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104 results for “Earth System Modeling”
Supporting data for manuscript: "Global-scale evaluation of coastal ocean alkalinity enhancement in a fully-coupled Earth system model"
<p>Supporting data for manuscript: "Global-scale evaluation of coastal<br> ocean alkalinity enhancement in a fully-coupled Earth system model"</p> <p>Authors: Julien Palmieri and Andrew Yool</p> <p>Institute: National Oceanography Centre, European Way, Southampton<br> SO14 3ZH, UK</p> <p>This repository consists of four main sets of files:</p> <p>1. Matlab scripts used for analysis, figure plotting and table<br> preparation</p> <p> Filenames of the format: Figure_??.m</p> <p>2. Raw netCDF output files from UKESM1 for six model experiments</p> <p> Filenames of the format: medusa_c*.nc</p> <p>3. BGCVal processed timeseries shelve files</p> <p> Filenames of the format: u-c*.shelve.txt</p> <p>4. CMM2 processed timeseries netCDF files</p> <p> Filenames of the format: c*_global.nc</p> <p>File sets 2-4 are read and processed by script files in file set 1<br> </p>
Assessment of equilibrium climate sensitivity of the Community Earth System Model version 2 through simulation of the Last Glacial Maximum
<p>Simulation data (TS, FSNT, and FLNT) and apap cloud feedback analysis for CESM2 LGM simulation</p> <p><strong>Simulation boundary condition files in 1-degree resolution: boundary_condition_files.zip</strong></p> <p><strong>Please cite: </strong></p> <p>Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Poulsen, C. J., Tierney, J. E., Lofverstrom, M., & DiNezio, P. (2021). Assessment of equilibrium climate sensitivity of the Community Earth System Model version 2 through simulation of the Last Glacial Maximum. <em>Geophysical Research Letters</em>, <em>n/a</em>(n/a), e2020GL091220. https://doi.org/10.1029/2020GL091220</p>
Data from: Gene trees, species trees and Earth history combine to shed light on the evolution of migration in a model avian system
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UVic earth system climate model data generated under MIS3 boundary conditions
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Pyrocumulonimbus Events over British Columbia in August 2017: Results from the NASA GEOS Earth System Model
Model data associated with the manuscript submitted in Atmospheric Chemistry and Physics Journal, titled, " Pyrocumulonimbus Events over British Columbia in 2017: The Long-term Transport and Radiative Impacts of Smoke Aerosols in the Stratosphere". Abstract. Interactions of meteorology with wildfires in British Columbia, Canada during August 2017 led to three major pyrocumulonimbus (pyroCb) events that resulted in the injection of large amounts of smoke aerosols and other combustion products at the local upper troposphere and lower stratosphere (UTLS). These plumes of UTLS smoke with elevated values of aerosol extinction and backscatter compared to the background state were readily tracked by multiple satellite-based instruments as they spread across the Northern Hemisphere (NH). The plumes resided in the lower stratosphere for about 8-10 months following the fire injections. To investigate the radiative impacts of these events on the Earth system, we performed a number of simulations with the Goddard Earth Observing System (GEOS) atmospheric general circulation model (AGCM). Observations from multiple remote-sensing instruments were used to calibrate the injection parameters (location, amount, composition, and heights) and optical properties of the smoke aerosols in the model. The resulting simulations of three-dimensional smoke transport were evaluated for a year from the day of injections using daily observations from OMPS-LP (Ozone Mapping Profiler Suite Limb Profiler). The model simulated rate of ascent, hemispheric spread, and residence time of the smoke aerosols in the stratosphere are in close agreement with OMPS-LP observations. We found that both aerosol self-lofting and the large-scale atmospheric motion play important roles in lifting the smoke plumes from near the tropopause altitudes (~12 km) to about 22-23 km into the atmosphere. Further, our estimations of the radiative impacts of the pyroCb-emitted smoke aerosols showed that the smoke caused additional warming of the atmosphere by about 0.6-1 W/m2 (zonal mean) that persisted for about 2-3 months after the injections in regions north of 40oN. The surface experienced a comparable magnitude of cooling. The atmospheric warming is mainly located in the stratosphere, coincident with the location of the smoke plumes, leading to an increase in zonal mean shortwave (SW) heating rates of 0.02-0.04 K/day during September 2017.
Water isotope data for "Simulation of early Eocene water isotopes using an Earth system model and its implication for past climate reconstruction"
<p><strong>iCESM1.2 simulated seawater oxygen isotopes for the Early Eocene</strong></p> <p><strong>Citation: </strong>Zhu, J., Poulsen, C. J., Otto-Bliesner, B. L., Liu, Z., Brady, E. C., & Noone, D. C. (2020). Simulation of early Eocene water isotopes using an Earth system model and its implication for past climate reconstruction. Earth and Planetary Science Letters, 537, 116164. <a href="https://doi.org/10.1016/j.epsl.2020.116164">https://doi.org/10.1016/j.epsl.2020.116164</a></p> <ul> <li>Data set includes climatology (12 months) sea-surface temperature (TEMP) and sea-surface oxygen isotope ratio (R18O) from four Eocene simulations with 1×, 3×, 6×, and 9× preindustrial level of CO2 (284.7 ppmv), and a preindustrial simulation.</li> <li>Climatology was calculated from averaging data over the last 100 years of each simulation.</li> <li>Seawater d18O = (R18O - 1.0) * 1000.0</li> <li>TEMP and R18O are on the POP ocean grid (~1°; see here: <a href="http://www.cesm.ucar.edu/models/cesm1.2/pop2/">http://www.cesm.ucar.edu/models/cesm1.2/pop2/</a>).</li> </ul>
Data for exploring topography-based methods for downscaling subgrid precipitation for use in Earth System Models
<p>Topography exerts major control on land surface processes. To improve representation of topographic impacts on land surface processes, a new topography-based subgrid structure has been introduced to the Energy Exascale Earth System Model representing the subgrid heterogeneity of surface elevation. Four topography-based methods of downscaling grid precipitation to the subgrids have been explored. The data utilized for the study include precipitation, surface elevation, and height rise data derived from wind speed and Brunt Vaisala parameter and outputs of downscaled precipitation and statistical metrics calculated in this study. Results show that utilizing hypsometric elevation of the subgrid landscape within the model grid cell improves downscaling of precipitation in mountainous areas. Furthermore, accounting for blocking of airflow further improves precipitation downscaling slightly in mountainous regions consistently across multiple grid sizes.</p> <p>The data files include:</p> <ol> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/daily_prism_precip.zip?versionId=be97ca8d-182a-4f1e-9ae3-9da3f2b87e24">daily_prism_precip.zip</a>: high resolution precipitation data (4 km) obtained from PRISM [Daly et al. 1994, Daly et al. 2008].</li> <li> <a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/dem_4km4.nc">dem_4km4.nc</a>: 4 km surface elevation data derived from high resolution surface elevation data (90 m) obtained from HydroSHEDS [Lehner et al. 2008, Lehner and Grill 2013]</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">fr_number.zip</a>: Height rise of airflow calculated from wind speed and Brunt Vaisala parameter derived from the North American Regional Reanalysis data.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_128km.zip?versionId=5f64ec8c-4018-4d97-ae1d-eb6f15ccc564">output_from_dwnscaling_methods_at_128km.zip</a>: Output data of the downscaling methods at 128 km spatial resolution.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_96km.zip?versionId=b2c67f80-9794-41cb-9986-a4c7259ccf1c">output_from_dwnscaling_methods_at_96km.zip</a>: Output data of the downscaling methods at 96 km spatial resolution. </li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_64km.zip?versionId=b83230a0-308e-4f90-971b-6636a5add796">output_from_dwnscaling_methods_at_64km.zip</a>: Output data of the downscaling methods at 64 km spatial resolution.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_32km.zip?versionId=cc9021cc-c3b4-4c04-a558-752c151c49ba">output_from_dwnscaling_methods_at_32km.zip</a>: Output data of the downscaling methods at 32 km spatial resolution. </li> <li>ppt_spatial_downscaling_daily_data_flatten_withFr_test_filt0_v3rev_64.py: Python code used to calculate downscaled precipitation data from aggregated grid precipitation data.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/stns_precip_2015.csv">stns_precip_2015.csv</a>: Precipitation data at rain gauge stations in the Conterminous US extracted from the Daymet station-level input datasets are used for evaluation of the downscaled results </li> </ol> <p>Other datasets used to calculate wind speed and Brunt Vaisala parameter were extracted from the North American Regional Reanalysis (NARR) including wind speed, temperature, surface pressure, specific humidity and relative humidity [Mesinger et al. 2006].</p> <p> </p> <p><strong>References:</strong></p> <p>Daly, C., et al. (1994). "A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain." Journal of Applied Meteorology <strong>33</strong>(2): 140-158. </p> <p>Daly, C., et al. (2008). "Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States." International Journal of Climatology <strong>28</strong>(15): 2031-2064.</p> <p>Lehner, B., et al. (2008). "New Global Hydrography Derived From Spaceborne Elevation Data." Eos, Transactions American Geophysical Union <strong>89</strong>(10): 93-94.</p> <p>Lehner, B. and G. Grill (2013). "Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems." Hydrological Processes <strong>27</strong>(15): 2171-2186.</p> <p>Mesinger, F., et al. (2006). "NORTH AMERICAN REGIONAL REANALYSIS." Bulletin of the American Meteorological Society <strong>87</strong>(3): 343-360.</p>
Supplemental data for "Initial land use/cover distribution substantially affects global carbon and local temperature projections in the integrated Earth System Model." Article published as Global Biogeochemical Cycles publication 2019B006383
<p>These are supporting data for Global Biogeochemical Cycles publication 2019B006383: "Initial land use/cover distribution substantially affects global carbon and local temperature projections in the integrated Earth System Model." They include data for all of the regular and supplemental figures.</p>
Improved representation of clouds in the atmospheric component LMDZ6A of the IPSL Earth system model IPSL-CM6A : Source codes and supporting files
<p>Source codes and supporting files of the paper by J-B Madeleine et al., 2020, entitled "Improved representation of clouds in the atmospheric component LMDZ6A of the IPSL Earth system model IPSL-CM6A" published in the Journal of Advances in Modeling Earth Systems. See the README file for more information.</p>
Unstructured global to coastal wave modeling for the Energy Exascale Earth System Model - 1/2 degree WaveWatchIII configuration files
<p>This dataset contains the mesh and model configuration information for a WaveWatchIII run using a 1/2 degree structured grid.</p> <ul> <li>glo_30m.bot <ul> <li>Bottom depth file for a 1/2 degree structured grid</li> </ul> </li> <li>glo_30m.mask <ul> <li>Mask file for a 1/2 degree structured grid</li> </ul> </li> <li>obstructions_local.glo_30m.in <ul> <li>local obstructions file for use with UOST source term switch</li> </ul> </li> <li>obstructions_shadow.glo_30m.in <ul> <li>shadow obstructions file for use with UOST source term switch</li> </ul> </li> <li>ww3_grid.inp <ul> <li>Input file for the ww3_grid pre-processing program. This file specifies many of the model configuration settings.</li> </ul> </li> <li>ww3_shel.inp <ul> <li>Input file for the ww3_shel program.</li> </ul> </li> </ul>
Dataset to reproduce the figures in "Parameterizing the Impact of Unresolved Temperature Variability on the Large-Scale Density Field: Part 2. Modeling." in Journal of Advances in Modeling Earth Systems (JAMES)
Ocean circulation models have systematic errors in large-scale horizontal density gradients due to estimating the grid-cell-mean density by applying the nonlinear seawater equation of state to the grid-cell-mean water properties. In frontal regions where unresolved subgrid-scale (SGS) fluctuations are significant, dynamically relevant errors in the representation of current systems can result. A previous study developed a novel and computationally efficient parameterization of the unresolved SGS temperature variance andresulting density correction. This parameterization was empirically validated but not tested in an ocean model. In this study, we implement deterministic and stochastic variants of this parameterization in the pressure-gradient force term of a coupled ocean-sea ice configuration of CESM-MOM6 and perform a suite of hindcast sensitivity experiments to investigate the ocean response. The parameterization leads to coherent changes in the large-scale ocean circulation and hydrography, particularly in the Nordic Seas and Labrador Sea, which are attributable in large part to changes in the seasonally varying upper-ocean exchange through Denmark Strait. In addition, the separated Gulf Stream strengthens and shifts equatorward, reducing a common bias in coarse-resolution ocean models. The ocean response to the deterministic and stochastic variants of the parameterization is qualitatively, albeit not quantitatively, similar, yet qualitative differences are found in various regions.
Data for "Improvements in wintertime surface temperature variability in the Community Earth System Model version 2 (CESM2) related to the representation of snow density"
<p>This dataset contains all the postprocessed data required to reproduce the figures in the publication Simpson et al (2022) "Improvements in wintertime surface temperature variability in the Community Earth System Model version 2 (CESM2) related to the representation of snow density", in the Journal of Advances in Modelling the Earth System.</p>
U-Surf: a global 1km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
<p>High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset to represent the spatial heterogeneity of urban surfaces and their biophysical properties. This deficiency has long obstructed the development of urban-resolving Earth System Models (ESMs) and ultra-high-resolution urban climate modeling, particularly at large scales. Here, we present a first-of-its-kind 1km-resolution present-day (circa-2020) global continuous urban surface parameter dataset – U-Surf. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for developing dataset requirements, U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for UCMs at the facet- and canopy-level. Our high-resolution U-Surf dataset significantly improves the representation of the urban land heterogeneity both within and across cities globally. U-Surf provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs, enables detailed city-to-city comparisons across the globe, and supports the next-generation kilometer-resolution Earth system modeling across scales. U-Surf parameters can be easily converted or adapted to various types of UCMs, such as those embedded in weather and regional climate models, as well as air quality models. The fundamental urban surface constraints provided by U-Surf are also relevant as features for machine learning models and can have other broad-scale applications for socioeconomic, public health, and urban planning contexts. We expect U-Surf to promote the research frontier on urban systems science, climate-sensitive urban design, and coupled human-Earth systems in the future.</p> <p>The complete list of parameters is presented in the table below.</p> <table> <tbody> <tr> <td>Category</td> <td>Parameter</td> <td>Notes</td> </tr> <tr> <td>Radiative</td> <td>Roof | Impervious | Pervious canyon floor | Wall emissivity</td> <td> </td> </tr> <tr> <td> </td> <td>Roof | Impervious | Pervious canyon floor | Wall albedo</td> <td> </td> </tr> <tr> <td>Morphological</td> <td>Roof | Pervious fraction</td> <td>Roof fraction is w.r.t. urban horizontal surface, and pervious fraction is w.r.t. canyon floor (i.e. pervious and impervious canyon floor).</td> </tr> <tr> <td> </td> <td>Building height</td> <td>Unit: m; Height of wind in the canyon is simply set as half of the building height in CLMU.</td> </tr> <tr> <td> </td> <td>Canyon height-to-width ratio</td> <td> </td> </tr> <tr> <td> </td> <td>Urban percentage</td> <td> </td> </tr> <tr> <td>Thermal</td> <td>Roof | Wall thickness</td> <td>Unit: m</td> </tr> <tr> <td> </td> <td>Roof | Impervious canyon floor | Wall thermal conductivity</td> <td>Unit: W/m*K</td> </tr> <tr> <td> </td> <td>Roof | Impervious canyon floor | Wall volumetric heat capacity</td> <td>Unit: J/m^3*K</td> </tr> <tr> <td> </td> <td>Number of impervious canyon floor layer</td> <td> </td> </tr> <tr> <td> </td> <td>Minimum | Maximum interior building temperature</td> <td>Unit: K</td> </tr> <tr> <td> </td> <td>Air conditioning adoption rate</td> <td> </td> </tr> </tbody> </table> <p> </p> <p>Radiative and morphological parameters are presented in the format of both .tif and .nc to accommodate different needs for the urban climate modeling community. Thermal parameters adapted from CLMU are available in a single .nc file. A CESM-compatiable surface dataset and a time-variant urban dataset (including P_AC and T_BUILDING_MAX; Li et al., 2024) at standard resolution (0.9375°x1.25°) are included for direct simulation use. Note that the urban percentage used to create the surface dataset comes from the PCT_URBAN parameter calculated in U-Surf, but users can input their own urban extent data to generate a customized surface dataset. The raw 1-km data can be easily aggregated/regridded to other resolution as needed.</p> <p> </p> <p><strong>Version 1.1 updates:</strong></p> <p>1. Fill part of the data gaps in Asia. </p> <p>2. Change the aggregation method of some parameters to be facet-area weighted in the 1deg surfdata.</p>
Spatially explicit re-harmonized terrestrial carbon densities for calibrating Integrated human-Earth System Models
<p>Soil and vegetation carbon densities play a critical role in global and regional human-Earth system models. These densities affect variables such as land use change emissions and also influence land use change pathways under climate mitigation scenarios where terrestrial carbon is assigned a carbon price. Recently, more spatially explicit, fine resolution data have become available for both soil and vegetation carbon. However, for models to effectively use these data the fine resolution data need to be reharmonized to initial land use and land cover conditions represented by these models. Without such reharmonization the carbon values may be very inaccurate for particular land types and places where the source data and the model disagree on the land use/cover type. Here we present reharmonized soil and vegetation carbon densities both at the grid cell level at 5 arcmin resolution and also aggregated to 235 water sheds for 4 different land use and 15 land cover types. These data are particularly useful as initial land carbon conditions for global Multisectoral Dynamic Models (MSD). Moreover, these data include six different statistical states calculated using distinct resampling methods for each of the land use, land cover types. These statistical states are used to define a range of possible carbon values for each land classification, and any state can be used for defining initial conditions of soil and vegetation carbon in MSD models. We make use of these statistical states to calculate spatially distinct uncertainties in the carbon densities by land type. We have implemented these data in a state-of-the-art multi sector dynamics model, namely the Global Change Analysis Model (GCAM), and show that these new data improve several land use responses in the model, especially when terrestrial carbon is assigned a carbon price. The statistical states in our data are validated against similar estimates in the literature both at a grid cell level and at a regional level. </p> <p>This is a data record which corresponds to the paper "Spatially explicit re-harmonized terrestrial carbon densities for calibrating Integrated Multisectoral Models" (Narayan et al. 2023, under review)</p> <p>We have now also added a tabular version of the dataset aggregated to GTAP's AEZ definitions as opposed to GCAM's GLUs</p> <p> </p>
Ocean Dynamics in the DOE Energy, Exascale, Earth System Model (E3SM)
<p>Climate research at the U.S. Department of Energy (DOE) includes the development of ocean, sea-ice, atmosphere, land-vegetation and land-ice models. The ability to run high-resolution global simulations efficiently on the world’s largest computers is a priority for the DOE. This movie shows simulations from the variable-resolution ocean model, the Model for Prediction Across Scales (MPAS-Ocean), which is developed at Los Alamos National Laboratory. MPAS-Ocean is a component of the DOE’s newly released Energy, Exascale, Earth System Model (E3SM). Applications of E3SM include the simulation of 20th-century and future climate scenarios, as well as special configurations where model resolution is enhanced in regions of particular interest, like coastal areas, the Arctic, or below Antarctic ice shelves.</p> <p>Website: <a href="https://e3sm.org">https://e3sm.org</a>. </p>
Assessing acetone for the GISS ModelE2.1 Earth system model
<p>The simulated three-dimensional distributions of acetone from each simulation described in the paper (Baseline, sensitivity simulations in Table 1, and Nudged_ATom) are available in the zip files below, grouped by simulation:</p> <p>Each zip file contains a series of netCDF format files with filenames: {month}_5yrAvg_Acetone_{simulation}.nc, for example, <a href="https://urldefense.proofpoint.com/v2/url?u=http-3A__JUN-5F5yrAvg-5FAcetone-5FChem-5FPar2.0.nc&d=DwMFaQ&c=009klHSCxuh5AI1vNQzSO0KGjl4nbi2Q0M1QLJX9BeE&r=AW5CBwXzwRZfFpcXrchhGJYviihO8BJyAZekNlH15N4&m=tMBMWYi2ngTehrdwTk8qd9ZLcw3W5QqDEOLFI-OsoUHkrSSiiBk6fUbSw2OBQdRa&s=Yeqlp_vtfNOpWQcBGoIeiEDlbA_pc1JwfYT8ghUFVgA&e=">JUN_5yrAvg_Acetone_Chem_Par2.0.nc</a>, where each file is a climatological average over 5 years of repeated forcing conditions.</p> <p>The exception is the transient-forcing simulation "Nudged_ATom", which contains single-month averages of acetone from JUL 2016 through MAY 2018, to cover the ATom observational period. The file names for that simulation are of the form: {month}_{year}_Acetone_Nudged_ATom.nc. Acetone is in ppbv units and given on the model's native grid and vertical levels. These are hybrid sigma levels, but nominal pressure middles and edges are given in the plm and ple variables, respectively, and the grid box surface areas are also provided.</p> <p> </p> <p>GISS_MODELE_Acetone_Work.tar.gz contains:</p> <p>GISS_MODELE_Acetone_Work/ = the GISS ModelE directory used to perform simulations. With model codes in model/ and run instructions (called “rundecks”) in decks/. That decks directory also contains each simulation’s pre-compiled executable.</p> <p>The mapping of paper simulation name to the rundeck is as follows:</p> <table> <tbody> <tr> <td> <p>Paper Simulation Name</p> </td> <td> <p>ModelE “rundeck”</p> </td> </tr> <tr> <td> <p>Baseline</p> </td> <td> <p>E3acetOn3g.R</p> </td> </tr> <tr> <td> <p>Nudged_ATom</p> </td> <td> <p>E3acetOn3g_tranAtom.R</p> </td> </tr> <tr> <td> <p>Chem_Par2.0</p> </td> <td> <p>E3acetOn3g_ParDbl.R</p> </td> </tr> <tr> <td> <p>Chem_Terp0</p> </td> <td> <p>E3acetOn3g_Terp0.R</p> </td> </tr> <tr> <td> <p>Chem_Par0.5</p> </td> <td> <p>E3acetOn3g_ParHalf.R</p> </td> </tr> <tr> <td> <p>Dep_f<sub>0</sub>0</p> </td> <td> <p>E3acetOn3g_f00.R</p> </td> </tr> <tr> <td> <p>Ocn_2.0</p> </td> <td> <p>E3acetOn3g_Ocn30.R</p> </td> </tr> <tr> <td> <p>Veg_0.7</p> </td> <td> <p>E3acetOn3g_Meg07.R</p> </td> </tr> <tr> <td> <p>Chem_Cl0</p> </td> <td> <p>E3acetOn3i.R</p> </td> </tr> <tr> <td> <p>BB_2.0</p> </td> <td> <p>E3acetOn3g_bbDbl.R</p> </td> </tr> </tbody> </table> <p> </p> <p>INPUT_FILES.tar.gz contains: the modelE chemistry input instruction tables referenced in the (*.R) rundecks. </p> <p> </p> <p>SCRIPTS.tar.gz contains:</p> <p>SCRIPTS/extract_and_convert.ksh = used to extract model output to provide in the zip files in this archive.</p> <p>SCRIPTS/PLOTTING/ = python programs used to make the paper figures</p> <p> </p> <p>MODELE_RAW_OUTPUT_VARIABLES.tar.gz contains: the modelE output files used as input to the plotting/analysis programs.</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 6
<p>Future projections of precipitation by the BMlinear model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 10
<p>Extra data of the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia" that did not fit in their respective deposits:</p> <p>Future projections of:</p> <p>Precipitation by all CNN models (BMlinear, BM1, BM10, BMdense) forced by the UKESM1-0-LL GCM.</p> <p>2-meter maximum and minimum temperatures by the BM1 model forced by the NorESM2-MM and UKESM1-0-LL GCMs.</p> <p>2-meter mean temperature by the BMlinear and BM1 models forced by the NorESM2-MM and UKESM1-0-LL GCMs.</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 4
<p>Future projections of 2-meter mean temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
EMIT L4 Earth System Model Products V001
The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take measurements of the sunlit regions of interest between 52° N latitude and 52° S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) [EMIT Open Data Portal](https://earth.jpl.nasa.gov/emit/data/data-portal/coverage-and-forecasts/).The EMIT Level 4 Earth System Model (EMITL4ESM) Version 1 data product provides radiative forcing outputs, along with other ancillary outputs generated from different Earth System Models (ESMs). ESMs are complex models that integrate relevant physical, chemical, biological, and human components to simulate multiple aspects of large-scale systems on Earth. Multiple models, input mineral maps, meteorology inputs, and emissions/concentration scenarios are examined for the model runs contained within this data product. Models currently utilized include the Community Earth System Model 2 ([CESM2](https://www.cesm.ucar.edu/models/cesm2)) and the Goddard Institute for Space Studies (GISS) model. Some ESM runs utilize reference surface mineral maps from the literature dating back to 2007; others rely on the EMIT L3 Aggregated Mineral Spectral Abundance and Uncertainty 0.5 Deg ([EMITL3ASA](https://doi.org/10.5067/EMIT/EMITL3ASA.001)) data as inputs. Each EMITL4ESM granule represents a single ESM run with a Network Common Data Format 4 (netCDF-4) file for each variable. A total of 12 Science Dataset (SDS) layers or variables are provided for each model run. For some SDS layers or variables, multiple layers based on inclusion of model minerology inputs are provided in their netCDF files. The layers/variables table below details which variables contain the extra layers. Metadata flags for Earth System Model, Resolution, Surface Mineral Map, External Meteorology, Time Period, and Emissions/Concentration Scenario indicate the key parameters for each granule. A table outlining each variable in detail can be found in the [EMIT Science Data System Level 4 repository](https://github.com/emit-sds/emit-sds-l4/blob/main/data/L4_varnames.csv).Known Issues* Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe.
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.