Maize Drought Hazard in the Northeast Farming Region of China: Unprecedented Events in the Current Climate

Chris Kent Met Office Hadley Centre, Exeter, United Kingdom

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Edward Pope Met Office Hadley Centre, Exeter, United Kingdom

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Nick Dunstone Met Office Hadley Centre, Exeter, United Kingdom

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Adam A. Scaife Met Office Hadley Centre, Exeter, United Kingdom
College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom

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Zhan Tian Southern University of Science and Technology, Shenzhen, China

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Robin Clark Met Office Hadley Centre, Exeter, United Kingdom

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Lixia Zhang Institute of Atmospheric Physics, Beijing, China

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Jemma Davie Met Office Hadley Centre, Exeter, United Kingdom

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Kirsty Lewis Met Office Hadley Centre, Exeter, United Kingdom

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Abstract

The Northeast Farming Region (NFR) of China is a critically important area of maize cultivation accounting for ~30% of national production. It is predominantly rain fed, meaning that adverse climate conditions such as drought can significantly affect productivity. Forewarning of such events, to improve contingency planning, could therefore be highly beneficial to the agricultural sector. For this, an improved estimate of drought exposure, and the associated large-scale circulation patterns, is of critical importance. We address these important questions by employing a large ensemble of initialized climate model simulations. These simulations provide 80 times as many summers as the equivalent observational dataset and highlight several limitations of the recent observational record. For example, the chance of a drought greater in area than any current observed event is approximately 5% per year, suggesting the risk of a major drought is significantly underestimated if based solely on recent events. The combination of a weakened East Asian jet stream and intensified subpolar jet are found to be associated with severe NFR drought through enhanced upper-level convergence and anomalous descent, reducing moisture and suppressing precipitation. We identify a strong 500-hPa geopotential height anomaly dipole pattern as a useful metric to identify this mechanism for relevance to seasonal predictability. This work can inform policy planning and decision-making through an improved understanding of the near-term climate exposure and form the basis of new climate services.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JAMC-D-19-0096.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chris Kent, chris.kent@metoffice.gov.uk

Abstract

The Northeast Farming Region (NFR) of China is a critically important area of maize cultivation accounting for ~30% of national production. It is predominantly rain fed, meaning that adverse climate conditions such as drought can significantly affect productivity. Forewarning of such events, to improve contingency planning, could therefore be highly beneficial to the agricultural sector. For this, an improved estimate of drought exposure, and the associated large-scale circulation patterns, is of critical importance. We address these important questions by employing a large ensemble of initialized climate model simulations. These simulations provide 80 times as many summers as the equivalent observational dataset and highlight several limitations of the recent observational record. For example, the chance of a drought greater in area than any current observed event is approximately 5% per year, suggesting the risk of a major drought is significantly underestimated if based solely on recent events. The combination of a weakened East Asian jet stream and intensified subpolar jet are found to be associated with severe NFR drought through enhanced upper-level convergence and anomalous descent, reducing moisture and suppressing precipitation. We identify a strong 500-hPa geopotential height anomaly dipole pattern as a useful metric to identify this mechanism for relevance to seasonal predictability. This work can inform policy planning and decision-making through an improved understanding of the near-term climate exposure and form the basis of new climate services.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JAMC-D-19-0096.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chris Kent, chris.kent@metoffice.gov.uk

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  • Bett, P. E., and Coauthors, 2017: Skill and reliability of seasonal forecasts for the Chinese energy sector. J. Appl. Meteor. Climatol., 56, 30993114, https://doi.org/10.1175/JAMC-D-17-0070.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camp, J., and Coauthors, 2019: The western Pacific subtropical high and tropical cyclone landfall: Seasonal forecasts using the Met Office GloSea5 system. Quart. J. Roy. Meteor. Soc., 145, 105116, https://doi.org/10.1002/qj.3407.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cui, K., and S. P. Shoemaker, 2018: A look at food security in China. npj Sci. Food, 2, 4, https://doi.org/10.1038/s41538-018-0012-x.

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Du, T., S. Kang, X. Zhang, and J. Zhang, 2014: China’s food security is threatened by the unsustainable use of water resources in North and Northwest China. Food Energy Secur., 3, 718, https://doi.org/10.1002/fes3.40.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunstone, N., and Coauthors, 2018: Skilful seasonal predictions of Summer European rainfall. Geophys. Res. Lett., 45, 32463254, https://doi.org/10.1002/2017GL076337.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • FAO, 2018: FAOSTAT Database Collections. Food and Agriculture Organization of the United Nations, accessed 7 December 2018, http://www.fao.org/faostat/en/#data/QC.

  • Gao, Z., Z.-Z. Hu, B. Jha, S. Yang, J. Zhu, B. Shen, and R. Zhang, 2014a: Variability and predictability of Northeast China climate during 1948–2012. Climate Dyn., 43, 787804, https://doi.org/10.1007/s00382-013-1944-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, Z., Z. Z. Hu, J. Zhu, S. Yang, R. H. Zhang, Z. Xiao, and B. Jha, 2014b: Variability of summer rainfall in Northeast China and its connection with spring rainfall variability in the Huang-Huai region and Indian Ocean SST. J. Climate, 27, 70867101, https://doi.org/10.1175/JCLI-D-14-00217.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ghose, B., 2014: Food security and food self-sufficiency in China: From past to 2050. Food Energy Secur., 3, 8695, https://doi.org/10.1002/fes3.48.

  • Guo, J., H. Chen, X. Zhang, Y. Zhao, K. Mao, N. Li, and L. Zhu, 2018: A dataset of agro-meteorological disaster-affected area and grain loss in China (1949–2015). Science Data Bank, accessed 29 August 2018, https://doi.org/10.11922/sciencedb.540.

    • Crossref
    • Export Citation
  • Hao, Z., X. Yuan, Y. Xia, F. Hao, and V. P. Singh, 2017: An overview of drought monitoring and prediction systems at regional and global scales. Bull. Amer. Meteor. Soc., 98, 18791896, https://doi.org/10.1175/BAMS-D-15-00149.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hardiman, S. C., and Coauthors, 2018: The asymmetric response of Yangtze river basin summer rainfall to El Niño/La Niña. Environ. Res. Lett., 13, 024015, https://doi.org/10.1088/1748-9326/aaa172.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister, 2014: Updated high-resolution grids of monthly climatic observations—The CRU TS3.10 Dataset. Int. J. Climatol., 34, 623642, https://doi.org/10.1002/joc.3711.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawkins, E., and R. Sutton, 2009: The potential to narrow uncertainty in regional climate predictions. Bull. Amer. Meteor. Soc., 90, 10951108, https://doi.org/10.1175/2009BAMS2607.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hewitt, C., and N. Golding, 2018: Development and pull-through of climate science to services in China. Adv. Atmos. Sci., 35, 905908, https://doi.org/10.1007/s00376-018-7255-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hewitt, C., S. Mason, and D. Walland, 2012: The global framework for climate services. Nat. Climate Change, 2, 831832, https://doi.org/10.1038/nclimate1745.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, X., and R. Lu, 2016: The meridional displacement of the summer Asian jet, Silk Road pattern, and tropical SST anomalies. J. Climate, 29, 37533766, https://doi.org/10.1175/JCLI-D-15-0541.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, D. Q., J. Zhu, Y. C. Zhang, and A. N. Huang, 2014: The different configurations of the East Asian polar front jet and subtropical jet and the associated rainfall anomalies over eastern China in summer. J. Climate, 27, 82058220, https://doi.org/10.1175/JCLI-D-14-00067.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kent, C., E. Pope, V. Thompson, K. Lewis, A. A. Scaife, and N. Dunstone, 2017: Using climate model simulations to assess the current climate risk to maize production. Environ. Res. Lett., 12, 054012, https://doi.org/10.1088/1748-9326/aa6cb9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutti, R., R. Furrer, C. Tebaldi, J. Cermak, and G. A. Meehl, 2010: Challenges in combining projections from multiple climate models. J. Climate, 23, 27392758, https://doi.org/10.1175/2009JCLI3361.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kobayashi, S., and Coauthors, 2015: The JRA-55 Reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 548, https://doi.org/10.2151/jmsj.2015-001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, C., and Coauthors, 2016: Skillful seasonal prediction of Yangtze River valley summer rainfall. Environ. Res. Lett., 11, 094002, https://doi.org/10.1088/1748-9326/11/9/094002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, C., R. Lu, P. E. Bett, A. A. Scaife, and N. Martin, 2018: Skillful seasonal forecasts of summer surface air temperature in western China by Global Seasonal Forecast System version 5. Adv. Atmos. Sci., 35, 955964, https://doi.org/10.1007/s00376-018-7291-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, X., C. Li, R. Lu, and A. A. Scaife, 2018: Predictable and unpredictable components of the summer East Asia–Pacific teleconnection pattern. Adv. Atmos. Sci., 35, 13721380, https://doi.org/10.1007/s00376-018-7305-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Z., and R. Lu, 2005: Interannual meridional displacement of the East Asian upper-tropospheric jet stream in summer. Adv. Atmos. Sci., 22, 199, https://doi.org/10.1007/BF02918509.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, B., and Coauthors, 2018: An extreme negative Indian Ocean dipole event in 2016: dynamics and predictability. Climate Dyn., 51, 89100, https://doi.org/10.1007/s00382-017-3908-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, R., 2002: Indices of the summertime western North Pacific subtropical high. Adv. Atmos. Sci., 19, 10041028, https://doi.org/10.1007/s00376-002-0061-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martin, G. M., A. Chevuturi, R. E. Comer, N. J. Dunstone, A. A. Scaife, and D. Zhang, 2019: Predictability of South China Sea summer monsoon onset. Adv. Atmos. Sci., 36, 253260, https://doi.org/10.1007/s00376-018-8100-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration to time scales. Proc. Eighth Conf. on Applied Climatology, Anaheim, CA, Amer. Meteor. Soc., 179–184.

  • Meng, E. C. H., R. Hu, X. Shi, and S. Zhang, 2006: Maize in China: Production systems, constraints, and research priorities. International Maize and Wheat Improvement Center (CIMMYT) Rep., 77 pp., https://core.ac.uk/download/pdf/7052615.pdf.

  • Monfreda, C., N. Ramankutty, and J. A. Foley, 2008: Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochem. Cycles, 22, GB1022, https://doi.org/10.1029/2007GB002947.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NBSC, 2018: Annual data by province. National Bureau of Statistics of China, accessed 1 June 2018, http://www.stats.gov.cn/english/.

  • Qian, W., X. Shan, D. Chen, C. Zhu, and Y. Zhu, 2012: Droughts near the northern fringe of the East Asian summer monsoon in China during 1470–2003. Climatic Change, 110, 373383, https://doi.org/10.1007/s10584-011-0096-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qin, D., J. Zhang, C. Shan, and L. Song, 2015: China National Assessment Report on Risk Management and Adaptation of Climate Extremes and Disasters. Refined ed. Science Press, 123 pp.

  • Schneider, U., A. Becker, P. Finger, A. Meyer-Christoffer, B. Rudolf, and M. Ziese, 2011: GPCC Full Data Reanalysis version 6.0 at 0.5°: Monthly land-surface precipitation from rain-gauges built on GTS-based and historic data. GPCC, https://doi.org/10.5676/DWD_GPCC/FD_M_V7_050.

    • Crossref
    • Export Citation
  • Shen, B., Z. Lin, R. Lu, and Y. Lian, 2011: Circulation anomalies associated with interannual variation of early- and late-summer precipitation in Northeast China. Sci. China Earth Sci., 54, 10951104, https://doi.org/10.1007/s11430-011-4173-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shen, C., W. C. Wang, Z. Hao, and W. Gong, 2007: Exceptional drought events over eastern China during the last five centuries. Climatic Change, 85, 453471, https://doi.org/10.1007/s10584-007-9283-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, L., B. Shen, B. Sui, and B. Huang, 2017: The influences of East Asian monsoon on summer precipitation in Northeast China. Climate Dyn., 48, 16471659, https://doi.org/10.1007/s00382-016-3165-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, V., N. J. Dunstone, A. A. Scaife, D. M. Smith, J. M. Slingo, S. Brown, and S. E. Belcher, 2017: High risk of unprecedented UK rainfall in the current climate. Nat. Commun., 8, 107, https://doi.org/10.1038/s41467-017-00275-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, V., N. J. Dunstone, A. A. Scaife, D. M. Smith, S. C. Hardiman, H. L. Ren, B. Lu, and S. E. Belcher, 2019: Risk and dynamics of unprecedented hot months in South East China. Climate Dyn., 52, 2585259, https://doi.org/10.1007/s00382-018-4281-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., A. Dai, G. van der Schrier, P. D. Jones, J. Barichivich, K. R. Briffa, and J. Sheffield, 2014: Global warming and changes in drought. Nat. Climate Change, 4, 1722, https://doi.org/10.1038/nclimate2067.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vicente-Serrano, S. M., S. Beguería, and J. I. López-Moreno, 2010: A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Climate, 23, 16961718, https://doi.org/10.1175/2009JCLI2909.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weedon, G. P., G. Balsamo, N. Bellouin, S. Gomes, M. J. Best, and P. Viterbo, 2014: The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resour. Res., 50, 75057514, https://doi.org/10.1002/2014WR015638.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, K. D., and Coauthors, 2015: The Met Office Global Coupled Model 2.0 (GC2) configuration. Geosci. Model Dev., 8, 15091524, https://doi.org/10.5194/gmd-8-1509-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • World Meteorological Organization, 2012: Standardized precipitation index user guide. WMO Doc. 1090, 24 pp., https://library.wmo.int/doc_num.php?explnum_id=7768.

  • Xie, N., J. Xin, and S. Liu, 2014: China’s regional meteorological disaster loss analysis and evaluation based on grey cluster model. Nat. Hazards, 71, 10671089, https://doi.org/10.1007/s11069-013-0662-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, X., Q. Ge, J. Zheng, E. Dai, X. Zhang, S. He, and G. Liu, 2013: Agricultural drought risk analysis based on three main crops in prefecture-level cities in the monsoon region of east China. Nat. Hazards, 66, 12571272, https://doi.org/10.1007/s11069-012-0549-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yan, P., D. Huang, J. Zhu, X. Kuang, and Y. Huang, 2019: The decadal shift of the long persistent rainfall over the northern part of China and the associated ocean conditions. Int. J. Climatol., 39, 30433056, https://doi.org/10.1002/joc.6001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yatagai, A., K. Kamiguchi, O. Arakawa, A. Hamada, N. Yasutomi, and A. Kitoh, 2012: APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull. Amer. Meteor. Soc., 93, 14011415, https://doi.org/10.1175/BAMS-D-11-00122.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., 2004: Risk assessment of drought disaster in the maize-growing region of Songliao Plain, China. Agric. Ecosyst. Environ., 102, 133153, https://doi.org/10.1016/j.agee.2003.08.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., Z. Zhang, and F. Tao, 2017: Performance of temperature-related weather index for agricultural insurance of three main crops in China. Int. J. Disaster Risk Sci., 8, 7890, https://doi.org/10.1007/s13753-017-0115-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, J., J. Guo, Y. Xu, and J. Mu, 2015: Effects of climate change on cultivation patterns of spring maize and its climatic suitability in Northeast China. Agric. Ecosyst. Environ., 202, 178187, https://doi.org/10.1016/j.agee.2015.01.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, J., J. Zhou, L. Yang, W. Hou, and G. Feng, 2018: Inter-annual and inter-decadal variability of early- and late-summer precipitation over northeast China and their background circulation. Int. J. Climatol., 38, 28802888, https://doi.org/10.1002/joc.5470.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, T., B. Wu, and L. Dong, 2014: Advances in research of ENSO changes and the associated impacts on Asian-Pacific climate. Asia-Pac. J. Atmos. Sci., 50, 405422, https://doi.org/10.1007/s13143-014-0043-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
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