Abstract
The validation of regional climate models is usually based on the intercomparison of the model's mean climate with the observed climatology. Albeit a prerequisite for the use of the model in a predictive mode, a successful validation of this type does not strictly test the model's ability to simulate anomalous conditions as might be associated with anthropogenic climate change. Here, we explore an alternate strategy, whereby the model's ability to reproduce the observed interannual variability is tested. The model utilized is an operational numerical weather prediction model of the German Weather Service, and it is tested for its use over East Asia and Japan in a series of 5 month-long January simulations. The model is used in a domain of 5100×5100km2, has a horizontal resolution of 56km, and 20 levels in the vertical. It is driven at its boundaries by the European Center for Medium-Range Weather Forecast (ECMWF) analysis.
In validating the integrations, particular emphasis is put on the precipitation fields. For validation we use three different observational data sets: a terrestrial analysis from rain gauges, including the Automated Meteorological Data Acquisition System (AMeDAS) data of the Japan Meteorological Agency, the gridded data set of the Global Precipitation Climatology Project (GPCP), which over sea is largely based upon satellite information, and the ECMWF Re-Analysis (ERA) data set, which is produced by a model in an assimilation mode.
It is demonstrated that the synoptic-scale evolution of individual low-pressure systems within the modeling domain is deterministically controlled by the lateral boundary conditions. Precipitation-spatially averaged over selected subdomains-compares remarkably well with the observations, both in terms of the monthly amounts and of the temporal evolution throughout the integration period. Using the strategy of a previous study, we analyze the year-to-year variations of the model results, both for the dynamical and precipitation fields. It is found that the modeling error is substantially smaller than the typical year-to-year fluctuations of the interannual variability. Implications of this result, concerning the model's use as a tool for down-scaling climate change, are also discussed.
