Prioritization of global climate models using fuzzy analytic hierarchy process and reliability index
- 50 Downloads
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.
We gratefully acknowledge all the CMIP5 modelers and Indian Meteorological Department (IMD), New Delhi, for providing data sets for this work.
This study received financial grants provided by National Innovations on Climate Resilient Agriculture (NICRA), Indian Council of Agricultural Research, New Delhi.
- 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):827–832CrossRefGoogle Scholar
- 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):143–147CrossRefGoogle Scholar
- Brun F, Wallach D, Makowski D, Jones JW (2006) Working with dynamic crop models: evaluation, analysis, parameterization, and applications. ElsevierGoogle Scholar
- Chaturvedi RK, Joshi J, Jayaraman M, Bala G, Ravindranath NH (2012) Multi-model climate change projections for India under representative concentration pathways. Curr Sci:791–802Google Scholar
- Dettinger MD (2005) From climate-change spaghetti to climate-change distributions for 21st-century California. San Francisco Estuary and Watershed Science 3(1)Google Scholar
- 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):1258–1281CrossRefGoogle Scholar
- 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
- 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):683–699Google Scholar
- 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):14Google Scholar
- Raju KS, Kumar N, D (2014) Ranking of global climate models for India using multicriterion analysis. Clim Res 60:103–117Google Scholar
- 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):3268–3273CrossRefGoogle Scholar
- Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New YorkGoogle Scholar
- 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):517–532CrossRefGoogle Scholar
- 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):27–34Google Scholar