Content as published on 9.3.2007.

1. Introduction to the System 3 Ocean Analysis

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The main purpose of the ocean analysis at ECMWF is to provide initial conditions for the extended range forecasts (seasonal and monthly). The ECMWF seasonal and monthly forecasting systems are based on a coupled ocean-atmosphere general circulation model that predicts both the lower boundary conditions (namely SSTs) and their impact on the atmospheric circulation. The quality of monthly and seasonal forecasts is determined by the the various components of the system (the ocean initialization, the coupled model, the ensemble generation and the calibration strategy), which are closely interrelated:

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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 atmopspheric 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. At ECMWF, the ocean model is HOPE (Hamburg Ocean Primitive Equation) and the assimilation system is based on an OI (Optimal interpolation) scheme.

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Data assimilation has a favourable impact on the skill of seasonal forecasts of SST, especially in the western Pacific, where the forecast skill is improved, especially in the first 3 months, is improved by using data assimilation in the initialization of the ocean (Alves et al 2003, Balmaseda et al 2007).

1.2 The System 3 ocean analysis system

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The S3 ocean analysis has several innovative features, including an on-line bias correction algorithm, the assimilation of salinity data on T-surfaces and assimilation of global seas level trends. Two main criteria have been considered in the design of the assimilation algorithm: making optimal use of the observation information at the same time as avoiding spurious climate variability in the resulting ocean reanalys09.03.200709.03.2007is given in Balmaseda et al. , 2007.

The surface fluxes of heat, momentum and fresh water are an important component of the ocean analysis system. In S3, these are provided by ERA40 for the period 1959-2002 and by the operational system thereafter (ERA40/OPS). The five simultaneous ocean analyses are created by adding perturbations, commensurate with the estimated uncertainty, to the wind stress while the model is being integrated forward from one analysis time to the next. The wind perturbations have been revised in S3 to represent the perceived uncertainty in ERA40/OPS wind stress.

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ECMWF has produced operationally daily global ocean analyses to provide initial conditions for the seasonal forecasting system since 1997. There have been two versions of the ocean analysis, linked to the operational seasonal forecasting system. System 1 (S1) started in 1997 (Alves et al., 2003) and provided the initial conditions for the first ECMWF operational seasonal forecasting system (Stockdale et al., 1998). System 2 (S2) was introduced in 2001 (Balmaseda 2004), and has provided initial conditions for the ECMWF operational seasonal forecasts since 2002 (Anderson et al. 2003, Oldenborgh et al. 2005a,b, Vialard et al. 2005). A comparison between S2 and S1 ocean analyses is given in Balmaseda 2004. In 2004 an extension of S2 was introduced in order to initialize the monthly forecasting system (Balmaseda 2005, Vitart 2005). In summer 2006 the S3 ocean analysis was implemented operationally (Balmaseda et al 2007).

Although originally developed solely to provide ocean initial conditions for the seasonal forecast system, the scope of the ocean analysis at ECMWF has been slowly widening with time. Initially the length of the historical record of ocean initial conditions was not too long, since the hindcasts were mainly used to estimate the bias of the coupled model, which, although seasonally dependent, could be robustly estimated with a limited set of integrations. For instance, in the first seasonal forecasting system (S1), only 5 years of calibrating hindcasts were used. However, it was also necessary to provide an estimation of the forecast skill, and that required a larger historical sample, including as wide a range of climate conditions as possible. There was also the realization that calibrating not only the mean, but also the variance of the coupled model forecasts could lead to improved reliability of the seasonal forecasts. Besides, with the advent of multi-model activities it is clear that the robust bayesian combination and calibration of multi-model forecasts needs long records of realizations. All of these applications pointed towards the need for a long historical ocean reanalysis that could provide consistent initial conditions for the "calibrating" coupled hindcasts. And with a long record it is possible to start trying some decadal forecasts, as in the ENACT and ENSEMBLES projects, which try to assess the predictability at the decadal time scales.

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The first-guess is obtained from integrating the HOPE ocean model from one analysis time to the next, forced by ERA40/OPS fluxes (ERA40 fluxes from the period January 1959 to June 2002 and NWP operational analysis thereafter). In S2 the fluxes were from ERA15/OPS, but the wind stresses were not directly used: instead, the wind stress was derived from the analyzed winds using an off-line bulk formula. The representation of the upper ocean interannual variability is improved when using the ERA40 wind stress (Uppala et al., 2006), although the stresses are biased weak in the equatorial Pacific. The fresh water flux from ERA-40 (Precipitation - Evaporation, denoted P-E) is known to be inaccurate. S3 uses a better but by no means perfect estimate, obtained by 'correcting' the ERA-40 precipitation values (Troccoli and Kallberg 2004)..

3. The Ocean Observations and Quality Control

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Subsurface temperature observations that are available in near-real-time are currently provided by the TAO/TRITON and PIRATA arrays in the equatorial region (McPhaden 1998, Servain et al 1998) and the global Volunteer Observing Ship (VOS) programme which provides XBT (eXpandable BathyThermographs) measurements mainly along merchant shipping routes. More recently, observations are provided by the ARGO network of drifting profilers. Salinity data is available from ARGO and from the TRITON moorings.

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1. Daily averaging. First of all, daily averages are created if applicable: if some site reports more frequently than once per day, daily averages are created (this is the case for the TRITON moorings, which report hourly).
2. Black list of coastal observations. Data in the vecinity of the coast are rejected, as a way of accounting for representativeness error
3. Background check. A level-by-level check between the distance between model values and observations in relation to the error statistics.
4. Buddy check. A consistency test between observations is peformedperformed.
5. Super-obbing. Profiles which are close in space and time are superobbed, following the same criteria as in Smith et al., 1991.
6. Completness of profiles. in S3 there is an additional check for completeness of the profiles: a profile is considered incomplete, and therefore rejected, if the sparsity of the remaining observations in the vertical is judged insufficient to resolve the vertical temperature gradients. (An observation profile will be rejected if the temperature difference between consecutive levels is larger than 5 deg C or if it contains a vertical temperature gradient larger than 0.1 deg C/m).

The observation coverage and the quality control decisions for the different assimilation cycles can be seen here. The figures show that thanks to the ARGO system the coverage of salinity is now comparable to that of temperature, and it is almost global. In recent times the TRITON moorings in the West Pacific and Indian ocean also provide salinity in real time. The PIRATA and TAO moorings report only temperature in real time, even when the sensors are also able to measure salinity. Most of the data from XBTs and Mooring are superobbed, whilst the ARGO profiles are often partially rejected by our QC system.

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An accurate initial SST is of great importance for the accuracy of the forecasts. In the present system, we09.03we .200709.03. 2007l#Reynolds">Reynolds et al 2002). The analysis is linearly interpolated to daily values before use. Because the SST analysis is produced only once a week (on Mondays, with a valid central time of the previous Wednesday), and because we need the following Wednesday to produce the interpolated value for a Thursday, this implies a delay of up to 11 days in the availability of the SST field. Daily SST fields are available in near real time from the NWP system, but these are less stable, and there are significant inconsistencies in the data from different periods. We prefer to use the higher quality data, even if it is slightly delayed. Despite using what we consider to be the best SST analysis available, there are still significant errors, estimated to be of the order of 0.3 K.

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When in 2004 the monthly forecasting system became operational at ECMWF, it was necessary to produce near-real-time ocean initial conditions, since the typical 11 days delay of the reanalysis product was not adequate for the monthly system. To this end, an early delivery ocean analysis system was introduced in May 2004. Details of the timing of the near real time S2 ocean analysis are given in Balmaseda 2005. In what follows, we denote the Near Real Time stream as NRT, and for BRT we refer to the reanalysis, which runs 11 days Behind Real Time. The BRT and NRT analysis systems are almost identical: they only differ in the details of the assimilation cycle and in the earlier cut-off of the assimilation windows. The BRT product is a continuation of the historical ocean reanalysis, while the NRT only exists for the recent period

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The figure below shows schematically the schedule followed in the production of the NRT and BRT ocean analyses in S3 (which is sligthly diffrent slightly different from that described in Balmaseda 2005). Every day, the BRT ocean analysis is advanced by 1 day, starting from the BRT analysis from the previous day (D0-12 in fig) to produce the BRT analysis at day D0-11. If the 10-day assimilation cycle is due on that day (D0-12), the OI analysis is performed using a 10-day centered window,

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Similarly, the NRT ocean analysis is also produced daily. Starting from BRT (D0-12), the model is integrated forward 12 days, so bringing it up to real time. All the available observations during that period are used. During the 12 days there are always two assimilation cycles. The first assimilation takes place at (D0-12), using a 10-day window centred at D0-12 (W1), and the assimilation increment using the incremental update method is computed . This increment is only applied for the next 7 days, at which time the second assimilation W2 is performed (i.e. at D0-5), using observations from the 10-day window as shown in fig 5. The corresponding increment is then computed , and applied during the next 5 days of the integration. Effectively, W2 only has 9 days worth of data, since the daily averages for day D0 are not available at the time of the operational production. The assimilation windows W1 and W2 overlap for 3 days. The observations within this overlapping period, although used in two different analysis cycles are not given extra weight, since the increment of the first assimilation cycle is not applied to completion.

5. Ocean Analysis Products

The ECMWF ocean analysis system produces daily analysis of the global ocean for temperturetemperature, salinity, velocity, sea level and other derived variables. The reanalysis stream spans the period 1959 up to 11 days behind real time. The realtime real-time stream started it August 2006.

The following products are displayed in the web:

• Horizonal Horizontal maps of SST, Sea Level, Depth of 20 deg Isotherm, Surface Salinity, Zonal Wind Stress and Averaged Temperature and Salinity over the upper 300m.
• Zonal sections along the equator of temperature.
• Meridonal Meridional temperature sections at 165E, 140W and 30W.
• Time-Longitude sections along the equator of SST, Sea Level, Depth of 20 degrees Isotherm, Zonal Wind Stress.
• Observation coverage maps and quality control decisions.

Both the full fields and the anomalies respect the 1981-2005 are displayed. The maps represent daily fields from the real-time analysis, and weekly and monthly from the reanalysis stream. The monthly fields go back to 1959. The weekly fields are only displayed since January 2007. For the realtimereal time, only the last 30 daily fields are displayed.

6. Ocean Archive

The operational ocean analysis is archived in the MARS mars under stream=OCEA, and expver=0001 and system=3.

As for all the ocean data archived in MARS, the leveltype=depth (dp).

The ocean reanalisys is archived as type=OR

The realt ime real-time ocean analysis is archived as type=AN

Time and date atributes

For the ocean analysis and reanalysis products, time=0,step=0, and date=YYYYMMDD as the verifying date.

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The ocean data is archived in GRIB using the ECMWF local table 2 version 151 and GRIB extension in definition 4. The most common parameters are:

• 129 for potential temperturetemperature
• 130 for salinity
• 131 for zonal velocity
• 132 for meridional velocity
• 133 for vertical velocity
• 145 for sea level
• 163 for depth of 20 degree isotherm
• 148 for mixed layer depth
• 164 for averaged temperature in upper 300m
• 175 for averaged salinity in upper 300m
• 153 for zonal wind stress
• 154 for meridional wind stress

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• PROD=INST for instantaneous fields.
• PROD=TACC for accumulated fields (only for OR).
• PROD=TIMS for timeseries (only for ReanlysisReanalysis) .
• PROD=TAVG for time averaged fields

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• method=1 (for data assimilation initialization)
• number=0 for centered unperturbed analysis.(n=1/2/3/4 for the perturbed analysis)
• system=3 (system 3 operational analsyisanalysis)
• levellist: needed for H sections
• latitude: needed for Z sections
• longitude: needed for M sections
• range: time range (in hours) need needed for TIMS and TAVG products.

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• Mars retrieval of Sea Surface Temperature and Surface Salinity from the real time analysis, for the 2nd of March 2007. Only the centered analysis (number=0):

  retrieve,
number=0,
system=3,
time=00:00:00,
date=2007-03-02,
section=h,
levtype=dp,
method=1,
param=129.151/130.151,
levelist=5.0,
stream=ocea,
expver=1,
type=an,
product=inst,
class=od

• Mars retrieval of accumulated sea level and depth of 20 deg Isotherm from the ocean reanalysis for the 1st November 1982:

  retrieve,
number=0,
system=3,
time=00:00:00,
date=1982-11-01,
section=h,
levtype=dp,
method=1,
param=163.151/145.151,
levelist=0,
stream=ocea,
expver=1,
type=or,
product=tacc,
class=od

• Mars retrieval of an intantaneous instantaneous zonal section of temperature along the equator, from the reanalysis, all 5 ensemble members:

  retrieve,
number=0/1/2/3/4,
system=3,
time=00:00:00,
date=1982-11-01,
stream=ocea,
section=z,
latitude=0,
levtype=dp,
expver=1,
method=1,
class=od,
product=inst,
type=or,
param=129.151

• Mars retrieval of an instantaneous meridional section of zonal velocity at 140W, from the realtime real-time analysis. The user needs to indicate the closer model longitude:

  retrieve,
longitude=220.078,
number=0,
system=3,
time=00:00:00,
date=1982-11-07,
stream=ocea,
section=m,
levtype=dp,
expver=1,
method=1,
type=or,
product=inst,
class=od,
param=131.151

• Mars retrieval of timesies timeseries of averaged temperature in the upper 300m along the equator from the reanalysis, for the period 19870101-19870201:

  retrieve,
number=0,
system=3,
time=00:00:00,
date=1987-02-01,
range=720,
section=z,
latitude=0,
levtype=dp,
method=1,
param=164.151,
levelist=0,
stream=ocea,
expver=1,
product=tims,
type=or,
class=od

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Alves O., M. Balmaseda, D Anderson, T Stockdale, 2003, Sensitivity of dynamical seasonal forecasts to ocean initial conditions. Q. J. R. Meteorol. Soc.,130, 647-668. Also ECMWF Technical Memorandum 369.

Anderson, D. L. T., and M. Balmaseda, 2005: Overview of ocean models at ECMWF. ECMWF Seminar Proceedings. Seminar on Recent developments in numerical methods for atmospheric and ocean modelling, 6-10 September 2004, 103-111.

Anderson, D. L. T., T. Stockdale, M. Balmaseda, L. Ferranti, F. Vitart, P. Doblas-Reyes, R. Hagedorn, T. Jung, A. Vidard, A. Troccoli and T. Palmer, 2003: Comparison of the ECMWF seasonal forecast Systems 1 and 2, including the relative performance for the 1997/8 El Nino. ECMWF Technical Memorandum 404.

Anderson, D. L. T., T. Stockdale, M. Balmaseda, L. Ferranti, F. Vitart, F. Molteni, F. Doblas-Reyes, K. Mogensen and A. Vidard , 2006. Development of the ECMWF seasonal forecast System 3. ECMWF Technical Memorandum 503.

Balmaseda, M.A., 2004: Ocean data assimilation for seasonal forecasts. ECMWF Seminar Proceedings. Seminar on Recent developments in data assimilation for atmosphere and ocean, 8-12 September 2003, 301-326.

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Balmaseda, M.A., A. Vidard and D.L.T. Anderson, 2007: The ECMWF System 3 Ocean Analysis System. ECMWF Technical Memorandum 408508.

Bloom, S. C., Takacs, L. L., Da Silva, A. M. and Ledvina, D., 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 1256-1271.

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