| 12 Jun 2026
Simulating enhanced ocean alkalinity experiments in a high-latitude fjord using nested ROMS simulations coupled with MARBL biogeochemistry
Abstract. Ocean-based carbon dioxide removal (CDR) technologies have the potential to make significant contributions to climate change mitigation, yet more research is needed to deepen our understanding of their effectiveness and safety. One proposed method, ocean alkalinity enhancement (OAE), involves increasing seawater alkalinity to promote additional carbon uptake and long-term storage in the ocean. Ocean models are crucial tools to accompany OAE field trials and research, as alkalinity signals are rapidly diluted, and observations alone cannot capture the spatiotemporal scales at which interventions evolve. The C-Star open source regional ocean-biogeochemical modeling system is designed to support OAE research and quantification. Here, we present results from deploying C-Star in a nested regional modeling configuration established for Hvalfjörður, a fjord located in western Iceland. We compare the model solution with observations collected during a 2024 field campaign. These include repeated measurements of the fjord’s physical and chemical state, as well as a tracer release and sampling program used to assess the model’s ability to reproduce tracer transport and dispersal. We find that the model captures key features of the circulation in the fjord, including tidally driven currents and sea-surface height variations, tracer dispersal, and seasonal stratification. Next, we use C-Star to simulate six 96 hr OAE releases in the fjord under varying seasonal, tidal, and weather conditions. The OAE experiments produce a ~1 km2 plume with detectable anomalies in alkalinity, pH and pCO2 during the release. The uptake of CO2 from the atmosphere is 0.05 to 0.15 mol of carbon absorbed per mol of added alkalinity during and four days following the release, and surface winds and seasonal stratification are key for both alkalinity dispersal and air-sea gas exchange. The model exhibits background biases in alkalinity, dissolved inorganic carbon (DIC), and nutrients, arising from limitations in initial and boundary conditions and representation of in situ biogeochemical processes. While these biases are a target for improvement, we show that they do not significantly degrade the model’s ability to simulate OAE-relevant anomalies. Overall, this work enhances confidence in the applicability of C-Star nested model domains as OAE research and field-trial support tools in fjord and estuary systems.
Review of Heede et al.
In this study authors construct ROMS model configuration for high latitude fjord Hvalfjörður. The model performs generally well in simulating the physics, although some biases were reported. Using MARBL biogeochemistry module, idealized OAE experiments were then performed on this model domain to understand the feasibility of this set up in quantifying CDR effect, which in general is difficult by solely relying on observations.
Keeping in mind the recent push for mCDR through OAE, this kind of regional model set up is a timely work which will benefit over all OAE-mCDR community. However, the present version of the manuscript is lacking discussion on the other works this field which needs to be addressed before publication. For example, I see Laurent et al. (2026) is cited, but this was not discussed in discussion. They have done a very similar work on the other side of Atlantic, using similar nested ROMS model configuration. These examples need to be discussed.
The current OAE simulations represent highly simplified approach by adding NaOH which instantaneously elevates the TA concentration. While this is fine for idealized simulations, it is important to discuss if this kind of material would be suitable/feasible in practical deployments. As far as I understand, most real-life deployments are based on alkaline mineral addition, so they are in particulate/slurry forms, which undergoes dissolution at different rates depending on a number of variables. This generates TA that would then be available for CDR. These limitations need to be mentioned in the discussion.
Other comments:
The model underestimates productivity in the surface layer, and remineralization at surface to subsurface depths. This is likely due to iron limitation in the model leading to low PP. Is it possible to show (in supplement) the OM (POC) concentration in the water column, PP production and POC flux to bottom if the relevant model outputs/obs are available?
Line 129: what is NHy?
Line 160-161: briefly state the benefit of dual tracer, either here or in the discussion.
Line 191: typo. ‘biogeochemistry’
Line 228: What would be the unit of I ? Is it the added alkalinity?
Line 248: ηmax ?
Line 347: For readers, please explain how the salinity-normalized DIC, Alk was calculated, and what information it provides.
Line 391: There’s no Fig 3f. 10f?
Line 393: No Fig S3g.
Line 414-416: It is interesting that for Nov simulations CO2 uptake is higher, even though model simulation shows lower fraction of added alkalinity within the surface layers for Nov compared to July (Fig S11). Fig 11 a,c shows higher cumulative CO2 uptake in Nov. But b,c show DIC anomaly is higher in July compared to Nov. I am having hard time understanding this, and was expecting the DIC signal to be other way around. Could you clarify how the patterns observed in Fig 11 emerges? I see later that due to mixed condition in Nov, even though lower frac of added alkalinity remains in the surface, CO2 draw down continues. Still, I did not understand how the integrated DIC anomalies are higher in July than Nov.
Fig 11e: would it be more appropriate to write as ∑ΔDIC / ∑ΔALK ?
Fig. S10: mention units for oxygen.