Dale Partridge

Increasing the complexity of regional weather and climate models by developing coupled environmental prediction systems improves their performance, particularly in coastal areas where equilibrium assumptions between Earth system components break down. By allowing consistency between earth system components, they also unlock new insights on multi-hazard processes with benefits for enhanced forecasting. We present recent advances in the regional coupled environmental prediction system developed in the UK through the release of the Regional Coupled Suite – UK Coupled domain version 4 (RCS-UKC4) configuration. This includes implementation of the new Regional Atmosphere and Land configuration (RAL3.3) alongside updates to all model components relative to previous releases. RCS-UKC4 also supports enhanced online simulation of river flows and coupling to a biogeochemistry model. New functionality including running near-real-time ensemble forecasts and climate hindcasts is demonstrated. We first examine the effects of changing atmospheric and land configurations in both multi-annual simulations and short-term forecasts and assess the quality of river flows. RAL3.3 shows a beneficial increase in shortwave radiation reaching the ocean in summer months and a beneficial reduction in wind speed, which is slightly further reduced with wave coupling. Simulated river discharge has good skill in the northern and western regions of the UK, whilst there is too much variability for rivers in the southeast. Next, we introduce ensemble forecasts and show RCS-UKC4 has good wave forecast skill during storms compared to the current operational wave-only ensemble. This may partly reflect a good representation of tidal current/wave/wind interactions. Coupling can either increase or decrease the ensemble spread in screen temperature relative to atmosphere-only ensemble simulations, depending on whether latent heat flux or radiative heat flux dominates the spread in near-surface fluxes. Finally, we demonstrate that higher frequency (10 min coupling) enables new prediction capability with a good representation of high frequency sea surface height variability linked with weather disturbances.

The state of the marine ecosystem can be estimated by a combination of numerical models and satellite observations through data assimilation (DA) methods. Satellite data representing phytoplankton chlorophyll are typically used in operational marine ecosystem prediction. These data are derived from ocean colour measured by optical satellite observations. Recently a new phytoplankton carbon product, from the ESA funded BICEP project made available from the UK CEDA Archive, has been derived through a novel processing of ocean colour. This novel carbon product captures aspects of phytoplankton biomass composition that chlorophyll alone does not represent. Here, we investigate the effects of assimilating the new carbon product on the modelling of the marine ecosystem. The investigation is carried out in a newly developed global ensemble DA system for the marine ecosystem using a coupled ocean-biogeochemistry model, NEMO-MEDUSA, and the Parallel Data Assimilation Framework. With the ensemble DA system, the evaluation can take the time-dependent uncertainty of the marine ecosystem and the reliability of the ensemble into account. We demonstrate that, compared with assimilating only the chlorophyll product, which may degrade the seasonality of phytoplankton carbon, assimilating the new carbon product can provide different patterns of adjustment and seasonal anomalies in phytoplankton concentrations, surface pCO ₂ , and oxygen. Our findings reveal that simultaneously assimilating both phytoplankton chlorophyll and carbon products in a complex marine ecosystem yields more balanced estimates of phytoplankton biomass than assimilating a single phytoplankton product.

Increasing the complexity of regional weather and climate models by developing coupled environmental prediction systems increases prediction skills in coastal areas, where equilibrium assumptions between Earth system components break down. By allowing consistency between earth system components, they also unlock new insights on multi-hazard processes with benefits for enhanced forecasting. We present recent advances in the regional coupled environmental prediction system developed in the UK through the release of the Regional Coupled Suite – UK Coupled domain version 4 (RCS-UKC4) configuration. This includes implementation of the new Regional Atmosphere and Land configuration (RAL3.3) alongside updates to all model components relative to previous releases. RCS-UKC4 also supports enhanced online simulation of river flows and coupling to a biogeochemistry model. New functionality including running near-real time ensemble forecasts and climate hindcasts is demonstrated. We first examine the effects of changing atmospheric and land configurations in both multi-annual simulations and short-term forecasts and assess the quality of river flows. RAL3.3 shows a beneficial increase in shortwave radiation reaching the ocean in summer months and a beneficial reduction in wind speed, which is slightly further reduced with wave coupling. River discharges have good skill in the northern and western regions of the UK, whilst the southeast rivers show too much variability. In a second part, we introduce ensemble forescasts, and show RCS-UKC4 has good skill in terms of wave forecasts during storms compared to the current operational ensemble: it shows lower root mean square error thanks to a good representation of tidal current/wave/wind interactions. Coupling can either increase or decrease the ensemble spread in screen temperature relative to atmosphere-only ensemble simulations, depending on whether latent heat flux or radiative heat flux dominates the spread in near-surface fluxes. Finally, we demonstrate that higher frequency (10-minute coupling) enables new prediction capability with a good representation of high frequency sea surface height variability linked with weather disturbances.

This study outlines the development and testing of a Digital Twin Ocean (DTO) framework, aimed at improving coastal ocean forecasts through the use of autonomous underwater gliders. A fleet of gliders were deployed in the western English Channel during August-September 2024 to collect measurements of temperature, salinity, chlorophyll and oxygen, aiming to track the movement of the harmful algal bloom Karenia mikimotoi. Measurements were assimilated into a very high resolution (1.5 km) numerical forecast model, with an implementation of biogeochemistry data assimilation for this purpose. The model forecast was then used by a probabilistic uncertainty model to plan a series of waypoints to navigate the glider fleet towards features of interest. By utilising a continuous feedback loop of measurement, prediction, guidance, and refinement a system with real time coupling between the real ocean environment and its digital counterpart has been established. Building upon a prior pilot study of Ford et al. (2022), this work improves every element of the system to address several limitations of the prior configuration. Whilst a bloom was present in the wider area, measurements and modeling suggest it didn't enter the glider operation zone. Despite this and other operational challenges the mission clearly demonstrates the benefits of such a system. The ability to simultaneously track multiple features of interest, namely chlorophyll maxima and oxygen minima, would not have been possible with a single glider resulting in significant benefits to the system. Furthermore, the improvement to biogeochemical forecasting has been demonstrated through a series of post mission experiments, highlighting the advantages of high temporal resolution observations and increased spatial resolution of the model.

Increasing the complexity of regional weather and climate models by developing coupled environmental prediction systems increases prediction skills in coastal areas, where equilibrium assumptions between Earth system components break down. By allowing consistency between earth system components, they also unlock new insights on multi-hazard processes with benefits for enhanced forecasting. We present recent advances in the regional coupled environmental prediction system developed in the UK through the release of the Regional Coupled Suite – UK Coupled domain version 4 (RCS-UKC4) configuration. This includes implementation of the new Regional Atmosphere and Land configuration (RAL3.3) alongside updates to all model components relative to previous releases. RCS-UKC4 also supports enhanced online simulation of river flows and coupling to a biogeochemistry model. New functionality including running near-real time ensemble forecasts and climate hindcasts is demonstrated. We first examine the effects of changing atmospheric and land configurations in both multi-annual simulations and short-term forecasts and assess the quality of river flows. RAL3.3 shows a beneficial increase in shortwave radiation reaching the ocean in summer months and a beneficial reduction in wind speed, which is slightly further reduced with wave coupling. River discharges have good skill in the northern and western regions of the UK, whilst the southeast rivers show too much variability. In a second part, we introduce ensemble forescasts, and show RCS-UKC4 has good skill in terms of wave forecasts during storms compared to the current operational ensemble: it shows lower root mean square error thanks to a good representation of tidal current/wave/wind interactions. Coupling can either increase or decrease the ensemble spread in screen temperature relative to atmosphere-only ensemble simulations, depending on whether latent heat flux or radiative heat flux dominates the spread in near-surface fluxes. Finally, we demonstrate that higher frequency (10-minute coupling) enables new prediction capability with a good representation of high frequency sea surface height variability linked with weather disturbances.

The state of the marine ecosystem can be estimated by a combination of numerical models and satellite observations through data assimilation (DA) methods. Satellite data representing phytoplankton chlorophyll are typically used in operational marine ecosystem prediction. These data are derived from ocean colour from optical satellite observations. Recently a novel phytoplankton carbon product, from the ESA funded BICEP project available from the UK CEDA Archive, has been derived through an alternate processing of ocean colour. With the novel carbon product, the phytoplankton biomass is represented more directly than relying on the chlorophyll. Here, we investigate the effects of assimilating the new carbon product on the modelling of the marine ecosystem. The investigation is carried out in a newly developed global ensemble DA system for the marine ecosystem using a coupled ocean-biogeochemistry model, NEMO-MEDUSA, and the Parallel Data Assimilation Framework. With the ensemble DA system, the evaluation can take the time-dependent uncertainty of the marine ecosystem and the reliability of the ensemble into account. We demonstrate that, compared with solely assimilating chlorophyll product, with the new carbon product the DA can provide different patterns of adjustments in the phytoplankton concentration and seasonal anomalies. Our findings reveal that simultaneously assimilating both phytoplankton chlorophyll and carbon products in a complex marine ecosystem yields more accurate and balanced estimates of phytoplankton biomass than assimilating a single phytoplankton product.

Increasing the complexity of regional weather and climate models by developing coupled environmental prediction systems increases prediction skills in coastal areas, where equilibrium assumptions between Earth system components break down. By allowing consistency between earth system components, they also unlock new insights on multi-hazard processes with benefits for enhanced forecasting. We present recent advances in the regional coupled environmental prediction system developed in the UK through the release of the Regional Coupled Suite – UK Coupled domain version 4 (RCS-UKC4) configuration. This includes implementation of the new Regional Atmosphere and Land configuration (RAL3.3) alongside updates to all model components relative to previous releases. RCS-UKC4 also supports enhanced online simulation of river flows and coupling to a biogeochemistry model. New functionality including running near-real time ensemble forecasts and climate hindcasts is demonstrated. We first examine the effects of changing atmospheric and land configurations in both multi-annual simulations and short-term forecasts and assess the quality of river flows. RAL3.3 shows a beneficial increase in shortwave radiation reaching the ocean in summer months and a beneficial reduction in wind speed, which is slightly further reduced with wave coupling. River discharges have good skill in the northern and western regions of the UK, whilst the southeast rivers show too much variability. In a second part, we introduce ensemble forescasts, and show RCS-UKC4 has good skill in terms of wave forecasts during storms compared to the current operational ensemble: it shows lower root mean square error thanks to a good representation of tidal current/wave/wind interactions. Coupling can either increase or decrease the ensemble spread in screen temperature relative to atmosphere-only ensemble simulations, depending on whether latent heat flux or radiative heat flux dominates the spread in near-surface fluxes. Finally, we demonstrate that higher frequency (10-minute coupling) enables new prediction capability with a good representation of high frequency sea surface height variability linked with weather disturbances.

The state of the marine ecosystem can be estimated by a combination of numerical models and satellite observations through data assimilation (DA) methods. Satellite data representing phytoplankton chlorophyll are typically used in operational marine ecosystem prediction. These data are derived from ocean colour from optical satellite observations. Recently a novel phytoplankton carbon product, from the ESA funded BICEP project available from the UK CEDA Archive, has been derived through an alternate processing of ocean colour. With the novel carbon product, the phytoplankton biomass is represented more directly than relying on the chlorophyll. Here, we investigate the effects of assimilating the new carbon product on the modelling of the marine ecosystem. The investigation is carried out in a newly developed global ensemble DA system for the marine ecosystem using a coupled ocean-biogeochemistry model, NEMO-MEDUSA, and the Parallel Data Assimilation Framework. With the ensemble DA system, the evaluation can take the time-dependent uncertainty of the marine ecosystem and the reliability of the ensemble into account. We demonstrate that, compared with solely assimilating chlorophyll product, with the new carbon product the DA can provide different patterns of adjustments in the phytoplankton concentration and seasonal anomalies. Our findings reveal that simultaneously assimilating both phytoplankton chlorophyll and carbon products in a complex marine ecosystem yields more accurate and balanced estimates of phytoplankton biomass than assimilating a single phytoplankton product.

The state of the marine ecosystem can be estimated by a combination of numerical models and satellite observations through data assimilation (DA) methods. Satellite data representing phytoplankton chlorophyll are typically used in operational marine ecosystem prediction. These data are derived from ocean colour from optical satellite observations. Recently a novel phytoplankton carbon product, from the ESA funded BICEP project available from the UK CEDA Archive, has been derived through an alternate processing of ocean colour. With the novel carbon product, the phytoplankton biomass is represented more directly than relying on the chlorophyll. Here, we investigate the effects of assimilating the new carbon product on the modelling of the marine ecosystem. The investigation is carried out in a newly developed global ensemble DA system for the marine ecosystem using a coupled ocean-biogeochemistry model, NEMO-MEDUSA, and the Parallel Data Assimilation Framework. With the ensemble DA system, the evaluation can take the time-dependent uncertainty of the marine ecosystem and the reliability of the ensemble into account. We demonstrate that, compared with solely assimilating chlorophyll product, with the new carbon product the DA can provide different patterns of adjustments in the phytoplankton concentration and seasonal anomalies. Our findings reveal that simultaneously assimilating both phytoplankton chlorophyll and carbon products in a complex marine ecosystem yields more accurate and balanced estimates of phytoplankton biomass than assimilating a single phytoplankton product.

This study outlines the development and testing of a Digital Twin Ocean (DTO) framework, aimed at improving coastal ocean forecasts through the use of autonomous underwater gliders. A fleet of gliders were deployed in the western English Channel during August–September 2024 to collect measurements of temperature, salinity, chlorophyll and oxygen, aiming to track the movement of the harmful algal bloom Karenia mikimotoi. Measurements were assimilated into a very high resolution (1.5 km) numerical forecast model, with an implementation of biogeochemistry data assimilation for this purpose. The model forecast was then used by a probabilistic uncertainty model to plan a series of waypoints to navigate the glider fleet towards features of interest. By utilising a continuous feedback loop of measurement, prediction, guidance, and refinement a system with real time coupling between the real ocean environment and its digital counterpart has been established. Building upon a prior pilot study of Ford et al. (2022), this work improves every element of the system to addresses several limitations of the prior configuration. Whilst a bloom was present in the wider area, measurements and modeling suggest it didn't enter the glider operation zone. Despite this and other operational challenges the mission clearly demonstrates the benefits of such a system. The ability to simultaneously track multiple features of interest, namely chlorophyll and oxygen, would not have been possible with a single glider resulting in significant benefits to the system. Furthermore, the improvement to biogeochemical forecasting has been demonstrated through a series of post mission experiments, highlighting the advantages of high temporal resolution observations and increased spatial resolution of the model.