The spatial and temporal distribution of rainfall variability is crucial in determining the socioeconomic conditions in the arid regions of the world. A reliable prediction of the rainfall variability is thus an important component of a disaster mitigation system. In addition, analysis of the underlying processes of such natural variability in the regional hydrological cycle provides clues to understand the mechanism of the desertification. In the present study, we discuss the region called “Asir” in the southwestern part of the Kingdom of Saudi Arabia to understand the influence of soil and vegetable on the rainfall variability. The Asir region enjoys a relatively good seasonal rainfall in spring and summer. .
The southern part of the study region exhibits higher amount of inter-annual variability, most of which is confined in the first half of a year. The tropical climate variability from the Indian and Pacific Oceans, viz. the Indian Ocean Dipole and the El Niño-Southern Oscillation, dominantly influence the rainfall patterns through the atmospheric teleconnections. Besides, the changes in the Mediterranean Sea and the variability originating from the extra-tropical regions are found to influence the rainfall anomalies of the region.
We are now undertaking numerical simulations of rainfall and meteorology using a regional atmospheric model (RAMS) and ECMWF data in 2000, under an assumption of various soil and vegetation conditions. The most powerful computer called the “Earth Simulator” provides us a scope to resolve complicated terrains and complex surface conditions in supper-high resolution (1km x 1km), using our original parallel computing scheme called “Time Splitting” method. The TS method divides a total simulation period into a combination of the same number of simulation tasks as the number of CPU clusters, and performs each task of a fine grid independently with initial and boundary conditions supplied from a large grid simulation of the conventional nesting scheme. The computational speed of TS method is linearly proportional with the number of CPU clusters.
Together with this regional modeling effort, we will include in our study a range of ocean and atmosphere models with regional to global extent. The synergic effort will provide us a better basis for understanding as well as predicting the climate variability in the study region. The knowledge gained from the study will be helpful in designing a new system for ameliorating socioeconomic as well as agricultural conditions in semi-arid and arid regions.