| 29 Jul 2026
A new ocean model configuration for management of central North Pacific Marine Resources (MOM6-COBALT-HIC6kv1.0)
Abstract. Despite often being referred to as an “ocean desert”, the North Pacific Subtropical Gyre (NPSG) supports economically valuable fisheries worth hundreds of millions of dollars annually, including Pacific bigeye tuna (Thunnus obesus) and other high-value pelagic species. The waters in and around the Hawaiian Islands support valuable and culturally significant nearshore fisheries and coral reef ecosystems and fuel ocean recreation and tourism valued in the billions. To be useful in understanding and managing these interacting resources, models must adequately represent ocean dynamics across multiple scales, from basin-wide circulation patterns to finer-scale island-ocean interactions. We evaluate the capacity of an ocean and biogeochemical model with 6 km horizontal grid spacing, a resolution considered “high” for climate-scale applications, across these scales. After a number of adjustments, including but not limited to calibrating light attenuation to subtropical observations and adjusting wind forcing to better reflect wind-current feedbacks, the model demonstrates strong skill in capturing basin-scale pelagic habitat features, including seasonal sea surface temperature anomalies (r > 0.95), accurate Transition Zone Chlorophyll Front migration and anomalies, and oxycline structure critical for bigeye tuna habitat compression. It also captures key features of the high seas ecosystems as represented by observations at station ALOHA, including exceptionally deep chlorophyll maxima (DCM) and seasonal variations and trends in acidification, though modest biases are evident. At the scale of island-ocean interactions, the model captures heightened eddy kinetic energy (EKE) downstream of the Hawaiian Islands and elevated productivity associated with the island mass effect. Finally, in nearshore regions, the model captures 88 % of the temperature variance observed at coral bleaching and habitat monitoring stations despite limited resolution. Overall, the configuration provides a robust foundation for a range of marine resource applications. Potential directions for future improvement include incorporating time-varying biogeochemical boundary conditions, refining salinity dynamics and carbonate chemistry representation, along with continued enhancement of nearshore circulation processes.
Competing interests: At least one of the (co-)authors serves as topic editor for the special issue to which this paper belongs.
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This manuscript presents a new high-resolution coupled physical-biogeochemical model configuration (MOM6-COBALT-HIC6k) for the central North Pacific and Hawaiian Islands region. The stated objective is to provide an ocean forecasting and projection framework that can support living marine resource management, including applications to pelagic fisheries and ecosystem assessments.
The manuscript's principal strengths are the large model domain for the particular study area, relatively high horizontal resolution, comprehensive physical and biogeochemical validation effort, and the multi-scale evaluation strategy spanning basin-scale, mesoscale, and nearshore environments. The model reproduces many ecosystem-relevant physical and biogeochemical variables, including temperature, salinity, oxygen, chlorophyll, nutrients, mixed-layer properties, and sea-surface height. Particularly notable strengths include the realistic representation of the oxycline and oxygen minimum zone structure, the reproduction of mesoscale variability patterns, and the assessment of model performance across seasonal to decadal timescales. The authors also demonstrate generally realistic vertical structure and temporal variability across a wide range of observational datasets, providing confidence that the model captures key physical and biogeochemical processes relevant to regional ecosystem dynamics.
My main concern is the suitability of the evaluation for the intended fisheries management applications. In particular, the manuscript devotes extensive attention to chlorophyll and nitrate-phosphate state variables while providing little information on zooplankton biomass, despite motivating the model in the context of pelagic fisheries and tuna habitat applications. Certain aspects of the biogeochemical model configuration could also be described in greater detail. In addition, further discussion of nutrient limitation, particularly the roles of iron and silicate, and information on computational performance would strengthen the manuscript.
Overall, I find the model configuration valuable and relevant for the GMD readership. The model development is solid, the evaluation is extensive, and the manuscript is generally well written.
General Comments
1. Model suitability for fisheries applications
The manuscript is motivated partly by applications to bigeye tuna and other pelagic fishes. However, fish production and fisheries yields are not solely determined by primary productivity. Several studies have shown that mesozooplankton productivity, trophic transfer efficiency, and secondary production are stronger predictors of fisheries production than phytoplankton biomass indicators such as chlorophyll concentration alone (e.g., Friedland et al., 2012; Stock et al., 2017). In addition, fisheries and ecosystem models such as those described by Kearney et al. (2012) and Dueri et al. (2014) rely on the representation of zooplankton and mesozooplankton prey fields to connect lower-trophic-level ecosystem dynamics with fish production and habitat suitability. I appreciate that Table 1 includes standing-stock estimates for microzooplankton and mesozooplankton, which provide useful information on the mean state of the food web. However, standing stocks alone provide only a partial assessment of model skill. Given the fisheries-oriented motivation of the study, I encourage the authors to provide, where observational datasets are available, a more comprehensive evaluation of zooplankton biomass and secondary production, including information on their spatial distributions and temporal variability. Such diagnostics would help demonstrate whether the model reproduces prey fields that are directly relevant to higher trophic levels and fisheries applications.
2. Biogeochemical model description
The physical model configuration is generally well described. However, the description of the biogeochemical model would benefit from additional information on the zooplankton component. How many zooplankton groups are represented and how is zooplankton mortality parameterized? Given the relevance of zooplankton to both grazing control of phytoplankton and prey availability for higher trophic levels, a brief discussion of whether mortality includes a density-dependent "higher-predation closure" term representing unresolved predators would be particularly useful for the fisheries applications outlook of the model.
3. Nutrient limitation
The manuscript evaluates nitrate and phosphate but provides little discussion of iron and silicate. Iron modulates diazotroph activity (as mentioned in the discussion) and silicate controls diatom growth. Even if iron and silicate are not dominant limiting nutrients within the focal region, the authors should justify why they were excluded from the evaluation.
Specific comments
Introduction
Line 102: “The computational expense of these models must also be manageable enough to enable sufficient simulations”
For a model development and evaluation paper in GMD, I would appreciate information.
Such information is useful for readers considering adoption of the configuration and reproducibility.
Methods
Line 214: The statement regarding lower phosphate concentrations provides an opportunity to mention diazotroph dynamics and their role in regional nutrient cycling earlier in the article.
Line 215: Please clarify what is meant by "biological tracers which equilibrate rapidly." How quickly do these tracers converge?
Lines 343, 382: Please justify the omission of iron and silicate from the nutrient evaluation.
Results
Figure 4i: Could the authors comment on the relatively large temperature differences near 200 m depth within the subtropical interior?
Lines 507-508: The model appears to maintain excessive chlorophyll concentrations at depth relative to observations. Could this issue be revisited in the discussion?
Figure 9f: The large spring chlorophyll discrepancies in the northwestern domain deserve additional explanation.
Figure 10f: Similarly, the surface nitrate differences in the northwestern domain warrant discussion.
Discussion
Lines 809-810: “Integrated biological metrics… (Table 1)” Agreement in integrated carbon stocks alone is not sufficient to demonstrate skill in zooplankton dynamics and potential fisheries applications.
Line 926: “Potential mechanisms underlying this bias include inadequate top-down control from light-dependent grazing by microzooplankton”. This further motivates inclusion of zooplankton evaluation, if observational datasets are available.
Refrences
Dueri, S., Bopp, L. and Maury, O., 2014. Projecting the impacts of climate change on skipjack tuna abundance and spatial distribution. Global change biology, 20(3), pp.742-753.
Friedland KD, Stock C, Drinkwater KF, Link JS, Leaf RT, Shank BV, Rose JM, Pilskaln CH, Fogarty MJ. Pathways between primary production and fisheries yields of large marine ecosystems. PloS one. 2012 Jan 20;7(1):e28945.
Kearney, K.A., Stock, C., Aydin, K. and Sarmiento, J.L., 2012. Coupling planktonic ecosystem and fisheries food web models for a pelagic ecosystem: Description and validation for the subarctic Pacific. Ecological Modelling, 237, pp.43-62.
Stock, C.A., John, J.G., Rykaczewski, R.R., Asch, R.G., Cheung, W.W.L., Dunne, J.P., Friedland, K.D., Lam, V.W.Y., Sarmiento, J.L., & Watson, R.A. (2017). Reconciling fisheries catch and ocean productivity. PNAS, 114, E1441-E1449.