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29 results for “Climate index”
2-meter Universal Thermal Climate Index (UTCI) and Human Heat Health Index (H3I) hazard for Austin, Texas
<p>Universal Thermal Climate Index (UTCI) is a physiological temperature that is widely used in biometeorological studies to assess the heat stress felt by humans. UTCI considers the shortwave and longwave radiation incident on humans from the six cubical directions as well as air temperature, humidity, wind speed and clothing. As a part of NOAA National Integrated Heat Health Information System (NIHHIS) and NASA Interdisciplinary Research in Earth Science (IDS) project, we have generated the UTCI data for Austin, Texas and surrounding peri-urban area at 2-meters spatial resolution for the year 2017. Details on data generation and methodology can be found in Kamath et al., (2023) but are summarized here. </p> <p><strong>1. Datasets and model used</strong></p> <p>The solar and longwave environmental irradiance geometry (SOLWEIG) model was used to simulate shadows, mean radiant temperature (T<sub>MRT</sub>) and the UTCI (Lindberg et al., 2008). T<sub>MRT</sub> is the equivalent temperature due to exposure to absorbed shortwave and longwave radiation from all directions in a standing position. SOLWEIG was forced using near-surface ERA-5 data available at a spatial resolution of 0.25°x 0.25°. Building, vegetation heights, and digital terrain model were again derived from 3DEP LiDAR point cloud data. SOLWEIG was run using the urban multi-scale environment predictor (UMEP) (Lindberg et al., 2018) plug-in with QGIS. </p> <p><strong>2. Data availability</strong></p> <p>Diurnal UTCI data were calculated for typical meteorological clear sky days corresponding to Summer and Fall. The typical clear sky day was selected using the 10-year Typical meteorological Year (TMY) for Austin, Texas (30.2672° N, 97.7431° W) provided by National Solar Radiation Database (NSRDB). More details on TMY files can be found at: https://nsrdb.nrel.gov/data-sets/tmy</p> <p>Additionally, data is developed for heat hazard for daytime Human Heat Health Index (H3I) calculation as defined by Kamath et al., (2023). Briefly, this heat hazard is defined as the fraction of the day when the UTCI exceeds certain threshold. The threshold used to calculate heat hazard for Summer and Fall were 35° C and 32°C, respectively that imply strong heat stress (Jendritzky et al., 2012). Note that UTCI is on a different scale compared to air temperature, and could yield different heat stress levels.</p> <p><strong>3. Data format</strong></p> <p>The georeferenced UTCI and heat hazard data are available in the geoTIFF file format. The files can be readily visualized using GIS software such as QGIS and ArcGIS, as well as programing languages such as Python.</p> <p> <strong>4. Companion dataset</strong></p> <p>Based on the calculated UTCI here, the potential locations for tree planting were calculated to increase the shade to reduce heat vulnerability for Austin, Texas. [https://doi.org/10.5281/zenodo.6363494]</p> <p><strong>References</strong></p> <ol> <li>Kamath, H. G., Martilli, A., Singh, M., Brooks, T., Lanza, K., Bixler, R. P., ... & Niyogi, D. (2023). Human heat health index (H3I) for holistic assessment of heat hazard and mitigation strategies beyond urban heat islands. Urban Climate, 52, 101675.</li> <li>Lindberg, F., Holmer, B., & Thorsson, S. (2008). SOLWEIG 1.0–Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. <em>International journal of biometeorology</em>, <em>52</em>, 697-713.</li> <li>Lindberg, F., Grimmond, C. S. B., Gabey, A., Huang, B., Kent, C. W., Sun, T., ... & Zhang, Z. (2018). Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services. <em>Environmental modelling & software</em>, <em>99</em>, 70-87.</li> <li>Jendritzky, G., de Dear, R., & Havenith, G. (2012). UTCI—why another thermal index?. <em>International journal of biometeorology</em>, <em>56</em>, 421-428.</li> <li>Bixler, R. P., Coudert, M., Richter, S. M., Jones, J. M., Llanes Pulido, C., Akhavan, N., ... & Niyogi, D. (2022). Reflexive co-production for urban resilience: Guiding framework and experiences from Austin, Texas. Frontiers in Sustainable Cities, 4, 1015630.</li> <li>Lanza, K., Jones, J., Acuña, F., Coudert, M., Bixler, R. P., Kamath, H., & Niyogi, D. (2023). Heat vulnerability of Latino and Black residents in a low-income community and their recommended adaptation strategies: A qualitative study. <em>Urban Climate</em>, <em>51</em>, 101656.</li> </ol>
Dataset for "Future projections for the Antarctic ice sheet until the year 2300 with a climate-index method"
<p>Dataset for the paper "Future projections for the Antarctic ice sheet until the year 2300 with a climate-index method" (Journal of Glaciology, <a href="https://doi.org/10.1017/jog.2023.41">doi: 10.1017/jog.2023.41</a>).</p> <p>Please see the README for details.</p> <p>V1.1: Run-specs header files for SICOPOLIS added. README updated.<br>V1: Initial upload.</p> <p>* * * * * * *</p> <p>Users should cite the original publication when using all or parts of these data.</p>
North Atlantic Oscillation (NAO) climate index hidden in ocean generated secondary microseisms
<p>Datatsets associated with "North Atlantic Oscillation (NAO) climate index hidden in ocean generated secondary microseisms". The data include the daily seismic cross-correlograms for station pairs located on land and at the seafloor offshore Ireland, 3D models used for the numerical simulations and the associated synthetic seismic data.</p>
Data for "Improved constraints on hematite refractive index for estimating climatic effects of dust aerosols"
<p>This repository contains calculated/simulated data on the imaginary part of the complex refractive index, single scattering albedo, and/or optical depth for dust aerosols in the visible band or at the wavelength of 550 nm.</p> <p>For detailed information on (1) the acquisition and utilization of this data, (2) comprehensive configurations for model simulations, (3) the principal findings, and (4) the methodology employed to achieve these findings, please refer to the article authored by Li, Mahowald et al. (2024; Commun. Earth Environ).</p> <p>Other datasets, including the code and laboratory observations presented in the paper, can be found elsewhere (refer to the Data and Code Availability sections of the paper).</p> <p>For any clarification regarding the data and code, inquiries related to the publication, or potential collaboration, please contact Longlei Li (<a href="mailto:ll859@cornell.edu">ll859@cornell.edu</a>) or Natalie M. Mahowald (<a href="mailto:mahowald@cornell.edu">mahowald@cornell.edu</a>).</p>
CLIMATE CHANGE EFFECTS ON A SUBTROPICAL COASTAL SHALLOW LAKE FROM HEATWAVE INDEXES
<p>This zipped folder contains the files used to generate the results of this article, submitted to the journal Earth Systems and Environment.</p>
TMax index: spatial heterogeneity of climate change as an experiential basis for skepticism
<p>To evaluate how the spatial heterogeneity of climate change affects the public’s willingness to accept scientific results that the climate is changing, we propose an index that accurately measures local changes in climate based on the number of days per year for which the year of the record high temperature is more recent than the year of the record low temperature. TMax index is calculated using the Global Historical Climatology Network (GHCN) dataset. Please refer to the PNAS paper for details on how the index is calculated.</p>
Data and scripts for figures in Walton & Huntingford, "Little Evidence of Hysteresis in Regional Precipitation, When Indexed by Global Temperature Rise and Fall in an Overshoot Climate Simulation"
<p>The datasets included here are of the plotted data from the figures of the paper entitled "Little Evidence of Hysteresis in Regional Precipitation, When Indexed by Global Temperature Rise and Fall in an Overshoot Climate Simulation", submitted for publication to Environmental Research Letters. Scripts used for plotting and analysis are also included.</p>
TreeGOER Global Zones: Global atlas for the Climatic Moisture Index (CMI), Maximum Climatological Water Deficit (MCWD) and the number of months with average temperature > 10 degrees C (Tmo10)
<p>The <strong>TreeGOER (Tree Globally Observed Environmental Ranges)</strong> database documents the environmental ranges for 48,129 tree species and is available from files archived at <a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a>. Details on the preparation of this database from 30 arc-second global grid layers are provided by Kindt, R. (2023). <strong>TreeGOER: A database with globally observed environmental ranges for 48,129 tree species</strong>. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>. The atlas from this archive was designed to be used together with TreeGOER and possibly also with the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database (Kindt et al. <a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) to allow users to filter suitable tree species based on environmental conditions of the planting site.</p> <p><strong>TreeGOER</strong> includes a file (<em>TreeGOER_Tmo10_classes.txt</em>) that documents the distribution of species in zones defined by the number of months with average temperature > 10 degrees C. <strong>TreeGOER</strong> also includes a file (<em>TreeGOER_CMI_classes.txt</em>) that documents the distribution of species in zones defined by the Climatic Moisture Index (CMI). The atlas provided here shows the global distribution of the Tmo10 zones and CMI zones at high resolution on six sheets each, including three sheets in the northern hemisphere and three sheets in the southern hemisphere.</p> <p>The atlas also includes six sheets that show the global distribution of the Maximum Climatological Water Deficit (MCWD), another environmental variable covered by the <strong>TreeGOER</strong> database.</p> <p> </p> <table> <tbody> <tr> <td><strong>Zone</strong></td> <td><strong>Classes</strong></td> <td><strong>Comment</strong></td> </tr> <tr> <td>Tmo10</td> <td> Tmo10 = 12 + Bio06 >= 18</td> <td>tropical (minimum temperature of coldest month 18 degrees C or higher)</td> </tr> <tr> <td> </td> <td>Tmo10 = 12 + Bio06< 18</td> <td>tropical (minimum temperature of coldest month less than 18 degrees C)</td> </tr> <tr> <td> </td> <td>8 ≤ Tmo10 < 12</td> <td>subtropical</td> </tr> <tr> <td> </td> <td>4 ≤ Tmo10 < 8</td> <td>temperate</td> </tr> <tr> <td> </td> <td>1 ≤ Tmo10 < 4</td> <td>boreal</td> </tr> <tr> <td> </td> <td>Tmo10 < 1</td> <td>polar</td> </tr> <tr> <td>CMI</td> <td>CMI ≥ 0.5</td> <td>P >= 2 * PET</td> </tr> <tr> <td> </td> <td>0 ≤ CMI < 0.5</td> <td>PET <= P < 2 * PET</td> </tr> <tr> <td> </td> <td>−0.35 ≤ CMI < 0</td> <td>0.65 <= P/PET < 1</td> </tr> <tr> <td> </td> <td>−0.5 ≤ CMI < −0.35</td> <td>dry sub-humid</td> </tr> <tr> <td> </td> <td>−0.8 ≤ CMI < −0.5</td> <td>semi-arid</td> </tr> <tr> <td> </td> <td>−0.95 ≤ CMI < −0.8</td> <td>arid</td> </tr> <tr> <td> </td> <td>CMI < −0.95</td> <td> hyper-arid</td> </tr> <tr> <td>MCWD</td> <td>MCWD ≤ -100</td> <td> </td> </tr> <tr> <td> </td> <td>−200 ≤ MCWD < −100</td> <td> </td> </tr> <tr> <td> </td> <td>−400 ≤ MCWD < −200</td> <td> </td> </tr> <tr> <td> </td> <td>−600 ≤ MCWD < −400 </td> <td> </td> </tr> <tr> <td> </td> <td>−800 ≤ MCWD < −600</td> <td> </td> </tr> <tr> <td> </td> <td>−1000 ≤ MCWD < −800</td> <td> </td> </tr> <tr> <td> </td> <td>−1250 ≤ MCWD < −1000</td> <td> </td> </tr> <tr> <td> </td> <td>−1500 ≤ MCWD < −1250</td> <td> </td> </tr> <tr> <td> </td> <td>−1750 ≤ MCWD < −1500 </td> <td> </td> </tr> <tr> <td> </td> <td>−2000 ≤ MCWD < −1750 </td> <td> </td> </tr> <tr> <td> </td> <td>−2500 ≤ MCWD < −2000 </td> <td> </td> </tr> </tbody> </table> <p> </p> <p>A fourth map series in the atlas combines information from the Climatic Moisture Index with the distribution of 52,602 cities that were included in the CitiesGOER database, available from <a href="https://doi.org/10.5281/zenodo.8175429">https://doi.org/10.5281/zenodo.8175429</a><a name="_Hlk141002106"></a><br></p> <p>Maps were created from the environmental raster layers used to create the TreeGOER via the <a href="https://cran.r-project.org/web/packages/terra/">terra package</a> (Hijmans et al. 2022, version 1.7-46) in the <a href="https://cran.r-project.org/">R 4.2.1 environment</a>.</p> <p>Added country boundaries were obtained from <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/">Natural Earth</a> as <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_countries.zip">Admin 0 – countries vector layers</a> (version 5.1.1). Also added after obtaining them from Natural Earth were <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_boundary_lines_disputed_areas.zip">Admin 0 – Breakaway, Disputed areas</a> (version 5.1.0, coloured yellow in the atlas), <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_roads.zip">Roads</a> (version 5.0.0, coloured red in the atlas) and <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/physical/ne_10m_lakes.zip">Lakes</a> (version 5.0.0, coloured darkblue in the atlas).</p> <p>For countries where the GlobalUsefulNativeTrees database included subnational levels, boundaries were added and depicted as dot-dash lines. These subnational levels correspond to level 3 boundaries in the World Geographical Scheme for Recording Plant Distributions. These were obtained from <a href="https://github.com/tdwg/wgsrpd">https://github.com/tdwg/wgsrpd</a>. Check <a href="https://github.com/tdwg/wgsrpd/blob/master/109-488-1-ED/2nd%20Edition/TDWG_geo2.pdf">Brummit 2001</a> for details such as the maps shown at the end of this document.</p> <p>When using the TreeGOER Global Zones atlas in your work, cite this depository and the following:</p> <ul> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> </ul> <p> </p> <p>The development of the TreeGOER Global Zones atlas (including development of version 2024.06) was supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by <strong>Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia</strong> to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project and through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em>, by the <strong>Bezos Earth Fund</strong> to the <em>Bezos Quality Tree Seed for Africa in Kenya and Rwanda</em> project and by the <strong>German International Climate Initiative (IKI)</strong> to the regional tree seed programme on <em>The Right Tree for the Right Place for the Right Purpose in Africa</em>.</p> <p> </p>
Рис. 6. МоΔеΛирование экоΛогических ниш коΛораΔского жука ΔΛя ΔаΛьневосточного, европейского и североамериканского ареаΛов метоΔом метрического Δвухмерного шкаΛирования с применением коэффициента Жаккара Fig. 6. Models of ecological niches of the Colorado potato beetle for the Far Eastern, European, and North-American habitats (metric multidimensional scaling, Jaccard index) in Comparative characterization of the ecology of native (Henosepilachna vigintioctomaculata) and invasive (Leptinoatrsa decemlineata) species under the conditions of the monsoon climate in the southern part of the Russian Far East
Рис. 6. МоΔеΛирование экоΛогических ниш коΛораΔского жука ΔΛя ΔаΛьневосточного, европейского и североамериканского ареаΛов метоΔом метрического Δвухмерного шкаΛирования с применением коэффициента Жаккара Fig. 6. Models of ecological niches of the Colorado potato beetle for the Far Eastern, European, and North-American habitats (metric multidimensional scaling, Jaccard index)
ECLIPS 1.1 - European CLimate Index ProjectionS database
<p>Based on the available bias corrected regional climate model results, which were created under the CORDEX project we calculated several climate indices both for past and future. (http://cordex.org/data-access/bias-adjusted-rcm-data/)</p> <p>This database contains ascii files for climate index maps with a horizontal resolution of 0.11 degree ( on a regular grid ) for Europe.</p> <p>Five GCM-RCM pairs for RCP4.5 and RCP8.5 scenarios are available for three future periods: 2041-2060, 2061-2080, 2081-2100 and one past period :1961-1990</p> <p>Additionally multi-model mean maps are available for the period 1961-1990 as a reference.</p> <p>The attached document gives information about the calculated indices and climate models ( Research paper is in prep.)</p> <p>21 zip files covers the database: 5-5 for each RCP scenarios (1 for each model) + 1 for the past period.</p> <p>File name conventions: <GCM name> <RCP scenario> <RCM name> <Index name> <period></p> <p>(GCM and RCM names are following the CORDEX name conventions)</p> <p> </p> <p>Contact: dobor.laura@gmail.com</p> <p> </p> <p> </p>
Figure 4 in Influence of Climatic Factors on the Interannual Changes of Gonadosomatic Index of the Red Mullet Mullus barbatus ponticus in the Coastal Crimean Waters
Figure 4. The relative size distribution of females (1) and males (2) in the spawning of 2016 -2019 (gonads on V maturity stage).
Figure 6 in Influence of Climatic Factors on the Interannual Changes of Gonadosomatic Index of the Red Mullet Mullus barbatus ponticus in the Coastal Crimean Waters
Figure 6. The average weight – length relationship for the red mullet that lived in the coastal waters of Crimea in the spring and summer of 2016–2019.
Figure 2 in Influence of Climatic Factors on the Interannual Changes of Gonadosomatic Index of the Red Mullet Mullus barbatus ponticus in the Coastal Crimean Waters
Figure 2. The average annual GSI values (1) of females and males of red mullet, the average monthly temperature of water (2) during the spawning period, and their standard deviations.
Dataset for the climate-related financial policy index (CRFPI)
<p>Data on the climate-related financial policy index (CRFPI) - comprising the global climate-related financial policies adopted globally and the bindingness of the policy - are provided for 74 countries from 2000 to 2020. The data include the index values from four statistical models used to calculate the composite index as described in D’Orazio and Thole 2022. The four alternative statistical approaches were designed to experiment with alternative weighting assumptions and illustrate how sensitive the proposed index is to changes in the steps followed to construct it. The index data shed light on countries’ engagement in climate-related financial planning and highlight policy gaps in relevant policy sectors.</p> <p> </p>
CESM and FOCI model data as supplementary data for Climate Index Collection based on model data (CICMoD)
<p>The Community Earth System Model (CESM) and the Flexible Ocean and Climate Infrastructure (FOCI) are both fully-coupled, global climate models that provide state-of-the-art computer simulations of the Earth's past, present, and future climate states.</p> <p>This dataset contains results from control runs with conditions of year 1850 without additional external forcing for 1000 years and 999 years for FOCI and CESM, respectively.</p> <p>Included features are:</p> <ul> <li>sea surface temperature</li> <li>surface air temperature</li> <li>sea level pressure</li> <li>sea surface salinity</li> <li>geopotential height (500mb)</li> <li>precipitation</li> </ul>
Climate extreme Index (CEI) Values for six continents.
Drought duration was surprisingly limned as the trenchant environmental factor concerning phenophase shift. The yield increase rate was higher in America and Australia than in other continents. The study recommends redesigning regional integration between crop-livestock-forestry systems and constructing local water reservoirs for better adaptability of crops toward SSCT. Moreover, the results have implications for agroforestry systems to use wind barriers not only as an anti-errant but also as SSCT-protectant. Therefore, we recommend the integration of the least water-absorbing, native horticultural trees along with the maize crop to protect plants from extreme temperature fluctuations and to protect soil erosion against extreme precipitation and wind.
A climate risk index for marine life
<p><a>Climate change is impacting virtually all marine life.</a> Adaptation strategies will require a robust understanding of the risk to species and ecosystems and how those propagate to human societies. We develop a unified and spatially explicit index to comprehensively evaluate the climate risks to marine life. Under high emissions (SSP5-8.5), almost 90% of ~25,000 species are at high or critical risk, with species at risk across 85% of their native distributions. One-tenth of the ocean contains ecosystems where the aggregated climate risk, endemism, and extinction threat of their constituent species are high. Climate change poses the greatest risk for exploited species in low-income countries with high dependence on fisheries. Mitigating emissions (SSP1-2.6) reduces the risk for virtually all species (98.2%), enhances ecosystem stability, and disproportionally benefits food-insecure populations in low-income countries. Our climate risk assessment can help prioritize vulnerable species and ecosystems for climate-adapted marine conservation and fisheries management efforts. </p>
Figure 1 in Influence of Climatic Factors on the Interannual Changes of Gonadosomatic Index of the Red Mullet Mullus barbatus ponticus in the Coastal Crimean Waters
Figure 1. Scheme of the study area and location of sampling points.
A climate risk index for marine life
Open the record for dataset details and reuse information.
Normalized Difference Vegetation Index (NDVI):BAC: Biodiversity and Climate
Climate changes forecast for our region by GCM???s and shifts in biodiversity and composition each have the potential to alter ecosystem functioning; their interactive effects are unknown. The "BAC" experiment is designed to determine the direct and interactive effects of plant species numbers, plant community composition, temperature, and precipitation on 11 productivity, C and N dynamics, stability, and plant, microbe, and insect species abundances in CDR grassland ecosystems.
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.