Abstract
This study makes use of temperature and precipitation from CMIP5 climate model output for climate change application studies over the Indian region during the summer monsoon season (JJAS). Bias correction of temperature and precipitation from CMIP5 GCM simulation results with respect to observation is discussed in detail. The non-linear statistical bias correction is a suitable bias correction method for climate change data because it is simple and does not add up artificial uncertainties to the impact assessment of climate change scenarios for climate change application studies (agricultural production changes) in the future. The simple statistical bias correction uses observational constraints on the GCM baseline, and the projected results are scaled with respect to the changing magnitude in future scenarios, varying from one model to the other. Two types of bias correction techniques are shown here: (1) a simple bias correction using a percentile-based quantile-mapping algorithm and (2) a simple but improved bias correction method, a cumulative distribution function (CDF; Weibull distribution function)-based quantile-mapping algorithm. This study shows that the percentile-based quantile mapping method gives results similar to the CDF (Weibull)-based quantile mapping method, and both the methods are comparable. The bias correction is applied on temperature and precipitation variables for present climate and future projected data to make use of it in a simple statistical model to understand the future changes in crop production over the Indian region during the summer monsoon season. In total, 12 CMIP5 models are used for Historical (1901–2005), RCP4.5 (2005–2100), and RCP8.5 (2005–2100) scenarios. The climate index from each CMIP5 model and the observed agricultural yield index over the Indian region are used in a regression model to project the changes in the agricultural yield over India from RCP4.5 and RCP8.5 scenarios. The results revealed a better convergence of model projections in the bias corrected data compared to the uncorrected data. The study can be extended to localized regional domains aimed at understanding the changes in the agricultural productivity in the future with an agro-economy or a simple statistical model. The statistical model indicated that the total food grain yield is going to increase over the Indian region in the future, the increase in the total food grain yield is approximately 50 kg/ ha for the RCP4.5 scenario from 2001 until the end of 2100, and the increase in the total food grain yield is approximately 90 kg/ha for the RCP8.5 scenario from 2001 until the end of 2100. There are many studies using bias correction techniques, but this study applies the bias correction technique to future climate scenario data from CMIP5 models and applied it to crop statistics to find future crop yield changes over the Indian region.
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References
Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, Nelkin E (2003) The version 2 global precipitation climatology project (GPCP), monthly precipitation analysis (1979-present). J Hydrometeor 4:1147–1167
Aggarwal PK, Sinha SK (1993) Effect of probable increase in carbon dioxide and temperature on productivity of wheat in India. J Agric Meteorol 48:811–814
DES, 2010 Agricultural statistics at a glance, Directorate of Economics and Statistics, Department of Agriculture and cooperation, Ministry of Agriculture, Government of India, New Delhi
Dhanya CT, A Gupta (2014) Improved bias correction method using wavelet decomposition. Int J Civil Eng Res 5(1):1–8. ISSN:2278–3652
Durman CF, Gregory JM, Hassel DC, Jones RG, Murphy JM (2001) A comparison of extreme European daily precipitation simulated by a global and a regional climate model for present and future climates. Q J R Meteorol Soc 127:1005–1015
Gadgil S (1996) Climate change and agriculture—an Indian perspective. In: Abrol YR, Gadgil S, Pant GB (eds) Climate variability and agriculture. Narosa, New Delhi, India, p 1–18
Gadgil S, Rupa Kumar K (2006) The Asian monsoon—agriculture and economy. In: Wang B (ed) The Asian Monsoon. Springer Praxis, Heidelberg, p 651–683. doi:10.1007/3-540-37722-0_18
IPCC (2001a) Climate change 2001: technical summary, contribution of Working Group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press
IPCC (2001b) Climate change 2001: impacts, adaptation and vulnerability, contribution of Working Group II to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press
IPCC (2007a) 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
IPCC (2007b) Climate change 2007: the scientific basis. Cambridge University Press
IPCC (2007c) Climate change 2007: impacts, adaptation and vulnerability. Cambridge University Press
Johnson FM, Sharma A (2009) Measurement of GCM skill in predicting variables relevant for hydro climatological assessments. J Clim 22:4373–4382. doi:10.1175/2009JCLI2681.1
Johnson FM, Sharma A (2012) A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations. Water Resour Res 48:1–16. doi:10.1029/2011WR010464
Jones PD, New M, Parker DE, Martin S, Rigor IG (1999) Surface air temperature and its variations over the last 150 years. Rev Geophys 37:173–199
Kothawale DR, Rupa Kumar K (2005) On the recent changes in surface temperature trends over India. Geophys Res Lett 32:L18714
Krishna Kumar K, Rupa Kumar K, Ashrit R, Deshpande NR, Hansen JW (2004) Climate impacts on Indian agriculture. Int J Climatol 24:1375–1393
Kumar KS, Parikh J (1998) Climate change impacts on Indian agriculture: the Ricardian approach. In: Dinar A, Mendelsohn R, Evenson R, Parikh J, Sanghi A, Kumar K, McKinsey J, Lonergan S (eds) Measuring the impact of climate change on Indian agriculture. World Bank
Leander R, Buishand TA (2007) Re-sampling of regional climate model output for the simulation of extreme river flows. J Hydrol 332:487–496
Leander R, Buishand T, van den Hurk B, de Wit M (2008) Estimated changes in flood quantiles of the river Meuse from resampling of regional climate model output. J Hydrol 351:331–343
Ojha R, Nageshkumar D, Sharma A, Mehrotra R (2013) Assessing severe drought and wet events over India in a future climate using a nested bias correction approach. J Hydrol Eng: 760–772. doi:10.1061/(ASCE)HE.1943-5584.0000585
Pant GB, Rupa Kumar K (1997) Climates of India. Wiley, 320 pp
Parthasarathy B, Rupakumar K, Munot AA (1992) Forecast of rainy season food grain production based on monsoon rainfall. Indian J Agric Sci 62:1–8
Parthasarathy B, Munot AA, Kothawale DR (1995) All India monthly and seasonal rainfall series: 1871–1993. Theor Appl Climatol 49:217–224
Piani C, Sanderson B, Giorgi F, Frame DJ, Christiansen C, Allen MR (2007) Regional probabilistic forecasts from a multithousand, multi-model ensemble of simulations. J Geophys Res 112:D24108. doi:10.1029/2007JD008712
Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99:187–192
Prasanna V (2014) Impact of monsoon rainfall on the total food grain yield over India. Journal of Earth System Science: Volume 123(5):1129–1145
Preethi B, Revadekar JV (2012) Kharif food grain yield and daily summer monsoon precipitation over India. Int J Climatol 33:1978–1986
Van der Linden P, Mitchell JFB (eds) (2009) Climate change and its impacts: summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, 160 pp. http://ensembles-eu.metoffice.com/docs/Ensembles_final_report_Nov09.pdf
Webster PJ, Magana VO, Palmer TN, Shukla J, Thomas RA, Yanai M, Yasunari T (1998) Monsoons: processes, predictability and the prospects of prediction. J Geophys Res 103:14451–14510
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Prasanna, V. Statistical bias correction method applied on CMIP5 datasets over the Indian region during the summer monsoon season for climate change applications. Theor Appl Climatol 131, 471–488 (2018). https://doi.org/10.1007/s00704-016-1974-8
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DOI: https://doi.org/10.1007/s00704-016-1974-8