Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
173
datasets available to search
ShareScore release 0.7.1
Dataset results
173 results for “surface flux”
TSSCXG-17: Global Gridded Dataset of Surface Ocean pCO2 and Air-Sea CO2 Flux (1993-2020)
<p>This dataset presents a global gridded reconstruction of the partial pressure of CO2 (pCO2) in the surface ocean and the corresponding air-sea CO2 flux, covering the period from 1993 to 2020. Developed to enhance understanding of climate change and the global carbon cycle, this dataset addresses gaps in oceanic carbon flux data through innovative machine learning techniques. The reconstruction process integrates in situ observations, satellite data, and reanalysis products, employing a three-step algorithm involving dimensionality reduction, clustering, and regression.</p>
ATASF: Tri-hourly anomaly of tropical atmospheric surface fluxes.
<p><strong>ATASF dataset</strong> is a product that contains the tri-hourly surface anomalies of the entire tropical region (180°W - 178.125°E/33.3328°S – 31.4281°N) of the heat fluxes and of the incident and reflected radiation. These are calculated from 20th Century Reanalysis V2c data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at <a href="http://www.esrl.noaa.gov/psd/">http://www.esrl.noaa.gov/psd/</a>. This product contains the variables:</p> <ol> <li>Tri-hourly convective precipitation rate (kg m^{-2} s)</li> <li>Tri-hourly downward longwave radiation flux at surface (w m^{-2})</li> <li>Tri-hourly downward solar radiation flux at surface (w m^{-2})</li> <li>Tri-hourly upward longwave radiation flux at surface (w m^{-2})</li> <li>Tri-hourly upward solar radiation flux at surface (w m^{-2})</li> <li>Tri-hourly latent heat net flux at surface (w m^{-2})</li> <li>Tri-hourly sensible heat net flux at surface (w m^{-2})</li> </ol> <p>All these 3D grids have a period of time from 1851-01-01 00:00:00 to 2014-12-31 21:00:00, with a spatial resolution of 1.875 x 1.904732 degrees. The objective of this dataset is to facilitate researchers to study the anomalies of these 7 parameters on the ocean surface during the occurrence of short and medium duration physical processes such as low pressures, cold fronts, hurricanes and others.</p>
Air-Sea fluxes of CO2 in the Indian Ocean between 1985 and 2018: A synthesis based on Observation-based surface CO2, hindcast and atmospheric inversion models.
<p>This data set contains 14 hindcast models (CCSM-WHOI.nc, CESC_ETHZ.nc, CNRM-ESM2-1.nc, EC_Earth3.nc, FESOM_REcoM_LR.nc, MOM6_Princeton.nc, MPIOM_HAMOCC.nc; MRI-ESM2-1.nc, NorESM-OC1.2.nc, ORCA1-LIM3-PISCES.nc, ORCA025-EOMAR.nc, Plankotom12, INCOIS-BIO-ROMS.nc, ROMS-NYUAD.nc), nine empirical models (CMEMS-LSCE-FFNN.nc, CSIRML6.nc, Jena-MLS.nc, JMAMLR.nc, Spco2_LDEO_HPD.nc, SOMFNN.nc, NIES-MLR3.nc, UOEX-WAT20.nc, OceanSODAETHZ.nc) and CO2 flux climatology data (CO2_Climatology.nc). This data set also has two atmospheric inversion models output and those are - MACTM (MACTM.nc) and CAMSv20r1 (CMSv20r1.zip format and inside the zip folder files are .nc format).</p>
Flux data kit, a comprehensive data set of ecosystem fluxes for land surface modelling
<blockquote> <p>Newer versions (>= v3.0) are released by Benjamin Stocker and can be found at his Zenodo account at: https://zenodo.org/records/10885934</p> </blockquote> <p>The Flux data kit is an effort to expand upon the existing work by Ukolla et a. (2022) to synthesize various sources of ecosystem flux data (i.e. the PLUMBER2 data set, gathered from all major networks). We further expand upon the original data set by integrating data which was either expanded upon (temporally) or where sites were added (e.g. the integration of ICOS data).</p> <p>The effort uses the FluxnetLSM package by the above mentioned authors, as well as their general workflow. In contrast to the PLUMBER2 data set we do not apply stringent quality control, and all quality control on the availability of variables and/or their duration <em>should be done by the user</em>. Furthermore, we include both leaf area index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in the netcdf output, where PLUMBER2 only provided LAI. On all other parts the formatting and naming conventions as well as quality control specifications remain the same as in PLUMBER2. We therefore refer to Ukolla et al. (2022) for details.</p> <p><strong>Data included</strong></p> <p>The data included consists of three files, containing different versions of the same data. The FLUXDATAKIT_LSM.tar.gz file contains compressed netCDF files compatible with the ALMA scheme for land surface modelling. The FLUXDATAKIT_FLUXNET.tar.gz file contains data in a CSV format according to the FLUXNET specifications. And finally the rsofun_driver_data_clean.rds file is a compressed serialized R file containing data formatted for use with the `rsofun` R package.</p> <p><strong>Data generation</strong></p> <p>Data is generated using the FluxDataKit project. Although this project is not meant for continuous releases, and no support is provided in using this code with data provided AS IS, it might still be useful to some:</p> <p><a href="https://github.com/geco-bern/FluxDataKit">https://github.com/geco-bern/FluxDataKit</a></p> <p>The data can be further complimented using the FluxnetEO dataset, which is accessible through the package with the same name as found here:</p> <p><a href="https://github.com/geco-bern/FluxnetEO">https://github.com/geco-bern/FluxnetEO</a></p> <p><strong>Acknowledgements</strong></p> <p>The flux data kit is part of the LEMONTREE project and funded by Schmidt Futures and under the umbrella of the Virtual Earth System Research Institute (VESRI).</p> <p><strong>References:</strong></p> <ul> <li>Ukkola, Anna M., Gab Abramowitz, and Martin G. De Kauwe. "A flux tower dataset tailored for land model evaluation." Earth System Science Data 14.2 (2022): 449-461.</li> </ul>
Iberian Summer Surface Temperature and Fluxes for Energy Balance
<p>This dataset holds selected postprocessed files for surface temperature and fluxes involved in the surface energy balance.</p><p>Four WRF experiments nested in ERA-Interim were prepared. The first one (N) was configured as in standard numerical downscaling experiments using the Noah LSM. The second one (D), with the same parameterizations, included a step of 3DVAR data assimilation every 6 hours. The third and the fourth ones (S and C) are similar to N and D but use a diffusive soil scheme instead of NOAH LSM. The experiments covered the period 2010-2014 after a year of spin-up (2019). </p><p>The following 3-hourly files are included:</p><ul><li>Tsoil: soil temperature for the first 2 top levels of the surface.</li><li>T2: 2 metre temperature. </li><li>Latent: Latent heat flux.</li><li>Sensible: Sensible heat flux.</li><li>NetSW: net short-wave radiation flux at the surface.</li><li>NetLW: net long-wave radiation flux at the surface.</li><li>GRDFLX: ground flux toward lower layers of the soil.</li></ul><p>The <i>N, D, C or S </i>characters in the file names indicate whether the files come from the WRF N, D, C or S experiments. The files include a table including the 3-hourly data for each grid point over the Iberian Peninsula: year | month | day | hour | V1 | ... | V2058 </p><p>The 2058 grid points included in each file are listed in the same order as in the file WRFmask_points_withoutUrban_withLandType.dat </p><p> </p><p> </p>
Surface carbon, water and energy fluxes measured by eddy covariance at 3 sites within the Alaska Peatlands Experiment and Bonanza Creek Experimental Forest 2013-2016
These data are simultaneous and continuous measurements of carbon, water and energy fluxes of the terrestrial landscape. These fluxes are major regulatory drivers of the boreal climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. At the APEX project site, within Bonanza Creek Experimental Forest, this monitoring is repeated across a chronosequence of permafrost degradation; the Black Spruce site is an area of stable permafrost with intact black spruce forest (APEX gamma site), the Thermokarst site is an active thermokarst zone with considerable tree mortality (APEX betaSW site), the Fen site is within a stable treeless fen with deep active layer depth (APEX apexcon,low, and ele sites). The main variables being monitored are the instananeous fluxes of CO2, water vapor and surface energy (shortwave, longwave and net radiation), secondary variables included photosynthetically active radiation (PAR), air and soil temperatures, rainfall, snow depth, soil moisture content, wind direction and speed, and average atmospheric concentrations of CO2 and H2O through the year. Our site naming scheme is as follows: 1) gamma = Black Spruce site = YF_2472, 2) betaSW = Thermokarst site= BC_5166, 3) (apexcon+apexele+apexlow) = Fen site = BC_FEN
Parameterizing Subgrid Variations of Land Surface Heat Fluxes to the Atmosphere Improves Land Precipitation Simulation with the NCAR CESM1.2
<p>The dataset is the output of CESM that is used in the paper "Parameterizing Subgrid Variations of Land Surface Heat Fluxes to the Atmosphere Improves Land Precipitation Simulation with the NCAR CESM1.2" submitted to <em>Geophysical Research Letters</em>.</p>
IPSL_pictrl_surface_fluxes
<p>20 years of daily average outputs from the IPSL_CM5A-LR coupled climate model, in its pre-industrial control configuration. Variables included are zonal and meridional wind stress, net surface heat and freshwater fluxes. </p>
Data from: Carbon dioxide and methane fluxes from different surface types in a created urban wetland
<p><span>Many wetlands have been drained due to urbanization, agriculture, forestry or other purposes, which has resulted in losing their ecosystem services. To protect receiving waters and to achieve services such as flood control and stormwater quality mitigation, new wetlands are created in urbanized areas. However, our knowledge of greenhouse gas exchange in newly created wetlands in urban areas is currently limited. In this paper we present measurements carried out at a created urban wetland in boreal climate.</span></p> <p><span>We conducted measurements of ecosystem CO<sub>2 </sub>flux (NEE) and CH<sub>4</sub> flux (F<sub>CH4</sub>) at the constructed stormwater wetland Gateway in Nummela, Vihti, Southern Finland using eddy covariance (EC) technique. The measurements were commenced the fourth year after construction and lasted for one full year and two subsequent growing seasons. Besides ecosystem scale fluxes measured by EC tower, the diffusive CO<sub>2 </sub>and<sub> </sub>CH<sub>4</sub> fluxes from the open-water area (F<sub>w</sub>_CO<sub>2</sub> and F<sub>w</sub>_CH<sub>4, </sub>respectively) were modelled based on measurements of CO<sub>2 </sub>and<sub> </sub>CH<sub>4 </sub>concentration in the water. Fluxes from vegetated area were estimated by applying a simple mixing model using above-mentioned fluxes and footprint-weighted fractional area. The half-hourly footprint-weighted contribution of diffusive fluxes from open water ranged from 0 to 25.5 % in year 2013.</span></p> <p><span>The annual NEE of the studied wetland was 8.0 g C-CO<sub>2 </sub>m<sup>-2</sup> yr<sup>-1 </sup>with the 95 % confidence interval between<sup> </sup>-18.9 and 34.9 g C-CO<sub>2 </sub>m<sup>-2</sup> yr<sup>-1 </sup>and F<sub>CH4 </sub>was 3.9 g C-CH<sub>4</sub> m<sup>-2</sup> yr<sup>-1</sup> with the 95 % confidence interval between 3.75 and 4.07 g C-CH<sub>4</sub> m<sup>-2</sup> yr<sup>-1</sup>. The ecosystem sequestered CO<sub>2 </sub>during summer months (June-August), while the rest of the year it was a CO<sub>2</sub> source. CH<sub>4</sub> displayed strong seasonal dynamics, higher in summer and lower in winter, with a sporadic emission episode in the end of May 2013. Both CH<sub>4 </sub>and CO<sub>2 </sub>fluxes<sub>, </sub>especially those obtained from vegetated area, exhibited strong diurnal<sub> </sub>cycle during summer with synchronized peaks around noon. The annual F<sub>w</sub>_CO<sub>2 </sub>was 297.5 g C-CO<sub>2 </sub>m<sup>-2</sup> yr<sup>-1 </sup>and F<sub>w</sub>_CH<sub>4 </sub>was 1.73 g C-CH<sub>4 </sub>m<sup>-2</sup> yr<sup>-1</sup>. The peak diffusive CH<sub>4</sub> flux was 137.6 nmol C-CH<sub>4</sub> m<sup>-2</sup> s<sup>-1</sup>, which was<sup> </sup>synchronized with the F<sub>CH4</sub>.</span></p> <p><span>Overall, during the monitored time period, the established stormwater wetland had a climate warming effect with 0.263 kg CO<sub>2</sub>-eq m<sup>-2</sup> yr<sup>-1 </sup>of<sup> </sup>which 89 % was contributed by CH<sub>4</sub>. The radiative forcing of the open-water exceeded the vegetation area (1.194 kg CO<sub>2</sub>-eq m<sup>-2</sup> yr<sup>-1</sup> and<sup> </sup>0.111 kg CO<sub>2</sub>-eq m<sup>-2</sup> yr<sup>-1</sup>, respectively), which implies that, when considering solely the climate impact of a created wetland over a 100-year horizon, it would be more beneficial to design and establish wetlands with large patches of emergent vegetation, and to limit the areas of open-water to the minimum necessitated by other desired ecosystem services.</span></p>
The role of bottom friction in mediating the response of the Weddell Gyre circulation to changes in surface stress and buoyancy fluxes
<p>This repository contains code to reproduce the figures in the paper titled "The role of bottom friction in mediating the response of the Weddell Gyre circulation to changes in surface stress and buoyancy fluxes" published in the Journal of Physical Oceanography (DOI: <a href="https://doi.org/10.1175/JPO-D-23-0165.1">https://doi.org/10.1175/JPO-D-23-0165.1</a>), as well as the barotropic vorticity budget terms for each of simulation in the paper.</p>
Outputs from new LBA simulations. Part II: Interactive surface fluxes
<p>MIMICA model outputs for new LBA simulations. The case, model configuration and output files are all described in the attached TRANSITION_setup.pdf file. This is the second of a three part dataset containing results from simulations in which surface fluxes were calculated interactively (see below for a very quick overview of all data).</p> <p>Three sets of data outputs are available: One corresponding to the original, idealized LBA case, one in which cold pools have been suppressed by nudging temperature and moisture tendencies below cloud base(_nocp extension), and one in which the fixed surface fluxes have been replaced by interactive fluxes (_sst extension). For each set of simulations, five horizontal grid spacings were employed to test the sensitivity of the results to resolution (specified as file name extensions): 1.6 km (64 grid points), 800 m (128 grid points), 400 m (256 grid points), 200 m (512 grid points) and 100 m (1024 grid points). Finally, for each case, a series of various output files are available, including time series of domain averaged quantities (T_S), time series of horizontally averaged quantities (profiles_tot.nc), and two-dimensional horizontal slices extracted at various altitudes (slice_z_XXXX.nc). Note that all files present except T_S follow the NetCDF standard.</p>
caneill et al. The polar transition from alpha to beta regions set by a surface buoyancy flux inversion.
<p>NEMO outputs used for the paper Caneill, R., Roquet, F., Madec, G., and Nycander, J. (2022). The polar transition from alpha to beta regions set by a surface buoyancy flux inversion. Journal of Physical Oceanography.</p> <p><a href="https://doi.org/10.1175/JPO-D-21-0295.1">https://doi.org/10.1175/JPO-D-21-0295.1</a></p> <p>Abstract:</p> <p>The stratification is primarily controlled by temperature in subtropical regions (alpha-ocean), and by salinity in subpolar regions (beta-ocean). Between these two regions lies a transition zone, often characterized by deep mixed layers in winter and responsible for the ventilation of intermediate or deep layers. While of primary interest, no consensus on what controls its position exists yet. Amongst the potential candidates, we find the wind distribution, air-sea fluxes or the nonlinear cabbeling effect. Using an ocean general circulation model in an idealized basin configuration, a sensitivity analysis is performed testing different equations of state. More precisely, the thermal expansion coefficient (TEC) temperature dependence is explored, changing the impact of heat fluxes on buoyancy fluxes in a series of experiments. The transition zone is found to be located at the position where the sign of the surface buoyancy flux reverses to become positive, in the subpolar region, while wind or cabbeling are found of secondary importance. This inversion becomes possible because the TEC is reducing at low temperature, enhancing in return the relative impact of freshwater fluxes on the buoyancy forcing at high latitudes. When the TEC is made artificially larger at low temperature, the freshwater flux required to produce a positive buoyancy flux increases and the transition zone moves poleward. These experiments demonstrate the important role of competing heat and freshwater fluxes in setting the position of the transition zone. This competition is primarily influenced by the spatial variations of the TEC linked to meridional variations of the surface temperature.</p> <p> </p> <p>Download data:</p> <p>Due to their large size, the data have been split into multiple zip files. To extract them, follow the following commands:</p> <pre><code class="language-bash">zip -s 0 EXP_main.zip --out out.zip unzip out.zip</code></pre> <p> </p>
On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Trained Models
<p>Trained machine learning models and scaling values used in the paper "On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model."</p>
Climate Impacts of Parameterizing Subgrid Partitioning of Land Surface Heat Fluxes to the Atmosphere with the NCAR CESM1.2
<p>The modified code as well as the CAM5 output for all the simulations in this study (V0 for the CTL run, CON1 for the EXP run, and PCON1R for EXP_COR run).</p> <p>The CESM1.2.1-CAM5.3 source code can be downloaded through the CESM official website https://www.cesm.ucar.edu/models/cesm1.2/cesm/doc/usersguide/x290.html#download_ccsm_code. Its output files are named in V0*.nc.</p> <p>The modified code for the EXP run in the study is in CON1.tar, with its CAM5 output files named in CON1*.nc.</p> <p>The modified code for the EXP_COR run in the study is in PCON1R.tar, with its CAM5 output files named in PCON1R*.nc</p>
Sea surface Dimethylsulfide Concentration and Emission Flux over the North Atlantic Ocean
<p>The data represent the monthly climatology of sea surface Dimythylsulfide (DMS) concentration and sea-to-air Dimythylsulfide flux (FDMS) over the North Atlantic Ocean at 0.25°×0.25° spatial resolution. DMS data were obtained by applying a machine learning predictive algorithm based on Gaussian process regression (GPR) to model the distribution of daily DMS concentrations in the North Atlantic waters over 24 years (1998-2021). FDMS derived from the predicted DMS concentrations (GPR) and Goddijn-Murphy et al. (2012) parametrization. The scripts are authored by K. Mansour. For more information, please contact me at k.mansour@isac.cnr.it.</p>
Turbulent flux measurements of the near-surface and residual-layer small particle events
<p>According to recent field studies, almost half of the New Particle Formation (NPF) events occur aloft, in a residual layer, near the top of the boundary layer. Therefore, measurements of the meteorological parameters, precursor gas concentrations, and aerosol loadings conducted at the ground level are often not representative of the conditions where the NPFs take place. This paper presents new measurements obtained during the Turbulent Flux Measurements of the Residual Layer Nucleation Particles, conducted at the Southern Great Plains research site. Vertical turbulent fluxes of 3–10 nm-sized particles were measured using a sonic anemometer and two condensation particle counters with nominal cutoff diameters of 3 nm and10 nm mounted at the top of the 10-m telescoping tower. Aerosol number size distribution (5 to 300 nm) was determined through the ground-based Scanning Mobility Particle Sizers. The size selected (15 to 50 nm) particle hygroscopicity was derived with the Humidified Tandem Differential Mobility Analyzer. The ground-level observations were supplemented by vertically-resolved measurements of horizontal and vertical wind speed and aerosol backscatter. The data analysis suggests that 1) turbulent flux measurements of 3-10 nm particles can distinguish between near-surface and residual-layer small particle events; 2) sub-50 nm particles had a hygroscopicity value of 0.2, suggesting that organic compounds dominate atmospheric nanoparticle chemical composition at the site; and 3) current methodologies are inadequate for estimating dry deposition velocity of sub-10 nm particles because it is not feasible to measure particle concentration very near the surface, in the diffusion sublayer.</p>
Components of Ocean Surface Heat Flux
<p>Shows the components of surface heat flux: solar/shortwave radiation, back/longwave radiation, sensible heat flux(conduction), and latent heat flux(evaporation).</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>
A flux tower site attribute dataset intended for land surface modeling
<p><span>Land surface models (LSMs) should have reliable forcing, validation, and surface attribute data as the foundation for effective model development and improvement. Eddy covariance flux tower data are considered the benchmarking data for LSMs. We conducted a comprehensive quality screening of the existing reprocessed flux tower dataset, including the proportion of gap-filled data, external disturbances, and energy balance closure (EBC), leading to 90 high-quality sites. For these sites, we collected vegetation, soil, topography information, and wind speed measurement height from literature, regional networks, and Biological, Ancillary, Disturbance, and Metadata (BADM) files. Then we obtained the final flux tower attribute dataset by global data product complement and plant functional types (PFTs) classification.</span></p> <p><span>The dataset comprises a total of 90 NetCDF files. Each site's data is formatted within a NetCDF file named according to the site name, database, and attributes (vegetation, soil, topography, and reference height), such as ‘AT-Neu_FLUXNET2015_Veg_Soil_ Topography_ReferenceHeight.nc’. This dataset addresses the lack of site attribute data to some extent, reduces uncertainty in LSMs data source, and aids in diagnosing parameter as well as process deficiencies.</span></p>
Effects of surface fluxes on the moist potential vorticity distribution in the tropical cyclone boundary layer
<p>Hourly model outputs (t=150-240 hrs) from five axisymmetric simulations of tropical cyclones are provided as follows :</p> <ol> <li>cm1_test40_ver2 (referred to as CONTROL in the manuscript)</li> <li>cm1_test41_ver2 (referred to as H2.0 in the manuscript)</li> <li>cm1_test42_ver2 (referred to as H0.5 in the manuscript)</li> <li>cm1_test43_ver2 (referred to as M2.0 in the manuscript)</li> <li>cm1_test44_ver2 (referred to as M0.5 in the manuscript)</li> </ol> <p>Model outputs from the 3D simulation are interpolated to cylindrical coordinates and saved individually for each variable in binary format, and these are provided for t=150-240 hrs as follows:</p> <ol> <li>cm1_test13_ver2 (referred to as 3D-TC in the manuscript)</li> </ol> <p>Jupyter notebooks are also provided to read and analyze processed outputs from the datasets described above and plot the figures included in the manuscript. The datasets used in "<em>figure_05.ipynb</em>", "<em>figure_06.ipynb</em>", and "<em>figure_07.ipynb</em>" are large and can be made available by the authors upon request.</p>
ScienceDex guides
Understand access before you commit
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.