The Forecast Ocean Assimilation Model (FOAM) is the Met Office's operational, coupled ocean–sea ice system, which produces analyses and short-range forecasts at global and regional scales each day for various stakeholders, including defence, marine navigation and science users. This paper describes and evaluates the impacts of recent model and data assimilation (DA) updates on global FOAM when compared to its current operational version. The model updates include the use of the TEOS10 formulation for the seawater equation of state, with improved ocean model settings in the Southern Ocean and the implementation of a new sea ice model. Updates to the DA include an increase in the number of DA minimisation iterations, an improved specification of observation errors for sea surface temperature and sea level anomaly (SLA), and optimisations of the DA computational efficiency. Large-scale DA corrections for temperature have also been removed to prevent an inconsistent projection of the SLA DA signal onto large-scale temperature at depth. For 1-year runs at 1/12° resolution, the new FOAM system shows a 40 % improvement in observation-minus-background (OmB) statistics for SLA and subsurface temperatures relative to the current system in eddy-rich regions, which result in a similar level of improvement for ocean currents. To evaluate potential impacts on the pre-Argo period, 1-year experiments at 1/4° resolution are run withholding profiles of temperature and salinity observations in both new and current FOAM systems. Limited to the assimilation of only surface data, OmB statistics for SLA, temperature and salinity in the new FOAM system can reach improvements up to 90 % in the Southern Hemisphere relative to the current system, resulting in more temporally consistent ocean transport and heat content results. Therefore, it is expected that the model and DA updates will lead to more potential for use of FOAM reanalyses in climate studies, particularly in the pre-Argo period, and will provide improved ocean–sea ice initial conditions to FOAM as well as to the Met Office short-range and seasonal coupled ocean–atmosphere–land–sea ice forecasting systems.

In the last 2 decades, UK research institutes have led a wide range of developments in marine data assimilation (MDA), covering areas from operational applications in physics and biogeochemistry to fundamental theory. We highlight the emergence of strong collaboration in the UK MDA community over this period and the increasing unification of its tools. We focus on identifying the MDA stakeholder community and current/future areas of impact, as well as current trends and future opportunities. This includes the rapid growth of machine learning (ML)/artificial intelligence (AI) and digital-twin applications. We articulate a vision for the future, including the need for future types of observational data (whether planned missions or hypothetical) and how the community should respond to increases in computational power and new computer architectures (e.g. exascale computing). We contrast the requirements of different MDA areas, including physics, biogeochemistry, and coupled data assimilation (DA). Although the specifics of the vision depend on each area, common themes emerge. We advocate for balanced redistribution of new computational capability among increased model resolution, model complexity, more sophisticated DA algorithms, and uncertainty representation (e.g. ensembles). We also advocate for integrated approaches, such as strongly coupled DA (ocean–atmosphere, physics–biogeochemistry, and ocean–sea ice) and the use of ML/AI components (e.g. for multivariate increment balancing, bias correction, model emulation, observation re-gridding, or fusion).

The Met Office Forecast Ocean Assimilation Model (FOAM) ocean-sea-ice analysis and forecasting operational system has been using an ORCA tripolar grid with 1/4 degrees horizontal grid spacing since December 2008. Surface boundary forcing is provided by numerical weather prediction fields from the operational global atmosphere Met Office Unified Model. We present results from a 2-year simulation using a 1/12 degrees global ocean-sea-ice model configuration while keeping a 1/4 degrees data assimilation (DA) set-up. We also describe recent operational data assimilation enhancements that are included in our 1/4 degrees control and 1/12 degrees simulations: a new bias-correction term for sea-level anomaly assimilation and a revised pressure correction algorithm. The primary effect of the first is to decrease the mean and variability of sea-level anomaly increments at high latitudes, whereas the second significantly reduces the vertical velocity standard deviation in the tropical Pacific. The level of improvement achieved with the higher resolution configuration is moderate but consistently satisfactory when measured using neighbourhood verification metrics that provide fairer quantitative comparisons between gridded model fields at different spatial resolutions than traditional root-mean-square metrics. A comparison of the eddy kinetic energy from each configuration and an observation-based product highlights the regions where further system developments are most needed. center dot$$ \bullet $$ A new satellite altimeter bias-correction term reduces the mean and variability of sea-level anomaly increments at high latitudes. A revised pressure correction algorithm significantly reduces the vertical velocity standard deviation in the tropical Pacific. center dot$$ \bullet $$ The higher resolution configuration shows moderate but satisfactory improvements (e.g., lower sea-surface temperture continuous ranked probability score, SST CRPS; see figure) using neighbourhood verification metrics. center dot$$ \bullet $$ A comparison of the zonal eddy kinetic energy from ORCA025 and ORCA12 configurations and an observation-based product highlights the regions where further system developments are most needed. image

Sea surface temperature is an essential variable for oceanography, meteorology, and climatology. In situ and satellite observations are used together in creating products that meet the requirements for both near-real-time and retrospective, consistent data sets. In situ measurements, particularly those of drifting buoys and moorings, are used to validate satellite sea surface temperature retrievals, and in some cases are also used to define those retrievals. The validation strategy should have clear objectives and be designed accordingly. A checklist of eight aspects to consider in designing a validation strategy is discussed, the question of independence being particularly crucial. Validation can and should assess both the retrieval characteristics and the uncertainty model attributed to the retrieval. The other usage of in situ data is in blending with satellite information to create higher level products, such as gap-filled analyses of sea surface temperature. While daily analyses are typical, the scope for capturing subdaily variability is discussed. For product validation, particularly of blended analyses, we emphasize the value of producers reserving an agreed set of in situ data; this is to help drive real reductions in product uncertainty to meet the more stringent emerging requirements for sea surface temperature observation in the context of coupled weather and climate models.

We have developed a global ocean and sea-ice ensemble forecasting system based on the operational forecasting ocean assimilation model (FOAM) system run at the Met Office. The ocean model Nucleus for European Modelling of the Ocean (NEMO) and the community ice code (CICE) sea-ice model are run at 1/4 circle$$ {}<^>{\circ } $$ resolution and the system assimilates data using a three-dimensional variational assimilation (3DVar) version of NEMOVAR. This data assimilation (DA) system can perform hybrid ensemble/variational assimilation. A 36-member ensemble of hybrid ensemble variational assimilation systems with perturbed observations (values and locations) has been set up, with each member forced at the surface by a separate member of the Met Office Global-Regional Ensemble Prediction System (MOGREPS-G). The unperturbed member is forced by atmospheric fields from the Met Office operational numerical weather prediction (NWP) deterministic system. The system includes stochastic model perturbations and a relaxation to prior spread (RTPS) inflation scheme. A control run of the system using an ensemble of 3DVars is shown to be generally reliable for Sea-Level Anomaly (SLA), temperature, and salinity (the ensemble spread being a good representation of the uncertainty in the ensemble mean), although the ensemble is underspread in eddying regions. The ensemble mean gives a 4% reduction in error in SLA compared with the deterministic 3DVar system currently used operationally. The system was tested with different weights for the ensemble component of the hybrid background-error covariance matrix and different inflation factors. The best results, in terms of short-range forecast error and ensemble reliability statistics, were obtained with hybrid three-dimensional ensemble variational DA (3DEnVar). The RTPS inflation scheme is shown to be beneficial in producing an appropriate ensemble spread in response to hybrid DA. 3DEnVar with an ensemble hybrid weight of 0.8 leads to a reduction of 20% (5%) in the ensemble mean error for SLA (profile temperature and salinity) compared with an ensemble of standard 3DVars.

The importance of oceans for atmospheric forecasts as well as climate simulations is being increasingly recognised with the advent of coupled ocean / atmosphere forecast models. Having comparable resolutions in both domains maximises the benefits for a given computational cost. The Met Office has recently upgraded its operational global ocean-only model from an eddy permitting 1/4 degree tripolar grid (ORCA025) to the eddy resolving 1/12 degree ORCA12 configuration while retaining 1/4 degree data assimilation. We will present a description of the ocean-only ORCA12 system, FOAM-ORCA12, alongside some initial results. Qualitatively, FOAM-ORCA12 seems to represent better (than FOAM-ORCA025) the details of mesoscale features in SST and surface currents. Overall, traditional statistical results suggest that the new FOAM-ORCA12 system performs similarly or slightly worse than the pre-existing FOAM-ORCA025. However, it is known that comparisons of models running at different resolutions suffer from a double penalty effect, whereby higher-resolution models are penalised more than lower-resolution models for features that are offset in time and space. Neighbourhood verification methods seek to make a fairer comparison using a common spatial scale for both models and it can be seen that, as neighbourhood sizes increase, ORCA12 consistently has lower continuous ranked probability scores (CRPS) than ORCA025. CRPS measures the accuracy of the pseudo-ensemble created by the neighbourhood method and generalises the mean absolute error measure for deterministic forecasts. The focus over the next year will be on diagnosing the performance of both the model and assimilation. A planned development that is expected to enhance the system is the update of the background-error covariances used for data assimilation.

The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system generates global, daily, gap-filled foundation sea surface temperature (SST) fields from satellite data and in situ observations. The SSTs have uncertainty information provided with them and an ice concentration (IC) analysis is also produced. Additionally, a global, hourly diurnal skin SST product is output each day. The system is run in near real time to produce data for use in applications such as numerical weather prediction. Data production is monitored routinely and outputs are available from the Copernicus Marine Environment Monitoring Service (CMEMS; marine.copernicus.eu). As an operational product, the OSTIA system is continuously under development. For example, since the original descriptor paper was published, the underlying data assimilation scheme that is used to generate the foundation SST analyses has been updated. Various publications have described these changes but a full description is not available in a single place. This technical note focuses on the production of the foundation SST and IC analyses by OSTIA and aims to provide a comprehensive description of the current system configuration.

We describe a variational bias‐correction system for satellite sea‐surface temperature (SST) data that includes the use of “observations‐of‐bias”. The bias‐correction scheme is designed to work in the historical period, when good quality low‐bias reference data were scarce, but can also take advantage of reference data when they are available. In testing with a simple Lorenz 63 model, our new scheme outperformed traditional variational bias correction. When compared with an offline bias‐correction method, the new scheme showed superior performance both when the bias was large and when reference observations were sparse. The bias‐correction scheme has also been tested using a three‐year assimilative run (2008–2010) of the Nucleus for European Modeling of the Ocean (NEMO) ocean general circulation model, with reference data from the Advanced Along Track Scanning Radiometer (AATSR) instrument withheld in 2009. In these tests, the new scheme was found to be more robust to missing reference observations than an offline scheme. Against AATSR data, the new bias‐correction method had lower biases and root‐mean‐square (RMS) errors than an offline scheme, but was degraded relative to a pure variational technique. However, in comparisons with drifting buoys, the new scheme outperformed both offline and pure variational methods.

Measuring Sea‐Surface Salinity (SSS) from space is a relatively recent technique that relies on L‐band radiometry that has evolved to a point where useful information is provided every few days. The impact of assimilating satellite SSS data is investigated using the global FOAM ocean forecasting system. This system assimilates daily satellite SSS products from the ESA Soil Moisture and Ocean Salinity (SMOS), NASA Aquarius and Soil Moisture Active Passive (SMAP) missions equatorward of 40°N/S, in addition to other observing systems. The data are assimilated using a 3D‐Var scheme that includes an observation bias correction scheme to estimate spatially and temporally varying bias estimates for each SSS satellite.The SSS assimilation is tested over 2 years and 3 months covering the 2015/2016 El Niño with assessment focussed on the tropical regions, particularly in the Pacific. Consistent reductions in root‐mean‐square errors are found in all tropical regions from each of the satellite SSS datasets. The largest improvements of up to about 8% are found in the tropical Pacific from assimilating both the SMOS and SMAP datasets together. The largest impact is in the intertropical convergence zone (ITCZ) in the central Pacific during 2015 and early 2016 where the surface salinity is reduced by about 0.03 pss on average, correcting for too little precipitation. A smaller magnitude, large‐scale reduction in the SSS is also seen in the tropical Pacific that increases the modelled surface stratification and leads to reduced vertical mixing. Changes to the SSS in the ITCZ lead to changes in the meridional gradients of SSS that affects the sea surface height and surface currents, both of which are improved in the SMOS assimilation experiment compared to externally produced observation‐based datasets. The results suggest that assimilation of SMOS and SMAP satellite data is now of an appropriate quality for operational implementation.

The quality of a short-term ocean forecast relies on its initialisation. However, operational shelf-seas forecasting systems tend to assimilate fewer observation types than non-tidal global systems. For shelf-seas systems, the challenge is to incorporate observations into the 3D ocean state within a model with complex vertical co-ordinates and large-amplitude variations in the sea surface height due to tides. In this paper, we describe the first use of altimeter and in situ profile observations to improve the initialisation of an operational shelf-seas forecasting system. FOAM-Shelf v9 is a 7 km horizontal resolution ocean model covering the European North-West Shelf (NWS) seas. We have adapted our assimilation scheme in this system to account for spatially- and temporally-varying vertical coordinates. Throughout the domain, there is now assimilation of in situ profile measurements of temperature and salinity and satellite and in situ sea surface temperature observations. Additionally, in deep water regions ( > 700 m) southward of 60 degrees N, the system assimilates newly-available altimeter observations tailored for use in coastal models. With this new system, gross biases are significantly reduced, the sub-surface root-mean-square (RMS) temperature and salinity errors are reduced by > 25%, and there is an increase in the number of eddying structures providing a better qualitative match to observation-derived surface current products.

The European Reanalysis of Global Climate Observations 2 (ERA-CLIM2) is a European Union Seventh Framework Project started in January 2014 and due to be completed in December 2017. It aims to produce coupled reanalyses, which are physically consistent datasets 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 twentieth century. ERA-CLIM2 has led to the generation of the first European ensemble of coupled ocean, sea ice, land, and atmosphere reanalyses of the twentieth century. The project has funded work to rescue and prepare observations and to advance the data-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.

We describe the physical model component of the standard Coastal Ocean version 5 configuration (CO5) of the European north-west shelf (NWS). CO5 was developed jointly between the Met Office and the National Oceanography Centre. CO5 is designed with the seamless approach in mind, which allows for modelling of multiple timescales for a variety of applications from short-range ocean forecasting to climate projections. The configuration constitutes the basis of the latest update to the ocean and data assimilation components of the Met Office's operational Forecast Ocean Assimilation Model (FOAM) for the NWS. A 30.5-year non-assimilating control hindcast of CO5 was integrated from January 1981 to June 2012. Sensitivity simulations were conducted with reference to the control run. The control run is compared against a previous non-assimilating Proudman Oceanographic Laboratory Coastal Ocean Modelling System (POLCOMS) hindcast of the NWS. The CO5 control hindcast is shown to have much reduced biases compared to POLCOMS. Emphasis in the system description is weighted to updates in CO5 over previous versions. Updates include an increase in vertical resolution, a new vertical coordinate stretching function, the replacement of climatological riverine sources with the pan-European hydrological model E-HYPE, a new Baltic boundary condition and switching from directly imposed atmospheric model boundary fluxes to calculating the fluxes within the model using a bulk formula. Sensitivity tests of the updates are detailed with a view toward attributing observed changes in the new system from the previous system and suggesting future directions of research to further improve the system.

An operational system for producing a global diurnally varying analysis of skin sea‐surface temperature (SST) has been developed at the Met Office. Skin SST is formulated as the sum of a foundation temperature, a warm‐layer temperature difference, and a thermal skin temperature difference. Foundation temperature is taken from the Operational Sea surface Temperature and sea Ice Analysis (OSTIA) system, while numerical models are used for the warm layer and thermal skin layer. Both the thermal skin layer and warm‐layer models are forced using outputs from the Met Office's numerical weather prediction system. Data assimilation is used to improve estimates of the warm layer, with observations coming from the geostationary SEVIRI and GOES‐W instruments, as well as the polar orbiting NOAA‐AVHRR sensors. The SST observations of these instruments are converted to observations of the warm‐layer temperature difference by subtracting an instrument‐specific foundation SST calculated using just night‐time data from that sensor. A quality control procedure removes warm‐layer observations where the satellite‐specific foundation estimates are less robust due to lack of data.Validation of the analysis system has been performed by comparing the model to the assimilated satellite data, and to independent near‐surface observations from Argo floats. The mean state of the system was assessed via a comparison to a climatology generated from drifting buoys. Results from the validation show that the system does a good job of replicating the climatology and that assimilation improves the analysis when assessed against Argo. However, the system has been found to underestimate the diurnal range of skin SST by approximately 0.1–0.3 °C on average. Data from the system are available free of charge from the Copernicus Marine Environment Monitoring Service.

This article describes the implementation of an incremental first guess at an appropriate time three-dimensional variational (3DVAR) data assimilation scheme, NEMOVAR, in the Met Office's operational 1/4 degree global ocean model. NEMOVAR assimilates observations of sea-surface temperature (SST), sea-surface height (SSH), in situ temperature and salinity profiles and sea ice concentration. The Met Office is the first centre to implement NEMOVAR at 1/4 degree and the required developments are discussed, with particular focus on the specification of the background-error covariances.Background-error correlations in NEMOVAR are modelled using a diffusion operator. The horizontal background-error correlations for temperature, salinity and sea ice concentration are parametrized using the Rossby radius, which produces relatively short correlation length-scales at mid to high latitudes, while a flow-dependent mixed-layer depth parametrization is used to define the vertical length-scales for the 3D variables.Results from a one-year reanalysis with NEMOVAR are presented and compared with the preceding operational data assimilation scheme at the Met Office. NEMOVAR is shown to provide significant improvements to SST, SSH and sea ice concentration fields, with the largest improvements seen in regions of high variability such as eddy shedding and frontal regions and the marginal ice zone. This improvement is associated with shorter correlation length-scales in the extratropics and an improved fit to observations in NEMOVAR. Some degradation to subsurface temperature and salinity fields where data are sparse is identified and this will be the focus of future improvements to the system.

A variational data assimilation system based on an incremental 4D‐Var approach is proposed for use with a zero‐dimensional model of the diurnal cycle of sea surface temperature (SST). Traditional 4D‐Var, which seeks to find the initial state of a system, is not appropriate for diurnal SST which is a wind and heat flux driven system that has only a limited memory of its prior state. Instead the proposed assimilation system corrects both the initial SST and the heat and wind fluxes applied throughout the day. The assimilation system is tested using ensembles in a set of idealized twin experiments. In these tests controlling parameters are varied around reasonable “default” values with the quality of the analyses assessed against a known “truth”. Within our tests data assimilation is shown to improve diurnal SST under most circumstances. Analyzed heat fluxes are also sometimes improved, although the improvement is much less than that observed for diurnal SST. The system was not found to improve the wind stress. The only circumstances where diurnal SST was not found to be improved by the assimilation were where either observational errors were large (greater than 0.5 K in our tests), or biases in the observations were too big (less than −0.3 K or greater than 0.2 K). The non‐Gaussian behavior of the wind stress was found to have an impact on the assimilation in low‐wind conditions and under these conditions the best analyses were obtained by artificially inflating the observation error.