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147 results for “CO2 flux”
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Half-hourly growing season, chamber-based, CO2 flux data, with dark daytime measurements, 2014
This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warmign affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieved using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011. This data contains CO2 fluxes measured using an automated chamber system that measures net CO2 exchange (NEE). Measurements are made every ~1.5 hours and modeled half-hourly. Half hour ecoystem respiration is modeled using an exponential Q10 relationship when light conditions are low (PAR<5umol/m2/s) and using a hyperbolic light relationship when PAR>5umol/m2/s. Addditional dark daytime Reco measurements were used to define the Q10 model. GPP is calculated as the difference between NEE and Reco.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Weekly dark CO2 fluxes, 2014
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. How does warming impact the phenology of dominant plant species? This data contains manual CO2 flux measurements collected using a dark, portable CO2 flux chamber. These measurements are intended to estimate daytime ecosystem respiration to provide some validation for ecosystem respiration modeled with an exponential temperature response from clear, automated CO2 flux chambers at this same site. Dark flux measurements were taken weekly from 25 April 2014 to 23 Sep 2014.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Off Plot Soil Incubation By Depth II - Soil CO2 Fluxes 2013-2014
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. We investigated C and nitrogen (N) mineralization within the soil profile by incubating soil cores collected adjacent to (but not within) the CiPEHR site. These soil cores spanned the entire active layer and approximately 30 cm of permafrost. Soil cores were separated into 10 cm depth intervals and incubated for 241 days at 15 degC and field moisture was maintained with water additions. This dataset contains CO2 flux rates measured periodically throughout the incubation.
Soil carbon flux: BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
Representing Model Uncertainty for Global Atmospheric CO2 Flux Inversions Using ECMWF-IFS-46R1
<p>Data used in the work "Representing Model Uncertainty for Global Atmospheric CO2 Flux Inversions Using ECMWF-IFS-46R1" - McNorton et al. (2020)</p> <p>All data generated using version 46R1 of the Integrated Forecast System based at the European Centre for Medium-Range Weather Forecasts, with work funded as part of the European Commission CO2 Human Emissions Project.</p> <p>Data includes global total standard errors for the total column CO2 mixing ratios at 3 hourly intervals for 2015 and both total column and surface transport errors at hourly intervals for January and July 2015, derived from a 50 member ensemble. It is suggested that the data are used by the inverse modelling community to account for transport model errors.</p> <p>Please view the README.txt file for a full description.</p> <p> </p> <p>###########################<br> ## EXPERIMENTAL SETUP ##<br> ###########################</p> <p># FLUXES #</p> <p>CHE-EDGAR-2015 EMISSIONS<br> CHE-TIER-2-FIRE/OCEAN<br> ONLINE CHTESSEL BIOGENIC FLUXES (FOR TRANSPORT ERROR THESE USE THE CONTROL MEMBER FLUXES)</p> <p># MODEL #</p> <p>IFS-CYCLE 46R1<br> RESOLUTION TCO399 (~25km)<br> 137 VERTICAL LEVELS<br> ALL DATA PROVIDED HERE ARE EITHER COLUMN INTEGRATED MIXING RATIO (XCO2) OR SURFACE (LEVEL 137)<br> ALL DATA PROVIDED HERE ARE STANDARD DEVIATION ACROSS 50 ENSEMBLE MEMBERS<br> </p>
Dataset: Ideas and perspectives: patterns of soil CO2, CH4, and N2O fluxes along an altitudinal gradient - a pilot study from an Ecuadorian neotropical montane forest
<p>These datasets contain data from (i) soil CO<sub>2</sub>, CH<sub>4,</sub> and N<sub>2</sub>O flux measurements (ii) physicochemical soil properties, and (iii) soil temperature and moisture of four tropical forests located on the western flanks of the Andes in northern Ecuador.</p> <p>A manuscript using these datasets has been submitted to Biogeosciences under the title: <strong><em>"Ideas and perspectives: patterns of soil CO2, CH4, and N2O fluxes along an altitudinal gradient - a pilot study from an Ecuadorian neotropical montane forest"</em></strong>.</p>
The winter subset of the Saildrone 2021-2022 Mission to the Gulf Stream used for the publication "The importance of contemporaneous measurements for regional air-sea CO2 flux estimates"
<p>Data from the Saildrone 2021-2022 observational mission to the Gulf Stream. These data are published to accompany the publication "The importance of contemporaneous measurements for regional air-sea CO<sub>2</sub> flux estimates." Included in this dataset are the primary and processed variables used throughout the paper. The data associated with each saildrone is named by the drone number. Additionally, included in the structure for each drone are the gas transfer velocities for each scenario, MBL atmospheric CO2 interpolated to the time and location of the drone, and ERA-5 wind speed, sea level pressure, significant wave height, and drag coefficient interpolated to the time and location of the drone. These variables are used to calculate CO<sub>2</sub> fluxes for each scenario and are named as follows: "F" + gas transfer velocity equation used (DM18 or W14) + drone ID + scenario. Scenario A-D correspond to those outlined in the paper. Scenarios E and F correspond to the calculation of air-sea fluxes using all saildrone observed variables except for atmospheric CO<sub>2</sub> (from MBL product) and significant wave height (from ERA-5), respectively. </p>
CO and CO2 mixing ratios and flux measurements from the Amazon tropical rainforest
<p>CO and CO2 mixing ratio and flux measurements from the Amazon tropical rainforest</p><p>This dataset belongs to the manuscript 'The emission of CO from tropical rain forest soils', submitted to the journal Biogeosciences in December 2023.</p><p>More details on this dataset can be found in this manuscript:</p><p>https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2746/egusphere-2023-2746.pdf</p><p> For questions, please reach out to Hella van Asperen: hasperen@bgc.mpg-jena.de</p><p>########################################</p><p>Plateau tower CO and CO2 mixing ratio measurements</p><p>Plateau tower CO and CO2 mixing ratio measurements took place in a dry season campaign (28 Sep- 7 Oct 2020) and a wet season campaign (11-18 May 2021) at the K34 tower at field site ZF2 in the Amazon rain forest (-2.60898, -60.209106). Due to a problem in the beginning of the dry season campaign, the measurements at the tower were continued until outside the campaign period, until 18 October 2020. Measurements were performed by a Spectronus FTIR analyzer. Concentrations were measured at 3 heights (5,15 and 36m) every half hour. Canopy height is ~28m.</p><p>########################################</p><p>Valley CO and CO2 mixing ratio measurements</p><p>Valley CO and CO2 mixing ratio measurements took place in a dry season campaign (28 Sep- 7 Oct 2020) and a wet season campaign (11-18 May 2021) at a valley close to the K34 tower at field site ZF2 (-2.600026, -60.217079). Since no electricity was available, automatic battery-driven bag sampling was performed during the night at 3h time intervals, with 4 measurements per night from a 1m height inlet (~18:00, ~21:00, ~0:00, ~3:00). Bag samples were measured the following morning by a Spectronus FTIR-analyzer. </p><p>########################################</p><p>Plateau and valley chamber CO and CO2 fluxes</p><p>Flux chamber measurements were performed over soil and litter together on the plateau and in the valley at field site ZF2, in a dry season campaign (28 Sep- 7 Oct 2020) and a wet season campaign (11-18 May 2021). Five soil collars were installed in the valley, and five on the plateau. Each collar was measured 3 times during each campaign week (on different days). Measurements from the same collar are indicated as (for example) V1A, V1B, V1C. After each flux chamber measurement, soil moisture and soil temperature was measured.</p>
N2O and CO2 fluxes and related environmental conditions in response to manure and synthetic fertilization treatments, with and without a urease inhibitor
<p>Agricultural best management practices (BMPs) intended to solve one environmental challenge may have unintended climate impacts. For example, manure injection is often promoted for its potential to reduce runoff and N loss as NH<sub>3</sub>, but the practice has been shown to increase N<sub>2</sub>O, a powerful GHG, compared to surface application. Urease inhibitor application with N fertilizer is another BMP that can enhance N retention by reducing NH<sub>3</sub> emissions, but its impact on N<sub>2</sub>O emissions is mixed. Thus, we measured N<sub>2</sub>O, CO<sub>2</sub>, soil mineral N availability, soil moisture, soil temperature, and yield in a two-year perennial hayfield trial with four fertilization treatments (manure injection, manure broadcast, synthetic urea, and control) applied with or without a urease inhibitor in Alburgh, VT (n=4 treatment replicates; 32 total subplots). This dataset includes the daily N<sub>2</sub>O and CO<sub>2</sub> fluxes in every subplot on each sampling day in our trial, along with cumulative emissions for 2020 and 2021. We also include the other variables that we measured (listed above) and relevant weather data (precipitation and air temperature). Note that during the second treatment application in 2021, the incorrect treatment was applied to one synthetic urea subplot. We therefore excluded this subplot from our analysis of daily fluxes after 30 July 2021 (n=3 synthetic urea replicates). Details about our sampling methods are available in the corresponding manuscript.</p>
Data from the micrometeorological tower of the Antarctic Modeling Observation System (ATMOS) project of the 40th Brazilian Antarctic Operation (OPERANTAR XL) to calculate the CO2 flux (FCO2)
<p>Micrometeorological tower data obtained by the "Antarctic Modeling Observation System" (ATMOS) project. These data were collected in the southern summer of 2021/2022 during the 40th Brazilian Antarctic Operation (OPERANTAR XL) and were used to calculate CO2 fluxes (FCO2).</p> <p>A 9 m high metal micrometeorological tower was installed on the bow of the Polar Ship Almirante Maximiano, 8.75 m above sea level, for sampling atmospheric variables. In the tower, the Motion Pack II was installed, on the main shaft of the tower, to determine the ship's movement in the orthogonal directions xp, yp and zp of the platform's coordinate system, at a frequency of 20 Hz. As well as, a GPS (Global Positioning System) and an electronic compass, to determine the ship's geographic position and speed. The movement of the ship influences wind speed measurements, and as a solution, a wind speed correction was carried out.<br>An IRGASON sensor (Campell Scientific®) was placed on the secondary shaft of the tower, configured to perform measurements at 20 Hz. The IRGASON has an open path infrared gas analyzer that measures the concentrations of CO2 and water vapor (H2O), a three-dimensional sonic anemometer, which measures the 3 vector components of the wind, and a thermohygrometer that measures air temperature and humidity. From the IRGASON collections, it is possible to calculate the CO2 flux using the Vortex Covariance method.</p>
Dataset for a manuscript entitled Variation in CO2 and CH4 Fluxes Among Land Cover Types in Heterogeneous Arctic Tundra in Northeastern Siberia
<p>Dataset for the manuscript Variation in CO<sub>2</sub> and CH<sub>4</sub> Fluxes Among Land Cover Types in Heterogeneous Arctic Tundra in Northeastern Siberia authored by Sari Juutinen, Mika Aurela, Juha-Pekka Tuovinen, Viktor Ivakhov, Maiju Linkosalmi, Aleksi Räsänen, Tarmo Virtanen, Juha Mikola, Johanna Nyman, Emmi Vähä, Marina Loskutova, Alexander Makshtas, and Tuomas Laurila</p> <p>Dataset consists of CO2 and CH4 flux data measured mainly using chamber method in arctic tundra</p>
OceanSODA-ETHZ-v2: Surface ocean sea-air CO2 fluxes from 1982 to 2022 (8-day by 0.25° x 0.25°)
<div> <h2>Product information</h2> <table> <tbody> <tr> <td><strong>Product name</strong></td> <td>OceanSODA-ETHZ-v2</td> <td> </td> </tr> <tr> <td><strong>SOCOM-style name</strong></td> <td>OSETHZv2-NN</td> <td> </td> </tr> <tr> <td><strong>Product version</strong></td> <td>v2024r01</td> <td> </td> </tr> <tr> <td><strong>Coverage</strong></td> <td>1982-01-01 — 2022-12-31</td> <td>Global ice free ocean</td> </tr> <tr> <td><strong>Resolution</strong></td> <td>8-day</td> <td>0.25° x 0.25°</td> </tr> <tr> <td><strong>Contact</strong></td> <td><a href="mailto:nicolas.gruber@env.ethz.ch">nicolas.gruber@env.ethz.ch</a></td> <td>OR <a href="mailto:luke.gregor@usys.ethz.ch">luke.gregor@usys.ethz.ch</a></td> </tr> </tbody> </table> <h2>Variables</h2> <p>Each netCDF contains only one variable. The metadata also has a start and end time as well as min/max lat/lon.<br>The information about the settings used in PyCO2SYS is also included in each file's metadata.</p> <table> <tbody> <tr> <th>Folder</th> <th>Variable name</th> <th>Units</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td><code>fgco2</code></td> <td><em>F</em><em>C</em><em>O</em><sub>2</sub><sup>MBL</sup></td> <td>mmol m<sup>−2</sup> day<sup>−1</sup></td> <td>Sea-air <em>C</em><em>O</em><sub>2</sub> flux computed using Marine Boundary Layer <em>x</em>CO<sub>2</sub></td> </tr> <tr> <td><code>fgco2CT</code></td> <td><em>F</em><em>C</em><em>O</em><sub>2</sub><sup>CT</sup></td> <td>mmol m<sup>−2</sup> day<sup>−1</sup></td> <td>Sea-air <em>C</em><em>O</em><sub>2</sub> flux computed using CarbonTracker <em>x</em>CO<sub>2</sub></td> </tr> <tr> <td><code>fgco2_global</code></td> <td>globally integrated <em>F</em><em>C</em><em>O</em><sub>2</sub></td> <td>PgC yr<sup>−1</sup></td> <td>Surface — atmosphere ocean fugacity of <em>C</em><em>O</em><sub>2</sub></td> </tr> <tr> <td><code>dfco2</code></td> <td><em>Δ</em><em>f</em><em>C</em><em>O</em><sub>2</sub></td> <td><em>μ</em>atm</td> <td>Surface — atmosphere ocean fugacity of <em>C</em><em>O</em><sub>2</sub></td> </tr> <tr> <td><code>sfco2</code></td> <td><em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup></td> <td><em>μ</em>atm</td> <td>Surface ocean fugacity of <em>C</em><em>O</em><sub>2</sub></td> </tr> <tr> <td><code>sfco2_uncert</code></td> <td><em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup> uncertainty</td> <td><em>μ</em>atm</td> <td>Uncertainty of surface ocean fugacity of <em>C</em><em>O</em><sub>2</sub> based on 5.7 ⨉ ensemble spread</td> </tr> <tr> <td><code>kw</code></td> <td><em>k</em><sub><em>w</em></sub></td> <td>cm hr<sup>−1</sup></td> <td>Gas transfer velocity</td> </tr> <tr> <td><code>sol</code></td> <td><em>K</em><sub>0</sub></td> <td>mol L<sup>−1</sup> atm<sup>−1</sup></td> <td>Solubility of <em>C</em><em>O</em><sub>2</sub> in seawater</td> </tr> <tr> <td><code>ice</code></td> <td><em>i</em><em>c</em><em>e</em></td> <td>fraction</td> <td>Sea-ice fraction</td> </tr> </tbody> </table> <h2>File structure</h2> <p>The file naming structure is <code><variable>_<decade>_OceanSODAETHZv2.2024rXX.nc</code>. Data are grouped by decade, with each file being about 800 MB. Not all files contain the same number of years (e.g., 1982-1989). The size for each variable (1982-2022) is between 2.5 and 3.5 GB.</p> <h2>Product description</h2> <p>This dataset contains a global gridded dataset of sea-air carbon dioxide fluxes (<em>F</em><em>C</em><em>O</em><sub>2</sub>) for subseasonal to decadal studies. The product is available from 1982 through 2022 and is at an 8-daily by 0.25° by 0.25° resolution. FCO2 is calculated using the bulk formulation of flux:</p> <p>(<em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup>−<em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>a</em><em>t</em><em>m</em></sup>) ⋅ <em>k</em><sub><em>w</em></sub> ⋅ <em>K</em><sub>0</sub> ⋅ (1−<em>i</em><em>c</em><em>e</em>)</p> <p>where (<em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup> − <em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>a</em><em>t</em><em>m</em></sup>) is the sea-air difference in fugacity of carbon dioxide, <em>k</em><sub><em>w</em></sub> is the gas transfer velocity, <em>K</em><sub>0</sub> is the solubility of CO2 in seawater, and ice is the sea-ice fraction.</p> <p>The surface ocean fugacity of carbon dioxide (<em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup>) is calculated using the surface Ocean Carbon dioxide Neural Network (OceanCarbNN). This two-step approach first estimates an 8-day climatology of <em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup> based on climatological drivers. The second step uses the output of the first step as a prior estimate of <em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup> which is then adjusted to a time-varying product. An important novelty of this method is that the first step uses Gradient Boosted Trees (low bias, low variance, but unable to extrapolate) while the second step uses Neural Networks (able to extrapolate, but higher variance). We use 35 ensemble members to make these estimates. The average of the ensemble members is used as the final estimate of <em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup>. Further, the standard deviation of the ensemble members can be scaled (× 5.7) to be used as an analog of the uncertainty.</p> <p>The atmospheric fugacity of carbon dioxide (<em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>a</em><em>t</em><em>m</em></sup>) is calculated using the NOAA Marine Boundary Layer (MBL) product OR the NOAA CarbonTracker 2022 product (CT), ERA5 sea level pressure. <em>k</em><sub><em>w</em></sub> is calculated using the Wanninkhof (2014) formulation, but we adjust the coefficient of gas transfer for the ERA5 wind reanalysis product (0.274) so that the global mean kw = 16.5 cm/hr. <em>K</em><sub>0</sub> is calculated using the Weiss (1974) formulation.</p> </div>
MIROC4-ACTM CO2 Inversion flux (2001-2022)
<p>This dataset is prepared for GCP CO2-2023 assessment. </p> <p>Inversion Details are in:</p> <div> <div>Chandra, N., Patra, P. K., Niwa, Y., Ito, A., Iida, Y., Goto, D., Morimoto, S., Kondo, M., Takigawa, M., Hajima, T., and Watanabe, M.: Estimated regional CO<sub>2</sub> flux and uncertainty based on an ensemble of atmospheric CO<sub>2</sub> inversions, Atmos. Chem. Phys., 22, 9215–9243, https://doi.org/10.5194/acp-22-9215-2022, 2022</div> <div> </div> <div>MIROC4-ACTM Details:</div> <div><span>Patra, P. K., Takigawa, M., Watanabe, S., Chandra, N., Ishijima, K., and Yamashita, Y.: Improved Chemical Tracer Simulation by MIROC4.0-based Atmospheric Chemistry-Transport Model (MIROC4-ACTM), SOLA, 14, 91–96, <a href="https://doi.org/10.2151/sola.2018-016">https://doi.org/10.2151/sola.2018-016</a>, 2018. </span></div> <div> </div> <div> </div> </div>
Partitioning of water and CO2 fluxes at NEON sites into soil and plant components: a five-year dataset for spatial and temporal analysis
<p>This dataset includes estimates of transpiration, evaporation, soil respiration, and plant net photosynthesis obtained using five partitioning approaches. Flux components are available at 47 NEON sites over a period of five years. Additional meteorological inputs and water-use efficiency data are also included.</p>
Congo Basin forest soil CO2, CH4 and N2O flux data.
<p><span>Tropical forests play an important role in the greenhouse gas exchange between biosphere and atmosphere. Despite holding the second largest tropical forest globally, the Congo basin is generally understudied and ground based greenhouse gas flux data are lacking. In this study, high frequency measurements spanning of sixteen months from automated and manual soil chambers are combined, to characterize spatio-temporal variability in soil greenhouse gas fluxes from a lowland tropical forest in Yangambi, in the Congo Basin. </span></p>
Data from: Spatial variation of soil CO2, CH4 and N2O fluxes across topographical positions in tropical forests of the Guiana Shield in Ecosystems
<p>Data from: Spatial variation of soil CO2, CH4 and N2O fluxes across topographical positions in tropical forests of the Guiana Shield in Ecosystems</p>
Modelling alternative harvest effects on soil CO2 and CH4 fluxes from peatland forests [dataset]
<p>This contains the forest floor soil respiration data and the water level data used in the model simulations in the paper titled "Modelling alternative harvest effects on soil CO2 and CH4 fluxes from peatland forests " (Li et al, 2024; <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.scitotenv.2024.175257" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.scitotenv.2024.175257</a>)</p>
CO2 fluxes measurement data set for the lower segment of the River Noce, Italy
<p>This data set reports on data recorded during four measurement campaigns in the lower segment of the River Noce, Italy.<br>The measurements were aimed at characterising CO2 fluxes along the river, and were conducted using a combination of loggers (time-resolved data) and sampling (space-resolved data).<br>The data were collected in the Santa Giustina reservoir (upstream reservoir), along the residual flow reaches that extend between the Santa Giustina reservoir and the outlet of the hydropower diversion system in Mezzocorona, and downstream of the diversion outlet.</p> <p>The data is provided as .csv and Matlab .mat files.<br>For more information about the data set and data acquisition, please contact the author or refer to the following article:</p> <p><br>Dolcetti, G., Piccolroaz, S., Bruno, M. C., Calamita, E., Larsen, S., Zolezzi, G. & Siviglia, A. Quantification of carbopeaking and CO2 fluxes in a regulated Alpine river.</p>
Data for "Impact of prior terrestrial carbon flux on atmospheric CO2 concentration simulation"
<p>Data for "Impact of prior terrestrial carbon flux on atmospheric CO2 concentration simulation"</p>
Mycorrhizal effects on decomposition and soil CO2 flux depend on changes in nitrogen availability during forest succession
<p>Mycorrhizal fungi play a central role in plant nutrition and nutrient cycling, yet our understanding on their effects on free-living microbes, soil carbon (C) decomposition and soil CO2 fluxes remains limited.</p> <p>Here we used trenches lined with mesh screens of varying sizes to isolate mycorrhizal hyphal effects on soil C dynamics in subtropical successional forests.</p> <p>We found that the presence of mycorrhizal hyphae suppressed soil CO2 fluxes by 17% in early-successional forests, but enhanced CO2 losses by 20% and 32% in mid- and late-successional forests, respectively. The inhibitory effects of mycorrhizal fungi on soil CO2 fluxes in the young stands were associated with changes in soil nitrogen (N) mineralization and microbial activities, suggesting that competition between mycorrhizae and saprotrophs for N likely suppressed soil C decomposition. In the mid- and late-successional stands, mycorrhizal enhancement of CO2 release from soil likely resulted from both hyphal respiration and mycorrhizal-induced acceleration of organic matter decay.</p> <p>Synthesis. Our results highlight the sensitivity of mycorrhizal fungi-saprotroph interactions to shifts in nutrient availability and demand, with important consequences for soil carbon dynamics particularly in ecosystems with low nutrient conditions. Incorporating such interactions into models should improve the simulations of forest biogeochemical cycles under global change.</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.