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Coupling of a Regional Climate Model with a Crop Development Model and Evaluation of the Coupled Model across China

Abstract

In this study, the CERES (Crop Estimation through Resource and Environment Synthesis) crop model was coupled with CLM3.5, the land module of the regional climate model RegCM4. The new coupled model was named RegCM4 CERES; and in this model, crop type was further divided into winter wheat, spring wheat, spring maize, summer maize, early rice, late rice, single rice, and other crop types based on each distribution fraction. The development of each crop sub-type was simulated by the corresponding crop model separately, with each planting and harvesting date. A simulation test using RegCM4 CERES was conducted across China from 1999 to 2008; a control test was also performed using the original RegCM4. Data on crop LAI (leaf area index), soil moisture at 10 cm depth, precipitation, and 2 m air temperature were collected to evaluate the performance of RegCM4 CERES. The evaluation provided comparison of single-station time series, regional distributions, seasonal variations, and statistical indices for RegCM4 CERES. The results revealed that the coupled model had an excellent ability to simulate the phonological changes and spatial variations in crops. The consideration of dynamic crop development in RegCM4 CERES corrected the wet bias of the original RegCM4 over North China and the cold bias over South China. However, the degree of improvement was minimal and the statistical indices for RegCM4 CERES were roughly the same as the original RegCM4.

概要

本文中, CERES作物模型(Crop Estimation through Resource and Environment Synthesis)与区域气候模式RegCM4的陆面模块CLM3.5实现了双向耦合. 新耦合模式被命名为RegCM4_CERES, 在该模式中, 网格内的作物类型根据实际作物分布比例被进一步划分为冬小麦、春小麦、春玉米、夏玉米、早稻、晚稻、单季稻与其他作物类型八类. 每一类作物次类型根据各自的种植、收获日期利用相应的作物模型进行独立模拟. 本文利用新建立的RegCM4_CERES模式, 针对中国区域进行了自1999年至2008年的模拟试验. 与之相对应地, 本文也利用原RegCM4模式进行了相同设置的控制试验. 台站观测的叶面积指数、10cm深土壤湿度、降水、2m高气温等要素被用于RegCM4_CERES模式的性能评估. 评估工作主要提供了RegCM4_CERES模式在单站时间序列、区域空间分布、典型区内季节变率及各统计量等方面的评估结果. 结果表明, RegCM4_CERES模式在模拟各类作物的物候变化与空间分布方面有着优秀的模拟能力. 考虑了作物生长发育过程的RegCM4_CERES模式纠正了原RegCM4模式在华北的湿偏差和华南的冷偏差, 但其改善程度十分有限, 两个模式在典型区内的统计指数几乎保持一致.

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References

  • Chen, F., and Z. H. Xie, 2011: Effects of crop growth and development on land surface fluxes. Adv. Atmos. Sci., 28(4), 927–944, https://doi.org/10.1007/s00376-010-0105-1.

    Article  Google Scholar 

  • Chen, F., and Z. H. Xie, 2012: Effects of crop growth and development on regional climate: A case study over East Asian monsoon area. Climate Dyn., 38, 2291–2305, https://doi.org/10.1007/s00382-011-1125-y.

    Article  Google Scholar 

  • Chen, F., and Z. H. Xie, 2013: An evaluation of RegCM3 CERES for regional climate modeling in China. Adv. Atmos. Sci., 30(4), 1187–1200, https://doi.org/10.1007/s00376-012-2114-8.

    Article  Google Scholar 

  • de Noblet-Ducoudré, N., S. Gervois, P. Ciais, N. Viovy, N. Bresson, B. Seguin, and A. Perrier, 2004: Coupling the soilvegetation-atmosphere-transfer scheme ORCHIDEE to the agronomy model STICS to study the influence of croplands on the European carbon and water budgets. Agronomie, 24, 397–407, https://doi.org/10.1051/agro:2004038.

    Article  Google Scholar 

  • Gao, X. J., Y. Shi, and F. Giorgi, 2016: Comparison of convective parameterizations in RegCM4 experiments over China with CLM as the land surface model. Atmospheric and Oceanic Science Letters, 9(4), 246–254, https://doi.org/10.1080/16742834.2016.1172938.

    Article  Google Scholar 

  • Gao, X. J., Y. Shi, Z. Y. Han, M. L. Wang, J. Wu, D. F. Zhang, Y. Xu, and F. Giorgi, 2017: Performance of RegCM4 over major river basins in China. Adv. Atmos. Sci., 34(4), 441–455, https://doi.org/10.1007/s00376-016-6179-7.

    Article  Google Scholar 

  • Gervois, S., N. de Noblet-Ducoudré, N. Viovy, and P. Ciais, 2004: Including croplands in a global biosphere model: Methodology and evaluation at specific sites. Earth Interactions, 8(16), 1–25, https://doi.org/10.1175/1087-3562(2004)8<1:ICIAGB>2.0.CO;2.

    Article  Google Scholar 

  • Giorgi, F., and R. O. Anyah, 2012: The road towards RegCM4. Climate Res., 52, 3–6, https://doi.org/10.3354/cr01089.

    Article  Google Scholar 

  • Giorgi, F., M. Marinucci, G. T. Bates, and G. de Canio, 1993: Development of a second-generation regional climate model (RegCM2). Part II: Convective processes and assimilation of lateral boundary conditions. Mon. Wea. Rev., 121, 2814–2832, https://doi.org/10.1175/1520-0493(1993)121<2814:DOASGR>2.0.CO;2.

    Google Scholar 

  • Jones, C. A., and J. R. Kiniry, 1986: CERES-Maize: A Simulation Model of Maize Growth and Development. TexasA&M University Press, 194 pp.

    Google Scholar 

  • Kiniry, J. R., and Coauthors, 1997: Evaluation of two maize models for nine U. S. locations. Agronomy Journal, 89, 421–426, https://doi.org/10.2134/agronj1997.00021962008900030009x.

    Article  Google Scholar 

  • Lawrence, P. J., and T. N. Chase, 2007: Representing a new MODIS consistent land surface in the Community Land Model (CLM 3. 0). J. Geophys. Res., 112: G01023, https://doi.org/10.1029/2006JG000168.

    Google Scholar 

  • Lei, H., D. Yang, E. Lokupitiya, and Y. Shen, 2010: Coupling land surface and crop growth models for predicting evapotranspiration and carbon exchange in wheat-maize rotation croplands. Biogeosciences, 7, 3363–3375, https://doi.org/10.5194/bg-7-3363-2010.

    Google Scholar 

  • Levis, S., G. B. Bonan, M. Vertenstein, and K. W. Oleson, 2004: The Community land model’s dynamic global vegetation model (CLM-DGVM): Technical description and user’s guide. NCAR Technical Note NCAR/TN-459+IA, 50 pp.

    Google Scholar 

  • Levis, S., G. B. Bonan, E. Kluzek, P. E. Thornton, A. Jones, W. J. Sacks, and C. J. Kucharik, 2012: Interactive crop management in the Community Earth System Model (CESM1): Seasonal influences on land-atmosphere fluxes. J. Climate, 25, 4839–4859, https://doi.org/10.1175/JCLI-D-11-00446.1.

    Article  Google Scholar 

  • Li, S., T. J. Wang, B. L. Zhuang, and Y. Han, 2009: Indirect radiative forcing and climatic effect of the anthropogenic nitrate aerosol on regional climate of China. Adv. Atmos. Sci., 26(3), 543–552, https://doi.org/10.1007/s00376-009-0543-9.

    Article  Google Scholar 

  • Li, Y., J. Zhou, W. Kinzelbach, G. D. Cheng, X. Li, and W. Z. Zhao, 2013: Coupling a SVAT heat and water flow model, a stomatal-photosynthesis model and a crop growth model to simulate energy, water and carbon fluxes in an irrigated maize ecosystem. Agricultural and Forest Meteorology, 176, 10–24, https://doi.org/10.1016/j.agrformet.2013.03.004.

    Article  Google Scholar 

  • Lokupitiya, E., and Coauthors, 2009: Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands. Biogeo sciences, 6, 969–986, https://doi.org/10.5194/bg-6-969-2009.

    Article  Google Scholar 

  • Lu, Y. Q., J. M. Jin, and L. M. Kueppers, 2015: Crop growth and irrigation interact to influence surface fluxes in a regional climate-cropland model (WRF3. 3-CLM4crop). Climate Dyn., 45(11–12), 3347–3363, https://doi.org/10.1007/s00382-015-2543-z.

    Article  Google Scholar 

  • Maruyama, A., and T. Kuwagata, 2010: Coupling land surface and crop growth models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies. Agricultural and Forest Meteorology, 150, 919–930.

    Article  Google Scholar 

  • McPherson, R. A., D. J. Stensrud, and K. C. Crawford, 2004: The impact of Oklahoma’s winter wheat belt on the mesoscale environment. Mon. Wea. Rev., 132, 405–421, https://doi.org/10.1175/1520-0493(2004)132<0405:TIOOWW>2.0.CO;2.

    Article  Google Scholar 

  • Oleson, K. W., and Coauthors, 2004: Technical description of the community land model (CLM). NCAR Technical Note NCAR/TN-461+STR, 174 pp, https://doi.org/10.5065/D6N877R0.

    Google Scholar 

  • Oleson, K. W., and Coauthors, 2008: Improvements to the Community Land Model and their impact on the hydrological cycle. J. Geophys. Res., 113: G01021, https://doi.org/10.1029/2007JG000563.

    Google Scholar 

  • Osborne, T., J. Slingo, D. Lawrence, and T. Wheeler, 2009: Examining the interaction of growing crops with local climate using a coupled crop-climate model. J. Climate, 22, 1393–1411, https://doi.org/10.1175/2008JCLI2494.1.

    Article  Google Scholar 

  • Pang, X. P., J. Letey, and L. Wu, 1997: Yield and nitrogen uptake prediction by CERES-Maize model under semiarid conditions. Soil Science Society of America Journal, 61, 254–256, https://doi.org/10.2136/sssaj1997.03615995006100010035x.

    Article  Google Scholar 

  • Prince, S. D., J. Haskett, M. Steininger, H. Strand, and R. Wright, 2001: Net primary production of U. S. Midwest croplands from agricultural harvest yield data. Ecological Applications, 11, 1194–1205, https://doi.org/10.1890/1051-0761(2001)011[1194:NPPOUS]2.0.CO;2.

    Google Scholar 

  • Qin, P. H., Z. H. Xie, and X. Yuan, 2013: Incorporating groundwater dynamics and surface/subsurface runoff mechanisms in regional climate modeling over river basins in China. Adv. Atmos. Sci., 30(4), 983–996, https://doi.org/10.1007/s00376-012-2095-7.

    Article  Google Scholar 

  • Tsarouchi, G. M., W. Buytaert, and A. Mijic, 2014: Coupling a land-surface model with a crop growth model to improve ET flux estimations in the Upper Ganges basin, India. Hydrology and Earth System Sciences, 18, 4223–4238, https://doi.org/10.5194/hess-18-4223-2014.

    Article  Google Scholar 

  • Tsuji, G. Y., G. Hoogenboom, and P. K. Thornton, 1998: Understanding Options for Agricultural Production. Springer, Dordrecht, 399 pp, https://doi.org/10.1007/978-94-017-3624-4.

    Book  Google Scholar 

  • Tsvetsinskaya, E. A., L. O. Mearns, and W. E. Easterling, 2001: Investigating the effect of seasonal plant growth and development in three-dimensional atmospheric simulations. Part II: Atmospheric response to crop growth and development. J. Climate, 14, 711–729, https://doi.org/10.1175/1520-0442(2001)014<0711:ITEOSP>2.0.CO;2.

    Google Scholar 

  • Twine, T. E., C. J. Kucharik, and J. A. Foley, 2004: Effects of land cover change on the energy and water balance of the Mississippi River Basin. Journal of Hydrometeorology, 5(4), 640–655, https://doi.org/10.1175/1525-7541(2004)005<0640:EOLCCO>2.0.CO;2.

    Article  Google Scholar 

  • Van den Hoof, C., E. Hanert, and P. L. Vidale, 2011: Simulating dynamic crop growth with an adapted land surface model-JULES-SUCROS: Model development and validation. Agricultural and Forest Meteorology, 151, 137–153, https://doi.org/10.1016/j.agrformet.2010.09.011.

    Article  Google Scholar 

  • Wang, X. J., G. J. Pang, M. X. Yang, and G. N. Wan, 2016: Effects of modified soil water-heat physics on RegCM4 simulations of climate over the Tibetan Plateau. J. Geophys. Res., 121(12), 6692–6712, https://doi.org/10.1002/2015JD024407.

    Google Scholar 

  • Xie, J. B., Y. J. Zeng, M. H. Zhang, and Z. H. Xie, 2016: Detection and attribution of the influence of climate change and human activity on hydrological cycle in China’s eastern monsoon area. Climatic and Environmental Research, 21(1), 87–98, https://doi.org/10.3878/j.issn.1006-9585.2015.15097. (in Chinese with English abstract)

    Google Scholar 

  • Yao, F. M., Y. L. Xu, E. D. Lin, M. Yokozawa, and J. H. Zhang, 2007: Assessing the impacts of climate change on rice yields in the main rice areas of China. Climatic Change, 80, 395–409, https://doi.org/10.1007/s10584-006-9122-6.

    Article  Google Scholar 

  • Zhang, F. C., D. H. Wang, and B. J. Qiu, 1987: Agricultural Phenology Atlas of China. Science Press, 202 pp. (in Chinese)

    Google Scholar 

  • Zou, J., and Z. H. Xie, 2012: The effects of the land-surface process parameterization of the RegCM4 on climate simulation in East Asia. Acta Meteorologica Sinica, 70(6), 1312–1326, https://doi.org/10.11676/qxxb2012.110. (in Chinese with English abstract)

    Google Scholar 

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Acknowledgements

This study was financially supported by the National Key R&D Program of China (Grant No. 2017 YFA0603702), the National Natural Science Foundation (Grant Nos. 41705046, 41606112 and 41571019), and the Key Research and Development Program of Shandong Province of China (Grant No. 2016JMRH0538).

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Correspondence to Jing Zou.

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Article Highlights

• The CERES crop model was coupled into the climate model RegCM4/CLM3.5.

• The new coupled model RegCM4 CERES divided the crop type of CLM further into eight sub-types with different farming systems.

• RegCM4 CERES corrected the bias of the original RegCM4 over China, but the degree of correction was minimal.

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Zou, J., Xie, Z., Zhan, C. et al. Coupling of a Regional Climate Model with a Crop Development Model and Evaluation of the Coupled Model across China. Adv. Atmos. Sci. 36, 527–540 (2019). https://doi.org/10.1007/s00376-018-8160-0

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  • DOI: https://doi.org/10.1007/s00376-018-8160-0

Key words

  • model evaluation
  • model coupling
  • crop development model
  • regional climate model
  • climate modeling

关键词

  • 模型评估
  • 模型耦合
  • 作物生长模型
  • 气候模式
  • 气候模拟