22.214.171.124 Skill of Models in Simulating Present Climate
Evaluating temperature and precipitation simulations over Antarctica is difficult due to sparse observations and often relies on numerical weather prediction (re)analyses. However, significant differences between those have been found, and comparisons with station observations show that the surface temperature can be subject to considerable biases (Connolley and Harangozo, 2001; Bromwich and Fogt, 2004). Marked improvement in the bias is seen after the satellite era (~1978) (Simmons et al., 2004), and parts of the bias are explained by the reanalyses’ smoothing of the sharp changes in the terrain near coastal stations. Satellite-derived monthly surface temperatures agree with antarctic station data with an accuracy of 3°C (Comiso, 2000). Precipitation evaluation is even more challenging and the different (re)analyses differ significantly (Connolley and Harangozo, 2001; Zou et al., 2004). Very few direct precipitation gauge and detailed snow accumulation data are available, and these are uncertain to varying degrees (see Section 4.6).
Major challenges face the simulation of the atmospheric conditions and precipitation patterns of the polar desert in the high interior of East Antarctica (Guo et al., 2003; Bromwich et al., 2004a; Pavolonis et al., 2004, Van de Berg et al., 2005). Driven by analysed boundary conditions, RCMs tend to show smaller temperature and precipitation biases in the Antarctic compared to the GCMs (Bailey and Lynch, 2000; Van Lipzig et al., 2002a,b; Van den Broeke and Van Lipzig, 2003; Bromwich et al., 2004b; Monaghan et al., 2006). Krinner et al. (1997) show the value of a stretched model grid with higher horizontal resolution over the Antarctic as compared to standard GCM formulations. Despite these promising developments, since the TAR there has been no coordinated comparison of the performance of GCMs, RCMs and other alternatives to global GCMs over Antarctica.