[논문]농업분야 활용을 위한 한반도 1km 격자형 SSP 기후변화 시나리오

허지나; 조재필; 조세라; 심교문; 김용석; 강민구; 오찬성; 서승범; 김응섭

doi:10.5532/kjafm.2024.26.1.1

[국내논문] 농업분야 활용을 위한 한반도 1km 격자형 SSP 기후변화 시나리오
SSP Climate Change Scenarios with 1km Resolution Over Korean Peninsula for Agricultural Uses

한국농림기상학회지 = Korean Journal of Agricultural and Forest Meteorology, v.26 no.1, 2024년, pp.1 - 30

허지나 (국립농업과학원 기후변화평가과) , 조재필 (유역통합관리연구원) , 조세라 (국립농업과학원 기후변화평가과) , 심교문 (국립농업과학원 기후변화평가과) , 김용석 (국립농업과학원 기후변화평가과) , 강민구 (국립농업과학원 기후변화평가과) , 오찬성 (유역통합관리연구원) , 서승범 (서울시립대학교) , 김응섭 (국립농업과학원 기후변화평가과)

초록
AI-Helper

국제사회는 IPCC를 중심으로 SSP (Shared Socioeconomic Pathways) 기후변화 시나리오를 새로운 온실가스 변화 경로로 채택하고, 신기후변화 시나리오 기반으로 다양한 규모와 형태로 기후변화를 전망하고 분석하고 있다. 국립농업과학원은 이러한 국제적 동향을 반영하고 농업부문 기후변화 적응대책 지원을 위한 노력의 일환으로 신규 온실가스 경로에 기반한 한반도 상세(1km) 기후변화 시나리오를 산출하였다. 본 논문은 2022년 "국가 기후변화 표준 시나리오" 로 인증받은 국립농업과학원의 SSP 기후변화 시나리오 자료를 소개하고, 기후변화 전망 결과를 보여주고자 한다. 한반도의 미래 기후 변화에 대한 전망 정보를 생산하기 위해 CMIP6에 참여한 18개의 GCM 모형에서 생산된 전지구 규모의 기후 자료를 과거기간(1985-2014)과 미래기간(2015-2100)에 대해 수집하고, 1km 격자형 한반도 전자기후도와 SQM 방법을 이용하여 한반도 영역에 대해 통계적 상세화를 수행하였다. 21세기 후반기(2071~2100년), 한반도의 연평균 최고, 최저기온은 온실가스 배출 정도에 따라 각각 2.6~6.1 ℃, 2.5~6.3 ℃ 상승하고, 연강수량은 21.5~38.7 % 상승하는 것으로 전망되었다. 저탄소 시나리오(SSP1-2.6)의 경우 기온과 강수량 상승이 적게 나타나, 탄소 배출을 감축하는 경우에 상승 폭을 억제할 수 있을 것으로 전망되었다. 21세기 후반기의 우리나라 평균 풍속과 일사량은 상대적으로 현재 대비 미래에 큰 변화가 없을 것으로 전망하고 있다. 이 자료는 기후변화에 따를 미래의 불확실성을 이해하고 기후변화 적응을 위한 합리적인 의사결정에 기여할 수 있을 것으로 기대된다.

Abstract ▼ AI-Helper

The international community adopts the SSP (Shared Socioeconomic Pathways) scenario as a new greenhouse gas emission pathway. As part of efforts to reflect these international trends and support for climate change adaptation measure in the agricultural sector, the National Institute of Agricultural Sciences (NAS) produced high-resolution (1 km) climate change scenarios for the Korean Peninsula based on SSP scenarios, certified as a "National Climate Change Standard Scenario" in 2022. This paper introduces SSP climate change scenario of the NAS and shows the results of the climate change projections. In order to produce future climate change scenarios, global climate data produced from 18 GCM models participating in CMIP6 were collected for the past (1985-2014) and future (2015-2100) periods, and were statistically downscaled for the Korean Peninsula using the digital climate maps with 1km resolution and the SQM method. In the end of the 21st century (2071-2100), the average annual maximum/minimum temperature of the Korean Peninsula is projected to increase by 2.6~6.1℃/2.5~6.3℃ and annual precipitation by 21.5~38.7% depending on scenarios. The increases in temperature and precipitation under the low-carbon scenario were smaller than those under high-carbon scenario. It is projected that the average wind speed and solar radiation over the analysis region will not change significantly in the end of the 21st century compared to the present. This data is expected to contribute to understanding future uncertainties due to climate change and contributing to rational decision-making for climate change adaptation.

주제어

표/그림 (26)

표 Table 1. CMIP6 GCMs used in this study
그림 Fig. S1. Time-series of the wind speed (m/s) under HIS (black), SSP1-2.6 (blue), SSP2-4.5 (green), SSP3-7.0 (yellow) and SSP5-8.5 (red) scenarios. The projection results of 18 GCM models are averaged.
그림 Fig. S2. Map of administrative districts of the Korean Peninsula.
그림 Fig. 1. 30-year (1981-2010) mean of maximum temperature (℃) for (a) OBS and (b) HIS averaged over 18 GCM models, and (c) its difference.
그림 Fig. 2. 30-year (1981-2010) mean of minimum temperature (℃) for (a) OBS and (b) HIS averaged over 18 GCM models, and (c) its difference.
그림 Fig. 3. 30-year (1981-2010) mean of annual precipitation (mm) for (a) OBS and (b) HIS averaged over 18 GCM models, and (c) its difference.
그림 Fig. 4. 30-year (1981-2010) mean of solar radiation (MJ/m²) for (a) OBS and (b) HIS averaged over 18 GCM models, and (c) its difference.
그림 Fig. 5. 30-year (1981-2010) mean of wind speed (m/s) for (a) OBS and (b) HIS averaged over 15 GCM models, and (c) its difference.
그림 Fig. S3. 30-year (1981-2010) mean of annual precipitation (mm) for OBS and HIS averaged over 18 GCM models, and individual models.
그림 Fig. 6. Time-series of the climate variables under HIS (black), SSP1-2.6 (blue), SSP2-4.5 (green), SSP3-7.0 (yellow) and SSP5-8.5 (red) scenarios. The shaded areas indicate the spread of each ensemble member of HIS (grey), SSP1-2.6 (light blue), SSP2-4.5 (light green), SSP3-7.0 (light yellow) and SSP5-8.5 (light red) scenarios.
표 Table 2. Spread of 18 GCM models (15 models for wind speed) for the HIS (1981-2010), the near future (FUT1: 2011-2040), far future (FUT2: 2041-2070), and distant future (FUT3: 2071-2100) under SSP scenarios
그림 Fig. 7. Change in maximum temperature (℃) for the near future (2011-2040), far future (2041-2070), distant future (2071-2100) under SSP scenarios compared to the Historical (1981-2010). The projection results of 18 GCM models are averaged. The dot indicates the confidence interval is 95% or higher.
그림 Fig. 8. Change in minimum temperature (℃) for the near future (2011-2040), far future (2041-2070), distant future (2071-2100) under SSP scenarios compared to the Historical (1981-2010). The projection results of 18 GCM models are averaged. The dot indicates the confidence interval is 95% or higher.
표 Table 3. Change in seasonal and annual mean of maximum temperature (℃) under SSP scenarios compared to one of historical period (1981-2010). The projection results of 18 GCM models are averaged for the near future (FUT1: 2011-2040), far future (FUT2: 2041-2070), distant future (FUT3: 2071-2100)
표 Table 4. Change in seasonal and annual mean of minimum temperature (℃) under SSP scenarios compared to one of historical period (1981-2010). The projection results of 18 GCM models are averaged for the near future (FUT1: 2011-2040), far future (FUT2: 2041-2070), distant future (FUT3: 2071-2100)
그림 Fig. 9. Change rate in annual precipitation (%) for the near future (2011-2040), far future (2041-2070), distant future (2071-2100) under SSP scenarios compared to the Historical (1981-2010). The projection results of 18 GCM models are averaged. The dot indicates the confidence interval is 95% or higher.
표 Table 5. Change rate in seasonal and annual precipiation (%) under SSP scenarios compared to one of historical period (1981-2010). The projection results of 18 GCM models are averaged for the near future (FUT1: 2011-2040), far future (FUT2: 2041-2070), distant future (FUT3: 2071-2100)
그림 Fig. 10. Change rate in solar radiation (%) for the near future (2011-2040), far future (2041-2070), distant future (2071-2100) under SSP scenarios compared to the Historical (1981-2010). The projection results of 18 GCM models are averaged. The dot indicates the confidence interval is 95% or higher.
표 Table 6. Change rate in seasonal and annual mean of solar radiation (%) under SSP scenarios compared to one of historical period (1981-2010). The projection results of 18 GCM models are averaged for the near future (FUT1: 2011-2040), far future (FUT2: 2041-2070), distant future (FUT3: 2071-2100)
그림 Fig. 11. Change rate in wind speed (%) for the near future (2011-2040), far future (2041-2070), distant future (2071-2100) under SSP scenarios compared to the Historical (1981-2010). The projection results of 15 GCM models are averaged. The dot indicates the confidence interval is 95% or higher.
표 Table 7. Change rate in seasonal and annual mean of wind speed (%) under SSP scenarios compared to one of historical period (1981-2010). The projection results of 15 GCM models are averaged for the near future (FUT1: 2011-2040), far future (FUT2: 2041-2070), distant future (FUT3: 2071-2100)
표 Table 8. Change in maximum temperature (℃) by province under SSP scenarios compared to one of historical period (1981-2010). The projection results of 18 GCM models are averaged for the near future (FUT1: 2011-2040), far future (FUT2: 2041-2070), distant future (FUT3: 2071-2100)
표 Table 9. Change in minimum temperature (℃) by province under SSP scenarios compared to one of historical period (1981-2010). The projection results of 18 GCM models are averaged for the near future (FUT1: 2011-2040), far future (FUT2: 2041-2070), distant future (FUT3: 2071-2100)
표 Table 10. Change rate in annual precipitation (%) by province under SSP scenarios compared to one of historical period (1981-2010). The projection results of 18 GCM models are averaged for the near future (FUT1: 2011-2040), far future (FUT2: 2041-2070), distant future (FUT3: 2071-2100)
표 Table 11. Change rate in solar radiation (%) by province under SSP scenarios compared to one of historical period (1981-2010). The projection results of 18 GCM models are averaged for the near future (FUT1: 2011-2040), far future (FUT2: 2041-2070), distant future (FUT3: 2071-2100)
표 Table 12. Change rate in wind speed (%) by province under SSP scenarios compared to one of historical period (1981-2010). The projection results of 15 GCM models are averaged for the near future (FUT1: 2011-2040), far future (FUT2: 2041-2070), distant future (FUT3: 2071-2100)

참고문헌 (26)

Boucher, O., S. Denvil, G. Levavasseur, A. Cozic,？A. Caubel, M.-A. Foujols, Y. Meurdesoif, P.？Cadule, M. Devilliers, E. Dupont, and T. Lurton,？2019: IPSL IPSL-CM6A-LR model output？prepared for CMIP6 ScenarioMIP. Earth System？Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1532
Cho, J., W. Cho, and I. Jung, 2018: RSQM:？Statistical downscaling toolkit for Climate Change？Scenario Using Nonparametric Quantile Mapping.？http://cran.r-project.org/web/packages/rSQM/index.html/ (Accessed 22 July, 2020).
Choi, S. K., J. Jeong, S. J. Yeob, M. Kim, J. H.？Kim, and M. K. Kim, 2020: Evaluating Changes？and Uncertainty of Nitrogen Load from Rice？Paddy according to the Climate Change Scenario？Multi-Model Ensemble. Journal of the Korean？Society of Agricultural Engineers 62(5), 47-62. (in？Korean with English abstract).
Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A？statistical-topographic model for mapping？climatological precipitation over mountainous terrain.？Journal of Applied Meteorology 33, 140-158.？https://doi.org/10.1175/1520-0450(1994)033 2.0.CO;2

상세보기
Dix, M., D. Bi, P. Dobrohotoff, R. Fiedler, I.？Harman, R. Law, C. Mackallah, S. Marsland, S.？O'Farrell, H. Rashid, J. Srbinovsky, A. Sullivan,？C. Trenham, P. Vohralik, I. Watterson, G.？Williams, M. Woodhouse, R. Bodman, Dias, F.？B., C.-M. Domingues, N. Hannah, A. Heerdegen,？A. Savita, S. Wales, C. Allen, K. Druken, B.？Evans, C. Richards, S.-M. Ridzwan, D. Roberts, J.？Smillie, K. Snow, M. Ward, and R. Yang, 2019:？CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP. Earth System？Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285
FAO, 2020: Climate change: Unpacking the burden？on food safety. Food safety and quality series No.？8, 176. Retrieved from https://www.fao.org/documents/card/en/c/ca8185en/.
Hur, J., J.-P. Cho, S. Jo, K.-M. Shim, Y.-S. Kim,？M.-G. Kang, C.-S. Oh, S.-B. Seo, and E.-S. Kim.？2023: Production of Digital Climate Maps with？1km resolution over Korean Peninsula using？Statistical Downscaling Model. Korean Journal of？Agricultural and Forest Meteorology 25(4),？404-414. (in Korean with English abstract).
IPCC, 2018: Summary for Policymakers. In: Global？Warming of 1.5℃. An IPCC Special Report on？the impacts of global warming of 1.5℃ above？pre-industrial levels and related global greenhouse？gas emission pathways, in the context of？strengthening the global response to the threat of？climate change, sustainable development, and？efforts to eradicate poverty [Masson-Delmotte, V.,？P. Zhai, H.-O. Portner, D. Roberts, J. Skea, P. R.？Shukla, A. Pirani, W. Moufouma-Okia, C. Pean,？R. Pidcock, S. Connors, J. B. R. Matthews, Y.？Chen, X. Zhou, M. I. Gomis, E. Lonnoy, T.？Maycock, M. Tignor, and T. Waterfield (eds.)？World Meteorological Organization, Geneva,？Switzerland, 32 pp.
IPCC, 2021: Summary for Policymakers. In: 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. In Press.
IPCC, 2023: Climate Change 2023: Synthesis Report.？Contribution of Working Groups I, II and III to？the Sixth Assessment Report of the Intergovernmental？Panel on Climate Change [Core Writing Team, H.？Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland,？184 pp., doi: 10.59327/IPCC/AR6-9789291691647.
John, J. G., C. Blanton, C. McHugh, A. Radhakrishnan,？K. Rand, H. Vahlenkamp, C. Wilson, N. T.？Zadeh, J. P. Dunne, R. Dussin, L. W. Horowitz,？J. P. Krasting, P. Lin, S. Malyshev, V. Naik, J.？Ploshay, E. Shevliakova, L. Silvers, C. Stock, M.？Winton, and Y. Zeng, 2018: NOAA-GFDL？GFDL-ESM4 model output prepared for CMIP6？ScenarioMIP. Earth System Grid Federation.？https://doi.org/10.22033/ESGF/CMIP6.1414
Kim, J. U., T. J. Kim, D. H. Kim, Y. H. Byun, E.？C. Chang, D. H. Cha, J. B. Ahn, S. and K. Min,？2022: Performance Evaluation and Future？Projection of East Asian Climate using SSP？Scenario-based CORDEX-East Asia Phase 2？Multi-RCM Simulation. Journal of Climate？Change Research 13(3), 339-35 (in Korean with？English abstract).
Kogo, B. K., L. Kumar, and R. Koech, 2021:？Climate change and variability in Kenya: a review？of impacts on agriculture and food security.？Environment, Development and Sustainability？23(1), 23-43.

상세보기
Kotir, J. H. 2011: Climate change and variability in？Sub-Saharan Africa: A review of current and？future trends and impacts on agriculture and food？security. Environment, Development and Sustainability？13(3), 587-605.
NIMS (National Institute of Meteorological Science),？2022: South Korea Detailed Climate Change？Outlook Report (Revised Edition), Korea, Jeju,？p1-77 (available at http://www.climate.go.kr/).
RDA (Rural Development Administration), 2021:？Technology Guide on Downscaling Data？Production for Agricultural Applications using SSP？Scenarios. 49p, Wanju, Jeonbuk, Korea.
Seferian, R. 2019: CNRM-CERFACS CNRM-ESM2-1？model output prepared for CMIP6 ScenarioMIP.？Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1395
Schupfner, M., K.-H. Wieners, F. Wachsmann, C.？Steger, M. Bittner, J. Jungclaus, B. Fruh, K.？Pankatz, M. Giorgetta, C. Reick, S. Legutke, M.？Esch, V. Gayler, H. Haak, P. de Vrese, T.？Raddatz, T. Mauritsen, J.-S. von Storch, J.？Behrens, V. Brovkin, M. Claussen, T. Crueger, I.？Fast, S. Fiedler, S. Hagemann, C. Hohenegger, T.？Jahns, S. Kloster, S. Kinne, G. Lasslop, L.？Kornblueh, J. Marotzke, D. Matei, K. Meraner, U.？Mikolajewicz, K. Modali, W. Muller, J. Nabel, D.？Notz, K. Peters, R. Pincus, H. Pohlmann, J.？Pongratz, S. Rast, H. Schmidt, R. Schnur, U.？Schulzweida, K. Six, B. Stevens, A. Voigt, and E.？Roeckner, 2020: CMIP6 CMIP DKRZ？MPI-ESM1-2-HR amip - RCM-forcing data. World？Data Center for Climate (WDCC) at DKRZ.？https://doi.org/10.26050/WDCC/RCM_CMIP6_AMIP-HR
Tang, Y., S. Rumbold, R. Ellis, D. Kelley, J.？Mulcahy, A. Sellar, J. Walton, and C. Jones,？2019: MOHC UKESM1.0-LL model output？prepared for CMIP6 CMIP historical. Earth？System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.6113
Voldoire, A. 2019: CNRM-CERFACS CNRM-CM6-1？model output prepared for CMIP6 ScenarioMIP.？Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1384
Volodin, E., E. Mortikov, A. Gritsun, V. Lykossov,？V. Galin, N. Diansky, A. Gusev, S. Kostrykin, N.？Iakovlev, A. Shestakova, and S. Emelina, 2019a:？INM INM-CM4-8 model output prepared for？CMIP6 ScenarioMIP. https://doi.org/10.22033/ESGF/CMIP6.12321
Volodin, E., E. Mortikov, A. Gritsun, V. Lykossov,？V. Galin, N. Diansky, A. Gusev, S. Kostrykin, N.？Iakovlev, A. Shestakova, and S. Emelina, 2019b:？INM INM-CM5-0 model output prepared for？CMIP6 ScenarioMIP. https://doi.org/10.22033/ESGF/CMIP6.12322
WHO, 2019: Food safety, climate change and the？role of WHO. from https://www.who.int/publications/i/item/food-safety-climate-change-and-the-role-of-who.？(Accessed 12 February 2022).
Wieners, K.-H., M. Giorgetta, J. Jungclaus, C. Reick,？M. Esch, M. Bittner, V. Gayler, H. Haak, P. de？Vrese, T. Raddatz, T. Mauritsen, J.-S. von Storch,？J. Behrens, V. Brovkin, M. Claussen, T. Crueger,？I. Fast, S. Fiedler, S. Hagemann, C. Hohenegger,？T. Jahns, S. Kloster, S. Kinne, G. Lasslop, L.？Kornblueh, J. Marotzke, D. Matei, K. Meraner, U.？Mikolajewicz, K. Modali, W. Muller, J. Nabel, D.？Notz, K. Peters-von Gehlen, R. Pincus, H.？Pohlmann, J. Pongratz, S. Rast, H. Schmidt, R.？Schnur, U. Schulzweida, K. Six, B. Stevens, A.？Voigt, and E. Roeckner, 2019: MPI-M？MPIESM1.2-LR model output prepared for CMIP6？ScenarioMIP. Earth System Grid Federation.？https://doi.org/10.22033/ESGF/CMIP6.793
Yukimoto, S., T. Koshiro, H. Kawai, N. Oshima, K.？Yoshida, S. Urakawa, H. Tsujino, M. Deushi, T.？Tanaka, M. Hosaka, H. Yoshimura, E. Shindo, R.？Mizuta, M. Ishii, A. Obata, and Y. Adachi, 2019:？MRI MRI-ESM2.0 model output prepared for？CMIP6 ScenarioMIP. Earth System Grid？Federation. https://doi.org/10.22033/ESGF/CMIP6.638
Ziehn, T., M. A. Chamberlain, R. M. Law, A.？Lenton, R. W. Bodman, M. Dix, L. Stevens,？Y.-P. Wang and J. Srbinovsky, 2020: The？Australian Earth System Model: ACCESS-ESM1.5.？Journal of Southern Hemisphere Earth Systems？Science 70(1), 193-214.

저자의 다른 논문 :

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증