Global Biogeochemical Cycles最新文献

The ocean is a source of atmospheric methane (CH₄), yet the impact of mesoscale processes on CH₄ cycling remains largely unconstrained. In this study, we combined high-resolution underway observations and site-specific geochemical analyses conducted in September 2020, with methane oxidation (MOx) rates measurements and molecular analysis in September 2022, to investigate the regulation of mesoscale eddies on CH₄ production, methanotrophic activity, and emission fluxes in the South China Sea (SCS). Underway observation revealed that cyclonic eddies (CEs) increased surface CH₄ concentrations, while anticyclonic eddies (AEs) generally exhibited lower CH₄ levels. CEs observed in September 2020 after summer, enhanced CH₄ production associated with phytoplankton by transporting coastal nitrate-rich waters into the eddy core. Particulate dimethylsulfoniopropionate (DMSP_p) produced by phytoplankton was identified as a significant source of CH₄ within the mixed layer based on the significant correlations between DMSP and CH₄ (r = 0.79; p < 0.01). In contrast, elevated MOx rates and pmoA gene abundance were observed in the AEs, driven by convergence and stratification of surface seawater caused by downwelling of water masses. Compared to reference sites, the CH₄ air–sea fluxes in CEs increased by 204%, whereas the CH₄ emission flux in AEs was reduced by 25.1%. Collectively, mesoscale eddies significantly influence CH₄ cycle by altering phytoplankton composition, nutrient dynamics and microbial communities, ultimately leading to the divergent CH₄ emissions. Our results illustrated the control of mesoscale eddies on CH₄ production and oxidation and highlighted the importance of physical processes on biogeochemical cycling and greenhouse gas emissions.

Widespread increases in lake browning, which affects primary production, have been observed in northern lakes. While lake browning is attributed to increases in terrestrially derived total organic carbon (TOC) and total iron (Fe), Fe does not consistently correlate with increasing TOC over time. This temporal mismatch between TOC and Fe indicates that we still do not fully understand the causes of lake browning, especially in the context of gradually changing climatic conditions. In this study, we utilized Fennoscandian 30-year (1990–2020) time series data for 102 lakes to describe possible reasons for the temporal decoupling between TOC and Fe. Using Bayesian mixed-effects models and wavelet coherence analysis, we found evidence for differential responses of TOC and Fe concentrations to changes in precipitation, temperature, and sulfur deposition. While TOC appeared more sensitive to the effects of precipitation, temperature and sulfur deposition in individual lakes, Fe concentrations were impacted by complex interactions among these environmental variables. Although TOC and Fe increased in most lakes in response to increased temperature and precipitation, 41% of the lakes—typically with larger catchment-to-lake area ratios and shorter water residence times—exhibited a declining trend in Fe. This analysis encompasses lakes of both significant and non-significant changes over time. This decline in Fe was associated with short-timescale (2–4 years) increases in precipitation, leading to a temporal decoupling between Fe and TOC. Our findings suggest that Fe concentrations do not increase uniformly with rising temperatures and increased precipitation, especially in regions where sulfur deposition has declined due to atmospheric recovery policies.

The flux of nutrients from continents to the oceans sustains oceanic primary productivity and is a fundamental component of the carbon cycle. In most regions of the world's oceans primary productivity is limited by the supply of nutrients. In particular, iron can become limiting in the open-ocean due to its low solubility. Glaciated continents have been suggested as an underappreciated source of iron to the high-latitude oceans. Yet, uncertainty remains regarding the magnitude and spatial variability of glacially derived nutrient fluxes, and the extent to which these nutrients impact open-ocean ecosystems. To quantify lithogenic fluxes at the West Greenland margin, we measured ²³²Th and ²³⁰Th in seawater and core-top sediments across the shelf and slope. Our results highlight a negative correlation between low-salinity waters and dissolved and particulate ²³²Th, suggesting a glacial source for this lithogenic isotope. We calculated dissolved ²³²Th fluxes 5–24 μg m⁻² yr⁻¹ (100–500 m depth), and sedimentary ²³²Th fluxes 105–711 μg m⁻² yr⁻¹, higher than typical open-ocean settings and similar to margin sites influenced by large inputs from aeolian dust and rivers. A sampling transect shows that dissolved ²³²Th fluxes increase toward Greenland, confirming that lithogenic inputs are sourced laterally from the margin. Using our ²³²Th fluxes, we estimate an elevated supply of dissolved Fe which extends over the continental slope toward the open ocean. This Fe flux is large enough to support much of the local primary productivity, highlighting the importance of lithogenic fluxes in supporting the marine ecosystem in high-latitude oceans.

We present the CH₄ and N₂O budgets for anthropogenic and natural sources and sinks of Australasia (Australia and New Zealand) from 2010 to 2019 using bottom-up and top-down methods, in line with the RECCAP-2 initiative, with extensions to 2022. We show that the bottom-up CH₄ budget for Australasia (2010–2019) was a net source of 14.1 ± 5.5 Tg CH₄ yr⁻¹, with Australia and New Zealand contributing 84% and 16%, respectively. Anthropogenic sources contributed 55% of all CH₄ emissions, the rest coming from natural sources, primarily wetlands. The bottom-up N₂O budget was a net source of 0.5 ± 0.3 Tg N₂O yr⁻¹, with Australia contributing the majority (92%), mainly from natural sources (82%). Australasia top-down CH₄ (10.4 ± 0.5 Tg CH₄ yr⁻¹) and N₂O budgets (0.8 ± 0.5 Tg N₂O yr⁻¹) differ in magnitude from the bottom-up budgets but remain consistent within their uncertainties. Similar consistency is observed for Australia, while New Zealand shows significant discrepancies, particularly for N₂O, where the bottom-up estimate is 71% higher than the top-down estimate. In terms of trends, bottom-up natural wetland CH₄ emissions increased in both countries between 2010 and 2019. CH₄ emissions from enteric fermentation slightly declined in Australia but increased in New Zealand. Soil N₂O emissions from nitrogen additions increased in both countries, with a significant rise in New Zealand driving the overall positive trend in anthropogenic emissions. These findings highlight critical sectors with large mitigation potential and the significance of monitoring natural sources for possible biogeochemical-climate feedback.

Deep chlorophyll maxima (DCMs) have long been studied in the northern hemisphere but have received less attention in the Southern Ocean. Their contribution to phytoplankton biomass and net primary productivity (NPP) is poorly resolved. Recently, the application of satellite NPP algorithms to biogeochemical (BGC)-Argo float data has improved vertically resolved NPP estimates. Using this approach on 12,700 BGC-Argo profiles south of 30°S, we report (1) subsurface (below the mixed layer) estimates of NPP, (2) the contribution of subsurface NPP to total NPP, and (3) the influence of DCMs and deep biomass maxima (DBMs) on (1) and (2). When DCMs are present (n = 2,119 profiles), subsurface NPP is 217 ± 106 mg C m⁻² day⁻¹ compared to 82 ± 92 mg C m⁻² day⁻¹ for all profiles. We further compare observations across seasons in four water masses from nitrate-limited oligotrophic waters north of the subtropical front to iron-limited regions further south, including the sea ice zone. Low-latitude DCMs (i.e., 30–44°S), show the highest contribution to column-integrated NPP. However, DCMs occur across all frontal zones and contribute significantly to total NPP when present. Rather than missing subsurface NPP associated with DCMs, the satellite Carbon-based Productivity Model (CbPM) tends to mistakenly assume DCMs below the mixed layer, overestimating NPP. This situation is somewhat ameliorated in the ferricline version of the CbPM due to better nutricline-euphotic depth alignment. Our results highlight the importance of understanding the vertical structure of phytoplankton stocks and productivity, with direct impacts on global NPP estimates and, ultimately, climate model projections.

Global assessments of ecosystem regeneration as a climate mitigation strategy have traditionally focused on CO₂, despite the acknowledgment that methane (CH₄) and nitrous oxide (N₂O) are also important greenhouse gases (GHGs). We conducted a meta-analysis of studies that measured soil CH₄ and N₂O fluxes in unmanaged, regenerating forested and savanna ecosystems, with a focus on understanding biome-specific differences in these GHG fluxes compared to a counterfactual of agricultural land use. We expected most regenerating ecosystems to act as small CH₄ sinks and relatively larger N₂O sources, with a net warming combined CH₄-N₂O effect. Three of the five forested biomes we studied followed this pattern: subtropical/tropical forest, subtropical/tropical savanna and temperate conifer forest (0.60 ± 0.30, 0.15 ± 0.06, and 0.83 ± 0.24 Mg CO₂e ha⁻¹ yr⁻¹, respectively). Results suggest that even after 100 years of regeneration, the radiative cooling of the climate from CO₂ sequestration in aboveground biomass exceeds the radiative warming driven by the net CH₄-N₂O effect among all ecosystems on average globally. We also found that the “climate opportunity benefit” of ecosystem regeneration—the difference in the net CH₄-N₂O effects of agriculture versus regeneration—yields a net cooling effect for all biomes. However, because the CH₄-N₂O effect diminishes the cooling effect of ecosystem regeneration, our results underscore that it is unsound to use ecosystem regeneration as a justification for continuing fossil fuel emissions.

Dissolved manganese (dMn) is an essential bioactive element required for marine organisms. Redox condition determines its solubility and its solid phase removal from seawater. It displays a typical scavenging type profile in the Indian Ocean with an elevated concentration in the Oxygen Minimum Zone (OMZ) of the Bay of Bengal (BoB). The surface dMn decreases southward in the BoB, and its concentration gradient correlates well with salinity because of the enormous riverine influx. Reductive dissolution of Iron-manganese (Fe-Mn) oxyhydroxides-rich sediments brought by the Ganga-Brahmaputra rivers enriches dMn in the bottom waters of the shelf regions (∼25 nM), which gets advected to the open ocean through cross-shelf transport. The atmospheric input is the prominent source of dMn in the BoB. Transport of the Indonesian Through Flow waters supplies high dMn in the surface waters of the Central Indian Ocean Basin. Internal cycling seems to control the dMn distribution in the water column in addition to its external sources. Water column denitrification increases dMn in the OMZ waters of the BoB through the reductive dissolution of sinking Mn oxide particles under the prevailing suboxic conditions. The presence of two sub-surface peaks of dMn associated with nitrite maxima suggests active denitrification in the OMZ waters of the BoB, similar to the Arabian Sea. The interaction of circulating fluid with subducting Fe-Mn-rich crusts enriches the deep water dMn in the Java Sumatra region. Further, the hydrothermal activity over the Southeast and Central Indian Ridges contributes significantly to the dMn budget of the deeper waters.

Marine oxygen deficient zones (ODZs) play a major role in the Earth's biogeochemical cycles and are responsible for nitrogen and sulfur removal from the oceans. Microbial-reducing reaction processes generate nitrite (NO₂⁻) and sulfur compounds as intermediaries that may accumulate in these zones. Current assessments on microbial transformations inside ODZs are based on shipboard measurements, and there are no well-resolved seasonal to annual observations or high-resolution vertical sampling that would characterize variability. Here, we propose an alternative statistical approach to analyze the raw output of the nitrate sensor from BGC-Argo floats with the ability to detect NO₂⁻ and thiosulfate (S₂O₃²⁻) concentrations in addition to nitrate. The new approach provides data with great vertical and spatiotemporal resolution. The method can be applied to UV-spectrometer output data from SUNAs and ISUS nitrate sensors commonly deployed on various observing platforms. We validated the technique in the field by matching shipboard NO₂⁻ bottle data with float data from the Eastern Tropical North Pacific (ETNP) and Eastern Tropical South Pacific (ETSP) ODZs. We then show a complete time series of three floats as study cases. The ability to detect NO₂⁻ and S₂O₃²⁻ concomitantly with other key chemical variables (i.e., oxygen, pH, and bio-optics) at such fine scale allows for novel insights into the nitrogen and sulfur cycling of ODZs and processes driving these cycles. This new approach will enable fine-scale remote quantification of NO₂⁻ and S₂O₃²⁻ to support a better understanding of the biogeochemical transformations happening inside these already-expanding deoxygenated regions.

Marine Carbon Dioxide Removal (mCDR) will likely play a role in efforts to keep global warming below 2°C. mCDR methods create a deficit in dissolved seawater CO₂ relative to the unperturbed counterfactual. This seawater CO₂ deficit induces either an uptake of atmospheric CO₂ or reduced CO₂ outgassing into the atmosphere. The immediate climatic benefit of mCDR depends on air-sea CO₂ equilibration before the CO₂ depleted seawater deficit in the surface ocean loses contact with the atmosphere through water mass ventilation. Air-sea CO₂ equilibration occurs over vast ocean regions, which are too large to constrain equilibration with current observational methods. As such, numerical modeling is needed to evaluate the spatial and temporal scales of air-sea CO₂ equilibration. This study employs the ACCESS-OM2 model at three resolutions (0.1°, 0.25°, and 1°) to evaluate the dependency of simulated equilibration timescales on model resolution. Results indicate that model resolution has limited influence on equilibration timescales in the tropics but exerts a more significant effect in polar regions. The main reason for the simulated differences is that different resolutions advect CO₂-deficient seawater into different locations (horizontally and vertically) where air-sea exchange can occur at different rates. The comparison of our results with simulations made with other ocean models further suggests that differences due to model resolution are smaller than differences between different models of similar resolutions. Our results are one step forward in evaluating the robustness of model-based assessments of air-sea CO₂ equilibration timescales.

Surface phytoplankton biomass (measured in mol C m⁻³) represents a critical parameter within the Earth System that is measured from space and simulated in Earth System Models. Under climate change, the current generation of Earth System Models agrees that low-latitude biomass will decline and high-latitude biomass will increase. However, on a regional scale, the magnitude, phenology and spatial pattern of these changes are highly inconsistent across models. We use machine learning to investigate the sources of the divergence and evaluate the realism of the simulations. We train Random Forests driven by environmental drivers to simulate surface phytoplankton biomass under both pre-industrial control and SSP5-8.5 scenarios. Outside the Arctic, the bulk of the changes in biomass are driven by rearrangements in the spatiotemporal distribution of environmental predictors. Large regional changes in models, however, are associated either with unrealistically low pre-industrial levels of macronutrients or unrealistically strong responses to those macronutrients. Within the Arctic, relationships between environmental predictors and biomass change under global warming. While increased light drives increased biomass, the effect is largest in models with a high nutrient bias. Feeding inputs from an ensemble of models to an emulator trained on observations predicts observed biomass better than the ensemble of the models does, highlighting the fact that models do not produce the correct relationships between environmental predictors and biomass. However, this technique does not yield mechanistically consistent predictions of biomass under climate change. Skepticism of large regional changes in surface phytoplankton biomass produced by individual models is warranted.