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29 results for “wave climate”
Adriatic Sea wind-wave climate years 1981-2010 and 2021-2050 (RCP4.5 and RCP8.5)
<p>Adriatic Sea mean annual 50th, 90th, 95th and 99th percentiles of the significant wave height (Hs) from WAVEWATCH III v6.07 (2 km) forced with 1-hour ERA5 wind fields statistically scaled to QQ-match COSMO-CLM fields (available at https://doi.org/10.5281/zenodo.6021380).</p> <p>Reference periods:</p> <p>1) Historical climate: years 1981-2010</p> <p>2) Future climate: years 2021-2050 (IPCC scenario RCP4.5 and RCP8.5)</p>
Statistical characterization of Andalusian wave climate for several combinations of Global Climate Models and Regional Climate Models and periods 2026 - 2045 and 2081 - 2100.
<p>The following text is an extract of the extended abstract entitled "<strong>Parametric Characterization of Wave Climate along the Andalusian Coast for Non-Stationary Stochastic Simulation</strong>" whose authors are Manuel Cobos, Pedro Magaña, Pedro Otiñar and Asunción Baquerizo, and that was included in proceedings of <em>39th IAHR World Congress</em> where this dataset is included.</p> <p><em>Processed data comes from PIMA Adapta Costas project (Ramírez et al., 2019), in particular, from projections of maritime climate for 2026-2045 and 2081-2100. Sea climate contains, among other information, time series of the significant wave height (H<sub>s</sub>) obtained for several combinations of GCM-RCM projections of EUR-11 for the RCP 8.5. GCM-RCM combinations ACCE, CMCC, CNRM, GFDL, HADG, IPSL, MIRO with a 0.1 degrees grid were used for the Atlantic facade while CNRM, HADG, IPSL, MIRO, MEDC, MPIE, ESM2, EART models with 1/11 degrees were used for the Mediterranean one. A total of 210 locations were analyzed, 54 at the Atlantic facade and 156 at the Mediterranean one (Figure 1). The data was bias adjusted using the Empirical Quantile Mapping (Déqué et al., 2007; Michelangeli et al., 2009). Information of the significant wave height and the dependence between the values at a given time with previous values with a VAR(q) model is already available. </em></p> <p><em>At each location, the methodology of Lira-Loarca et al. (2021) was applied, using the software described in Cobos et al. (2022a). More precisely, for every GCM-RCM (hereinafter, model n for n = 1, .., N where N = 7 for Atlantic data and N = 8 for the Mediterranean data), a non-stationary marginal distribution of H<sub>s</sub>, , assuming that the year was the largest periodicity of the climate, was fitted to data using a lognormal model for the central part and two generalized Pareto distribution for the lower and upper tails, as in Solari and Losada (2011). The non- stationarity is considered by assuming a decomposition of the parameters of the distribution and of the percentiles of the common end points of the interval into a trigonometric truncated expansion.</em></p> <p><em>In addition, the coefficients of the matrix, C<sub>n</sub>, of a VAR(q) model with q up to 92 hours were estimated. The ensemble multi-model characteristics of the data were obtained from the compound distributions and the weighted averaged matrix coefficients. </em></p> <p><em>Soon, the results of the peak period (T<sub>p</sub>) and mean incoming wave direction (ϑ<sub>m</sub>) and the coefficients of the multivariate VAR model will also be included.</em></p> <p> </p> <p> </p>
Two-wave Post-Disaster Survey on Climate Change Attitudes: Texas after Hurricane Harvey and the 2021 North American Winter Storms
<p><strong>Overview</strong></p> <p>This repository contains data needed to reproduce the analysis results from Chen et al. 2024. "Disaster Experience Mitigates the Partisan Divide on Climate Change: Evidence from Texas," <em>Global Environmental Change</em>. It is a study about climate change attitudes and experience with climate disasters across U.S. partisan groups. For details about the data, please see the published paper. Results reproduction code is available at <a href="https://github.com/tedhchen/floodStorm" target="_blank" rel="noopener">https://github.com/tedhchen/floodStorm</a>.</p> <p> </p> <p><strong>Data Set Details</strong></p> <p>`texas_climate_attitudes.csv` contains data from two waves of surveys of Democrats and Republicans living in Texas, with the following groups of variables.</p> <ul> <li>climate change attitudes</li> <li>self-reported exposure to climate disasters</li> <li>scientific information treatment condition and checks</li> <li>political leaning</li> <li>sociodemographics and residential location</li> <li>survey administration details</li> </ul> <p>`outage2021_data.RData` contains power outage data for counties and cities in Texas during Feb. 2020 and Feb. 2021.</p> <p>`outage2021_data_multithreshold.RData` contains power outage data for counties and cities in Texas during Feb. 2020 and Feb. 2021, aggregated to the county level based on different thresholds of uncertainty about which cities people live in.</p> <p>`gtrends_archive.RData` contains Google Trends data for "hurricane", "astros", and "power", in Texas between 2017 and 2021.</p> <p> </p> <p><strong>References</strong></p> <p>Please reference the original study when using this data set.</p> <p>Ted Hsuan Yun Chen, Christopher J. Fariss, Hwayong Shin, Xu Xu. 2024. "Disaster Experience Mitigates the Partisan Divide on Climate Change: Evidence from Texas." <em>Global Environmental Change</em>. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102918" target="_blank" rel="noopener">doi:10.1016/j.gloenvcha.2024.102918</a>.</p>
Waves Hindcast on the Senegalese Coast over the Last Four Decades (from 1980 to 2021). [A Dataset use in : SAMOU, M.S.; BERTIN, X.; SAKHO, I.; LAZAR, A.; SADIO, M.; DIOUF, M.B. Wave Climate Variability along the Coastlines of Senegal over the Last Four Decades. Atmosphere 2023]
<p>Computed from the WW3 Model, the last Four Decades Wave Hindcast is available on the Senegalese Coast through this present Dataset. Covering the period 1980 to 2021, this high resolution hindcast, both spatial (0.05x0.05) and temporal (1 h) provided all the wave parameters such as: the significant wave heights, the mean wave periods, the wave directions and the peak wave periods (to compute from wave frequencies) with an hourly interval.</p> <p>More details on this data (e.g., model implementation and validation) can be obtained in: SAMOU, M.S.; BERTIN, X.; SAKHO, I.; LAZAR, A.; SADIO, M.; DIOUF, M.B. Wave Climate Variability along the Coastlines of Senegal over the Last Four Decades. <em>Journal Atmosphere 2023</em>].</p>
Climate Record of Wintertime Wave-Affected Marginal Ice Zones in the Atlantic Arctic based on CryoSat-2,2010-2022
<p>This record contains data related to article "A 12-Year Climate Record of Wintertime Wave-Affected Marginal Ice Zones in the Atlantic Arctic based on CryoSat-2 ". Wave-affected marginal ice zone (MIZ) is an integral part of the sea ice cover, and a key region for air-ice-sea interactions in polar regions. We develop a novel retrieval algorithm for wave-affected MIZs based on the delay-Doppler radar altimeter onboard CryoSat-2 (CS2). CS2 waveform power and the waveform stack statistics are used to determine the MIZ locations. Based on the CS2 data since 2010, we generate a record of wave-affected MIZs in the Atlantic Arctic, spanning 12 winters between 2010 and 2022.</p> <p>The dataset contains two parts. First, the txt file contains retrieved MIZ locations obtained from each single track. Each column of data is represented by the corresponding CS2 product name, year, month, day, hour, regional flag (1:GS region,2:NS region,3:BS region), the start longitude and latitude of the marginal ice zone on each track, the end longitude and latitude of the marginal ice zone.</p> <p>Second,the gridded dataset is based on the along-track MIZ retrieval results, and it records the presence of the MIZ on the monthly basis. The latitude-longitude grid is adopted, with 2 degrees of longitude spacing and 1 degree of latitude spacing. Each file contains the following variables: the time, the MIZ flag, and the location flag. In this new version, we provide files in both NetCDF and GeoTIFF formats for users to download.</p>
Reanalysis and future wave climate projections of the wave climate of the Gulf of Riga 1993-2100
<h4><strong>Data sets</strong></h4><p>There are two data sets: (1) reanalysis (1993-2021) and (2) future projection (2015-2100).</p><p>The dataset provides gridded monthly mean values of the parameters of the wind waves in the Gulf of Riga, Baltic Sea. The variables of the dataset of the wave field state of the Gulf of Riga are as follows (Long name: <i>acronym</i>, <i>units</i>) </p><ul><li>Mean wave direction: <i>VMDR_WW, </i>°</li><li>Spectral significant wave height: <i>VHM0_WW, m</i></li><li>Spectral moment (0,1) of wave period or mean wave period: <i>VTM01_WW, s</i></li><li>Eastward wave energy flux: <i>WWEFu, W/m</i></li><li>Northward wave energy flux: <i>WWEFv, W/m</i></li></ul><p> </p><p>The grid size of the dataset is 101 (latitude) x 93 (longitude). The horizontal grid spacing is 1 nm. The time resolution of the dataset is monthly – the monthly mean value is provided in the 1st day of the month in the time dimension.</p><p>The original climatic calculations are based on the University of Latvia (UL) set-up of the SWAN model for the Gulf of Riga. The original output of the model run is hourly data series. </p><h4><strong>Reanalysis</strong></h4><p>Time period: 1993-2021, 29 years.</p><p>The main characteristics of the input data and approach for the reanalysis run are as follows: </p><ul><li>EMODNET2020 bathymetry.</li><li>Atmospheric forcing (eastward and northward components of the near surface wind) – ERA5 meteorology.</li><li>Ice conditions – LU HBM, see Frishfelds et. al. 2023.</li><li>Boundary conditions – Baltic Sea Wave Hindcast.</li></ul><h4><strong>Future climate projection</strong></h4><p>Time period: 2015-2100, 86 years.</p><p>The main characteristics of the input data and approach for the future wave climate projections run are as follows: </p><ul><li>Emodnet2020 bathymetry.</li><li>Atmospheric forcing (eastward and northward components of the near surface wind) 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>Ice conditions – LU HBM, see Frishfelds et. al. 2023. </li><li>Boundary conditions – fetch model according to Shore protection manual, 1984.</li></ul><h4><strong>References</strong></h4><p>Frishfelds, V., Cepīte-Frišfelde, D., Timuhins, A., Bethers, U., Sennikovs, J., Reanalysis and future climate projections of the physical state of the Gulf of Riga 1993-2100, Zenodo, <a href="https://zenodo.org/doi/10.5281/zenodo.8248942">10.5281/zenodo.8248942</a>, (2023).</p><p>Baltic Sea Wave Hindcast. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). doi: <a href="https://doi.org/10.48670/moi-00014">https://doi.org/10.48670/moi-00014</a>.</p><p>Shore protection manual, Army Corps of Engineers, Coastal Engineering Research Center (CERC), (1984).</p>
Dataset for Transitional Wave Climate Regions on Continental and Polar Coasts in a Warming World
<p>This dataset supports Oderiz et al. (2022) "Transitional Wave Climate Regions on Continental and Polar Coasts in a Warming World". Nature Climate Change.10.1038/s41558-022-01389-3</p> <p> </p> <p>We recomend users to also read:<br> Odériz, I., Silva, R., Mortlock, T. R., Mori, N., Shimura, T., et al. (2021). Natural variability and warming signals in global ocean wave climates. Geophysical Research Letters, 48, e2021GL093622. https://doi.org/10.1029/2021GL093622</p>
Data Storage Report. RODBreak - Wave run-up, overtopping and damage in rubble-mound breakwaters under oblique extreme wave conditions due to climate change scenarios
<p>Wave breaking / run-up / overtopping and their impact on the stability of rubble-mound breakwaters (both at trunk and roundhead) are not adequately characterized yet for climate change scenarios. The same happens with the influence of high-incidence angles on such phenomena.</p> <p>To study these phenomena a stretch of a rubble-mound breakwater (head and part of the adjoining trunk, with a slope of 1(V):2(H)) was built in the wave basin of the LUH, The trunk of the breakwater was 7.5 m long and the head had the same cross section as the exposed part of breakwater. The model was 9.0 m long, 0.82 m high and 3.0 m wide. The angle between the longitudinal axis of the breakwater and the tank wall was 70º. Two types of armour elements (rock and Antifer cubes) were tested.</p> <p>60 tests were carried out in this experiment to assess, under extreme wave conditions (wave steepness of 0.055) with different incidence wave angles (from 40º to 90º), the structure behaviour in what concerns wave run-up, wave overtopping and damage progression of the armour layer.</p> <p>The report describes the data collected in those tests as well as how such data is stored.</p>
Impact of Grid Resolution on Wave-mean Flow Interactions with High Resolution Mars Global Climate Model Simulations
<p>This dataset contains NetCDF files necessary to replicate results from the 2024 paper "<em>Impact of Grid Resolution on Wave-mean Flow Interactions with High Resolution Mars Global Climate Model Simulations</em>"</p> <p>The dataset contains NetCDF files with 1 year of zonally-averaged NASA Ames Mars Global Climate Model (MGCM) fields with 5-sol binning for each of the simulations presented in the paper: </p> <ul> <li>a "low-resolution" simulation with no parameterization for gravity waves</li> <li>a "high-resolution" simulation with no parameterization for gravity waves</li> <li>a "low-resolution" simulation with parameterizations for orographic and non-orographic gravity waves</li> </ul> <p>Also included are:</p> <ul> <li>a file describing the coordinates for the MGCM's vertical grids used in the study </li> <li> atmospheric fields not provided in the other NetCDF files and necessary to replicate figures 3 and supplemental figure FS2 from the paper.</li> <li>a README.txt detailing the content of each file in the dataset</li> </ul>
Climate Change Awareness in the Arab Barometer Wave 5 Survey
<p>The Arab Barometer Wave V 2018-2019 is based on a nationally representative probability sample of the population aged 18 and above. In most countries, the sample includes 2,400 citizens. The data were conducted in face-to-face public opinion surveys (CAPI and PAPI). See technical reports by country for country-specific information. You can find the data, codebooks and all relevant information on the Arab Barometer website.</p> <p>Our dataset contains country weighted counts of different answer options and the re-weighted values of the answers given to the Arab Barometer Wave 5 question:</p> <p>Q108 : How serious a problem do you think the following issues are: Is climate change a very serious problem, a somewhat serious problem, not a very serious problem, not at all a serious problem?</p> <p>Get the country averages and aggregates from Zenodo</p> <p>Get the plot in jpg or png from figshare.</p> <p> </p> <p>See the detailed PDF documentation.</p>
Ocean and ice with waves data for role of surface gravity waves in aquaplanet ocean climates
<p>This data corresponds to the runs analysed in the manscript: Role of Surface Gravity Waves in Aquaplanet Ocean Climates (JAMES, 2021).</p> <p>In this work, we present a set of idealised numerical experiments that demonstrate the thermodynamic and dynamic implications of surface gravity waves for the oceanic climate of an aquaplanet. We study the impact of accounting for modulations by such waves upon air-sea momentum fluxes, Langmuir circulation and the Stokes-Coriolis force.</p> <p>This dataset is made up of atmospheric, oceanic and surface gravity wave simulations. When uncompressed the total dataset is 1.6 TB, the ocean and ice with waves component is 564 GB. See below for further details.</p> <p>See the related works section for the corresponding datasets.</p>
Ocean and ice without waves data for role of surface gravity waves in aquaplanet ocean climates
<p>This data corresponds to the runs analysed in the manscript: Role of Surface Gravity Waves in Aquaplanet Ocean Climates (JAMES, 2021).</p> <p>In this work, we present a set of idealised numerical experiments that demonstrate the thermodynamic and dynamic implications of surface gravity waves for the oceanic climate of an aquaplanet. We study the impact of accounting for modulations by such waves upon air-sea momentum fluxes, Langmuir circulation and the Stokes-Coriolis force.</p> <p>This dataset is made up of atmospheric, oceanic and surface gravity wave simulations. When uncompressed the total dataset is 1.6 TB, the ocean and ice without waves component is 484 GB. See below for further details.</p> <p>See the related works section for the corresponding datasets.</p>
Ocean and ice without waves data for role of surface gravity waves in aquaplanet ocean climates
Open the record for dataset details and reuse information.
Ocean and ice with waves data for role of surface gravity waves in aquaplanet ocean climates
Open the record for dataset details and reuse information.
Ocean and ice spin-up data for role of surface gravity waves in aquaplanet ocean climates
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Atmospheric and surface gravity wave data for role of surface gravity waves in aquaplanet ocean climates
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Source code and datasets used to link new waves of plague outbreaks in medieval Europe to climate fluctuations affecting the reservoirs of the disease in Asia.
<p>The zipfile contains the project directory which includes the source code and datasets used in the paper on <strong>Climate-driven introduction of the Black Death and successive plague reintroductions into Europe</strong>, as published in <em>Proceedings of the National Academy of Sciences</em> (PNAS). Access the paper at http://www.doi.org/pnas.1412887112</p> <p>If you are not familiar with Clojure, Leiningen, and its project directory format, see http://clojure.org/getting_started for one of the IDE's to run the clojure code in, and use http://leiningen.org/ as the project / dependency manager.</p>
Radiative and climatic impacts of coarse aerosols scattering in the long-wave spectrum
<p>The data deposited on the site concern the output of the ARPEGE climate model at monthly resolution. This concerns the different variables used and analysed in the article. They include simulations with LW aerosol scattering (LWAS) and without aerosol scattering (NOLWAS). The data description, resolution and information on fields outputs are available in each NetCDF file submitted.</p>
Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux
<p>This is dataset of global climate model simulation used in the paper "Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux" by Shimura et al. (2021)</p> <p>Followings are the explanation of data file.</p> <p>*** File naming rule ***<br> {data_group_name}_Exp{experiment_name}_TCnumber{tropical_cyclone_case_number}.nc</p> <p> data_group_name<br> - atm<br> - track</p> <p> experiment_name<br> - Wind<br> - Wave<br> - SlabO</p> <p> tropical_cyclone_case_number<br> - 001<br> - 002<br> ...<br> - 099<br> - 100</p> <p>*** Description on each data group ***<br> <br> atm: three dimentional atmospheric velocity data<br> - level: pressure levels for vertical atmospheric data<br> - longitude: Longitude<br> - latitude: Latitude<br> - velocity_u_component: averaged atmospheric eastward velocity</p> <p> track: data around tropical cyclone track<br> - time: UTC time (YYYYMMDDHH)<br> - longitude_center: Longitude of typhoon center<br> - latitude_center: Latitude of typhoon center<br> - central_pressure: typhoon central pressure<br> - maximum_surface_wind: typhoon maximum surface wind speed<br> - longitude_sfc: Longitude for surface data around typhoon center<br> - latitude_sfc: Latitude for surface data around typhoon center<br> - surface_wind_u_component: surface eastward wind around typhoon<br> - surface_wind_v_component: surface northward wind around typhoon<br> - sea_level_pressure: sea level pressure around typhoon<br> - latent_heat_flux: surface upward latent heat flux<br> - sensible_heat_flux: surface upward sensible heat flux<br> - time_atm: UTC time (YYYYMMDDHH) for atmospheric data<br> - level: pressure levels for atmospheric data<br> - longitude_atm: Longitude for atmospheric data around typhoon center<br> - latitude_atm: Latitude for atmospheric data around typhoon center<br> - velocity_u_component: 3d eastward velocity around typhoon<br> - velocity_v_component: 3d northward velocity around typhoon</p> <p> </p>
FOCI model output used in the study by Ivanciu et al. - On the ridging of the South Atlantic Anticyclone over South Africa: the impact of Rossby wave breaking and of climate change
<p>This dataset contains the model output used in the analysis presented in the study by Ivanciu et al., 2022 - On the ridging of the South Atlantic Anticyclone over South Africa: the impact of Rossby wave breaking and of climate change. Four ensembles of three simulations each were performed with the global coupled climate model FOCI (Flexible Ocean and Climate Infrastructure, Matthes et al., 2020). Details about the ensembles can be found in the above-mentioned publication. The files containing "past" in their name belong to the ensemble "PAST", the files containing "future" in their name belong to the ensemble "FUTURE", the files containing "future_GHG" in their name belong to the ensemble "GHG" and the files containing "future_Ozone" in their name belong to the ensemble "OZONE" from the publication.</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.