Ocean Carbon Cycle Model Research Articles

Effects of depth‐ and CO2‐dependent C:N ratios of particulate organic matter (POM) on the marine carbon cycle

According to a recent study, C:N ratios of sinking particulate organic matter (POM) in the ocean appear to be higher than Redfield (7.1 instead of 6.6) and depth dependent (increase +0.2/km). Here we investigate the effects of vertically variable C:N element ratios on marine carbon fluxes and the air‐sea exchange of CO2 using a global ocean carbon cycle model (AAMOCC). For a steady‐state ocean, the results show that models using the constant classical Redfield ratio underestimate both, total inventory and vertical gradients of dissolved inorganic carbon (DIC). While the amount of additional DIC (+150 Gt C) is negligible compared to the high marine carbon inventory, the C:N depth dependence can reduce the ambient atmospheric pCO2 by 20 ppm, permanently. Moreover, the simulation of a future scenario, estimating a possible effect of CO2‐dependent C:N ratios of POM on the marine carbon cycle, has shown that even a moderate rise in the C:N element ratio of sinking POM, which is on the order of magnitude of natural variability, yields a considerably higher oceanic uptake of anthropogenic CO2 on timescales of decades to centuries. The assumption is based on a predicted increase in the production of highly carbon enriched transparent exopolymer particles (TEP) caused by rising atmospheric CO2 concentrations and enhanced nutrient limitation. However, counteracting a predicted decrease of the physical (solubility) CO2 pump as a consequence of global change, the effect in our scenario will alleviate further rising atmospheric CO2 concentrations rather than compensate a reduced physical uptake.

Evasion of CO 2 injected into the ocean in the context of CO 2 stabilization

The eventual evasion of injected CO 2 to the atmosphere is one consideration when assessing deep-sea disposal of CO 2 as a potential response option to climate change concerns. Evasion estimated using an ocean carbon cycle model is compared to long-term trajectories for future CO 2 emissions, including illustrative cases leading to stabilization of CO 2 concentration at various levels. Modeled residence time for CO 2 injected into the deep ocean exceeds the 100-year time-scale usually considered in scenarios for future emissions, and the potential impacts of climate change. Illustrative cases leading monotonically to constant CO 2 concentration have been highlighted by the Intergovernmental Panel on Climate Change to give guidance on possible timing of emission reductions that may be required to stabilize greenhouse gas concentrations at various levels. For stabilization cases considered, significant modeled evasion does not occur until long after CO 2 emissions have reached a maximum and begun to decline. Illustrative cases can also lead to a maximum in CO 2 concentration followed by a decline to slowly decreasing concentrations. In such cases, future injection of emissions into the deep ocean leads to lower maximum CO 2 concentration, with less effect on concentration later on in time.

Evaluation of ocean carbon cycle models with data‐based metrics

New radiocarbon and chlorofluorocarbon‐11 data from the World Ocean Circulation Experiment are used to assess a suite of 19 ocean carbon cycle models. We use the distributions and inventories of these tracers as quantitative metrics of model skill and find that only about a quarter of the suite is consistent with the new data‐based metrics. This should serve as a warning bell to the larger community that not all is well with current generation of ocean carbon cycle models. At the same time, this highlights the danger in simply using the available models to represent the state‐of‐the‐art modeling without considering the credibility of each model.

Enhancement of oceanic uptake of anthropogenic CO2 by macronutrient fertilization

A global three‐dimensional ocean carbon cycle model was used to investigate the use of macronutrient fertilization of the ocean to increase the oceanic uptake of CO2. To simulate macronutrient fertilization, phosphate was added to the 18°–50°S surface ocean. The carbon sequestration efficiency of fertilization was determined from the ratio of increased ocean uptake of anthropogenic CO2 to the rate of phosphate addition to the upper ocean (converted to carbon units using the C/P ratio of organic matter, 106). The model simulation produced a maximum efficiency of 78%. However, the simulations demonstrated that changes in calcium carbon production with macronutrient fertilization could significantly reduce carbon sequestration efficiency. When calcium carbonate production increases at the same rate as export production, the carbon sequestration efficiency is reduced by 25% when compared to a simulation where calcium carbonate production is held constant. The study also discusses several other potential process that could impact the efficiency phosphate fertilization to sequester carbon in the ocean and the potential consequences of large‐scale macronutrient fertilization of the ocean.

Natural and anthropogenic climate change: incorporating historical land cover change, vegetation dynamics and the global carbon cycle

This study explores natural and anthropogenic influences on the climate system, with an emphasis on the biogeophysical and biogeochemical effects of historical land cover change. The biogeophysical effect of land cover change is first subjected to a detailed sensitivity analysis in the context of the UVic Earth System Climate Model, a global climate model of intermediate complexity. Results show a global cooling in the range of –0.06 to –0.22 °C, though this effect is not found to be detectable in observed temperature trends. We then include the effects of natural forcings (volcanic aerosols, solar insolation variability and orbital changes) and other anthropogenic forcings (greenhouse gases and sulfate aerosols). Transient model runs from the year 1700 to 2000 are presented for each forcing individually as well as for combinations of forcings. We find that the UVic Model reproduces well the global temperature data when all forcings are included. These transient experiments are repeated using a dynamic vegetation model coupled interactively to the UVic Model. We find that dynamic vegetation acts as a positive feedback in the climate system for both the all-forcings and land cover change only model runs. Finally, the biogeochemical effect of land cover change is explored using a dynamically coupled inorganic ocean and terrestrial carbon cycle model. The carbon emissions from land cover change are found to enhance global temperatures by an amount that exceeds the biogeophysical cooling. The net effect of historical land cover change over this period is to increase global temperature by 0.15 °C.

How positive is the feedback between climate change and the carbon cycle?

Future climate change induced by atmospheric emissions of greenhouse gases is believed to have a large impact on the global carbon cycle. Several offline studies focusing either on the marine or on the terrestrial carbon cycle highlighted such potential effects. Two recent online studies, using ocean—atmosphere general circulation models coupled to land and ocean carbon cycle models, investigated in a consistent way the feedback between the climate change and the carbon cycle. These two studies used observed anthropogenic CO2 emissions for the 1860–1995 period and IPCC scenarios for the 1995–2100 period to force the climate – carbon cycle models. The study from the Hadley Centre group showed a very large positive feedback, atmospheric CO2 reaching 980 ppmv by 2100 if future climate impacts on the carbon cycle, but only about 700 ppmv if the carbon cycle is included but assumed to be insensitive to the climate change. The IPSL coupled climate – carbon cycle model simulated a much smaller positive feedback: climate impact on the carbon cycle leads by 2100 to an addition of less than 100 ppmv in the atmosphere. Here we perform a detailed feedback analysis to show that such differences are due to two key processes that are still poorly constrained in these coupled models: first Southern Ocean circulation, which primarily controls the geochemical uptake of CO2, and second vegetation and soil carbon response to global warming. Our analytical analysis reproduces remarkably the results obtained by the fully coupled models. Also it allows us to identify that, amongst the two processes mentioned above, the latter (the land response to global warming) is the one that essentially explains the differences between the IPSL and the Hadley results.

A simple global carbon and energy coupled cycle model for global warming simulation: sensitivity to the light saturation effect

A simple Earth system model, the Four-Spheres Cycle of Energy and Mass (4-SCEM) model, has been developed to simulate global warming due to anthropogenic CO₂ emission. The model consists of the Atmosphere—Earth Heat Cycle (AEHC) model, the Four Spheres Carbon Cycle (4-SCC) model, and their feedback processes. The AEHC model is a one-dimensional radiative convective model, which includes the greenhouse effect of CO₂ and H₂O, and one cloud layer. The 4-SCC model is a box-type carbon cycle model, which includes biospheric CO₂ fertilization, vegetation area variation, the vegetation light saturation effect and the HILDA oceanic carbon cycle model. The feedback processes between carbon cycle and climate considered in the model are temperature dependencies of water vapor content, soil decomposition and ocean surface chemistry. The future status of the global carbon cycle and climate was simulated up to the year 2100 based on the “business as usual” (IS92a) emission scenario, followed by a linear decline in emissions to zero in the year 2200. The atmospheric CO₂ concentration reaches 645 ppmv in 2100 and a peak of 760 ppmv approximately in the year 2170, and becomes a steady state with 600 ppmv. The projected CO₂ concentration was lower than those of the past carbon cycle studies, because we included the light saturation effect of vegetation. The sensitivity analysis showed that uncertainties derived from the light saturation effect of vegetation and land use CO₂ emissions were the primary cause of uncertainties in projecting future CO₂ concentrations. The climate feedback effects showed rather small sensitivities compared with the impacts of those two effects. Satellite-based net primary production trends analyses can somewhat decrease the uncertainty in quantifying CO₂ emissions due to land use changes. On the other hand, as the estimated parameter in vegetation light saturation was poorly constrained, we have to quantify and constrain the effect more accurately.

How positive is the feedback between climate change and the carbon cycle?

Future climate change induced by atmospheric emissions of greenhouse gases is believed to have a large impact on the global carbon cycle. Several offline studies focusing either on the marine or on the terrestrial carbon cycle highlighted such potential effects. Two recent online studies, using ocean—atmosphere general circulation models coupled to land and ocean carbon cycle models, investigated in a consistent way the feedback between the climate change and the carbon cycle. These two studies used observed anthropogenic CO2 emissions for the 1860–1995 period and IPCC scenarios for the 1995–2100 period to force the climate – carbon cycle models. The study from the Hadley Centre group showed a very large positive feedback, atmospheric CO2 reaching 980 ppmv by 2100 if future climate impacts on the carbon cycle, but only about 700 ppmv if the carbon cycle is included but assumed to be insensitive to the climate change. The IPSL coupled climate – carbon cycle model simulated a much smaller positive feedback: climate impact on the carbon cycle leads by 2100 to an addition of less than 100 ppmv in the atmosphere. Here we perform a detailed feedback analysis to show that such differences are due to two key processes that are still poorly constrained in these coupled models: first Southern Ocean circulation, which primarily controls the geochemical uptake of CO2, and second vegetation and soil carbon response to global warming. Our analytical analysis reproduces remarkably the results obtained by the fully coupled models. Also it allows us to identify that, amongst the two processes mentioned above, the latter (the land response to global warming) is the one that essentially explains the differences between the IPSL and the Hadley results.

A simple global carbon and energy coupled cycle model for global warming simulation: sensitivity to the light saturation effect

A simple Earth system model, the Four-Spheres Cycle of Energy and Mass (4-SCEM) model, has been developed to simulate global warming due to anthropogenic CO2 emission. The model consists of the Atmosphere—Earth Heat Cycle (AEHC) model, the Four Spheres Carbon Cycle (4-SCC) model, and their feedback processes. The AEHC model is a one-dimensional radiative convective model, which includes the greenhouse effect of CO2 and H2O, and one cloud layer. The 4-SCC model is a box-type carbon cycle model, which includes biospheric CO2 fertilization, vegetation area variation, the vegetation light saturation effect and the HILDA oceanic carbon cycle model. The feedback processes between carbon cycle and climate considered in the model are temperature dependencies of water vapor content, soil decomposition and ocean surface chemistry. The future status of the global carbon cycle and climate was simulated up to the year 2100 based on the “business as usual” (IS92a) emission scenario, followed by a linear decline in emissions to zero in the year 2200. The atmospheric CO2 concentration reaches 645 ppmv in 2100 and a peak of 760 ppmv approximately in the year 2170, and becomes a steady state with 600 ppmv. The projected CO2 concentration was lower than those of the past carbon cycle studies, because we included the light saturation effect of vegetation. The sensitivity analysis showed that uncertainties derived from the light saturation effect of vegetation and land use CO2 emissions were the primary cause of uncertainties in projecting future CO2 concentrations. The climate feedback effects showed rather small sensitivities compared with the impacts of those two effects. Satellite-based net primary production trends analyses can somewhat decrease the uncertainty in quantifying CO2 emissions due to land use changes. On the other hand, as the estimated parameter in vegetation light saturation was poorly constrained, we have to quantify and constrain the effect more accurately.

Carbon cycling and climate change during the last glacial cycle inferred from the isotope records using an ocean biogeochemical carbon cycle model

We construct a simple, vertical one-dimensional ocean model coupled with carbon biogeochemical cycle model in order to study the carbon cycle in association with the glacial–interglacial cycle. The model is time-integrated by using the atmospheric CO 2 concentration and the marine carbon isotope record of the surface and the deep water during the last 130,000 years. Temporal variation of the mean upwelling rate, productivity of organic carbon, productivity of carbonate carbon, and the terrestrial carbon storage are obtained from the mass balances of the total carbon, the 13C of dissolved inorganic carbon (DIC), the total alkalinity, and the dissolved phosphate. Variation of the terrestrial biomass size is similar to the δ 18O curve obtained from deep-sea sediments, suggesting that the terrestrial biomass change has been influenced by climate change. As far as we know, this is the first attempt to reconstruct the temporal variation of the terrestrial carbon storage during the last 130,000 years. The obtained terrestrial carbon storage at the last glacial maximum is within the range estimated in previous studies. Vertical mixing of the ocean is weakened and the bioproductivity decreases globally during the glacial intervals. Although the obtained variations of organic carbon and carbonate productivities are similar to each other, remarkable increases at the deglaciations are found only in carbonate productivity. The variation of nutrient concentration in the intermediate water differs from that in the deep water, while the variations of the total alkalinity in the intermediate and the deep water are in phase. The glacial ocean is characterized by higher alkalinity throughout the water column and lower gradient of DIC and nutrient. This lower gradient, resulting from lower upwelling rate and organic carbon productivity, results in a redistribution of DIC and nutrient between the intermediate and the deep water, but not change the total stock of DIC. On the other hand, higher alkalinity in the glacial intervals results in an increase in the total stock of dissolved inorganic carbon in the ocean because of increased solubility of atmospheric CO 2 from higher alkalinity throughout the water column. Temporal variations in alkalinity are characterized by abrupt decreases corresponding to the deglaciations every 100,000 years, resulting from a remarkable increase in carbonate productivity. This might indicate a relationship with the variation of atmospheric CO 2 level, which shows a gradual decrease through the glacial interval and an abrupt increase at the deglaciations. Higher alkalinity in the glacial intervals would be a result of an integral effect of the lower net carbonate burial rate during those periods. On the other hand, the redistribution of nutrients and dissolved carbon between the intermediate and the deep water shows shorter-term fluctuations. This would not change the atmospheric CO 2 level significantly, probably because the integral effect of net carbonate burial may not be enough to change the total alkalinity over the water column.

Dimethylsulfoniopropionate (DMSP) and dimethylsulfide (DMS) sea surface distributions simulated from a global three‐dimensional ocean carbon cycle model

A global model for surface dimethylsulfide (DMS) and particulate dimethylsulfoniopropionate (DMSP) (pDMS) distributions is presented. The main goals of this work were to be able to predict the regional distribution of the air‐sea fluxes of DMS and to predict eventually their future evolution with climate change. Diagnostic relationships have been established from data sets obtained during the ALBATROSS and EUMELI cruises carried out in the Atlantic Ocean. These equations nonlinearly relate DMS and pDMSP concentrations to chlorophyll concentrations and to the trophic status of surface waters. This model has been embedded in the global ocean carbon cycle model Institut Pierre et Simon Laplace‐Ocean Carbon Cycle Model version 2 (ISPL‐OCCM2), a simple plankton model coupled to a global three‐dimensional ocean general circulation model. Predicted global distributions and seasonal variations of surface chlorophyll are in good agreement with the observations, except in the equatorial Pacific Ocean and, to a lesser extent, in the Southern Ocean. In these regions, simulated surface chlorophyll concentrations are strongly overestimated, most likely because limitations of the biological production by nutrients like iron or silicate are not considered. The model predicts surface DMS and pDMSP concentrations, which compare reasonably well with the observations. However, in the high latitudes, seasonal variations are underestimated, especially in the Ross and Weddell Seas where observed very elevated concentrations of DMS due to spring and summer blooms of Phaeocystis cannot be reproduced by the model. The global annual flux of DMS predicted by lPSL‐OCCM2 ranges from 17 to 26.7 Tg S yr−1 depending on the formulation for gas exchange coefficient. About one third of this flux is located in the subtropical/subpolar frontal zone of the Southern Ocean, which plays a critical role in the sulfur cycle. Furthermore, model results suggest that the Southern Ocean, south of the Polar Front, could be a rather modest source of DMS for the atmosphere.

A quasi‐one‐dimensional coupled climate‐carbon cycle model: 2. The carbon cycle component

A quasi‐one‐dimensional, coupled climate‐carbon cycle model is presented which consists of two polar domains and one nonpolar domain. The model simulates the distribution of dissolved inorganic carbon (DIC), alkalinity, phosphate, dissolved oxygen, and temperature and contains a biological pump with production of organic tissue, calcite, and arogonite. Bottom water is conditioned in one polar domain through interaction with the atmosphere and convective mixing and is injected into the lower portion of the nonpolar domain. Intermediate water flows into the other polar domain and upwells. Successful simultaneous simulation of the observed distribution of all the tracers (including isotope ratios) requires (1) an upwelling velocity in the nonpolar domain that peaks around 2 m yr−1 at a depth of 1 km, with a gradual decrease above and below this depth; (2) effective vertical diffusion coefficients for temperature and other tracers that are different in the upper 0.5 km; and (3) a carbonate carbon to organic carbon production ratio of only 0.09. These requirements are consistent with physical considerations and/or observational evidence. In particular, observational data combined with a consideration of mixing along isopycnal surfaces and model results both indicate that the effective vertical diffusion coefficient in the upper ocean should be smallest for temperature and largest for oxygen, with the values for alkalinity and phosphate modestly smaller than for DIC. The model parameters obtained by tuning the model to preindustrial tracer distributions also provide the best (and generally excellent) fit to observed transient isotope changes. Interactions between alkalinity and DIC modulate the effect on steady state atmospheric pCO2 of changes in the model parameters. However, the model uptake of anthropogenic CO2 and the computed atmospheric CO2 variation to year 2200 are remarkably insensitive to the choice of model mixing parameters, given that these are assumed to be constant during a given simulation. Finally, the sensitivity of the model atmospheric pCO2 to changes in the temperature of warm ocean surface matches that obtained by three‐dimensional ocean carbon cycle models.

Production and export in a global ocean ecosystem model

The Hadley Centre Ocean Carbon Cycle (HadOCC) model is a coupled physical–biogeochemical model of the ocean carbon cycle. It features an explicit representation of the marine ecosystem, which is assumed to be limited by nitrogen availability. The biogeochemical compartments are dissolved nutrient, total CO 2, total alkalinity, phytoplankton, zooplankton and detritus. The results of the standard simulation are presented. The annual primary production predicted by the model ( 47.7 Gt C yr −1 ) compares well to the estimates made by Longhurst et al. (1995, J. Plankton Res., 17, 1245) and Antoine et al. (1996, Global Biogeochem. Cycles, 10, 57). The HadOCC model finds high production in the sub-polar North Pacific and North Atlantic Oceans, and around the Antarctic convergence, and low production in the sub-tropical gyres. However in disagreement with the observations of Longhurst et al. and Antoine et al., the model predicts very high production in the eastern equatorial Pacific Ocean. The export flux of carbon in the model agrees well with data from deep-water sediment traps. In order to examine the factors controlling production in the ocean, additional simulations have been run. A nutrient-restoring simulation confirms that the areas with the highest primary production are those with the greatest nutrient supply. A reduced wind-stress experiment demonstrates that the high production found in the equatorial Pacific is driven by excessive upwelling of nutrient-rich water. Three further simulations show that nutrient supply at high latitudes, and hence production there, is sensitive to the parameters and climatological forcings of the mixed layer sub-model.

An off‐line 3D model of anthropogenic CO2 uptake by the oceans

A key unanswered question in global climate research is the extent to which the oceans absorb and redistribute anthropogenic CO2 (ΔDICant). We apply an offline ocean carbon cycle model, one that uses atmospheric CO2 concentrations and simplified ocean ventilation estimates. We estimate ocean ventilation by introducing the full age distributions of water masses obtained from a global ocean circulation model. The two model cases we use are known to respectively underestimate and overestimate observed ventilation rates, thereby providing upper and lower bounds on CO2 uptake. The error in determining ΔDICant is reduced significantly by minimising the number of tunable parameters. The development of the oceanic ΔDICant inventory is resolved from the beginning of industrialisation to the present time. According to the model, 177Gt anthropogenic carbon have been locked up in the oceans by 1999, corresponding to an annual uptake rate of 3.9GtC*a−1 during 1999. Uncertainties in our estimated oceanic CO2 uptake are discussed.

Marine Biogeochemical Modeling: Recent Advances and Future Challenges

One of the central objectives of the Joint Global Ocean Flux Study (JGOFS) is to use data from the extensive field programs to evaluate and improve numerical ocean carbon-cycle models. Substantial improvements are required if we are to achieve a better understanding of present-day biogeochemical properties and processes in the ocean and to predict potential future responses to perturbations resulting from human activities. We have made significant progress in this regard and expect even greater strides over the next decade as the synthesis of JGOFS data sets is completed and disseminated to the broader scientific community.

A model of the iron cycle in the ocean

The IRONEX II in situ fertilization experiment showed decisively that availability of iron limits the growth of phytoplankton in equatorial Pacific surface waters. High‐nutrient low‐chlorophyll (HNLC) surface waters, potentially iron limited, are also found in the North Pacific and the Southern Ocean. A model of seawater iron geochemistry has been incorporated into a global ocean circulation and carbon cycle model and tuned to match the observed ocean Fe distribution. The model reproduces the observed HNLC areas in the equatorial Pacific and Southern Ocean but predicts nutrient depletion in the North Pacific (a region of high dust fluxes from Asia). The model is based on the idea that the concentrations of organic iron‐binding ligands in seawater control the subsurface Fe concentration and predicts that a majority (70–80%) of the global carbon export production can be sustained by upwelling of dissolved iron in seawater rather than by atmospheric deposition. The ocean iron cycle increases the efficiency of iron utilization in the biological pump, buffers new production against interannual fluctuations in iron deposition, and contributes several new intrinsic timescales to the ocean carbon cycle: several hundred years for the ocean iron concentration and perhaps several thousand for the concentration of ligand in the deep sea. Ocean recycling of Fe appears to play a major role in determining the strength of the biological pump in the ocean and the pCO2 of the atmosphere.

Modeling the effects of snowpack on heterotrophic respiration across northern temperate and high latitude regions: Comparison with measurements of atmospheric carbon dioxide in high latitudes

Simulations by global terrestrial biogeochemical models (TBMs) consistently underestimate the concentration of atmospheric carbon dioxide (CO2 at high latitude monitoring stations during the non-growing season. We hypothesized that heterotrophic respiration is underestimated during the nongrowing season primarily because TBMs do not generally consider the insulative effects of snowpack on soil temperature. To evaluate this hypothesis, we compared the performance of baseline and modified versions of three TBMs in simulating the seasonal cycle of atmospheric CO2 at high latitude CO2 monitoring stations; the modified version maintained soil temperature at 0 °C when modeled snowpack was present. The three TBMs include the Carnegie-Ames-Stanford Approach (CASA), Century, and the Terrestrial Ecosystem Model (TEM). In comparison with the baseline simulation of each model, the snowpack simulations caused higher releases of CO2 between November and March and greater uptake of CO2 between June and August for latitudes north of 30° N. We coupled the monthly estimates of CO2 exchange, the seasonal carbon dioxide flux fields generated by the HAMOCC3 seasonal ocean carbon cycle model, and fossil fuel source fields derived from standard sources to the three-dimensional atmospheric transport model TM2 forced by observed winds to simulate the seasonal cycle of atmospheric CO2 at each of seven high latitude monitoring stations. In comparison to the CO2 concentrations simulated with the baseline fluxes of each TBM, concentrations simulated using the snowpack fluxes are generally in better agreement with observed concentrations between August and March at each of the monitoring stations. Thus, representation of the insulative effects of snowpack in TBMs generally improves simulation of atmospheric CO2 concentrations in high latitudes during both the late growing season and nongrowing season. These simulations highlight the global importance of biogeochemical processes during the nongrowing season in estimating carbon balance of ecosystems in northern high and temperate latitudes.

Nutrient trapping in the equatorial Pacific: The ocean circulation solution

Nutrient trapping is a chronic problem found in global carbon cycle models with particle‐only remineralization schemes. It is defined as the excess of subsurface nutrient concentrations relative to observations and occurs principally in the eastern equatorial Pacific. Previous studies reduced excess simulated nutrients by increasing the complexity of modeled biogeochemistry, i.e., by adding pools for nutrients (and carbon) either in dissolved organic form or as plankton. Conversely, our study suggests that deficiencies in modeled circulation fields from global coarse‐resolution ocean models are mostly responsible. This new interpretation stems from our use of an ocean general circulation model with higher resolution, which offers a more realistic equatorial circulation. We used the same biogeochemical model Hamburg ocean carbon cycle model, version 3, as in some of the previous studies. Our model‐predicted distribution of PO43− in the equatorial Pacific agrees reasonably well with the observations both at the surface and in the subsurface. Subsurface PO43− concentrations in our model's eastern equatorial Pacific exceed observations by, at most, 15%, unlike coarser‐resolution models. Improvement is due to enhanced meridional resolution (0.5°) near the equator, which allows the model to simulate a vigorous equatorial undercurrent that brings in low‐nutrient water from the western basin. Furthermore, the model upwells no nutrient‐rich abyssal water into the surface equatorial Pacific. Our results suggest that dissolved organic carbon plays a minor role in the carbon budget of the equatorial Pacific.

On ocean carbon-cycle model comparison

On ocean carbon-cycle model comparison

A global oceanic sediment model for long‐term climate studies

A chemically reactive 10‐layer sediment module was coupled to a geochemical ocean general circulation model (the Hamburg Oceanic Carbon Cycle Model). The sediment model includes four solid sediment components (CaCO3, opal, organic carbon, and clay), and five pore water substances (dissolved inorganic carbon, total alkalinity, PO43−, O2, Si(OH)4) plus corresponding species containing 13C and 14C instead of 12C. The processes, namely, particle deposition, pore water reactions, pore water diffusion and interaction with the open water column, vertical sediment advection, sediment accumulation, and bioturbation, are simulated through basic parametrizations. For the water column part the Si and C cycles are coupled by a formulation of the “rain ratio” Si:C(CaCO3):C(POC), where POC is particulate organic carbon, in biogenic particle export production, with CaCO3 frustrule production growing in parallel to a weakening of opal production during progressing deficiency of dissolved silicate in the surface layer. For two preindustrial velocity fields the model reproduces major features of observed water column and sediment tracer distributions parallel to a correct preindustrial CO2 level close to 280 ppm. The model reacts sensitively to the formulation of the POC flux parametrization, the rain ratio, as well as the solubility of opal but is fairly insensitive to changes in the bioturbation rate as well as the amount of clay deposition. A simulation of the sediment distribution by use of a velocity field, which represents the ocean at conditions during the last glacial maximum, yields realistic glacial‐interglacial changes for the Atlantic Ocean, while discrepancies remain for the Indo‐Pacific region. A significant decrease of the atmospheric pCO2 could be achieved through an additional change of water column inventories by a change in weathering input of Si and alkalinity.