Southeast Asia
Area-mean precipitation over Southeast Asia increases in most MMD model simulations, with a median change of about 7% in all seasons (Table 11.1), but the projected seasonal changes vary strongly within the region. The seasonal confidence intervals based on the methods of Tebaldi et al. (2004a,b) are similar for DJF and JJA (roughly –4% to 17%). The strongest and most consistent increases broadly follow the ITCZ, lying over northern Indonesia and Indochina in JJA, and over southern Indonesia and Papua New Guinea in DJF (Figure 11.9). Away from the ITCZ, precipitation decreases are often simulated. The pattern is broadly one of wet season rainfall increase and dry season decrease.
Earlier studies of precipitation change in the area in some cases have suggested a worse inter-model agreement than found for the MMD models. Both Giorgi et al. (2001a) and Ruosteenoja et al. (2003) find inconsistency in the simulated direction of precipitation change in the region, but a relatively narrow range of possible changes; similar results were found over an Indonesian domain by Boer and Faqih (2004). Compositing the projections from a range of earlier simulations forced by the IS92a scenario, Hulme and Sheard (1999a,b) find a pattern of rainfall increase across northern Indonesia and the Philippines, and decrease over the southern Indonesian archipelago. More recently, Boer and Faqih (2004) compared patterns of change across Indonesia from five AOGCMS and obtained highly contrasting results. They conclude that ‘no generalisation could be made on the impact of global warming on rainfall’ in the region.
The regional high-resolution simulations of McGregor et al. (1998), McGregor and Dix (2001) and AIACC (2004) have demonstrated the potential for significant local variation in projected precipitation change. The simulations showed considerable regional detail in the simulated patterns of change, but little consistency across the three simulations. The authors related this result to significant deficiencies in the current-climate simulations of the models for this region.
Rainfall variability will be affected by changes in ENSO and its effect on monsoon variability, but this is not well understood (see Section 10.3). However, as Boer and Faqih (2004) note, those parts of Indonesia that experience a mean rainfall decrease are likely to also experience increases in drought risk. The region is also likely to share the general tendency for daily extreme precipitation to become more intense under enhanced greenhouse conditions, particularly where the mean precipitation is projected to increase. This has been demonstrated in a range of global and regional studies (see Section 10.3), but needs explicit study for the Southeast Asian region.
The northern part of the Southeast Asian region will be affected by any change in tropical cyclone characteristics. As noted in Section 10.3, there is evidence in general of likely increases in tropical cyclone intensity, but less consistency about how occurrence will change (see also Walsh, 2004). The likely increase in intensity (precipitation and winds) is supported for the northwest Pacific (and other regions) by the recent modelling study of Knutson and Tuleya (2004). The high-resolution time-slice modelling experiment of Hasegawa and Emori (2005) also demonstrates an increase in tropical cyclone precipitation in the western North Pacific, but not an increase in tropical cyclone intensity. Wu and Wang (2004) examined possible changes in tracks in the northwest Pacific due to changes in steering flow in two Geophysical Fluid Dynamics Laboratory (GFDL) enhanced greenhouse gas experiments. Tracks moved more north-easterly, possibly reducing tropical cyclone frequency in the Southeast Asian region. Since most of the tropical cyclones form along the monsoon trough and are also influenced by ENSO, changes in the occurrence, intensity and characteristics of tropical cyclones and their interannual variability will be affected by changes in ENSO (see Section 10.3).
Box 11.3: Climatic Change in Mountain Regions
Although mountains differ considerably from one region to another, one common feature is the complexity of their topography. Related characteristics include rapid and systematic changes in climatic parameters, in particular temperature and precipitation, over very short distances (Becker and Bugmann, 1997); greatly enhanced direct runoff and erosion; systematic variation of other climatic (e.g., radiation) and environmental (e.g., soil types) factors. In some mountain regions, it has been shown that temperature trends and anomalies have an elevation dependence (Giorgi et al., 1997), a feature that is not, however, systematically observed in all upland areas (e.g., Vuille and Bradley, 2000, for the Andes).
Few model simulations have attempted to directly address issues related specifically to future climatic change in mountain regions, primarily because the current spatial resolution of GCMs and even RCMs is generally too crude to adequately represent the topographic detail of most mountain regions and other climate-relevant features such as land cover that are important determinants in modulating climate in the mountains (Beniston et al., 2003). High-resolution RCM simulations (5-km and 1-km grid scales) are used for specific investigations of processes such as surface runoff, infiltration, evaporation and extreme events such as precipitation (Weisman et al., 1997; Walser and Schär, 2004; Kanada et al., 2005; Yasunaga et al., 2006) and damaging wind storms (Goyette et al., 2003), but these simulations are too costly to operate in a ‘climate mode’. Because of the highly complex terrain, empirical and statistical downscaling techniques have often been seen as a very valuable tool to generate climate change information for mountainous regions (e.g., Benestad, 2005; Hanssen-Bauer et al., 2005).
Projections of changes in precipitation patterns in mountains are unreliable in most GCMs because the controls of topography on precipitation are not adequately represented. In addition, it is now recognised that the superimposed effects of natural modes of climatic variability such as ENSO or the NAO can perturb mean precipitation patterns on time scales ranging from seasons to decades (Beniston and Jungo, 2001). Even though there has been progress in reproducing some of these mechanisms in coupled ocean- atmosphere models (Osborn et al., 1999), deficiencies remain and prevent a good simulation of these large-scale modes of variability (see also Section 8.4). However, several studies indicate that the higher resolution of RCMs and GCMs can represent observed mesoscale patterns of the precipitation climate that are not resolved in coarse-resolution GCMs (Frei et al., 2003; Kanada et al., 2005; Schmidli et al., 2006; Yasunaga et al., 2006).
Snow and ice are, for many mountain ranges, a key component of the hydrological cycle, and the seasonal character and amount of runoff is closely linked to cryospheric processes. In temperate mountain regions, the snowpack is often close to its melting point, so that it may respond rapidly to minor changes in temperature. As warming increases in the future, regions where snowfall is the current norm will increasingly experience precipitation in the form of rain (e.g., Leung et al., 2004). For every degree celsius increase in temperature, the snow line will on average rise by about 150 m. Although the snow line is difficult to determine in the field, it is established that at lower elevations the snow line is very likely to rise by more than this simple average estimate (e.g., Martin et al., 1994; Vincent, 2002; Gerbaux et al., 2005; see also Section 4.2). Beniston et al. (2003) show that for a 4°C shift in mean winter temperatures in the European Alps, as projected by recent RCM simulations for climatic change in Europe under the A2 emissions scenario, snow duration is likely to be reduced by 50% at altitudes near 2,000 m and by 95% at levels below 1,000 m. Where some models predict an increase in winter precipitation, this increase does not compensate for the effect of changing temperature. Similar reductions in snow cover that will affect other mountain regions of the world will have a number of implications, in particular for early seasonal runoff (e.g., Beniston, 2003), and the triggering of the annual cycle of mountain vegetation (Cayan et al., 2001; Keller et al., 2005).
Because mountains are the source region for over 50% of the globe’s rivers, the impacts of climatic change on mountain hydrology not only affect the mountains themselves but also populated lowland regions that depend on mountain water resources for domestic, agricultural, energy and industrial supply. Water resources for populated lowland regions are influenced by mountain climates and vegetation; shifts in intra-annual precipitation regimes could lead to critical water amounts resulting in greater flood or drought episodes (e.g., Barnett et al., 2005; Graham et al., 2007).