scholarly journals Summer monsoon rainfall variability over North East regions of India and its association with Eurasian snow, Atlantic Sea Surface temperature and Arctic Oscillation

2016 ◽  
Vol 49 (7-8) ◽  
pp. 2545-2556 ◽  
Author(s):  
Amita Prabhu ◽  
Jaiho Oh ◽  
In-won Kim ◽  
R. H. Kripalani ◽  
A. K. Mitra ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Summer Monsoon ◽  
Arctic Oscillation ◽  
Monsoon Rainfall ◽  
Rainfall Variability ◽  
Summer Monsoon Rainfall ◽  
Sea Surface ◽  
North East ◽  
Eurasian Snow

scholarly journals Relationship between Indian summer monsoon rainfall and sea surface temperature anomalies over equatorial central and eastern Pacific

MAUSAM ◽  
2021 ◽  
Vol 49 (2) ◽  
pp. 229-234
Author(s):  
V. THAPLIYAL ◽  
M. RAJEEVAN ◽  
S. R. PATIL
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
Monsoon Season ◽  
Summer Monsoon Rainfall ◽  
Sea Surface ◽  
Sst Anomalies

Sea surface temperature (SST) variations over the three key regions over equatorial Pacific, viz., Nino (1+2), Nino 3 and Nino 4 and their relationships with Indian summer monsoon rainfall have been examined in this study. On monthly scale, SST anomalies over the three key regions show an oscillatory type of lagged correlations with Indian monsoon rainfall, positive correlations almost one year before the monsoon season (CC's are of the order of 0.3) which gradually change to significant negative correlation peaking in September/October during/after the monsoon season. The variations on seasonal scale also exhibit the same pattern of monthly variations but more smooth in nature. Composites of similar monsoon years show that during deficient (excess) monsoon years SST anomalies over all the three regions have warmer (cooler) trend which starts about 6 months prior to monsoon season. Tendencies of SST anomalies from previous winter (DJF) to summer (MAM) seasons over Nino 3 and Nino 4 regions are better predictors than EI-Nino categories currently being used in IMD's operational LRF model. By using tendency of SST over EI- Nino -4 region, in place of the category of EI-Nino, the 16 parameter operational Power Regression Model of IMD has been modified. The new forecast model shows better reduction in the forecast error.

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