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2,837 results for “climate data”
Climate data for D1 data loggers (CR23X and CR1000), 2000 - ongoing, daily.
Climatological data were collected from a Niwot Ridge climate station (D1) throughout the year. From July 5 2000 through October 23 2013, data were recorded using a Campbell Instruments CR23X data logger. Subsequently, data were recorded using a Campbell Instruments CR1000 data logger. Maximum and minimum values are recorded instantaneously, with a sampling interval of 5 seconds. Daily averages and totals were calculated from 17,280 individual measurements. This instrument was programmed to generate both hourly and daily output. Beginning in June 25 2009, 10 minute output was also generated. Daily output generated by the logger ended in 2014. Beginning in 2014, daily values were calculated from the native resolution data (ten minute).
Climate data for saddle data loggers (CR23X and CR1000), 2009 - 2021, hourly.
Climatological data were collected from the saddle climate station on Niwot Ridge (3525 m elevation) throughout the year. From 2000-06-24 to 2012-03-24, data were recorded using a Campbell Instruments CR23X data logger. Subsequently, data were recorded using a Campbell Instruments CR1000 data logger. This data set includes data beginning in 2009. Maximum and minimum values were recorded instantaneously, with a sampling interval of 5 seconds. Hourly means and totals were calculated from 720 individual measurements. The CR23X logger was programmed to generate both hourly and daily output. The CR1000 logger generated daily, hourly, and minute data until September 2014, and 10 minute and minute data thereafter. This dataset discontinued, see methods for instructions on accessing the 10 minute data instead.
Data set for Global quantitative synthesis of ecosystem functioning across climatic zones and ecosystem types
<p>Dataset used in the publication: " Global quantitative synthesis of ecosystem functioning across climatic zones and ecosystem types". The dataset gathers estimates of ecosystem standing stocks (biomass, organic carbon, detritus), fluxes (GPP, ER, NEP) and process rates (decomposition and carbon uptake rates) for eight broad ecosystem types (forest, grassland, agroecosystem, desert, stream, lake, pelagic and benthic marine ecosystems) in five broad climatic zones (arctic, boreal, arid, temperate, tropical, arid).</p> <p>The scripts to produce the figures and the statistics of the publication are released along with the txt version of the data, which file is uploaded when running the script.</p>
Paleoclimate Data-Model Comparison and the Role of Climate Forcings over the Past 1500 Years
<p>The past 1500 years provide a valuable opportunity to study the response of the climate system to external forcings. However, the integration of paleoclimate proxies with climate modeling is critical to improving the understanding of climate dynamics. In this paper, a climate system model and proxy records are therefore used to study the role of natural and anthropogenic forcings in driving the global climate. The inverse and forward approaches to paleoclimate data-model comparison are applied, and sources of uncertainty are identified and discussed. In the first of two case studies, the climate model simulations are compared with multiproxy temperature reconstructions. Robust solar and volcanic signals are detected in Southern Hemisphere temperatures, with a possible volcanic signal detected in the Northern Hemisphere. The anthropogenic signal dominates during the industrial period. It is also found that seasonal and geographical biases may cause multiproxy reconstructions to overestimate the magnitude of the long-term preindustrial cooling trend. In the second case study, the model simulations are compared with a coral d18O record from the central Pacific Ocean. It is found that greenhouse gases, solar irradiance, and volcanic eruptions all influence the mean state of the central Pacific, but there is no evidence that natural or anthropogenic forcings have any systematic impact on El Nino-Southern Oscillation. The proxy climate relationship is found to change over time, challenging the assumption of stationarity that underlies the interpretation of paleoclimate proxies. These case studies demonstrate the value of paleoclimate data-model comparison but also highlight the limitations of current techniques and demonstrate the need to develop alternative approaches.</p>
C3-EURO4M-MEDARE Mediterranean historical climate data - v.2
<p>Historical surface climate data files and meta-data for stations in Mediterranean North Africa and Middle East areas (1852-2008).</p>
Supplementary data to: "A European Monsoon-like climate in a Warmhouse World"
<p>Supplementary information belonging to the manuscript titled "A European Monsoon-like climate in a Warmhouse World" by Nick van Horebeek and colleagues, containing the following information:</p> <ul> <li>"Campanile_D47_sample_data_calc.csv" - A file containing all clumped isotope measurements carried out for this study</li> <li>"Campanile_D47_season_data_calc.xlsx" - A file containing seasonal means and uncertainties of temperature and d18Osw based on clumped isotope measurements carried out for this study</li> <li>"Campanile_d18O_season_data_calc.csv" - A file containing all incrementally sampled oxygen and carbon isotope values with seasonal characterization.</li> <li>"Intra-growthline_variability_edit.png" - An image showing the variability in d18O values repeatedly measured in the same location in the shell</li> <li>"Campanile_Winter_growth_stop_images.zip" - A folder containing all shell images used in the publication</li> <li>"Campanile_Data_figure_S1.xlsx" - A file containing the dataset needed to produce the supplementary figure showing variability in oxygen isotope values (S1).</li> <li>"SI_Campanile_d18O_d13C_depth_rev1.png" - A plot showing the variability in d18O and d13C values along the shell</li> <li>"Campanile_clumped_season_plot_rev3.r" - Script used to process clumped isotope data for seasonal statistics and plotting</li> <li>"Campanile_clumped_d18O_plots_rev2.r" - Script used to process and plot seasonal statistics and isotope data + uncertainty against shell age.</li> </ul>
Data files for figures in "Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability" by Bird et al.
<p>The data files for figures in <i>Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability</i> by Bird, Bodeker and Clem. The files required to create each figure in the paper and in the supplementary material are described in a readme.txt file which is also provided below:</p><p><strong>Figure 1</strong></p><p>The background image was obtained from the 'NaturalEarthFeature' function of the python Cartopy library (Figure1_background.png). The data required to generate the plots shown in Figure 1 are provided in the Figure1.nc file:</p><ul><li>The latitudes and longitudes for the 10,000 training sites are provided in Training_location_latitudes and Training_location_longitudes variables. </li><li>The latitudes and longitudes for the 8 sites used to demonstrate the ability of the CNN to generalise spatially are provided in the Validation_location_latitudes and Validation_location_longitudes variables. </li><li>The block maxima at each of the 8 sites are provided in the Location_1year_block_maxima variable.</li><li>The GEV fits at 0°C are provided in the GEV_fit_at_0.0C variable.</li><li>The GEV fits at 1.5°C are provided in the GEV_fit_at_1.5C variable.</li></ul><p><strong>Figure 2</strong></p><ul><li>The 1-in-100 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named Figure2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-100 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named Figure2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure 3</strong></p><ul><li>The cumulative distribution functions (CDFs) shown in the lower four panels are provided as text files listing the ARI in years and the daily total precipitation depth in mm. These files are named CDF_<lat>_<long>.dat where <lat> is the latitude and <long> is the longitude. Files for each region are zipped into .7z files named Figure3_<region>.7z where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The latitudes and longitudes for the upper panels can be inferred from the file names for each region.</li></ul><p><strong>Figure 4</strong></p><p>The data for each panel are provided in a netCDF file named Figure4_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 5</strong></p><p>The data are provided as text files named Figure5_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.</p><p><strong>Figure 6</strong></p><p>The data are provided as text files named Figure6_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the precipitation depths and the sensitivities at ARIs of 10, 20, 50, 100, and 200 years. </p><p><strong>Figure 7</strong></p><p>The data for each panel are provided in a netCDF file named Figure7_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 8</strong></p><p>The data are provided as text files named Figure8_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the global surface temperature anomaly (°C) and the average negative log likelihood.</p><p><strong>Figure S1 and Figure S3</strong></p><p>The data are provided as text files named Figure_S1_and_S3_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes its contents.</p><p><strong>Figure S2</strong></p><ul><li>The 1-in-20 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named FigureS2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-20 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named FigureS2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure S4</strong></p><ul><li>The block maxima for each site are provided in text files named FigureS4_blockmaxima_siteA.txt and FigureS4_blockmaxima_siteB.txt. A header at the top of each column described the column contents. </li><li>The GEV-derived curves for each site are provided in text files named FigureS4_gevcurves_siteA.txt and FigureS4_gevcurves_siteB.txt. A header at the top of each column described the column contents. </li></ul><p><strong>Figure S5</strong></p><p>There are no data associated with this figure. This figure was made using Microsoft Powerpoint.</p><p><strong>Figure S6</strong></p><p>The data are provided as text files named FigureS6_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes the files contents.</p>
LAMASUS NUTS-level climate data 01/2007-12/2016
<p>This dataset comprises spatial and temporal climate data aggregated from the CHELSA-W5E5 v1.0 data to the NUTS regions across Europe, covering the period from January 2007 to December 2016. The dataset consists of four files, each corresponding to a different level of NUTS coding (NUTS 0-3).</p> <p>For each file, the following columns are included:</p> <ol> <li>NUTS Code: The unique identifier for the NUTS region.</li> <li>Year: The year of the data point.</li> <li>Month: The month of the data point.</li> <li>Precipitation (pr): The mean precipitation in kilograms per square meter per second for the given month, year, and region.</li> <li>Maximum Temperature (tasmax): The surface maximum temperature in degrees Celsius for the given month, year, and region.</li> <li>Minimum Temperature (tasmin): The surface minimum temperature in degrees Celsius for the given month, year, and region.</li> </ol> <p>The temporal dimension is divided into years and months, ranging from January 2007 to December 2016. The spatial dimension is identified by NUTS codes, with granularity ranging from level 0 to level 3.</p> <div> <p>This dataset has been created as part of LAMASUS Project under the scope of Deliverable 3.2 titled "Database on EU policies and payments for agriculture, forest, and other LUM related drivers ". The data is directly linked to the work described on pages 49-50, belonging to section 3.5 Climate data. The full text of the deliverable can be accessed via: <a href="https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf">https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf.</a></p> <p>Please note that this dataset is intended for research and analysis in the fields of climatology, environmental science, and related disciplines. Users are encouraged to cite this dataset appropriately if utilized in academic or scientific publications.</p> </div>
Data from Yellow Sigatoka monitoring methods in the subtropical climate of southern Brazil
<h2>Description of the data and file structure</h2> <p>In this study four methods of disease monitoring were tested under field conditions: Biological Pre Warning (BPW); Stage of Evolution (SE); youngest Leaf Spotted (YLS); Infection Index (II). The BPW system evaluates the youngest leaves (2, 3, and 4), assigning a value for each type of lesion present, as well as for intensity of the lesion on the leaves (BUREAU et al., 1992). In the dataset is cited as the variable gross sum (points).</p> <p>The SE evaluates more leaves (1, 2, 3, 4, and 5) and scores only the most advanced symptoms of leaf disease, but without considering lesion intensity (GANRY et al., 2008). The SE calculation also corrects the gross sum of the disease according to leaf emission. The leaf emission rate was calculated using the Brun scale, which evaluates cigar leaf growth in decimals from 0.0 to 0.8. In the dataset is cited as the variable corrected gross sum (points).</p> <p>YLS is evaluated as the first leaf that has 10 spots with gray centers (CARLIER et al., 2003). In the dataset is cited as the variable YLS, which means the leaf position counted from the top to the botton of the plant (leaf number 3, leaf number 4...).</p> <p>Sigatoka Infection Index is quantified by assessing the severity of banana leaf disease using the Stover scale, with indexes from 0 to 50%, by means of the following formula: Infection Index =% (IF): [Σn × b / (N- 1) × T] × 100, in which: n = the number of leaves at each Stover scale level; b = degree according to the scale; N = the number of degrees employed in the scale (6); T = the total number of leaves evaluated (CARLIER et al., 2003). In the dataset is cited as the variable Infection index that should be understood like the severity of this leaf disease.</p> <p>In the second phase of the study, two monitoring methods were applied in commercial orchards in order to compare the standard model (Biological Pre-Warning – BPW) with the alternative method selected in the experimental phase (Youngest Leaf Spotted – YLS). The methods were applied, as described before in three sites in Criciúma (site 1) and Siderópolis (sites 2 e 3), municipalities in the southern coast of the state of Santa Catarina, from March 2016 to November 2018. During this period, 37 disease evaluations were performed at each location.</p> <p>Disease data of the experimental area were submitted to descriptive analysis and Pearson correlation at 5% probability of error. Disease progress curves were also plotted. The disease development data in commercial orchards were analyzed by plotting disease progress curves for BPW and by frequency distribution (%) for the YLS variable during all period of the experiment.</p>
Climate Solutions Explorer - hazard, impacts and exposure data
<p><a name="_GoBack"></a>The Climate Solutions Explorer website maps and presents information about mitigation pathways, avoided climate impacts, vulnerabilities and risks arising from development and climate change. <a href="https://www.climate-solutions-explorer.eu"><strong>www.climate-solutions-explorer.eu</strong></a></p> <p>Using the latest data, state-of-the-art models were used to assess the future trends of indicators of development- and climate-induced challenges.</p> <p>Updated gridded global climate and impact model data are based on CMIP6 and CMIP5 projections, using a subset of models from the ISIMIP project that have been consistently downscaled and bias-corrected. The data includes various indicators (~42) relating to extremes of precipitation and temperature (e.g. from Expert Team on Climate Change Detection and Indices), hydrological variables including runoff and discharge, heat stress (from wet bulb temperature) events (multiple statistics and durations), and cooling degree days, as well as further indicators relating to air pollution (PM2.5 from the GAINs model), and crop yields and natural habitat land-use change (biodiversity pressure) from the GLOBIOM model.</p> <p>Indicators were calculated at a spatial resolution of 0.5° (approximately 50km at the equator), and subsequently spatially aggregated to the country level – from which population and land area exposure to the impacts were calculated. This has enabled the country-by-country comparison of national climate impacts and avoided exposure. Impacts were calculated at global mean temperature intervals, i.e. 1.2, 1.5, 2, 2.5, 3, and 3.5 °C, compared to a pre-industrial climate.<br><br></p> <p><strong>The dataset includes: </strong></p> <ul> <li>Global gridded projections (in netCDF format) of all the climate impact indicators at 0.5° spatial resolution, at global warming levels of 1.2, 1.5, 2, 2.5, 3, and 3.5 °C<br><br>For each GWL, maps for the absolute indicator values, the relative difference, and the scores are provided. The naming format is: cse_[short_indicator_name]_[ssp]_[gwl]_[metric].nc4. Please note that the Greenland ice sheet and the desert areas have been masked out for the hydrology indicators for these datasets.<br><br></li> <li>Intermediate output data, including gridded maps of absolute values, relative differences, and scores for all ensemble members, as well as gridded maps of the multi-model ensemble statistics for the global warming levels and the reference period <br><br>For the ensemble member data, the naming format is [gcm]_[ssp/rcp]_[gwl]_[short_indicator_name]_global_[start_year]_[end_year].nc4 or [ghm]_[gcm]_[ssp/rcp]_[gwl]_[soc]_[short_indicator_name]_global_[start_year]_[end_year]_[metric].nc4 for the hydrology indicators. <br><br></li> <li>Tabular data (.csv) aggregating the indicators to country (or region) level, for both hazards and exposure, population and land-area weighted<br><br>The .zip archives ‘table_output_climate_exposure_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_climate_exposure_land_air_pollution.zip’ contains the table data for theland and air pollution indicators. <br><br></li> <li>Tabular data (.csv) for avoided impacts by mitigating to 1.5 °C (land and population exposure)<br><br>The .zip archives ‘table_output_avoided_impacts_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_avoided_impacts_land_air_pollution.zip’ contains the table data for the land and air pollution indicators.</li> </ul> <p> </p> <p>Further details are available on the Data Story page – <a href="http://www.climate-solutions-explorer.eu/story/data">www.climate-solutions-explorer.eu/story/data</a>. A detailed description of the methodology and the calculation of the ISIMIP-derived indicators has been published in <a title="Global warming levels indicators of climate change and hotspots of exposure" href="https://doi.org/10.1088/2752-5295/ad8300" target="_blank" rel="noopener">Werning, M. et al. (2024).</a></p> <p> </p> <p><strong>Release notes (v1.1)</strong></p> <p>Changes in this version:</p> <ul> <li>Only table output data for the land and air pollution indicators have been changed, all other indicator data remain unchanged from v1.0</li> <li>Updated land and air pollution indicators to use scaled population data to match the latest SSP population projections from the Wittgenstein Center from 2023</li> <li>Fixed issue with the region mask for the EU</li> <li>Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions</li> </ul> <p> </p> <p><strong>Release notes (v1.0)</strong></p> <p>Changes in this version:</p> <ul> <li>Fixed calculation of the indicator “Drought intensity” (both for the version using discharge and run-off)</li> <li>Masked out the Greenland ice sheet and the desert areas for the global gridded projections for the hydrology indicators in the final output files</li> <li>Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions</li> <li>Used scaled population data to match the latest SSP population projections from the Wittgenstein Center from <a>2023</a></li> <li>Added the indicator ‘Heatwave days’</li> <li>Added intermediate outputs for all ensemble members for energy, hydrology, precipitation, and temperature indicators<br><br></li> </ul> <p><strong>Release Notes (v0.4)</strong></p> <p>Changes in this version:</p> <ul> <li>Removed ssp and metric from variable name in netCDF files</li> <li>Removed obsolete coordinates in netCDF files for 'Drought intensity'</li> <li>Added intermediate outputs for energy, hydrology, precipitation, and temperature indicators</li> </ul> <div> </div>
Data Files for Climate-based Maize Loss Rate Simulations
<p>This archive contains data files from an <a href="../records/13356711">open source pipeline</a> looking at how crop insurance rates may change in the future within the US Corn Belt using <a href="https://www.sciencedirect.com/science/article/pii/S0034425715001637">SCYM</a> and <a href="https://www.chc.ucsb.edu/data/chc-cmip6">CHC-CMIP6</a>. These are available under a Creative Commons license. Unless otherwise specified, these report on SSP245.</p> <p>See README for more details including column-level description of each resource. Funded by the <a href="https://dse.berkeley.edu/">Eric and Wendy Schmidt Center for Data Science and Environment</a> at the University of California, Berkeley.</p>
Data for paper publication "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3"
<p>The dataset presented here is related to the article by Leon-Marcos et al. 2025: "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3" accepted for publication in GMD. It comprises global fields of the FESOM2.1-REcoM3 biogeochemistry model tracers employed to calculate the ocean biomolecule concentration that serve as input data for the aerosol model. Additionally, the ECHAM6.3–HAM2.3 code of the marine aerosol implementation and the required scripts to run the model experiments are provided here. The aerosol-climate model simulation results of the marine aerosol emission, as well as the evaluation of the model results compared to observations, are also included. For further information, please refer to the attached data description. </p> <p> </p> <h2> </h2>
An experimental data set on the thermal and fluid dynamic performance of double skin facades (DSFs) subjected to various controlled boundary conditions through the use of a climate simulator facility
<p>Double skin facades (DSFs) are building envelope systems defined by complex phenomena and non-linear-processes that make characterizing their performance a non-trivial task. In an effort to enable the scientific community to access experimental data for further analysis or model validation purposes, we release together with the open-access paper entitled “<strong><em>Laboratory testbed and methods for flexible characterization of the thermal and fluid dynamic behavior of double skin facades” (</em></strong><a href="https://doi.org/10.1016/j.buildenv.2021.108700"><strong><em>https://doi.org/10.1016/j.buildenv.2021.108700</em></strong></a><strong><em>)</em></strong>, a set of experimental data collected during tests carried out with the use of the newly developed testbed. The data contains the results of a series of tests where various configurations of a full-scale DSF mock-up that have been subjected to different boundary conditions replicated in a climate simulator. The database contains a guide in the form of the file ‘Guide.pdf’, which explains how to read data, presents a schematic drawing of sensor layout, and provides more information on sensors’ positions. Further information on the original aims of the experiments, methods, and other data can be found in the article mentioned above, which becomes an essential tool to understand how to read and interpret the experimental data fully. The following collection of experimental data are provided:</p> <ul> <li>32 steady-state measurements where the following factors were changed: ventilation mode (indoor and outdoor air curtain), solar irradiance (0, 400, 600, and 800 Wm<sup>-2</sup>), outdoor chamber temperature (10, 20, 30, and 40 ℃), cavity depth (20, 30, 40 and 60 cm) and venetian blinds position (no blinds, closed blinds, θ=45 º, and open blinds) [file names: ‘Taguchi_4Lx4F_L16_I-I.csv’ and ‘Taguchi 4Lx4F_L16_O-O.csv’],</li> <li>Dynamic profile measurements corresponding to a typical hot summer day [Dynamic_profile_measurements.csv] and</li> <li>Calibration data [Callibration.csv].</li> </ul> <p>Any inquires on the experimental data<em> can be sent </em>to: aleksandar.jankovic@ntnu.no</p>
Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets
<p><strong>Sydney morphology and land surface dataset</strong></p> <p>This dataset for Sydney, Australia, represents land cover, building morphology, vegetation morphology and other parameters appropriate for input into local or mesoscale urban climate models.</p> <p>The dataset is provided in netCDF4 and GeoTiff formats.</p> <p>Associated manuscript:</p> <blockquote> <p><a href="https://doi.org/10.3389/fenvs.2022.866398">A transformation in city-descriptive input data for urban climate models</a></p> </blockquote> <p>Citation for the open dataset:<br> - Lipson, M., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets (v1.01), <a href="https://doi.org/10.5281/zenodo.6579061">https://doi.org/10.5281/zenodo.6579061</a>, 2022.</p> <p>Citation for the associated manuscript:<br> - Lipson, M. J., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: A Transformation in City-Descriptive Input Data for Urban Climate Models, Frontiers in Environmental Science, 10, <a href="https://doi.org/10.3389/fenvs.2022.866398">https://doi.org/10.3389/fenvs.2022.866398</a>, 2022.</p> <p>Location of associated processing code:<br> - <a href="https://github.com/matlipson/geoscape_processing_public.git">https://github.com/matlipson/geoscape_processing_public.git</a></p> <p><strong>Acknowledgments</strong></p> <p>We gratefully acknowledge the Australian Urban Research Infrastructure Network (AURIN) and Geoscape Australia for <br> providing the datasets necessary for this study, drawing on Geoscape Buildings, Surface Cover and Trees datasets, <br> © Geoscape Australia, 2020: https://geoscape.com.au/legal/data-copyright-and-disclaimer/. <br> This research was supported by the Australian Research Council (ARC) Centre of Excellence for Climate System Science <br> (grant CE110001028), the ARC Centre of Excellence for Climate Extremes (grant CE170100023). </p> <p> </p>
Data and analysis script for "The (non)effect of personalization in climate texts on credibility of climate scientists: A case study on sustainable travel"
<p>Dataset and analysis script for the article "<strong>The (non)effect of personalization in climate texts on credibility of climate scientists</strong><strong>: A case study on sustainable travel</strong>", under review at Geoscience Communication (https://doi.org/10.5194/egusphere-2024-543)</p>
Data from Davison et al. (2024) Changes in Danish bird communities over four decades of climate and land-use change
<p>Environmental and biodiversity data associated with the article: <br><strong>Davison, C. W., Rahbek, C., & Morueta-Holme, N. (2024) Changes in Danish bird communities over four decades of climate and land-use change. <em>Oikos. </em></strong>https://doi.org/10.1111/oik.10697</p> <p>Data on local bird species richness, functional diversity, temporal and spatial turnover (beta diversity), abundance, and biomass at volunteer led survey routes across Denmark. Matched habitat data (from volunteers) and historical climate data (E-OBS). Bird observations are a subset of the Common Bird Monitoring programme (DOF – BirdLife Denmark) that include routes surveyed in the summer season, spanning ≥10 years, and with full GPS coordinates. This excel document contains all of the derived (and anomysied) data used in the final analyses and includes metadata describing the variables.</p> <p>Climate and trait data were obtained from open-access databases (see references). Metadata is included in the excel file.</p> <ul> <li><strong>Danish Common Bird Monitoring programme</strong> – Eskildsen, D. P., Vikstrøm, T., & Jørgensen, M. F. (2021). Overvågning af de almindelige fuglearter i Danmark 1975-2020. <em>Dansk Ornitologisk Forening</em>.</li> <li><strong>E-OBS European gridded climate data</strong> – Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., & New, M. (2008). A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. <em>Journal of Geophysical Research Atmospheres</em>, <em>113</em>(20). https://doi.org/10.1029/2008JD010201</li> <li><strong>AVONET bird traits data</strong> – Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Yang, J., Neate-Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., Walkden, P. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño-Centellas, F., Leandro-Silva, V., Claramunt, S., Darski, B., … Schleunning, M. (2022). AVONET: morphological, ecological and geographical data for all birds. <em>Ecology Letters</em>, <em>25</em>(3), 581–597. https://doi.org/10.1111/ele.13898</li> </ul> <p> </p>
A Novel Framework to Harmonise Satellite Data Series for Climate Applications: Matchups, Calibration Parameters and Residuals
<p>The datasets included with this archive supplement the journal article:</p> <p>Giering, R.; Quast, R.; Mittaz, J.P.D.; Hunt, S.E.; Harris, P.M.; Woolliams, E.R.; Merchant, C.J. A Novel Framework to Harmonise Satellite Data Series for Climate Applications. <em>Remote Sens. 2019</em>, <strong>11</strong>, 1002. doi:<a href="https://doi.org/10.3390/rs11091002">10.3390/rs11091002</a>.</p> <p>The archive includes a README file with further explanations.</p>
Supplementary Data: Global rise in forest fire emissions linked to climate change in the extratropics
<p>Supplementary Data for the paper "Global rise in forest fire emissions linked to climate change in the extratropics" by Jones et al. (2024, <em>Science</em>).</p> <p>The records include mapped pyromes and data and code used to delineate the pyromes.</p> <h3><strong>Mapped Pyromes</strong></h3> <p>The data records include mapped pyromes in three forms:</p> <ol> <li><strong>Shapefile</strong> (Jones_etal_2024_Global_Forest_Pyromes.shp.zip). Vector features in shapefile format containing data fields <em>pyrome ID</em> and <em>pyrome name</em>. The zipped file contains .shp, .dbf, .prj, .shx files.</li> <li><strong>Lower-resolution NetCDF </strong>(Jones_etal_2024_Global_Forest_Pyromes_Qdeg.nc). NetCDF version 4 file containing gridded values of <em>pyrome ID</em> at quarter-degree resolution.</li> <li><strong>Higher-resolution NetCDF</strong> (Jones_etal_2024_Global_Forest_Pyromes_005deg.nc). NetCDF version 4 file containing gridded values of <em>pyrome ID</em> at 0.05 degree resolution.</li> </ol> <p>Shapefiles are accessible via GIS programmes such as QGIS or ArcGIS. All files .shp, .dbf, .prj, .shx files must be stored in a single directory</p> <p>NetCDF files can be access by a variety of programming languages such as Python and R. For quick visualisations and access to the data structure, we suggest using the Panoply tool https://www.giss.nasa.gov/tools/panoply/.</p> <h3><strong>Correlation Data</strong></h3> <p>The data records (Correlation_Qdeg.zip) include gridded quarter-degree correlations between forest burned area (BA) and each of the following variables:</p> <ul> <li><em><strong>Fire weather index</strong></em></li> <li><em><strong>Atmospheric instability (continuous Haines index)</strong></em></li> <li><em><strong>Lightning flash density</strong></em></li> <li><em><strong>Soil moisture</strong></em></li> <li><em><strong>Vegetation productivity (Normalised Difference Vegetation Index)</strong></em></li> <li><em><strong>Population density</strong></em></li> <li><em><strong>Cropland cover</strong></em></li> <li><em><strong>Pasture cover</strong></em></li> <li><em><strong>Road density</strong></em></li> <li><em><strong>Potential fuel loads - surface fuels</strong></em></li> <li><em><strong>Potential fuel loads - shrub fuels</strong></em></li> <li><em><strong>Potential fuel loads - canopy and ladder fuels</strong></em></li> <li><em><strong>Terrain ruggedness index</strong></em></li> <li><em><strong>Forest area density</strong></em></li> </ul> <p>The BA data derive from MODIS MCD64A1 collection 6.1 (Giglio et al., 2018). BA data for forests is masked using the MODIS MOD44B product (DiMiceli et al., 2021) with a 30% tree cover threshold. The predictor data derive from multiple sources as desribed by Jones et al. (2024). See Supplementary Methods and Materials.</p> <p>The gridded correlations data are provided in Hierarchical Data Format version 5 (.hdf5) files, zipped to Correlation_Qdeg.zip. File names describe the variables used.<em> Cropland_Pasture_Qdeg.hdf5 </em>contains data for both cropland and pasture. Each file contains layers describing the Spearman's rho (ρ) correlation coefficient and the related p-value.</p> <p>As explained and justified by Jones et al. (2024), the correlation structure used depends on the variable (see Supplementary Methods and Materials) as per the following categories:</p> <ul> <li><strong><em>Fire Weather Index, Atmospheric Instability, and Lightning Flash Density:</em></strong> Monthly correlation between forest BA and each variable across all fire season months in the period 2001-2021 at the quarter-degree resolution.</li> <li><strong><em>Soil Moisture:</em></strong> Inter-annual correlation between (i) mean soil moisture during the fire season and (ii) accumulated forest BA during the fire season at quarter-degree resolution across years 2001-2021. </li> <li><strong><em>Vegetation Productivity (NDVI):</em></strong> Inter-annual correlation between (i) mean NDVI during the prior growing season and (ii) accumulated forest BA during the fire season at quarter-degree resolution across years 2001-2021. </li> <li><strong><em>Population Density, Cropland Cover, Pasture Cover, Road Density, T</em></strong><strong><em>errain Ruggedness Index, Forest Area Density: </em></strong>Spatial correlation between mean annual forest BA and each variable across the 0.05° cells within each quarter-degree cell during 2001-2021.</li> </ul> <p>Note that these grids are provided for insights into spatial variation in the input correlation data. Pyromes are defined based on correlations fitted on the spatial scale of Olson ecoregions, not quarter-degree grid cells (see further details below).</p> <h3><strong>Clustering Code</strong></h3> <p>DEMO_Clustering.zip contains R Statistics code for clustering forest ecoregions into pyromes based on correlations observed between forest BA and 14 predictors at regional level. The <em>Input</em> directory contains a .RData data frame with correlations between forest BA and each predictor for ecoregions. For demonstrative purposes the code is applied to cluster forest ecorgions of North America into pyromes. The <em>Regions</em> directory contains ecoregions of North America in shapefile format. The <em>Output</em> directory contains output generated by M. Jones, which can be used for validation purposes once other users have trialled the code.</p>
Data, Analytical Code, and Model Outputs From: Restoration Treatments Enhance Tree Growth and Alter Climatic Constraints During Extreme Drought
<p>This archive includes data (forest inventories, tree ring measurements, climate variables), statistical code, model outputs, and a preprint copy of Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive</p>
Replication data for "Climate change may induce connectivity loss and mountaintop extinction in Central American forests"
<p>Model code and predictor data underlying the publication "<strong>Climate change may induce connectivity loss and mountaintop extinction in Central American forests</strong>".</p>
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.