A A Munot
Articles written in Journal of Earth System Science
Volume 102 Issue 1 March 1993 pp 89-104
Utilizing data for the long period 1871–1990, variation in the relationships between Indian monsoon rainfall (
It is observed that while the relationships of IMR with SSTT-1, SSTT0 and SSTT2 exist almost throughout the whole period, that with SOIT-1 exists for 1942–1990, with SOIT0 for 1871–1921 and 1957–1990 and with SOIT2, for 1871–1921 only. The relationships that exist with SOIT-1, SOIT2, SSTT-1, SSTT2 and with SSTT0 (for period 1931–1990) are found to be very good and those that exist with SOIT0 for periods 1871–1921 and 1957–90 and for SSTT0 for the period 1871–1930 are good. It is thus seen that the relationships of SOIT-1, SOIT0 and SOIT2 with IMR do not correspond well with those of SSTT-1, SSTT0 and SSTT2 with IMR respectively, even though SOI and SST are closely related to each other for all the seasons. SOIT-1 and SSTT-1 can continue to be used as predictors for IMRDuring the whole period, IMR is found to play a passive, i.e. of being influenced or anticipated by SSTT-1 as well as an active role, i.e. of influencing or anticipating SSTT2. This implies a complex and perhaps non-linear interaction between IMR and SST tendency from DJF to MAM. Possibly, this is a part of the larger interaction between Asian monsoon rainfall and the tropical Pacific. A possible physical mechanism for the interaction is indicated.
Volume 102 Issue 1 March 1993 pp 121-155
The Indian summer monsoon rainfall is known to have considerable spatial variability, which imposes some limitations on the all-India mean widely used at present. To prepare a spatially coherent monsoon rainfall series for the largest possible area, fourteen subdivisions covering the northwestern and central parts of India (about 55% of the total area of the country), having similar rainfall characteristics and associations with regional/global circulation parameters are merged and their area-weighted means computed, to form monthly and seasonal Homogeneous Indian Monsoon (HIM) rainfall series for the period 1871–1990. This paper includes a listing of monthly and seasonal rainfall of HIM region. HIM rainfall series has been statistically analysed to understand its characteristics, variability and teleconnections for long-range prediction.
HIM rainfall series isfound to be homogeneous, Gaussian distributed and free from persistence. The mean (R) rainfall is 757 mm (87% of annual) and standard deviation (
To delineate the changes in the climatic regime of the Indian summer monsoon, sliding correlation coefficients (CCs) between HIM rainfall series and (i) Bombay msl pressure, (ii) Darwin msl pressure and (iii) Northern Hemisphere surface air temperature over the period 1871–1990 have been examined. The 31-year sliding CCs showed the systematic turning points of positive and negative CCs around the years, 1900 and 1940. In the light of other corroborative evidences, these turning points seem to delineate ‘meridional’ monsoon regime during 1871–1900 and 1940–1990 and ‘zonal’ monsoon regime during 1901–1940. The monsoon signal is particularly dominant in many regional and global circulation parameters, during 1951–1990.
Using the teleconnections of
Volume 107 Issue 1 March 1998 pp 91-95
An Ocean-Atmosphere Index (OAI) for ENSO is developed using data on Southern Oscillation Index (SOI) and sea surface temperature (SST) over eastern equatorial Pacific. Seasonal values of OAI, SOI and SST have been computed for the seasons September-October-November (SON), December-January-February (DJF), March-April-May (MAM) and June-July-August (JJA). Similarly SON to DJF, DJF to MAM, MAM to JJA and JJA to SON tendencies have been worked out for SOI, SST and OAI. The relationships between Indian Monsoon Rainfall (IMR) and SOI/SST/OAI, (i) for the seasons SON, DJF and MAM before and after the monsoon and JJA concurrent with the monsoon and (ii) for SON to DJF and DJF to MAM tendencies before and after the monsoon, and MAM to JJA tendency concurrent with the monsoon have been explored. It is found that IMR is more influenced by SOI before the monsoon than it is influenced by SST before the monsoon and IMR affects SST after monsoon more strongly than it affects SOI after the monsoon. It is also observed that DJF to MAM tendencies for SOI, SST and OAI before monsoon are significantly related to IMR, among which the relationship between IMR and DJF to MAM tendency for OAI is the best.
Volume 107 Issue 2 June 1998 pp 107-119
Temporal distribution of southwest monsoon (June –September) rainfall is very useful for the country’s agriculture and food grain production. It contributes more than 75% of India’s annual rainfall. In view of this, an attempt has been made here to understand the performance of the monthly rainfall for June, July, August and September when the seasonal rainfall is reported as excess, deficient or normal. To know the dependence of seasonal rainfall on monthly rainfall, the probabilities of occurrence of excess, deficient and normal monsoon when June, July, August and also June + July and August + September rainfall is reported to be excess or deficient, are worked out using the long homogenous series of 124 years (1871-–1994) data of monthly and seasonal rainfall of 29 meteorological sub-divisions of the plain regions of India.
In excess monsoon years, the average percentage contribution of each monsoon month to the long term mean (1871–1994) seasonal rainfall (June –September) is more than that of the normal while in the deficient years it is less than normal. This is noticed in all 29 meteorological sub-divisions. From the probability analysis, it is seen that there is a rare possibility of occurrence of seasonal rainfall to be excess/deficient when the monthly rainfall of any month is deficient/excess.
Volume 116 Issue 1 February 2007 pp 73-79
The search for new parameters for predicting the all India summer monsoon rainfall (AISMR) has been an important aspect of long range prediction of AISMR. In recent years NCEP/NCAR reanalysis has improved the geographical coverage and availability of the data and this can be easily updated. In this study using NCEP/NCAR reanalysis data on temperature, zonal and meridional wind at different pressure levels, few predictors are identified and a prediction scheme is developed for predicting AISMR. The regression coeffcients are computed by stepwise multiple regression procedure. The final equation explained 87% of the variance with multiple correlation coeffcient (MCC), 0.934. The estimated rainfall in the El-Nino year of 1997 was -1.7% as against actual of 4.4%. The estimated rainfall deficiency in both the recent deficient years of 2002 and 2004 were -19.5% and -8.5% as against observed -20.4% and -11.5% respectively.
Volume 120 Issue 4 August 2011 pp 713-721
We developed a ring-width chronology of teak (