
m s
pF is the projected rainfall under emission scenario-s, model-m,
grid-i, year-t and month-b, ,
, ,
, b
t i
m s
F is future climate from the GCM under
scenario-s, model-m, grid i, year-t and month-b, and ,
, ,
b i
m s
B is baseline climate
from GCM under scenario-s, model-m, grid-i, and month-b. Since we have 14 GCMs and each GCM has two set of future climate t1=2021-2030 and t2=2051-
2060, overall we will have 140 rainfall data for each period of time. Using data we develop distribution of future climate for the two periods.
The emission scenarios selected for this study are SRESA2 and SRESB1. These two scenarios were selected as they reflect current understanding and knowledge about
underlying uncertainties in the emissions. SRESA2 describes a very heterogeneous world. The underlying theme is self-reliance and preservation of local identities.
Fertility patterns across regions converge very slowly, which results in continuously increasing global population. Economic development is primarily regionally oriented
and per capita economic growth and technological change are more fragmented and slow. SRESB1 describes a convergent world with the same global population that
peaks in mid-century and declines thereafter, rapid change in economic structures toward a service and information economy, with reduction in material intensity, and
the introduction of clean and resource-efficient technology IPCC, 2000. With these characteristics, the SRESA2 will lead to higher future GHG emissions while
SRESB1 leads to lower future GHG emissions. Thus SRESB1 was defined as a policy scenario, while SRESA2 as a reference scenario.
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