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@comment{{This file has been generated by bib2bib 1.95}}
@comment{{Command line: /usr/bin/bib2bib --quiet -c 'not journal:"Discussions"' -c 'not journal:"Polymer Science"' -c year=1997 -c $type="ARTICLE" -oc lmd_LEGACY1997.txt -ob lmd_LEGACY1997.bib /home/WWW/LMD/public/}}
  author = {{Vintzileos}, A. and {Sadourny}, R.},
  title = {{A General Interface between an Atmospheric General Circulation Model and Underlying Ocean and Land Surface Models: Delocalized Physics Scheme}},
  journal = {Monthly Weather Review},
  year = 1997,
  volume = 125,
  pages = {926},
  doi = {10.1175/1520-0493(1997)125<0926:AGIBAA>2.0.CO;2},
  adsurl = {},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
  author = {{Harzallah}, A. and {Sadourny}, R.},
  title = {{Observed lead-lag relationships between Indian summer monsoon and some meteorological variables}},
  journal = {Climate Dynamics},
  year = 1997,
  volume = 13,
  pages = {635-648},
  abstract = {{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 20{\deg}N (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.
  doi = {10.1007/s003820050187},
  adsurl = {},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
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