the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Jul 2025
A Digital Twin Ocean: Can we improve Coastal Ocean Forecasts using targeted Marine Autonomy?
Abstract. 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.
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Status: open (until 18 Oct 2025)
- RC1: 'Comment on egusphere-2025-3346', Anonymous Referee #1, 27 Aug 2025 reply
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General comments
The article describes the use of glider BGC and physical targeted observations to improve coastal ocean forecasts in the western English Channel. The gliders are driven in real time based on an automatic procedure to optimize the sampling of chlorophyl maximum and oxygen minimum associated with spring bloom.
Targeted observations from autonomous platforms to improve monitoring and forecast of the coastal ocean is a very promising approach for different real time applications. This paper addresses its implementation in real time with a coupled physical and BGC ocean monitoring and forecasting system to track specific events and drive the gliders in the area of interest. It follows a previous paper from Ford et al. (2022) which was the proof of concept with some identified limitations. Here, the model resolution is increased and three gliders instead of one are sampling the area and are assimilated in real time.
This paper focus on the sensitivity of the BGC analysis, forecasts and glider automated path to the model increased resolution from 7 km to 1.5 km and the assimilation of better qualified observations.
Unfortunately, the paper in its current status lacks some validation of the sensitivity experiments to support the model estimate improvements mentioned in the conclusion. There is no diagnostic to evaluate the realism of the different simulations between each others. The impact of a higher resolution model and assimilation of DT versus NRT observations is analysed by comparing the different simulations with the simulation at high resolution with the DT observations assimilated, including the glider ones. There is a need to show that the changes between the simulations are associated to more realistic analysis and forecasts, at least for the oxygen minimum and chlorophyl maximum. This could be achieved by comparing the analysis and forecasts to the assimilated observations and if possible, to other independent observations, as done in Ford et al. (2022).
I would recommend major revision before publication.
Specific comments:
l.13: “features of interest, namely chlorophyll and oxygen”: the later ones are variables not features. From my understanding, the feature of interest in this paper is the spring bloom associated with oxygen minimum and chlorophyll-a maximum.
L.27: making our observations more efficient and through the use of low-carbon autonomous platforms, such as ocean gliders (Testor et al. (2019)): “and” can be removed.
l.49: Three issues in previous study by Ford et al. are mentionned as improved in this paper but I did not find where the reduction of “biases in the observational source” is addressed in this paper.
l.140: …from satellite the physics observations… -> …from satellite, the physical observations…
l.175: Does NEMO and ERSEM shares the same spatial resolution at 1.5 km in the AMM15 configuration?
l.223: I would add for clarity some information in the sentence, even if mentioned later: …transect based sampling of the feature of interest … disregarding forecast uncertainty …
l.299: Does the other in-situ observations than the glider ones are also DT quality in AMM15-DT?
l.303-312: In this section the impact of glider data assimilation is discussed but the simulations that are compared to infer it are AMM15-DT and AMM15-NoG-“NRT”. Those simulations differ also in processing level of the assimilated data sets, in addition to the glider assimilation. The comparison should be computed against the AMM15-NRT so the differences could solely be attributed to glider data assimilation.
Figure 4: “depth average”: Can you specify over which range of depth the average is computed?
l.351: the impact of model differences on the glider: Do you mean on the glider path?
l.369-372: Can you also interpret the different paths of the gliders in AMM15-NRT and AMM15-NoG by looking at the Chl-a maximum and Oxygen minimum locations in the 2 simulations? Which of the two path is the better sampling the different extrema?
l.387: The improved realism of the simulation with the increased resolution is not shown in the paper. Only differences between the simulations are analysed.
l.396: I do not find where the impact on glider path of using DT mode observations instead of NRT ones is shown. Figure 10 shows the different paths for the simulation with/without glider NRT observations assimilated.
Technical corrections
l.10: to addresses
I would suggest checking the format of references in the text. For example, line 24: “… within marine autonomy Ford et al. (2022).” could be changed to ” … within marine autonomy (Ford et al., 2022).”
l.68: assoiated -> associated