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150 results for “climate extreme”
Data from: Extreme climate-induced life-history plasticity in an amphibian
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Environmental data from: Potential distributions of invasive vertebrates in the Iberian Peninsula under projected changes in climate extreme events
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Data from: Extreme climate events counteract the effects of climate and land-use changes in Alpine treelines
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Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Antwerp Berchem, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Antwerp Berchem (51°12'00"N 4°26'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Storyline data used in the paper "Storylines reveal contrasting thermodynamic effects of climate change on 2020/21 East Asian cold extremes"
<p>We provide the storyline data (in NetCDF format) used in the paper:”<strong>Storylines reveal contrasting thermodynamic effects of climate change on 2020/21 East Asian cold extremes”. </strong>The data is structured five .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climates. The data includes the five ensemble members (E1 to E5) and ensemble mean variables at winter season (DJF) in 2020/2021.</p> <p>Files of simulation ensemble member data are named as:</p> <p><span> </span>“AWICM1_ssp370/hist_f{begin year}_n2017_T20e24_{variable name}_E{ensemble member}_DJF-{years}_dailymean.nc”</p> <p>Files of simulation ensemble-mean data are names as:</p> <p>“AWICM1_ssp370/hist_f{begin year}_n2017_T20e24_{variable name}_DJF-{years}_ensmean.nc”</p> <p>Files of free-run (CMIP6) data are names as:</p> <p>“freerun_{variable name}_DJF-{year}_ensmean_31days-runmean_11years-ydaymean.nc”</p> <p>Variables includes:<span> </span></p> <ul> <li>Mean 2m Temperature (t2m)</li> </ul> <ul> <li>Downward net surface solar radiation (srads)</li> </ul> <ul> <li>Total cloud cover (aclcov)</li> <li>Downward solar radiation at clear sky (rsdscs)</li> <li>sea ice concentration (friac)</li> </ul> <p>Only for present climate:</p> <ul> <li>Zonal/meridional wind at 850hPa (u850,v850)</li> <li>500 hPa Geopotential Height (z500)</li> </ul> <p><span> </span></p>
Open data for "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate"
<p>______________________________________________________<br> This page contains public-domain data required to reconstruct simulation results in the manuscript "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate," submitted by the following author.</p> <p>Author: Hisashi SATO (JAMSTEC) <br> email : hsatoscb_(at)_gmail.com</p> <p>______________________________________________________<br> 1. Folder "Code"<br> Detailed descriptions are available on the code. </p> <p>1-1. MachineLearningComparison.R<br> Machine learning programs using random forest (RF), naive Bayes classifier (NV), and support vector machine (SVM) algorithms.</p> <p>1-2. Analyse_MapSimilarity.R<br> Calculate coincidences of simulated potential natural vegetation (PNV) maps simulated by different models.</p> <p>1-3. Visualize_VCE.R<br> Generating VCE (Visualize Climate Image) for training CNN models.</p> <p>1-4. Visualize_Maps.R<br> Visualizing global PNV maps.</p> <p>1-5. Visualize_ClimateHistgrams.R<br> Visualizing histograms of climate datasets.</p> <p>______________________________________________________<br> 2. Folder "Input"</p> <p>2-1. Unified_BIOCLIM_WorldClim.csv<br> Input data for the current climate.<br> This file contains the following variables.<br> lon Longitude at the center of the grid<br> lat Latitude at the center of the grid<br> bio1~19 Average climate indices from BIOCLIM (AveI)<br> CDD~WSDI Extreme climate indices (CEI)<br> c1~c16 Fraction of PNV from MODIS data<br> tavg01~tavg12 Monthly mean air temperature from January to December (Ave)<br> prec01~prec12 Monthly precipitation from January to December (Ave)</p> <p>2-2. Unified_BIOCLIM_WorldClimFutureRCP85.csv<br> Input data for future climate (@RCP8.5)<br> Including variables are the same as Unified_BIOCLIM_WorldClim.csv</p> <p>2-3. BIOCLIM_RefNo.csv<br> This CSV file contains the following information for each grid.<br> lat: Latitude at the center of the grid<br> lon: Longitude at the center of the grid<br> latNo: Latitude number corresponding to the image file name<br> lonNo: Longitude number corresponding to the image file name<br> lineNo: No use. Don't mind.<br> vegNo: Most dominant PNV based on the Unified_BIOCLIM_WorldClim.csv</p> <p>______________________________________________________<br> 3. Folder "Output"</p> <p>3-1. PNV_sim<br> 3-2. PNV_sim_RCP85.csv<br> Current and future PNV maps from various models. These files are the main output files from the code MachineLearningComparison.R. For PNV maps from CNN models (m4p1~6) were supplemented. Detailed methods to build CNN models, please refer to the following manuscript.<br> Sato, H. & T. Ise (2022). "Predicting global terrestrial biomes with the LeNet convolutional neural network." Geoscientific Model Development 15(7): 3121-3132.</p> <p>Labels indicate combinations of machine-learning-algorithm and dataset for training the model. For example, In case of "m1p1", that column shows the simulation result of models trained with randomForest (RF) algorithm and Ave dataset.<br> m1: randomForest (RF)<br> m2: Support vector machine (SVM)<br> m3: Naive Bayes (NB)<br> m4: Convolutional Neural Network (CNN), which is NOT analysed in this code<br> p1: Ave<br> p2: Ave + CEI<br> p3: Ave + CEIpart<br> p4: AveI <br> p5: AveI + CEI<br> p6: AveI + CEIpart</p>
Extreme boreal fire risks under historical and future climates in Finland
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Extreme events changes over China under 1.5-4°C global warming targets: projected by an ensemble of regional climate model simulations
<p>This file is for the upload of data for 2019JD031057R.</p>
Responses of hydrological extremes to future climate change and forest disturbance in snow-dominated watersheds of southern British Columbia
<p>1. Future hydrological predictions in watershed 241, Camp, Greata, and Trepanier watersheds, including daily hydrometeorological predictions and aggregated future hydrometeorological metrics. The future climate data are from six GCMs and under three distinct development pathways.</p> <p>2. Trends of multiple future hydrological signatures (i.e., high and low flows) in watershed 241, Camp, Greata, and Trepanier watersheds.</p>
Data for: Extreme shifts in habitat suitability under contemporary climate change for a high-Arctic herbivore
<p>Data and code associated with MaxEnt analyses to quantify shifts in habitat suitability of muskoxen in the Northeast Greenland National Park. Details on how to use the files are provided in the README.docx file</p>
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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.