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238 results for “carbon fluxes”
Eddy covariance (EC) vertical carbon fluxes from a Georgia tidal salt marsh from 2014 to 2024
We present our methodology and data for science ready vertical carbon fluxes from a Spartina alterniflora tidal salt marsh as part of the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) site on Sapelo Island, Georgia, USA. Vertical carbon fluxes were measured through the eddy covariance (EC) method from 2014 to 2024. The EC flux tower was located on the western side of Sapelo Island bounded by the Duplin River and Barn Creek. The proportional influence of marsh habitats on the flux measurements were 4% tall, 38% short, and 58% medium height form Spartina alterniflora. We present the net ecosystem exchange (NEE), ecosystem respiration (ER), and gross primary production (GPP) at 30-minute fluxes (μmol CO2 m-2 s-1), daily averages (μmol CO2 m-2 s-1) and totals (g C m-2 day-1), and annual (g C m-2 year-1) quantities. We provide estimated uncertainty for each flux at each integrated timescale as 95% confidence intervals. Providing open access to 10-year carbon flux datasets can facilitate collaboration for advancing regional and global blue carbon synthesis and scale-up studies.
Time series of carbon dioxide and methane fluxes measured with eddy covariance for Falling Creek Reservoir in southwestern Virginia, USA during 2020-2025
We measured carbon dioxide and methane flux exchange with the atmosphere at the deepest site of Falling Creek Reservoir (Vinton, Virginia, USA) every 30 minutes from 04 April 2020 to 31 December 2025. Falling Creek Reservoir is a drinking water supply reservoir owned and managed by the Western Virginia Water Authority (WVWA) as a primary drinking water source. The dataset consists of micrometeorological and flux data collected using an eddy covariance system (LiCor Biosciences, Lincoln, Nebraska, USA) and analyzed with associated Eddy Pro software (Eddy Pro Version 7.0.6), including carbon dioxide, methane, and water vapor. All analysis scripts are included for data processing and quality assurance/quality control following best practices.
Time series of methane and carbon dioxide diffusive fluxes using an Ultraportable Greenhouse Gas Analyzer (UGGA) for Falling Creek Reservoir and Beaverdam Reservoir in southwestern Virginia, USA during 2018-2025
Diffusive fluxes of methane and carbon dioxide were measured using an Ultraportable Greenhouse Gas Analyzer (UGGA) at the surface of Falling Creek Reservoir (FCR) and Beaverdam Reservoir (BVR; Vinton, Virginia, USA). FCR and BVR are owned and operated by the Western Virginia Water Authority as drinking water sources for Roanoke, Virginia. The dataset consists of calculated diffusive fluxes of methane and carbon dioxide measured at the deepest site of the reservoir adjacent to the dam (2018–2025) and additional reservoir upstream sites in FCR (2018, 2023) and BVR (2022). In 2025, two littoral sites were measured at the northernmost wetland inflow to FCR. Measurements were collected approximately fortnightly in FCR throughout the summer stratified periods of 2018–2021 and 2023-2025, while measurements from BVR were only taken in 2018 and 2022-2024.
Time series of carbon dioxide fluxes measured with eddy covariance for Danjiangkou Reservoir in Hubei Province, China during 2022-2024
This dataset contains half-hourly micrometeorological and eddy covariance flux measurements of carbon dioxide (CO₂) collected over the water surface of the Danjiangkou Reservoir in Hubei Province, China, from April 2022 to November 2024. The eddy covariance tower was installed at the deepest point of the reservoir, which serves as a critical water source for water supply and regional ecological functions in the middle reaches of the Yangtze River. Measurements were obtained using a LI-COR eddy covariance system (LI-COR Biosciences, Lincoln, NE, USA), and fluxes were calculated using EddyPro software (version 7.0.6). The dataset includes CO₂ and CH₄ fluxes as well as supporting micrometeorological, radiation, and water temperature measurements. All data were processed following established best practices for eddy covariance measurements, including comprehensive quality assurance and quality control procedures, which are fully documented and included with the dataset.
Ecosystem-level Carbon dioxide fluxes in two long-term experimental wet sedge tundra sites near Toolik Lake, AK, ARC LTER 1994.
Ecosystem-level Carbon dioxide fluxes were measured in two long-term experimental wet sedge tundra sites near Toolik Lake, AK. Experimental treatments at each site included factorial nitrogenXphosphorus, greenhouse and shade house and were begun in 1985 (Sag site) or in 1988 (Toolik sites). Fluxes were measured on quadrats that were later sampled for biomass and leaf area.
Majadas de Tietar: Ecosystem level and understorey carbon, water, and energy fluxes in a Mediterranean tree-grass ecosystem
<p>This dataset contains a subset of measurements collected at the experimental site Majadas de Tietar. We collected ecosystem level and understorey carbon, water, and energy fluxes in a Mediterranean Savanna using the eddy covariance technique and a series of meteorological sensors for the time period December 2015 - February 2018. The dataset is used for the development of a series of R packages including 'bigleaf' (Knauer et al., 2018).</p> <p>The experimental site is collected in Majadas de Tietar (Casals et al., 2009) located in western Spain (39°56′25″N 5°46′29″W). The ecosystem is a typical “Iberic Dehesa”, which is characterized by an herbaceous stratum of native pasture and sparse trees, for the majority (~98%) Quercus ilex. The tree density is about 20–25 trees/ha, the fractional cover of trees is about 20%, mean DBH of 46 cm, and a canopy height of about 8 m. (El-Madany et al., 2018). The herbaceous layer is composed of native annual species of the three main functional plant forms (grasses, forbs and legumes), whose fractional cover varies seasonally and is characterized by important inter-annual variations in the seasonal dynamics related to the onset of the dry period.</p> <p>Fluxes were measured with the eddy covariance technique with two different systems, one at ecosystem scale to characterize the fluxes of the whole ecosystem (15.5 m above ground), and one at 1.65 m above ground in an open space to measure the fluxes of the well-established understory grass layer.</p> <p>The description of the set-up, equipment and processing used to calculate ecosystem scale fluxes are described in El-Madany et al., (2018), while for the understory tower can be found in Perez-Priego et al., (2017).</p> <p>The dataset is composed of two files: 'ESLMa_MainTower', which is the ecosystem eddy covariance system, and 'ESLMa_SubCanopy', which is the understory eddy covariance system. The dataset contains half-hourly, processed eddy covariance of the ecosystem and understory tower, as well as the main biometeorological data used in the big-leaf package (net radiation, soil heat fluxes, horizontal wind velocity, atmospheric pressure, precipitation, air temperature). All the processing was conducted with EddyPro software (version 5.2.0, LI-COR Biosciences Inc., Lincoln, NE, USA) and the ustar filtering, gap-filling and partitioning with the R package REddyProc (Wutzler et al., 2018). The variables and the units are described in the Readme.txt file released with the dataset.</p> <p><strong>References</strong></p> <p>Casals, P. et al., 2009. Soil CO2 efflux and extractable organic carbon fractions under simulated precipitation events in a Mediterranean Dehesa. Soil Biol. Biochem. 41, 1915–1922. <a href="https://doi.org/10.1016/j.soilbio.2009.06.015">https://doi.org/10.1016/j.soilbio.2009.06.015</a>.</p> <p>El-Madany, T.S.,et al., 2018. Drivers of spatio-temporal variability of carbon dioxide and energy fluxes in a Mediterranean savanna ecosystem 21. <a href="https://doi.org/10.1016/j.agrformet.2018.07.010">https://doi.org/10.1016/j.agrformet.2018.07.010</a></p> <p>Knauer, J., et al., 2018. bigleaf - An R package for the calculation of physical and physiological ecosystem properties from eddy covariance data. PLOS ONE, doi:10.1371/journal.pone.0201114</p> <p>Perez-Priego O, et al., 2017. Evaluation of eddy covariance latent heat fluxes with independent lysimeter and sapflow estimates in a Mediterranean savannah ecosystem. Agricultural and Forest Meteorology. 236: 87-99. doi: 10.1016/j.agrformet.2017.01.009.</p> <p>Wutzler, T., et al., 2018. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosciences Discuss., p. 1-39.</p> <p> </p>
In Situ Carbon Dioxide and Methane Flux Measurements Using Opaque Chambers in a Sedge Fen Wetland (US-Los Lost Creek AmeriFlux Site, Wisconsin, Summer 2015)
This dataset contains in situ measurements of carbon dioxide (CO₂) and methane (CH₄) fluxes, collected using opaque closed chambers at the US-Los Lost Creek AmeriFlux fen shrub wetland site in northern Wisconsin during summer 2015. These data were collected to characterize variability of day-time mid-summer methane soil fluxes across the sampling area of the eddy covariance flux tower.
Carbon dioxide flux measurements and plot photographs from Arctic LTER Heath Tundra herbivore exclosures, Toolik Field Station, Alaska 2013
Ecosystem carbon dioxide (CO2) flux light response curves were measured from Arctic LTER heath tundra herbivore exclosures. Plot photographs were taken of each subplot using five consumer grade red, green and blue (RGB) wavelength camera. Structure from motion (SFM) photogrammetric method was then used to derive canopy structure. This file contains the CO2, normalized difference vegetation index (NDVI) data and photographs for each plot.
Carbon flux from aquatic ecosystems of the Arctic Coastal Plain along the Beaufort Sea, Alaska, 2010-2018
Multiple aquatic ecosystems (pond, lake, river, lagoon, ocean) on the Arctic Coastal Plain (ACP) near Utqiaġvik, AK were visited to determine their relative contribution to landscape-level atmospheric CO2 flux and how this may have changed over time. pCO2 (partial pressure of carbon dioxide) was monitored in late summer (late July to mid-August) over a period of four years (2013, 2015, 2017, 2018) from open water areas and is related to habitat type, dissolved organic carbon (DOC) and environmental factors (temperature, radiation, rainfall). Data include both daily averages from most sites, as well as spatial representation of pCO2 in Elson Lagoon and diel cycles of pCO2 from a tundra pond. Pond NEP (net ecosystem production) is estimated by free water metabolism and presented as daily estimates over a four summer period.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Half-hourly growing season, chamber-based, CO2 flux data, 2009-2021
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. This data contains CO2 fluxes measured using an automated chamber system that measures net ecosystem CO2 exchange (NEE). Measurements are made every ~1.5 hours and modeled half-hourly. Half hour ecosystem 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. GPP is calculated as the difference between NEE and Reco.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Growing season, chamber-based, CO2 flux data, 2009-2021
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 warming 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 set includes measured values of CO2 fluxes during the growing season.
Aquatic biofilm autotrohic index, carbon dioxide flux, and environmental conditions for the APEX water table experiment 2021-2023
To better understand linkages between hydrology and ecosystem carbon flux in northern aquatic ecosystems, we evaluated the relationship between plant communities, biofilm development, and carbon dioxide (CO2) exchange following long-term changes in hydrology in an Alaskan fen. We quantified seasonal variation in biofilm composition and CO2 exchange in response to lowered and raised water-table position (relative to a control) during years with varying levels of background dissolved organic carbon (DOC). We then used nutrient-diffusing substrates to evaluate cause-effect relationships between changes in plant subsidies (i.e., leachates) and biofilm composition among water-table treatments. We found that background DOC concentration determined whether plant subsidies promoted net autotrophy or heterotrophy on nutrient diffusing substrates. In conditions where background DOC was <= 40 mg L-1, plant subsidies promoted an autotrophic biofilm. Conversely, when background DOC concentration was >= 50 mg L-1, plant subsidies promoted heterotrophy. Greater light attenuation associated with elevated levels of DOC may have overwhelmed the stimulatory effect of nutrients on autotrophic microbes by constraining photosynthesis while simultaneously allowing heterotrophs to outcompete autotrophs for available nutrients. At the ecosystem level, conditions that favored an autotrophic biofilm resulted in net CO2 uptake among all water-table treatments, whereas the site was a net source of CO2 to the atmosphere in conditions that supported greater heterotrophy. Taken together, these findings show that hydrologic history interacts with changes in dominant plant functional groups to alter biofilm composition, which has consequences for ecosystem CO2 exchange.
Anchor sampling of inorganic carbon (DIC) and dissolved oxygen (DO) at the flux tower tidal creek in the Duplin River
An anchor sampling of DIC and DO level at the mouth of a first order creek of the Duplin was conducted seasonally in order to estimate the metabolism and CO2/O2 dynamics of water when it floods the marsh where the GCE LTER project operates an eddy covariance tower.
Soil nitrous oxide and carbon dioxide flux data for Niwot Ridge and Loch Vale watershed, 1994.
Fluxes of carbon dioxide and nitrous oxide from snow-covered alpine soils were measured at Niwot Ridge. Six sites characterized by relatively shallow snowpacks were sampled in 1993. A total of 27 sites were sampled in 1994. Nine of the 1994 sites were located in the naturally shallow snowfield sampled in 1993, 9 sites were located in a formerly shallow snowpack site where snow depth was augmented by the construction of a 2.8-m high, 60-m long snowfence, and the 9 remaining sites were located in a naturally deep snowpack. Concentrations of N2O and CO2 at the soil surface were measured monthly from January until March, biweekly through April, and weekly until snowmelt was complete. Elevated levels of CO2 under the snowpack, suggesting microbial activity, were first observed under the shallow snowpacks in early March of 1993. N2O production under snow was first observed in April 1993, when soil temperatures had warmed above -3 degrees C. In 1994 shallow snowpack sites exhibited diminished and sporadic production of both CO2 and N2O, apparently due to the inconsistent snow cover compared to 1993. The snowfence sites exhibited increased CO2 and N2O fluxes beginning in February 1994. Both CO2 and N2O fluxes from the snowfence site were similar to those measured under the naturally deep snowpack. These data suggest that the timing and depth of snow cover during the alpine winter control microbial activity by insulating soils from extreme air temperatures. To obtain a regional perspective on subnivean trace gas fluxes, both CO2 and N2O samples were determined at sites below treeline on Niwot Ridge and at Loch Vale in Rocky Mountain National Park.
Vertical fluxes of particulate carbon, nitrogen and phosphorus from a sediment trap deployed west of Palmer Station, Antarctica at a depth of 170 meters, 1992-2019.
Particulate organic matter is exported from the upper ocean euphotic zone in the form of large sinking particles and as dissolved material. Particle fluxes to depth link the surface and mesopelagic realm and supply food to the benthos. Sedimentation flux is typically measured with sediment traps of various designs. Palmer LTER has deployed a time-series trap near 64.5degrees S, 66.0degrees W since late 1992. The trap is moored in 300 m depth and collects sinking particles at 150 m. Deployments and analyses were performed by David Karl, University of Hawaii until 2002 when Hugh Ducklow took over the sediment trap operations.Sedimentation at the PAL site of the West Antarctic Peninsula demonstrates extreme seasonality, with a well-defined pulse in the Austral summer following sea ice retreat. Daily sedimentation rates during the summer flux event are among the highest recorded globally. During the Austral winter when the ocean is covered by sea ice and shrouded in darkness, fluxes are among the lowest observed anywhere. Sedimentation rates at PAL typically vary by 4 orders of magnitude. There is also order of magnitude variability in the total annual flux (area under the curve).
Carbon and water fluxes in a cork oak woodland in Central Portugal
<p>The Data set contains eddy covariance measurements of carbon and water fluxes and ancillary measurements observed at a cork oak woodland (<em>Quercus suber </em>L.) in central Portugal. The climate is Mediterranean, with mild, wet winters and hot, dry summers.</p> <p>Data are available in the file data.csv (UTF-8 encoding), the description of the variables and units are available in the file meta.csv (UTF-8 encoding).</p> <p>Further description of the site, methods and data processing can be viewed in the files metadata.pdf and metadata.csv</p> <p> </p>
Seafloor organic carbon flux output from the NEMO-MEDUSA model
<p>This output was produced by a simulation using a coupled ocean physics and marine biogeochemistry model. The physical ocean submodel was the Nucleus for European Modeling of the Ocean (NEMO) physical ocean model (Madec, 2014), run here in a global 1/12-degree resolution configuration (ORCA0083). The marine biogeochemistry submodel was the Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification (MEDUSA-2), an intermediate-complexity plankton ecosystem model (Yool et al., 2013). The horizontal resolution of this configuration of NEMO has non-uniform grid cells ranging 2 to 9 km in size (mean 7.5 km), with 75 vertical depth levels (31 levels between the surface and 200 m depth). Sea-ice is represented in the model by the Louvian‐la‐Neuve Ice Model (LIM2) (Fichefet, & Maqueda, M. a. M., 1997; Goosse & Fichefet, 1999). The configuration was forced at the air-sea interface with version 5.2 of the DRAKKAR forcing set (DFS) (Brodeau et al., 2010). DFS 5.2 is based on ERA40 reanalysis data, comprising of 6‐hourly means for wind, humidity, and atmospheric temperature, daily means for radiative fluxes (both longwave and shortwave), and monthly means for precipitation. A monthly climatology was used for river runoff, taken from the CORE2 reanalysis (Brodeau et al., 2010; Timmermann et al., 2005). The resulting model hindcast was created using this forcing set for the period 1958–2015, with marine biogeochemistry initialised in 1990.</p> <p>This archive includes the flux of organic carbon reaching the seafloor and the area of the grid cells for the global domain. In MEDUSA, the seafloor flux is the sum of slow- and fast-sinking detrital particles that reach the base of the water column and enter the benthic submodel of MEDUSA. In general, away from shallow water regions (< 200 m), this flux is dominated by fast-sinking material produced by ecological processes associated with the large components of MEDUSA.</p> <p>The specific subset of output used was drawn from the decadal period 2006-2015, and was regridded from the non-uniform ORCA0083 grid to a regular 1/12-degree grid. Output processing was undertaken by A. Yool (axy@noc.ac.uk; National Oceanography Centre, Southampton UK).</p> <p>In addition to the netCDF files, text file dumps of their contents are included to assist with interpretation.</p> <p>References:</p> <p>Brodeau, L., Barnier, B., Treguier, A.‐M., Penduff, T., & Gulev, S. (2010). An ERA40‐based atmospheric forcing for global ocean circulation models. Ocean Modelling, 31, 88–104.</p> <p>Fichefet, T., & Maqueda, M. a. M. (1997). Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. Journal of Geophysical Research, Oceans, 102, 12,609–12,646.</p> <p>Goosse, H., & Fichefet, T. (1999). Importance of ice‐ocean interactions for the global ocean circulation: A model study. Journal of Geophysical Research, Oceans, 104, 23,337–23,355.</p> <p>Kelly, S., Popova, E., Aksenov, Y., Marsh, R., & Yool, A. (2018). Lagrangian modeling of Arctic Ocean circulation pathways: Impact of advection on spread of pollutants. J. Geophys. Res. Oceans, 123, 2882‐2902, doi: 10.1002/2017JC013460.</p> <p>Madec, G. (2014). "NEMO Ocean engine" (draft edition r5171) "NEMO Ocean engine" (draft edition r5171). Note du Pôle de modélisation, Institut Pierre‐Simon Laplace (IPSL), France, 27, 1288–1619.</p> <p>Timmermann, R., Goosse, H., Madec, G., Fichefet, T., Ethe, C., & Dulière, V. (2005). On the representation of high latitude processes in the ORCA‐LIM global coupled sea ice–ocean model. Ocean Modelling, 8, 175–201.</p> <p>Yool, A., Popova, E.E. and Anderson, T.R. (2013). MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies. Geoscientific Model Development 6, 1767-1811, doi: 10.5194/gmd-6-1767-2013.</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>
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.
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.