| 12 Feb 2026
Decadal biogeochemical predictions for the bottom marine environment of the Northeast U.S. Continental Shelf
Abstract. The Gulf of Maine and the surrounding Northeast U.S. Continental Shelf are experiencing rapid marine environmental change arising from complex regional dynamics that challenge near-term (1–10 years) predictive capabilities for valuable living marine resources. Here, using a high-resolution regional ocean model, we demonstrate skilful decadal forecasts of ocean bottom habitat characteristics including bottom temperature, dissolved oxygen (O2), pH and aragonite saturation state (Ωar). Bottom temperature and pH predictions show substantial skill driven primarily by radiatively forced warming and carbon uptake trends, while bottom O2 and Ωar predictions benefit more from initialization due to stronger internal variability. Retrospective forecasts successfully predicted observed historical changes in water masses and environmental properties, including recent cooling/freshening transitions driven by replacement of Warm Slope Water with Labrador Slope Water. This water mass variability also modulates biogeochemical conditions and ocean acidification buffering capacity, with our recent forecasts indicating that benefits from the expected respite from rapid warming might be tempered by challenges posed by rapid acidification. The demonstrated predictability of coupled physical-biogeochemical processes supports developing integrated prediction systems for climate-informed marine resource management.
Competing interests: At least one of the (co-)authors serves as editor for the special issue to which this paper belongs.
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Review for Koul et al. – Decadal biogeochemical predictions for the bottom marine environment of the Northeast U.S. Continental Shelf
This manuscript presents a decadal scale forecast for the Gulf of Maine, describing the skill of hindcast models that can be used to parse mechanisms useful for predicting changes to temperature and other water quality conditions that are important for marine biota. The baseline model performs favorably against multiple sources of regridded observational data and is a step towards operationalizing longer-term forecasts that would be important for fishery and ecosystem management purposes.
Overall, the paper is well written and does a good job describing skill assessments and limitations of multiple approaches. It would also benefit the paper to further describe the dataset errorbars presented, specifically what they correspond to (spatio-temporal variability?) and how they were computed/propagated after regridding. After reading the paper, it is my understanding that substantial degradation in predictive skill is unavoidable after ~2 years of simulations into future conditions (Figure 4), inherently limiting the forecast window of this approach since it will necessarily be forced to guess at realistic boundary conditions that influence the projected results. However, this was a bit difficult to discern from my read of the paper, and ties together with some general confusion about differences between the NWA12_HIND and NWA12_HIST model simulations, particularly with respect to boundary conditions and how they are being used for forecast analyses. As it reads in the methods section, I am unsure if NWA12_HIST simulations are exactly equivalent to NWA12_HIND but are simply missing an initialization period? If so, I remain confused about how the authors are considering the impacts of unavailable future boundary conditions to influence their model results, particularly with some of the lags found in Figure 5 when attempting to simulate accurate probabilistic predictions of multiple variables. I believe that many of these points, some of which are addressed in the detailed comments below, are not insurmountable and could be more thoroughly addressed by the authors before publication.
Detailed Comments
Line 105: Suggest striking “however” to make sentence flow better.
Figure 1: Suggest modifying aspect ratio (widen) panel a to improve readability, a little difficult to see any detail for bathymetry like that present in the Northeast Channel. May also be beneficial to include arrows showing sources of Warm Slope, Labrador Slope, and Gulf Stream water masses. What do the errorbars correspond to, just standard deviations over the regridded datasets? I’m unsure what the implications may be for normalizing temperature to the variables in d-f, as it may imply that a stronger direct relationship between unit changes in O2, pH, and omega for changes in temperature when the mechanisms are more complex. I also am a bit confused by the choice to invert temperature on panel d, wouldn’t it be more beneficial to show that increases in oxygen anomalies are correlated with decreases in temperature and vice versa? As plotted now, it requires a much more careful reading of the plot to confirm this.
Line 243-249: Are you still referring to Figure 3 in this paragraph? Or Figure 1?
Line 254-259: This explanation seems plausible, but as you note the large increase in bottom oxygen concentrations in 2015 seem to also be driven by a large positive winter MLD anomaly (Fig. S5) and that does seem to match well with Fishbot data. Are there other plausible explanations for the rapid declines in prediction skill?
Line 292-293: Missing end quote, but also suggest rewording to “… ask about forecast reliability of anomalously warm or cool conditions.”
Line 303-323: Perhaps this is also addressed in the discussion, but it seems from looking at Figure 5 that the model is most limited by transition periods and its own internal variability that increases the persistence of ongoing trends. For all variables shown, there are numerous examples of high confidence projected by the model for the persistence of a trend, followed by an eventual transition 1-2 years later (or sometimes missed altogether as seen in the 2010s for pH and omega). I don’t think that this necessarily indicates a critical lack of model skill, but it does warrant some further elaboration and discussion here.
Line 377-379: I would agree that the model does largely predict past variability well for temperature and salinity, excluding a few outliers in the 2000s, and would be curious to know if the bottom temperature skill is comparable to the surface temperature skill referenced in Koul et al. (2024). It seems like a bit of a stretch to say that it captured past variations in pH and omega (Figure 1) given the relatively limited dataset.
Line 391: suggest replacing “however” with “therefore”
Line 393-398: This sentence is a bit of a weak conclusion to some impressive work in the paper. Suggest striking or finding other references that you can use to stress the importance of probabilistic forecasting on longer timescales.
Line 466: Please define or provide citation for SSP585.
Figure S5: Typo of Fishbot in caption and would be helpful to define what errorbars shown correspond to.
General figure comments: Some parts of the figures can be a little difficult to read (e.g., axis labels in Fig 1-2, 4, legend and skill metrics in Fig. 2), recommend doing a quick reformat to make text more visible and consistent among them