Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

Application of Land Initialization and its Impact in KMA's Operational Climate Prediction System

현업 기후예측시스템에서의 지면초기화 적용에 따른 예측 민감도 분석

  • Lim, Somin (Climate Model Development Team, Operational Systems Development Department, National Institute of Meteorological Sciences) ;
  • Hyun, Yu-Kyung (Climate Model Development Team, Operational Systems Development Department, National Institute of Meteorological Sciences) ;
  • Ji, Heesook (Climate Model Development Team, Operational Systems Development Department, National Institute of Meteorological Sciences) ;
  • Lee, Johan (Climate Model Development Team, Operational Systems Development Department, National Institute of Meteorological Sciences)
  • 임소민 (국립기상과학원 현업운영개발부 기후모델개발팀) ;
  • 현유경 (국립기상과학원 현업운영개발부 기후모델개발팀) ;
  • 지희숙 (국립기상과학원 현업운영개발부 기후모델개발팀) ;
  • 이조한 (국립기상과학원 현업운영개발부 기후모델개발팀)
  • Received : 2021.08.01
  • Accepted : 2021.09.16
  • Published : 2021.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, the impact of soil moisture initialization in GloSea5, the operational climate prediction system of the Korea Meteorological Administration (KMA), has been investigated for the period of 1991~2010. To overcome the large uncertainties of soil moisture in the reanalysis, JRA55 reanalysis and CMAP precipitation were used as input of JULES land surface model and produced soil moisture initial field. Overall, both mean and variability were initialized drier and smaller than before, and the changes in the surface temperature and pressure in boreal summer and winter were examined using ensemble prediction data. More realistic soil moisture had a significant impact, especially within 2 months. The decreasing (increasing) soil moisture induced increases (decreases) of temperature and decreases (increases) of sea-level pressure in boreal summer and its impacts were maintained for 3~4 months. During the boreal winter, its effect was less significant than in boreal summer and maintained for about 2 months. On the other hand, the changes of surface temperature were more noticeable in the southern hemisphere, and the relationship between temperature and soil moisture was the same as the boreal summer. It has been noted that the impact of land initialization is more evident in the summer hemispheres, and this is expected to improve the simulation of summer heat wave in the KMA's operational climate prediction system.

Keywords

Acknowledgement

이 연구는 기상청 국립기상과학원 「기후예측 현업 시스템 개발」 (KMA2018-00322)의 지원으로 수행되었습니다.

References

  1. Berrisford, P., D. P. Dee, M. Fielding, M. Fuentes, P. W. Kallberg, S. Kobayashi, and S. Uppala, 2009: The ERA-Interim archive. ERA Report Series, Tech. Rep. 16 pp.
  2. Best, M. J., and Coauthors, 2011: The Joint UK Land Environment Simulator (JULES), model description - Part 1: Energy and water fluxes. Geosci. Model Dev., 4, 677-699, doi:10.5194/gmd-4-677-2011.
  3. Bowler, N. E., A. Arribas, S. E. Beare, K. R. Mylne, and G. J. Shutts, 2009: The local ETKF and SKEB: Upgrades to the MOGREPS short-range ensemble prediction system. Q. J. R. Meteorol. Soc., 135, 767-776, doi:10.1002/qj.394.
  4. Brown, A., S. Milton, M. Cullen, B. Golding, J. Mitchell, and A. Shelly, 2012: Unified modeling and prediction of weather and climate: A 25-year journey. Bull. Amer. Meteor. Soc., 93, 1865-1877, doi:10.1175/BAMSD-12-00018.1.
  5. Dirmeyer, P. A., 2003: The role of the land surface background state in climate predictability. J. Hydrometeor., 4, 599-610, doi:10.1175/1525-7541(2003)004<0599:TROTLS>2.0.CO;2.
  6. Ebita, A., and Coauthors, 2011: The Japanese 55-year reanalysis "JRA-55": An interim report. SOLA, 7, 149-152, doi:10.2151/sola.2011-038.
  7. Entin, J. K., A. Robock, K. Y. Vinnikov, S. E. Hollinger, S. Liu, and A. Namkhai, 2000: Temporal and spatial scales of observed soil moisture variations in the extratropics. J. Geophys. Res. Atmos., 105, 11865-11877, doi:10.1029/2000JD900051.
  8. Fischer, E. M., S. I. Seneviratne, P. L. Vidale, D. Luthi, and C. Schar, 2007: Soil moisture-atmosphere interactions during the 2003 European summer heat wave. J. Climate, 20, 5081-5099, doi:10.1175/JCLI4288.1.
  9. Giorgi, F., and R. Francisco, 2000: Uncertainties in regional climate change prediction: a regional analysis of ensemble simulations with the HADCM2 coupled AOGCM. Climate Dyn., 16, 169-182, doi:10.1007/PL00013733.
  10. Hunke, E. C., and W. H. Lipscomb, 2010: CICE: the Los Alamos sea ice model documentation and software user's manual, Version 4.1, LA-CC-06-012. Tech. rep., Los Alamos National Laboratory, 76 pp.
  11. Hwang, S. O., and Coauthors, 2020: Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. National Institute Meteorological Sciences, 186 pp [Available online at https://policy.nl.go.kr/search/searchDetail.do?rec_key=SH1_UMO20201209700&kwd=] (in Korean).
  12. Hyun, Y.-K., J. Park, J. Lee, S. Lim, S.-I. Heo, H. Ham, S.-M. Lee, H.-S. Ji, and Y. Kim, 2020: Reliability assessment of temperature and precipitation seasonal probability in current climate prediction systems. Atmosphere, 30, 141-154, doi:10.14191/Atmos.2020.30.2.141 (in Korean with English abstract).
  13. Jeong, J.-H., H. W. Linderholm, S.-H. Woo, C. Folland, B.-M. Kim, S.-J. Kim, and D. Chen, 2013: Impacts of snow initialization on subseasonal forecasts of surfa ce a ir tempera ture for the cold sea son. J. Climate, 26, 1956-1972, doi:10.1175/JCLI-D-12-00159.1.
  14. Jeong, J.-H., and Coauthors, 2017: The status and prospect of seasonal climate prediction of climate over Korea and East Asia: A review. Asia-Pac. J. Atmos. Sci., 53, 149-173, doi:10.1007/s13143-017-0008-5.
  15. Joo, J. Y., M. Choi, S. W. Jung, and S. O. Lee, 2010: Prediction of soil moisture using hydrometeorological data in Selmacheon. KSCE J. CEER, 30, 437-444 (in Korean with English abstract).
  16. Kim, S. O., M.-S. Suh, and K. Chong-Heum, 2005: Climatological characteristics in the variation of soil temperature in Korea. J. Korean Earth Sci. Soc., 26, 93- 105 (in Korean with English abstract).
  17. Koster, R. D., and M. J. Suarez, 2003: Impact of land surface initialization on seasonal precipitation and temperature prediction. J. Hydrometeor., 4, 16.
  18. Koster, R. D., M. J. Suarez, P. Liu, U. Jambor, A. Berg, M. Kistler, R. Reichle, M. Rodell, and J. Famiglietti, 2004: Realistic initialization of land surface states: Impacts on subseasonal forecast skill. J. Hydrometeor., 5, 1049-1063, doi:10.1175/JHM-387.1.
  19. Koster, R. D., and Coauthors, 2010: Contribution of land surface initialization to subseasonal forecast skill: First results from a multi-model experiment. Geophys. Res. Lett., 37, L02402, doi:10.1029/2009GL041677.
  20. Koster, R. D., and Coauthors, 2011: The second phase of the Global Land-Atmosphere Coupling Experiment: Soil moisture contributions to subseasonal forecast skill. J. Hydrometeor., 12, 805-822, doi:10.1175/2011JHM1365.1.
  21. Lea, D. J., I. Mirouze, M. J. Martin, R. R. King, A. Hines, D. Walters, and M. Thurlow, 2015: Assessing a new coupled data assimilation system based on the Met Office coupled atmosphere-land-ocean-sea ice model. Mon. Wea. Rev., 143, 4678-4694, doi:10.1175/MWRD-15-0174.1.
  22. MacLachlan, C., and Coauthors, 2015: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Q. J. R. Meteorol. Soc., 141, 1072-1084, doi:10.1002/qj.2396.
  23. Madec, G., 2008: NEMO Ocean Engine. Note du Pole de Modelisation, 27, Institute Pierre-Simon Laplace (IPSL), 300 pp.
  24. Milly, P. C. D., and K. A. Dunne, 2001: Trends in evaporation and surface cooling in the Mississippi River Basin. Geophys. Res. Lett., 28, 1219-1222, doi:10.1029/2000GL012321.
  25. National Academies of Sciences, Engineering, and Medicine, 2016: Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. The National Academies Press. 350 pp, doi:10.17226/21873.
  26. Reichle, R. H., and R. D. Koster, 2004: Bias reduction in short records of satellite soil moisture. Geophys. Res. Lett., 31, L19501, doi:10.1029/2004GL020938.
  27. Reichle, R. H., G. De Lannoy, R. D. Koster, W. T. Crow, J. S. Kimball, and Q. Liu. 2020: SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update, Version 5. NASA National Snow and Ice Data Center Distributed Active Archive Center, doi:10.5067/0D8JT6S27BS9.
  28. Robertson, A. W., A. Kumar, M. Pena, and F. Vitart, 2015: Improving and promoting subseasonal to seasonal prediction. Bull. Amer. Meteor. Soc., 96, ES49-ES53, doi:10.1175/BAMS-D-14-00139.1.
  29. Sehler, R., J. Li, J. T. Reager, and H. Ye, 2019: Investigating relationship between soil moisture and precipitation globally using remote sensing observations. J. Contemp. Water Res. Edu., 168, 106-118, doi:10.1111/j.1936-704X.2019.03324.x.
  30. Seo, E., M.-I. Lee, J.-H. Jeong, H.-S. Kang, and D.-J. Won, 2016: Improvement of soil moisture initialization for a global seasonal forecast system. Atmosphere, 26, 35-45, doi:10.14191/Atmos.2016.26.1.035 (in Korean with English abstract).
  31. Seo, E., and Coauthors, 2019: Impact of soil moisture initialization on boreal summer subseasonal forecasts: mid-latitude surface air temperature and heat wave events. Climate Dyn., 52, 1695-1709, doi:10.1007/s00382-018-4221-4.
  32. Seo, E., M.-I. Lee, S. D. Schubert, R. D. Koster, and H.-S. Kang, 2020: Investigation of the 2016 Eurasia heat wave as an event of the recent warming. Environ. Res. Lett., 15, 114018, doi:10.1088/1748-9326/abbbae.
  33. Tennant, W. J., G. J. Shutts, A. Arribas, and S. A. Thompson, 2011: Using a stochastic kinetic energy backscatter scheme to improve MOGREPS probabilistic forecast skill. Mon. Wea. Rev., 139, 1190-1206, doi:10.1175/2010MWR3430.1.
  34. Vitart, F., A. W. Robertson, and D. L. T. Anderson, 2012: Subseasonal to Seasonal Prediction Project: Bridging the gap between weather and climate. WMO Bulletin, 61, 23-28.
  35. Walters, D. N., and Coauthors, 2011: The Met Office Unified Model Global Atmosphere 3.0/3.1 and JULES Global Land 3.0/3.1 configurations. Geosci. Model Dev., 4, 919-941, doi:10.5194/gmd-4-919-2011.
  36. White, C. J., and Coauthors, 2017: Potential applications of subseasonal-to-seasonal (S2S) predictions. Meteor. Appl., 24, 315-325, doi:10.1002/met.1654.
  37. Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 2539-2558, doi:10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2.
  38. Zhang, T., R. G. Barry, D. Gilichinsky, S. S. Bykhovets, V. A. Sorokovikov, and J. Ye, 2001: An amplified signal of climatic change in soil temperatures during the last century at Irkutsk, Russia. Climatic Change, 49, 41-76, doi:10.1023/A:1010790203146.