188.8.131.52 Nested Regional Climate Models
The principle behind nested modelling is that, consistent with the large-scale atmospheric circulation, realistic regional climate information can be generated by integrating an RCM if the following premises are satisfied: time-varying large-scale atmospheric fields (winds, temperature and moisture) are supplied as lateral boundary conditions (LBCs) and SST and sea ice as lower boundary conditions; the control from the LBCs keeps the interior solution of the RCM consistent with the driving atmospheric circulation; and sub-grid scale physical processes are suitably parametrized, including fine-scale surface forcing such as orography, land-sea contrast and land use.
A typical RCM grid for climate change projections is around 50 km, although some climate simulations have been performed using grids of 15 or 20 km (e.g., Leung et al., 2003, 2004; Christensen and Christensen, 2004; Kleinn et al., 2005). Recently, projections of climate changes for East Asia were completed with a 5-km non-hydrostatic RCM (Kanada et al., 2005; Yoshizaki et al., 2005; Yasunaga et al., 2006), but only for short simulations. Following the trend in global modelling, RCMs are increasingly coupled interactively with other components of the climate system, such as regional ocean and sea ice (e.g., Bailey and Lynch 2000; Döscher et al., 2002; Rinke et al., 2003; Bailey et al., 2004; Meier et al., 2004; Sasaki et al., 2006a), hydrology, and with interactive vegetation (Gao and Yu, 1998; Xue et al., 2000).
Multi-decadal RCM experiments are becoming standard (e.g., Whetton et al., 2000; Kwon et al., 2003; Leung et al., 2004; Kjellström et al., 2007; Plummer et al., 2006), including the use of ensembles (Christensen et al., 2002), enabling a more thorough validation and exploration of projected changes. In multi-year ensemble simulations driven by reanalyses of atmospheric observations, Vidale et al. (2003) show that RCMs have skill in reproducing interannual variability in precipitation and surface air temperature. The use of ensemble simulations has enabled quantitative estimates regarding the sources of uncertainty in projections of regional climate changes (Rowell, 2006 (Errata); Déqué et al., 2005, 2007; Beniston et al., 2007; Frei et al., 2006; Graham et al., 2007). Combining information from four RCM simulations, Christensen et al. (2001) and Rummukainen et al. (2003) demonstrate that it is feasible to explore not only uncertainties related to projections in the mean climate state, but also for higher-order statistics.
The difficulties associated with the implementation of LBCs in nested models are well documented (e.g., Davies, 1976; Warner et al., 1997). As time progresses in a climate simulation, the RCM solution gradually turns from an initial-value problem more into a boundary value problem. The mathematical interpretation is that nested models represent a fundamentally ill-posed boundary value problem (Staniforth, 1997; Laprise, 2003). The control exerted by LBCs on the internal solution generated by RCMs appears to vary with the size of the computational domain (e.g., Rinke and Dethloff, 2000), as well as location and season (e.g., Caya and Biner, 2004). In some applications, the flow developing within the RCM domain may become inconsistent with the driving LBC. This may (Jones et al., 1997) or may not (Caya and Biner, 2004) affect climate statistics. Normally, RCMs are only driven by LBCs with high time resolution to capture the temporal variations of large-scale flow. Some RCMs also use nudging or relaxation of large scales in the interior of the domain (e.g., Kida et al., 1991; Biner et al., 2000; von Storch et al., 2000). This has proved useful to minimise the distortion of the large scales in RCMs (von Storch et al., 2000; Mabuchi et al., 2002; Miguez-Macho et al., 2004), although it can also hide model biases. One-way RCM-GCM coupling is mostly used, although recently a two-way nested RCM has been developed (Lorenz and Jacob, 2005) thus achieving interaction with the global atmosphere as with variable-resolution AGCMs.
The ability of RCMs to simulate the regional climate depends strongly on the realism of the large-scale circulation that is provided by the LBCs (e.g., Pan et al., 2001). Latif et al. (2001) and Davey et al. (2002) show that strong biases in the tropical climatology of AOGCMs can negatively affect downscaling studies for several regions of the world. Nonetheless, the reliability of nested models, that is, their ability to generate meaningful fine-scale structures that are absent in the LBCs, is clear. A number of studies have shown that the climate statistics of atmospheric small scales can be re-created with the right amplitude and spatial distribution, even if these small scales are absent in the LBCs (Denis et al., 2002, 2003; Antic et al., 2005; Dimitrijevic and Laprise, 2005). This implies that RCMs can add value at small scales to climate statistics when driven by AOGCMs with accurate large scales. Overall, the skill at simulating current climate has improved with the MMD AOGCMs (Chapter 8), which will lead to higher quality LBCs for RCMs.