Ensemble Simulation of Indian Summer Monsoon Rainfall by an Atmospheric General Circulation Model

Abstract

This study examines the fidelity of the Meteorological Research Institute (MRI) atmospheric general circulation model (AGCM), ensemble runs forced with observed sea surface temperature (SST), in simulating Indian summer monsoon rainfall (ISMR), and its interannual variation. Despite the simple ensemble mean (SEM) capturing essential features of climatological ISMR pattern and its extreme ISMR anomalies, it still shows certain systematic bias in simulating mean seasonal variation of rainfall over the Asia-Pacific region. Concurrently, the ISMR interannual variability throughout the analysis period is not adequately represented.A bias-correction is applied to remove this bias by deriving weights for the member simulations for each Julian day separately at every grid point through multiple linear regression of their daily rainfall, against corresponding observation over a 23-year 'training phase' (out of the total 24-year analysis period). Thereafter, at every grid point, for each Julian day of the remaining 1-year 'forecast phase', the bias-removed ensemble mean (BREM) is computed as an optimal linear combination of weighted member simulations. In cross validation, each year in the analysis period is treated successively as the 'forecast phase', with the remaining 23 years included in the corresponding training phase. The methodology minimises the systematic bias in mean seasonal variation of rainfall, and BREM consequently improves upon SEM in simulating the mean ISMR pattern, and its interannual variability over the entire analysis period.The skill of BREM in forecasting the ISMR, and its intraseasonal variability is validated, for the severe monsoon drought of 2002. The effective removal of climatological bias brings out the realistic precipitation response in BREM to fluctuations in SST boundary forcing. As a result, BREM captures the seasonal rainfall anomaly pattern of 2002, and markedly improves its intraseasonal evolution. Apart from the basic skill of the AGCM ensemble system, pronounced equatorial SST impact in modulating the monsoon circulation during 2002, played a seminal role in the success of the methodology. The analysis also underlines the importance of mean seasonal variation, not only for capturing ISMR climatology, and its interannual variation but for improving its intraseasonal variability as well. With the aid of realistic SST forecasts, this methodology has the application potential for dynamical prediction of ISMR.

Dynamical simulation of Indian summer monsoon circulation, rainfall and its interannual variability using a high resolution atmospheric general circulation model

“…If we compare the drought and excess years in the SFM simulations with in our simulations for the common period 1985-2004, we find : (1) drought is simulated in 1994 in SFM as it was in our present study and that of Sajani et al (2007) and (2) monsoon excess years are simulated in SFM for 1988 and 1998 as in our study. The matching of results in category-wise basis between NCEP T170 and NCEP-SFM is not surprising as the two models have similar dynamics and physical parameterization schemes except that the SFM has lower resolution than T170.…”

“…(1987 and 2002) fell under drought category, which was an improvement over SEM simulations. From this we tend to believe that perhaps the procedure followed in BREM simulations may improve the prediction of droughts as it happened in the study of Sajani et al (2007). We have as yet not used BREM procedure in our study and cannot say whether following that procedure, hindcast of our model would also produce better matching of drought/excess years between simulations and observations.…”

“…E-mail: satyabanb@cdac.in better seasonal simulations in the tropics over sufficiently large areal and seasonal average basis. Beginning from 1980, several Atmospheric General Circulation Models (AGCMs) have been used in different countries to simulate the Indian summer monsoon (ISM) circulation and Indian summer monsoon rainfall (ISMR) with different resolutions and model physics (Fennessy et al, 1994;Goswami, 1998;Sabre et al, 2000;Wang et al, 2005;Sajani et al, 2007;Rajendran et al, 2008 and several others). The studies of Gadgil and Sajani (1998) and Kang et al (2002), which analysed the Atmospheric Model Intercomparison Project (AMIP) model results, showed that most of the AGCMs are capable of simulating the regional structure of the ISM circulation and precipitation regimes.…”

Prediction of winter precipitation over northwest India using ocean heat fluxes

“…The details of MLR can be found in Sajani et al (2007). It is obvious that if the number of predictors is comparatively more then there is a high possibility of having multi-co-linearity and over fitting problems among the predictors that may affect the estimated coefficient adversely (Gowariker et al 1989;Delsole and Shukla 2002).…”

“…Therefore, instead of considering all the predictors, it is worthwhile to screen the predictors to avoid such problems based on principal component analysis (PCA). Sajani et al (2007) introduced PCA based on regression for improved prediction. In this method, the first principal component (PC) explains the high amount of variance retained for the multi-linear regression.…”