Carbon content, carbon fixation yield and dissolved organic carbon release from diverse marine nitrifiers

Nitrifying microorganisms, including ammonia-oxidizing archaea, ammonia-oxidizing bacteria and nitrite-oxidizing bacteria, are the most abundant chemoautotrophs in the ocean and play an important role in the global carbon cycle by fixing dissolved inorganic carbon (DIC) into biomass. The release of organic compounds by these microbes is less well known but may represent an as-yet unaccounted source of dissolved organic carbon (DOC) available to heterotrophic marine food webs. Here, we provide measurements of cellular carbon and nitrogen quotas, DIC fixation yields and DOC release of ten phylogenetically diverse marine nitrifiers grown in multiple culture conditions. All investigated strains released DOC during growth, making up on average 5-15% of the fixed DIC. Neither substrate concentration nor temperature affected the proportion of fixed DIC released as DOC, but release rates varied between closely related species. Our results also indicate previous studies may have underestimated DIC fixation yields of marine nitrite oxidizers due to partial decoupling of nitrite oxidation from CO2 fixation, and due to lower observed yields in artificial compared to natural seawater medium. The results of this study provide values for biogeochemical models of the global carbon cycle, and help to further constrain the implications of nitrification-fueled chemoautotrophy for marine food-web functioning and the biological sequestration of carbon in the ocean.

A Global Ocean Oxygen Database and Atlas for Assessing and Predicting Deoxygenation and Ocean Health in the Open and Coastal Ocean

In this paper, we outline the need for a coordinated international effort toward the building of an open-access Global Ocean Oxygen Database and ATlas (GO2DAT) complying with the FAIR principles (Findable, Accessible, Interoperable, and Reusable). GO2DAT will combine data from the coastal and open ocean, as measured by the chemical Winkler titration method or by sensors (e.g., optodes, electrodes) from Eulerian and Lagrangian platforms (e.g., ships, moorings, profiling floats, gliders, ships of opportunities, marine mammals, cabled observatories). GO2DAT will further adopt a community-agreed, fully documented metadata format and a consistent quality control (QC) procedure and quality flagging (QF) system. GO2DAT will serve to support the development of advanced data analysis and biogeochemical models for improving our mapping, understanding and forecasting capabilities for ocean O2 changes and deoxygenation trends. It will offer the opportunity to develop quality-controlled data synthesis products with unprecedented spatial (vertical and horizontal) and temporal (sub-seasonal to multi-decadal) resolution. These products will support model assessment, improvement and evaluation as well as the development of climate and ocean health indicators. They will further support the decision-making processes associated with the emerging blue economy, the conservation of marine resources and their associated ecosystem services and the development of management tools required by a diverse community of users (e.g., environmental agencies, aquaculture, and fishing sectors). A better knowledge base of the spatial and temporal variations of marine O2 will improve our understanding of the ocean O2 budget, and allow better quantification of the Earth’s carbon and heat budgets. With the ever-increasing need to protect and sustainably manage ocean services, GO2DAT will allow scientists to fully harness the increasing volumes of O2 data already delivered by the expanding global ocean observing system and enable smooth incorporation of much higher quantities of data from autonomous platforms in the open ocean and coastal areas into comprehensive data products in the years to come. This paper aims at engaging the community (e.g., scientists, data managers, policy makers, service users) toward the development of GO2DAT within the framework of the UN Global Ocean Oxygen Decade (GOOD) program recently endorsed by IOC-UNESCO. A roadmap toward GO2DAT is proposed highlighting the efforts needed (e.g., in terms of human resources).

Downscaling CMIP6 Global Solutions to Regional Ocean Carbon Model: Connecting the Mississippi, Gulf of Mexico, and Global Ocean

Coupled physical-biogeochemical models can significantly reduce uncertainties in estimating the spatial and temporal patterns of the ocean carbon system. Challenges of applying a coupled physical-biogeochemical model in the regional ocean include the reasonable prescription of carbon model boundary conditions, lack of in situ observations, and the oversimplification of certain biogeochemical processes. In this study, we applied a coupled physical-biogeochemical model (Regional Ocean Modelling System, ROMS) to the Gulf of Mexico (GoM) and achieved an unprecedented 20-year high-resolution (5 km, 1/22°) hindcast covering the period of 2000–2019. The model’s biogeochemical cycle is driven by the Coupled Model Intercomparison Project 6-Community Earth System Model 2 products (CMIP6-CESM2) and incorporates the dynamics of dissolved organic carbon (DOC) pools as well as the formation and dissolution of carbonate minerals. Model outputs include generally interested carbon system variables, such as pCO2, pH, aragonite saturation state (ΩArag), calcite saturation state (ΩCalc), CO2 air-sea flux, carbon burial rate, etc. The model’s robustness is evaluated via extensive model-data comparison against buoy, remote sensing-based Machine Learning (ML) predictions, and ship-based measurements. Model results reveal that the GoM water has been experiencing an ~ 0.0016 yr−1 decrease in surface pH over the past two decades, accompanied by a ~ 1.66 µatm yr−1 increase in sea surface pCO2. The air-sea CO2 exchange estimation confirms that the river-dominated northern GoM is a substantial carbon sink. The open water of GoM, affected mainly by the thermal effect, is a carbon source during summer and a carbon sink for the rest of the year. Sensitivity experiments are conducted to evaluate the impacts from river inputs and the global ocean via model boundaries. Our results show that the coastal ocean carbon cycle is dominated by enormous carbon inputs from the Mississippi River and nutrient-stimulated biological activities, and the carbon system condition of the open ocean is primarily driven by inputs from the Caribbean Sea via Yucatan Channel.

Soil related developments of the Biome-BGCMuSo v6.2 terrestrial ecosystem model by integrating crop model components

Terrestrial biogeochemical models are essential tools to quantify climate-carbon cycle feedback and plant-soil relations from local to global scale. In this study, theoretical basis is provided for the latest version of Biome-BGCMuSo biogeochemical model (version 6.2). Biome-BGCMuSo is a branch of the original Biome-BGC model with a large number of developments and structural changes. Earlier model versions performed poorly in terms of soil water content (SWC) dynamics in different environments. Moreover, lack of detailed nitrogen cycle representation was a major limitation of the model. Since problems associated with these internal drivers might influence the final results and parameter estimation, additional structural improvements were necessary. During the developments we took advantage of experiences from the crop modeller community where internal process representation has a long history. In this paper the improved soil hydrology and soil carbon/nitrogen cycle calculation methods are described in detail. Capabilities of the Biome-BGCMuSo v6.2 model are demonstrated via case studies focusing on soil hydrology and soil organic carbon content estimation. Soil hydrology related results are compared to observation data from an experimental lysimeter station. The results indicate improved performance for Biome-BGCMuSo v6.2 compared to v4.0 (explained variance increased from 0.121 to 0.8 for SWC, and from 0.084 to 0.46 for soil evaporation; bias changed from −0.047 to 0.007 m3 m−3 for SWC, and from −0.68 mm day−1 to −0.2 mm day−1 for soil evaporation). Sensitivity analysis and optimization of the decomposition scheme is presented to support practical application of the model. The improved version of Biome-BGCMuSo has the ability to provide more realistic soil hydrology representation and nitrification/denitrification process estimation which represents a major milestone.

Settling of highly porous and impermeable particles in linear stratification: implications for marine aggregates

The settling velocity of porous particles in linear stratification is affected by the diffusive exchange between interstitial and ambient water. The extent to which buoyancy and interstitial mass adaptation alters the settling velocity depends on the ratio of the diffusive and viscous time scales. We conducted schlieren experiments and lattice Boltzmann simulations for highly porous (95 %) but impermeable spheres settling in linear stratification. For a parameter range that resembles marine porous particles, ‘marine aggregates’, i.e. low Reynolds numbers ( $0.05\leq \textit {Re}\leq 10$ ), intermediate Froude numbers ( $0.1\leq \textit {Fr}\leq 100$ ) and Schmidt number of salt ( $\textit {Sc}=700$ ), we observe delayed mass adaptation of the interstitial fluid due to lower-density fluid being dragged by a particle that forms a density boundary layer around the particle. The boundary layer buffers the diffusive exchange of stratifying agent with the ambient fluid, leading to an enhanced density contrast of the interstitial pore fluid. Stratification-related drag enhancement by means of additional buoyancy of dragging lighter fluid and buoyancy-induced vorticity resembles earlier findings for solid spheres. However, the exchange between density boundary layer and pore fluid substantially increases stratification drag for small $\textit {Fr}$ . To estimate the effect of stratification on marine aggregates settling in the ocean, we derived scaling laws and show that small particles ( $\leq$ 0.5 mm) experience enhanced drag which increases retention times by 10 % while larger porous particle (>0.5 mm) settling is dominated by delayed mass adaptation that diminishes settling velocity by 10 % up to almost 100 %. The derived relationships facilitate the integration of stratification-dependent settling velocities into biogeochemical models.

Evaluation of LandscapeDNDC Model Predictions of CO2 and N2O Fluxes from an Oak Forest in SE England

Process-based biogeochemical models are valuable tools to evaluate impacts of environmental or management changes on the greenhouse gas (GHG) balance of forest ecosystems. We evaluated LandscapeDNDC, a process-based model developed to simulate carbon (C), nitrogen (N) and water cycling at ecosystem and regional scales, against eddy covariance and soil chamber measurements of CO2 and N2O fluxes in an 80-year-old deciduous oak forest. We compared two LandscapeDNDC vegetation modules: PSIM (Physiological Simulation Model), which includes the understorey explicitly, and PnET (Photosynthesis–Evapotranspiration Model), which does not. Species parameters for both modules were adjusted to match local measurements. LandscapeDNDC was able to reproduce daily micro-climatic conditions, which serve as input for the vegetation modules. The PSIM and PnET modules reproduced mean annual net CO2 uptake to within 1% and 15% of the measured values by balancing gains and losses in seasonal patterns with respect to measurements, although inter-annual variations were not well reproduced. The PSIM module indicated that the understorey contributed up to 21% to CO2 fluxes. Mean annual soil CO2 fluxes were underestimated by 32% using PnET and overestimated by 26% with PSIM; both modules simulated annual soil N2O fluxes within the measured range but with less interannual variation. Including stand structure information improved the model, but further improvements are required for the model to predict forest GHG balances and their inter-annual variability following climatic or management changes.

Ecological stoichiometry as a foundation for omics-enabled biogeochemical models of soil organic matter decomposition

Coupled biogeochemical cycles drive ecosystem ecology by influencing individual-to-community scale behaviors; yet the development of process-based models that accurately capture these dynamics remains elusive. Soil organic matter (SOM) decomposition in particular is influenced by resource stoichiometry that dictates microbial nutrient acquisition (‘ecological stoichiometry’). Despite its basis in biogeochemical modeling, ecological stoichiometry is only implicitly considered in high-resolution microbial investigations and the metabolic models they inform. State-of-science SOM decomposition models in both fields have advanced largely separately, but they agree on a need to move beyond seminal pool-based models. This presents an opportunity and a challenge to maximize the strengths of various models across different scales and environmental contexts. To address this challenge, we contend that ecological stoichiometry provides a framework for merging biogeochemical and microbiological models, as both explicitly consider substrate chemistries that are the basis of ecological stoichiometry as applied to SOM decomposition. We highlight two gaps that limit our understanding of SOM decomposition: (1) understanding how individual microorganisms alter metabolic strategies in response to substrate stoichiometry and (2) translating this knowledge to the scale of biogeochemical models. We suggest iterative information exchange to refine the objectives of high-resolution investigations and to specify limited dynamics for representation in large-scale models, resulting in a new class of omics-enabled biogeochemical models. Assimilating theoretical and modelling frameworks from different scientific domains is the next frontier in SOM decomposition modelling; advancing technologies in the context of stoichiometric theory provides a consistent framework for interpreting molecular data, and further distilling this information into tractable SOM decomposition models.

Multivariate trait analysis reveals diatom plasticity constrained to a reduced set of biological axes

Trait-based approaches to phytoplankton ecology have gained traction in recent decades as phenotypic traits are incorporated into ecological and biogeochemical models. Here, we use high-throughput phenotyping to explore both intra- and interspecific constraints on trait combinations that are expressed in the cosmopolitan marine diatom genus Thalassiosira. We demonstrate that within Thalassiosira, phenotypic diversity cannot be predicted from genotypic diversity, and moreover, plasticity can create highly divergent phenotypes that are incongruent with taxonomic grouping. Significantly, multivariate phenotypes can be represented in reduced dimensional space using principal component analysis with 77.7% of the variance captured by two orthogonal axes, here termed a ‘trait-scape’. Furthermore, this trait-scape can be recovered with a reduced set of traits. Plastic responses to the new environments expanded phenotypic trait values and the trait-scape, however, the overall pattern of response to the new environments was similar between strains and many trait correlations remained constant. These findings demonstrate that trait-scapes can be used to reveal common constraints on multi-trait plasticity in phytoplankton with divergent underlying phenotypes. Understanding how to integrate trait correlational constraints and trade-offs into theoretical frameworks like biogeochemical models will be critical to predict how microbial responses to environmental change will impact elemental cycling now and into the future.

Mapping manifestations of parametric uncertainty in projected pelagic oxygen concentrations back to contemporary local model fidelity

Pelagic biogeochemical models (BGCMs) have matured into generic components of Earth System Models. BGCMs mimic the effects of marine biota on oceanic nutrient, carbon and oxygen cycles. They rely on parameters that are adjusted to match observed conditions. Such parameters are key to determining the models’ responses to changing environmental conditions. However, many of these parameters are difficult to constrain and constitute a major source of uncertainty in BGCM projections. Here we use, for the first time, variance-based sensitivity analyses to map BGCM parameter uncertainties onto their respective local manifestation in model entities (such as oceanic oxygen concentrations) for both contemporary climate and climate projections. The mapping effectively relates local uncertainties of projections to the uncertainty of specific parameters. Further, it identifies contemporary benchmarking regions, where the uncertainties of specific parameters manifest themselves, thereby facilitating an effective parameter refinement and a reduction of the associated uncertainty. Our results demonstrate that the parameters that are linked to uncertainties in projections may differ from those parameters that facilitate model conformity with present-day observations. In summary, we present a practical approach to the general question of where present-day model fidelity may be indicative for reliable projections.

Theoretical insights from upscaling Michaelis–Menten microbial dynamics in biogeochemical models: a dimensionless approach

Leading an effective response to the accelerating crisis of anthropogenic climate change will require improved understanding of global carbon cycling. A critical source of uncertainty in Earth system models (ESMs) is the role of microbes in mediating both the formation and decomposition of soil organic matter, and hence in determining patterns of CO2 efflux. Traditionally, ESMs model carbon turnover as a first-order process impacted primarily by abiotic factors, whereas contemporary biogeochemical models often explicitly represent the microbial biomass and enzyme pools as the active agents of decomposition. However, the combination of non-linear microbial kinetics and ecological heterogeneity across space and time guarantees that upscaled dynamics will violate mean-field assumptions via Jensen's inequality. Violations of mean-field assumptions mean that parameter estimates from models fit to upscaled data (e.g., eddy covariance towers) are likely systematically biased. Likewise, predictions of CO2 efflux from models conditioned on mean-field values will also be biased. Here we present a generic mathematical analysis of upscaling Michaelis–Menten kinetics under heterogeneity and provide solutions in dimensionless form. We illustrate how our dimensionless form facilitates qualitative insight into the significance of this scale transition and argue that it will facilitate cross-site intercomparisons of flux data. We also identify the critical terms that need to be constrained in order to unbias parameter estimates.