the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Aug 2025
RADIv2, an Adaptable and Versatile Diagenetic Model for Coastal and Open-Ocean Sediments
Abstract. Ocean biogeochemistry is being altered by anthropogenic processes such as warming, acidification, eutrophication, and deoxygenation. Global-ocean biogeochemistry models are essential for investigating present and projecting future conditions, yet they often lack detailed representations of seafloor processes, despite the seafloor’s important role in material exchange between the biosphere and geosphere. To improve the representation of exchange across the sediment-water interface, we present RADIv2, a flexible and computationally efficient diagenetic model designed to simulate benthic biogeochemical processes across a range of marine environments, from coastal zones to abyssal plains. RADIv2 incorporates key features such as benthic methane cycling, a hydrodynamically controlled diffusive boundary layer thickness and porewater dispersion to the original RADI model, which enhance its ability to capture sediment-water exchange under varied environmental conditions. Using RADIv2, we develop and validate a regression-based metamodel that predicts benthic solute fluxes (oxygen, dissolved inorganic carbon, and alkalinity). This metamodel provides a universal and computationally efficient alternative to full-complexity coupled water column-sediment biogeochemical models at the global scale. Ultimately, this approach improves the representation of global biogeochemical cycles in ocean models by improving the parameterization of sediment-water exchange.
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Status: open (until 10 Oct 2025)
- RC1: 'Comment on egusphere-2025-2244', Anonymous Referee #1, 03 Sep 2025 reply
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Overview
Van der Zant et al. present RADIv2, an updated version of the RADIv1 1D early diagenesis sediment model described by Sulpis et al., (2022). Specifically, they have included improved parameterizations for diffusive boundary layer thickness and pore water dispersion, and added methane cycling. They also added features that allow ensembles to be run, and that allow unknown parameters to be optimized to better fit in situ data. They validate RADIv2 from in situ measurements across a range of different locations, then use RADIv2 outputs to develop a metamodel. This metamodel parameterizes dissolved oxygen, dissolved inorganic carbon, and total alkalinity fluxes in terms of temperature, bottom velocity, bottom-water calcite saturation state, and organic and inorganic carbon fluxes. They suggest that this metamodel could be used to represent more complex sediment dynamics than are currently included in global ocean biogeochemical models (GOBMs), without incurring the significant computational expense of including a fully process based model.
General comments
I think this paper adds some very useful features to RADIv1 and seeks to address a critical issue by demonstrating a pathway towards including more realistic sediment biogeochemical cycling in GOBMs. It’s well written, and I think the authors have done a great job in finding so many datasets to compare RADIv2 output against as I know that useful datasets for calibrating such a model are not readily available. Overall, I think it’s a strong paper. Below are some suggestions primarily focused on validation and the associated discussion which could improve it further.
I think this paper could be strengthened by adding more discussion to the sections validating RADIv2 against in situ observations. The applicability of RADIv2 and the resulting metamodel to a wide range of environments depends on these sections, hence I think they are particularly important.
Comparisons between the modelled and observed fluxes currently focus on whether the modelled fluxes lie within the range of observational values. However, it seems like in several cases the model means lie the observed values. Performing some kind of statistical test to determine whether the model differs significantly from the observations or not would strengthen the case that the model does capture most of what’s happening in the environment, and that the variations attributed to transient processes aren’t as important. If they do differ significantly, it might indicate that these differences are due to more than just these transient variations, or that these variations occur so frequently that they need to be accounted for.
I also think the discussion about the discrepancies between the model output and the observational data could dive deeper into factors that might be affecting these differences on the modelling side. Currently much of this discussion is framed around how the values the model is being compared to are outliers in the wider literature (e.g., the North Sea values for remineralization rates were lower than rates reported from other shelves, the Berelson et al., (2003) values for Monterey Bay are lower than the global-mean Corg remineralization rates for continental shelf environments). I think that if the point of this section is to demonstrate that RADIv2 can capture conditions across a range of different environments then it’s important to focus the discussion on how it compares to the specific environment it’s being tested against rather than against more generic conditions.
Diving more into the factors in the model that could be driving these discrepancies could also offer more insight into ways that the RADIv2 could be improved. In particular, I think that more discussion of why RADIv2 overestimates the POC remineralization rate at each site would be useful, as it seems like these overestimations could be responsible for the modelled O2, TA, and DIC fluxes tending to be higher than the observed values. Could this indicate that the POC fluxes used for the model are too high? How are the remineralization rate constants chosen for each site, and how does changing them affect both the remineralization rates and the partitioning of remineralization between aerobic and anaerobic pathways? There are also other parameters that could affect these fluxes, such as changes in porosity or the temperature dependence of redox reaction rates. I understand that RADIv2 has a lot of dials to turn, and the data necessary to really constrain it doesn’t exist and so these questions are probably impossible to answer definitively. However, given the importance of this section I think more discussion about why it overestimates remineralization would help strengthen this work.
I think it would also be useful to check the metamodel against the experimental data, rather than only comparing it to RADIv2 output. The ultimate goal of the metamodel is to simulate the world, hence I think it’s important to see how it does against observational data in addition to how well it replicates the results of RADIv2.
The explanation of how the metamodel was developed could be made clearer by specifying which results the regressions were performed on. If they were performed on the same outputs used to validate the model, then I think it would be helpful for that to be stated. If that is the case, was there thought given to running RADIv2 over a wider parameter space and performing regressions on those results? One area that could potentially be beneficial to explore would be more tropical, carbonate rich environments. Assessing the performance of a metamodel developed over a wider parameter space against both RADIv2 outputs and the validation sites presented here would provide a better basis for assessing how well the metamodel would fare over the diverse range of environments present within a GOBM.
I think the structure of the paper could be changed slightly to improve clarity. Section 3.1 to 3.5 are all about model validation, so I would suggest renaming section 3 to model validation. I would then add a distinct metamodel section, which could include parts of what is written in section 3, and 3.6
Overall, I think this is a strong piece of work that highlights a path forward and makes valuable contributions towards addressing a large problem in current GOBMs. The suggestions above are intended to help strengthen the validation and discussion in order to further improve the work and maximize its impact. I congratulate the authors for tackling this difficult problem, and look forward to seeing this work develop further.
Specific comments
Line 59: generic is repeated twice
Line 107: How is this incorporated within RADIv2? Manually setting them based on literature values?
Line 112: Does RADIv2 use the same carbonate dissolution/precipitation scheme as RADIv1? If so, I think it’s worth stating here, as RADIv1 is setup for relatively cold temperatures. If not, I think it would be useful to describe the differences given the importance of precipitation and dissolution to sediment biogeochemistry.
Line 256: Move the first bracket to after ITTC et al., so that it reads … ITTC et al., (2011).
Line 296: It could be helpful to be a bit more specific here. Is it the climate change-induced warming itself that could outpace the capacity, or is it the effects of warming? If so, what effects specifically?
Line 336:
Line 348: A citation would be handy for the 12 mmol C m-2 d-1 number.
Line 392: The text refers to Luff et al. (2000) while the figure reports values from Epping et al. (2002). Should there be another reference in here?
Line 426: Capitalise the T variables.
Line 457: By keeping track of organic carbon pools with different labilities, won’t RADIv2 capture some of this implicitly? The more labile stuff will be consumed closer to the surface, resulting in a reduction in lability with depth.
Figures & tables
Figure 1: The fitted line in 1a is difficult to make out. It might be worth changing the colour and style so it stands out a bit more. Switching the panels may also improve the flow so that they appear in the order that they’re discussed.
Figure 2:Changing the x axis labels so they’re in alphabetical order would make it easier to find the reference each bar refers to. You could also put all of the references in a single table, either in the text or supplementary, so that you didn’t have to list them in each caption.
If the data’s available and n is large enough that they’re not too noisy, it would be interesting to see the bars as violin plots to better get a sense of the data. Are the long tails in the in situ data caused by just a few extreme points, or do some of these data sets of some kind of bimodality? Are the RADIv2 results relatively normal?
Could the table be presented as a figure as well? Given the number of different values reported it might make it easier to quickly compare different values.
Figures 3-5: Same comment about violin plots, though I think a table’s fine for just a few values. The figure panels for 4 and 5 could potentially be narrowed. The colours in figure 2 correspond to the parameter being plotted. It would be good to keep these colours consistent in the other plots.
Figure 6: The x labels have RADI2v instead of RADIv2. All the other figures with oxygen fluxes (which I think were into the sediment) have reported them as positive values while here they’re reported as negative values. I think changing the other figures to have negative values would help make the presentation of this paper more consistent, and highlight that the oxygen fluxes are in the opposite direction to the TA and DIC fluxes.
Table 2: Is saturation state calculated from TA and DIC? If so, it might be worth stating it specifically given the larger uncertainties that can occur if you calculate it from other pairs of parameters.
It seems like all the organic carbon is now either fast or slow, whereas RADIv1 had refractory organic carbon. Has that been removed from the model? If so, I think this change should be mentioned somewhere.