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34 results for “RCP8.5”
Northwest Europe NEMO-ERSEM ocean model hindcast and climate projection under RCP8.5
<p>Dataset of model hindcast and climate projection data from a NEMO-ERSEM simulation of the 7km-resolution Atlantic Margin Model (AMM7). Model description and data are presented in </p> <p>Wakelin, S. L., Y. Artioli, J. T. Holt, M. Butenschön, and J. Blackford (2020), Controls on near-bed oxygen concentration on the Northwest European Continental Shelf under a potential future climate scenario, Progress in Oceanography, 102400. doi: https://doi.org/10.1016/j.pocean.2020.102400.</p> <p>Coupled NEMO-ERSEM model simulations are used to study temperature, salinity and near-bed oxygen concentrations on the northwest European Continental Shelf (NWES). Data are from a hindcast (1980 to 2007) and a climate projection (1980 to 2099) under the RCP8.5 climate emissions scenario.</p> <p>The climate projection (1980 to 2099) under the RCP8.5 climate emissions scenario is described as experiment E1 in</p> <p>Holt, J., J. Polton, J. Huthnance, S. Wakelin, E. O'Dea, J. Harle, A. Yool, Y. Artioli, J. Blackford, J. Siddorn, and M. Inall (2018), Climate-Driven Change in the North Atlantic and Arctic Oceans Can Greatly Reduce the Circulation of the North Sea, Geophysical Research Letters, 45(21), 11,827-811,836. doi: 10.1029/2018gl078878.</p> <p>The dataset consists of </p> <ul> <li>Hindcast simulation data</li> </ul> <ol> <li>AMM7_hindcast_3D_S_1980_2007.nc - monthly mean salinity fields.</li> <li>AMM7_hindcast_3D_T_1980_2007.nc - monthly mean temperature fields.</li> <li>AMM7_hindcast_near_bed_O2o_1980_2007.nc - near-bed oxygen concentrations on the NWES.</li> </ol> <ul> <li>Climate projection data</li> </ul> <ol> <li>AMM7_RCP8_5_3D_S_1980_2099.nc - monthly mean salinity fields.</li> <li>AMM7_RCP8_5_3D_T_1980_2099.nc - monthly mean temperature fields.</li> <li>AMM7_RCP8_5_3D_U_1980_2099.nc - monthly mean eastwards currents.</li> <li>AMM7_RCP8_5_3D_V_1980_2099.nc - monthly mean northwards currents.</li> <li>AMM7_RCP8_5_near_bed_1980_2099.nc - monthly mean near-bed oxygen concentrations and near-bed bacterial respiration on the NWES.</li> <li>AMM7_RCP8_5_netPP_1980_2099.nc - monthly mean depth integrated net primary production.</li> </ol>
Ensemble of NEMO present-day (1989-2009) and future (2080-2100 under RCP8.5) ocean properties and ice shelf melt rates in the Amundsen Sea
<p>Model outputs used in <a href="https://www.essoar.org/doi/10.1002/essoar.10511482.3">Jourdain et al. (GRL, 2022)</a></p> <p>The output files consist of monthly climatologies over either 1989-2009 or 2080-2100. The file names have the form:</p> <p><strong>climato_monthly_AMUXL12-GNJ002_<simu>_<group>_1989_2009.nc</strong>, where :</p> <ul> <li><simu> is either : <ul> <li>"BM02MAR" (ensemble member A, present-day),</li> <li>"BM03MAR" (ensemble member B, present-day),</li> <li>"BM04MAR" (ensemble member C, present-day),</li> <li>"BM02MARrcp85" (ensemble member A, future for both surface and lateral boundaries),</li> <li>"BM03MARrcp85" (ensemble member B, future for surface BUT NOT for lateral boundaries),</li> <li>"BM03MARrcBDY" (ensemble member B, future for both surface and lateral boundaries),</li> <li>"BM04MARrcp85" (ensemble member C, future for both surface and lateral boundaries),</li> </ul> </li> <li><group> is either : <ul> <li>"SBC" (surface boundary conditions),</li> <li>"icemod" (sea ice variables),</li> <li>"gridT" (temperature, salinity),</li> <li>"gridU" (zonal velocities),</li> <li>"gridV" (meridional velocities).</li> </ul> </li> </ul> <p>Grid information in:</p> <ul> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2019-05-24.nc (ensemble member A),</li> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2020-07-15_v02_ICB380.nc (ensemble members B & C).</li> </ul> <p>where:</p> <ul> <li>glamt : longitude</li> <li>gphit: latitude</li> <li>e1t, e2t, e3t_0 : mesh size (in meters) along x, y, z</li> <li>tmask = 1 for ocean mesh, = 0 otherwise (land, continental ice).</li> </ul> <p> </p> <p><strong>Acknowledgments:</strong> This work was granted access to the HPC resources of CINES (occigen) under the allocation A0100106035 attributed by GENCI.</p>
Adriatic Sea wind-wave climate years 1981-2010 and 2021-2050 (RCP4.5 and RCP8.5)
<p>Adriatic Sea mean annual 50th, 90th, 95th and 99th percentiles of the significant wave height (Hs) from WAVEWATCH III v6.07 (2 km) forced with 1-hour ERA5 wind fields statistically scaled to QQ-match COSMO-CLM fields (available at https://doi.org/10.5281/zenodo.6021380).</p> <p>Reference periods:</p> <p>1) Historical climate: years 1981-2010</p> <p>2) Future climate: years 2021-2050 (IPCC scenario RCP4.5 and RCP8.5)</p>
Adriatic Sea wind climate years 1981-2010 and 2021-2050 (RCP4.5 and RCP8.5)
<p>Adriatic Sea mean annual 50th, 90th, 95th and 99th percentiles of the sea surface wind speed (10-m height U10) from 1-hour ERA5 fields (25 km) statistically scaled to QQ-match COSMO-CLM fields (8 km).</p> <p>Reference periods:</p> <p>1) Historical climate: years 1981-2010</p> <p>2) Future climate: years 2021-2050 (IPCC scenario RCP4.5 and RCP8.5)</p>
Adriatic Sea wind and wave time series years 1981-2010 and 2021-2050 (RCP4.5 and RCP8.5)
<p>Wind and wave time series for 27 stations in the Adriatic Sea.</p> <p>Variables:</p> <p>- 10-m height wind speed (wnd) and wind direction (wnddir) from 1-hour ERA5 fields (25 km) statistically scaled to QQ-match COSMO-CLM fields (8 km)</p> <p>- significant wave height (hs) and peak wave period (tp) from WAVEWATCH III v6.07 (2 km) forced with the scaled ERA5 wind fields</p> <p>Reference periods:</p> <p>- Historical climate: years 1981-2010</p> <p>- Future climate: years 2021-2050 (IPCC scenario RCP4.5 and RCP8.5)</p>
Soybean yield projections in Europe under historical (1981-2010) and future climate (2050-2059 and 2090-2099 for RCP4.5 and RCP8.5)
<p><strong>General information</strong></p> <p>This dataset contains soybean yield projections in Europe under historical (1981-2010) and future climate with moderate (RCP 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. The data has been generated by <em>Guilpart et al. (2022) Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, Nature Food. </em>All details can be found in this paper. A brief summary is provided below.</p> <p><strong>Summary of soybean yield projections methodology</strong></p> <p>Yield projections have been performed using data-driven relationships between climate and soybean yield derived from machine-learning (Random Forest). The Random Forest model was trained using (i) the the global dataset of historical yields updated version (Iizumi et al. 2014a), which includes grid-wise soybean yields worldwide with the grid size of 1.125 degree over 1981-2010, and (ii) the global retrospective meteorological forcing dataset tailored for agricultural application (GRASP, Iizumi et al. 2014b), which covers the period 1961–2010 at the same spatial resolution as yield data, i.e. a grid size of 1.125 degree. Time-detrended soybean yield data was related (using Random Forest) to 35 climate variables defined at a monthly time step over the seven months of the soybean growing season, plus the fraction of irrigated area, i.e. a total of 36 variables. The 35 climate variables are monthly mean daily minimum and maximum temperatures (<em>Tmin</em> and <em>Tmax</em>, degree Celsius), monthly total precipitation (<em>rain</em>, mm month<sup>-1</sup>), monthly mean daily total solar radiation (<em>solar</em>, MJ m<sup>-2</sup> day<sup>-1</sup>), monthly mean air vapor pressure (VP, hPa). The fitted model showed high R² (higher than 0.9) and low RMSE (0.35 t ha<sup>-1</sup>) between observed and predicted yields based on cross-validation.</p> <p>Then, soybean yield projections under historical over whole Europe have been performed using the GRASP climate data, and yield projections under future climate have been performed using 16 climate change scenarios consisting of bias-corrected data of eight Global Circulation Models (GCM; GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MRI-CGCM3, and NorESM1-M, used in the Coupled Model Intercomparison phase 5 (CMIP5) and two Representative Concentration Pathways (RCPs; 4.5 and 8.5 W m<sup>-2</sup>). Soybean growing season used for projections is April to October. All projections assumed irrigated fraction equals to zero. Projections are shown only on agricultural area (cropland plus pasture), in the year 2000. Soybean yield is expressed in tons per hectare.</p> <p><strong>Files description</strong></p> <ul> <li><em>RF_soybean_historical_GRASP_median_1981_2010.nc</em> : random forest projections of soybean yield in Europe for the historical (1981-2010) period using GRASP climate data. This file contains the median yield (in tons per hectare) over 1981-2010.</li> <li><em>RF_soybean_rcp45_median_2050_2059.nc : </em>random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li><em>RF_soybean_rcp45_median_2090_2099.nc : </em>random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> <li><em>RF_soybean_rcp85_median_2050_2059.nc : </em>random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li><em>RF_soybean_rcp85_median_2090_2099.nc : </em>random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> </ul> <p><strong>References</strong></p> <p>Guilpart N. <em>et al.</em> (2022)<strong> </strong>Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, <em>Nature Food</em>.</p> <p>Iizumi T. <em>et al.</em> (2014a) Historical changes in global yields: Major cereal and legume crops from 1982 to 2006. <em>Glob. Ecol. Biogeogr.</em> 23, 346–357.</p> <p>Iizumi T. <em>et al</em>. (2014b). A meteorological forcing data set for global crop modeling: Development, evaluation, and intercomparison. <em>J. Geophys. Res. Atmos. Res.</em> 119, 363–384.</p>
Projected surface mass balance of the Juneau Icefield, Southeast Alaska (2030 to 2060, RCP8.5)
<p>This dataset contains the modelled projected surface mass balance of the Juneau Icefield in Southeast Alaska for two global climate models (GFDL-CM3 and NCAR-CCSM4) for the RCP8.5 emissions scenario from 2030 to 2060. The simulated surface mass balance was produced using COSIPY - a coupled snowpack and ice surface energy and mass balance model. The input climate data was originally dynamically downscaled in Lader et al., (2020, <em>J. Appl. Meteor. Climatol.)</em></p> <p>This data supplements the manuscript "<em>Surface mass balance modelling of the Juneau Icefield highlights the potential for rapid ice loss by the mid-21st century</em>" submitted to the Journal of Glaciology. </p>
Wave Parameters - North Atlantic Ocean - Period 2091-2100 - RCP8.5 - MODEL: Wavewatch III - Global Driver: ACCESS
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <p>ACCESS (Australian Community Climate and Earth System Simulator)</p> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10°</li> <li>northernmost latitude = 42°</li> <li>westernmost longitude = -70°</li> <li>easternmost longitude = -5°</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Wave Parameters - North Atlantic Ocean - Period 2036-2045 - RCP8.5 - MODEL: Wavewatch III - Global Driver: ACCESS
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <p>ACCESS (Australian Community Climate and Earth System Simulator)</p> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10°</li> <li>northernmost latitude = 42°</li> <li>westernmost longitude = -70°</li> <li>easternmost longitude = -5°</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Wave Parameters - North Atlantic Ocean - Period 2036-2045 - RCP8.5 - MODEL: Wavewatch III - Global Driver: HadGEM
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <ul> <li>HadGEM (Hadley Centre Global Environmental Model)</li> </ul> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10.</li> <li>northernmost latitude = 42.</li> <li>westernmost longitude = -70.</li> <li>easternmost longitude = -5.</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Wave Parameters - North Atlantic Ocean - Period 2081-2099 - RCP8.5 - MODEL: Wavewatch III - Global Driver: HadGEM
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <ul> <li>HadGEM (Hadley Centre Global Environmental Model)</li> </ul> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10°</li> <li>northernmost latitude = 42°</li> <li>westernmost longitude = -70°</li> <li>easternmost longitude = -5°</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Data de Verema de la vinya a la D.O. Pla de Bages a llarg del segle XXI a l'escenari de canvi climàtic RCP8.5 (format TIFF)
<p>Distribució espacial del valor mitjà de la data de verema de la vinya (dia de l'any) a la D.O. Pla de Bages per l’escenari de canvi climàtic intensiu RCP 8.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005. </p>
Data de Floració de la vinya a la D.O. Pla Bages a llarg del segle XXI a l'escenari de canvi climàtic RCP8.5 (format TIFF)
<p> </p> <p>Distribució espacial del valor mitjà de la data de floració de la vinya (dia de l'any) a la D.O. Pla de Bages per l’escenari de canvi climàtic intensiu RCP 8.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005. </p>
Amplitud tèrmica diària a la fase de maduració de la vinya a la D.O. Pla de Bages a llarg del segle XXI a l'escenari de canvi climàtic RCP8.5 (format TIFF)
<p>Distribució espacial del valor mitjà de la amplitud tèrmica diària durant la fase de maduració de la vinya (indicador climàtic DTRIII) a la D.O. Pla de Bages per l’escenari de canvi climàtic moderat RCP 8.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005. </p>
Nits tropicals a la fase de maduració de la vinya a la D.O. Empordà (Alt Empordà) a llarg del segle XXI a l'escenari de canvi climàtic RCP8.5 (format TIFF)
<p>Distribució espacial del valor mitjà de nits tropicals a la fase de maduració de la vinya (indicador climàtic NT20III) a la D.O. Alt Empordà (Alt Empordà) per l’escenari de canvi climàtic intensiu RCP 8.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005.</p>
Temperatura mínima diària a la fase de maduració de la vinya a la D.O. Empordà (Alt Empordà) a llarg del segle XXI a l'escenari de canvi climàtic RCP8.5 (format TIFF)
<p>Distribució espacial del valor mitjà de la temperatura mínima diària de la vinya (indicador climàtic TNIII) a la D.O. Alt Empordà (Alt Empordà) per l’escenari de canvi climàtic intensiu RCP 8.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005.</p>
Nits tropicals a la fase de maduració de la vinya a la D.O. Empordà (Baix Empordà) a llarg del segle XXI a l'escenari de canvi climàtic RCP8.5 (format TIFF)
<p>Distribució espacial del valor mitjà de les nits tropicals durant la fase de maduració de la vinya (indicador climàtic NT20III) a la D.O. Empordà (Baix Empordà) per l’escenari de canvi climàtic intensiu RCP 8.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005.</p>
Amplitud tèrmica a la fase de maduració de la vinya a la D.O. Empordà (Baix Empordà) a llarg del segle XXI a l'escenari de canvi climàtic RCP8.5 (format TIFF)
<p>Distribució espacial del valor mitjà de la amplitud tèrmica diària durant la fase de maduració de la vinya (indicador climàtic DTRIII) a la D.O. Empordà (Baix Empordà) per l’escenari de canvi climàtic intensiu RCP 8.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005.</p>
Temperatura mínima diària a la fase de maduració de la vinya a la D.O. Empordà (Baix Empordà) a llarg del segle XXI a l'escenari de canvi climàtic RCP8.5 (format TIFF)
<p>Distribució espacial del valor mitjà de les temperatura mínima durant la fase de maduració de la vinya (indicador climàtic TNIII) a la D.O. Empordà (Baix Empordà) per l’escenari de canvi climàtic intensiu RCP 8.5 projectat per a les dècades dels anys 2030, 2050, 2070 i 2090. A dalt a l’esquerra es mostra l’escenari control on es representa el valor mitjà del període 1972-2005.</p>
Model simulated potential natural vegetation state in the western US under preindustrial, historic, and future (RCP8.5) atmospheric conditions using multiple parameterizations of the dynamic vegetation model TRIFFID.
<p>DATA DESCRIPTION<br> Author contact information:<br> Linnia R. Hawkins<br> Oregon State University<br> lhawkins@oregonstate.edu; linnia.hawkins@gmail.com<br> Data supporting 2019 Journal of Advances in Modeling Earth Systems publication</p> <p>Simulations of the equilibrium vegetation distribution in the western US performed with the climate model HadAM3p-HadRM3p-MOSES2-TRIFFID</p> <p>step1: Identify Influential Parameters<br> All files labeled step1.<br> EXPERIMENT DESCRIPTION: Data used in step 1: identify influential parameters <br> sensitivity experiment adjusting one parameter at a time 38 individual parameters were adjusted to 7 values, equally spaced<br> over a defined plausible range. For reference nine simulations with the default model parameterization are included, initiated with unique initial potential temperature perturbations. </p> <p>The data contains the vegetation state variables at the end of four-year simulations (January 2004 to December 2007) during which two equilibrium time steps with the dynamic vegetation model TRIFFID (Cox et al., 2001). The results are averaged over three simulations initiated with unique atmospheric potential temperature perturbations.</p> <p>FILE DESCRIPTION:<br> NETCDF: Each netcdf file contains the fractional coverage (field1391), leaf area index (field1392), and the canopy height (field1393) for 5 plant functional types (PFTs: broadleaf, needleleaf, c3 grass, c4 grass, shrub) simulated for November 29, 2007. </p> <p> File labeling scheme: <br> step1_parameter_settingindex_3ICave.nc</p> <p> parameter: the name of the only model parameter adjusted<br> setting index: the index of the parameter setting (1-7)<br> index of 1 references the lowest plausible parameter setting<br> index of 7 references the highest plausible parameter setting<br> index of 4 references the parameter setting half way between the lowest and highest plausible parameter settings. <br> 3ICave: references that the results have been averaged over 3 initial conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> </p> <p>step2: ParameterSensitivity<br> all files labeled step2<br> EXPERIMENT DESCRIPTION:<br> Data used in step 2: parameter sensitivity <br> Perturbed Parameter Experiment (PPE) simultaneously adjusting 18 parameters.<br> Latin hypercube sampling was employed to generate 250 unique parameterizations references with a SETID (ranging from 1-359)<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p>FILE DESCRIPTION:<br> NETCDF: files contain either the fractional coverage (field1391) or the above ground biomass (field1512) for 5 plant functional types (PFTs) simulated for November 1907.</p> <p> File labeling scheme: <br> step2_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]</p> <p>TXT: files contain a list of the model parameterizations (for each PFT and variable) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p>RESTART: restart_region_TPPE_c374_1903-12-01.nc<br> The restart file contains the model state variables after spinup. This file was used to initiate all model simulations in step2.</p> <p>step3: ParameterSetSelection<br> All files labeled step3<br> EXPERIMENT DESCRIPTION:<br> Data used in step 3: parameter set selection<br> PPE simultaneously adjusting 10 parameters. <br> Latin hypercube sampling was employed to generate 140 unique model parameterizations, referenced with a SETID (ranging from 3-276).<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p><br> FILE DESCRIPTION:<br> NETCDF: files contain either the biomass (field1512) fractional coverage (field1391) canopy height (field1393) for 5 plant functional types (PFTs) simulated for November 1907 or the net primary productivity (NPP; item3262_monthly_mean) for December 1903 through November 1907. </p> <p> File labeling scheme: <br> step3_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable(s) contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> item3262_monthly_mean – Net primary productivity – units: (kgC/m2/sec) – all PFTs</p> <p>TXT: files contain a list of the model parameterizations (for each PFT) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p><br> production_runs<br> files labeled PI, historical, and future<br> EXPERIMENT DESCRIPTION:<br> Data simulated in the production runs. Model spinup was performed under preindustrial conditions with 10 unique model parameterizations (pset0-pset9). The resulting vegetation distribution for each parameterization after spinup are included and labeled PIrestarts. These restarts were used to initiate (or restart) the simulations under historic and future (RCP8.5) climate conditions. Files labeled historic contains the simulated vegetation state after 5 year simulations (2004-09-01 to 2009-08-30) with one TRIFFID equilibrium round occurring at the end. Files labeled future contain the simulated vegetation state after 5 year simulations (2054-09-01 to 2059-08-30) with one TRIFFID equilibrium round occurring at the end. </p> <p>FILE DESCRIPTION:<br> NETCDF: files contain the fractional coverage (field1391), leaf area index (field1392), canopy height (field1393), and biomass(field1512) for 5 plant functional types (in the order broadleaf, needleleaf, C3 grass, C4 grass, shrub).</p> <p><br> File labeling scheme:<br> pset* where * refers to the model parameterization 0-9<br> files were simulated with the model parameterization *, initiated with a unique initial condition (perturbation to the potential temperature field). </p> <p> Variables (PIrestart)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf] <br> field1391_1 – fractional coverage of PFT – units: fraction – [needleleaf]<br> field1391_2 – fractional coverage of PFT – units: fraction – [c3grass]<br> field1391_3 – fractional coverage of PFT – units: fraction – [c4grass]<br> field1391_4 – fractional coverage of PFT – units: fraction – [shrub]<br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf]<br> field1392_1 – leaf area index of PFT – units: m2/m2 – [needleleaf]<br> field1392_2 – leaf area index of PFT – units: m2/m2 – [c3grass]<br> field1392_3 – leaf area index of PFT – units: m2/m2 – [c4grass]<br> field1392_4 – leaf area index of PFT – units: m2/m2 – [shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf]<br> field1393_1 – canopy height of PFT – units: meters – [needleleaf]<br> field1393_2 – canopy height of PFT – units: meters – [c3grass]<br> field1393_3 – canopy height of PFT – units: meters – [c4grass]<br> field1393_4 – canopy height of PFT – units: meters – [shrub]<br> </p> <p> Variables (historic/future)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> <br> </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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