Influence of global sea‐surface temperature on ultra‐low‐frequency variability in Indian summer monsoon rainfall

2020 ◽  
Vol 146 (727) ◽  
pp. 904-921
Author(s):  
Nirupam Karmakar ◽  
Arindam Chakraborty ◽  
Ravi S. Nanjundiah
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Indian Summer Monsoon ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
Low Frequency ◽  
Summer Monsoon Rainfall ◽  
Sea Surface ◽  
Low Frequency Variability ◽  
Global Sea Surface Temperature

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.

scholarly journals Impact of Madden-Julian oscillations on the Indian summer monsoon sub-divisional rainfalls

MAUSAM ◽  
2021 ◽  
Vol 59 (2) ◽  
pp. 195-210
Author(s):  
K. SEETHARAM
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Seasonal Variations ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Summer Monsoon Rainfall ◽  
Dominant Mode ◽  
Oscillation Phenomenon

Indian summer monsoon rainfall exhibits inter-seasonal variations in the time scales of 2-7 years which are linked to quasi-biennial oscillations and El nino-Southern Oscillation phenomenon and also intra-seasonal variations in the time-scale of 30-60 days which are linked to activity of MJO which emerged as a dominant mode of intra-seasonal oscillations of Indian summer monsoon rainfall in addition to the other modes of low frequency oscillations. In this scenario, the inter and intra seasonal variability of 29 meteorological sub-divisional rainfalls has been investigated by correlating the MJO indices at 10 different longitudes covering Indian, Pacific and Atlantic Oceans with cumulative sub-divisional summer monsoon rainfall (1979 – 2000). The results were discussed.

scholarly journals The influence of low-frequency variability and long-term trends in North Atlantic sea surface temperature on Irish waters

2009 ◽  
Vol 66 (7) ◽  
pp. 1480-1489 ◽  
Author(s):  
Heather Cannaby ◽  
Y. Sinan Hüsrevoğlu
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic ◽  
Low Frequency ◽  
Warming Trend ◽  
Sea Surface ◽  
Low Frequency Variability ◽  
Warm Anomaly ◽  
Long Term Trends

Abstract Cannaby, H., and Hüsrevoğlu, Y. S. 2009. The influence of low-frequency variability and long-term trends in North Atlantic sea surface temperature on Irish waters. – ICES Journal of Marine Science, 66: 1480–1489. Sea surface temperature (SST) time-series collected in Irish waters between 1850 and 2007 exhibit a warming trend averaging 0.3°C. The strongest warming has occurred since 1994, with the warmest years in the record being 2005, 2006, and 2007. The warming trend is superimposed on significant interannual to multidecadal-scale variability, linked to basin-scale oscillations of the ocean–atmosphere system. The dominant modes of low-frequency variability in North Atlantic SST records, investigated using an empirical orthogonal function (EOF) analysis, correspond to the Atlantic Multidecadal Oscillation (AMO), the East Atlantic Pattern (EAP), and the North Atlantic Oscillation (NAO) index, respectively, accounting for 23, 16, and 9% of the total variance in the dataset. Interannual variability in Irish SST records is dominated by the AMO, which, currently in its warm phase, explains approximately half of the current warm anomaly in the record. The EAP and the NAO influence variability in Irish SST time-series on a smaller scale, with the EAP also contributing to the current warm anomaly. After resolving the prevalent oscillatory modes of variability in the SST record, the underlying warming trend compares well with the global greenhouse effect warming trend. The anthropogenic contribution to the current warm anomaly in Irish SSTs was estimated at 0.41°C for 2006, and this is predicted to increase annually.

scholarly journals Predictive relationships between Indian Ocean sea surface temperatures and Indian summer monsoon rainfall

MAUSAM ◽  
2022 ◽  
Vol 53 (3) ◽  
pp. 337-348
Author(s):  
M. RAJEEVAN ◽  
D. S. PAI ◽  
V. THAPLIYAL
Keyword(s):  
Indian Ocean ◽  
Indian Summer Monsoon ◽  
Arabian Sea ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
Summer Monsoon Rainfall ◽  
Sea Surface ◽  
South Indian Ocean ◽  
South Indian ◽  
Sst Anomalies

Monthly sea surface temperature (SST) data of 49 years (1950-98) have been analysed to examine the relationship of SST anomalies in the Indian Ocean with Indian summer monsoon rainfall (ISMR) and to derive useful predictors for long-range forecasts of ISMR. There is significant positive relationship between ISMR and SST anomalies over the Arabian Sea during November to January and also in May. SST anomalies over southeast Indian Ocean during February to March and over North Pacific during May are also positively correlated with ISMR. The composite analysis revealed that in Non-ENSO drought years (1966, 1968, 1974 and 1979) negative SST anomalies are observed over south Indian Ocean from February which slowly spread towards equator during the subsequent months. These negative SST anomalies which persist during the monsoon season may be playing an important role in modulating ISMR especially in non-ENSO years.   We have derived two indices, ARBSST (SST anomalies in Arabian Sea averaged over 15o - 25o N, 50o -70o E      and November-December-January) and SIOSST (SST anomalies over south Indian Ocean averaged over 15o -30o S,      70o -110o E and February and March) as useful predictors for the long-range forecasts of ISMR. The correlation coefficient (for the period 1950-98) of ARBSST and SIOSST with ISMR is 0.45 and 0.46 respectively which is statistically significant at 99.9 % level. SIOSST index has shown consistently stable relationship with ISMR. However the ARBSST index showed significant correlation with ISMR only after 1976.

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