Editorial Type:
Article
Article Type:
Research Article

Diagnostics of Climate Model Biases in Summer Temperature and Warm-Season Insolation for the Simulation of Regional Paddy Rice Yield in Japan

Toshichika Iizumi Agro-Meteorology Division, National Institute for Agro-Environmental Sciences, Tsukuba, Japan

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Motoki Nishimori Agro-Meteorology Division, National Institute for Agro-Environmental Sciences, Tsukuba, Japan

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Masayuki Yokozawa Agro-Meteorology Division, National Institute for Agro-Environmental Sciences, Tsukuba, Japan

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Print Publication:
01 Apr 2010
DOI:
https://doi.org/10.1175/2009JAMC2225.1
Page(s):
574–591
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Abstract

This study quantifies the ranges of climate model biases in surface air temperature for July and August (summer temperature) and daily total insolation for May–October (warm-season insolation) that can give simulated regional paddy rice yields with a bias within ±2.5% of the 20-yr mean observed regional yield. The following four sets of three meteorological elements (daily maximum and minimum temperatures and daily total insolation) from daily climate model outputs were used as meteorological inputs for a large-scale crop model for irrigated paddy rice: 1) raw climate model outputs of all meteorological elements, 2) bias-corrected temperatures and raw climate model outputs of insolation, 3) bias-corrected insolation and raw climate model outputs of temperatures, and 4) bias-corrected climate model outputs of all meteorological elements. These meteorological inputs were sourced from seven coupled general circulation models, one regional climate model, and one reanalysis dataset. Crop model simulations with artificially biased meteorological inputs were also used. By using the approximation formula derived from these crop model simulation results and the Monte Carlo simulation technique, it was found that climate model outputs with biases within ±0.6°C and ±3% for summer temperature and warm-season insolation, respectively, could result in a simulated regional paddy rice yield with a bias within ±2.5% of the 20-yr mean observed regional yield. The simulated regional yield was less biased not only when the biases of two meteorological inputs were small but also when the cold or warm bias of summer temperature and the overestimation of warm-season insolation were balanced through the crop model processes. The methodology presented here will lead to a better and more comprehensive understanding of the nature of error propagation from a climate model to an application model and will facilitate the selection of climate models suitable for specific applications.

Corresponding author address: Toshichika Iizumi, Agro-Meteorology Division, National Institute for Agro-Environmental Sciences, 3-1-3 Kannondai, Tsukuba, Ibaraki 305-8604, Japan. Email: iizumit@affrc.go.jp

Abstract

This study quantifies the ranges of climate model biases in surface air temperature for July and August (summer temperature) and daily total insolation for May–October (warm-season insolation) that can give simulated regional paddy rice yields with a bias within ±2.5% of the 20-yr mean observed regional yield. The following four sets of three meteorological elements (daily maximum and minimum temperatures and daily total insolation) from daily climate model outputs were used as meteorological inputs for a large-scale crop model for irrigated paddy rice: 1) raw climate model outputs of all meteorological elements, 2) bias-corrected temperatures and raw climate model outputs of insolation, 3) bias-corrected insolation and raw climate model outputs of temperatures, and 4) bias-corrected climate model outputs of all meteorological elements. These meteorological inputs were sourced from seven coupled general circulation models, one regional climate model, and one reanalysis dataset. Crop model simulations with artificially biased meteorological inputs were also used. By using the approximation formula derived from these crop model simulation results and the Monte Carlo simulation technique, it was found that climate model outputs with biases within ±0.6°C and ±3% for summer temperature and warm-season insolation, respectively, could result in a simulated regional paddy rice yield with a bias within ±2.5% of the 20-yr mean observed regional yield. The simulated regional yield was less biased not only when the biases of two meteorological inputs were small but also when the cold or warm bias of summer temperature and the overestimation of warm-season insolation were balanced through the crop model processes. The methodology presented here will lead to a better and more comprehensive understanding of the nature of error propagation from a climate model to an application model and will facilitate the selection of climate models suitable for specific applications.

Corresponding author address: Toshichika Iizumi, Agro-Meteorology Division, National Institute for Agro-Environmental Sciences, 3-1-3 Kannondai, Tsukuba, Ibaraki 305-8604, Japan. Email: iizumit@affrc.go.jp

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  • Arnell, N. W., D. A. Hudson, and R. G. Jones, 2003: Climate change scenarios from a regional climate model: Estimating change in runoff in southern Africa. J. Geophys. Res., 108 , 4519. doi:10.1029/2002JD002782.

    • Search Google Scholar
    • Export Citation

  • Baigorria, G. A., J. W. Jones, D-W. Shin, A. Mishra, and J. J. O’Brien, 2007: Assessing uncertainties in crop model simulations using daily bias-corrected regional circulation model outputs. Climate Res., 34 , 211–222.

    • Search Google Scholar
    • Export Citation

  • Carter, T. R., and Coauthors, 2007: General guidelines on the use of scenario data for climate impact and adaptation assessment version 2. IPCC Task Group on Scenarios for Climate Impact Assessment, 66 pp. [Available online at http://ipcc-ddc.cru.uea.ac.uk].

    • Search Google Scholar
    • Export Citation

  • Challinor, A. J., T. R. Wheeler, P. Q. Craufurd, J. M. Slingo, and D. I. F. Grimes, 2004: Design and optimization of a large-area process-based model for annual crops. Agric. For. Meteor., 124 , 99–120.

    • Search Google Scholar
    • Export Citation

  • Challinor, A. J., T. R. Wheeler, J. M. Slingo, P. Q. Craufurd, and D. I. F. Grimes, 2005: Simulation of crop yields using ERA-40: Limits to skill and nonstationarity in weather–yield relationships. J. Appl. Meteor., 44 , 516–531.

    • Search Google Scholar
    • Export Citation

  • Delworth, T. L., and Coauthors, 2006: GFDL’s CM2 global coupled climate models. Part I: Formulation and simulation characteristics. J. Climate, 19 , 643–674.

    • Search Google Scholar
    • Export Citation

  • Flato, G. M., G. J. Boer, W. G. Lee, N. A. McFarlane, D. Ramsden, M. C. Reader, and A. J. Weaver, 2000: The Canadian Centre for Climate Modelling and Analysis global coupled model and its climate. Climate Dyn., 16 , 451–467.

    • Search Google Scholar
    • Export Citation

  • Gordon, H. B., and Coauthors, 2002: The CSIRO Mk3 climate system model. CSIRO Atmospheric Research Tech. Paper 60, 130 pp.

  • GSI, 1998: User’s guide for numerical map (in Japanese). Revised 2nd ed. Japan Map Center, Tokyo, Japan, 500 pp.

  • Hanasaki, N., S. Kanae, and T. Oki, 2006: A reservoir operation scheme for global river routing models. J. Hydrol., 327 , 22–41.

  • Hansen, J. W., A. Challinor, A. Ines, T. Wheeler, and V. Moron, 2006: Translating climate forecasts into agricultural terms: Advances and challenges. Climate Res., 33 , 27–41.

    • Search Google Scholar
    • Export Citation

  • Hasumi, H., and S. Emori, Eds. 2004: K-1 coupled GCM (MIROC) description. K-1 Tech. Rep. 1, Center for Climate System Research, University of Tokyo, 34 pp.

    • Search Google Scholar
    • Export Citation

  • Horie, T., H. Nakagawa, H. G. S. Centeno, and M. J. Kropff, 1995: The rice crop simulation model SIMRIW and its testing. Modeling the Impact of Climate Change on Rice Production in Asia, R. B. Matthews, M. J. Kropff, and D. Bachelet, Eds., IRRI and CAB International, 51–66.

    • Search Google Scholar
    • Export Citation

  • Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, X. Dai, K. Maskell, and C. A. Johnson, Eds. 2001: Climate Change 2001: The Scientific Basis. Cambridge University Press, 881 pp.

    • Search Google Scholar
    • Export Citation

  • Iizumi, T., M. Nishimori, and M. Yokozawa, 2008: Combined equations for estimating global solar radiation: Projection of radiation field over Japan under global warming condition by statistical downscaling. J. Agric. Meteor., 64 , 9–23.

    • Search Google Scholar
    • Export Citation

  • Iizumi, T., M. Yokozawa, and M. Nishimori, 2009a: Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: Application of a Bayesian approach. Agric. For. Meteor., 149 , 333–348.

    • Search Google Scholar
    • Export Citation

  • Iizumi, T., M. Yokozawa, and M. Nishimori, 2009b: Development of impact functions on regional paddy rice yield in Japan for integrated impact assessment models. J. Agric. Meteor., 65 , 179–190.

    • Search Google Scholar
    • Export Citation

  • Ines, A. V. M., and J. W. Hansen, 2006: Bias correction of daily GCM rainfall for crop simulation studies. Agric. For. Meteor., 138 , 44–53.

    • Search Google Scholar
    • Export Citation

  • Jha, M., Z. Pan, E. S. Takle, and R. Gu, 2004: The impacts of climate change on stream flow in the upper Mississippi River Basin: A regional climate model perspective. J. Geophys. Res., 109 , D09105. doi:10.1029/2003JD003686.

    • Search Google Scholar
    • Export Citation

  • Jones, J. W., and Coauthors, 2003: The DSSAT cropping system model. Eur. J. Agron., 18 , 235–265.

  • Kurihara, K., and Coauthors, 2005: Projection of climatic change over Japan due to global warming by high-resolution regional climate model in MRI. SOLA, 1 , 97–100.

    • Search Google Scholar
    • Export Citation

  • MAFF, 1998: New method for calculating normal yield of paddy rice per decade (in Japanese). Association of Agriculture and Forestry Statistics, 143 pp.

    • Search Google Scholar
    • Export Citation

  • Mearns, L. O., T. Mavromatis, E. Tsvetsinskaya, C. Hays, and W. Easterling, 1999: Comparative responses of EPIC and CERES crop models to high and low spatial resolution climate change scenarios. J. Geophys. Res., 104 , (D6). 6623–6646.

    • Search Google Scholar
    • Export Citation

  • Mearns, L. O., F. Giorgi, P. Whetton, D. Pabon, M. Hulme, and M. Lal, 2003: Guidelines for use of climate scenarios developed from regional climate model experiments. IPCC Task Group on Scenarios for Climate Impact Assessment, 38 pp. [Available online at http://ipcc-ddc.cru.uea.ac.uk].

    • Search Google Scholar
    • Export Citation

  • Meehl, G. A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J. F. B. Mitchell, R. J. Stouffer, and K. E. Taylor, 2007: The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc., 88 , 1383–1394.

    • Search Google Scholar
    • Export Citation

  • Nakagawa, H., T. Horie, and T. Matsui, 2003: Effects of climate change on rice production and adaptive technologies. Rice Science: Innovations and Impact for Livelihood, T. W. Mew et al., Eds., IRRI and Chinese Academy of Engineering and Chinese Academy of Agricultural Sciences, 635–658.

    • Search Google Scholar
    • Export Citation

  • NPD/JMA, 1997: Outline of the operational numerical weather prediction of the Japan Meteorological Agency. Japan Meteorological Agency, Tokyo, Japan, 126 pp.

    • Search Google Scholar
    • Export Citation

  • Ohta, S., and A. Kimura, 2007: Impacts of climate changes on the temperature of paddy waters and suitable land for rice cultivation in Japan. Agric. For. Meteor., 147 , 186–198.

    • Search Google Scholar
    • Export Citation

  • Okada, M., T. Iizumi, M. Nishimori, and M. Yokozawa, 2009: Mesh climate change data of Japan Ver. 2 for climate change impact assessments under IPCC SRES A1B and A2. J. Agric. Meteor., 65 , 97–109.

    • Search Google Scholar
    • Export Citation

  • Onogi, K., and Coauthors, 2007: The JRA-25 Reanalysis. J. Meteor. Soc. Japan, 85 , 369–432.

  • Parton, W. J., D. S. Schimel, C. V. Cole, and D. S. Ojima, 1987: Analysis of factors controlling soil organic matter levels in Great Plains grasslands. Soil Sci. Soc. Amer. J., 51 , 1173–1179.

    • Search Google Scholar
    • Export Citation

  • Parton, W. J., J. W. B. Stewart, and C. V. Cole, 1988: Dynamics of C, N, P and S in grassland soils: A model. Biogeochemistry, 5 , 109–131.

    • Search Google Scholar
    • Export Citation

  • R Development Core Team, 2006: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 1671 pp.

    • Search Google Scholar
    • Export Citation

  • Reichler, T., and J. Kim, 2008: How well do coupled models simulate today’s climate? Bull. Amer. Meteor. Soc., 89 , 303–311.

  • Seino, H., 1993: An estimation of distribution of meteorological elements using GIS and AMeDAS data. J. Agric. Meteor., 48 , 379–383.

  • Tao, F., M. Yokozawa, and Z. Zhang, 2009: Modelling the impacts of weather and climate variability on crop productivity over a large area: A new process-based model development, optimization, and uncertainties analysis. Agric. For. Meteor., 149 , 831–850.

    • Search Google Scholar
    • Export Citation

  • Taylor, K., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106 , (D7). 7183–7192.

  • Xu, T., L. White, D. Hui, and Y. Luo, 2006: Probabilistic inversion of a terrestrial ecosystem model: Analysis of uncertainty in parameter estimation and model prediction. Global Biogeochem. Cycles, 20 , GB2007. doi:10.1029/2005GB002468.

    • Search Google Scholar
    • Export Citation

  • Yamamura, K., and M. Yokozawa, 2002: Prediction of a geographical shift in the prevalence of rice stripe virus disease transmitted by the small brown planthopper, Laodelphax striatellus (Fallen) (Hemiptera: Delphacidae), under global warming. Appl. Entomol. Zool., 37 , 181–190.

    • Search Google Scholar
    • Export Citation

  • Yamamura, K., M. Yokozawa, M. Nishimori, Y. Ueda, and T. Yokosuka, 2006: How to analyze long-term insect population dynamics under climate change: Fifty-year data of three insect pests in paddy fields. Popul. Ecol., 48 , 31–48.

    • Search Google Scholar
    • Export Citation

  • Yokozawa, M., T. Iizumi, A. Kotera, T. Sakamoto, and H. Nakagawa, 2006: Development of crop model for paddy rice to estimate prefectural-scale average yield (in Japanese). Proc. Joint Meeting on Environmental Engineering in Agriculture, Sapporo, Japan, Society of Eco-Engineering, 1.

    • Search Google Scholar
    • Export Citation

  • Yukimoto, S., and A. Noda, 2002: Improvements of the Meteorological Research Institute global ocean-atmosphere Coupled GCM (MRI CGCM2) and its climate sensitivity. Center for Global Environmental Research (CGER)’s Super Computer Activity Rep. 10, 161 pp.

    • Search Google Scholar
    • Export Citation

  • Yukimoto, S., and Coauthors, 2001: The new Meteorological Research Institute Coupled GCM (MRI-CGCM2)–Model climate and variability. Pap. Meteor. Geophys., 51 , 47–88.

    • Search Google Scholar
    • Export Citation
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