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1,836 results for “flux”
Costal operating wind farms: two datasets with concurrent SCADA, LiDAR and turbulent fluxes
<p>This data collection consists of two datasets from a micrometeorological experiment conducted in two distinct operating wind farms in a coastal area of the northeast region of Brazil, called Pedra do Sal Wind Farm (UEPS) and Beberibe Wind Farm (UEBB). These wind farms are located on the northeast coast of Brazil where meteorological conditions are strongly influenced by trade winds and sea breeze. Both datasets represent a full-year of measurements from August/2013 to July/2014.</p> <p>On both operating wind farms it was commissioned a fully instrumented IEC-compliant 100m met mast, with five levels of first-class calibrated cup anemometers and one level (100m) with 3D sonic anemometer. Additionally at UEPS there's an extra 3D sonic at 20m height on the met mast, as well as a VAISALA LEOSPHERE Windcube8 doppler wind lidar with a range up to 500m height and located 2.5D upwind of one of the wind turbines.</p> <p>The Pedra do Sal wind farm (UEPS) has an installed capacity of 18MW, with 20 Enercon E-44 installed at 55m a.g.l. At Beberibe wind farm (UEBB) there are 32 Enercon E-48 wind turbines installed at 75m a.g.l. The dataset includes 10min SCADA data for all wind turbines on both wind farms.</p> <p>This dataset has a high-quality combination of meteorological, SCADA and turbulent flux data of two operating wind farms in Brazil. During a full-year of measurements both datasets had a high data recovery rate (see attached tables). The dataset has already been used to assess the impact of atmospheric stability on the wind farm performance, as well as the effect of mesoscale patterns on the wind profile and wind farm power production. Recirculation of the sea breeze and the development of an internal boundary layer upwind the wind turbines were also characterized.</p> <p>For more details on the experimental layout, wind turbine locations, meso and microcale wind conditions and any other information not stated in the NetCDF4 files, please refer to the reference material or contact one of the authors.</p> <p> </p>
Leaf carbonyl sulfide flux data from Stunt Ranch in 2013
<p>Version 1.0.1: Added citation to the publication. Note that if you are using Version 1.0, there is no need to switch because no change has been made to the data.</p> <p>This repository contains data and code used to reproduce results in the following work:</p> <p>Sun, W., Maseyk, K., Lett, C., & Seibt, U. (2024). Restricted internal diffusion weakens transpiration–photosynthesis coupling during heatwaves: Evidence from leaf carbonyl sulphide exchange. <em>Plant, Cell & Environment</em>, <em>47</em>(5), 1813–1833. <a href="https://doi.org/10.1111/pce.14840" target="_blank" rel="noopener">https://doi.org/10.1111/pce.14840</a></p> <p>The data product contains leaf chamber measurements of water vapor, carbon dioxide (CO2), and carbonyl sulfide (COS) fluxes and auxiliary biometeorological variables. For details, see README.md.</p> <p>This repository is licensed under either the MIT License or the CC-BY 4.0 License. You may choose the license that best suits your intended use case. By accepting the license, you are granted permission to use this repository without requiring additional written consent from the authors. If you use this data set, we kindly ask that you give fair credit to the authors by citing or acknowledging this work as appropriate.</p>
Autochamber CH4 Fluxes and δ13C Values at Stordalen Mire
<p>Autochamber-based CH<sub>4</sub> fluxes and δ<sup>13</sup>C values measured with a Tunable Infrared Laser Direct Absorption Spectrometer (TILDAS, Aerodyne Research Inc.); and ancillary data, including CO<sub>2</sub> fluxes (measured with a LGR Greenhouse Gas Analyzer), temperatures, atmospheric pressure, and photosynthetically active radiation (PAR).</p> <p>In addition to the data published here, data from 2011 is also available in the supplementary files to <a href="https://doi.org/10.1038/nature13798"><strong>McCalley et al. (2014)</strong></a> under the <strong><a href="https://static-content.springer.com/esm/art%3A10.1038%2Fnature13798/MediaObjects/41586_2014_BFnature13798_MOESM61_ESM.xlsx">Source data to Fig. 1</a></strong> link.</p> <p> </p> <p>METHODS:</p> <p>Methane fluxes were measured using a system of 8 automatic gas-sampling chambers made of transparent Lexan (n=3 each in the palsa and bog habitats, and n=2 in the fen habitat). Chambers were initially installed in the three habitat types at Stordalen Mire in 2001 (Bäckstrand et al., 2008) and the chamber lids were replaced in 2011 with the current design, similar to that described by Bubier et al 2003. Chambers cover an area of 0.2 m<sup>2</sup> (45 cm x 45 cm), with a height ranging from 15-75 cm depending on habitat vegetation. At the Palsa and bog site the chamber base is flush with the ground and the chamber lid (15 cm in height) lifts clear of the base between closures. At the fen site the chamber base is raised 50–60 cm on lexon skirts to accommodate large stature vegetation. The chambers are instrumented with thermocouples measuring air and surface ground temperature, and water table depth and thaw depth are measured manually 3–5 times per week. The chambers are connected to the gas analysis system, located in an adjacent temperature-controlled cabin, by 3/8” Dekoron tubing through which air is circulated at approximately 2.5 L min<sup>-1</sup>. Each chamber lid is closed once every 3 hours for a period of 8 min, with a 5 min flush period before and after lid closure.</p> <p>We measured methane concentration using a Tunable Infrared Laser Direct Absorption Spectrometers (TILDAS, Aerodyne Research Inc.) connected to the main chamber circulation using ¼” Dekoron tubing (McCalley et al 2014). Calibrations were done every 90 min using 3 calibration gases spanning the observed concentration range (1.8–10 ppm). For each autochamber closure we calculated flux using a method consistent with that detailed by Bäckstrand et al 2008 for CO<sub>2</sub> and total hydrocarbons, using a linear regression of changing headspace CH<sub>4</sub> concentration over a period of 2.5 min. Eight 2.5 min regressions were calculated, staggered by 15 sec, and the most linear fit (highest r<sup>2</sup>) was then used to calculate flux. Daily average flux for each chamber was used to calculate daily flux and standard error for each cover type.</p> <p><em>References:</em></p> <p>Bäckstrand, K., Crill, P. M., Mastepanov, M., Christensen, T. R. & Bastviken, D. Total hydrocarbon flux dynamics at a subarctic mire in northern Sweden. <em>Journal of Geophysical Research</em> <strong>113</strong>, (2008).</p> <p>Bubier, J. L., Crill, P. M., Mosedale, A., Frolking, S. & Linder, E. Peatland responses to varying interannual moisture conditions as measured by automatic CO<sub>2</sub> chambers. <em>Global Biogeochemical Cycles</em> <strong>17</strong>, (2003).</p> <p>McCalley, C.K., B.J. Woodcroft, S.B. Hodgkins, R.A. Wehr, E-H. Kim, R. Mondav, P.M. Crill, J.P. Chanton, V.I. Rich, G.W. Tyson, S.R. Saleska (2014), Methane dynamics regulated by microbial community response to permafrost thaw, <em>Nature</em>, 514:478-481, doi:10.1038/nature13798.</p> <p> </p> <p>FILES:</p> <p>Files are named with the year or date range, followed by a suffix indicating data resolution:</p> <ul> <li>*<strong>_CH4output_clean_ckm.txt</strong> - Individual measurements of CH<sub>4</sub> fluxes (CH4Flux), CO<sub>2</sub> fluxes (CO2flux; for select years), and δ<sup>13</sup>C signature of emitted CH<sub>4</sub> (Flux13CH4) for each chamber closure. CH4FluxRsq is the R<sup>2</sup> value of the linear fit used to calculate CH<sub>4</sub> flux, CO2Rsq is the R<sup>2</sup> value of the linear fit used to calculate CO<sub>2</sub> flux, and Flux13CH4_stdev is the standard deviation of the δ<sup>13</sup>C signature (standard deviation of the intercept of the Keeling plot).</li> <li>*<strong>_DailyCH4output_ckm.txt</strong> - Daily average CH<sub>4</sub> fluxes (CH4Flux) and δ<sup>13</sup>C values (13CH4), grouped by site: Palsa, Bog, Fen, and Chamber 9 (bog/fen transition); along with standard deviations (stdev) and standard errors (se) of the flux or δ<sup>13</sup>C for each site type. For the Palsa, Bog, and Fen sites, these averages are calculated by chamber (n=3 for Palsa and Bog, n=2 for Fen), so each chamber's daily average is calculated, and then a daily average for that site is calculated as the average of the chambers. For Chamber 9 (bog/fen intermediate; n=1 chamber), averages are calculated by day as there are no chamber replicates.</li> </ul> <p>MEASUREMENT UNITS (same for both file types):</p> <ul> <li>CH<sub>4</sub> flux: mg CH<sub>4</sub> m<sup>−2</sup> hr<sup>−1</sup></li> <li>CO<sub>2</sub> flux: mg C m<sup>−2</sup> h<sup>−1</sup></li> <li>δ<sup>13</sup>C: ‰</li> <li>Temperature: °C</li> <li>Air pressure: mbar</li> <li>PAR: µmol photons m<sup>−2</sup> s<sup>−1</sup></li> </ul> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br>This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.<br>Autochamber measurements between 2013 and 2017 were supported by a grant from the US National Science Foundation MacroSystems program (NSF EF 1241037, PI Varner).</p>
Data in: Reduced predation and energy flux in soil food webs by introduced tree species
<p>The introduction of non-native tree species has become a global concern and may disruptnative communities and related ecosystem functions. Soil food webs regulate organic matter decomposition and nutrient cycling in forests with their feeding activities, butevaluating consequences of tree species introduction on soil invertebrates is challengingdue to the complex trophic structure and wide range in body size of soil invertebrates. Here, we employed an energetic food web approach, and estimated the energy flux in soil food webs using a four-node model including soil meso- and macrofauna decomposers and predators. We examined pure and mixed stands of native European beech (<em>Fagus sylvatica</em>), introduced Douglas fir (<em>Pseudotsuga menziesii</em>) and native range-expanding Norway spruce (<em>Picea abies</em>) across site conditions. Compared to native forests, introduced tree species reduced total mass of macrofauna predators by 92% at sandy sites but not that of decomposers, suggesting trophic downgrading in soil food webs by Douglas fir. The energy flux in mixed forests was intermediate between respective monocultures, suggesting that tree mixtures mitigate potential negative impacts of introduced tree species on food web functioning. Across size classes, soil macrofauna responded more sensitively to changes in environmental conditions than soil mesofauna. Despite the lower total mass, the energy flux through mesofauna outweighed that through macrofauna when consideringenergy loss to predators, highlighting the importance of mesofauna for decomposition processes in forest soil food webs. Additionally, total energy flux positively correlated with species richness, pointing to the significance of soil biodiversity for trophic functionality. Overall, the study emphasizes the critical role of tree species composition, site conditionsand soil biodiversity in driving energy flux through soil food webs and maintaining forest ecosystem functions.</p>
Autochamber CH4 Fluxes at Stordalen Mire, 2014 (from LGR)
<p>METHODS:</p> <p>Fluxes were measured using a system of 8 automatic gas-sampling chambers made of transparent Lexan (n=3 each in the palsa and bog habitats, and n=2 in the fen habitat). Chambers were initially installed in the three habitat types at Stordalen Mire in 2001 (Bäckstrand et al., 2008) and the chamber lids were replaced in 2011 with the current design, similar to that described by Bubier et al 2003. Chambers cover an area of 0.2 m<sup>2</sup> (45 cm x 45 cm), with a height ranging from 15-75 cm depending on habitat vegetation. At the Palsa and bog site the chamber base is flush with the ground and the chamber lid (15 cm in height) lifts clear of the base between closures. At the fen site the chamber base is raised 50–60 cm on lexon skirts to accommodate large stature vegetation.</p> <p>The chambers are connected to the gas analysis system, located in an adjacent temperature-controlled cabin, by 3/8” Dekoron tubing through which air is circulated at approximately 2.5 L min<sup>-1</sup>. Each chamber lid is closed once every 3 hours for a period of 8 min, with a 5 min flush period before and after lid closure. Gas concentration in the chamber headspace was measured with a Los Gatos Research (LGR) Fast Greenhouse Gas Analyzer, with timing control and data acquisition using a Campbell CR10x (Holmes et al., 2022).</p> <p><em>References:</em></p> <p>Bäckstrand, K., Crill, P. M., Mastepanov, M., Christensen, T. R. & Bastviken, D. Total hydrocarbon flux dynamics at a subarctic mire in northern Sweden. <em>Journal of Geophysical Research</em> <strong>113</strong>, (2008).</p> <p>Bubier, J. L., Crill, P. M., Mosedale, A., Frolking, S. & Linder, E. Peatland responses to varying interannual moisture conditions as measured by automatic CO<sub>2</sub> chambers. <em>Global Biogeochemical Cycles</em> <strong>17</strong>, (2003).</p> <div> <div>Holmes, M. E., Crill, P. M., Burnett, W. C., McCalley, C. K., Wilson, R. M., Frolking, S., Chang, K. ‐Y., Riley, W. J., Varner, R. K., Hodgkins, S. B., IsoGenie Project Coordinators, IsoGenie Field Team, McNichol, A. P., Saleska, S. R., Rich, V. I., Chanton, J. P. (2022). Carbon accumulation, flux, and fate in Stordalen Mire, a permafrost peatland in transition. <em>Global Biogeochemical Cycles</em>, 36, e2021GB007113, doi:10.1029/2021GB007113.</div> </div> <p>McCalley, C.K., B.J. Woodcroft, S.B. Hodgkins, R.A. Wehr, E-H. Kim, R. Mondav, P.M. Crill, J.P. Chanton, V.I. Rich, G.W. Tyson, S.R. Saleska (2014), Methane dynamics regulated by microbial community response to permafrost thaw, <em>Nature</em>, 514:478-481, doi:10.1038/nature13798.</p> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br>This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.<br>These autochamber measurements were also supported by a grant from the US National Science Foundation MacroSystems program (NSF EF 1241037, PI Varner).</p>
Daily Anomalies of the Surface Atmospheric Fluxes of the Brazilian Northeast (DASAF-BNE)
<p>This dataset contains the daily anomalies of the main atmospheric fluxes throughout the Brazilian NE region. Geographically it is framed at 42.5°W - 29.75°E/20.5°S - 1.25°N, in the time range from 1979-01-01 12:00:00 to 2017-12-31 12:00:00. The DASAF-BNE dataset with a spatial resolution of 1 degree was the basis for the calculation of the daily anomalies, they were interpolated by the bilinear method to obtain a resolution of 0.25 degrees.</p>
Global Carbon Budget 2022, surface ocean fugactiy of CO2 (fCO2) and air-sea CO2 flux of individual Global ocean biogechemical models and surface ocean fCO2-based data-products
<p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (data-products).</strong><br> There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p> <p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of data-products and GOBMs and with the adjustments described in the Global Carbon Budget 2022 (https://doi.org/10.5194/essd-14-4811-2022, section C3), are available in the Global Carbon Budget 2022 spreadsheet.</strong></p> <p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2022 paper (https://doi.org/10.5194/essd-14-4811-2022), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.17 GtC yr-1, Tropics: 0.16 GtC yr-1, South: 0.32 GtC yr-1, see GCB 2022 paper, section 2.4.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because adjustments were applied only for global fluxes.</p> <p><strong>What is in the files?</strong></p> <p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):</p> <p><br> fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, south<br> fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br> sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br> area: Area per pixel, dimensions: latitude, longitude<br> area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p> <p>(2) The files for the GOBMs contain the following fields, for simulation A (‘contemporary simulation’, including effects of rising CO2, climate change and variability) and simulation B (‘control simulation’, constant CO2, no climate change and variability). Temporal resolution: monthly</p> <p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br> sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br> area: Area per pixel, dimensions: latitude, longitude</p> <p><br> (3) One file ‘GCB-2022_OceanModel_RegionalBreakdown_1959-2021.nc’ with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual.</p> <p><br> <strong>Fair data use statement:</strong><br> The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br> <strong>Citation:</strong> Please cite the Global Carbon Budget 2022 (Friedlingstein et al., 2022, ESSD, https://doi.org/10.5194/essd-14-4811-2022) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2022 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).<br> <strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: “We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output.”<br> <strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p> <p><br> Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudget.org/</p> <p> </p>
Model output used in the manuscript "Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency"
<p>This *.zip file contains the model output from seasonal variability experiments using the NPZD-DOP GEOMAR biogeochemical model (<a href="https://doi.org/10.1016/j.pocean.2010.05.002" target="_blank" rel="noopener">Kriest et al., 2010</a>) coupled with the MITgcm 2.8deg ocean circulation via the transport matrix method (<a href="https://doi.org/10.1016/j.ocemod.2004.04.002" target="_blank" rel="noopener">Khatiwala et al., 2005</a>; <a href="https://doi.org/10.1029/2007GB002923" target="_blank" rel="noopener">Khatiwala, 2007</a>; <a href="https://doi.org/10.5281/zenodo.1246300" target="_blank" rel="noopener">Khatiwala, 2018</a>).</p> <p>These model outputs are presented and discussed in the Preprint "<em>Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency</em>", published by Geophysical Research Letters (<a href="https://doi.org/10.1029/2023GL107050" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original model. For this matter, we also refer you to <a href="https://doi.org/10.1029/2021GB007101" target="_blank" rel="noopener">de Melo Viríssimo et al. (2022)</a>.</p> <p>All files uploaded were generated from simulations run by the authors, except: the grid file, the salinity field, and the temperature field, which came with the model; and the density fields, who were computed from the MITgcm 2.8deg transport matrix by Dr Rafaelle Bernadello, using a TEOS-10 Matlab routine (<a href="http://www.teos-10.org/">http://www.teos-10.org/</a>).</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p>
Sensible heat fluxes control cloud trail strength
<p>This dataset contains a minimal set of data used to create figures in Johnston et al. (2023) Sensible Heat Fluxes Control Cloud Trail Strength, Quarterly Journal of the Royal Meteorological Society. Additional liquid water path data from the control experiment (H250E250) is also provided to better visualise the cloud field in this central experiment.</p>
4.5 years of peatland forest N2O flux data data measured using automatic chambers
<p>The data contains daily mean N2O fluxes and supporting environmental data from June 2015 to September 2019. The measurement site is Lettosuo peatland forest (ICOS associate site, FI-Let) located in Tammela, Finland. The site is nutrient-rich and drained for forestry in 1969. Light selection harvest was done at the automatic chamber location in March 2016. Six automatic chambers operated year-round and each chamber measured N2O flux once in an hour. Environmental variables were measured close to the automatic chambers or in the nearest automatic weather station. For more information about the site and automatic chamber system, see Koskinen et al., (2014) and Korkiakoski et al. (2017, 2020).</p>
SeaFlux v2023: harmonised sea-air CO2 fluxes from surface pCO2 data products using a standardised approach
<p><strong>BE SURE TO DOWNLOAD 2023.02</strong></p> <p>See the additional notes for updates on the products. </p> <p>Fluxes calculated using the standardized approach:</p> <p> \(F\text{CO}_2=K_0 \cdot K_w \cdot (p\text{CO}_2^\text{sea} - p\text{CO}_2^\text{atm})\ \cdot (1 - [ice])\).</p> <p>We provide each of the components to this equation to reduce the potential for errors in fluxes due to methodological differences.</p> <p>The netCDF files contain the following data (<strong>note that only bold names have been updated in v2023</strong>): </p> <ul> <li>fgco2_all_winds_products: the sea-air CO2 flux for all spCO2 products (6) and <em>kw</em> from all wind products (5). </li> <li>fgco2_global:<strong> </strong>the globally integrated sea-air CO2 fluxes for all spCO2 products (6) and <em>kw</em> from all wind products (6)</li> <li><strong>sol:</strong> \(K_0\) is calculated using the Weiss (1974) parameterization with EN4 salinity and OISST temperatures </li> <li><strong>kw:</strong> \(k_w\) is calculated for winds with each being scaled independently to a 14-C bomb flux estimate of 16.5 cm/hr using the quadratic formulation by Wanninkhof (1992). <ul> <li>CCMPv2</li> <li>ERA5</li> <li>JRA55</li> <li>NCEP1</li> <li>NCEP2</li> </ul> </li> <li>spco2_SOCOM_unfilled<em>: </em>\(p\text{CO}_2^\text{sea}\) downloaded from various sources contains the following products: <ul> <li>CMEMS_FFNN</li> <li>CSIR_ML6</li> <li>JENA_MLS</li> <li>JMA_MLR</li> <li>MPI_SOMFFN</li> <li>NIES_FNN</li> </ul> </li> <li>spco2_filler<em>: </em>scaled version of the Landschützer et al. (2020) climatology used to fill missing regions of <em>spco2_SOCOM_unfilled</em></li> <li><strong>fco2atm: </strong>\(p\text{CO}_2^\text{atm}\) is calculated from NOAA's marine boundary layer product with ERA5 mean sea level pressure corrected for pH2O. The virial coefficient is then applied to pCO2atm</li> <li><strong>ice: </strong>\([ice]\) is the ice fraction from the OISST product</li> <li><strong>area_ocean:</strong><em> </em>the surface area of the ocean including the fractional area of the coastal regions</li> <li><strong>seafrac: </strong>the fraction of a pixel that is ocean</li> </ul> <p><strong><em>Units are listed in the metadata of each of the netCDF variables. </em></strong></p>
Accessible Oceans: Auditory Display. Net flux of CO2 between Ocean and Atmosphere
<p>The seven tracks make up an auditory display of the net flux of carbon dioxide between the ocean and the atmosphere. The seven tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/RhkRBKbRTueE2F86b5QS">listen online here</a>.</p> <p>The data comes from the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) and the display is based on the OOI Nugget developed by Dr. Leslie Smith. (<a href="https://datalab.marine.rutgers.edu/ooi-nuggets/co2-flux/">https://datalab.marine.rutgers.edu/ooi-nuggets/co2-flux/</a>)</p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></p>
Groundwater-derived nutrient fluxes and offshore mixing rates along the New Jersey coast 21-23
Radium isotopes are natural tracers useful for studying the magnitude of groundwater discharge and the transport and fate of nutrients in the coastal ocean. We collected radium and nutrient samples from groundwater and surface waters along the southern New Jersey coast to calculate the flux of groundwater-derived nutrients and coastal mixing rates. These data serve as baselines for assessing future changes in the magnitude and quality of groundwater discharge driven by human activity and climate change.
Greenhouse gas fluxes and concentrations and associated habitat data in western Dane County, Wisconsin, USA, streams during the 2018 growing season
Streams are often sources of carbon dioxide (CO2) and methane (CH4), particularly in agricultural regions where sediment and organic matter inputs can be substantial. Floods are occurring more often and more intensely in southern Wisconsin, one such agricultural region, due to climate change and few studies have investigated how floods impact stream CO2 and CH4 fluxes and concentrations. I compared concentrations and fluxes of CO2 and CH4 with greater than 30 variables representing in-stream and watershed attributes at 10 sites in mixed agricultural and suburban locations in southern Wisconsin. Sampling was conducted 10 times at each site during the growing season (May-November) in 2018
GLEON DC-FLUX Lake Mendota floating chamber carbon dioxide flux, 2017 - 2018
Campaign to measure diel cycle of lake-atmosphere carbon dioxide flux in different seasons. This is part of a larger synthesis working group for GLEON called DC-FLUX. Floating chamber with in-situ CO2 sensor was deployed between July 2017 and April 2018 over four campaigns of three-hourly samples taken by two chambers with three replicates each by boat and in one campaign, also near shore. These data are being synthesized with similar measurements made in multiple lakes for a forthcoming manuscript.
Stream chemistry concentrations and fluxes using proportional sampling in the Andrews Experimental Forest, 1968 to present
Stream chemistry sampling and analysis was initiated at the H.J. Andrews Experimental Forest in 1968 in two small watersheds (Watersheds 9,10). Sampling has expanded as additional paired watershed studies (Watersheds 1, 2, 6, 7, 8) and monitoring (Mack, Lookout Cr) were initiated over time. Water samples are collected proportionally to streamflow as a function of stage height and composited at each stream gauging site. Composite sample periods are generally three weeks and 3 one-week samples are commonly composited in the lab before analysis. Water samples are analyzed at the Cooperative Chemical Analytical Lab (CCAL) (http://www.ccal.oregonstate.edu/ ). Concentrations of analytes include dissolved and particulate nitrogen, phosphorus, carbon, as well pH, conductivity, suspended sediment, and full suite of cations and anions. Monthly and annual mean concentrations are calculated by weighting 3-week periods by streamflow. Fluxes are calculated using concentrations and flow. The original objective was to examine the nutrient budgets for small watersheds and to evaluate changes in average concentrations and fluxes following timber harvest in comparison with unharvested reference watersheds. This study is conducted in conjunction with Andrew's precipitation chemistry (CP002) and the U.S. National Atmospheric Deposition Program (NADP).
Precipitation and dry deposition chemistry concentrations and fluxes, Andrews Experimental Forest, 1969 to present
Collection and analyses of precipitation chemistry were initiated in 1969 at the low-elevation Primary Met site, and in 1973 at a mid-elevation Hi-15 site. Rain collection samples accumulate from one week to three weeks in bulk and NADP type collectors and then are transported to Cooperative Chemical Analytical Laboratory (CCAL) for analysis. Analytes include nitrogen, phosphorus, carbon, and cations and anions as well as pH, conductivity, alkalinity and particulate sediment. Concentration and volume of precipitation are combined for inflow. Dry deposition chemistry concentrations began in 1989 and are analyzed 2-4 times per year at one site. The original objectives were to evaluate precipitation chemistry inputs versus chemistry outputs in streamflow from forested watersheds. The study has evolved into a general monitoring effort for precipitation chemistry that is among the least contaminated of any within the USA. This study is conducted in conjunction with Andrews streamflow chemistry (CF002) and the U.S. National Atmospheric Deposition Program (NADP).
Eddy Flux Measurements, Tussock Station, Imnavait Creek, Alaska - 2006
The Biocomplexity Station was established in 2005 to measure landscape-level carbon, water and energy balances at Imnavait Creek, Alaska. The station is now contributing valuable data to the Arctic Observing Network that was established at two nearby stations. These will form part of a network of observatories with Abisko (Sweden), Zackenburg (Greenland) and a location in the Canadian High Arctic which will provide further data points as part of the International Polar Year. This particular part of the project focuses on simultaneous measurements of carbon, water and energy fluxes of the terrestrial landscape at hourly, daily, seasonal and multi-year time scales. These are the major regulatory drivers of the Arctic climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. We will provide a comprehensive description of the state of the regional Arctic system with respect to these variables, its overall regulation and controlling features and its interaction with the global system.
Eddy Flux Measurements, Ridge Station, Imnavait Creek, Alaska - 2007
In contribution to the Arctic Observing Network, the researchers have established two observatories of landscape-level carbon, water and energy balances at Imnavait Creek, Alaska and at Pleistocene Park near Cherskii, Russia. These will form part of a network of observatories with Abisko (Sweden), Zackenburg (Greenland) and a location in the Canadian High Arctic which will provide further data points as part of the International Polar Year. This particular part of the project focuses on simultaneous measurements of carbon, water and energy fluxes of the terrestrial landscape at hourly, daily, seasonal and multi-year time scales. These are the major regulatory drivers of the Arctic climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. We will provide a comprehensive description of the state of the regional Arctic system with respect to these variables, its overall regulation and controlling features and its interaction with the global system.In support of these objectives, a 3m eddy covariance station was established on a low ridge adjacent to Imnavait Creek, Alaska. This station has been continuously monitoring carbon dioxide, water vapor, energy fluxes and various micrometeorological variables.
Eddy Flux Measurements, Ridge Station, Imnavait Creek, Alaska - 2008
In contribution to the Arctic Observing Network, the researchers have established two observatories of landscape-level carbon, water and energy balances at Imnavait Creek, Alaska and at Pleistocene Park near Cherskii, Russia. These will form part of a network of observatories with Abisko (Sweden), Zackenburg (Greenland) and a location in the Canadian High Arctic which will provide further data points as part of the International Polar Year. This particular part of the project focuses on simultaneous measurements of carbon, water and energy fluxes of the terrestrial landscape at hourly, daily, seasonal and multi-year time scales. These are the major regulatory drivers of the Arctic climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. We will provide a comprehensive description of the state of the regional Arctic system with respect to these variables, its overall regulation and controlling features and its interaction with the global system. In support of these objectives, a 3m eddy covariance station was established on a low ridge adjacent to Imnavait Creek, Alaska. This station has been continuously monitoring carbon dioxide, water vapor, energy fluxes and various micro-meteorological variables.
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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.