Abstract:
The meteorological parameters Rainfall and temperature, simulated using Global Climate Models (GCMs), serve as a key inputs for hydrological models in studying catchment response to climate scenarios. GCM simulations of rainfall and temperature, however, are uncertain due to model structure, scenarios and initial conditions, which results in biased outcomes if used for impact assessment without due consideration of the uncertaintiesThis is mainly due to three factors: natural variability, model uncertainty, and GHG emission scenario uncertainty. In general, scenarioand model configuration uncertainty dominate for long term climate change, especiallyat the global scale. The contribution of internal variability increases for near term projections and for higher order climate statistics. Downscaling uncertainty is significant for variables primarily affected by local processes, such as summer convective precipitation. It is argued that because of these sources of uncertainty, the climate prediction problemshould be addressed in a probabilistic, rather than deterministic way. Resolving inter—model differences could reduce uncertainty significantly, but there is still a large irreducible uncertainty due to climate variability in the near—term and, particularly for temperature, future emissions scenarios in the long—term.
