Ocean Reanalysis/analysis

ECMWF estimates the state of the global ocean via the operational system OCEAN5.  The OCEAN5 system is a global eddy-permitting ocean-sea ice ensemble (5 members) reanalysis-analysis system. It comprises a Behind-Real-Time (BRT) component, that was used for production of Ocean ReAnalysis System 5 (ORAS5) and provides an estimate of the historical ocean state from 1979 to present (with a few days delay); and a Real-Time (RT) component, that provides the latest ocean conditions for NWP applications. A backward extension of ORAS5 is also available from 1958 onwards. The OCEAN5 system replaced the OCEAN4 system and now provide ocean and sea-ice initial conditions for all ECMWF coupled forecasting systems.

Graphical Products:

Upgrades in OCEAN5

The OCEAN5 has been developed based on ORAP5 (Zuo et al., 2017a), a prototype system which was developed within the EU funded research projects MyOcean and MyOcean2. Compared to OCEAN4, it includes many upgrades in both model and data assimilation method, as well as in source/use of forcing and observation data sets. The ocean model resolution in OCEAN5 has been increased to 0.25 degree in the horizontal and 75 levels in the vertical. It includes a prognostic thermodynamic-dynamic sea-ice model (LIM2) with assimilation of sea-ice concentration data. It also includes surface waves effects in the exchange of momentum and turbulent kinetic energy (Janssen et al., 2013). Another important novelty in the OCEAN5 is a  new generic ensemble generation scheme (Zuo et al., 2017b) that accounts for both observation and forcing errors.

The table below summarizes upgrades in the ocean reanalysis ORAS5 as produced by the OCEAN5-BRT component (DA method changes are not listed here).


Ocean horizontal resolution ORCA 1 degree ORCA 0.25 degree
Ocean vertical resolution L42 L75
Ocean model

NEMO v3.0

NEMO v3.4

Sea ice model No sea-ice model LIM2
Forcing Direct surface fluxes from ERA40 and ERA-Interim ERA-Interim with bulk formula + WAVE forcing
Observation OIv2 SST, EN3 in-situ, AVISO SLA HadISST2 SST, OSTIA SIC, EN4 in-situ, AVISO DT2014 SLA
bias correction multi-scales bias correction multi-scales bias correction with ensemble approach
Reanalysis Period 1959-present 1979-present (with spin-up from 1958 onwards)
Ensemble size 5 members 5 members with additional perturbations in forcings, observations and initial conditions

Background Information:

Initialisation of coupled forecasts and ocean monitoring

The main purpose of the ocean analysis at ECMWF is to provide initial conditions for ECMWF's coupled forecasting system. Following an strategy of Earth system approach, all ECMWF forecasts are now produced with a coupled atmospheric-ocean-sea ice model. The OCEAN5-RT system provides the ocean and sea-ice initial conditions for the medium-range and monthly ensemble forecast (ENS, since November 2016), the extended range forecasts (Seasonal, since November 2017), the high-resolution deterministic forecast (HRES, since June 2018). The OCEAN5-RT also provides sea-ice concentration for the atmospheric analysis system since Cy45r1. Ocean reanalyses produced by OCEAN5-BRT are valuable resources for climate monitoring, climate variability studies,  and verification of climate models. One important task for the OCEAN5 system is to provide continuously monitoring of the recent ocean state, including contribute to the international Ocean Reanalyses Inter-comparison Real-Time (ORIP-RT) project.

Ocean and Sea-ice initial conditions are the main source of predictability at seasonal time scales, being especially important in the prediction of ENSO (El Niño Southern Oscillation) and its impact on the climate system. More generally, the correct initialisation of the upper ocean thermal structure is considered instrumental in the prediction of the tropical SST at seasonal timescales. At the monthly time scales, the prediction of phenomena such as the MJO (Madden Julian Oscillation) requires the correct representation of the ocean-atmosphere interactions.

A historical ocean reanalysis is required to provide initial conditions for the calibration of the coupled forecasts. The a-posteriori calibration of model output requires an estimate of the model climatology, which is obtained by performing a series of coupled hindcasts during some historical period. A historical record of hindcasts is also needed for skill assessment. The interannual variability represented by ocean reanalysis will have an impact on both the calibration and on the assessment of the skill. The ocean reanalysis product ORAS5 now provides  historical ocean states from 1979 onwards. The period 1993-2016 is used to initialise the calibrating hindcasts of the SEAS5 seasonal forecasting system.

An ensemble of ocean and sea-ice analyses (five in total) is performed to sample the uncertainty in the ocean and sea-ice initial conditions. The ensemble of initial conditions provided by the five analyses contributes to the creation of the ensemble of forecasts for the probabilistic predictions at medium, monthly and seasonal ranges. The ensemble can also be used to estimate the uncertainty in the climate signals from the ocean reanalysis. The five ensemble members in OCEAN5 are generated by sampling uncertainty in the forcing fields, in the observation locations, and in the ocean state chosen to initialise the reanalysis.

The ingredients: ocean model, surface forcing, data assimilation and ocean observations

Ocean initial conditions for the global ocean could in principle be estimated by forcing an ocean model with atmospheric fluxes of heat, momentum and fresh water. However both ocean models and atmospheric fluxes are far from perfect, and the estimation thus obtained (first guess) can have substantial uncertainty. In order to improve the estimation of the state of the ocean, this first guess is combined with ocean observations via a data assimilation procedure.

The current operational ocean estimation (OCEAN5) is based in the NEMO ocean model coupled to the LIM2 sea-ice model. Observations of different types are then assimilated through the NEMOVAR data assimilation system. This is the first time that a dynamic thermodynamic sea-ice model is used in the ECMWF operational ocean analysis. A schematic diagram of the OCEAN5 system is shown below.

The ocean and sea-ice model: the community ocean model NEMO version 3.4.1 has been used for OCEAN5 in a global configuration ORCA025.L75, a tripolar grid which allows eddy to be represented approximately between 50 S and 50N. Model horizontal resolution is approximately 25 km in the tropics, and increases to 9 km in the Arctic. There are 75 vertical levels, with level spacing increasing from 1 m at the surface to 200 m in the deep ocean. The sea-ice model used to represent the dynamic and thermodynamic sea-ice evolution in OCEAN5 is LIM2 (the Louvain-la-Neuve sea-ice model version 2), which is part of the NEMO ocean model. This sea-ice model has two layers of ice and one layer of snow, and uses a rheological model to describe the internal ice dynamics.

Surface forcing fluxes: heat, momentum and fresh water fluxes are used to force the ocean model and to produce the first guess or the state of the ocean. The source of forcing fluxes used in OCEAN5 is shown in the diagram below. Prior to 1979, the surface forcing fluxes are from the ERA-40 atmospheric reanalysis. From the period 1979-2014, the surface forcing fluxes are from ERA-Interim reanalysis. From 2015 onwards,  OCEAN5 started to use surface forcing fluxes derived from the ECMWF operational NWP.

The ocean data assimilation system: the NEMOVAR in its 3D-Var FGAT configuration is used to assimilate temperature and salinity profiles, sea-ice concentration (SIC) and altimeter derived along-track sea-level anomalies (SLA) data. The sea surface temperature (SST) data is also used to constrain ocean upper temperature through a simple nudging scheme.  The assimilation window is 5 days for the OCEAN5-BRT stream while a variable length window is used for the OCEAN5-RT stream (for more information about the streams see below).  A multi-scales bias correction scheme is used to correct the model/forcing errors. The bias correction is also needed to ameliorate the spurious variability that can arise from changes in the global ocean observing system.

Ocean Observations: are used to improve the ocean estimate given by the first guess. The figure above offers an schematic view of the different data sources used in OCEAN5. The following types of ocean observation are used:

  • Temperature and salinity profiles (T/S). The profiles are from the quality-controlled EN4 with Expendable BathyThermograph (XBT) and Mechanical bathythermograph (MBT) depth corrections until May 2015, and from Global Telecommunications System (GTS) thereafter. The T/S profiles consisting of the several data-types: XBTs and MBT; CTDs; moorings; Argo floats; and APBs (or mammal-based). 
  • Altimeter derived sea level anomalies (SLA). The along-track SLA data used in OCEAN5 is the AVISO DT2014 (reprocessed until December 2014, then NRT) data set, now distributed by Copernicus Marine Environment Monitoring Service (CMEMS). The along track product is available from 1993 onwards and assimilated via NEMOVAR. Global mean values from the AVISO gridded maps of sea level anomaly (MSLA) are also used to constrain the global fresh water budget.
  • Sea-Ice Concentration (SIC). From 1979 until August 1981, the SIC is taken from the ERA-40 archive. From September 1981 until December 1984, SIC is taken from the NCEP OI v2 weekly product (Reynolds in the figure). From January 1985 until December 2007, SIC is taken from the OSTIA reprocessed data set. From January 2008 onwards, the OSTIA operational SIC is used.
  • Sea Surface Temperature (SST). From 1979 until December 2007, the SST are taken from the HadISSTv2 product. From January 2008 onwards, the OSTIA operational SST is used. The SST information is used to constrain the upper level ocean temperature via a newtonian relaxation scheme.

The reanalysis and real time streams

The ECMWF OCEAN5 system includes BRT and RT components for estimation of both historical ocean state (reanalysis) as well as the real-time ocean state (analysis).

  • OCEAN5-BRT: The Behind-Real-Time component of OCEAN5 produces a historical ocean reanalysis from 19790101 to present (ORAS5), updated every 5 days with a delay varying from 7 to 11 days.  It is used to initialise the coupled hindcasts needed for calibration of coupled model output.
  • OCEAN5-RT: The Real-Time component of OCEAN5 provides estimation of  the latest ocean states. It is initialized from the the last BRT analysis and brings the ocean analysis up to real time every day using a variable assimilation window.  The Real-Time analysis is used to initialise all ECMWF's coupled forecasts.

The figure below shows schematically the operational schedule followed in the production of the two streams. The OCEAN5-BRT uses a 5-day assimilation window and is updated every 5 days with a minimum delay of 7 days,  in order to wait for the arrival of ocean observations (in particular, the NRT sea level anomaly data). The OCEAN5-RT analysis is updated daily using a variable assimilation window of 8 to 12 days: starting from the last BRT analysis, it brings the RT analysis forward up to current conditions, to produced ocean states suitable to initialise the coupled forecast. This RT extension contains 2 assimilation cycles (Chunk) with a variable second assimilation window. The RT extension is always initialized from the last day of the BRT analysis and synchronically switches to the new initialization whenever the BRT analysis updates, hence the variable assimilation window. In practice, the OCEAN5 RT analysis is launched every day at 14Z to produce a daily analysis valid for 0Z for the following day.


The data archive

The OCEAN5 data are not yet archived in MARS.  The first member of ocean and sea-ice reanalysis data (ORAS5) from 1958 onwards is published by C3S through their CDS service, with a delay of 1-month to real-time.  ORAS5 data from 1993 onwards (in netCDF format) is also available through the CMEMS data portal with a delay of one year. This data is distributed in its native grid as well as in a reduced grid (1x1 degree) and as monthly mean fields, as ECMWF's contribution to the CMEMS GLO-RAN and GLO-RAN 2 project. Ocean variables released in this service are listed in the Table below. Like ORAS4, the full ORAS5 dataset (1958-2018, monthly mean of all 5 ensemble members) has been published through the Integrated Climate Data Center - ICDC at University of Hamburg.

Cite the data

When using the data set in a paper, please cite the following:

Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, 2019.

Contact: Hao Zuo or Magdalena Balmaseda


Variable names

Variable long names and units


Potential Temperature [K]


Salinity [PSU]


Zonal Velocity [m/s]


Meridional Velocity [m/s]


Sea Surface Temperature [C]


Sea Surface Salinity [PSU]


Sea Surface Height [m]


Sea Ice zonal velocity [m/s]


Sea Ice meridional velocity [m/s]


Sea Ice Concentration


Sea Ice Thickness [m]


Mixed Layer Depth 0.01 [m]


Net Downward Heat Flux [W/m2]


Net Upward Water Flux [Kg/m2/s]


Wind Stress along i-axis [N/m2]


Wind Stress along j-axis [N/m2]



Further reading

H. Zuo, M. A. Balmaseda, S. Tietsche,  K. Mogensen, and  M. Mayer. The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019.

H. Zuo, M. A. Balmaseda, K. Mogensen, S. Tietsche, OCEAN5: the ECMWF Ocean Reanalysis System and its Real-Time analysis component, 2018, ECMWF Technical Memorandum, 823, 2018.

H. Zuo, M. A. Balmaseda, and K. Mogensen. The new eddy-permitting ORAP5 ocean reanalysis: description, evaluation and uncertainties in climate signals. Climate Dynamics, 49(3):791–811, aug 2017a. ISSN 1432-0894. doi: 10.1007/s00382-015-2675-1.

H. Zuo, M. A. Balmaseda, E. D. Boisseson, S. Hirahara, M. Chrust, and P. D. Rosnay. A generic ensemble generation scheme for data assimilation and ocean analysis. ECMWF Technical Memorandum, 795, 2017b.

P. Janssen, O. Brevivik, K. Mogensen, F. Vitart, M. A. Balmaseda, J. R. Bidlot, S. Keeley, M. Leutecher, L. Magnusson, and F. Molteni. Air-sea interaction and surface waves. ECMWF Technical Memorandum, 712, 2013