lmd_LEGACY1997_abstracts.html
1997 .
(2 publications)A. Vintzileos and R. Sadourny. A General Interface between an Atmospheric General Circulation Model and Underlying Ocean and Land Surface Models: Delocalized Physics Scheme. Monthly Weather Review, 125:926, 1997. [ bib | DOI | ADS link ]
A. Harzallah and R. Sadourny. Observed lead-lag relationships between Indian summer monsoon and some meteorological variables. Climate Dynamics, 13:635-648, 1997. [ bib | DOI | ADS link ]
Lagged relationships between the Indian summer monsoon and several climate variables are investigated. The variables examined are gridded fields of snow cover (14 years), sea surface temperature (41 years) and 500 hPa geopotential height north of 20degN (42 years). We also used series of global air temperature (108 years) and Southern Oscillation index (112 years). Precipitation over all India during June-September over a 112 year period are used as Indian monsoon index. Emphasis is put on early monsoon precursors. In agreement with the tendency for a low frequency oscillation in the ocean-atmosphere system, several precursor patterns are identified as early as the year preceding the monsoon. The most important key regions and seasons of largest correlations are selected and the corresponding series are used to perform a monsoon prediction. The prediction shows however a relatively moderate score mainly due to the not highly significant correlations. To improve the predictions we filtered the variables into their biennial (1.5-3.5 years) and low frequency (3.5-7.5 years) modes. Correlations between the monsoon and the filtered variables are higher than those obtained without filtering especially for the biennial mode. The two modes are out-of-phase before the monsoon and in-phase during and after. This phasing is found in all variables except for snow cover for which the two modes are in-phase before the monsoon and out-of-phase during and after. It is suggested that such phasing may be important for the formation of snow and could explain the higher correlations when variables are concomitant or are lagging the monsoon. Early predictions of the monsoon based on those two modes show improved scores with highly significant correlations with the actual monsoon.