Estimation of Future Changes in Precipitation over Shikoku Island in Japan using Pseudo Global Warming Downscaling and Statistical Bias Correction Approaches
To reduce the bias propagation from parent GCMs, a simple method called as Pseudo Global Warming Downscale (PGW-DS) was proposed. PGW-DS is the same as the conventional DD but the future model boundary conditions are obtained by adding the monthly mean differences between the future and the past climates simulated by the selected GCMs from Coupled Model Inter-comparison Project phase-3 (CMIP3) to reanalysis data. This method was formulated under the assumption of which future boundary conditions are assumed to be a linear coupling of the reanalysis data and the difference component of the global warming estimated by GCMs. As a result, this method allows a comparison of the climate in the present year and that in a PGW year, which is similar to the control year in terms of the inter-annual variation but includes future climatology.
In this study, we apply the PGW-DS approach to a small island called Shikoku in Japan. Regional (grid resolution of 24 km) and local (grid resolution of 6 km) model simulations for both historical (1980-2010) verification run and PGW simulation were performed with a state-of-the-art atmospheric model, Weather Research and Forecasting (WRF-ARW). The initial conditions and boundary conditions were obtained from ERA-interim reanalysis data. Results were compared with rainfall gauges and the high resolution daily gridded precipitation data set for Japan (APHRO_JP-V1207).
Results for annual climatology showed that dynamical downscaling captured spatial and temporal distributions well owing to the direct influence of the spatial resolution enhancement. This indicated the role of topography effect in obtaining reasonable rainfall over this region. However, monthly climatology of precipitation showed discrepancies especially underestimation of rainfall during July-September. This period is associated with heavy rainfalls due to typhoon events. Presently, a statistical bias correction method is being implemented for both historical and PGW simulation results to improve the biases identified in historical output. The merged method aims to provide better science-based information of future changes of precipitation and discharges. The issues and results will be presented during the conference.