Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
617
datasets available to search
ShareScore release 0.7.1
Dataset results
617 results for “Climate models”
Developing crown width model for mixed forests using soil, climate, and stand factors
<ol> <li>The tree crown is a useful measure of tree vigor and is highly relevant to a tree's environmental adaptability. Crown allometry depends on environmental and stand conditions. Several studies have focused on the effects of climate change and competitive intensity on the crown, but the regulatory role of soil resources and diversity on crown allometry and carbon allocation has been neglected.</li> <li>Data from 20,994 trees in 232 mixed forests collected between 2011 and 2019 was located near four major mountain ranges in northeast China. The proposed crown width model includes the stand developmental stage, soil, climate, competition intensity, species mixture, species diversity, structural diversity, and their interactions.</li> <li>We observed that the cross-species allometric scaling exponent does not conform to the universal scaling law. Our results showed that crown width increased with increasing soil bulk density, quadratic mean diameter, and coefficient of diameter variation but decreased with increasing de Martonne aridity index, basal area, Simpson index, and species mixture. The interaction of quadratic mean diameter and soil bulk density had a significant negative effect on crown width. The influence of a particular factor within the interaction term on crown width was modulated by the gradients of other factors. Furthermore, soil bulk density contributed more to crown width modeling than the aridity index, and structural diversity had a greater effect on crown width than species diversity.</li> <li> <em>Synthesis</em>. Our results provide new insights into the environmental variability of crown allometry in mixed forests under global change, which is critical for improving regional and global estimates of forest biomass and carbon stocks.</li> </ol>
GLobAl building MOrphology dataset for URban climate modelling
<p>GLobAl building MOrphology dataset for URban climate modelling (GLAMOUR) offers the building footprint and height files at the resolution of 100 m in global urban centers.</p> <ul> <li>the `BH_100m` contains the building height files where each file is named as `BH_{lon_start}_{lon_end}_{lat_start}_{lat_end}.tif`.</li> <li>the `BF_100m` contains the building footprint files where each file is named as `BF_{lon_start}_{lon_end}_{lat_start}_{lat_end}.tif`.</li> </ul> <p>Here `lon_start`, `lon_end`, `lat_start`, `lat_end` denote the starting and ending positions of the longitude and latitude of target mapping areas.</p> <p>To avoid possible confusion, it should be clarified that the 'building footprint' in GLAMOUR represents the 'building surface fraction', i.e., the ratio of building plan area to total plan area.</p> <p> </p> <p>We also offer the snapshot of source code used for the generation of the GLAMOUR dataset including:</p> <ul> <li>`GC_ROI_def.py` defines regions of interest (ROI) used in the mapping of the GLAMOUR dataset.</li> <li>`GC_user_download.py` retrieves satellite images including Sentinel-1/2, NASADEM and Copernicus DEM from Google Earth Engine and exports them into Google Cloud Storage.</li> <li>`GC_master_pred.py` downloads exported data records from Google Cloud Storage and then performs the estimation of building footprint and height using Tensorflow-based models.</li> <li>`GC_postprocess.py` performs postprocessing on initial estimations by pixel masking with the World Settlement Footprint layer for 2019 (WSF2019).</li> <li>`GC_postprocess_agg.py` aggregates masked patches into larger tiles contained in the GLAMOUR dataset.</li> </ul>
Converging findings of climate models and satellite observations on the positive impact of European forests on cloud cover
<p>Overview:<br>This repository hosts a comprehensive dataset resulting from a Space for Time (S4T) analysis (<em>Duveiller et al. 2018</em>). The dataset spans monthly data from 2004 to 2014, providing detailed insights into cloud cover dynamics and land cover characteristics. Leveraging observations from the Cloud CCI MODIS-Aqua dataset (<em>Stengel et al. 2017</em>) and RegCM5 (<em>Giorgi et al. 2023</em>) model outputs at 0.05 degrees resolution, it offers valuable resources for researchers studying atmospheric and terrestrial interactions.</p> <p>Contents:</p> <p>s4t_ESACCI.zip:<br>Output of the space-for-time algorithm applied to the Global MODIS-Aqua cloud cover data for low, medium, and high clouds.<br>s4t_RegCM5.zip:<br>Output of the space for time algorithm applied to the European RegCM5 cloud data for low, medium, and high clouds.<br>Variables:</p> <p>Cloud Area Fractions:<br>Includes low (cll), medium (clm), and high (clh) cloud area fractions, expressed as percentages.<br>Cloud layers are categorized based on cloud top pressure (CTP), following the convention of the International Satellite Cloud Climatology Project.</p>
Data for: A protocol for model intercomparison of impacts of Marine Cloud Brightening Climate Intervention
<p>Replication data for "A protocol for model intercomparison of impacts of Marine Cloud Brightening Climate Intervention" submitted to Geophysical Model Development.</p>
Climate biogeography of Arabidopsis thaliana: Linking distribution models and individual variation
<p>Patterns of individual variation are key to testing hypotheses about the mechanisms underlying biogeographic patterns. If species distributions are determined by environmental constraints, then populations near range margins may have reduced performance and be adapted to harsher environments. Model organisms are potentially important systems for biogeographical studies, given the available range‐wide natural history collections, and the importance of providing biogeographical context to their genetic and phenotypic diversity. We fit occurrence records to climate data and then projected the distribution of <em>Arabidopsis</em> under the last glacial maximum, current, and future climates. We confronted model predictions with individual performance measured on 2194 herbarium specimens, and we asked whether predicted suitability was associated with life history and genomic variation measured on ~900 natural accessions. The most important climate variables constraining the <em>Arabidopsis</em> distribution were winter cold in northern and high-elevation regions and summer heat in southern regions. Herbarium specimens from regions with lower habitat suitability in both northern and southern regions were smaller, supporting the hypothesis that the distribution of <em>Arabidopsis</em> is constrained by climate‐associated factors. Climate anomalies partly explained interannual variation in herbarium specimen size, but these did not closely correspond to local limiting factors identified in the distribution model. Late‐flowering genotypes were absent from the lowest suitability regions, suggesting slower life histories are only viable closer to the centre of the realized niche. We identified glacial refugia farther north than previously recognized, as well as refugia concordant with previous population genetic findings. Lower latitude populations, known to be genetically distinct, are most threatened by future climate change. The recently colonized range of Arabidopsis was well‐predicted by our native‐range model applied to certain regions but not others, suggesting it has colonized novel climates. Integration of distribution models with performance data from vast natural history collections is a route forward for testing biogeographical hypotheses about species distributions and their relationship with evolutionary fitness across large scales.</p>
Supporting Data for "Climate Sensitivity and Relative Humidity Changes in Global Storm-Resolving Model Simulations of Climate Change"
<p>Code and netcdf files of processed X-SHiELD and CMIP6 simulations to reproduce the figures of Timothy M. Merlis, Kai-Yuan Cheng, Ilai Guendelman, Lucas Harris, Christopher S. Bretherton, Maximilien Bolot, Linjiong Zhou, Alex Kaltenbaugh, Spencer K. Clark, Gabriel A. Vecchi, and Stephan Fueglistaler (2024): "Climate Sensitivity and Relative Humidity Changes in Global Storm-Resolving Model Simulations of Climate Change".</p>
Modelling 21st-century refugia and the impact of climate change on Amazonia's largest primates
<p>Edaphic and vegetation conditions can render climatically suitable sites inadequate for a species to persist, constraining the amount of suitable habitat and the possibilities of tracking preferred climatic conditions as they shift in response to climate change. We combined climatic and remotely sensed data to model current and future distributions of nine extant taxa of ateline primates across the Amazon basin. We used the models to identify and quantify potential range changes and refugia of suitable habitats from the present to the latter half of the 21st century.<strong> </strong>We applied an ensemble forecasting approach for species distribution models using 596 spatially rarefied occurrences. We parameterised these models by combining reflectance data from a basin‐wide Landsat TM/ETM+ image composite, three sets of bioclimatic layers containing data for the current period, and two different (moderate and worst-case) climate change scenarios for 2041-2070. Eight out of nine taxa are likely to experience pronounced range losses, with seven of them predicted to lose over 50% of their currently suitable habitats irrespective of climate change scenarios. Modelled ateline richness exhibited a broad decrease in high-richness areas and a possible redistribution along the northernmost parts of western Amazonia. Refugia from 21st-century climate change for the whole complex was mostly concentrated in western Amazonia, especially in its southern part. We identified hotspots of vulnerability to climate change and 21st-century refugia for all Amazonian atelines while accounting for habitat characteristics that are important to guarantee the continued existence of suitable habitats for these strictly arboreal taxa. Increasing the understanding of climate change impacts on Amazonia's largest primates can help to inform spatial conservation planning decisions and management to sustain forest-dwelling biodiversity over large areas such as Amazonia.</p>
Model output for a storyline analysis of hurricane Irma's precipitation under various levels of climate warming
<p>Understanding how extreme weather, such as tropical cyclones, will change with future climate warming is an interesting computational challenge. Here, the hindcast approach is used to create different storylines of a particular tropical cyclone, Hurricane Irma (2017). Using the Community Atmosphere Model, we explore how Irma's precipitation would change under various levels of climate warming. Analysis is focused on a 48-hour period where the simulated hurricane tracks reasonably represent Irma's observed track. Under future scenarios of 2 K, 3 K, and 4 K global average surface temperature increase above pre-industrial levels, the mean 3-hourly rainfall rates in the simulated storms increase by 3-7%/K compared to present. This change increases in magnitude for the 95th and 99th percentile 3-hourly rates, which intensify by 10-13%/K and 17-21%/K, respectively. Over Florida, the simulated mean rainfall accumulations increase by 16-26%/K, with local maxima increasing by 18-43%/K. All percent changes increase monotonically with warming level.</p>
Data and Code for "A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Flood Timing" Paper Submission to AGU GRL
<p>This contains the emergent constraint code, the offline CMIP6 hydrology data, and the shapefiles for each region used in the paper "A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Flood Timing" submitted to AGU GRL.</p>
Impact of host climate model on contrail cirrus effective radiative forcing estimates
<p>Data to reproduce the figures in the article 'Impact of host climate model on contrail cirrus effective radiative forcing estimates'.</p>
" Description and evaluation of a new contrail cirrus 2 parameterization in the ARPEGE-Climat atmospheric 3 model " datasets
<p>This repository contains the data files used for the analyses presented in the paper. The dataset includes variables of interest for the two main simulations (CONTFREE and CONTNUDGED) for the year 2019. </p> <ul> <li>"totcon" variable represents the integrated contrail cirrus coverage.</li> <li>"rst", respectively "rstcotra", represent the net downward shortwave radiation at the top of the atmosphere for the radiative call with contrails perturbation, respectively without perturbation. The difference between these two variables provides the contrail cirrus net downward shortwave radiation contribution. </li> <li>"rlut", respectively "rlutcotra", represent the net upward longwave radiation at the top of the atmosphere for the radiative call with contrails perturbation, respectively without perturbation. The difference between these two variables provides the contrail cirrus net upward longwave radiation contribution. </li> <li>"pissr" represents the probability of the gridbox being ice supersaturated. This variable is provided for pressure levels 200,225, and 250hPa.</li> <li>"clhcalipso" represents the integrated coverage of "high clouds" (>400hPa). </li> </ul>
Climate model (CM2.6) and regional model (ACM) processed output used to investigate the physical drivers and biogeochemical effects of the weakening of the northwest North Atlantic Shelfbreak Jet (Garcia-Suarez & Fennel., 2024; JAMES)
<p>Key processed output from the climate model GFDL CM2.6 and the regional Atlantic Canada model (ACM) used to investigate the physical drivers and the biogeochemical effects of the weakening of the shelfbreak jet in the northwest North Atlantic Ocean. The dataset includes all model variables required to reproduce the key results in <em>Garcia-Suarez & Fennel (2024, JAMES)</em>. See <em>GarciaSuarezandFennel_JAMES_CM26_ACM_data_README_v2.txt</em> for more details.</p>
Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux
<p>This is dataset of global climate model simulation used in the paper "Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux" by Shimura et al. (2021)</p> <p>Followings are the explanation of data file.</p> <p>*** File naming rule ***<br> {data_group_name}_Exp{experiment_name}_TCnumber{tropical_cyclone_case_number}.nc</p> <p> data_group_name<br> - atm<br> - track</p> <p> experiment_name<br> - Wind<br> - Wave<br> - SlabO</p> <p> tropical_cyclone_case_number<br> - 001<br> - 002<br> ...<br> - 099<br> - 100</p> <p>*** Description on each data group ***<br> <br> atm: three dimentional atmospheric velocity data<br> - level: pressure levels for vertical atmospheric data<br> - longitude: Longitude<br> - latitude: Latitude<br> - velocity_u_component: averaged atmospheric eastward velocity</p> <p> track: data around tropical cyclone track<br> - time: UTC time (YYYYMMDDHH)<br> - longitude_center: Longitude of typhoon center<br> - latitude_center: Latitude of typhoon center<br> - central_pressure: typhoon central pressure<br> - maximum_surface_wind: typhoon maximum surface wind speed<br> - longitude_sfc: Longitude for surface data around typhoon center<br> - latitude_sfc: Latitude for surface data around typhoon center<br> - surface_wind_u_component: surface eastward wind around typhoon<br> - surface_wind_v_component: surface northward wind around typhoon<br> - sea_level_pressure: sea level pressure around typhoon<br> - latent_heat_flux: surface upward latent heat flux<br> - sensible_heat_flux: surface upward sensible heat flux<br> - time_atm: UTC time (YYYYMMDDHH) for atmospheric data<br> - level: pressure levels for atmospheric data<br> - longitude_atm: Longitude for atmospheric data around typhoon center<br> - latitude_atm: Latitude for atmospheric data around typhoon center<br> - velocity_u_component: 3d eastward velocity around typhoon<br> - velocity_v_component: 3d northward velocity around typhoon</p> <p> </p>
Data assimilation products by using multiple climate model simulations and different combinations of proxies
<p>This dataset of the climate reconstruction by data assimilation using isotope ratios provides annual surface air temperature, precipitation amount, and other climate variables during 850–2000.</p> <p>Two isotopes-incorporated atmospheric general circulation models and 129 isotopic proxy data (65 corals, 43 ice cores, and 21 tree-ring cellulose) were used in this study. There are nine experiments using three type of simulations and three combinations of proxies.</p> <p>The associated publication: Shoji, S., Okazaki, A., & Yoshimura, K. (2020). Impact of proxies and prior estimates on data assimilation using isotope ratios for the climate reconstruction of the last millennium. (submitted to Earth and Space Science)</p> <p>[Data structure]<br> X(lon) x Y(lat) x Z(2) x Variables(8) x Year(1151)<br> Z(1): analyses<br> Z(2): priors</p>
Evidence for niche conservatism in alpine beetles under a climate-driven species pump model
<p>Aim</p> <p>Past glacial climate cycles have generated lineage diversity in alpine habitats, acting as a climate-driven species pump. It is not clear how much this process contributes to ecological diversification of alpine species. To examine this problem, we test patterns of genetic and phenotypic divergence in two co-distributed species complexes of flightless alpine ground beetles. Greater differentiation in ecologically-important functional traits would indicate that ecological selection is an outcome of oscillating climate change, whereas greater differentiation in non-ecological traits would indicate niche conservatism.</p> <p>Location</p> <p>The Cascades Range and Trinity Mountains of western North America.</p> <p>Taxon</p> <p>Members of the <i>Nebria paradisi</i> and <i>N. vandykei</i> species complexes (Insecta: Coleoptera: Carabidae: Nebriinae)</p> <p>Methods</p> <p>We generated genome-wide single nucleotide polymorphism data and mitochondrial sequence data, as well as morphological and physiological data, to compare populations spanning the range of both species. Phylogenetic and population genetic analyses were used to infer the relationships among taxa and populations within each species complex, as well as historical population demography. Support vector machines were used to test for classification of taxa and populations based on ecomorphological, ecophysiological, and male reproductive traits. Mantel tests were then used to assess statistical associations between phenotypic and genetic divergence among populations.</p> <p>Results</p> <p>The <i>N. vandykei</i> and <i>N. paradisi</i> species complexes are each comprised of genetically distinctive populations exhibiting long-term demographic declines. Each phylogeny supports multiple monophyletic groups with geographical cohesion. By examining phenotypic traits among populations in both species' complexes, we show that reproductive trait divergence can discriminate species and population status more effectively than ecomorphological or ecophysiological traits. Reproductive and genetic divergence are significantly correlated in the <i>N. vandykei</i> species complex.</p> <p>Main Conclusions</p> <p>We found limited evidence of ecological selection acting on functional traits. Instead, reproductive and genetic divergence evolved among isolated populations in both species complexes, suggesting niche conservatism may be a common outcome in alpine species diversification.</p>
[Dataset] Polar and Topographic Amplifications of Inter-model Spread of Surface Temperature in Climate Models
<p>All the raw CMIP5 and CMIP6 model data used in this work are available at <a href="https://data.ceda.ac.uk/badc/cmip5/data/cmip5/output1">https://data.ceda.ac.uk/badc/cmip5/data/cmip5/output1</a> and <a href="https://esgf-node.llnl.gov/search/cmip6/">https://esgf-node.llnl.gov/search/cmip6/</a> respectively.</p> <p>The data uploaded here is the processed data that support and lead to the described results in the manuscript entitled "Polar and Topographic Amplifications of Inter-model Spread of Surface Temperature in Climate Models"</p> <p>Figure numbers shown in brackets are associated figures in the manuscript.</p> <p><br> cmip5_sdev.nc (the first row of figure 1 )<br> cmip6_sdev.nc (the second row figure 1 )</p> <p>cmip6_np_decompose.nc (figure 2 and figure 6)<br> cmip6_sp_decompose.nc (figure 3 and figure 7)<br> cmip6_tp_decompose.nc (figure 4 and figure 8)<br> cmip6_clt.nc (figure 5)<br> cmip6_np_energy_transport.nc (the first column of figure 9)<br> cmip6_sp_energy_transport.nc (the second column of figure 9)<br> cmip6_tp_energy_transport.nc (the third column of figure 9)</p> <p><br> cmip5_np_decompose.nc (figure 10 and figure 14 in appendix)<br> cmip5_sp_decompose.nc (figure 11 and figure 15 in appendix)<br> cmip5_tp_decompose.nc (figure 12 and figure 16 in appendix)<br> cmip5_clt.nc (figure 13 in appendix)<br> cmip5_np_energy_transport.nc (the first column of figure 17 in appendix)<br> cmip5_sp_energy_transport.nc (the second column of figure 17 in appendix)<br> cmip5_tp_energy_transport.nc (the third column of figure 17 in appendix)<br> </p>
Stratigraphic and Isotopic Evolution of the Martian Polar Caps from Paleo-Climate Models
<p>The data in this folder is a collection of outputs or manipulated variables from the LMD-MGCM simulations presented in figures in the JGR article "Stratigraphic and Isotopic Evolution of the Martian Polar Caps from Paleo-Climate Models."<br> </p>
Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations
<p>Data for article "Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations"</p>
Antarctic surface climate and surface mass balance in the Community Earth System Model version 2 (1850-2100) - AWS data
<p>This Antarctica AWS temperature and wind speed dataset was compiled by Alexandra Gossart and Niels Souverijns (<a href="https://doi.org/10.1175/JCLI-D-19-0030.1">https://doi.org/10.1175/JCLI-D-19-0030.1</a>).</p>
Glacier model simulations of moraine building forced by interannual variability in climate
<p>A set of 2,000-year simulations of moraine building by a glacier flowing through a synthetic alpine landscape forced by interannual variability in weather imposed on an otherwise stable climate. Moraine relief is shown for a standard deviation in mean annual air temperature (dT) of 0.5°C, 1.5°C, and 3.0°C around a long-term mean of 7.0°C. Simulations were made using the ice-flow model iSOSIA (Egholm et al., 2011, <em>Geomorphology</em>).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.