Arthur Vidard

The ECMWF Ocean Analysis System: ORA-S3

Abstract A new operational ocean analysis/reanalysis system (ORA-S3) has been implemented at ECMWF. The reanalysis, started from 1 January 1959, is continuously maintained up to 11 days behind real time and is used to initialize seasonal forecasts as well as to provide a historical representation of the ocean for climate studies. It has several innovative features, including an online bias-correction algorithm, the assimilation of salinity data on temperature surfaces, and the assimilation of altimeter-derived sea level anomalies and global sea level trends. It is designed to reduce spurious climate variability in the resulting ocean reanalysis due to the nonstationary nature of the observing system, while still taking advantage of the observation information. The new analysis system is compared with the previous operational version; the equatorial temperature biases are reduced and equatorial currents are improved. The impact of assimilation in the ocean state is discussed by diagnosis of the assimilatio...

Tropical Atlantic SST prediction with coupled ocean-atmosphere GCMs

Abstract Variations in tropical Atlantic SST are an important factor in seasonal forecasts in the region and beyond. An analysis is given of the capabilities of the latest generation of coupled GCM seasonal forecast systems to predict tropical Atlantic SST anomalies. Skill above that of persistence is demonstrated in both the northern tropical and equatorial Atlantic, but not farther south. The inability of the coupled models to correctly represent the mean seasonal cycle is a major problem in attempts to forecast equatorial SST anomalies in the boreal summer. Even when forced with observed SST, atmosphere models have significant failings in this area. The quality of ocean initial conditions for coupled model forecasts is also a cause for concern, and the adequacy of the near-equatorial ocean observing system is in doubt. A multimodel approach improves forecast skill only modestly, and large errors remain in the southern tropical Atlantic. There is still much scope for improving forecasts of tropical Atla...

Impact of Argo on analyses of the global ocean

The impact of Argo on ECMWF operational ocean analyses has been assessed by conducting a set of observing system experiments spanning the period 2001-2006. The experiments evaluate the information content of Argo temperature and salinity data, gauged in terms of influence on the ocean state and the skill of seasonal forecasts. The salinity data from Argo is instrumental in correcting the salinity of the ECMWF analysis on a basin-wide scale. The effect of Argo temperature is noticeable in the Indian, Atlantic and Southern Oceans. In the Pacific, the impact is modest, being confined to the South American coast. The information content of Argo combines well with the altimeter information in most regions. The impact of Argo on seasonal forecasts of sea surface temperature has been assessed for the period 2001-2006. The assimilation of Argo improves seasonal forecast skill in most areas, although the reduction of the error is small.

Historical reconstruction of the Atlantic Meridional Overturning Circulation from the ECMWF operational ocean reanalysis

A reconstruction of the Atlantic Meridional Overturning Circulation (MOC) for theperiod 1959-2006 has been derived from the ECMWF operational ocean reanalysis. The reconstruction shows a wide range of time-variability, including a downward trend. At 26N, both the MOC intensity and changes in its vertical structure are in good agreement with previous estimates based on trans-Atlantic surveys. At 50N, the MOC and strength of the subpolar gyre are correlated at interannual time scales, but show opposite secular trends. Heat transport variability is highly correlated with the MOC but shows a smaller trend due to the warming of the upper ocean, which partially compensates for the weakening of the circulation. Results from sensitivity experiments show that although the time-varying upper boundary forcing provides useful MOC information, the sequential assimilation of ocean data further improves the MOC estimation by increasing both the mean and the time variability.

Salinity Assimilation Using S(T): Covariance Relationships

Assimilation of salinity into ocean and climate general circulation models is a very important problem. Argo data now provide far more salinity observations than ever before. In addition, a good analysis of salinity over time in ocean reanalyses can give important results for understanding climate change. Here it is shown from the historical ocean database that over large regions of the globe (mainly midlatitudes and lower latitudes) variance of salinity on an isotherm S(T ) is often less than variance measured at a particular depth S(z). It is also shown that the dominant temporal variations in S(T ) occur more slowly than variations in S(z), based on power spectra from the Bermuda time series. From ocean models it is shown that the horizontal spatial covariance of S(T ) often has larger scales than S(z). These observations suggest an assimilation method based on analyzing S(T ). An algorithm for assimilating salinity data on isotherms is then presented, and it is shown how this algorithm produces orthogonal salinity increments to those produced during the assimilation of temperature profiles. It is argued that the larger space and time scales can be used for the S(T ) assimilation, leading to better use of scarce salinity observations. Results of applying the salinity assimilation algorithm to a single analysis time within the ECMWF seasonal forecasting ocean model are also shown. The separate salinity increments coming from temperature and salinity data are identified, and the independence of these increments is demonstrated. Results of an ocean reanalysis with this method will appear in a future paper.

Assimilation of Altimeter Data in the ECMWF Ocean Analysis System 3

The latest version of the ECMWF ocean analysis system was recently introduced into operational use. This not only provides initial conditions for the monthly and seasonal forecast systems but also creates a historical reanalysis. For the first time, altimeter data are used in the ECMWF operational ocean analysis. However, making good use of altimetric sea level information was not easy and several difficulties had to be overcome. Various strategies were tried and compared. Attempts to use mean sea level from gravimetric satellites were also tried, but no satisfactory method of using this data was found. The altimetric data used show a marked rising trend that cannot be directly represented in the model as the Boussinesq approximation is used. A strategy for dealing with the trend is given. Results of Observing System Experiments (OSEs) with and without altimeter data are described and results are compared to illustrate the benefits of using altimetry. Although the results are positive, further developments are needed to more fully utilize the data.

The EU-FP7 ERA-CLIM2 Project Contribution to Advancing Science and Production of Earth System Climate Reanalyses

ERA-CLIM2 is a European Union Seventh Framework Project started in January 2014. It aims to produce coupled reanalyses, which are physically consistent data sets describing the evolution of the global atmosphere, ocean, land-surface, cryosphere and the carbon cycle. ERA-CLIM2 has contributed to advancing the capacity for producing state-of-the-art climate reanalyses that extend back to the early 20th century. It has led to the generation of the first ensemble of coupled ocean, sea-ice, land and atmosphere reanalyses of the 20th century. The project has funded work to rescue and prepare observations, and to advance the data51 nassimilation systems required to generate operational reanalyses, such as the ones planned by the European Union Copernicus Climate Change Service. This paper summarizes the main goals of the project, discusses some of its main areas of activities, and presents some of its key results.

On model error in variational data assimilation

Abstract The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find the initial condition. The optimal solution (analysis) error arises due to the errors in the input data (background and observation errors). Under the Gaussian assumption the optimal solution error covariance can be constructed using the Hessian of the auxiliary data assimilation problem. The aim of this paper is to study the evolution of model errors via data assimilation. The optimal solution error covariances are derived in the case of imperfect model and for the weak constraint formulation, when the model euations determine the cost functional.