ALBERT

Description: Nitrogen is a key limiting nutrient that influences marine productivity and carbon sequestration in the ocean via the biological pump. In this study, we present the first estimates of nitrogen cycling in a coupled 3D ocean-biogeochemistry-isotope model forced with realistic boundary conditions from the Last Glacial Maximum (LGM) ~21,000 years before present constrained by nitrogen isotopes. The model predicts a large decrease in nitrogen loss rates due to higher oxygen concentrations in the thermocline and sea level drop, and, as a response, reduced nitrogen fixation. Model experiments are performed to evaluate effects of hypothesized increases of atmospheric iron fluxes and oceanic phosphorus inventory relative to present-day conditions. Enhanced atmospheric iron deposition, which is required to reproduce observations, fuels export production in the Southern Ocean causing increased deep ocean nutrient storage. This reduces transport of preformed nutrients to the tropics via mode waters, thereby decreasing productivity, oxygen deficient zones, and water column N-loss there. A larger global phosphorus inventory up to 15% cannot be excluded from the currently available nitrogen isotope data. It stimulates additional nitrogen fixation that increases the global oceanic nitrogen inventory, productivity, and water column N-loss. Among our sensitivity simulations, the best agreements with nitrogen isotope data from LGM sediments indicate that water column and sedimentary N-loss were reduced by 17–62% and 35–69%, respectively, relative to preindustrial values. Our model demonstrates that multiple processes alter the nitrogen isotopic signal in most locations, which creates large uncertainties when quantitatively constraining individual nitrogen cycling processes. One key uncertainty is nitrogen fixation, which decreases by 25–65% in the model during the LGM mainly in response to reduced N-loss, due to the lack of observations in the open ocean most notably in the tropical and subtropical southern hemisphere. Nevertheless, the model estimated large increase to the global nitrate inventory of 6.5–22% suggests it may play an important role enhancing the biological carbon pump that contributes to lower atmospheric CO2 during the LGM.

Description: The size of the bio-available (i.e. "fixed") nitrogen inventory in the ocean influences global marine productivity and the biological carbon pump. Despite its importance, the pre-industrial rates for the major source and sink terms of the oceanic fixed nitrogen budget, N2 fixation and denitrification, respectively, are not well known. However, these processes leave distinguishable imprints on the ratio of stable nitrogen isotopes, δ15N, which can therefore help to infer their patterns and rates. Here we use δ15N observations from the water column and a new database of seafloor measurements to constrain rates of N2 fixation and denitrification predicted by a global three-dimensional Model of Ocean Biogeochemistry and Isotopes (MOBI). Sensitivity experiments were performed to quantify uncertainties associated with the isotope effect of denitrification in the water column and sediments. They show that the level of nitrate utilization in suboxic zones, that is the balance between nitrate consumption by denitrification and nitrate replenishment by mixing (dilution effect), significantly affects the isotope effect of water column denitrification and thus global mean δ15NO3−. Experiments with lower levels of nitrate utilization within the suboxic zone (i.e. higher residual water column nitrate concentrations, ranging from 20–32 μM) require higher ratios of benthic to water column denitrification (BD:WCD = 0.75–1.4, respectively), to satisfy the global mean NO3− and δ15NO3− constraints in the modern ocean. This suggests that nitrate utilization in suboxic zones play an important role in global nitrogen isotope cycling. Increasing the net fractionation factor for benthic denitrification (ϵBD = 0–4‰) requires even higher ratios of benthic to water column denitrification (BD:WCD = 1.4–3.5, respectively). The model experiments that best reproduce observed seafloor δ15N support the middle to high-end estimates for the net fractionation factor of benthic denitrification (ϵBD = 2–4‰). Assuming a balanced fixed nitrogen budget, we estimate that pre-industrial rates of N2 fixation, water column denitrification, and benthic denitrification were approximately 195–345, 65–75, and 130–270 Tg N yr−1, respectively. Although uncertainties still exist, these results suggest that previous estimates of N2 fixation have been significantly underestimated and the residence time for oceanic fixed nitrogen is between ~ 1500–3000 yr.

Description: We describe and test a new model of biological marine silicate cycling, implemented in the Kiel Marine Biogeochemical Model version 3 (KMBM3), embedded in the University of Victoria Earth System Climate Model (UVic ESCM) version 2.9. This new model adds diatoms, which are a key component of the biological carbon pump, to an existing ecosystem model. This new model combines previously published parameterizations of a diatom functional type, opal production and export with a novel, temperature-dependent dissolution scheme. Modelled steady-state biogeochemical rates, carbon and nutrient distributions are similar to those found in previous model versions. The new model performs well against independent ocean biogeochemical indicators and captures the large-scale features of the marine silica cycle to a degree comparable to similar Earth system models. Furthermore, it is computationally efficient, allowing both fully coupled, long-timescale transient simulations and “offline” transport matrix spinups. We assess the fully coupled model against modern ocean observations, the historical record starting from 1960 and a business-as-usual atmospheric CO2 forcing to the year 2300. The model simulates a global decline in net primary production (NPP) of 1.4 % having occurred since the 1960s, with the strongest declines in the tropics, northern midlatitudes and Southern Ocean. The simulated global decline in NPP reverses after the year 2100 (forced by the extended RCP8.5 CO2 concentration scenario), and NPP returns to 98 % of the pre-industrial rate by 2300. This recovery is dominated by increasing primary production in the Southern Ocean, mostly by calcifying phytoplankton. Large increases in calcifying phytoplankton in the Southern Ocean offset a decline in the low latitudes, producing a global net calcite export in 2300 that varies only slightly from pre-industrial rates. Diatom distribution moves southward in our simulations, following the receding Antarctic ice front, but diatoms are outcompeted by calcifiers across most of their pre-industrial Southern Ocean habitat. Global opal export production thus drops to 75 % of its pre-industrial value by 2300. Model nutrients such as phosphate, silicate and nitrate build up along the Southern Ocean particle export pathway, but dissolved iron (for which ocean sources are held constant) increases in the upper ocean. This different behaviour of iron is attributed to a reduction of low-latitude NPP (and consequently, a reduction in both uptake and export and particle, including calcite scavenging), an increase in seawater temperatures (raising the solubility of particulate iron) and stratification that “traps” the iron near the surface. These results are meant to serve as a baseline for sensitivity assessments to be undertaken with this model in the future.

Description: We describe and test a new model of biological marine silicate cycling, implemented in the Kiel Marine Biogeochemical Model version 3 (KMBM3), embedded in the University of Victoria Earth System Climate Model (UVic ESCM) version 2.9. This new model adds diatoms, which are a key component of the biological carbon pump, to an existing ecosystem model. This new model combines previously published parameterizations of a diatom functional type, opal production and export with a novel, temperature-dependent dissolution scheme. Modelled steady-state biogeochemical rates, carbon and nutrient distributions are similar to those found in previous model versions. The new model performs well against independent ocean biogeochemical indicators and captures the large-scale features of the marine silica cycle to a degree comparable to similar Earth system models. Furthermore, it is computationally efficient, allowing both fully coupled, long-timescale transient simulations and “offline” transport matrix spinups. We assess the fully coupled model against modern ocean observations, the historical record starting from 1960 and a business-as-usual atmospheric CO2 forcing to the year 2300. The model simulates a global decline in net primary production (NPP) of 1.4 % having occurred since the 1960s, with the strongest declines in the tropics, northern midlatitudes and Southern Ocean. The simulated global decline in NPP reverses after the year 2100 (forced by the extended RCP8.5 CO2 concentration scenario), and NPP returns to 98 % of the pre-industrial rate by 2300. This recovery is dominated by increasing primary production in the Southern Ocean, mostly by calcifying phytoplankton. Large increases in calcifying phytoplankton in the Southern Ocean offset a decline in the low latitudes, producing a global net calcite export in 2300 that varies only slightly from pre-industrial rates. Diatom distribution moves southward in our simulations, following the receding Antarctic ice front, but diatoms are outcompeted by calcifiers across most of their pre-industrial Southern Ocean habitat. Global opal export production thus drops to 75 % of its pre-industrial value by 2300. Model nutrients such as phosphate, silicate and nitrate build up along the Southern Ocean particle export pathway, but dissolved iron (for which ocean sources are held constant) increases in the upper ocean. This different behaviour of iron is attributed to a reduction of low-latitude NPP (and consequently, a reduction in both uptake and export and particle, including calcite scavenging), an increase in seawater temperatures (raising the solubility of particulate iron) and stratification that “traps” the iron near the surface. These results are meant to serve as a baseline for sensitivity assessments to be undertaken with this model in the future.

Description: Marine particulate organic carbon-13 stable isotope ratios (δ13CPOC) provide insights in understanding carbon cycling through the atmosphere, ocean, and biosphere. They have been used to trace the input of anthropogenic carbon in the marine ecosystem due to the distinct isotopically light signature of anthropogenic emissions. However, δ13CPOC is also significantly altered during photosynthesis by phytoplankton, which complicates its interpretation. For such purposes, robust spatio-temporal coverage of δ13CP OC observations is essential. We collected all such available data sets, merged and homogenized them to provide the largest available marine δ13CPOC data set (Verwega et al., 2021). The data set consists of 4732 data points covering all major ocean basins beginning in the 1960s. We describe the compiled raw data, compare different observational methods, and provide key insights in the temporal and spatial distribution that is consistent with previously observed patterns. The main different sample collection methods (bottle, intake, net, trap) are generally consistent with each other when comparing within regions. An analysis of 1990s mean δ13CP OC values in an meridional section accross the Atlantic Ocean shows relatively high values (≥ −22 ‰) in the low latitudes (〈 30°) trending towards lower values in the Arctic Ocean (∼ −24 ‰) and Southern Ocean (≤ −28 ‰). The temporal trend since the 1960s shows a decrease of mean δ13CPOC by more than 3 ‰ in all basins except for the Southern Ocean which shows a weaker trend but contains relatively poor multi-decadal coverage.

Description: Earth System Sciences have been generating increasingly larger amounts of heterogeneous data in recent years. We identify the need to combine Earth System Sciences with Data Sciences, and give our perspective on how this could be accomplished within the sub-field of Marine Sciences. Marine data hold abundant information and insights that Data Science techniques can reveal. There is high demand and potential to combine skills and knowledge from Marine and Data Sciences to best take advantage of the vast amount of marine data. This can be accomplished by establishing Marine Data Science as a new research discipline. Marine Data Science is an interface science that applies Data Science tools to extract information, knowledge, and insights from the exponentially increasing body of marine data. Marine Data Scientists need to be trained Data Scientists with a broad basic understanding of Marine Sciences and expertise in knowledge transfer. Marine Data Science doctoral researchers need targeted training for these specific skills, a crucial component of which is co-supervision from both parental sciences. They also might face challenges of scientific recognition and lack of an established academic career path. In this paper, we, Marine and Data Scientists at different stages of their academic career, present perspectives to define Marine Data Science as a distinct discipline. We draw on experiences of a Doctoral Research School, MarDATA, dedicated to training a cohort of early career Marine Data Scientists. We characterize the methods of Marine Data Science as a toolbox including skills from their two parental sciences. All of these aim to analyze and interpret marine data, which build the foundation of Marine Data Science.

Description: The ability of marine diazotrophs to fix dinitrogen gas (N₂) is one of the most influential yet enigmatic processes in the ocean. With their activity diazotrophs support biological production by fixing about 100-200 Tg N/yr of bioavailable nitrogen (N), an essential limiting nutrient. Despite their important role, the factors that control the distribution of diazotrophs and their ability to fix N₂ are not fully elucidated. We discuss insights that can be gained from the emerging picture of a wide geographical distribution of marine diazotrophs and provide a critical assessment of environmental (bottom-up) versus trophic (top-down) controls. We present a simplified theoretical framework to understand how top-down control affects competition for resources that determine ecological niches. Selective grazing on non-fixing phytoplankton is identified as a critical process that can broaden the ability of diazotrophs to compete for resources in top-down controlled systems and explain an expanded ecological niche for diazotrophy. Our simplified analysis predicts a larger importance of top-down control in nutrient-rich systems where grazing controls the faster growing phytoplankton, allowing the slower growing diazotrophs to become established. However, these predictions require corroboration by experimental and field data, together with the identification of specific traits of organisms and associated trade-offs related to selective top-down control. Elucidation of these factors could greatly improve our predictive capability for marine N2 fixation. The susceptibility of this key biogeochemical process to future changes may not only be determined by changes in environmental conditions but also via changes in the ecological interactions.

Description: Probability density functions (PDFs) provide information about the probability of a random variable taking on a specific value. In geoscience, data distributions are often expressed by a parametric estimation of their PDF, such as, for example, a Gaussian distribution. At present there is growing attention towards the analysis of non-parametric estimation of PDFs, where no prior assumptions about the type of PDF are required. A common tool for such non-parametric estimation is a kernel density estimator (KDE). Existing KDEs are valuable but problematic because of the difficulty of objectively specifying optimal bandwidths for the individual kernels. In this study, we designed and developed a new implementation of a diffusion-based KDE as an open source Python tool to make diffusion-based KDE accessible for general use. Our new diffusion-based KDE provides (1) consistency at the boundaries, (2) better resolution of multimodal data, and (3) a family of KDEs with different smoothing intensities. We demonstrate our tool on artificial data with multiple and boundary-close modes and on real marine biogeochemical data, and compare our results against other popular KDE methods. We also provide an example for how our approach can be efficiently utilized for the derivation of plankton size spectra in ecological research. Our estimator is able to detect relevant multiple modes and it resolves modes that are located closely to a boundary of the observed data interval. Furthermore, our approach produces a smooth graph that is robust to noise and outliers. The convergence rate is comparable to that of the Gaussian estimator, but with a generally smaller error. This is most notable for small data sets with up to around 5000 data points. We discuss the general applicability and advantages of such KDEs for data–model comparison in geoscience.

Description: Nitrogen (N) is a crucial limiting nutrient for phytoplankton growth in the ocean. The main source of bioavailable N in the ocean is delivered by N2-fixing diazotrophs in the surface layer. Since field observation of N2 fixation are spatially and temporally sparse, the fundamental processes and mechanisms controlling N2 fixation are not well understood and constrained. Here, we implement benthic denitrification in an Earth System Model of intermediate complexity (UVic-ESCM 2.9) coupled to an optimality-based plankton ecosystem model (OPEM v1.1). Benthic denitrification occurs mostly in coastal upwelling regions and on shallow continental shelves, and is the largest N-loss process in the global ocean. We calibrate our model against three different combinations of observed Chl, NO3-, PO43-, O2 and N* = NO3- −16PO43- +2.9. The inclusion of N* provides a powerful constraint on biogeochemical model behavior. Our new model version including benthic denitrification simulates higher global rates of N2 fixation with a more realistic distribution extending to higher latitudes that are supported by independent estimates based on geochemical data. Oxygen deficient zone volume and water column denitrification rates are reduced in the new version, indicating that including benthic denitrification may improve global biogeochemical models that commonly overestimate anoxic zones. With the improved representation of the ocean N cycle, our new model configuration also yields better global net primary production (NPP) when compared to the independent datasets not included in the calibration. Benthic denitrification plays an important role shaping N2 fixation and NPP throughout the global ocean in our model, and should be considered when evaluating and predicting their response to environmental change.