Prioritization of global climate models using fuzzy analytic hierarchy process and reliability index

Abstract

Climate scenarios derived from the global climate models (GCMs) are used for climate change impact studies in several sectors including agriculture, hydrological, and health. Globally, more than 50 climate models exist and choosing suitable models based on reproducibility of observed weather for a study region is a challenging task. This step is important to reduce uncertainty. This study compared the simulation performance of 12 global climate models for temperatures and rainfall in past 30 years over Indian region. For this, Priority index from Fuzzy Analytic Hierarchy Process (FAHP) and Reliability index were used and both methods were compared. Study revealed all 12 models overestimated minimum and maximum temperatures in most regions of India, which resulted in hot bias especially in northern region. However, models showed significant cold bias for the Himalayan region. In general, simulated rainfall was underestimated by many GCMs. The analysis indicated that FAHP method is good to shortlist GCMs based on their spatial and temporal performance in reproducing observed weather. Among 12 models, NORESM1 model has performed better in reproducing maximum temperature. The IPSL-LR, FIO-ESM, GFDL-CM3, and MIROC5 models have performed better for minimum temperature. In case of rainfall, CSIRO, MIROC5, HADGEM2, GFDL-ESM 2 M, and IPSL-LR have performed better as compared to other models.

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References

  1. Aich V, Liersch S, Vetter T, Huang S, Tecklenburg J, Hoffmann P, Koch H, Fournet S, Krysanova V, Müller EN, Hattermann FF (2014) Comparing impacts of climate change on streamflow in four large African river basins. Hydrol Earth Syst Sc 18(4):1305–1321

    Article  Google Scholar 

  2. Asseng S, Ewert F, Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, Thorburn PJ, Rötter RP, Cammarano D, Brisson N, Basso B, Martre P, Aggarwal PK, Angulo C, Bertuzzi P, Biernath C, Challinor AJ, Doltra J, Gayler S, Goldberg R, Grant R, Heng L, Hooker J, Hunt LA, Ingwersen J, Izaurralde RC, Kersebaum KC, Müller C, Naresh Kumar S, Nendel C, O’Leary G, Olesen JE, Osborne TM, Palosuo T, Priesack E, Ripoche D, Semenov MA, Shcherbak I, Steduto P, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Travasso M, Waha K, Wallach D, White JW, Williams JR, Wolf J (2013) Uncertainty in simulating wheat yields under climate change. Nat Clim Chang 3(9):827–832

    Article  Google Scholar 

  3. Asseng S, Ewert F, Martre P, Rötter RP, Lobell DB, Cammarano D, Kimball BA, Ottman MJ, Wall GW, White JW, Reynolds MP, Alderman PD, Prasad PVV, Aggarwal PK, Anothai J, Basso B, Biernath C, Challinor AJ, de Sanctis G, Doltra J, Fereres E, Garcia-Vila M, Gayler S, Hoogenboom G, Hunt LA, Izaurralde RC, Jabloun M, Jones CD, Kersebaum KC, Koehler AK, Müller C, Naresh Kumar S, Nendel C, O’Leary G, Olesen JE, Palosuo T, Priesack E, Eyshi Rezaei E, Ruane AC, Semenov MA, Shcherbak I, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Thorburn PJ, Waha K, Wang E, Wallach D, Wolf J, Zhao Z, Zhu Y (2015) Rising temperatures reduce global wheat production. Nat Clim Chang 5(2):143–147

    Article  Google Scholar 

  4. Barnett TP, Pierce DW, Hidalgo HG, Bonfils C, Santer BD, Das T et al (2008) Human-induced changes in the hydrology of the western United States. science 319(5866):1080–1083

    Article  Google Scholar 

  5. Bhattacharjee PS, Zaitchik BF (2015) Perspectives on CMIP5 model performance in the Nile River headwaters regions. Int J Climatol 35(14):4262–4275

    Article  Google Scholar 

  6. Brekke LD, Dettinger MD, Maurer EP, Anderson M (2008) Significance of model credibility in estimating climate projection distributions for regional hydroclimatological risk assessments. Clim Chang 89(3–4):371–394

    Article  Google Scholar 

  7. Brun F, Wallach D, Makowski D, Jones JW (2006) Working with dynamic crop models: evaluation, analysis, parameterization, and applications. Elsevier

  8. Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Sets Systems 17(1):233–247

    Article  Google Scholar 

  9. Cannon AJ (2015) Selecting GCM scenarios that span the range of changes in a multimodel ensemble: application to CMIP5 climate extremes indices. J Clim 28(3):1260–1267

    Article  Google Scholar 

  10. Challinor AJ, Simelton ES, Fraser EDG, Hemming D, Collins M (2010) Increased crop failure due to climate change: assessing adaptation options using models and socio-economic data for wheat in China. Environ Res Lett 5:034012

    Article  Google Scholar 

  11. Chang D-Y (1996) Applications of the extent analysis method on fuzzy AHP. EJOR 95(3):649–655

    Article  Google Scholar 

  12. Chaturvedi RK, Joshi J, Jayaraman M, Bala G, Ravindranath NH (2012) Multi-model climate change projections for India under representative concentration pathways. Curr Sci:791–802

  13. Chou SW, Chang YC (2008) The implementation factors that influence the ERP (enterprise resource planning) benefits. Decis Support Syst 46(1):149–157

    Article  Google Scholar 

  14. Coquard J, Duffy PB, Taylor KE, Iorio JP (2004) Present and future surface climate in the western USA as simulated by 15 global climate models. Clim Dyn 23(5):455–472

    Article  Google Scholar 

  15. Dettinger MD (2005) From climate-change spaghetti to climate-change distributions for 21st-century California. San Francisco Estuary and Watershed Science 3(1)

  16. Fleisher DH, Condori B, Quiroz R, Alva A, Asseng S, Barreda C, Bindi M, Boote KJ, Ferrise R, Franke AC, Govindakrishnan PM, Harahagazwe D, Hoogenboom G, Naresh Kumar S, Merante P, Nendel C, Olesen JE, Parker PS, Raes D, Raymundo R, Ruane AC, Stockle C, Supit I, Vanuytrecht E, Wolf J, Woli P (2017) A potato model intercomparison across varying climates and productivity levels. Glob Change Biol 23(3):1258–1281

    Article  Google Scholar 

  17. Giorgi F, Mearns LO (1991) Approaches to the simulation of regional climate change: a review. Rev Geophys 29(2):191–216

    Article  Google Scholar 

  18. Hajat S, Vardoulakis S, Heaviside C, Eggen B (2014) Climate change effects on human health: projections of temperature-related mortality for the UK during the 2020s, 2050s and 2080s. J Epidemiol Community Health 68(7):641–648

    Article  Google Scholar 

  19. Houle D, Bouffard A, Duchesne L, Logan Harvey R (2012) Projections of future soil temperature and water content for three southern Quebec forested sites. J Clim 25(21):7690–7701

    Article  Google Scholar 

  20. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex andP.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp, doi:https://doi.org/10.1017/CBO9781107415324

  21. Islam A, Sikka AK, Saha B, Singh A (2012) Streamflow response to climate change in the Brahmani River basin, India. Water Res Manag 26(6):1409–1424

    Article  Google Scholar 

  22. Kilincci O, Onal SA (2011) Fuzzy AHP approach for supplier selection in a washing machine company. Expert Syst Appl 38(8):9656–9664

    Article  Google Scholar 

  23. Lutz AF, ter Maat HW, Biemans H, Shrestha AB, Wester P, Immerzeel WW (2016) Selecting representative climate models for climate change impact studies: an advanced envelope-based selection approach. Int J Climatol 36(12):3988–4005

    Article  Google Scholar 

  24. Milly PC, Dunne KA, Vecchia AV (2005) Global pattern of trends in streamflow and water availability in a changing climate. Nature 438(7066):347–350

    Article  Google Scholar 

  25. Naresh Kumar S, Aggarwal PK, Saxena R, Rani S, Jain S, Chauhan N (2013) An assessment of regional vulnerability of rice to climate change in India. Clim Chang 118(3–4):683–699

    Google Scholar 

  26. Naresh Kumar S, Aggarwal PK, Rani DS, Saxena R, Chauhan N, Jain S (2014) Vulnerability of wheat production to climate change in India. Clim Res 59(3):173–187

    Article  Google Scholar 

  27. Parry M, Rosenzweig C, Iglesias A, Livermore M, Fischer G (2004) Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Glob Environ Change 53–67(171):14

    Google Scholar 

  28. Perez J, Menendez M, Mendez FJ, Losada IJ (2014) Evaluating the performance of CMIP3 and CMIP5 global climate models over the north-East Atlantic region. Clim Dyn 43(9–10):2663–2680

    Article  Google Scholar 

  29. Raju KS, Kumar N, D (2014) Ranking of global climate models for India using multicriterion analysis. Clim Res 60:103–117

  30. Raju KS, Kumar DN (2015) Ranking general circulation models for India using TOPSIS. J Water Clim Change 6(2):288–299

    Article  Google Scholar 

  31. Raju KS, Kumar DN (2016) Selection of global climate models for India using cluster analysis. J Water Clim Change 7(4):764–774

    Article  Google Scholar 

  32. Rojas R, Feyen L, Watkiss P (2013) Climate change and river floods in the European Union: socio-economic consequences and the costs and benefits of adaptation. Glob Environ Chang 23(6):1737–1751

    Article  Google Scholar 

  33. Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C, Arneth A, Boote KJ, Folberth C, Glotter M, Khabarov N, Neumann K, Piontek F, Pugh TAM, Schmid E, Stehfest E, Yang H, Jones JW (2014) Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc Natl Acad Sci 111(9):3268–3273

    Article  Google Scholar 

  34. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York

    Google Scholar 

  35. Shashikanth K, Salvi K, Ghosh S, Rajendran K (2014) Do CMIP5 simulations of Indian summer monsoon rainfall differ from those of CMIP3? Atmos Sci Let 15:79–85

    Article  Google Scholar 

  36. Su F, Duan X, Chen D, Hao Z, Cuo L (2013) Evaluation of the global climate models in the CMIP5 over the Tibetan plateau. J Clim 26(10):3187–3208

    Article  Google Scholar 

  37. Teklesadik AD, Alemayehu T, van Griensven A, Kumar R, Liersch S, Eisner S, Tecklenburg J, Ewunte S, Wang X (2017) Inter-model comparison of hydrological impacts of climate change on the upper Blue Nile basin using ensemble of hydrological models and global climate models. Clim Chang 141(3):517–532

    Article  Google Scholar 

  38. Thor J, Ding SH, Kamaruddin S (2013) Comparison of multi criteria decision making methods from the maintenance alternative selection perspective. The Int J Eng Sc 2(6):27–34

    Google Scholar 

  39. Wilcke RA, Bärring L (2016) Selecting regional climate scenarios for impact modelling studies. Environ Modell Soft 78:191–201

    Article  Google Scholar 

  40. Willmott CJ (1981) On the validation of models. Phy Geo 2(2):184–194

    Article  Google Scholar 

  41. Xuan W, Ma C, Kang L, Gu H, Pan S, Xu YP (2017) Evaluating historical simulations of CMIP5 GCMs for key climatic variables in Zhejiang Province, China. Theor Appl Climatol 128(1–2):207–222

    Article  Google Scholar 

  42. Ying X, Chong-Hai X (2012) Preliminary assessment of simulations of climate changes over China by CMIP5 multi-models. Atmos Oceanic Sci Lett 5(6):489–494

    Article  Google Scholar 

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Acknowledgements

We gratefully acknowledge all the CMIP5 modelers and Indian Meteorological Department (IMD), New Delhi, for providing data sets for this work.

Funding

This study received financial grants provided by National Innovations on Climate Resilient Agriculture (NICRA), Indian Council of Agricultural Research, New Delhi.

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Correspondence to S. Naresh Kumar.

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Panjwani, S., Naresh Kumar, S., Ahuja, L. et al. Prioritization of global climate models using fuzzy analytic hierarchy process and reliability index. Theor Appl Climatol 137, 2381–2392 (2019). https://doi.org/10.1007/s00704-018-2707-y

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