Seasonal relationships between indian summer monsoon rainfall and the southern oscillation

Parthasarathy, Balaji; Pant, G. B.

doi:10.1002/joc.3370050404

Cited by 137 publications

(68 citation statements)

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“…The ENSO association with foodgrain production can best be understood with reference to previous research describing the relationship between ENSO and SMR (Parthasarathy and Pant, 1985;Gregory, 1989). The conventional description of the ENSO-induced teleconnection response in the summer monsoon is through large-scale east-west shifts in the tropical Walker circulation, normally located in the western Pacific, toward the anomalously warm waters in the central and eastern Pacific.…”

Section: Enso Rainfall and Foodgrain Productionmentioning

confidence: 99%

“…The inter-annual monsoon rainfall variability in India leads to large-scale droughts and floods, resulting in a major effect on Indian foodgrain production (Parthasarathy and Pant, 1985;Parthasarathy et al, 1992) and on the economy of the country (Gadgil et al, 1999). In addition, intra-seasonal variation in rainfall distribution, temperature, radiation regimes, soil moisture, and wet and dry spells also affect agricultural production and national food security (Ramakrishna et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

“…The relationship between the El Niño-southern oscillation (ENSO) and variations in Indian summer monsoon rainfall (SMR) has been demonstrated by many studies (Troup, 1965;Sikka, 1980;Angell, 1981;Pant and Parthasarathy, 1981;Mooley and Parthasarathy, 1983;Bhalme et al, 1983;Carpenter, 1982, 1983;Shukla and Paolino, 1983;Parthasarathy and Pant, 1985;Gregory, 1989;Webster and Yang, 1992;Webster et al, 1998). The ENSO-monsoon teleconnections involve significant simultaneous relationships between monsoon rainfall and various ENSO indices (Krishna Kumar et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

“…The ENSO-monsoon teleconnections involve significant simultaneous relationships between monsoon rainfall and various ENSO indices (Krishna Kumar et al, 1995). It has generally been observed that negative southern oscillation index (SOI) (Bhalme and Jadhav, 1984;Parthasarathy and Pant, 1985) and warmer sea-surface temperature (SST) in the central and eastern equatorial Pacific are associated with lower monsoon rainfall and drought conditions (Angell, 1981;Khandekar and Neralla, 1984;Parthasarathy et al, 1988b;De, 1999), reducing crop production over India.…”

Section: Introductionmentioning

confidence: 99%

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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: Enso Rainfall and Foodgrain Productionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of El Niño–southern oscillation on Indian foodgrain production

Selvaraju

2003

Intl Journal of Climatology

135

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“…Accurate long range prediction of winter monsoon rainfall can improve planning to mitigate the adverse impacts of rainfall variability and will be beneficial to policy makers and farmers both. Further, many researchers have studied the predictability/trends of the Indian summer monsoon [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] using dynamical as well as statistical models. For more than one century, the prediction of Indian Monsoon Rainfall has been based on empirical models [2,3,[17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Southern and Tropical Indian Ocean SST: A Possible Predictor of Winter Monsoon Rainfall over South India

Shukla¹,

Rai²,

Pandey³

2013

ACS

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The complexities in the relationship between winter monsoon rainfall (WMR) over South India and Sea Surface temperature (SST) variability in the southern and tropical Indian Ocean (STIO) are evaluated statistically. The data of the time period of our study D)) by averageing SST for the regions having maximum correlation (positive or negative) with WMRI index. These SST indices are used to predict the WMRI using linear and multivariate linear regression models. In addition, we also attempted to detect a dynamic link for the predictability of WMRI using Nino 3.4 index. The predictive skill of these indices is tested by error analysis and Willmott's index.

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Forecasting the Spatial Variability of the Indian Monsoon Rainfall Using Canonical Correlation

Prasad

Singh

1996

Int. J. Climatol.

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The application of a canonical correlation model to the long‐range forecast of the spatial variability of the Indian monsoon (June–September) rainfall has been demonstrated. The predictands used in the model are the summer monsoon rainfall of 29 contiguous meteorological subdivisions of India and the predictors are the 500 hPa ridge axis position over India for April, the Darwin surface pressure tendency (April– January), the sea‐surface temperature of the central and eastern equatorial Pacific for the five successive months preceding the monsoon (January to May) and the rainfall of the southernmost subdivision of India (Kerala) for April. The model is developed on 30 years (1939–1968) of data and tested on 16 independent years thereafter. The model demonstrates positive skill for the large contiguous meteorological subdivisions of India using the first canonical mode (found significant). The root‐mean‐square error and the absolute error between the observed and the predicted rainfall for different meteorological subdivisions are of the order of 1 cm. The high skill score (≥0ċ3) is found particularly for the meteorological subdivisions lying in west‐central India. The performance of the model appears to be better than that of the multiple regression model developed earlier by Prasad and Singh. The combined model (containing the first and the second canonical modes) appears to perform even better than that of the single model. These results, therefore, seem to be important in view of the long‐range forecast of the spatial variability of the Indian monsoon rainfall.

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Seasonal relationships between indian summer monsoon rainfall and the southern oscillation

Cited by 137 publications

References 18 publications

Impact of El Niño–southern oscillation on Indian foodgrain production

Impact of El Niño–southern oscillation on Indian foodgrain production

Southern and Tropical Indian Ocean SST: A Possible Predictor of Winter Monsoon Rainfall over South India

Forecasting the Spatial Variability of the Indian Monsoon Rainfall Using Canonical Correlation

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