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.
226
datasets available to search
ShareScore release 0.7.1
Dataset results
226 results for “intercomparison”
INTERACT-II (INTERcomparison of Aerosol and Cloud Tracking - II)
<p>Following the previous efforts of INTERACT (INTERcomparison of Aerosol and Cloud Tracking), the INTERACT-II campaign used multi-wavelength Raman lidar measurements to assess the performance of an automatic compact micro-pulse lidar (MiniMPL) and two ceilometers (CL51 and CS135) in providing reliable information about optical and geometric atmospheric aerosol properties. The campaign took place at the CNR-IMAA Atmospheric Observatory (760 ma.s.l.; 40.60<sup>∘</sup> N, 15.72<sup>∘</sup> E) in the framework of ACTRIS-2 (Aerosol Clouds Trace gases Research InfraStructure) H2020 project. Co-located simultaneous measurements involving a MiniMPL, two ceilometers and two EARLINET multi-wavelength Raman lidars were performed from July to December 2016.</p> <p>All the data from the CIAO lidars, the MiniMPL and from theCHM15k, CS135 and the CT25K ceilometers, operating collocated and simultaneously during the INTERACT-II campaign, are provided here. Additional files for the correction of the MiniMPL incomplere overlap are also provided.</p> <p>The results of the campaign are described in detail in Madonna et al., 2018 (<a href="https://amt.copernicus.org/articles/11/2459/2018/">https://amt.copernicus.org/articles/11/2459/201</a>8/).</p>
Climate Forcing due to Future Ozone Changes: An intercomparison of metrics and methods
<p>The data provided in this repository relates to a paper on ozone radiative forcing submitted for publication in Atmos. Chem. Phys., as part of the TOAR-II special issue (<a href="https://acp.copernicus.org/articles/special_issue1256.html">ACP – Special issue – Tropospheric Ozone Assessment Report Phase II (TOAR-II) Community Special Issue (ACP/AMT/BG/GMD inter-journal SI)</a>). The paper is entitled "<span>Climate Forcing due to Future Ozone Changes</span><span>: An intercomparison of metrics and methods" by authors <span><span>William J. Collins</span></span><span><span>,</span> <span>Fiona M. O’Connor</span></span><span><span>, </span><span>Connor R. Barker</span></span><span><span>, </span><span>Rachael E. Byrom</span></span><span><span>, </span><span>Sebastian D. Eastham</span></span><span><span>,</span> <span>Øivind Hodnebrog</span></span><span><span>, Patrick Jöckel</span></span><span><span>, </span><span>Eloise A. Marais</span></span><span><span>, </span><span>Mariano Mertens</span></span><span><span>, Gunnar Myhre</span></span><span><span>, Matthias Nützel</span></span><span><span>, Dirk Olivié</span></span><span><span>, Ragnhild </span><span>Bieltvedt</span><span> Skeie</span></span><span><span>5</span></span><span><span>, Laura Stecher</span></span><span><span>, Larry W. Horowitz</span></span><span><span>, Vaishali Naik</span></span><span><span>, Gregory Faluvegi</span></span><span><span>, Ulas Im</span></span><span><span>, Lee T. Murray</span></span><span><span>, Drew Shindell</span></span><span><span>, Kostas Tsigaridis</span></span><span><span>, Nathan Luke Abraham</span></span><span><span>, James Keeble.</span></span></span></p>
Simulations from the LPJmL3.5 dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the LPJmL3.5 dynamic global vegetation model 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 each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Simulations from the ORCHIDEE dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the ORCHIDEE dynamic global vegetation model 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>
Simulations from the JULES dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the JULES dynamic global vegetation model 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 1.875 x 1.25 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
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>
`hectordata` inputs: Reduced Complexity Model Intercomparison (RCMIP) Phase 1 Emissions and Concentrations
<p>The attached data was downloaded on April 30, 2020 from <a href="https://www.rcmip.org/">https://www.rcmip.org/</a>. These data have no formal citation or DOI. Since we use this specific version as inputs to the `hectordata` R package (<a href="https://github.com/JGCRI/hectordata">https://github.com/JGCRI/hectordata</a>), we offer these as an archived resource. The `hectordata` package is used to prepare the inputs used in the simple climate model Hector (<a href="https://github.com/JGCRI/hector">https://github.com/JGCRI/hector</a>).</p> <p>The datasets contained were listed as "Version 4.0.0, 31st December 2019" for both Emissions and Concentrations. Units are described in the files.</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>
Data from Glacier Model Intercomparison Project Phase 3 (GlacierMIP3)
<p>This dataset presents the data from the third phase of <a href="https://climate-cryosphere.org/glaciermip/">GlacierMIP</a> (GlacierMIP3: Equilibration of glaciers under different climate states). It includes regional glacier volume and area projections as submitted by the glacier modelling groups. Additionally, it features post-processed and aggregated data derived from GlacierMIP3, or in combination with other studies, which is used for the analyses and visualisations presented in the following manuscript: </p> <p><em>Zekollari*, H., Schuster*, L., Maussion, F., Hock, R., Marzeion, B., Rounce, D. R., Compagno, L., Fujita, K., Huss, M., James, M., Kraaijenbrink, P. D. A., Lipscomb, W. H., Minallah, S., Oberrauch, M., Van Tricht, L., Champollion, N., Edwards, T., Farinotti, D., Immerzeel, W., Leguy, G., Sakai, A. (under review): Glacier preservation doubled by limiting warming to 1.5°C. Preprint available at <a href="https://doi.org/10.31223/X51T5W">https://doi.org/10.31223/X51T5W</a>, 2024.</em><br><em>*Harry Zekollari and Lilian Schuster contributed equally to this dataset and the manuscript above.<br><br></em>If you use the data, please cite this Zenodo dataset and the above study. <em><br></em><br>More info in <em>README_data.pdf</em>. For information about the GlacierMIP3 experimental design, please refer to the<em> GlacierMIP3_protocol.pdf</em>. The code used to generate the postprocessed data and to conduct the analyses for the manuscript mentioned above is available at <a href="https://github.com/GlacierMIP/GlacierMIP3]">https://github.com/GlacierMIP/GlacierMIP3</a>.<br><br></p> <p>To assist potential data users, we have included a jupyter notebook (gmip3_data_example_use_cases.ipynb) that guides you through some simple use cases. This notebook can be directly run when clicking on this <a href="https://drive.google.com/file/d/1xbhXZwT3sQydAGi8rSjEXRKdKhFosW6d/view?usp=sharing">link</a>. Please note that you will need to log in to your Google account and, if you haven't already done so, install Google Colaboratory. The data will then be automatically downloaded to your account.</p> <p>We may adapt the data structure and improve the documentation during the review phase. If you have any questions or suggestions, please contact us (lilian.schuster@uibk.ac.at, harry.zekollari@vub.be).</p> <p>----<br>difference version v2 to v1.0: only the files <em>README_data.pdf</em> and the <em>lowess*_regional_glacier_temp_ch.csv</em> were changed according to the resubmission of the manuscript</p>
Data from PV module energy rating standard IEC 61853-3 intercomparison
<p>This is the data from PV module energy rating standard IEC 61853-3 intercomparison.</p> <p>Details can be found in:</p> <p>M. R. Vogt, S. Riechelmann, A. M. Gracia-Amillo, A. Driesse, A. Kokka, K. Maham, P. Kärhä, R. Kenny, C. Schinke, K. Bothe, J. C. Blakesley, E. Music, F. Plag, G. Friesen, G. Corbellini, N. Riedel-Lyngskær, R. Valckenborg, M. Schweiger, W. Herrmann, „PV module energy rating standard IEC 61853-3 intercomparison and best practice guidelines for implementation and validation”, accepted IEEE JPV. DOI (identifier) 10.1109/JPHOTOV.2021.3135258</p>
Data set for the paper: Intercomparison of ocean colour algorithms for picophytoplankton carbon in the ocean
<p>This dataset contains the phytoplankton carbon,Cphy, obtained from in situ counts of phytoplankton cells using ow cytometry presented in the paper [13]. The location and time of the samples have been matched with the satelllite data in the Ocean Colour Climate Change Initiative (OCCCI) dataset. This dataset is the match between the in situ Cphy and the products from using the OCCCI inputs (i.e. chlorophyll concentration, backscattering coecient, phytoplankton absorption) with 6 different algorithms. This document describes the dataset details: data sources, computation of Cphy, selected data.</p>
Dataset: Intercomparison of flux, gradient, and variance-based optical turbulence ($C_n^2$) parameterizations
<p>This repository contains the dataset for the manuscript</p> <p>Pierzyna, M, et al. "Intercomparison of flux, gradient, and variance-based optical turbulence (Cn2) parameterizations." <em>Applied Optics</em>, 2024. <a href="https://doi.org/10.1364/AO.519942">https://doi.org/10.1364/AO.519942</a></p> <p>The data is organized in the following structure:</p> <ul> <li>`met_cn2_*_10m.nc`: netCDF files containing Cn2 estimated from meteorological data obtained at the CESAR site<br> using the flux-based and gradient-based methods at the 10 m level.</li> <li>`wrf_cn2_*.nc`: netCDF files containing Cn2 estimated from WRF model output using the variance-based method (80m)<br> and flux, gradient, and variance-based methods (10m).</li> <li>`wrf_meteo_*.nc`: netCDF files containing a cross-section of CESAR site extracted from WRF model output. This data<br> serves as input for `wrf_cn2_*.nc` files.</li> </ul>
DEM Intercomparison eXercise (DEMIX) - Maps of completeness criteria scores for global DEMs
<h2>Introduction</h2> <p>This introduction gives a brief overview of the context in which the dataset has been produced. Readers curious about the detailed standards and procedures described in this section are encouraged to open the resources linked to this dataset.</p> <h3>The Digital Elevation Model Intercomparison eXercise (DEMIX)</h3> <p>This work is part of the Digital Elevation Model Intercomparison eXercise (DEMIX), initiated by the <a href="https://ceos.org/ourwork/workinggroups/wgcv/current-activites/#:~:text=DEMIX%3A%20Digital%20Elevation%20Model%20Intercomparison,elevation%20model%20for%20their%20application.">Committee on Earth Observation Satellites (CEOS)</a>. This initiative aims at "<a href="https://isprs-archives.copernicus.org/articles/XLIII-B4-2021/395/2021/">providing harmonised terminology and methods, as well as practical guidelines and results allowing the intercomparison of continental or global Digital Elevation Models (DEM)</a>" (Strobl et al., 2021). Several publications have defined the framework of DEMIX, from <a href="https://doi.org/10.3390/rs13183581">the terminology and definitions</a> (Guth et al., 2021) to the <a href="https://doi.org/10.1109/TGRS.2024.3368015">DEM ranking methods</a> (Bielski et al., 2024). An additional methodology paper has been publicated regarding the assessment of <a href="https://doi.org/10.3390/ijgi13030096">planimetric displacements between DEMs</a> (Riazanoff et al., 2024), which are a common source of biases in DEM comparisons.</p> <h3>The DEMIX grid</h3> <p>Studies performed within the DEMIX framework rely on the <a href="../records/7504791">DEMIX grid</a> (Guth et al., 2023), a geodetic grid (EPSG:4326) dividing the world in areas of approximately 10x10km. These standard areas are called DEMIX tiles, and can be precisely located thanks to their identifier.</p> <h3>Criteria and scores</h3> <p>Within DEMIX, several criteria have been defined to assess the quality of DEMs. These criteria take as input a DEM and a DEMIX tile, and provide as output the score of the DEM for this specific tile. Repeating this process over several DEMs and DEMIX tiles of interest allow for a comparison of scores, leading to a ranking of DEMs. <a href="https://doi.org/10.1109/TGRS.2024.3368015">DEMIX rankings are based on the Randomized Complete Block Design (RCBD)</a> (Bielski et al., 2024).</p> <h2>This dataset</h2> <p>This dataset is composed of global maps of one map per (DEM, criterion) tuple. Each GeoTIFF map can be superimposed with the <a href="../records/7504791">DEMIX grid</a> (Guth et al., 2023) in a GIS (tested in QGIS 3.16).</p> <h3>Completeness criteria</h3> <p>The completeness criteria have originally been defined by Peter Strobl. A brief description of each criterion is given in the next table. Please see the column "Original document" and files of this repository for the complete definitions.</p> <table> <tbody> <tr> <td><strong>Criterion</strong></td> <td><strong>Description</strong></td> <td><strong>Requirements</strong></td> <td><strong> Original document</strong></td> </tr> <tr> <td>A01 - Product fractional cover</td> <td>Fraction of a DEMIX tile <strong>covered</strong> by the DEM product</td> <td>None</td> <td>See document "DEMIX_CDD-A01_20211103.docx"</td> </tr> <tr> <td>A02 - Valid data fraction</td> <td>Fraction of a DEMIX tile <strong>covered</strong> by <strong>valid </strong>pixels of the DEM product</td> <td>"No data" or "void" value in metadata</td> <td>See document "DEMIX_CDD-A02_20211103.docx"</td> </tr> <tr> <td>A03 - Primary data fraction</td> <td>Fraction of a DEMIX tile <strong>covered </strong>by <strong>valid </strong>pixels generated from the <strong>main source of data</strong> of the DEM product</td> <td>"No data" or "void" value in metadata + source data/editing mask</td> <td>See document "DEMIX_CDD-A03_20211103.docx"</td> </tr> <tr> <td>A04 - Valid land fraction</td> <td>Fraction of a DEMIX tile <strong>covered </strong>by <strong>valid </strong>pixels of <strong>land </strong>of the DEM product</td> <td>"No data" or "void" value in metadata + water body mask</td> <td>See document "DEMIX_CDD-A04_20211103.docx"</td> </tr> <tr> <td>A05 - Primary land fraction</td> <td>Fraction of a DEMIX tile <strong>covered </strong>by <strong>valid </strong>pixels of <strong>land </strong>generated from the <strong>main source of data </strong>of the DEM product</td> <td>"No data" or "void" value in metadata + water body mask + source data/editing mask</td> <td>See document "DEMIX_CDD-A05_20211103.docx"</td> </tr> </tbody> </table> <h3>DEMs and ancillary data</h3> <p>The following DEM products and ancillary layers have been used to generate the dataset.</p> <table> <tbody> <tr> <td><strong>Identifier</strong></td> <td><strong>Used layers</strong></td> <td><strong>Data access</strong></td> </tr> <tr> <td> <p>ASTGTM v003</p> </td> <td>ASTER GDEM elevations (dem.tif) + editing / source masks (num.tif)</td> <td><a href="https://lpdaac.usgs.gov/products/astgtmv003/">https://lpdaac.usgs.gov/products/astgtmv003/</a></td> </tr> <tr> <td> <p>ASTWBD v001</p> </td> <td>ASTER GDEM water body mask (att.tif)</td> <td><a href="https://lpdaac.usgs.gov/products/astwbdv001/">https://lpdaac.usgs.gov/products/astwbdv001/</a></td> </tr> <tr> <td> <p>AW3D30 v2003</p> </td> <td>ALOS World 3D elevations (DSM.tif) + editing / source / water body masks (MSK.tif)</td> <td><a href="https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm">https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm</a></td> </tr> <tr> <td>COP-DEM_GLO-30-DGED v2019_1</td> <td>Copernicus DEM GLO-30 elevations (DEM.tif) + editing (EDM.tif) + source (SRC.tif) + water body (WBM.tif) masks</td> <td><a href="https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model">https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model</a></td> </tr> <tr> <td>COP-DEM_GLO-90-DGED v2019_1</td> <td>Copernicus DEM GLO-90 elevations (DEM.tif) + editing (EDM.tif) + source (SRC.tif) + water body (WBM.tif) masks</td> <td><a href="https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model">https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model</a></td> </tr> <tr> <td> <p>NASADEM_HGT v001</p> </td> <td>NASADEM elevations (.hgt) + editing / source (.num) + water body (.swb) masks</td> <td><a href="https://lpdaac.usgs.gov/products/nasadem_hgtv001/">https://lpdaac.usgs.gov/products/nasadem_hgtv001/</a></td> </tr> <tr> <td> <p>SRTMGL1 v003</p> </td> <td>SRTMGL1 elevations (.hgt)</td> <td><a href="https://lpdaac.usgs.gov/products/srtmgl1v003/">https://lpdaac.usgs.gov/products/srtmgl1v003/</a></td> </tr> <tr> <td> <p>SRTMGL1N v003</p> </td> <td>SRTMGL1 editing / source / water body masks (.num)</td> <td><a href="https://lpdaac.usgs.gov/products/srtmgl1nv003/">https://lpdaac.usgs.gov/products/srtmgl1nv003/</a></td> </tr> </tbody> </table> <h3>Computation of scores</h3> <p>For each DEMIX tile and DEM, each "fractional cover" has been computed using the following procedure:</p> <ol> <li><strong>Crop DEMIX tile layers</strong> - The tiles of each DEM layer (elevations, editing, sources and water bodies) are cropped to the extent of the DEMIX tile.</li> <li><strong>Compute standardized layers</strong><strong> </strong>- Given the cropped DEM layers, four standardized layers are produced, which are: <ul> <li>Heights layer - Containing the heights of the DEM</li> <li>Land/water mask layer - Indicating whether DEM pixels are land or water: <ul> <li>0 = NO_DATA</li> <li>1 = BACKGROUND</li> <li>2 = INVALID</li> <li>3 = WATER</li> <li>4 = LAND</li> </ul> </li> <li>Source mask layer - Indicating the source data of DEM heights (or "edited" value): <ul> <li>0 = NO_DATA</li> <li>1 = BACKGROUND</li> <li>2 = INVALID</li> <li>3 = PRIMARY_DATA</li> <li>4 = EXTERNAL_DATA</li> <li>5 = EDITED</li> </ul> </li> <li>Valid mask layer - Indicating if the DEM pixels are valid or not: <ul> <li>0 = NO_DATA</li> <li>1 = BACKGROUND</li> <li>2 = INVALID</li> <li>3 = VALID</li> </ul> </li> </ul> </li> <li><strong>Retrieve pixel number N</strong><em><strong> </strong>-<strong> </strong></em>The total pixel number N is computed for one of the layers (all layers have the same number of pixels).</li> <li><strong>Retrieve criterion pixel number C </strong>-<strong> </strong>The criterion pixel number C is computed based on the standard layers, more precisely: <ul> <li>A01 - Product fractional cover - Number of pixels of <strong>valid mask layer equal to 1, 2 or 3</strong></li> <li>A02 - Valid data fraction - Number of pixels of <strong>valid mask layer equal to 3</strong></li> <li>A03 - Primary data fraction - Number of pixels of <strong>source mask layer equal to 3</strong></li> <li>A04 - Valid land fraction - Number of pixels of <strong>land/water mask layer equal to 4</strong></li> <li>A05 - Primary land fraction - Number of pixels of <strong>source mask layer equal to 3</strong> and<strong> land/water mask layer equal to 4</strong></li> </ul> </li> <li><strong>Compute the final score S</strong><strong> </strong>- The final score S is expressed as the following percentage: <strong>S = ceil(C/N*100)</strong></li> </ol> <h2>Known issues</h2> <p>The "SRTMGL1N v003" is known to have "tile repeating issues", where part of the data is wrongly flagged as water. This issue has been reported with no particular response from the providers of the DEM (see <a href="https://forum.earthdata.nasa.gov/viewtopic.php?t=2752">https://forum.earthdata.nasa.gov/viewtopic.php?t=2752</a>).</p> <p><strong>References:</strong></p> <ul> <li>Guth, P.L.; Strobl, P.; Gross, K.; Riazanoff, S. <em>DEMIX 10k Tile Data Set (1.0)</em> [Data set]. Zenodo 2023. <a href="https://doi.org/10.5281/zenodo.7504791" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7504791</a></li> <li>Guth, P.L.; Van Niekerk, A.; Grohmann, C.H.; Muller, J.-P.; Hawker, L.; Florinsky, I.V.; Gesch, D.; Reuter, H.I.; Herrera-Cruz, V.; Riazanoff, S.; López-Vázquez, C.; Carabajal, C.C.; Albinet, C.; Strobl, P. <em>Digital Elevation Models: Terminology and Definitions</em>. Remote Sens. 2021, 13, 3581. <a href="https://doi.org/10.3390/rs13183581">https://doi.org/10.3390/rs13183581</a></li> <li>Riazanoff, S.; Corseaux, A.; Albinet, C.; Strobl, P.A.; López-Vázquez, C.; Guth, P.L.; Tadono, T. <em>Best BiCubic Method to Compute the Planimetric Misregistration between Images with Sub-Pixel Accuracy: Application to Digital Elevation Models</em>. <em>ISPRS Int. J. Geo-Inf.</em> 2024, <em>13</em>, 96. <a href="https://doi.org/10.3390/ijgi13030096">https://doi.org/10.3390/ijgi13030096</a></li> <li>Bielski, C.; López-Vázquez, C.; Grohmann, C.H.; Guth, P.L.; Hawker, L.; Gesch, D.; Trevisani, S.; Herrera-Cruz, V.; Riazanoff, S.; Corseaux, A.; Reuter, H.I.; Strobl, P.A.; <em>Novel Approach for Ranking DEMs: Copernicus DEM Improves One Arc Second Open Global Topography</em> in <em>IEEE Transactions on Geoscience and Remote Sensing</em>, vol. 62, pp. 1-22, 2024, Art no. 4503922. <a href="https://doi.org/10.1109/TGRS.2024.3368015">https://doi.org/10.1109/TGRS.2024.3368015</a></li> <li>Strobl, P.A.; Bielski, C.; Guth, P.L.; Grohmann, C.H.; Muller, J.P.; López-Vázquez, C.; Gesch, D.B.; Amatulli, G.; Riazanoff, S.; Carabajal, C. The Digital Elevation Model Intercomparison eXperiment DEMIX, a community based approach at global DEM benchmarking. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2021, XLIII-B4-2021, 395–400. <a href="https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021">https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021</a></li> </ul>
Results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison
<p>This archive provides the forcing data and ice sheet model output produced as part of the publication "Design and results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison", published in The Cryosphere, https://www.the-cryosphere.net/12/1433/2018/</p> <p>Goelzer, H., Nowicki, S., Edwards, T., Beckley, M., Abe-Ouchi, A., Aschwanden, A., Calov, R., Gagliardini, O., Gillet-Chaulet, F., Golledge, N. R., Gregory, J., Greve, R., Humbert, A., Huybrechts, P., Kennedy, J. H., Larour, E., Lipscomb, W. H., Le clec´h, S., Lee, V., Morlighem, M., Pattyn, F., Payne, A. J., Rodehacke, C., Rückamp, M., Saito, F., Schlegel, N., Seroussi, H., Shepherd, A., Sun, S., van de Wal, R., and Ziemen, F. A.: Design and results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison, The Cryosphere, 12, 1433-1460, 2018, doi:10.5194/tc-12-1433-2018.</p> <p>Contact: Heiko Goelzer, h.goelzer@uu.nl</p> <p>Further information on ISMIP6 and initMIP-Greenland can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=InitMIP-Greenland</p> <p>Users should cite the original publication when using all or part of the data. <br> In order to document CMIP6’s scientific impact and enable ongoing support of CMIP, users are also obligated to acknowledge CMIP6, ISMIP6 and the participating modelling groups.</p> <p><br> *** Important note ***<br> For consistency with future ISMIP6 intercomparison exercises and some observational data sets, we have re-gridded all output to a diagnostic grid following the EPSG:3413 specifications, which differs from the grid originally used to distribute the forcing data. We also provide the forcing data conservatively interpolated to the new grid. </p> <p><br> Archive overview<br> ----------------<br> README.txt - this information</p> <p>dSMB.zip - The original surface mass balance anomaly forcing data and description<br> dSMB/<br> dsmb_01B13_ISMIP6_v2.nc<br> dsmb_05B13_ISMIP6_v2.nc<br> dsmb_10B13_ISMIP6_v2.nc<br> dsmb_20B13_ISMIP6_v2.nc<br> README_dSMB_v2.txt</p> <p>dSMB_epsg3413.zip - The surface mass balance anomaly forcing data and description, interpolated to the new grid on EPSG:3413<br> dSMB_epsg3413/<br> dsmb_01e3413_ISMIP6_v2.nc<br> dsmb_05e3413_ISMIP6_v2.nc<br> dsmb_10e3413_ISMIP6_v2.nc<br> dsmb_20e3413_ISMIP6_v2.nc<br> README_dSMB_v2_epsg3413.txt</p> <p><group>_<model>_<experiment>.zip - The model output per group, model and experiment (init, ctrl, asmb)<br> <group1>_<model1>_init/<br> acabf_GIS_<group1>_<model1>_init.nc<br> ...<br> <group1>_<model1>_ctrl/<br> acabf_GIS_<group1>_<model1>_ctrl.nc<br> ...<br> <group1>_<model1>_asmb/<br> acabf_GIS_<group1>_<model1>_asmb.nc<br> ...</p> <p><group1>_<model2>_init/<br> ...<br> <group1>_<model2>_ctrl/<br> ...<br> <group1>_<model2>_asmb/<br> ...</p> <p><group2>_<model1>_init/<br> ...<br> <group2>_<model1>_ctrl/<br> ... <br> <group2>_<model1>_asmb/</p> <p>...</p> <p> </p> <p>The following script may be used to download the content of the archive.</p> <p>#!/bin/bash<br> wget https://zenodo.org/record/1173088/files/README.txt<br> wget https://zenodo.org/record/1173088/files/dSMB_epsg3413.zip<br> wget https://zenodo.org/record/1173088/files/dSMB.zip<br> <br> for amodel in ARC_PISM AWI_ISSM1 AWI_ISSM2 BGC_BISICLES1 BGC_BISICLES2 BGC_BISICLES3 DMI_PISM1 DMI_PISM2 DMI_PISM3 DMI_PISM4 DMI_PISM5 IGE_ELMER1 IGE_ELMER2 ILTS_SICOPOLIS ILTSPIK_SICOPOLIS IMAU_IMAUICE1 IMAU_IMAUICE2 IMAU_IMAUICE3 JPL_ISSM LANL_CISM LSCE_GRISLI MIROC_ICIES1 MIROC_ICIES2 MPIM_PISM UAF_PISM1 UAF_PISM2 UAF_PISM3 UAF_PISM4 UAF_PISM5 UAF_PISM6 UCIJPL_ISSM ULB_FETISH1 ULB_FETISH2 VUB_GISM1 VUB_GISM2; do</p> <p>wget https://zenodo.org/record/1173088/files/${amodel}_init.zip<br> wget https://zenodo.org/record/1173088/files/${amodel}_ctrl.zip<br> wget https://zenodo.org/record/1173088/files/${amodel}_asmb.zip</p> <p>done</p> <p> </p>
Model outputs: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP)
<p>This dataset contains the fire model outputs of emissions for 34 species (elements, compounds, and classes of compounds) as described in the following:</p> <p>Li, F., Val Martin, M., Hantson, S., Andreae, M. O., Arneth, A., Lasslop, G., Yue, C., Bachelet, D., Forrest, M., Kaiser, J. W., Kluzek, E., Liu, X., Melton, J. R., Ward, D. S., Darmenov, A., Hickler, T., Ichoku, C., Magi, B. I., Sitch, S., van der Werf, G. R., Wiedinmyer, C., and Rabin, S.: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP), <em>Atmos. Chem. Phys. Discuss.</em>, https://doi.org/10.5194/acp-2019-37, accepted pending technical corrections, 2019.</p> <p>See Readme for more information.</p>
Dataset for the preprint: "Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich"
<p>Dataset supporting the submission of the manuscript titled "Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich" to the to the international journal "Biogeosciences".</p> <p><strong>Meteorological data</strong></p> <p>Hourly aggregated meteorological dataset for the urban area of Zurich, originating from two urban stations: Kaserne (8°32'/47°23'), which is a station of the Swiss national air pollution monitoring network NABEL, and Hardau II (8°30'/47°23'), which is a station established for the ICOS-Cities project. Zurich Kaserne is located in a large courtyard. Wind and global radiation are measured on top of a four-storey building. Wind is measured at 35 m and global radiation at 27 m above ground. Hardau II station is established on the top of a high-rise building (110 m a.g.l.). Meteorological observation gaps were filled using Copernicus ERA5-Land data.</p> <p>Data format: comma separated values (csv)</p> <p>Time step: 60 min (aggregated)</p> <p>Time stamp: yyyy-MM-dd HH:mm, UTC, end of aggregated period</p> <p>Period: 01/2022–09/2023</p> <p>Monthly mean atmospheric CO2 concentration data derived from the ICOS-Cities Hardau II station (07/2022–09/2023) and the Beromunster station (11/2012–02/2022). Observation gaps were filled using Copernicus ERA5-Land data.</p> <p>Further information on the dataset can be found in the submitted manuscript. </p> <p>Data format: comma separated values (csv)</p> <p>Time step: Monthly (mean)</p> <p>Time stamp: yyyy-MM-dd </p> <p>Period: 11/2012–09/2023</p> <table> <tbody> <tr> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Acronym</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Height above ground</strong></p> </td> <td> <p><strong>Location</strong></p> </td> <td> <p><strong>Geographic location</strong></p> </td> </tr> <tr> <td> <p>Global radiation</p> </td> <td> <p>G</p> </td> <td> <p>W m-2</p> </td> <td> <p>27 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Air temperature</p> </td> <td> <p>Tair</p> </td> <td> <p>Degrees Celsius</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Relative humidity</p> </td> <td> <p>RH</p> </td> <td> <p>%</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Air pressure</p> </td> <td> <p>P</p> </td> <td> <p>hPa</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Wind speed</p> </td> <td> <p>u</p> </td> <td> <p>m s-1</p> </td> <td> <p>35 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Precipitation</p> </td> <td> <p>R</p> </td> <td> <p>mm</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Downward longwave radiation</p> </td> <td> <p>LW</p> </td> <td> <p>W m-2</p> </td> <td> <p>110 m</p> </td> <td> <p>Hardau II, ERA-5</p> </td> <td> <p>8°30'/47°23'</p> </td> </tr> <tr> <td> <p>Soil temperature</p> </td> <td> <p>Tsoil</p> </td> <td> <p>Degrees Celsius</p> </td> <td> <p>-0.15 m</p> </td> <td> <p>Parks</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Soil water content</p> </td> <td> <p>SWC</p> </td> <td> <p>m3 m-3</p> </td> <td> <p>-0.15 m</p> </td> <td> <p>Parks</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Atmospheric CO2 concentration</p> </td> <td> <p>CO2</p> </td> <td> <p>ppmv</p> </td> <td> <p>2 m</p> </td> <td> <p>Hardau II, Beromunster, ERA-5</p> </td> <td> <p>8°30'/47°23', 8°10'/47°11</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>In-situ ecophysiological data</strong></p> <p>In-situ ecophysiology measurements performed on park trees and lawns in the city of Zurich during the ICOS-Cities project.</p> <p>LAI (leaf area index) was measured in dense <em>Platanus</em> sp. tree stands, found only in Bullingerhof and Hardaupark, during sunny conditions using a ceptometer (SS1 SunScan, Delta-T Devices).</p> <p>Sap flow was measured at six trees (<em>Platanus</em> sp., <em>Tilia</em> sp.), at Bullingerhof, Hardaupark and Fritschiwiese, with heat pulse sap flow sensors (3 x 3 cm probes, Implexx Sense), providing continuous measurements at 10-min sampling intervals. Daily aggregated sap flux densities (cm3 cm−2 d−1) were calculated from the 10-min data using the sensor inner thermistors, averaged for the six sampled trees.</p> <p>Soil and grass respiration were measured using a portable CO2 soil efflux system equipped with a 20 cm diameter survey chamber (LI-8200-01S, LI-COR Biosciences) and a CO2/H2O analyser (LI-870, LI-COR Biosciences). The observations originate from a total of 10 soil collars (Bullingerhof, Hardaupark, Fritschiwiese, Heiligfeld) separated to undisturbed grass collars (Reco, μmol CO2 m-2 s-1) and collars where the aboveground grass was clipped (Rsoil, μmol CO2 m-2 s-1).</p> <p>Further information on the dataset can be found in the submitted manuscript.</p> <p>Data format: comma separated values (csv)</p> <p>Time stamp: yyyy-MM-dd</p> <p>Period: 04/2022–09/2023</p> <p> </p> <p><strong>Land cover map</strong></p> <p>Land cover map of part of Zurich urban area. Datasets used to derive this map:</p> <ul> <li>· Land Use Cadastre of the Canton of Zurich (https://www.geolion.zh.ch/geodatensatz/show?gdsid=443)</li> <li>· Urban Atlas (https://doi.org/10.2909/fb4dffa1-6ceb-4cc0-8372-1ed354c285e6)</li> <li>· Vegetation Height Model (VHM) from the Swiss federal forest inventory (https://opendata.swiss/de/dataset/vegetationshohenmodell-lfi)</li> <li>· Forest Mixture from the Swiss Federal Forest Inventory (https://opendata.swiss/de/dataset/waldmischungsgrad-lfi)</li> </ul> <p>Further information on the dataset can be found in the submitted manuscript.</p> <p> </p> <p>Data specifications:</p> <p>CRS: EPSG:32632 - WGS 84 / UTM zone 32N - Projected</p> <p>Spatial Extent: 461972.1, 5246490.4 : 463972.1, 5248490.4</p> <p>Temporal Extent: 2023</p> <p>Units: meters</p> <p>Width: 2000</p> <p>Height: 2000</p> <p>Bands: 1</p> <p>Pixel Size: 1,-1</p> <p>Data type: Float32 - Thirty two bit floating point</p> <p>GDAL Driver Description: GTiff</p> <p>GDAL Driver Metadata: GeoTIFF</p> <p> </p> <p>Legend:</p> <p>30 Grass</p> <p>40 Crops</p> <p>50 Paved</p> <p>60 Buildings</p> <p>70 Deciduous trees</p> <p>80 Water</p> <p> </p> <p><strong>CO2 fluxes</strong></p> <p>Hourly mean CO2 fluxes estimated by the models diFUME, JSBACH, SUEWS and VPRM for the trees and lawns of the Zurich urban parks: Bullingerhof, Hardaupark, Fritschiwiese and Heiligfeld. GPP stands for gross primary productivity, Reco for ecosystem respiration and NEE for net ecosystem exchange. All fluxes are in units: μmol CO2 m-2 s-1.</p> <p>The parameter sets used by each model are presented in the Tables below. Further information on the dataset can be found in the submitted manuscript.</p> <p>Parameters used by diFUME model</p> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>A_max</p> </td> <td> <p>15</p> </td> <td> <p>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>maximum leaf gross photosynthetic rate</p> </td> </tr> <tr> <td> <p>a</p> </td> <td> <p>0.045</p> </td> <td> <p>mol CO<sub>2</sub> mol<sup>-1</sup> PAR</p> </td> <td> <p>quantum yield for CO2 assimilation</p> </td> </tr> <tr> <td> <p>a_1</p> </td> <td> <p>25</p> </td> <td> <p>N/A</p> </td> <td> <p>empirical coefficient in Leuning (1995) model</p> </td> </tr> <tr> <td> <p>b_</p> </td> <td> <p>0.65</p> </td> <td> <p>N/A</p> </td> <td> <p>empirical coefficient in β-factor formula</p> </td> </tr> <tr> <td> <p>b_1</p> </td> <td> <p>5</p> </td> <td> <p>N/A</p> </td> <td> <p>empirical coefficient</p> </td> </tr> <tr> <td> <p>D_o</p> </td> <td> <p>0.3</p> </td> <td> <p>kPa</p> </td> <td> <p>empirically determined coefficient for the VPD scalar inside Leuning (1995) model</p> </td> </tr> <tr> <td> <p>D_sc</p> </td> <td> <p>1</p> </td> <td> <p>N/A</p> </td> <td> <p>daylight scalar for dark respiration inhibition during day (1: no inhibition)</p> </td> </tr> <tr> <td> <p>E_0</p> </td> <td> <p>487.75</p> </td> <td> <p>K</p> </td> <td> <p>temperature sensitivity parameter for soil respiration</p> </td> </tr> <tr> <td> <p>g_o</p> </td> <td> <p>0.01</p> </td> <td> <p>mol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>residual stomatal conductance for CO2 (g_s when Anet = 0, PAR = 0).</p> </td> </tr> <tr> <td> <p>Q_10</p> </td> <td> <p>1.85</p> </td> <td> <p>N/A</p> </td> <td> <p>temperature sensitivity of leaf respiration</p> </td> </tr> <tr> <td> <p>R_(l,ref)</p> </td> <td> <p>1.53</p> </td> <td> <p>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>reference leaf respiration at Tair = 25 °C</p> </td> </tr> <tr> <td> <p>R_(S,ref)</p> </td> <td> <p>2.49</p> </td> <td> <p>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>reference soil respiration at Tsoil = 10 °C</p> </td> </tr> <tr> <td> <p>T_opt</p> </td> <td> <p>23</p> </td> <td> <p>°C</p> </td> <td> <p>optimum air temperature for gross photosynthesis</p> </td> </tr> <tr> <td> <p>T_0</p> </td> <td> <p>-46</p> </td> <td> <p>°C</p> </td> <td> <p>low-temperature limit for soil respiration</p> </td> </tr> <tr> <td> <p>T_(ref,S)</p> </td> <td> <p>10</p> </td> <td> <p>°C</p> </td> <td> <p>reference soil temperature for soil respiration</p> </td> </tr> <tr> <td> <p>T_(ref,l)</p> </td> <td> <p>25</p> </td> <td> <p>°C</p> </td> <td> <p>reference air temperature for leaf respiration</p> </td> </tr> <tr> <td> <p>W</p> </td> <td> <p>10</p> </td> <td> <p>°C</p> </td> <td> <p>width of the bell-shape curve at f(T_air ) = 0.5</p> </td> </tr> <tr> <td> <p>θ_ref</p> </td> <td> <p>0.4</p> </td> <td> <p>m<sup>3</sup> m<sup>-3</sup></p> </td> <td> <p>saturated soil volumetric water content </p> </td> </tr> <tr> <td> <p>θ_g</p> </td> <td> <p>0.1</p> </td> <td> <p>m<sup>3</sup> m<sup>-3</sup></p> </td> <td> <p>minimum soil volumetric water content, limit to stomatal conductance</p> </td> </tr> <tr> <td> <p>θ_0</p> </td> <td> <p>0.04</p> </td> <td> <p>m<sup>3</sup> m<sup>-3</sup></p> </td> <td> <p>minimum soil volumetric water content, limit to soil respiration</p> </td> </tr> </tbody> </table> <p> </p> <p>Parameters used by JSBACH model</p> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Trees</strong></p> </td> <td> <p><strong>Lawn</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>J_max</p> </td> <td> <p>104.5</p> </td> <td> <p>148.6</p> </td> <td> <p>μmol(CO<sub>2</sub>) m<sup>-2</sup>(leaf) s<sup>-1</sup></p> </td> <td> <p>Maximum electron transport rate at 25 °C</p> </td> </tr> <tr> <td> <p>T_alt</p> </td> <td> <p>4.0–4.5</p> </td> <td> <p> </p> </td> <td> <p>°C</p> </td> <td> <p>Alternation temperature</p> </td> </tr> <tr> <td> <p>θ_cap</p> </td> <td> <p>0.32– 0.39</p> </td> <td> <p>0.32– 0.34</p> </td> <td> <p>m m<sup>-1</sup></p> </td> <td> <p>Volumetric soil field capacity</p> </td> </tr> <tr> <td> <p>θ_pwp</p> </td> <td> <p>0.13– 0.21</p> </td> <td> <p>0.135–0.165</p> </td> <td> <p>m m<sup>-1</sup></p> </td> <td> <p>Volumetric wilting point</p> </td> </tr> <tr> <td> <p>V_max</p> </td> <td> <p>55.0</p> </td> <td> <p>78.2</p> </td> <td> <p>μmol(CO<sub>2</sub>) m<sup>-2</sup>(leaf) s<sup>-1</sup></p> </td> <td> <p>Maximum carboxylation rate at 25 °C</p> </td> </tr> <tr> <td> <p>z_root</p> </td> <td> <p>0.5</p> </td> <td> <p>0.12</p> </td> <td> <p>m</p> </td> <td> <p>Root depth</p> </td> </tr> <tr> <td> <p>CC</p> </td> <td> <p>1.25</p> </td> <td> <p>1.25</p> </td> <td> <p>N/A</p> </td> <td> <p>Relative cost to produce one carbon</p> </td> </tr> <tr> <td> <p>f_faeces</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>N/A</p> </td> <td> <p>Fraction of carbon from herbivore faeces that goes into the green litter pool</p> </td> </tr> <tr> <td> <p>f_leaf</p> </td> <td> <p>0.4</p> </td> <td> <p>0.4</p> </td> <td> <p>N/A</p> </td> <td> <p>A fixed fraction of canopy maintenance respiration that makes up the dark respiration</p> </td> </tr> <tr> <td> <p>k</p> </td> <td> <p>0.1</p> </td> <td> <p>0.09</p> </td> <td> <p>N/A</p> </td> <td> <p>LAI growth rate during growth phase</p> </td> </tr> <tr> <td> <p>LAI_max</p> </td> <td> <p>3.6–4.1</p> </td> <td> <p>3.0</p> </td> <td> <p>m2 m-2</p> </td> <td> <p>Maximum leaf area index</p> </td> </tr> <tr> <td> <p>p</p> </td> <td> <p>veg:</p> <p>0.004</p> <p>rest:</p> <p>0.1</p> </td> <td> <p>growth:</p> <p>0.1</p> <p>dry:</p> <p>0.015</p> </td> <td> <p>N/A</p> </td> <td> <p>LAI shedding rate (trees: vegetative and rest phase; grass: growth and dry season)</p> </td> </tr> <tr> <td> <p>r_d</p> </td> <td> <p>0.605</p> </td> <td> <p>0.8602</p> </td> <td> <p>μmol(CO2) m-2(leaf) s-1</p> </td> <td> <p>Dark respiration at 25 °C, fraction of Vmax</p> </td> </tr> </tbody> </table> <p> </p> <p>Parameters used by SUEWS model</p> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Trees</strong></p> </td> <td> <p><strong>Lawn</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>f_i</p> </td> <td> <p>0.21</p> </td> <td> <p>0.18</p> </td> <td> <p>N/A</p> </td> <td> <p>Fraction of each vegetation type i</p> </td> </tr> <tr> <td> <p>F_(pho,max,i)</p> </td> <td> <p>8.3</p> </td> <td> <p>8.92</p> </td> <td> <p>μmol m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>Maximum potential photosynthesis</p> </td> </tr> <tr> <td> <p>LAI_(max,i)</p> </td> <td> <p>4.8</p> </td> <td> <p>3</p> </td> <td> <p>m<sup>2</sup> m<sup>-2</sup></p> </td> <td> <p>Full leaf-on summertime value</p> </td> </tr> <tr> <td> <p>LAI_(min,i)</p> </td> <td> <p>0.66</p> </td> <td> <p>1.6</p> </td> <td> <p>m<sup>2</sup> m<sup>-2</sup></p> </td> <td> <p>Leaf-off wintertime value</p> </td> </tr> <tr> <td> <p>T_L</p> </td> <td> <p>-10</p> </td> <td> <p>-10</p> </td> <td> <p>°C</p> </td> <td> <p>Lower air temperature limit</p> </td> </tr> <tr> <td> <p>T_H</p> </td> <td> <p>55</p> </td> <td> <p>55</p> </td> <td> <p>°C</p> </td> <td> <p>Upper air temperature limit</p> </td> </tr> <tr> <td> <p>G_5</p> </td> <td> <p>30</p> </td> <td> <p>30</p> </td> <td> <p>°C</p> </td> <td> <p>Parameter related to temperature dependence</p> </td> </tr> <tr> <td> <p>G_3</p> </td> <td> <p>0.66</p> </td> <td> <p>0.538</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter related to VPD dependence</p> </td> </tr> <tr> <td> <p>G_4</p> </td> <td> <p>0.89</p> </td> <td> <p>0.87</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter related to VPD dependence</p> </td> </tr> <tr> <td> <p>G_6</p> </td> <td> <p>0.36</p> </td> <td> <p>0.55</p> </td> <td> <p>mm<sup>-1</sup></p> </td> <td> <p>Parameter related to soil moisture dependence</p> </td> </tr> <tr> <td> <p>G_2</p> </td> <td> <p>477</p> </td> <td> <p>263.5</p> </td> <td> <p>W m<sup>-2</sup></p> </td> <td> <p>Parameter related to dependence</p> </td> </tr> <tr> <td> <p>Δθ_WP</p> </td> <td> <p>132.5</p> </td> <td> <p>143</p> </td> <td> <p>mm</p> </td> <td> <p>Wilting point deficit</p> </td> </tr> <tr> <td> <p>K_(↓max)</p> </td> <td> <p>1200</p> </td> <td> <p>1200</p> </td> <td> <p>W m<sup>-2</sup></p> </td> <td> <p>Maximum incoming shortwave radiation</p> </td> </tr> <tr> <td> <p>a_i</p> </td> <td> <p>0.78</p> </td> <td> <p>1.7</p> </td> <td> <p>N/A</p> </td> <td> <p>Empirical soil and vegetation respiration coefficient</p> </td> </tr> <tr> <td> <p>b_i</p> </td> <td> <p>0.08</p> </td> <td> <p>0.06</p> </td> <td> <p>N/A</p> </td> <td> <p>Empirical soil and vegetation respiration coefficient</p> </td> </tr> <tr> <td> <p>ω_(1,GDD,i)</p> </td> <td> <p>0.04</p> </td> <td> <p>0.04</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>ω_(2,GDD,i)</p> </td> <td> <p>0.0005</p> </td> <td> <p>0.0005</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>ω_(1,SDD,i)</p> </td> <td> <p>-1.5</p> </td> <td> <p>-1.5</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>ω_(1,SDD,i)</p> </td> <td> <p>0.0025</p> </td> <td> <p>0.0025</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>GDD</p> </td> <td> <p>300</p> </td> <td> <p>300</p> </td> <td> <p>days</p> </td> <td> <p>The growing degree days (GDD) needed for full capacity of the leaf area index</p> </td> </tr> <tr> <td> <p>SDD</p> </td> <td> <p>-300</p> </td> <td> <p>-300</p> </td> <td> <p>days</p> </td> <td> <p>The senescence degree days (SDD) needed to initiate leaf off</p> </td> </tr> <tr> <td> <p>T_(base,GDD)</p> </td> <td> <p>5</p> </td> <td> <p>5</p> </td> <td> <p>°C</p> </td> <td> <p>Base Temperature for initiating growing degree days (GDD) for leaf growth</p> </td> </tr> <tr> <td> <p>T_(base,SDD)</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>°C</p> </td> <td> <p>Base temperature for initiating senescence degree days (SDD) for leaf off</p> </td> </tr> </tbody> </table> <p> </p> <p><span>Parameters used by VPRM model</span></p> <table> <tbody> <tr> <td> <p><strong><span>Parameter</span></strong></p> </td> <td> <p><strong><span>Trees</span></strong></p> </td> <td> <p><strong><span>Lawn</span></strong></p> </td> <td> <p><strong><span>Units</span></strong></p> </td> <td> <p><strong><span>Description</span></strong></p> </td> </tr> <tr> <td> <p><span>λ</span></p> </td> <td> <div> <p><span>-0.16</span></p> </div> </td> <td> <div> <p><span>-0.13</span></p> </div> </td> <td> <div> <p><span>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></span></p> </div> </td> <td> <div> <p><span>light use efficiency</span></p> </div> </td> </tr> <tr> <td> <p><span>PAR_0</span></p> </td> <td> <div> <p><span>356.99</span></p> </div> </td> <td> <div> <p><span>545.61</span></p> </div> </td> <td> <div> <p><span>μmol m<sup>-2</sup> s<sup>-1</sup></span></p> </div> </td> <td> <div> <p><span>half-saturation value</span></p> </div> </td> </tr> <tr> <td> <p><span>α</span></p> </td> <td> <div> <p><span>0.22</span></p> </div> </td> <td> <div> <p><span>0.40</span></p> </div> </td> <td> <div> <p><span>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup> /<sup>0</sup>C</span></p> </div> </td> <td> <div> <p><span>empirical coefficient</span></p> </div> </td> </tr> <tr> <td> <p><span>β</span></p> </td> <td> <div> <p><span>1.09</span></p> </div> </td> <td> <div> <p><span>0.42</span></p> </div> </td> <td> <div> <p><span>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></span></p> </div> </td> <td> <div> <p><span>empirical coefficient</span></p> </div> </td> </tr> <tr> <td> <p><span>T_max</span></p> </td> <td> <div> <p><span>40</span></p> </div> </td> <td> <div> <p><span>40</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>maximum temperature for photosynthesis</span></p> </div> </td> </tr> <tr> <td> <p><span>T_min</span></p> </td> <td> <div> <p><span>0</span></p> </div> </td> <td> <div> <p><span>2</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>minimum temperature for photosynthesis</span></p> </div> </td> </tr> <tr> <td> <p><span>T_opt</span></p> </td> <td> <div> <p><span>20</span></p> </div> </td> <td> <div> <p><span>18</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>optimal temperature for photosynthesis</span></p> </div> </td> </tr> <tr> <td> <p><span>T_low</span></p> </td> <td> <div> <p><span>0</span></p> </div> </td> <td> <div> <p><span>0</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>to account for the persistence of soil respiration in winter</span></p> </div> </td> </tr> </tbody> </table> <p><span> </span></p> <p>Data format: comma separated values (csv)</p> <p>Time step: 60 min (aggregated)</p> <p>Time stamp: yyyy-MM-dd HH:mm, UTC, end of aggregated period</p> <p>Period: 01/2022–09/2023</p>
MARv3.10 outputs: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates
<p>MARv3.10 outputs used in:</p> <p><em>Mottram, R., Hansen, N., Kittel, C., van Wessem, M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-333, accepted, 2020.</em></p> <ul> <li>MARv3.10 forced by ERA-Interim outputs with monthly values of SMB and components (kg m<sup>-2</sup> month<sup>-1</sup>), and (near-) surface temperature (°C) over the Antarctic ice sheet (1981--2018)</li> <li>Grid file used in MAR simulation</li> </ul> <p>Be carreful that the unit metadata in the netcdf files from SMB and its components are uncorrect. <strong>Values are in kg m<sup>-2</sup> month<sup>-1</sup></strong> instead of kg m<sup>-2</sup> day<sup>-1</sup>.<br> <br> If you need other variables or output frequencies from MAR, write me (c2kittel@gmail.com) and I will be glad to help you. I will also be happy to share the scripts I have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR outputs. However, note that these outputs are now considered as deprecated since new outputs using a more recent model version (MARv3.11) and forcing (ERA5) have been published (see Kittel et al., 2021: https://tc.copernicus.org/articles/15/1215/2021/).<br> <br> Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below that contained informations related to MAR. In order to document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact C. Kittel and C. Agosta to add their works in the list of MAR-related publications. </p> <p>"We thank the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations."</p> <p>You should also refer to and cite the following paper:</p> <p><em>Mottram, R., Hansen, N., Kittel, C., van Wessem, M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-333, accepted, 2020.</em></p>
Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)
<p>Model output of the global eddy-rich configuration used in the Geoscientific Model Development publication: "Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)" </p> <p>Abstract: This paper describes the global eddying ocean-sea ice simulation produced at the Euro-Mediterranean Center on Climate Change (CMCC) obtained following the experimental design of the Ocean Model Intercomparison Project phase 2 (OMIP2). The eddy-rich model is based on the NEMOv3.6 framework, with a global horizontal resolution of 1/16° and 98 vertical levels, and was originally designed for an operational short-term ocean forecasting system. Here, it is driven by one multi-decadal cycle of the prescribed JRA55-do atmospheric reanalysis and runoff dataset in order to perform a long-term benchmarking experiment.<br> To access the accuracy of simulated 3D ocean fields, and highlight the relative benefits of mesoscale activities, the GLOB16 performances are evaluated via a selection of key climate metrics against observational datasets and two other NEMO configurations at lower resolutions: an eddy-permitting resolution (ORCA025) and a non-eddying resolution (ORCA1) designed to form the ocean-sea ice component of the fully coupled CMCC climate model. <br> The well-known biases in the low-resolution simulations are significantly improved in the high-resolution model. The evolution and spatial pattern of large-scale features (such as sea surface temperature biases and winter mixed layer structure) in GLOB16 are generally better reproduced, and the large-scale circulation is remarkably improved compared to the low-resolution oceans. We find that eddying resolution is an advantage in resolving the structure of western boundary currents, the overturning cells, and flow through key passages. GLOB16 might be an appropriate tool for ocean climate modeling effort, even though the benefit of eddying resolution does not provide unambiguous advances for all ocean variables in all regions.<br> </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.