D. J. Lea

Skillful long‐range prediction of European and North American winters

This work was supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101), the UK Public Weather Service research program, and the European Union Framework 7 SPECS project. Leon Hermanson was funded as part of his Research Fellowship by Willis as part of Willis Research Network (WRN).

Forecasting the ocean state using NEMO:The new FOAM system

The Forecasting Ocean Assimilation Model (FOAM) deep ocean analysis and forecasting system has been running operationally at the Met Office for over 10 years.The system has recently been transitioned to use the Nucleus for European Modelling of the Ocean (NEMO) community model as its core ocean component. This paper gives an end-to-end description of the FOAM-NEMO operational system and presents some preliminary assessment of operational and hindcast integrations including verification statistics against observations and forecast verification against model best guess fields.Validation of the sea surface height fields is presented, which suggests that the system captures and tracks the major mesoscale features of the ocean circulation reasonably well, with some evidence of improvement in higher-resolution configurations.

Assessing the impact of observations on ocean forecasts and reanalyses: Part 1, Global studies

Under GODAE OceanView the operational ocean modelling community has developed a suite of global ocean forecast, reanalysis and analysis systems. Each system has a critical dependence on ocean observations – routinely assimilating observations of in-situ temperature and salinity, and satellite sea-level anomaly and sea surface temperature. This paper demonstrates the value and impact of ocean observations to three global eddy-permitting forecast systems, one global eddy-permitting model-independent analysis system, one eddy-resolving reanalysis system, and two seasonal prediction systems. All systems have been used to assess the impact of Argo profiles, including scenarios with no Argo data, and a degraded Argo array – unanimously concluding that Argo is a critical data set – the most critical for seasonal prediction, and as critical as satellite altimetry for eddy-permitting applications. Most systems show that TAO data are as important as Argo in the tropical Pacific, and that XBT data have an impact that is comparable to other data types in the vicinity of XBT transects. It is clear that no currently available data type is redundant. On the contrary, the components of the global ocean observing system complement each other remarkably well, providing sufficient information to monitor and forecast the global ocean.

Status and future of data assimilation in operational oceanography

The GODAE OceanView systems use various data assimilation algorithms, including 3DVar, EnOI, EnKF and the SEEK filter with a fixed basis, using different time windows. The main outputs of the operational data assimilation systems, the increments, have been compared for February 2014 in various regions. The eddy-permitting systems’ increments are similar in a number of the regions, indicating similar forecast errors are being corrected, while the eddy-resolving systems represent smaller-scale structures in the mid-latitude regions investigated and appear to have smaller biases. Monthly average temperature increments show significant SST biases, particularly in the systems which assimilate swath satellite SST data, indicating systematic errors in the surface heat fluxes and the way in which they are propagated vertically by the ocean models. On-going developments to the data assimilation systems include improvements to the specification of error covariances, improving assimilation of data near the equator, and understanding the effect of assimilation on the Atlantic Meridional Overturning Circulation. Longer term developments are expected to include the implementation of more advanced algorithms to make use of flow-dependent error covariance information. Assimilation of new data sources over the coming years, such as wide-swath altimetry, is also expected to improve the accuracy of ocean state estimates and forecasts provided by the GODAE OceanView systems.

Progress and challenges in short- to medium-range coupled prediction

The availability of GODAE Oceanview-type ocean forecast systems provides the opportunity to develop high-resolution, short- to medium-range coupled prediction systems. Several groups have undertaken the first experiments based on relatively unsophisticated approaches. Progress is being driven at the institutional level targeting a range of applications that represent their respective national interests with clear overlaps and opportunities for information exchange and collaboration. The applications include forecasting of the general circulation, hurricanes, extra-tropical storms, high-latitude weather and coastal air–sea interaction. In some cases, research has moved beyond case and sensitivity studies to controlled experiments to obtain statistically significant metrics and operational predictions.

How well can we measure the ocean's mean dynamic topography from space?

Recent gravity missions have produced a dramatic improvement in our ability to measure the oceans mean dynamic topography (MDT) from space. To fully exploit this oceanic observation, however, we must quantify its error. To establish a baseline, we first assess the error budget for an MDT calculated using a 3rd generation GOCE geoid and the CLS01 mean sea surface (MSS). With these products, we can resolve MDT spatial scales down to 250 km with an accuracy of 1.7 cm, with the MSS and geoid making similar contributions to the total error. For spatial scales within the range 133–250 km the error is 3.0 cm, with the geoid making the greatest contribution. For the smallest resolvable spatial scales (80–133 km) the total error is 16.4 cm, with geoid error accounting for almost all of this. Relative to this baseline, the most recent versions of the geoid and MSS fields reduce the long and short-wavelength errors by 0.9 and 3.2 cm, respectively, but they have little impact in the medium-wavelength band. The newer MSS is responsible for most of the long-wavelength improvement, while for the short-wavelength component it is the geoid. We find that while the formal geoid errors have reasonable global mean values they fail capture the regional variations in error magnitude, which depend on the steepness of the sea floor topography.

Assessing equatorial surface currents in the FOAM Global and Indian Ocean models against observations from the global tropical moored buoy array

Surface currents from 2007–2008 hindcasts of the Forecast Ocean Assimilation Model (FOAM) Global and Indian Ocean models are assessed against observations at 46 global tropical moored buoy array sites. Zonal (u) currents are less challenging to model than meridional flows (v) due to their lower frequency variability. The assimilative global model has reasonable skill for zonal currents but less skill for meridional currents. The assimilative models have higher skill than the corresponding non-assimilative models. A too-strong westward bias of the order of 20cm/s is evident along the equator in all model versionsused in this study. No extra skill is evident in the high resolution (1/12°) regional model compared to the coarser resolution (1/4°) global model.

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 assimilation 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.