Influence of anthropogenic warming on extremes in the Indian summer monsoon using cluster analysis
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Thursday, 8 January 2015: 9:30 AM
125AB (Phoenix Convention Center - West and North Buildings)
The South Asian Summer Monsoon directly affects the lives of over 1/6th of the world's population. With greater than 50% of agricultural lands dependent on rain-fed agriculture, rainfall variability during the monsoon season adversely impacts crop yields in addition to water availability in the subcontinent. The summer monsoon is characterized by a dominant 30-60 day mode of intraseasonal variability causing the occurrence of wet and dry spells over a substantial portion of India during the peak-monsoon months (July-August). We use a 1°x1° gridded rainfall dataset (1951-2011) from the Indian Meteorological Department to quantify changes in the mean and intraseasonal variability of daily summer monsoon rainfall across India. Using a non-parametric statistical methodology to account for temporal correlation in the time-series, we find a statistically significant decreasing trend in rainfall and increasing trend in variability in many regions, and changes in the characteristics of wet and dry spells. The observed changes in wet and dry extremes during the monsoon season are relevant for managing climate-related risks, with particular relevance for water resources, agriculture, disaster preparedness, and infrastructure planning.
Using geopotential heights from the NCEP reanalysis dataset, we apply the Self-Organizing Maps (SOMs) approach (cluster analysis) to define typical upper (200mb) and lower-level (850mb) atmospheric patterns associated with extreme wet and dry conditions in the different sub-regions within India. We identify the extreme wet and dry spell patterns from the precipitation composites associated with the SOM patterns. Next, we link the changing frequency of occurrence of the associated atmospheric patterns and increasing moisture availability to the changes in the characteristics of these extremes in the observational period. Lastly, we compare the changes in the frequency of occurrence of these atmospheric patterns in the historical and pre-industrial simulations from a single GCM to examine the influence of anthropogenic warming on these extremes.