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To improve the predictive capability of ecosystem biogeochemical models (EBMs), we discuss the feasibility of formulating biogeochemical processes using physical rules that have underpinned the many successes in computational physics and chemistry. We argue that the currently popular empirically based approaches, such as multiplicative empirical response functions and the law of the minimum, will not lead to EBM formulations that can be continuously refined to incorporate improved mechanistic understanding and empirical observations of biogeochemical processes. Instead, we propose that EBM parameterizations, as a lossy data compression problem, can be better formulated using established physical rules widely used in computational physics and chemistry, and different biogeochemical processes can be more robustly integrated within a reactive-transport framework. Through several examples, we demonstrate how mathematical representations derived from physical rules can improve understanding of relevant biogeochemical processes and enable more effective communication between modelers, observationalists, and experimentalists regarding essential questions, such as what measurements are needed to meaningfully inform models and how can models generate new process-level hypotheses to test in empirical studies. Finally, while empirical models with more parameters are often less robust, physical rules-based models can be more robust and show lower predictive equifinality, stemming from their enhanced consistency in representations of processes, interactions and spatial scaling.

Redox processes, aqueous and solid-phase chemistry, and pH dynamics are key drivers of subsurface biogeochemical cycling and methanogenesis in terrestrial and wetland ecosystems but are typically not included in terrestrial carbon cycle models. These omissions may introduce errors when simulating systems where redox interactions and pH fluctuations are important, such as wetlands where saturation of soils can produce anoxic conditions and coastal systems where sulfate inputs from seawater can influence biogeochemistry. Integrating cycling of redox-sensitive elements could therefore allow models to better represent key elements of carbon cycling and greenhouse gas production. We describe a model framework that couples the Energy Exascale Earth System Model (E3SM) Land Model (ELM) with PFLOTRAN biogeochemistry, allowing geochemical processes and redox interactions to be integrated with land surface model simulations. We implemented a reaction network including aerobic decomposition, fermentation, sulfate reduction, sulfide oxidation, methanogenesis, and methanotrophy as well as pH dynamics along with iron oxide and iron sulfide mineral precipitation and dissolution. We simulated biogeochemical cycling in tidal wetlands subject to either saltwater or freshwater inputs driven by tidal hydrological dynamics. In simulations with saltwater tidal inputs, sulfate reduction led to accumulation of sulfide, higher dissolved inorganic carbon concentrations, lower dissolved organic carbon concentrations, and lower methane emissions than simulations with freshwater tidal inputs. Model simulations compared well with measured porewater concentrations and surface gas emissions from coastal wetlands in the Northeastern United States. These results demonstrate how simulating geochemical reaction networks can improve land surface model simulations of subsurface biogeochemistry and carbon cycling.

Increased urbanization has reduced the amount of green space, resulting in a reduced carbon sink potential across urban landscapes. Through the use of biogeochemical modeling, different land use scenarios have been developed and run for the future (2020–2099) to compare and quantify the potential for change in carbon and water dynamics by having more tree cover and reducing impervious surfaces or turf lawns in Lehigh Valley, PA. These results show that the effect of deforestation is larger than the effect of reforestation. Due to young-stand age trees having a lower capacity for carbon storage than mature trees, the loss of the mature trees has a more immediate impact. The conversion of lawns or impervious surfaces to forests has somewhat similar effects, although the higher nutrients of lawns allow the forest to grow better. However, replacing impervious surfaces with trees reduces runoff more. This study shows that within the city of Bethlehem, the most socially vulnerable area benefits the most from increasing the number of trees. When converting 25% of the impervious area to forest, South Bethlehem significantly increased its vegetation carbon, productivity, and carbon storage, reduced its runoff, and generally created a safer and cleaner environment for residents. [ABSTRACT FROM AUTHOR]

Manganese (Mn) is a redox‐active micronutrient that has been shown to accelerate plant litter decomposition; however, the effect of Mn‐promoted decomposition on soil C storage is unclear. We present a novel biogeochemical model simulating how Mn bioavailability influences soil organic C (SOC) stocks in a soil profile (<50 cm) within a temperate forest. In our model, foliar Mn increased in response to increasing soluble Mn released through Mn‐oxide (birnessite) dissolution in mineral soil layers. The ensuing Mn enrichment in leaf litter redistributed Mn to the surface forest floor layer, promoted enzymatic oxidation of lignin, and decreased SOC stocks. Total SOC loss was partially mitigated by accumulation of lignin‐oxidation products as mineral‐associated organic C. We also explored how Mn‐driven changes to C storage interacted with effects of N deposition and warming. Nitrogen enrichment inhibited Mn‐dependent lignin degradation, increasing SOC stocks and weakening their dependence on Mn bioavailability. Warming stimulated decomposition and reduced C stocks but was less effective at low Mn bioavailability. Our model results suggest that SOC stocks are sensitive to Mn bioavailability because increased plant uptake redistributes Mn to surface soils where it can enhance litter decomposition. Based on our simulations, we predict that Mn becomes limiting to litter decomposition where Mn is poorly soluble. Depletion of bioavailable Mn or other cofactors that are critical to decomposition could limit the response of organic C stocks to warming over time, but quantitative projections of the role of Mn bioavailability in regulating decomposition requires additional measurements to constrain model uncertainties. Plain Language Summary: Carbon that is removed from the atmosphere by plants and stored in soils has the potential to partially offset greenhouse gas emissions and mitigate climate change. However, predictions of soil carbon storage are challenged by limited understanding of complex interactions between biological and geochemical processes that influence how quickly organic matter decomposes in the soil. We developed a novel model that simulates how cycling of the micronutrient manganese between soils and plants impacts carbon storage in a soil profile. Although it has been demonstrated that manganese is involved in breakdown of lignin, an important component of plant litter, its effects on soil carbon storage are unknown. We also explore how effects of manganese interact with two other major global perturbations: warming and nitrogen deposition to soils. From our model results, we determine that high levels of manganese uptake by plants ultimately generate manganese‐rich surface soils that promote litter decomposition and decrease soil carbon storage. Nitrogen deposition increased carbon storage by inhibiting effects of manganese on decomposition. Decomposition increased with warming but was inhibited by low manganese bioavailability. Our model provides a novel framework for simulating how soil properties that control micronutrient availability to plants can impact soil carbon dynamics. Key Points: We developed a biogeochemical model simulating how manganese bioavailability impacts soil carbon storage in temperate forestsManganese redistribution to surface soils by plants enhanced decomposition and decreased carbon storage in the soil profileOur model predicts that low manganese bioavailability may generate nutrient limitation that decreases warming effects on decomposition [ABSTRACT FROM AUTHOR]

The Arctic Ocean is surrounded by land that feeds highly seasonal rivers with water enriched in high concentrations of dissolved and particulate organic carbon (DOC and POC). Explicit estimates of the flux of organic carbon across the land-ocean interface are difficult to quantify and many interdependent processes makes source attribution difficult. A high-resolution 3-D biogeochemical model was built for the lower Yukon River and coastal ocean to estimate biogeochemical cycling across the land-ocean continuum. The model solves for complex reactions related to organic carbon transformation, including mechanistic photodegradation and multi-reactivity microbial processing, DOC-POC flocculation, and phytoplankton dynamics. The baseline DOC and POC flux out of the delta from April to September 2019, was 977 and 536 Gg C (∼80% of the annual total), but only 50% of the DOC and 25% of the POC exited the plume across the 10 m isobath. Microbial breakdown of DOC accounted for a net loss of 168 Gg C (17% of delta export) within the plume and photodegradation accounted for a net loss of 46.6 Gg C DOC (5% of delta export) in 2019. Flocculation decreased the total organic carbon flux by only 6.4 Gg C (∼1%), while POC sinking accounted for 63.3 Gg C (10%) settling in the plume. The loss of chromophoric dissolved organic matter due to photodegradation increased the light available for phytoplankton growth throughout the coastal ocean, demonstrating the secondary effects that organic carbon reactions can have on biological processes and the net coastal carbon flux.

Grazing dynamics are one of the most poorly constrained components of the marine carbon cycle. We use inverse modeling to infer the distribution of community‐integrated zooplankton grazing dynamics based on the ability of different grazing formulations to recreate the satellite‐observed seasonal cycle in phytoplankton biomass after controlling for physical and bottom‐up controls. We find large spatial variability in the optimal community‐integrated half saturation concentration for grazing (K1/2), with lower (higher) values required in more oligotrophic (eutrophic) biomes. This leads to a strong sigmoidal relationship between observed mean‐annual phytoplankton biomass and the optimally inferred grazing parameterization. This relationship can be used to help constrain, validate and/or parameterize next‐generation biogeochemical models. Plain Language Summary: To improve predictions of the ocean's ability to feed a growing human population and buffer a changing climate, we need to improve our understanding of what happens to carbon once it is absorbed into the surface ocean. One of the largest knowledge gaps in marine carbon cycling is the role of zooplankton grazing. The rate at which zooplankton graze phytoplankton modifies the size and seasonal evolution of phytoplankton populations and in turn, the associated rates of net primary production at the base of the food‐web, secondary production of grazers (an indicator of fisheries potential) and export production (the biological sequestration of carbon). However, regional differences in grazing, which are difficult to measure outside of the laboratory, remain poorly constrained by observations and thus difficult to model. Here, we run a suite of model simulations, which each simulate grazing differently, then compare the results to infer which grazing dynamics best match observations. We find that there is dramatic spatial variability in how zooplankton, as a community, appear to be grazing and that this variability maps well onto observed phytoplankton abundance, suggesting that the type of zooplankton present may be determined by the amount of prey available. Key Points: Oligotrophic (eutrophic) biomes exhibit more (less) efficient community‐integrated grazing, characteristic of micro‐ (meso‐) zooplanktonWe find a strong link between observed mean‐annual phytoplankton biomass and the grazing dynamics required to recreate its seasonal cycleA type III functional response typically does a better job recreating observed phytoplankton seasonal cycles than a type II response [ABSTRACT FROM AUTHOR]

Within oxygen minimum zones, anaerobic processes transform bioavailable nitrogen (N) into the gases dinitrogen (N2) and nitrous oxide (N2O), a potent greenhouse gas. Mesoscale eddies in these regions create heterogeneity in dissolved N tracers and O2 concentrations, influencing nonlinear N cycle reactions that depend on them. Here, we use an eddy‐resolving model of the Eastern Tropical South Pacific to show that eddies enhance N2 production by between 43% and 64% at the expense of reducing N2O production by between 94% and 104% due to both the steep increase of progressive denitrification steps at vanishing oxygen, and the more effective inhibition of N2O consumption relative to production. Our findings reveal the critical role of eddies in shaping the N cycle of oxygen minimum zones, which is not currently represented by coarse models used for climate studies. Plain Language Summary: Nitrogen limits photosynthesis over large swaths of the ocean. Oxygen minimum zones are important oceanic environments where the presence of low O2 concentrations allow anaerobic microbial communities to transform bioavailable forms of nitrogen into the gases dinitrogen (N2) and nitrous oxide (N2O, a powerful greenhouse gas), which together lead to nitrogen loss from the oceans. The large scale ocean circulation acts to maintain these oxygen minimum zones, and the gradients in nitrogen and oxygen concentrations surrounding them. Turbulence also generates eddies and filaments which stir these gradients, promoting chemical heterogeneity. However, it is uncertain how eddies impact the nitrogen transformation rates dependent on nitrogen and oxygen tracer concentrations. Using a high resolution ocean model of the Eastern Tropical South Pacific oxygen minimum zone, we explore the contributions from eddies to total N2 and N2O production. We highlight that roughly half of total N2 production is caused by the presence of eddies, while N2O production is drastically reduced or even consumed. These findings expand our understanding of the ways in which eddies alter nitrogen loss from the oceans, and challenge the ability of non‐eddy resolving models to accurately represent N2 and N2O production in a changing ocean. Key Points: A high resolution model is used to estimate the effect of mesoscale eddies on the N cycle in a major oxygen minimum zoneEddies simultaneously enhance fixed N loss and suppress nitrous oxide productionThese eddy effects result from the progressive inhibition of the denitrification steps by oxygen concentrations [ABSTRACT FROM AUTHOR]

Abstract Grazing dynamics are one of the most poorly constrained components of the marine carbon cycle. We use inverse modeling to infer the distribution of community‐integrated zooplankton grazing dynamics based on the ability of different grazing formulations to recreate the satellite‐observed seasonal cycle in phytoplankton biomass after controlling for physical and bottom‐up controls. We find large spatial variability in the optimal community‐integrated half saturation concentration for grazing (K1/2), with lower (higher) values required in more oligotrophic (eutrophic) biomes. This leads to a strong sigmoidal relationship between observed mean‐annual phytoplankton biomass and the optimally inferred grazing parameterization. This relationship can be used to help constrain, validate and/or parameterize next‐generation biogeochemical models.

Abstract Within oxygen minimum zones, anaerobic processes transform bioavailable nitrogen (N) into the gases dinitrogen (N2) and nitrous oxide (N2O), a potent greenhouse gas. Mesoscale eddies in these regions create heterogeneity in dissolved N tracers and O2 concentrations, influencing nonlinear N cycle reactions that depend on them. Here, we use an eddy‐resolving model of the Eastern Tropical South Pacific to show that eddies enhance N2 production by between 43% and 64% at the expense of reducing N2O production by between 94% and 104% due to both the steep increase of progressive denitrification steps at vanishing oxygen, and the more effective inhibition of N2O consumption relative to production. Our findings reveal the critical role of eddies in shaping the N cycle of oxygen minimum zones, which is not currently represented by coarse models used for climate studies.

Abstract Redox processes, aqueous and solid‐phase chemistry, and pH dynamics are key drivers of subsurface biogeochemical cycling and methanogenesis in terrestrial and wetland ecosystems but are typically not included in terrestrial carbon cycle models. These omissions may introduce errors when simulating systems where redox interactions and pH fluctuations are important, such as wetlands where saturation of soils can produce anoxic conditions and coastal systems where sulfate inputs from seawater can influence biogeochemistry. Integrating cycling of redox‐sensitive elements could therefore allow models to better represent key elements of carbon cycling and greenhouse gas production. We describe a model framework that couples the Energy Exascale Earth System Model (E3SM) Land Model (ELM) with PFLOTRAN biogeochemistry, allowing geochemical processes and redox interactions to be integrated with land surface model simulations. We implemented a reaction network including aerobic decomposition, fermentation, sulfate reduction, sulfide oxidation, methanogenesis, and methanotrophy as well as pH dynamics along with iron oxide and iron sulfide mineral precipitation and dissolution. We simulated biogeochemical cycling in tidal wetlands subject to either saltwater or freshwater inputs driven by tidal hydrological dynamics. In simulations with saltwater tidal inputs, sulfate reduction led to accumulation of sulfide, higher dissolved inorganic carbon concentrations, lower dissolved organic carbon concentrations, and lower methane emissions than simulations with freshwater tidal inputs. Model simulations compared well with measured porewater concentrations and surface gas emissions from coastal wetlands in the Northeastern United States. These results demonstrate how simulating geochemical reaction networks can improve land surface model simulations of subsurface biogeochemistry and carbon cycling.

Liebig's law of the minimum (LLM) is often used to interpret empirical biological growth data and model multiple substrates co-limited growth. However, its mechanistic foundation is rarely discussed, even though its validity has been questioned since its introduction in the 1820s. Here we first show that LLM is a crude approximation of the law of mass action, the state of art theory of biochemical reactions, and the LLM model is less accurate than two other approximations of the law of mass action: the synthesizing unit model and the additive model. We corroborate this conclusion using empirical data sets of algae and plants grown under two co-limiting substrates. Based on our analysis, we show that when growth is modeled directly as a function of substrate uptake, the LLM model improperly restricts the organism to be of fixed elemental stoichiometry, making it incapable of consistently resolving biological adaptation, ecological evolution, and community assembly. When growth is modeled as a function of the cellular nutrient quota, the LLM model may obtain good results at the risk of incorrect model parameters as compared to those inferred from the more accurate synthesizing unit model. However, biogeochemical models that implement these three formulations are needed to evaluate which formulation is acceptably accurate and their impacts on predicted long-term ecosystem dynamics. In particular, studies are needed that explore the extent to which parameter calibration can rescue model performance when the mechanistic representation of a biogeochemical process is known to be deficient.

Increased urbanization has reduced the amount of green space, resulting in a reduced carbon sink potential across urban landscapes. Through the use of biogeochemical modeling, different land use scenarios have been developed and run for the future (2020–2099) to compare and quantify the potential for change in carbon and water dynamics by having more tree cover and reducing impervious surfaces or turf lawns in Lehigh Valley, PA. These results show that the effect of deforestation is larger than the effect of reforestation. Due to young-stand age trees having a lower capacity for carbon storage than mature trees, the loss of the mature trees has a more immediate impact. The conversion of lawns or impervious surfaces to forests has somewhat similar effects, although the higher nutrients of lawns allow the forest to grow better. However, replacing impervious surfaces with trees reduces runoff more. This study shows that within the city of Bethlehem, the most socially vulnerable area benefits the most from increasing the number of trees. When converting 25% of the impervious area to forest, South Bethlehem significantly increased its vegetation carbon, productivity, and carbon storage, reduced its runoff, and generally created a safer and cleaner environment for residents.

Phytoplankton growth is controlled by multiple environmental drivers, which are all modified by climate change. While numerous experimental studies identify interactive effects between drivers, large‐scale ocean biogeochemistry models mostly account for growth responses to each driver separately and leave the results of these experimental multiple‐driver studies largely unused. Here, we amend phytoplankton growth functions in a biogeochemical model by dual‐driver interactions (CO2 and temperature, CO2 and light), based on data of a published meta‐analysis on multiple‐driver laboratory experiments. The effect of this parametrization on phytoplankton biomass and community composition is tested using present‐day and future high‐emission (SSP5‐8.5) climate forcing. While the projected decrease in future total global phytoplankton biomass in simulations with driver interactions is similar to that in control simulations without driver interactions (5%–6%), interactive driver effects are group‐specific. Globally, diatom biomass decreases more with interactive effects compared with the control simulation (−8.1% with interactions vs. no change without interactions). Small‐phytoplankton biomass, by contrast, decreases less with on‐going climate change when the model accounts for driver interactions (−5.0% vs. −9.0%). The response of global coccolithophore biomass to future climate conditions is even reversed when interactions are considered (+33.2% instead of −10.8%). Regionally, the largest difference in the future phytoplankton community composition between the simulations with and without driver interactions is detected in the Southern Ocean, where diatom biomass decreases (−7.5%) instead of increases (+14.5%), raising the share of small phytoplankton and coccolithophores of total phytoplankton biomass. Hence, interactive effects impact the phytoplankton community structure and related biogeochemical fluxes in a future ocean. Our approach is a first step to integrate the mechanistic understanding of interacting driver effects on phytoplankton growth gained by numerous laboratory experiments into a global ocean biogeochemistry model, aiming toward more realistic future projections of phytoplankton biomass and community composition. [ABSTRACT FROM AUTHOR]

Oceanic emissions of nitrous oxide (N2O) account for roughly one‐third of all natural sources to the atmosphere. Hot‐spots of N2O outgassing occur over oxygen minimum zones (OMZs), where the presence of steep oxygen gradients surrounding anoxic waters leads to enhanced N2O production from both nitrification and denitrification. However, the relative contributions from these pathways to N2O production and outgassing in these regions remains poorly constrained, in part due to shared intermediary nitrogen tracers, and the tight coupling of denitrification sources and sinks. To shed light on this problem, we embed a new, mechanistic model of the OMZ nitrogen cycle within a three‐dimensional eddy‐resolving physical‐biogeochemical model of the Eastern Tropical South Pacific (ETSP), tracking contributions from remote advection, atmospheric exchange, and local nitrification and denitrification. The model indicates that net N2O production from denitrification is approximately one order of magnitude greater than nitrification within the ETSP OMZ. However, only ∼32% of denitrification‐derived N2O production ultimately outgasses to the atmosphere in this region (contributing ∼36% of the air‐sea N2O flux on an annual basis), while the remaining is exported out of the domain. Instead, remotely produced N2O advected into the OMZ region accounts for roughly half (∼57%) of the total N2O outgassing, with smaller contributions from nitrification (∼7%). Our results suggests that, together with enhanced production by denitrification, upwelling of remotely derived N2O contributes the most to N2O outgassing over the ETSP OMZ. Key Points: In the eastern tropical South Pacific Oxygen Minimum Zone, denitrification is the dominant source of N2O productionTropical subsurface currents supply N2O to the region, fueling N2O emissions to the atmosphereSignificant amounts of locally produced N2O escape outgassing and are exported to the subtropical gyre [ABSTRACT FROM AUTHOR]

Hypoxia in coastal seas is a severe threat to marine ecosystems, with the Baltic Sea exhibiting the largest hypoxic areas worldwide. While perennial oxygen deficiency in the deep basins is a component of environmental assessments, seasonal oxygen deficiency in shallow areas is not routinely assessed. Current measurements alone cannot provide the spatio-temporal resolution needed for highly dynamic seasonal oxygen deficiency, making estimations on its duration and extent uncertain. Utilizing long-term 3D model simulations with a horizontal resolution of 3 nautical miles, we analyzed the development of seasonal oxygen deficiency in the western Baltic Sea. Different metrics (near-bottom area, water volume, duration, and frequency) and critical oxygen concentrations were analyzed for exemplary sub-basins as defined by the Helsinki Commission. Our results indicate that the extent of seasonal oxygen deficiency has continually increased in the second half of the twentieth century until the end of the 1980s and slightly decreased in the last two decades. In the 1950s, the spatial extent of oxygen deficiency was still at a low plateau before increasing, indicating that this period could be suitable as a reference period representing a good status, including naturally occurring oxygen deficiency. Overall, seasonal oxygen deficiency is a suitable indicator for describing the ecological status of the western Baltic Sea. For an application as eutrophication indicator in shallow areas, a sufficient spatio-temporal resolution of seasonal oxygen deficiency is needed which can be gained by a combination of model simulations and measurements. Further analysis is needed to integrate in situ measurements and model results to obtain the most reliable approach. [ABSTRACT FROM AUTHOR]

Multiple factors influence the spatial and temporal chlorophyll‐a concentration of marine systems. The Hudson Bay Complex has historically been seen as a large, low‐production inland sea situated in the north of Canada. However, recent field campaigns, for the BaySys project, have provided new data on primary production in the bay. Due to the Hudson Bay complex's positioning, it experiences seasonal sea‐ice cover and has many rivers draining into it, resulting in a unique estuarine‐like environment. We use the biogeochemical model BLINGv0 + DIC, coupled to the online regional physical oceanographic and sea‐ice models, NEMOv3.6 and LIM2, respectively, forced with two bias‐corrected Coupled Model Intercomparison Project 5 climate forcings (MIROC5 and MRI) to simulate the base of the ecosystem. The simulations were evaluated with chlorophyll‐a satellite imagery and observations collected in 2018 and analyzed with Empirical Orthogonal Functions to understand the underlying physical forcings and key areas of chlorophyll‐a concentration distribution. The evaluation showed that both simulations successfully reproduced the sea‐ice melt, from west to east and formation, from north to south and correlated well with spatial bloom patterns. The main drivers of phytoplankton growth are the seasonal light and nutrient levels (48% and 54%), the mixed layer depth dynamics (18% and 14%), nutrient supply from rivers (13% and 8%), and sea ice production (7%) for the MIROC5 and MRI simulations, respectively. The sea‐ice dynamics and river runoff played a significant role in the system's productivity. Therefore, with future climate change and increased river regulation projects, up to 20% of overall chlorophyll‐a may be negatively impacted. Plain Language Summary: The Hudson Bay Complex (HBC) is an inland sub‐Arctic sea that is covered by sea ice from December to June. Many rivers drain into the bay, making it a relatively fresh marine environment. Due to its size, difficulty to access, and seasonal ice cover, it is a challenging environment to sample. A recent Hudson Bay system study, BaySys, has allowed us to understand that the HBC is more productive than previously thought. Using regional numerical ocean, sea ice, and biological models, we can simulate the biological productivity in this area with two different climate forcings. Comparing the simulations with satellite and in situ observations from 2018, we can see that our models simulate sea‐ice production and the spatial and temporal pattern of chlorophyll a relatively well. Using a statistical method called Empirical Orthogonal Functions, we could identify that the physical components influencing this region's phytoplankton growth are the mixed layer depth, river runoff into the area and sea‐ice melt and freeze cycle. The HBC expects to experience significant climate change and changes in river runoff in the future. Any changes in these two physical forcings can negatively impact up to 20% of the overall chlorophyll a distribution patterns. Key Points: Physical and biogeochemical models can be used to understand the underlying drivers of primary productivityThe biogeochemical model, BLINGv0 + DIC, simulates the spatial and temporal distributions of chlorophyll a within the Hudson Bay ComplexMixed layer depth dynamics, river runoff, and sea‐ice production were observed to have the largest impact on simulated chlorophyll a [ABSTRACT FROM AUTHOR]

We examine the effects of the submesoscale in mediating the response to projected warming of phytoplankton new production and export using idealized biogeochemical tracers in a high‐resolution regional model of the Porcupine Abyssal Plain region of the North Atlantic. We quantify submesoscale effects by comparing our control run to an integration in which submesoscale motions have been suppressed using increased viscosity. Annual new production is slightly reduced by submesoscale motions in a climate representative of the early 21st‐century and slightly increased by submesoscale motions in a climate representative of the late 21st‐century. The warmer climate at the end of the 21st century reduces resolved submesoscale activity by a factor of 2–3. Resolving the submesoscale, however, does not strongly impact the projected reduction in annual production under representative warming. Organic carbon export from the surface ocean includes both direct sinking of detritus (the biological gravitational pump) and advective transport mediated pathways; the sinking component is larger than advectively mediated vertical transport by up to an order of magnitude across a wide range of imposed sinking rates. The submesoscales are responsible for most of the advective carbon export, however, which is thus largely reduced in a warmer climate. In summary, our results demonstrate that resolving more of the submesoscale has a modest effect on present‐day new production, a small effect on simulated reductions in new production under global warming, and a large effect on advectively mediated export fluxes. Plain Language Summary: We examine the effects of a warmer climate on phytoplankton growth and the sinking of organic matter in the ocean using numerical simulations of a region of the northeastern North Atlantic. We quantify the effects of physical motions at scales below 25 km (submesoscales) by suppressing them in some simulations. In this region, annual phytoplankton growth is slightly reduced when including these motions in the current climate and slightly increased when including them in the warmer climate. The submesoscale motions are less energetic in the warmer climate at the end of the 21st century. The projected reduction in phytoplankton growth over the 21st century due to climate change, however, is not very sensitive to the inclusion of submesoscales in our simulations. The transfer of organic matter from surface to depth is due to both sinking of particles and vertical motions of the water. Submesoscales are responsible for most of vertical transfer of organic matter by vertical water movement, which is largely reduced in a warmer climate. Therefore, global climate models that do not explicitly represent the submesoscale are likely to be accurate for phytoplankton growth but not for the downward transport of organic matter. Key Points: For the Porcupine Abyssal Plain region, submesoscales drive a 3% reduction in annual production in an early 21st‐century climateProjected changes in annual production over the 21st century are similar regardless of including submesoscalesIncluding submesoscales increases advectively mediated carbon export fluxes by 58%–70% [ABSTRACT FROM AUTHOR]

Eastward zonal jets at intermediate depths of 300–800 m connect the oxygen‐rich western boundary of the Atlantic basin with the oxygen minimum zones (OMZs) on the eastern boundary. They are not well represented in climate models because the low horizontal resolution of these models yields excessive viscosity. We use two physical‐biogeochemical model configurations of the Tropical Atlantic to show that the increase in resolution results in more robust intermediate zonal jets and a better representation of the OMZs. The OMZ structure is distorted at low‐resolution as surface, westward jets advect low‐oxygen waters from the eastern boundary much further west than in the climatology. The emergence of robust eastward jets in the high‐resolution run alleviate this problem and reproduce the Atlantic OMZs more accurately. The asymmetry between westward and eastward jets occurs because the former are associated with homogenous potential vorticity regions originating in the eastern boundary while the latter are associated with potential vorticity gradients. Intermediate, eastward jets constrain the westward expansion of the OMZs by supplying oxygen to their western edge. Within the OMZs, higher resolution allows a better representation of the boundary current system and eddying processes at depth which redistribute of low oxygen values from the productive eastern boundary. Basin‐scale, high‐resolution simulations reproduce more accurately the transfer of energy across scales that results in robust zonal jets as well as their impact on the ocean biogeochemistry. Accurate model predictions provide a pathway to disentangle natural and anthropogenic causes of ocean deoxygenation. Plain Language Summary: Long‐term averages of ocean velocities reveal the existence of east‐west, alternating currents along multiple latitudes. These currents are difficult to observe and model because of their small speeds at great depths. Despite their low intensity, in the long‐term they can transport tracers across the ocean basins with oxygen being a very important one as it provides conditions for aerobic respiration in so‐called oxygen minimum zones (OMZs) on the eastern side of the basin. Long‐term measurements show that oxygen concentrations are decreasing in various regions of the ocean and that OMZs are expanding, which can be a problem as these regions may become inhospitable for aerobic life. That is why we need to understand the processes that supply oxygen to OMZs and are important for their evolution with time. Models can be used as tools for testing hypotheses regarding the expansion or contraction of OMZs in the future. However, models must be shown to correctly simulate the dynamics and biogeochemistry of the region as a whole. Our results show that these intermediate east‐west current systems are important in structuring the OMZs and that higher‐resolution, basin‐scale simulations are necessary to correctly simulate their impact on oxygen concentrations in the ocean. Key Points: Intermediate, eastward zonal jets are an important oxygen supply route to the oxygen minimum zones and modulate their westward extentRobust, intermediate zonal jets emerge in a high‐resolution (3 km), basin‐scale simulation with robust eddying motions at depthA correct representation of the zonal jets in climate models is key for reliable, long‐term forecasts of ocean deoxygenation [ABSTRACT FROM AUTHOR]

Abstract Eastward zonal jets at intermediate depths of 300–800 m connect the oxygen‐rich western boundary of the Atlantic basin with the oxygen minimum zones (OMZs) on the eastern boundary. They are not well represented in climate models because the low horizontal resolution of these models yields excessive viscosity. We use two physical‐biogeochemical model configurations of the Tropical Atlantic to show that the increase in resolution results in more robust intermediate zonal jets and a better representation of the OMZs. The OMZ structure is distorted at low‐resolution as surface, westward jets advect low‐oxygen waters from the eastern boundary much further west than in the climatology. The emergence of robust eastward jets in the high‐resolution run alleviate this problem and reproduce the Atlantic OMZs more accurately. The asymmetry between westward and eastward jets occurs because the former are associated with homogenous potential vorticity regions originating in the eastern boundary while the latter are associated with potential vorticity gradients. Intermediate, eastward jets constrain the westward expansion of the OMZs by supplying oxygen to their western edge. Within the OMZs, higher resolution allows a better representation of the boundary current system and eddying processes at depth which redistribute of low oxygen values from the productive eastern boundary. Basin‐scale, high‐resolution simulations reproduce more accurately the transfer of energy across scales that results in robust zonal jets as well as their impact on the ocean biogeochemistry. Accurate model predictions provide a pathway to disentangle natural and anthropogenic causes of ocean deoxygenation.

In deciduous forests, spring leaf development and fall leaf senescence regulate the timing and duration of photosynthesis and transpiration. Being able to model these dates is therefore critical to accurately representing ecosystem processes in biogeochemical models. Despite this, there has been relatively little effort to improve internal phenology predictions in widely used biogeochemical models. Here, we optimized the phenology algorithms in a regionally developed biogeochemical model (PnET-CN) using phenology data from eight mid-latitude PhenoCam sites in eastern North America. We then performed a sensitivity analysis to determine how the optimization affected future predictions of carbon, water, and nitrogen cycling at Bartlett Experimental Forest, New Hampshire. Compared to the original PnET-CN phenology models, our new spring and fall models resulted in shorter season lengths and more abrupt transitions that were more representative of observations. The new phenology models affected daily estimates and interannual variability of modeled carbon exchange, but they did not have a large influence on the magnitude or long-term trends of annual totals. Under future climate projections, our new phenology models predict larger shifts in season length in the fall (1.1–3.2 days decade−1) compared to the spring (0.9–1.5 days decade−1). However, for every day the season was longer, spring had twice the effect on annual carbon and water exchange totals compared to the fall. These findings highlight the importance of accurately modeling season length for future projections of carbon and water cycling. [ABSTRACT FROM AUTHOR]

A next-generation biogeochemical model was developed to explore the impact of the native water source on microbially induced desaturation and precipitation (MIDP) via denitrification. MIDP is a non-disruptive, nature-based ground improvement technique that offers the promise of cost-effective mitigation of earthquake-induced soil liquefaction under and adjacent to existing structures. MIDP leverages native soil bacteria to reduce the potential for liquefaction triggering in the short term through biogenic gas generation (treatment completed within hours to days) and over a longer term through calcium carbonate precipitation (treatment completed in weeks to months). This next-generation biogeochemical model expands earlier modeling to consider multi-phase speciation, bacterial competition, inhibition, and precipitation. This biogeochemical model was used to explore the impact of varying treatment recipes on MIDP products and by-products in a natural seawater environment. The case study presented herein demonstrates the importance of optimizing treatment recipes to minimize unwanted by-products (e.g., H2S production) or incomplete denitrification (e.g., nitrate and nitrite accumulation). [ABSTRACT FROM AUTHOR]

Background: Globally, rice systems are a major source of atmospheric CH4 and for major rice‐producing countries, such as Vietnam, CH4 as well as N2O emissions from agricultural land used for rice production may represent about one‐fourth of total national anthropogenic greenhouse gas (GHG) emissions. However, national‐scale estimates of GHG emissions from rice systems are uncertain with regard to its magnitude, spatial distribution, and seasonality. Aims: Here, we used the biogeochemical model LandscapeDNDC to calculate emissions of CH4 and N2O from rice systems in Vietnam (Tier 3 IPCC approach). Our objectives were to identify hotspot regions of emissions and to assess the contribution of N2O to the total non‐CO2 (CH4+N2O) GHG balance of rice systems as well as the seasonal and interannual variability of fluxes in dependence of uncertain input data on field management. Methods: The biogeochemical model LandscapeDNDC model was linked to publicly available information on climate, soils, and land management (fertilization, irrigation, crop rotation) for calculating a national inventory in daily time steps of CH4 and N2O emissions from rice systems at a spatial resolution of 0.083° × 0.083°. Uncertainty in management practices related to fertilization, use of harvest residues or irrigation water, and its effects on simulated CH4 and N2O fluxes was accounted for by Latin Hypercube Sampling of probability distribution functions. Results: Our study shows that CH4 and N2O fluxes from rice systems in Vietnam are highly seasonal, with national CH4 and N2O emissions totaling to about 2600 Gg CH4 year–1 and 42 Gg N2O year–1, respectively. Highest emissions were simulated for double and triple rice cropping systems in the Mekong Delta region. Yield‐scaled emissions varied largely in a range of 300–3000 kg CO2‐eq Mg–1 year–1, with CH4 emissions during the rice season(s) dominating (>82%) the total annual non‐CO2 GHG balance of rice systems. In our study, uncertainty in field management information (nitrogen fertilization, ratio synthetic to organic fertilization, residue management, availability of irrigation water) were major drivers of uncertainty of the national CH4 and N2O emission inventory. Conclusions: Our study shows that Tier 3 approaches, that is, process‐oriented model approaches combined with GIS databases, for estimating national‐scale GHG emissions from rice systems are ready to be applied at national scale. Generally, this approach is powerful as it allows to identify regions with elevated emissions, thereby accounting not only for CH4, but as well for N2O emissions. However, our study also shows that specifically better information on land management is required to narrowing uncertainties. [ABSTRACT FROM AUTHOR]

Biogenetic sulfide dimethylsulfide (DMS) plays a major role on the global climate, especially in Arctic Ocean. Accurate simulate DMS concentration is an important task. Here we introduced both biogeochemical depth-averaged model G93 and its extension model-one dimensional DMS model. Both surface concentrations, vertical profiles of chlorophyll (CHL) and DMS are simulated using the two models within southern Greenland Sea (0°E–10°E, 70°N–75°N) during year 2012. As the input data for the models simulations, the spatial monthly mean of methodology forcings including sea surface temperature (SST), wind speed (WIND), cloud cover (CLD), sea ice concentration (ICE) and mixed layer depth (MLD) are calculated. Satellite 8-day time series of chlorophyll-a (CHL) are used as observation data for CHL related parameter calibrations. Simó's imperial formula is used as the monthly DMS observation data. The Genetic Algorithm technique is used for the parameter calibrations. The simulation results show that the most DMS related surface concentrations exhibit the normal distributions with peak during May. CHL, DMS and DMSP (dimethylsulphoniopropionate) vertical profiles are obtained for July, August and September in year 2012. CHL had the higher variation of subsurface concentration maximum (SCM) in July with the lower surface concentration value. DMS had surface higher and subsurface lower profile for the all three months. DMSP also had subsurface high in July. The SCM CHL diurnal variation in the subsurface also can be resulted from diurnal changes in MLD and vertical mixing variations, plus photolysis and wind-driven ventilations. [ABSTRACT FROM AUTHOR]

This paper presents a novel chain model named soil–food–human (SFH) for clarifying the biogeochemical cascades among the triple challenges of cadmium contamination, food safety, and related public health effect. The model was developed based on the integration of spatial distribution pattern of soil environment and the biogeochemical process of cadmium in soil–rice–human health, and it was validated through a case study. In soil environment terms, SFH predicted the spatial distribution of soil properties with an average prediction accuracy of 82.28%. In food production terms, the SFH can identify the safe production zones for planting rice and unsafe area for adjusting cropping systems with a relative error of 39.41%. In food consumption terms, SFH mapped the high-resolution map of cadmium exposure dose, which gives a new solution to assess the food safety risks for self-sufficient populations. For the health effect of rice cadmium exposure, SFH simulated the spatiotemporal pattern of urinary cadmium based on toxicokinetic which revealed the health effect of rice cadmium exposure. The chain model provides a new insight in understanding the biogeochemical cascades between food production, food safety, and public health, making it possible to develop a comprehensive strategy to tackle cadmium pollution in soil–rice–human health system.

Using total suspended matter (TSM), particulate organic carbon (POC), and particulate nitrogen data, this study investigated the potential vertical POC flux and transport in the South Yellow Sea (SYS). The biogenic production and resuspension fraction (i.e., the proportion of resuspended particles in TSM) were estimated using an ecosystem model and a vertical mixing model. They were verified against reported sediment trap and primary productivity data. The estimates of resuspension fraction showed substantial uncertainty of 50% in summer likely owing to the potential errors of model parameter estimation and the influence of other unexplored biophysical processes such as biological degradation, upwelling, and monsoons; however, the estimates of resuspension fraction showed less uncertainty in other seasons (10% of the resuspension fraction. It will be necessary to acquire additional field data covering a larger spatiotemporal scale to establish an integrated network of the SYS carbon budget.

Primary production in the Central Arctic Ocean (CAO) is limited by light and bioavailable nutrients. With the decline of the sea‐ice cover in recent decades, and the resulting increase in light availability, nitrate limitation has been speculated to become more prominent. We used an eddy‐permitting biogeochemical model simulation to estimate nitrate advective fluxes at different spatio‐temporal scales (synoptic, mesoscale and sub‐mesoscale) over the 1985–2015 period. We found that the pan‐Arctic continental slope contributes disproportionately to the Dissolved Inorganic Nitrogen supply and that this supply is intensifying through two main processes: lateral eddy transport and upwelling. Despite this increasing supply in nitrate and an intensification of ocean dynamics, the nutrient supply is decreasing everywhere else in the central basins and the simulation indicates that the CAO is still shifting from light to nutrient limitation. Plain Language Summary: Microscopic algae called phytoplankton are the base of the trophic chain, sustaining the entire Arctic Ocean (AO) ecosystem. In the central parts of the AO, multi‐year sea‐ice used to limit transmission of light in the surface ocean and therefore control phytoplankton growth and primary productivity. However, the massive loss in sea‐ice during the last 3 decades allowed more and more light to penetrate the water column, making nutrient availability the main bottom‐up control of the AO productivity. A major part of the bio‐available nutrients reaching the surface in the central AO are transported with ocean currents from the adjacent North Atlantic and Pacific and from deeper water masses. Using a biogeochemical model resolving processes at high spatial resolution, we were able to quantify the different transport pathways of nutrients with ocean currents and revealed that despite increasing supply along the anticlockwise flowing boundary current, the central AO is still running into more severe nutrient limitation. Key Points: The pan‐Arctic continental slope contributes disproportionately to the transport of nutrients in the Central Arctic Ocean (CAO)The nutrient supply intensifies along the slope through upwelling and lateral eddy transport but decreases in the interior basinsDespite an intensification of ocean dynamics, the CAO is still shifting from a light‐limited to a nutrient‐limited system [ABSTRACT FROM AUTHOR]

Worldwide, rice production contributes about 10% of total greenhouse gas (GHG) emissions from the agricultural sector, mainly due to CH4 emissions from continuously flooded fields. Alternate Wetting and Drying (AWD) is a promising crop technology for mitigating CH4 emissions and reducing the irrigation water currently being applied in many of the world's top rice‐producing countries. However, decreased emissions of CH4 may be partially counterbalanced by increased N2O emissions. In this case study for the Philippines, the national mitigation potential of AWD is explored using the process‐based biogeochemical model LandscapeDNDC. Simulated mean annual CH4 emissions under conventional rice production for the time period 2000–2011 are estimated as 1,180 ± 163 Gg CH4 yr−1. During the cropping season, this is about +16% higher than a former estimate using emission factors. Scenario simulations of nationwide introduction of AWD in irrigated landscapes suggest a considerable decrease in CH4 emissions by −23%, while N2O emissions are only increased by +8%. Irrespective of field management, at national scale, the radiative forcing of irrigated rice production is always dominated by CH4 (>95%). The reduction potential of GHG emissions depends on, for example, number of crops per year, residue management, amount of applied irrigation water, and sand content. Seasonal weather conditions also play an important role since the mitigation potential of AWD is almost double as high in dry as compared to wet seasons. Furthermore, this study demonstrates the importance of temporal continuity, considering off‐season emissions and the long‐term development of GHG emissions across multiple years. Plain Language Summary: Worldwide, rice production contributes to about 10% of total greenhouse gas emissions of the agricultural sector mainly due to CH4 emissions from fields that are continuously flooded. Alternate Wetting and Drying (AWD) is an alternative cropping practice where fields are irrigated a few days after the disappearance of the ponded water. This study explores the mitigation potential of nationwide introduction of AWD in the Philippines. Results from the application of a process‐based model suggest a considerable decrease in CH4 emissions by −23%. Compared to N2O, CH4 is responsible for more than 95% of the total radiative forcing under conventional or AWD field management. Key Points: Nationwide, Alternate Wetting and Drying (AWD) reduces CH4 emissions by −23%N2O emissions contribute to less than 5% to the total radiative forcing under conventional or AWD field managementMitigation of AWD depends on, for example, seasonal weather conditions, cropping intensity, irrigation, residue management, and soil texture [ABSTRACT FROM AUTHOR]

Mitigation of greenhouse gas emissions from agriculture requires an understanding of spatial‐temporal dynamics of nitrous oxide (N2O) emissions. Process‐based models can quantify N2O emissions from agricultural soils but have rarely been applied to regions with highly diverse agriculture. In this study, a process‐based biogeochemical model, DeNitrification‐DeComposition (DNDC), was applied to quantify spatial‐temporal dynamics of direct N2O emissions from California cropland employing a wide range of cropping systems. DNDC simulated direct N2O emissions from nitrogen (N) inputs through applications of synthetic fertilizers and crop residues during 2000–2015 by linking the model with a spatial‐temporal differentiated database containing data on weather, crop areas, soil properties, and management. Simulated direct N2O emissions ranged from 3,830 to 7,875 tonnes N2O‐N yr−1, representing 0.73%–1.21% of the N inputs. N2O emission rates were higher for hay and field crops and lower for orchard and vineyard. State cropland total N2O emissions showed a decreasing trend primarily driven by reductions of cropland area and N inputs, the trend toward growing more orchard, and changes in irrigation. Annual direct N2O emissions declined by 47% from 2000 to 2015. Simulations showed N2O emission variations could be explained not only by cropland area and N fertilizer inputs but also climate, soil properties, and management besides N fertilization. The detailed spatial‐temporal emission dynamics and driving factors provide knowledge toward effective N2O mitigation and highlight the importance of coupling process‐based models with high‐resolution data for characterizing the spatial‐temporal variability of N2O emissions in regions with diverse croplands. Plain Language Summary: Nitrous oxide (N2O) is the third most important greenhouse gas and an important ozone‐depleting substance. It is challenging to quantify N2O emissions from croplands in regions with highly diverse agriculture. In this study, a process‐based biogeochemical model, DeNitrification‐DeComposition (DNDC), was applied to quantify spatial‐temporal dynamics of direct N2O emissions from California cropland, which has a wide range of cropping systems. DNDC simulated direct N2O emissions from nitrogen (N) inputs from synthetic fertilizers and crop residues during 2000–2015. Simulated direct N2O emissions ranged from 3,830 to 7,875 tonnes N yr−1, representing 0.73%–1.21% of the N inputs from synthetic fertilizers and crop residues. Simulated annual direct N2O emissions from California cropland declined by 47% from 2000 to 2015. This decreasing trend could be explained by reductions of cropland area and associated N inputs, the trend toward growing more orchard crops, and changes in irrigation. This study characterized spatial‐temporal dynamics of N2O emissions from California cropland, and provided knowledge toward effective N2O mitigation. It also highlights the importance of coupling process‐based models with high‐resolution data to quantify the spatial‐temporal variability of N2O emissions in regions with complicated agricultural systems. Key Points: Direct nitrous oxide (N2O) emission from N inputs of fertilizers and residues ranged from 3,830 to 7,875 ton N yr−1 from 2000 to 2015 in California croplandSimulated annual direct N2O emission decreased by 47% from 2000 to 2015, primarily driven by changes in land use and farming managementCoupling model with environmental and activity database improved characterization of direct N2O emissions from California cropland [ABSTRACT FROM AUTHOR]

Abstract Mitigation of greenhouse gas emissions from agriculture requires an understanding of spatial‐temporal dynamics of nitrous oxide (N2O) emissions. Process‐based models can quantify N2O emissions from agricultural soils but have rarely been applied to regions with highly diverse agriculture. In this study, a process‐based biogeochemical model, DeNitrification‐DeComposition (DNDC), was applied to quantify spatial‐temporal dynamics of direct N2O emissions from California cropland employing a wide range of cropping systems. DNDC simulated direct N2O emissions from nitrogen (N) inputs through applications of synthetic fertilizers and crop residues during 2000–2015 by linking the model with a spatial‐temporal differentiated database containing data on weather, crop areas, soil properties, and management. Simulated direct N2O emissions ranged from 3,830 to 7,875 tonnes N2O‐N yr−1, representing 0.73%–1.21% of the N inputs. N2O emission rates were higher for hay and field crops and lower for orchard and vineyard. State cropland total N2O emissions showed a decreasing trend primarily driven by reductions of cropland area and N inputs, the trend toward growing more orchard, and changes in irrigation. Annual direct N2O emissions declined by 47% from 2000 to 2015. Simulations showed N2O emission variations could be explained not only by cropland area and N fertilizer inputs but also climate, soil properties, and management besides N fertilization. The detailed spatial‐temporal emission dynamics and driving factors provide knowledge toward effective N2O mitigation and highlight the importance of coupling process‐based models with high‐resolution data for characterizing the spatial‐temporal variability of N2O emissions in regions with diverse croplands.

Abstract The Ordovician (Hirnantian; 445 Ma) hosts the second most severe mass extinction in Earth history, coinciding with Gondwanan glaciation and increased geochemical evidence for marine anoxia. It remains unclear whether cooling, expanded oxygen deficiency, or a combination drove the Late Ordovician Mass Extinction (LOME). Here, we present combined iodine and sulfur isotope geochemical data from three globally distributed carbonate successions to constrain changes in local and global marine redox conditions. Iodine records suggest locally anoxic conditions were potentially pervasive on shallow carbonate shelves, while sulfur isotopes suggest a reduction in global euxinic (anoxic and sulfidic) conditions. Late Katian sulfate‐sulfur isotope data show a large negative excursion that initiated during elevated sea level and continued through peak Hirnantian glaciation. Geochemical box modeling suggests a combination of decreasing pyrite burial and increasing weathering are required to drive the observed negative excursion suggesting a ∼3% decrease of global seafloor euxinia during the Late Ordovician. The sulfur datasets provide further evidence that this trend was followed by increases in euxinia which coincided with eustatic sea‐level rise during subsequent deglaciation in the late Hirnantian. A persistence of shelf anoxia against a backdrop of waning then waxing global euxinia was linked to the two LOME pulses. These results place important constraints on local and global marine redox conditions throughout the Late Ordovician and suggest that non‐sulfidic shelfal anoxia—along with glacioeustatic sea level and climatic cooling—were important environmental stressors that worsened conditions for marine fauna, resulting in the second‐largest mass extinction in Earth history and the only example during an icehouse climate.

The Miocene period saw substantially warmer Earth surface temperatures than today, particularly during a period of global warming called the Mid Miocene Climatic Optimum (MMCO; ∼17–15 Ma). However, the long‐term drivers of Miocene climate remain poorly understood. By using a new continuous climate‐biogeochemical model (SCION), we can investigate the interaction between volcanism, climate and biogeochemical cycles through the Miocene. We identify high tectonic CO2 degassing rates and further emissions associated with the emplacement of the Columbia River Basalt Group as the primary driver of the background warmth and the MMCO respectively. We also find that enhanced weathering of the basaltic terrane and input of explosive volcanic ash to the oceans are not sufficient to drive the immediate cooling following the MMCO and suggest that another mechanism, perhaps the change in ocean chemistry due to massive evaporite deposition, was responsible. Plain Language Summary: The Miocene period was much warmer than today, with the Mid Miocene Climatic Optimum (MMCO, roughly 17–15 million years ago) especially warm. Due to the high surface temperatures, comparisons to projected climatic conditions as a result of anthropogenic climate change have been drawn. However, the drivers of climate during the Miocene are not well understood. By using a new type of climate model, we investigate the impact volcanic eruptions had on the period, and link the extreme warmth of the MMCO with greenhouse gas release from the eruption of the Columbia River Basalts Group (CRBG). We find weathering of the CRBG does not explain the cooling at the end of the MMCO, and so discuss other potential explanations such as evaporite deposition. Key Points: A new climate‐biogeochemical model allows investigation of drivers of climate change in the MioceneColumbia River Basalt Group (CRBG) degassing is sufficient to have caused the Mid Miocene Climatic Optimum (MMCO)Weathering of CRBG insufficient to drive cooling after the MMCO. This may be linked to evaporite deposition and changes to marine chemistry [ABSTRACT FROM AUTHOR]

The Southern California Bight (SCB), an eastern boundary upwelling system, is impacted by global warming, acidification, and oxygen loss and receives anthropogenic nutrients from a coastal population of 20 million people. We describe the configuration, forcing, and validation of a realistic, submesoscale‐resolving ocean model as a tool to investigate coastal eutrophication. This modeling system represents an important achievement because it strikes a balance of capturing the forcing by U.S. Pacific Coast‐wide phenomena, while representing the bathymetric features and submesoscale circulation that affect the transport of nutrients from natural and human sources. Moreover, the model allows simulations at time scales that approach the interannual frequencies of ocean variability. The model simulation is evaluated against a broad suite of observational data throughout the SCB, showing realistic depiction of the mean state and its variability with satellite and in situ measurements of state variables and biogeochemical rates. The simulation reproduces the main structure of the seasonal upwelling front, the mean current patterns, the dispersion of wastewater plumes, as well as their seasonal variability. Furthermore, it reproduces the mean distributions of key biogeochemical and ecosystem properties and their variability. Biogeochemical rates reproduced by the model, such as primary production and nitrification, are also consistent with measured rates. This validation exercise demonstrates the utility of using fine‐scale resolution modeling and local observations to identify, investigate, and communicate uncertainty to stakeholders to support management decisions on local anthropogenic nutrient discharges to coastal zones. Plain Language Summary: We applied and validated an ocean numerical model to investigate the effects of land‐based and atmospheric nutrient loading on coastal eutrophication and its effects on carbon, nitrogen, and oxygen cycles of the Southern California Bight, an upwelling‐dominated marine embayment on the U.S. West Coast. The model is capable of high‐resolution, multiyear hindcast simulations, which enable investigations to disentangle natural variability, climate change, and local human pressures that accelerate land‐based and atmospheric nutrient loads. The model performance assessment illustrates that it faithfully reproduces monitored ocean properties related to algal blooms, oxygen, and water acidity, among others, that can be traced to land‐based and atmospheric inputs of nutrients and carbon from human activities. The model performance assessment helps to constrain uncertainties in predictions to support ongoing conversations on approaches to reduce the effects of climate change, including considerations of management of local nutrient and carbon inputs. Key Points: The model reproduces gradients of physical and biogeochemical properties in the SCB, including seasonal, interannual, and ENSO variabilityResolving submesoscale currents increases the vertical flux of nutrients and allows realistic reproduction of wastewater plume circulationWastewater and riverine inputs are consistent with intensified nutrients, productivity, export, and remineralization nearshore [ABSTRACT FROM AUTHOR]

Fe(II) oxidation coupled to nitrate reduction is a widely observed metabolism. However, to what extent the observed Fe(II) oxidation is driven enzymatically or abiotically by metabolically produced nitrite remains puzzling. To distinguish between biotic and abiotic reactions, we cultivated the mixotrophic nitrate-reducing Fe(II)-oxidizing Acidovorax strain BoFeN1 over a wide range of temperatures and compared it to abiotic Fe(II) oxidation by nitrite at temperatures up to 60°C. The collected experimental data were subsequently analyzed through biogeochemical modeling. At 5°C, BoFeN1 cultures consumed acetate and reduced nitrate but did not significantly oxidize Fe(II). Abiotic Fe(II) oxidation by nitrite at different temperatures showed an Arrhenius-type behavior with an activation energy of 80±7 kJ/mol. Above 40°C, the kinetics of Fe(II) oxidation were abiotically driven, whereas at 30°C, where BoFeN1 can actively metabolize, the model-based interpretation strongly suggested that an enzymatic pathway was responsible for a large fraction (ca. 62%) of the oxidation. This result was reproduced even when no additional carbon source was present. Our results show that at below 30°C, i.e. at temperatures representing most natural environments, biological Fe(II) oxidation was largely responsible for overall Fe(II) oxidation, while abiotic Fe(II) oxidation by nitrite played a less important role. [ABSTRACT FROM AUTHOR]

Soil microbes drive decomposition of soil organic matter (SOM) and regulate soil carbon (C) dynamics. Process‐based models have been developed to quantify changes in soil organic carbon (SOC) and carbon dioxide (CO2) fluxes in agricultural ecosystems. However, microbial processes related to SOM decomposition have not been, or are inadequately, represented in these models, limiting predictions of SOC responses to changes in microbial activities. In this study, we developed a microbial‐mediated decomposition model based on a widely used biogeochemical model, DeNitrification‐DeComposition (DNDC), to simulate C dynamics in agricultural ecosystems. The model simulates organic matter decomposition, soil respiration, and SOC formation by simulating microbial and enzyme dynamics and their controls on decomposition, and considering impacts of climate, soil, crop, and farming management practices (FMPs) on C dynamics. When evaluated against field observations of net ecosystem CO2 exchange (NEE) and SOC change in two winter wheat systems, the model successfully captured both NEE and SOC changes under different FMPs. Inclusion of microbial processes improved the model's performance in simulating peak CO2 fluxes induced by residue return, primarily by capturing priming effects of residue inputs. We also investigated impacts of microbial physiology, SOM, and FMPs on soil C dynamics. Our results demonstrated that residue or manure input drove microbial activity and predominantly regulated the CO2 fluxes, and manure amendment largely regulated long‐term SOC change. The microbial physiology had considerable impacts on the microbial activities and soil C dynamics, emphasizing the necessity of considering microbial physiology and activities when assessing soil C dynamics in agricultural ecosystems. Plain Language Summary: Soil microbes drive decomposition of soil organic matter (SOM) and regulate soil carbon (C) dynamics. Process‐based models are useful tools for quantifying changes in soil organic carbon (SOC) and carbon dioxide (CO2) fluxes in agricultural ecosystems. However, microbial processes related to SOM decomposition have not been, or are inadequately, represented in these models, limiting predictions of SOC responses to changes in microbial activities. We developed a microbial‐mediated decomposition model based on a widely used biogeochemical model, DeNitrification‐DeComposition (DNDC), to simulate C dynamics in agricultural ecosystems. The model simulates organic matter decomposition, soil respiration, and SOC formation by simulating microbial dynamics and controls on decomposition, and considering impacts of climate, soil, crop, and farming management practices (FMPs) on C dynamics. We also investigated impacts of microbial physiology, SOM, and FMPs on soil C dynamics. Our results demonstrated that residue or manure input drove microbial activity and predominantly regulated CO2 fluxes, and manure amendment largely regulated long‐term SOC change. The microbial physiology had considerable impacts on microbial activities and soil C dynamics, emphasizing the necessity of considering microbial physiology and activities when assessing soil C dynamics in agricultural ecosystems. These results provide insights in simulating microbial‐mediated soil C dynamics in agricultural ecosystems. Key Points: A biogeochemical model was improved to simulate microbe‐driven soil organic matter (SOM) decomposition and carbon dynamics in agricultural ecosystemsThe model simulates carbon dynamics by simulating both microbial controls and impacts of soil, crop, and farming management practicesCarbon input drives microbial activity and exerts large impacts on carbon dynamics while microbes play a central role in decomposing SOM [ABSTRACT FROM AUTHOR]

Abstract 21st‐century modeling of greenhouse gas (GHG) emissions from bioenergy crops is necessary to quantify the extent to which bioenergy production can mitigate climate change. For over 30 years, the Century‐based biogeochemical models have provided the preeminent framework for belowground carbon and nitrogen cycling in ecosystem and earth system models. While monthly Century and the daily time‐step version of Century (DayCent) have advanced our ability to predict the sustainability of bioenergy crop production, new advances in feedstock generation, and our empirical understanding of sources and sinks of GHGs in soils call for a re‐visitation of DayCent's core model structures. Here, we evaluate current challenges with modeling soil carbon dynamics, trace gas fluxes, and drought and age‐related impacts on bioenergy crop productivity. We propose coupling a microbial process‐based soil organic carbon and nitrogen model with DayCent to improve soil carbon dynamics. We describe recent improvements to DayCent for simulating unique plant structural and physiological attributes of perennial bioenergy grasses. Finally, we propose a method for using machine learning to identify key parameters for simulating N2O emissions. Our efforts are focused on meeting the needs for modeling bioenergy crops; however, many updates reviewed and suggested to DayCent will be broadly applicable to other systems.

Abstract Soil microbes drive decomposition of soil organic matter (SOM) and regulate soil carbon (C) dynamics. Process‐based models have been developed to quantify changes in soil organic carbon (SOC) and carbon dioxide (CO2) fluxes in agricultural ecosystems. However, microbial processes related to SOM decomposition have not been, or are inadequately, represented in these models, limiting predictions of SOC responses to changes in microbial activities. In this study, we developed a microbial‐mediated decomposition model based on a widely used biogeochemical model, DeNitrification‐DeComposition (DNDC), to simulate C dynamics in agricultural ecosystems. The model simulates organic matter decomposition, soil respiration, and SOC formation by simulating microbial and enzyme dynamics and their controls on decomposition, and considering impacts of climate, soil, crop, and farming management practices (FMPs) on C dynamics. When evaluated against field observations of net ecosystem CO2 exchange (NEE) and SOC change in two winter wheat systems, the model successfully captured both NEE and SOC changes under different FMPs. Inclusion of microbial processes improved the model's performance in simulating peak CO2 fluxes induced by residue return, primarily by capturing priming effects of residue inputs. We also investigated impacts of microbial physiology, SOM, and FMPs on soil C dynamics. Our results demonstrated that residue or manure input drove microbial activity and predominantly regulated the CO2 fluxes, and manure amendment largely regulated long‐term SOC change. The microbial physiology had considerable impacts on the microbial activities and soil C dynamics, emphasizing the necessity of considering microbial physiology and activities when assessing soil C dynamics in agricultural ecosystems.

Environmental omics and molecular-biological data have been proposed to yield improved quantitative predictions of biogeochemical processes. The abundances of functional genes and transcripts relate to the number of cells and activity of microorganisms. However, whether molecular-biological data can be quantitatively linked to reaction rates remains an open question. We present an enzyme-based denitrification model that simulates concentrations of transcription factors, functional-gene transcripts, enzymes, and solutes. We calibrated the model using experimental data from a well-controlled batch experiment with the denitrifier Paracoccous denitrificans. The model accurately predicts denitrification rates and measured transcript dynamics. The relationship between simulated transcript concentrations and reaction rates exhibits strong non-linearity and hysteresis related to the faster dynamics of gene transcription and substrate consumption, relative to enzyme production and decay. Hence, assuming a unique relationship between transcript-to-gene ratios and reaction rates, as frequently suggested, may be an erroneous simplification. Comparing model results of our enzyme-based model to those of a classical Monod-type model reveals that both formulations perform equally well with respect to nitrogen species, indicating only a low benefit of integrating molecular-biological data for estimating denitrification rates. Nonetheless, the enzyme-based model is a valuable tool to improve our mechanistic understanding of the relationship between biomolecular quantities and reaction rates. Furthermore, our results highlight that both enzyme kinetics (i.e., substrate limitation and inhibition) and gene expression or enzyme dynamics are important controls on denitrification rates.

Understanding patterns and seasonal variations of excessive nutrients in surface water from anthropogenic activities is important for pollution control. In this study, we developed an integrated biogeochemical modeling framework for nitrogen exchanges among the atmosphere, terrestrial, and aquatic ecosystems. A land surface model, a terrestrial nitrogen cycle model, and a riverine hydrodynamics model incorporated with a river temperature model were consolidated and driven by multiple nitrogen sources related to anthropogenic activities. We estimated the global nitrogen loading and transporting in global rivers, with consideration of seasonal variations, and the validation demonstrates the reliability of the proposed model. The total dissolved inorganic nitrogen (DIN) flow rate is accumulated following rivers and it has high total DIN loads even in regions with low population density but large basin area, such as those at high latitudes. This study successfully improves estimation of nitrogen loading on global scale with consideration of seasonal variation. Our results show consistent trends with the observed data of DIN concentrations in global rivers, where all above variables are greatly affected by seasonal variations. The results also reflect the monthly-variant nitrogen inputs help produce closer DIN concentration estimates to observations, which will possibly stress the need for further study on seasonal variability of anthropogenic emissions. [ABSTRACT FROM AUTHOR]

Environmental omics and molecular-biological data have been proposed to yield improved quantitative predictions of biogeochemical processes. The abundances of functional genes and transcripts relate to the number of cells and activity of microorganisms. However, whether molecular-biological data can be quantitatively linked to reaction rates remains an open question. We present an enzyme-based denitrification model that simulates concentrations of transcription factors, functional-gene transcripts, enzymes, and solutes. We calibrated the model using experimental data from a well-controlled batch experiment with the denitrifier Paracoccous denitrificans. The model accurately predicts denitrification rates and measured transcript dynamics. The relationship between simulated transcript concentrations and reaction rates exhibits strong non-linearity and hysteresis related to the faster dynamics of gene transcription and substrate consumption, relative to enzyme production and decay. Hence, assuming a unique relationship between transcript-to-gene ratios and reaction rates, as frequently suggested, may be an erroneous simplification. Comparing model results of our enzyme-based model to those of a classical Monod-type model reveals that both formulations perform equally well with respect to nitrogen species, indicating only a low benefit of integrating molecular-biological data for estimating denitrification rates. Nonetheless, the enzyme-based model is a valuable tool to improve our mechanistic understanding of the relationship between biomolecular quantities and reaction rates. Furthermore, our results highlight that both enzyme kinetics (i.e., substrate limitation and inhibition) and gene expression or enzyme dynamics are important controls on denitrification rates. [ABSTRACT FROM AUTHOR]

With global population growth and climate change, food security and global warming have emerged as two major challenges to agricultural development. Plastic film mulching (PM) has long been used to improve yields in rain-fed agricultural systems, but few studies have focused on soil gas emissions from mulched rainfed potatoes on a long-term and regional scale. This study integrated field data with the Denitrification-Decomposition (DNDC) model to evaluate the impacts of PM on potato yields, greenhouse gas (GHG) and ammonia (NH 3) emissions in rainfed agricultural systems in China. We found that PM increased potato yield by 39.7 % (1505 kg ha−1), carbon dioxide (CO 2) emissions by 15.4 % (123 kg CO 2 eq ha−1), nitrous oxide (N 2 O) emissions by 47.8 % (1016 kg CO 2 eq ha−1), and global warming potential (GWP) by 38.9 % (1030 kg CO 2 eq ha−1), while NH 3 volatilization decreased by 33.9 % (8.4 kg NH 3 ha−1), and methane (CH 4) emissions were little changed compared to CK. Specifically, the yield after PM significantly increased in South China (SC), North China (NC), and Northwest China (NWC), with increases of 66.1 % (2429 kg ha−1), 44.1 % (1173 kg ha−1), and 43.6 % (956 kg ha−1) compared to CK, respectively. The increase in GWP and greenhouse gas emission intensity (GHGI) under PM was more pronounced in the Northeast China (NEC) and NWC regions, with respective increases of 57.1 % and 60.2 % in GWP, 16.9 % and 10.3 % in GHGI. While in the Middle and Lower reaches of the Yangtze River (MLYR) and SC, PM decreased GHGI with 10.2 % and 31.1 %, respectively. PM significantly reduced NH 3 emissions in all regions and these reductions were most significant in Southwest China (SWC), SCand MLYR, which were 41 %, 38.0 %, and 38.0 % lower than CK, respectively. In addition, climatic and edaphic variables were the main contributors to GHG and NH 3 emissions. In conclusion, it is appropriate to promote the use of PM in the MLYR and SC regions, because of the ability to increase yields while reducing environmental impacts (lower GHGI and NH 3 emissions). The findings provide a theoretical basis for sustainable agricultural production of PM potatoes. [Display omitted] • Plastic mulch boosts potato yield and CO 2 , N 2 O emissions, reduces NH 3 emissions. • Global warming potential is 38.9 % higher with mulching compared to without. • Climatic and edaphic factors primarily influence GHG and NH 3 emissions. [ABSTRACT FROM AUTHOR]

The ocean coastal‐shelf‐slope ecosystem west of the Antarctic Peninsula (WAP) is a biologically productive region that could potentially act as a large sink of atmospheric carbon dioxide. The duration of the sea‐ice season in the WAP shows large interannual variability. However, quantifying the mechanisms by which sea ice impacts biological productivity and surface dissolved inorganic carbon (DIC) remains a challenge due to the lack of data early in the phytoplankton growth season. In this study, we implemented a circulation, sea‐ice, and biogeochemistry model (MITgcm‐REcoM2) to study the effect of sea ice on phytoplankton blooms and surface DIC. Results were compared with satellite sea‐ice and ocean color, and research ship surveys from the Palmer Long‐Term Ecological Research (LTER) program. The simulations suggest that the annual sea‐ice cycle has an important role in the seasonal DIC drawdown. In years of early sea‐ice retreat, there is a longer growth season leading to larger seasonally integrated net primary production (NPP). Part of the biological uptake of DIC by phytoplankton, however, is counteracted by increased oceanic uptake of atmospheric CO2. Despite lower seasonal NPP, years of late sea‐ice retreat show larger DIC drawdown, attributed to lower air‐sea CO2 fluxes and increased dilution by sea‐ice melt. The role of dissolved iron and iron limitation on WAP phytoplankton also remains a challenge due to the lack of data. The model results suggest sediments and glacial meltwater are the main sources in the coastal and shelf regions, with sediments being more influential in the northern coast. Plain Language Summary: Some coastal ocean areas of Antarctica, like the West Antarctic Peninsula (WAP), have high biological productivity, which indicates they could absorb more atmospheric CO2. Studying these regions is hard since weather, clouds and sea ice make satellite and cruise data collection challenging. Using models is an alternative to fill in the gaps in the data. In this study, we used an ocean model that simulates circulation, sea ice, biological productivity, and the nutrient cycle in the WAP to study how much sea ice and biology influence the carbon cycle. We find that in years when the ice season is long there is a smaller flux of carbon between ocean and atmosphere, and more dilution of the surface waters by sea ice melt. Although the amount of inorganic carbon in the surface ocean is low by the end of the productive season, this does not reflect more CO2 being taken up or more biological productivity. In years of shorter sea ice season, in contrast, the ocean takes up more atmospheric CO2 and has a longer productive season. This is because the surface ocean is exposed for longer, so gas transfer happens more easily and there is more light available for photosynthesis. Key Points: Longer growth season in years of early sea‐ice retreat show higher seasonally integrated NPP, despite lower chlorophyll in JanuarySea ice is important for DIC drawdown as it influences the duration of phytoplankton bloom, air‐sea CO2 fluxes, and dilution by meltwaterIn the WAP, sedimentary iron has a larger role in the northern coast and shelf, glacial meltwater likely the main source in the south [ABSTRACT FROM AUTHOR]

Soils are a source of atmospheric nitrogen oxides (NOx), especially in regions with significant cropland where nitrogen (N) fertilizers are used to enhance crop yields. The magnitude of soil NOx emissions, however, varies substantially by region, depending on the local land use pattern and management activities. We estimated soil NOx emissions in California based on the DeNitrification‐DeComposition (DNDC) biogeochemical model, linked to a detailed spatial‐temporal differentiated California‐specific database. The DNDC‐generated surface fluxes were used in the Community Multiscale Air Quality (CMAQ) model to evaluate impacts of soil NOx emissions on formation of ambient particulate (PM2.5) nitrate in the San Joaquin Valley (SJV) where cropland is the dominant land use. The DNDC‐generated soil NOx emissions contribute approximately 1.1% of total anthropogenic NOx emissions in California, at an emission rate of roughly 24 t day−1 (as NO2) statewide and 9 t day−1 in the SJV. Cropland is the dominant source of soil NOx emissions in California, contributing nearly 60% of statewide soil NOx emissions, driven principally by fertilizer use. The PM2.5 nitrate concentrations simulated by CMAQ using the DNDC‐generated soil NOx emissions are compatible with those observed in the SJV, suggesting that soil NOx emissions have limited impacts on PM2.5 nitrate formation in the atmosphere. Our emission and air quality modeling results are further supported by long‐term ambient NOx‐to‐carbon monoxide (CO) and satellite NO2 data analyses in the SJV, which showed diurnal, monthly, and annual trends consistent with characteristics of NOx sources dominated by traffic combustion in both urban and agricultural regions. Plain Language Summary: Nitrogen oxides (NOx) are air pollutants that can react with other chemicals in the air to form fine particulate matter and ozone, both of which pose adverse impacts to human health and the environment. Control of NOx emissions is critical to improving air quality in California. Soils are a known NOx source, especially in agricultural areas where large amounts of nitrogen fertilizers are used to increase crop yields. The nitrogen chemicals in soil can be converted into various nitrogen gases, including NOx, by soil microorganisms. The contribution of soil emissions to the total NOx budget varies by region, depending on land uses and management activities. This study modeled soil NOx emissions from different land covers in California and evaluated impacts of soil NOx emissions on the formation of ambient particulate nitrate in the San Joaquin Valley (SJV), where cropland is the dominant land use. Our results indicate that soil NOx is a relatively minor fraction of the total NOx budget in California and has a minor effect on atmospheric concentrations of particulate nitrate in the SJV. Ambient and satellite data analyses show traffic combustions being the dominate source of NOx emissions in both urban and agricultural areas of the SJV. Key Points: Soil NOx is a relatively minor fraction of the total NOx emissions in California, driven primarily by nitrogen fertilizer use in croplandImpact of soil NOx emissions on ambient PM2.5 nitrate formation is limited in the San Joaquin Valley of CaliforniaAmbient and satellite data analyses show vehicular emissions dominating NOx emissions in both urban and agricultural areas of California [ABSTRACT FROM AUTHOR]

Processes that occur at mesoscale and submesoscale features such as eddies and fronts are important for marine ecosystem dynamics and biogeochemical fluxes. However, their impacts on the fate of biogenic organic carbon in coastal oceans are not well quantified because physical and biological interactions at such features are very complex with short time-and small spatial scales variability. As part of the California Current Ecosystem Long-Term Ecological Research (CCE-LTER) Process studies in the southern California Current in October 2008, we sampled across a strong temperature and chlorophyll front ('A-Front') separating water masses with distinct hydrographic and biogeochemical characteristics and a modified biological assemblage at the frontal interface. Thorpe-scale analyses of the hydrographic data from a free-fall moving vessel profiler suggested an increased diapycnal diffusive nitrate flux at the front zone. Based on these field data, we use data-driven diagnostic biogeochemical models to quantify how the front-induced physical mixing influenced the production, grazing and transport of phytoplankton carbon in the southern California Current. Our results suggest that enhanced diffusive diapycnal fluxes of nutrients stimulated phytoplankton primary production at the front; this effect, together with reduced microzooplankton grazing, increased net growth of the phytoplankton community leading to locally enhanced biomass of large phytoplankton, such as diatoms, in the frontal zone. © 2012 The Author.

Understanding how a changing boreal fire regime is likely to influence regional climate requires detailed information about fire effects on the surface radiation budget. We used time series of satellite observations of surface albedo from 2000–2011 and fire perimeters since 1970 to study post-fire changes in surface net shortwave radiation along a latitudinal transect in central Canada. Fire-induced surface shortwave forcing (SSF) integrated over an annual cycle for the first 30 years after fire was similar (−4.1 W m−2 with a 95% confidence interval of −4.5 to −3.7 W m−2) between southern and northern boreal regions. The lack of a latitudinal difference in SSF was caused by counteracting latitudinal trends in seasonal contributions. Spring (March, April, and May) SSF increased with latitude, from −7.2 W m−2 in the south to −10.1 W m−2in the north, primarily because of delayed snow melt, which amplified albedo differences between unburned forests and recovering stands. In contrast, winter incoming solar radiation and summer albedo change decreased from south to north, resulting in a decreasing latitudinal trend in winter and summer SSF. Vegetation recovery was slower in the north, leading to smaller increases in summer albedo during the first decade after fire, and a prolonged phase of elevated spring albedo during the second decade. Our results indicate that fires reduce surface net shortwave radiation considerably for many boreal forest ecosystems in North America, providing further evidence that disturbance-mediated shifts in surface energy exchange need to be considered in efforts to manage these forests for climate change mitigation.

21st‐century modeling of greenhouse gas (GHG) emissions from bioenergy crops is necessary to quantify the extent to which bioenergy production can mitigate climate change. For over 30 years, the Century‐based biogeochemical models have provided the preeminent framework for belowground carbon and nitrogen cycling in ecosystem and earth system models. While monthly Century and the daily time‐step version of Century (DayCent) have advanced our ability to predict the sustainability of bioenergy crop production, new advances in feedstock generation, and our empirical understanding of sources and sinks of GHGs in soils call for a re‐visitation of DayCent's core model structures. Here, we evaluate current challenges with modeling soil carbon dynamics, trace gas fluxes, and drought and age‐related impacts on bioenergy crop productivity. We propose coupling a microbial process‐based soil organic carbon and nitrogen model with DayCent to improve soil carbon dynamics. We describe recent improvements to DayCent for simulating unique plant structural and physiological attributes of perennial bioenergy grasses. Finally, we propose a method for using machine learning to identify key parameters for simulating N2O emissions. Our efforts are focused on meeting the needs for modeling bioenergy crops; however, many updates reviewed and suggested to DayCent will be broadly applicable to other systems. [ABSTRACT FROM AUTHOR]

The isotopic composition of Si in biogenic silica (BSi), such as opal buried in the oceans' sediments, has changed over time. Paleorecords suggest that the isotopic composition, described in terms of δ30Si, was generally much lower during glacial times than today. There is consensus that this variability is attributable to differing environmental conditions at the respective time of BSi production and sedimentation. The detailed links between environmental conditions and the isotopic composition of BSi in the sediments remain, however, poorly constrained. In this study, we explore the effects of a suite of offset boundary conditions during the Last Glacial Maximum (LGM) on the isotopic composition of BSi archived in sediments in an Earth System Model of intermediate complexity (EMIC). Our model results suggest that a change in the isotopic composition of Si supply to the glacial ocean is sufficient to explain the observed overall low(er) glacial δ30Si in BSi. All other processes explored trigger model responses of either wrong sign or magnitude or are inconsistent with a recent estimate of bottom water oxygenation in the Atlantic Sector of the Southern Ocean. Caveats, mainly associated with generic uncertainties in today's pelagic biogeochemical modules, remain. Key Points: We simulate the isotopic composition of biogenic silica archived in the ocean's sedimentOur simulations with an Earth System Model compare modern climate to glacial conditionsOut of several hypotheses tested, altering the isotopic composition of runoff during the LGM is most consistent with proxies [ABSTRACT FROM AUTHOR]

The multifaceted interface of plant roots, microbes, water, and soil can be considered a critical zone within the Critical Zone as it is host to many important dynamically linked processes, including the promotion of nutrient cycling through absorption and rhizodeposition, interaction and feedbacks with microorganisms and fungi, root‐facilitated hydraulic redistribution, and soil carbon dynamics. Such important processes in the Critical Zone have not been fully characterized and modeled in an ecohydrologic framework linking above‐ground natural and/or anthropogenic processes to below‐ground biogeochemical cycling. Specifically, the relation between root exudates and nutrient cycling remains an open challenge. Here we present the model REWT (Root Exudation in Watershed‐scale Transport) to demonstrate the systematic modeling of root exudation in an interconnected ecohydrologic framework. REWT incorporates an explicit dynamic root exudation transport model, nutrient absorption, and coupled microbial processes within the framework of a validated ecohydrologic model. Model simulations demonstrate the influence of root exudation of glucose, a polysaccharide that serves as fuel for microbes, and flavonoids, which can act as a biological nitrification inhibitor on microbial processes linked to soil carbon and nitrogen cycling. To demonstrate the capabilities of this theoretical framework, we parameterize REWT for corn and soybean crops in the Midwestern United States, and simulations indicate that rates of nitrification and respiration were substantially altered compared to model simulations in which root exudation was not explicitly included. This work demonstrates the importance of systematically incorporating root exudates into hydrobiogeochemical models and can serve to inform experimental design for active root zone processes. Plain Language Summary: Root exudation is the highly complex and variable process by which roots release carbon and other nutrients into the surrounding soil to influence microbial activity and biogeochemical conditions to their benefit. It is a process common to all types of plants and trees and has important implications for nutrient cycling and soil health. Here we develop a model, REWT (Root Exudation in Watershed‐scale Transport), which couples exudation with moisture transport and microbial transformation of nutrients in agricultural and natural soils in an effort to more accurately capture dynamic biogeochemical processes in the Critical Zone. We present REWT simulations for corn and soybean crops in the Midwestern United States. These demonstrate that explicitly incorporating root exudation into hydrobiogeochemical models can result in substantial increases or decreases of microbial biomass, rate of nitrification, and total nitrate leached by the soil system, in this case on the order of ±4%, up to 100% decrease, and up to 16% decrease, respectively. REWT simulations are not only promising for future work related to plant breeding and modeling of biogeochemical cycling in natural and agroecosystems; they also shed light on the need for more empirical work related to rhizosphere processes, particularly the measurement of root exudation rates. Key Points: Roots influence subsurface biogeochemical dynamics and mediate microbial dynamics and associated geochemical feedbacks via exudationRoot exudates both enhance microbial activity, such as through glucose production, and inhibit it, such as through production of flavonoidsHere, the highest sensitivity of exudation's effect on nutrient dynamics results from nitrification inhibition through flavonoid exudation [ABSTRACT FROM AUTHOR]