11.8.1.2 Skill of Models in Simulating Present Climate

Many processes are still poorly understood and thus continue to pose a challenge for climate models (ACIA, 2005). In addition, evaluating simulations of the Arctic is difficult because of the uncertainty in the observations. The few available observations are sparsely distributed in space and time and different data sets often differ considerably (Serreze and Hurst, 2000; ACIA, 2005; Liu et al., 2005; Wyser and Jones, 2005). This holds especially for precipitation measurements, which are problematic in cold environments (Goodison et al., 1998; Bogdanova et al., 2002).

Few pan-arctic atmospheric RCMs are in use. When driven by analysed lateral and sea ice boundary conditions, RCMs tend to show smaller temperature and precipitation biases in the Arctic compared to GCMs, indicating that sea ice simulation biases and biases originating from lower latitudes contribute substantially to the contamination of GCM results in the Arctic (e.g., Dethloff et al., 2001; Wei et al., 2002; Lynch et al., 2003; Semmler et al., 2005). However, even under a very constrained experimental design, there can be considerable across-model scatter in RCM simulations (Tjernström et al., 2005; Rinke et al., 2006). The construction of coupled atmosphere-ice-ocean RCMs for the Arctic is a recent development (Maslanik et al., 2000; Debernard et al., 2003; Rinke et al., 2003; Mikolajewicz et al., 2005).