scholarly journals Influence of Madden-Julian Oscillation on water budget transported by the Somali low-level jet and the associated Indian summer monsoon rainfall

2013 ◽  
Vol 49 (10) ◽  
pp. 6474-6485 ◽  
Author(s):  
Paulina Ordonez ◽  
Pedro Ribera ◽  
David Gallego ◽  
Cristina Pena-Ortiz
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Water Budget ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
Summer Monsoon Rainfall ◽  
Madden Julian Oscillation ◽  
Low Level ◽  
Low Level Jet

scholarly journals The Intraseasonal Fluctuation of Indian Summer Monsoon Rainfall and its Relation with Monsoon Intraseasonal Oscillation (MISO) and Madden Julian Oscillation (MJO)

2021 ◽  
Author(s):  
Avijit Dey ◽  
R. Chattopadhyay ◽  
S. Joseph ◽  
M. Kaur ◽  
R. Mandal ◽  
...  
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
Intraseasonal Oscillation ◽  
Summer Monsoon Rainfall ◽  
Madden Julian Oscillation ◽  
Phase 2 ◽  
Seasonal Fluctuations ◽  
Northward Propagation

Abstract The intra-seasonal fluctuations of Indian summer monsoon rainfall (ISMR) are mainly controlled by northward propagating Monsoon Intra-seasonal Oscillation (MISO) and eastward propagating Madden Julian Oscillation (MJO). In the current study, we examine the relationship between the intra-seasonal fluctuations (active and break spells) of ISMR with the phase propagation and amplitude of MISO and MJO. We notice that active spells generally occur during MISO phase 2–5 (MJO phase 3–6), and break spells mainly occur during MISO phase 6–8 (MJO phase 6–8 and 1). The association of active/break spells with MISO phases is more prominent than with MJO phases. We show the phase composite of unfiltered and regression based reconstructed rainfall for eight MISO and MJO phases, and the same is consistent with the earlier findings. We notice that the reconstructed field shows a systematic and well-organised northward propagation compared to the unfiltered field. Phase composite also indicates that there is a lead-lag relationship between MISO and MJO phases. MISO phase composite shows more robust northward propagation than the MJO phase composite. MISO reconstructed rainfall explained more percentage variance than MJO reconstructed rainfall with reference to 20–90 days filtered rainfall. It is found that long active (> 7 days) predominantly occurs when either MISO or MJO, or both of them are active, and the associated signal is somewhere in between phase 2–5. A long break occurs when either one or both of them are feeble, or even though associated signals are strong, they are primarily located in phases 1, 6, 7 and 8.

scholarly journals Activity of Madden Julian Oscillation during 2002 and 2006 – A comparative analysis

MAUSAM ◽  
2021 ◽  
Vol 59 (3) ◽  
pp. 321-326
Author(s):  
MEDHA KHOLE
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Seasonal Variability ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
Monsoon Season ◽  
Rainfall Variability ◽  
Summer Monsoon Rainfall ◽  
Equatorial Region ◽  
Madden Julian Oscillation

The Indian summer monsoon is characterized by very significant intra-seasonal variability. Madden-Julian Oscillation (MJO) is one of the dominant modes of the intra-seasonal variability of the Indian summer monsoon rainfall.  The activity of Madden Julian Oscillation during the monsoon seasons of the two years of contrasting intra-seasonal rainfall variability has been examined in terms of rainfall activity over India and eastward propagation of convection in the near-equatorial region. The study shows the contrasting nature, viz., in the monsoon season of 2002, eastward mode dominated whereas in 2006, it remained suppressed.

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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