Assessing impact of climate change on season length in Karnataka for IPCC SRES scenarios



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Changes in seasons and season length are an indicator, as well as an effect, of climate change. Seasonal change profoundly affects the balance of life in ecosystems and impacts essential human activities such as agriculture and irrigation. This study investigates the uncertainty of season length in Karnataka state, India, due to the choice of scenarios, season type and number of seasons. Based on the type of season, the monthly sequences of variables (predictors) were selected from datasets of NCEP and Canadian General Circulation Model (CGCM3). Seasonal stratifications were carried out on the selected predictors using K-means clustering technique. The results of cluster analysis revealed increase in average, wet season length in A2, A1B and B1 scenarios towards the end of 21st century. The increase in season length was higher for A2 scenario whereas it was the least for B1 scenario. COMMIT scenario did not show any change in season length. However, no change in average warm and cold season length was observed across the four scenarios considered. The number of seasons was increased from 2 to 5. The results of the analysis revealed that no distinct cluster could be obtained when the number of seasons was increased beyond three.


Citation: Anandhi, Aavudai. “Assessing Impact of Climate Change on Season Length in Karnataka for IPCC SRES Scenarios.” Journal of Earth System Science 119, no. 4 (August 2010): 447–60.


General circulation model, Third generation Canadian coupled global climate model, K-means clustering, Uncertainty, IPCC SRES scenarios, Climate change, Season length