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We apply principles of Gibbs phase plane chemistry across the entire ocean-atmosphere interface to investigate aerosol generation and geophysical transfer issues. Marine surface tension differences comprise a tangential pressure field controlling trace gas fluxes, primary organic inputs, and sea spray salt injections, in addition to heat and momentum fluxes. Mapping follows from the organic microlayer composition, now represented in ocean system models. Organic functional variations drive the microforcing, leading to (1) reduced turbulence and (by extension) laminar gas-energy diffusion; plus (2) altered bubble film mass emission into the boundary layer. Interfacial chemical behaviors are, therefore, closely reviewed as the background. We focus on phase transitions among two dimensional “solid, liquid, and gaseous” states serving as elasticity indicators. From the pool of dissolved organic carbon (DOC) only proteins and lipids appear to occupy significant atmospheric interfacial areas. The literature suggests albumin and stearic acid as the best proxies, and we distribute them through ecodynamic simulation. Consensus bulk distributions are obtained to control their adsorptive equilibria. We devise parameterizations for both the planar free energy and equation of state, relating excess coverage to the surface pressure and its modulus. Constant settings for the molecular surrogates are drawn from laboratory study and successfully reproduce surfactant solid-to-gas occurrence in compression experiments. Since DOC functionality measurements are rare, we group them into super-ecological province tables to verify aqueous concentration estimates. Outputs are then fed into a coverage, tension, elasticity code. The resulting two dimensional pressure contours cross a critical range for the regulation of precursor piston velocity, bubble breakage, and primary aerosol sources plus ripple damping. Concepts extend the water-air adsorption theory currently embodied in our OCEANFILMS aerosol emissions package, and the two approaches could be inserted into Earth System Models together. Uncertainties in the logic include kinetic and thermochemical factors operating at multiple scales.

Flowing waters have a unique role in supporting global biodiversity, biogeochemical cycles and human societies(1-5). Although the importance of permanent watercourses is well recognized, the prevalence, value and fate of non-perennial rivers and streams that periodically cease to flow tend to be overlooked, if not ignored(6-8). This oversight contributes to the degradation of the main source of water and livelihood for millions of people(5). Here we predict that water ceases to flow for at least one day per year along 51-60 per cent of the world's rivers by length, demonstrating that non-perennial rivers and streams are the rule rather than the exception on Earth. Leveraging global information on the hydrology, climate, geology and surrounding land cover of the Earth's river network, we show that non-perennial rivers occur within all climates and biomes, and on every continent. Our findings challenge the assumptions underpinning foundational river concepts across scientific disciplines(9). To understand and adequately manage the world's flowing waters, their biodiversity and functional integrity, a paradigm shift is needed towards a new conceptual model of rivers that includes flow intermittence. By mapping the distribution of non-perennial rivers and streams, we provide a stepping-stone towards addressing this grand challenge in freshwater science.

Accurate and high-resolution spatial soil information is crucial for efficient and sustainable land use, management, and conservation. Since the establishment of digital soil mapping (DSM) and the GlobalSoilMap working group, significant advances have been made in terms of the availability and quality of spatial soil information globally. However, accurately predicting soil variation over large and complex areas with limited samples remains a challenge, especially for China, which has diverse soil landscapes. To address this challenge, we utilised 11 209 representative multi-source legacy soil profiles (including the Second National Soil Survey of China, the World Soil Information Service, the First National Soil Survey of China, and regional databases) and high-resolution soil-forming environment characterisation. Using advanced ensemble machine learning and a high-performance parallel-computing strategy, we developed comprehensive maps of 23 soil physical and chemical properties at six standard depth layers from 0 to 2 m in China at a 90 m spatial resolution (China dataset of soil properties for land surface modelling version 2, CSDLv2). Data-splitting and independent-sample validation strategies were employed to evaluate the accuracy of the predicted maps' quality. The results showed that the predicted maps were significantly more accurate and detailed compared to traditional soil type linkage methods (i.e. CSDLv1, the first version of the dataset), SoilGrids 2.0, and HWSD 2.0 products, effectively representing the spatial variation of soil properties across China. The prediction accuracy of soil properties at all depth intervals ranged from good to moderate, with median model efficiency coefficients for most soil properties ranging from 0.29 to 0.70 during data-splitting validation and from 0.25 to 0.84 during independent-sample validation. The wide range between the 5 % lower and 95 % upper prediction limits may indicate substantial room for improvement in current predictions. The relative importance of environmental covariates in predictions varied with soil property and depth, indicating the complexity of interactions among multiple factors in the soil formation processes. As the soil profiles used in this study mainly originate from the Second National Soil Survey of China, conducted during the 1970s and 1980s, they could provide new perspectives on soil changes, together with existing maps based on soil profiles from the 2010s. The findings of this study make important contributions to the GlobalSoilMap project and can also be used for regional Earth system modelling and land surface modelling to better represent the role of soil in hydrological and biogeochemical cycles in China. This dataset is freely available at https://www.scidb.cn/s/ZZJzAz (last access: 17 November 2024​​​​​​​) or https://doi.org/10.11888/Terre.tpdc.301235 (Shi and Shangguan, 2024).

China ranks in the highest position for nitrogen (N) fertilizer consumption in the world. Although N fertilizer use has greatly contributed to the China's food production, this has also caused an unprecedented alteration in the biogeochemical cycles and endangered terrestrial and aquatic ecosystems. Existing use of N fertilizers in China, as shown by digital maps, is usually coarse in resolution and intermittently covered with a biased gridded dataset. Here, we have reconstructed a historical, annual N fertilizer use dataset in China and resampled it to 5 km×5 km resolution, covering the period from 1952 to 2018 by integrating improved cropland maps. Results showed that most of the N input was directly applied as N-only fertilizer, while the contribution from compound fertilizers has ranged between 16 % and 24 % since 1980. The national total N fertilizer input increased from 0.06 Tg N yr−1 (0.05 g N m−2 yr−1) in 1952 to 31.15 Tg N yr−1 (18.83 g N m−2 yr−1) in 2014 and then decreased to 28.31 Tg N yr−1 (17.06 g N m−2 yr−1) in 2018. Despite the total N input decreasing by 9.1 % (2.84 Tg N yr−1) from 2014 to 2018, the N input from compound fertilizers has increased by 6 % (0.43 Tg N yr−1) during the corresponding period. The previous Food and Agriculture Organization (FAO) data-based N fertilizer products in China overestimated N use in low cropland coverage areas but underestimated N use in high cropland coverage areas. However, our newly reconstructed data have not only corrected the existing biases and improved the spatial distribution but have also shown that vegetable and other crops (e.g., orchards), but not grain crops, are the most intensively fertilized crops in China, implying the importance of quantifying greenhouse gas (GHG) emissions from these croplands. We argue that the reconstructed, spatially explicit N fertilizer use data in this study are expected to contribute to better understanding of biogeochemical cycles, including the simulations of GHG emissions and food production in China. The spatially explicit N fertilizer use and the crop-specific N fertilizer use datasets are available via an open data repository (https://doi.org/10.6084/m9.figshare.21371469.v1; Yu, 2022).

Vegetation impacts on ecosystem functioning are mediated by mycorrhizas, plant–fungal associations formed by most plant species. Ecosystems dominated by distinct mycorrhizal types differ strongly in their biogeochemistry. Quantitative analyses of mycorrhizal impacts on ecosystem functioning are hindered by the scarcity of information on mycorrhizal distributions. Here we present global, high-resolution maps of vegetation biomass distribution by dominant mycorrhizal associations. Arbuscular, ectomycorrhizal, and ericoid mycorrhizal vegetation store, respectively, 241 ± 15, 100 ± 17, and 7 ± 1.8 GT carbon in aboveground biomass, whereas non-mycorrhizal vegetation stores 29 ± 5.5 GT carbon. Soil carbon stocks in both topsoil and subsoil are positively related to the community-level biomass fraction of ectomycorrhizal plants, though the strength of this relationship varies across biomes. We show that human-induced transformations of Earth’s ecosystems have reduced ectomycorrhizal vegetation, with potential ramifications to terrestrial carbon stocks. Our work provides a benchmark for spatially explicit and globally quantitative assessments of mycorrhizal impacts on ecosystem functioning and biogeochemical cycling. Mycorrhizas—mutualistic relationships formed between fungi and most plant species—are functionally linked to soil carbon stocks. Here the authors map the global distribution of mycorrhizal plants and quantify links between mycorrhizal vegetation patterns and terrestrial carbon stocks.

Ground ice melt caused by climate-induced permafrost degradation may trigger significant ecological change, damage infrastructure, and alter biogeochemical cycles. The fundamental ground ice mapping for Canada is now >20 years old and does not include significant new insights gained from recent field- and remote-sensing-based studies. New modelling incorporating paleogeography is presented in this paper to depict the distribution of three ground ice types (relict ice, segregated ice, and wedge ice) in northern Canada. The modelling uses an expert-system approach in a geographic information system (GIS), founded in conceptual principles gained from empirically based research, to predict ground ice abundance in near-surface permafrost. Datasets of surficial geology, deglaciation, paleovegetation, glacial lake and marine limits, and modern permafrost distribution allow representations in the models of paleoclimatic shifts, tree line migration, marine and glacial lake inundation, and terrestrial emergence, and their effect on ground ice abundance. The model outputs are generally consistent with field observations, indicating abundant relict ice in the western Arctic, where it has remained preserved since deglaciation in thick glacigenic sediments in continuous permafrost. Segregated ice is widely distributed in fine-grained deposits, occurring in the highest abundance in glacial lake and marine sediments. The modelled abundance of wedge ice largely reflects the exposure time of terrain to low air temperatures in tundra environments following deglaciation or marine/glacial lake inundation and is thus highest in the western Arctic. Holocene environmental changes result in reduced ice abundance where the tree line advanced during warmer periods. Published observations of thaw slumps and massive ice exposures, segregated ice and associated landforms, and ice wedges allow a favourable preliminary assessment of the models, and the results are generally comparable with the previous ground ice mapping for Canada. However, the model outputs are more spatially explicit and better reflect observed ground ice conditions in many regions. Synthetic modelling products that incorporated the previous ground ice information may therefore include inaccuracies. The presented modelling approach is a significant advance in permafrost mapping, but additional field observations and volumetric ice estimates from more areas in Canada are required to improve calibration and validation of small-scale ground ice modelling. The ground ice maps from this paper are available in the supplement in GeoTIFF format.

The assembly and analysis of complete genomes and large genomic fragments have tripled the number of known ocean viruses and uncovered the potentially important roles they play in nitrogen and sulfur cycling. Viral diversity in the oceans Ocean viruses profoundly impact microbial community composition and metabolic activity in the oceans, thereby affecting global-scale biogeochemical cycling. Owing to sampling and cultivation challenges, viral diversity remains poorly described at the genome level, such that less than one per cent of observed surface-ocean viruses are 'known'. Information on viruses of the deep ocean is particularly scarce. Here, Matthew Sullivan and colleagues report the assembly of complete genomes and large genomic fragments from both surface- and deep-ocean viruses sampled during the Tara Oceans and Malaspina research expeditions. The resulting Global Oceans Viromes dataset roughly triples known ocean viral populations and doubles known candidate bacterial and archaeal viral genera. Using this global map, the study predicts viral hosts and identifies viral auxiliary metabolic genes, most of which were previously unknown. Ocean microbes drive biogeochemical cycling on a global scale 1 . However, this cycling is constrained by viruses that affect community composition, metabolic activity, and evolutionary trajectories 2 , 3 . Owing to challenges with the sampling and cultivation of viruses, genome-level viral diversity remains poorly described and grossly understudied, with less than 1% of observed surface-ocean viruses known 4 . Here we assemble complete genomes and large genomic fragments from both surface- and deep-ocean viruses sampled during the Tara Oceans and Malaspina research expeditions 5 , 6 , and analyse the resulting ‘global ocean virome’ dataset to present a global map of abundant, double-stranded DNA viruses complete with genomic and ecological contexts. A total of 15,222 epipelagic and mesopelagic viral populations were identified, comprising 867 viral clusters (defined as approximately genus-level groups 7 , 8 ). This roughly triples the number of known ocean viral populations 4 and doubles the number of candidate bacterial and archaeal virus genera 8 , providing a near-complete sampling of epipelagic communities at both the population and viral-cluster level. We found that 38 of the 867 viral clusters were locally or globally abundant, together accounting for nearly half of the viral populations in any global ocean virome sample. While two-thirds of these clusters represent newly described viruses lacking any cultivated representative, most could be computationally linked to dominant, ecologically relevant microbial hosts. Moreover, we identified 243 viral-encoded auxiliary metabolic genes, of which only 95 were previously known. Deeper analyses of four of these auxiliary metabolic genes ( dsrC , soxYZ , P-II (also known as glnB ) and amoC ) revealed that abundant viruses may directly manipulate sulfur and nitrogen cycling throughout the epipelagic ocean. This viral catalog and functional analyses provide a necessary foundation for the meaningful integration of viruses into ecosystem models where they act as key players in nutrient cycling and trophic networks.

Plant functional types (PFTs) are used to represent vegetation distribution in land surface models (LSMs). Previous studies have shown large differences in the geographical distribution of PFTs currently used in various LSMs, which may arise from the differences in the underlying land cover products but also the methods used to map or reclassify land cover data to the PFTs that a given LSM represents. There are large uncertainties associated with existing PFT mapping methods since they are largely based on expert judgement and therefore are subjective. In this study, we propose a new approach to inform the mapping or the cross-walking process using analyses from sub-pixel fractional error matrices, which allows for a quantitative assessment of the fractional composition of the land cover categories in a dataset. We use the Climate Change Initiative (CCI) land cover product produced by the European Space Agency (ESA). Previous work has shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an improved land cover distribution compared to that from the GLC2000 dataset currently used in the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. A tree cover fraction dataset and a fine-resolution land cover map over Canada are used to compute the sub-pixel fractional composition of the land cover classes in ESA-CCI, which is then used to create a cross-walking table for mapping the ESA-CCI land cover categories to nine PFTs represented in the CLASSIC model. There are large differences between the new PFT distributions and those currently used in the model. Offline simulations performed with the CLASSIC model using the ESA-CCI-based PFTs show improved winter albedo compared to that based on the GLC2000 dataset. This emphasizes the importance of accurate representation of vegetation distribution for realistic simulation of surface albedo in LSMs. Results in this study suggest that the sub-pixel fractional composition analyses are an effective way to reduce uncertainties in the PFT mapping process and therefore, to some extent, objectify the otherwise subjective process.

Groundwater mixing dynamics play a crucial role in the biogeochemical cycling of shallow wetlands. In this paper, we conducted groundwater simulations to investigate the combined effects of evaporation and local heterogeneity on mixing dynamics in shallow wetland sediments. The results show that evaporation causes groundwater and solutes to upwell from deep sediments to the surface. As the solute reaches the surface, evaporation enhances the accumulation of the solute near the surface, resulting in a higher solute concentration than in deep sediments. Mapping of flow topology reveals that local heterogeneity generates spatially varied mixing patterns mainly along preferential flow pathways. The upwelling of groundwater induced by surface evaporation through heterogeneous sediments is likely to create distinct mixing hotspots that differ spatially from those generated by lateral preferential flows driven by large‐scale hydraulic gradients, which enhances the overall mixing in the subsurface. These findings have strong implications for biogeochemical processing in wetlands. Plain Language Summary In shallow wetlands, groundwater mixing and exchange have been identified as critical factors affecting biogeochemical cycling and transformation in sediments. Our results for the first time demonstrate evaporation causes a significant upwelling of groundwater and solutes from deep sediments to the surface. As the solute reaches the surface, evaporation enhances the accumulation of the solute near the surface, resulting in a higher solute concentration than in deep sediments. Mapping of flow topology, including the Okubo‐Weiss parameter and dilution index, reveals that evaporation and local heterogeneity generates dynamic mixing patterns along preferential flow pathways. Such mixing mechanisms would strongly affect biogeochemical conditions in near‐surface sediments of shallow wetlands, which have strong implications for wetland ecosystems. Key Points Evaporation causes groundwater and solutes to upwell from deep sediments to the surface, resulting in near‐surface solute accumulation Heterogeneity causes strain‐dominated and vorticity‐dominated flow regions to coexist at small spatial scales along preferential flow paths Upwelling groundwater creates distinct mixing hotspots that differ spatially from those generated by large‐scale hydraulic gradients