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

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Abstract

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.

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References

  • Alves O, Balmaseda M, Anderson D, Stockdale T (2004) Sensitivity of dynamical seasonal forecasts to ocean initial conditions. Q J R Meteorol Soc 130:647–668

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 DC

    Google Scholar 

  • 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 248–258

  • Balmaseda MA, Anderson D (2009) Impact of initialization strategies and observations on seasonal forecast skill. Geophys Res Lett 36:L01701. doi:10.1029/2008GL035561

    Article  Google Scholar 

  • 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 133:167–179

    Article  Google Scholar 

  • 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

  • Balmaseda MA, Mogensen K, Weaver AT (2013) Evaluation of the ECMWF ocean reanalysis system ORAS4. Q J R Meteorol Soc 131:1132–1161

    Article  Google Scholar 

  • Balmaseda MA et al (2015) The Ocean Reanalyses Intercomparison Project (ORA-IP). J Oper Oceanogr 8:80–97

    Article  Google Scholar 

  • 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 11–15

  • 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 126:1013–1021

    Article  Google Scholar 

  • 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 130:873–893

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Bloom SC, Takacs LL, Da Silva AM, Ledvina D (1996) Data assimilation using incremental analysis updates. Mon Weather Rev 124:1256–1271

    Article  Google Scholar 

  • 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 89:1111–1125

    Article  Google Scholar 

  • 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 

  • 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):775–803

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

  • 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

    Article  Google Scholar 

  • 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 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Hu S, Fedorov AV (2016) An exceptional easterly wind burst stalling El Niño of 2014. PNAS. doi:10.1073/pnas.1514182113

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 23:4901–4925

    Article  Google Scholar 

  • 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 126:1022–1034

    Article  Google Scholar 

  • Jin FF (1997) An equatorial ocean recharge paradigm for ENSO. Part I: conceptual model. J Atmos Sci 54:811–829

    Article  Google Scholar 

  • 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 83:1631–1643

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Kumar A, Hu ZZ (2014) Interannual and interdecadal variability of ocean temperature along the equatorial Pacific in conjunction with ENSO. Clim Dyn 42:1243–1258

    Article  Google Scholar 

  • Large WG, Yeager SG (2009) The global climatology of an interannually varying air–sea flux data set. Clim Dyn 33:341–364

    Article  Google Scholar 

  • Lee T, Awaji T, Balmaseda MA, Grenier E, Stammer D (2009) Ocean state estimation for climate research. Oceanography 22:160–167

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 DC

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Madec G (2008) NEMO ocean engine. Note du Pole de mode´lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27 ISSN No 1288–1619

  • Martin MJ et al (2015) Status and future of data assimilation in operational oceanography. J Oper Oceanogr 8:s28–48

    Article  Google Scholar 

  • Masson D, Knutti R (2011) Climate model genealogy. Geophys Res Lett 38:L08703. doi:10.1029/2011GL046864

    Article  Google Scholar 

  • 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

  • McPhaden MJ et al (1998) The tropical ocean–global atmosphere (TOGA) observing system: a decade of progress. J Geophys Res 103:14169–14240

    Article  Google Scholar 

  • McPhaden MJ et al (2009) RAMA: the research moored array for African–Asian–Australian monsoon analysis and prediction. Bull Am Meteorol Soc 90:459–480

    Article  Google Scholar 

  • 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 13:3551–3559

    Article  Google Scholar 

  • 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 68:29744

    Article  Google Scholar 

  • Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite SST analysis for climate. J Clim 15:1609–1625

    Article  Google Scholar 

  • Reynolds RW, Smith TM, Liu C, Chelton DB, Casey KS, Schlax MG (2007) Daily high-resolution blended analyses for sea surface temperature. J Clim 20:5473–5496

    Article  Google Scholar 

  • Rienecker MM et al (2011) MERRA—NASA’s modern-era retrospective analysis for research and applications. J Clim 24:3624–3648

    Article  Google Scholar 

  • Rosati A, Miyakoda K, Gudgel R (1997) The impact of ocean initial conditions on ENSO forecasting with a coupled model. Mon Weather Rev 125:754–772

    Article  Google Scholar 

  • Saha S et al (2006) The NCEP climate forecast system. J Clim 19:3483–3517

    Article  Google Scholar 

  • Saha S et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1057

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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):7994–8016

    Article  Google Scholar 

  • 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 30

  • 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 37:2511–2539

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 Dyn

  • 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-306

  • 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 

  • 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

    Article  Google Scholar 

  • Xue Y et al (2012) A comparative analysis of upper ocean heat content variability from an ensemble of operational ocean reanalyses. J Clim 25:6905–6929

    Article  Google Scholar 

  • 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 

  • 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):650–661

    Article  Google Scholar 

  • Yin Y, Alves O, Oke PR (2011) An ensemble ocean data assimilation system for seasonal prediction. Mon Weather Rev 139:786–808

    Article  Google Scholar 

  • Zebiak SE (1989) Oceanic heat content variability and El Niñocycles. J Phs Oceanogr 19:475–486

    Article  Google Scholar 

  • 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 135:3541–3564

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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Acknowledgements

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.

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Authors and Affiliations

  1. Climate Prediction Center, NCEP/NWS/NOAA, 5830 University Research Court, Room 3005, College Park, MD, 20740, USA

    Yan Xue, C. Wen & A. Kumar

  2. European Center for Medium-Range Weather Forecasts, Reading, UK

    M. Balmaseda

  3. Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan

    Y. Fujii

  4. Geophysical Fluid Dynamics Laboratory, NOAA/OAR, Princeton, NJ, USA

    X. Yang & R. Gudgel

  5. Goddard Space Flight Center, NASA, Greenbelt, MD, USA

    G. Vernieres

  6. Bureau of Meteorology, Melbourne, Australia

    O. Alves

  7. Met Office, Exeter, Devon, UK

    M. Martin & I. Ascione

  8. Mercator Ocean, Ramonville-Saint-Agne, France

    C. Desportes

  9. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    T. Lee

  10. Innovim, LLC, Greenbelt, MD, USA

    C. Wen

  11. Japan Meteorological Agency, Tokyo, Japan

    I. Ishikawa

  12. University of California at Los Angeles, Los Angeles, CA, USA

    T. Lee

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  1. Yan Xue
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  2. C. Wen
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  7. M. Martin
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Corresponding author

Correspondence to Yan Xue.

Appendix

Appendix

A brief description of each ORA is provided below:

1.1 NCEP Global Ocean Data Assimilation System (GODAS)

The operational GODAS was implemented at NCEP in 2003 (Behringer and Xue 2004), providing oceanic initial conditions for the NCEP Climate Forecast System from 1979 to present (Saha et al. 2006). GODAS is based on the Geophysical Fluid Dynamics Laboratory’s Modular Ocean Model version 3 (MOM3) (1° with 0.3° equatorial refinement and 40 levels) forced by the NCEP/DOE Reanalysis daily surface fluxes (Kanamitsu et al. 2002) and a 3-D variational (3DVAR) data assimilation scheme (Behringer et al. 1998). Observed temperature and synthetic salinity profiles, derived from a climatological T/S relationship, are assimilated. The model SST is nudged strongly to the NOAA weekly OI SST (Reynolds et al. 2002) (with a relaxation coefficient equivalent to time scale of 5 days). The GODAS is updated daily in real-time with a 2-day delay, and pentad and monthly averages are used for real-time ocean monitoring (http://www.cpc.ncep.noaa.gov/products/GODAS).

1.2 NCEP Climate Forecast System Reanalysis (CFSR)

A weakly coupled reanalysis for the atmosphere, ocean, sea ice and land, known as the CFSR (Saha et al. 2010), was implemented in 2011, and was used to provide atmospheric and oceanic initial conditions for the NCEP Climate Forecast System version 2 from 1979 to present (Saha et al. 2014). The oceanic component of CFSR includes many advances over that of GODAS (Xue et al. 2011): (a) the Modular Ocean Model version 4 (MOM4) at 1/2° with 1/4° equatorial refinement and 40 vertical levels with an interactive sea-ice model, (b) 6 h coupled model forecast as the first guess, (c) inclusion of the mean climatological river runoff, (d) high spatial (0.5°) and temporal (hourly) model outputs, and (e) strong relaxation to the NOAA daily OI SST product (Reynolds et al. 2007).

1.3 European Centre for Medium-Range Weather Forecasts (ECMWF) ocean reanalysis

The ECMWF ocean reanalysis, referred to as ORAS4, was implemented in 2010 (Balmaseda et al. 2013), and it spans the period 1958 to present and provides ocean initial conditions for the ECMWF coupled ensemble prediction system (medium range, monthly and seasonal). ORAS4 is based on the NEMO ocean model (Madec 2008) forced by daily surface fluxes from the ECMWF reanalysis until 2010 and from operational NWP thereafter. Observations of temperature profiles, salinity profiles, and along-track altimeter-derived sea-level anomalies are assimilated into the ocean model via the NEMOVAR system, a multivariate 3DVAR system. In addition, global mean SST and sea-level variations are used to modify the heat and fresh-water budget, respectively. A bias correction scheme is used to adjust temperature, salinity and pressure gradient (Balmaseda et al. 2007). In addition, a conservative (20-year time-scale) relaxation to temperature and salinity climatological values from the World Ocean Atlas 2005 (WOA05; Antonov et al. 2006; Locarnini et al. 2006) is applied throughout the water column.

ORAS4 uses the so-called ORCA1 horizontal discretization (1° with 0.3° equatorial refinement) and has 42 vertical levels. The analysis cycle is 10 days; every 10 days, the NEMO model is integrated forward forced by daily-averaged surface fluxes, relaxed to sea-surface temperature (SST) and bias corrected to produce the first guess and background trajectory. The assimilation increment computed by 3DVAR is applied using the Incremental Analysis Update (IAU, Bloom et al. 1996) with constant weights during a second model integration spanning the same time window as for the first guess.

1.4 Japan Meteorological Agency (JMA) ocean reanalysis

The JMA ocean reanalysis, referred to as MOVE/MRI.COM-G2, was implemented in June 2015 for ocean initialization of the JMA seasonal forecast model. The system uses the MRI Community Ocean Model version 3 (MRI.COM3) with horizontal resolutions 1° longitude and 0.5° latitude with 0.3° equatorial refinement and 52 vertical levels. The model is driven by surface fluxes from JRA-55 (Ebita et al. 2011) except latent and sensible heat fluxes and wind stress are calculated from the bulk formulae of Large and Yeager (2009). The analysis scheme is a multivariate 3DVAR analysis scheme with vertically coupled Temperature–Salinity Empirical Orthogonal Function (EOF) modal decomposition of a background error covariance matrix (Fujii and Kamachi 2003; Fujii et al. 2005). The system assimilates observed temperature and salinity profiles, gridded SST data and along-track satellite SSH anomaly. A sequential bias correction scheme is adopted (Fujii et al., 2015). Details about an experimental version of the system can be found in Toyoda et al. (2013).

1.5 Geophysical Fluid Dynamics Laboratory (GFDL) ocean reanalysis

The GFDL ocean reanalysis, referred to as the ensemble coupled data assimilation (ECDA) system, consists of an Ensemble Kalman Filter applied to GFDL’s second generation fully coupled climate model CM2.1 (Zhang et al. 2007). In ECDA, the probability distribution function (PDF) of climate states is represented by combination of the prior PDF approximately derived from 12 ensemble coupled model simulations and the observational PDF. The ECDA covers the period 1960 to present and is being updated monthly for GFDL’s seasonal-to-decadal experimental forecasts (Yang et al. 2013, Vecchi et al. 2014). The ocean component is the MOM4 configured with 50 vertical levels and 1° horizontal resolution, telescoping to 1/3° meridional spacing near the equator. The atmospheric component has a resolution of 2.5° × 2° with 25 vertical levels. The first guess is from a fully coupled model where the atmosphere component is constrained by winds, sea level pressure and temperature data from the NCEP/DOE reanalysis (Kanamitsu et al. 2002). For the ocean component, observed temperature and salinity profiles, pseudo salinity profiles (Chang et al. 2011) and the weekly OISST (Reynolds et al. 2002) are assimilated. The altimetry SSH data is used for the generation of pseudo salinity profiles, but not directly assimilated in the current analysis. A comprehensive assessment of the 1960–2010 oceanic variability in ECDA can be found in Chang et al. (2013).

1.6 National Aeronautics and Space Administration (NASA) ocean reanalysis

The NASA ocean analysis uses the GEOS-5 coupled atmosphere–ocean general circulation model based on MOM4 (0.5° with 1/4° equatorial refinement and 40 levels) and the GEOS-5 AGCM (1° x 1.25° with 72 levels) model. The atmosphere is constrained by the atmospheric fields from MERRA (Rienecker et al. 2011) and the first guess for the ocean comes from a coupled forecast (Vernieres et al. 2012). The ocean data assimilation uses a multivariate ensemble optimal interpolation (EnOI) to infer background-error covariances from a static ensemble of 50 model state-vector EOFs. Observed temperature and salinity profiles, and along-track sea level anomalies from AVISO are assimilated daily. Synthetic salinity profiles are generated for all temperature-only observations. More details are available at http://gmao.gsfc.nasa.gov/research/oceanassim/.

1.7 Bureau of Meteorology (BOM) ocean reanalysis

The BOM ocean reanalysis, referred to as PEODAS (POAMA Ensemble Ocean Data Assimilation System, http://poama.bom.gov.au/research/assim/index.htm), is an approximate form of ensemble Kalman filter system (Yin et al. 2011) wherein a single (central) analysis is computed utilizing an ensemble-based covariance and each of the perturbed ensemble members is nudged toward the central analysis to control the ensemble spread and constrain the mean. Observed temperature and salinity profiles are assimilated every 3 days, and corrections to the ocean currents are generated based on the ensemble covariance.

1.8 Met Office (MET) ocean reanalysis

The Met Office ocean analysis is based on the Forecasting Ocean Assimilation Model (FOAM v12) system (Blockley et al. 2014). It uses the NEMO ocean model coupled with the CICE sea-ice model configured with a 1/4 degree resolution with 75 vertical levels, forced by 3-hourly frequency heat and freshwater fluxes and hourly winds from the Met Office Numerical Weather Prediction system. The NEMOVAR scheme is run in 3DVar-FGAT (First-Guess-At-Appropriate-Time) mode with a 1-day time window (Waters et al. 2015). The system assimilates in situ temperature and salinity profiles, in situ sea surface temperature (SST) data, satellite SST data, along-track satellite altimeter SSH anomaly and satellite sea-ice concentration data. A model bias correction scheme (Bell et al. 2004) is employed to improve the assimilation of data in the tropics. The FOAM system runs on a daily basis producing analyses in near real-time for initialization of 7 day forecasts, and it also produces ocean reanalyses as part of the GloSea5 seasonal forecasting system (MacLachlan et al. 2015). The only differences between the reanalysis system and the NRT system used at the time of the analysis carried out in this paper are the source of the surface fluxes and observations. Reanalyzed surface fluxes from the ERA-Interim product are used, and reprocessed versions of the real-time observational data-sets are assimilated. Monthly means are from the GloSea5 reanalysis for the period 1993–2013, and from the NRT FOAM system from January 2014 onwards.

1.9 Mercator-ocean (MERCATOR) reanalysis

The Mercator-ocean reanalysis, referred to as Glorys2v3, is based on the NEMO 3.1, with a ORCA025 configuration, 75 levels, the TKE model for the vertical physics, the LIM2 sea-ice model, and is forced by ERA-Interim atmospheric variables (Lellouche et al. 2013). The surface boundary conditions are prescribed to the model using the CORE bulk formulation. The data assimilation method relies on a reduced order Kalman Filter based on the SEEK formulation. A 3DVar bias correction is implemented to correct large-scale temperature and salinity biases. The increments are applied with an Incremental Analysis Update and a low-pass filter, which gives a smooth model integration. The assimilated data consist of satellite SST and SLA data, in situ temperature and salinity profiles, and sea ice concentration. The model innovation (observation minus model equivalent) is computed at appropriate time (FGAT, First Guess at Appropriate Time). Further details are available at http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-009-011-017.pdf. The real-time product, from 2014 onward, is similar to Glorys2v3 but with ECMWF forcings and 50 levels.

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Xue, Y., Wen, C., Kumar, A. et al. A real-time ocean reanalyses intercomparison project in the context of tropical pacific observing system and ENSO monitoring. Clim Dyn 49, 3647–3672 (2017). https://doi.org/10.1007/s00382-017-3535-y

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