8.3.4.2 Land Hydrology

The evaluation of the hydrological component of climate models has mainly been conducted uncoupled from AOGCMs (Bowling et al., 2003; Nijssen et al., 2003; Boone et al., 2004). This is due in part to the difficulties of evaluating runoff simulations across a range of climate models due to variations in rainfall, snowmelt and net radiation. Some attempts have, however, been made. Arora (2001) used the AMIP-2 framework to show that the Canadian Climate Model’s simulation of the global hydrological cycle compared well to observations, but regional variations in rainfall and runoff led to differences at the basin scale. Gerten et al. (2004) evaluated the hydrological performance of the Lund-Potsdam-Jena (LPJ) model and showed that the model performed well in the simulation of runoff and evapotranspiration compared to other global hydrological models, although the version of LPJ assessed had been enhanced to improve the simulation of hydrology over the versions used by Sitch et al. (2003).

Milly et al. (2005) made use of the MMD, which contains results from recent models, to investigate whether observed 20th-century trends in regional land hydrology could be attributed to variations in atmospheric composition and solar irradiance. Their analysis, based on an ensemble of 26 integrations of 20th-century climate from nine climate models, showed that at regional scales these models simulated observed streamflow measurements with good qualitative skill. Further, the models demonstrated highly significant quantitative skill in identifying the regional runoff trends indicated by 165 long-term stream gauges. They concluded that the impact of changes in atmospheric composition and solar irradiance on observed streamflow was, at least in part, predictable. This is an important scientific advance: it suggests that despite limitations in the hydrological parametrizations included in climate models, these models can capture observed changes in 20th-century streamflow associated with atmospheric composition and solar irradiance changes. This enhances confidence in the use of these models for future projection.