Comparing climate projections for Asia, East Asia and South Korea

아시아 대륙, 동아시아, 대한민국을 대상으로 다른 공간적 규모의 기후변화시나리오 예측 비교

  • Received : 2017.01.01
  • Accepted : 2017.04.16
  • Published : 2017.04.30


Many studies on climate change and its impacts use a single climate scenario. However, one climate scenario may not accurately predict the potential impacts of climate change. We estimated temperature and precipitation changes by 2070 using 17 of the CMIP5 Global Climate Models (GCMs) and two emission scenarios for three spatial domains: the Asian continent, six East Asia countries, and South Korea. For South Korea, the range of increased minimum temperature was lower than for the ranges of the larger regions, but the range of projected future precipitation was higher. The range of increased minimum temperatures was between $1.3^{\circ}C$ and $5.2^{\circ}C$, and the change in precipitation ranged from - 42.4 mm (- 3.2%) and + 389.8 mm (+ 29.6%) for South Korea. The range of increased minimum temperatures was between $2.3^{\circ}C$ and $8.5^{\circ}C$ for East Asia countries and was between $2.1^{\circ}C$ and $7.4^{\circ}C$ for the Asian continent, and the change in precipitation ranged from 28.8 mm (+ 6.3%) and 156.8 mm (+ 34.3%) for East Asia countries and from 32.4 mm (+ 5.5%) and 126.2 mm (+ 21.3%) for the Asian continent. We suggest climate change studies in South Korea should not use a single GCM or only an ensemble climate model's output and we recommend to use GFDL-CM3 and INMCM4 GCMs to bracket projected change for use in other national climate change studies to represent the range of projected future climate conditions.

우리나라의 많은 기후변화 관련 영향 평가 연구들이 기상청에서 제공하는 기후변화 시나리오를 이용하고 있지만, 하나의 기후 시나리오로 기후변화의 잠정적인 영향을 정확히 예측하기에는 한계가 있다. 본 연구는 세 가지의 지역적 스케일 - 아시아 대륙, 동아시아 6개국, 대한민국- 을 대상으로 두 가지 대표농도경로 시나리오에서 17개의 지역기후모델을 이용하여 현재와 2070년의 연간 최저 온도와 연간 강수량의 차이를 확인하였다. 대한민국의 경우 최저온도 증가량의 범위는 아시아 규모보다 작았으며 강수량 차이에 대한 편차는 아시아 규모보다 컸다. 최저온도 증가범위는 $1.3^{\circ}C$에서 $5.2^{\circ}C$이며, 연간 강수량 차이는 -42.4 mm (-3.2%) 에서 +389.8 mm (+ 29.6%) 로 기상청의 기후변화 시나리오는 긍정적 기후 시나리오의 예측값에 가까운 것으로 나타났다. 따라서 기후변화 및 관련 영향 평가 연구들은 다양한 기후변화 시나리오를 이용하여 그 예측 범위에 대비할 필요가 있으며, 본 연구 결과에 따라 GFDL-CM3와 INMCM4의 두 가지 기후모델을 이용하여 우리나라의 지구 온난화에 대한 잠정적인 영향을 평가하기를 권한다.


Supported by : Korea Ministry of Environment


  1. Araujo MB, New M. 2007. Ensemble forecasting of species distributions. Trends in Ecology & Evolution. 22(1): 42-47.
  2. Arnell NW, Brown S, Gosling SN, Gottschalk P, Hinkel J, Huntingford C, Lloyd-Hughes B, Lowe JA, Nicholls RJ, Osborn TJ, Osborne TM, Rose GA, Smith P, Wheeler TR, Zelazowski P. 2016. The impacts of climate change across the globe: A multisectoral assessment. Climatic Change. 134: 457-474.
  3. Butt N, Possingham HP, De Los Rios C, Maggini R, Fuller RA, Maxwell SL, Watson JEM. 2016. Challenges in assessing the vulnerability of species to climate change to inform conservation actions. Biological Conservation. 199: 10-15.
  4. Choe H. 2015. Biodiversity conservation planning for South Korea: Predicting plant biodiversity dynamics under climate change and the impacts from forest conversion scenarios (Order No. 10036024). Available from Dissertations & Theses @ University of California; ProQuest Dissertations & Theses A&I. (1774020368). Retrieved from
  5. Choe H, Thorne JH, Hijmans R, Kim J, Kwon H, Seo C. 2017. Meta-corridor solutions for climate-vulnerable plant species groups in South Korea. Journal of Applied Ecology. DOI: 10.1111/1365-2664.12865.
  6. Elsen PR, Tingley MW. 2015. Global mountain topography and the fate of montane species under climate change. Nature Clim Change. 5: 772-776.
  7. ENES. 2016. CMIP5 Models and Grid Resolution. Available: Last accessed, December 6, 2016.
  8. Giorgi F, Torma C, Coppola E, Ban N, Schar C, Somot S. 2016. Enhanced summer convective rainfall at alpine high elevations in response to climate warming. Nature Geoscience. doi:10.1038/ngeo2761
  9. Hallegatte S. 2009. Strategies to adapt to an uncertain climate change. Global Environmental Change. 19: 240-247.
  10. Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Brönnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM. 2013. Observations: Atmosphere and Surface. In: 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 TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  11. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology. 25: 1965-1978.
  12. Keppel G, Van Niel KP, Wardell-Johnson GW, Yates CJ, Byrne M, Mucina L, Schut AGT, Hopper SD, Franklin SE. 2012. Refugia: Identifying and understanding safe havens for biodiversity under climate change. Global Ecology and Biogeography. 21: 393-404.
  13. Kim HG, Lee DK, Jung H, Kil SH, Park JH, Park C, Tanaka R, Seo C, Kim H, Kong W, Oh K, Choi J, Oh YJ, Hwang G, Song CK. 2016. Finding key vulnerable areas by a climate change vulnerability assessment. Natural Hazards. 81: 1683-1732.
  14. Kim KH, Cho J. 2016. Predicting potential epidemics of rice diseases in Korea using multi-model ensembles for assessment of climate change impacts with uncertainty information. Climatic Change. 134: 327-339.
  15. Kim SY, Park C, Park JH, Lee DK. 2015. Estimating Effects of Climate Change on Ski Industry - The Case of Ski Resorts in South Korea -. J. Environ. Impact Assess. 24(5): 432-443. [Korean Literature]
  16. KMA. 2012. Climate Change Outlook Report on the Korean Peninsula (한반도 기후변화 전 망 보 고 서 ). Korea Meteorological Administration. No. 11-1360000-000861-01. [Korean Literature]
  17. Lee SA, Lee SH, Ji SY, Choi JY. 2016. Predicting change of suitable plantation of Schisandra chinensis with ensemble of climate change scenario. J. Environ. Impact Assess. 25(1): 77-87. [Korean Literature]
  18. Millar CI, Stephenson NL. 2015. Temperate forest health in an era of emerging megadisturbance. Science. 349: 823-826.
  19. Millar CI, Stephenson NL, Stephens SL. 2007. Climate change and forests of the future: Managing in the face of uncertainty. Ecological Applications. 17(8): 2145-2151.
  20. Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins M, Stainforth DA. 2004. Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature. 430: 768-772.
  21. Park HC, Lee JH, Lee GG. 2014. Predicting the suitable habitat of the Pinus pumila under climate change. J. Environ. Impact Assess. 23(5): 379-392. [Korean Literature]
  22. Park HC, Lee JH, Lee GG, Um GJ. 2015. Environmental features of the distribution areas and climate sensitivity assesment of korean fir and khinghan fir. J. Environ. Impact Assess. 24(3): 260-277. [Korean Literature]
  23. Prieto-Torres DA, Navarro-Siguenza AG, Santiago-Alarcon D, Rojas-Soto OR. 2016. Response of the endangered tropical dry forests to climate change and the role of mexican protected areas for their conservation. Global Change Biology. 22: 364-379.
  24. Riahi K, Rao S, Krey V, Cho C, Chirkov V, Fischer G, Kindermann G, Nakicenovic N, Rafaj P. 2011. RCP 8.5-a scenario of comparatively high greenhouse gas emissions. Climatic Change. 109: 33-57.
  25. Sanderson BM, Oleson KW, Strand WG, Lehner F, O'Neill BC. 2015. A new ensemble of gcm simulations to assess avoided impacts in a climate mitigation scenario. Climatic Change. DOI 10.1007/s10584-015-1567-z.
  26. SCENIC. 2014. Primer on climate data and global climate models. Southwest Climate and Environmental Information Collaborative.
  27. Shin YH, Jung HC. 2015. Assessing uncertainty in future climate change in Northeast Asia using multiple CMIP5 GCMs with four RCP scenarios. J. Environ. Impact Assess. 24(3): 205-216. [Korean Literature]
  28. Taylor KE, Stouffer RJ, Meehl GA. 2012. An overview of cmip5 and the experiment design. Bulletin of the American Meteorological Society. 93: 485-498.
  29. Tebaldi C, Knutti R. 2007. The use of the multimodel ensemble in probabilistic climate projections. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 365:2053-2075.
  30. Thomson AM, Calvin KV, Smith SJ, Kyle GP, Volke A, Patel P, Delgado-Arias S, Bond-Lamberty B, Wise MA, Clarke LE, Edmonds JA. 2011. Rcp4.5: A pathway for stabilization of radiative forcing by 2100. Climatic Change. 109: 77-94.
  31. Thorne JH, Boynton RM, Flint LE, Flint AL. 2015. The magnitude and spatial patterns of historical and future hydrologic change in california's watersheds. Ecosphere. 6(2):24.
  32. van Vuuren D, Edmonds JA, Kainuma M, Riahi K, Thomson AM, Hibbard KA, Hurtt G, Kram T, Krey V, Lamarque J-F, Masui T, Meinhausen M, Nakicenovic N, Smith SJ, Rose SK. 2011. The representative concentration pathways: An overview. Climatic Change. 109: 5-31.
  33. Webster M. 2003. Communicating climate change uncertainty to policy-makers and the public. Climatic Change. 61: 1-8.
  34. Wheeler HY. 2015. Asia. Available: Last accessed, November 27, 2016.