Evidence of Secular Variations in Indian Monsoon Rainfall–Circulation Relationships

Parthasarathy, Balaji; Kumar, Krishan; Munot, A. A.

doi:10.1175/1520-0442(1991)004<0927:eosvii>2.0.co;2

Cited by 135 publications

(73 citation statements)

References 21 publications

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“…Recent studies have indicated a weakened ENSO-monsoon relationship during the last decade due to epochal variations in monsoon rainfall (Kripalani and Kulkarni, 1997), changes in ENSO characteristics (Parthasarathy et al, 1991;Krishna Kumar et al, 1999a) and shifts in Walker circulation anomalies and increased surface temperature over Eurasia (Krishna Kumar et al, 1999b). Troup (1965) recognized a statistical non-stationarity between the Asian monsoon and ENSO even four decades earlier.…”

Section: Introductionmentioning

confidence: 99%

Impact of El Niño–southern oscillation on Indian foodgrain production

Selvaraju

2003

Intl Journal of Climatology

135

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The impact of El Niño-southern oscillation (ENSO) on Indian foodgrain production was analysed for the period 1950-99. The inverse relationship between sea-surface temperature (SST) anomalies from June to August (JJA) over the NINO3 sector of the tropical Pacific Ocean and Indian foodgrain production anomalies (r = −0.50) was significant at the 1% level. During the warm ENSO phase, the total foodgrain production frequently decreased (12 out of 13 years) by 1 to 15%. In 10 out of 13 cold ENSO-phase years, the total foodgrain production increased from normal. The relationship between the SST-based NINO3 ENSO index and the Kharif season (June-September) foodgrain production anomalies (r = −0.52) was greater than for the Rabi season (October-February) foodgrain production (r = −0.27). The ENSO impact on rice production was greatest among the individual crops. The average drop in rice (Kharif season crop) production during a warm ENSO-phase year was 3.4 million tonnes (7%). In a cold ENSO-phase year the average production increase was 1.3 million tones (3%). Wheat (Rabi season crop) production was also influenced by ENSO, as it depends on the carryover soil water storage from the Kharif season. Sorghum and chickpea production are not significantly influenced by ENSO extremes.Inter-annual fluctuation of the gross value of Indian foodgrain production was very large, reducing up to US$2183 million in a warm ENSO year and increasing up to US$1251 million in a cold ENSO year. On average, a warm ENSO year costs US$773 million and a cold ENSO year had a financial gain of US$437 million from normal.The cumulative probability distributions of foodgrain production anomalies during cold and warm ENSO phases are shifted positively or negatively, relative to the neutral distribution. The warm ENSO forcing significantly (1% level) reduced the probability of above-average production. The cold ENSO forcing moderately increased the above-average foodgrain production over the neutral ENSO phase (5% level). A simple conditional probability forecast based on annual and JJA NINO3 SST predicted the category of foodgrain production in 11 of the 14 years. The results demonstrated that the relationship between NINO3 ENSO index and foodgrain production could be used for agricultural applications and policy decisions on food security for the rapidly growing population in India.

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Section: Introductionmentioning

confidence: 99%

Impact of El Niño–southern oscillation on Indian foodgrain production

Selvaraju

2003

Intl Journal of Climatology

135

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“…(Pant and Rupa Kumar, 1997). Different combinations of these parameters have been used for the long-range prediction of AISMR and many regression models have been proposed from among these variables by several investigators (Gowariker et al, 1989(Gowariker et al, , 1991Hastenrath, 1991;Parthasarathy et al, 1991;Thapliyal, 1997). Many of them were selected subjectively according to the meteorological aspects of the parameters of the rainfall and have limitations in explaining the variance in rainfall.…”

Section: Introductionmentioning

confidence: 99%

“…There are also studies indicating some features regarding the spatial coherence of the correlation coefficients between these parameters and rainfall over the country. The high correlation coefficients are over northwestern and central India and the lowest over northeast and western peninsular India (Parthasarathy et al, 1991;Krishna Kumar et al, 1995). India, as a whole is too large a region to be treated as a single unit, implying that it is not spatially homogenous to rainfall (Normand, 1953;Rasmusson and Carpenter, 1983).…”

Section: Introductionmentioning

confidence: 99%

An empirical model for the seasonal prediction of southwest monsoon rainfall over Kerala, a meteorological subdivision of India

Nayagam¹,

Janardanan²,

Mohan³

2007

Intl Journal of Climatology

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ABSTRACT:There are several studies showing a skillful empirical prediction of the All India Summer Monsoon Rainfall (AISMR) based on various combination of parameters as the predictors. However, the southwest monsoon rainfall over Kerala, a meteorological subdivision of India, bears a considerably low correlation coefficient with the AISMR. This implies that the existing predictors in the long-range forecast models of the AISMR do not have much influence on the Kerala Summer Monsoon Rainfall (KSMR). This study attempts to examine the relationship of some ocean and atmospheric parameters with the rainfall and to formulate a linear multiple regression model for the long-range forecast over a small area like the Kerala. Parameters having significant correlation (significant at 1% level) with the KSMR were identified for the period 1961-1994. The consistency of the relationship between these parameters and the KSMR was checked by doing a 21-year sliding window correlation (significant at 5% level). Using a stepwise regression method, seven predictors, explaining a significant amount of variance in the KSMR were selected and a linear multiple regression model was developed. The parameters that explain the high inter-annual variability of the KSMR are specific humidity, sensible heat net flux, relative humidity, zonal wind at 70 and 10 hPa, meridional wind and geopotential height. The characteristics of the forecast and its reliability were studied by various statistical techniques such as, Durbin Watson statistics and variance inflation factor. The model has a multiple correlation of 0.943 and coefficient of determination of 88.8%. The root mean square error (RMSE) was 6.60% (15.80%), bias (BIAS) was −0.26% (6.20%), absolute error (ABSE) was 5.33% (13.15%) of mean rainfall for the training (test) period respectively. Climatological predictions were also made and the RMSE was (17.90%), BIAS (−5.40%) and ABSE (15.16%) of mean rainfall. The selected parameters were at least 2 months prior to the monsoon season and hence have predictive value.

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“…The Asian monsoon is an extremely complex phenomenon that encompasses variability over a wide range of spatial and temporal scales (Lau et al 2000). A number of observational studies (Parthasarathy et al 1991) have reported that the Asian monsoon system plays an important role as a major energy source for global-scale circulation at middle and low latitudes. The Indian summer monsoon is characterized by the largest northward advance of the ITCZ (locally known as the monsoon trough) within the Asian summer monsoon regime.…”

Section: Introductionmentioning

confidence: 99%

Features of the Indian Summer Monsoon 2004-Observed and Model Forecasts

Basu

Iyengar

2007

Journal of the Meteorological Society of Japan

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The observed and the model forecast rainfall and other atmospheric fields are compared To determine the skill of the National Centre for Medium Range Weather Forecasting (NCMRWF) model in forecasting precipitation and circulation features over India, we compared observed and forecast rainfall and other atmospheric fields for the boreal summer monsoon season of 2004. This season was an important period within the period 4 of the Coordinated Enhanced Observing Period (CEOP). For comparison of observed rainfall with predicted values, station values are averaged over the area represented by each grid point of the model. For other fields, comparisons were made between the analysis and forecast values at the same horizontal resolution. The model showed considerable skill in predicting the daily and seasonal accumulated rainfall amounts when averaged over the whole of India. The predicted allIndia average precipitation tends to increase in magnitude with increasing forecast period. The model forecasts that were undertaken especially for the CEOP project reproduced the important features of the summer monsoon over India. The onset of monsoon is captured in terms of both the convective precipitation and diabatic heating fields forecast by the model. Variations in monsoon activity between July and August are also well simulated in the monthly averaged forecast fields for these two months.

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Evidence of Secular Variations in Indian Monsoon Rainfall–Circulation Relationships

Cited by 135 publications

References 21 publications

Impact of El Niño–southern oscillation on Indian foodgrain production

Impact of El Niño–southern oscillation on Indian foodgrain production

An empirical model for the seasonal prediction of southwest monsoon rainfall over Kerala, a meteorological subdivision of India

Features of the Indian Summer Monsoon 2004-Observed and Model Forecasts

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