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202 results for “climate projections”

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zenodo40/100

Reanalysis and future climate projections of the physical state of the Gulf of Riga 1993-2100

Data sets There are two data sets: (1) reanalysis (1993-2021) and (2) future projection (2015-2100). Future projection data set is split into 10 files. The dataset provides gridded monthly mean values of physical parameters in the Gulf of Riga, Baltic Sea. The variables of the dataset of the physical state of the Gulf of Riga are as follows (Long name: acronym, units) <ul> <li>Potential temperature: thetao, °C</li> <li>Sea water salinity: s, g/kg</li> <li>Eastward sea water velocity: ocu, m/s</li> <li>Northward sea water velocity: ocv, m/s</li> <li>Deviation of sea-level from the mean sea level: zos, m</li> <li>Sea ice area fraction siconc, m2/ m2</li> <li>Sea ice thickness: sithick, m</li> <li>Bathymetry: bathymetry, m (included only in reanalysis data set)</li> </ul> The grid size of the dataset is 15 (depth) x 203 (latitude) x 187 (longitude). The horizontal grid spacing is 0.5 nm; the vertical grid has 15 depth layers – 2 m deep surface layer and 4 m step for deeper layers. The time resolution of the dataset is monthly – the monthly mean value is provided in the 1st day of the month in time dimension. The original climatic calculations are based on the University of Latvia (UL) set-up of the Hiromb-BOOS model routinely implemented for the operational oceanography in the Baltic Sea and the Gulf of Riga in Latvia. Its parametrization is empirically suited for climatical reanalysis in the Gulf of Riga domain. The original output of the model run is hourly data series. Reanalysis Time period: 1993-2021 (29 years). The main characteristics of the input data and approach for the reanalysis run are as follows: <ul> <li>EMODNET2020 bathymetry.</li> <li>Initial conditions – bias corrected Copernicus Marine Service (CMS).</li> <li>Atmospheric forcing – ERA5 meteorology with improved cloudiness.</li> <li>Boundary conditions from CMS 1993-2018 reanalysis and CMS operational archive (2019-2021) with bias correction for waterlevel in CMS forecast.</li> <li>River inflow – 15 main rivers taken into consideration according to E-HYPE hydrological model data. E-HYPE discharge multiplied by 0.75.</li> <li>Tides: astronomic calculations.</li> </ul> Future climate projection Time period: 2015-2100 (86 yrs). The main characteristics of the input data and approach for the future climate projections run are as follows: <ul> <li>Emodnet2020 bathymetry.</li> <li>Initial conditions – bias corrected Copernicus Marine Service (CMS).</li> <li>Boundary conditions from downscaled CMIP6 climate projection model NorESM2-MM_ssp585_r1i1p1f1 (search string – project:'CMIP6', source_id:'NorESM2-MM', experiment_id:'ssp585', variant_label:'r1i1p1f1').</li> <li>River inflow – 15 main rivers taken into consideration according to E-HYPE climatological model (SMHI_RCA4_HadGEM2-ES_rcp45). E-HYPE discharge multiplied by 1.093.</li> <li>The past period data was used for the downscaling: <ul> <li>ERA5 reanalysis data was used for the downscaling of the atmospheric forcing time series of CMIP6 climate projection model,</li> <li>CMS reanalysis model data was used for the downscaling  of the sea state time series.</li> </ul> </li> <li>Downscaled variables: eastward and northward components of the near surface wind, air temperature, air pressure, water temperature, water salinity, sea level.</li> </ul>

opencc-by-4.0Aug 2023View details →

zenodo40/100

NPJ Climate Action AR6 scenarios database submission histograms by model family and project

Based on submissions to the IPCC AR6 Scenarios Database, this datasets uses the metadata to construct histograms of the submitted scenarios by model family and project, noting the total submissions, vetted scenarios, and climate assessed scenarios. A total of 2304 scenarios were submitted to the global emissions database, of these, 618 did not passing vetting for sufficiently consistency with historical energy and emissions data, and a further 484 did not have sufficient data to perform a climate assessment, leaving a total of 1202 used in the primary assessment of scenarios.  The database based on the scenario metadata. The ‘model family’ was determined by removing version numbers from the full model name. The ‘project family’ was obtained using the ‘Scenario family’ variable in metadata, supplemented by manually checking against cited literature. The classification of vetted scenarios was based on the variable ‘Historical vetting’ and the climate assessment on the ‘Climate Category’. This version is based on version 1.0 of the AR6 scenarios database.

opencc-by-4.0Sep 2023View details →

dryad40/100

Projected increases in exposure to climate extremes across global vertebrate diversity hotspots

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publicJun 2025View details →

dryad40/100

Using remote sensing to quantify the additional climate benefits of California forest carbon offset projects

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publicSep 2022View details →

dryad40/100

Recommendations for quantifying and reducing uncertainty in climate projections of species distributions

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publicSep 2022View details →

zenodo36/100

RESCCUE (RESilience to cope with Climate Change in Urban arEas) EU Project - WP1 Data

These files contain the data generated for the Work Package 1 of the RESCCUE project. RESCCUE project was devised to analyse future urban impacts due to climate change so as to improve resilience of three target cities: Barcelona, Bristol and Lisbon. To achieve that, future climate projections and changes in extreme events were obtained at a local scale for the Work Package 1. Several past studies were analysed to identify all the climate variables and extreme events that could affect urban areas, e.g. heavy rainfall, heat waves and storm surge. All available meteorological observations in the considered areas were collected and filtered through several tests (general consistency, outliers and inhomogeneities) in order to handle datasets long enough and of good quality. As a way to obtain the best input possible, every valid station was extended in time by downscaling process with the ERA-Interim reanalysis. Future climate projections were obtained for ten different global climate models considering two of the main Representative Concentration Pathways (RCP4.5 and RCP8.5) established in the last IPCC report. These models were downscaled through a sophisticated statistical methods (analogous stratification and transfer functions among others) to project local climate according to the identified climate drivers: temperature, precipitation, wind, relative humidity, sea level pressure, potential evapotranspiration, snowfall, wave height and sea level; and for both climate and decadal timescales. Already downscaled models were first validated for the method and afterwards verified, obtaining small errors and good coherent simulations. Extreme events of the main climate drivers were obtained and analysed for both historical and future scenarios through the combination of several statistical methods as well as through the analysis of several teleconnectionpatterns. Derived events such as heat waves, drought, snowstorms, storm surges, wave height among others were afterwards inferred for climate, decadal and seasonal scale. FILES The data generated have been grouped into three different files, one for each studied area: the hydrological basin of the rivers Ter and Llobregat (the area that influences Barcelona), the geographical area between England and South Wales (the area that influences Bristol) and the Lisbon area. Each of the files contains a self-explanatory file detailing the structure of the information contained and the way in which it is provided. ABOUT THE RESCCUE PROJECT The RESCCUE project, Resilience to cope with Climate Change in Urban Areas, –a multisectorial approach focusing on water– aims to provide practical and innovative models and tools to end-users facing climate change challenges to build more resilient cities. The project provides tools to assess urban resilience from a multisectorial approach, for current and future climate scenarios and including multiple hazards. This holistic approach to urban resilience will enable city managers and urban systems operators to decide the optimal investments to cope with future situations. For more information, please visit <a href="http://www.resccue.eu/">www.resccue.eu</a>

opencc-by-4.0Feb 2020View details →

zenodo36/100

RCP8.5-ECEARTH-RACMO-LARSIM_ME Climate Flow Projection Data for German Waterways

The datasets provided here were produced as part of the IMPREX project for work package 4, task 4 &bdquo;Improving prediction on the climate scale“ and work package 9, task 3 “Case studies”. Analysis of the datasets are published in Deliverable 4.4 &bdquo;Estimation of hazards based on improved representation of highly vulnerable water resources of strategic importance on the climate scale“ (Falloon et al 2019). The aim was to study the impact of internal climate model variability and bias correction method on the climate change signal of relevant flow indicators for the German waterways Rhine, Elbe and Danube. To assess the impact of internal variability of the global climate model on future changes of flow, precipitation, temperature and global radiation of the 16-member ensemble generated with the RCM KNMI-RACMO2 driven by the GCM EC-EARTH 2.3 provided by WP3 of IMPREX were used. EC-EARTH was run 16 times from 1850 to 2100, each member starting from a slightly different initial state, under forcing of historical emissions until 2005 and the RCP8.5 greenhouse gas concentration pathway from 2006 onwards. Each of the EC-EARTH members was subsequently dynamically downscaled using KNMI-RACMO2 on a 0.11° (~12 km) resolved domain (Aalbers et al. 2018). To correct the systematic model biases of climate models different bias correction methods were applied: (1) no bias correction, (2) linear scaling (Lenderink et al. 2007) and (3) quantile-quantile mapping (Piani et al. 2010). Bias correction relationships were derived for five-day periods (for each variable and location, in total 73 bias correction relationships were derived) including 13 days before and after the considered five-day period (total window size was 31 days) from the observations and values of the regional climate simulations. The period used to estimate the bias correction relationships was 1971-2000. The hydrological model applied is called LARSIM-ME (ME – MittelEuropa = Central Europe) and is based in the model software LARSIM (Large Area Runoff SImulation Model) originally developed by Ludwig & Bremicker (2006). LARSIM-ME covers the catchments of the rivers Rhine, Elbe, Weser/Ems, Odra and Upper Danube. The total catchment size simulated by the model is approximately 800,000 km². The spatial resolution is 5 km x 5 km and the computational time-step is daily. As observed meteorological forcings, precipitation, air temperature and global radiation from the HYRAS data set (Rauthe et al. 2013) available for the 5 km x 5 km model grid and the period 1951-2015 were used. The hydrological model was calibrated using the automatic calibration scheme Shuffled Complex Evolution SCE-UA algorithm (Duan et al. 1994). For more details about the model see Meißner et al. (2017). The meteorological variables air temperature, precipitation and global radiation produced by the KNMI RACMO-EC-EARTH 16 member ensemble (period 1951-2100) were interpolated to a 25 km x 25 km grid and afterwards bias corrected with respect to the observation data (HYRAS) used for calibration of the hydrological model LARSIM. From this 25 km x 25 km grid the bias corrected variables were downscaled to the 5 km x 5 km model grid of LARSIM using monthly background climatology fields on the 5 km x 5 km target grid of the HYRAS dataset. The bias-corrected and downscaled data was then used as meteorological forcing of LARSIM to calculate flow projections for the rivers Rhine, Elbe and Upper Danube (up to the German/Austrian border). Dataset Q_OBS_DE.nc: Mean daily observed flow of the gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe for the period 1951–2017 stored as variable q_obs(time=24472, stations=8). Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG) <pre><code>float q_obs(time=24472, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "observed streamflow"; :coordinates = "lat lon";</code></pre> Dataset Q_HYRAS_LME.nc: Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from the HYRAS dataset stored as variable q_sim (time=23741, stations=8). Period 1951-2015, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe. <pre><code>float q_sim(time=23741, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "simulated streamflow"; :coordinates = "lat lon";</code></pre> Q_RCP85_ECEARTH_RACMO_[bc]_LME.nc: Mean daily projected flow of the hydrological model LARSIM-ME forced by 16 realizations of RCP8.5-ECEARTH-RACMO stored as variable q_sim(time=54787, realization=16, stations=8), first dimension time, second dimension realization and third dimension stations. Bias correction of meteorological forcings [bc]: NOBC: no bias correction, LS: linear scaling, QQMAP Quantile-Quantile Mapping. Period 1951-2100, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe. <pre><code>float q_sim(time=54787, realization=16, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "projected streamflow"; :coordinates = "lat lon";</code></pre> Literature Aalbers, E. E., G. Lenderink, E. van Meijgaard & B. J. J. M. van den Hurk (2018): Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability? Climate Dynamics 50(11), 4745-4766 Duan, Q., S. Sorooshian & V. K. Gupta (1994): Optimal use of the SCE-UA global optimization method for calibrating watershed models. Journal of Hydrology 158(3–4), 265-284 Falloon, P., K. Williams, J. Andreu, A. Solera, S. Suárez-Almiñana, B. Klein, D. Meissner, J. Hunink, J. Eekhout & J. de Vente (2019): Estimation of hazards based on improved representation of highly vulnerable water resources of strategic importance on the climate scale. Deliverable 4.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="https://imprex.eu/system/files/generated/files/resource/imprex-deliverablereport-d4-4-final-1.pdf">https://imprex.eu/system/files/generated/files/resource/imprex-deliverablereport-d4-4-final-1.pdf</a> Lenderink, G., A. Buishand & W. van Deursen (2007): Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrology and Earth System Sciences 11(3), 1143-1159 Ludwig, K. & M. Bremicker (2006): The Water Balance Model LARSIM –Design, Content and Applications. 22. C. Leibundgut, S. Demuth and J. Lange (Eds), Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp. Meißner, D., B. Klein & M. Ionita (2017): Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe. Hydrol. Earth Syst. Sci. 21(12), 6401 Piani, C., J. O. Haerter & E. Coppola (2010): Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology 99(1-2), 187-192 Rauthe, M., H. Steiner, U. Riediger, A. Mazurkiewicz & A. Gratzki (2013): A Central European precipitation climatology - Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS). Meteorologische Zeitschrift 22(3), 235-256

opencc-by-nc-sa-4.0Mar 2020View details →

dryad36/100

Data from: Developmental nutrition modulates metabolic responses to projected climate change

<ol> <li>Current policy has the world on track to experience around 3°C of warming by 2100. The responses of organisms to our warming world will be mediated by changes in physiological processes, including metabolic rate. Metabolic rate represents the energetic cost of living, and is fundamental to understanding the energy required to sustain populations. Current evidence indicates that animals have a limited capacity to adapt to warmer environments by reducing their metabolic rate. Consequently, animals may be more reliant on metabolic plasticity to ameliorate the thermodynamic effect of rising temperatures on physiological rates. However, metabolic plasticity is influenced by other environmental factors, including the nutritional quality of food. Elevated levels of atmospheric CO2 are expected to reduce the protein and increase the carbohydrate concentration in plants, but we do not know how this will affect the response of metabolic rate to climate warming.</li> <li>Here we test the interactive effects of developmental dietary protein and carbohydrate concentrations on the metabolic plasticity of adult Drosophila melanogaster in response to a 3°C increase in temperature while accounting for variation associated with body mass and activity (resting metabolic rate).</li> <li>We show that the thermal sensitivity of resting metabolic rate is modulated by developmental nutrition with animals reared on nutritionally poor, low-protein diets showing the greatest increase in resting metabolic rate in response to simulated climate warming. We also show that if the nutritional quality of resources is unaffected by climate change, then temperature-induced increases in resting metabolic rate will be offset by decreases in mass, but the absolute energy requirements of animals will be elevated relative to current conditions despite this. If, on the other hand, temperatures rise and resources become more calorie-dense and carbohydrate-rich, then the resting metabolic rate of animals will remain relatively unchanged, but decreases in mass and activity may drive down the absolute energy requirements of animals.</li> <li>In the absence of evolutionary adaptation, these findings suggest that the combined plastic response of physiological, morphological, and behavioural traits to temperature and nutrition may be an important determinant of the ultimate outcome of climate change for populations.</li> </ol>

opencc-zeroAug 2020View details →

dryad36/100

Data From: Conservation planning in an uncertain climate: identifying projects that remain valuable and feasible across future scenarios

Conservation actors face the challenge of allocating limited resources despite uncertainty about future climate. A key goal is to minimize the potential for negative outcomes under future scenarios. Thus, we address a global conservation challenge: how to allocate conservation investments given high uncertainty about future climate conditions. To that end, we present a method for identifying projects that remain valuable and feasible across climate scenarios and apply our framework to freshwater biodiversity conservation in the South-Central USA. We combine data from a recent high-resolution hydrologic planning tool and species distribution models to estimate the conservation feasibility and biodiversity value of river reaches below 38 major reservoirs in the Red River basin.We find that only 13% of sites have high conservation priority across all future climate scenarios and that spatial patterns of conservation priority largely reflect patterns of water availability and fish biodiversity.

opencc-zeroOct 2020View details →

zenodo36/100

Dataset for Bukovsky et al. (2021): "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections"

This dataset contains derived data and model data necessary for reproducing the results found in "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections" by Melissa S. Bukovsky, Jing Gao, Linda O. Mearns, and Brian C. O'Neill. This dataset contains data not otherwise available in other public archives, as noted in Bukovsky et al. (2021, Earth's Future; preprint available at https://doi.org/10.1002/essoar.10504141.2). That is, this dataset contains data from the land-use change simulations that are not part of NA-CORDEX (na-cordex.org), but which are complementary to those published in the NA-CORDEX archive.

opencc-by-4.0Dec 2020View details →

zenodo36/100

Dataset from the project entitled HimFunDiff. Related to research article: Global warming alters Himalayan alpine shrub growth dynamics and climate sensitivity.

We examined a total of 9 populations of Rhododendron anthopogon, which were located between 3200 m and 4200 m above sea level (asl). These populations were distributed across three geographically distant transects, with each transect consisting of three sites (along an elevation gradient). The transects are referred to as northern, intermediate, and southern, while the sites at each transect are categorized as low, mid, and high (as depicted in Thakur et al 2024). The northern transect exhibited colder temperatures and lower rainfall compared to the other two transects. On the other hand, the two remaining transects had relatively similar temperatures, but the southernmost transect received higher levels of precipitation. The mean annual temperature of these populations ranged from 2 °C to 5 °C from 2021 through 2022, while volumetric soil moisture levels varied from 0.198 to 0.377 based on onsite measurements using TMS4 dataloggers (Wild et al., 2019). We collected a total of 81 wood disc samples, with 9 samples obtained from each of the 9 sites studied (9 populations × 9 discs). The samples were collected by cutting a single piece from the thickest stem segment, approximately 5 cm in length, from 81 different mature and healthy individuals. Within each site, the 9 samples were obtained from three separate plots (three samples per plot), each covering an area of approximately 100 m2. The selection criteria for these plots included: (1) the presence of Rhododendron anthopogon as one of the dominant species; (2) minimal anthropogenic disturbance; and (3) the absence of large shrubs or trees. The sampled individuals within a plot were spaced at least 5 m apart from each other, and the plots themselves were at least 20 m apart. To prevent rapid drying, the cut stem samples were immediately placed in a wet paper towel. Within 48  hours of sampling, the stem samples underwent dehydration by being immersed in 50 % ethanol for the initial 3 days, followed by 70 % ethanol for the subsequent 7 to 10 days. After the ethanol dehydration process, the samples were air-dried for 72  hours and then stored in paper bags until further processing. Plant age and growth data for each of the sampled individuals were obtained following established protocols (Doležal et al., 2018). In the laboratory, we utilized a sledge microtome to cut cross-sections from each stem sample. These cross-sections were then stained with Astra Blue and Safranin and permanently affixed to microscope slides using Canada Balsam (Doležal et al., 2022). High-resolution images of the fixed sections were captured using an Olympus BX53 microscope equipped with an Olympus DP73 camera. The software CellSense Entry 1.9 was employed to analyse the best image obtained from each individual. We measured annual radial growth increments from pith to bark to the nearest micrometre.  More details are given in the article entitled  <h1>Global warming alters Himalayan alpine shrub growth dynamics and climate sensitivity. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.scitotenv.2024.170252" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.scitotenv.2024.170252</a></h1>

opencc-by-4.0Jan 2024View details →

dryad36/100

Monthly aggregated climate projections of IPSL-CM5A-LR ISIMIP2a fasttrack data for the BES SIM study

Climate projections used in the BES SIM study (Pereira et al. Science 2024). Netcdf files with average air surface temperature (tas) projections, minimum air surface temperature (tasmin), maximum air surface temperature (tasmax), total precipitation (pr), short wave downwelling radiation (rsds) and number of days with a precipitation above 1.0mm/day (wetday) bias corrected following Hempel et al. (2013), from ISIMIP2a fasttrack, for the model IPSL-CM5A-LR for each RCP scenario, with each file being named after the respective scenario (RCP2.6, RCP6.0, RCP8.5). Monthly means (monmean) or sums (monsum) are provided in a 0.5-degree horizontal resolution from the year 1901 to 2099.

opencc-zeroFeb 2024View details →

dryad36/100

Data for: Climate warming and projected loss of thermal habitat volume in lake populations of brook trout

We applied an ensemble of climate warming models and the seasonal temperature profile model for lakes (STM) to assess changes in brook trout thermal habitat volume (THV) among lakes (N=100) within a large, protected area under two climate warming scenarios, RCP 4.5 and RCP 8.5. Brook trout thermal habitat was defined as 9-17°C. Climate warming projections for the balance of this century, regardless of RCP category, will result in the loss of brook trout habitat in lakes that range widely in size. THV loss will be most extensive in lakes that are relatively shallow given their surface area. By 2071-2100 under RCP 4.5, the 90th percentile of THV loss = 31% vs. 63% under RCP 8.5. By the century's end under RCP 8.5, the protected area landscape will be a matrix of lakes with some serving as climate refugia (with reduced THV) and others having severe reductions in THV (>90th percentile THV loss).

opencc-zeroMar 2024View details →

zenodo36/100

Jupyter Notebook and comprising data for GRL2023GL106264R: Understanding the Cascade: Removing GCM biases improves dynamically downscaled climate projections

This notebook and attendant files allows users to interface with a small subset of the data used to create the data in GRL2023GL106264R. Also feel free to check out the overall description of the non-bias corrected dynamically downscaled GCMs in WUS-D3 here: https://zenodo.org/records/10635867. This DOI also contains version of WRF 4.1.3 allowing for yearly CH4, CO2, and N2O updates, as well as a 360-day calendar version.

opencc-by-4.0Mar 2024View details →

zenodo36/100

Calibrated and uncalibrated projection data from the paper "Assessing observational constraints on future European climate in an out-of-sample framework"

Individual uncalibrated and calibrated projections for each of the five methods (A-E) in the following folders: MethodA_proj/ MethodB_proj/ MethodC_proj/ MethodD_proj/ MethodE_proj/   Also included are the out-of-sample data from the "pseudo-observations" (taken from CMIP6 models) used for the verification (see paper for full details): FUTUREverif/

opencc-by-4.0Apr 2024View details →

zenodo36/100

Data and Code for "A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Flood Timing" Paper Submission to AGU GRL

This contains the emergent constraint code, the offline CMIP6 hydrology data, and the shapefiles for each region used in the paper "A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Flood Timing" submitted to AGU GRL.

opencc-by-4.0Apr 2024View details →

zenodo36/100

Projecting climate and socioeconomic contributions to global flood-induced displacements using a data-driven approach - Data Supplement

This contribution contains data and analysis scripts for the manuscript "Projecting climate and socioeconomic contributions to global flood-induced displacements using a data-driven approach" by Lenin Del Rio Amador, Mathieu Boudreault and David A. Carozza.

opencc-by-4.0Nov 2024View details →

zenodo36/100

FORSITE-Clim Europe: European-wide climate indicators for historical periods and climate projections at high resolution

<h2>Overview</h2> This meteorological data set consists of climatologies (climate indicators) on 30-year average basis for Europe and covers two historical periods as well as two periods for three selected climate scenarios with a high spatial resolution of less than 1 km. The two 30-year periods provided for the observations allow the analysis of the climate change that has already happened.  Resolution: 30x30 arcsec Projection: EPSG 4326 Extent for historical data: 10.67°W – 47.67°E, 33.68°N – 71.33°N Extent for scenario data: 10.67°W – 39.33°E, 33.68°N – 71.33°N Periods for historical data: 1961-1990 and 1991-2020 Periods for scenario data: 2036-2065 and 2071-2100 Format: GeoTIFF List of climatologies (climate indicators)   <table> <tbody> <tr> <td> # </td> <td> Short name </td> <td> Name </td> <td> Description </td> <td> Unit </td> <td> Yearly (Y) or monthly (M) </td> </tr> <tr> <td> 1 </td> <td> tasmin </td> <td> Average daily minimum temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 2 </td> <td> tasmax </td> <td> Average daily maximum temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 3 </td> <td> tas </td> <td> Average temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 4 </td> <td> tas_warmest_month </td> <td> Average temperature mean in the warmest month </td> <td> Calculation of the mean temperature over the climate period for all months and then selection of the highest value for the warmest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 5 </td> <td> tas_coldest_month </td> <td> Average temperature mean in the coldest month </td> <td> Calculation of the mean temperature over the climate period for all months and then selection of the lowest value for the coldest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 6 </td> <td> tasmin_coldest_month </td> <td> Average temperature minimum in the coldest month </td> <td> Calculation of the mean minimum temperature over the climate period for all months and then selection of the lowest value for the coldest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 7 </td> <td> tasmax_warmest_month </td> <td> Average temperature maximum in the warmest month </td> <td> Calculation of the mean maximum temperature over the climate period for all months and then selection of the highest value for the warmest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 10 </td> <td> GSL </td> <td> Average length of the growing season </td> <td> The growing season is the duration in days of the longest continuous period of days with an average temperature of at least 5°C. However, an earlier or later period of such warm days is included in the growing season if it lasts longer than the sum of all intervening cooler days </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 12 </td> <td> GDD </td> <td> Average Growing Degree Days per year above 5°C </td> <td> Σ(Tmean – 5°C) per year.  </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 13 </td> <td> FD_first </td> <td> Average date of the first frost occurrence </td> <td> Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate </td> <td> day of year </td> <td> Y </td> </tr> <tr> <td> 14 </td> <td> FD_last </td> <td> Average date of the last frost occurrence </td> <td> Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate </td> <td> day of year </td> <td> Y </td> </tr> <tr> <td> 20 </td> <td> GLO_hori </td> <td> Average sum of global radiation </td> <td>   </td> <td> kWh </td> <td> Y, M </td> </tr> <tr> <td> 33 </td> <td> pr </td> <td> Average precipitation sum </td> <td>   </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 39 </td> <td> ET0 </td> <td> Average annual potential evapotranspiration </td> <td> Calculation according to FAO Penman-Monteith: fao.org/3/X0490E/x0490e08.htm </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 40 </td> <td> WBAL </td> <td> Average climatic water balance </td> <td> Precipitation minus potential evapotranspiration </td> <td> mm </td> <td> Y, M </td> </tr> </tbody> </table> <h2>Data sources</h2> The raw historical data is a combination or extension of daily CHELSA (Climatologies at high resolution for the earth’s land surface areas) with ERA5-Land to fully cover 1961-2020.  <ul> <li>CHELSA-W5E5 v1.0 (https://doi.org/10.5194/essd-15-2445-2023) for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), maximum temperature (tasmax) and minimum temperature (tasmin) for the period 1979-2016</li> <li>CHELSA V2.1 for climatological average monthly wind speed (sfcWind_01, ..., sfcWind_12) and for climatological mean vapor pressure deficit (vpd_01, ..., vpd_12)</li> <li>ERA5-Land for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), dew point (tds), and wind speed (sfcWind) for the period 1961-2020</li> <li>v2.0: WorldClim version 2.1 for climatological monthly minimum, maximum, and average temperatures.</li> </ul> Climate models from EURO-CORDEX (doi.org/10.1007/s10113-013-0499-2): <ul> <li>MPI-M-MPI-ESM-LR_rcp45_r1i1p1_CLMcom-CCLM4-8-17</li> <li>MPI-M-MPI-ESM-LR_rcp85_r1i1p1_CLMcom-CCLM4-8-17</li> <li>ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4</li> </ul>

opencc-by-4.0Feb 2024View details →

zenodo36/100

Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations

Data for article "Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations"

opencc-by-4.0Jan 2022View details →

dryad36/100

Evolution of thermal physiology alters the projected range of threespine stickleback under climate change

Species distribution models (SDMs) are widely used to predict range shifts but could be unreliable under climate change scenarios because they do not account for evolution. The thermal physiology of a species is a key determinant of range and thus incorporating thermal trait evolution into SDMs might be expected to alter projected ranges. We identified a genetic basis for physiological and behavioural traits that evolve in response to temperature change in natural populations of threespine stickleback (Gasterosteus aculeatus). Using these data, we created geographic range projections using a mechanistic niche envelope approach under two climate change scenarios. Under both scenarios, trait data was either static ('no evolution' model), allowed to evolve at observed evolutionary rates for the trait ('evolution' model), or allowed to evolve at a rate of evolution scaled in association with the variance that is explained by quantitative trait loci (QTL; 'scaled evolution' models). We show that incorporating these traits and their evolution substantially altered the projected ranges for a widespread panmictic marine population, with over 7-fold increases in area under climate change projections when traits are allowed to evolve. Evolution-informed SDMs should improve the precision of forecasting range dynamics under climate change, and aid in their application to management and the protection of biodiversity.

opencc-zeroApr 2022View details →

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record