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3,113 results for “Extremism”
Spatial patterns of extreme precipitation and their changes under ~2 °C global warming: A large-ensemble study of the western US: Data Release
<p>This dataset supports the analysis in Rupp et al. (2022). The dataset consists of 17,223 data files containing the water year (WY) maximum of the daily-averaged precipitation rate simulated with the HadRM3p regional climate model configured for the western United States. Each file contains the WY maxima across the model domain for a single WY, single model parameterization, and single set of initial conditions. Please refer to Hawkins et al. (2019) and Rupp et al. (2022) for a description of how the climate model data were generated.</p>
Projected changes in droughts and extreme droughts in Great Britain are strongly influenced by the choice of drought index: UKCP18-based bias adjusted potential evapotranspiration
<p>Potential evapotranspiration calculated from the UKCP18 RCM ensemble using the Penman-Monteith method as implemented by Robinson et al. (2017) and bias adjusted using Lange et al. (2019). This dataset was used for analysis of future drought characteristics in Reyniers et al. (2022). Details on the bias adjustment of this potential evapotranspiration dataset, as well as bias-adjusted precipitation and temperature from the same climate model ensemble, can be found in Reyniers et al. (2025).</p> <p>---</p> <p>Reyniers, N., Osborn, T. J., Addor, N., and Darch, G.: Projected changes in droughts and extreme droughts in Great Britain strongly influenced by the choice of drought index, Hydrol. Earth Syst. Sci., 27, 1151–1171, https://doi.org/10.5194/hess-27-1151-2023, 2023.</p> <p>Reyniers, N., Zha, Q., Addor, N., Osborn, T. J., Forstenhäusler, N., and He, Y.: Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain, Earth Syst. Sci. Data, 17, 2113–2133, https://doi.org/10.5194/essd-17-2113-2025, 2025. </p> <p>Robinson, E. L., Blyth, E. M., Clark, D. B., Finch, J., Rudd, A. C. (2017). Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data. HESS, <em>21</em>(2), 1189-1224.</p> <p>Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). <em>GMD,</em> <em>12</em>(7), 3055-3070.</p>
Projected changes in droughts and extreme droughts in Great Britain are strongly influenced by the choice of drought index: UKCP18-based SPI and SPEI data
<p>Standardised Precipitation Index (SPI; McKee et al., 1993) and Standardised Precipitation Evapotranspiration Index (SPEI; Vicente-Serrano et al., 2009) computed from UKCP18 Strand 3 simulations (Met Office Hadley Centre, 2018).</p> <p>This data was produced for the study by Reyniers et al. (<em>in prep</em>) analysing (diferences in) drought projections using these indicators. The methodology used to produce this data can be found there if/when the paper is accepted, however do not hesitate to reach out with any further questions. Please note the RCM data was bias adjusted prior to SPI and SPEI computation. There is one file per ensemble member containing the full simulated period on a monthly time step, using aggregation periods of 1, 3, 6, 12, 24 and 36 months for the computation of SP(E)I.</p> <p><strong>References</strong></p> <p>McKee, T. B., Doesken, N. J., Kleist, J., et al.: The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference on Applied Climatology, vol. 17, pp. 179–183, Boston, 1993</p> <p>Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. Centre for Environmental Data Analysis, <em>date of citation</em>. <a href="https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604">https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604</a></p> <p>Reyniers, N., Osborn, T. J., Addor, N., Darch, G.: Projected changes in droughts and extreme droughts in Great<br> Britain are strongly influenced by the choice of drought index. Hydrology and Earth System Sciences, <em>in prep. for HESS</em></p> <p>Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index, Journal of Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, 2009.</p>
Pre- and post-oviposition behavioural strategies to protect eggs against extreme winter cold in an insect with maternal care
<p>Data set and R script supporting the publication entitled "<strong>Pre- and post-oviposition behavioural strategies to protect eggs against extreme winter cold in an insect with maternal care" </strong>by Jean-Claude Tourneur, Claire Cole, Jess Vickruck, Simon Dupont and Joël Meunier.</p> <ul> <li>Script Earwig oviposition - Zenodo v2.R = R script allowing to conduct the stats and obtain the figures presented in the manuscript</li> <li>Part I - Zenodo.txt = Data set of the first part of the experiment about the location of females and eggs until oviposition (included)</li> <li>Part II - Zenodo v2.txt = Data set of the second part of the experiment about the location of eggs after oviposition</li> <li>Readme.txt = details of the variables present in the 2 data sets</li> </ul>
Supporting data for ``Summer-Winter Contrast in the Response of Precipitation Extremes to Climate Change over Northern Hemisphere Land'"
<p>Here we have the processed data used in the preprint ``'Summer-Winter Contrast in the Response of Precipitation Extremes to Climate Change over Northern Hemisphere Land''</p> <p>The README.md file includes explanations about the data in the repository.</p>
Supplementary Material: Climate-sensitive disease outbreaks in the aftermath of extreme climatic events: a scoping review
<p><strong>Supplemental experimental procedures</strong></p> <p><em>General Information</em></p> <p>Here we provide the data extraction of the studies retrieved for the scoping review "Climate-sensitive disease outbreaks in the aftermath of extreme climatic events" following PRISMA-ScR guidelines. Data were extracted for the following variables: title, first author, year of publication, country/region studied, extreme climate event, extreme climate event name (tropical cyclones are often named e.g. Typhoon Haiyan), Index used to measure climate anomaly, extreme climate event definition, text description of extreme climate event, disease, outbreak definition, time period of the study, data source, baseline/reference period, study design, statistics, outcome, outcome quantification, outbreak risk, qualitative description of extreme climate event and outbreak risk, time lag. Outcome was defined as either disease cases or incidence. </p>
The Extreme Mechanics of Viscoelastic Metamaterials
<p>This dataset contains all data as used for the paper 'The Extreme Mechanics of Viscoelastic Metamaterials'. It provides example files on viscoelastic modelling in Abaqus and on deriving viscoelastic material properties,</p> <p> </p> <p><strong>Abstract:</strong></p> <p>Mechanical metamaterials made of flexible building blocks can exhibit a plethora of extreme mechanical responses, such as negative elastic constants, shape-changes, programmability and memory. To date, dissipation has largely remained overlooked for such flexible metamaterials. As a matter of fact, extensive care has often been devoted in the constitutive materials’ choice to avoid strong dissipative effects. However, in an increasing number of scenarios, where metamaterials are loaded dynamically, dissipation can not be ignored. In this review, we show that the interplay between mechanical instabilities and viscoelasticity can be crucial and can be harnessed to obtain new functionalities. We first show that this interplay is key to understanding the dynamical behaviour of flexible dissipative metamaterials that use buckling and snapping as functional mechanisms. We further discuss the new opportunities that spatial patterning of viscoelastic properties offer for the design of mechanical metamaterials with properties that depend on loading rate.</p>
Extreme hydrometeorological events, a challenge for gravimetric and seismology networks
<p>“Data supporting the paper published by Earth's Future: "Extreme hydrometeorological events, a challenge for gravimetry and seismological networks" by Van Camp, de Viron, Dassargues, Delobbe, Chanard, and Gobron, doi: 10.1029/2022EF002737, 2022.</p> <p>The data contains:</p> <p>1) A NETCDF mvc_data_EF.nc file, and the Python mvc_data_EF.py code for reading it. The nc file contains the time series from and around the Membach station, Belgium, as recorded during the July 2021 flood of the Vesdre River. Time in second since 2021-01-01 00:00:</p> <p>-Gravity [nm/s²]: from the superconducting gravimeter (after correcting for tidal and atmospheric pressure effects),</p> <p>To convert it into mm water: use factor -1/0.39 [mm/nm/s²]</p> <p>-Seismology [counts]: from the broadband Guralp CMG-3ESP seismometer.</p> <p>Calibration factor: 838.86 [count/µm/s]</p> <p>-Rain [mm water]: data inferred from the weather radar</p> <p>-Flow [m³/s]: water entering the Eupen reservoir</p> <p>2) An ASCII file SG_C021.TSF contains the complete recording of the superconducting gravimeter GWR#C021, since 1995-10-01. The data are corrected for polar motion, tidal, and atmospheric pressure effects (local admittance of -3.3 nm/s²/hPa), and instrumental drift. This drift is determined by using 323 absolute gravity measurements performed at the Membach station by the FG5#202 gravimeter since January 1996.</p> <p>3) PDF Document "<em>Supplement 1 Other seismic spectra during high flow events</em>" providing moving window spectra of seismic recordings during other significant floods in the Vesdre Valley</p> <p>4) PDF document "<em>Supplement 2_GRACE_GLDAS_GNSS</em>" providing the observations from GNSS and GRACE, and the predictions of the GLDAS hydrological model during the July 2021 event.</p>
Limited legacy effects of extreme multi-year drought on carbon and nitrogen cycling in a mesic grassland
<p>The intensification of drought throughout the US Great Plains has the potential to have large impacts on grassland functioning, as has been shown with dramatic losses of plant productivity annually. Yet, we have a poor understanding of how grassland functioning responds after drought ends. This study examined how belowground nutrient cycling responds after drought and whether legacy effects persist post-drought. We assessed the two-year recovery of nutrient cycling processes following a four-year experimental drought in a mesic grassland by comparing two different growing season drought treatments - chronic (each rainfall event reduced by 66%) and intense (all rain eliminated until 45% of annual rainfall was achieved) – to the control (ambient precipitation) treatment. At the beginning of the first growing season post-drought, we found that in situ soil CO<sub>2</sub> efflux and laboratory-based soil microbial respiration were reduced by 42% and 22% respectively in the intense drought treatment compared to the control, but both measures had recovered by mid-season (July) and remained similar to the control treatment in the second post-drought year. We also found that extractable soil ammonium and total inorganic N were elevated throughout the growing season in the first year after drought in the intense treatment. However, these differences in inorganic N pools did not persist during the growing season of the second year post-drought. The remaining measures of C and N cycling in both drought treatments showed no post-drought treatment effects. Thus, although we observed short-term legacy effects following the intense drought, C and N cycling returned to levels comparable to non-droughted grassland within a single growing season regardless of whether the drought was intense or chronic in nature. Overall, these results suggest that key aspects of C and N cycling in mesic tallgrass prairie do not exhibit persistent legacies from four years of experimentally-induced drought.</p>
Extreme sea level rise along the Indian Ocean coastline: Observations and 21st century projections
<p>This file include all the data sets used to make the figures in the paper " <strong><em>Extreme sea level rise along the Indian Ocean coastline: Observations and 21<sup>st</sup> century projections</em></strong> "</p>
DATASET - Improving Remote Sensing of Extreme Events with Machine Learning: Application to IASI LST Retrievals
<p>Data for experiments presented in the paper "Improving Remote Sensing of Extreme Events with Machine Learning: Application to IASI LST Retrievals" </p>
Mehrabi et al. 2022. Research priorities for global food security under extreme events. Supplementary data and code.
<p>Data and script for reproducing the final results shown in Mehrabi et al., Research priorities for global food security under extreme events, One Earth (2022), https://doi.org/10.1016/j.oneear.2022.06.008.</p> <p>Simply download and read the Mehrabi2022_EEGFS.pdf or the Mehrabi2022_EEGFS.Rmd file from which it was created.</p>
Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation
<p>The flood day products were derived from GRACE Terrestrial Water Storage and Extreme Precipitation. We used GRACE terrestrial water storage and precipitation data combined with high-frequency filtering, anomaly detection and flood potential index methods to successfully extract historical flood days globally between Apr. 1st, 2002, and Aug. 31st, 2016, and further compared and validated the results with Dartmouth Flood Observatory (DFO) data, Global Runoff Data Centre (GRDC) discharge data, news reports and social media data. The results showed that GRACE-based flood days could cover 81% of the flood events in the DFO database, 87% of flood events extracted by MODIS and supplement many additional flood events not recorded by the DFO. Moreover, the probability of detection greater than or equal to 0.5 reached 62% among 261 river basins compared to flood events derived from the GRDC discharge data. These detection capabilities and detection results are both good. We finally provided flood day products with 1° spatial resolution covering the range of 60°S—60°N from Apr. 1st, 2002, to Aug. 31st, 2016. This research provides a data foundation for the mechanistic analysis and attribution of global flood events.</p>
Chameleon biogeographic dispersal associated with extreme life history strategies
<div class="t-landing__text-wall "> <p>This dataset contains data and code that support the results in Weil, S.-S., Gallien, L., Lavergne, S., Börger, L., Hassler, G., Nicolaï, Michaël P. J., Allen, William L. (2022) Chameleon biogeographic dispersal associated with extreme life history strategies (DOI<strong>: </strong><span>10.1111/ecog.06323</span>).</p> <p>We used species distribution, phylogenetic and life history trait data of 181 chameleons to determine the relationship between three traits (coastal distribution, body size, position on the fast/slow life history continuum) and past dispersal probability on an evolutionary timescale using trait-dependent biogeographic models.</p> <p>We found that all three traits were associated with past biogeographical movements. Lineages having coastal distributions and those with large bodies had higher dispersal probabilities. Interestingly, chameleons with either very fast or very slow life history were more successful dispersers than species with an intermediate strategy. Together, the three traits "coastal, large-bodied and extreme life history" form a dispersal syndrome.</p> </div>
Fig. 25. Lycosa piochardi Simon, 1876, habitat extremes. A in Lycosa Latreille, 1804 (Araneae, Lycosidae) of Israel, with a note on Geolycosa Montgomery, 1904
Fig. 25. Lycosa piochardi Simon, 1876, habitat extremes. A. Alpine tragacanth steppe, Mt. Hermon. B. Natural woodland, Odem Forest. C. Sandy desert, western Negev. D. Dead Sea oasis, En Gedi. Photos by I. Armiach Steinpress.
FIG. 2 in Relations between metatarsal proximal extremity parameters and weight and height at the withers of the dromedary (Camelus dromedarius Linnaeus, 1758) in the Sahraoui and Targui "breeds".
FIG. 2. — Principal component analysis (PCA), graphs for seven parameters per bone, 43 right metatarsal bones.A, graph of variables;B, scatterplot of individuals; C, 95% confidence ellipsis for breed; D, 95% confidence ellipsis for sex; E, 95% confidence ellipsis for breed and sex. Abbreviations: BpT, proximal width with T at the extremity for the metatarsal; BW, body weight; Dim, Factor of the PCA (Dim 1 = Factor 1, Dim 2 = Factor 2); DpT, proximal depth with T at the extremity for the metatarsal; F, female; GC, surface for the great cuneiform bone; HW, height at the withers; M, male; NC1, great cranial articular surface for the cuboid bone; NC2, little caudal articular surface for the cuboid bone; S, Sahraoui breed; SA, total proximal articular surface; SF, female Sahraoui; SM, male Sahraoui; T, Targui breed; TF, female Targui; TM, male Targui.
Extreme weather events threaten biodiversity and functions of river ecosystems: dataset for conducting the meta-analysis
<p>This repository contains the code and dataset to replicate the meta-analysis conducted by Sabater et al. entitled "Extreme weather events threaten biodiversity and functions of river ecosystems: evidence from a meta-analysis"</p> <p>Metadata:</p> <p>- metaanalysis_GlobalEvidenceRivers_Rscript.R - R Script to conduct the meta-analysis</p> <p>- structural_resp.csv - table with data to perform the species richness, density, and biomass meta-analysis. It includes the mean, SD (or SE), and sample number of the studies included in the meta-analysis, as well as information on the paper authors, year of publication, and type of study (experimental or observational). It also includes co-variates and the author who subtracts the information from the paper.</p> <p>- functional_resp.csv - table with data to perform the primary productivity, respiration, and decomposition meta-analysis. It includes the mean, SD (or SE), and sample number of the studies included in the meta-analysis, as well as information on the paper authors, year of publication, and type of study (experimental or observational). It also includes co-variates and the author who subtracts the information from the paper.</p> <p>-refMap.csv - Geographical information of the papers included in the meta-analysis.</p>
Dataset in support of the study "Getting the leaves right matters for estimating temperature extremes"
<p>Dataset in support of the study “Getting the leaves right matters for estimating temperature extremes"</p>
Comparing climatic suitability and niche distances to explain populations responses to extreme climatic events
<p><span>Habitat suitability calculated from Species Distribution Models (SDMs) has been used to assess population performance, but empirical studies have provided weak or inconclusive support to this approach. Novel approaches measuring population distances to niche centroid and margin in environmental space have been recently proposed to explain population performance, particularly when populations experience exceptional environmental conditions that may place them outside of the species niche. Here, we use data of co-occurring species' decay, gathered after an extreme drought event occurring in the SE of the Iberian Peninsula which highly affected rich semiarid shrubland communities, to compare the relationship between population decay (mortality and remaining green canopy) and (1) distances between populations' location and species niche margin and centroid in the environmental space, and (2) climatic suitability estimated from frequently used SDMs (here MaxEnt) considering both the extreme climatic episode and the average reference climatic period before this. We found that both SDMs-derived suitability and distances to species niche properly predict populations performance when considering the reference climatic period; but climatic suitability failed to predict performance considering the extreme climate period. In addition, while distance to niche margins accurately predict both mortality and remaining green canopy responses, centroid distances failed to explain mortality, suggesting that indexes containing information about the position to niche margin (inside or outside) are better to predict binary responses. We conclude that the location of populations in the environmental space is consistent with performance responses to extreme drought. Niche distances appear to be a more efficient approach than the use of climate suitability indices derived from more frequently used SDMs to explain population performance when dealing with environmental conditions that are located outside the species environmental niche. The use of this alternative metrics may be particularly useful when designing</span><span> conservation measures to mitigate impacts of shifting environmental conditions.</span></p>
Coastal extreme sea levels in the Caribbean Sea induced by tropical cyclones
<p>Previous version contained the return levels of coastal significant wave height and sea surface elevation along the Caribbean coastlines corresponding to the periods of 10, 50, 100, 200 and 500 years. The return periods have been computed fitting a Generalised Pareto Distribution to a set of hydrodynamic-wave coupled ocean simulations forced with 1000 synthetic tropical cyclones. See the paper Martin et al (under review) for details. </p> <p> The file (Return_levels.mat) contains a Matlab table (with header names) indicating latitude, longitude and the return levels described above, named as Hs (significant wave height) and SSE (sea surface elevation). </p> <p>Three other datasets have been included, with the subsample of the Tropical Cyclones selected for the study (Subsample.mat), the geographic data of the coastal grid points used for our analysis (Coastaline.mat), and finally a dataset with the results of the analysis presented in the paper (Results_4_runs.mat). All datasets are provided in a .mat file, generated using Matlab. </p> <p>First dataset contains a Matlab table (with header names) indicating latitude and longitude, radius of maximum wind speed, minimum pressure and maximum wind speed a long the track of the Tropical Cyclone, for the 1000 samples selected. </p> <p>Second dataset provides both Latitude and Longitude of the coastal grid points used for the analysis. </p> <p>The last dataset contains a Matlab table (with header names) where the first column represents the Tropical Cyclone, columns 2,3 and 4 contain the maximum of sea surface elevation (SSE) during the lifetime of the Tropical Cyclone for each coastal point, for the 3 decoupled runs, wind-forced only, pressure-forced only and wind and pressure respectively. The next 4 columns contain the values of the variables for the coupled simulations with WWM-III, maximum significant wave height (Hs), maximum SSE, median of Peak Direction (Dp) and median of Peak period (Tp). All these variables contain a description in the dataset as variable information, and are provided for all coastal points of our grid. </p>
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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.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.