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57 results for “Carbon sink”
Weakening of carbon sink on the Qinghai–Tibet Plateau
<p>The file "<a href="https://zenodo.org/api/files/478b7152-858e-48cb-89a1-c7942474578b/Code%20for%20improved%20IBIS%20model.rar">Code for improved IBIS model.rar</a>"is the improved IBIS model code applied for the paper "Weakening of carbon sink on the Qinghai–Tibet Plateau" which have been published in Geoderma. The data files include all data the paper applied in the context.</p>
Variability in the global ocean carbon sink from 1959-2020 by correcting models with observations (LDEO-HPD)
<p><strong>* The latest versions of this dataset are maintained and available here: <a href="https://zenodo.org/record/7901433">https://zenodo.org/record/7901433</a> *</strong></p> <p>The ocean reduces human impact on the climate by absorbing and sequestering CO2. From 1950s to the 1980s, observations of pCO2 and related ocean carbon variables were sparse and uncertain. Thus, global ocean biogeochemical models (GOBMs) have been the basis for quantifying the ocean carbon sink. The LDEO-Hybrid Physics Data product (LDEO-HPD) interpolates sparse surface ocean pCO2 data to global coverage by using GOBMs as priors, applying machine learning to estimate full-coverage corrections. The largest component of the GOBM corrections are climatological. This is consistent with recent findings of large seasonal discrepancies in GOBMs, but contrasts the long-held view that interannual variability is a major source of GOBM error. This supports extension of the LDEO-HPD pCO2 product back to 1959, using a climatology of model-observation misfits prior to 1982. Consistent with previous studies for 1980 onward, air-sea CO2 fluxes for 1959-2020 demonstrate response to atmospheric pCO2 growth and volcanic eruptions.</p> <p>This data is the final reconstruction of air-sea CO2 fluxes for 1959-2020 using the mean pCO2 from the corrected models. Both annual flux time series and spatially explicit fluxes are included. RIVERINE CARBON EFFLUX ADJUSTMENTS ARE NOT INCLUDED WITHIN THESE FILES. File metadata provides units.</p>
Simulated climate change reduced the capacity of lichen-dominated biocrusts to act as carbon sinks in two semi-arid Mediterranean ecosystems
<p> Biocrust gas exchange measurements used as input data for this study. The methods are described in detail in the related identifier paper.</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>
Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change (public)
<p><strong>Title</strong>: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change</p> <p><strong>Contact:</strong> Viola Heinrich (viola.heinrich@bristol.ac.uk)</p> <p><strong>This repository contains</strong>:</p> <ol> <li>Zipped folder:<strong> Fig1_data_input.zip</strong> - all the files needed to produce Figure 1a-e of the main paper. Set the working directory to folder containing the file and use the script "Fig1a_f_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 1 - these files are in the format "<strong><driver>_assessment_v2.csv</strong>". The columns in the files are: A: age of secondary forest; B: 50th percentile (median) of the modal Aboveground Biomass (AGB) value for the given age (note, units are in biomass not carbon: Mg/ha/yr); C: The bias-corrected AGB value, calculated by subtracting the lowest AGB value in column B such that the AGB data starts at or near 0Mg/ha/yr at age 1. D: the number of secondary forest pixels observed to have the given age, E: "Threshold" : the threshold limits of the given driver e.g. 0 Fires in fire_assessmentv2.csv implies the corresponding secondary forest pixels experienced 0 fires throughout the analysis period. The folder also contains the output regrowth models seen in Figure 1 in the format "<strong>regrowth_model_<driver_threshold>.RData" </strong>where driver_threshold refers to the driving variable name and the associated threshold limit for the given driver.</li> <li>Zipped folder:<strong> Fig2_regions_outline.zip</strong> - contains the boundaries of the 4 regions identified in Figure 2a of the main paper in a shapefile (.shp) format and the corresponding file formats needed to produce and load a shapefile. </li> <li>Zipped folder: <strong>Fig1g_2b_e_variable_importance.zip</strong> - contains the output files of the random forest analysis assessing the variable importance for the whole Amazon ("whole_Amazon" subfolder) and for the different regions identified in Figure2a. Files are given as .RDS files that can be loaded in R and the corresponding figures produced using the script "Fig1g_2b_e_plot.R". Files start with the region of interest e.g. "whole_Amazon" or "NE_sector". Middle part of the filename - importance_conditionalTrue/False - this determines whether the importance was calculated using the conditional permutation (True) or not (False). The end of the file name - seed<NUM> - denotes the number of the random seed that was set to extract the sample data. e.g. whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS - shows the conditional permutation importance assessment using a sample size of 2500 when the setseed parameter was set to 200 to extract a random sample representing the whole Amazon. The remaining files are the random forest output - as .RDS file. Please note the code to produce the random forest model and the importance assessment has not been included here - this code takes multiple days to run, so only the input and outputs have been included here. Please contact the corresponding author (see end) for more information on this. </li> <li>Zipped folder: <strong>Fig3_data_input.zip</strong> - all the files needed to produce Figure 3a-d of the main paper. Set the working directory to folder containing the file and use the script "Fig3_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 3 - these files are in the format "<strong><REGION>-Group.csv</strong>". See bullet point 1 for explanations for the columns in the file. Again column E -"threshold" denotes the code used to identify the the 4 subclasses of regrowth seen in the Figure. Where 11 = No disturbance; 12 = Only burning; 21 = Only (multiple) deforestations; 22 = Both burning and multiple deforestations as disturbance. The code takes data in AGB and converts to AGC. The folder also contains the output regrowth models seen in Figure 3 in the format <strong>"regrowth_model_<region_disturbance_type>.RData" </strong>where region_disturbance refers to the region and the type of disturbance experienced. </li> <li> Zipped folder: <strong>Fig4_5_carbon_sink_2017.zip </strong>- Contains two subfolders: a) <strong>Map_aggre_0.1deg</strong> -this folder contains .tiff files (and associated files) of the losses, gains and net change in AGC between 2016 - 2017 in secondary forests in Amazonia - this has been aggregated to 0.1 degree grid cells so each cell contains the total sum of the losses/gains experienced by secondary forests in that 0.1degree grid cell. b) <strong>secondary_forest_by_region_and_disturbance </strong>- this folder contains .tiff files (and associated files) of the secondary forest data at the original resolution (30m) for 2016 and 2017 split up according to the regions identified in Figure 2, and the type of disturbance (if any). The associated files include a .dbf file which includes additional data [read "README.txt" file in folder] - upon loading the data in a GIS software - the age of the secondary forest pixel will be displayed - open the attribute table to see more data associated with that given pixel e.g. modelled associated AGB for a given pixel. Files in this folder can be used to make Figure 4d and Figure 5 - see script "Fig4_Fig5_plot.R" in the code repository (see below). </li> </ol> <p><strong>Code: </strong>The corresponding code mentioned here can be access here: <a href="https://github.com/heinrichTrees/secondary-forest-regrowth-amazon-public">heinrichTrees/secondary-forest-regrowth-amazon-public (github.com)</a></p> <p><strong>Data usage: </strong>When using any code or data in this repository or another related to this study please cite Heinrich et al.2021 and the original paper as well as the DOI of this repository. </p> <p>If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)</p>
Data set for "Drought response of the boreal forest carbon sink is driven by understory-tree composition"
<p>This data set is a compilation of 1) environmental conditions, 2) biometric- and chamber-based annual CO<sub>2</sub> fluxes, 3) vegetation phenological greenness, and 4) forest-floor environmental conditions, all measured over the Krycklan Catchment Study (KCS, <a href="https://www.slu.se/Krycklan">https://www.slu.se/Krycklan</a>), a multi-scale long-term monitored boreal catchment spanning 68 km<sup>2</sup> in northern Sweden.</p> <p>The environmental measurements cover the period 1991–2020. Specifically, meteorological conditions measured close to the central part of the KCS at the Svartberget reference climate station (64°14′N, 19°46′E, 225 m.a.s.l.) included air temperature at 1.7 m above ground (Ta, °C), global radiation at 1.7 m above ground (Rg, MJ m<sup>-2</sup>), and precipitation (P, mm). Drought conditions were characterized by the Standardized Precipitation Evapotranspiration Index (SPEI) computed at 3-month time scale. SPEI was retrieved from the 0.5° gridded dataset supplied in the Global SPEI Database (SPEIbase v2.8, <a href="https://spei.csic.es/database.html">https://spei.csic.es/database.html</a>). The data set comprises monthly values obtained during the long-term reference period 1991–2020 (LT<sub>91–20</sub>), the baseline period 2016–2017 (BL<sub>16–17</sub>), and the drought year 2018 (D<sub>18</sub>). The standardized anomaly (ɀ-score) was used to identify extreme environmental measurements during both the BL<sub>16–17 </sub>and D<sub>18</sub> periods relative to the LT<sub>91–20 </sub>period.</p> <p>Annual CO<sub>2</sub> flux estimates were collected in 50 forest stands located across the KCS during the period 2016–2018 using biometric- and chamber-based methods. However, to prevent confounding effects, one forest stand that was subjected to thinning operations in spring 2018 was excluded from the analysis. The selected forest stands encompassed different landscape attributes such as 1) soil type (i.e., sediment and till), 2) dominant tree species (i.e., pine and spruce), and 3) stand age classes (i.e., initiation, young, middle-aged, mature, and old-growth stands). The annual CO<sub>2</sub> fluxes included the net ecosystem production (NEP) and its component fluxes, i.e., net primary production (NPP), total heterotrophic respiration (RH), net primary production of trees (NPP<sub>t</sub>) and its above- and belowground components (ANPP<sub>t</sub> and BNPP<sub>t</sub>, respectively), and net primary production of understory (NPP<sub>u</sub>) and its above- and belowground components (ANPP<sub>u</sub> and BNPP<sub>u</sub>, respectively). The impact of drought on annual CO<sub>2</sub> fluxes was evaluated by calculating both the absolute and relative anomalies (∆X and δX, respectively) of D<sub>18</sub> relative to BL<sub>16–17</sub>. To identify the temporal shift of the dominant contributor to ∆NEP, a moving-window correlation was conducted between the absolute anomaly of NEP (∆NEP) and the absolute anomalies of understory and tree NPP (∆NPP<sub>u</sub> and ∆NPP<sub>t</sub>, respectively), using a 7-forest-stand window with 1-forest-stand step.</p> <p>The study assessed the phenological greenness of the understory and trees in a ⁓110 years-old mixed-species forest stand in the central part of the KCS from 2016 to 2018. The greenness index (gcc) was derived from hourly images collected through digital repeat photography at the Integrated Carbon Observation System (ICOS) Svartberget ecosystem station (SE-Svb, 64°15′N, 19°46′E, 270 m.a.s.l., <a href="https://www.icos-sweden.se/svartberget">https://www.icos-sweden.se/svartberget</a>). Web cameras were used to capture images below- and above-tree canopy to define the gcc index for understory (gcc<sub>u</sub>) and trees (gcc<sub>t</sub>), respectively. The gcc<sub>u</sub> and gcc<sub>t</sub> values were then normalized (0–1) to describe the seasonal minimum and maximum of vegetation biomass development. A locally estimated scatterplot smoothing (loess) curve fit was then used through the normalized data points to improve visualization. The impact of drought on mean estimates of gcc<sub>u</sub> and gcc<sub>t</sub> during the growing season was evaluated by calculating the absolute and relative anomalies (∆X and δX, respectively) of D<sub>18</sub> relative to BL<sub>16–17</sub>.</p> <p>Environmental conditions at the forest-floor interface were measured in each of the 50 forest stands located across the KCS during the period 2016–2018. As before, one forest stand that was subjected to thinning operations in spring 2018 was excluded from the analysis to prevent confounding effects. The measured conditions included the below-canopy air temperature (Ta<sub>bc</sub>, °C), soil temperature at 10 cm depth (Ts, °C), and soil volumetric water content at 5 cm depth (SWC, %). The data set includes mean monthly and mean May-August values estimated during the BL<sub>16–17</sub> and D<sub>18</sub> periods, for which the absolute and relative anomalies (∆X and δX, respectively) were calculated.</p> <p>This data set consists of four Microsoft Excel workbooks:</p> <p>1_dataset_environmental_conditions.xlxs</p> <p>2_dataset_biometric_&_chamber-based_CO2_fluxes.xlxs</p> <p>3_dataset_vegetation_phenological_greenness.xlxs</p> <p>4_dataset_forest-floor_environmental_conditions.xlxs</p> <p>Further details can be found in Martínez-García et al. “Drought response of the boreal forest carbon sink is driven by understory-tree composition” (Nature Geoscience, <a href="https://doi.org/10.1038/s41561-024-01374-9" target="_blank" rel="noopener noreferrer">https://doi.org/10.1038/s41561-024-01374-9</a>).</p> <p>Contact information:</p> <p>Ph.D. Eduardo Martínez García<sup>1,2</sup> (<a href="mailto:eduardo.martinez@slu.se">eduardo.martinez@slu.se</a>, <a href="eduardo.martinezgarcia@luke.fi">eduardo.martinezgarcia@luke.fi</a>, <a href="mailto:edu.martinez.garcia@gmail.com">edu.martinez.garcia@gmail.com</a>)</p> <p>Professor Matthias Peichl<sup>1</sup> (<a href="mailto:matthias.peichl@slu.se">matthias.peichl@slu.se</a>)</p> <p><sup>1</sup> Department of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgränd 17, SE-901 83, Umeå, Sweden</p> <p><sup>2</sup> Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790, Helsinki, Finland</p>
Are terrestrial biosphere models fit for simulating the global land carbon sink?
<p>This repository contains the data and code required for reproducing the results presented in the paper "Are terrestrial biosphere models fit for simulating the global land carbon sink?" by Seiler et al., 2021. The study evaluates an ensemble of terrestrial biosphere models (<a href="https://sites.exeter.ac.uk/trendy/">TRENDY</a>; v9; S3 simulations) against a wide range of reference data using the Automated Model Benchmarking R package (AMBER; version 1.1.1). The only requirement for reproducing our results is access to a Linux machine with <a href="https://docs.conda.io">conda</a>, an open-source package management system and environment management system, installed. Follow the steps described in the <em>readme</em> file to install AMBER and run the analysis. The repository also contains all output produced by our analysis. </p>
Relevant data for publication 'Atmospheric phosphorus deposition amplifies carbon sinks in simulations of a tropical forest in Central Africa' Goll et al.
<p>Plotting scripts and processed output from ORCHIDEE-CNP. The version of ORCHIDEE is available here: https://doi.org/10.14768/391825ae-d257-4365-9820-30ea1940914c</p>
The carbon sink potential of southern China after two decades of afforestation
<p>Geological data were provided by China Geological Survey in shapefile with lithology and lithological age in the attribute table. The "class" means the lithology class, where "1" denotes a classification dominated by Dolomite; "2" is a classification dominated by Limestone; "3" represents a classification dominated by Clastic; "4" means water and "5" denotes a classification dominated by Carbonate rocks. "symbol" represents the lithological age.</p> <p>The geomorphological units (Cheng and Zhou, 2014) can be downloaded at the National Tibetan Plateau Third Pole Environment Data Center (https://data.tpdc.ac.cn/en/data/ecb4889a-8d85-4a64-a426-2c74f59fe14f/?q=geomor) in shapefile. It includes 5 types in the attribute table named TypeNames: Flat, Hills, Low relief, Moderate relief, High relief.</p> <p>Hydrological data is available at https://www.webmap.cn/commres.do?method=result25W in shapefile with 3 elements: rivers, lakes, springs and so on. "HYDA" represents the lakes, "HYDL" is rivers, and "HYDP" is springs and wells.</p> <p>Climate data include mean annual precipitation (MAP, mm), mean annual temperature (MAT, °C), aridity index (aridity), humidity index (im), >0°C accumulated temperature (aat0dem, °C-days) and >10°C accumulated temperature (aat10dem, °C-days). MAP and MAT are at a resolution of 1km x 1km from 2000-2015. Aridity index (aridity), humidity index (im), >0°C accumulated temperature (aat0dem, °C-days) and >10°C accumulated temperature (aat10dem, °C-days) are at a resolution of 500m x 500m.</p> <p>Soil properties are also available at the Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (https://www.resdc.cn/) at a resolution of 1km x 1km. Where names of the soil order codes represent as follows: 10: Alfisols. 11: Semi-alfisol. 13: Xerosol. 15: Primitive soil. 16: Semi-hydric soil. 17: Hydric soil. 18: Saline-alkali soil. 19: Anthrosols. 20: Alpine soil. 21: Ferralsols. 22: Cities. 23: Rocks. 24: Lakes and reservoirs. 25: Rivers. 26: Sand bars and islands in rivers. 27: Glacier and snow cover. 28: Coral reefs and sea islands. 30: Coastal salt farm/aquaculture farm. Soil texture includes clay content (%), silt content (%), and sand content (%).</p> <p>DEM (ASTGTM2_dem) is also available at the Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (https://www.resdc.cn/) at a resolution of 30m x 30m.</p> <p>This dataset is the percentage of above-ground biomass carbon carrying capacity reached in the eight provinces of southern China and in different forest types from 2002 to 2017 at the resolution of 500m x 500m, with the urban and water areas, cropland, and the southeast margin of the Tibet Plateau masked. The dataset takes values ranging from 0%-100%. 0% represents the highest carbon sequestration potential, while 100% represents carbon sequestration has reached saturation. The dataset can locate areas where vegetation has not yet reached its full potential, which is significant for the implementation and adjustment of ecological engineering. The dataset is publicly available.</p>
Datasets for China's carbon sink estimates
<p>Here are the carbon fluxes estimated by the Carbon Cycle Data Assimilation System (CCDAS) with the assimilation of in-situ CO<sub>2</sub> and multiple satellite observations (i.e. soil moisture, FAPAR), JAMSTEC MIROC-ACTM v1, and a statistic approach from forest inventory. These data sets are used for estimating the carbon sink of China.</p>
Data and code from paper: The carbon sink of secondary and degraded humid tropical forests
<p>This repository contains the data and code produced for the following paper:</p> <p><strong>Title: </strong>The carbon sink of recovering secondary and degraded humid tropical forests</p> <p><strong>Contact:</strong> Viola Heinrich (viola.heinrich@bristol.ac.uk)</p> <p><strong>Please note:</strong></p> <ul> <li> throughout repository where files include reference to: <...<strong>congo_basin</strong>...> this refers to the <strong>Central Africa </strong>region as it is termed in the main paper.</li> <li>the <strong>code</strong> <strong>has not been amended</strong> for wider use and still contains set working directories for use with University of Bristol systems, you will need to change these for the scripts to run. </li> </ul> <p>The data produced in this project were produced using a combination of programming languages due to differences in the author's preferences and expertise. Overall, the initial data analysis was carried out in (i) Google Earth Engine, and (ii) Arcpy (Python3.6.10). Most of the post-processing of the initial data was then carried out in <strong>R (v3.6) for which the code and output datasets are available here.</strong></p> <p>To access the code used in <strong>Google Earth Engine</strong> that was used to produce and export data from the Tropical Moist Forest dataset (e.g. Years Since Last Disturbance of secondary/degraded forest), please follow the link: https://code.earthengine.google.com/d303fc21e7b57a8fc259e0ee2b58bfb4 </p> <p>This repository contains the following zipped folders:</p> <ul> <li><strong>data_folder</strong>: this folder contains further folders with all the data produced for this paper.</li> </ul> <ol> <li>Fig1_data_models: All data needed to produce Figure 1 of the main paper, including an .RDS version of the 6 main regrowth models produced for this paper (secondary and degraded forests in the three regions). These are the files beginning with "<strong>regrowthModel_..RDS</strong>. Additionally, the folder includes the dataframe files originally from GeoTiff files that were used to extract the Aboveground Biomass in old-growth (undisturbed forests) > e.g. the subfolder "amazon_basin_oldG_AGB" contains the .dbf files representing the AGB in old-growth forest pixels. There are 4 files as the Amazon was split up into 4 sections for computational reasons. Similarly, the Central Africa region (here referred to as congo_basin) was split up into 2 regions.</li> <li>Fig2_data_models_plus_exFig3_to_5: The data needed to produce Figure 2 in the main paper as well as the Extended Data Figures 3 to 5. This includes .RDS versions of the regrowth models for secondary and degraded forests in the three regions for the different variables considered (files beginning with "<strong>regrowthModel_..RDS</strong>) e.g. "regrowtModel_borneo_deg_MaxTemo_low.rds", refers to the regrowth model shown in Figure 2c - the regrowth model for Bornean degraded forests for the variable "Maximum Temperature", where "low" refers to the lowest temperature range considered in the study. As before, files are provided giving information on the AGB in old-growth forests for each region within different conditions of each driving variable. </li> <li>Fig4: All the data needed to produce Figure 4 (and Supplementary Figure 18) of the main paper. This includes the file "regrowth_in_all_basins_by_country_input_data.csv", which contains data on the total number of cells for each forest type for each Years Since Last Disturbance (YSLD) in each region.</li> <li>Extended_dataFig1_input: The input for Extended Data Figure 1, including the values derived from other studies used in this comparison as well as additional notes/comments on how the data were assessed.</li> <li>Extended_dataFig2_input: the input data used to determine the standardised coefficients seen in the Extended Data Figure 2.</li> <li>Extended_data_table_inputs: The inputs for the Extended Data Tables 1 and 2. Inputs include the dataframe files (.dbf), of key variables that were extracted from the GeoTiff files. Only the .dbf files have been included here to limit excessively large data being uploaded. </li> </ol> <ul> <li><strong>code_folder.zip</strong>: The code in this folder was used to produce the main figures and results for the extended data tables shown in the paper. <ul> <li>this folder also contains a file "example_code_read_in_models.R" which provides an example of how best to read in the regrowth models for each region and forest type to extract important information such as the: (i) average growth rate in the first 20 years of analysis, (ii) all AGCs as a function of YSLD, and (iii) the estimated time it takes to reach the asymptote. </li> </ul> </li> </ul> <p><strong>Data and Code usage:</strong> When using any code or data in this repository or another related to this study please cite Heinrich et al. and the original paper as well as the DOI of this repository. </p> <p>Further source data in .xlsx format were also submitted with the main manuscript.</p> <p>If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)</p>
Supplementary Data for the publication "High economic costs of reduced carbon sinks and declining biome stability in Central American forests"
<p>Supplementary data from the DGVM simulations underlying the main figures presented in the publication.</p> <p>Naming convention: {variable}_{aggregation period}-{comparison period [only relevant for bsprob]}_{climate model}-{climate scenario}.tif</p> <p>Variables are:</p> <ul> <li>bsprob-Snell2013ed = biome shift probability (biomization adjusted from Snell et al. 2013) [%]</li> <li>nee = net ecosystem exchange [kgC/m2/year]</li> </ul> <p>Global climate models include GFDL = GFDL-ESM4 and IPSL= IPSL-CM6A-LR. Climate scenarios refer to the shared socioeconomic pathways (SSP) SSP126= SSP1-2.6 and SSP370= SSP3-7.0.</p>
Carbon-sink potential of continuous alfalfa agriculture lowered by short-term nitrous oxide emission events
Open the record for dataset details and reuse information.
Data from: Oyster reefs as carbon sources and sinks
Carbon burial is increasingly valued as a service provided by threatened vegetated coastal habitats. Similarly, shellfish reefs contain significant pools of carbon and are globally endangered, yet considerable uncertainty remains regarding shellfish reefs' role as sources (+) or sinks (−) of atmospheric CO2. While CO2 release is a by-product of carbonate shell production (then burial), shellfish also facilitate atmospheric-CO2 drawdown via filtration and rapid biodeposition of carbon-fixing primary producers. We provide a framework to account for the dual burial of inorganic and organic carbon, and demonstrate that decade-old experimental reefs on intertidal sandflats were net sources of CO2 (7.1 ± 1.2 MgC ha−1 yr−1 (µ ± s.e.)) resulting from predominantly carbonate deposition, whereas shallow subtidal reefs (−1.0 ± 0.4 MgC ha−1 yr−1) and saltmarsh-fringing reefs (−1.3 ± 0.4 MgC ha−1 yr−1) were dominated by organic-carbon-rich sediments and functioned as net carbon sinks (on par with vegetated coastal habitats). These landscape-level differences reflect gradients in shellfish growth, survivorship and shell bioerosion. Notably, down-core carbon concentrations in 100- to 4000-year-old reefs mirrored experimental-reef data, suggesting our results are relevant over centennial to millennial scales, although we note that these natural reefs appeared to function as slight carbon sources (0.5 ± 0.3 MgC ha−1 yr−1). Globally, the historical mining of the top metre of shellfish reefs may have reintroduced more than 400 000 000 Mg of organic carbon into estuaries. Importantly, reef formation and destruction do not have reciprocal, counterbalancing impacts on atmospheric CO2 since excavated organic material may be remineralized while shell may experience continued preservation through reburial. Thus, protection of existing reefs could be considered as one component of climate mitigation programmes focused on the coastal zone.
A drained nutrient-poor peatland forest in boreal Sweden constitutes a net carbon sink after integrating terrestrial and aquatic fluxes
<div>In this study, we estimated the net ecosystem carbon balance (NECB) from a nutrient‐poor drained peatland forest and an adjacent natural mire in northern Sweden by integrating terrestrial carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>) fluxes with aquatic losses of dissolved organic C (DOC) and inorganic C based on eddy covariance and stream discharge measurements, respectively, over two hydrological years. Each variable presented was measured during each experimental period in sites.</div>
Supporting data: Divergent Estimates of China's Forest Carbon Sink Can Not Be Well Coordinated based on a Collective Analysis
<p>Differences in the carbon sink results obtained from the inventory method, DGVMs, ATM, and EC were compensated using relevant data under a harmonized definition of the NetFCsink. All the carbon flux results used have been stored in an xlsx file, categorized according to the corresponding method.</p>
Visualization and quantification of carbon 'rusty sink' by rice root iron plaque: mechanisms, functions, and global implications
<p><span>Paddies contain 78% higher organic carbon (C) stocks than adjacent upland soils, and iron (Fe) plaque formation on rice roots is one of the mechanisms that traps C. The process sequence, extent and global relevance of this C stabilization mechanism under oxic/anoxic conditions remains unclear. We quantified and localized the contribution of Fe plaque to C stabilization in a microoxic area (</span><span>rice </span><span>rhizosphere) and</span> <span>evaluated the role of this C trap toward global C sequestration in paddy soils. Visualization and localization of pH by imaging with planar optodes, enzyme activities by zymography,</span> <span>and root exudation by 14C imaging, as well as upscale modeling enabled linkage of three groups of rhizosphere processes that are responsible for C stabilization from the micro- (root) to the macro- (ecosystem) level. The 14C activity in soil (reflecting stabilization of rhizodeposits) with Fe2+ addition was 1.4−1.5 times higher than that in the control and phosphate addition soils. Perfect co-localization of the hotspots of β-glucosidase activity (by zymography) with exudation showed that labile C and high enzyme activities were localized within Fe plaques. </span><span>Fe</span><span>2+</span> <span>addition </span><span>to </span><span>soil and its</span><span> microbial oxidation to Fe3+ by radial oxygen release from rice roots increased </span><span>Fe</span><span> plaque (Fe3+) formation by 1.7−2.5 times. The C trapped</span><span> by </span><span>Fe plaque was 1.1 times higher after Fe2+ addition. Therefore, Fe plaque formed from amorphous and complex Fe on root surface act as a "rusty sink" for C. Upscaling by model revealed the global significance of C preservation within Fe3+ complexes in paddy soils. Considering the area of coverage of paddy soils globally, radial oxygen loss from roots and bacterial Fe oxidation may trap up to 130 Mg C in Fe plaques per rice season. This represents an important annual surplus of new and stable C to the existing C pool</span> <span>under long-term rice cropping.</span></p>
Model simulation results for "Enhanced seasonal amplitude of atmospheric CO2 by the changing Southern Ocean carbon sink"
<p>This dataset contains the seasonal variations of monthly mean atmospheric CO<sub>2</sub> concentration derived from GEOS-Chem model simulations during 2000-2016. Monthly terrestrial CO2 fluxes derived from CLM4.5-CN, used as an input dataset for the GEOS-Chem simulations, are also included.</p> <p>There are six sets of GEOS-Chem simulation results; "ctrl", "BIOfix", "OCNfix", and "FFfix" are the main experiments to evaluate the effects of changes in terrestrial CO<sub>2</sub> fluxes, air-sea CO<sub>2</sub> fluxes, and fossil fuel CO<sub>2</sub> emissions on the seasonal amplitude of atmospheric CO<sub>2</sub> over the globe; "ALLfix" and "OCNfix_SO" are additional experiments for identifying the effects of changes in the other factors (i.e., atmospheric transport and biomass burning) and regional changes in air-sea fluxes in the Southern Ocean. </p> <p>Detailed explanations for each simulation are described in the main text.</p> <p>*We recommend contacting us first if you want to utilize the dataset for study (yjm921@gmail.com).</p>
Data from: Forest carbon sink in the U.S. (1870–2012) driven by substitution of forest ecosystem service flows
Open the record for dataset details and reuse information.
Assessing the ocean carbon sink: Assimilation of temperature and salinity into a global ocean biogeochemistry model
<p>Data underlying figures in manuscript draft.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.