Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

Seasonal Forecasting of Tropical Storms using GloSea5 Hindcast

기후예측시스템(GloSea5) 열대성저기압 계절예측 특성

  • Lee, Sang-Min (Operational Systems Development Department, National Institute of Meteorological Sciences) ;
  • Lee, Jo-Han (Operational Systems Development Department, National Institute of Meteorological Sciences) ;
  • Ko, A-Reum (Convergence Meteorological Research Department, National Institute of Meteorological Sciences) ;
  • Hyun, Yu-Kyung (Operational Systems Development Department, National Institute of Meteorological Sciences) ;
  • Kim, YoonJae (Operational Systems Development Department, National Institute of Meteorological Sciences)
  • 이상민 (국립기상과학원 현업운영개발부) ;
  • 이조한 (국립기상과학원 현업운영개발부) ;
  • 고아름 (국립기상과학원 용합기술연구부) ;
  • 현유경 (국립기상과학원 현업운영개발부) ;
  • 김윤재 (국립기상과학원 현업운영개발부)
  • Received : 2020.04.17
  • Accepted : 2020.07.19
  • Published : 2020.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Seasonal predictability and variability of tropical storms (TCs) simulated in the Global Seasonal Forecast System version 5 (GloSea5) of the Korea Meteorological Administration (KMA) is assessed in Northern Hemisphere in 1996~2009. In the KMA, the GloSea5-Global Atmosphere version 3.0 (GloSea5-GA3) that was previously operated was switched to the GloSea5-Global Coupled version 2.0 (GloSea5-GC2) with data assimilation system since May 2016. In this study, frequency, track, duration, and strength of the TCs in the North Indian Ocean, Western Pacific, Eastern Pacific, and North Atlantic regions derived from the GloSea5-GC2 and GloSea5-GA3 are examined against the best track data during the research period. In general, the GloSea5 shows a good skill for the prediction of seasonally averaged number of the TCs in the Eastern and Western Pacific regions, but underestimation of those in the North Atlantic region. Both the GloSea5-GA3 and GC2 are not able to predict the recurvature of the TCs in the North Western Pacific Ocean (NWPO), which implies that there is no skill for the prediction of landfalls in the Korean peninsula. The GloSea5-GC2 has higher skills for predictability and variability of the TCs than the GloSea5-GA3, although continuous improvements in the operational system for seasonal forecast are still necessary to simulate TCs more realistically in the future.

Keywords

References

  1. Bell, R., K. Hodges, P. L. Vidale, J. Strachan, and M. Roberts, 2014: Simulation of the global ENSO-tropical cyclone teleconnection by a high-resolution coupled general circulation model. J. Climate, 27, 6404-6422, doi:10.1175/jcli-d-13-00559.1.
  2. 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.
  3. 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.
  4. Byun, H.-R., 2009: Comparative analysis of the drought diagnosis and related systems. J. Korean Soc. Hazard Mitig., 9, 7-18 (in Korean).
  5. 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.
  6. Camargo, S. J., A. H. Sobel, A. G. Barnston, and P. J. Klotzbach, 2010: The Influence of natural climate variability on tropical cyclones, and seasonal forecasts of tropical cyclone activity. In J. C. L. Chan et al. Eds., Global Perspectives on Tropical Cyclones, World Scientific, 325-360, doi:10.1142/9789814293488 0011.
  7. 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. Q. J. R. Meteorol. Soc., 141, 2206-2219, doi:10.1002/qj.2516.
  8. Chan, J. C. L., J.-E. Shi, and C.-M. Lam, 1998: Seasonal forecasting of tropical cyclone activity over the western North Pacific and the South China Sea. Wea. Forecast., 13, 997-1004, doi:10.1175/1520-0434(1998)013<0997:SFOTCA>2.0.CO;2.
  9. Chan, J. C. L., J.-E. Shi, and K. S. Liu, 2001: Improvements in the seasonal forecasting of tropical cyclone activity over the western North Pacific. Wea. Forecast., 16, 491-498, doi:10.1175/1520-0434(2001)016<0491:IITSFO>2.0.CO;2.
  10. Davies, T., M. J. P. Cullen, A. J. Malcolm, M. H. Mawson, A. Staniforth, A. A. White, and N. Wood, 2005: A new dynamical core for the Met Office's global and regional modelling of the atmosphere. Q. J. R. Meteorol. Soc., 131, 1759-1782, doi:10.1256/qj.04.101, 2005.
  11. Finan, C., H. Wang, and J. Schemm, 2017: CFSv2-based hybrid dynamical-statistical model for week 3 to 4 forecast of Atlantic/Pacific tropical storm activity. Prepints, 42nd NOAA Annual Climate Diagnostics and Prediction Workshop, Norman, OK, Science and Technology Infusion Climate Bulletin, NOAA's National Weather Service, 49-52.
  12. Gray, W. M., 1984: Atlantic seasonal hurricane frequency. Part II: Forecasting its variability. Mon. Wea. Rev., 112, 1669-1683, doi:10.1175/1520-0493(1984)112<1669:ASHFPI>2.0.CO;2.
  13. Ho, C.-H., J.-H. Kim, H.-S. Kim, W. Choi, M.-H. Lee, H.-D. Yoo, T.-R. Kim, and S. Park, 2013: Technical note on a track-pattern-based model for predicting seasonal tropical cyclone activity over the western North Pacific. Adv. Atmos. Sci., 30, 1260-1274, doi:10.1007/s00376-013-2237-6.
  14. Hodges, K. I., 1995: Feature tracking on the unit sphere. Mon. Wea. Rev., 123, 3458-3465, doi:10.1175/1520-0493(1995)123<3458:FTOTUS>2.0.CO;2.
  15. Hodges, K. I., 1996: Spherical nonparametric estimators applied to the UGAMP model integration for AMIP. Mon. Wea. Rev., 124, 2914-2932, doi:10.1175/1520-0493(1996)124<2914:SNEATT>2.0.CO;2.
  16. 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.
  17. Hunke, E. C., and W. H. Lipscomb, 2010: CICE: The sea ice model documentation and software user's manual, version 4.1. Tech. Rep. LA-CC-06-012, 76 pp.
  18. Janiga, M. A., C. J. Schreck, J. A. Ridout, M. Flatau, N. P. Barton, E. J. Metzger, and C. A. Reynolds, 2018: Subseasonal forecasts of convectively coupled equatorial waves and the MJO: activity and predictive skill. Mon. Wea. Rev., 146, 2337-2360, doi:10.1175/MWR-D-17-0261.1.
  19. 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 $CO_2$ doubling in the GFDL CM2.5 high-resolution coupled climate model. J. Climate, 27, 8034-8054, doi:10.1175/jcli-d-13-00475.1.
  20. Kim, H.-S., C.-H. Ho, J.-H. Kim, and P.-S. Chu, 2012: Track-pattern-based model for seasonal prediction of tropical cyclone activity in the western North Pacific. J. Climate, 25, 4660-4678, doi:10.1175/JCLI-D-11-00236.1.
  21. 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.
  22. LaRow, T. E., L. Stefanova, D.-W. Shin, and S. Cocke, 2010: Seasonal Atlantic tropical cyclone hindcasting/forecasting using two sea surface temperature datasets. Geophys. Res. Lett., 37, L02804, doi:10.1029/2009gl041459.
  23. Lee, C.-Y., S. J. Camargo, F. Vitart, A. H. Sobel, and M. K. Tippett, 2018: Subseasonal tropical cyclone genesis prediction and MJO in the S2S dataset. Wea. Forecasting, 33, 967-988, doi:10.1175/WAF-D-17-0165.1.
  24. Lee, S.-M., H.-S. Kang, Y.-H. Kim, Y.-H. Byun, and C. H. Cho, 2016: Verification and comparison of forecast skill between Global Seasonal Forecasting System version 5 and Unified Model during 2014. Atmosphere, 26, 59-72. doi:10.14191/Atmos.2016.26.1.059 (in Korean with English abstract).
  25. Lim, S.-M., Y.-K. Hyun, H.-S. Kang, and S.-W. Yeh, 2018: Prediction skill of East Asian precipitation and temperature associated with El Nino in GloSea5 hindcast data. Atmosphere, 28, 37-51, doi:10.14191/Atmos.2018.28.1.037 (in Korean with English abstract).
  26. MacLachlan, C., and Coauthors, 2014: 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.
  27. Madec, G., 2008: NEMO ocean engine. Note du Pole de modelisation No. 27, Institut Pierre-Simon Laplace (IPSL), 300 pp.
  28. Megann, A., D. Storkey, Y. Aksenov, S. Alderson, D. Calvert, T. Graham, P. Hyder, J. Siddorn, and B. Sinha, 2014: GO 5.0: the joint NERC-Met Office NEMO global ocean model for use in coupled and forced applications. Geosci. Model Dev., 7, 1069-1092, doi:10.5194/gmd-7-1069-2014.
  29. Molteni, F., and Coauthors, 2011: The new ECMWF seasonal forecast system (system 4). ECMWF Tech. Memo. No. 656, 49 pp.
  30. Nicholls, N., 1979: A possible method for predicting seasonal tropical cyclone activity in the Australian region. Mon. Wea. Rev., 107, 1221-1224, doi:10.1175/1520-0493(1979)107<1221:APMFPS>2.0.CO;2.
  31. Pan, L. L., and Coauthors, 2015: Thunderstorms enhance tropospheric ozone by wrapping and shedding stratospheric air. Geophy. Res. Lett., 41, 7785-7790, doi:10.1002/2014gl061921.
  32. Pielke, R. A., Jr., and R. A. Pielke Sr., 1997: Hurricanes: Their Nature and Impacts on Society. Wiley, 279 pp.
  33. Rae, J. G. L., H. T. Hewitt, A. B. Keen, J. K. Ridley, A. E. West, C. M. Harris, E. C. Hunke, and D. N. Walters, 2015: Development of Global Sea Ice 6.0 CICE configuration for the Met Office Global Coupled Model. Geosci. Model Dev. Discuss., 8, 2529-2554, doi:10.5194/gmdd-8-2529-2015.
  34. Schreck, C. J., J. Molinari, and A. Aiyyer, 2012: A global view of equatorial waves and tropical cyclogenesis. Mon. Wea. Rev., 140, 774-788, doi:10.1175/MWR-D-11-00110.1.
  35. Shaevitz, D. A., and Coauthors, 2014: Characteristics of tropical cyclones in high-resolution models in the present climate. J. Adv. Model. Earth Sy., 6, 1154-1172, doi:10.1002/2014MS000372.
  36. Southern, R. L., 1979: The global socio-economic impact of tropical cyclones. Aust. Meteor. Mag., 27, 175-195.
  37. 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.
  38. Valcke, S., R. Budich, M. Carter, E. Guilyardi, M.-A. Foujols, M. Lautenschlager, R. Redler, L. Steenman-Clark, and N. Wedi, 2006: The PRISM Software Framework and the OASIS Coupler. Proc. The 18 Annual BMRC Modelling Workshop, Melbourne [Available online at http://hdl.handle.net/11858/00-001M-0000-0028-52D2-5].
  39. Vecchi, G. A., and G. Villarini, 2014: Next season's hurricanes. Science, 343, 618-619, doi:10.1126/science.1247759.
  40. Vecchi, G. A., and Coauthors, 2014: On the seasonal forecasting of regional tropical cyclone activity. J. Climate, 27, 7994-8016, doi:10.1175/jcli-d-14-00158.1.
  41. Villarini, G., and G. A. Vecchi, 2013: Multiseason lead forecast of the North Atlantic power dissipation index (PDI) and accumulated cyclone energy (ACE). J. Climate, 26, 3631-3643, doi:10.1175/jcli-d-12-00448.1.
  42. Vitart, F., M. R. Huddleston, M. Deque, D. Peake, T. N. Palmer, T. N. Stockdale, M. K. Davey, S. Ineson, and A. Weisheimer, 2007: Dynamically-based seasonal forecasts of Atlantic tropical storm activity issued in June by EUROSIP. Geophys. Res. Lett., 34, L16815, doi:10.1029/2007gl030740.
  43. Vitart, F., and A. W. Robertson, 2018: The Sub-seasonal to seasonal prediction project (S2S) and the prediction of extreme events. npj Clim. Atmos. Sci., 1, 3, doi:10.1038/s41612-0013-0.
  44. Walters, D., and Coauthors, 2017: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations. Geosci. Model Dev., 10, 1487-1520, doi:10.5194/gmd-10-1487-2017.
  45. Wang, H., and Coauthors, 2014: How well do global climate models simulate the variability of Atlantic tropical cyclones associated with ENSO? J. Climate, 27, 5673-5692, doi:10.1175/jcli-d-13-00625.1.
  46. Wang, Z., W. Li, M. S. Peng, X, Jiang, R. McTaggart-Cowan, and C. Davis, 2018: Predictive skill and predictability of North Atlantic tropical cyclogenesis in different synoptic flow regimes. J. Atmos. Sci., 75, 361-378. https://doi.org/10.1175/JAS-D-17-0094.1
  47. Wheeler, M., and G. N. Kiladis, 1999: Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber-frequency domain. J. Atmos. Sci., 56, 374-399. https://doi.org/10.1175/1520-0469(1999)056<0374:CCEWAO>2.0.CO;2
  48. Williams, K. D., and Coauthors, 2015: The Met Office Global Coupled model 2.0 (GC2) configuration. Geosci. Model Dev., 8, 1509-1524, doi:10.5194/gmd-88-1509-2015.
  49. Yamaguchi, M., F. Vitart, S. Maeda, and Y. Takaya, 2016: Were one-month global ensembles capable of predicting inactive TC activity in the western North Pacific basin during early 2016? Proc. 2016 Fall Meeting of Meteorological Society of Japan (In Japanese).
  50. Zhang, G., and Z. Wang, 2019: North Atlantic Rossby wave breaking during the hurricane season: association with tropical and extratropical variability. J. Climate, 32, 3777-3801, doi:10.1175/JCLI-D-18-0299.1.
  51. Zhang, G., Z. Wang, M. S. Peng, and G. Magnusdottir, 2017: Characteristics and impacts of extratropical Rossby wave breaking during the Atlantic hurricane season. J. Climate, 30, 2363-2379, doi:10.1175/JCLI-D-16-0425.1.
  52. Zhao, M., I. M. Held, and G. A. Vecchi, 2010: Retrospective forecasts of the hurricane season using a global atmospheric model assuming persistence of SST anomalies. Mon. Wea. Rev., 138, 3858-3868, doi:10.1175/2010mwr3366.1.