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 Long-Lead Statistical Forecasts of the Indian Summer Monsoon Rainfall Based on Causal Precursors

2019 ◽  
Vol 34 (5) ◽  
pp. 1377-1394 ◽  
Author(s):  
G. Di Capua ◽  
M. Kretschmer ◽  
J. Runge ◽  
A. Alessandri ◽  
R. V. Donner ◽  
...  
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
A Priori ◽  
Low Frequency ◽  
India Meteorological Department ◽  
Internal Variability ◽  
Summer Monsoon Rainfall ◽  
Lead Times

Abstract Skillful forecasts of the Indian summer monsoon rainfall (ISMR) at long lead times (4–5 months in advance) pose great challenges due to strong internal variability of the monsoon system and nonstationarity of climatic drivers. Here, we use an advanced causal discovery algorithm coupled with a response-guided detection step to detect low-frequency, remote processes that provide sources of predictability for the ISMR. The algorithm identifies causal precursors without any a priori assumptions, apart from the selected variables and lead times. Using these causal precursors, a statistical hindcast model is formulated to predict seasonal ISMR that yields valuable skill with correlation coefficient (CC) ~0.8 at a 4-month lead time. The causal precursors identified are generally in agreement with statistical predictors conventionally used by the India Meteorological Department (IMD); however, our methodology provides precursors that are automatically updated, providing emerging new patterns. Analyzing ENSO-positive and ENSO-negative years separately helps to identify the different mechanisms at play during different years and may help to understand the strong nonstationarity of ISMR precursors over time. We construct operational forecasts for both shorter (2-month) and longer (4-month) lead times and show significant skill over the 1981–2004 period (CC ~0.4) for both lead times, comparable with that of IMD predictions (CC ~0.3). Our method is objective and automatized and can be trained for specific regions and time scales that are of interest to stakeholders, providing the potential to improve seasonal ISMR forecasts.

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