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726 results for “global model”
Simulations from the LPJ-GUESS dynamic global vegetation model v3.0 for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the LPJ-GUESS dynamic global vegetation model v3.0 are provided for two global simulations. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for mortality, leaf phenological turnover and fine root phenological turnover were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
JUMP - Data collection - Part II: Zonal jets using three different approaches, laboratory - Global Climate Models - observations.
<p>The formation of large scale structures in three-dimensional (3D) turbulent flows. How small-scale dynamics organize in turbulent flows to grow large scale coherent circulation? is at the heart of fundamental studies in fluid dynamics. It appears to be equally important for our understanding of atmospheric dynamics, oceanography, meteorology and more generally geophysical fluid dynamics. Here, we deliver a data collection that <strong>(1)</strong> gathers measurements of 3D turbulent flows that emulate planetary atmospheres of the gas giants. Turbulent flows are explored using three different approaches, laboratory experiments, numerical simulations and direct planetary observations. All data set are computed in order to easily extract flow properties, i.e. high resolution maps of the different velocity components and flow vorticity (useful for further diagnostic). The data collected are fully discribed in Cabanes et al GRL (2020) "Revealing the intensity of turbulent energy transfer in planetary atmospheres" and can be used to compute <strong>(2)</strong> theoretical diagnostics with the numerical codes that allow to reveal the physical meaning of flow measurements. Numerical codes are available on https://github.com/scabanes</p> <p>We deliver (1) data collection and (2) numerical codes in the following files attached:</p> <p>(1) Data collection:</p> <ul> <li>A PDF file named <strong>JUMP-zonal-jets-data-collection-GRL.pdf</strong> that describes the following data files and nomenclature.</li> <li>A zip File of the velocity fields in the lab, interpolated on Polar and Cartesian grids <ul> <li><strong>JUMP-JetsInTheLab.zip</strong></li> </ul> </li> <li>A netcdf file of velocity fields of our Saturn reference simulation <ul> <li><strong>uvData-SRS-istep-312000-nstep-50-niz-12.nc</strong></li> </ul> </li> <li>Two netcdf files of velocity fields from Cassini observations of Jupiter<strong> </strong> <ul> <li><strong>uvData-JupObs-istep-0-nstep-4-niz-1.nc</strong></li> <li><strong>StatisticalData-JupObs.nc</strong></li> </ul> </li> <li>A zip file of potential vorticity profiles for Saturn and Jupiter observations <ul> <li><strong>IPV-QGPV-Jupiter-Saturn.zip</strong></li> </ul> </li> </ul> <p>(2) Numerical codes:</p> <ul> <li>Codes for statistical analysis in spherical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> <li>Codes for statistical analysis in cylindrical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> <li>Codes for statistical analysis in cartesian geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> </ul> <p> </p> <p>The purpose of this data collection is to reveal statistical properties of planetary flows. By computing the same analysis on different data sets the researcher allows direct confrontation of planetary observations with idealized laboratory and numerical models. Idealized models are specially designed to sweep on a large array of parameters in order to understand what parameters control planetary global circulation. The data collected and generated by the researcher deliver <strong>(1)</strong> velocity measurements of 3D turbulent flows using the different approaches (observations-laboratory-numerics) and <strong>(2)</strong> guidelines to compute the appropriate statistical analysis through the PTST. Here, the ground-breaking novelty is that the researcher deliver the possibility to compute statistical diagnostics adapted to the different geometries: the spherical geometry of planetary flows, i.e. 2D latitude-longitude maps, the cylindrical geometry of laboratory experiments, i.e. 2D flows in a rotating cylindrical tank, and the Cartesian geometry of idealized numerical simulations. Indeed, the math behind each statistical diagnostics must account for the different geometrical configurations in order to properly confront the different approaches. The PTST is also designed to be easily re-used by different communities such as experimentalists, numericists and atmosphericists that deal with 3D or 2D turbulent flows.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 797012.</p>
AgMIP-Wheat multi-model ensemble simulations on climate change impact and adaptation for 60 representative global locations
<p>This is model output from the Agricultural Model Intercomparison and Improvement Project for wheat (AgMIP-Wheat) dataset of multi-model ensemble simulations for 60 representative global locations under different climate scenarios.</p> <p>The data have been generated following the modeling protocol of Asseng et al. (2019) and Liu et al. (2019).</p> <p>References</p> <p>Asseng, S. et al. (2019). Climate change impact and adaptation for wheat protein. Glob Chang Biol 25, 155-173, doi:10.1111/gcb.14481</p> <p>Liu, B. et al. (2019). Global wheat production with 1.5 and 2.0°C above pre-industrial warming. Global Change Biol 25, 1428-1444, doi:10.1111/gcb.14542</p> <p> </p>
Daily ocean bottom pressure anomalies 2007-2009 from global numerical models
<p>Data supplement to: Schindelegger, M., Harker, A. A., Ponte, R. M., Dobslaw, H., & Salstein, D. A. (2021). Convergence of daily GRACE solutions and models of submonthly ocean bottom pressure variability. <em>Journal of Geophysical Research: Oceans</em>, 126, e2020JC017031. <a href="https://doi.org/10.1029/2020JC017031">https://doi.org/10.1029/2020JC017031</a></p> <p><strong>Contents:</strong></p> <p>Daily ocean bottom pressure anomalies over 2007-2009 obtained from two global forward simulations:</p> <ol> <li><strong>DEBOT</strong> (<em>David Einspigel Barotropic Ocean Tide Model</em>): 1/3° horizontal grid spacing, single-layer model</li> <li><strong>MITgcm LLC270</strong> (<em>Massachusetts Institute of Technology general circulation model, Lat-Lon-Cap 270</em>): nominal 1/3° horizontal grid spacing, 50 vertical layers</li> </ol> <p>Common specifications:</p> <ul> <li>Yearly files (<em>yyyy</em>) for each model run (<em>DEBOT_OBP_n180_yyyy.nc</em>, <em>LLC270_OBP_n180_yyyy.nc</em>)</li> <li>Temporal mean 2007-2009 reduced</li> <li>Daily fields centered at 12 UTC</li> <li>Units: cm of equivalent water height</li> <li>Data given on regular 1° grid</li> <li>Synthesized from spherical harmonic expansion truncated at degree 180 (<em>n180</em>)</li> <li>Degree 1 terms: included</li> <li>Static atmospheric contribution to bottom pressure: included</li> <li>Atmospheric forcing: ERA-Interim (6-hourly)</li> </ul> <p> </p> <p>Contact: M. Schindelegger (schindelegger@igg.uni-bonn.de)</p>
Carbonyl Sulfide (OCS/COS) and Carbon Disulfide (CS2): global modelled marine surface concentrations and emissions, 2000-2019
<p>This dataset contains a global ocean emission inventory of the sulfur-containing trace gases carbonyl sulfide (OCS/COS) and carbon disulfide (CS2). It covers the period 2000-2019, and includes a monthly average and an average diel cycle for each month for sea surface concentrations and emissions to the atmosphere. The spatial resolution is 2.8° x 2.8° at the equator (T42 grid), the depth extends from the surface to the mixed layer depth.</p> <p>Carbonyl sulfide (OCS) is the most abundant, long-lived sulphur gas in the atmosphere and a major supplier of sulfur to the stratospheric sulfate aerosol layer. The short-lived gas carbon disulfide (CS<sub>2</sub>) is oxidized to OCS and constitutes a major indirect source to the atmospheric budget of OCS. We encourage the use of the data provided here as input for atmospheric modelling studies to further assess the atmospheric OCS budget and the role of OCS in climate.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL soybean simulations
<p>This data set contains output data from simulations with the model LPJmL for soybean as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL rice simulations
<p>This data set contains output data from simulations with the model LPJmL for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL maize simulations
<p>This data set contains output data from simulations with the model LPJmL for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
Next generation global ice-ocean-biogeochemistry coupled model with 13C-cycling (GFDL MOM5-BLING13C)
<p> </p> <p>======= DESCRIPTION =======</p> <p>This is the model output supporting our paper <em>A next generation ocean carbon isotope model for climate studies I: Steady state controls on ocean <sup>13</sup>C</em> (2021 Global Biogeochemical Cycles).</p> <p>This model output simulates the transient response of ocean carbon biogeochemistry to anthropogenic CO<sub>2</sub> and <sup>13</sup>CO<sub>2</sub> atmospheric emissions with a nominal lateral resolution of 1° and 50 vertical levels. The model uses the NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) MOM5 coupled to the NOAA-GFDL Biogeochemistry with Light Iron Nutrients and Gas (BLING) with <sup>13</sup>C-cycling. Atmospheric forcing is prescribed using the repeating annual cycle of the Common Ocean Reference Experiment version 2 normal year forcing dataset (COREv2-NYF). The implementation of <sup>13</sup>C-cycling applies isotopic fractionations during air-sea gas exchange, photosynthetic production of organic matter, and formation of calcium carbonate. The sensitivity of dissolved inorganic <sup>13</sup>C in the ocean to the CO<sub>2</sub> gas exchange rate is explored by repeating the simulation twice, once using the latest OMIP-CMIP6 protocol for the k-U<sub>10 </sub>parameterization (standard) and once using the previous OCMIP2 protocol (fast-gas-exchange).</p> <p> </p> <p>Files information:</p> <ul> <li><strong>ocean_static.nc</strong>: Static fields (longitude, latitude, area).</li> <li><strong>1990-2002.ocean_month.nc</strong>: Monthly output between 1990 and 2002 of ocean physical variables (temperature, salinity, averaged mixed layer depth, maximum mixed layer depth).</li> <li><strong>1990-2002.ocean_bling_trc_month_CMIP6.nc</strong>: Monthly output between 1990 and 2002 of biogeochemical variables* for the simulation using the OMIP-CMIP6 air-sea gas exchange protocol.</li> <li><strong>1970_1989_d13c_org_mldave_CMIP6.nc</strong>: Monthly output between 1970 and 1989 of d<sup>13</sup>C of organic matter averaged over the mixed layer.</li> <li><strong>1990-2002.ocean_bling_trc_month_OCMIP2.nc</strong>: Monthly output between 1990 and 2002 of biogeochemical variables* for the simulation using the OCMIP2 air-sea gas exchange protocol.</li> </ul> <p>* Biogeochemical variables are dissolved inorganic carbon, dissolved inorganic carbon-13, oxygen, and dissolved inorganic phosphate.</p> <p> </p> <p>======= HOW TO CITE =======</p> <p>This model output can be freely distributed, but please cite it using the following paper:</p> <p>Claret, M., Sonnerup, R. E., & Quay, P. D. (2021). A next generation ocean carbon isotope model for climate studies I: Steady state controls on ocean <sup>13</sup>C. <em>Global Biogeochemical Cycles</em>, 35, e2020GB006757. <a href="https://doi.org/10.1029/2020GB006757">https://doi.org/10.1029/2020GB006757</a></p> <p> </p> <p>======= ACKNOWLEDGEMENTS =======</p> <p>This work was funded by the National Science Foundation (NSF-OCE 1356756 and NSF-OCE 1829796). We would also like to acknowledge high-performance computing support from Cheyenne (<a href="https://doi.org/10.5065/D6RX99HX">doi:10.5065/D6RX99HX</a>) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the NSF.</p> <p> </p> <p>======= QUESTIONS AND REQUESTS? =======</p> <p>Please contact Mariona Claret (mclaret@uw.edu) or Rolf Sonnerup (rolf@uw.edu).</p> <p> </p>
Global Carbon Budget 2023, 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 (fCO2-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 fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2023 (https://doi.org/10.5194/essd-15-5301-2023), are available in the Global Carbon Budget 2023 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 2023 paper (https://doi.org/10.5194/essd-15-5301-2023), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2023 paper, section 2.5.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some 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):<br><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<br><br>(3) One file 'GCB-2023_OceanModel_RegionalBreakdown_1959-2022.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual.</p><p><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 2023 (Friedlingstein et al., 2023, ESSD, https://doi.org/10.5194/essd-15-5301-2023) 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 2023 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>Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional 3D 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://globalcarbonbudgetdata.org/closed-access-requests.html</p>
Global scientific research commons under the Nagoya Protocol: Towards a collaborative economy model for the sharing of basic research assets
<p>This paper aims to get a better understanding of the motivational and transaction cost features of<br /> building global scientific research commons, with a view to contributing to the debate on the design of<br /> appropriate policy measures under the recently adopted Nagoya Protocol. For this purpose, the paper<br /> analyses the results of a world-wide survey of managers and users of microbial culture collections, which<br /> focused on the role of social and internalized motivations, organizational networks and external<br /> incentives in promoting the public availability of upstream research assets. Overall, the study confirms<br /> the hypotheses of the social production model of information and shareable goods, but it also shows the<br /> need to complete this model. For the sharing of materials, the underlying collaborative economy in<br /> excess capacity plays a key role in addition to the social production, while for data, competitive pressures<br /> amongst scientists tend to play a bigger role.</p>
Supplementary Data: Global fits of GUT-scale SUSY models with GAMBIT (arXiv:1705.07935)
<p>Supplementary Data</p> <p><em>Global fits of GUT-scale SUSY models with GAMBIT</em><br> <em>arXiv:1705.07935</em></p> <p>The files in this record contain data for the CMSSM, NUHM1 and NUHM2 models considered in the GAMBIT "Round 1" GUT-scale SUSY paper.</p> <p>For each model, there are</p> <ul> <li>A number of YAML files, each corresponding to a different set of sampling parameters and/or priors</li> <li>A set of YAML files used for postprocessing: CMSSM_intermediate.yaml, CMSSM.yaml, NUHM1.yaml and NUHM2.yaml</li> <li>A final hdf5 file, containing the combined results of all sampling runs</li> <li>An example pip file, for producing plots from the hdf5 file using pippi</li> <li>SLHA1 and SLHA2 files for the best-fit point in each subregion of the fit. These can be found inside the tarball best_fits_SLHA.tar.gz.</li> </ul> <p>The record also contains</p> <ul> <li>StandardModel_SLHA2_scan.yaml and StandardModel_SLHA2_postprocessing.yaml, two universal YAML fragments included from other yaml files</li> <li>gambit_preamble.py, a collection of python functions used for in-line data processing in the pip files</li> </ul> <p>The different YAML files corresponding to different samplers and/or priors follow the naming scheme [model]_[scanner]_[prior]_[slice]_[special].yaml, where</p> <ul> <li>model = CMSSM, NUHM1, NUHM2</li> <li>scanner = Diver, MN</li> <li>prior = log, flat</li> <li>slice = pmu, nmu (positive or negative mu)</li> <li>special = sqcoann, slcoann, [blank] (squark co-annihilation, slepton co-annihilation, or bulk)</li> </ul> <p>A few caveats to keep in mind:</p> <ol> <li> <p>For each model, the final hdf5 results file included here was generated in the following way:</p> <ul> <li>carry out initial runs using YAML files following the naming scheme above</li> <li>combine the resulting hdf5 output files into a single file, using gambit/Printers/scripts/combine_hdf5.py</li> <li>postprocess the samples to remove all points more than 5 sigma from the current best fit, using [model]_strip.yaml</li> <li>postprocess the samples to include a new likelihood term for LHC Run II searches, and to recompute the FlavBit likelihoods (these were buggy in a pre-release version of GAMBIT). For the CMSSM, this happened in two steps, due to persistent flavour bugs, using CMSSM_intermediate.yaml and CMSSM.yaml. For the NUHM1 and NUHM2, this was done in a single step each, using NUHM1.yaml and NUHM2.yaml.</li> </ul> </li> <li> <p>It is not necessary to repeat the steps listed in point 1 when running new scans; the LHC Run II likelihoods can be included in the original YAML file, so that no postprocessing step is required.</p> </li> <li> <p>The YAML files that we give here are updated compared to the ones that we used when generating the hdf5 file, in order to match the set of available options in the release version of GAMBIT 1.0.0. The included physics and numerics are however identical.</p> </li> <li> <p>The YAML files are designed to work with the tagged release of GAMBIT 1.0.0, and the pip files are tested with pippi 2.0, commit 2ab061a8. They may or may not work with later versions of either software (but you can of course always obtain the version that they do work with via the git history).</p> </li> <li> <p>The pip file for each model is an example only. Users wishing to reproduce the more advanced plots in any of the GAMBIT papers should contact us for tips or scripts, or experiment for themselves. Many of these scripts are in multiple parts and require undocumented manual interventions and steps in order to implement various plot-specific customisations, so please don't expect the same level of polish as for files provided here or in the GAMBIT repo.</p> </li> </ol>
Global Surface Ozone Concentration Dataset 1990-2017 Mapped at Fine Resolution through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output
<p>This global surface ozone concentration dataset corresponds to the data developed in this paper:</p> <p>DeLang, M. N., J. S. Becker, K.-L. Chang, M. L. Serre, O. R. Cooper, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, S. Cleland, E. Collins, M. Brauer, and J. J. West (2021) Mapping yearly fine resolution global surface ozone through the Bayesian Maximum Entropy data fusion of observations and model output for 1990-2017, <em>Environmental Science & Technology</em>, 55, 4389-4398, doi: 10.1021/acs.est.0c07742.</p> <p>Ozone concentrations are estimated as described in the paper, with output shown for the Ozone Season Daily Maximum 8-hr metric (OSDMA8) for each year between 1990 and 2017, at 0.1 degree spatial resolution. Ozone is estimated through data fusion of output from several global models, with observations of ozone collected by TOAR. The data fusion involves application of the M3Fusion method to create a multi-model composite of several global models, followed by BME data fusion, as described in the paper. </p> <p>The *.nc file contains the latitude, longitude, ozone concentration estimate, and estimated variance for each 0.1 x 0.1 degree grid cell.</p> <p>Please contact Jason West (jasonwest@unc.edu) with questions about the dataset. We'd like to hear from you to know how you're using the data!</p> <p> </p> <p> </p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2001_2005)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2006_2010)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global soil type dataset for WRF-ARW model, based on HWSD version 2
<p>Global soil type dataset, based on HWSD ("Harmonized World Soil Database", version 2.0), suitable for meteorological model WRF-ARW.</p> <ul> <li>spatial resolution: 30 arc seconds by 30 arc seconds (about 1km)</li> <li>original data (HWSD 2.0) <ul> <li><a href="https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_RASTER.zip">https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_RASTER.zip</a></li> <li><a href="https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_DB.zip">https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_DB.zip</a></li> <li>https://gaez.fao.org/pages/hwsd</li> <li>documentation: Nachtergaele, Freddy, et al. Harmonized world soil database version 2.0. Food and Agriculture Organization of the United Nations, 2023. https://www.fao.org/3/cc3823en/cc3823en.pdf</li> </ul> </li> <li>the original 7 soil layers (0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, 80–100 cm, 100–150 cm and 150–200 cm) have been remapped to the 2 layers required by WRF (topsoil 0-30 cm, botsoil 30-200 cm)</li> <li>the original Soil Mapping Units (SMU) have been remapped to the 16 soil categories used by WRF: <ul> <li>the depth-weighted averages of the content of clay, silt and sand lead to 12 texture-based categories (Sand, Loamy sand, Sandy loam, Silt loam, Silt, Loam, Sandy clay loam, Silty clay loam, Clay loam, Sandy clay, Silty clay, Clay), as defined by USDA;</li> <li>category "Organic material" is assigned where the average content of organic carbon exceeds the threshold of 25%;</li> <li>where the content of clay, silt and sand is not defined, HWSD special categories are mapped to the WRF last 3 categories, as follows: <ul> <li>"Water bodies" to "Water", </li> <li>"Rock outcrops" and "Rocky sublayers" to "Bedrock", </li> <li>"Land ice and glaciers", "Dunes/shifting sands", "Salt flats", and "Other" to "Other"</li> </ul> </li> </ul> </li> </ul> <p>The dataset is provided in three ways:</p> <ol> <li>two global files (SoilType_depth<T>to<B>cm.tif), one for each layer; format is GeoTIFF, compatible with <a href="https://github.com/openwfm/convert_geotiff" target="_blank" rel="noopener"><em>convert_geotiff</em></a>, a commandline utility for converting data from GeoTIFF to geogrid format used by WRF;</li> <li>16 tiles, 8 for each layer, each covering 90 degrees by 90 degrees (SoilType_depth<T>to<B>cm_lon<W>to<E>deg_lat<S>to<N>deg.tif); format is GeoTIFF;</li> <li>two compressed folders, hwsd_toplayer.zip and hwsd_bottomlayer.zip, each including 648 tiles in binary format and an "index" ASCII file, following the Geogrid data format and naming convention, as described <a href="https://www2.mmm.ucar.edu/wrf/users/tutorial/presentation_pdfs/202101/duda_wps_advanced.pdf">here</a>.</li> </ol> <p>Soil categories are coded as follows</p> <table> <tbody> <tr> <td><strong>code</strong></td> <td><strong>category</strong></td> </tr> <tr> <td>1</td> <td>sand</td> </tr> <tr> <td>2</td> <td>loamy sand</td> </tr> <tr> <td>3</td> <td>sandy loam</td> </tr> <tr> <td>4</td> <td>silt loam</td> </tr> <tr> <td>5</td> <td>silt</td> </tr> <tr> <td>6</td> <td>loam</td> </tr> <tr> <td>7</td> <td>sandy clay loam</td> </tr> <tr> <td>8</td> <td>silty clay loam</td> </tr> <tr> <td>9</td> <td>clay loam</td> </tr> <tr> <td>10</td> <td>sandy clay</td> </tr> <tr> <td>11</td> <td>silty clay</td> </tr> <tr> <td>12</td> <td>clay</td> </tr> <tr> <td>13</td> <td>organic material</td> </tr> <tr> <td>14</td> <td>water</td> </tr> <tr> <td>15</td> <td>bedrock</td> </tr> <tr> <td>16</td> <td>other</td> </tr> </tbody> </table> <p> </p>
Supporting data for "Global Model of Atmospheric Chlorate on Earth" by Chan et al.
<p>Model code, simulation outputs, observation tables, and Python scripts for reproducing the analysis results/ figures presented in "Global Model of Atmospheric Chlorate on Earth" by Yuk-Chun Chan et al. Please refer to the publication and readme.txt for more information.</p>
Global monthly sectoral water withdrawal and allocation datasets (QUAlloc, water use and allocation model) at 10 km spatial resolution
<p>Output data of water withdrawals and water allocation per water source from the sectoral water use and allocation model (QUAlloc).</p> <p>Dataset properties:</p> <ul> <li>spatial resolution: 10 km (global-scale)</li> <li>temporal resolution: monthly time-step</li> <li>period: 1980 - 2019</li> <li>units: m3/month</li> </ul> <p>Output datasets:<br> <data_type>_<sector_name>_allocated_to_<source_type>_monthlyTot_1980_2019.nc</p> <ul> <li><data_type><br> <ul> <li>"withdrawal": refers to the water that is withdrawn at a water source level to satisfy the demands within an allocation zone</li> <li>"demand": refers to the withdrawn water that is supplied to each location (cell) where there are demands to satisfy</li> </ul> </li> <li><sector_name> <ul> <li>"domestic"</li> <li>"irrigation"</li> <li>"livestock"</li> <li>"manufacture"</li> <li>"thermoelectric"</li> </ul> </li> <li><source_type> <ul> <li>"renewable_surfacewater": refers to water obtained from the surface water system components (e.g., direct runoff, base flow, interflow, etc.)</li> <li>"renewable_groundwater": refers to water obtained from aquifers that are recharged by percolation from the upper soil layers</li> <li>"nonrenewable_groundwater": refers to water obtained from aquifers not replenished on a human time scale</li> </ul> </li> </ul> <p>The sectoral water use and allocation model used, QUAlloc, can be found at: https://github.com/SustainableWaterSystems/QUAlloc.</p>
Global Carbon Budget 2024, surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogeochemical models and surface ocean fCO2-based data-products
<p><strong>v2 update: </strong></p> <ul> <li>update to data in UoEX-UEPFFNU fCO2-product</li> <li>fix of lat-lon issue in Jena-MLS fCO2-product</li> <li>minor fixes to metadata in fCO2-products</li> </ul> <p><br>The v2 data is used for the final published version of the Global Carbon Budget 2024.</p> <p>-----------------</p> <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 (fCO2-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 fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2024 (https://essd.copernicus.org/preprints/essd-2024-519), are available in the Global Carbon Budget 2024 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 14 of the Global Carbon Budget 2024 paper (https://essd.copernicus.org/preprints/essd-2024-519), the river flux adjustment needs to be added to the CO2 flux estimated from the fCO2-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2024 paper). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some 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):<br><br>fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: global, 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>(3) One file 'GCB-2024_OceanModel_RegionalBreakdown_1959-2023.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Regions: North, tropics, south. Temporal resolution: annual.</p> <p><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 2024 (Friedlingstein et al., 2024, ESSD, https://essd.copernicus.org/preprints/essd-2024-519) 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 2024 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).</p> <p><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>Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional 3D 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://globalcarbonbudgetdata.org/closed-access-requests.html</p>
WINTERC-G: a global upper mantle thermochemical model from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data
<p>WINTERC-G: A global, temperature and compositional model of the lithosphere<br> and upper mantle.<br> Version: v5.4, December 2020, J. Fullea, S. Lebedev, Z. Martinec, N. Celli<br> <br> Contact: Javier Fullea (jfullea@ucm.es)<br> Facultad de Fisica,<br> Universidad Complutense de Madrid (UCM),<br> Spain<br> ////////<br> Geophysics Section,<br> Dublin Institute for Advanced Studies<br> Dublin, Ireland<br> </p> <p>TYPE:<br> This contains files with:<br> i) the model directly on the triangular grid solved for in the surface wave inversion.</p> <p> ii) an interpolated grid at 0.5 deg lateral resolution for the density and density discontinuities used in the gravity field data inversion<br> </p> <p>If you have any questions regarding the methodology or the construction<br> of the model, please contact the authors. If you use the model, we would<br> request that you cite the reference indicated below, and appreciate<br> your feedback regarding the model and its application.</p> <p>Citation:</p> <p>Fullea, J., Lebedev, S., Martinec, Z., & Celli, N. L. (2021). WINTERC-G: mapping the upper mantle thermochemical heterogeneity from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data. Geophysical Journal International, 226(1), 146-191.</p> <p>*******************************<br> Summary: construction of the model.<br> WINTERC-G is a Waveform tomography and Gravity (geoid and gravity anomalies and gradiometric measurements<br> from ESA's GOCE mission) INversion model of the TEmpeRature and Composition of the lithosphere and upper mantle at<br> global scale. WINTERC-G is based on upon the integrated geophysical-petrological<br> approach LitMod (Afonso et al., 2008; Fullea et al. 2009) and, hence, all<br> relevant mantle rock physical properties modelled (seismic velocities and density) are<br> computed within a thermodynamically self-consistent framework allowing for a direct<br> parameterization in terms of the temperature and composition of the lithosphere-upper<br> mantle. The inversion is a two-step procedure. In a first step, we invert surface-wave, Rayleigh and Love<br> fundamental mode dispersion curves from a high resolution global dataset measured using waveform inversion,<br> along with surface heat flow and elevation (isostasy) for temperature and crustal structure<br> using a point-wise, non-linear, gradient-search inversion<br> over a triangular grid with an average 225 km lateral inter-knot spacing. In a second step we<br> use a fully parallelized spherical harmonic formalism to invert satellite gravity field data in<br> order to refine the initial crustal density and mantle composition distributions from the step 1<br> for a fixed temperature field.</p> <p>The parameter space in step 1 includes crust (densities and S-wave velocities for a three-layered crust)<br> and mantle variables (the depth of the thermal Lithosphere-Athenosphere-Boundary,<br> the thickness of the sublithospheric thermal buffer, the sublithospheric temperatures at 3 different<br> equispaced nodes down to 400 km, the lithospheric and sublithospheric mantle compositon, and<br> the the radial anisotropy at the 3 crustal layers and at 56, 80, 110, 150, 200, 260, 330,<br> and 400 km depths.</p> <p>The parameter space in step 2 is defined by the average crustal density, and the<br> mantle composition in the lithosphere and sublithosphere.<br> We use the output crustal density from step 1 as the<br> initial value in step 2 inversion. Mantle densities are derived based on the output temperature<br> field from step 1 (kept fixed) and the bulk mantle composition inversion variables.</p> <p> </p> <p> </p> <p>*******************************</p> <p>This archive contains the following files:<br> README (this file)<br> WINTERC-G_Vp-Vs.lis (triangular grid)<br> WINTERC-G_rad_anis_Vs.lis (triangular grid)<br> WINTERC-G_Temperature.lis (triangular grid)<br> WINTERC-G_Density.lis (triangular grid)<br> WINTERC-G_LAB.lis (triangular grid)<br> WINTERC_T_rho_1D.z (1D average model of temperature and density)<br> rho_*_out.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_continental.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Ice.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Bed.xyz (0.5 deg egular grid for gravity field)<br> Global_Moho_WINTERC-G.xyz (0.5 deg egular grid for gravity field)</p> <p><br> Files in the triangular grid with an average 225 km lateral inter-knot spacing (12232 grid points):</p> <p>* WINTERC-G_Vp-Vs.lis: Vp and Vs (in km/s) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) Vp (km/s) Vs(km/s)<br> 5640 93.72 4.135 -5.0 3.91 2.11</p> <p><br> * WINTERC-G_rad_anis_Vs.lis: radial anisotropy, (Vsh-Vsv)/Vs_iso (in %) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) anisotropy (%)</p> <p>* WINTERC-G_Temperature.lis: temperature (in ºC) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth (km, <0 downwards) T (ºC) dT (%) dT(K) <br> 6437 297.20 -2.524 -259.000 1431.9 -1.91 -27.9<br> The anomalies dT are in % and K with respect to the 1D model in WINTERC_T_rho_1D.z (column 2).</p> <p>* WINTERC-G_Density.lis: density (in kg/m3) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) rho (kg/m3) drho(%) drho(kg/m3)<br> The anomalies drho are in % and kg/m3 with respect to the 1D model in WINTERC_T_rho_1D.z (column 3).</p> <p>* WINTERC_T_rho_1D.z: 1D average model of temperature (column 2 in ºC) and density (column 3 in kg/m3) with a vertical grid step of 2 km <br> 5.00000000 0.0000000000000000 6.0259973839110526<br> 3.00000000 0.0000000000000000 38.960571309690394<br> 1.00000000 0.33634006819423840 174.42296045978722<br> -1.00000000 3.8888495253719624 1692.8437489147236<br> -3.00000000 23.974111923225379 1863.8834351235944<br> -5.00000000 47.727920701943034 2568.2414495590924<br> -7.00000000 89.633398074381162 2819.8386016341910<br> -9.00000000 137.01489361657013 2839.5325893195904<br> -11.0000000 182.35233447017222 2897.6600872935287<br> -13.0000000 224.46247069572485 2945.2036923862997<br> -15.0000000 260.63395547331390 3069.6809340323475<br> -17.0000000 292.28175449521456 3132.4574175461721<br> -19.0000000 322.29571965406632 3145.5747337463940<br> -21.0000000 351.58698283375054 3157.2401512748038<br> -23.0000000 380.30002225705056 3177.0000420059773<br> -25.0000000 408.50259805632055 3183.6651032398490<br> -27.0000000 436.22632217636487 3190.9586785996116<br> -29.0000000 463.48733903170023 3198.9369509456310<br> -31.0000000 490.29841705549831 3209.7229872383764<br> -33.0000000 516.71149258457456 3221.6329506091679<br> ...</p> <p>Files in the interpolated regular grid at 0.5 deg lateral resolution used for gravity field data inversion:</p> <p> * rho_c_out.xyz: average crustal density<br> * rho_submoho_out.xyz: mantle density below the Moho discontinuity<br> * rho_*_out.xyz: mantle density defined at different model depths: 20, 35, 56, 80, 110, 150, 200, 260, 330 and 400 km.</p> <p> Format for the density files:<br> # longitude latitude density (kg/m3)<br> <br> Files containing layer discontinuities:</p> <p> * ETOPO2_km_continental.xyz: surface elevation including ice sheet and 0 in marine areas (km, <0 upwards)</p> <p> * ETOPO2_km_depth_Ice.xyz: surface elevation including ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * ETOPO2_km_depth_Bed.xyz: bedrock surface elevation without ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * Global_Moho_WINTERC-G.xyz: crust-mantle discontinuity depth (km, >0 downwards)</p> <p> Format for the discontinuity files:<br> # longitude latitude depth (km)<br> <br> <br> The gravity field in WINTERC-G is computed using an spherical harmonic formalism and a model discretization<br> in 13 layers with laterally varying density. The first 7 layers are characterized by top and bottom boundaries with laterally varying radius whereas the last 6 layers are defined by top and bottom boundaries with constant radius:</p> <p>1/ Water: from ETOPO2_km_continental.xyz to ETOPO2_km_depth_Ice.xyz with rho=1030 kg/m3 (constant vertically)</p> <p>2/ Ice: from ETOPO2_km_depth_Ice.xyz to ETOPO2_km_depth_Bed.xyz with rho=910 kg/m3 (constant vertically)</p> <p>3/ Crust: from ETOPO2_km_depth_Bed to Global_Moho_WINTERC-G.xyz with rho=rho_c_out.xyz (constant vertically)</p> <p>4/ submoho-20km: from Global_Moho_WINTERC-G.xyz to z_20km (file with 20 km everywhere except where z_moho>20km) with rho=rho_submoho_out.xyz (top) and rho=rho_20km_out.xyz (bottom)</p> <p>5/ 20km-36km: from z_20km (file with 20 km everywhere except where z_moho>20km) to z_36km (file with 36 km everywhere except where z_moho>36km) with rho=rho_20km_out.xyz (top) and rho=rho_36km_out.xyz (bottom)</p> <p>6/ 36km-56km: from z_36km (file with 36 km everywhere except where z_moho>36km) to z_56km (file with 56 km everywhere except where z_moho>56km) with rho=rho_36km_out.xyz (top) and rho=rho_56km_out.xyz (bottom)</p> <p>7/ 56km-80km: from z_56km (file with 56 km everywhere except where z_moho>56km) to 80 km depth with rho=rho_56km_out.xyz (top) and rho=rho_80km_out.xyz (bottom)</p> <p>The next 6 layers are computed using the constant radius option:</p> <p>8/ 80km-110km: from z=80km to z=110 km with rho=rho_80km_out.xyz (top) and rho=rho_110km_out.xyz (bottom)</p> <p>9/ 110km-150km: from z=110km to z=150 km with rho=rho_110km_out.xyz (top) and rho=rho_150km_out.xyz (bottom)</p> <p>10/ 150km-200km: from z=150km to z=200 km with rho=rho_150km_out.xyz (top) and rho=rho_200km_out.xyz (bottom)</p> <p>11/ 200km-260km: from z=200km to z=260 km with rho=rho_200km_out.xyz (top) and rho=rho_260km_out.xyz (bottom)</p> <p>12/ 260km-330km: from z=260km to z=330 km with rho=rho_260km_out.xyz (top) and rho=rho_330km_out.xyz (bottom)</p> <p>13/ 330km-400km: from z=330km to z=400 km with rho=rho_330km_out.xyz (top) and rho=rho_400km_out.xyz (bottom)</p> <p> </p> <p> </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)
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DANDI Archive for NWB datasets
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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.