Climate Chan CLIMATE HISTORY AND FUTURES
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.
44 Based on the two scenarios described above, in the next 100 years, concentrations of
CO
2
in the atmosphere under the reference emission scenario would be more than double, while under the policy emission scenario only 1.5 times the current
condition. Similarly for other gases CH
4
and N
2
O; Table 3.2. Concentration of SO
2,
which counters the effect of greenhouse gases, would not change significantly Table 3.3.
Table 3.3:
.
Gas Concentrations ppmv
2000 2025
2050 2100
CO
2
: SRESA2: Best guess
370 440
535 825
: Range 370
430-450 515-555
760-890 SRESB1: Best guess
370 420
460 550
: Range 370
410-430 450-470
510-590 CH
4
: SRESA2: Best guess
1600 2250
2850 4300
: Range 1600
2200-2300 2700-3000
3800-4800 SRESB1: Best guess
1600 2050
2250 2200
: Range 1600
2000-2100 2150-2350
2100-2300 N
2
O:
SRESA2: Best guess 316
344 375
452 SRESB1: Best guess
316 340
360 395
Under increased GHG conditions, it was estimated that global temperature will consistently increase by about 0.027
C per year, while sea level increased by about 0.6 cm per year Table 3.4. Historical records show that over the last 100 years the
global sea level has increased between 0.10-0.25 cm per year Warrick et al., 1996.
Table 3.4 Temperature
o
C and Sea Level Rise cm, with reference to 1990
2000 2025
2050 2100
Temperature: SRESA2: Best guess
0.2 0.5
1.2 2.9
: Range 0.15-0.25
0.3-0.7 0.8-1.6
2.0-4.1 SRESB1: Best guess
0.2 0.7
1.1 1.9
: Range 0.15-0.25
0.5-0.9 0.7-1.6
1.2-2.7
Sea Level: SRESA2: Best guess
2 10
21 60
: Range 0-4.0
4.0-20 9.0-41
25-112 SRESB1: Best guess
2 10
21 48
: Range 0-4.0
4.0-22 9.0-42
18-85
In this study we assess the potential risk of the Semarang city to be exposed to extreme rainfall under current and future climate. The extreme rainfall was defined
using climate hazards data flood and drought. The extreme rainfall is defined as rainfall where its intensity is more than the critical threshold. For wet season DJF,
if the intensity is more than critical threshold, Semarang city is very likely to be exposed to flood hazard. While for dry season JJA, if the intensity is less than the
45 critical threshold, Semarang city is very likely to be exposed to drought hazard.
This analysis suggest that the critical threshold of rainfall for wet season is 302 mm equivalent to quartile 3, Q3, and for dry season is 84 mm also equivalent to
quartile 3, Q3. Methodology for defining the critical threshold is described in Chapter 6.
It was found that probability of having rainfall more than Q3 in wet season DJF generally decreased in the future particularly in the centre of the city, irrespective of
the emission scenarios Figure 3.11. For the dry season, probability to have rainfall less than Q3 in coastal area is higher under current climate than that under changing
climate. This suggests that wet season rainfall in the future tended to be lower than current climate while dry season rainfall tended to be higher than the current climate.
This finding is not in conformity with other study of Naylor et al. 2007. Further analysis using improved methodology may be required to assess future climate trend
in the Semarang city. The use dynamic and statistical downscaling technique is strongly recommended to study climate change scenario in small area such as
Semarang City. Current approach may be appropriate to be applied for regional study.
46
110.25 110.3
110.35 110.4
110.45 110.5
110.55 -7.13
-7.08 -7.03
-6.98 -6.93
0.00 0.20
0.40 0.60
0.80 1.00
DJF-Q3
110.25 110.3
110.35 110.4
110.45 110.5
110.55 -7.13
-7.08 -7.03
-6.98 -6.93
0.00 0.20
0.40 0.60
0.80 1.00
DJF-Q3
110.25 110.3
110.35 110.4
110.45 110.5
110.55 -7.13
-7.08 -7.03
-6.98 -6.93
0.00 0.20
0.40 0.60
0.80 1.00
DJF-Q3
110.25 110.3
110.35 110.4
110.45 110.5
110.55 -7.13
-7.08 -7.03
-6.98 -6.93
0.00 0.20
0.40 0.60
0.80 1.00
DJF-Q3
110.25 110.3
110.35 110.4
110.45 110.5
110.55 -7.13
-7.08 -7.03
-6.98 -6.93
0.00 0.20
0.40 0.60
0.80 1.00
DJF-Q3
Baseline A2 2025
B1 2050 B1 2025
A2 2050
47
110.25 110.3
110.35 110.4
110.45 110.5
110.55 -7.13
-7.08 -7.03
-6.98 -6.93
0.00 0.20
0.40 0.60
0.80 1.00
JJA-Q3
110.25 110.3
110.35 110.4
110.45 110.5
110.55 -7.13
-7.08 -7.03
-6.98 -6.93
0.00 0.08
0.16 0.24
0.32 0.40
0.48 0.56
0.64 0.72
0.80 0.88
0.96
JJA-Q3
110.25 110.3
110.35 110.4
110.45 110.5
110.55 -7.13
-7.08 -7.03
-6.98 -6.93
0.00 0.08
0.16 0.24
0.32 0.40
0.48 0.56
0.64 0.72
0.80 0.88
0.96
JJA-Q3
110.25 110.3
110.35 110.4
110.45 110.5
110.55 -7.13
-7.08 -7.03
-6.98 -6.93
0.00 0.08
0.16 0.24
0.32 0.40
0.48 0.56
0.64 0.72
0.80 0.88
0.96
JJA-Q3
110.25 110.3
110.35 110.4
110.45 110.5
110.55 -7.13
-7.08 -7.03
-6.98 -6.93
0.00 0.08
0.16 0.24
0.32 0.40
0.48 0.56
0.64 0.72
0.80 0.88
0.96
JJA-Q3
Figure 3.11: Probability to have rainfall more than Q3 in wet season DJF and less than Q3 in dry season JJA under two emission scenarios A2 2025
B1 2050 B1 2025
A2 2050 Baseline
48