ALBERT

Description: The McMurray Formation of northern Alberta in Canada contains multiscale complex geologic features that were partially formed in a fluvial-estuarine depositional environment. The inclined heterolithic strata deposited as part of fluvial point bars contain continuous centimeter-scale features that are important for flow characterization of steam-assisted gravity drainage processes. These channels are common, extensive, and imbricated over many square kilometers. Modeling the detailed facies in such depositional systems requires a methodology that reflects heterogeneity over many scales. This article presents an object-based facies modeling technique that (1) reproduces the geometry of multiscale geologic architectural elements seen in the McMurray Formation outcrops and (2) provides a grid-free framework that models these geologic objects without relating them to a grid system. The grid-free object-based modeling can be applied to any depositional environment and allows for the complete preservation of architectural information for consistent application to any gridding scheme, local grid refinements, downscaling, upscaling, drape surface, locally variable azimuths, property trend modeling, and flexible model interaction and manipulation. Features millimeters thick or kilometers in extent are represented very efficiently in the same model.

Description: The sinking and decomposition of particulate organic matter are critical processes in the ocean's biological pump, but are poorly understood and crudely represented in biogeochemical models. Here we present a mechanistic model for particle fluxes in the ocean that solves the evolution of the particle size distribution with depth. The model can represent a wide range of particle flux profiles, depending on the surface particle size distribution, the relationships between particle size, mass and velocity, and the rate of particle mass loss during decomposition. Spatially variable flux profiles are embedded in a data-constrained ocean circulation model, where the most uncertain parameters governing particle dynamics are tuned to achieve an optimal fit to the global distribution of phosphate. The resolution of spatially variable particle sizes has a significant effect on modeled organic matter production rates, increasing production in oligotrophic regions and decreasing production in eutrophic regions compared to a model that assumes spatially uniform particle sizes and sinking fluxes. The mechanistic particle model can reproduce global nutrient distributions better than, and sediment trap fluxes as well as, other commonly used empirical formulas. However, these independent data constraints cannot be simultaneously matched in a closed P budget commonly assumed in ocean models. Through a systematic addition of model processes, we show that the apparent discrepancy between particle flux and nutrient data can be resolved through P burial, but only if that burial is associated with a slowly decaying component of organic matter as might be achieved through protection by ballast minerals. Moreover, the model solution that best matches both datasets requires a larger rate of P burial (and compensating inputs) than have been previously estimated. Our results imply a marine PO4 inventory with a residence time of a few thousand years, similar to that of the relatively dynamic N cycle.

Description: A major impediment to understanding long-term changes in the marine nitrogen (N) cycle is the persistent uncertainty about the rates, distribution, and sensitivity of its largest fluxes in the modern ocean. We use a global 3-dimensional ocean circulation model to obtain the first estimate of marine denitrification rates that is maximally consistent with available observations of nitrate deficits and the nitrogen isotopic ratio of ocean nitrate. We find a global rate of marine denitrification in suboxic waters and sediments of 120–240 Tg N yr−1, which is lower than most other recent estimates. The difference stems from the ability to represent the 3-D spatial structure of suboxic zones, where denitrification rates of 50–77 Tg N yr−1 result in up to 50% depletion of nitrate. This depletion reduces the effect of local isotopic enrichment on the rest of the ocean, allowing the N isotope ratio of oceanic nitrate to be achieved with a sedimentary denitrification rate about 1.3–2.3 times that of suboxic zones. This balance of N losses between sediments and suboxic zones is shown to obey a simple relationship between isotope fractionation and the degree of nitrate consumption in the core of the suboxic zones. The global denitrification rates derived here suggest that the marine nitrogen budget is likely close to balanced.

Description: A major impediment to understanding long-term changes in the marine nitrogen (N) cycle is the persistent uncertainty about the rates, distribution, and sensitivity of its largest fluxes in the modern ocean. We use a global ocean circulation model to obtain the first 3-D estimate of marine denitrification rates that is maximally consistent with available observations of nitrate deficits and the nitrogen isotopic ratio of oceanic nitrate. We find a global rate of marine denitrification in suboxic waters and sediments of 120–240 Tg N yr−1, which is lower than many other recent estimates. The difference stems from the ability to represent the 3-D spatial structure of suboxic zones, where denitrification rates of 50–77 Tg N yr−1 result in up to 50% depletion of nitrate. This depletion reduces the effect of local isotopic enrichment on the rest of the ocean, allowing the N isotope ratio of oceanic nitrate to be achieved with a sedimentary denitrification rate about 1.3–2.3 times that of suboxic zones. This balance of N losses between sediments and suboxic zones is shown to obey a simple relationship between isotope fractionation and the degree of nitrate consumption in the core of the suboxic zones. The global denitrification rates derived here suggest that the marine nitrogen budget is likely close to balanced.

Description: The sinking and decomposition of particulate organic matter are critical processes in the ocean's biological pump, but are poorly understood and crudely represented in biogeochemical models. Here we present a mechanistic particle remineralization and sinking model (PRiSM) that solves the evolution of the particle size distribution with depth. The model can represent a wide range of particle flux profiles, depending on the surface particle size distribution, the relationships between particle size, mass and sinking velocity, and the rate of particle mass loss during decomposition. The particle flux model is embedded in a data-constrained ocean circulation and biogeochemical model with a simple P cycle. Surface particle size distributions are derived from satellite remote sensing, and the remaining uncertain parameters governing particle dynamics are tuned to achieve an optimal fit to the global distribution of phosphate. The resolution of spatially variable particle sizes has a significant effect on modeled organic matter production rates, increasing production in oligotrophic regions and decreasing production in eutrophic regions compared to a model that assumes spatially uniform particle sizes and sinking speeds. The mechanistic particle model can reproduce global nutrient distributions better than, and sediment trap fluxes as well as, other commonly used empirical formulas. However, these two independent data constraints cannot be simultaneously matched in a closed P budget commonly assumed in ocean models. Through a systematic addition of model processes, we show that the apparent discrepancy between particle flux and nutrient data can be resolved through P burial, but only if that burial is associated with a slowly decaying component of organic matter such as might be achieved through protection by ballast minerals. Moreover, the model solution that best matches both data sets requires a larger rate of P burial (and compensating inputs) than have been previously estimated. Our results imply a marine P inventory with a residence time of a few thousand years, similar to that of the dynamic N cycle.

Description: There are a number of hypotheses concerning the environmental controls on marine nitrogen fixation (NF). Most of these hypotheses have not been assessed against direct measurements on the global scale. In this study, we use ~ 500 depth-integrated field measurements of NF covering the Pacific and Atlantic oceans to test whether the spatial variance of these measurements can be explained by the commonly hypothesized environmental controls, including measurement-based surface solar radiation, mixed layer depth, average solar radiation in the mixed layer, sea surface temperature, wind speed, surface nitrate and phosphate concentrations, surface excess phosphate (P*) concentration and subsurface minimum dissolved oxygen (in upper 500 m), as well as model-based P* convergence and atmospheric dust deposition. By conducting simple linear regression and stepwise multiple linear regression (MLR) analyses, surface solar radiation (or sea surface temperature) and subsurface minimum dissolved oxygen are identified as the predictors that explain the most spatial variance in the observed NF data, although it is unclear why the observed NF decreases when the level of subsurface minimum dissolved oxygen is higher than ~ 150 μM. Dust deposition and wind speed do not appear to influence the spatial patterns of NF on global scale. The weak correlation between the observed NF and the P* convergence and concentrations suggests that the available data currently remain insufficient to fully support the hypothesis that spatial variability in denitrification is the principal control on spatial variability in marine NF. By applying the MLR-derived equation, we estimate the global-integrated NF at 74 (error range 51–110) Tg N yr−1 in the open ocean, acknowledging that it could be substantially higher as the 15N2-assimilation method used by most of the field samples underestimates NF. More field NF samples in the Pacific and Indian oceans, particularly in the oxygen minimum zones, are needed to reduce uncertainties in our conclusion.