Indian summer monsoon simulation studies with different orographic representations in a spectral GCM

Abstract

AbstractThe current study focuses on two aspects of the representation of orography in a spectral general circulation model (GCM). The most important issue is the reduction of the ripple effect, especially the oceanic surface deformations, which can create tendency for unrealistic oceanic rainfall patches in the model climate. In this study, seven types of digital filters were tried at horizontal resolution equivalent to spectral truncation at T80 to identify the most effective filter. Digital filtering results in much greater reduction in orographic barrier, and hence, the blocking effect compared to mean orography. The second point is retention of the barrier effect. In the past, several attempts have been made to address this. Techniques have been developed in the past to construct an optimal orography, to enable more region‐specific enhancement of mountains with less impact over oceans and plains. These techniques include site‐specific kernels and the cost‐function‐minimization. An attempt has been made in the current study to develop a digitally filtered, locally enhanced, optimal orography called the Filtered‐Modified Orography (FMO), for its application to Indian summer monsoon simulation. A number of sensitivity experiments have been conducted to determine the possible impacts of FMO on the seasonal simulation and the medium‐range prediction of Indian summer monsoon. Results of this study show that two‐dimensional (2‐D) Lanczos filter is the most effective in reducing the ripples and the associated errors in the orographic representation. FMO is able to enhance the peaks of the Himalayan range and Western Ghats, and in consequence, it produces a better simulation of Indian summer monsoon circulation features and the associated rainfall in the seasonal and medium time scale. This procedure for local enhancement is found to be more beneficial than global enhancement of orography in a spectral GCM so far as the regional weather prediction is concerned. Copyright © 2008 Royal Meteorological Society