Spatial Patterns of Climate Change in India

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A presentation made at a National Level Technical Symposium on Climate Change patterns in India in the 21st Century.

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  • India’s population dependency
  • Spatial Patterns of Climate Change in India

    1. 1. Identification of Spatial Patterns of Climate Change in India for the 21st Century M. Prasanth N. Khalid Ahmed Senior Undergraduate, B. Tech Senior Undergraduate, B. Tech Department of Production Engineering Department of Chemical Engineering National Institute of Technology National Institute of Technology Tiruchirapalli – 620015 Tiruchirapalli – 620015. Mentor: Padma Shri. Dr. N. Roddam Narasimha, FRS Victor Albertson Distinguished Professor of Atmospheric Sciences Centre for Atmospheric and Oceanic Sciences Indian Institute of Science Bangalore, India. Moments 2010, NIT – Trichy, India, 31 January 2010
    2. 2. Why study this problem? • Climate change could cause major environmental changes. • Agriculture, Forestry, Wetlands and fisheries – occupations based on soil condition • Crucial Factors: – Water based eco-systems depending on monsoon rains. Hence, study of climate change is essential to predict monsoonal rains. – Future prediction of droughts and adverse conditions is important and has direct impact on policy implications.
    3. 3. Previous Works • Observed data • Focuses only on precipitation S. N. Srivatsava et. al • Observed data • Focuses only of precipitation U. S. De et. al. • Observed data • Focuses only on temperature change S. K. Dash et. al • Observed data • Focuses only on monsoonal rains D. R. Kothwale et. al • Model data • Focuses on monsoonal change onlyDiffenbaugh et. al • Observed data • Focuses only on monsoonal changes Emori et. al • Model data • Focuses on precipitation and specific humidity • Simple models, hence not accurate and reliable. B. Wang et. al
    4. 4. What is new in our work? • Extensive analysis using 19 global climate models – run on Supercomputers – Fourth assessment report (AR4) of the UN based Intergovernmental Panel for Climate Change (IPCC) – No work done on India using any of the IPCC supercomputer models • Precipitation, Temperature change, Specific and Relative humidity – All variables considered unlike previous works, focusing only on monsoonal precipitation • Introduction of Net Water Budget = Precipitation – Evaporation – Used as Drought Index in our work • Introduction of Standard Euclidian Distance – for calculating the magnitude of response of climate change in various parts of India – predicts the regions which are vulnerable to climate change in future
    5. 5. Details of Analysis • India – land region between 68oE to 96oE and 8oN to 36oN • 19 CMIP3 atmosphere-ocean general circulation models – Multi model database – All models participated in the Fourth assessment of IPCC. • SRES A1B scenario is considered – CO2 concentration increases to 720 parts per million in 2100 – Characterized by a balanced emphasis on all energy sources.
    6. 6. Results and Discussions Anomalies and Percentage changes in the annual mean precipitation (P), evaporation (E) and precipitation minus evaporation (P-E). Number of models with P-E values positive, negative or close to zero. Blue bar indicates negative P-E value (drier), green bar indicates positive P-E value (wetter), and brown bar shows model with P-E value close to 0. Changes in the Annual Mean P-E anomaly
    7. 7. Changes in the Annual Temperature AnomalyChanges in the annual mean specific humidity (at a height of 2m) anomaly.
    8. 8. Aggregate Standard Euclidean Distance (SED) scores (dimensionless) for 30 year segments of the 21st century. SED – measure of vulnerability to climate change Contributions of each of the variables to the Standard Euclidean Distance (SED)
    9. 9. References 1. IPCC, Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change (eds J. T. Houghton et al.), Cambridge University Press, New York, USA, 2001. 2. Meehl, G. A., Washington, W. M., Collins, W. D., Arblaster, J. M., Hu, A. X., Buja, L. E., Strand, W. G., and Teng, H. Y., How Much More Global Warming and Sea Level Rise?. Science, 2005, 307, 1769–1772. 3. Diffenbaugh, N. S., Pal, J. S., Trapp, R. J., and Giorgi. F., Fine-scale processes regulate the response of extreme events of global climate change. Proc. Natl. Acad. Sci. USA, 2005, 102, 15774–15778. 4. Meehl, G. A., Arblaster, J. M., and Tebaldi, C., Understanding future patterns of increased precipitation intensity in climate model simulations. Geophys. Res. Lett., 2005, 32, L18719. 5. Tebaldi, C., Hayhoe, K., Arblaster, J. M., and Meehl, G. A., Going to the extremes: An intercomparison of model-simulated historical and future changes in extreme events. Climatic Change, 2006, 79, 185–211. 6. Brooks, H. E., Lee, J. W., and Craven, J. P., The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data. Atmos. Res., 2003, 67, 73–94. 7. Zwiers, F. W., and Kharin, V. V., Changes in the extremes of the climate simulated by CCC GCM2 under CO2 doubling. J. Climate, 1998, 11, 2200–2222. 8. Kharin, V. V., and Zwiers, F. W., changes in the extremes in an ensemble of transient climate simulation with a coupled atmosphere-ocean GCM. J. Climate, 2000, 13, 3760–3788. 9. Bell, J. L., Sloan, L. C., and Snyder, M. A., Regional changes in extreme climatic events. J. Climate, 2004, 17, 81–87. 10. Pal, J. S., Giorgi, F., and Bi, X., Consistency of recent European summer precipitation trends and extremes with future regional climate projections. Geophys. Res. Lett., 2004, 31, L13202. 11. Christensen, J. H., and Christensen, O. B., Climate modelling: Severe summertime flooding in Europe. Nature, 2003, 421, 805–806. 12. Patz, J. A., Campbell-Lendrum, D., Holloway, T., and Foley, J. A., Impact of regional climate change on Human Health. Nature, 2005, 438, 310–317.
    10. 10. We kindly acknowledge: • US Department of Space and Energy and NASA Goddard Institute for Space Studies, USA – for making available the model data for performing this analysis. • Dr. V. Rajaraman, Director, Supercomputing Research Centre, IISc, Bangalore – for providing necessary computing facilities for running these models. • Indian Academy of Science, Bangalore – for funding our stay and providing resources to carry out this research.

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