Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

The Seasonal Forecast Characteristics of Tropical Cyclones from the KMA's Global Seasonal Forecasting System (GloSea6-GC3.2)

기상청 기후예측시스템(GloSea6-GC3.2)의 열대저기압 계절 예측 특성

  • Sang-Min Lee (Climate Research Division, National Institute of Meteorological Sciences) ;
  • Yu-Kyung Hyun (Climate Research Division, National Institute of Meteorological Sciences) ;
  • Beomcheol Shin (Climate Research Division, National Institute of Meteorological Sciences) ;
  • Heesook Ji (Climate Research Division, National Institute of Meteorological Sciences) ;
  • Johan Lee (Climate Research Division, National Institute of Meteorological Sciences) ;
  • Seung-On Hwang (Climate Research Division, National Institute of Meteorological Sciences) ;
  • Kyung-On Boo (Climate Research Division, National Institute of Meteorological Sciences)
  • 이상민 (국립기상과학원 기후연구부) ;
  • 현유경 (국립기상과학원 기후연구부) ;
  • 신범철 (국립기상과학원 기후연구부) ;
  • 지희숙 (국립기상과학원 기후연구부) ;
  • 이조한 (국립기상과학원 기후연구부) ;
  • 황승언 (국립기상과학원 기후연구부) ;
  • 부경온 (국립기상과학원 기후연구부)
  • Received : 2024.02.09
  • Accepted : 2024.03.12
  • Published : 2024.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

The seasonal forecast skill of tropical cyclones (TCs) in the Northern Hemisphere from the Korea Meteorological Administration (KMA) Global Seasonal Forecast System version 6 (GloSea6) hindcast has been verified for the period 1993 to 2016. The operational climate prediction system at KMA was upgraded from GloSea5 to GloSea6 in 2022, therefore further validation was warranted for the seasonal predictability and variability of this new system for TC forecasts. In this study, we examine the frequency, track density, duration, and strength of TCs in the North Indian Ocean, the western North Pacific, the eastern North Pacific, and the North Atlantic against the best track data. This methodology follows a previous study covering the period 1996 to 2009 published in 2020. GloSea6 indicates a higher frequency of TC generation compared to observations in the western North Pacific and the eastern North Pacific, suggesting the possibility of more TC generation than GloSea5. Additionally, GloSea6 exhibits better interannual variability of TC frequency, which shows relatively good correlation with observations in the North Atlantic and the western North Pacific. Regarding TC intensity, GloSea6 still underestimates the minimum surface pressures and maximum wind speeds from TCs, as is common among most climate models due to lower horizontal resolutions. However, GloSea6 is likely capable of simulating slightly stronger TCs than GloSea5, partly attributed to more frequent 6-hourly outputs compared to the previous daily outputs.

Keywords

Acknowledgement

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

References

  1. Ahn, S.-H., K.-J. Park, J.-Y. Kim, and B.-J. Kim, 2015: The characteristics of the frequency and damage for meteorological disasters in Korea. J. Korean Soc. Hazard Mitig., 15, 133-144, doi:10.9798/KOSHAM.2015.15.2.133 (in Korean with English abstract).
  2. Basconcillo, J., and I.-J. Moon, 2023: Increasing peak intensity of tropical cyclones passing through the Korean Peninsula. Sci. Rep., 13, 5097, doi:10.1038/s41598-023-32020-w.
  3. Bengtsson, L., K. I. Hodges, M. Esch, N. Keenlyside, L. Kornblueh, J.-J. Luo, and T. Yamagata, 2007: How may tropical cyclones change in a warmer climate? Tellus A, 59, 539-561, doi:10.1111/j.1600-0870.2007.00251.x.
  4. Camargo, S. J., 2013: Global and regional aspects of tropical cyclone activity in the CMIP5 models. J. Climate, 26, 9880-9902, doi:10.1175/jcli-d-12-00549.1.
  5. Camargo, S. J., and Coauthors, 2023: An update on the influence of natural climate variability and anthropogenic climate change on tropical cyclones. Trop. Cyclone Res. Rev., 12, 216-239, doi:10.1016/j.tcrr.2023.10.001.
  6. Camp, J., M. Roberts, C. MacLachlan, E. Wallace, L. Hermanson, A. Brookshaw, A. Arribas, and A. A. Scaife, 2015: Seasonal forecasting of tropical storms using the Met Office GloSea5 seasonal forecast system. Quart. J. Roy. Meteorol. Soc., 141, 2206-2219, doi: 10.1002/qj.2516.
  7. Ho, C.-H., B.-G. Kim, B.-M. Kim, D.-S. R. Park, C.-K. Park, S.-W. Son, J.-H. Jeong, and D.-H. Cha, 2023: Review of the weather hazard research: Focused on typhoon, heavy rain, drought, heat wave, cold surge, heavy snow, and strong gust. Atmosphere, 33, 223-246, doi:10.14191/Atmos.2023.33.2.223 (in Korean with English abstract).
  8. Hodges, K. I., 1999: Adaptive constraints for feature tracking. Mon. Wea. Rev., 127, 1362-1373, doi:10.1175/1520-0493(1999)127<1362:ACFFT>2.0.CO;2.
  9. 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).
  10. Kang, H., C. Son, J. Park, S. Jang, and J. Kim, 2018: A Study on the time-periodic characteristics of changes in typhoon activities and typhoon-induced rainfall over the Korean peninsula. J. Korean. Soc. Hazard Mitig., 18, 395-402, doi:10.9798/KOSHAM.2018.18.7.395 (in Korean with English abstract).
  11. Kim, H., J. Lee, Y.-K. Hyun, and S.-O. Hwang, 2021: 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).
  12. Kim, H.-S., G. A. Vecchi, T. R. Knutson, W. G. Anderson, T. L. Delworth, A. Rosati, F. Zeng, and M. Zhao, 2014: Tropical cyclone simulation and response to CO2 doubling in the GFDL CM2.5 high-resolution coupled climate model. J. Climate, 27, 8034-8054, doi: 10.1175/jcli-d-13-00475.1.
  13. Kim, J., S.-O. Hwang, S.-S. Kim, I. Oh, and D.-J. Ham, 2022: Algorithms for determining Korea Meteorological Administration (KMA)'s official typhoon best tracks in the National Typhoon Center. Atmosphere, 32, 381-394, doi:10.14191/Atmos.2022.32.4.381 (in Korean with English abstract).
  14. Landsea, C. W., and J. L. Franklin, 2013: Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Wea. Rev., 141, 3576-3592, doi:10.1175/mwr-d-12-00254.1.
  15. Lee, S.-M., J.-H. Lee, A-R. Ko, Y.-K. Hyun, and Y. Kim, 2020: Seasonal Forecasting of Tropical Storms using GloSea5 Hindcast. Atmosphere, 30, 209-220, doi: 10.14191/Atmos.2020.30.3.209 (in Korean with English abstract).
  16. Na, H., and W.-S. Jung, 2020: Autumn Typhoon Affecting the Korean Peninsula - Past and present characteristics. J. Korean Soc. Atmos. Environ., 36, 482-491, doi:10.5572/KOSAE.2020.36.4.482 (in Korean with English abstract).
  17. Park, D.-S. R., E. Seo, M. Lee, D.-H. Cha, D. Kim, C.-H. Ho, M.-I. Lee, H.-S. Kim, and S.-K. Min, 2024: Sea surface temperature warming to inhibit mitigation of tropical cyclone destructiveness over East Asia in El Nino . Npj. Clim. Atmos. Sci., 7, 24, doi:10.1038/s41612-023-00556-3.
  18. Strachan, J., P. L. Vidale, K. Hodges, M. Roberts, and M.-E. Demory, 2013: Investigating global tropical cyclone activity with a hierarchy of AGCMs: The role of model resolution. J. Climate, 26, 133-152, doi:10.1175/jcli-d-12-00012.1.
  19. Utsumi, N., and H. Kim, 2022: Observed influence of anthropogenic climate change on tropical cyclone heavy rainfall. Nat. Clim. Chang., 12, 436-440, doi: 10.1038/s41558-022-01344-2.
  20. Walters, D. N., and Coauthors, 2019: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations. Geosci. Model Dev., 12, 1909-1963, doi:10.5194/gmd-12-1909-2019.
  21. WMO, 2021: WMO atlas of mortality and economic losses from weather, climate and water extremes (1970~2019). WMO-No. 1267, World Meteorological Organization, 90 pp [Available online at https://library.wmo.int/viewer/57564/download?file=1267_Atlas_of_Mortality_en.pdf&type=pdf&navigator=1].