the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Jan 2026
Complexity in Biogeochemical Models: Consequences for the Biological Carbon Pump
Abstract. Ocean biogeochemical models underpin projections of future marine ecosystem change, including anticipated shifts in the biological carbon pump (BCP) and broader biogeochemical cycles. However, their outputs remain highly sensitive to model complexity and parameterisation choices. Here, we evaluate five configurations of the Pelagic Interaction Scheme for Carbon and Ecosystem Studies (PISCES) to quantify intramodel variability in net primary productivity (NPP), carbon export (Cexp), and export efficiency (e-ratio) over the 21st century under the high emissions RCP8.5 scenario. The tested PISCES configurations differed from the standard model through distinct modifications to phytoplankton growth processes, but are forced by identical physical variables, representing an ensemble opportunity. All configurations resolve NPP and Cexp within the range of remote-sensing variability. The more complex Quota-based configurations produce 15–21 (10–18) Pg C yr-1 more NPP than the simpler Monod-quota models in the reference (future) period, but this increase, driven by elevated small phytoplankton biomass, does not enhance Cexp, yielding lower e-ratios (0.14–0.17) than in the Monod-quota configurations (~0.25). The introduction of a picophytoplankton functional type (PFT) emerges as one of the most influential parameterisation choices. It drives opposing future NPP responses between 30–60º N/S, an increase in the Monod-quota configurations versus a decline in the Quota-based ones, as well as contrasting latitudinal trends in Cexp within the same region. Other parameterisations, such as a low-iron scheme, an added diazotroph PFT, and explicit manganese cycling, exert more modest, regionally confined effects under high emissions scenarios, influencing NPP and Cexp primarily at biome scales rather than driving large-scale divergence in model behaviour.
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Status: open (until 17 Mar 2026)
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RC1: 'Comment on egusphere-2025-6505', John Dunne, 18 Jan 2026
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AC1: 'Reply on RC1', Jonathan Rogerson, 29 Jan 2026
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Firstly, we thank the reviewer, John Dunne, for their insightful and positive comments on the manuscript. Aside from several minor corrections to wording and figure captions, two main points are raised. The first concerns an exploration of intramodel variability in the spatial patterns of nitrogen and phosphorus, as well as their projected future changes. The second, more limited point, relates to a discussion of the degree of variability resolved in our model ensemble relative to CMIP ensembles more generally. We address these comments and suggestions in detail below.
Response:
54 - Yes, would agree that giving a date or year is more suitable than simply the word ‘present’ in adding to the long-term readability of the work.
162 - For this comment I am not exactly sure how to respond. In our methodology, we state that all the PISCES configurations were forced with identical physical outputs from the IPSL _CM5A_LR climate model and beyond the historical run, we used the high emissions RCP8.5 scenario. So we are using the scenarios that were used within CMIP5.
192 - In lines 185 – 190, we give a brief description of the P5M configuration and state that: “…incorporating manganese (Mn), following Hawco et al. (2022), which included its role in limiting phytoplankton productivity in the Southern Ocean, where observations show Mn as either a primary or co-limiting micronutrient alongside Fe…”. So in brief, Mn and Zn addition impacts phytoplankton growth by imposing an additional nutrient limitation term, alongside the other micro- and macronutrients already represented.
Fig. (1) - Ah, thank you for pointing this out. In our supplementary we show the biome spatial map with all the regions labelled. I will amend the caption then to the following:
Figure 1: Model and remote-sensing (RMT) estimates of (a) NPP and (b) Cexp, integrated over each RECCAP2 biome (refer to Supp. S1). Black bars indicate ±1 standard deviation across the remote-sensing ensemble.
The biome map was placed in the supplementary material solely to limit the number of figures in the main manuscript.
Fig. (2) - Your suggestion of adding the remote sensing (RMT) means is something we considered and I, as well as my fellow co-authors, can definitely see the value in doing so. However, we opted for a per biome breakdown when comparing NPP and Cexp of PISCES and RMT to champion the fact that the modifications to some of the configurations were highly regionally specific: e.g. diazotrophs in the subtropical gyres in P6Z or Mn and Zn impacts in the Southern Ocean for P5M.
Pertaining to one of your major comments, we adopt your idea when looking at the N and P representations in the different configurations
As for the caption and the time ranges, we do state in the methodology: “For this study, we conducted our analysis of the BCP using averaged model outputs over two time windows, the ‘reference’ (1986-2005) and ‘future’ (2091-2100)”. We rigidly stick to this nomenclature throughout the manuscript and thus implicitly have the date ranges accounted for in the various tables and plots. Am sure personal preferences prevail, but hope this justification suffices.
Major comment #1: N and P representations
Please refer to the figure showing the latitudinal upper-ocean N and P inventories integrated over the upper 100 m for the present period, as well as the corresponding relative future changes for the five PISCES configurations. We also include data from the World Ocean Atlas 2023 (WOA23) for comparison. As all models were forced with identical physical forcing, intramodel variability in the simulated N and P inventories arises primarily from differences in biogeochemical formulations and, potentially, model tuning.
Addressing the latter first, we are unable to directly quantify the degree to which model tuning contributes to the spread in the reference period for N and P inventories across the different configurations. The inclusion of additional biogeochemical processes or parameters in the various PISCES configurations inevitably requires some degree of tuning, with the implicit assumption that individual modelling teams calibrate their configurations to best represent the present-day (reference) state. While I do not have explicit documentation of the tuning procedures employed for each configuration, correspondence with the respective lead authors indicates that the various PISCES configurations were tuned to reasonably reproduce present-day conditions.
Differences in biogeochemical parameterisations are likely the main cause for intramodel variability in the N and P inventories. In the figure, N and P exhibit consistent spatial patterns across all configurations for the reference period (panels a, b), as well as broadly similar future trends (panels c, d). For the reference period, N and P inventories are systematically higher in the Monod-quota configurations than in the Quota-based configurations; however, all configurations closely reproduce both the spatial patterns and magnitudes of the WOA23 data. Differences in the absolute magnitudes of N and P inventories, particularly between the standard and Quota-based versions, likely reflect the flexible stoichiometry inherent to the quota models and, potentially, the larger contribution of small phytoplankton biomass with higher nutrient affinities. Together, these factors result in slightly lower standing inventories of N and P in the quota-based configurations, owing to more efficient nutrient uptake.
Despite small differences in the magnitudes for the reference period and future N and P inventories, all configurations simulate similar future shifts, with N and P inventories decreasing by -11.78 ± 1.85% and -12.39 ± 1.28%, respectively. The small standard deviations indicate that variability in N and P is similarly resolved across configurations. Consequently, variability in Cexp is more likely driven by differences in phytoplankton assemblages and their interactions with zooplankton, as discussed in the manuscript.
The intention is to add the figure to our supplementary section and within the discussion section to mention that N/P variability are not likely a driving factor of Cexp variability in our study, referencing of course the figure.
I hope this explanation is satisfactory and addresses all your comments on the matter.
Major comment #2: Variability in CMIP models vs our ensemble
I fully agree with your comment. While the magnitude of variability for relative change in NPP and Cexp is similar for our ensemble vs CMIP5, it does not suggest that our ensemble captures the diversity in model complexity present within CMIP. As you mention, in CMIP, the physical models differ along with the manner in which N and P are represented, as well as other nutrients and functional types. What is interesting nonetheless is that through only modifications to the growth processes controlling phytoplankton, we create a similar spread to CMIP, underscoring if nothing else the sensitivity of these parameterisations and processes within biogeochemical models.
502-503 - On this, thank you for the paper recommendation and will nuance this phrase as well as add the appropriate reference. As I understand, from your comment reduced export is a consequence, not the driver.
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AC1: 'Reply on RC1', Jonathan Rogerson, 29 Jan 2026
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The manuscript “Complexity in Biogeochemical Models: Consequences for the Biological Carbon Pump” by Rogerson et al compares 5 biogeochemical formulations of the PISCES model in the same physical model to explore the implications of varying complexity of phytoplankton physiological assumptions on model representation of primary production and carbon export out of the euphotic zone. The experimental design is well designed and executed and the manuscript is very well written. My only criticism is that the manuscript does not provide an analysis/comparison of the latitudinal structure of surface/mixed layer/euphotic zone nitrate and phosphate in the present day with observations and the change under the future projections. These patterns and their relative change seem key to explaining the differences between model representation of Cexp patterns and their change under climate warming and the degree to which the structural uncertainty described here captures the range in CMIP models in general.
Specific comments:
54 - “the present” is constantly changing. Better to use “2023”
162 - it would be helpful to add which configurations were used for CMIP5 and CMIP6 given their prominence in previous climate change intercomparisons as point of reference.
192 - It would be helpful to add a sentence on the impact of adding Mn and Zn modulation of the ecosystem.
Figure 1: The acronym definitions for each region should be provided.
Figure 2: it would be helpful to add the Model and Remote Sensing means and range estimates here. Also, the caption should provide the time ranges used for both the model averages and delta values.
317 - before moving on to the sensitivity to climate change, it is important to understand the differences in model representation of surface nitrate and phosphate as these are the most robust observational constraints and often the levers through which changes are manifest, particularly the latitudinal structure of the Southern Ocean and Equatorial Pacific high nutrient regions and the amount of residual phosphate in the subtropical north Pacific after nitrate exhaustion as a constraint on nitrogen fixation, both of which have been shown in previous CMIP comparisons to vary between models, particularly under changes in relative iron limitation. Alternatively, insofar as the nitrate and phosphate concentrations in all the models is similar (as perhaps suggested in the similarity of structure in Cexp figure 2), then that vastly simplifies the interpretation. It would also be important to add the degree to which all these configurations were tuned to represent the same average surface nitrate and phosphate concentrations. Moving to the sensitivity to climate change question, the delta nitrate and phosphate in the projections is also an important point of analysis and constraint.
399 - adding the suggested surface nitrate and phosphate analysis would improve the robustness of these conclusions dramatically.
443 - it would be interesting to know how the average euphotic zone or mixed layer ammonia values vary between the models and compare to observations as another constraint on the relative robustness of the different models.
498 - The CMIP models also have radically different representations of Cexp latitudinal structure through surface nitrate and phosphate and underlying physical model differences. I would be surprised if this present ensemble captured much of that variability in Seferain et al 2020, but the degree to which it does capture the CMIP structural uncertainty should be explicit as contextualization of the discussion here.
502-503 - The logic in the assertion that “the overall decline in carbon export reduces the ocean’s capacity to sequester CO2 from the atmosphere.” seems reversed… The enhanced stratification increases the ocean’s sequestration of remineralized carbon and the associated nutrients, which then leads to reduced supply of nutrients to the surface and reduced Cexp as a consequence of the enhanced efficiency of the biological carbon pump. See https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023GB007859 for a detailed discussion.