한국농림기상학회지 (Korean Journal of Agricultural and Forest Meteorology)

  • 제24권4호
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  • Pages.218-233
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  • 2022
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  • 1229-5671(pISSN)
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  • 2288-1859(eISSN)
한국농림기상학회 (Korean Society of Agricultural and Forest Meteorology)
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PNU CGCM-WRF Chain을 이용한 우리나라 콩의 고온해 및 저온해에 대한 예측성 검증

Evaluating the Predictability of Heat and Cold Damages of Soybean in South Korea using PNU CGCM -WRF Chain

  • 최명주 (부산대학교 BK21 지구환경시스템 교육연구단 대기환경과학과) ;
  • 안중배 (부산대학교 대기환경과학과) ;
  • 김영현 (부산대학교 대기환경과학과) ;
  • 정민경 (부산대학교 BK21 지구환경시스템 교육연구단 대기환경과학과) ;
  • 심교문 (국립농업과학원 기후변화평가과) ;
  • 허지나 (국립농업과학원 기후변화평가과) ;
  • 조세라 (국립농업과학원 기후변화평가과)
  • Myeong-Ju, Choi (Department of Atmospheric Sciences, BK21 School of Earth and Environmental Systems, Pusan National University) ;
  • Joong-Bae, Ahn (Department of Atmospheric Sciences, Pusan National University) ;
  • Young-Hyun, Kim (Department of Atmospheric Sciences, Pusan National University) ;
  • Min-Kyung, Jung (Department of Atmospheric Sciences, BK21 School of Earth and Environmental Systems, Pusan National University) ;
  • Kyo-Moon, Shim (Climate Change Assessment Division, National Institute of Agricultural Sciences) ;
  • Jina, Hur (Climate Change Assessment Division, National Institute of Agricultural Sciences) ;
  • Sera, Jo (Climate Change Assessment Division, National Institute of Agricultural Sciences)
  • 투고 : 2022.09.14
  • 심사 : 2022.11.17
  • 발행 : 2022.12.30
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초록

본 연구에서는 Pusan National University Coupled General Circulation Model-Weather Research and Forecasting (PNU CGCM-WRF)에서 생산된 hindcast 자료(1986~2020)를 이용하여 우리나라의 주요 곡물 중 하나인 콩의 생육단계별 고온해 및 저온해 발생일수의 예측성을 평가하였다. 예측성을 평가하는 방법으로는 Normalized Standard Deviations (NSD), Root Mean Square Error (RMSE), Hit Rate (HR), Heidke Skill Score (HSS)이다. 이를 위해 먼저 콩의 고온해 및 저온해를 정의하는 변수인 일 최고기온(Tmax) 및 일 최저기온(Tmin)의 모의성능을 검증하였다. 그 결과 1~5월(01RUN~05RUN)의 초기조건을 가지고 시작하는 월에 따라 다소 차이가 있지만, Variance Scaling 방법을 적용하여 보정한 결과가 보정전보다 관측과 유사하게 나타났으며, 보정한 3~10월의 Tmax 및 Tmin에 대한 모의성능은 전반적으로 01RUN~05RUN에 Simple Composite Method (SCM)을 적용하여 평균한 결과(ENS)에서 높게 나타났다. 또한, 콩의 생육시기별 고온해 및 저온해 발생일수의 지역적 패턴과 특성을 관측과 비교하였을 때 모형이 잘 모의하고 있다. ENS에서 콩의 고온해(저온해)에 대한 HR과 HSS는 생육시기 별로 0.45~0.75, 0.02~0.10(0.49~0.76, -0.04~0.11)의 범위를 가진다. 결론적으로, PNU CGCM-WRF chain의 01RUN~05RUN 및 ENS는 우리나라 콩의 생육시기별 고온해 및 저온해를 예측할 수 있는 성능을 가지고 있다.

The long-term (1986~2020) predictability of the number of days of heat and cold damages for each growth stage of soybean is evaluated using the daily maximum and minimum temperature (Tmax and Tmin) data produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF). The Predictability evaluation methods for the number of days of damages are Normalized Standard Deviations (NSD), Root Mean Square Error (RMSE), Hit Rate (HR), and Heidke Skill Score (HSS). First, we verified the simulation performance of the Tmax and Tmin, which are the variables that define the heat and cold damages of soybean. As a result, although there are some differences depending on the month starting with initial conditions from January (01RUN) to May (05RUN), the result after a systematic bias correction by the Variance Scaling method is similar to the observation compared to the bias-uncorrected one. The simulation performance for correction Tmax and Tmin from March to October is overall high in the results (ENS) averaged by applying the Simple Composite Method (SCM) from 01RUN to 05RUN. In addition, the model well simulates the regional patterns and characteristics of the number of days of heat and cold damages by according to the growth stages of soybean, compared with observations. In ENS, HR and HSS for heat damage (cold damage) of soybean have ranged from 0.45~0.75, 0.02~0.10 (0.49~0.76, -0.04~0.11) during each growth stage. In conclusion, 01RUN~05RUN and ENS of PNU CGCM-WRF Chain have the reasonable performance to predict heat and cold damages for each growth stage of soybean in South Korea.

키워드

과제정보

본 연구는 농촌진흥청 연구사업(세부과제번호: PJ01489102)의 지원으로 수행되었습니다.

참고문헌

  1. Ahn, J.-B., and J.-A. Lee, 2001: Numerical study on the role of sea-ice using ocean general circulation model. The Sea: Journal of the Korean Society of Oceanography 6(4), 225-233. (in Korean with English abstract)
  2. Ahn, J.-B., and J.-L. Lee, 2015: Comparative Study on the Seasonal Predictability Dependency of Boreal Winter 2m Temperature and Sea Surface Temperature on CGCM Initial Conditions. Atmosphere 25(2), 353-366. doi:10.14191/Atmos.2015.25.2.353 (in Korean with English abstract)
  3. Ahn, J.-B., and J.-L. Lee, and S. R. Jo, 2018a: Evaluation of PNU CGCM Ensemble Forecast System for Boreal Winter Temperature over South Korea. Atmosphere 28(4), 509-520. doi:10.14191/Atmos.2018.28.4.509 (in Korean with English abstract)
  4. Ahn, J.-B., K.-M. Shim, M.-P. Jung, H.-G. Jeong, Y.-H. Kim, and E.-S. Kim, 2018b: Predictability of Temperature over South Korea in PNU CGCM and WRF Hindcast. Atmosphere 28(4), 479-490. doi: 10.14191/Atmos.2018.28.4.479 (in Korean with English abstract)
  5. Ahn, J.-B., S. R. Jo, M.-S. Suh, D.-H. Cha, D.-K. Lee, S.-Y. Hong, S.-K. Min, S.-C. Park, H.-S. Kang, and K.-M. Shim, 2016: Changes of Precipitation Extremes over South Korea Projected by the 5 RCMs under RCP Scenarios. Asia-Pacific Journal of Atmospheric Sciences 52(2), 223-236. doi: 10.1007/s13143-016-0021-0
  6. Bonan, G. B., 1998: The land surface climatology of the NCAR land surface model coupled to the NCAR community climate model. Journal of Climate 11(6), 1307-1326. https://doi.org/10.1175/1520-0442(1998)011<1307:TLSCOT>2.0.CO;2
  7. Chen, F., and J. Dudhia, 2001: Coupling an Advanced Land Surface-Hydrology Model with the Penn State-NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity. Monthly Weather Review 129(4), 569-585. doi:10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2
  8. Chung, S. O., 2010: Simulating evapotranspiration and yield responses of rice to climate change using FAO-AquaCrop. Journal of the Korean Society of Agricultural Engineers 52(3), 57-64. doi:10.5389/KSAE.2010.52.3.057 (in Korean with English abstract)
  9. Dudhia, J., 1989: Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model. Journal of Atmospheric Sciences 46(20), 3077-3107. doi:10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2
  10. Gibson, L. R., and R. E. Mullen, 1996: Influence of day and night temperature on soybean seed yield. Crop Science 36(1), 98-104. doi:10.2135/cropsci1996.0011183X003600010018x
  11. Hong, S.-Y., J. Dudhia, and S.-H. Chen, 2004: A Revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Monthly Weather Review 132(1), 103-120. doi:10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2
  12. Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly Weather Review 134(9), 2318-2341. doi:10.1175/MWR3199.1
  13. Hunke, E. C., and J. K. Dukowicz, 1997: An Elastic-Viscous-Plastic model for sea ice dynamics. Journal of Physical Oceanography 27(9), 1849-1867. doi:10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2
  14. Hur, J. N., J. H. Park, K. M. Shim, Y. S. Kim, and S. R. Jo, 2020: A High-Resolution Agro-Climatic Dataset for Assessment of Climate Change over South Korea. Korean Journal of Agricultural and Forest Meteorology 22(3), 128-134. (in Korean with English abstract)
  15. Jang, S.-H., C.-S. Ryu, Y.-S. Kang, J.-W. Park, T.-Y. Kim, K.-S. Kang, M.-J. Park, H.-C. Baek, Y.-H. Park, D.-W. Kang, K. Zou, M.-C. Kim, Y.-J. Kwon, S.-A. Han, and T.-H. Jun, 2021: Estimation of Fresh Weight and Leaf Area Index of Soybean (Glycine max) Using Multi-year Spectral Data. Korean Journal of Agricultural and Forest Meteorology 23(4), 329-339. doi:10.5532/KJAFM.2019.21.4.327 (in Korean with English abstract)
  16. Kain, J. S., 2004: The Kain-Fritsch convective parameterization: An update. Journal of applied meteorology 43(1), 170-181. doi:10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2
  17. Kang, S.-T., J.-S. Lee, I.-Y. Baek, W.-Y. Han, D.-C. Shin, and M.-G. Choung, 2014: Effect of Rainfall in Harvest Period on Seed Quality in Soybean. Korea Soybean Digest 31(1), 1-10. (in Korean with English abstract)
  18. Kiehl, J. T., J. J. Hack, G. B. Bonan, B. A. Boville, B. P. Briegleb, D. L. Williamson, and P. J. Rasch, 1996: Description of the NCAR Community Climate Model (CCM3). NCAR Technical note, NCAR/TN-420+STR,NCAR/TN-420+STR, 152 pp. doi:10.5065/D6FF3Q99
  19. Kim, C.-K., H.-K. Jeong, S.-H. Han, J.-S. Kim, and D.-H. Moon, 2013: Impacts and countermeasures of climate change on food supply in Korea. Korea Rural Economic Institute, 176pp. [Available online at https://repository.krei.re.kr/handle/2018.oak/21015]
  20. Kim, D.-J., and J.-H. Kim, 2018: An Outlook of Changes in the Flowering Dates and Low Temperature after Flowering under the RCP8.5 Projected Climate Condition. Korean Journal of Agricultural and Forest Meteorology 20(4), 313-320. doi:10.5532/KJAFM.2018.20.4.313 (in Korean with English abstract)
  21. Kim, M.-R., 2011: The Status of Korea's Rice Industry and the Rice Processing Industry. Food Industry and Nutrition 16(1), 22-26. (in Korean)
  22. Kim, S.-M., and S.-M. Kim, 2013: Agricultural production base disaster vulnerability assessment due to meteorological disasterst. Magazine of the Korean Society of Agricultural Engineers 55(1), 40-46. (in Korean)
  23. Kim, S.-Y., I.-H. Heo, and S.-H. Lee, 2010: Impacts of Temperature Rising on Changing of Cultivation Area of Apple in Korea. Journal of the Korean association of regional geographers 16(3), 201-215. (in Korean with English abstract)
  24. Kim, Y.-H., E.-S. Kim, M.-J. Choi, K.-M. Shim, and J.-B. Ahn, 2019: Evaluation of Long-Term Seasonal Predictability of Heatwave over South Korea Using PNU CGCM-WRF Chain. Atmosphere 29(5), 671-687. doi:10.14191/Atmos.2019.29.5.671 (in Korean with English abstract)
  25. Kim, Y.-H., M.-J. Choi, K.-M. Shim, J. N. Hur, S. R. Jo, and J.-B. Ahn, 2021: A Study on the Predictability of the Number of Days of Heat and Cold Damages by Growth Stages of Rice Using PNU CGCM-WRF Chain in South Korea. Atmosphere 31(5), 577-592. doi:10.14191/Atmos.2021.31.5.577 (in Korean with English abstract)
  26. KMA (Korea Meteorological Administration), 2021: Annual report for 2018 extreme climate. Korea Meteorological Administration, 198pp. (in Korean)
  27. Lee, B.-U., 2009: The global food crisis and the importance of crop protection. Life and Agrochemicals 30(1), 20-23. (in Korean)
  28. Lee, B.-U., 2012: Impacts and responses of climate change on global agriculture. KOREA RURAL ECONOMIC INSTITUTE (KREI) research reports 146, 79-94.
  29. Lee, D. H., S.-K. Min, J. H. Jin, J.-W. Lee, D.-H. Cha, M.-S. Suh, J.-B. Ahn, S.-Y. Hong, H.-S. Kang, and M. S. Joh, 2017: Thermodynamic and dynamic contributions to future changes in summer precipitation over Northeast Asia and Korea: a multi-RCM study. Climate Dynamics 49(11), 4121-4139. https://doi.org/10.1007/s00382-017-3566-4
  30. Lee, J.-W., Y. J. Jang, K.-K. Ko, and J.-K. Park, 2013: Effects of Meteorological Elements in the Production of Food Crops: Focused on Regression Analysis using Panel Data. Journal of Environmental Science International 22(9), 1171-1180. doi:10.5322/JESI.2013.22.9.1171 (in Korean with English abstract)
  31. Lee, K.-M., H.-J. Baek, S.-H. Park, H.-S. Kang, and C.-H. Cho, 2012: Future projection of changes in extreme temperatures using high resolution regional climate change scenario in the Republic of Korea. Journal of the Korean Geographical Society 47(2), 208-225. (in Korean with English abstract)
  32. Lee, S. H., I. H. Heo, K. M. Lee, S. Y. Kim, Y. S. Lee, and W. T. Kwon, 2008: Impacts of climate change on phonology and growth of crops: In the case of Naju. Journal of the Korean Geographical Society 43(1), 20-35. (in Korean with English abstract)
  33. Lee, Y.-H., W.-G. Sang, J.-I. Cho, and M.-C. Seo, 2019: Effects of high temperature on soybean physiology, protein and oil content, and yield. Korean Journal of Crop Science 64(4), 395-405. doi:10.7740/kjcs.2019.64.4.395 (in Korean with English abstract)
  34. Lesk, C., P. Rowhani, and N. Ramankutty, 2016: Influence of extreme weather disasters on global crop production. Nature 529(7584), 84-87. doi:10.1038/nature16467
  35. Lim, M. S., K. Y. Shin, J. G. Woo, Y. S. Kwon, S. W. Jang, W. B. Kim, J. N. Lee, J. T. Lee, H. J. Kwon, J. T. Seo, J. H. Ahn, Y. G. Kang, Y. I. Ham, M. Kwon, and K. R. Ryu, 2000: Vegetable cultivation technique in highland area. Kwahakwonyae press, Seoul, 52-56.
  36. Min, S.-K., S. Legutke, A. Hense, U. Cubasch, W.-T. Kwon, J.-H. Oh, and U. Schlese, 2006: East Asian climate change in the 21st century as simulated by the coupled climate model ECHO-G under IPCC SRES scenarios. Journal of the Meteorological Society of Japan. Ser. II 84(1), 1-26. doi:10.2151/jmsj.84.1
  37. Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research: Atmospheres 102(D14), 16663-16682. doi:10.1029/97JD00237
  38. Myeong, S. J., 2018: Impact of Climate Change Related Natural Disasters on Rice Production in South Korea. Journal of the Korean Society of Hazard Mitigation 18(7), 53-60. doi:10.9798/KOSHAM.2018.18.7.53 (in Korean with English Abstract)
  39. NIMS (National Institute of Meteorological Science), 2012: Global Climate Change Report 2012 for Response to "IPCC Fifth Assessment Report", 59-89. (in Korean)
  40. Pacanowski, R. C., and S. M. Griffies, cited 1998: MOM 3.0 manual. NOAA/GFDL [Available online at http://www.gfdl.noaa.gov/ocean-model]
  41. Park, C. Y., D.-H. Cha, G. Y. Kim, G. Lee, D.-K. Lee, M.-S. Suh, S.-Y. Hong, J.-B. Ahn, and S.-K. Min, 2020: Evaluation of summer precipitation over Far East Asia and South Korea simulated by multiple regional climate models. International Journal of Climatology 40(4), 2270-2284.
  42. Park, C. Y., S.-K. Min, D. H. Lee, D.-H. Cha, M.-S. Suh, H.-S. Kang, S.-Y. Hong, D.-K. Lee, H.-J. Baek, K.-O. Boo, and W.-T. Kwon, 2016: Evaluation of multiple regional climate models for summer climate extremes over East Asia. Climate Dynamics 46(7), 2469-2486.
  43. Park, J. H., S. K. Kim, Y. S. Shin, M. I. Ahn, and Y. K. Han, 2014: Implementation of a Weather Hazard Warning System at a Catchment Scale. Korean Journal of Agricultural and Forest Meteorology 16(4), 389-395. doi:10.5532/KJAFM.2014.16.4.389 (in Korean with English abstract)
  44. Park, K. Y., 2015: Soybean cultivation technology : Overcoming disasters by disseminating new soybean raising seeding technology. - The planting time can be adjusted according to the weather conditions, and there is no need to be decapitation during the growing season -, Understanding the growth characteristics of soybeans. Korea Soybean Society 326, 5-6.
  45. Park, P.-S., 2008: The status of Korea's rice industry and the rice processing industry. Food preservation and processing industry 7(2), 3-19.
  46. Park, S. H., J. S. Lee, M, H. Seo, and J. S. Lee, 2002: Radish Cultivation. Rural Development Administration, Suwon, 39-40.
  47. Paulson, C. A., 1970: The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. Journal of Applied Meteorology and Climatology 9(6), 857-861. doi:10.1175/1520-0450(1970)009<0857:TMROWS>2.0.CO;2
  48. RDA (Rural Development Administration), 2016: A Research on Agro-meteorological Disaster Hazards Mapping. National Academy of Agricultural Science. (in Korean)
  49. RDA (Rural Development Administration), 2018a: Final report of Development of production prospects and risk assessment evaluation technology for decreasing climate disaster on upland crops. National Academy of Agricultural Science. (in Korean)
  50. RDA (Rural Development Administration), 2018b: Technical support datas for reduce of heatwave and drought damage for food crops. National Academy of Agricultural Science. (in Korean)
  51. Seo, H.-C., S.-K. Kim, Y.-S. Lee, and Y.-C. Cho, 2006: Geographical Shift of Quality Soybean Production Area in Northern Gyeonggi Province by Year 2100. Korean Journal of Agricultural and Forest Meteorology 8(4), 242-249. (in Korean with English abstract)
  52. Shim, K.-M., J.-T. Lee, Y.-S. Lee, and G.-Y. Kim, 2003: Traits of Agro-meteorological Disasters in 20th Century Korea. Korean Journal of Agricultural and Forest Meteorology 5(4), 255-260. (in Korean with English abstract)
  53. Shim, K.-M., Y.-S. Kim, M.-P. Jung, I.-T. Choi, and S.-H. Min, 2014: Agro-climatic Zonal Characteristics of the Frequency of Abnormal Duration of Sunshine in South Korea. Korean Journal of Agricultural and Forest Meteorology 16(1), 83-91. doi:10.5532/KJAFM.2014.16.1.83 (in Korean with English abstract)
  54. Shim, K.-M., Y.-S. Kim, M.-P. Jung, J. W. Kim, M. S. Park, S. H. Hong, and K. K. Kang, 2018: Recent Changes in the Frequency of Occurrence of Extreme Weather Events in South Korea. Journal of Climate Change Research 9(4), 461-470. doi:10.15531/KSCCR.2018.9.4.461 (in Korean with English abstract)
  55. Sin, Y.-H., 2013: Research on global food security and climate change in agriculture. Water for future 46(7), 66-71.
  56. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.-Y. Huang, W. Wang, and J. G. Powers, 2008: A description of the advanced research WRF version 3. NCAR Technical note-475+STR, 125pp.
  57. Son, I.-C., K.-H. Moon, E. Y. Song, S. J. Oh, H. H. Seo, and J. Y. Yang, 2015: Effects of differentiated temperature based on growing season temperature on growth and physiological response in Chinese cabbage 'Chunkwang'. Korean Korean Journal of Agricultural and Forest Meteorology 17(3), 254-260. doi:10.5532/KJAFM.2015.17.3.254 (in Korean with English abstract)
  58. Sun, J., and J. B. Ahn, 2011: A GCM-Based Forecasting Model for the Landfall of Tropical Cyclones in China. Advances in atmospheric sciences 28(5), 1049-1055. doi:10.1007/s00376-011-0122-8
  59. Sun, J., and J. B. Ahn, 2015: Dynamical Seasonal Predictability of the Arctic Oscillation Using a CGCM. International Journal of Climatology 35(7), 1342-1353. doi:10.1002/joc.4060
  60. Teutschbein, C., and J. Seibert, 2012: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of hydrology 456, 12-29. https://doi.org/10.1016/j.jhydrol.2012.05.052
  61. Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467pp. 
  62. Yun, J. I., 1999: Agricultural Meteorology. Arche Publishing House. 15-21. (in Korean)