Journal of Earth System Science

, Volume 119, Issue 4, pp 447–460 | Cite as

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

  • Aavudai Anandhi
Article

Abstract

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.

Keywords

General circulation model (GCM) third generation Canadian coupled global climate model (CGCM3) SRES A1B, A2, B1 and COMMIT scenarios K-means clustering uncertainty 

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References

  1. Alley R, Berntsen T, Bindoff N L, Chen Z, Chidthaisong A, Friedlingstein P, Gregory J, Hegerl G, Heimann M, Hewitson B, Hoskins B, Joos F, Jouzel J, Kattsov V, Lohmann U, Manning M, Matsuno T, Molina M, Nicholls N, Overpeck J, Qin D, Raga G, Ramaswamy V, Ren J, Rusticucci M, Solomon S, Somerville R, Stocker T F, Stott P, Stouffer R J, Whetton P, Wood R A and Wratt D 2007 Climate Change 2007: The Physical Science Basis. Summary for Policy makers; Report of the Intergovernmental Panel on Climate Change, Geneva, CH.Google Scholar
  2. Anandhi A 2007 Impact assessment of climate change on hydrometeorology of Indian river basin for IPCC SRES scenarios; PhD thesis, Indian Institute of Science, India.Google Scholar
  3. Anandhi A, Srinivas V V, Nanjundiah R S and Kumar D N 2008 Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine; Int. J. Climatol. 28(3) 401–420.CrossRefGoogle Scholar
  4. Argiriou A A, Kassomenos P A and Lykoudis S P 2004 On the methods for the delimitation of seasons: Water, Air, and Soil Pollution; Focus 4 65–74.Google Scholar
  5. Chakraborty S, Tiedemann A V and Teng P S 2000 Climate change: Potential impact on plant diseases; Environmental Pollution 108 317–326.CrossRefGoogle Scholar
  6. Christidis N, Stott P A, Brown S, Karoly D J and Caesar J 2007 Human contribution to the lengthening of the growing season during 1950–99; J. Climate 20 5441–5454.CrossRefGoogle Scholar
  7. Cleugh H A, Miller J M and Böhm M 1998 Direct mechanical effects of wind on crops; Agroforestry Systems 41(1) 85–112, 10.1023/A:1006067721039.CrossRefGoogle Scholar
  8. Doty B and Kinter J L III 1993 The grid analysis and display system (GrADS): A desktop tool for earth science visualization; American Geophysical Union 1993 Fall Meeting, San Fransico, CA, 6–10 December.Google Scholar
  9. Fix E and Hodges J L 1951 Discriminatory analysis: Nonparametric discrimination: Consistency properties; USAF School of Aviation Medicine, Project 21-49-004, Report 4.Google Scholar
  10. Jones G S, Jones A, Roberts D L, Stott P A and Williams K D 2005 Sensitivity of global scale climate change attribution results to inclusion of fossil fuel black carbon aerosol; Geophys. Res. Lett. 32 L14701, doi: 10.1029/2005GL023370.CrossRefGoogle Scholar
  11. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo K C, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R and Joseph D 1996 The NCEP/NCAR 40-year reanalysis project; Bull. Amer. Meteor. Soc. 77(3) 437–471.CrossRefGoogle Scholar
  12. Kendall M G 1951 Regression structure and functional relationship, Part I; Biometrika 38 11–25.Google Scholar
  13. Kripalani R H, Oh J H, Kulkarni A, Sabade S S and Chaudhari H S 2007 South Asian summer monsoon precipitation variability: Coupled climate model simulations and projections under IPCC AR4; Theoretical and Applied Climatology 90 133–159.CrossRefGoogle Scholar
  14. Lamb H H 1972 British Isles weather types and a register of daily sequence of circulation patterns, 1861–1971; Geophysical Memoir 116 HMSO, London, pp. 85.Google Scholar
  15. Leach C 1979 Introduction to statistics: A nonparametric approach for the social sciences (New York: Wiley).Google Scholar
  16. MacQueen J 1967 Some methods for classification and analysis of multivariate observation; In: Proceedings of the fifth Berkeley Symposium on mathematical statistics and probability, (eds) Le Cam L M and Neyman J (Berkeley: University of California Press) 1 281–297.Google Scholar
  17. Nakicenovic N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Grübler A, Jung T Y, Kram T, La Rovere E L, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Raihi K, Roehrl A, Rogner H H, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, van Rooijen S, Victor N and Dadi Z 2000 IPCC Special report on emissions scenarios, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 599.Google Scholar
  18. Patterson D T, Westbrook J K, Joyce R J V, Lingren P D and Rogasik J 1999 Weeds, insects and diseases; Climatic Change 43 711–727.CrossRefGoogle Scholar
  19. Pearson K 1896 Mathematical contributions to the Theory of Evolution III Regression Heredity and Panmixia; Philosophical Transactions of the Royal Society of London Series 187 253–318.CrossRefGoogle Scholar
  20. Peng S, Huang J, Sheeshy J E, Laza R C, Visperas R M, Zhong X, Centeno G S, Khush G S and Cassman K G 2004 Rice yields decline with higher night temperature from global warming; Proceedings of National Academy of Science, USA 101 9971–9975.CrossRefGoogle Scholar
  21. Press W H, Teukolsky S A, Vetterling W T and Flannery B P 1992 Numerical recipes in Fortran 77: The art of scientific computing (New York: Cambridge University Press).Google Scholar
  22. Rajeevan M, Bhate J, Kale J D and Lal B 2005 Development of a high resolution daily gridded rainfall data for the Indian Region (version 2), Meteorol. Monogr. Climatol. 22/2005, India Meteorol. Dept., New Delhi.Google Scholar
  23. Rajeevan M, Bhate J, Kale J D and Lal B 2006 High resolution daily gridded rainfall data for the Indian region: Analysis of break and active monsoon spells; Curr. Sci. 91(3) 296–306.Google Scholar
  24. Rupa Kumar K, Sahai A K, Krishna Kumar K, Patwardhan S K, Mishra P K, Revadekar J V, Kamala K and Pant G B 2006 High-resolution climate change scenarios for India for the 21st century; Curr. Sci. 90 334–344.Google Scholar
  25. Sailor D J, Smith M and Hart M 2008 Climate change implications for wind power resources in the Northwest United States; Renewable Energy 33 2393–2406.CrossRefGoogle Scholar
  26. Spearman C E 1904a ’General intelligence’ objectively determined and measured; Am. J. Psychol. 5 201–293.CrossRefGoogle Scholar
  27. Spearman C E 1904b Proof and measurement of association between two things; Am. J. Psychol. 15 72–101.CrossRefGoogle Scholar
  28. Tripathi S, Srinivas V V and Nanjundiah R S 2006 Downscaling of precipitation for climate change scenarios: A support vector machine approach; J. Hydrol. 330 621–640, doi: 10.1016/j.jhydrol.2006.04.030.CrossRefGoogle Scholar
  29. Tuller S E 1990 Standard seasons; Int. J. Biomet. 34 181–188.CrossRefGoogle Scholar
  30. Winkler J A, Palutikof J P, Andresen J A and Goodess C M 1997 The simulation of daily temperature time series from GCM output Part II: Sensitivity analysis of an empirical transfer function methodology; J. Climate 10(10) 2514–2532.CrossRefGoogle Scholar
  31. Yadav R K, Rupa Kumar K and Rajeevan M 2007 Role of Indian Ocean sea surface temperatures in modulating northwest Indian winter precipitation variability; Theoretical and Applied Climatology 87 73–83, doi: 10.1007/s00704005-0221.CrossRefGoogle Scholar
  32. Yadav R K, Rupa Kumar K and Rajeevan M 2009a Increasing influence of ENSO and decreasing influence of AO/NAO in the recent decades over northwest India winter precipitation; J. Geophys. Res.-Atmos. 114 D12112, doi: 10.1029/2008JD011318.CrossRefGoogle Scholar
  33. Yadav R K, Rupa Kumar K and Rajeevan M 2009b Out-ofphase relationships between convection over north-west India and warm-pool region during winter season; Int. J. Climatol. 29 1330–1338, doi: 10.1002/joc.1783.CrossRefGoogle Scholar
  34. Yadav R K, Rupa Kumar K and Rajeevan M 2010a Climate change scenarios for northwest India winter season; Quaternary International 213 12–19.CrossRefGoogle Scholar
  35. Yadav R K, Yoo J H, Kucharski F and Abid M A 2010b Why is ENSO influencing northwest India winter precipitation in recent decades?; J. Climate (in press).Google Scholar

Copyright information

© Indian Academy of Sciences 2010

Authors and Affiliations

  • Aavudai Anandhi
    • 1
  1. 1.CUNY Institute for Sustainable Cities/Hunter CollegeCity University of New YorkNew YorkUSA

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