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53 results for “Earth system data”
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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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>
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>
Supporting data for Loik et al. 2017 Wavelength-Selective Solar Photovoltaic Systems: Powering greenhouses for plant growth at the food-energy-water nexus. Earth's Future
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DSCOVR EPIC Level 2 Vegetation Earth System Data Record (VESDR), Version 2
DSCOVR_EPIC_L2_VESDR_02 is the Deep Space Climate ObserVatoRy (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Level 2 Vegetation Earth System Data Record (VESDR), Version 2 data product. It provides Leaf Area Index (LAI) and diurnal courses of Normalized Difference Vegetation Index (NDVI), Sunlit Leaf Area Index (SLAI), Fraction of incident Photosynthetically Active Radiation (400-700 nm) absorbed by the vegetation (FPAR), Directional Area Scattering Function (DASF), Earth Reflector Type Index (ERTI) and Canopy Scattering Coefficient at 443 nm, 551 nm, 680 nm and 779 nm. The VESDR files also include Solar Zenith Angle (SZA), Solar Azimuthal Angle (SAA), View Zenith (VZA), and Azimuthal (VAA) angles at the same temporal and spatial resolutions. The parameters are projected on eight regional 10 km SIN grids and available at 65 to 110 min temporal frequency. The version 2 VESDR product is generated from the upstream DSCOVR EPIC L2 MAIAC (Multi-Angle Implementation of Atmospheric Correction version 2) surface reflectance product. FPAR, LAI, and SLAI help monitor variability and change in global vegetation due to climate and anthropogenic influences, modeling climate, carbon, and water cycles and improving forecasting of near-surface weather. DASF provides information critical to accounting for structural contributions to measurements of leaf biochemistry from remote sensing. The canopy scattering coefficient is the Fraction of intercepted radiation reflected from or diffusely transmitted through the vegetation. This parameter is strongly correlated with leaf albedo, which depends on leaf biochemical constituents. We also provide two ancillary science data products: "Version 2 10 km Land Cover Type" and "Version 2 Distribution of Land Cover Types within 10 km EPIC pixel." The products were derived from 500m Moderate Resolution Imaging Spectroradiometer (MODIS) land cover type 3 product (MCDLCHKM), which was generated from 2008, 2009, and 2010 land cover products (MCD12Q1, v051). A detailed description of the VESDR and ancillary science data products can be found in "VESDR Science Data Product Guide, version 2". Section "USER'S GUIDE" provides links to this document as well as to the two ancillary science data products.
Aquaplanet experiment data for Webb, M. J., & Lock, A. P. (2020). Testing a physical hypothesis for the relationship between climate sen-sitivity and double-ITCZ bias in climate models.Journal of Advances in Modeling Earth Systems, 12,e2019MS001999.https://doi.org/10.1029/2019MS001999
<p><strong>Aquaplanet experiment data from Webb and Lock (2020)</strong></p> <p><br> Webb, M. J., & Lock, A. P. (2020). Testing a physical hypothesis for the relationship between climate sen-sitivity and double-ITCZ bias in climate models.Journal of Advances in Modeling Earth Systems, 12,e2019MS001999.https://doi.org/10.1029/2019MS001999</p> <p>CSV files containing data from Figs 1(b) and 2(a-d)</p> <p>Figure 2b:</p> <p>APEQ.Precipitation_mmperday.zonal.csv<br> APEQ_2LW_Cloud.Precipitation_mmperday.zonal.csv<br> APEQ_3LW_Cloud.Precipitation_mmperday.zonal.csv</p> <p>Figure 3a:</p> <p>APEQ.w700.zonal.csv<br> APEQ_3LW_Cloud.w700.zonal.csv<br> APEQ_2LW_Cloud.w700.zonal.csv</p> <p>Figure 3b:</p> <p>APEQ.Estimated_Inversion_Strength_K.zonal.csv<br> APEQ_2LW_Cloud.Estimated_Inversion_Strength_K.zonal.csv<br> APEQ_3LW_Cloud.Estimated_Inversion_Strength_K.zonal.csv</p> <p>Figure 3c:</p> <p>APEQ.Net_CRE_Wperm2.zonal.csv<br> APEQ_2LW_Cloud.Net_CRE_Wperm2.zonal.csv<br> APEQ_3LW_Cloud.Net_CRE_Wperm2.zonal.csv</p> <p>Figure 3d:</p> <p>APEQ4K-APEQ.Net_CRE_Feedback_Wperm2perK.zonal.csv<br> APEQ4K_2LW_Cloud-APEQ_2LW_Cloud.Net_CRE_Feedback_Wperm2perK.zonal.csv<br> APEQ4K_3LW_Cloud-APEQ_3LW_Cloud.Net_CRE_Feedback_Wperm2perK.zonal.csv</p> <p>Any queries please contact Mark Webb mark.webb@metoffice.gov.uk</p> <p> </p>
Observational data for "A New Lake Classification System based on Thermal Profiles to Better Understand the Most Dominant Lake Type on Earth"
<p>Previously unpublished continuous water temperature data used in the study "A New Lake Classification System based on Thermal Profiles to Better Understand the Most Dominant Lake Type on Earth". Table S1 links to previously published data in other manuscripts.</p>
All data for: Megaherbivore impacts on ecosystem and Earth system functioning: The current state of the science
<p>Megaherbivores (adult body mass >1000 kg) are suggested to disproportionately shape ecosystem and Earth system functioning. We systematically reviewed the empirical basis for this general thesis and for the more specific hypotheses that (i) megaherbivores have disproportionately larger effects on Earth system functioning than their smaller counterparts, (ii) this is true for all extant megaherbivore species and (iii) their effects vary along environmental gradients. We furthermore explored possible biases in our understanding of megaherbivore impacts. We found that there are too few studies to quantitatively evaluate the general thesis or any of the hypotheses for all but the African savanna elephant. Following this finding, we performed a qualitative vote counting analysis. Our synthesis of this analysis suggests that megaherbivores can elicit strong impacts on e.g. vegetation structure, and biodiversity and all the elephant species promote seed dispersal. We were however unable to evaluate whether these effects are disproportionate to smaller large herbivores. Although environmental conditions can mediate megaherbivore impact, few studies quantified the effect of rainfall or soil fertility on megaherbivore impacts, precluding prediction of megaherbivore effects on the Earth system, particularly under future climates. Moreover, our review highlights major taxonomic, thematic and geographic biases in our understanding of megaherbivore effects. Most of the studies focused on African savanna elephant impacts on vegetation structure and biodiversity, with other megaherbivores and Earth system functions comparatively neglected. Studies were also biased towards semi-arid and relatively fertile systems, with the arid, high-rainfall and/or nutrient-poor parts of the megaherbivores' distribution ranges largely unrepresented. Our findings highlight that the empirical basis of our understanding of the ecological effects of extant megaherbivores is still limited for all species, except African savanna elephant, and that our current understanding is biased towards certain environmental and geographic areas. We further outline a detailed, urgently needed avenue for future research.</p>
All data for: Megaherbivore impacts on ecosystem and Earth system functioning: The current state of the science
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Polar Visible Imaging System (VIS) Earth Camera Images, Calibrated (E0), 4 min Data
Instrument Functional Description: The VIS Instrument is a Set of three Low Light Level Cameras. Two of these Cameras share primary and some secondary Optics and are designed to provide Images of the Nighttime Auroral Oval at Visible Wavelengths. A Third Camera is used to monitor the Directions of the Fields-of-View of the Auroral Cameras with respect to the sunlit Earth and return Global Images of the Auroral Oval at Ultraviolet Wavelengths. The VIS Instrumentation produces an Auroral Image of 256 × 256 Pixels approximately every 24 s dependent on the Integration Time and Filter selected. The Fields-of-View of the two Nighttime Auroral Cameras are 5.6 × 6.3° and 2.8 × 3.3° for the Low and Medium Resolution Cameras, respectively. The Medium Resolution Camera was never activated. One or more Earth Camera Images of 256 × 256 Pixels are produced every 5 min, depending on the commanded Mode. The Field-of-View of the Earth Camera is approximately 20 × 20°. See: http://vis.physics.uiowa.edu/vis/vis_description/vis_description.htmlx Reference: Frank, L.A., J.B. Sigwarth, J.D. Craven, J.P. Cravens, J.S. Dolan, M.R. Dvorsky, J.D. Harvey, P.K. Hardebeck, and D. Muller, The Visible Imaging System (VIS) for the Polar Spacecraft, Space Science Review, Vol. 71, pp. 297-328, 1995. Data Set Description: The VIS Earth Camera Data Set comprises all Earth Camera Images for the selected Time Period. Full Coordinate Information is included for Viewer Orientation. In addition, a Rotation Matrix and a Table of Distortion-correcting Look Direction Unit Vectors are provided for the Purpose of calculating Coordinates for every Pixel. To facilitate viewing of the Images, a Mapping of Pixel Value to a recommended Color Table based on the Characteristics of the selected Filter will be included with each Image. A Relative Intensity Scale is provided through an Uncompressed Count Table. Approximate Intensity Levels in kiloRayleighs are given in an Intensity Table. For detailed Information on Intensities, see Sensitivities_and_Intensities.txt at https://cdaweb.gsfc.nasa.gov/Polar_VIS_docs/SENSITIVITIES_AND_INTENSITIES.TXT. Supporting Software is available at: http://vis.physics.uiowa.edu/vis/software/ Included is an IDL Program that displays the Images with the recommended Color Bar, provides approximate Intensities, Coordinate Data for each Pixel, and includes multiple Options for Image Manipulation.
ScienceDex guides
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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.