대기 (Atmosphere)

  • 제34권1호
  • /
  • Pages.55-67
  • /
  • 2024
  • /
  • 1598-3560(pISSN)
  • /
  • 2288-3266(eISSN)
한국기상학회 (Korean Meteorological Society)
권호 표지
DOI QR코드

DOI QR Code

기상청 기후예측시스템(GloSea)의 앙상블 확대를 통해 살펴본 신호대잡음의 역설적 특징(Signal-to-Noise Paradox)과 예측 스킬의 한계

Characteristics of Signal-to-Noise Paradox and Limits of Potential Predictive Skill in the KMA's Climate Prediction System (GloSea) through Ensemble Expansion

  • 현유경 (국립기상과학원 기후연구부 기후모델개발팀) ;
  • 박연희 (국립기상과학원 기후연구부 기후모델개발팀) ;
  • 이조한 (국립기상과학원 기후연구부 기후모델개발팀) ;
  • 지희숙 (국립기상과학원 기후연구부 기후모델개발팀) ;
  • 부경온 (국립기상과학원 기후연구부 기후모델개발팀)
  • Yu-Kyung Hyun (Climate Model Development Team, Climate Research Department, National Institute of Meteorological Sciences) ;
  • Yeon-Hee Park (Climate Model Development Team, Climate Research Department, National Institute of Meteorological Sciences) ;
  • Johan Lee (Climate Model Development Team, Climate Research Department, National Institute of Meteorological Sciences) ;
  • Hee-Sook Ji (Climate Model Development Team, Climate Research Department, National Institute of Meteorological Sciences) ;
  • Kyung-On Boo (Climate Model Development Team, Climate Research Department, National Institute of Meteorological Sciences)
  • 투고 : 2023.11.04
  • 심사 : 2023.11.28
  • 발행 : 2024.02.29
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This paper aims to provide a detailed introduction to the concept of the Ratio of Predictable Component (RPC) and the Signal-to-Noise Paradox. Then, we derive insights from them by exploring the paradoxical features by conducting a seasonal and regional analysis through ensemble expansion in KMA's climate prediction system (GloSea). We also provide an explanation of the ensemble generation method, with a specific focus on stochastic physics. Through this study, we can provide the predictability limits of our forecasting system, and find way to enhance it. On a global scale, RPC reaches a value of 1 when the ensemble is expanded to a maximum of 56 members, underlining the significance of ensemble expansion in the climate prediction system. The feature indicating RPC paradoxically exceeding 1 becomes particularly evident in the winter North Atlantic and the summer North Pacific. In the Siberian Continent, predictability is notably low, persisting even as the ensemble size increases. This region, characterized by a low RPC, is considered challenging for making reliable predictions, highlighting the need for further improvement in the model and initialization processes related to land processes. In contrast, the tropical ocean demonstrates robust predictability while maintaining an RPC of 1. Through this study, we have brought to attention the limitations of potential predictability within the climate prediction system, emphasizing the necessity of leveraging predictable signals with high RPC values. We also underscore the importance of continuous efforts aimed at improving models and initializations to overcome these limitations.

키워드

과제정보

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

참고문헌

  1. Ahn, J.-B., J. Lee, and S. Jo, 2018: Evaluation of PNU CGCM ensemble forecast system for boreal winter temperature over South Korea. Atmosphere, 28, 509-520, doi:10.14191/Atmos.2018.28.4.509 (in Korean with English abstract).
  2. Beare, S., W. Tennant, and C. Sanchez, 2019: Stochastic physics code in the UM. MetOffice Unified Model Doc., 81, 28 pp.
  3. Berner, J., G. J. Shutts, M. Leutbecher, and T. N. Palmer, 2009: A spectral stochastic kinetic energey backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. J. Atmos. Sci., 66, 603-626, doi:10.1175/2008JAS2677.1.
  4. Boer, G. J., 2011: Decadal potential predictability of twenty-first century climate. Clim. Dyn., 36, 1119-1133, doi:10.1007/s00382-010-0747-9.
  5. 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. Quart. J. Roy. Meteor. Soc., 135, 767-776, doi:10.1002/qj.394.
  6. Buizza, R., M. Milleer, and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 125, 2887-2908, doi:10.1002/qj.49712556006.
  7. Charron, M., G. Pellerin, L. Spacek, P. L. Houtekamer, N. Gagnon, H. L. Mitchell, and L. Michelin, 2010: Toward random sampling of model error in the Canadian ensemble prediction system. Mon. Wea. Rev., 138, 1877-1901, doi:10.1175/2009MWR3187.1.
  8. Eade, R., D. Smith, A. Scaife, E. Wallace, N. Dunstone, L. Hermanson, and N. Robinson, 2014: Do seasonal-to-decadal climate predictions underestimate the predictability of the real world? Geophys. Res. Lett., 41, 5620-5628, doi:10.1002/2014GL061146.
  9. Ham, H., D. Won, and Y.-S. Lee, 2017: Performance assessment of weekly ensemble prediction data at seasonal forecast system with high resolution. Atmosphere, 27, 261-276, doi:10.14191/Atmos.2017.27.3.216 (in Korean with English abstract).
  10. Ham, H., S.-M. Lee, Y.-K. Hyun, and Y. Kim, 2019: Performance assessment of monthly ensemble prediction data based on improvement of climate prediction system at KMA. Atmosphere, 29, 149-164, doi:10.14191/Atmos.2019.29.2.149 (in Korean with English abstract).
  11. 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).
  12. Hyun, Y.-K., and Coauthors, 2022: The KMA Global Seasonal forecasting system (GloSea6) - Part 2: Climatological mean bias characteristics. Atmosphere, 32, 87-101, doi:10.14191/Atmos.2022.32.2.087 (in Korean with English abstract).
  13. Kang, D., and M. I. Lee, 2017: Increase in the potential predictability of the Arctic Oscillation via intensified teleconnection with ENSO after the mid-1990s. Climate Dynamics, 49, 2147-2160, doi:10.1007/s00382-016-3436-5.
  14. Kim, S.-W., 2019: Optimal ensemble size for Sub-seasonal to Seasonal (S2S) prediction system. Unpublished master's thesis, Seoul National University, 46 pp [Available online at https://s-space.snu.ac.kr/handle/10371/161653].
  15. Kim, H., J. Lee, Y.-K. Hyun, and S.-O. Hwang, 2021a: The KMA Global Seasonal forecasting system (GloSea6) - Part 1: Operational System and Improvements. Atmosphere, 31, 341-359, doi:10.14191/Atmos.2021.31.3.341 (in Korean with English abstract).
  16. Kim, J.-Y., Y.-K. Hyun, J. Lee, and B.-C. Shin, 2021b: Assessment on East Asian summer monsoon simulation by improved Global Coupled (GC) model. Atmosphere, 31, 563-576, doi:10.14191/Atmos.2021.31.5.563 (in Korean with English abstract).
  17. Murphy, J. M., 1990: Assessment of the practical utility of extended range ensemble forecasts. Quart. J. Roy. Meteorol. Soc., 116, 89-125, doi:10.1002/qj.49711649105.
  18. Palmer, T. N. and R. Buizza, F. Doblas-Reyes, T. Jung, M. Leutbecher, G. J. Shutts, M. Steinheimer, A. Weisheimer, 2009: Stochastic Parametrization and Model Uncertainty. ECMWF Tech. Mem., 598, 42 pp.
  19. Park, Y.-H., Y.-K. Hyun, S.-I. Heo, and H.-S. Ji, 2021: Assessment of the prediction performance of ensemble size-related in GloSea5 hindcast data. Atmosphere, 31, 511-523, doi: 10.14191/Atmos.2021.31.5.511 (in Korean with English abstract).
  20. Raynaud, L., and F. Bouttier, 2017: The impact of horizontal resolution and ensemble size for convective-scale probabilistic forecasts. Quart. J. Roy. Meteor. Soc., 143, 3037-3047, doi:10.1002/qj.3159.
  21. Sanchez, C., K. D. Williams, and M. Collins, 2016: Improved stochastic physics schemes for global weather and climate models. Quart. J. Roy. Meteor. Soc., 142, 147-159, doi:10.1002/qj.2640.
  22. Scaife, A. A., and Coauthors, 2014: Skillful long-range prediction of European and North American winters. Geophys. Res. Lett., 41, 2514-2519, doi:10.1002/2014GL059637.
  23. Scaife, A. A., and D. Smith, 2018: A signal-to-noise paradox in climate science. npj Climate Atmos. Sci., 1, 28, doi:10.1038/s41612-018-0038-4.
  24. Shutts, G., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 131, 3079-3102, doi:10.1256/qj.04.106.
  25. Shutts, G. J., and T. N. Palmer, 2007: Convective forcing fluctuations in a cloud-resolving model: Relevance to the stochastic parametrization problem. J. Climate, 20, 187-202, doi:10.1175/JCLI3954.1.
  26. Talagrand, O., R. Vautard, and B. Strauss, 1997: Evaluation of probabilistic prediction system. In Proc. of Workshop on Predictability, ECMWF, Reading, UK, 1-25.
  27. 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.
  28. Turner, J., T. Phillips, J. S. Hosking, G. J. Marshall, and A. Orr, 2012: The amundsen sea low. Int. J. Climatol., 33, 1818-1829, doi:10.1002/joc.3558.
  29. Weisheimer, A., D. Decremer, D. MacLeod, C. O'Reilly, T. N. Stockdale, S. Johnson, and T. N. Palmer, 2019: How confident are predictability estimates of the winter North Atlantic Oscillation? Quart. J. Roy. Meteor. Soc., 145, 140-159, doi:10.1002/qj.3446.
  30. Weisheimer, A., 2020: Assessing the skill of long range forecast and the predictability paradox. Virtual training course: Predictability and Ensemble Forecast Systems, ECMWF, Reading, UK, 38 pp.