한국농림기상학회지 (Korean Journal of Agricultural and Forest Meteorology)

  • 제26권3호
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  • Pages.209-218
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  • 2024
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  • 1229-5671(pISSN)
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  • 2288-1859(eISSN)
한국농림기상학회 (Korean Society of Agricultural and Forest Meteorology)
권호 표지
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PNU/RDA 전지구-한반도 앙상블 장기기후 예측자료 소개 및 평가

Introduction and Evaluation of the Pusan National University/Rural Development Administration Global-Korea Ensemble Long-range Climate Forecast Data

  • 조세라 (국립농업과학원 기후변화평가과) ;
  • 이준리 (울산과학기술원 지구환경도시건설공학과) ;
  • 김응섭 (국립농업과학원 기후변화평가과) ;
  • 안중배 (부산대학교 대기환경과학과) ;
  • 허지나 (국립농업과학원 기후변화평가과) ;
  • 김용석 (국립농업과학원 기후변화평가과) ;
  • 심교문 (국립농업과학원 기후변화평가과)
  • Sera Jo (Climate Change Assessment Division, National Institute of Agricultural Sciences) ;
  • Joonlee Lee (Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology) ;
  • Eung-Sup Kim (Climate Change Assessment Division, National Institute of Agricultural Sciences) ;
  • Joong-Bae Ahn (Department of Atmospheric Sciences, Pusan National University) ;
  • Jina Hur (Climate Change Assessment Division, National Institute of Agricultural Sciences) ;
  • Yongseok Kim (Climate Change Assessment Division, National Institute of Agricultural Sciences) ;
  • Kyo-Moon Shim (Climate Change Assessment Division, National Institute of Agricultural Sciences)
  • 투고 : 2024.09.13
  • 심사 : 2024.09.30
  • 발행 : 2024.09.30
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초록

농촌진흥청 국립농업과학원은 공동연구를 통해 개발한 Pusan National University/Rural Development Administration (PNU/RDA) 전지구-한반도 앙상블 장기예측시스템을 운영 중이다. 이 시스템은 1~6개월의 미래 상세기후예측자료를 생산한다. 일최고, 일최저, 일평균기온, 강수량 등 20종의 변수로 구성되어 있으며, 농업예측 분야에서 필요로 하는 일사량, 토양수분, 지중온도 등과 같은 농업기상 변수를 포함한다. 시간해상도는 일단위이며, 공간해상도는 5km 간격의 격자형태로, 지점형태로 값을 추출(내삽)하거나 행정구역 평균하여 활용이 가능하다. 최종 생산된 상세기후예측자료의 계절별 평년 기온 및 강수분포를 살펴봤을 때, 평년값을 관측과 비슷한 값으로 나타냈으며 공간적 분포 또한 상세한 지형적 효과를 반영하여 관측과 유사하게 모의하여 신뢰성을 입증하였다. 따라서 국립농업과학원의 장기(1~6개월) 상세기후예측 자료는 농업 전망 및 계획 수립에 유용한 자료로 활용될 수 있을 것으로 기대된다. 이러한 상세기후예측자료는 국립농업과학원 기후변화평가과를 통해 제공받을 수 있다.

The National Institute of Agricultural Sciences (NAS) operates in-house long-range climate forecasting system to support the agricultural use of climate forecast data. This system, developed through collaborative research with Pusan National University, is based on the PNU/RDA Coupled General Circulation Model (CGCM) and includes the regional climate model WRF (Weather Research and Forecasting). It generates detailed climate forecast data for periods ranging from 1 to 6 months, covering 20 key variables such as daily maximum, minimum, and average temperatures, precipitation, and agricultural meteorological elements like solar radiation, soil moisture, and ground temperature-factors essential for agricultural forecasting. The data are provided at a daily temporal resolution with a spatial resolution of a 5km grid, which can be used in point form (interpolated) or averaged across administrative regions. The system's seasonal temperature and precipitation forecasts align closely with observed climatological data, accurately reflecting spatial and topographical influences, confirming its reliability. These long-range forecasts from NAS are expected to offer valuable insights for agricultural planning and decision-making. The detailed forecast data can be accessed through the Climate Change Assessment Division of NAS.

키워드

과제정보

본 연구는 농촌진흥청 "신농업기후변화대응체계구축사업(과제번호: RS-2024-00400632)"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 H. J. Kim, 2014: Improvement of 1-month lead predictability of the wintertime AO using a realistically varying solar constant for a CGCM. Meteorological Applications 21(2), 415-418. doi:10.1002/met.1372.
  3. Ahn, J. B., and J. 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)
  4. Ahn, J. B., K. M. Shim, M. P. Jung, H. G. Jeong, Y. H. Kim, and E. S. Kim, 2018a: 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., J. Lee, and S. Jo, 2018b: 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).
  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. doi:10.1175/1520-0442(1998)011<1307:TLSCOT>2.0.CO;2.
  7. Cannon, A. J., S. R. Sobie, and T. Q. Murdock, 2015: Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes?. Journal of Climate 28(17), 6938-6959. doi:10.1175/JCLI-D-14-00754.1.
  8. Choi, M. J., J. B. Ahn, Y. H. Kim, M. K. Jung, K. M. Shim, J. Hur, and S. Jo, 2022: Evaluating the Predictability of Heat and Cold Damages of Soybean in South Korea using PNU CGCM-WRF Chain. Korean Journal of Agricultural and Forest Meteorology 24(4), 218-233. doi:10.5532/KJAFM.2022.24.4.218. (in Korean with English Abstract).
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. Hur, J., J. H. Park, K. M. Shim, Y. S. Kim, and S. 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. doi:10.5532/KJAFM.2020.22.3.12. (in Korean with English Abstract)
  15. Hur, J., Y. S. Kim, S. Jo, K. M. Shim, J. B. Ahn, M. J. Choi, Y. H. Kim, M. Kang, and W. J. Choi, 2021: Estimation of Waxy Corn Harvest Date over South Korea Using PNU CGCM-WRF Chain. Korean Journal of Agricultural and Forest Meteorology 23(4), 405-414. doi:10.5532/KJAFM.2021.23.4.405. (in Korean with English Abstract).
  16. Hyun, Y. K., J. Lee, B. Shin, Y. Choi, J. Y. Kim, S. M. Lee, H. S. Ji, K. O. Boo, S. Lim, H. Kim, Y. Ryu, Y. H. Park, H. S. Park, S. H. Hyun, and S. O. Hwang, 2022: The KMA Global Seasonal forecasting system (GloSea6) - Part 2: Climatological Mean Bias Characteristics. Atmosphere 32(2), 87-101. doi:10.14191/ATMOS.2022.32.2.087. (in Korean with English Abstract)
  17. IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2391 pp. doi:10.1017/9781009157896.
  18. Jo, S., K. M. Shim, J. Hur, Y. S. Kim, and J. B. Ahn, 2020: Future changes of agro-climate and heat extremes over S. Korea at 2 and 3℃ global warming levels with CORDEX-EA phase 2 projection. Atmosphere 11(12), 1336. doi:10.3390/atmos11121336.
  19. Jo, S., J. L. Lee, K. M. Shim, J. B. Ahn, J. Hur, Y. S. Kim, W. J. Choi, and M. Kang, 2022: The Advanced Bias Correction Method based on Quantile Mapping for Long-Range Ensemble Climate Prediction for Improved Applicability in the Agriculture Field. Korean Journal of Agricultural and Forest Meteorology 24(3), 155-163. doi:10.5532/KJAFM.2022.24.3.155. (in Korean with English Abstract)
  20. 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.
  21. 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, 152 pp. doi:10.5065/D6FF3Q99.
  22. Kim, H. J., and J. B. Ahn, 2015: Improvement in Prediction of the Arctic Oscillation with a Realistic Ocean Initial Condition in a CGCM. Journal of Climate 28(22), 8951-8967. doi:10.1175/JCLI-D-14-00457.1
  23. 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)
  24. Kim, H., J. Lee, Y. K. Hyun, and S. O. Hwang, 2021a: The KMA Global Seasonal Forecasting System (GloSea6) - Part 1: Operational System and Improvements. Atmosphere 31(3), 341-359. doi:10.14191/ATMOS.2021.31.3.341. (in Korean with English abstract)
  25. Kim, Y. H., M. J. Choi, K. M. Shim, J. Hur, S Jo, S., and J. B. Ahn, 2021b: 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. Kim, E. S., J. B. Ahn, K. M. Shim, J. Hur, S. Jo, M. S. Suh, D. H. Cha, S. K. Min, and H. S. Kang, 2023: Projections of suitable cultivation area for major fruit trees and climate-type in South Korea under representative concentration pathway scenarios using the ensemble of high-resolution regional climate models. International Journal of Climatology 43(10), 4552-4571. Doi:10.1002/joc.8102.
  27. Kim, E. S., V. N. Kryjov, and J. B. Ahn, 2024: Seasonal prediction and simulation of the cold surges over the Korean Peninsula using a CGCM. Theoretical and Applied Climatology 155(3), 1793-1806. doi:10.1007/s00704-023-04731-7.
  28. Lee, B. W., 2012: Impact and response of climate change on global agriculture. World Agriculture No. 146. Korea Rural Economic Research Institute, 1-16.
  29. Lee, J., M. I. Lee, and J. B. Ahn, 2022: Importance of ocean initial conditions of late autumn on winter seasonal prediction skill in atmosphere-land-ocean-sea ice coupled forecast system. Climate Dynamics 58, 3427-3440. doi:10.1007/s00382-021-06106-y.
  30. Meza, F. J., and D. Silva, 2009: Dynamic adaptation of maize and wheat production to climate change. Climatic Change 94(1), 143-156. doi:10.1007/s10584-009-9544-z.
  31. 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.
  32. Pacanowski, R. C., and S. M. Griffies, 1998: MOM 3.0 manual. NOAA/GFDL. Available online at http://www.gfdl.noaa.gov/ocean-model
  33. 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)
  34. Shim, K. M., M. J. Jung, Y. S. Kim, I. T. Choi, H. J. Kim, and K. K. Kang, 2016: Some Meteorological Anomalies and their Relationships with Rice Yield for El Nino Years in South Korea. Korean Journal of Agricultural and Forest Meteorology, 18(3), 143-150. doi:10.5532/KJAFM.2016.18.3.143. (in Korean with English abstract)
  35. 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 Tech. Note, NCAR/TN- 475+STR, 125pp.
  36. 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.
  37. Sun, J. Q., and J. B. Ahn, 2011: A GCM-based forecasting model for the landfall of tropical cyclones in China. Advances in atmospheric sciences 28, 1049-1055. doi:10.1007/s00376-011-0122-8.
  38. Sun, J. Q., 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.
  39. Yun S. H., J. N. Im, J. T. Lee, K. M. Shim, and K. H. Hwang, 2001: Climate Change and Coping with Vulnerability of Agricultural Productivity. Korean Journal of Agricultural and Forest Meteorology 3(4), 220-237. (in Korean with English abstract)