Working Group I: The Scientific Basis

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10.5.2 Simulations of Climate Change

Since the SAR, several multi-year RCM simulations of anthropogenic climate change, either from equilibrium experiments or for time slices of transient simulations, have become available (Appendix 10.3). Mean climate

Figure 10.13: Percentage change in mean seasonal rainfall under 2xCO2 conditions as simulated by a GCM (a) and a RCM (b) for a region around Victoria, Australia. Areas of change statistically significant at the 5% confidence level are shaded. Whetton et al. (2001).

An important issue when analysing RCM simulations of climate change is the significance of the modelled responses. To date RCM simulations have been mostly aimed at evaluating models and processes rather than producing projections and, as such, they have been relatively short (10 years or less). At short time-scales, natural climate variability may mask all but the largest responses. For example, in an analysis of 10-year RCM simulations over Europe, Machenhauer et al. (1998) concluded that generally only the full area averaged seasonal mean surface temperature responses were statistically significant, and in only a few cases were sub-domain deviations from the mean response significant. The changes in precipitation were highly variable in space, and, in each season, they were only significant in those few sub-areas having the largest changes. Similar results were documented by Pan et al. (2000) and Kato et al. (2001) for the USA and East Asia, respectively. Hence, 30-year samples may be required to confidently assess the mesoscale response of a RCM (Jones et al., 1997). Partly to improve signal to noise definition, a transient RCM simulation of 140 years duration was recently conducted (Hennessy et al., 1998; McGregor et al., 1999).

Despite the limitations in simulation length, most RCM experiments clearly indicate that, while the large-scale patterns of surface climate change in the nested and driving models are similar, the mesoscale details of the simulated changes can be quite different. For example, significantly different patterns of temperature and rainfall changes were found in a regional climate change simulation for Australia (Whetton et al., 2001). This was most clearly seen in mountainous areas (Figure 10.13). Winter rainfall in southern Victoria increased in the RCM simulation, but decreased in the driving GCM. High resolution topographical modification of the regional precipitation change signal in nested RCM simulations has been documented in other studies (Jones et al., 1997; Giorgi et al., 1998; Machenhauer et al., 1998; Kato et al., 2001).

The response in an RCM can also be modified by changes in regional feedbacks. In a 20 year nested climate change experiment for the Indian monsoon region, Hassell and Jones (1999) showed that a maximum anomaly of 5°C seen in central northern India in the GCM simulation was reduced and moved to the north-west in the nested RCM, with a secondary maximum appearing to the south-east (Figure 10.14). The shift of the main maximum was attributed to deficiencies in the GCM control climate that promoted excessive drying of the soil in North-west India. The secondary maximum was attributed to a complex response involving the RCM’s better representation of the flow patterns in southern India resulting from an improved representation of the Western Ghats mountains. In this instance, it was argued that the improved realism of the RCM’s control simulation increases confidence in its response.

The high resolution representation of mountainous areas in an RCM has made it possible to show that the simulated surface air temperature change signal due to 23CO2 concentration could have a marked elevation dependency, resulting in more pronounced warming at high elevations than low elevations as shown in Figure 10.15 (Giorgi et al., 1997). This is primarily caused by a depletion of the snow pack in enhanced GHG conditions and the associated snow albedo feedback mechanism, and it is consistent with observed temperature trends for anomalous warm winters over the alpine region. A similar elevation modulation of the climate change signal has been confirmed in later studies utilising both RCMs and GCMs (e.g., Leung and Ghan, 1999b; Fyfe and Flato, 1999).

The impact of land-use changes on regional climate has been addressed in RCM simulations (e.g., Wei and Fu, 1998; Pan et al., 1999; Pielke et al., 1999; Chase et al., 2000). Land-use changes due to human activities could induce climate modifications, at the regional and local scale, of magnitude similar to the observed climatic changes during the last century (Pielke et al., 1999; Chase et al., 2000). The issue of regional climate modification by land-use change has been little explored within the context of the global change debate and, because of its potential importance, is in need of further examination.

Figure 10.14: Simulated surface air temperature anomaly (°C) for JJA, Indian monsoon region. GHG (2040 to 2060) minus control 20 year average for (a) GCM and (b) RCM. From Hassel and Jones (1999).

Figure 10.15: Difference between 2xCO2 and control run surface air temperature as a function of elevation over the Alpine sub-region for the four seasons. Units are °C. From Giorgi et al. (1997). Climate variability and extreme events

Changes in climate variability between control and 2xCO2 simulations with a nested RCM for the Great Plains of the USA have been reported (Mearns, 1999; Mearns et al., 1999). There is indication of significant decreases in daily temperature variability in winter and increases in temperature variability in summer. These changes are very similar to those of the driving GCM, while changes in variability of precipitation are quite different in the nested and driving models, particularly in summer, with increases being more pronounced in the RCM. Similar results have been documented over the Iberian Peninsula (Gallardo et al., 1999).

Different studies have analysed changes in the frequency of heavy precipitation events in enhanced GHG climate conditions over the European region (Schär et al., 1996; Frei et al., 1998; Durman et al., 2001). They all indicate an increase of up to several tens of percentage points in the frequency of occurrence of precipitation events exceeding 30 mm/day, with these increases being less than those simulated by the driving GCMs (see also Jones et al., 1997). In a transient RCM simulation for 1961 to 2100 over south-eastern Australia, substantial increases were found in the frequency of extreme daily rainfall and days of extreme high maximum temperature (Hennessy et al., 1998), In this long simulation, changes in the frequency of long-duration extreme events (such as droughts) were identified. Finally, increases in the number of typhoons reaching mainland China and in the number of heavy rain days were reported for enhanced GHG conditions in RCM simulations over East Asia (Gao et al., 2001).

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