Arctic Ocean sediments reveal permafrost thaw and carbon release during three large warming events of the past 27,000 years.
We evaluated global soil organic carbon (SOC) stocks and turnover time predictions from a global land model (ELMv1\uffe2\uff80\uff90ECA) integrated in an Earth System Model (E3SM) by comparing them with observed soil bulk and \uffce\uff9414C values around the world. We analyzed observed and simulated SOC stocks and \uffce\uff9414C values using machine learning methods at the Earth System Model grid cell scale (~200\uffc2\uffa0km). In grid cells with sufficient observations, the model provided reasonable estimates of soil carbon stocks across soil depth and \uffce\uff9414C values near the surface but underestimated \uffce\uff9414C at depth. Among many explanatory variables, soil albedo index, soil order, plant function type, air temperature, and SOC content were major factors affecting predicted SOC \uffce\uff9414C values. The influences of soil albedo index, soil order, and air temperature were primarily important in the shallow subsurface (\uffe2\uff89\uffa430\uffc2\uffa0cm). We also performed sensitivity studies using different vertical root distributions and decomposition turnover times and compared to observed SOC stock and \uffce\uff9414C profiles. The analyses support the role of vegetation in affecting soil carbon turnover, particularly in deep soil, possibly through supplying fresh carbon and degrading physical\uffe2\uff80\uff90chemical protection of SOC via root activities. Allowing for grid cell\uffe2\uff80\uff90specific rooting and decomposition rates substantially reduced discrepancies between observed and predicted \uffce\uff9414C values and SOC content. Our results highlight the need for more explicit representation of roots, microbes, and soil physical protection in land models. Arctic Ocean sediments reveal permafrost thaw and carbon release during three large warming events of the past 27,000 years.
Nitrous oxide (N2O) emissions from grazing animal excreta are estimated to be responsible for 1.5 Tg of the total 6.7 Tg of anthropogenic N2O emissions. This study was conducted to determine the in situ effect of incorporating biochar, into soil, on N2O emissions from bovine urine patches and associated pasture uptake of N. The effects of biochar rate (0\uffe2\uff80\uff9330 t ha\uffe2\uff88\uff921), following soil incorporation, were investigated on ruminant urine\uffe2\uff80\uff90derived N2O fluxes, N uptake by pasture, and pasture yield. During an 86\uffe2\uff80\uff90d spring\uffe2\uff80\uff90summer period, where irrigation and rainfall occurred, the N2O fluxes from 15N labeled ruminant urine patches were reduced by >50%, after incorporating 30 t ha\uffe2\uff88\uff921 of biochar. Taking into account the N2O emissions from the control plots, 30 t ha\uffe2\uff88\uff921 of biochar reduced the N2O emission factor from urine by 70%. The atom% 15N enrichment of the N2O emitted was lower in the 30 t ha\uffe2\uff88\uff921 biochar treatment, indicating less urine\uffe2\uff80\uff90N contributed to the N2O flux. Soil NO3\uffe2\uff88\uff92\uffe2\uff80\uff90N concentrations were lower with increasing biochar rate during the first 30 d following urine deposition. No differences occurred, due to biochar addition, with respect to dry matter yields, herbage N content, or recovery of 15N applied in herbage. Incorporating biochar into the soil can significantly diminish ruminant urine\uffe2\uff80\uff90derived N2O emissions. Further work is required to determine the persistence of the observed effect and to fully understand the mechanism(s) of the observed reduction in N2O fluxes.
", "keywords": ["bovine urine", "550", "Nitrogen", "Nitrous Oxide", "Urine", "Soil", "ANZSRC::0702 Animal Production", "ANZSRC::0503 Soil Sciences", "Animals", "Humans", "biochar", "Weather", "2. Zero hunger", "nitrous oxide", "ANZSRC::31 Biological sciences", "emissions", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water", "ANZSRC::050304 Soil Chemistry (excl. Carbon Sequestration Science)", "ANZSRC::37 Earth sciences", "13. Climate action", "Charcoal", "ANZSRC::41 Environmental sciences", "0401 agriculture", " forestry", " and fisheries", "Cattle", "Volatilization"]}, "links": [{"href": "https://doi.org/10.2134/jeq2010.0419"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Environmental%20Quality", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2134/jeq2010.0419", "name": "item", "description": "10.2134/jeq2010.0419", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2134/jeq2010.0419"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2011-03-01T00:00:00Z"}}, {"id": "10.5194/gmd-2021-98", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:52Z", "type": "Journal Article", "created": "2021-11-30", "title": "Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations", "description": "Abstract. The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world's growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires efficient models and data to monitor land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1 to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30\u2009arcsec (\u223c1\u2009km). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database version 1.2 (HWSDv1.2), and generic crop information. The setup with a generic crop is chosen as a baseline for a future satellite-based data assimilation system. The relative temporal variability in daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active\u2013Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean temporal correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the short-term and inter-annual variability, with a mean anomaly correlation of 0.46 for daily biomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model adequately simulates crop production and soil moisture and provides a suitable setup for subsequent satellite-based data assimilation.
Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar\u2013von Caemmerer\u2013Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation\u2013transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model builds on the theory developed in Prentice et\u00a0al. (2014) and Wang et\u00a0al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8\u2009d mean, 126 sites) \u2013 similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106\u2013122\u2009Pg\u2009C\u2009yr\u22121 (mean of 2001\u20132011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting \u2013 rather than prescribing \u2013 light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).
Abstract. Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, the relative contributions of the world\u2019s major dust source regions to the global dust cycle remain poorly constrained. This problem hinders accounting for the potentially large impact of regional differences in dust properties on clouds, the Earth's energy balance, and terrestrial and marine biogeochemical cycles. Here, we constrain the contribution of each of the world\u2019s main dust source regions to the global dust cycle. We use an analytical framework that integrates an ensemble of global model simulations with observationally informed constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth. We obtain a data set that constrains the relative contribution of each of nine major source regions to size-resolved dust emission, atmospheric loading, optical depth, concentration, and deposition flux. We find that the 22\u201329\u2009Tg (one standard error range) global loading of dust with geometric diameter up to 20\u2009\u03bcm is partitioned as follows: North African source regions contribute ~50\u2009% (11\u201315\u2009Tg), Asian source regions contribute ~40\u2009% (8\u201313\u2009Tg), and North American and Southern Hemisphere regions contribute ~10\u2009% (1.8\u20133.2\u2009Tg). Current models might on average be overestimating the contribution of North African sources to atmospheric dust loading at ~65\u2009%, while underestimating the contribution of Asian dust at ~30\u2009%. However, both our results and current models could be affected by unquantified biases, such as due to errors in separating dust aerosol optical depth from that produced by other aerosol species in remote sensing retrievals in poorly observed desert regions. Our results further show that each source region's dust loading peaks in local spring and summer, which is partially driven by increased dust lifetime in those seasons. We also quantify the dust deposition flux to the Amazon rainforest to be ~10\u2009Tg/year, which is a factor of 2\u20133 less than inferred from satellite data by previous work that likely overestimated dust deposition by underestimating the dust mass extinction efficiency. The data obtained in this paper can be used to obtain improved constraints on dust impacts on clouds, climate, biogeochemical cycles, and other parts of the Earth system.
Mineral dust particles suspended in the atmosphere span more than three orders of magnitude in diameter, from less than 0.1 \u00b5m to more than 100 \u00b5m. This wide size range makes dust a unique aerosol species with the ability to interact with many aspects of the Earth system, including radiation, clouds, hydrology, atmospheric chemistry, and biogeochemistry. This review focuses on coarse and super-coarse dust aerosols, which we respectively define as dust particles with a diameter between 2.5 - 10 \u00b5m and 10 - 62.5 \u00b5m. We review several lines of observational evidence indicating that coarse and super-coarse dust particles are transported farther than previously expected and that the abundance of these particles is substantially underestimated in current global models. We synthesize previous studies that used observations, theories, and model simulations to highlight the impacts of coarse and super-coarse dust aerosols on the Earth system, including their effects on dust-radiation interactions, dust-cloud interactions, and atmospheric chemistry, and biogeochemistry. In addition, we examine several limitations in the representation of coarse and super-coarse dust aerosols in current model simulations and in remote-sensing retrievals. Because these limitations substantially contribute to the uncertainties in simulating the abundance and impacts of coarse and super-coarse dust aerosols, we offer some recommendations to facilitate future studies. Overall, we conclude that an accurate representation of coarse and super-coarse properties is critical in understanding the overall impacts of dust aerosols on the Earth system.
Abstract. Quantifying the dust optical depth (DOD) and its uncertainty across spatiotemporal scales is key to understanding and constraining the dust cycle and its interactions with the Earth System. This study quantifies the DOD along with its monthly and year-to-year variability between 2003 and 2017 at global and regional levels based on the MIDAS (ModIs Dust AeroSol) dataset, which combines Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua retrievals and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis products. We also describe the annual and seasonal geographical distributions of DOD across the main dust source regions and transport pathways. MIDAS provides columnar mid-visible (550\u2009nm) DOD at fine spatial resolution (0.1\u2218\u00d70.1\u2218), expanding the current observational capabilities for monitoring the highly variable spatiotemporal features of the dust burden. We obtain a global DOD of 0.032\u00b10.003 \u2013 approximately a quarter (23.4\u2009%\u00b12.4\u2009%) of the global aerosol optical depth (AOD) \u2013 with about 1\u00a0order of magnitude more DOD in the Northern Hemisphere (0.056\u00b10.004; 31.8\u2009%\u00b12.7\u2009%) than in the Southern Hemisphere (0.008\u00b10.001; 8.2\u2009%\u00b11.1\u2009%) and about 3.5 times more DOD over land (0.070\u00b10.005) than over ocean (0.019\u00b10.002). The Northern Hemisphere monthly DOD is highly correlated with the corresponding monthly AOD (R2=0.94) and contributes 20\u2009% to 48\u2009% of it, both indicating a dominant dust contribution. In contrast, the contribution of dust to the monthly AOD does not exceed 17\u2009% in the Southern Hemisphere, although the uncertainty in this region is larger. Among the major dust sources of the planet, the maximum DODs (\u223c1.2) are recorded in the Bod\u00e9l\u00e9 Depression of the northern Lake Chad Basin, whereas moderate-to-high intensities are encountered in the Western Sahara (boreal summer), along the eastern parts of the Middle East (boreal summer) and in the Taklamakan Desert (spring). Over oceans, major long-range dust transport is observed primarily along the tropical Atlantic (intensified during boreal summer) and secondarily in the North Pacific (intensified during boreal spring). Our calculated global and regional averages and associated uncertainties are consistent with some but not all recent observation-based studies. Our work provides a simple yet flexible method to estimate consistent uncertainties across spatiotemporal scales, which will enhance the use of the MIDAS dataset in a variety of future studies.
Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2\uffc2\uffa0m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1\uffe2\uff80\uff90km2resolution for 0\uffe2\uff80\uff935 and 5\uffe2\uff80\uff9315\uffc2\uffa0cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1\uffe2\uff80\uff90km2pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse\uffe2\uff80\uff90grained air temperature estimates from ERA5\uffe2\uff80\uff90Land (an atmospheric reanalysis by the European Centre for Medium\uffe2\uff80\uff90Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\uffc2\uffb0C (mean\uffc2\uffa0=\uffc2\uffa03.0\uffc2\uffa0\uffc2\uffb1\uffc2\uffa02.1\uffc2\uffb0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6\uffc2\uffa0\uffc2\uffb1\uffc2\uffa02.3\uffc2\uffb0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (\uffe2\uff88\uff920.7\uffc2\uffa0\uffc2\uffb1\uffc2\uffa02.3\uffc2\uffb0C). The observed substantial and biome\uffe2\uff80\uff90specific offsets emphasize that the projected impacts of climate and climate change on near\uffe2\uff80\uff90surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil\uffe2\uff80\uff90related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications. Abstract. The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world's growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires efficient models and data to monitor land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1 to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30\u2009arcsec (\u223c1\u2009km). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database version 1.2 (HWSDv1.2), and generic crop information. The setup with a generic crop is chosen as a baseline for a future satellite-based data assimilation system. The relative temporal variability in daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active\u2013Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean temporal correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the short-term and inter-annual variability, with a mean anomaly correlation of 0.46 for daily biomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model adequately simulates crop production and soil moisture and provides a suitable setup for subsequent satellite-based data assimilation.
We evaluated global soil organic carbon (SOC) stocks and turnover time predictions from a global land model (ELMv1\uffe2\uff80\uff90ECA) integrated in an Earth System Model (E3SM) by comparing them with observed soil bulk and \uffce\uff9414C values around the world. We analyzed observed and simulated SOC stocks and \uffce\uff9414C values using machine learning methods at the Earth System Model grid cell scale (~200\uffc2\uffa0km). In grid cells with sufficient observations, the model provided reasonable estimates of soil carbon stocks across soil depth and \uffce\uff9414C values near the surface but underestimated \uffce\uff9414C at depth. Among many explanatory variables, soil albedo index, soil order, plant function type, air temperature, and SOC content were major factors affecting predicted SOC \uffce\uff9414C values. The influences of soil albedo index, soil order, and air temperature were primarily important in the shallow subsurface (\uffe2\uff89\uffa430\uffc2\uffa0cm). We also performed sensitivity studies using different vertical root distributions and decomposition turnover times and compared to observed SOC stock and \uffce\uff9414C profiles. The analyses support the role of vegetation in affecting soil carbon turnover, particularly in deep soil, possibly through supplying fresh carbon and degrading physical\uffe2\uff80\uff90chemical protection of SOC via root activities. Allowing for grid cell\uffe2\uff80\uff90specific rooting and decomposition rates substantially reduced discrepancies between observed and predicted \uffce\uff9414C values and SOC content. Our results highlight the need for more explicit representation of roots, microbes, and soil physical protection in land models. Arctic Ocean sediments reveal permafrost thaw and carbon release during three large warming events of the past 27,000 years. Arctic Ocean sediments reveal permafrost thaw and carbon release during three large warming events of the past 27,000 years.