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Accuracy of Short-Term Ocean Prediction and the Effect of Atmosphere-Ocean Coupling on KMA Global Seasonal Forecast System (GloSea5) During the Development of Ocean Stratification

기상청 계절예측시스템(GloSea5)의 해양성층 강화시기 단기 해양예측 정확도 및 대기-해양 접합효과

  • Jeong, Yeong Yun (Typhoon Research Center/Graduate School of Interdisciplinary Program in Marine Meteorology, Jeju National University) ;
  • Moon, Il-Ju (Typhoon Research Center/Graduate School of Interdisciplinary Program in Marine Meteorology, Jeju National University) ;
  • Chang, Pil-Hun (National Institute of Meteorological Sciences)
  • 정영윤 (제주대학교 해양기상학협동과정/태풍연구센터) ;
  • 문일주 (제주대학교 해양기상학협동과정/태풍연구센터) ;
  • 장필훈 (국립기상과학원)
  • Received : 2016.07.20
  • Accepted : 2016.11.03
  • Published : 2016.12.31

Abstract

This study investigates the accuracy of short-term ocean predictions during the development of ocean stratification for the Korea Meteorological Administration (KMA) Global Seasonal Forecast System version 5 (GloSea5) as well as the effect of atmosphere-ocean coupling on the predictions through a series of sensitive numerical experiments. Model performance is evaluated using the marine meteorological buoys at seas around the Korean peninsular (KP), Tropical Atmosphere Ocean project (TAO) buoys over the tropical Pacific ocean, and ARGO floats data over the western North Pacific for boreal winter (February) and spring (May). Sensitive experiments are conducted using an ocean-atmosphere coupled model (i.e., GloSea5) and an uncoupled ocean model (Nucleus for European Modelling of the Ocean, NEMO) and their results are compared. The verification results revealed an overall good performance for the SST predictions over the tropical Pacific ocean and near the Korean marginal seas, in which the Root Mean Square Errors (RMSE) were $0.31{\sim}0.45^{\circ}C$ and $0.74{\sim}1.11^{\circ}C$ respectively, except oceanic front regions with large spatial and temporal SST variations (the maximum error reached up to $3^{\circ}C$). The sensitive numerical experiments showed that GloSea5 outperformed NEMO over the tropical Pacific in terms of bias and RMSE analysis, while NEMO outperformed GloSea5 near the KP regions. These results suggest that the atmosphere-ocean coupling substantially influences the short-term ocean forecast over the tropical Pacific, while other factors such as atmospheric forcing and the accuracy of simulated local current are more important than the coupling effect for the KP regions being far from tropics during the development of ocean stratification.

Keywords

References

  1. Arribas, A., and Coauthors, 2011: The GloSea4 Ensemble Prediction system fore seasonal forecasting. Mon. Wea. Rev., 139, 1891-1910. https://doi.org/10.1175/2010MWR3615.1
  2. 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. Meteor. Soc., 141, 2206-2219. https://doi.org/10.1002/qj.2516
  3. Cho, Y. K., and K. Kim, 1996: Seasonal variation of the East Korea Warm Current and its relation with the cold water. La Mer, 34, 172-182.
  4. Choi, B. J., D. S. Byun, and K. H. Lee, 2012: Satellitealtimeter-derived East Sea surface currents: Estimation, description and variability pattern, The Sea, 17, 225-242 (in Korean with English abstract). https://doi.org/10.7850/jkso.2012.17.4.225
  5. Donlon, C. J., M. Martin, J. Stark, J. Roberts-Jones, E. Fiedler, and W. Wimmer, 2012: The operational sea surface temperature and sea ice analysis (OSTIA) system. Remote Sens. Environ., 116, 140-158. https://doi.org/10.1016/j.rse.2010.10.017
  6. Graham, R. J., M. Gordon, P. J. McLean, S. Ineson, M. R. Huddleston, M. K. Davey, A. Brookshaw, and R. T. H. Barnes, 2005: A performance comparison of coupled and uncoupled versions of the Met Office seasonal prediction general circulation model. Tellus, 57A, 320-339.
  7. Jung, M.-I., S.-W. Son, J. Choi, and H.-S. Kang, 2015:Assessment of 6-Month Lead Prediction Skill of the GloSea5 Hindcast Experiment. Atmosphere, 25, 323-337 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2015.25.2.323
  8. Kang, H.-S., K.-O. Boo, and C. Cho, 2011: Introduction to the KMA-Met Office joint seasonal forecasting system and evaluation of its hindcast ensemble simulations. In 36th NOAA Annual Climate Diagonostics and Prediction Workshop, 3-6.
  9. Kim, K., Y.-K. Cho, B.-J. Choi, Y.-G. Kim, and R. C. Beardsley, 2002: Sea level variability at Ulleung Island in the East (Japan) Sea. J. Geophys. Res., 107, 3015. https://doi.org/10.1029/2001JC000895
  10. Kim, S.-J., S.-H. Woo, B.-M. Kim, and S.-D. Hur, 2011:Trends in sea surface temperature (SST) change near the Korean peninsula for the past 130 years. Ocean Polar Res., 33, 281-290. https://doi.org/10.4217/OPR.2011.33.3.281
  11. Lee, K.-J., and M. H. Kwon, 2015: A prediction of Northeast Asian summer precipitation using Teleconnection. Atmosphere, 25, 179-183 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2015.25.1.179
  12. Lee, S. H., D. S. Byun, B. J. Choi, and E. Lee, 2009: Estimation of the surface currents using mean dynamic topography and satellite altimeter data in the East Sea. The Sea, 14, 195-204 (in Korean with English abstract).
  13. MacLachlan, C., and Coauthors, 2014: Global Seasonal forecast system version 5 (GloSea5): A high resolution seasonal forecast system. Quart. J. Roy. Meteor. Soc., 141, 1072-1084.
  14. Madec, G., and the NEMO team, 2008: NEMO ocean engine. Note du Pole de modelisation de l'Institut-Pierre-Simon Laplace, 27, 401 pp.
  15. Martin, M. J., A. Hines, and M. J. Bell, 2007: Data assimilation in the FOAM operational short-range ocean forecasting system: A description of the scheme and its impact. Quart. J. Roy. Meteor. Soc., 133, 981-995. https://doi.org/10.1002/qj.74
  16. Minobe, S., A. Sako, and M. Nakamura, 2004: Interannual to interdecadal variability in the Japan Sea based on a new gridded upper water temperature dataset. J. Phys. Oceanogr., 34, 2382-2397. https://doi.org/10.1175/JPO2627.1