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37 results for “climate map”
Mapping of aridity and its connections with climate classes and climate desertification in future scenarios – Brazilian semi-arid region
<p>This database comes from the article entitled ''Mapping of aridity and its connections with climate classes and climate desertification in future scenarios –Brazilian semi-arid region'' (https://seer.ufu.br/index.php/sociedadenatureza /article/view/67666/36193). We provide data on aridity and desertification index for the current scenario and future projections considering changes in climate.</p>
Global potential invasion maps of traded birds under climate and land-cover change
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Data from: Maps and additional figures for climate change refugia hotspots for priority species: A case study in East Africa
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Data from: Monitoring and predictive mapping of floristic biodiversity along a climatic gradient in ENSO's terrestrial core region, NW Peru
<p>This is the data from the publication "Monitoring and predictive mapping of floristic biodiversity along a climatic gradient in ENSO's terrestrial core region, NW Peru" (<a href="http://onlinelibrary.wiley.com/doi/10.1111/ecog.05091/abstract">http://onlinelibrary.wiley.com/doi/10.1111/ecog.05091/abstract</a>).</p> <p>The code (including figures, appendices and the manuscript) can be found directly in the <a href="https://github.com/jannes-m/2020-enso-tdf">GitHub repository</a>.</p> <p><strong>Data sources and description</strong></p> <p>Column descriptions for all tables can be found in <em>variable_description.ods. </em>Following tables are stored in <em>tables.gpkg</em>:</p> <ol> <li>plot_species_matrix_2011: Plot species matrix recorded in 2011</li> <li>plot_species_matrix_2012: Plot species matrix recorded in 2012.</li> <li>plot_species_matrix_2016: Plot species matrix recorded in 2016.</li> <li>plot_species_matrix_2017: Plot species matrix recorded in 2017.</li> <li>lifeform: Lifeforms of the recorded species</li> <li>plot_variables: Variables specific to the plots such as height of the first tree layer, cover of dead wood, etc.</li> <li>soil: Edaphic variables.</li> <li>topography: Topographic variables.</li> <li>streets: Streets and dirt tracks in the study area.</li> <li>towns: Polygons displaying the outline of the cities Paita, Piura and Chulucanas.</li> <li>rivers: Lines displaying the major rivers in the study area.</li> <li>study_area: Outline of the study area.</li> <li>peru: Outline of Peru.</li> <li>neighbors: Outline of Peru's neighbors (Bolivia, Brazil, Chile, Colombia, Ecuador).</li> <li>coast: Coastal strip of and close to the study area.</li> <li>precipitation: Precipitation measured at the three climatic stations (Paita, Piura, Chulucanas).</li> <li>experiment_count: species counted per visit (irrigation-fertilization experiment).</li> <li>experiment_irrigation: Rain input by time during the irrigation-fertilization experiment.</li> <li>experiment_cover: Cover of each plant species per visit and per experimental plot (irrigation-fertilization experiment).</li> </ol>
Distribution maps and climatic niches analysis of Heliconius butterflies
<p>Distribution maps of <em>Heliconius</em> butterflies and climatic niches analysis between co-occurring and hybridizing species.</p>
FIGURE 1. Map showing the localities from where specimens have been studied. 1 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)
FIGURE 1. Map showing the localities from where specimens have been studied. 1. Sümela, Maçka, Trabzon, northeastern Anatolia, Turkey, 2. Çataldere, Kaptanpaşa, Rize, northeastern Anatolia, Turkey, 3. 11 km southwest of Hemşin, Rize, northeastern Anatolia, Turkey, 4. Ayder Plateau, Çamlıhemşin, Rize, northeastern Anatolia, Turkey, 5. Hatila Plateau, Artvin, northeastern Anatolia, Turkey, 6. 16 km northeast of Ortacalar, Arhavi, Artvin, northeastern Anatolia, Turkey, 7. Subaşı Village, Hopa, Artvin, northeastern Anatolia, Turkey, 8. Cankurtaran, Hopa, Artvin, northeastern Anatolia, Turkey, 9. Karagöl, Borçka, Artvin, northeastern Anatolia, Turkey, 10. Charnaly, Georgia
Fig. 1. Maps displaying all 290 in Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data
Fig. 1. Maps displaying all 290 occurrence points used for Myosotis pygmy species group niche modelling (Supplementary Table S1). Maps, clockwise from top: World, New Zealand, Campbell Island, and southern South America. Colour represents a priori species: M. antarctica (pink circles); M. drucei (dark blue circles); M. pygmaea (green circles); M. brevis (yellow circles); M. glauca (light blue circles); M. "Volcanic Plateau" (grey triangles).
Mapping Tree Species Drought Sensitivity Under Climate Change
<p>Forests cover approximately 30% of Earth's land surface, absorb more carbon than all other terrestrial ecosystems, and provide trillions of dollars' worth of ecosystem services (Food and Agriculture Organization of the United Nations, 2005). However, climate change-induced droughts pose a significant threat to these vital ecosystems. As climate change intensifies, it is critical for our planning and management that we understand how and where trees will be the most threatened. Previous research has examined the effects of these droughts on forests at a global scale, but these large-scale analyses are not particularly helpful for land managers who often focus on specific regions and only a limited number of species. Our project addresses this gap by assessing species-specific sensitivity to increasingly severe and frequent droughts, considering the variations within their ranges. This localized information is crucial for land managers to develop targeted conservation strategies. By analyzing species-specific data, we demonstrate that the impacts of drier conditions are not uniform across or within species. Our findings suggest that effective management strategies must adopt a multifaceted and area-specific approach. To make our findings easily usable, we developed an interactive dashboard for land managers and the public. Here, users can find species-specific sensitivity maps that highlight the areas of greatest concern within manageable spaces, providing a valuable tool for informed decision-making. Our project contributes to the understanding of the potential future drought impacts on forests and emphasizes the need for targeted conservation efforts to mitigate the consequences of climate change on these essential ecosystems.</p>
STORM Climate Indices Maps
<p>These data sets contains climate indices (for definitions please see README file) determined with the Climate Data Operator (cdo) software calculated based on EURO-CORDEX regional climate model projections using RCP4.5 and RCP8.5 for the period 2036-2065 as well as the time period 1971-2000 (baseline climate). The indices are based on an ensemble of EURO-CORDEX runs (see README file) after determining the indices based on each run individually, and are available for an area around the STORM pilot sites (located in Mellor, UK; Troia, Portugal; Rome, Italy; Rethymno, Greece; and Ephesus, Turkey). For more information and terms of use please consult the README file.</p>
Maps for Soil loss by water from climate change scenarios for Austria
<p><span>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</span></p> <p><span>This dataset contains the change of modelled annual soil loss rates for changing R-factor according to RCP4.5 and RPC8.5 climate scenarios, relative to modelled soil loss in the base scenario, using R-factor calculated for the 1990-2021 period. For each climate scenario, four periods were considered: 1991-2020, 2021-2040, 2041-2060 and 2061-2080. The RUSLE-based soil loss calculations were done according to the SERENA/EJP-Soil soil erosion cookbook and are described in the respective project deliverables D3.3 and D3.4.</span></p>
Data from "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps"
<p>The following files were used as data and analysis in the article "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps" (<a href="https://doi.org/10.1029/2022EF003211">https://doi.org/10.1029/2022EF003211</a>). In the study, we applied self-organizing maps (SOMs) as an automated machine-learning approach to characterize the large-scale meteorological patterns (LSMP) and associated frequency and intensity of discrete extratropical cyclone (ETC) events over the northeastern U.S. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during 1980 - 2019. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMP and ETC events identified in the SOM. Please see the published paper for more details. Here we have archived: </p> <p>- data pre-processing scripts</p> <p>- code to run the self-organizing map analysis</p> <p>- code to calculate the SOM and ETC statistics</p> <p>- composites of 500-hPa geopotential height for each dataset as organized by the SOM</p> <p>- ETC tracking script and tracking output for each dataset</p> <p>- SOM output for each dataset </p>
Mapping Tree Species Drought Sensitivity Under Climate Change
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Blue Carbon-based Natural Climate Solutions, Priority Maps for the U.S., 2006-2011
This dataset contains shapefiles showing location of tidal wetland parcels with the potential for net greenhouse gas removal if restored from current mapped condition to unimpeded tidal wetlands. These maps focus on managed lands in the contiguous United States along the ocean coasts and show impounded wetlands where reconnecting tidal flow could diminish methane production. The maps include current dominant wetland type, restoration category, potential removal of atmospheric greenhouse gases in units of mass carbon dioxide with estimates of uncertainty.
Aquarius Official Release Level 3 Sea Surface Density Standard Mapped Image Monthly Climatology Data V5.0
Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, sea surface densityproduct for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.
Synthetic Assessment of Global Distribution of Vulnerability to Climate Change: Maps and Data, 2005, 2050, and 2100
The Synthetic Assessment of Global Distribution of Vulnerability to Climate Change: Maps and Data, 2005, 2050, and 2100 data set consist of maps and vulnerability index to climate change of 100 countries based on the Vulnerability-Resilience Indicator Model (VRIM), which not only presents sensitivity to climate change stresses but allows the division of indicators into components that reflects sensitivity and adaptive capacity. It was produced in collaboration with the Wesleyan University, Joint Global Change Research Institute, University of Illinois and the Columbia University Center for International Earth Science Information Network (CIESIN).
Net Solar Heating Resource Maps for US Climates: 2020 and 2000
<p>These files contain U.S. national map packages (ESRI ArcMap 10.5) for 2000 and 2020, including:</p> <ol> <li>Heating season length and intensity (monthly heating degree-days evaluated from a base temperature of 18.3 ºC);</li> <li>Residential heating energy needs (monthly kWh per household);</li> <li>Solar heating resources on 10m<sup>2</sup> surfaces of optimal tilt (monthly Wh);</li> <li>Optimal south-facing tilt values for solar heat collection (angular degrees above horizontal);</li> <li>Net solar heating resources on 10m<sup>2</sup> optimally-tilted collector surfaces (NSHR<sub>10</sub>) (monthly MWh per household and sums over 10km x 10km sectors);</li> <li>Proportions of the NSHR<sub>10</sub> provided by diffuse radiation (monthly percentage by location);</li> <li>Collector areas needed to intercept solar radiation equal to household heating needs (monthly m<sup>2</sup>);</li> <li>Median absolute deviations in the NSHR<sub>10</sub> obtained with twelve consecutive years of solar radiation data (annual MWh and percentage).</li> </ol> <p>Please see associated publication (Rempel et al. 2020) for source data and methodological details.</p> <p> </p>
South-East Asian Region (SEAR): Sea Basin Landscape Mapping for Paleoclimatology & Recent Climate Change Impacts
The objective of this work includes the coastal scenario, risks and development of coastal paleoclimatology through landscape mapping; by highlighting the devastating climate change impacts that might result in tsunami in South-East Asian sea basin with short or no-awareness period; despite the facts that the Southeast Asia region is generally poor being encompassed by twelve countries along with the Indian and Pacific Oceans. Special payable concern to the Bay of Bengal has been paid that can dictate region’s climate to certain extent. The ecosystems’ impact due to climate change and global warming -can bring direct variables and affects in –salinity, temperature, river flow, runoff, soil characteristics, erosion, nutrition level and water quality. The landscape mapping can address the system infrastructure requirements to the SEAR’s sea basin attainable by the annual monsoons, the Southwest and the Northeast Monsoons. How recent meteorological and geodynamic-genetic events can result in adverse economical damages and significant losses of lives are also drawn. This work monitors on Sea-level rise gets projected under global warming. The most brainstorming findings from climate change issues are how the high latitudes for SEAR Sea Basins’ are likely to experience greater warming than the global mean and warming,- And how the hydrological cycle gets found responsible for bringing more floods and more droughts in- causing huge devastating changes for environmental factors in coastal zones
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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.