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The Korean Earth Science Society (한국지구과학회)
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Prediction Performance of Ocean Temperature and Salinity in Global Seasonal Forecast System Version 5 (GloSea5) on ARGO Float Data

  • Jieun Wie (Division of Science Education and Institute of Fusion Science, Jeonbuk National University) ;
  • Jae-Young Byon (Forecast Research Department, National Institute of Meteorological Sciences) ;
  • Byung-Kwon Moon (Division of Science Education and Institute of Fusion Science, Jeonbuk National University)
  • Received : 2024.08.02
  • Accepted : 2024.08.21
  • Published : 2024.08.31
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Abstract

The ocean is linked to long-term climate variability, but there are very few methods to assess the short-term performance of forecast models. This study analyzes the short-term prediction performance regarding ocean temperature and salinity of the Global Seasonal prediction system version 5 (GloSea5). GloSea5 is a historical climate re-creation (2001-2010) performed on the 1st, 9th, 17th, and 25th of each month. It comprises three ensembles. High-resolution hindcasts from the three ensembles were compared with the Array for Real-Time Geostrophic Oceanography (ARGO) float data for the period 2001-2010. The horizontal position was preprocessed to match the ARGO float data and the vertical layer to the GloSea5 data. The root mean square error (RMSE), Brier Score (BS), and Brier Skill Score (BSS) were calculated for short-term forecast periods with a lead-time of 10 days. The results show that sea surface temperature (SST) has a large RMSE in the western boundary current region in Pacific and Atlantic Oceans and Antarctic Circumpolar Current region, and sea surface salinity (SSS) has significant errors in the tropics with high precipitation, with both variables having the largest errors in the Atlantic. SST and SSS had larger errors during the fall for the NINO3.4 region and during the summer for the East Sea. Computing the BS and BSS for ocean temperature and salinity in the NINO3.4 region revealed that forecast skill decreases with increasing lead-time for SST, but not for SSS. The preprocessing of GloSea5 forecasts to match the ARGO float data applied in this study, and the evaluation methods for forecast models using the BS and BSS, could be applied to evaluate other forecast models and/or variables.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Government of Korea (MSIT) (No. 2022R1A2C100 8858) and Research and Development for Korea Meteorological Administration (KMA) Weather, Climate, and Earth system Services (NIMS-2016-3100). This work was also funded by the Korea Meteorological Administration Research and Development Program "Developing Operational Marine Forecasting System" under Grant (KMA2018-00420).

References

  1. Best, M.J., Pryor, M., Clark, D.B., Rooney, G.G., Essery, R.L.H., Menard, C.B., Edwards, J.M., Hendry, M.A., Porson, A., Gedney, N., Mercado, L.M., Sitch, S., Blyth, E., Boucher, O., Cox, P.M., Grimmond, C.S.B., and Harding, R.J., 2011, The Joint UK Land Environment Simulator (JULES), model description - Part 1: Energy and water fluxes. Geoscientific Model Development, 4, 677-699, https://doi.org/10.5194/gmd-4-677-2011.
  2. Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., and Shelly, A., 2012, Unified modeling and prediction of weather and climate: A 25-year journey. Bulletin of the American Meteorological Society, 93, 1865-1877. https://doi.org/10.1175/Bams-D-12-00018.1.
  3. Camp, J., Bett, P.E., Golding, N., Hewitt, C.D., Mitchell, T.D., and Scaife, A.A., 2020, Verification of the 2019 GloSea5 seasonal tropical cyclone landfall forecast for East China. Journal of Meteorological Research, 34, 917-925. https://doi.org/10.1007/s13351-020-0043-5.
  4. Camp, J., Roberts, M., MacLachlan, C., Wallace, E., Hermanson, L., Brookshaw, A., Arribas, A., and Scaife, A.A., 2015, Seasonal forecasting of tropical storms using the Met Office GloSea5 seasonal forecast system. Quarterly Journal of the Royal Meteorological Society, 141, 2206-2219. https://doi.org/10.1002/qj.2516.
  5. Cho, A., Song, H., Tak, Y.J., Yeh, S.W., An, S.I., Lee, S.M., Ji, H.S., and Hyun, Y.K., 2022, Atmosphere-driven cold SST biases over the western North Pacific in the GloSea5 seasonal forecast system. Climate Dynamics, 59, 2571-2584, https://doi.org/10.1007/s00382-022-06228-x.
  6. Hunke, E.C., and Lipscomb, W.H., 2010, CICE: The sea ice model documentation and software user's manual, version 4.1, Technical report LA-CC-06-012. Los Alamos National Laboratory: Los Alamos, NM, https://csdms.colorado.edu/w/images/CICE_documentation_and_software_user's_manual.pdf.
  7. Jung, M.-I., Son, S.-W., Choi, J., and Kang, H.-S., 2015, Assessment of 6-month lead prediction skill of the GloSea5 hindcast experiment. Atmosphere. Korean Meteorological Society, 25(2), 323-337, https://doi.org/10.14191/Atmos.2015.25.2.323. (in Korean)
  8. Lee, H., Moon, B.-K., Kim, H.-K., Wie, J., Park, H.J., Chang, P.-H., Lee, J., and Kim, Y., 2021, Multivariable integrated evaluation of GloSea5 ocean hindcasting. Journal of the Korean Earth Science Society, 42(6), 605-622, https://doi.org/10.5467/JKESS.2021.42.6.605.
  9. Lee, S.-M., Lee, J.-H., Ko, A.-R., Hyun, Y.-K., and Kim, Y.J., 2020, Seasonal forecasting of tropical storms using GloSea5 hindcast. Atmosphere. Korean Meteorological Society, 30(3), 209-220, https://doi.org/10.14191/Atmos.2020.30.3.209. (in Korean)
  10. Lim, S.-M., Hyun, Y.-K., Kang, H.-S., and Yeh, S.-W., 2018, Prediction skill of East Asian precipitation and temperature associated with El Nino in GloSea5 hindcast data. Atmosphere. Korean Meteorological Society, 28(1), 37-51. https://doi.org/10.14191/Atmos.2018.28.1.037. (in Korean)
  11. MacLachlan, C., Arribas, A., Peterson, K.A., Maidens, A., Fereday, D., Scaife, A.A., Gordon, M., Vellinga, M., Williams, A., Comer, R.E., Camp, J., Xavier, P., and Madec, G., 2015, Global seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Quarterly Journal of the Royal Meteorological Society, 141, 1072-1084. https://doi.org/10.1002/qj.2396.
  12. Madec, G., and the NEMO team, 2008, NEMO ocean engine. Note du Pole de modelisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619. https://epic.awi.de/id/eprint/39698/1/NEMO_book_v6039.pdf.
  13. Walters, D.N., Best, M.J., Bushell, A.C., Copsey, D., Edwards, J.M., Falloon, P.D., Harris, C.M., Lock, A.P., Manners, J.C., Morcrette, C.J., Roberts, M.J., Stratton, R.A., Webster, S., Wilkinson, J.M., Willett, M.R., Boutle, I.A., Earnshaw, P.D., Hill, P.G., MacLachlan, C., Martin, G.M., Moufouma-Okia, W., Palmer, M.D., Petch, J.C., Rooney, G.G., Scaife, A.A., and Williams, K.D., 2011, The Met Office Unified Model Global Atmosphere 3.0/3.1 and JULES Global Land 3.0/3.1 configurations. Geoscientific Model Development, 4, 919-941. https://doi.org/10.5194/gmd-4-919-2011.
  14. Yang, G.Y., Feng, X.B., and Hodges, K., 2023, Seasonal and interannual variation of equatorial waves in ERA5 and GloSea5. Quarterly Journal of the Royal Meteorological Society, 149, 1109-1134. https://doi.org/10.1002/qj.4460.