1. Introduction to the System 3 Ocean Analysis

1.1 Ocean initial conditions, ocean reanalysis and data assimilation

1.2 The System 3 ocean analysis system

1.3 History of ocean analysis at ECMWF

1.1 Ocean initial conditions, ocean reanalysis and data assimilation

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:

• Ocean initial conditions are the main source of predictability at seasonal time scales, and their are especially important in the prediction of ENSO (El Niño Southern Oscillation) and its impact on the climate system. The correct initialization of the upper ocean thermal structure is considered instrumental in the prediction of the tropical SST at seasonal timescales with dynamical models. 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 seasonal 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 (typically 10-20 years). 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.
• An ensemble of ocean analyses (five in total) is performed to sample the uncertainty in the ocean initial conditions. The ensemble of ocean initial conditions provided by the five analyses contributes to the creation of the ensemble of forecasts for the probabilistic predictions at monthly and seasonal ranges.

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 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.

Data assimilation has a large impact on the mean state of the first guess, and consistently reduces the bias.The impact is more especially noticeable in the tropics, where both the mean state and the interannual variability is improved of the ocean analysis is improved by data assimilation.

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

The new operational ECWMF ocean analysis system (system 3 or S3) consists of two analysis streams:

• A historical reanalysis from 1959 which is 09.03.2007e (BRT). It is used to initialize seasonal forecasts.
• An early delivery ocean analysis, produced daily in near real time (NRT), used to initialize the monthly forecasts.

Although the historical reanalysis goes back to 1959, only the period 1981-2005 is used to initialize the calibrating hindcasts of the S3 seasonal forecasting system (Anderson et al., 2007). The earlier period of S3 ocean analysis will be used to initialize seasonal and decadal predictions within the ENSEMBLES project. As well as providing initial conditions for coupled model forecasts, the S3 ocean re-analysis, based on the synthesis of surface and subsurface ocean observations, surface fluxes from atmospheric analyses and reanalyses, and a general circulation ocean model, constitutes an important resource for climate variability studies.

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.

1.3 History of ocean analysis at ECMWF

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.

At the other end of the spectrum, moving towards the shorter time scales, we have the monthly and medium range forecasting activities, where the demand for ocean initial conditions is increasing. Monthly forecasting activities have stirred quite some interest in the last few years, and the benefits of having an active ocean in the forecasting system has been demonstrated (Vitart et al. 2006, Woolnough et al. 2007). The monthly forecasting system uses the same ocean model as the seasonal forecasting system though there are some differences in the way the ocean initial conditions are produced. In the near future it is planned that the medium range EPS forecasts will also be performed with a coupled model, with the consequent need for real-time ocean initial conditions. The EPS will also have a need for the historical ocean reanalysis, since the reliable prediction of extreme events also requires hindcasts or re-forecasting activities just as is done now for monthly and seasonal forecasting.

In order to accommodate these different demands, the S3 ECMWF ocean analysis has been designed to deliver two kinds of analysis products: a delayed product 11 days Behind Real Time (BRT), used to initialize the seasonal forecasts, and an early delivery product, which runs in Near Real Time (NRT), to be used by the monthly forecasts, and which in the near future is envisaged to be used by the medium range forecasting system as well.