Journal of Geophysical Research: Biogeosciences最新文献

Over the past two decades, numerous studies have emphasized the importance of including organic matter (OM) in land surface models (LSMs) to accurately represent soil thermal and hydrological properties. This is particularly relevant in Arctic regions, where organic-rich soils are widespread. Consequently, most LSMs incorporate parameterizations that account for OM effects, although these implementations are often simplified. Recent advancements in global soil data sets now enable more precise modeling of soil properties by providing detailed inputs for soil composition and physical characteristics. This study focuses on the refinement of the representation of soil organic and mineral content and the revision of the parameterizations of heat capacity, thermal conductivity, and porosity in the ORCHIDEE LSM, using data from the SoilGrids 250m v2.0 database. The updated model is evaluated across multiple Arctic and boreal sites and compared against two earlier versions: (a) a Bulk version that neglects OM effects on the thermal processes and (b) a simplified version with a basic OM prescription. Results show that incorporating OM into thermal processes modeling significantly improves soil temperature simulations, particularly under the soil surface in the critical zone. For some sites, root mean square errors (RMSE) are reduced by up to 50% compared to the Bulk version, especially during the snow-free summer months. These findings highlight the value of high-resolution soil data sets, such as SoilGrids, for improving simulations of thermal dynamics in carbon-rich Arctic soils.

Arctic warming is altering vegetation and carbon dynamics with global implications, yet Earth System Model (ESM) predictions in the Arctic remain highly uncertain, in part due to historically limited data for model parameterization and validation. As such, ESMs typically represent Arctic ecosystems in an oversimplified manner. Recently, nine plant functional types (PFTs) designed to realistically represent tundra vegetation were integrated into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) and parameterized using plot-scale observations from a single site. Additional evaluation was needed to determine their transferability across the Arctic. Here, we evaluated whether refined representation of tundra vegetation improved model accuracy by conducting spatially explicit 100 × 100 m resolution ELM simulations on Alaska's Seward Peninsula. Simulations with the default two-PFT configuration and with the nine Arctic-specific PFTs were benchmarked against observations of net ecosystem exchange, gross primary production, and aboveground biomass from multiple data streams including an eddy covariance flux tower, flux chambers, and aircraft and unoccupied aerial system hyperspectral remote sensing. Evaluation revealed that Arctic-specific PFT simulations produced more realistic landscape-level carbon exchanges, and better captured observed heterogeneity in biomass and productivity, explaining 60%–70% of spatial variance (R² = 0.6–0.7) compared to just 12%–18% (R² = 0.12–0.18) with the default configuration. However, the refined model failed to reproduce observed aboveground biomass for highly productive alder-willow communities, requiring further evaluation of carbon allocation parameterizations for tall shrubs that are increasingly expanding across tundra landscapes. Our results demonstrate that enhanced representation of vegetation heterogeneity boosts predictive understanding of tundra carbon dynamics, facilitating regional to pan-Arctic model and remote-sensing scaling.

The extent of projected ocean acidification is partly dependent on the natural variability of marine carbonate chemistry—which is higher in coastal systems than in the open ocean. However, there are limited empirical studies quantifying the rate, magnitude and drivers of coastal environmental variability, preventing accurate assessments for how species and their associated communities may respond to projected climate change. Here, we quantified the annual variability of pH, temperature and dissolved oxygen in a coralline algae reef, a globally distributed biodiverse habitat that may be one of the most sensitive to projected climate change. We found that coralline algae and their communities are exposed to pH values as low as those projected for 2100 (even under a low emission scenario) for 63% of the year, including most of autumn and all of winter. Annual fluctuations in pH ranged by 0.46 units, with identifiable patterns at diel to seasonal timescales driven by various biogeochemical factors. Biologically driven patterns in dissolved oxygen and pH were coupled at multiple periodicities, and temperature was coupled to pH during the winter. Tidal cycling additionally modulated biological forcing of pH, increasing the complexity of intra-seasonal pH variability. Forecasting this environmental variability to the future led to projections of new pH extremes well beyond all IPCC emission scenarios. However, persistent long-term exposure to low pH may increase the acclimation and adaptation potential of coralline algae and their associated communities, providing a level of optimism for the continued survival of this habitat despite sensitivity to projected climate change.

This study evaluates the effects of ground-based logging on soil properties and microarthropod biodiversity in Mediterranean beech (Fagus sylvatica L.) forests, with a focus on recovery over a 5-year period. Fieldwork was conducted in three study areas in southern Italy to compare three site types: recently harvested (2021, NEW), harvested 5 years prior (2017, OLD), and unharvested control (>40 years ago, CON). Soil samples were collected from skid trails (DIST) and from adjacent, not-trafficked areas (UND) to assess bulk density, penetration resistance, shear strength, organic matter content, and the QBS-ar index of microarthropod biodiversity. Logging machinery significantly compacted the soil in DIST_NEW, increasing bulk density by 19% compared with UND_NEW. Penetration resistance tripled (0.26 vs. 0.09 MPa in CON), and shear strength rose from 1.96 to 7.18 t m⁻². The QBS-ar index declined by 50% in comparison to the CON areas, indicating substantial biodiversity loss. After 5 years, partial recovery was observed in DIST_OLD, with bulk density and penetration resistance decreasing by 13% and 8%, respectively, though values remained above those in CON sites. These results highlight the lasting impacts of forest operations on soil health and microarthropod biodiversity. To reduce degradation, strategies such as permanent skid trail planning or use of protective technologies like logging mats are recommended. Continued research into deeper soil layers and various soil types is essential to guide more sustainable forest management practices.

Reservoirs with thicker sediments generally exhibit elevated carbon emissions; however, this relationship may not necessarily persist when emissions are assessed per unit thickness. A comprehensive understanding of this relationship is essential for accurately evaluating carbon emissions across various sedimentation patterns. This study proposes the concept of carbon emission intensity (CEI), defined as carbon emissions per unit mass or volume of sediment, evaluates its correlation with sediment thickness (ST) using 58 global data sets and clarifies the impact of ST on CEI through controlled incubation experiments. The results revealed a strong negative logarithmic correlation between CEI and ST (R² = 0.88), indicating that increasing ST substantially reduces CEI values. The observed trend can be attributed to the potential of thicker sediment layers to restrict oxygen penetration, promoting organic carbon (OC) burial. The incubation experiments reinforced this mechanism, revealing that although thicker sediments generated higher absolute carbon emissions, their CEI values were consistently lower. Oxygen penetration and active OC decomposition were found to be limited to the top few centimeters of sediment, independent of the total ST. The findings indicate that dam construction in narrow river sections could be strategically favorable, as these areas are more likely to accumulate thicker sediments characterized by lower CEI, reducing overall carbon emissions.

Unvegetated intertidal sediments are increasingly recognized as contributors to coastal carbon storage, yet their organic carbon burial potential remains poorly constrained. This study examines spatial and temporal patterns of carbon accumulation in unvegetated intertidal flats of Ōhiwa Harbor, New Zealand, using surface sediments and three radiocarbon-dated cores spanning up to ∼7,700 yrs. Within the harbor, five distinct sedimentary facies were identified, each displaying unique sediment characteristics and patterns of organic carbon burial. Mud-rich, low-energy facies, including rippled and bioturbated muds, consistently showed higher organic carbon density and burial rates compared to sandy, more dynamic facies. Estimated carbon stocks in the upper meter of sediment range from 44 to 120 t C ha⁻¹, comparable to or exceeding those of many vegetated coastal habitats. Temporal changes in facies distribution driven by estuarine processes and variations in sediment supply led to significant long-term fluctuations in organic carbon burial. These results demonstrate that organic carbon storage in unvegetated intertidal flats is highly heterogeneous and controlled by the persistence of fine-grained depositional environments. A facies-based framework offers a process-driven approach to assessing and managing blue-carbon potential in estuarine systems increasingly altered by climate and land-use change.

Denitrification and anaerobic ammonium oxidation (anammox) are key processes in mitigating nitrogen (N) pollution in river ecosystems. However, there has been insufficient investigation of the riverine N-removal processes on the Qinghai-Tibet Plateau (QTP). This study investigated the driving mechanisms of denitrification and anammox in 23 small rivers using multiple approaches (remote sensing, ¹⁵N pairing, and molecular techniques). The rivers span large elevational and climatic gradients, offering an ideal window for studying the riverine N-removal processes, especially in the context of global change. The denitrification rates were 1.37 ± 2.88 mg N/kg/d in summer and 0.21 ± 0.26 mg N/kg/d in winter, which dominated the total riverine N removal (96% and 73%, respectively). Structural equation models (SEM) revealed that geographic factors (elevation and land use), water properties (water temperature, pH, etc.), sediment parameters (moisture, NO₃⁻-N, NH₄⁺-N, etc.), and microbial gene abundances collectively explained over 60% of the variation in N-removal rates, with sediment properties being the primary regulating factors in summer. In winter, SEM showed that the contribution of geographic factors increased. We proposed a framework combining cross-perspective methods for revealing in-river N removal mechanisms and predicted that, under global change, N removal processes in rivers on the QTP are likely to intensify in the future. The integrated approaches systematically offered critical insights into the processes and drivers of biogeochemical N cycling on the QTP.

Environmental observatory networks are fundamental in advancing scientific understanding of biogeochemical processes. FLUXNET is a global network of regional eddy covariance networks that measure ecosystem-scale exchanges of greenhouse gases (e.g., CO₂, CH₄, H₂O) and energy between the biosphere and the atmosphere. MexFlux is the eddy covariance network of Mexico, a megadiverse country with many underrepresented ecosystems within FLUXNET. This study evaluates the spatial representativeness of MexFlux by assessing its ability to capture the statistical and spatial heterogeneity of annual gross primary productivity (GPP) and evapotranspiration (ET) within Mexico. We tested four network configurations: the historical distribution of MexFlux sites (MexFlux-H, n = 33), an expanded network with 20 additional sites (MexFlux + 20, 53 sites), MexFlux sites with publicly available data (MexFlux-P, n = 20), and a hypothetical optimized design with only 25 sites (MexFlux25, n = 25). Results show that MexFlux-H and MexFlux-P overrepresent regions with GPP values between 250 and 600 gC m⁻² yr⁻¹ and ET of 200–1,200 mm yr⁻¹. MexFlux + 20 demonstrates that adding 20 strategically located sites improves the representativeness of MexFlux while preserving the historical distribution of the network. The configuration of MexFlux25 highlights that a few but strategically distributed sites are an alternative way to enhance the representativeness of the network. Mountain regions, tropical forests, and urban sites may remain underrepresented in any network configuration, highlighting the challenges of monitoring efforts in this country. Our framework integrates distributions, copulas, semivariograms, and upscaling to highlight the value of a multidimensional assessment of spatial representativeness, which applies to other regional FLUXNET networks.

Ocean alkalinity enhancement (OAE) is a marine carbon dioxide (CO₂) removal strategy that relies on lowering the ocean's pCO₂ via the addition of alkaline materials to facilitate enhanced CO₂ uptake with the potential for durable, long-term, storage. This strategy has gained recent scientific and private sector attention as a possible component of climate mitigation portfolios, yet many research questions remain. This work describes an analysis of historical reconstructions of regional carbonate chemistry developed via application of machine learning algorithms to an ocean reanalysis product. Model skill assessment demonstrated excellent performance when compared to regional observations, and this work focuses on four carbonate system variables that may influence OAE applications: total scale pH, calcite saturation state, the theoretical molar change in dissolved inorganic carbon associated with a molar change in total alkalinity (ΔDIC/ΔTA), and the timescale of CO₂ equilibrium of the surface mixed layer ($��{\tau }_{{\text{CO}}_2}�$). These metrics were combined into a suitability index to quantify locations and times of year more favorable for OAE. Much of the US Northeast Shelf and Slope region has seasonally similar suitability for small-scale OAE applications, with nearshore environments exhibiting high suitability year-round. Lagrangian particle tracking experiments show strong reductions in ΔDIC/ΔTA and increases in $��{\tau }_{{\text{CO}}_2}�$ due to horizontal and vertical transport, suggesting that when water motion is accounted for, reduced efficiency and longer equilibration times may impact successful observations of carbon uptake and storage. This analysis and framework were developed with publicly available tools, data sets, and global data products allowing for global scalability and application.