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35 results for “carbon budget”
Carbon Budget at the Harvard Forest 1992-2015
How, where, and why carbon (C) moves into and out of an ecosystem through time are long-standing questions in biogeochemistry. Here, we bring together hundreds of thousands of C-cycle observations at the Harvard Forest in central Massachusetts, USA, a mid-latitude landscape dominated by 80–120-year-old closed-canopy forests. These data answered four questions: (i) where and how much C is presently stored in dominant forest types; (ii) what are current rates of C accrual or loss; (iii) what biotic and abiotic factors contribute to variability in these rates; and (iv) is climate change affecting the forest’s C cycle? Harvard Forest is an active C sink resulting from forest regrowth following land abandonment. Soil and tree biomass comprise nearly equal portions of existing C stocks. Net primary production (NPP) averaged 750–970 g C m-2 yr-1; belowground NPP contributed 30–60% of the total. Mineral soil C measured in the same inventory plots in 1992 and 2013 were too heterogeneous to detect change in soil-C pools; however, radiocarbon data suggest a small but persistent sink of 10–30 g C m-2 yr-1. Net ecosystem production (NEP) in hardwood stands averaged ~300 g C m-2 yr-1. NEP in hemlock-dominated forests averaged ~450 g C m-2 yr-1 prior to infestation by the hemlock woolly adelgid (HWA) in 2013, and then became a net C source. Stand dynamics and climate change in the last three decades enhanced the C sink in hardwood stands; NPP increased 26% between 2000–2014 (p = 0.02) and NEP increased 93% between 1992–2015 (p = 0.13). Compared to long-term global change experiments at the Harvard Forest, the C sink in regrowing biomass equaled or exceeded C cycle modifications imposed by soil warming, N saturation, and hemlock removal. Median forest biomass in the surrounding ecoregion was only 78% of that at the Harvard Forest due to higher timber harvesting rates across the region. Results of this synthesis and comparison to simulation models suggest that forests across the reg
Steady state carbon, nitrogen, phosphorus, and water budgets for twelve mature ecosystems ranging from prairie to forest and from the arctic to the tropics
We use the Multiple Element Limitation (MEL) model to examine the responses of twelve ecosystems - from the arctic to the tropics and from grasslands to forests - to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in annual precipitation. The ecosystems we simulated include moist acidic tundra, shrub tundra, and wet sedge tundra near Toolik Lake, Alaska, alpine dry meadow tundra near Niwot Ridge, Colorado, restored tallgrass prairie near Kellogg Biological Station, Michigan, native tallgrass prairie at the Konza Prairie, Kansas, upland and lowland boreal forest near Bonanza Creek, Alaska, temperate coniferous forest in HJ Andrews Experimental Forest, Oregon, a northern hardwood forest in Hubbard Brook Experimental Forest, New Hampshire, a transition oak-maple forest in Harvard Forest, Massachusetts, and lowland tropical rainforest near Caxiuanã National Forest, Pará, Brazil. For each of the twelve sites, we run six 100-year simulations beginning from the calibrated steady state (72 simulations total). The six simulations are: (1) increasing CO2 from 400 to 800 μmol mol-1, (2) warming from current temperatures to current plus 3.5oC, (3) decreasing precipitation from 100% to 80% of the current annual rate, (4) increasing precipitation from 100% to 120% of the current annual rate, (5) doubling of CO2, 3.5oC warming, and 20% decrease in precipitation, and (6) doubling of CO2, 3.5oC warming, and 20% increase in precipitation. The carbon, nitrogen, phosphorus, and water budgets presented here are used to calibrate the MEL model prior to running the climate change simulations. Citations and calculations for the data presented here are described in the individual site html files included in this dataset.
FAB 1: Forests and Biodiversity Experiment - High density diversity experiment: carbon budget data
This data represents changes in above and belowground C pools in young stands six years after the initiation of the Forests and Biodiversity experiment (FAB1) in 2013, consisting of high density plots of one, two, five, or 12 tree species planted in a common garden. Trees were planted to represent a range of native functional diversity, including needle-leaf conifer and broadleaf deciduous species as well as ectomycorrhizal and arbuscular mycorrhizal species. We quantified the effects of species richness, phylogenetic diversity, and functional diversity on aboveground C accumulation, as well as on soil C accumulation, fine root C, and soil aggregation. To assess the role of the microbial community in mediating these effects, we further compared changes in soil C pools to phospholipid fatty acids (PLFAs) profiles collected in 2016.
Global Carbon Budget 2022, surface ocean fugactiy of CO2 (fCO2) and air-sea CO2 flux of individual Global ocean biogechemical models and surface ocean fCO2-based data-products
<p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (data-products).</strong><br> There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p> <p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of data-products and GOBMs and with the adjustments described in the Global Carbon Budget 2022 (https://doi.org/10.5194/essd-14-4811-2022, section C3), are available in the Global Carbon Budget 2022 spreadsheet.</strong></p> <p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2022 paper (https://doi.org/10.5194/essd-14-4811-2022), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.17 GtC yr-1, Tropics: 0.16 GtC yr-1, South: 0.32 GtC yr-1, see GCB 2022 paper, section 2.4.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because adjustments were applied only for global fluxes.</p> <p><strong>What is in the files?</strong></p> <p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):</p> <p><br> fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, south<br> fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br> sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br> area: Area per pixel, dimensions: latitude, longitude<br> area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p> <p>(2) The files for the GOBMs contain the following fields, for simulation A (‘contemporary simulation’, including effects of rising CO2, climate change and variability) and simulation B (‘control simulation’, constant CO2, no climate change and variability). Temporal resolution: monthly</p> <p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br> sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br> area: Area per pixel, dimensions: latitude, longitude</p> <p><br> (3) One file ‘GCB-2022_OceanModel_RegionalBreakdown_1959-2021.nc’ with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual.</p> <p><br> <strong>Fair data use statement:</strong><br> The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br> <strong>Citation:</strong> Please cite the Global Carbon Budget 2022 (Friedlingstein et al., 2022, ESSD, https://doi.org/10.5194/essd-14-4811-2022) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2022 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).<br> <strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: “We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output.”<br> <strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p> <p><br> Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudget.org/</p> <p> </p>
Global Carbon Budget 2023, surface ocean fugactiy of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogechemical models and surface ocean fCO2-based data-products
<p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products).</strong><br>There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p><p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2023 (https://doi.org/10.5194/essd-15-5301-2023), are available in the Global Carbon Budget 2023 spreadsheet.</strong></p><p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2023 paper (https://doi.org/10.5194/essd-15-5301-2023), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2023 paper, section 2.5.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes.</p><p><strong>What is in the files?</strong></p><p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):<br><br>fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, south<br>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude<br>area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p><p>(2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly</p><p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude<br><br>(3) One file 'GCB-2023_OceanModel_RegionalBreakdown_1959-2022.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual.</p><p><strong>Fair data use statement:</strong><br>The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br><strong>Citation:</strong> Please cite the Global Carbon Budget 2023 (Friedlingstein et al., 2023, ESSD, https://doi.org/10.5194/essd-15-5301-2023) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2023 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).<br><strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output."<br><strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p><p>Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional 3D output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html</p>
Global Carbon Budget 2024, surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogeochemical models and surface ocean fCO2-based data-products
<p><strong>v2 update: </strong></p> <ul> <li>update to data in UoEX-UEPFFNU fCO2-product</li> <li>fix of lat-lon issue in Jena-MLS fCO2-product</li> <li>minor fixes to metadata in fCO2-products</li> </ul> <p><br>The v2 data is used for the final published version of the Global Carbon Budget 2024.</p> <p>-----------------</p> <p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products).</strong><br>There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p> <p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2024 (https://essd.copernicus.org/preprints/essd-2024-519), are available in the Global Carbon Budget 2024 spreadsheet.</strong></p> <p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 14 of the Global Carbon Budget 2024 paper (https://essd.copernicus.org/preprints/essd-2024-519), the river flux adjustment needs to be added to the CO2 flux estimated from the fCO2-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2024 paper). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes.</p> <p><strong>What is in the files?</strong></p> <p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):<br><br>fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: global, north, tropics, south<br>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude<br>area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p> <p>(2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly</p> <p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude</p> <p>(3) One file 'GCB-2024_OceanModel_RegionalBreakdown_1959-2023.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Regions: North, tropics, south. Temporal resolution: annual.</p> <p><strong>Fair data use statement:</strong><br>The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br><strong>Citation:</strong> Please cite the Global Carbon Budget 2024 (Friedlingstein et al., 2024, ESSD, https://essd.copernicus.org/preprints/essd-2024-519) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2024 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).</p> <p><strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output."<br><strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p> <p>Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional 3D output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html</p>
Carbon Budget Scenarios for Ireland's Energy System, 2021-50
<p>Carbon Budget Scenarios for Ireland's Energy System, 2021-50, calculated with the TIMES-Ireland model.</p>
Dataset associated with the manuscript "A comprehensive assessment of anthropogenic and natural sources and sinks of Australasia's carbon budget" by Villalobos et al. (2023), part of the the second phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2).
<p>Dataset associated with the manuscript "A comprehensive assessment of anthropogenic and natural sources and sinks of Australasia’s carbon budget" by Villalobos et al. (2023), part of the the second phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2). </p>
Assessing Negative Carbon Dioxide Emissions from the Perspective of a National 'Fair Share' of the Remaining Global Carbon Budget: Supplementary Material
<p>Detailed calculations supporting the results in the published paper, <em>Assessing Negative Carbon Dioxide Emissions from the Perspective of a National 'Fair Share' of the Remaining Global Carbon Budget</em>, <a href="https://link.springer.com/journal/11027">Mitigation and Adaptation Strategies for Global Change</a>, DOI: <a href="https://doi.org/10.1007/s11027-019-09881-6">10.1007/s11027-019-09881-6</a>.</p> <ul> <li><strong>IE-CO2-Quota-2015.ods</strong>: Spreadsheet/workbook in <a href="http://opendocumentformat.org/">Open Document</a> format. Includes table and charts as presented in the paper. Prepared using <a href="http://www.libreoffice.org">LibreOffice</a> (v 5.0+). Should also be accessible also in Microsoft Excel, but some formatting or functionality may be lost.</li> <li><strong>IE-CO2-Quota-2015.ipynb</strong>: Mathematical background and cross-check of detailed calculations in interactive <a href="https://jupyter.org/">Jupyter notebook</a> format (coding in <a href="https://www.python.org/">python</a>).</li> <li><strong>IE-CO2-Quota-2015-ipynb.html</strong>: Static HTML version of the <strong>IE-CO2-Quota-2015.ipynb</strong> suitable for simple viewing/printing.</li> <li><strong>IE-CO2-Quota-2015-ipynb.pdf</strong>: Static version of the <strong>IE-CO2-Quota-2015.ipynb</strong> suitable for simple viewing/printing.</li> </ul>
Canadian fossil fuel production, greenhouse gas emissions, emissions targets and carbon budgets
<p>This spreadsheet shows the amounts of coal, oil and natural gas produced in Canada from 2010 to 2020 using governmental sources. It includes calculations of the corresponding emissions according to a life-cycle analysis. The total greenhouse gas emissions from fossil fuels extracted annually in Canada (including those burned abroad) are computed. McGlade and Ekins (2015) proposed budgets for the production of each type of fossil fuel in order to provide a 67% chance to limit warming to 2.0 °C by 2100. The proportion of each budget that is already spent is calculated. Emissions targets from 21 scenarios originating from five effort-sharing studies are compared with Canadian 2020 emissions to evaluate the difference. Carbon budgets from 18 scenarios originating from seven studies are compared with Canadian cumulative emissions to evaluate the percentage of the budgets within the period 2010-2050 already emitted.</p>
Data from: Mesophotic Foraminiferal-Algal Nodules play a role in the Red Sea carbonate budget
<p>Free-living mesophotic Foraminiferal-Algal Nodules (FANs) have been discovered along the coast of the northern Saudi Arabian Red Sea (NEOM region) where they form a novel benthic ecosystem in mesophotic water depths on the continental shelf. Being mostly spheroidal, the nodules are transported <em>en masse</em> down slope, into the deep water of the basin, where they stop accreting. Radiometric dating informs that FANs can be more than two thousand years old and that they collectively contribute up to 66 g m<sup>-2</sup> year<sup>-1</sup> to the mesophotic benthic carbonate budget and account for at least 980 megatons of CaCO<sub>3</sub>, a substantial contribution considering the depauperate production of carbonate by other means in this light-limited environment. Our findings advance the knowledge of mesophotic biodiversity and carbonate production, and provide data that will inform conservation policies in the Saudi Arabian Red Sea.</p>
Data from: Mesophotic Foraminiferal-Algal Nodules play a role in the Red Sea carbonate budget
Open the record for dataset details and reuse information.
Data from: Phenological mismatch with trees reduces wildflower carbon budgets
Interacting species can respond differently to climate change, causing unexpected consequences. Many understory wildflowers in deciduous forests leaf out and flower in the spring when light availability is highest before overstory canopy closure. Therefore, different phenological responses by understory and overstory species to increased spring temperature could have significant ecological implications. Pairing contemporary data with historical observations initiated by Henry David Thoreau (1850s), we found that overstory tree leaf out is more responsive to increased spring temperature than understory wildflower phenology, resulting in shorter periods of high light in the understory before wildflowers are shaded by tree canopies. Because of this overstory-understory mismatch, we estimate that wildflower spring carbon budgets in the northeastern United States were 12-26% larger during Thoreau's era and project a 10-48% reduction during this century. This underappreciated phenomenon may have already reduced wildflower fitness and could lead to future population declines in these ecologically important species.
Data from: The contribution of carbon budget to masting intervals in Veratrum album populations inhabiting different elevations
<p><strong>Premise</strong><strong>: </strong>Mast flowering/seeding is often more extreme in lower-resource environments, such as alpine compared to lowland habitats. We studied a masting herb which had less extreme masting at higher elevations, and tested if this difference could be explained by higher photosynthetic productivity and/or lower reproductive investment at the higher elevation sites.</p> <p><strong>Methods: </strong>We examined the relationship between flowering intervals and carbon budget (i.e., the balance between reproductive investment and annual carbon fixation) in a masting herb, <em>Veratrum album</em> subsp. <em>oxysepalum</em>, across five lowland and six alpine populations in northern Japan. We evaluated the previous flowering histories of individual plants based on rhizome morphology and analyzed the masting patterns of individual populations. Total mass of the reproductive organs, as a proxy of reproductive investment, was compared between the lowland and alpine populations. Annual carbon fixation was estimated based on photosynthetic capacity, total leaf area per plant, and seasonal transition of light availability.</p> <p><strong>Results: </strong>Interval between high-flowering years was shorter and total reproductive investment was smaller in the alpine than in the lowland populations. Owing to its high photosynthetic capacity and continuous bright conditions, annual carbon fixation per plant was 1.5 times greater at the alpine habitat than at the lowland habitat. These results suggest that <em>V. album</em> alpine populations have shorter flowering intervals than lowland populations due to faster recovery from energy loss after reproduction.</p> <p><strong>Conclusions: </strong>Our study demonstrated that masting intervals in <em>V. album</em> populations can be explained by habitat-specific carbon budget balances.</p>
OSCAR contribution to the Global Carbon Budget 2024
<p>Global and national CO2 emissions from land use and land cover change, and their sub-components, simulated with OSCAR v3.3 for the 2024 edition of the Global Carbon Budget.</p>
Aerosol-light interactions reduce the carbon budget imbalance - DGVM output
<p>DGVM (JULES and ORCHIDEE_DF) output associated with the publication 'Aerosol-light interactions reduce the carbon budget imbalance' (O'Sullivan et al., 2021 - Environmental Research Letters).</p> <p>Files contain monthly data over the period 1901-2017. There are 7 simulations for each model, with varying surface solar radiation in each case. Atmospheric CO<sub>2</sub> and all climate variables (except for incoming short-wave radiation) vary throughout all simulations.</p> <p>Historical: total and diffuse shortwave radiation depend on the solar zenith angle, temporally varying cloud cover and aerosol optical depth of the whole atmospheric column.</p> <p>FixedAero: Time-invariant (1901-1920 cycle) tropospheric aerosol and no stratospheric aerosol influence on total and diffuse shortwave radiation</p> <p>FixedTropAero: Time-invariant (1901-1920 cycle) tropospheric aerosol influence on total and diffuse shortwave radiation</p> <p>FixedStratAero: no stratospheric aerosol influence on total and diffuse shortwave radiation</p> <p>FixedAero_DF: Time-invariant (1901-1920 cycle) tropospheric aerosol and no stratospheric aerosol influence on diffuse shortwave radiation</p> <p>FixedTropAero_DF: Time-invariant (1901-1920 cycle) tropospheric aerosol influence on diffuse shortwave radiation</p> <p>FixedStratAero_DF: no stratospheric aerosol influence on diffuse shortwave radiation</p> <p> </p>
Soil dissolved organic carbon in terrestrial ecosystems: global budget, spatial distribution and controls
<p><strong>Aims: </strong>Soil dissolved organic carbon (DOC) is a primary form of labile carbon in terrestrial ecosystems and therefore plays a vital role in soil carbon cycling. This study aims to quantify the budgets of soil DOC at biome- and global levels and to examine the variations in soil DOC and their environmental controls. Location: Global Time period: 1981 - 2019 Method: We compiled a global dataset and analyzed the concentration and distribution of DOC across 10 biomes.</p> <p><strong>Results: </strong>Large variations in DOC are found among biomes across space and the soil DOC concentration declines exponentially along soil depths. Tundra has the highest soil DOC concentration in 0 - 30 cm soils (453.75 (95% confidence interval: 324.95 – 633.5) mg·kg-1); whereas tropical and temperate forests have relatively lower DOC concentrations, ranging from 30.20 (24.78 - 36.80) mg·kg-1 to 54.54 (49.77 – 59.77) mg·kg-1. DOC generally accounts for < 1% of total organic carbon in soils, and DOC in 0 - 30 cm contributes more than half of total DOC in 0 - 100 cm soil profile. Furthermore, variations in DOC are primarily controlled by soil texture, moisture, and total organic carbon.</p> <p><strong>Main conclusion: </strong>A global synthesis is combined with an empirical model to extrapolate the DOC concentration along soil profiles across the globe, and global budgets of DOC are estimated as 7.20 Pg C in top 0 - 30 cm and 12.97 Pg C in 0 - 100 cm, respectively, with a considerable variation among biomes. The strong soil texture control but weak TOC control on DOC variations suggest that the investigation of physical protection of soil organic carbon might need to expand to consider the labile C in soils. The global maps of DOC concentration serve as a benchmark for validating land surface models in estimating carbon storage in soils.</p>
Components of the complete budget for SAFE intensive carbon plots
<p><strong>Description: </strong></p> <p>Measured components of total carbon budget at SAFE project, values, with standard errors, for each 1-ha carbon plots for 11 plots investigated across a logging gradient from unlogged old-growth to heavily logged.<br> <br> These data are also published in below-ground carbon cycle in Riutta et al 2021 GBC and allocation of net primary productivity from Riutta et al 2019 GCB. This worksheet include two addititional carbon plots from Lambir Hills National Park (see Kho et al. 2013 JGR), which are not part of the SAFE Project. Below-ground carbon cycle data can be found at DOI 10.5281/zenodo.3266770 and leaf respiration at DOI 10.5281/zenodo.3247630.<br> <br> SAFE Intensive Carbon Plots, part of the Global Ecosystem Monitoring (GEM) network, see http://gem.tropicalforests.ox.ac.uk/. All the methods and installation is described in detail in the GEM Intensive Carbon Plots manual, available at http://gem.tropicalforests.ox.ac.uk/files/rainfor-gemmanual.v3.0.pdf.</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/113"><strong>Changing carbon dioxide and water budgets from deforestation and habitat modification</strong></a></p> <p><strong>Funding: </strong>These data were collected as part of research funded by:</p> <ul> <li>Sime Darby Foundation (Grant, SAFE Core data)</li> <li>European Research Council Advanced Investigator Grant, GEM-TRAIT (Grant, Grant number 321131)</li> <li>NERC Human-Modified Tropical Forests Programme: Biodiversity And Land-use Impacts on tropical ecosystem function (BALI) Project (Grant, NE/K016369/1)</li> <li>NERC standard grant: The multi-year impacts of the 2015/2016 El Niño on the carbon cycle of tropical forests worldwide (Grant, NE/P001092/1)</li> <li>HSBC Malaysia (Grant)</li> <li>The University of Zurich (Grant)</li> </ul> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <p> </p> <p><strong>Permits: </strong>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Council (Research licence JKM/MBs.1000-2/2 JLD.6 (76))</li> </ul> <p> </p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7307449">here</a></p> <p><strong>Files: </strong>This consists of 1 file: SAFE_CarbonBalanceComponents.xlsx</p> <p><strong>SAFE_CarbonBalanceComponents.xlsx</strong></p> <p>This file contains dataset metadata and 1 data tables:</p> <ol> <li> <p><strong>Carbon balance components data</strong> (described in worksheet Data)</p> <p>Description: Carbon balance components and carbon budget of intensive carbon plots at SAFE project</p> <p>Number of fields: 64</p> <p>Number of data rows: 11</p> <p>Fields:</p> <ul> <li><strong>ForestType</strong>: Old-growth or Logged (Field type: categorical)</li> <li><strong>SAFEPlotName</strong>: SAFE plot name, as in the SAFE Gazetteer (Field type: location)</li> <li><strong>PlotName</strong>: Plot name (used in field work) (Field type: id)</li> <li><strong>ForestPlotsCode</strong>: Plot code, as in the ForestPlots database (this should be used in publications, instead of plot name) (Field type: id)</li> <li><strong>WoodyNPP_Stem</strong>: Woody stem productivity (subcomponent of woody net primary productivity) (Field type: numeric)</li> <li><strong>WoodyNPP_CoarseRoot</strong>: Coarse root productivity (subcomponent of woody net primary productivity) (Field type: numeric)</li> <li><strong>WoodyNPP_BranchTurnover</strong>: Branch turnover productivity (subcomponent of woody net primary productivity) (Field type: numeric)</li> <li><strong>WoodyNPP_Total</strong>: Total woody net primary producivity (Field type: numeric)</li> <li><strong>CanopyNPP_Leaf</strong>: Leaf productivity (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Twig</strong>: Twig productivity (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Reproductive</strong>: Reproductive productivity, i.e. fruit, seed and flowers (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Miscellaneous</strong>: Unidentified canopy debris (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Herbivory</strong>: Leaf productivity lost to herbivory (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Total</strong>: Total canopy net primary producivty (Field type: numeric)</li> <li><strong>FineRootNPP</strong>: Fine root productivity (Field type: numeric)</li> <li><strong>TotalNPP_WithoutMycorrhiza</strong>: Total net primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>TotalNPP_WithMycorrhiza</strong>: Total net primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>GPP_WithoutMycorrhiza</strong>: Gross primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>GPP_WithMycorrhiza</strong>: Gross primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>R_Stem</strong>: Respiration from woody stems (Field type: numeric)</li> <li><strong>R_Leaf</strong>: Leaf Respiration (Field type: numeric)</li> <li><strong>R_FineRoots</strong>: Respiration from fine roots (Field type: numeric)</li> <li><strong>R_CoarseRoots</strong>: Respiration from coarse roots (Field type: numeric)</li> <li><strong>R_SOM</strong>: Respiration from soil organic matter (Field type: numeric)</li> <li><strong>R_Mycorrhiza</strong>: Respiration from mycorrhiza (Field type: numeric)</li> <li><strong>R_Litter</strong>: Respiration from litter layer (Field type: numeric)</li> <li><strong>R_Deadwood</strong>: Deadwood respiration (Field type: numeric)</li> <li><strong>R_auto</strong>: Total autotrophic respiration (Field type: numeric)</li> <li><strong>R_het</strong>: Total heterotrophic respiration (Field type: numeric)</li> <li><strong>R_eco</strong>: Total ecosystem respiration (Field type: numeric)</li> <li><strong>NEP_WithoutMycorrhiza</strong>: Total net ecosystem productivity (also known as net ecosystem exchange) without including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric)</li> <li><strong>NEP_WithMycorrhiza</strong>: Total net ecosystem productivity (also known as net ecosystem exchange) including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric)</li> <li><strong>AbovegroundBiomassCarbonStock</strong>: Plot above-ground biomass carbon stock (Field type: numeric)</li> <li><strong>CoarseRootBiomassCarbonStock</strong>: Biomass carbon stock of coarse roots (Field type: numeric)</li> <li><strong>SE_WoodyNPP_Stem</strong>: Standard error of woody stem productivity (Field type: numeric)</li> <li><strong>SE_WoodyNPP_CoarseRoot</strong>: Standard error of coarse root productivity (Field type: numeric)</li> <li><strong>SE_WoodyNPP_BranchTurnover</strong>: Standard error of branch turnover productivity (Field type: numeric)</li> <li><strong>SE_WoodyNPP_Total</strong>: Standard error of total woody net primary producivity (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Leaf</strong>: Standard error of leaf productivity (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Twig</strong>: Standard error of twig productivity (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Reproductive</strong>: Standard error of reproductive productivity, i.e. fruit, seed and flowers (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Miscellaneous</strong>: Standard error of unidentified canopy debris (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Herbivory</strong>: Standard error of leaf productivity lost to herbivory (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Total</strong>: Standard error of total canopy net primary producivty (Field type: numeric)</li> <li><strong>SE_FineRootNPP</strong>: Standard error of fine root productivity (Field type: numeric)</li> <li><strong>SE_TotalNPP_WithoutMycorrhiza</strong>: Standard error of total net primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>SE_TotalNPP_WithMycorrhiza</strong>: Standard error of total net primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>SE_GPP_WithoutMycorrhiza</strong>: Standard error of gross primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>SE_GPP_WithMycorrhiza</strong>: Standard error of gross primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>SE_R_Stem</strong>: Standard error of respiration from woody stems (Field type: numeric)</li> <li><strong>SE_R_Leaf</strong>: Standard error of leaf Respiration (Field type: numeric)</li> <li><strong>SE_R_FineRoots</strong>: Standard error of respiration from fine roots (Field type: numeric)</li> <li><strong>SE_R_CoarseRoots</strong>: Standard error of respiration from coarse roots (Field type: numeric)</li> <li><strong>SE_R_SOM</strong>: Standard error of respiration from soil organic matter (Field type: numeric)</li> <li><strong>SE_R_Mycorrhiza</strong>: Standard error of respiration from mycorrhiza (Field type: numeric)</li> <li><strong>SE_R_Litter</strong>: Standard error of litter layer respiration (Field type: numeric)</li> <li><strong>SE_R_Deadwood</strong>: Standard error of deadwood respiration (Field type: numeric)</li> <li><strong>SE_R_auto</strong>: Standard error of total autotrophic respiration (Field type: numeric)</li> <li><strong>SE_R_het</strong>: Standard error of total heterotrophic respiration (Field type: numeric)</li> <li><strong>SE_R_eco</strong>: Standard error of total ecosystem respiration (Field type: numeric)</li> <li><strong>SE_NEP_WithoutMycorrhiza</strong>: Standard error of total net ecosystem productivity (Field type: numeric)</li> <li><strong>SE_NEP_WithMycorrhiza</strong>: Standard error of total net ecosystem productivity (Field type: numeric)</li> <li><strong>SE_AbovegroundBiomassCarbonStock</strong>: Standard error of plot above-ground biomass carbon stock (Field type: numeric)</li> <li><strong>SE_CoarseRootBiomassCarbonStock</strong>: Standard error of biomass carbon stock of coarse roots (Field type: numeric)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2011-08-25 to 2018-07-17</p> <p><strong>Latitudinal extent: </strong>4.1830 to 5.0700</p> <p><strong>Longitudinal extent: </strong>114.0190 to 117.8200</p>
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