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71 results for “Mean Sea Level”
Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from various reanalysis datasets
<h1>Dataset Description</h1> <p>Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from several reanalysis datasets. This dataset is the result of an extension of the Jenkinson-Collison circulation type classification to the entire globe, including a modification of its original formulation for the southern hemisphere.</p> <p>A modified version of the IPCC-AR6 Reference Regions that excludes the intertropical range where the method is not applicable is also included, as used in the reference paper for global assessment.</p> <p>Further details in <a href="https://doi.org/10.1007/s00382-022-06658-7" target="_blank" rel="noopener">https://doi.org/10.1007/s00382-022-06658-7 </a></p> <h2>Note for version 1.1.0</h2> <p>This version corrects an issue in the previous release, which was incorrectly labeled as <em>version 0.1</em>. That version was incomplete due to the omission of previously existing files, and should be considered <strong>incomplete</strong>. Version 1.1.0 restores all original files alongside the newly added one, ensuring the dataset is now complete and consistent. We apologize for any inconvenience this may have caused and appreciate your understanding.</p>
NOAA Monthly Mean Sea Level Summary Data for the Key West Water Level Station (NOAA/NOS Co-OPS ID 8724580), Florida, USA, January 1913 - ongoing
Monthly Mean Sea Level Summary Data for the Key West, Florida, Water Level Station (NOAA/NOS CO-OPS ID 8724580). Data is in meters relative to the STND-Key West Station Datum.
Monthly mean sea level data (1921-2018) relative to NAVD88 for Boston, Massachusetts, NOAA/NOS
Monthly sea level data for NOAA/NOS station 8443970, Boston, Massachusetts. The tide station is located on the right side of the U.S. Coast Guard Building adjacent to Northern Avenue Bridge. NOAA/NOS Center for Operational Oceanographic Products and Services (CO-OPS)
Monthly mean sea level data (1912-2018) relative to NAVD88 for Portland, Maine, NOAA/NOS
Monthly sea level data for NOAA/NOS station 8418150, Portland, Maine. Tidal bench marks directions from north bound Interstate 295 in Portland, take the Waterfront Exit (Alt. U.S. 1) to Commercial Street, then continue NE along Commercial Street for 2.4 km (1.5 mi) to the Maine State Pier, the last pier- warehouse along the waterfront. The bench marks are located within 1.6 km (1 mi) radius of tide station. The tide gage is located in the south corner on the off shore end of the Maine State Pier. NOAA/NOS Center for Operational Oceanographic Products and Services (CO-OPS).
Mean sea level fields used within the GTSMip simulations
<ul> <li><em>TotalSeaLevel_MapsSROCC_rcp85_Perc50_zero1986to2005dflow_extrap.nc </em>provides mean sea level fields using as reference period. Sea level fields are computed from the sum of different contributors, including dynamic changes, thermal expansion, changes in gravitational fields, and contribution from glaciers and ice sheets. The different contributions are computed and combined using the probabilistic model described in Le Bars (2018). For the period 1950-2016, we use products based on observations for the Antarctic and Greenland ice sheets (Mouginot et al., 2019; Rignot et al., 2019), the glaciers (Marzeion et al., 2015), thermal expansion between 0 and 2000 m depth (Levitus et al., 2012), and climate-driven water storage (Humphrey & Gudmundsson, 2019). The ice sheets are assumed to be in equilibrium before 1979 for Antarctica and 1972 for Greenland because no data are available before these dates. For the period 2016-2050 we use sea-level rise projections based on the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) for the RCP8.5 scenario (Church et al., 2013), very similar to the SSP585 scenario used by the models as above. The redistribution of water in the ocean due to wind changes and local steric effects is taken from the CMIP5 models (i.e. ‘zos’ field for the entire period). The fingerprints for the ice sheets, glaciers and land water storage are from the AR5 assessment, and include the gravitational, rotational and Earth elastic response. For the dynamics of the Antarctic contribution we use the re-evaluation presented in the IPCC’s Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) (Oppenheimer et al., 2019). Additionally, we add the glacial isostatic adjustment from the ICE-6G model (Peltier et al., 2015) but do not consider other processes of vertical land motion, such as subsidence or tectonics. The uncertainty in mean sea level is removed by selecting the median of the sea level observations and projections distributions. Note that at the time the GTSM simulation were carried out the SLR projections based on CMIP6 were not yet available. To serve as input to GTSM, the files are converted from a water level to a pressure.</li> <li><em>ERAInterim_average_msl_neg_19491215_19510101.nc </em>provides a vertical reference based on the mean sea-level pressure field (MSLP) over 1986–2005 as calculated with GTSMv3.0 forced by ERA-Interim. This corrections is used to make the definition of MSL in GTSM more consistent with the vertical reference used in the SLR field .</li> </ul>
Global Mean Sea Level, Trajectory and Extrapolation
<p>Global Mean Sea Level, Trajectory and Extrapolation</p> <p>This file contains Global Mean Sea Level (GMSL) variations along, data for the quadratic fit (trajectory) to the GMSL variations, and an extrapolation of this trajectory to 2050.</p> <p>Column 1 provides the calendar year plus the decimal fraction of the current year. The GMSL variations(column 2) are computed at the NASA Goddard Space Flight Center under the auspices of the NASA Sea Level Change program. All units for sea level are in centimeters The GMSL was generated using the NASA-SSH Simple Gridded Sea Surface Height from Standardized Reference Missions Version 1: https://podaac.jpl.nasa.gov/dataset/NASA_SSH_REF_SIMPLE_GRID_V1. It combines Sea Surface Heights from the TOPEX/Poseidon, Jason-1, OSTM/Jason-2, HDR Jason-3, and Sentinel-6 Michael Freilich missions.</p> <p>In addition, the rate and acceleration are estimated from full record of GMSL relative to the midpoint of the record and then used to generate a quadratic fit to the data. This quadratic fit is provided in column 3. The rate associated with this quadratic fit at any time in the record is also provided (column 4). </p> <p>The parameters estimated from the quadratic fit are also used to generated an extrapolated time series out to 2050 (column 5). These are provided at yearly intervals. This is not a projection and is only considered an extrapolation of the current trajectory of GMSL variations. This also differs from Nerem et al. (2022) and Sweet et al. (2022) as additional signals are not removed from GMSL prior to estimating the rate and acceleration parameters. The yearly rate associated with this extrapolation is also provided (column 6).<br><br>If you use these data please cite:<br>Willis, J.K., Hamlington, B.D., and Fournier, S., Global Mean Sea Level Time Series, Trajectory and Extrapolation. Dataset access [YYYY-MM-DD] at 10.5281/zenodo.7702314.</p> <p>References:</p> <p>Nerem, R. S., Frederikse, T., & Hamlington, B. D. (2022). Extrapolating Empirical Models of Satellite‐Observed Global Mean Sea Level to Estimate Future Sea Level Change. <em>Earth's Future</em>, <em>10</em>(4), e2021EF002290.</p> <p>Sweet, W. V., Hamlington, B. D., Kopp, R. E., Weaver, C. P., Barnard, P. L., Bekaert, D., ... & Zuzak, C. (2022). <em>Global and regional sea level rise scenarios for the United States: updated mean projections and extreme water level probabilities along US coastlines</em>. Interagency Technical Report.</p>
Dublin's Corrected Mean Sea Level (1938-2016)
<p>These datasets have been created and published to accompany the paper "A newly reconciled data set for identifying sea level rise and variability in Dublin Bay" by Shoari Nejad et al 2021.</p> <p> Filename format: <dataset_name>_dublin_<variable_names>_<startyear>_to_<endyear>.csv.</p> <p>Time and date variables are described using standard names: Year, Month, Day, Hour, Minute, Date (dd/mm/yyyy). All times are UTC. Sea level variables are described in the format <variable_name>_<unit>_<datum>. Ordnance Datum Malin (ODM) is used with geoid model OSGM15. LAT is 2.599 m below ODM for Dublin Port. None of the datasets is adjusted for atmospheric effects. </p>
Probabilistic projections of mean sea level change in Finland by 2100
<p><strong>Paper describing the methods used to calculate these projections: Pellikka, H., Johansson, M. M., Nordman, M., and Ruosteenoja, K.: Probabilistic projections and past trends of sea level rise in Finland, Nat. Hazards Earth Syst. Sci., <a href="https://doi.org/10.5194/nhess-2022-230">https://doi.org/10.5194/nhess-2022-230</a>, 2023.</strong></p> <p>This dataset includes probability distributions of projected mean sea level in Finland in 2030, 2040, ... 2100, as well as time series of projected mean sea level 2005-2100. Data is provided for 13 tide gauge locations and 3 emission scenarios: low (RCP2.6 / SSP1-2.6), medium (RCP4.5 / SSP2-4.5), and high (RCP8.5 / SSP5-8.5).</p> <p>There are two data packages, <em>distributions.zip</em> and <em>timeseries.zip</em>. The data files included in these packages are tab- or space-delimited text files with the file extension .dat.</p> <p>All filenames start with a three-character code xxx that determines the tide gauge (1-letter symbol) and the emission scenario (2 digits). For example:</p> <p>v26 means Vaasa, low emission scenario (RCP2.6 / SSP1-2.6)<br> e45 means Helsinki, medium emission scenario (RCP4.5 / SSP2-4.5)<br> t85 means Turku, high emission scenario (RCP8.5 / SSP5-8.5)</p> <p>The letter symbols and locations of the tide gauges are, from north to south along the coast:</p> <p>a - Kemi (65.67 N, 24.52 E)<br> o - Oulu (65.04 N, 25.42 E)<br> b - Raahe (64.67 N, 24.41 E)<br> p - Pietarsaari (63.71 N, 22.69 E)<br> v - Vaasa (63.08 N, 21.57 E)<br> s - Kaskinen (62.34 N, 21.21 E)<br> m - Mäntyluoto (61.59 N, 21.46 E)<br> r - Rauma (61.13 N, 21.44 E)<br> t - Turku (60.43 N, 22.1 E)<br> d - Degerby (60.03 N, 20.38 E)<br> h - Hanko (59.82 N, 22.98 E)<br> e - Helsinki (60.15 N, 24.96 E)<br> f - Hamina (60.56 N, 27.18 E)</p> <p>1) <em>distributions.zip > xxx_fitdistr_yyyy.dat</em><br> These files include the probability distribution (probability density function) of projected mean sea level in year yyyy (2030, 2040, ... 2100). There are two columns: sea level and probability. Sea level values are millimetres in the Finnish N2000 height system.</p> <p>2)<em> timeseries.zip > xxx_timeseries.dat</em><br> These files include the time series of projected mean sea level in 2005-2100. The files have 8 columns: year and 7 sea level values representing different percentiles of the probability distribution. The percentiles are 1%, 5%, 17%, 50% (median), 83%, 95%, 99%. Sea level values are centimetres in the Finnish N2000 height system.</p> <p><strong>Please note that all projections for years other than 2100 are indicative and based on a simple 2nd order fit made to the current rate of mean sea level change and the projected mean sea level in 2100. In other words, the projections for intermediate years are based on the 2100 projections assuming constant acceleration in mean sea level change rates.</strong></p> <p>Example figures <em>distributions.png</em> and <em>timeseries.png</em> are included to illustrate the data. The Matlab script <em>slrfinland_figures.m</em> used to produce these figures is also included.</p>
Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines
<p><strong>Code and data for Section 2 of the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines</strong></p> <p><strong>Versions:</strong></p> <p>Version 1.1 This one:</p> <ul> <li>updated region names</li> </ul> <p>Version 1.0 <a href="https://doi.org/10.5281/zenodo.5951626">https://doi.org/10.5281/zenodo.5951626</a></p> <p>This repository contains the code and data needed to produce the trajectories, projections, and observations for the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines.</p> <p>The report can be found on <a href="https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html">https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html</a></p> <p>An interactive tool to study the observations, trajectories, and scenarios can be accessed from <a href="https://sealevel.nasa.gov/task-force-scenario-tool">https://sealevel.nasa.gov/task-force-scenario-tool</a></p> <p>Frequently-asked questions: <a href="https://sealevel.nasa.gov/faq/16/">https://sealevel.nasa.gov/faq/16/</a></p> <p><strong>Authors</strong></p> <ul> <li>William V. Sweet, NOAA National Ocean Service</li> <li>Benjamin D. Hamlington, NASA Jet Propulsion Laboratory</li> <li>Robert E. Kopp, Rutgers University</li> <li>Christopher P. Weaver, U.S. Environmental Protection Agency</li> <li>Patrick L. Barnard, U.S. Geological Survey</li> <li>Michael Craghan, U.S. Environmental Protection Agency</li> <li>Gregory Dusek, NOAA National Ocean Service</li> <li>Thomas Frederikse, NASA Jet Propulsion Laboratory</li> <li>Gregory Garner, Rutgers University</li> <li>Ayesha S. Genz, University of Hawai‘i at Mānoa, Cooperative Institute for Marine and Atmospheric Research</li> <li>John P. Krasting, NOAA Geophysical Fluid Dynamics Laboratory</li> <li>Eric Larour, NASA Jet Propulsion Laboratory</li> <li>Doug Marcy, NOAA National Ocean Service</li> <li>John J. Marra, NOAA National Centers for Environmental Information</li> <li>Jayantha Obeysekera, Florida International University</li> <li>Mark Osler, NOAA National Ocean Service</li> <li>Matthew Pendleton, Lynker</li> <li>Daniel Roman, NOAA National Ocean Service</li> <li>Lauren Schmied, FEMA Risk Management Directorate</li> <li>William C. Veatch, U.S. Army Corps of Engineers</li> <li>Kathleen D. White, U.S. Department of Defense</li> <li>Casey Zuzak, FEMA Risk Management Directorate</li> </ul> <p><strong>Contents</strong></p> <p>This data and code set contains the following directories:</p> <p><em>Results</em></p> <p>The <code>Results</code> folder contains the resulting projections, trajectories and observations from the report.</p> <ul> <li><code>TR_global_projections.nc</code>: GMSL projections, trajectory, and observations</li> <li><code>TR_regional_projections.nc</code>: Regional observations, projections and trajectories</li> <li><code>TR_local_projections.nc</code>: Local observations, projections and trajectories</li> <li><code>TR_gridded_projections.nc</code>: Gridded projections</li> </ul> <p>These files are in the NetCDF forrmat. To read the NetCDF files, many free software packages are available, including <a href="http://meteora.ucsd.edu/~pierce/ncview_home_page.html">ncview</a> and <a href="https://www.giss.nasa.gov/tools/panoply/">Panoply</a>. Free NetCDF packages are available to directly import the data into <a href="https://github.com/Alexander-Barth/NCDatasets.jl">Julia</a> and <a href="https://unidata.github.io/netcdf4-python/">Python</a> code.</p> <p><em>Code</em></p> <p>The <code>Code</code> folder contains all the computer code used to read and analyze the observations and the projections, and to generate the trajectories.</p> <p>To run this code, you need <a href="https://julialang.org/">Julia</a>. The code requires the Julia packages <code>CSV</code>, <code>Interpolations</code>, <code>JSON</code>, <code>LoopVectorization</code>, <code>MAT</code>, <code>NCDatasets</code>, <code>NetCDF</code>, <code>Plots</code>, <code>XLSX</code>, <code>LinearAlgebra</code>, and <code>Statistics</code>. They can be installed by pressing <code>]</code> at the Julia REPL and typing:</p> <pre><code>add CSV Interpolations JSON LoopVectorization MAT NCDatasets NetCDF Plots XLSX LinearAlgebra Statistics </code></pre> <p>This program also requires <a href="http://segal.ubi.pt/hector/">Hector</a>. Hector needs to be installed or compiled. In the file <code>Hector.jl</code> update the path to the Hector executable on lines 30 and 104.</p> <p>Run <code>Run_TR.jl</code> in the REPL or run <code>julia Run_TR.jl</code> from the command line to run the projections. The projections are then written to the <code>.\Data</code> directory.</p> <p>The folder contains the following files:</p> <ul> <li><code>Run_TR.jl</code>: This is the main routine that (eventually) calls all the functions to compute the projections.</li> <li><code>ConvertNCA5ToGrid.jl</code>: Converts the original NCA5 projections to a set of netCDF files that's used throughout this code</li> <li><code>ProcessObservations.jl</code>: Reads and processes the tide-gauge and altimetry observations</li> <li><code>GlobalProjections.jl</code>: Reads and processes the GMSL observations and projections, and computes the trajectory</li> <li><code>RegionalProjections.jl</code>: Reads and processes the regional projections and computes the trajectories</li> <li><code>LocalProjections.jl</code>: Reads and processes the local projections at the tide-gauge locations and computes the trajectories</li> <li><code>GriddedProjections.jl</code>: Reads the gridded NCA5 projections and add a GMSL baseline correction for the 2005 vs 2000 baseline</li> <li><code>SaveFigureData.jl</code>: Reads the results and writes text files for GMT</li> <li><code>Hector.jl</code>: Wrapper for <a href="http://segal.ubi.pt/hector/">Hector</a>, used to compute trends and uncertainties.</li> <li><code>Masks.jl</code>: Defines the region masks for each region.</li> </ul> <p><em>Data</em></p> <p>The <code>Data</code> directory contains the input data sets used during the computations. Please appropriately cite the input data if you use it. It contains the following:</p> <p>Directories:</p> <ul> <li><code>ClimIdx</code>: Map with climate indices (NAO, PDO, MEI) used to remove internal variability. All the indices come from NOAA <a href="https://psl.noaa.gov/data/climateindices/">Physical Sciences Laboratory (PSL)</a> and <a href="https://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml">NOAA Climate Prediction Centre (CPC)</a></li> <li><code>NCA5_projections</code> Contains the NCA5 projections for each scenario (Low, IntLow, Int, IntHigh, and High). For each scenario, the GMSL projections, projections at tide-gauge locations and on a 1-degree grid are provided.</li> </ul> <p>Files:</p> <ul> <li><code>basin_codes.nc</code>: Map with basin codes. from Eric Leuliette/NOAA. Data provided by the NOAA Laboratory for Satellite Altimetry.</li> <li><code>CDS_monthly_1993_2020.nc</code>: Monthly-mean sea level (1993-2020) from gridded altimetry. Obtained from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global">Copernicus Climate Data Store</a>. This dataset contains modified Copernicus Climate Change Service information [2020]</li> <li><code>enso_correction.mat</code>: GMSL correction for ENSO/PDO from Hamlington, B. D., Frederikse, T., Nerem, R. S., Fasullo, J. T., & Adhikari, S. (2020). Investigating the Acceleration of Regional Sea‐level Rise During the Satellite Altimeter Era. Geophysical Research Letters. <a href="https://doi.org/10.1029/2019GL086528">https://doi.org/10.1029/2019GL086528</a></li> <li><code>filelist_psmsl.txt</code>: List with PSMSL file names and PSMSL IDs. Obtained from the Permanent Service for Mean Sea Level (<a href="http://www.psmsl.org/">PSMSL</a>), 2021, Retrieved 29 Nov 2021. Simon J. Holgate, Andrew Matthews, Philip L. Woodworth, Lesley J. Rickards, Mark E. Tamisiea, Elizabeth Bradshaw, Peter R. Foden, Kathleen M. Gordon, Svetlana Jevrejeva, and Jeff Pugh (2013) New Data Systems and Products at the Permanent Service for Mean Sea Level. Journal of Coastal Research: Volume 29, Issue 3: pp. 493 – 504. <a href="https://doi.org/:10.2112/JCOASTRES-D-12-00175.1">https://doi.org/:10.2112/JCOASTRES-D-12-00175.1</a>.</li> <li><code>GEBCO_bathymetry_05.nc</code>: Bathymetry map of the global oceans from the General Bathymetric Chart of the Oceans (<a href="https://www.gebco.net/">GEBCO</a>). Source: GEBCO Compilation Group (2021) GEBCO 2021 Grid (<code>doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f</code>) The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>GIA_Caron_stats_05.nc</code>: Glacial Isostatic Adjustment estimates from Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., & Blewitt, G. (2018). GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science. Geophysical Research Letters, 45(5), 2203–2212. <a href="https://doi.org/10.1002/2017GL076644">https://doi.org/10.1002/2017GL076644</a>. The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>global_timeseries_measures.nc</code>: Time series of estimated 20th-century GMSL and its components, based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_ensembles.nc</code>: Ensemble GMSL reconstruction from tide-gauges based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_TPJAOS_5.0_199209_202106.txt</code>: Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1 [Data set]. NASA Physical Oceanography DAAC. <a href="https://doi.org/10.5067/GMSLM-TJ151">https://doi.org/10.5067/GMSLM-TJ151</a>. This altimetry dataset uses the methods as described in Beckley, B. D., Callahan, P. S., Hancock, D. W., Mitchum, G. T., & Ray, R. D. (2017). On the “Cal-Mode” Correction to TOPEX Satellite Altimetry and Its Effect on the Global Mean Sea Level Time Series. Journal of Geophysical Research: Oceans, 122(11), 8371–8384. <a href="https://doi.org/10.1002/2017JC013090">https://doi.org/10.1002/2017JC013090</a></li> <li><code>grd_1992_2020.nc</code>: Seafloor deformation due to contemporary GRD effects based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>region_mask.nc</code>: Mask with the definition of all regions.</li> <li><code>US_tg_monthly.xlsx</code>: Tide gauge observations from the NOAA tide gauge network</li> </ul> <p><em>GMT</em></p> <p>This directory contains the <a href="https://www.generic-mapping-tools.org/">GMT</a> scripts to make Figures 1.2, 2.1, 2.2, 2.6, and A.1.2 from the report. To generate the figures, make sure GMT is installed and run the Shell script in each directory.</p>
Mean sea-level pressure and related indices from control and tropical relaxation winter seasonal hindcasts using the Met Office GloSea5 system.
<p>© Crown Copyright, Met Office</p> <p>The accompanying data is made available under the terms of the Non-Commercial Government Licence (http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/).</p> <p>These data are the results of ensemble modelling simulations using the Met Office GloSea5 numerical seasonal prediction system. Files in (zipped) netcdf format contain winter seasonal-mean mean sea-level pressure (MSLP) fields for each member of specified ensemble and hindcast winter (December-February from 1993-94 to 2015-16). The files are named according to the specification of relaxation towards observational reanalysis in each. HCAST has no relaxation, ALL has tropical (approximately 22.5S to 22.5N) relaxation at all atmospheric heights, TROP is similar to ALL but with relaxation limited to below 18km and STRAT is similar to ALL but with relaxation limited to above 18km. The relaxation is designed to constrain the atmospheric state to be close to that which was observed within the domain over which it is applied. Details of the technique can be found in Maidens et al., 2021* and references therein.</p> <p>An additional text file is provided to give summary statistics of key MSLP-based winter indices.</p> <p> </p> <p>* Maidens, A., Knight, J. R., & Scaife, A. A. (2021). Tropical and stratospheric influences on winter atmospheric circulation patterns in the North Atlantic sector. Environmental Research Letters, 16, 024035. https://doi.org/10.1088/1748-9326/abd8aa</p> <p> </p>
Data & Code for Peak water levels rise less than mean sea level in tidal channels subject to depth convergence by deepening
<p>Data & Code for the article: "Peak water levels rise less than mean sea level in tidal channels subject to depth convergence by deepening"</p> <p>- Input-files used to run the models</p> <p>- Scripts used for the figures</p> <p>- Excel-based tool to calculate tidal response to channel deepening with system-specific parameters</p> <p>- ...</p>
California Current Ecosystem site, station NOAA Station 9410170, San Diego, CA, study of mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) in units of meter on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from California Current Ecosystem (CCE) contains mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) measurements in meter units and were aggregated to a monthly timescale.
California Current Ecosystem site, station NOAA Station 9410170, San Diego, CA, study of mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) in units of meter on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from California Current Ecosystem (CCE) contains mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) measurements in meter units and were aggregated to a yearly timescale.
Luquillo Experimental Forest site, station NOAA Station 9755371, San Juan, PR, study of mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) in units of meter on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Luquillo Experimental Forest (LUQ) contains mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) measurements in meter units and were aggregated to a monthly timescale.
Luquillo Experimental Forest site, station NOAA Station 9755371, San Juan, PR, study of mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) in units of meter on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Luquillo Experimental Forest (LUQ) contains mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) measurements in meter units and were aggregated to a yearly timescale.
Marcell Experimental Forest site, station Watershed S2, peatland water table bogwell site, study of water table elevation above mean sea level in units of meter on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Marcell Experimental Forest (MAR) contains water table elevation above mean sea level measurements in meter units and were aggregated to a monthly timescale.
Marcell Experimental Forest site, station Watershed S2, peatland water table bogwell site, study of water table elevation above mean sea level in units of meter on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Marcell Experimental Forest (MAR) contains water table elevation above mean sea level measurements in meter units and were aggregated to a yearly timescale.
Marcell Experimental Forest site, station Watershed S2, groundwater deepwell #202, study of water table elevation above mean sea level in units of meter on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Marcell Experimental Forest (MAR) contains water table elevation above mean sea level measurements in meter units and were aggregated to a monthly timescale.
Marcell Experimental Forest site, station Watershed S2, groundwater deepwell #202, study of water table elevation above mean sea level in units of meter on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Marcell Experimental Forest (MAR) contains water table elevation above mean sea level measurements in meter units and were aggregated to a yearly timescale.
Moorea Coral Reef site, station Papeete station, Moorea, study of mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) in units of meter on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Moorea Coral Reef (MCR) contains mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) measurements in meter units and were aggregated to a monthly timescale.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.