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Cited by 135

Ranking of global climate models for India using multicriterion analysis

Raju, KS, Nagesh Kumar, D·2014·Climate Research
DOI 10.3354/cr01222
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  • Previous research has suggested that choosing appropriate GCMs can help to decrease uncertainties in climate change projections.

    “Previous research has suggested that choosing appropriate GCMs can help to decrease uncertainties in climate change projections (Raju & Nagesh kumar 2014;McSweeney et al. 2015).”

    From Catchment response to climate change under CMIP6 scenarios: a case study of the Krishna River Basin (2024) · cites this paper as Raju & Nagesh kumar 2014

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  10. Kalnay E, Kanamitsu M, Kistler R, Collins W and others (1996) The NCEP/NCAR 40-Year Reanalysis Project. Bull Am Meteorol Soc 77: 437-471 · DOI
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  12. Macadam I, Pitman AJ, Whetton PH, Abramowitz G (2010) Ranking climate models by performance using actual values and anomalies: implications for climate change impact assessments. Geophys Res Lett 37: L16704, doi:10. 1029/ 2010GL043877 · DOI
  13. Maxino CC, McAvaney BJ, Pitman AJ, Perkins SE (2008) Ranking the AR4 climate models over the Murray- Darling Basin using simulated maximum temperature, minimum temperature and precipitation. Int J Climatol 28: 1097-1112 · DOI
  14. Meehl GA, Stocker TF, Collins WD, Friedlingstein P and others (2007) Global climate projections. In: Solomon S, Qin D, Manning M, Chen Z and others (eds) Climate Change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, p 747-846
  15. Mehrotra R, Sharma A (2010) Development and application of a multisite rainfall stochastic downscaling framework for climate change impact assessment. Water Resour Res 46: W07526, doi:10.1029/2009WR008423 · DOI
  16. Mehrotra R, Sharma A, Nagesh Kumar D, Reshmidevi TV (2013) Assessing future rainfall projections using multi- ple GCMs and a multi-site stochastic downscaling model. J Hydrol (Amst) 488: 84-100 · DOI
  17. Milton JS, Arnold JC (2007) Introduction to probability and statistics. Tata McGraw Hill, New Delhi
  18. Mujumdar PP, Ghosh S (2008) Modeling GCM and scenario uncertainty using a possibilistic approach: application to the Mahanadi River, India. Water Resour Res 44: W06407, doi: 10.1029/2007WR006137 · DOI
  19. Mujumdar PP, Nagesh Kumar D (2012) Floods in a changing climate: hydrologic modeling. International Hydrology Series. Cambridge University Press, Cambridge
  20. Ojha R, Kumar DN, Sharma A, Mehrotra R (2013) Assessing GCM convergence for the Indian region using the vari- able convergence score. J Hydrol Eng doi: 10. 1061/ (ASCE) HE.1943-5584.0000888 · DOI
  21. Perkins SE, Pitman AJ, Holbrook NJ, McAveney J (2007) Evaluation of the AR4 climate models' simulated daily maximum temperature, minimum temperature and pre- cipitation over Australia using probability density func- tions. J Clim 20: 4356-4376 · DOI
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  26. Raju KS, Nagesh Kumar D (2010) Multicriterion analysis in engineering and management. Prentice Hall of India, New Delhi
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Authors
Raju, K., & Nagesh Kumar, D.
Venue
Climate Research
Publisher
Inter-Research Science Center
Published
2014-06-17
Volume
60
Issue
2
Pages
103-117
ISSN
  • 0936-577X
  • 1616-1572
DOI
10.3354/cr01222
Cited by
135
References
2
Language
en

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