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238 results for “carbon fluxes”
Saltwater Coastal Carbon Flux Synthesis
We collated published and unpublished data from studies that measured net ecosystem exchange as carbon dioxide (CO2) fluxes (and methane, CH4, where available) from wetlands that varied in surface water and porewater salinity concentrations (both manipulative experiments and observations). Studies were selected to minimize the influence of exogenous covariates such as land-use, hydrologic, and water quality changes. We calculated minimum, maximum, and median values from time series of gross ecosystem productivity (GEP), ecosystem respiration (ERCO2, ERCH4), and net ecosystem productivity (NEP). We included data that were measured at daily and monthly scales. Whenever possible, we used measured porewater salinities. If porewater data were not available, we used measured surface water salinities. We calculated the change in salinity as the difference between ambient and elevated concentrations from experimental salinity additions, or as the differences in observed salinities between freshwater and brackish marshes or freshwater and saltwater marshes. We calculated the % difference of change in median values of GEP, ERCO2, ERCH4, and NEP using the following equation: Percent (%) Difference in Median = [(elevated-ambient) / |ambient|] x 100
Water, energy and carbon fluxes and ancillary meteorological measurements of four different urban landscapes in Phoenix, AZ during 2015
<p>Water, energy and carbon fluxes and ancillary meteorological measurements of four different urban landscapes in Phoenix, AZ during 2015. The measurements were done in three temporal not continuous deployment and in a permanent site as a reference. The urban landscape sites consisted in:</p> <ul> <li>A xeric landscape (XL), with measurements from 01/20/2015 to 03/13/2015.</li> <li>A parking lot (PL), with measurements from 05/19/2015 to 06/30/2015.</li> <li>A mesic landscape (ML), with measurements from 07/08/2015 to 09/18/2015.</li> <li>A reference suburban neighbourhood (REF), with measurements from 01/01/2015 to 12/13/2015.</li> </ul> <p>Water energy and carbon fluxes were processed using the software EdiRe. If additional data or information is needed, please contact the authors.</p> <p>The use of the datasets requires the citation of the next papers:</p> <p>- Templeton, N.P., Vivoni, E.R., Wang, Z-H., and Schreiner-McGraw, A.P. 2018. Quantifying Water and Energy Fluxes over Different Urban Land Covers in Phoenix, Arizona. Journal of Geophysical Research - Atmospheres. 123(4): 2111-2128.</p> <p>-Pérez-Ruiz, E. R., Vivoni, E. R. and Templeton, N. P. 2020. Urban land cover type determines the sensitivity of carbon dioxide fluxes to precipitation in Phoenix, Arizona. PLoS ONE 15(2): e0228537. https://doi.org/10.1371/journal.pone.0228537</p>
Du feu à l'eau: source and flux of dissolved black carbon from the Congo River
<p>Dissolved black carbon, dissolved organic carbon, stable carbon isotope data, and discharge data used for the analyses in "Du feu à l'eau: source and flux of dissolved black carbon from the Congo River".</p>
Herbivore assemblage as an important factor modulating grazing effects on ecosystem carbon fluxes in a meadow steppe in northeast China
<p>A better understanding of how grazing by large herbivores, as the major land use worldwide, affects grassland carbon fluxes is critical for predicting future uptake of CO<span>2</span> in terrestrial ecosystem. Previous studies have focused on individual herbivore species; it remains poorly understood as to if and how herbivore assemblage (single- vs. mixed-species) would alter the impact of grazers on grassland carbon fluxes. Here we examined the effects of moderate grazing by different herbivore assemblages on net ecosystem CO<span>2</span> exchange (NEE), gross ecosystem productivity (GEP) and ecosystem respiration (ER) over the growing season in two types of grassland communities in a meadow steppe. We found that herbivore assemblages significantly affected NEE, and the effects varied in the two types of grassland communities. Compared to un-grazed grassland, mixed grazing of sheep and cattle increased NEE (i.e., increased CO<span>2</span> uptake) over the growing season regardless of community type, while sheep- or cattle-only grazing increased NEE in the low diversity community and decreased it in the high diversity community. Herbivore assemblages altered the NEE primarily via changing GEP. The different effects of herbivore assemblages on GEP may be attributed mainly to grazing-induced changes in soil water availability and canopy light availability. Our study indicates that mixed grazing of sheep and cattle might be an important grazing management practice to improve plant aboveground productivity and help mitigate CO<span>2</span> emissions during the growing season. It should be particularly used in diverse plant communities, where it might increase grassland carbon sequestration.</p>
Data from: Carbon flux and forest dynamics: increased deadwood decomposition in tropical rainforest tree-fall gaps
<p>This study was carried out within an area of lowland, old growth dipterocarp rainforest in the Maliau Basin Conservation Area, Sabah, Malaysia (4° 44' 35" to 55" N and 116° 58' 10" to 30" E; mean annual rainfall 2838 mm ± 93 mm). On the 20<sup>th</sup> of July 2017, there was a storm at the study site, which generated winds speeds of 8.4 m/s (Fig. S1). These were among the strongest winds normally experienced in inland forests of the region, which placed extreme sheer stress on trees. Consequently, a large number of trees fell within the same 24-hour period in the study location. Ten tree-fall gaps (mean length: 32 m ± 2.8, mean width: 24.5 m ± 3; see table S1 for gap characteristics) created during this event were selected for use in this investigation, along with ten adjacent closed canopy sites, located 20 m from the edge of each gap. We took 10 hemispherical photos in each gap and closed canopy sites to quantity canopy openness at each location.</p>
Data from: Predicting peatland carbon fluxes from non-destructive plant traits
1. Determining the plant traits that best predict carbon (C) storage is increasingly important as global change drivers will affect plant species composition and ecosystem C cycling. Despite the critical role of peatlands in the global C cycle, trait-flux relationships in peatlands are relatively unknown. 2. We assessed the ability of four non-destructive plant traits to predict carbon dioxide (CO2) and methane (CH4) fluxes over two growing seasons in a temperate peatland in Ontario, Canada. We examined relationships between C-fluxes and leaf area, leaf persistence (deciduous, evergreen), growth form (woody, herbaceous), and aerenchyma tissue. To explore potential inconsistencies between different scales of data aggregation, traits were analysed at the level of plots, species and microsites. 3. CO2 fluxes showed a positive relationship with leaf area and leaf persistence, and a negative relationship with proportion of woody species. CH4 fluxes showed a positive relationship with aerenchyma and leaf area. The significance of trait-flux relationships differed based on whether data were averaged at the level of plot, species or microsite. 4. We recommend applying leaf area as a non-destructive trait to other systems where it is not ideal to measure traits destructively. A better understanding of the relationships between above and belowground traits is likely needed to further explain variation in ecosystem respiration and CH4 fluxes from plant traits.
Global Carbon Monoxide (CO) Flux Estimates for 2001-2015
<p>This data set contains Global carbon monoxide (CO) flux estimates for 2001-2015 partitioned into biomass burning (BB), fossil fuel (FF) and biogenic (BG) sources. The estimates were created at JPL/Caltech by Anthony Bloom using a Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithm (Bloom et al., 2015) applied to top-down CO fluxes obtained from inverse modeling using the GEOS-Chem (with adjoint) model and data from the Terra/MOPITT satellite (Jiang et al., 2017). The spatial resolution is 4.0 x 5.0 degrees lat/lon.</p> <p>Examples of the use of this data are described in Worden, J., et al., 2017 and Worden, H. et al., 2019.</p> <p>References:</p> <p>Bloom, A. A., J. Worden, Z. Jiang, H. Worden, T. Kurosu, C. Frankenberg, D. Schimel, (2015), Remote sensing constraints on South America fire traits by Bayesian fusion of atmospheric and surface data, Geophysical Research Letters, doi:10.1002/2014GL062584</p> <p>Jiang, Z., J. R. Worden, H. Worden, M. Deeter, D. B. A. Jones, A. F. Arellano, and D. K. Henze (2017), A 15-year record of CO emissions constrained by MOPITT CO observations, Atmos. Chem. Phys., 17(7), 4565–4583, doi:10.5194/acp-17-4565-2017.</p> <p>Worden, J. R., A.A. Bloom, S. Pandey, Z. Jiang, H.M. Worden, T.W. Walker, S. Houweling, T. Röckmann, (2017), Reduced biomass burning emissions reconcile conflicting estimates of the post-2006 atmospheric methane budget, Nature Communications, 8:2227, doi:10.1038/s41467-017-02246-0.</p> <p>Worden, H. M., Bloom, A. A., Worden, J. R., Jiang, Z., Marais, E., Stavrakou, T., Gaubert, B., and Lacey, F.: New Constraints on Biogenic Emissions using Satellite-Based Estimates of Carbon Monoxide Fluxes, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2019-377, in review, 2019.</p>
Southern Ocean Air-Sea Carbon Fluxes from Aircraft Observations: Modeling Datasets
The Southern Ocean plays an important role in determining atmospheric CO2, yet estimates of air-sea CO2 flux for the region diverge widely. We constrain Southern Ocean air-sea CO2 exchange by relating fluxes to horizontal and vertical CO2 gradients in atmospheric transport models and then apply atmospheric observations of these gradients to estimate fluxes. Aircraft-based measurements of the vertical atmospheric CO2 gradient provide robust flux constraints. We find an annual-mean flux of –0.55±0.23 Pg C yr–1 (net uptake) south of 45°S during 2009–2018. This is consistent with the mean of atmospheric-inversion estimates and surface-ocean pCO2-based products, but our data indicate stronger annual-mean uptake than suggested by recent interpretations of profiling-float observations.
Average Annual Atmospheric CO2 (ppm) with Average Regional Surface Downward Mass Flux of Carbon Dioxide Expressed as Carbon (molC_m²_yr)
<p>The fluctuation in both atmospheric CO2 and regional surface downward mass flux of carbon dioxide expressed as carbon. The annual data for both variable is taken from average monthly measured data.</p>
UFLUX 100m half-yearly carbon, water, and energy fluxes in Europe in 2019
<div> <div> <h3>UFLUX Ensemble Europe100m6monthly (European 100 6-monthly) in 2019</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using <strong>Deep Forest machine learning models</strong>. It integrates <strong>satellite-based Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. The UFLUX project includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>)</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>)</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>)</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>)</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>)</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul> </div> </div>
UFLUX 100m half-yearly carbon, water, and energy fluxes in Europe in 2020
<div> <div> <div> <h3>UFLUX Ensemble Europe100m6monthly (European 100 6-monthly) in 2020</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using <strong>Deep Forest machine learning models</strong>. It integrates <strong>satellite-based Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. The UFLUX project includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>)</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>)</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>)</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>)</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>)</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul> </div> </div> </div>
UFLUX 100m half-yearly carbon, water, and energy fluxes in Europe in 2021
<div> <div> <div> <div> <h3>UFLUX Ensemble Europe100m6monthly (European 100 6-monthly) in 2021</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using <strong>Deep Forest machine learning models</strong>. It integrates <strong>satellite-based Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. The UFLUX project includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>)</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>)</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>)</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>)</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>)</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul> </div> </div> </div> </div>
UFLUX 100m half-yearly carbon, water, and energy fluxes in Europe in 2022
<div> <div> <div> <div> <h3>UFLUX Ensemble Europe100m6monthly (European 100 6-monthly) in 2022</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European fluxes at 100 m spatial resolution</strong>, generated using <strong>Deep Forest machine learning models</strong>. It integrates <strong>satellite-based Sentinel-2 vegetation proxies NIRv</strong> with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. The UFLUX project includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>)</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>)</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>)</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>)</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>)</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul> </div> </div> </div> </div>
Supplementary files for the manuscript "The Timescale and Carbon Flux Recorded by Skarn Garnet from Gangdese Arc, Southern Tibet"
<p>This supplementary datafiles include Excel spreadsheets and MATLAB codes that are supplementary to the main manuscript. </p>
Disentangling Sources of Uncertainty in CLM5 Model Predictions: Water, Energy, and Carbon Fluxes at European Observation Sites
<p>The datasets include:</p> <ul> <li>EC data from Europement measurement sites in <a href="https://www.icos-cp.eu/data-products/2G60-ZHAK">ICOS</a>, <a href="https://fluxnet.org/login/?redirect_to=/data/download-data/">FLUXNETS</a>, and <a href="https://doi.org/10.34731/x9s3-Kr48">COSMOS-Europe</a>.</li> <li>Ensemble simulation data used for analysis</li> </ul> <p>The atmospheric forcings used in driving the model were all local measurements pre-processed using the script in GitHub repository <a href="https://github.com/FedoAIworld/CLM5-Disentangling-Uncertainty/tree/main/00_create_forcing_ds">CLM5-Disentangling-Uncertainty</a>.</p>
Combined CH4, N2O and CO2 fluxes budgets reveal a net carbon sink across a glacier-ocean continuum
<p>This dataset is supplument to a manuscript "Meltwater impacts CH4 and N2O fluxes across a glacier-ocean interface". </p> <p>Dataset includes </p> <ul> <li>timeseries.xlsx <ul> <li>A timeseries dataset conducted at Jökulsárlón Lagoon in Iceland. It includes CH<sub>4</sub> and N<sub>2</sub>O concentration, water flow and environmental variables. </li> </ul> </li> <li>discrete_v3.xlsx <ul> <li>Discrete samples conducted around Jökulsárlón Lagoon in Iceland It includes CH<sub>4</sub> and N<sub>2</sub>O concentration, nutrient. </li> <li>Add lat and lon (updated: 20 Nov 2024) </li> </ul> </li> </ul>
Litter–trapping tank bromeliads in five different forests: carbon and nutrient pools and fluxes
Bromeliads are the most abundant litter–trapping plants in Neotropical forest canopies. By intercepting litter, bromeliads obtain and retain nutrients before they reach the pedosphere. Here, we analyzed the litter captured and stored by tank bromeliads (TB) in five different forests along an elevation gradient in Mexico. Among those forests, carbon and nutrient pools and nitrogen fluxes in TB were estimated in a mangrove (MF) and a semi–deciduous tropical forest (SDTF). The composition of the litter trapped by TB along the gradient was similar to forest litterfall and was mainly composed of leaves. Most of the litter was captured in the dry season and we found a significant effect of projected plant area and the interaction between month and site on bromeliad litter capture. Moreover, litter stored in TB increased exponentially with projected plant area and differed between three studied species. In the MF (with ca. 2,700 TB ha<sup>-1</sup>), barely ca. 1% of annual litterfall is trapped by these plants, but even in the SDTF, with >10,000 TB ha<sup>-1</sup>, only ca. 2.4% is captured. We found that carbon and nitrogen pools in TB were small and represented < 1% of the carbon and nitrogen stored in forest aboveground biomass. Furthermore, the residence time of litter trapped in TB was not particularly large and was similar to that of litter on the forest floor. In light of our results, we conclude that in the studied forests the effect of TB on the forest carbon and nutrient cycle is negligible.
Sinking efficiency of cyanobacteria-derived particulate organic carbon from one eutrophic lake and global perspectives on carbon burial flux in subtropical shallow lakes
<p>It is the data that support the findings entitled "Sinking efficiency of cyanobacteria-derived particulate organic carbon from one eutrophic lake and global perspectives on carbon burial flux in subtropical shallow lakes” </p>
Dataset for the paper pyATM "An Automatic Differentiation Method for Surface Carbon Flux Inversion"
<p>Dataset for the paper pyATM "An Automatic Differentiation Method for Surface Carbon Flux Inversion"</p>
The 4-km monthly global air-sea carbon flux density dataset from 2000 to 2018
<p>We combined 24.6 million ocean observation data points, terabyte-level remote sensing images, and petabyte-level reanalysis data to create a standardized sample set containing more than 3 million records. We proposed a spatiotemporal feature-embedding machine-learning method to solve the issues of sparse data, missing data, and discontinuous changes existing in most current research. Our approach enables efficiently exploiting these massive datasets, leading to the first fully continuous and monthly global ocean partial pressure dataset covering the years 2000 to 2018. Based on the dataset, we presented a depiction of the 4-km monthly global air-sea carbon flux density that encompassed coastal oceans and characterized the ocean carbon budget for the period of 2000 to 2018 by integrating open datasets, including atmosphere partial pressure, wind speed, sea surface temperature, sea surface salinity.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.