| Summary: | Pre-monsoon principal components of circulation fields covering the South Asian subcontinent
were used as predictors for all-India summer monsoon rainfall (AISMR) during the period 1958-
1998. Predictive skill and stationarity of non-linear ensemble neural network and linear multiple
regression model relationships were assessed using a Monte Carlo-based resampling procedure.
Monsoon precursor signals represented by the PCs were investigated using a model sensitivity
analysis and comparisons were made with recent observational and general circulation modelling
studies.
Results suggest the presence of coherent, stable predictive relationships between AISMR and
the SLP field during November (median r=0.5, p=0.05), as well as between AISMR and the 200
hPa geopotential height field during May (median r=0.67, p < 0.005). The latter relationship was
essentially linear, and appears to be related to the winter-summer transition between a flow regime
dominated by the subtropical westerly jet stream to one dominated by the tropical easterly jet
stream, as well as to changes in strength and position of the upper tropospheric Tibetan anticyclone.
The former relationship was nonlinear, appearing in essentially the same form at the 850 hPa level
(median r=0.37, p=0.06). Possibly related to anomalous SSTs in the Arabian Sea, further work is
required to determine physical mechanisms responsible for predictive skill at this lead-time.
Weaker relationships were observed between summer monsoon rainfall and PCs of the SLP
field during May (median r=0.47, p=0.10) and PCs of the 850 hPa geopotential height field during
January (median r=0.40, p=0.10). May SLP-rainfall relationships were not significant until the
early 1970s but remained relatively stable for the remainder of the record. Spatially, maximum
correlations and model sensitivities were centred over northwest India and Pakistan, suggesting
a link with pre-monsoon heating and development of the heat low over Pakistan. Relationships
between January 850 hPa geopotential heights and rainfall were nonstationary, only showing significant
correlations during portions of the record. Significant differences in skill between neural
network and multiple linear regression models were present at this level and lead-time, consistent
with indications of nonlinearity and interactions between inputs suggested by the sensitivity analysis.
Further work is required to determine whether these relationships have physical meaning or
whether they are simply a statistical artifact. === Arts, Faculty of === Geography, Department of === Graduate
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