In this study, we make use of two versions of the Institut Pierre-Simon Laplace Climate Model (IPSL-CM). The first model, IPSL-CM5A-LR (Dufresne et al., 2013), has been used extensively for Phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al., 2012) and compared to other CMIP5 models in terms of its marine biogeochemistry response to climate change in Bopp et al. (2013). The second model is the newly developed IPSL-CM6A-LR (Boucher et al., 2020), used in the more recent CMIP6 (Eyring et al., 2016) and compared to other CMIP6 models in Kwiatkowski et al. (2020) for the response of marine ecosystem stressors to anthropogenic climate change.

Both IPSL models rely on the same atmospheric (LMDZ), ocean (NEMO) and land surface (ORCHIDEE) model components. However these model components have been substantially revised and upgraded in IPSL-CM6A-LR with respect to IPSL-CM5A-LR. The spatial resolution of the atmospheric model has also been increased from 96×95 points (mean resolution of 236 km) in longitude and latitude with 39 vertical layers in IPSL-CM5A-LR (Dufresne et al., 2013) to 144×143 points (mean resolution of 157 km) and 79 vertical layers in IPSL-CM6A-LR (Boucher et al., 2020). In addition, the nominal resolution of the ocean model has increased from 2∘ and 31 vertical layers to 1∘ and 75 vertical layers.

A detailed description of the changes to LMDZ, NEMO and ORCHIDEE is provided in Boucher et al. (2020). However, we note that the atmospheric general circulation model LMDZ6A (Hourdin et al., 2020) differs from LMDZ5A (Hourdin et al., 2013) in its inclusion of a new package of parameterisations for turbulence, convection and clouds. The NEMO ocean model comprises three components, i.e. ocean dynamics (NEMO-OPA), sea-ice dynamics and thermodynamics (NEMO-LIM), and marine biogeochemistry (NEMO-PISCES). All of these ocean components have been updated from IPSL-CM5A-LR to IPSL-CM6A-LR, from version 3.2 to version 3.6 of NEMO (Madec et al., 2017; Rousset et al., 2015; Aumont et al., 2015), with the addition of a nonlinear free surface, a parameterisation of mixing in the mixed layer due to submesoscale processes, an energy-constrained parameterisation of mixing due to internal tides for NEMO-OPA and a new multicategory halothermodynamic sea-ice model for NEMO-LIM. The changes in the marine biogeochemistry component (NEMO-PISCES) are described below. A detailed assessment of the key properties of the ocean and marine biogeochemical models as used in ESMs that have contributed to CMIP5 and CMIP6 (including IPSL models) is available in Séférian et al. (2020).

These two versions from the IPSL Climate Model family qualify as Earth system models as they include carbon cycle components for the land biosphere (ORCHIDEE) and the ocean (NEMO-PISCES). In the following we describe the PISCES model versions used in this study.

NEMO-PISCES is an ocean biogeochemical model that simulates marine biological productivity and describes the biogeochemical cycles of carbon, oxygen and the main limiting nutrients (P, N, Si, Fe). It is based on 24 prognostic tracers in its standard configuration, with two phytoplankton functional types (diatoms and nanophytoplankton) and two zooplankton size classes (micro- and mesozooplankton). PISCES is by nature a “Redfieldian” model; i.e. it assumes constant stoichiometric ratios for carbon, nitrogen and phosphorus in all organic compartments but considers flexible stoichiometry for iron and silica.

PISCES-v1 is used in IPSL-CM5A-LR and described in detail in Aumont and Bopp (2006), whereas PISCES-v2 is used in IPSL-CM6A-LR and described in Aumont et al. (2015). Despite being similar in terms of their overall architecture and number of prognostic tracers, PISCES-v2 differs from PISCES-v1 with an improved representation of iron cycling, phytoplankton growth and nutrient limitation, zooplankton grazing, the sinking of particles, external sources of nutrients, and the treatment of water–sediment interactions.

In both PISCES versions, the modelling of the nitrogen cycle relies on the explicit representation of nitrate and ammonium concentrations in seawater. It includes nitrification, which corresponds to the conversion of ammonium to nitrate and is assumed to be photo-inhibited and reduced in low-oxygen waters, as well as denitrification, when nitrate is used instead of oxygen for remineralisation in suboxic waters (Aumont et al., 2015). External sources of nitrogen in the ocean include riverine input (using Global NEWS 2 data sets – Mayorga et al., 2010 – for both PISCES versions), atmospheric deposition (using input4MIPs data – Hegglin et al., 2016 – in IPSL-CM6A-LR and output of the INCA model – Aumont et al., 2008 – for IPSL-CM5A-LR) and biological N fixation (see below). External sinks of nitrogen include denitrification and organic matter burial in the sediment (see Aumont and Bopp, 2006, and Aumont et al., 2015, for detailed descriptions).

In both PISCES versions, N fixation is represented implicitly as a source of ammonium, i.e. without an explicit diazotroph plankton functional type (Fig. 2). N fixation is restricted to warm waters (>20 ∘C) and increases exponentially with temperature following a Q10 value of 1.9 as for all autotrophic processes in PISCES (Aumont and Bopp, 2006; Aumont et al., 2015). N fixation is limited by the availability of light and iron and favoured in low-nitrogen (NO3 and NH4) environments. PISCES-v1 and PISCES-v2 differ in their treatment of phosphorus limitation on N fixation, which is absent in PISCES-v1 but combined with iron limitation in PISCES-v2. In PISCES-v1, due to the fixed stoichiometric ratios between carbon, nitrogen and phosphorus in all organic components, it is assumed that N fixation is accompanied by a release of inorganic phosphorus to account for the fact that diazotrophy-derived organic matter is much richer in N than the standard Redfield assumptions in the model. This additional P source is interpreted as deriving from the use of an unresolved dissolved organic phosphorus (DOP) pool by the implicit diazotrophs. Thus, in PISCES-v1, for every mole of N2 fixed by diazotrophy and instantaneously transferred into the ammonium pool, an additional 0.04 mol of phosphorus is added in the phosphate pool to represent the subsequent remineralisation of the diazotrophy-derived organic matter with an N : P ratio of 46 : 1 (Fig. 2a). In PISCES-v2, this parameterisation has been changed and instead includes an unresolved source of inorganic phosphorus from labile DOP, independently of N fixation. In strongly P-limited areas, diazotrophic cyanobacteria use DOP as a source of P, but this is the case also for other phytoplankton groups (Cotner and Wetzel, 1992; Paytan and McLaughlin, 2007). The parameterisation thus mimics this source of P, which depends on simulated dissolved organic matter concentrations and is inhibited when dissolved inorganic P is not limiting phytoplankton growth (Fig. 2b). In PISCES-v2, this P source is therefore not dependent on the rate of N fixation as it is in PISCES-v1.

Note that in all versions, the overall P inventory of the ocean is ensured by an annual restoring of the global mean PO4 concentration to its historical global value computed from the World Ocean Atlas 2001 for PISCES-v1 and World Ocean Atlas 2013 for PISCES-v2. This restoring term is applied everywhere (at all depths) and modifies phosphate concentration relatively, thus acting preferentially in the deep ocean where PO4 concentrations are higher. Overall, this term represents a restoring timescale of about 10 000 years (Aumont et al., 2015).

In this work, we use two modified versions of PISCES-v2. First, PISCES-quota is a newly developed version of PISCES, which accounts for flexible C : N : P stoichiometry. This is accompanied by the introduction of a new plankton functional type (picophytoplankton) and leads to a subsequent increase in the number of prognostic variables to 39 (compared to 24 in all other PISCES versions). A detailed description of PISCES-quota is provided in Kwiatkowski et al. (2018). As in PISCES-v1 and PISCES-v2, N fixation is parameterised implicitly, limited to waters with high light levels, low nitrogen, and adequate iron and phosphorus. However because PISCES-quota is non-Redfieldian, two major changes have been introduced (Fig. 2d): (1) N fixation consumes and is limited by the availability of an explicit dissolved organic phosphorus pool as seen in observations (Sohm and Capone, 2006; Orchard et al., 2010), and (2) the organic matter that is produced by diazotrophy is enriched in nitrogen with respect to phosphorus (with an N : P ratio of 46 : 1 vs. 16 : 1 for the canonical Redfield ratio). In PISCES-quota, implicit diazotrophy transfers nitrogen and phosphorus to three pools: particulate organic matter, dissolved organic matter, and ammonium and dissolved inorganic phosphorus, with a ratio of one-third each (Fig. 2d). Importantly, the temperature sensitivity of N fixation has been changed following Breitbarth et al. (2007), with a bell-shape response curve and a maximum N-fixation rate set at a thermal optimum of ∼ 26 ∘C (Fig. 2e).

Last and specifically for this study, we modified a version of PISCES-v2 in which only the parameterisation of N fixation was changed based on PISCES-quota (PISCES-v2fix, Fig. 2c). This newly developed parameterisation is inspired by PISCES-quota, except it assumes that the diazotrophy-produced material follows the Redfield stoichiometric ratio of N : P (i.e. 16 : 1) and uses the same release of additional inorganic phosphorus as in PISCES-v2 (Fig. 2c). PISCES-v2fix uses the same temperature-dependency as PISCES-quota (Fig. 2e).

IPSL-CM5A-LR and IPSL-CM6A-LR have been integrated following the CMIP5 (Taylor et al., 2012) and CMIP6 (Eyring et al., 2016) protocols, respectively. After long spin-up integrations in pre-industrial conditions, both Earth system models are run under historical forcing (from 1850 to 2005 or 2014) and then under high-emission scenarios (from 2006 or 2015 to 2100, following RCP8.5 for IPSL-CM5A-LR and SSP5-8.5 for IPSL-CM6A-LR). Details on historical and projection simulations with IPSL-CM5A-LR and IPSL-CM6A-LR are given in Dufresne et al. (2013) and Boucher et al. (2020), respectively.

In addition to these coupled Earth system simulations and to facilitate the assessment of the role of biogeochemical parameterisations, we performed a series of “offline” ocean biogeochemistry simulations under the same physical forcing. The four PISCES versions that were run offline are PISCES-v1, PISCES-v2, PISCES-v2fix and PISCES-quota. To do so, we used the physical output of IPSL-CM5A-LR under historical and RCP8.5 conditions (monthly means of ocean temperature, salinity, currents and mixed-layer depth) and forced these different PISCES versions over 1850–2100 so that the only differences between the offline simulations originate from the biogeochemical parameterisations.

Hereafter, the coupled simulations are referred to as IPSL-CM5A and IPSL-CM6A, whereas the offline simulations are referred as PISCES-v1, PISCES-v2, PISCES-v2fix and PISCES-quota. To facilitate the comparison of simulations run under different protocols, the same 20-year periods have been retained for historical (1986–2005) and future (2080–2099) baseline periods.

We compare here two successive generations of the IPSL Climate Model, IPSL-CM5A (Dufresne et al., 2013) and IPSL-CM6A (Boucher et al., 2020), forced over the 21st century by two similar high-emission scenarios (RCP8.5 and SSP5-8.5). The equilibrium climate sensitivity (ECS) has increased slightly, from 4.1 K in IPSL-CM5A to 4.8 K in IPSL-CM6A (Boucher et al., 2020), both values being in the upper range of ECS from climate models developed in the framework of CMIP5 and CMIP6 (Forster et al., 2019). As a consequence, the warming level for the global mean sea surface temperature under the high-emission scenarios is slightly higher in IPSL-CM6A (+3.57 ∘C) than in IPSL-CM5A (+3.27 ∘C) at the end of the 21st century.

As stated in the Introduction, the two climate models that we compare here simulate opposing projections of global oceanic NPP over the 21st century. In IPSL-CM5A, NPP decreases by 3.1 PgC yr-1 from 1986–2005 to 2080–2099 (under RCP8.5), whereas IPSL-CM6A simulates an increase in NPP by 2.9 PgC yr-1 over the same period (but under SSP5-8.5) (Table 1, Fig. 1). These NPP global changes represent a 9.1 % decrease and a 6.8 % increase for IPSL-CM5A and IPSL-CM6A, respectively.

The difference between the two model versions mostly arises from the tropical oceans (30∘ S–30∘ N), with a decrease of 2.6 Pg yr-1 (-14.3 %) and an increase of 1.9 Pg yr-1 (+7.3 %) in IPSL-CM5A and IPSL-CM6A, respectively (Table 1). The extra-tropical oceans also show notable differences between the two model versions, but these differences contribute much less to the global NPP changes (Table 1). At the regional scale, the largest differences between the two model versions are located in the oligotrophic gyres of all ocean basins, which show significant increases in NPP for IPSL-CM6A (up to +45 gC m-2 yr-1) but slight decreases or increases in IPSL-CM5A (Fig. 3). Elsewhere, the general patterns are similar across the model versions with NPP increasing at high latitudes and decreasing in the equatorial band and at the poleward borders of subtropical gyres (Fig. 3).

The localisation of NPP differences in the oligotrophic gyres and the similarity of the physical ocean response (not shown) to anthropogenic climate change point towards a role for nitrogen fixation in explaining the contrast between IPSL-CM5A and IPSL-CM6A in terms of NPP projections. Indeed, whereas N fixation slightly decreases in IPSL-CM5A (-7.3 TgN yr-1; -9 %) through the 21st century, it almost doubles in IPSL-CM6A (+77.9 TgN yr-1; +75 %) (Fig. 1, Table 1). Spatially, the regions in which the increases in N fixation are strongest coincide with the regions in which NPP also increases strongly in IPSL-CM6A (Fig. 3).

To further understand the role of N fixation in explaining the differences between IPSL-CM5A and IPSL-CM6A, we use additional offline simulations where the same ocean physical forcing is applied to different versions of the PISCES biogeochemical model. This enables a direct comparison of specific biogeochemical parameterisations within the same physical framework (see Sect. 2.3).

The PISCES-v1 version (used in IPSL-CM5A) forced in offline mode with the IPSL-CM5A output over 1850–2100 gives broadly similar results to those obtained with IPSL-CM5A, i.e a decrease in NPP and in N fixation of -13.2 % and -14.9 %, in 2080–2099, respectively (Table 1, Fig. 4a and b). Spatially, the NPP and N-fixation changes obtained with PISCES-v1 in offline mode strongly resemble those from IPSL-CM5A, as can be seen when comparing Fig. 3c and e to Fig. 4c and d. Interestingly, these similarities between the online and offline versions are also maintained for PISCES-v2. By using the same PISCES version (PISCES-v2; Aumont et al., 2015) as that in IPSL-CM6A but forced with the physical output of IPSL-CM5A under RCP8.5 (simulation PISCES-v2; see Sect. 2.3), we obtain a very similar response to that in IPSL-CM6A in terms of N fixation, with an increase of 76.1 TgN yr-1 (+68.1 %) from 1986–2005 to 2080–2099, as compared to 77.9 TgN yr-1 (+75 %) over the same period in IPSL-CM6A (Table 1, Fig. 4). This shows that the differences between IPSL-CM6A and IPSL-CM5A are robust in a common physical framework. Consequently, NPP also increases in PISCES-v2 (by +13.8 %) (Table 1, Fig. 4). As in IPSL-CM6A, the increase in NPP in this offline simulation is largely concentrated in the tropical oceans, where it reaches +17 % (Table 2), and is typically coincident with regions of increasing N fixation (Fig. 4).

In the PISCES-v2fix simulation, the temperature dependency of N fixation and the assumed N : P ratio of the implicit diazotrophs are modified from those in PISCES-v2 (Sect. 2.1), while the same forcing that derived from IPSL-CM5A is applied (Sect. 2.3). In this offline simulation, N fixation also increases over the 21st century (by 15.6 TgN yr-1 or 20.1 % at the end of the 21st century) but much less than in PISCES-v2. The increase in N fixation is small everywhere compared to PISCES-v2; there are even regions where N fixation decreases in PISCES-v2fix (west tropical Pacific, tropical Atlantic) (Fig. 5). Consequently, NPP changes are markedly different than those from PISCES-v2, with a global decrease of 1.6 % in 2080–2099 (Table 1), mostly located in the tropics (Fig. 4).

Lastly, we also compare these offline simulations with an offline simulation of the recently developed PISCES-quota model (Kwiatkowski et al., 2018), an advanced version of PISCES-v2 in which the assumption of fixed phytoplankton C : N : P stoichiometry has been relaxed. In this simulation, N fixation increases only slightly (by 6 TgN yr-1 or 6.5 % over the 21st century) whereas NPP decreases (by 2.2 PgC yr-1 or 5.7 %) over the 21st century. At regional scales, the patterns of N fixation display contrasting tendencies with decreases in the western Pacific, equatorial Atlantic and Indian Ocean and increases polewards of these regions. The regional changes in NPP resemble those simulated in IPSL-CM5A (Fig. 4). The differences between PISCES-quota and the two other offline simulations (PISCES-v2 and PISCES-v2fix) originate from the major developments in PISCES-quota such as variable C : N : P stoichiometry and the inclusion of a third phytoplankton functional type (picophytoplankton) as demonstrated in Kwiatkowski et al. (2018).

The comparison of our four offline simulations demonstrates the role of the response of N fixation in the evolution of NPP over the 21st century. Under the same physical forcing, N fixation changes by -14.9 %, +68.1 %, +20.1 % and +6.5 % over the 21st century, in PISCES-v1, PISCES-v2, PISCES-v2fix and PISCES-quota, respectively. The changes in global NPP over the same period are -13.1 %, +13.8 %, -1.6 % and -5.7 %, in the same four versions, respectively. The comparison of these offline simulations clearly links the response of global NPP to the parameterisation of N fixation and hence reinforces the assumption that differences in N fixation are the primary driver of the divergent IPSL-CM5A and IPSL-CM6A NPP projections.

The contrasting responses of N fixation in the IPSL-CM5A and IPSL-CM6A models (and thus between PISCES-v1 and PISCES-v2) are not entirely intuitive, as the two models share very similar parameterisations of N fixation (e.g. the same N-fixation temperature sensitivity) (Fig. 2). To explain these contrasting trends, we focus on an area located in the northwestern tropical Pacific (130–160∘ E, 10–20∘ N), where the response of nitrogen fixation diverges strongly between IPSL-CM5A and IPSL-CM6A (Fig. 5a), and exploit the comparison between the different offline versions of PISCES (PISCES-v1, PISCES-v2, PISCES-v2fix and PISCES-quota) that use an identical climate forcing. In this region, N fixation increases by 32 % in PISCES-v2 between 1986–2005 and 2080–2099 (of the same order of magnitude as in IPSL-CM6A, +45 %, not shown); decreases by 46 % in PISCES-v1; and decreases by 2 % and 10 % in PISCES-v2fix and PISCES-quota, respectively (Fig. 5b). Concurrently, sea surface temperature in this region increases by nearly 4 ∘C (between 1986–2005 and 2081–2100) to reach 31.5 ∘C at the end of the 21st century. In PISCES-v1 and PISCES-v2, this increase leads to a boost in N fixation by a factor of 2.1 (Fig. 5d). In PISCES-v2fix and PISCES-quota, on the contrary, the increase in temperature reduces N fixation by more than 30 % (Fig. 5d) due to the bell-shaped temperature sensitivity function of the Breitbarth et al. (2007) parameterisation.

In PISCES-v2, the limitation terms due to light, phosphate, iron and excess nitrate remain inoperative and N fixation responds almost exclusively to temperature (Fig. 5b). Indeed, NO3 concentrations remain close to zero (with N* values, defined as NO3 - 16*PO4, remaining slightly negative), and the LN term has no influence, allowing N fixation to increase in response to temperature. In PISCES-v1, on the other hand, the higher and increasing nitrate concentrations (Fig. 5e), resulting in positive N* values (Fig. 5h), lead to a limitation and even to a decrease in N fixation due to the LN term (limitation by excess inorganic nitrogen, Fig. 5f) (see Fig. 2b, Eq. 2).

The differential response of N fixation in PISCES-v1 and PISCES-v2 is thus related to how the parameterisation of N fixation simulates inorganic N and P inputs to surface waters (see Sect. 2.2). In PISCES-v1, surface waters in the oligotrophic subtropical gyres are P-limited for phytoplankton (N* being positive). Warming first increases N fixation, resulting in a continuous addition of N (through N fixation) at the expense of P. Nitrogen fixation is hence progressively limited by the accumulation of inorganic nitrogen and thus decreases. In PISCES-v2, warming also increases N fixation due to the same parameterisation of the thermal sensitivity of N fixation. But in this version, the sustained low addition of phosphate (which accounts for the implicit remineralisation of DOP and is independent of N-fixation rates) prevents any shift to P limitation. N* remains negative; inorganic N does not accumulate, and N fixation continues to increase.

In PISCES-v2fix and PISCES-quota, NO3 concentrations remain close to zero as in PISCES-v1 (Fig. 5e) and the excess nitrogen limitation term has no influence, remaining close to 1 (Fig. 5f). But contrary to PISCES-v1, N fixation decreases slightly (Fig. 5b) due to warming and the use of the bell-shape temperature sensitivity function (Fig. 5d).

Although the analysis presented here is limited to a small region of the western tropical Pacific (130–160∘ E, 10–20∘ N), we show the same contrasting behaviour between the different PISCES versions in other sub-tropical oligotrophic gyres (see Fig. A1 in the Appendix showing the same analysis in a region centred around the HOT station; 175–205∘ E, 15–25∘ N).

In summary, the simulated N-fixation response is highly sensitive to (1) the parameterisation used for the temperature sensitivity (PISCES-v1 and PISCES-v2 as compared to PISCES-v2fix and PISCES-quota) and (2) the respective evolution of NO3 and PO4 concentrations (PISCES-v1 as compared to PISCES-v2). To reiterate, it is the divergent responses of N fixation in oligotrophic gyres (Fig. 5a), where temperatures can exceed the optimal values in the Breitbarth et al. (2007) parameterisation (Fig. 2) and where NO3 accumulation can induce a decrease in the rate of N fixation, that explain the differences between the different versions of PISCES.

As the responses of the different versions of PISCES to anthropogenic climate change were so variable (see Sect. 3.1 and 3.2), we conducted a brief evaluation of these simulations over the historical period for NPP, nutrients (nitrate, phosphate) and N fixation to determine if any of the PISCES versions have significantly better performance scores (based on root-mean-square errors – RMSEs; see “Materials and methods”). We note that the outputs of IPSL-CM5A and IPSL-CM6A are evaluated in Séférian et al. (2020) alongside the other Earth system models of the CMIP5 and CMIP6 exercises.

A visual inspection of the bias maps for NPP, NO3 and PO4 concentrations and N fixation fails to highlight a single model version that outperforms the others, with similar regional biases for all PISCES variants (Fig. 6). All versions tend to underestimate NPP in the mid-latitudes of the Northern Hemisphere and overestimate NPP in the equatorial band and the Southern Ocean. For macro-nutrient concentrations, the regional biases are also similar, with a marked underestimation in the North Pacific and equatorial Pacific and an overestimation in the sub-Antarctic. Finally, comparison of simulated N-fixation rates to the measured estimates of Landolfi et al. (2018) shows a fairly widespread underestimation, for all versions of PISCES, in the northern subtropical gyre of the Pacific and in the equatorial Atlantic.

A more quantitative analysis using RMSE for each of the above fields confirms this visual impression with very similar RMSEs across all versions of PISCES. It can be noted, however, that the spatial distribution of NPP seems to be better reproduced in PISCES-quota, while the comparison of N-fixation rates gives the worst scores for PISCES-quota.

In conclusion this brief comparison with observations fails to distinguish between the different versions of the PISCES model used here. To go further, it would be necessary to compare the simulated trends over the historical period with time series obtained at marine stations (e.g. Hawaii Ocean Time-series programme – Karl and Church, 2014; Bermuda Atlantic Time-series Study – Lomas et al., 2013), or with reconstructions of the evolution of N fixation over the last century from palaeoceanographic proxies such as δ15N (Sherwood et al., 2014).

Another method using observations to constrain projection uncertainties, known as the emergent constraint approach, relies on relating observable trends or sensitivities across a model ensemble to future differences in model simulations (Allen and Ingram, 2002; Hall and Qu, 2006; Hall et al., 2019). This approach has been used extensively within the Earth sciences to constrain projections as diverse as climate sensitivity (Caldwell et al., 2018), snow–albedo feedbacks (Hall and Qu, 2006), precipitation extremes (O'Gorman, 2012; DeAngelis et al., 2016) and carbon cycle feedbacks (Cox et al., 2013; Wenzel et al., 2014; Goris et al., 2018; Terhaar et al., 2020). Interestingly, Kwiatkowski et al. (2017) applied an emergent constraint approach to CMIP5 NPP projections, finding an emergent relationship between the tropical (30∘ N–30∘ S) sensitivity of integrated NPP to El Niño–Southern Oscillation (ENSO) variability (Niño 3.4 SST anomalies) in pre-industrial control simulations and the tropical NPP response to 21st century warming under a high-emission scenario (RCP8.5). That is, models that exhibited heightened tropical NPP sensitivity to ENSO-driven SST fluctuations typically exhibited greater future NPP declines in response to climate change. The authors then used observational estimates of the NPP ENSO sensitivity to constrain the future NPP response.

The present study highlights that N fixation may represent a slow or threshold process that has the potential to limit the applicability of emergent constraint approaches to projections of NPP. Indeed, the NPP ENSO sensitivities of PISCES-v1, PISCES-v2 and PISCES-v2fix are shown to be highly similar in the first 50 years of historical simulations (-2.6 % ∘C-1, -2.8 % ∘C-1 and -3.3 % ∘C-1), and yet the 21st century NPP sensitivity of PISCES-v2 changes sign (+3.2 % ∘C-1), due to the enhanced role of N fixation, while the sensitivities of PISCES-v1 and PISCES-v2fix remain negative (-3.4 % ∘C-1 and -2.2 % ∘C-1) (Fig. 7). The extent to which the differing PISCES-v2 model behaviour across timescales challenges the previously identified emergent constraint is unknown, but it is possible that future changes move beyond what is constrainable with historical variability (e.g. Tagliabue et al., 2020). Interestingly, the tropical NPP sensitivity of PISCES-quota in the early historical period (-2.9 % ∘C-1) and under RCP8.5 (-2.8 % ∘C-1) is highly similar and in line with PISCES-v1 and PISCES-v2fix (Fig. 7). As such, there is strong reason to doubt whether the sensitivity of N fixation to climate change and hence the response of NPP, in PISCES-v2, are realistic. However, given all of the above, verifying the validity of the Kwiatkowski et al. (2017) NPP emergent constraint with multiple quota models is identified as a priority. It is urgent as well to verify the sensitivity of nitrogen fixation with more mechanistic models of N fixation (such as those of Pahlow et al., 2013, or Inomura et al., 2018).

The last question we explore is the potential implications of this large difference in NPP projections between IPSL-CM5A and IPSL-CM6A on (1) carbon export, (2) ocean deoxygenation and (3) impacts on plankton biomass.

As expected from NPP differences, the export of particulate organic matter at 100 m is strongly reduced in IPSL-CM5A (-17.1 %) at the end of the 21st century, whereas it is only slightly affected in IPSL-CM6A (-2.3 % in 2080–2099 as compared to 1986–2005) (Table 3). The same conclusions apply for the analysis of the offline simulations, with export changes reaching -19.6 %, +2.3 %, -10.9 % and -13.4 % in 2080–2099 (relative to 1986–2005) for PISCES-v1, PISCES-v2, PISCES-v2fix and PISCES-quota, respectively, in line with the already-mentioned NPP changes (-13.2 %, +13.8 %, -1.6 % and -5.7 % for the same four offline versions).

For ocean deoxygenation as well, the intensity of the subsurface signal we obtain with IPSL-CM5A and IPSL-CM6A may indeed be driven by the opposing NPP and export changes, even if the main drivers of ocean deoxygenation remain ocean warming and circulation/stratification changes. Whereas subsurface O2 concentrations only decrease by 8.2 mmol m-3 (-4.1 %) on average in IPSL-CM5A, they decrease by 17.96 mmol m-3 (-9.4 %) in IPSL-CM6A (Table 3). Spatially, the general patterns of subsurface ocean deoxygenation are similar across the two models, with the largest decreases, up to -60 mmol m-3 in 2080–2099, occurring in the North Atlantic, North Pacific and Southern Ocean (not shown). In the tropical oceans, the changes are weaker, from -20 to +20 mmol m-3 in IPSL-CM5A and from -20 to +10 mmol m-3 in IPSL-CM6A. Note that the regions where subsurface O2 concentrations increase in IPSL-CM5A, such as in the tropical Indian and Atlantic oceans, as discussed in Bopp et al. (2017), display on the contrary decreasing trends in IPSL-CM6A. We interpret this difference as due to the response to increasing NPP, increasing export of organic matter and the subsequent remineralisation-driven consumption of O2 at depth in IPSL-CM6A. An analysis of the offline simulations, all forced with the same physical fields, confirms the above hypothesis, i.e. a strong relationship between different export/NPP changes and the intensity of ocean deoxygenation in the subsurface ocean (Table 3).

Finally, the reduction in simulated plankton biomass in IPSL-CM5A (-6.7 % for phytoplankton and -14.9 % for zooplankton) is less extensive in IPSL-CM6A (-4.3 % and -10.7 % for phytoplankton and zooplankton, respectively; Table 3). It is interesting to note that despite opposite trends in projected NPP, both models simulate significant reductions in planktonic biomass and an associated trophic amplification, i.e. a larger decrease in zooplankton than phytoplankton (Lotze et al., 2019; Kwiatkowski et al., 2019). This suggests that the potential impact on projections of upper trophic levels (e.g. Tittensor et al., 2018) should not differ as much as the differences in NPP projections between IPSL-CM5A and IPSL-CM6A indicate for ecosystem models forced by biomass changes. It also implies that changes in NPP may not be a robust proxy for diagnosing the potential impact of anthropogenic climate change on marine ecosystems.