Long range daily ocean forecasts in the Indo-Pacific oceans with ACCESS-S1

References

• Balmaseda, M. A., Mogensen, K. and Weaver, A. T. 2013. Evaluation of the ECMWF ocean reanalysis system ORAS4. Q. J. R. Meteorol. Soc. 139, 1132–1161. doi:https://doi.org/10.1002/qj.2063
   Web of Science ®Google Scholar
• Bell, M. J., Lefèbvre, M., Le Traon, P.-Y., Smith, N. and Wilmer-Becker, K. 2009. GODAE: the global ocean data assimilation experiment. Oceanography. 22, 14–21. doi:https://doi.org/10.5670/oceanog.2009.62
   Web of Science ®Google Scholar
• Blockley, E. W., Martin, M. J., McLaren, A. J., Ryan, A. G., Waters, J. and co-authors. 2014. Recent development of the Met Office operational ocean forecasting system: An overview and assessment of the new global FOAM forecasts. Geosci. Model Dev. 7, 2613–2638. doi:https://doi.org/10.5194/gmd-7-2613-2014
   Web of Science ®Google Scholar
• Bowler, N., Arribas, A., Beare, S., Mylne, K. E. and Shutts, G. 2009. The local ETKF and SKEB: upgrades to the MOGREPS short-range ensemble prediction system. Q. J. R. Meteorol. Soc. 135, 767–776. doi:https://doi.org/10.1002/qj.394
   Web of Science ®Google Scholar
• Brassington, G. B., Freeman, J., Huang, X., Pugh, T., Oke, P. R. and co-authors. 2012. Ocean model, analysis and prediction system (OceanMAPS): version 2. CAWCR Technical Report 52, 110 pp. A research partnership between CSIRO and the Australian Bureau of Meteorology. Australia.
   Google Scholar
• Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E. and co-authors. 2012. The operational sea surface temperature and sea ice analysis (OSTIA) system. Remote Sens. Environ. 116, 140–158. doi:https://doi.org/10.1016/j.rse.2010.10.017
   Web of Science ®Google Scholar
• Fortin, V., Abaza, M., Anctil, F. and Turcotte, R. 2014. Why should ensemble spread match the RMSE of the ensemble mean? J. Hydrometeor. 15, 1708–1713.. doi:https://doi.org/10.1175/JHM-D-14-0008.1
   Web of Science ®Google Scholar
• Fujii, Y., Remy, E., Zuo, H., Oke, P. R., Halliwell, G. R. and co-authors. 2019. Observing system evaluation based on ocean data assimilation and prediction systems: on-going challenges and future vision for designing/supporting ocean observational networks. Front. Mar. Sci. 6, 417. doi:https://doi.org/10.3389/fmars.2019.00417
   Web of Science ®Google Scholar
• Hernandez, F., Blockley, E., Brassington, G. B., Davidson, F., Divakaran, P. and co-authors. 2015. Recent progress in performance evaluations and near real-time assessment of operational ocean products. J. Oper. Oceanogr. 8, s221–s238.
   Web of Science ®Google Scholar
• Hudson, D., Alves, O., Hendon, H. H., Lim, E., Liu, G. and co-authors. 2017. ACCESS-S1: The new bureau of meteorology multi-week to seasonal prediction system. J. South. Hemisphere Earth Syst. Sci. 67, 132–159. doi:https://doi.org/10.22499/3.6703.001
   Web of Science ®Google Scholar
• Large, W. G. and Yeager, S. G. 2009. The global climatology of an interannually varying air–sea flux data set. Clim. Dyn. 33, 341–364.. doi:https://doi.org/10.1007/s00382-008-0441-3
   Web of Science ®Google Scholar
• MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D. and co-authors. 2014. Global seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Q. J. R. Meteor. Soc. 141:1072–1084.
   Web of Science ®Google Scholar
• Madec, G. and the NEMO team 2016. NEMO ocean engine. Technical Report Note du Pole de od’Elisation No 27, ISSN No 1288-1619, Institut Pierre-Simon Laplace (IPSL), France.
   Google Scholar
• Martin, M. 2011. Ocean forecasting systems: product evaluation and skill. In Operational Oceanography in the 21st Century. Springer Science + Business Media B. V., pp. 611–632.
   Google Scholar
• Martin, M. J., Hines, A. and Bell, M. J. 2007. Data assimilation in the FOAM operational short-range ocean forecasting system: A description of the scheme and its impact. Q. J. R. Meteorol. Soc. 133, 981–995. doi:https://doi.org/10.1002/qj.74
   Web of Science ®Google Scholar
• Meehl, G. A., Goddard, L., Boer, G., Burgman, R., Branstator, G. and co-authors. 2014. Decadal climate prediction: An update from the trenches. Bull. Amer. Meteor. Soc. 95, 243–267. doi:https://doi.org/10.1175/BAMS-D-12-00241.1
   Web of Science ®Google Scholar
• Megann, A., Storkey, D., Aksenov, Y., Alderson, S., Calvert, D. and co-authors. 2014. GO5.0: The joint NERC–Met Office NEMO global ocean model for use in coupled and forced applications. Geosci. Model. Dev. 7, 1069–1092. doi:https://doi.org/10.5194/gmd-7-1069-2014
   Web of Science ®Google Scholar
• Pujol, M.-I., Faugère, Y., Taburet, G., Dupuy, S., Pelloquin, C. and co-authors. 2016. The new multi-mission altimeter data set reprocessed over 20 years. Ocean Sci. 12, 1067–1090.. doi:https://doi.org/10.5194/os-12-1067-2016
   Web of Science ®Google Scholar
• Rae, J. G. L., Hewitt, H. T., Keen, A. B., Ridley, J. K., West, A. E. and co-authors. 2015. Development of the Global Sea Ice 6.0 CICE configuration for the Met Office Global Coupled Model. Geosci. Model. Dev. 8, 2221–2230.. doi:https://doi.org/10.5194/gmd-8-2221-2015
   Web of Science ®Google Scholar
• Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S. and co-authors. 2007. Daily high-resolution blended analyses for sea surface temperature. J. Clim. 20, 5473–5496.. doi:https://doi.org/10.1175/2007JCLI1824.1
   Web of Science ®Google Scholar
• Schiller, A., Brassington, G. B., Oke, P., Cahill, M., Divakaran, P. and co-authors. 2020. Bluelink ocean forecasting Australia: 15 years of operational ocean service delivery with societal, economic and environmental benefits. J. Oper. Oceanogr. 13, 1–18.
   Web of Science ®Google Scholar
• Storto, A., Alvera-Azcárate, A., Balmaseda, M. A., Barth, A., Chevallier, M. and co-authors. 2019. Ocean reanalyses: recent advances and unsolved challenges. Front. Mar. Sci. 6, 418. doi:https://doi.org/10.3389/fmars.2019.00418
   Web of Science ®Google Scholar
• Usui, N., Ishizaki, S., Fujii, Y., Tsujino, H., Yasuda, T. and co-authors. 2006. Meteorological research institute multivariate ocean variational estimation (MOVE) system: some early results. Adv. Space Res. 37, 806–822. 022 doi:https://doi.org/10.1016/j.asr.2005.09.022
   Web of Science ®Google Scholar
• Walters, D., Brooks, M., Boutle, I., Melvin, T., Stratton, R. and co-authors. 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:https://doi.org/10.5194/gmd-10-1487-2017
   Web of Science ®Google Scholar
• Wang, G., Hudson, D., Yin, Y., Alves, O., Hendon, H. and co-authors. 2011. POAMA-2 SST skill assessment and beyond. CAWCR Research Letters, No. 6, 40–46, Bureau of Meteorology, Australia.
   Google Scholar
• Waters, J., Lea, D. J., Martin, M. J., Mirouze, I., Weaver, A. and co-authors. 2015. Implementing a variational data assimilation system in an operational 1/4 degree global ocean model. Q. J. R. Meteorol. Soc. 141, 333–349. doi:https://doi.org/10.1002/qj.2388
   Web of Science ®Google Scholar
• Wilkin, J. L. and Hunter, E. J. 2013. An assessment of the skill of realtime models of Mid-Atlantic Bight continental shelf circulation. J. Geophys. Res. Oceans 118, 2919–2933. 1002/jgrc.20223 doi:https://doi.org/10.1002/jgrc.20223
   Web of Science ®Google Scholar
• Williams, K. D., Harris, C. M., Bodas-Salcedo, A., Camp, J., Comer, R. E. and co-authors. 2015. The Met Office Global Coupled model 2.0 (GC2) configuration. Geosci. Model. Dev. 8, 1509–1524. doi:https://doi.org/10.5194/gmd-8-1509-2015
   Web of Science ®Google Scholar
• Woodham, R. H., Alves, O., Brassington, G. B., Robertson, R. and Kiss, A. 2015. Evaluation of ocean forecast performance for royal Australian navy exercise areas in the Tasman Sea. J. Oper Oceanogr. 8, 147–161.
   Web of Science ®Google Scholar
• Zhou, X., Luo, J., Alves, O. and Hendon, H. 2015. Comparison of GloSea5 and POAMA2.4 Hindcasts 1996-2009: Ocean Focus. Bureau Research Report-010.
   Google Scholar