Indian summer monsoon and El Nino

Mooley, D. A.; Parthasarathy, Balaji

doi:10.1007/bf02590143

Cited by 110 publications

(55 citation statements)

References 19 publications

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“…[3] The relationship between El Niño events and Indian monsoon has been studied by many researchers [Walker and Bliss, 1932;Sikka, 1980;Rasmusson and Carpenter, 1983;Barnett, 1983;Mooley and Parthasarathy, 1984;Kripalani and Kulkarni, 1997;Krishna Kumar et al, 1999;Krishnamurthy and Goswami, 2000;Kane, 2005;Annamalai and Liu, 2005]. Keshavamurthy [1982] studied the sensitivity of the SST anomalies over the equatorial Pacific Ocean using the GFDL atmospheric general circulation model.…”

Section: Introductionmentioning

confidence: 99%

On the El Niño‐Indian monsoon predictive relationships

Rajeevan

Pai

2007

Geophysical Research Letters

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Most of the severe droughts over India are associated with El Niño. However, only less than half of El Niño events are associated with deficient rainfall over India. In other El Niño years, Indian summer monsoon rainfall (ISMR) was either normal or excess. A recent study suggested that the El Niño events with the warmest SST anomalies in the central Pacific are more effective in focusing drought producing subsidence over India than events with the warmest SST anomalies in the eastern equatorial Pacific. They have further suggested that the incorporation of additional information on the spatial distribution of SST anomalies over the equatorial Pacific Ocean in the statistical models should improve monsoon forecast skill. The present study explores this hypothesis on the skill of monsoon forecasts by analyzing SST data for the period 1880–2004. The present study reveals that incorporation of information on the spatial pattern of SST anomalies (by incorporating the Trans Niño Index) does not improve the association between El Niño and Indian monsoon rainfall. Simply using the SST index over the central Pacific (Niño‐3.4) may be a better indicator for the association than the Combined Niño Index derived from Niño‐3 and Trans Niño Index (TNI).

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Section: Introductionmentioning

confidence: 99%

On the El Niño‐Indian monsoon predictive relationships

Rajeevan

Pai

2007

Geophysical Research Letters

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“…The Indian summer monsoon, which provides 75 to 90% of the total annual rainfall over most parts of India during the 4 months from June to September, is vital to India's agriculture (Parthasarathy et al, 1992). The Indian monsoon circulation influences more than 60% of the Earth's population (Webster et al, 1998) due to its large-scale impact on food production, power generation, drinking water supply and overall economy (Mooley et al, 1981;Mooley and Parthasarathy, 1983;Parthasarathy et al, 1988a).…”

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%

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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“…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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Indian summer monsoon and El Nino

Cited by 110 publications

References 19 publications

On the El Niño‐Indian monsoon predictive relationships

On the El Niño‐Indian monsoon predictive relationships

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

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