Working Group I: The Scientific Basis

Other reports in this collection Climate variability and extreme events

A number of studies have investigated the interannual variability in RCM simulations driven by analyses of observations over different regions (e.g., Lüthi et al., 1996 for Europe; Giorgi et al., 1996 and Giorgi and Shields 1999 for the continental USA; Sun et al., 1999 for East Africa; Small et al., 1999a for central Asia; Rinke et al., 1999 for the Arctic; van Lipzig, 1999 for Antarctica). These show that RCMs can reproduce well interannual anomalies of precipitation and surface air temperature, both in sign and magnitude, over sub-regions varying in size from a few hundred kilometres to about 1,000 km (Figure 10.11).

At the intra-seasonal scale, the timing and positioning of regional climatological features such as the East Asia rain belt and the Baiu front can be reproduced with a high degree of realism with an RCM (Fu et al., 1998). A good simulation of the intra-seasonal evolution of precipitation during the short rain season of East Africa has also been documented (Sun et al., 1999). However, at shorter time-scales, Dai et al. (1999) found that, despite a good simulation of average precipitation, significant problems were exhibited by an RCM simulation of the observed diurnal cycle of precipitation over different regions of the USA.
Only a few examples are available of analysis of variability in RCMs driven by GCMs. At the intra-seasonal scale, Bhaskaran et al. (1998) showed that the leading mode of sub-seasonal variability of the South Asia monsoon, a 30 to 50 day oscillation of circulation and precipitation anomalies, was more realistically captured by an RCM than the driving GCM. Hassell and Jones (1999) then showed that a nested RCM captured observed precipitation anomalies in the active break phases of the South Asia monsoon (5 to 10 periods of anomalous circulations and precipitation) that were absent from the driving GCM (Figure 10.12).

At the daily time-scale, some studies have shown that nested RCMs tend to simulate too many light precipitation events compared with station data (Christensen et al. 1998; Kato et al., 2001). However, RCMs produce more realistic statistics of heavy precipitation events than the driving GCMs, sometimes capturing extreme events entirely absent in the GCMs (Christensen et al., 1998; Jones, 1999). Part of this is due to the inherent disaggregation of grid-box mean values resulting from the RCM’s higher horizontal resolution. However, in one study, even when aggregated to the GCM grid scale, the RCM was closer to observations than the driving GCM (Durman et al., 2001).

Figure 10.11: Examples of seasonal precipitation anomalies simulated with RCMs driven by analyses of observations over different regions. In all cases the anomalies are calculated as the difference between the precipitation of an individual season and the average for the seasonal value for the entire simulation. (a) (top) Northwestern USA (NW), and (bottom) Upper Mississippi Basin (UMB) for a three year simulation (1993 to 1996) over the continental USA. The three pairs of observed (hollow bars) and simulated (solid bars) anomalies for each season are grouped in sequential order from 1993 to 1996. Units are percentage of the three-year seasonal average (from Giorgi and Shields, 1999, Figure 9). (b) Precipitation anomalies for twelve short-rains periods over Tanzania for the October-December season: (top) model simulation, and (bottom) observations. Units are mm. (From Sun et al., 1999).

Figure 10.12: Relative characteristics of break and active precipitation composites of the Indian monsoon as simulated by (a) GCM and (b) RCM. Each field is the difference in the break and active composite precipitation as a percentage of the full mean. Overlaid are the 850 hPa wind anomalies (break composite minus active composite, units ms-1). Regions marked where observed ratios are <-50% (central India) and >+50% (Tamil Nadu and north-eastern India) according to Hamilton (1977). From Hassel and Jones (1999).

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