Climate Dynamics

, Volume 49, Issue 11–12, pp 3647–3672 | Cite as

A real-time ocean reanalyses intercomparison project in the context of tropical pacific observing system and ENSO monitoring

  • Yan Xue
  • C. Wen
  • A. Kumar
  • M. Balmaseda
  • Y. Fujii
  • O. Alves
  • M. Martin
  • X. Yang
  • G. Vernieres
  • C. Desportes
  • T. Lee
  • I. Ascione
  • R. Gudgel
  • I. Ishikawa


An ensemble of nine operational ocean reanalyses (ORAs) is now routinely collected, and is used to monitor the consistency across the tropical Pacific temperature analyses in real-time in support of ENSO monitoring, diagnostics, and prediction. The ensemble approach allows a more reliable estimate of the signal as well as an estimation of the noise among analyses. The real-time estimation of signal-to-noise ratio assists the prediction of ENSO. The ensemble approach also enables us to estimate the impact of the Tropical Pacific Observing System (TPOS) on the estimation of ENSO-related oceanic indicators. The ensemble mean is shown to have a better accuracy than individual ORAs, suggesting the ensemble approach is an effective tool to reduce uncertainties in temperature analysis for ENSO. The ensemble spread, as a measure of uncertainties in ORAs, is shown to be partially linked to the data counts of in situ observations. Despite the constraints by TPOS data, uncertainties in ORAs are still large in the northwestern tropical Pacific, in the SPCZ region, as well as in the central and northeastern tropical Pacific. The uncertainties in total temperature reduced significantly in 2015 due to the recovery of the TAO/TRITON array to approach the value before the TAO crisis in 2012. However, the uncertainties in anomalous temperature remained much higher than the pre-2012 value, probably due to uncertainties in the reference climatology. This highlights the importance of the long-term stability of the observing system for anomaly monitoring. The current data assimilation systems tend to constrain the solution very locally near the buoy sites, potentially damaging the larger-scale dynamical consistency. So there is an urgent need to improve data assimilation systems so that they can optimize the observation information from TPOS and contribute to improved ENSO prediction.


Ocean reanalysis intercomparison Ocean data assimilation system Ocean initialization Tropical Pacific observing system TAO/TRITON ENSO monitoring and prediction Argo floats Ensemble approach Signal to noise ratio 



We would like to thank support from the NOAA Climate Observation Division of Climate Program Office for this study. We also thank the anonymous reviwers and Dr. Stephen G. Penny and Dr. Zeng-Zhen Hu for the internal review of this paper. The scientific results and conclusions, as well as any view or opinions expressed herein, are those of the author(s) and do not necessarily reflect the views of NWS, NOAA, or the Department of Commerce.


  1. Alves O, Balmaseda M, Anderson D, Stockdale T (2004) Sensitivity of dynamical seasonal forecasts to ocean initial conditions. Q J R Meteorol Soc 130:647–668CrossRefGoogle Scholar
  2. Ando K, Matsumoto T, Nagahama T, Ueki I, Takatsuki Y, Kuroda Y (2005) Drift characteristics of a moored conductive-temperature depth sensor and correction salinity data. J Atmos Ocean Technol 22:282–291. doi: 10.1175/JTECH1704.1 CrossRefGoogle Scholar
  3. Antonov JI, Locarnini RA, Boyer TP, Mishonov AV, Garcia HE (2006) Salinity. In: World Ocean Atlas 2005, Levitus S (eds) NOAA Atlas NESDIS 62, vol 2. US Government Printing Office, Washington DCGoogle Scholar
  4. Argo Science Team (2001) The global array of profiling floats, observing the ocean in the 21st century. In: Koblinsky CJ, Smith NR (eds) Australian Bureau of Meteorology, pp 248–258Google Scholar
  5. Balmaseda MA, Anderson D (2009) Impact of initialization strategies and observations on seasonal forecast skill. Geophys Res Lett 36:L01701. doi: 10.1029/2008GL035561 CrossRefGoogle Scholar
  6. Balmaseda MA, Dee D, Vidard A, Anderson DLT (2007) A multivariate treatment of bias for sequential data assimilation: application to the tropical oceans. Q J R Meteorol Soc 133:167–179CrossRefGoogle Scholar
  7. Balmaseda MA et al (2010) Role of the ocean observing system in an end-to-end seasonal forecasting system. In: Proceedings of OceanObs’09: Sustained Ocean Observations and Information for Society (Vol. 1), Venice, Italy, 21–25 September 2009, Hall J, Harrison DE, Stammer D (eds) ESA Publication WPP-306. doi: 10.5270/OceanObs09.pp.03
  8. Balmaseda MA, Mogensen K, Weaver AT (2013) Evaluation of the ECMWF ocean reanalysis system ORAS4. Q J R Meteorol Soc 131:1132–1161CrossRefGoogle Scholar
  9. Balmaseda MA et al (2015) The Ocean Reanalyses Intercomparison Project (ORA-IP). J Oper Oceanogr 8:80–97CrossRefGoogle Scholar
  10. Behringer DW, Xue Y (2004) Evaluation of the global ocean data assimilation system at NCEP. In: The Pacific Ocean. Eighth symposium on integrated observing and assimilation system for atmosphere, ocean, and land surface, AMS 84th annual meeting, Washington State Convention and Trade Center, Seattle, Washington, DC, pp 11–15Google Scholar
  11. Behringer DW, Ji M, Leetmaa A (1998) An improved coupled model for ENSO prediction and implications for ocean initialization. Part I: the ocean data assimilation system. Mon Weather Rev 126:1013–1021CrossRefGoogle Scholar
  12. Bell MJ, Martin MJ, Nichols NK (2004) Assimilation of data into an ocean model with systematic errors near the equator. Q J R Meteorol Soc 130:873–893CrossRefGoogle Scholar
  13. Blockley EW, Martin MJ, McLaren A J, Ryan AG, Waters J, Lea DJ, Mirouze I, Peterson KA, Sellar A, Storkey D (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: 10.5194/gmd-7-2613-2014 CrossRefGoogle Scholar
  14. Bloom SC, Takacs LL, Da Silva AM, Ledvina D (1996) Data assimilation using incremental analysis updates. Mon Weather Rev 124:1256–1271CrossRefGoogle Scholar
  15. Bourle`s B, Lumpkin R, McPhaden MJ, Hernandez F, Nobre P, Campos E, Yu L, Planton S, Busalacchi AJ, Moura AD, Servain J, Trotte J (2008) The PIRATA program: history, accomplishments, and future directions. Bull Am Met Soc 89:1111–1125CrossRefGoogle Scholar
  16. Chang YS, Rosati A, Zhang S (2011) A construction of pseudo salinity profiles for the global ocean: method and evaluation. J Geophys Res 116:C02002. doi: 10.1029/2010JC006386 Google Scholar
  17. Chang YS, Zhang S, Rosati A, Delworth TL, Stern WF (2013) An assessment of oceanic variability for 1960–2010 from the GFDL ensemble coupled data assimilation. Clim Dyn 40(3–4):775–803CrossRefGoogle Scholar
  18. Ebita A et al (2011) The Japanese 55-year Reanalysis “JRA-55”: an interim report. SOLA 7:149–152. doi: 10.2151/sola.2011-038 CrossRefGoogle Scholar
  19. Fujii Y, Kamachi M (2003) Three-dimensional analysis of temperature and salinity in the equatorial Pacific using a variational method with vertical coupled temperature-salinity empirical orthogonal function modes. J Geophys Res 108. doi: 10.1029/2002JC001745
  20. Fujii Y, Ishizaki S, Kamachi M (2005) Application of nonlinear constraints in a three-dimensional variational ocean analysis. J Oceanogr 61:655–662. doi: 10.1007/s10872-005-0073-8 CrossRefGoogle Scholar
  21. Fujii Y, Kamachi M, Nakaegawa T, Yasuda T, Yamanaka G, Toyoda T, Ando K, Matsumoto S (2011) Assimilating ocean observation data for ENSO monitoring and forecasting. In: Hannachi A (ed) Climate variability, INTECH, Rijeka (ISBN: 979-953-307-236-3)Google Scholar
  22. Fujii Y et al (2015a) Evaluation of the tropical pacific observing system from the ocean data assimilation perspective. Q J R Meteorol Soc 141:2481–2496. doi: 10.1002/qj.2579 CrossRefGoogle Scholar
  23. Fujii Y, Ogawa K, Brassington GB, Ando K, Yasuda T, Kuragano T (2015b) Evaluating the impacts of the tropical Pacific observing system on the ocean analysis fields in the global ocean data assimilation system for operational seasonal forecasts in JMA. J Oper Oceanogr 8:25–39. doi: 10.1080/1755876X.2015.1014640 CrossRefGoogle Scholar
  24. Hu S, Fedorov AV (2016) An exceptional easterly wind burst stalling El Niño of 2014. PNAS. doi: 10.1073/pnas.1514182113 Google Scholar
  25. Hu ZZ, Kumar A (2015) Influence of availability of TAO data on NCEP ocean data assimilation systems along the equatorial Pacific. J Geophys Res (Ocean) 120:5534–5544 doi: 10.1002/2015JC010913 CrossRefGoogle Scholar
  26. Hu ZZ, Kumar A, Huang B (2016) Spatial distribution and the interdecadal change of leading modes of heat budget of the mixed-layer in the tropical Pacific and the association with ENSO. Clim Dyn 46:1753–1768. doi: 10.1007/s00382-015-2672-4 CrossRefGoogle Scholar
  27. Huang B, Xue Y, Zhang X, Kumar A, McPhaden MJ (2010) The NCEP GODAS ocean analysis of the tropical Pacific mixed layer heat budget on seasonal to interannual time scales. J Clim 23:4901–4925CrossRefGoogle Scholar
  28. Ji M, Behringer DW, Leetmaa A (1998) An improved coupled model for ENSO prediction and implications for ocean initialization. Part II: the coupled model. Mon Weather Rev 126:1022–1034CrossRefGoogle Scholar
  29. Jin FF (1997) An equatorial ocean recharge paradigm for ENSO. Part I: conceptual model. J Atmos Sci 54:811–829CrossRefGoogle Scholar
  30. Kanamitsu M, Ebitsuzaki W, Woolen J, Yang SK, Hnilo JJ, Fiorino M, Potter GL (2002) NCEP-DOE AMIP-II reanalysis (R-2). Bull Am Met Soc 83:1631–1643CrossRefGoogle Scholar
  31. Kumar A, Hu ZZ (2012) Uncertainty in the ocean-atmosphere feedbacks associated with ENSO in the reanalysis products. Clim Dyn 39:575–588. doi: 10.1007/s00382-011-1104-3 CrossRefGoogle Scholar
  32. Kumar A, Hu ZZ (2014) Interannual and interdecadal variability of ocean temperature along the equatorial Pacific in conjunction with ENSO. Clim Dyn 42:1243–1258CrossRefGoogle Scholar
  33. Large WG, Yeager SG (2009) The global climatology of an interannually varying air–sea flux data set. Clim Dyn 33:341–364CrossRefGoogle Scholar
  34. Lee T, Awaji T, Balmaseda MA, Grenier E, Stammer D (2009) Ocean state estimation for climate research. Oceanography 22:160–167CrossRefGoogle Scholar
  35. Lellouche JM et al (2013) Evaluation of global monitoring and forecasting systems at Mercator Océan. Ocean Sci 9:57–81. doi: 10.5194/os-9-57-2013 CrossRefGoogle Scholar
  36. Locarnini RA, Mishonov AV, Antonov JI, Boyer TP, Garcia HE (2006) Temperature. In: World Ocean Atlas 2005, Levitus S (eds) NOAA Atlas NESDIS 61, vol 1. US Government Printing Office, Washington DCGoogle Scholar
  37. MacLachlan C et al (2015) 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 CrossRefGoogle Scholar
  38. Madec G (2008) NEMO ocean engine. Note du Pole de mode´lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27 ISSN No 1288–1619Google Scholar
  39. Martin MJ et al (2015) Status and future of data assimilation in operational oceanography. J Oper Oceanogr 8:s28–48CrossRefGoogle Scholar
  40. Masson D, Knutti R (2011) Climate model genealogy. Geophys Res Lett 38:L08703. doi: 10.1029/2011GL046864 CrossRefGoogle Scholar
  41. McPhaden MJ (2012) A 21st century shift in the relationship between ENSO SST and warm water volume anomalies. Geophys Res Lett 39. doi: 10.1029/2012GL051826
  42. McPhaden MJ et al (1998) The tropical ocean–global atmosphere (TOGA) observing system: a decade of progress. J Geophys Res 103:14169–14240CrossRefGoogle Scholar
  43. McPhaden MJ et al (2009) RAMA: the research moored array for African–Asian–Australian monsoon analysis and prediction. Bull Am Meteorol Soc 90:459–480CrossRefGoogle Scholar
  44. Meinen CS, McPhaden MJ (2000) Observations of warm water volume changes in the equatorial Pacific and their relationship to El Niñoand La Niña. J Clim 13:3551–3559CrossRefGoogle Scholar
  45. Mirouze I, Blockley EW, Lea DJ, Martin MJ, Bell MJ (2016) A multiple length scale correlation operator with application to ocean data assimilation. Tellus A 68:29744CrossRefGoogle Scholar
  46. Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite SST analysis for climate. J Clim 15:1609–1625CrossRefGoogle Scholar
  47. Reynolds RW, Smith TM, Liu C, Chelton DB, Casey KS, Schlax MG (2007) Daily high-resolution blended analyses for sea surface temperature. J Clim 20:5473–5496CrossRefGoogle Scholar
  48. Rienecker MM et al (2011) MERRA—NASA’s modern-era retrospective analysis for research and applications. J Clim 24:3624–3648CrossRefGoogle Scholar
  49. Rosati A, Miyakoda K, Gudgel R (1997) The impact of ocean initial conditions on ENSO forecasting with a coupled model. Mon Weather Rev 125:754–772CrossRefGoogle Scholar
  50. Saha S et al (2006) The NCEP climate forecast system. J Clim 19:3483–3517CrossRefGoogle Scholar
  51. Saha S et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1057CrossRefGoogle Scholar
  52. Saha S et al (2014) The NCEP climate forecast system versions 2. J Clim 27(482):2185–2208. doi: 10.1175/JCLI-D-12-00823.1 CrossRefGoogle Scholar
  53. Stockdale TN, Anderson D, Balmaseda MA, Doblas-Reyes F, Ferranti L, Mogensen K, Palmer TN, Molteni F, Vitart F (2011) ECMWF seasonal forecast system 3 and its prediction of sea surface temperature. Clim Dyn. doi: 10.1007/s00382-010-0947-3 Google Scholar
  54. Toyoda T, Fujii Y, Yasuda T, Usui N, Iwao T, Kuragano T, Kamachi M (2013) Improved analysis of seasonal-interannual fields using a global ocean data assimilation system. Theoret Appl Mech Jpn 61:31–48. doi: 10.11345/nctam.61.31 Google Scholar
  55. Valdivieso M et al (2015) An assessment of air–sea heat fluxes from ocean and coupled reanalyses. Clim Dyn. doi: 10.1007/s00382-015-2843-3 Google Scholar
  56. Vecchi GA, Delworth T, Gudgel R, Kapnick S, Rosati A, Wittenberg AT, Zeng F, Anderson W, Balaji V, Dixon K, Jia L, Kim H-S, Krishnamurthy L, Msadek R, Stern WF, Underwood SD, Villarini G, Yang X, Zhang S (2014) On the seasonal forecasting of regional tropical cyclone activity. J Clim 27(21):7994–8016CrossRefGoogle Scholar
  57. Vernieres G, Keppenne C, Rienecker MM, Jacob J, Kovach R (2012) The GEOS-ODAS, description and evaluation. NASA Tech. Rep. Series on Global Modeling and Data Assimilation, NASA/TM–2012–104606, vol 30Google Scholar
  58. Wang W, Xie P, Yoo SH, Xue Y, Kumar A, Wu X (2011) An assessment of the surface climate in the NCEP climate forecast system reanalysis. Clim Dyn 37:2511–2539CrossRefGoogle Scholar
  59. Waters J, Lea DJ, Martin MJ, Mirouze I, Weaver A, While J (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: 10.1002/qj.2388 CrossRefGoogle Scholar
  60. Wen C, Xue Y, Kumar A, Behringer DW, Yu L (2016) How do uncertainties in NCEP R2 and CFSR surface fluxes impact tropical ocean simulations? Cond Accept Clim DynGoogle Scholar
  61. Xue Y et al (2010) Ocean state estimation for global ocean monitoring: ENSO and beyond ENSO. In: Hall J, Harrison DE, Stammer D (eds) Proceedings of oceanObs’09: sustained ocean observations and information for society (vol 2), Venice, Italy, 21–25 September 2009, ESA Publication WPP-306Google Scholar
  62. Xue Y, Kumar A (2016) Evolution of the 2015/16 El Niño and historical perspective since 1979. Sci China Earth Sci. doi: 10.1007/s11430-016-0106-9 Google Scholar
  63. Xue Y, Huang B, Hu ZZ, Kumar A, Wen C, Behringer DW, Nadiga S (2011) An assessment of oceanic variability in the NCEP climate forecast system reanalysis. Clim Dyn 37:2511–2539. doi: 10.1007/s00382-010-0954-4 CrossRefGoogle Scholar
  64. Xue Y et al (2012) A comparative analysis of upper ocean heat content variability from an ensemble of operational ocean reanalyses. J Clim 25:6905–6929CrossRefGoogle Scholar
  65. Xue Y, Wen C, Yang X, Behringer D, Kumar A, Vecchi G, Rosati A, Gudgel R (2015) Evaluation of tropical Pacific observing system using NCEP and GFDL ocean data assimilation systems. Clim Dyn. doi: 10.1007/s00382-015-2743-6 Google Scholar
  66. Yang X, Rosati A, Zhang S, Delworth TL, Gudgel RG, Zhang R, Vecchi G, Anderson W, Chang Y-S, DelSole T, Dixon K, Msadek R, Stern WF, Wittenberg A, Zeng F (2013) A predictable AMO-like pattern in the GFDL fully coupled ensemble initialization and decadal forecasting system. J Clim 26(2):650–661CrossRefGoogle Scholar
  67. Yin Y, Alves O, Oke PR (2011) An ensemble ocean data assimilation system for seasonal prediction. Mon Weather Rev 139:786–808CrossRefGoogle Scholar
  68. Zebiak SE (1989) Oceanic heat content variability and El Niñocycles. J Phs Oceanogr 19:475–486CrossRefGoogle Scholar
  69. Zhang S, Harrison MJ, Rosati A, Wittenberg A (2007) System design and evaluation of coupled ensemble data assimilation for global oceanic studies. Mon Weather Rev 135:3541–3564CrossRefGoogle Scholar
  70. Zhu J, Huang B, Marx L, Kinter JL III, Balmaseda MA, Zhang RH, Hu ZZ (2012) Ensemble ENSO hindcasts initialized from multiple ocean analyses. Geophys Res Lett 39:L09602. doi: 10.1029/2012GL051503 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg (outside the USA) 2017

Authors and Affiliations

  • Yan Xue
    • 1
  • C. Wen
    • 1
    • 10
  • A. Kumar
    • 1
  • M. Balmaseda
    • 2
  • Y. Fujii
    • 3
  • O. Alves
    • 6
  • M. Martin
    • 7
  • X. Yang
    • 4
  • G. Vernieres
    • 5
  • C. Desportes
    • 8
  • T. Lee
    • 9
    • 12
  • I. Ascione
    • 7
  • R. Gudgel
    • 4
  • I. Ishikawa
    • 11
  1. 1.Climate Prediction Center, NCEP/NWS/NOAACollege ParkUSA
  2. 2.European Center for Medium-Range Weather ForecastsReadingUK
  3. 3.Meteorological Research Institute, Japan Meteorological AgencyTsukubaJapan
  4. 4.Geophysical Fluid Dynamics LaboratoryNOAA/OARPrincetonUSA
  5. 5.Goddard Space Flight Center, NASAGreenbeltUSA
  6. 6.Bureau of MeteorologyMelbourneAustralia
  7. 7.Met OfficeExeterUK
  8. 8.Mercator OceanRamonville-Saint-AgneFrance
  9. 9.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA
  10. 10.Innovim, LLCGreenbeltUSA
  11. 11.Japan Meteorological AgencyTokyoJapan
  12. 12.University of California at Los AngelesLos AngelesUSA

Personalised recommendations