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97 results for “Climate Change Projections”
List of capacity building resources for climate change adaptation created by EU-funded projects
<p>This dataset is the result of collaborative work for Deliverable 1.3 (WP1; T1.3) of the AGORA project. It compiles resources from projects funded by the European Commission under the last two Framework Programmes (Horizon 2020 and Horizon Europe) and focused on climate change adaptation. The resources identified and analysed include training materials, guidelines and interactive digital platforms designed for various target groups.</p>
Projection of temperature-related mortality in 854 European cities under climate change and adaptation scenarios
<p>This repository contains the data and results from the paper <strong>Estimating future heat-related and cold-related mortality under climate change, demographic and adaptation scenarios in 854 European cities</strong> published in <em>Nature Medicine</em> (<a href="https://doi.org/10.1038/s41591-024-03452-2">https://doi.org/10.1038/s41591-024-03452-2</a>).</p> <p>It provides projections of excess death rates and burden for the period 2015-2099 for five age groups in 854 cities across 30 countries, under three Shared Socioeconomic Pathway (SSP) scenarios, and four adaptation scenarios. The results include point estimates for five-year periods and four global warming levels, along with 95% empirical confidence intervals. </p> <p>The fully reproducible analysis code using the data and producing the results included in this repository is provided in <a href="https://github.com/PierreMasselot/EUcityProj" target="_blank" rel="noopener">GitHub</a>. The results can be visualised and explored in a dedicated <a href="https://ehm-lab.shinyapps.io/vistemphip/">Shiny app</a>.</p> <h3>Content</h3> <p>This repository contains three zip files, each with an internal codebook:</p> <ul> <li><em>data.zip</em>: contains the input data necessary to run the analysis. It includes historical and projected daily temperature at the city level, age-group specific projections of population and survival rates at the country level, and exposure-response functions extracted from another Zenodo repository (<a href="https://doi.org/10.5281/zenodo.10288665" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10288665</a>). This file also include a script showing how each dataset was extracted for the purpose of this projection study.</li> <li><em>results_csv.zip</em>: contains the full results from the health impact projections. It includes one file for each combination of geographical level (city, country, region or European wide) and scale of reporting (five year periods or global warming levels). </li> <li><em>results_parquet.zip</em>: contains the same information as the <em>results_csv.zip</em> but in a parquet format. This allows for more efficient storage and data reading.</li> </ul> <p>It is recommended to only download <em>results_csv.zip</em> for a quick exploration of the results, or only <em>results_parquet.zip</em> when the results are to be loaded into a software for deeper analysis.</p> <p> </p>
Associating Land Cover Changes with Climate Sensitive Infection in Fennoscandia, as part of the CLINF project: Example on Tick-Borne Diseases
<p>The data was used as part of the IJERPH article below. The GeoJSON and shapefile ZIP archive are two versions of the same geometries to represent geographically the districts whole of Fennoscandia and the Russian districts of Leningrad, St Petersburg, Vologda, Arkhangelsk, Nenetsia, Murmansk, Karelia, and Komi, making up 69 districts used for the analysis.</p> <p>Leibovici DG, Bylund H, Björkman C, Tokarevich N, Thierfelder T, Evengård B, Quegan S (2021). Associating Land Cover Changes with Patterns of Incidences of Climate Sensitive Infections: An Example on Tick-Borne Diseases in the Nordic Area. <strong><em>International Journal of Environmental Research and Public Health, 18(20):10963. <a href="https://doi.org/10.3390/ijerph182010963">doi:10.3390/ijerph182010963</a></em></strong></p> <p>Special Issue: <a href="https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects">https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects</a></p> <p> </p>
Data Visualization - Final Project - Global Climate Change
<p>This Project is part of the course work for Data visualization DATS 6401. In this project, I have created webpage to show data analysis on Global Climate Change. D3 & Google Visualization API is used for all visualization graphs in the webpage.</p>
Survey answers to identify barriers and enablers to climate change adaptation solutions (as part of the Adaptation AGORA project)
<p><span>This dataset s the result of collaborative work for Deliverable 4.1 (WP4; T4.1) of the Adaptation AGORA project. This survey aimed to capture the key factors supporting or hindering adaptation practitioners experienced with engaging citizens and stakeholders in climate change adaptation initiatives. </span></p> <p><span>The survey targeted <span>European adaptation practitioners, i.e., all professionals in charge of implementing climate change adaptation initiatives, and more particularly, those involved in collaborative processes engaging stakeholders and citizens </span><span>at the local and/or regional scale.</span></span></p> <p><span><span>The survev protocol can be found here: Euro-Mediterranean Center for Climate Change, University of Geneva, Stockholm Environment Institute, Barcelona Supercomputing Center, & Agenzia per la Promozione della Ricerca Europea. (2024). Protocol to carry out surveys to identify barriers and enablers to climate change adaptation solutions. Zenodo. <a href="https://doi.org/10.5281/zenodo.13385305" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13385305</a></span></span></p>
Data provided for the Preenacting Climate Change Scenarios project 2021
<p>CMIP6 model output data processed using the scripts provided here: https://github.com/lukasbrunner/preenact/</p>
Water risks to hydropower projects in the face of climate change
<p>This repository hosts the main outputs from an analysis using the <a href="https://waterriskfilter.org/">WWF Water Risk Filter</a> to demonstrate how one such tool can be used to screen for a variety of risks at a global scale, including risks to riverine ecosystems from both climate change and hydropower as well as risks to hydropower projects — and operators, owners, and investors — from climate change and potential regulatory or reputational risk arising from negative impacts to ecosystems. The study <a href="https://www.mdpi.com/2073-4441/14/5/721">Using the WWF Water Risk Filter to Screen Existing and Projected Hydropower Projects for Climate and Biodiversity Risks (DOI 10.3390/w14050721) </a>was published in the special issue of the MDPI journal Water: <a href="https://www.mdpi.com/journal/water/special_issues/hydrometeorological_hazards">"Hydro-Meteorological Hazards under Climate Change"</a>.</p> <p>This product incorporates data from the GRanD v1.3 database which is © Global Water System Project (2011), and from the FHReD database beta version, both datasets available at <a href="http://globaldamwatch.org/">globaldamwatch.org</a> . The source code used in this study is available at <a href="https://github.com/rafaexx/hydropowerClimateChange">https://github.com/rafaexx/hydropowerClimateChange</a></p> <p>See the interactive maps using this data at <a href="https://rcamargo.shinyapps.io/HydropowerClimateChange">https://rcamargo.shinyapps.io/HydropowerClimateChange</a></p>
Climate based seed zones for Mexico: spatial grids to guide reforestation under observed and projected climate change
<p>This database entry provides climate-based seed zone system for Mexico to address climate change observed over the last 30 years and projected climate change for the 2050s. The database corresponds to a journal publication by Castellanos-Acuña et al. (2018), available at https://doi.org/10.1007/s11056-017-9620-6. This seed zone classification is based on bands of two climate variables that have often been shown to drive genetic adaptation of tree species: mean coldest month temperature (MCMT), and an aridity index (AHM). MCMT was divided into ten bands of 3°C intervals, with the limits of these bands being, temperatures below <2°C, 2-5°, 5-8°, 8-11°, 11-14°, 14-17°, 17-20°, 20-23°, 23-26°, >26°C. AHM was divided into seven bands with intervals that are approximately equal width under a log-transformation: <20, 20-30, 30-45, 45-65, 65-95, 95-140, and >140 °C/mm. The gridded files provided in this database entry, the classes are coded as integer numbers, with the last digit representing the AHM class (1-7) and the first or first and second digit representing the MCMT class (1-10).</p>
Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing
<p>The dataset contains supporting data for the paper submitted to The Cryosphere "Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing".<br><br>OGGM_area_projections.nc contains data for Figure 3.<br>OGGM_volume_projections contains data for Figure 4.</p> <p>OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5.</p> <p>OGGM_solid_ice_discharge_regions.nc contains data for Figure 6.</p> <p>OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7.</p> <p>OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.</p>
Expansion of coccidioidomycosis (Valley fever) endemic regions in the United States in response to climate change: projections of disease incidence
<p>This file contains estimations of coccidioidomycosis (Valley fever) incidence data in cases per 100,000 population per year for the contemporary time period and projections throughout the 21st century in response to RCP4.5 and RCP8.5 climate scenarios, associated with the publication:</p> <p>Gorris, M. E., Treseder, K. K., Zender, C. S., and Randerson, J. T. (2019). Expansion of coccidioidomycosis endemic regions in the United States in response to climate change. <em>GeoHealth</em>. </p> <p>The data is reported for each county in the conterminous US with its associated FIPS code (Column 1), county name (Column 2), state FIPS code (Column 3), and state name (Column 4). Column 5 contains the estimation of mean annual Valley fever incidence averaged from 2000-2015. Column 6-8 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for RCP4.5 climate scenario. Likewise, Columns 9-11 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for the RCP8.5 climate scenario. </p> <p>Details about how the incidence data was calculated may be read in the Methods subsection of the paper under "Modeling of current and future mean annual Valley fever incidence". The data provided here was used to create Figure 7 and Supporting Information Figure S5. Counties that have non-zero incidence are considered endemic by our climate-constrained niche model, so this data may also be used to create portions of Figures 3, 4, and S3. </p>
Projected distribution of invasive plant species in the tropical Andes under climate change
<p>Distribution maps of 11 invasive species now and in the future (2040-70). The projections were the result of the assembly of three algorithms: Adaptive Boosting (AdaBoost), Boosted Regression Trees (BRT), and Extreme Gradient Boosting (XGBoost). Future projections were made for three global circulation models and three climate change scenarios, each with low (SSP126), medium (SSP370), and high (SSP585) levels of carbon emission.</p> <p>Habitat suitability and presence/absence maps are also included. The threshold for establishing a species as present was determined to be the value that maximized the TSS. </p> <p>For more information, see the article accompanying the dataset by González-Trujillo et al. Mapping the threat: Projecting invasive plant distribution in the tropical Andes under climate change</p> <p>List of modeled invasive plant species and their known impacts in the tropics.</p> <table> <tbody> <tr> <td> <p><strong>Species </strong></p> </td> <td> <p><strong>Biogeographic origin</strong></p> </td> <td> <p><strong>Impacts </strong></p> </td> <td> <p><strong>References</strong></p> </td> <td> <p><strong>GBIF data (DOIs)</strong></p> </td> </tr> <tr> <td> <p><em>Acacia decurrens </em></p> </td> <td> <p>Australian</p> </td> <td> <p>Create regular layers of litter on the ground, inhibit or redirect successional processes, inhibit the expression of seed banks, and limit resource supply, leading to displacement of native plants and animals and increasing the frequency of fires.</p> </td> <td> <p> (Cárdenas López et al., 2017; Le Maitre et al., 2011)</p> </td> <td> <p>https://doi.org/10.15468/dl.mjyxhw</p> </td> </tr> <tr> <td> <p><em>Acacia melanoxylon</em></p> </td> <td> <p>Australian</p> </td> <td> <p>Alter the structure and function of their ecosystems, thereby displacing their native flora. It also causes soil erosion and alters hydrological cycles, negatively affecting agriculture.</p> </td> <td> <p>(Kumschick and Jansen, 2023; Le Maitre et al., 2011)</p> <p> </p> </td> <td> <p>https://doi.org/10.15468/dl.4cugnk</p> </td> </tr> <tr> <td> <p><em>Arundo donax</em></p> <p><em> </em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter<em> </em>the natural vegetation structure, outcompete native plant species and diminish the diversity and abundance of animals such as arthropods and birds. It also drives out soil, fuels forest fires, displaces native species, and increases the invasion of ticks that affect livestock.</p> </td> <td> <p>(Cárdenas López et al., 2017; Girotto et al., 2021; Lambert et al., 2010)</p> </td> <td> <p>https://doi.org/10.15468/dl.bfep4t</p> </td> </tr> <tr> <td> <p><em>Genista monspessulana</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter fire regime and nutrient cycling displace native species and decrease native diversity by forming dense monospecific stands. It also facilitates the establishment of other invasive species and produces seeds that are toxic to livestock and humans.</p> </td> <td> <p>(Cárdenas López et al., 2017; Herrera et al., 2016; Pauchard et al., 2008)</p> </td> <td> <p>https://doi.org/10.15468/dl.gyhnxh</p> </td> </tr> <tr> <td> <p><em>Hedychium coronarium </em></p> </td> <td> <p>Indo-Malesian</p> </td> <td> <p>Alter hydrological and nutrient cycles in soil. It forms thickets that suppress the successional and regeneration processes of native species, thus affecting the native flora and crops.</p> </td> <td> <p>(Cárdenas López et al., 2017; Costa et al., 2019)</p> </td> <td> <p>https://doi.org/10.15468/dl.6z2jgb</p> </td> </tr> <tr> <td> <p><em>Melinis minutiflora</em></p> </td> <td> <p>African</p> </td> <td> <p>Increases the occurrence of fires, displaces native species, and alters soil properties and decomposition. It also inhibits the growth of native species.</p> </td> <td> <p>(Cárdenas López et al., 2017; Nogueira et al., 2019; Sandoval et al., 2022)</p> </td> <td> <p>https://doi.org/10.15468/dl.fsqwsv</p> </td> </tr> <tr> <td> <p><em>Pteridium aquilinum</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter vegetation success processes affect crops and cause livestock poisoning. It also produces acids that inhibit root growth in native and cultivated species.</p> </td> <td> <p> (Berget et al., 2015; Cárdenas López et al., 2017; Valdez-Ramírez et al., 2020)</p> <p> </p> </td> <td> <p>https://doi.org/10.15468/dl.sp4uuv</p> </td> </tr> <tr> <td> <p><em>Ricinus communis</em></p> </td> <td> <p>African</p> </td> <td> <p>Alter vegetation success processes affect crops and cause livestock poisoning. It also produces acids that inhibit root growth in native and cultivated species.</p> </td> <td> <p>(Cárdenas López et al., 2017; Sandoval et al., 2022; Silva and Fabricante, 2022)</p> </td> <td> <p>https://doi.org/10.15468/dl.dhbphb</p> </td> </tr> <tr> <td> <p><em>Senecio madagascariensis</em></p> </td> <td> <p>African</p> </td> <td> <p>Alter soil nutrient cycles, damage to agricultural crops, and outcompete native species. It also contains substances that are toxic to both animals and humans. </p> </td> <td> <p>(Wijayabandara et al., 2021)</p> </td> <td> <p>https://doi.org/10.15468/dl.7e8eyx</p> </td> </tr> <tr> <td> <p><em>Thunbergia alata</em></p> </td> <td> <p>African</p> </td> <td> <p>Displace native species and reduce habitat heterogeneity, thereby affecting the structure and function of native ecosystems.</p> </td> <td> <p>(Cárdenas López et al., 2017; Quijano-Abril et al., 2021)</p> </td> <td> <p>https://doi.org/10.15468/dl.g9zybc</p> </td> </tr> <tr> <td> <p><em>Ulex europeaus</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Dry soil and increase the occurrence of fires. Inhibits vegetative growth, including pastures in agricultural and livestock lands.</p> </td> <td> <p>(Anderson and Anderson, 2009; Cárdenas López et al., 2017)</p> </td> <td> <p>https://doi.org/10.15468/dl.6642q9</p> </td> </tr> </tbody> </table>
Twiter Dataset on climate change discussions: COP27, IPCC, climate refugees and Doñana - Clint project
<p><strong>CLINT Data</strong></p> <p>This repository contains the date used in the project CLINT and the paper "<a href="https://arxiv.org/abs/2410.21187">A cross-platform analysis of polarization and echo chambers in climate change discussions</a>" </p> <p><strong>Open Twitter Data</strong></p> <p>We used the Twitter’s search to gather historical tweets and the streaming API to follow specified accounts and also collect in real-time tweets that mention specific keywords. To comply with <a href="https://developer.twitter.com/en/developer-terms/agreement-and-policy">Twitter’s Terms of Service</a>, we are only publicly releasing the tweet IDs of the collected tweets. The data is released for non-commercial research use. </p> <p><strong>With Twitter's changes to its Academic API policies, it’s no longer possible to collect or rehydrate tweets </strong><strong>as we usually did, however we open data in case at some point it will become feasible to do it.</strong></p> <table> <tbody> <tr> <td> </td> <td><strong>IPCC</strong></td> <td><strong>Doñana</strong></td> <td><strong>Climate Refugees</strong></td> <td><strong>COP27</strong></td> </tr> <tr> <td><strong>Number of tweets</strong></td> <td>352,723 </td> <td>1,487,425</td> <td>1,938,932</td> <td>6,225,508 </td> </tr> <tr> <td><strong>Number of authors</strong></td> <td>157,056</td> <td>290,782</td> <td>841,454 </td> <td>1,351,903 </td> </tr> <tr> <td><strong>First tweet date</strong></td> <td>2023-03-18</td> <td>2019-01-01</td> <td>2008-03-10 </td> <td>2022-09-01 </td> </tr> <tr> <td><strong>Last tweet date</strong></td> <td>2023-03-26</td> <td>2023-04-30</td> <td>2022-12-31 </td> <td>2022-11-27</td> </tr> </tbody> </table> <p> </p>
Model projection of the effect of climate change and fishing pressure on key species of the South East Asia Seas
<p>The dataset contain Projection from the Size-Spectra Bioclimatic Envelop Model (SS-DBEM), this work was part of the GCRF Blue communities Programme (www.blue-communities.org). The model provides distribution and abundance and/or biomass of fish and other species of commercial interest under climate change and fishing pressure. The model outputs are yearly abundance/biomass on a 0.5-by-0.5 degree grid, covering the period from 2000 to 2098. Further description of the model and relevant references are listed in the following file: Guide-fish-model-output-use.docx</p> <p>The model was run under two climate scenario: RCP4.5 and RCP8.5, with different combinations of fishing pressure expressed as the Maximum Sustainable Yield (MSY) for the following values: 0 (no fishing, climate change alone will cause variation in fish biomass), 1 (sustainable fishing), 2, 3 (overfishing), and, 4 (overfishing with destructive practice). The intent is not to reproduce current fishing level but to provide a range of scenarios with which the future of fisheries can be explored.</p> <p>We projected fish species that were identified as key in the South East Asia seas region by our regional partners.The full list is provided in document: Fish-list-modelguide.xlsx</p> <p>There are 4 zip files that contain the model outputs of in either abundance (number of fish) or biomass grams of fish) for the two climate scenario. For example Biomass-RCP45.zip will contain model outputs in biomass for projections under RCP4.5 and all MSY. within the zip files are .csv files of the outputs for each species under the 5 MSY (0 to 4), the individual file names identify the species (identified by a 6digit code), the output provided (abundance or biomass), the RCP (8.5 or 4.5), and the MSY (0, 1, 2, 3, or 4). For example the file labelled 600107-Abundance-rcp85-msy4.csv contains the outputs for species 600107 (Skipjack tuna, <em>Katsuwonnus pelamis</em>), as abundance, under RCP8.5 with MSY4. Headers indicate what is in each column (latitude, longitude and year).</p> <p> </p> <p>Note: some knowledge of Python, R, or a similar software is recommended to ensure easy of use.</p>
Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"
<p>This dataset is associated with the following publication:</p> <p>Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, <em>Astronomy and Astrophysics</em>, vol. 665, 2022. doi:10.1051/0004-6361/202142493.</p> <p>In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with:</p> <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> <p>Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations:<br> - 'ds_sel': contains monthly timeseries of selected intersecting data<br> - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10)<br> - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10)<br> - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10) </p> <p>The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2.</p> <p>The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error.</p> <p>The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures.</p> <p>Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a> </p> <p> </p>
Projected climate and canopy change lead to thermophilization and homogenization of forest floor vegetation in a hotspot of plant species richness, Berchtesgaden National Park, Bavaria, Germany
Mountain forests are plant diversity hotspots, but changing climate and increasing forest disturbances will likely lead to far-reaching plant community change. Projecting future change, however, is challenging for forest understory plants, which respond to forest structure and composition as well as climate. Here, we jointly assessed effects of both climate and forest change, including wind and bark beetle disturbances, using the process-based simulation model iLand in a protected landscape in the northern Alps (Berchtesgaden National Park, Germany), asking: (1) How do understory plant communities respond to 21st-century change in a topographically complex mountain landscape, representing a hotspot of plant species richness? (2) How important are climatic changes (i.e., direct climate effects) versus forest structure and composition changes (i.e., indirect climate effects and recovery from past land use) in driving understory responses at landscape scales? Stacked individual species distribution models fit with climate, forest, and soil predictors (248 species currently present in the landscape, derived from 150 field plots stratified by elevation and forest development, overall AUC = 0.86) were driven with projected climate (RCP4.5 and RCP8.5) and modeled forest variables to predict plant community change. Nearly all species persisted in the landscape in 2050, but on average 8% of the species pool was lost by the end of the century. By 2100, landscape mean species richness and understory cover declined (-13% and -8%, respectively), warm-adapted species increasingly dominated plant communities (i.e., thermophilization, +12%), and plot-level turnover was high (62%). Subalpine forests experienced the greatest richness declines (-16%), most thermophilization (+17%), and highest turnover (67%), resulting in plant community homogenization across elevation zones. Climate rather than forest change was the dominant driver of understory responses. The magnitude of unabated 2
Hubbard Brook Experimental Forest: In-situ Nitrogen Mineralization and Nitrification measurements for 4 winter climate change projects
These data are from four separate projects undertaken between 1997 and 2017. The first of these are two snow manipulation (freeze) projects: 1) In 1997, as part of a study of the relationships between snow depth, soil freezing and nutrient cycling, we established eight 10 x 10-m plots located within four stands; two dominated (80%) by sugar maple (SM1 and SM2) and two dominated by yellow birch(YB1 and YB2), with one snow reduction (shoveling) and one reference plot in each stand. 2) In 2001, we established eight new 10-m x 10-m plots (4 treatment, 4 reference) in four new sites; two high elevation, north facing and (East Kineo and West Kineo) two low elevation, south facing (Upper Valley and Lower Valley) maple-beech-birch stands. To establish plots, we cleared minor amounts of understory vegetation from all (both treatment and reference) plots (to facilitate shoveling). Treatments (keeping plots snow free by shoveling through the end of January) were applied in the winters of 1997/98, 1998/99, 2002/2003 and 2003/2004. The Climate Gradient Project was established in October 2010. Here we evaluated relationships between snow depth, soil freezing and nutrient cycling along an elevation/aspect gradient that created variation in climate with little variation in soils or vegetation. We established 6 20 x 20-m plots (intensive plots) and 14 10 x 10-m plots (extensive plots), with eight of the plots facing north and twelve facing south. The Ice Storm project was designed to evaluate the damage and changes ice storms cause to northern hardwood forests in forest structure, nutrient cycling and carbon storage. Ten 20x30 meter plots were established in a predominately sugar maple stand, with 4 icing treatments and 2 control plots. The treatments are as follows: Low (0.25"), Mid (0.5"), Midx2 (0.5") 2 Years in a row, High: (0.75"), Control. The icing treatment was conducted in the winter of 2015-2016, with a second year of icing on the Midx2 treatments plots in the winter of 20
Code and data for publication "Assessing carbon cycle projections from complex and simple models under SSP scenarios" published in "Climatic Change"
<p>Data and scripts for the article "Assessing carbon cycle projections from complex and simple models under SSP scenarios" by I. Melnikova, P. Ciais, O. Boucher and K. Tanaka was accepted for publication in Climatic Change (https://doi.org/10.1007/s10584-023-03639-5)</p><p> </p><p>We use bash, CDO, and python.</p><p>SSP2.xlsx contains preprocessed annual estimates of climate and carbon cycle variables from ESMs and SCMs used in the paper.</p><p>Two bash scripts contain preprocessing cdo commands for ESM output.s SCMs were preprocessed directly in python.</p><p>Jupyter notebook (python) contains preprocessing of data and plotting of all figures of the manuscript. The folder "additional" contains some more Excel files needed to run Jupyter-Notebook. Please adapt the folder names.</p><p>If you have any questions, please contact the corresponding author Irina MELNIKOVA at melnikova . irina@nies.go.jp</p><p> </p>
Projected trophic changes in species-carrying capacities under climate change scenarios
<p>Climate controls the amount of energy available for plants, which in turn determines the quantity of resources available for animals. It follows that when climate changes, so should trophic communities. Using a novel modeling approach, we investigate how bird and mammal trophic communities might disassemble and reassemble under 21<sup>st</sup> century climate changes. We show that trophic structures are expected to undergo profound changes globally, chiefly in the tropics and across high latitudes in the northern hemisphere. This trophic reorganization of communities is characterized by shifts in species richness within trophic guilds. While some guilds might face population collapses, others are projected to find new opportunities to maintain stable populations in previously inhospitable areas. The proposed models offer a tool for projecting and understanding the trophic ramifications of climate change, highlighting their potential in guiding future research and conservation efforts.</p>
Literature review of the enablers and barriers to stakeholder and citizen engagement in climate change adaptation process (as part of Adaptation AGORA project)
<p>This dataset is the result of collaborative work for Deliverable 4.1 (WP4; T4.1) of the Adaptation AGORA project. This database was used to conduct a literature review of the enablers and barriers to stakeholder and citizen engagement in climate change adaptation process. It contains 123 papers retrived from Web of Science Databse in June 2023. <span>We used a keyword search to identify and select articles that fell within the scope of our research, with each article containing at least one keyword related to climate change adaptation solutions, climate change, co-production, citizen and stakeholder involvement and factors (enablers and barriers). </span></p> <p><span>We divided the coding framework into four main sections:</span></p> <ul> <li> <p><span>Section 1 collected basic information about the paper (i.e., date, journal, authors, type of study and methods for data collection). </span></p> </li> <li> <p><span>Section 2 sought to better understand the adaptation initiatives treated in the paper. Here, we analysed 5 variables (the adaptation solutions type, sectors, benefits, scale, and location). </span></p> </li> <li> <p><span>Section 3 collected characteristics of the climate change adaptation co-production process, including the definition of co-production, the type of the co-production process, its outputs, and the methods used to engage stakeholders. </span></p> </li> <li> <p><span>Section 4 described the factors that enable or hinder the co-production process and their influence on different aspects of the process. After naming and defining each driver, we recorded the main type of factor, its impact, origin, and spatial and temporal scale of influence; the stakeholders who were responsible for and influenced by the factor, and the impacts on the various steps and outcomes of the co-production process.</span></p> <span> </span></li> </ul>
Summer Rainfall Scenarios and Climate Change Factor Projections over Wanzhou County, China
<p>This dataset consists of rainfall scenarios and ensemble projections of extreme daily rainfall and mean summer season rainfall over Wanzhou County, China.</p> <p><strong>Precipitation Reference Period (1979-2018)</strong></p> <p>The reference scenario rainfall covers the period of 1979-2018, and is derived from the China Meteorological Forcing Dataset (https://data.tpdc.ac.cn/en/data/8028b944-daaa-4511-8769-965612652c49/). The extreme daily rainfall (in mm/day) is derived from Gumbel distributions fitted to monthly maximum daily rainfall covering the months of June to August. A spatial distribution of return periods from 2, 5, 10 20, 50 and 100 years for this scenario were derived and included in this dataset. The mean seasonal rainfall scenario covers the average daily rainfall (in mm/day) for the months of May to July to represent antecedent rainfall conditions of that could trigger shallow landslides during the summer season.</p> <ul> <li>Spatial extent: Wanzhou County, China</li> <li>Spatial Resolution: 0.1 degrees x 0.1 degrees</li> <li>Time period: 1979-2018</li> <li>Data Format: .csv files (.xyz file extensions)</li> <li>Variable: Rainfall (pr)</li> <li>Units: mm/day </li> </ul> <p><strong>Ensemble Projections and Climate Change Factors</strong></p> <p>The ensemble climate change projections cover two periods: Mid-21st Century (2021-2060) and Late-21st Century (2061-2100). The influence of climate change is assessed through climate change factors that represent a multiplicative factor of change between present and future climate model outputs. The ensemble projections are the mean climate change factor derived from four bias-corrected Regional Climate Model outputs. The ensemble consisted of the results REMO2015 and RegCM4 models that dynamically downscaled HadGEM2-ES, MPI-ESM-ML, and MPI-ESM-MR model outputs (https://esgf-data.dkrz.de/search/cordex-dkrz/). The bias correction was performed using the quantile delta method. An empirical transfer function for daily rainfall was used to derive the mean seasonal rainfall scenario, while a parametric (Gumbel distribution) transfer function was used to derive on the monthly maxima for the extreme daily rainfall scenarios.</p> <ul> <li>Spatial extent: Wanzhou County, China</li> <li>Spatial Resolution: 0.22 degrees x 0.22 degrees</li> <li>Time periods: Mid-21st Century (2021-2060) & Late-21st Century (2061-2100)</li> <li>Data Format: .csv files</li> <li>Variable: Climate Change Factor (ccf)</li> <li>Unit: Dimensionless</li> <li>Included ensemble projection statistics: <ul> <li>Standard deviation (sd)</li> <li>Coefficient of Variation (cv)</li> </ul> </li> </ul>
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.