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HadGEM3-RA 지역기후모델을 이용한 CORDEX 동아시아 2단계 지역의 기온과 강수 모의 평가

Evaluation of Temperature and Precipitation over CORDEX-EA Phase 2 Domain using Regional Climate Model HadGEM3-RA

  • 변재영 (기상청 국립기상과학원) ;
  • 김태준 (기상청 국립기상과학원) ;
  • 김진욱 (기상청 국립기상과학원) ;
  • 김도현 (기상청 국립기상과학원)
  • Byon, Jae-Young (National Institute of Meteorological Sciences, Korea Meteorological Administration) ;
  • Kim, Tae-Jun (National Institute of Meteorological Sciences, Korea Meteorological Administration) ;
  • Kim, Jin-Uk (National Institute of Meteorological Sciences, Korea Meteorological Administration) ;
  • Kim, Do-Hyun (National Institute of Meteorological Sciences, Korea Meteorological Administration)
  • 투고 : 2022.03.24
  • 심사 : 2022.04.28
  • 발행 : 2022.06.30

초록

본 연구는 영국기상청에서 개발된 지역기후모델 Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA)로부터 모의된 동아시아 지역의 기온과 강수 결과를 평가하였다. HadGEM3-RA는 Coordinated Regional climate Downscaling Experiment-East Asia (CORDEX-EA) Phase II 영역에서 15년 (2000-2014년) 모의되었다. 동아시아 여름 몬순에 의한 HadGEM3-RA 강수대 분포는 Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of water resources (APHRODITE) 자료와 잘 일치한다. 그러나, 동남아시아 강수는 과대 모의하며 남한에서는 과소 모의한다. 특히 모의된 여름철 강수량과 APHRODITE 강수량은 남한지역에서 가장 낮은 상관 계수와 가장 큰 오차크기(RMSE)를 보인다. 동아시아 기온 예측은 과소 모의하며 겨울철 오차가 가장 크다. 남한 기온 예측은 봄 동안 가장 큰 과소 모의 오차를 나타냈다. 국지적 예측성을 평가하기 위하여 서울기상관측소 ASOS 자료와 비교한 기온과 강수의 시계열은 여름철 강수와 겨울철 기온이 과소 모의하는 공간 평균된 검증 결과와 유사하였다. 특히 여름철 강수량 증가시 과소 모의 오차가 증가하였다. 겨울철 기온은 저온에서는 과소 모의하나 고온은 과대 모의하는 경향이 나타났다. 극한기후지수 비교 결과는 폭염은 과대 모의하여, 집중호우는 과소 모의하는 오차가 나타났다. 수평해상도25km로 모의된 HadGEM3-RA는 중규모 대류계와 지형성 강수 예측에서 한계를 보였다. 본 연구는 지역기후모델 예측성 개선을 위한 초기 자료 개선, 해상도 향상, 물리 과정의 개선이 필요함을 지시한다.

This study evaluates the temperature and precipitation results in East Asia simulated from the Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA) developed by the UK Met Office. The HadGEM3-RA is conducted in the Coordinated Regional climate Downscaling Experiment-East Asia (CORDEX-EA) Phase II domain for 15 year (2000-2014). The spatial distribution of rainbands produced from the HadGEM3-RA by the summer monsoon is in good agreement with the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of water resources (APRODITE) data over the East Asia. But, precipitation amount is overestimated in Southeast Asia and underestimated over the Korean Peninsula. In particular, the simulated summer rainfall and APRODITE data show the least correlation coefficient and the maximum value of root mean square error in South Korea. Prediction of temperature in Southeast Asia shows underestimation with a maximum error during winter season, while it appears the largest underestimation in South Korea during spring season. In order to evaluate local predictability, the time series of temperature and precipitation compared to the ASOS data of the Seoul Meteorological Station is similar to the spatial average verification results in which the summer precipitation and winter temperature underestimate. Especially, the underestimation of the rainfall increases when the amounts of precipitation increase in summer. The winter temperature tends to underestimate at low temperature, while it overestimates at high temperature. The results of the extreme climate index comparison show that heat wave is overestimated and heavy rainfall is underestimated. The HadGEM3-RA simulated with a horizontal resolution of 25 km shows limitations in the prediction of mesoscale convective system and topographic precipitation. This study indicates that improvement of initial data, horizontal resolution, and physical process are necessary to improve predictability of regional climate model.

키워드

과제정보

이 연구는 기상청 국립기상과학원 「기상업무지원기술개발연구」 "신기후체제 대응 기후변화시나리오개발 평가(KMA2018-00321)"의 지원으로 수행되었습니다.

참고문헌

  1. Baek, H. J., and Coauthors, 2013, Climate change in the 21st century simulated by HadGEM2-AO under representative concentration pathways. Asia-Pacific Journal of Atmospheric Sciences, 49, 603-618. https://doi.org/10.1007/s13143-013-0053-7
  2. Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Menard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J., 2011, The Joint UK Land Environment Simulator (JULES), model description - Part 1: Energy and water fluxes, Geoscientific Model Development, 4, 677-699. https://doi.org/10.5194/gmd-4-677-2011
  3. Cha, D. H., D. K. Lee, and S. Y. Hong, 2008, Impact of boundary layer processes on seasonal simulation of the East Asian summer monsoon using a regional climate model. Meteorology and Atmospheric Physics, 100, 53-72. https://doi.org/10.1007/s00703-008-0295-6
  4. Choi, Y.-W., and J.-B. Ahn,, 2017, Impact of cumulus parameterization schemes on the regional climate simulation for the domain of CORDEX-East Asia Phase 2 using WRF Model. Atmosphere, 27(1), 105-118. https://doi.org/10.14191/Atmos.2017.27.1.105
  5. Cusack, S.S., Edwards, J.M., Kershaw, R., 1999, Estimating the subgrid variance of saturation, and its parameterization for use in a GCM cloud scheme. Monthly Weather Review, 125, 3057-3076.
  6. Davis, T., J. P. Cullen, A. J. Malcolm, M. H. Mawson, A. Staniforth, A. A. White, and N. Wood, 2005, A new dynamical core for the Met Office's global and regional climate modelling of the atmosphere. Quarterly Journal of Royal Meteorological Society, 131, 1759-1782. https://doi.org/10.1256/qj.04.101
  7. Edwards, J. M. and Slingo, A., 1996, Studies with a flexible new radiation code. I: Choosing a configuration for a largescale model. Quarterly Journal of the Royal Meteorological Society, 122, 689-719. https://doi.org/10.1002/qj.49712253107
  8. Fu, C., S. Wang, Z. Xiong, AW. Gutowski, D. K. Lee, J. L. Mc-Gregor, Y. Sato, H. Kato, J.-W. Kim, and M.-S. Suh, 2005: Regional climate model intercomparison project for Asia. Bulletin of American Meteorological Society, 77, 437-471. https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2
  9. Gregory, D., Rowntree, P.R., 1990, A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Monthly Weather Review, 118, 1483-1506. https://doi.org/10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2
  10. Giorgi, F., E. Coppola, F. Solmon, L. Mariotti, and others, 2012, RegCM4: model description and preliminary test over multi CORDEX domains. Climate Research, 52, 7-29. https://doi.org/10.3354/cr01018
  11. Grant, A. L. M., Brown, A. R., 1999, A similarity hypothesis for shallow-cumulus transports, Quarterly Journal of the Royal Meteorological Society, 125, 1913-1936. https://doi.org/10.1002/qj.49712555802
  12. 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, International Journal of Climatology, 34, 623-642. https://doi.org/10.1002/joc.3711
  13. Hong, S. Y., N.-K. Moon, K.-S. S. Lim, and J.-W. Kim, 2010, Future climate change scenarios over Korea using a mulit-nested downscaling system: A pilot study, Asia-Pacific Journal of Atmospheric Sciences, 46(4), 425-435. https://doi.org/10.1007/s13143-010-0024-1
  14. Hong, S. Y., H. Park, H. B. Cheong, J. E. E. Kim, M. S. Koo, J. Jang, S. Ham, S. O. Hwang, B. K. Park, E. C. Chang, and H. Li, 2013, The global/regional integrated model system (GRIMs). Asia-Pacific Journal of Atmospheric Sciences, 49, 219-243. https://doi.org/10.1007/s13143-013-0023-0
  15. IPCC, 2013: Climate Change 2013, The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T. F. et al. Eds., Cambridge University Press, 1585pp.
  16. IPCC, 2021, Climate Change 2021: The Physical Sciences Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Climate Change, Cambridge University Press, In Press.
  17. Jacob, D., and Coauthors, 2007, An inter-comparison of regional climate models for Europe: model performance in present-day climate. Climatic Change, 81, 31-52. https://doi.org/10.1007/s10584-006-9213-4
  18. Jo, S., J.-B. Ahn, D.-H. Cha, S.-K. Min, M.-S. Suh, Y.-H. Byun, and J.-U. Kim, 2019, The Koppen-Trewartha climate-type changes over the CORDEX-East Asia Phase 2 domain under 2 and 3℃ global warming. Geophysical Research Letters, 46, 14030-14041. https://doi.org/10.1029/2019gl085452
  19. Kim, J., B. Guan, D. E. Waliser, R. D. Ferraro, J. L. Case, T. Iguchi, E. Kemp, W. Putman, W. Wang, D. Wu, and B. Tian, 2018, Winter precipitation characteristics in Western US related to atmospheric river landfalls: observations and model evaluations. Climate Dynamics, 50, 231-248. https://doi.org/10.1007/s00382-017-3601-5
  20. Kim, J.-U., T.-J. Kim, D.-H. Kim, K.-O. Boo, J.-W. Kim, D.-H. Cha, S.-K. Min, and Y.-H. Kim, 2020, Evaluation of performance and uncertainty for multi-GCM & RCM over CORDEX-East Asia Phase 2 region. Atmosphere, (in Korean)
  21. Kimoto, M., 2005, Simulated change of the East Asian circulation under the global warming scenario. Geophysical Research Letters, 32, L16701, doi:10.1029/2005GL023383.
  22. KMA, 2018, Korean climate change assessment report 2018, Korea Meteorological Administration, 172 pp.
  23. Lee, D.-K., and M.-S. Suh, 2000: Ten-year East Asian summer monsoon simulation using a regional climate model (RegCM2). Journal of Geophysical Research, 105(D24), 29565-29577. https://doi.org/10.1029/2000JD900438
  24. Lee, J-W., S.-Y. Hong, E.-C. Chang, M.-S. Suh, and H.-S. Kang, 2014, Assessment of future climate change over East Asia due to the RCP scenarios downscaled by GRIMs-RMP. Climate Dynamics, 42, 733-747. https://doi.org/10.1007/s00382-013-1841-6
  25. Lock, A. P., Brown, A. R., Bush, M. R., Martin, G. M., and Smith, R. N. B., 2000, A new boundary layer mixing scheme. Part ?: Scheme description and single-column model tests, Monthly Weather Review, 128, 3187-3199. https://doi.org/10.1175/1520-0493(2000)128<3187:ANBLMS>2.0.CO;2
  26. Mearns, L. O., Arritt, R., Biner, S. Bukovsky, M. S., McGinnis, S., Sain, S., et al., 2012, The North American Regional Climate Change Assessment Program: Overview of Phase I results. Bulletin of American Meteorological Society, 93(9), 1337-1362. https://doi.org/10.1175/BAMS-D-11-00223.1
  27. Meehl, G. A., G. J. Boer, C. Covey, M. Latif, and R. J. Stouffer, 2000: The coupled model intercomparison project (CMIP). Bulletin of American Meteorological Society, 81, 313-318. https://doi.org/10.1175/1520-0477(2000)081<0313:TCMIPC>2.3.CO;2
  28. Oh, S.-G., M.-S. Suh, D.-H. Cha, and S.-J. Choi, 2011: Simulation Skills of RegCM4 for Regional Climate over CORDEX East Asia driven by HadGEM2-AO. Journal of Korean Earth Sciences Society, 32(7), 732-749. (in Korean). https://doi.org/10.5467/JKESS.2011.32.7.732
  29. Oh, S.-G., J.-H. Park, S.-H. Lee, and M.-S. Suh, 2014, Assessment of the RegCM4 over East Asia and future precipitation change adapted to the RCP scenarios. Journal of Geophysical. Research., 119, 2913-2927. https://doi.org/10.1002/2013JD020693
  30. Park, J.-H., S.-G. Oh, and M.-S. Suh, 2013, Impacts of boundary conditions on the precipitation simulation of RegCM4 in the CORDEX East Asia domain. Journal of Geophysical Research, 118, 1652-1667. https://doi.org/10.1002/jgrd.50159
  31. Sellar, A. A., C.G. Jones, J. Mulcahy, Y. Tang, A. Yool, A. Wiltshire, F.M. O'connor, M. Stringer, R. Hill, J. Palmieri, S. Woodward, L. de Mora, T. Kuhlbrodt, S. Rumbold, D.I. Kelley, R. Ellis, C.E. Johnson, J. Walton, N.L. Abraham, M.B. Andrews, T. Andrews, A.T. Archibald, S. Berthou, E. Burke, E. Blockley, K. Carslaw, M. Dalvi, J. Edwards, G.A. Folberth, A.J. Hewitt, B. Johnson, A. Jones, C.D. Jones, J. Keeble, S. Liddicoat, O. Morgenstern, R.J. Parker, V. Predoi, E. Robertson, A. Siahaan, R.S. Smith, R. Swaminathan, M.T. Woodhouse, G. Zeng, G. and M. Zerroukat, 2019: UKESM1: Description and evaluation of the UK Earth System Model. Journal of Advances in Modeling Earth Systems, https://doi.org/10.1029/2019MS001739.
  32. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005, A description of the advanced research WRF version 2. NCAR/TN-468+STR, 88 pp.
  33. Smith, R. N., 1990, A scheme for predicting layer clouds and their water content in a general circulation model. Quarterly Journal of the Royal Meteorological Society, 116, 371-386.
  34. Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single domain. Journal of Geophysical Research: Atmospheres, 106, 7183-7192. https://doi.org/10.1029/2000JD900719
  35. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012, An overview of CMIP5 and the experiment design, Bulletin of American Meteorological Society, 93(4), 485-498. https://doi.org/10.1175/BAMS-D-11-00094.1
  36. Torma, C. Giorgi, F., and Coppola, E., 2015, Added value of regional climate modeling over areas characterized by complex terrain precipitation over the Alps. Journal of Geophysical Research: Atmospheres, 120, 3957-3972. https://doi.org/10.1002/2014JD022781
  37. Wang, S.-Y., R. R. Gillies, E. S. Takle, and W. J. Gutowski Jr., 2009, Evaluation of precipitation in the inter-mountain region as simulated by NARCCAP regional climate models. Geophysical Research Letters, 36, L11704, doi:10.1029/2009GL037930.
  38. Webster, S., A. R. Brown, D. R. Cameron, and C. P. Jones, 2003, Improvements to the representation of orography in the Met Office Unified Model. Quarterly Journal of the Royal Meteorological Society, 129, 1989-2010. https://doi.org/10.1256/qj.02.133
  39. Wilson, D. R. and Ballard, S. P., 1999, A microphysically based precipitation scheme for the UK Meteorological Office Unified Model, Quarterly Journal of the Royal Meteorological Society, 125, 1607-1636. https://doi.org/10.1002/qj.49712555707
  40. Yatagai A, Kaminguchi K, Arakawa O, Hamada A, Yasutomi N, Kitoh A, 2012, APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bulletin of American Meteorological Society, 93, 1401-1415. https://doi.org/10.1175/BAMS-D-11-00122.1