the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 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.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-2920', Anonymous Referee #1, 08 Jul 2026
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RC2: 'Comment on egusphere-2026-2920', Anonymous Referee #2, 14 Aug 2026
Stated goal: Use C-Star nested regional ocean modeling to simulate the background flow and carbonate system in Hvalfjörður, Iceland, evaluate the model against observational data, and use it to simulate OAE deployments in the fjord.
This manuscript addresses an important need for high-resolution, site-specific modeling tools to support the design and monitoring, reporting, and verification (MRV) of ocean alkalinity enhancement (OAE) field trials. Unfortunately, there is not sufficient methodological detail or evaluation provided to sufficiently judge at this stage and there are some concerns with the results because of that confusion. The comments below are offered to help strengthen the model evaluation, clarify several methodological choices, and improve the overall presentation. With these edits, the paper maybe be suitable for publication in this journal.
Major Comments
- The manuscript would benefit from a fuller discussion of prior and related work that this modeling framework builds on. Situating C-Star relative to previous regional ocean modeling and mCDR simulation studies will help readers assess the novelty and contribution of this work.
- Section 2.2 states that river forcing is excluded from the two outermost nests and included only for the two innermost nests. Please clarify the reasoning (e.g., data availability constraints) and discuss the implications for the initial and boundary conditions of the inner nests, which are derived from the outer "parent" nests. Additionally, please justify — and, where possible, validate — the choice to source surface iron, NOx, NHy, ammonium, and DOM from the CESM simulation rather than a more regionally resolved product.
- Section 2.5 describes the OAE experimental setup (a 4.5% NaOH solution applied at 30 L/min over 96 hours). This design is well suited to testing the model's ability to produce an observable OAE signal. To strengthen the manuscript, please add justification for the chosen alkalinity source, flow rate, and perturbation duration, and discuss the real-world feasibility of this style of alkalinity addition for field deployment.
- The boundary conditions draw on GLORYS for physics and on GLODAPv2/WOA for biogeochemistry, and neither BGC product is particularly well resolved regionally. Please discuss the coverage of these BGC products around Iceland, the rationale for pairing disconnected physical and BGC boundary products, whether a more regional or empirical BGC product was considered, and how sensitive the results are to these boundary-condition choices.
- Please report the prescribed river end-member values used for each tracer, and clarify whether temperature was included as an end-member variable. Total alkalinity in rivers influenced by glacial or ice meltwater is difficult to constrain given the limited available data — please note this as a limitation and discuss how it may affect the results.
- Please expand the model evaluation description in the Methods to specify which performance metrics were used and why. A substantial literature exists on multi-metric regional-model evaluation (see, e.g., Kessouri et al., 2021); adopting quantitative bias and skill metrics — rather than the largely descriptive, qualitative comparisons currently presented — would strengthen the manuscript and align it with the rigor increasingly expected for MRV protocols.
- If direct CO2 observations are available, please include a comparison against them in the evaluation section.
- Please clarify what the approximately 60 mmol/m3 bias (as Table 2 appears to be limited to July 2024 only) corresponds to in terms of pCO2 or CO2 uptake, and how this bias is being factored into the MRV process. Consider expanding Table 2 to cover a longer period than a single month.
- Please add methodological detail on which biogeochemical processes are represented in MARBL — for example, whether calcium carbonate burial is included — and note any known biases from the existing literature that would help readers interpret this paper's results.
- Please provide more methodological detail on how salinity normalization was performed in this estuarine setting, given that vertical mixing in seasonally stratified shelf environments continually resets density and complicates this calculation.
- Please discuss whether current observational methods could detect the simulated signal in practice, including over what timeframe and spatial extent this would be feasible.
- Since the model output allows full carbon tracking, please consider adding a carbon budget showing where the added carbon ends up, whether CO2 is the only carbon species affected or others change as well, and the transported/advected component.
- Given the small magnitude of the simulated OAE signal, please compare it against the model's internal variability (e.g., using a perturbed-initial-condition ensemble) to demonstrate that the signal is distinguishable from natural variability.
- Please describe how the ensemble was constructed, particularly with respect to addressing the signal-to-noise ratio that affects the results, and expand the Methods accordingly.
- Table 2 suggests that the DIC and TA biases act in opposing directions and partially cancel. Please discuss how the authors plan to address these conflicting biases, which appear to contribute to a pCO2 anomaly error that is large relative to the intervention signal.
- Please report the total simulation length and describe the spin-up procedure, given the stated importance of initial conditions.
- The conclusion that circulation-driven variability — particularly wind-driven variability — is the dominant source of variability in short-term pH excursions and near-field pCO2 anomalies would be better supported by a quantitative budget analysis. Please add this analysis or temper the claim accordingly.
Minor Comments
- Table S2: Please add detail on how the salinity normalization for alkalinity and DIC was computed.
- Line 235: Please clarify the rationale for multiplying theta by I (the intervention term).
- Lines 294–297: Please clarify the connection between the salinity biases noted in the outermost and innermost fjord and the river-forcing configuration — for example, whether the outer-nest bias reflects the absence of river forcing and the inner-nest bias reflects limited river-data coverage. Additionally, the statement that "simulated estuarine circulation in the fjord is too weak" appears to be in tension with the SF6 results (Figs. S6–S7), where modeled average SF6 concentrations are consistently lower than observed — a pattern that would suggest the opposite. Please reconcile this discrepancy or clarify the reasoning.
- Lines 330–331: The bulk fjord characteristics appear reasonably well captured by the SF6 results, though a magnitude difference exists between some timestamps in Figs. S6 and S7. The discussion of potential sampling bias as a contributing explanation is a reasonable and sufficient treatment.
- Line 391: Figure 3f is referenced in the text but does not appear to be included — please add.
- Line 393: Figure S3g is referenced in the text but does not appear to be included — please add.
- Line 514: Please provide additional quantitative support for the claim that the seasonal salinity evolution is captured by the model.
Citation: https://doi.org/10.5194/egusphere-2026-2920-RC2
Data sets
Seasonal oceanography of Hvalfjörður in southwest Iceland 2024-2025 - CTD data S. Harðardóttir et al. https://doi.org/10.17882/110439
Seasonal oceanography of Hvalfjörður in southwest Iceland 2024-2025 – discrete samples S. Harðardóttir et al. https://doi.org/10.17882/110401
Seasonal oceanography of Hvalfjörður in southwest Iceland 2024-2025 – mooring data S. Harðardóttir et al. https://doi.org/10.17882/113246
Model code and software
C-Star in Hvalfjordur Ulla Heede https://cworthy-ocean.github.io/C-Star-in-Hvalfjordur/
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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.