National Action Plan for Climate Change Adaptation RAN-API - Synthesis Report
6
except for some areas in Maluku colored blue, within a period of approximately 10 years ranging from 1998 to 2008 .
a CDF curve with a threshold value for highest 1 daily rainfall chances based on TRMM satellite data for the period of 1998-2008. b The distribution of value changes in extreme daily
rainfall chances in the TRMM data for the 2003-2008 period relative to the value of opportunities in 1998-2002 period.
2.2 Climate Change Projection Based on IPCC-AR4 Models
Climate projection result is highly dependent on the scenarios of increase in greenhouse gases GHGs concentration in the atmosphere which is based on the assumption of global
socio-economic development and the main technologies that support it. In the IPCC-AR4, the scenarios used are based on the Special Report on Emissions Scenarios SRES.
2.2.1 Projection on Increase in Surface Temperature
IPCC-AR4 models assume that the temperature rise is caused predominantly by the effects of GHGs spread evenly in the atmosphere, therefore the projected average increase in
temperature for the region of Malang in East Java can represent all regions in Indonesia. As shown in the figure, it can be said the projected increase in average surface temperature
throughout Indonesia due to GHGs until the period of 2020-2050 is approximately 0.8 - 1°C relative to recent climatic period in the 20th century Bappenas, 2010c.
Average surface temperature projections for the area of Malang,
East Java based on IPCC-AR4 model after going through downscaling
process. Showing also historical data from 1951 to 2010 and the results
of GCM model simulations for the 20th century and projections for the
three SRES scenarios B1, A1B, and A2. Monthly time series data was
first smoothed to show the long-term trend KLH, 2012a
National Action Plan for Climate Change Adaptation RAN-API - Synthesis Report
7
2.2.2 Projection on Rainfall Changes
IPCC-AR4 models generally show changes in rainfall patterns are more varied in Indonesia, both temporally and spatially. Projection analysis based on the output of seven GCM on average
showed no significant change for the period of 2020 to 2050 Bappenas, 2010c. This indicates that, up to 2020-2050 period, natural climate variability is more involved than the effects of
GHGs in determining changes in rainfall. However, the changes leading up to and after 2050 needs more attention.
SNC report KLH, 2010 shows the trend of fourteen GCM models to the changes in seasonal rainfall in Indonesia based on two emission scenarios SRES A2 and B1 for 2025 and 2050. The
models that are part of the two scenarios agree that there is a trend towards reduced rainfall in June-July-August JJA and the transition to the September-October-November SON in Java
and Nusa Tenggara Islands. In addition, the models also agree that Java and Nusa Tenggara Islands have increased rainfall in December-January-February DJF. This trend is likely to
contrast with the projection for most areas in the other islands.
2.2.3 Projection on Sea Level Rise
The increase in sea level sea level riseSLR provides a huge potential threat to Indonesia which is an archipelagic country consisting many islands and small islands. In 2050, SLR
due to global warming is projected to reach 35-40 cm relative to year 2000. Based on these projections, the maximum SLR including the dynamics of melting ice in Indonesia can reach
up to 175 cm in 2100 Bappenas, 2010b.
Estimates of the rate of increase in sea level in Indonesia based on the model that takes into account the dynamics of melting ice Bappenas, 2010b
2.2.4 Projection on Weather and Climate Extreme Events
Analysis on extreme events projection is not easy to do because it requires plenty of time for analysis and more detailed data. Therefore, it can be understood that a comprehensive study
related to extreme events in Indonesia is still very limited.
