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695 results for “model output”
MITgcm model setup and output for "Modeling ocean circulation in the Bellingshausen Sea"
<p>MITgcm model setup and output for "Modeling ocean circulation in the Bellingshausen Sea".</p> <p>Here, it contains the results of the Amundsen and the Bellingshausen Sea from 1992 to 2020. </p> <p><Changes from run260 to this model><br> This is improved version of run260 with 70 vertical layers. <br> To adjust melt rate of the George VI ices shelf, we change the values of heat transfer coefficient γT similar to run260.</p> <p>This experiment was conducted in two separate sessions due to changes in timestep (150 -> 120).<br> We can refer to input/data.diagnostics for the details of the model output name.<br> Outputs are the monthly mean.</p> <p><br> <strong>(Contents)</strong></p> <p>ABSmodel_1992_2009_code.tar.gz (code to run this simulation between 1992 and 2009)</p> <p>ABSmodel_2010_2020_code.tar.gz (code to run this simulation between 2010 and 2020)</p> <p>ABSmodel_1992_2009_input.tar.gz (input file required for this simulation between 1992 and 2009)</p> <p>ABSmodel_2010_2020_input.tar.gz (input file required for this simulation between 1992 and 2009)</p> <p>ABSmodel_1992_2009_results.tar.gz</p> <p>ABSmodel_2010_2020_results.tar.gz</p> <p>(due to size limit of 50GB, please check https://ecco.jpl.nasa.gov/drive/files/ECCO2/LLC1080_REG_AMS/Hyogo_et_al_2022 for complete model output. Complete datasets can also be obtained by rerunning the simulation.)</p> <p><strong>(How to build and run)</strong><br> mkdir build<br> ./../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas_tokyo3 -mpi -mods ../code/<br> make depend<br> make -j 16<br> cd ..</p> <p>mkdir test<br> cd test<br> ln -sf ../input/* .<br> ln -sf /forcing/era_xx/ .<br> cp ../build/mitgcmuv .<br> qsub run_omp_high_t1.pbs</p>
Postprocessed trajectory output for ice cloud microphysics - ICON and CLaMS-Ice models
<p>Postprocessed output from a trajectory module implemented in ICON v 2.3.0. The trajectories track density, temperature, pressure, specific humidity, cloud ice mass and number mixing ratios, cloud liquid mass and number mixing ratios, graupel mass and number mixing ratios, and ice sedimentation mass and number mixing ratios both into and out of the parcel. They are initiated over the Sichuan basin and allowed to flow for 51 hours westward during which the cross India into the Arabian Sea. This trajectory output is also used to run an offline microphysics box model, CLaMS-Ice. qih-Nih* files contain histograms of ice mass mixing ratio (qi) and ice crystal number concentration (Ni); het-hom-pre* files contain process tendencies from heterogeneous nucleation, homogeneous nucleation, and preexisting ice in CLaMS-Ice; qippmvNi-TRHi* files contain qi and Ni versus a range of cirrus temperatures and a range of supersaturations with respect to ice; qi_ppmv_abs* and Ni_abs* files contain probability distributions of qi and Ni differences over absolute time; and qi_ppmv_norm* and Ni_norm* files contain probability distributions of qi and Ni differences over normalized time. Suffixes in all cases indicate the cloud microphysical setup that the trajectory values were used to run with 1M = one-moment scheme, 2M = two-moment scheme, Tf = temperature fluctuation parameterization in CLaMS-Ice simulations, and noSHflux = no pseudo-mixing tendency included in CLaMS-Ice simulations.</p>
CryoGridLite: Model output of pan-Arctic simulations at 1° resolution from 1700 to 2020
<p><strong>CryoGridLite</strong> is a lightweight version of the more complex and process-rich <strong>CryoGrid Community model</strong> (<a href="https://gmd.copernicus.org/preprints/gmd-2022-127/">Westermann et al. 2022</a>).</p> <p>This archive contains the following output datasets which were produced by the model.</p> <p><code>./SETUP.zip</code> The parameters varied in the ensemble simulations.<br> <code>./RESULTS.zip</code> The output variables. An overview of the output variables is provided below and in the README.md file of the archived code.</p> <p>The model code can be found here: <a href="https://zenodo.org/record/6619537">10.5281/zenodo.6619537</a></p> <p>Input data required for pan-Arctic simulations can be found here: <a href="https://zenodo.org/record/6619212">10.5281/zenodo.6619212</a></p> <table> <thead> <tr> <th>Variable</th> <th>Coordinates</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><strong>H_lat_sum</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total latent heat content in the entire ground column (550m) [J/m²]</td> </tr> <tr> <td><strong>H_sen_sum</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total sensible heat content in the entire ground column (550m) [J/m²]</td> </tr> <tr> <td><strong>H_tot_sum</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total heat content in the entire ground column (550m) [J/m²]</td> </tr> <tr> <td><strong>H_lat_50m</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total latent heat content in the uppermost 50m of the ground column [J/m²]</td> </tr> <tr> <td><strong>H_sen_50m</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total sensible heat content in the uppermost 50m of the ground column [J/m²]</td> </tr> <tr> <td><strong>H_tot_50m</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Total heat content in the uppermost 50m of the ground column [J/m²]</td> </tr> <tr> <td><strong>ThawedGround_10m</strong></td> <td><em>(tile, time, longitude, latitude)</em></td> <td>Maximum vertically integrated depth of thawed ground (T>0°C) in the uppermost 10m of the ground column [m]</td> </tr> <tr> <td><strong>T_av_all</strong></td> <td><em>(tile, time, longitude, latitude, depth)</em></td> <td>Mean annual ground temperature [°C]</td> </tr> <tr> <td><strong>T_min_all</strong></td> <td><em>(tile, time, longitude, latitude, depth)</em></td> <td>Minimum annual ground temperature [°C]</td> </tr> <tr> <td><strong>T_max_all</strong></td> <td><em>(tile, time, longitude, latitude, depth)</em></td> <td>Maximum annual ground temperature [°C]</td> </tr> <tr> <td><strong>LandArea</strong></td> <td><em>(latitude, longitude)</em></td> <td>Total area covered by land within the model grid cell [km²]</td> </tr> </tbody> </table>
Model run output for Warren & Kite 202x
<p>Model run output for oxygen loss modeling and 40Ar degassing modeling for Warren & Kite 202x (in preparation). Full timeseries output for individual runs in zipped file. Summary data in individual csvs. File names with "nomelt" in the title refer to baseline model runs. File names containing "melt" are model runs including runaway greenhouse surface melting. Code for running full model and generating figures from publication available at: https://github.com/aowarren/Venus_O2</p>
Example of input and output datasets of the cost module implemented in the SHERPA modelling tool
<p><em>Integrated Assessment Model provides a useful framework for evaluating different aspects of air quality policies, spanning from abatement measures to emissions, concentrations, health impacts and costs. These models are then useful to provide a holistic view of the impacts of policies, so that ex-ante one can evaluate how various policies will impact air concentrations, health benefits and implementation costs. Among these Integrated Assessment Models, SHERPA (Screening for High Emission Potentials on Air) has been recently used to evaluate the impact of policies, covering all aspects from measures to health, but without being able to provide the dimension related to abatement measures costs. These three files dataset provide an example of the ability of the new cost module in SHERPA to calculate the cost of emission reductions.</em></p>
Quantifying research interests in 7,521 mammalian species with h-index: a case study (model output)
<p>Model results for Quantifying research interests in 7,521 mammalian species with h-index: a case study</p>
Output files of the 3-D thermal modeling in the Alaska subduction zonne
<p>Data products (μ'=0.01275) for ‘Temperature distribution for interplate seismic events in the Alaska subduction zone based on 3-D thermal modeling’ by Kaya Iwamoto, Nobuaki Suenaga and Shoichi Yoshioka.</p>
Occurrence datasets, model outputs, and R script for 12 termite species used for niche modeling
<p>The advent of citizen-science databases in conjunction with museum specimen locality information has exponentially increased the power and accuracy of ecological niche modeling (ENM). Increased occurrence data has provided colossal potential to understand the distributions of lesser known or endangered species, including arthropods. Although niche modeling of termites has been conducted in the context of invasive and pest species, few studies have been performed to understand the distribution of basal termite genera. Using specimen records from the American Museum of Natural History (AMNH) as well as locality databases, we generated ecological niche models for 12 basal termite species belonging to six genera and three families. We extracted environmental data from the Worldclim 19 bioclimatic dataset v2, along with SoilGrids datasets and generated models using MaxEnt. We chose Optimal models based on partial Receiving Operating characteristic (pROC) and omission rate criterion and determined variable importance using permutation analysis. We also calculated response curves to understand changes in suitability with changes in environmental variables. Optimal models for our 12 termite species ranged in complexity, but no discernible pattern was noted among genera, families, or geographic range. Permutation analysis revealed that habitat suitability is affected predominantly by seasonal or monthly temperature and precipitation variation. Our findings not only highlight the efficacy of largely citizen-science and museum-based datasets, but our models provide a baseline for predictions of future abundance of lesser-known arthropod species in the face of habitat destruction and climate change.</p>
Model output dataset for Ardyna et al. (2022) Wildfire aerosol deposition likely amplified a summertime Arctic phytoplankton bloom
<p>Daily July and August 2014 aerosol optical depth (AODVIS) and nitrogen deposition (NDEP) output from simulations using Community Earth System Model. Data owner: Diana Bernstein (diana.bernstein@usm.edu).</p>
Diazotroph Elemental Use Efficiency Model Output
<p>Dataset containing model output from the PISCES QUOTA model for three experiments performed to explore the impact of global change on the niche and physiology of two explicit marine diazotrophs. The model included diazotroph specific thermal performance curves for growth and elemental use efficiencies (both iron and phosphorus). The experiments were run using the IPCC high emissions RCP8.5 scenario. The explicit diazotroph model for PISCES QUOTA can be found here: https://github.com/lewiswrightson/PISCES-QUOTA-P6Z</p>
Raw output data from ColabFold modelling for the paper 'Interaction of C21ORF2 with a domain of NEK1 mutated in human diseases is vital for NEK1 function in human cells'
<p><strong>Raw output data from ColabFold modelling for the paper 'Interaction of C21ORF2 with a domain of NEK1 mutated in human diseases is vital for NEK1 function in human cells'</strong></p> <p><strong>File descriptions:</strong></p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_1_model_1_fixed.pdb</strong><br> ColabFold output PDB file - Rank 1 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_2_model_2_fixed.pdb</strong><br> ColabFold output PDB file - Rank 2 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_3_model_4_fixed.pdb</strong><br> ColabFold output PDB file - Rank 3 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_4_model_3_fixed.pdb</strong><br> ColabFold output PDB file - Rank 4 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_5_model_5_fixed.pdb</strong><br> ColabFold output PDB file - Rank 5 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_coverage.png</strong><br> ColabFold output chart - MSA sequence coverage</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_PAE.png</strong><br> ColabFold output chart - PAE for each model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_plddt.png</strong><br> ColabFold output chart - predicted IDDT per position</p> <p><strong>Supplementary Excel file 1</strong><br> List of residues predicted to be involved in intermolecular interactions, and the type of interaction (based on PDB files for each models, generated using BIOVIA Discovery Studio 2021)</p>
Model output of: Excitation of an MJO event in response to a transient sea surface warming in Super-Parameterized CAM
<p>Recent studies have suggested that the Madden-Julian Oscillation (MJO) could be generated in the atmospheric adjustment to equatorial heating anomalies in models properly resolving moist convection. Here we use Super-Parameterized CAM to simulate atmospheric response to transient sea surface warming in the equatorial Indian Ocean, which leads to the excitation of an MJO event, robust across different ensemble members, in addition to the much-expected equatorial Rossby and Kelvin waves. A moist static energy (MSE) budget analysis suggests that longwave and surface turbulent latent heat flux anomalies are predominantly responsible for the MJO excitation; however, these terms oppose its eastward propagation. In contrast, advection of MSE generally weakens the MJO amplitude, but contributes to its eastward propagation. These results highlight the role of the MJO in the moist atmospheric adjustment to equatorial heating, with implications for the MJO mechanisms and prediction.</p>
ModelE simulation output used in the study "Severe Global Cooling After Volcanic Supereruptions? The Answer Hinges on Unknown Aerosol Size" in Journal of Climate (2024)
<p>The included files are the GISS ModelE output needed to replicate the figures in McGraw et al 2023, "Severe Global Cooling After Volcanic Supereruptions? The Answer Hinges on Unknown Aerosol Size"</p> <p>Most of the data herein is output from GISS ModelE2.2 simulations that did not include interactive aerosol microphysics and chemistry. Instead, aerosol extinction and effective radius were input into the model from scaled Easy Volcanic Aerosol [Toohey et al, GMD 2016] output, as described in this study's Methods section. To calculate volcanic temperature impacts and forcings at combinations of injected sulfur mass and peak effective radius (Reff) that were not simulated, we used 2D linear interpolation with the scipy function 'Rbf'.</p> <p>Separately included is output from GISS ModelE2.1 with MATRIX interactive aerosol microphysics and chemistry [Bauer et al, ACP 2008]. Note that the injections were scaled to match that a 6.5 Tg sulfur (S) injection in ModelE2.1/MATRIX best replicated the aerosol optical depth (AOD) and effective radius observations of the 1991 Pinatubo event despite this injection being most commonly considered an 9 Tg S injection. Hence, to produce the 1000 Tg S eruption, a 722 Tg S injected was simulated. Such a mismatch has been found in other GCMs (eg Mills et al, JGRA 2016) and may be due to aerosol quick-removal processes not represented in these models.</p> <p>Please note that simulated eruption masses are in this dataset listed in units of Tg S, but in the publication are in Tg SO2 (Tg S x 2).</p> <p>Data from other modeling studies included in Fig. 1 and tree ring estimates in Figs. S2 & S4 can be found within the cited studies.</p> <p>For additional information, please contact zachary.mcgraw@columbia.edu</p>
Model outputs for wild ungulates occurrence and hunting yield abundance at European scale
<p>These are the outputs of a model for the occurrence and hunting yield (HY) density of wild ungulates not only for widely distributed species in Europe, but also for those ones which have a constrained distribution and ii) to compare the output of occurrence with observed HY. Random Forest function was used for modelling occurrence of species. We used occurrence data available from the past 30 years, and HY data (period 2015-2020) from records collected by <em>ENETWILD</em>. Like previous models based on HY, the response variable was the maximum number of wild ruminants annually hunted in 2015-2020 hunting seasons divided by the area (km<sup>2</sup>) of the corresponding administrative unit (HY density). Models based on HY were statistically downscaled to make predictions to 10x10km squares. Occurrence data models indicated a good predictive performance for most species, showing that the model framework proposed have improved results in comparison to previous models. The transferability of models into new regions was limited by the exposure of species to environmental conditions. As for HY models, the calibration plots showed a good and linear predictive performance for widely distributed species, as well as constrained distributed species. Overall, our results were consistent with the expected abundance distribution of widely distributed species.</p>
Basic graben GeoModeller model and relevant MCUE outputs
<p>Companion dataset to the paper:</p> <p><em>Monte Carlo Simulations for Uncertainty Estimation in 3D Geological Modeling, A Guide for Disturbance Distribution Selection and Parameterization, </em>Evren Pakyuz-Charrier, Mark Lindsay, Vitaliy Ogarko, Jeremie Giraud, Mark Jessell</p> <p>This dataset contains three archives pertaining to Monte Carlo Uncertainty Estimation (MCUE) in implicit 3D geological models:</p> <ol> <li><strong><em>Graben_base.7z</em></strong> is a GeoModeller three dimensional model of the Mansfield syncline area (Victoria, Australia). This model is natively available in every GeoModeller install under the name of "Tutorial case study H".</li> <li><strong><em>MCUE_Graben_nopoletransform.7z</em></strong> is the MCUE output for non pole transfomed foliations.</li> <li><strong><em>MCUE_Graben_poletransform.7z</em></strong> is the MCUE output for non transfomed foliations.</li> </ol> <p>In the output archive files:</p> <ul> <li>xml files are the GeoModeller project saves, these can be opened with Intrepid's free viewer for GeoModeller 3.3 3.4 (available @: http://www.intrepid-geophysics.com/ig/index.php?page=geomodeller-update)</li> <li>.vo, .isi and .vop1 files are the lithological voxet exports in binary GOCAD format, these can be opened with the aforementionned viewer or Geocando (available @: https://sourceforge.net/projects/geocando)</li> <li>.vtk files are the MCUE uncertainty indexes outputs, these can be opened with Paraview (available @: https://www.paraview.org/download)</li> <li>.csv file is the MCUE probabilistic model output, it can be opened with any text editor /spreadsheet</li> </ul>
Mansfield (Victoria, Australia) area original GeoModeller model and relevant MCUE outputs
<p>Companion dataset to the paper:</p> <p><em>Monte Carlo Simulations for Uncertainty Estimation in 3D Geological Modeling, A Guide for Disturbance Distribution Selection and Parameterization</em><em>, </em>Evren Pakyuz-Charrier, Mark Lindsay, Vitaliy Ogarko, Jeremie Giraud, Mark Jessell</p> <p>This dataset contains three archives pertaining to Monte Carlo Uncertainty Estimation (MCUE) in implicit 3D geological models:</p> <ol> <li><strong><em>Mansfield_Base.7z</em></strong> is a GeoModeller three dimensional model of the Mansfield syncline area (Victoria, Australia). This model is natively available in every GeoModeller install under the name of "Tutorial case study H".</li> <li><strong><em>MCUE_Mansfield_nopoletransform.7z</em></strong> is the MCUE output for non pole transfomed foliations.</li> <li><strong><em>MCUE_Mansfield_poletransform.7z</em></strong> is the MCUE output for non transfomed foliations.</li> </ol> <p>In the output archive files:</p> <ul> <li>xml files are the GeoModeller project saves, these can be opened with Intrepid's free viewer for GeoModeller 3.3 3.4 (available @: http://www.intrepid-geophysics.com/ig/index.php?page=geomodeller-update)</li> <li>.vo, .isi and .vop1 files are the lithological voxet exports in binary GOCAD format, these can be opened with the aforementionned viewer or Geocando (available @: https://sourceforge.net/projects/geocando)</li> <li>.vtk files are the MCUE uncertainty indexes outputs, these can be opened with Paraview (available @:https://www.paraview.org/download)</li> <li>.csv file is the MCUE probabilistic model output, it can be opened with any text editor /spreadsheet</li> </ul> <p> </p>
Glacial ice sheet extent effects on tidal mixing and the global overturning circulation - Model Output
<p>This dataset contains the output from the tide model and climate model simulations from the publication Wilmes et al. (2018) "Glacial ice sheet extent effects on tidal mixing and the global overturning circulation" submitted to Paleoceanography. The user is referred to the paper for details on the methodology.</p> <p>Dissipation files:</p> <p>Files beginning with "diss" contain tidal dissipation files calculated from the OTIS tide model output at 1/8th deg using the direct method. Files with the M2 constituent only are in .mat format and extend from 86deg S to 89deg N whereas the files containing all constituents (M2, S2, K1 and O1) are in netcdf format and extend from 90deg S to 90deg N. These files regridded and are used as the climate model tidal forcing.</p> <p>Dissipation file list:</p> <p>diss_dir_ze_1_8_rtp_21kyrBP_i6g_-I1.5_-t_8299008.nc Dissipation for LGM ICE-6G ZE ITdrag 1/8th deg<br> diss_dir_ze_1_8_rtp_21kyrBP_i5g_-I1.5_-t_8299031.nc Dissipation for LGM ICE-5G ZE ITdrag 1/8th deg<br> diss_dir_ze_1_8_rtp_00kyrBP_-I1.5_pdsal_8299034.nc Dissipation for PD ZE ITdrag 1/8th deg</p> <p>diss_dir_js_1_8_rtop_21kyrBP_i6g_-t_-I6.0_7673000.nc Dissipation for LGM ICE-6G JS ITdrag 1/8th deg<br> diss_dir_js_1_8_rtop_21kyrBP_i5g_-t_-I6.0_7672999.nc Dissipation for LGM ICE-5G JS ITdrag 1/8th deg<br> diss_dir_js_1_8_rtop_00kyrBP_-I6.0_7672998.nc Dissipation for PD JS ITdrag 1/8th deg</p> <p>diss_dir_ze_m2_1_8_rtp_21kyrBP_i5g_blk5_NH_lmsk_-I1.5_8299652.mat M2 dissipation for LGM ICE-5G blk1 + NH ICE-6G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_i5g_blk5_-I1.5_8299534.mat M2 dissipation for LGM ICE-5G blk5 ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_i5g_blk4_-I1.5_8299533.mat M2 dissipation for LGM ICE-5G blk4 ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_i5g_blk3_-I1.5_8299531.mat M2 dissipation for LGM ICE-5G blk3 ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_i5g_blk2_-I1.5_8299530.mat M2 dissipation for LGM ICE-5G blk2 ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_i5g_blk1_-I1.5_8299529.mat M2 dissipation for LGM ICE-5G blk1 ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_140mSLD_i6g_lmsk_-I1.5_8299543.mat M2 dissipation for PD 140mSLD ICE-6G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_140mSLD_i5g_lmsk_-I1.5_8299542.mat M2 dissipation for PD 140mSLD ICE-5G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_130mSLD_i6g_lmsk_-I1.5_8299544.mat M2 dissipation for PD 130mSLD ICE-6G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_130mSLD_i5g_lmsk_-I1.5_8299541.mat M2 dissipation for PD 130mSLD ICE-5G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_120mSLD_i6g_lmsk_-I1.5_8299545.mat M2 dissipation for PD 120mSLD ICE-6G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_120mSLD_i5g_lmsk_-I1.5_8299540.mat M2 dissipation for PD 120mSLD ICE-5G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_110mSLD_i6g_lmsk_-I1.5_8299546.mat M2 dissipation for PD 110mSLD ICE-6G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_110mSLD_i5g_lmsk_-I1.5_8299539.mat M2 dissipation for PD 110mSLD ICE-5G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_100mSLD_i6g_lmsk_-I1.5_8299547.mat M2 dissipation for PD 100mSLD ICE-6G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_100mSLD_i5g_lmsk_-I1.5_8299538.mat M2 dissipation for PD 100mSLD ICE-5G land mask ZE ITdrag 1/8th deg<br> diss_dir_ze_m2_1_8_rtp_21kyrBP_120mSLD_-I1.5_8299537.mat M2 dissipation for PD 120mSLD JS ITdrag 1/8th deg</p> <p> </p> <p>Climate model output:</p> <p>UVic climate model output for all simulations in the paper has been compressed using tar and zip. Each folder contains the output yearly averages (tavg.xxx.nc) which have been used in the results section of the paper. The model input files are located in /data. The tidal input file is in /data/O_tideenrg_green.nc. Furthermore included are restart files (rest.xxx.nc), model code in /code, and the model exectuables.</p> <p>Climate mode output list:</p> <p>preind_tidal_ze_00kyr_rtop_-1.5_8299034_dir.tgz Output from PIC<br> lgm_tidal_ze_21kyr_i6g_rtop_-1.5_8299008_dir_tau_lgm.tgz Output from LGM_i6gT_lgmW<br> lgm_tidal_ze_21kyr_i6g_rtop_-1.5_8299008_dir.tgz Output from LGM_i6gT_pdW<br> lgm_tidal_ze_21kyr_i5g_rtop_-1.5_8299031_dir_tau_lgm.tgz Output from LGM_i5gT_lgmW<br> lgm_tidal_ze_21kyr_i5g_rtop_-1.5_8299031_dir.tgz Output from LGM_i5gT_pdW<br> lgm_tidal_ze_00kyr_rtop_-1.5_8299034_dir_tau_lgm.tgz Output from LGM_pdT_lgmW<br> lgm_tidal_ze_00kyr_rtop_-1.5_8299034_dir.tgz Output from LGM_pdT_pdW</p> <p>preind_tidal_js_1_2_rtp_00kyrBP_-I1.0_7881173.tgz Output from PIC_1_2_rtp82<br> preind_js_1_2_SandS8.2_00kyrBP_82SNcb_-I1.0_8317333_dir.tgz Output from PIC_1_2_SS82<br> lgm_tidal_js_1_2_SandS8.2_00kyrBP_120mSLD_82SNcb_-t_-I1.0_8317331_dir.tgz Output from LGM_1_2_SS82_sldT<br> lgm_tidal_js_1_2_SandS8.2_00kyrBP_82SNcb_-I1.0_8317333_dir.tgz Output from LGM_1_2_SS82_pdT<br> lgm_tidal_js_1_2_rtop_00kyrBP_120mSLD_82SN_-t_-I1.0_8315693.tgz Output from LGM_1_2_rtp82_sldT<br> lgm_tidal_js_1_2_rtop_00kyrBP_82SN_pdsal_-I1.0_8315702.tgz Output from LGM_1_2_rtp82_pdT<br> <br> </p> <p> </p> <p> </p>
Model output: CICE6-WIM Antarctic sea ice data (2010--2019)
<p>CICE6 model with waves-in-ice module (WIM) output analysed with k-means clustering to identify the Antarctic marginal ice zone. Daily data at a 1-degree horizontal resolution of the variables used to inform k-means and the physical processes which drive the Antarctic marginal ice zone are included. The model was spun-up over 2005–2009 and outputs were analysed over 2010–2019.</p> <p> </p> <p>For further details, see </p> <p>Day, N. S., Bennetts. L. G., O'Farrell, S. P., Alberello, A., & Montiel, F. (2023) Analysis of the Antarctic Marginal Ice Zone Based on Unsupervised Classification of Standalone Sea Ice Model Data.</p> <p> </p> <p>Data files:</p> <ul> <li><code>iceh_{year}.nc</code>: gridded NetCDF files containing the training variables for k-mean clustering and the fitted clusters.</li> <li><code>forcing_variables_2010_2019.csv</code>: flat file containing variables of CICE6-WIM forcing and sea ice dynamics.</li> <li><code>fsd_processes_2010_2019.csv</code>: flat file containing each term of the prognostic floe size distribution model in CICE6.</li> </ul> <p> </p> <p>Scripts:</p> <ul> <li><code>ice_in</code>: CICE namelist</li> <li>Waves-in-Ice Module: <ul> <li><code>ice_floe.F90</code>: Appends the WIM to CICE6</li> <li><code>m_prams_waveice.F90</code>: wave-ice parameters</li> <li><code>m_waveattn.F90</code>: wave attenuation from sea ice</li> <li><code>m_fzero.F90</code>: useful mathematical functions and solvers</li> <li><code>m_waveice.F90</code>: wave-ice physics</li> </ul> </li> </ul> <p>To implement the WIM into CICE6, subroutines within ice_floe.F90 should be called prior calling the floe size distribution subroutines in ice_step_mod.F90. For further details/assistance in implementing the WIM please contact Noah Day (<code>noah.day@adelaide.edu.au</code>).</p>
Model output derived from SWE pacemaker experiments using MIROC6-AGCM
<p><strong>This data set is model output derived from numerical simulations using MIROC6-AGCM (Onuma et al., 2024, Journal of Climate).</strong></p> <p><strong>The contents are as below.</strong></p> <p>- <strong>python</strong>: Python scripts for the visualization<br><br>- <strong>figure</strong>: PNG files created by the Python scripts<br><br>- <strong>data</strong>: model output data for the Python scripts. The data directory includes the below contents.<br><br>-- <strong>LMIP</strong>: Output data simulated using MATSIRO6 with GSWP3 reanalysis data (LMIP experiment) are included. Monthly time-series data and regional averages for 1901-2010 derived from LMIP experiment (netCDF and csv files, respectively).</p> <p>-- <strong>AMIP</strong>: Output data simulated using MIROC6 with prescribed sea surface temperature and sea ice concentration (AMIP experiment) are included. Monthly time-series data and regional averages for 1901-2010 derived from AMIP ensemble experiments (netCDF and csv files, respectively).</p> <p>-- <strong>AMIP+SWE</strong>: Output data simulated using MIROC6 with prescribed sea surface temperature, sea ice concentration and snow water equivalent (AMIP+SWE experiment) are included. Monthly time-series data and regional averages for 1901-2010 derived from AMIP+SWE ensemble experiments (netCDF and csv files, respectively). Difference in a surface energy budget between AMIP+SWE and AMIP is also included (feedback_*.csv).</p> <p>-- <strong>AS-A</strong>: Statistical results analyzed from output data in AMIP+SWE and AMIP experiments (csv files). R_a (R_b) means the correlation coefficients of the outputs in AMIP+SWE (AMIP) for inter-annual change from 1901 to 2010 with those in LMIP. P means p-value. RMSD_a (RMSD_b) means the root mean square difference of the outputs in AMIP+SWE (AMIP) for inter-annual change from 1901 to 2010 with those in LMIP. Tr_a (Tr_b, Tr_ref) means the inter-annual trends from 1901 to 2010 in AMIP+SWE (AMIP, LMIP).<br><br>-- <strong>else</strong>: CMIP6 amip multi-models output, GlobSnow3 observation data and mask files for the regional averages. CMIP6 and GlobSnow3 data have been processed for analysis.</p> <p>*Regarding AMIP and AMIP+SWE data, the uploaded data are limited to ensemble mean data and run01 data due to data size that can be uploaded. If you need model outputs from run02 to run10, please contact Yukihiko Onuma (yonuma613@gmail.com). </p> <p><strong>The abbreviations of the output files are below.</strong><br>alb: land surface albedo<br>clt: total cloud cover fraction<br>ebflx: soil evaporation<br>gdt: surface air temperature (GSWP3)<br>hfls: latent heat flux<br>hfss: sensible heat flux<br>mrsos: surface soil moisture<br>pr: total precipitation<br>rlds: downward longwave radiation at the surface<br>rldscs: clear-sky downward longwave radiation at the surface<br>rls: net longwave radiation at the surface<br>rlus: upward longwave radiation at the surface<br>rsds: downward shortwave radiation at the surface<br>rsdscs: clear-sky downward shortwave radiation at the surface<br>rss: net shortwave radiation at the surface<br>rsus: upward shortwave radiation at the surface<br>snc: snow cover fraction<br>snow: snow water equivalent<br>T2: surface air temperature<br>tsl_z0: surface soil temperature (top 0.05 m)<br>tsl_z1: sub-surface soil temperature (top 0.05-0.25 m)</p>
Data, models, and outputs for an agent-based hydro-economic modeling study in an intensively irrigated region of the U.S. High Plains
<p>In this dataset, we include all the models developed for the study "An integrated modeling approach to simulate human-crop-groundwater interactions in intensively irrigated regions", which is published in Environmental Modelling & Software (<a href="https://doi.org/10.1016/j.envsoft.2024.106120">https://doi.org/10.1016/j.envsoft.2024.106120</a>). Additionally, we provide all the data used in this study. Below, you will find a description of the contents of each file:</p> <ul> <li>abm_modflow.zip: This file includes the agent-based hydro-economic model (ABM-MODFLOW), including model inputs and outputs, Python post-processing scripts, and the Windows batch script for the integration process.</li> <li>modflow.zip: This file contains the standalone MODFLOW model files. Each folder includes files for individual simulation periods, starting with a steady-state model for the predevelopment period, followed by seven transient models.</li> <li>modflow_rs.zip: This file contains the MODFLOW-RS model files. Given that remote sensing data is provided for years from 1984 onwards, only models for the post-1980 simulation periods are included. For model files corresponding to years prior to 1980, refer to modflow.zip.</li> <li>Figurers.zip: This file includes all data and Python scripts used to produce the figures in the main text.</li> <li>Tables.zip: This file contains all tables and the associated data included in the main text.</li> <li>Supporting_Info_Figures: This file includes all data and Python scripts used to produce the figures in the Supporting Information.</li> <li>Supporting_Info_Tables: This file contains all tables and the associated data included in the Supporting Information.</li> <li>Supporting_Info_Videos: This file stores Videos S1 and S2 of the Supporting Information, displaying the historical development of irrigation wells and groundwater-fed irrigated lands in the study area from 1946 to 2018.</li> </ul>
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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
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.