DOI QR코드

DOI QR Code

리눅스 기반 U.K. 지구시스템모형을 이용한 동아시아 SO2 배출에 따른 기상장 변화

Changes in Meteorological Variables by SO2 Emissions over East Asia using a Linux-based U.K. Earth System Model

  • 윤대옥 (충북대학교 지구과학교육과) ;
  • 송형규 (충북대학교 지구과학교육과) ;
  • 이조한 (국립기상과학원 현업운영개발부)
  • Youn, Daeok (Department of Earth Science Education, Chungbuk National University) ;
  • Song, Hyunggyu (Department of Earth Science Education, Chungbuk National University) ;
  • Lee, Johan (Climate Model Development Team, Operational Systems Development Department, National Institute of Meteorological Sciences)
  • 투고 : 2022.02.08
  • 심사 : 2022.02.17
  • 발행 : 2022.02.28

초록

본 연구는 기상청에서 운용 중인 영국 the United Kingdom Earth System Model (UKESM)을 리눅스 클러스터에 설치하여 과거 28년 기간에 대해 적분을 수행하고, 추가적인 수치 실험을 수행하여 얻은 결과와 비교한다. 설치한 UKESM은 저해상도 버전이지만, 대류권 대기 화학-에어로졸 과정과 성층권 오존 화학 과정을 동시에 모의하는 United Kingdom Chemistry and Aerosol (UKCA) 모듈을 포함하고 있는 최신 버전이다. 본 연구에 사용된 UKCA가 포함된 UKESM (UKESM-UKCA)은 전체 대기에서의 화학, 에어로졸, 구름, 복사 과정이 연동된 모델이다. CMIP5 기존 배출량 자료를 사용하는 UKESM 기준 적분 수치 모의와 함께, 동아시아 지역 이산화황(SO2) 배출이 기상장에 미치는 영향을 평가하기 위하여 CMIP5 SO2 배출량 대신 최신의 REAS 배출자료로 교체한 실험 적분 수치 모의를 수행하였다. 두 수치 모의의 기간은 모두 1982년 1월 1일부터 2009년 12월 31일까지 총 28년이며, 모델 결과는 동아시아 지역 에어로졸 광학 두께, 2-m 온도, 강수 강도의 시간 평균값과 시간 변화 경향의 공간 분포를 분석하고 관측자료와 비교하였다. 모델에서 얻어진 온도와 강수 강도의 공간 분포 패턴은 관측자료와 전반적으로 유사하였다. 또한 UKESM에서 모의된 오존 농도와 오존전량의 공간 분포도 위성 관측 자료와 분포 패턴이 일치하였다. 두 UKESM 실험 적분 모의 결과로 얻어진 온도와 강수 강도의 선형 변화 경향의 비교를 통해, 동아시아 지역 SO2 지면 배출은 서태평양과 중국 북부지역에 대한 온도와 강수량의 변화 경향에 중요한 요인임을 확인할 수 있었다. 본 연구를 통해 슈퍼컴퓨터에서만 운용되던 UKESM이 리눅스 클러스터 컴퓨팅 환경에도 설치되어 운용이 가능하다는 점을 제시한다. 대기 환경 및 탄소순환을 연구하는 다양한 분야의 연구자들에게도 대기-해양-지면-해빙이 상호작용하는 UKESM와 같은 지구시스템모델이 활용될 가능성과 접근성이 높아졌다.

This study presents a software full setup and the following test execution times in a Linux cluster for the United Kingdom Earth System Model (UKESM) and then compares the model results from control and experimental simulations of the UKESM relative to various observations. Despite its low resolution, the latest version of the UKESM can simulate tropospheric chemistry-aerosol processes and the stratospheric ozone chemistry using the United Kingdom Chemistry and Aerosol (UKCA) module. The UKESM with UKCA (UKESM-UKCA) can treat atmospheric chemistryaerosol-cloud-radiation interactions throughout the whole atmosphere. In addition to the control UKESM run with the default CMIP5 SO2 emission dataset, an experimental run was conducted to evaluate the aerosol effects on meteorology by changing atmospheric SO2 loading with the newest REAS data over East Asia. The simulation period of the two model runs was 28 years, from January 1, 1982 to December 31, 2009. Spatial distributions of monthly mean aerosol optical depth, 2-m temperature, and precipitation intensity from model simulations and observations over East Asia were compared. The spatial patterns of surface temperature and precipitation from the two model simulations were generally in reasonable agreement with the observations. The simulated ozone concentration and total column ozone also agreed reasonably with the ERA5 reanalyzed one. Comparisons of spatial patterns and linear trends led to the conclusion that the model simulation with the newest SO2 emission dataset over East Asia showed better temporal changes in temperature and precipitation over the western Pacific and inland China. Our results are in line with previous finding that SO2 emissions over East Asia are an important factor for the atmospheric environment and climate change. This study confirms that the UKESM can be installed and operated in a Linux cluster-computing environment. Thus, researchers in various fields would have better access to the UKESM, which can handle the carbon cycle and atmospheric environment on Earth with interactions between the atmosphere, ocean, sea ice, and land.

키워드

과제정보

이 연구는 기상청 <기후 및 기후변화 감시·예측정보 응용 기술개발> (KMI 2020-01110)의 지원으로 수행되었습니다. 본 논문의 완성도를 높이도록 검토 의견과 조언을 주신 편집위원님과 두 분의 심사위원님께 감사드립니다.

참고문헌

  1. Abraham, N.L., 2021, United Kingdom Chemistry and Aerosol (UKCA) Technical Description, Met Office, Exter, UK, 149 p
  2. Ackermann, I.J., Hass, H., Memmesheimer, M., 1998, Modal aerosol dynamics model for Europe: development and first applications, Atmospheric Environment, 32(17), 2981-2999. https://doi.org/10.1016/S1352-2310(98)00006-5
  3. Albrecht, B.A., 1989, Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227-1230. https://doi.org/10.1126/science.245.4923.1227
  4. Andres, R.J. and Kasgnoc, A.D., 1998, A time-averaged inventory of subaerial volcanic sulfur emissions, Journal of Geophysical Research, 103(D19), 25251-25261. https://doi.org/10.1029/98JD02091
  5. Annette K.M., Paul R.F., Adrian A.H. et al., 2018, Aerosol-cloud interactions in mixed-phase convective clouds-Part 1: Aerosol perturbations, Atmospheric Chemistry and Physics, 18(5), 3119-3145. https://doi.org/10.5194/acp-18-3119-2018
  6. Archibald, A.T., O'Connor, F.M., Abraham, N.L. et al., 2020, Description and evaluation of the UKCA stratosphere-troposphere chemistry (UKCA StratTrop) as implemented in UKESM1, Geoscientific Model Development, 13, 1223-1266, doi: 10.5194/gmd-13-1223-2020.
  7. Baijun, T., Duane, E. Waliser, R. et al., 2011, Modulation of Atlantic aerosols by the Madden-Julian Oscillation, Journal of Geophysical Research, 116(D15). 1-12.
  8. Carver, G.D., Brown, P.D., and Wild, O., 1997, The ASAD atmospheric chemistry integration package and chemical reaction database, Computer Physics Communications, 105, 197-215. https://doi.org/10.1016/S0010-4655(97)00056-8
  9. Christensen, M.W., Jones, W.K., and Stier, P., 2020, Aerosols enhance cloud lifetime and brightness along the stratus-to-cumulus transition, Proceedings of the National Academy of Sciences, 117(30), 17591-17598. https://doi.org/10.1073/pnas.1921231117
  10. Collins, W.J., Bellouin, N., Doutriaux-Boucher, M. et al., 2011, Development and evaluation of an Earth-system model-HadGEM2, Geoscientific Model Development, 4, 1051-1075. https://doi.org/10.5194/gmd-4-1051-2011
  11. Craig, A., Valcke, S., and Coquart, L., 2017, Development and performance of a new version of the OASIS coupler, OASIS3-MCT_3.0, Geoscientific Model Development, 10(9), 3297-3308. https://doi.org/10.5194/gmd-10-3297-2017
  12. Cullen, M.J.P., 1993, The Unified Forecast/Climate Model, Meteorological Magazine, 122, 81-94.
  13. Danabasoglu, G., Lamarque, J.F., Bacmeister, J. et al., 2020, The Community Earth System Model Version 2 (CESM2), Journal of Advances in Modeling Earth Systems, 12(2), 1-35.
  14. Davies, T., Cullen, M.J.P., Malcolm, A.J. et al., 2005, A new dynamical core for the Met Office's global and regional modelling of the atmosphere, Quarterly Journal of the Royal Meteorological Society, 131(608), 1759-1782. https://doi.org/10.1256/qj.04.101
  15. Dunne J.P., John, J.G., Adcroft, A.J. et al., 2012, GFDL's ESM2 global coupled climate-carbon Earth System Models. Part I: Physical formulation and baseline simulation characteristics, Journal of Climate, 25, 6646-6665. https://doi.org/10.1175/JCLI-D-11-00560.1
  16. Dunne J.P., John, J.G., Shevliakova, E. et al., 2013, GFDL's ESM2 global coupled climate-carbon Earth System Models. Part II: Carbon system formulation and baseline simulation characteristics, Journal of Climate, 26, 2247-2267. https://doi.org/10.1175/JCLI-D-12-00150.1
  17. Edwards, J.M. and Slingo, A., 1996, Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model. Quarterly Journal of the Royal Meteorological Society, 122, 689-719. https://doi.org/10.1002/qj.49712253107
  18. Gates, W.L., 1992, AMIP: The Atmospheric Model Intercomparison Project, Bulletin of the American Meteorological Society, 73(12), 1962-1970. https://doi.org/10.1175/1520-0477(1992)073<1962:ATAMIP>2.0.CO;2
  19. Giorgetta, M.A., Jungclaus, J., Reick, C.H. et al., 2013, Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5, Journal of Advances in Modeling Earth Systems, 5, 572-597. https://doi.org/10.1002/jame.20038
  20. Gobron N., 2011, Envisat's Medium Resolution Imaging Spectrometer (MERIS) Algorithm Theoretical Basis Document: FAPAR and Rectified Channels over Terrestrial Surfaces, Publications Office of the European Union, Luxembourg, 27 p.
  21. Griffies, S.M., Danabasoglu, G., Durack. P.J. et al., 2016, OMIP contribution to CMIP6: Experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project, Geoscientific Model Development, 9, 3231-3296. https://doi.org/10.5194/gmd-9-3231-2016
  22. Guido, W., James, R., Louis, G. et al., 2010, The improved Global Fire Emissions Database (GFED) version 3: Contribution of savanna, forest, deforestation, and peat fires to the global fire emissions budget, Geophysical Research Abstracts, 12, 1 p.
  23. Hazeleger, W., Severijns, C., Semmler, T. et al, 2010, ECEarth A Seamless Earth-System Prediction Approach in Action, Bulletin of the American Meteorological Society, 91, 1357-1363. https://doi.org/10.1175/2010BAMS2877.1
  24. Hersbach, H., Bell, B., Berrisford, P. et al., 2020, The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999-2049. https://doi.org/10.1002/qj.3803
  25. Hurrell, J.W., Holland, M.M., Gent, P.R. et al, 2013, The Community Earth System Model: A Framework for Collaborative Research, Bulletin of American Meteorological Society, 94(9), 1339-1360. https://doi.org/10.1175/BAMS-D-12-00121.1
  26. Hunke, Elizabeth, and Lipscomb, W., 2010, CICE: The Los Alamos Sea Ice Model Documentation and Software User's Manual Version 4.1, LA-CC-06-012, 76 p.
  27. Kawamiya, M., Hajima, T., Tachiiri, K. et al., 2020, Two decades of Earth system modeling with an emphasis on Model for Interdisciplinary Research on Climate (MIROC). Progress in Earth and Planetary Science, 7, 1-13, doi:10.1186/s40645-020-00369-5
  28. Kim, A.H., Yum, S.S., Jang, D.Y., 2018, Sensitivity test of the parameterization method of cloud droplet activation process in model simulation of cloud formation, Atmosphere, 28(2), 211-222. (in Korean) https://doi.org/10.14191/ATMOS.2018.28.2.211
  29. Kim, H.R., Hong, J.W., Lim, Y.J. et al, 2019, Evaluation of JULES land surface model based on in-situ data of NIMS flux sites, Atmosphere. Korean Meteorological Society, 29(4), 355-365. (in Korean)
  30. Kim, S.Y., Park, S.M., and Son, S.W., 2021, Evaluation of the total column ozone and tropospheric ozone in the CCMI-1 models over East Asia, Journal of Climate Change Research, 12, 215-229. (in Korean) https://doi.org/10.15531/KSCCR.2021.12.3.215
  31. Kurokawa, J. and Ohara, T., 2020, Long-term historical trends in air pollutant emissions in Asia: Regional Emission inventory in ASia (REAS) version 3, Atmospheric Chemistry and Physics, 20, 12761-12793. https://doi.org/10.5194/acp-20-12761-2020
  32. Lary, D.J., and Pyle, J.A., 1991, Diffuse radiation, twilight, and photochemistry-II, Journal of Atmospheric Chemistry, 13, 393-406. https://doi.org/10.1007/BF00057754
  33. Lee, S.B. and Ahn, J.B., 2014, Sensitivity study of simulated sea-ice concentration and thickness using a Global Sea-Ice Model (CICE), Atmosphere, 24(4), 555-563. (in Korean) https://doi.org/10.14191/Atmos.2014.24.4.555
  34. Liu, S., Xing, J., Zhao, B. et al., 2019, Understanding of aerosol-climate interactions in China: Aerosol impacts on solar radiation, temperature, cloud, and precipitation and its changes under future climate and emission scenarios, Current Pollution Report, 5, 36-51. https://doi.org/10.1007/s40726-019-00107-6
  35. Madec, G, Delecluse, P, and Imbard, M, 1998, OPA 8.1 Ocean general circulation model reference manual, Note du Pole de Modelisation, 91 p.
  36. Mann, G.W., Carslaw, K.S., Spracklen, D.V. et al., 2010, Description and evaluation of GLOMAP-mode: A modal global aerosol microphysics model for the UKCA composition-climate model, Geoscientific Model Development, 3, 519-551. https://doi.org/10.5194/gmd-3-519-2010
  37. Masson-Delmotte, V.P., Zhai, A., Pirani, S.L. et al., 2021, IPCC: Climate Change 2021: The Physical Science Basis, Cambridge University Press, UK, 3949 p.
  38. Matthew, W.C., William, K.J., and Philip, S., 2020, Aerosols enhance cloud lifetime and brightness along the stratus-to-cumulus transition, Proceedings of the National Academy of Sciences, 117(30), 17591-17598, doi:10.1073/pnas.1921231117.
  39. Meehl, G.A., Boer, G.J., Covey, C. et al, 2000, The Coupled Model Intercomparison Project (CMIP), Bulletin of the American Meteorological Society, 81(2). 313-318. https://doi.org/10.1175/1520-0477(2000)081<0313:TCMIPC>2.3.CO;2
  40. Menon, S., Unger, N., Koch, D. et al., 2008, Aerosol climate effects and air quality impacts from 1980 to 2030, Environmental Research Letters, 3, 1-12.
  41. Morgenstern, O., Braesicke, P., O'Connor, F.M. et al., 2009, Evaluation of the new UKCA climate-composition model-Part 1: The stratosphere, Geoscientific Model Development, 2, 43-57. https://doi.org/10.5194/gmd-2-43-2009
  42. Nadine G., Bernard P., Frederic M. et al., 2007, Evaluation of the MERIS/ENVISAT FAPAR product, Advances in Space Research, 39(1), 105-115. https://doi.org/10.1016/j.asr.2006.02.048
  43. Neu, J.L., Prather, M.J., and Penner, J.E., 2007, Global atmospheric chemistry: Integrating over fractional cloud cover, Journal of Geophysical Research-Atmospheres, 112(D11), 1-12.
  44. Oliver, H.J., Shin, M., Fitzpatrick, B. et al., Cylc-a workflow engine, available at: http://cylc.github.io/cylc/ (last access: 2 February 2022).
  45. Pazmino, A., Godin-Beekmann, S., Hauchecorne, A. et al., 2018, Multiple symptoms of total ozone recovery inside the Antarctic vortex during austral spring, Atmospheric Chemistry and Physics, 18, 7557-7572. https://doi.org/10.5194/acp-18-7557-2018
  46. Schell, B., Ackermann, I.J., and Has, H., 2001, Modeling the formation of secondary organic aerosol within a comprehensive air quality model system, Journal of Geophysical Research, 106(D22), 275-293. https://doi.org/10.1029/2000JA000066
  47. Seinfeld, J.H. and Pandis, S.N., 2016, Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, John Wiley & Sons, Hoboken, USA, 1152 p.
  48. Sellar, A.A., Jones, C.G., Mulcahy, J.P. et al., 2019, UKESM1: Description and evaluation of the U.K. Earth system model, Journal of Advances in Modeling Earth Systems, 11, 4513-4558. https://doi.org/10.1029/2019ms001739
  49. Shin, M., Fitzpatrick, B., Clark, A. et al., Rose: a framework for managing and running meteorological suites, available at: http://metomi.github.io/rose/doc/rose.html/ (last access: 2 February 2022).
  50. Shin, M., Fitzpatrick, B., Matthews, D. et al., FCM: Flexible Configuration Management, available at: http://metomi.github.io/fcm/doc/ (last access: 2 February 2022).
  51. Staniforth, A., Melvin, T., and Wood, N., 2014, GungHo! A new dynamical core for the Unified Model, Met office, Exeter, UK, 15 p.
  52. Tian, B., Waliser, D.E., Kahn, R.A. et al., 2011, Modulation of Atlantic aerosols by the Madden-Julian oscillation, Journal of Geophysical Research, 116(D15108), 1-12.
  53. Twomey, S., 1974, Pollution and the planetary albedo, Atmospheric Environment, 8(12), 1251-1256. https://doi.org/10.1016/0004-6981(74)90004-3
  54. van der Werf, G.R., Randerson, J.T., Giglio, L., et al., 2010, Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009), Atmospheric Chemistry and Physics, 10, 11707-11735. https://doi.org/10.5194/acp-10-11707-2010
  55. Veefkind, J.P., Leeuw, G., Stammes, P. et al., 2000, Regional distribution of aerosol over land, derived from ATSR-2 and GOME, Remote Sensing of Environment, 74(3), 377-386. https://doi.org/10.1016/s0034-4257(00)00106-1
  56. Walters, D.N., Best, M.J., Bushell, A.C. et al., 2011, The Met Office Unified Model Global Atmosphere 3.0/3.1 and JULES Global Land 3.0/3.1 configurations, Geoscientific Model Development, 4, 919-941. doi: 10.5194/gmd-4-919-2011
  57. Wild, O. and Prather, M.J., 2000, Excitation of the primary tropospheric chemical mode in a three-dimensional model, Journal of Geophysical Research, 105(D20), 24647-24660. https://doi.org/10.1029/2000JD900399
  58. Wild, O., Zhu, X., and Prather, M.J., 2000, Fast-J: Accurate Simulation of In- and Below-Cloud Photolysis in Tropospheric Chemical Models, Journal of Atmospheric Chemistry, 37, 245-282. https://doi.org/10.1023/A:1006415919030
  59. Wood, N., Staniforth, A., White, A. et al., 2014, An inherently mass-conserving semi-implicit semi-Lagrangian discretization of the deep-atmosphere global nonhydrostatic equations, Quarterly Journal of the Royal Meteorological Society, 140, 1505-1520. https://doi.org/10.1002/qj.2235