Perceptible changes in Indian summer monsoon rainfall in relation to Indian Monsoon Index

2017 ◽  
Vol 157 ◽  
pp. 83-92 ◽  
Author(s):  
C.V. Naidu ◽  
A. Dharma Raju ◽  
P. Vinay Kumar ◽  
G.Ch. Satyanarayana
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Monsoon Rainfall ◽  
Indian Monsoon ◽  
Indian Summer Monsoon Rainfall ◽  
Summer Monsoon Rainfall ◽  
Monsoon Index

scholarly journals Relationship between tropospheric thickness anomalies and Indian summer monsoon rainfall

MAUSAM ◽  
2021 ◽  
Vol 47 (4) ◽  
pp. 377-382
Author(s):  
DHANNA SINGH ◽  
C.V.V. BHADRAM ◽  
G.S. MANDAL
Keyword(s):  
Multiple Regression ◽  
Long Range ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Monsoon Rainfall ◽  
Indian Monsoon ◽  
Indian Summer Monsoon Rainfall ◽  
Summer Monsoon Rainfall ◽  
Indian Monsoon Rainfall ◽  
Linear Correlations

ABSTRACT .The tropospheric mean monthly thickness anomalies of northern Indian stations of selected layers for the months April to July for a 28 years (1968-95) period have been analysed. The thickness anomalies of April and May exhibit significant persistence through July. Also the thickness anomalies of different layers for the months May-July are found to have generally significant (5% to 0.1% level) linear correlations with the succeeding all India seasonal monsoon rainfall. Out of different layers and all the months analysed, the thickness anomalies of 850-300 and 850-100 hPa layers for May are found to have maximum correlations (significant at 0.1% level). From linear and multiple regression results, 850-300 hPa thickness anomaly is seen to be a useful predictor for long range prediction of Indian monsoon rainfall.  

scholarly journals The Role of the Western Arabian Sea Upwelling in Indian Monsoon Rainfall Variability

2008 ◽  
Vol 21 (21) ◽  
pp. 5603-5623 ◽  
Author(s):  
Takeshi Izumo ◽  
Clémentde Boyer Montégut ◽  
Jing-Jia Luo ◽  
Swadhin K. Behera ◽  
Sébastien Masson ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Arabian Sea ◽  
Monsoon Rainfall ◽  
Indian Monsoon ◽  
Indian Summer Monsoon Rainfall ◽  
Summer Monsoon Rainfall ◽  
Indian Monsoon Rainfall ◽  
Precipitation Anomalies

Abstract The Indian summer monsoon rainfall has complex, regionally heterogeneous, interannual variations with huge socioeconomic impacts, but the underlying mechanisms remain uncertain. The upwelling along the Somalia and Oman coasts starts in late spring, peaks during the summer monsoon, and strongly cools the sea surface temperature (SST) in the western Arabian Sea. They restrict the westward extent of the Indian Ocean warm pool, which is the main moisture source for the monsoon rainfall. Thus, variations of the Somalia–Oman upwelling can have significant impacts on the moisture transport toward India. Here the authors use both observations and an advanced coupled atmosphere–ocean general circulation model to show that a decrease in upwelling strengthens monsoon rainfall along the west coast of India by increasing the SST along the Somalia–Oman coasts, and thus local evaporation and water vapor transport toward the Indian Western Ghats (mountains). Further observational analysis reveals that such decreases in upwelling are caused by anomalously weak southwesterly winds in late spring over the Arabian Sea that are due to warm SST/increased precipitation anomalies over the Seychelles–Chagos thermocline ridge of the southwestern Indian Ocean (and vice versa for years with strong upwelling/weak west Indian summer monsoon rainfall). The latter SST/precipitation anomalies are often related to El Niño conditions and the strength of the Indonesian–Australian monsoon during the previous winter. This sheds new light on the ability to forecast the poorly predicted Indian monsoon rainfall on a regional scale, helped by a proper ocean observing/forecasting system in the western tropical Indian Ocean.

Dependence of Indian summer monsoon rainfall forecast skill of CFSv2 on initial conditions and the role of bias in SST boundary forcing

2021 ◽  
Author(s):  
Stella Jes Varghese ◽  
Kavirajan Rajendran ◽  
Sajani Surendran ◽  
Arindam Chakraborty
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Lead Time ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
Initial Conditions ◽  
Summer Monsoon Rainfall ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Central Pacific

<p>Indian summer monsoon seasonal reforecasts by CFSv2, initiated from January (4-month lead time, L4) through May (0-month lead time, L0) initial conditions (ICs), are analysed to investigate causes for the highest Indian summer monsoon rainfall (ISMR) forecast skill of CFSv2 with February (3-month lead time, L3) ICs. Although theory suggests forecast skill should degrade with increase in lead-time, CFSv2 shows highest skill with L3, due to its forecasting of ISMR excess of 1983 which other ICs failed to forecast. In contrast to observation, in CFSv2, ISMR extremes are largely decided by sea surface temperature (SST) variation over central Pacific (NINO3.4) associated with El Niño-Southern Oscillation (ENSO), where ISMR excess (deficit) is associated with La Niña (El Niño) or cooling (warming) over NINO3.4. In 1983, CFSv2 with L3 ICs forecasted strong La Niña during summer, which resulted in 1983 ISMR excess. In contrast, in observation, near normal SSTs prevailed over NINO3.4 and ISMR excess was due to variation of convection over equatorial Indian Ocean, which CFSv2 fails to capture with all ICs. CFSv2 reforecasts with late-April/early-May ICs are found to have highest deterministic ISMR forecast skill, if 1983 is excluded and Indian monsoon seasonal biases are also reduced. During the transitional ENSO in Boreal summer of 1983, faster and intense cooling of NINO3.4 SSTs in L3, could be due to larger dynamical drift with longer lead time of forecasting, compared to L0. Boreal summer ENSO forecast skill is also found to be lowest for L3 which gradually decreases from June to September. Rainfall occurrence with strong cold bias over NINO3.4, is because of the existence of stronger ocean-atmosphere coupling in CFSv2, but with a shift of the SST-rainfall relationship pattern to slightly colder SSTs than the observed. Our analysis suggests the need for a systematic approach to minimize bias in SST boundary forcing in CFSv2, to achieve improved ISMR forecasts.</p>

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