Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
284
datasets available to search
ShareScore release 0.9.0
Dataset results
284 results for “Future climate”
Dataset - Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks
<p>This data is complementary to the paper by Leijnse et al. 2022 "Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks" <br> https://doi.org/10.5194/nhess-2021-181</p> <p>This data is made available in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE</p> <p>For questions about the data ask: tim.leijnse@deltares.nl</p> <p>For more information about the tool to generate the used synthetic tracks TCWiSE see: <a href="https://www.deltares.nl/en/software/tcwise/">https://www.deltares.nl/en/software/tcwise/</a></p> <p> </p>
Calculated moisture sources for the Yangtse River Valley for past, present and future climate using a Lagrangian moisture source diagnostic
<p>This dataset contains calculated moisture sources for the Yangtse River Valley (110–122°E and 27–33°N, eastern China) for past, present and future climate using a Lagrangian moisture source diagnostic. The dataset comprises gridded monthly moisture source data files and monthly time series files for a Last Glacial Maximum (LGM) simulation and a Pre-Industrial reference simulation (PRE) with CAM5.1 using prescribed sea surface temperatures, and a control simulation (CTL, 2001-2010) and a climate scenario run with representative concentration pathway 6 (RCP, 2061-2070) with the coupled NorESM-1M model. Each file covers a 10-year time period, computed with the Lagrangian moisture source diagnostic WaterSip (Sodemann et al., 2008).</p>
Climate Forcing due to Future Ozone Changes: An intercomparison of metrics and methods
<p>The data provided in this repository relates to a paper on ozone radiative forcing submitted for publication in Atmos. Chem. Phys., as part of the TOAR-II special issue (<a href="https://acp.copernicus.org/articles/special_issue1256.html">ACP – Special issue – Tropospheric Ozone Assessment Report Phase II (TOAR-II) Community Special Issue (ACP/AMT/BG/GMD inter-journal SI)</a>). The paper is entitled "<span>Climate Forcing due to Future Ozone Changes</span><span>: An intercomparison of metrics and methods" by authors <span><span>William J. Collins</span></span><span><span>,</span> <span>Fiona M. O’Connor</span></span><span><span>, </span><span>Connor R. Barker</span></span><span><span>, </span><span>Rachael E. Byrom</span></span><span><span>, </span><span>Sebastian D. Eastham</span></span><span><span>,</span> <span>Øivind Hodnebrog</span></span><span><span>, Patrick Jöckel</span></span><span><span>, </span><span>Eloise A. Marais</span></span><span><span>, </span><span>Mariano Mertens</span></span><span><span>, Gunnar Myhre</span></span><span><span>, Matthias Nützel</span></span><span><span>, Dirk Olivié</span></span><span><span>, Ragnhild </span><span>Bieltvedt</span><span> Skeie</span></span><span><span>5</span></span><span><span>, Laura Stecher</span></span><span><span>, Larry W. Horowitz</span></span><span><span>, Vaishali Naik</span></span><span><span>, Gregory Faluvegi</span></span><span><span>, Ulas Im</span></span><span><span>, Lee T. Murray</span></span><span><span>, Drew Shindell</span></span><span><span>, Kostas Tsigaridis</span></span><span><span>, Nathan Luke Abraham</span></span><span><span>, James Keeble.</span></span></span></p>
Climate change and terrigenous inputs decrease the efficiency of the future Arctic Ocean's biological carbon pump
<p>This repository contains the post-processed model outputs underlying the main figures in the paper "Climate change and terrigenous inputs decrease the efficiency of the future Arctic Ocean’s biological carbon pump" by Oziel et al. in Nature Climate Change (https://doi.org/10.1038/s41558-024-02233-6). The repository also contains the jupyter notebooks (python) scripts used to produce the figures, the custom model code as well as the mesh informations to reproduce the model run.</p>
Past and future effects of climate on the metapopulation dynamics of a NorthEast Atlantic seabird across two centuries
<p>Datasets required to run code for contribution:</p> <p>Past and future effects of climate on the metapopulation dynamics of a NorthEast Atlantic seabird across two centuries</p> <p>Jana WE Jeglinski, Holly I Niven, Sarah Wanless, Robert T. Barrett, Mike P. Harris, Jochen Dierschke and Jason Matthiopoulos</p> <p>Extension of a Bayesian metapopulation model fit to colony census data for all Northeast Atlantic colonies of the Northern gannet (<em>Morus bassanus</em>) described in Jeglinski et al. (2023) to investigate mechanistic relationships with climate and forecast metapopulation dynamics under two climate scenarios. </p>
Current and future global distribution of potential biomes under climate change scenarios
<p>Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023) conditions are calculated on historical long term averages (1979 - 2013), while future projections cover two different epochs: 2040 - 2060 and 2061 - 2080.</p> <p>Files are named according to the following naming convention, e.g.:</p> <ul> <li>biomes_graminoid.and.forb.tundra.rcp85_p_1km_a_20610101_20801231_go_epsg.4326_v20230410</li> </ul> <p>with the following fields:</p> <ul> <li>generic theme: <strong>biomes</strong>,</li> <li>variable name: <strong>graminoid.and.forb.tundra.rcp85</strong>,</li> <li>variable type, e.g. probability ("<strong>p</strong>"), hard class ("<strong>c</strong>"), model deviation ("<strong>md</strong>")</li> <li>spatial resolution: <strong>1km</strong>,</li> <li>depth reference, e.g. below ("<strong>b</strong>"), above ("<strong>a</strong>") ground or at surface ("<strong>s</strong>"),</li> <li>begin time (YYYYMMDD): <strong>20610101</strong>,</li> <li>end time: <strong>20801231</strong>,</li> <li>bounding box, e.g. global land without Antarctica ("<strong>go</strong>"),</li> <li>EPSG code: <strong>epsg.4326</strong>,</li> <li>version code, e.g. creation date: <strong>v20230410</strong>.</li> </ul> <p>We provide probability and hard class layers using a revised classification system of the <a href="https://www.jstor.org/stable/2846196">BIOME 6000 project</a> explained in the work of <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a>. The 20 classes from this classification system have then been aggregated in 6 biome classes following the <a href="https://global-ecosystems.org/page/typology">IUCN Global Ecosystem Typology</a> classification system.</p> <p>For probability layers, the uncertainty (model deviation: <strong>md</strong>) is calculated as the standard deviation of the predicted values of the base learners of the ensemble model. The higher the standard deviation the more uncertain the model is regarding the right value to assign to the pixel.</p> <p>For hard class layers the uncertainty is calculated using the margin of victory (<a href="https://doi.org/10.1016/j.rse.2020.112148">Calderón-Loor et al., 2021</a>) defined as the difference between the first and the second highest class probability value in a given pixel. High values would be measures of low uncertainty, while low values would indicate a high uncertainty. It is highly recommended to use the <strong>md </strong>layers to properly interpret the results of the map.</p> <p>Styling files are provided in both <em><strong>.SLD</strong></em> and <em><strong>.QML</strong></em> format; two different styling files are provided for the uncertainty of the probability layers and the hard classes due to the different interpretation of the chosen uncertainty metrics.</p> <p>The R scripts and a tutorial will be uploaded to the <a href="https://github.com/Envirometrix/PNVmaps">PNVmaps Github repository</a>, where previous versions of the biomes maps from <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a> is currently hosted. To cite the maps and the methodology, it is possible to refer to the scientific publication:</p> <p>Bonannella C, Hengl T, Parente L, de Bruin S. 2023. Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation. PeerJ 11:e15593 <a href="https://doi.org/10.7717/peerj.15593">https://doi.org/10.7717/peerj.15593</a></p>
Simulated forest dynamics (2016-2100) for six future climate-fire scenarios and five representative landscapes in Greater Yellowstone, USA
We simulated fire (incorporating fuels feedbacks) and forest dynamics on five landscapes spanning the Greater Yellowstone Ecosystem (GYE) to ask: (1) How and where are forest landscapes likely to change with 21st-century warming and fire activity? (2) Are future forest changes gradual or abrupt, and do forest attributes change synchronously or sequentially? (3) Can forest declines be averted by mid-21st-century stabilization of atmospheric greenhouse gas (GHG) concentrations? We used the spatially explicit individual-based forest model iLand to track multiple attributes (forest extent, stand age, tree density, basal area, aboveground carbon stocks, dominant forest types, species occupancy) through 2100 for six climate scenarios. The five study landscapes are representative of dominant forest types and environmental gradients of the Northern Rockies; collectively, they encompass nearly 300,000 ha, of which 279,488 ha are potentially stockable with trees. This data set contains annual landscape-level output data for simulations to 2100 with 6 climate scenarios (3 general circulation models x 2 representative concentration pathways) x 5 landscapes x 20 iterations of simulated fires. We include the data and R scripts used for the analyses of abrupt change in the publication associated with these data; all other analyses used standard functions in R.
WAT02 Climate legacies determine grassland responses to future rainfall regimes
Climate variability and periodic droughts have complex effects on carbon (C) fluxes, with uncertain implications for ecosystem C balance under a changing climate. Responses to climate change can be modulated by persistent effects of climate history on plant communities, soil microbial activity, and nutrient cycling (i.e., legacies). To assess how legacies of past precipitation regimes influence tallgrass prairie C cycling under new precipitation regimes, we modified a long-term irrigation experiment that simulated a wetter climate for >25 years. We reversed irrigated and control (ambient precipitation) treatments in some plots and imposed an experimental drought in plots with a history of irrigation or ambient precipitation to assess how climate legacies affect aboveground net primary productivity (ANPP), soil respiration, and selected soil C pools. Legacy effects of elevated precipitation (irrigation) included higher C fluxes and altered labile soil C pools, and in some cases altered sensitivity to new climate treatments. Indeed, decades of irrigation reduced the sensitivity of both ANPP and soil respiration to drought compared with controls. Positive legacy effects of irrigation on ANPP persisted for at least 3 years following treatment reversal, were apparent in both wet and dry years, and were associated with altered plant functional composition. In contrast, legacy effects on soil respiration were comparatively short-lived and did not manifest under natural or experimentally-imposed “wet years,” suggesting that legacy effects on CO2 efflux are contingent on current conditions. Although total soil C remained similar across treatments, long-term irrigation increased labile soil C and the sensitivity of microbial biomass C to drought. Importantly, the magnitude of legacy effects for all response variables varied with topography, suggesting that landscape can modulate the strength and direction of climate legacies. Our results demonstrate the role of climate his
plan4res public dataset for case study 3 "Cost of RES integration and impact of climate change for the European Electricity System in a future world with high shares of renewable energy sources"
<p>The objective of the plan4res project is to provide a well-structured and highly modular modelling framework to enable consistent insights into the different needs of future energy system. Three case studies will highlight the potentials of this framework by dealing with different aspects of a future energy systems.<br> Case study 3 will focus on cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources. Ist overall objectives are to identify the Cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources will be the main focus of case study 3.<br> The present dataset contains all the public data built for this case study.</p> <p>The related documentation is included in plan4res deliverable D4.5 </p> <pre>https://doi.org/10.5281/zenodo.3785010</pre>
Soybean yield projections in Europe under historical (1981-2010) and future climate (2050-2059 and 2090-2099 for RCP4.5 and RCP8.5)
<p><strong>General information</strong></p> <p>This dataset contains soybean yield projections in Europe under historical (1981-2010) and future climate with moderate (RCP 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. The data has been generated by <em>Guilpart et al. (2022) Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, Nature Food. </em>All details can be found in this paper. A brief summary is provided below.</p> <p><strong>Summary of soybean yield projections methodology</strong></p> <p>Yield projections have been performed using data-driven relationships between climate and soybean yield derived from machine-learning (Random Forest). The Random Forest model was trained using (i) the the global dataset of historical yields updated version (Iizumi et al. 2014a), which includes grid-wise soybean yields worldwide with the grid size of 1.125 degree over 1981-2010, and (ii) the global retrospective meteorological forcing dataset tailored for agricultural application (GRASP, Iizumi et al. 2014b), which covers the period 1961–2010 at the same spatial resolution as yield data, i.e. a grid size of 1.125 degree. Time-detrended soybean yield data was related (using Random Forest) to 35 climate variables defined at a monthly time step over the seven months of the soybean growing season, plus the fraction of irrigated area, i.e. a total of 36 variables. The 35 climate variables are monthly mean daily minimum and maximum temperatures (<em>Tmin</em> and <em>Tmax</em>, degree Celsius), monthly total precipitation (<em>rain</em>, mm month<sup>-1</sup>), monthly mean daily total solar radiation (<em>solar</em>, MJ m<sup>-2</sup> day<sup>-1</sup>), monthly mean air vapor pressure (VP, hPa). The fitted model showed high R² (higher than 0.9) and low RMSE (0.35 t ha<sup>-1</sup>) between observed and predicted yields based on cross-validation.</p> <p>Then, soybean yield projections under historical over whole Europe have been performed using the GRASP climate data, and yield projections under future climate have been performed using 16 climate change scenarios consisting of bias-corrected data of eight Global Circulation Models (GCM; GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MRI-CGCM3, and NorESM1-M, used in the Coupled Model Intercomparison phase 5 (CMIP5) and two Representative Concentration Pathways (RCPs; 4.5 and 8.5 W m<sup>-2</sup>). Soybean growing season used for projections is April to October. All projections assumed irrigated fraction equals to zero. Projections are shown only on agricultural area (cropland plus pasture), in the year 2000. Soybean yield is expressed in tons per hectare.</p> <p><strong>Files description</strong></p> <ul> <li><em>RF_soybean_historical_GRASP_median_1981_2010.nc</em> : random forest projections of soybean yield in Europe for the historical (1981-2010) period using GRASP climate data. This file contains the median yield (in tons per hectare) over 1981-2010.</li> <li><em>RF_soybean_rcp45_median_2050_2059.nc : </em>random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li><em>RF_soybean_rcp45_median_2090_2099.nc : </em>random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> <li><em>RF_soybean_rcp85_median_2050_2059.nc : </em>random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li><em>RF_soybean_rcp85_median_2090_2099.nc : </em>random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> </ul> <p><strong>References</strong></p> <p>Guilpart N. <em>et al.</em> (2022)<strong> </strong>Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, <em>Nature Food</em>.</p> <p>Iizumi T. <em>et al.</em> (2014a) Historical changes in global yields: Major cereal and legume crops from 1982 to 2006. <em>Glob. Ecol. Biogeogr.</em> 23, 346–357.</p> <p>Iizumi T. <em>et al</em>. (2014b). A meteorological forcing data set for global crop modeling: Development, evaluation, and intercomparison. <em>J. Geophys. Res. Atmos. Res.</em> 119, 363–384.</p>
Precipitation objects under the current and future climate: WRF 6-km hydroclimate simulation of the western US
<p>This folder includes the precipitation objects that are used in the following manuscript:</p> <p>Chen et al., Sharpening of Cold Season Storms over the Western US.</p> <p>It is generated using WRF V3.8 at PNNL. A historical simulation ("NARR") is done for 1981-2010, and five future simulations ("CanESM2", "CESM1-CAM5", "GFDL-ESM2M", "HadGEM2-ES", "MPI-ESM-MR") are done for 2041-2070 using the Pseudo Global Warming (PGW) approach. For the WRF model configuration and the simulation details, please refer to the abovementioned manuscript and Chen et al. (2018).</p> <p>This is the preliminary version of the dataset that contains the precipitation object features as analyzed in the manuscript. More data (including the WRF raw precipitation output) and the finalized scripts will be included here before the manuscript is published.</p> <p> </p> <p>Reference:</p> <p>Chen, X., L. R. Ruby, Y. Gao, Y. Liu, M. Wigmosta, and M. Richmond (2018), Predictability of Extreme Precipitation in Western U.S. Watersheds Based on Atmospheric River Occurrence, Intensity, and Duration, <em>Geophys. Res. Lett.</em> doi: <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018GL079831">10.1029/2018GL079831</a></p> <p>Chen, X., L. R. Ruby, Y. Gao, Y. Liu, and M. Wigmosta (2023), Sharpening of Cold Season Storms over the Western US, Nat. Clim. Change. doi: <a href="https://www.nature.com/articles/s41558-022-01578-0">10.1038/s41558-022-01578-0</a> </p>
Data for: A severe landslide event in the Alpine foreland under possible future climate and land-use changes
<p>Data underlying manuscript and supplementary figures of the corresponding publication, as well as the scripts to conduct the final analyses.</p>
Scripts and datas for "Climate-driven projections of future global wetlands extent"
<p>Computations scripts (1, 2), associated input dataset (3), and output datasets for wetland fractions (4, 5) used and presented in the study:</p> <p><em><strong>L. Hardouin, B. Decharme, J. Colin, C. Delire: </strong>Climate-driven projections of future global wetlands extent.</em></p> <p>The calculation and input scripts include:<br><em>1_var_comput </em>: Calculation of the main variables used to diagnose wetlands: depth of the "active" layer d_wtl, liquid water content w_l, ice content and maximum content in the layer d_wtl.</p> <p><em>2_TOPMODEL </em>: The scripts used to diagnose the wetland fraction and to calibrate the models using the TOPMODEL approach. In this folder, the mean, maximum, minimum, standard deviation and skewness datasets of the topographic indices at the grid-cell level are also included.</p> <p><em>3_alpha_and_beta </em>: Calibrated alpha and beta parameters used to obtain the historical and projected wetland fractions with the calibrated version.</p> <p>The outputs datasets contain:</p> <p><em>4_fwtl_model_period </em>: The fraction of wetlands computed from each model in the calibrated version, for the historical period and the 4 SSPs scenarios presented in the submitted work.</p> <p><em>5_not_calibrated_fwtl_model_period </em>: The fraction of wetlands computed from each model in the uncalibrated version with alpha=0.65, for the historical period and the 4 SSPs scenarios, where only the historical period is used in the submitted work.</p> <p> </p> <p>Additional data not created by the authors are needed to reproduce the study (see the Open research section in the submitted article). Feel free to contact the authors (lucas.hardouin@meteo.fr) for any help or questions.</p>
Interpreting future climate conditions in Brazilian cities – Dashboard and EPW files
<h3>(English)</h3> <h1>1. Introduction</h1> <p>This project aims to address the impacts of climate change on the built environment by developing a set of future Brazilian EPW (Energy Plus Weather Format) files and a dashboard to interpret and evaluate the data. The future climate files were obtained using the Future Weather Generator (FWG) [1] with climate projections for Brazilian cities, integrating these projections into a code pipeline for automation. In this part of the project, thermal comfort indices, such as the Universal Thermal Climate Index (UTCI) and the Discomfort Index (DI), were also evaluated to understand future thermal comfort conditions. The methodology followed the structure available in the <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> repository:</p> <ol> <li>Climate-One-Building (COB) web-scrapping for all available Brazilian EPW files (we recommend doing this carefully so as not to damage the COB infrastructure);</li> <li>Automatic organisation of all EPW files in a folder, extracting them from the ZIP format;</li> <li>Simulation of future climate files using FutureWeatherGenerator [1] in a line of code with default parameters (shown in Table 1);</li> <li>Organisation of all available EPWs (original and simulated) in a single database;</li> <li>Calculation of thermal comfort indices using pythermalcomfort [2].</li> </ol> <p>The main objective is to provide researchers, policymakers and professionals with a comprehensive tool for assessing and mitigating the impacts of climate change in different Brazilian cities, offering accurate data for thermal comfort and energy efficiency modelling. The methodology involves generating future EPW files, validating them against existing literature and visualising the results through a user-friendly dashboard. The study highlights the importance of adaptive and climate-resilient strategies in urban planning and building design. Expected climate changes in Brazil include increased dry bulb temperature and variations in relative humidity, radiation and wind speed in the different bioclimatic zones.</p> <p>The dashboard has been designed to simplify the visualisation of future climate data, focusing on the main climate variables, thermal comfort indices and data visualisation. It allows users to filter by city and automatically calculate all the indices, providing detailed analyses and comparisons of different scenarios. By offering a free, open-access, multi-platform, extensible, customisable and easy-to-maintain tool, the project aims to facilitate continuous updates, new features and corrections. This tool supports decision-making in public policy and urban planning, promoting a more sustainable and resilient built environment in the face of climate change.</p> <p> </p> <h1>2. Further details on the methodology</h1> <p>Details on how the indices were selected and how the study was conducted may be found in Vaz et al. [3]. The GitHub repository in <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> [4] also includes details on the step-by-step procedures.</p> <h3>Table 1 - Parameters used in the FWG simulation:</h3> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Data used in the simulations</strong></p> </td> </tr> <tr> <td> <p>Base files</p> </td> <td> <p>578 cities from COB</p> </td> </tr> <tr> <td> <p>CMIP-6 models</p> </td> <td> <p>BCC-CSM2-MR, CAS-ESM2.0, CMCC-ESM2, CNRM-CM6.1-HR, CNRM-ESM2.1, EC-Earth3, EC-Earth3-Veg, MIROC-ES2H, MIROC6, MRI-ESM2.0, UKESM1.0-LL</p> </td> </tr> <tr> <td> <p>Grid</p> </td> <td> <p>Bilinear interpolation of the four nearest points</p> </td> </tr> <tr> <td> <p>Month transition smoothness</p> </td> <td> <p>72 hours</p> </td> </tr> <tr> <td> <p>Apply variable limits</p> </td> <td> <p>True</p> </td> </tr> <tr> <td> <p>Scenarios</p> </td> <td> <p>A total of nine scenarios: One baseline for 2021 and eight future files (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 for 2050 and 2080)</p> </td> </tr> <tr> <td> <p>Solar hour correction</p> </td> <td> <p>Made by day</p> </td> </tr> <tr> <td> <p>Diffuse irradiation model</p> </td> <td> <p>Engerer, 2015</p> </td> </tr> </tbody> </table> <p> </p> <h1>3. References</h1> <p>[1] E. Rodrigues, M.S. Fernandes, D. Carvalho, Future weather generator for building performance research: An open-source morphing tool and an application, Building and Environment 233 (2023) 110104. https://doi.org/10.1016/j.buildenv.2023.110104.</p> <p>[2] F. Tartarini, S. Schiavon, pythermalcomfort: A Python package for thermal comfort research, SoftwareX 12 (2020) 100578. https://doi.org/10.1016/j.softx.2020.100578.</p> <p>[3] Vaz, I.C.M.; Ghisi, E.; Thives, L.P.; Vieira, A.S.; Rupp, R.F.; da Rosa, A.S.; Flores, R.A.; Bastos, M.B.; Marinoski, D.L.; Silva, A.S.; Weeber, M.; Invidiata, A. (2024). Dashboard for interpreting future climate files used in the simulation of buildings – an outdoor thermal comfort approach. Under submission.</p> <p>[4] Future EPW Analysis - A pipeline of processes aimed at providing future EPW files based on existing models from the literature. Available at: https://github.com/igorcmvaz/future-EPW-analysis.</p> <p> </p> <h1>Current version of the dashboard: 1.0.0.</h1> <h1>Available at <a title="Dashboard comfort - 1.0.0." href="https://app.powerbi.com/view?r=eyJrIjoiNWI0ZTk5YjMtZjA5Ny00ZjE3LTk2ZDUtNDA1OThhNWQ3NWYxIiwidCI6ImZhNzk1MzFjLThjZTUtNGJkMy05N2VlLTI0NWU2ZWUyNjZiOCJ9" target="_blank" rel="noopener">Dashboard Comfort.</a></h1> <p>Suggestions for improvements can be made directly in the GitHub repository at <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> or sent to igorcmvaz@gmail.com.</p> <p> </p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p> </p> <h3>(Português-BR)</h3> <h1>1. Introdução</h1> <p>Este projeto tem como objetivo abordar os impactos das mudanças climáticas no ambiente construído, desenvolvendo um conjunto de futuros arquivos EPW (Energy Plus Weather Format) brasileiros e um <em>dashboard</em> para interpretar e avaliar os dados. Os arquivos climáticos futuros foram obtidos com o Future Weather Generator (FWG) [1] com projeções climáticas para cidades brasileiras, integrando essas projeções a um pipeline de código para automação. Nessa parte do projeto, os índices de conforto térmico, como o Universal Thermal Climate Index (UTCI) e o Discomfort Index (DI), também foram avaliados para entender as condições futuras de conforto térmico. A metodologia seguiu a estrutura que está disponível no repositório <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a>:</p> <ol> <li>Web-scrapping do Climate-One-Building (COB) para todos os arquivos EPW brasileiros disponíveis (recomendamos fazer isso com cuidado para não prejudicar a infraestrutura do COB);</li> <li>Organização automática de todos os arquivos EPW em uma pasta, extraindo-os do formato ZIP;</li> <li>Simulação dos arquivos climáticos futuros por meio do FutureWeatherGenerator [1] em linha de código com parâmetros padrão (mostrados na Tabela 1);</li> <li>Organização de todos os EPW disponíveis (originais e simulados) em um único banco de dados;</li> <li>Cálculo dos índices de conforto térmico com o pythermalcomfort [2].</li> </ol> <p>O objetivo principal é fornecer a pesquisadores, formuladores de políticas e profissionais uma ferramenta abrangente para avaliar e mitigar os impactos das mudanças climáticas em diferentes cidades brasileiras, oferecendo dados precisos para modelagem de conforto térmico e eficiência energética. A metodologia envolve a geração de futuros arquivos EPW, validando-os com a literatura existente e visualizando os resultados por meio de um <em>dashboard</em> de fácil utilização. O estudo destaca a importância de estratégias adaptativas e resistentes ao clima no planejamento urbano e no projeto de edificações. As mudanças climáticas esperadas no Brasil incluem o aumento da temperatura de bulbo seco e variações na umidade relativa, radiação e velocidade do vento nas diferentes zonas bioclimáticas.</p> <p>O <em>dashboard</em> foi projetado para simplificar a visualização dos dados climáticos futuros, concentrando-se nas principais variáveis climáticas, índices de conforto térmico e visualização dos dados. Ele permite que os usuários filtrem por cidade e calculem automaticamente todos os índices, fornecendo análises detalhadas e comparações de diferentes cenários. Ao oferecer uma ferramenta gratuita, de acesso aberto, multiplataforma, extensível, personalizável e de fácil manutenção, o projeto visa a facilitar atualizações contínuas, novos recursos e correções. Essa ferramenta apoia a tomada de decisões em políticas públicas e planejamento urbano, promovendo um ambiente construído mais sustentável e resiliente em face das mudanças climáticas.</p> <p> </p> <h1>2. Mais detalhes sobre a metodologia</h1> <p>Detalhes sobre a seleção dos índices de conforto e como o estudo foi conduzido podem ser encontrados em Vaz et al. [3]. O repositório GitHub em <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> [4] também inclui detalhes sobre os procedimentos passo a passo.</p> <h3>Tabela 1 - Parâmetros usados na simulação do FWG</h3> <table> <tbody> <tr> <td> <p><strong>Parâmetro</strong></p> </td> <td> <p><strong>Dados utilizados na simulação</strong></p> </td> </tr> <tr> <td> <p>Arquivos base</p> </td> <td> <p>578 cidades do COB</p> </td> </tr> <tr> <td> <p>Modelos CMIP-6</p> </td> <td> <p>BCC-CSM2-MR, CAS-ESM2.0, CMCC-ESM2, CNRM-CM6.1-HR, CNRM-ESM2.1, EC-Earth3, EC-Earth3-Veg, MIROC-ES2H, MIROC6, MRI-ESM2.0, UKESM1.0-LL</p> </td> </tr> <tr> <td> <p>Malha</p> </td> <td> <p>Interpolação bilinear dos quatro pontos mais próximos</p> </td> </tr> <tr> <td> <p>Suavização da transição mensal</p> </td> <td> <p>72 horas</p> </td> </tr> <tr> <td> <p>Aplicar limites das variáveis</p> </td> <td> <p>Sim</p> </td> </tr> <tr> <td> <p>Cenários</p> </td> <td> <p>Total de nove cenários: Um arquivo base em 2021 e oito arquivos futuros (SSP1-2.6, SSP2-4.5, SSP3-7.0 e SSP5-8.5 para 2050 e 2080)</p> </td> </tr> <tr> <td> <p>Correção de hora solar</p> </td> <td> <p>Feita por dia</p> </td> </tr> <tr> <td> <p>Modelo de radiação difusa</p> </td> <td> <p>Engerer (2015)</p> </td> </tr> </tbody> </table> <p> </p> <h1>3. Referências</h1> <p>[1] E. Rodrigues, M.S. Fernandes, D. Carvalho, Future weather generator for building performance research: An open-source morphing tool and an application, Building and Environment 233 (2023) 110104. https://doi.org/10.1016/j.buildenv.2023.110104.</p> <p>[2] F. Tartarini, S. Schiavon, pythermalcomfort: A Python package for thermal comfort research, SoftwareX 12 (2020) 100578. https://doi.org/10.1016/j.softx.2020.100578.</p> <p>[3] Vaz, I.C.M.; Ghisi, E.; Thives, L.P.; Vieira, A.S.; Rupp, R.F.; da Rosa, A.S.; Flores, R.A.; Bastos, M.B.; Marinoski, D.L.; Silva, A.S.; Weeber, M.; Invidiata, A. (2024). Dashboard for interpreting future climate files used in the simulation of buildings – an outdoor thermal comfort approach. Under submission.</p> <p>[4] Future EPW Analysis - A pipeline of processes aimed at providing future EPW files based on existing models from the literature. Available at: https://github.com/igorcmvaz/future-EPW-analysis.</p> <p> </p> <h1>Versão atual do <em>dashboard</em>: 1.0.0. </h1> <h1>Disponível em <a title="Dashboard comfort - 1.0.0." href="https://app.powerbi.com/view?r=eyJrIjoiNWI0ZTk5YjMtZjA5Ny00ZjE3LTk2ZDUtNDA1OThhNWQ3NWYxIiwidCI6ImZhNzk1MzFjLThjZTUtNGJkMy05N2VlLTI0NWU2ZWUyNjZiOCJ9" target="_blank" rel="noopener">Dashboard conforto.</a></h1> <p>As sugestões de melhorias podem ser feitas diretamente no repositório do GitHub em <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> ou enviadas para igorcmvaz@gmail.com.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</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>
Powering the Circular Future: Climate Change and Economic Perspectives on Second-Life Batteries in the Belgian Context - Supporting Information S2 and S3
<p>The data contains the databases used to calculate the climate change impacts of second-life batteries including full Life Cycle Inventory data published in the article entitled "Powering the Circular Future: Climate Change and Economic Perspectives on Second-Life Batteries in the Belgian Context".</p> <p>The second file S3 contains the economic data and the climate change impacts of the same article.</p> <p>In version 2.0 of S2, a sensitivity analysis and more detail is added in the results.</p> <p> </p>
Supporting data: "RECEIPT D7.3: Future climate scenarios: sea level rise and sea ice extent"
<p>This is the supporting data for deliverable D7.3 from work package 7 ( Sea level rise, infrastructure and coastal flooding ) of the RECEIPT H2020 project (No 820712).</p> <p>Deliverable 7.3 describes the development of future SLR (sea level rise) and sea ice extent scenarios. Each SLR contributor (e.g. thermal expansion, instability of Antarctic and Greenland ice sheets, melting glaciers, ocean circulation and land water storage) are included in the assessment (KNMI, Task 7.4). Sea ice extent is derived from CMIP5/6.</p> <p>This dataset is composed of three compressed files:</p> <p>cmip5_zos_zostoga_v2.zip and cmip6_zos_zostoga_v2.zip: Netcdf files of ocean thermal expansion and ocean dynamics computed from zos and zostoga data from the ESGF nodes.</p> <p>data_RECEIPT_D73.zip: Netcdf files of three sea level scenarios. Data is provided globally but scenarios are designed for the European coast.</p>
Population dynamics shifts by Climate Change: High resolution future mid-century trends for South America.
<p>Köppen - Geiger scripts and resulting datasets for the publication entitled "Population dynamics shifts by Climate Change: High resolution future mid-century trends for South America." This scripts can be adapted to any geographic scale and region. Works with climate change scenarios.</p> <p>Original publication: <a href="https://doi.org/10.1016/j.gloplacha.2023.104155">https://doi.org/10.1016/j.gloplacha.2023.104155</a></p> <p>Dataset description</p> <p><strong>Scripts.rar</strong>: R Scripts used in this publication, as well they are reproducible</p> <p><strong>Readme_Köppen.txt</strong>: README file that explain the requisites and data formatting to run the scripts</p> <p><strong>Output datasets.zip</strong>: Output GIS datasets of this publication. Coordinate system GCS WGS 1984</p> <p> </p>
Data: Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models
<p>These data accompany the publication "Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models". The data are organized as follows:</p> <p>- two files with time series (csv) of hourly near-surface climate and surface energy balance values for Neumayer station (ice shelf, East Antarctic ice sheet) and automatic weather station S5 (southwest Greenland ice sheet)</p> <p>- two files (nc) with monthly melt fields from the regional climate model RACMO2.3p2 forced by ERA5 over Greenland (0.05-degree resolution) and Antarctica (0.25-degree resolution)</p> <p>- two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) or the historical period (1950-2014)</p> <p>- two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) for the future emission scenario SSP5-8.5 (2015-2099)</p>
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>
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.