We use the dynamical ocean model NEMO 4.0.4 (Nucleus for European Modelling of the Ocean; Madec, 2008), coupled with the sea-ice model SI³ (Vancoppenolle et al., 2009) and the biogeochemical model PISCESv2_RC (Pelagic-Interactions Scheme for Carbon and Ecosystem Studies; Aumont et al., 2015, 2017). Hereinafter, we will use the terms NEMO and PISCES to refer to NEMO 4.0.4 and the version of PISCESv2_RC released alongside it (Table 1). NEMO and PISCES are coupled through the Tracers in Ocean Paradigm (TOP) tracer manager (NEMO TOP Working Group, 2018), which performs advective and diffusive transports of both physical and biogeochemical tracers (see details in Supplement Sect. S1).

Global simulations were run on the ORCA1L75 grid (∼ 1° horizontal resolution, 75 non-linear vertical levels) with a 45-minute time step for ocean dynamics and biogeochemistry. The simulation followed the Ocean Model Intercomparison Project protocol (OMIP-2; Tsujino et al., 2018) and was forced by the JRA-55 atmospheric reanalysis (Kobayashi et al., 2015). After initialisation, biogeochemistry was spun up for 1302 years under recurring circulation and fixed preindustrial atmospheric CO₂. The subsequent transient simulation included rising atmospheric CO₂ concentrations over 1850–2019. Only results from the final 1958–2019 forcing period are analysed here, focusing on the North Atlantic. For comparison with satellite products, a monthly climatology over 1998–2019 was used. Further details are given in Supplement Sect. S1.

PISCES simulates marine planktonic food webs and the biogeochemical cycling of key elements (carbon, nitrogen, phosphorus, silicon, and iron) using 24 tracers that represent living, detrital and inorganic compartments. Here we briefly describe the representation of POC cycling in the model, introducing tracer names in italics.

POC is represented by six tracers: two phytoplankton types – miscellaneous small phytoplankton (phymisc) and diatoms (phydiat); two zooplankton types – microzooplankton (zmicro) and mesozooplankton (zmeso); and two detrital pools – small (sdetoc) and large (ldetoc) particles. Of these, only detrital tracers sink, with fixed sinking velocities of 2 and 50 m d⁻¹ for sdetoc and ldetoc, respectively. Phytoplankton growth depends on light availability and uptake of nitrate, ammonium, phosphate, dissolved iron and, for diatoms, silicate. Phytoplankton is lost through zooplankton grazing and linear and quadratic mortality terms, the latter representing aggregation upon bloom demise. Both zooplankton types prey on phytoplankton and sdetoc with different preferences. Mesozooplankton additionally predates on microzooplankton and consumes sdetoc and ldetoc via flux feeding.

Detritus is produced mainly through plankton mortality and zooplankton inefficient (“sloppy”) feeding, with partitioning between sdetoc and ldetoc depending on the organism and process involved. Detrital particles are degraded to DOC using the variable reactivity scheme of Aumont et al. (2017), which allows decay rate constants (k, d⁻¹) to vary in space and time. This scheme prescribes a maximum k of 0.035 d⁻¹ at 0 °C for freshly produced detritus in the upper mixed layer. With increasing depth, k decreases as sinking particles lose their most reactive components and become increasingly refractory. This decrease is not prescribed explicitly but emerges from the balance between particle sinking, the local supply of fresh detritus, and the progressive degradation of 15 lability classes implicitly represented by a gamma distribution. As a result, the vertical profile of k varies among regions, seasons, and detrital size classes. Detritus decay rates also increase as a power function of temperature, approximately doubling every 10 °C (Q10 of 1.9). Following PISCES terminology, degradation of sdetoc and ldetoc is referred to as remineralisation, although detrital POC is first converted to DOC before reaching dissolved inorganic carbon (DIC).

Mesozooplankton flux feeding and fragmentation are key features in PISCESv2 (Aumont et al., 2017). Given that the current parameterisation differs markedly from previous model versions (Gehlen et al., 2006), here we provide an updated description. At each time step, a variable fraction of mesozooplankton biomass engages in flux feeding. This fraction is diagnosed as the ratio of flux-feeding ingestion to total mesozooplankton ingestion. Flux feeding rates are calculated as the product of flux-feeding mesozooplankton biomass, a flux-feeding cross-section parameter, and the sinking flux of each detrital tracer (Supplement Sect. S1.2, Eq. S4). The flux-feeding parameter corresponds to a biomass-normalised area of 3 m² (mol C)⁻¹, roughly representative of temperate and subpolar flux feeders (Jackson, 1993; Stemmann et al., 2004b; Stukel et al., 2019).

Fragmentation of large detritus by mesozooplankton is expressed as a variable proportion of flux feeding rates, resulting in additional attenuation of ldetoc fluxes and transfer to sdetoc. The ratio between fragmentation and flux feeding increases, from 0.2 to around 2, as a function of the ratio of biogenic silica to ldetoc concentrations. This dependence is intended to represent the greater fragility of large detritus composed of diatom aggregates compared to more compact forms such as faecal pellets.

We diagnosed the full budgets of sdetoc and ldetoc by outputting all individual terms from their prognostic equations, including sources (production processes), sinks (consumption processes) and transports (Fig. 1). Transports include gravitational sinking and tridimensional advection and diffusion. Diffusion – equivalent to mixing – represents transport by subgrid scale ocean dynamics. Budgets were computed separately for the epipelagic and mesopelagic layer in three regions outlined in Fig. 2: the Subpolar North Atlantic (SPNA), the Transition Area (Trans_Area) and the Subtropical North Atlantic (STNA). The boundary between the two layers, Zprod, is defined as the depth at which the annual mean vertical profile of primary production (PP) falls below 1 % of its maximum. For each region, Zprod was fixed to ensure that budgets were computed over a consistent vertical domain over the seasonal cycle. The choice of a biome-specific Zprod, rather than a fixed reference depth, avoids conflating productive-layer structure with mesopelagic transfer when comparing regions. For instance, a uniform reference depth of 100 m would underestimate export in the SPNA, where substantial net POC removal occurs above 100 m, and overestimate export in the STNA, where photosynthetic production extends below 100 m (Palevsky and Doney, 2018, 2021; Wilson et al., 2022; Walker and Palevsky, 2025).

For a given region and layer, the budget equation takes the generic form 1δ[X]δt=Sources-Sinks-GS-ADV-DIFF where Sources and Sinks represent vertically integrated rates averaged over each region. Transport terms, namely gravitational sinking (GS), advection (ADV) and diffusion (DIFF), are expressed as the divergence of the fluxes along the lateral and vertical directions. Divergence corresponds to the net difference between input and output fluxes through the domain boundaries, divided by the domain length in the corresponding direction. Hereafter, fluxes across a bounding surface are denoted by F followed by a subscript (e.g., FGS, FADV, FDIFF), whereas GS, DIFF and ADV denote the associated flux divergences. Subscripts Z and L indicate the vertical and lateral components, respectively (e.g., FADVZ, FADVL, DIFF_Z, DIFF_L).

We verified budget closure by comparing the simulated tracer tendency δ[X]δt with the sum of all diagnosed source, sink and transport terms over each domain. Budgets were closed both over successive model time steps (within ±6×10-4 % error) and over monthly periods.

Below, we provide a qualitative representation of the detrital POC budgets (Eqs. 2 and 3). The full prognostic equations for sdetoc and ldetoc, explicitly showing their controlling parameters, are provided in Supplement Sect. S1.2 and Table S2.1, as these equations have been described previously by Aumont et al. (2015, 2017). Superscript numbers are used as labels to link terms to Table S2.1 and Fig. 1: 2δ[sdetoc]δt=(mortalities1+zmicro non assimilation2+bacterial breakdown of ldetoc3+DOC aggregation4+zmeso fragmentation of ldetoc5)-(zmicro ingestion6+zmeso ingestion and flux feeding7+remineralization8+sdetoc aggregation9)-(ADVL10+ADVZ11+DIFFL12+DIFFZ13+GS14)3δ[ldetoc]δt=(mortalities15+meso non assimilation16+sdetoc aggregation9+DOC aggregation17)-(zmeso flux feeding18+zmeso fragmentation of ldetoc5+remineralization19+bacterial breakdown of ldetoc3)-(ADVL20+ADVZ21+DIFFL22+DIFFZ23+GS24)

For clarity, we lumped together some budget terms that are small and/or follow similar dynamics: (i) sdetoc consumption by phagotrophic ingestion and flux feeding; (ii) physical aggregation processes, and (iii) mortality terms. Finally, note that processes that interconvert between sdetoc and ldetoc appear in both budgets with opposite signs.

We summarised spatial variations in vertical POC fluxes using three common metrics. Export efficiency (EE; Buesseler, 1998), also referred to as export ratio, is defined as the ratio between POC export flux through the bottom of the productive (epipelagic) layer and the net primary production rate integrated over that layer (NPP): 4EE=FZprodNPP

Transfer efficiency (TE; François et al., 2002) quantifies the fractional transfer of exported POC from the base of the productive layer to a deeper reference depth – in our case, the base of the mesopelagic layer (1000 m): 5TE=FZ1000FZprod

The product of EE and TE is the particulate carbon pump efficiency (Buesseler and Boyd, 2009) at 1000 m: 6EE×TE=FZprodNPP×F1000FZprod=F1000NPP Vertical POC fluxes at the same two depths bounding the mesopelagic domain were also used to calculate Martin's b exponent (Martin et al., 1987), often referred to as the flux attenuation coefficient: 7b=-ln⁡(FZ1000/FZprod)ln⁡(1000/Zprod)

Stronger vertical attenuation of POC fluxes corresponds to lower TE and larger positive values of b. All metrics were calculated using annual-mean climatological fluxes for each region to avoid issues associated with non-steady-state seasonal dynamics (Giering et al., 2017) (see Sects. 3.4 and 4.3).

To assess differences in attenuation among POC pools, Eqs. (4)–(6) were applied both to detrital POC fluxes alone (gravitational, diffusive and advective fluxes of sdetoc and ldetoc) and to total POC fluxes, which additionally include the diffusive and advective vertical transports of plankton tracers.

We evaluated model performance using gridded observational products for mixed layer depth (MLD), vertically integrated net primary production (NPP), phytoplankton carbon biomass, mesozooplankton carbon and small POC (sPOC) stocks, the latter assessed in both the epi- and mesopelagic layers. After regridding the observational products onto the model grid (ORCA1_L75), model skill was assessed using standard metrics applied to (i) spatial patterns of annual-mean fields (Fig. 2) and (ii) monthly climatological seasonal cycles averaged over each region (Fig. 3). Key characteristics and references for each observational dataset are summarised in Table 2. Further evaluation results, including skill metrics (Table S2.3) and seasonal maps comparing simulated and observed fields (Figs. S3.2–3.3), are presented in the Supplement Sect. S3.

While some simulated variables have a good correspondence in the observations – most notably MLD and NPP – other comparisons require simplifying assumptions, which are described here. First, modelled phytoplankton is evaluated using a satellite product that resolves three phytoplankton size classes. For this comparison, we assume that satellite microphytoplankton corresponds to model diatoms, and that the sum of pico- and nanophytoplankton corresponds to miscellaneous phytoplankton in the model. This group includes mostly haptophytes and cyanobacteria and, to some extent, dinoflagellates. Although this assumption introduces some uncertainty, it is broadly consistent with wide evidence from size-fractionated biomass measurements, ecophysiological studies, pigment signatures, and bio-optical data, all of which underpin satellite algorithms (Uitz et al., 2006, 2008) and model parameterisations. Second, we assume that the sPOC estimated from bio-optical data corresponds to the sum of four POC tracers with nominal sizes smaller than 100 µm: the two phytoplankton classes, microzooplankton, and small detritus, as previously discussed (Galí et al., 2022). Finally, mesozooplankton is evaluated using an observations-based machine learning product specifically developed for comparison with PISCES mesozooplankton fields (Clerc et al., 2024). No suitable gridded observational products were found for microzooplankton and large detrital POC concentrations.

In the case of sPOC, we used two estimates based on the same observational dataset. The approach of Galí et al. (2022), based on the variable ratio between POC and particulate backscattering, was used to estimate POC between 0–1000 m. For completeness, the method of Koestner et al. (2024), based on the relationship between POC, particulate backscattering and chlorophyll a, was used to estimate POC between the surface and the euphotic layer depth (below which gridded chlorophyll a data is not available). Hereafter, we refer to these sPOC estimates as G22 and K24, respectively. Additional details on these datasets are provided in Supplement Sect. S1.3.

Simulated export fluxes were evaluated separately using a subsampling approach designed to account for the sparse and uneven spatiotemporal coverage of the observations. Two complementary data compilations were used (Table 2): (i) Shallow POC export fluxes measured with the ²³⁴Th disequilibrium method, mostly at 100–150 m (Le Moigne et al., 2013); (ii) full water-column POC fluxes (Mouw et al., 2016), here restricted to sediment–trap estimates to avoid overlap with the ²³⁴Th dataset. Trap deployments longer than 1 month were discarded.

Evaluation results confirm that PISCES captures the main biogeochemical features of the North Atlantic basin, despite some season- and region-dependent biases (Table 3). To investigate how these patterns translate into POC vertical structure, this section documents the distribution of POC components and their corresponding fluxes and remineralisation processes from the epipelagic to the mesopelagic layer.

On an annual basis, total gravitational export at the base of the productive layer (Zprod) is similar in the transition and subpolar areas (3.9–4.3 mmol C m⁻² d⁻¹), and much lower in the subtropical area (1.3 mmol C m⁻² d⁻¹) (Fig. 4). In addition, a substantial amount of POC is exported through turbulent diffusion (vertical mixing) of plankton in the SPNA (1.4 mmol C m⁻² d⁻¹) and the Trans_Area (0.8 mmol C m⁻² d⁻¹). Smaller exports arise from the diffusion of detrital POC, especially sdetoc, while vertical advection of particles is negligible at the regional scale examined here.

Vertical flux profiles vary widely across regions and POC tracers (Fig. 4). Gravitational sdetoc fluxes attenuate rapidly in the upper mesopelagic and more slowly below ∼ 400 m. By contrast, ldetoc shows deeper flux maxima, greater penetration through the upper mesopelagic, and gradual attenuation throughout the mesopelagic. Consequently, the proportion of export fluxes carried by sdetoc increases with depth in the mesopelagic. Diffusive plankton fluxes attenuate at shallower depths than gravitational ones. Yet, their penetration deepens towards the SPNA, consistent with deeper mixing in winter (Figs. S1 and S2).

Small detritus is the most abundant POC fraction in the mesopelagic layer, where it accounts for 52 % (SPNA) to 70 % (STNA) of the POC pool. The ldetoc fraction usually is ≤ 10 % of total POC and smaller than total plankton (Fig. 4a, d, g).

Vertical profiles of detritus decay rates also vary by detritus type and region (Fig. 4c, f and i). The integrated decay rate of detritus (sdetoc + ldetoc) between the surface and 1000 m is highest in the Trans_Area (13.9 mmol C m⁻² d⁻¹) compared to the other areas (8.4–8.6 mmol C m⁻² d⁻¹), mirroring NPP patterns. Small detritus drive most of the detritus decay, with higher sdetoc contributions in the epipelagic (85 %–92 %) than in the mesopelagic (53 %–61 %). To characterise distinct regimes, we calculated the percentage of POC degradation occurring in the epipelagic layer relative to the 0–1000 m integral. In SPNA, 58 % (sdetoc) and 28 % (ldetoc) of decay occurs in the epipelagic, concurrent with low temperatures, strong ldetoc export and shallow Zprod. In contrast, the STNA shows 93 % (sdetoc) and 56 % (ldetoc) epipelagic decay, aligned with warm temperatures, reduced ldetoc export, and deep Zprod.

Detritus decay rates result from the product of tracer concentration and the degradation rate constant k, which varies with depth and temperature (Fig. 7; see also Sect. 2.2). For sdetoc, 0 °C-normalized k typically ranges 0.010–0.023 d⁻¹ in the epipelagic and decreases sharply to ∼ 0.002 d⁻¹ at 1000 m; for ldetoc, k typically ranges 0.027–0.035 d⁻¹ in the epipelagic and 0.015–0.020 d⁻¹ at 1000 m. Thus, in PISCES, sdetoc becomes refractory at shallower depths than ldetoc, explaining the limited change in sdetoc concentration and sinking flux below ∼ 400 m compared to ldetoc (Fig. 4).

While the change in reactivity is best described by 0 °C-normalized k, the actual in-situ rates are differently affected by temperature across regions and depths (Fig. 7), which here we quantify using the enhancement factor with respect to k at 0 °C. In the epipelagic, mean thermal enhancement factors are 1.7, 3.2 and 4.5 in the SPNA, Trans_Area and STNA, respectively. Smaller thermal enhancement factors (1.5–2.5) are found in the mesopelagic. Thus, temperature control on k exacerbates the vertical gradients in reactivity at low latitudes.

Regional contrasts in POC vertical structure reflect distinct combinations of particle composition, export pathways, and remineralisation regimes (Figs. 4 and 7). To quantify how these differences arise in PISCES, we now examine the annual climatological budgets of detrital POC in the three regions of interest. We place particular emphasis on how regional differences in productive-layer depth, particle size structure, and export pathways shape mesopelagic budgets (Fig. 8).

For sdetoc, the relative contribution of gravitational inputs increases toward lower latitudes (Fig. 8a), from 36 % (SPNA) to 74 % (STNA). In contrast, the supply of sdetoc by the mesopelagic food web shows the opposite pattern and exceeds 50 % of total supply in the SPNA, driven primarily by mesozooplankton fragmentation of ldetoc (16 %), plankton mortalities (20 %), and microzooplankton non-assimilation (9 %). In the Trans_Area, plankton mortalities dominate biological supply (14 %), followed by non-assimilation (8 %), bacterial breakdown of ldetoc (7 %), and mesozooplankton fragmentation (6 %). In the STNA, bacterial breakdown of ldetoc (7 %) and abiotic DOC aggregation (12 %) contribute relatively more, whereas contributions from mortalities and microzooplankton non-assimilation are reduced. Removal of sdetoc is dominated by remineralisation in all regions (> 70 %), reaching 87 % in the STNA. Mesozooplankton removal via phagotrophic ingestion and flux feeding is also significant, peaking at 17 % in the Trans_Area, whereas microzooplankton removal remains minor (≤ 5 %).

For ldetoc, gravitational inputs dominate in all regions, with smaller contributions from plankton mortalities and mesozooplankton non-assimilation (Fig. 8b). As for sdetoc, the importance of these biological sources diminishes southward in favour of sinking. Removal of ldetoc is dominated by remineralisation, but at lower fractions (50 %–75 %) than sdetoc. In the SPNA, nearly half of ldetoc removal occurs through mesozooplankton flux feeding (30 %) and fragmentation into sdetoc (15 %). Mesozooplankton-mediated transformation rates decrease toward lower latitudes, especially fragmentation, which in the Trans_Area contributes half as much to sdetoc budgets as in the SPNA. Bacterial disaggregation of ldetoc is generally minor, reaching 7 % in the STNA.

Overall, both sdetoc- and ldetoc-specific processes jointly regulate the magnitude and evolution of regional detrital POC stocks in PISCES. Supply pathways are more heterogeneous for sdetoc, whereas removal pathways are more diverse for ldetoc. This asymmetry largely reflects ldetoc transformation by fragmentation and disaggregation processes, which act as sinks for ldetoc while simultaneously supplying sdetoc. The indirect contribution of plankton diffusion (mixing) also affects sdetoc differentially across regions (Fig. 4 b, e, i).

Building on the annual-mean budgets presented in Sect. 3.3, we now examine how the processes controlling mesopelagic detrital POC vary over the seasonal cycle in PISCES. Monthly climatological budgets show that, consistent with the seasonality of physical forcing, temporal variability increases with latitude: it is minimal in the STNA but pronounced in the SPNA (Figs. 9, S5, S6 and vertical black lines in Fig. 8).

Gravitational sinking of large detritus during the high-latitude bloom and post-bloom period is the main seasonal event. The peak in gravitational ldetoc supply (June) is rapidly followed by maxima in ldetoc losses through remineralisation, mesozooplankton flux feeding and fragmentation. Enhanced mesozooplankton activity also generates secondary ldetoc sources via mortality and sloppy feeding. In contrast, sdetoc budgets exhibit a smoother seasonal succession, with sources shifting from mortalities and zooplankton non-assimilation in spring to ldetoc fragmentation in summer. Alongside dominant removal by remineralisation, sdetoc sinks transition gradually from microzooplankton ingestion to mesozooplankton ingestion and flux feeding through spring and summer.

Seasonal decomposition is also necessary to understand the role of vertical diffusive (mixing) fluxes and their contribution to detrital food webs. On an annual basis, vertical diffusion represents a significant net source of mesopelagic POC in the SPNA and the Trans_Area, whereas in the STNA, diffusive fluxes primarily redistribute POC within the epipelagic layer. Diffusive fluxes of POC are, however, largely driven by plankton and are therefore not included directly in detrital budgets. Instead, vertically mixed plankton enters detrital budgets through mortalities and sloppy feeding (Eqs. 1 and 2). In the SPNA, plankton diffusion supplies POC to the mesopelagic throughout the year and peaks in April (5 mmol m⁻² d⁻¹), when it is three times larger than total gravitational inputs (Supplement Sect. S4.2 file S4.2). Between December and April, when the MLD exceeds Zprod, the ratio between [mortalities + non-assimilation] and plankton diffusion is 0.70 ± 0.27. Between May and October, this ratio increases to 7.0 ± 5.1. Thus, detrainment of plankton from the epipelagic layer dominates detritus supply (largely as sdetoc) before the spring bloom, whereas biological processing of sdetoc derived from large detritus fragmentation dominates during the post-bloom season. Similar, though weaker, patterns of diffusive supply occur in the Trans_Area.

Direct diffusion of detritus is comparatively small, with the largest contribution occurring in the Trans_Area (7 %). In the SPNA, diffusive fluxes of detritus switch sign over the seasonal cycle (Fig. 9e), resulting in a weak net annual supply (Fig. 8a). In late autumn and early winter, mixed-layer deepening entrains mesopelagic detritus into the epipelagic layer, whereas in spring, re-stratification promotes detrainment of detrital POC from the epipelagic to the mesopelagic layer.

To synthesise the behaviour of the particulate C pump, we compare export and transfer efficiency metrics across regions (Fig. 10). Additionally, attenuation coefficients “b” (Martin et al., 1987) between Zprod and 1000 m are reported in Fig. 11. All metrics are based on annual mean fluxes.

Considering gravitational fluxes only, PISCES-simulated export efficiency (EE) decreases with latitude, from 21 % in the SPNA to 8 % in the STNA. In contrast, TE varies non-monotonically with latitude and is lowest in the Trans_Area (13 %), intermediate in the SPNA (17 %), and highest in the subtropical area (23 %). As a result, overall particulate C pump efficiency at 1000 m, expressed as EExTE, is highest in the subpolar area (3.6 %) and lower in the transition (1.5 %) and subtropical (1.8 %) areas.

Given the substantial contribution of non-gravitational fluxes in NEMO–PISCES, especially plankton diffusion (Fig. 4), we re-computed EE and TE for the total POC vertical flux, including gravitational, diffusive and advective components. In the subpolar and transition areas, diffusive plankton fluxes are comparable in magnitude to gravitational ones at Zprod but become negligible at 1000 m (Fig. 4). Therefore, their inclusion increases EE but decreases TE. In the SPNA, where the greatest non-gravitational fluxes occur, accounting for all vertical transport pathways increases EE by 8 % but reduces TE by 3 %, resulting in a modest increase in overall efficiency (EExTE) from 3.6 % to 4.0 %.

Evaluation against ²³⁴Th- and sediment trap–based estimates indicates that PISCES reproduces the order of magnitude and broad seasonality of epipelagic export fluxes in the North Atlantic, while exhibiting regionally distinct biases. In the SPNA and at PAP, model–data agreement generally improves with depth and toward summer. At BATS/OFP the model captures the weak seasonal cycle but overestimates export at 150 m, particularly in winter–spring. According to PISCES, large detritus drives 67 % of the gravitational flux at 100 m in the SPNA, exceeding the range (37 %–64 %) estimated in the western subpolar gyre by Wang and Fennel (2022). The latter study further suggests that the contribution of large detritus increases with depth, opposite to model estimates (Fig. 4). In the eastern subtropical Atlantic at 260 m, the contribution of slow-sinking (likely small) particles was > 60 % during most of the year (Alonso-González et al., 2010), roughly consistent with ∼ 50 % in PISCES at 200 m (Fig. 11). Model-data discrepancies possibly reflect a combination of observational uncertainties (Buesseler et al., 2007; Bishop et al., 2012) and limitations in how the model represents POC export using rigid relationships between size classes and sinking velocities (Jackson and Burd, 2015; Iversen and Lampitt, 2020; Cael et al., 2021)

Vertical gradients in simulated export are usually strongest near Zprod (Palevsky and Doney, 2018; Buesseler et al., 2020) implying intense flux attenuation in the lower epipelagic and upper mesopelagic layers. Thus, uncertainties in the mathematical representation of export and attenuation processes, together with small spatiotemporal mismatches between simulations and observations, can lead to substantial model-data deviations. Importantly, it remains unclear whether observational export estimates from sediment traps and the ²³⁴Th technique isolate gravitational sinking alone (Fig. 5) or can also account for downward transport by diffusion and advection (and, if so, how efficiently). We therefore interpret shallow export comparisons as quantitative consistency checks rather than direct validation (Aumont et al., 2017), and analyse mesopelagic POC inputs in terms of the total simulated downward POC flux, integrating gravitational sinking of detritus with diffusive and advective transport of both detrital and living particles.

Compared to gravitational fluxes, the transport of POC by vertical mixing has received little attention until recent years. Classical formulations of the biological pump have assumed vertical particle diffusion to be negligible at large scales (Martin et al., 1987), but oscillations in mixed layer depth can effectively entrain and detrain particles (Gardner et al., 1995). In models, these transports are diagnosed as diffusive fluxes, which our results show to be quantitatively relevant at large scales, in agreement with recent research (Bellacicco et al., 2025). In the NEMO-PISCES simulation (Fig. 4), POC diffusion amounts to 37 % (25 %) of annual gravitational fluxes in the SPNA (Trans_Area), in line with independent estimates. For instance, Dall'Olmo et al. (2016) inferred that the POC mixed-layer pump could represent ∼ 23 % of annual mean gravitational fluxes at high latitudes. Lacour et al. (2019) used biogeochemical Argo float profiles to quantify springtime net POC detrainment at 4.6 mmol C m⁻² d⁻¹ (55 mg C m⁻² d⁻¹) in the SPNA. Our corresponding estimate for March–May (4.3±1.1 mmol C m⁻² d⁻¹) is strikingly similar, despite relying on a fundamentally different approach.

Crucially, PISCES further suggests that over 85 % of this diffusive POC flux is associated with living plankton rather than detritus, reflecting the sharper vertical gradient of plankton biomass and their enhanced transport during convection events (Galí et al., 2026). Altogether, gravitational and diffusive export fluxes exhibit different seasonality and deliver to the mesopelagic layer distinct particle mixtures in terms of size, sinking speed, and composition. Differences in supplied POC entail different transformation pathways in the mesopelagic zone (Figs. 7–9 and 11), which are examined in the next section.

Zooplankton plays a central role in detritus processing in PISCES: 50 %–60 % of the gravitational input flux transits through zooplankton at mid and high latitudes, compared to ∼ 20 % in the subtropical area (Figs. 8 and 11). Importantly, zooplankton processing does not equate to POC flux attenuation, as a substantial and variable fraction returns to the sinking detritus pool via fragmentation, faecal pellet production and mortality (Figs. 8 and 9). As a result, POC recirculates within a mesopelagic detrital loop before final removal, primarily through bacterial decomposition (Fig. 11). In this section, we examine how zooplankton transformations and microbial degradation jointly shape mesopelagic detritus budgets, and how these simulated rates compare with the limited available observations.

A stringent evaluation is provided by comparison with observations at the PAP site (see Fig. 2), where Giering et al. (2014) (G14) were able to constrain, within reasonable uncertainty, POC flux attenuation and mesopelagic metabolism. Using July–August PISCES means for the SPNA and the Trans_Area and acknowledging imperfect correspondence between model- and observations-derived rates, we find that PISCES reproduces within ±15 % the magnitude of gravitational inputs, total zooplankton detritivory and bacterial degradation (Table 4). However, the model overestimates large-detritus fragmentation and the contribution of small detritus to POC decay. This discrepancy may partly reflect differences in pool definitions, as suspended POC in G14 is not fully equivalent to PISCES sdetoc, which accounts for suspended and slow-sinking fractions (Galí et al., 2022). Overall, this comparison is reassuring regarding simulated flux magnitudes, and further work is warranted to explore POC dynamics at the PAP site.

In PISCES, flux feeding and fragmentation of large detritus by mesozooplankton critically influence the vertical transfer of gravitational fluxes (Fig. 8), consistent with previous assessments (Mayor et al., 2020). Seasonal export pulses enhance mesozooplankton biomass and the proportion of flux feeders (Sect. 2.2; Supplement file 1), progressively intensifying removal of large sinking aggregates by mesozooplankton as the season advances (Fig. 9). Fragmentation of ldetoc into sdetoc additionally slows sinking fluxes, favouring microzooplankton consumption and microbial degradation over vertical transfer. Together, these trophic feedbacks limit mesopelagic TE during peak export periods (Fig. 9).

Although the functional representation of mesozooplankton activities in PISCES reasonably captures the magnitude of some observed process rates (Table 4), it necessarily simplifies the diversity of mesopelagic zooplankton communities and feeding strategies. In particular, the model cannot explicitly resolve the wide range of behaviours spanning flux-feeding Rhizaria to particle-attached copepods and other taxa that interact with sinking particles in distinct ways (Stukel et al., 2019; Mayor et al., 2020; Lampitt et al., 2023; Laget et al., 2024). As a result, the diagnosed trophic feedbacks should be interpreted as an emergent, functional representation of zooplankton–particle interactions rather than a taxonomically explicit description of mesopelagic food-web dynamics.

Large-detritus fragmentation deserves particular attention, as PISCES was the only CMIP6 model that explicitly represented this process (Henson et al., 2022). Because the model prescribes enhanced fragmentation of silica-rich aggregates, fragmentation is maximal in the SPNA (Figs. 8 and 9; see also Fig. 3d). From May to July, zooplanktonic fragmentation accounts for 19 % of ldetoc flux attenuation in the SPNA, with bacterial disaggregation contributing an additional 5 %. By comparison, Briggs et al. (2020) attributed to fragmentation 49 ± 22 % of fast-sinking particle flux attenuation during intense export events in the SPNA and the Southern Ocean. This discrepancy likely reflects differences in process definitions and temporal averaging. In particular, the attenuation attributed to fragmentation by Briggs et al. (2020) better matches the 55 % of ldetoc attenuation explained by the sum of flux feeding, fragmentation and bacterial disaggregation in PISCES. Further observational (Briggs et al., 2020; Wang and Fennel, 2022) and modelling efforts are needed to constrain fragmentation rates and mechanisms.

Despite the prominent role of zooplankton, most flux attenuation in PISCES proceeds through microbial degradation (Figs. 8, 9 and 11). Owing to the variable reactivity formulation, slow-sinking particles are degraded at shallower depths than fast-sinking ones. Simulated remineralisation rate constants (Fig. 7) fall within the wide range of in situ estimates, although natural and methodological variability preclude a conclusive assessment (Benner and Amon, 2015 and references therein; Belcher et al., 2016 and references therein; García-Martín et al., 2021). A more robust assessment is obtained from vertically-integrated POC degradation rates. In the mesopelagic SPNA, decay rates in May–June (7 mmol C m⁻² d⁻¹) resemble the GEOVIDE cruise mean (6.5 mmol C m⁻² d⁻¹; Lemaitre et al. (2018), and summer estimates at PAP also agree within 10 % (Table 4). Unfortunately, we are not aware of suitable datasets in the STNA, where available estimates reflect the respiration of POC+DOC (e.g., Ono et al., 2001) and therefore exceed POC decay simulated by PISCES.

The contribution of different particle classes to community metabolism remains poorly constrained in both epipelagic (García-Martín et al., 2021) and mesopelagic waters (Table 4). In PISCES, fast-sinking aggregates retain higher intrinsic reactivity (Fig. 7), whereas the larger stock of small detritus compensates for its lower reactivity. As a result, both particle types support comparable fractions of mesopelagic metabolism across regions (Fig. 11). Perhaps counterintuitively, the strongest sdetoc removal, both in absolute (2.6 mmol C m⁻² d⁻¹) and relative terms (61 %), occurs in the subpolar area, fuelled by particle diffusion and ldetoc fragmentation inputs. Overall, these results reinforce the emerging view that suspended and slow-sinking particles play a central role in the mesopelagic carbon cycling (Alonso-González et al., 2010; Baker et al., 2017; García-Martín et al., 2021; Wang and Fennel, 2022; Baumas and Bizic, 2024).

In PISCES, annual EE increases from 8 % to 29 % between subtropical and subpolar waters (Fig. 10). As found by Mouw et al. (2016), this gradient mirrors the latitudinal increase in diatom abundance (Fig. 2), which in the model rises from 7 % to 42 % (Fig. 10). Although the modelled latitudinal EE gradient falls within the range of current estimates (Villa-Alfageme et al., 2016; Siegel et al., 2023), EE is likely overestimated at mid and high latitudes because diatoms are overrepresented (Figs. 2 and 3; Table 3). Improved representation of phytoplankton functional groups and size structure in models is key to better constrain biological carbon export.

Our results further underscore the importance of including non-gravitational POC fluxes in carbon pump assessments. Neglecting these fluxes, especially those driven by vertical mixing, can substantially underestimate total POC export at high latitudes (Figs. 4 and 11). These unaccounted fluxes bias EE and TE metrics (Fig. 10) and affect interpretations of mesopelagic food-web metabolism and its seasonality (Figs. 9, S5 and S6) (Dall'Olmo et al., 2016; Lacour et al., 2019). Current model intercomparison frameworks (e.g. CMIP) rely on simulated gravitational fluxes at standard depths (Palevsky and Doney, 2018; Henson et al., 2022; Wilson et al., 2022). Thus, our findings suggest that non-gravitational POC transports, which are internally simulated by models, should be included in standard model intercomparison metrics.

Compared to the mechanisms controlling EE (see Sect. 4.1), the factors regulating mesopelagic TE remain more controversial. Mechanistic interpretations have alternately emphasised lower lability of exported POC in subtropical areas, leading to a latitudinal decrease in TE (Henson et al., 2012; Guidi et al., 2015), or reduced organic matter decay in cold mesopelagic waters, leading to a latitudinal increase in TE (DeVries and Weber, 2017). In PISCES, mesopelagic TE does not vary monotonically from subtropical to subpolar waters, nor with the depth of the productive layer, but instead exhibits a minimum at midlatitudes (Fig. 10). Lowest TE coincides with highest productivity (Figs. 10 and 11), broadly consistent with the climatological patterns reported by Mouw et al. (2016). This relationship also resembles the opposite interannual trends in NPP and export relative to mesopelagic transfer, reported by Lomas et al. (2010) in the Sargasso Sea. Nevertheless, the occurrence of maximum TE in the STNA and intermediate TE in the SPNA indicates that mesopelagic transfer is not controlled by productivity or productive-layer depth alone. As we shall see, these patterns cannot be attributed to a single dominant factor.

Examination of the mechanisms driving mesopelagic TE in PISCES is informative because the model represents the interplay between variable POC reactivity and temperature-dependent decay rates (Fig. 7), as well as zooplankton detritivory. First, exported POC – especially sdetoc – is least reactive in the STNA, reflecting deeper and warmer Zprod (Fig. 4i), consistent with Henson et al. (2012) and Guidi et al. (2015). Second, mesopelagic POC decay rate constants also increase toward low latitudes, primarily due to temperature effects, consistent with Marsay et al. (2015) and DeVries and Weber (2017). Third, simulated detritivory rates decrease toward low latitudes (Fig. 11), but their relative importance peaks in the Trans_Area (Fig. 8), concurrent with the highest proportion of flux feeders (Supplement file S1). As a result of these interacting mechanisms, detritus turnover times with respect to both microbial and zooplanktonic removal (calculated as the quotient between stocks and removal rates; Fig. 11) are shortest in the Trans_Area, and much longer in the STNA. While these outcomes remain model-dependent and subject to known biases (Table 3), they help reconciling the lability- and temperature-based hypotheses, yielding a more nuanced understanding of mesopelagic TE variability (Marsay et al., 2015), and further highlight that zooplankton processes must be explicitly accounted for.

Previous studies showed that considering the seasonal cycle is key when linking EE and TE observations and models (Ceballos-Romero et al., 2016; de Melo Viríssimo et al., 2024). In this study, however, we refrain from reporting these metrics at intra-annual scale for the following reasons: (i) for EE, there is a regionally varying time lag between NPP, detritus accumulation, and export rates (Figs. 2–4); (ii) in the case of the TE metric, distortion arises from the gradual vertical propagation of sinking POC fluxes (Giering et al., 2017), and from the lagged response of food-web processes that drive flux attenuation. In the highly seasonal SPNA, for example, sources exceed sinks by 25 % in May, and sinks exceed sources by 23 % in August (Fig. 9). Much larger imbalances may occur when examining smaller regions and non-climatological data. In summary, seasonal departure from steady state is an inherent feature of model-simulated POC budgets (Oliver et al., 2025), and helps explain difficulties in interpreting short-term in situ measurements (Giering et al., 2017).

In the previous sections, we have dissected the drivers of epipelagic export and mesopelagic POC budgets in PISCES. Here, we synthesise the resulting process-level understanding with the biases identified through model evaluation (Figs. 2–6) to provide a unified diagnosis of model performance across regions (Table 3) and outline pathways for model improvement.

In the subpolar area, simulated primary production and sPOC show small-to-moderate negative biases, while a clear bias in POC export cannot be identified (compare Figs. 5 and 6). Total phytoplankton biomass is reasonably simulated, but substantial positive biases in diatoms and mesozooplankton likely lead to an overestimation of large detritus export (Wang and Fennel, 2022). Correcting NPP underestimation would tend to increase sPOC stocks and exports. In contrast, correcting diatom overestimation would favour smaller classes of phytoplankton and detritus, lowering export efficiency. Addressing these opposing biases would likely improve model realism. In the mesopelagic SPNA, simulated sPOC stocks and export fluxes remain broadly consistent with what would be expected given epipelagic inputs (Table 3). The limited available observations of POC decay are reasonably reproduced (Lemaitre et al., 2018), but large detritus fragmentation may be underestimated (Briggs et al., 2020). Uncertainties in the model representation of fragmentation will be addressed through sensitivity analyses in forthcoming work (Orihuela-García et al., 2026).

In the midlatitude Trans_Area, epipelagic and mesopelagic biases differ in nature. Although diatoms are overrepresented at the expense of miscellaneous phytoplankton, NPP is accurately simulated, and no evident biases emerge in epipelagic sPOC stock and export fluxes, suggesting bias compensation. However, sPOC underestimation increases between epipelagic and upper mesopelagic waters, indicating excessive detritus removal. This is consistent with the overestimation of microbial POC degradation inferred from the comparison with PAP (Table 4) and would contribute to an underestimation of TE. Such a bias is not readily apparent from export flux comparisons alone, highlighting the value of evaluating models against diverse flux and stock datasets.

In the subtropical area, PISCES severely underestimates NPP, a bias that propagates to epipelagic sPOC stocks (Galí et al., 2022). Export production is instead overestimated, suggesting a strong positive bias in EE – at least according to BATS/OFP data at 150 m (Lomas et al., 2010). Below 500 m, POC fluxes are no longer overestimated (Fig. 6) despite the positive bias in shallow export (Fig. 5e). This suggests that mesopelagic TE is underestimated, consistent with the increasing underestimation of sPOC with depth, also found in the Trans_Area. Together, these results indicate offsetting biases between excessive EE in the epipelagic and insufficient TE in the mesopelagic. Increasing NPP would likely exacerbate the positive bias in shallow export. This bias would further propagate vertically if mesopelagic transfer were corrected upward. Thus, PISCES' representation of POC cycling in epi- and upper mesopelagic waters in oligotrophic areas may require revision.

Observed biases arise not only from model parameterisations but also from the omission of biological functional groups and/or processes. Several mechanisms known to influence mesopelagic carbon cycling are not explicitly represented in the model used here, including: zooplankton vertical migrations (Carr et al., 2008; Jónasdóttir et al., 2015; Gorgues et al., 2019); the diversity of zooplankton feeding strategies (Stemmann et al., 2004b, 2004a; Kiørboe, 2011; Mayor et al., 2020; Lampitt et al., 2023; Laget et al., 2024); and the distinct roles of free-living and particle-attached bacteria, carbon fixation by chemotrophic prokaryotes, and protist bacterivory (Arístegui et al., 2009; Herndl et al., 2023). For example, including zooplankton migration would likely enhance deep carbon export and mesopelagic sPOC stocks, while reducing NPP and epipelagic sPOC stocks (Aumont et al., 2018; Gorgues et al., 2019). These changes would entail region- and depth-dependent effects on model biases (Table 3). While incorporating missing processes might help bridge the gaps between the model and observations, it would increase model complexity and require extensive re-tuning to maintain consistency across coupled biogeochemical pathways.