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735 results for “Sea level”
Data from: Transcriptomics reveal transgenerational effects in purple sea urchin embryos: adult acclimation to upwelling conditions alters the response of their progeny to differential pCO2 levels
Understanding the mechanisms with which organisms can respond to a rapidly changing ocean is an important research priority in marine sciences, especially in light of recent predictions regarding the pace of ocean change in the coming decades. Transgenerational effects, in which the experience of the parental generation can shape the phenotype of their offspring, may serve as such a mechanism. In this study, adult purple sea urchins, Strongylocentrotus purpuratus, were conditioned to regionally and ecologically relevant pCO2 levels and temperatures representative of upwelling (low temperature, high pCO2) and non-upwelling (average temperature, low pCO2) conditions typical of coastal upwelling regions in the California Current System. Following 4.5 months of conditioning, adults were spawned and offspring were raised under either high or low pCO2 levels, to examine the role of maternal effects. Using RNA-seq and comparative transcriptomics, our results indicate that differential conditioning of the adults had an effect on the gene expression patterns of the progeny during the gastrula stage of early development. For example, maternal conditioning under upwelling conditions intensified the transcriptomic response of the progeny when they were raised under high versus low pCO2 conditions. Additionally, mothers that experienced upwelling conditions produced larger progeny. The overall findings of this study are complex, but do suggest that transgenerational plasticity in situ could act as an important mechanism by which populations might keep pace with rapid environmental change.
Supporting data for "Synthesizing long-term sea level rise projections - the MAGICC sea level model v2.0"
<p>This is supporting data and configuration information to reproduce results from the MAGICC sea level model (DOI: 10.5281/zenodo.572395) presented in Nauels et al. (2017), using version 7.0 beta of the simple climate carbon-cycle model MAGICC (Meinshausen et al. 2011). For a compiled or source code version of MAGICC including the sea level model (git hash: c5c4e05518ed99f2bd53e2c6e68d238bbd6f17ec), please contact alexander.nauels@climate-energy-college.org.</p> <p>MAGICC input data and CMIP5 reference datasets are provided as a zip-file.</p> <p><br> REFERENCE DATASETS</p> <p>For MAGICC version 7.0 beta, the ocean model has been updated to emulate CMIP5 ocean temperatures and thermal expansion. The calibration results shown in Nauels et al. (2017) are based on potential ocean temperature (thetao) and thermal expansion (zostoga) reference datasets that are provided the 'data' directory. Reference datasets for the other sea level components have to be requested from the authors of the corresponding studies (Marzeion et al. 2014, Fettweis et al. 2013, Nick et al. 2013, Ligtenberg 2013, Levermann 2014). All relevant CMIP5 MAGICC input (.IN) is provided in the 'run' directory.</p> <p><br> MAGICC MODEL CONFIGURATION</p> <p>To customize a MAGICC run, namelist entries have to be modified in the configuration file 'MAGCFG_USER.CFG'. In order to select a CMIP5 model specific MAGICC ocean calibration, the model setup has to be called with the 'FILE_TUNINGMODEL_XX' entry, e.g. 'OCNTUNE_CCSM4' for the CCSM4 model. Automatically, the corresponding initial ocean temperature profile and model specific ocean layer area fractions will be applied. For prescribing the respective surface air temperatures, the namelist flag 'CORE_PRESCRTEMP_APPLY' has to be set to 1, with the model specific temperature dataset defined by 'FILE_PRESCR_SURFACETEMP', e.g. 'CORE_PRESCRTEMP_CMIP5_CCSM4_RCP85.IN'. The following MAGICC namelist entries have to be adapted in order to fully reproduce CMIP5 consistent results presented in Nauels et al. (2017): </p> <p>[...]<br> e.g. FILE_TUNINGMODEL_1 = "OCNTUNE_CCSM4",<br> [...]<br> CORE_SWITCH_TEMPADJUST_OCN2ATM = 1,<br> CORE_SWITCH_OCN_TEMPPROFILE = 2,<br> CORE_SWITCH_OCN_AREAFACTOR = 1,<br> CORE_PRESCRTEMP_APPLY = 1,<br> e.g. FILE_PRESCR_SURFACETEMP = "CORE_PRESCRTEMP_CMIP5_CCSM4_RCP85.IN",<br> [...]<br> OUT_TEMPERATURE = 1,<br> OUT_TEMPOCEANLAYERS = 1,<br> OUT_SEALEVEL = 1,<br> OUT_PARAMETERS = 1,<br> [...]<br> OUT_ASCII_BINARY = "ASCII",<br> [...]</p> <p>MAGICC output is stored in the 'out' directory. Depending on the flag 'OUT_ASCII_BINARY', either ASCII or BINARY files are produced for the output parameters which are set to 1 in the namelist, e.g. 'OUT_SEALEVEL = 1'. </p> <p><br> MAGICC SEA LEVEL MODEL LICENSE</p> <p>This source code of the MAGICC sea level model is distributed under a Creative Commons Attribution-ShareAlike 4.0 license (https://creativecommons.org/licenses/by-sa/4.0/legalcode).</p> <p><br> MAGICC PARENT MODEL LICENSES</p> <p>The MAGICC executable is provided under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license (https://creativecommons.org/licenses/by-nc-sa/3.0/). The MAGICC source code is available under a separate license agreement. Any derivatives have to be fed back to the MAGICC developers, so that users of future MAGICC versions can have the benefit of applying the model alterations, enhancements etc. Furthermore, we would like you to provide feedback, bug reports and development suggestions.</p> <p><br> REFERENCES</p> <p>Fettweis, X., Franco, B., Tedesco, M., van Angelen, J. H., Lenaerts, J. T. M., van den Broeke, M. R., and Gallée, H.: Estimating the Greenland ice sheet surface mass balance contribution to future sea level rise using the regional atmospheric climate model MAR, The Cryosphere, 7, 469–489, 2013.</p> <p>Levermann, A.,Winkelmann, R., Nowicki, S., Fastook, J. L., Frieler, K., Greve, R., Hellmer, H. H., Martin, M. A., Meinshausen, M., Mengel, M., Payne, A. J., Pollard, D., Sato, T., Timmermann, R., Wang, W. L., and Bindschadler, R. A.: Projecting Antarctic ice discharge using response functions from SeaRISE ice-sheet models, Earth Syst. Dynam., 5, 271–293, 2014.</p> <p>Ligtenberg, S. R. M., van de Berg, W. J., van den Broeke, M. R., Rae, J. G. L., and van Meijgaard, E.: Future surface mass balance of the Antarctic ice sheet and its influence on sea level change, simulated by a regional atmospheric climate model, Climate Dynamics, 41, 867–884, 2013.</p> <p>Meinshausen, M., Raper, S. C. B., and Wigley, T. M. L.: Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 - Part 1: Model description and calibration, Atmospheric Chemistry and Physics, 11, 1417–1456, 2011.</p> <p>Nauels, A., Meinshausen, M., Mengel, M., Lorbacher, K., and Wigley, T. M. L.: Synthesizing long-term sea level rise projections – the MAGICC sea level model v2.0, Geosci. Model Dev., 2017.</p> <p>Nick, F. M., Vieli, A., Andersen, M. L., Joughin, I., Payne, A., Edwards, T. L., Pattyn, F., and van de Wal, R. S. W.: Future sea-level rise from Greenland’s main outlet glaciers in a warming climate, Nature, 497, 235–238, 2013.</p>
Pollen data: Influences of sea level changes and volcanic eruptions on Holocene vegetation in Tonga
<p><strong>Aim</strong>:</p> <p>To investigate mid- to late-Holocene vegetation changes on low-lying coastal areas in Tonga and how changing sea level and recurrent volcanic eruptions have influenced vegetation dynamics on four islands of the Tongan Archipelago (South Pacific).</p> <p><strong>Methods: </strong></p> <p>To investigate past vegetation and environmental change at Ngofe Marsh ('Uta Vava'u) we examined palynomorphs (pollen and spores), charcoal (fire), and sediment characteristics (volcanic activity) from a 6.7-m long sediment core. Radiocarbon dating indicated the sediments were deposited over the last 7700 years. We integrated the Ngofe Marsh data with similar previously published data from Avai'o'vuna Swamp on Pangaimotu Island, Lotofoa Swamp on Foa Island, and Finemui Swamp on Ha'afeva Island. Plant taxa were categorised as littoral, mangrove, rainforest, successional/ disturbance, and wetland groups and linear models were used to examine relationships between vegetation, relative sea-level change, and volcanic eruptions (tephra).</p> <p><strong>Results</strong>:</p> <p>Relative sea-level change has impacted vegetation on three of the four islands investigated. Volcanic eruptions were not identified as a driver of vegetation change. Rainforest decline does not appear to be driven by sea-level changes or volcanic eruptions. From all sites analysed, vegetation at Finemui Swamp was most sensitive to changes in relative sea level.</p> <p><strong>Conclusions: </strong></p> <p>While vegetation on low-lying Pacific islands is sensitive to changing sea levels, island characteristics, such as size and elevation, are also likely to be important factors that mediate specific island responses to drivers of change.</p>
Data: Salt marsh litter quality and decomposition under sea-level rise scenarios: from leaves to fine absorptive roots
<p>litter chemical characteristic in salt marshes, including fine absorptive roots, fine transportive roots, rhizomes and leaves. </p> <p>mass loss of litter and chemical characteristics of those litter under sea level scenarios (manipulated in situ)</p>
Data and code for: Sea level rise causes shorebird population collapse before habitat drowns
<p>Sea level rise causes habitat loss and is considered to be a key threat to coastal species globally. Sea level rise also reduces habitat quality, potentially threatening populations already before habitat drowns and is lost. The extent and timing of changes in habitat quality for wildlife actively adapting to sea level rise, and how this affects population numbers under different emission scenarios, is unknown. Here, we combine long-term field data with models of sea level rise, marsh geomorphology, adaptive behaviour, and population dynamics to show that habitat quality is already declining on three islands due to increased flooding of shorebird nests. Also, population collapses are projected well before habitat drowns. Habitat loss, a widely used proxy, thus severely underestimates population impacts of sea level rise and coastal species will suffer much sooner than previously thought. Despite shorebirds adapting by moving to higher grounds, sea level rise will result in up to 79% fewer birds in a century, eventually leading to extinction in their prime habitat. Local gas mining exacerbates matters, as deep soil subsidence makes habitat even more vulnerable to sea level rise, effectively halving the window of opportunity for conservation action. Climate change ultimately jeopardizes the biodiversity value of this UNESCO World Heritage Area, and nature management needs to take this long-term perspective on board by in the short-term, boosting the accretion of tidal marshes or developing flood-safe alternative habitat elsewhere.</p>
Marinoan Snowball Earth: The Impact of Deglaciation Duration on the Sea-Level History of Continental Margins
<p>Paleogeographies, ice histories, and predicted relative sea level output are available here as Matlab (.mat) files. Complementary text (.txt) files are included for latitude, longitude, and time. For different file formats or any questions to their use, please contact the corresponding author, Freya K Morris at: morrisfreya15@outlook.com</p>
Contrasting sensitivity of weathering proxies to Quaternary climate and sea-level fluctuations on the southern slope of the South China Sea
<p>Tropical marginal seas host important sedimentary archives that may be exploited to reveal past changes in continental erosion, chemical weathering, and ocean dynamics. However, these records can be challenging to interpret due to the complex interactions between climate and particulate transport across ocean margins. For the southern South China Sea over the last 90 kyr, we observe a contrasting temporal relationship between the deposition of clay minerals and magnetic minerals, which were associated with two different hydrodynamic modes. Fine-grained clay minerals can be carried in suspension by ocean currents, leading to a rapid response to regional climate-driven inputs. In contrast, changes in magnetic mineralogy were linked to glacial-interglacial sea-level variability, from which we infer a control by bedload transport and resuspension. Overall, this study indicates that the transfer pathways and mechanisms imparted by varying hydrodynamic conditions exert a substantial influence on the distribution of terrigenous material in continental margin sediments.</p>
MIS 5e relative sea-level index points along the Pacific coast of North America
<p>This spreadsheet contains data and metadata on relative sea-level index points and associated ages for the Pacific coast of North America. This is Version 1.1, updated after the peer-review of the associated paper.</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>
Data for "Sea-level stability over geologic time owing to limited deep subduction of hydrated mantle"
<p>This repository contains data to reproduce the results displayed in the manuscript : "Sea-level stability over geologic time owing to limited deep subduction of hydrated mantle" by Cerpa, N. G., Arcay, D., & Padron-Navarta J. A.<br> The data includes the P-T paths and the slab-water retention for the 56 modeled subduction transects. </p>
Rethinking Sea-Level Projections using Families and Timing Differences
<p>Dataset for publication "Rethinking Sea-Level Projections using Families and Timing Differences"</p>
GNSS and levelling data to detect ground deformation along the Upper Adriatic Sea coastal area (Italy)
<p>This geodetic dataset includes both Global Navigation Satellite System (GNSS) and levelling data. GNSS measurements were recorded by continuous stations managed by public institutions and private companies, while levelling measurements were obtained by the use of benchmarks managed by ENI S.p.A. </p> <p>This dataset is used in the manuscript entitled "Multi-technique geodetic detection of onshore and offshore subsidence along the Upper Adriatic Sea coasts" to estimate deformation around the littoral area of Ravenna (Italy) (Polcari et al., 2022). The GNSS data, from permanent stations RAVE, PCTA, FIUN and ANGA covers the period from around 1998 to 2018. The files in .csv format contain displacement time series with respect to the Adria-fixed reference frame and for PCTA, FIUN and ANGA also with respect to RAVE GNSS station.</p> <p>The levelling data refer to campaigns that took place in 2002, 2003, 2004, 2005, 2007, 2009, 2011, 2014, and 2017. The file named <em>Original.csv</em> contains the original height measurements for each benchmark, while the file named <em>Ref.RAVE.csv</em> contains the mean velocity and the displacement calculated for all 147 benchmarks. In this last file the data were scaled with respect to the mean velocity of the benchmark located near the RAVE station.</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>
Evaluation of the local sea-level budget at tide gauges since 1958
<p>### Data supplement for 'Evaluation of the local sea-level budget at tide gauges since 1958'</p> <p><br> Authors: Jinping Wang, John A. Church, Xuebin Zhang, Jonathan M. Gregory, Laure Zanna and Xianyao Chen</p> <p>Created 9/5/2020<br> Please email wangjinping@ouc.edu for questions.</p> <p>----------------------------------------------------------------------------------------------------<br> ### PLEASE CITE THE APPROPRIATE PAPERS WHEN USING THIS DATA ###<br> Please cite 'Evaluation of the local sea-level budget at tide gauges since 1958' when using this data set.<br> Our results heavily relies on previous work, and please acknowledge the previous work by citing the original sources of the data.<br> The Data Availability Statement section of 'Evaluation of the local sea-level budget at tide gauges since 1958' contains the full list of sources of all the original data.</p> <p>----------------------------------------------------------------------------------------------------<br> This data supplement contains the following files:</p> <p>separate_components_rsl.mat <br> Spatial patterns of relative sea level trend (mm/yr) are provided for individual component.</p> <p>lon = longitude<br> lat = latitude<br> sdsl = sterodynamic sea level<br> barystatic_grd = the sum of all barystatic-GRD fingerprints<br> glacier = glacier contribution including charted and uncharted glaciers<br> ice_sheet = the sum of Greenland and Antarctic ice sheets<br> gia = glacial isostatic adjustment<br> tws = terrestrial water storage<br> sum = the sum of all contributions</p> <p><br> GMSL.mat<br> Global mean time series of sea level and each components (mm).</p> <p>The variable names are the same with those in separate_components_rsl.mat <br> recons_mean = the ensemble mean of GMSL reconstructions (including Church & White, 2011; Dangendorf et al., 2019; Frederikse et al., 2020; Hay et al., 2015)</p> <p><br> components_TG_locations.mat<br> All tide gauge observations and the individual contribution (mmm/yr).</p> <p>The variable names are the same with those in separate_components_rsl.mat <br> tgobs = tide gauge observations<br> tgobs_ovlm = tide gauge observations with other VLM correction applied</p> <p><br> VLM_components.mat<br> VLM estimate and its individual components (mm/yr).</p> <p>vlm_total = total VLM<br> vlm_bgrd = barystatic-GRD VLM component<br> vlm_gia = GIA-related VLM component<br> vlm_other = other local VLM component</p> <p>vlm_total_uncertianty90 = 90% confidence level for total VLM<br> vlm_bgrd = 90% confidence level for barystatic-GRD VLM component<br> vlm_gia = 90% confidence level for GIA-related VLM component<br> vlm_other = 90% confidence level for other local VLM component</p>
Datasets for "A Detection of the Sea Level Fingerprint of Greenland Ice Sheet Melt" Coulson et al., 2022, Science
<p>Sea surface height (SSH) change altimetry-derived datasets and model predictions for:<br> "A Detection of the Sea Level Fingerprint of Greenland Ice Sheet Melt", Submitted to Science January 2022. Sophie Coulson, Sönke Dangendorf, Jerry X. Mitrovica, Mark E. Tamisiea, Linda Pan, David T. Sandwell.<br> Email: slcoulson@lanl.gov, sdangendorf1@tulane.edu</p> <p>See README for file details.</p>
A chromosome-level genome assembly of the highly heterozygous sea urchin Echinometra sp. EZ reveals adaptation in the regulatory regions of stress response genes
<p><em>Echinometra</em> is the most widespread genus of sea urchin and has been the focus of a wide range of studies in ecology, speciation, and reproduction. However, available genetic data for this genus are generally limited to a few select loci. Here, we present a chromosome-level genome assembly based on 10x Genomics, PacBio, and Hi-C sequencing for <em>Echinometra</em> sp. EZ from the Persian/Arabian Gulf. The genome is assembled into 210 scaffolds totaling 817.8 Mb with an N50 of 39.5 Mb. From this assembly we determined that the <em>E</em>. sp. EZ genome consists of 2n = 42 chromosomes. BUSCO analysis showed that 95.3% of BUSCO genes were complete. ab initio and transcript-informed gene modeling and annotation identified 29,<span>405</span> genes, including a conserved Hox cluster. <em>E.</em> sp. EZ can be found in high-temperature and high-salinity environments, and we therefore compared gene families and transcription factors associated with environmental stress response ("defensome") with other echinoid species with similar high-quality genomic resources. While the number of defensome genes was broadly similar for all species, we identified strong signatures of positive selection in non-coding elements near genes involved in environmental response pathways as well as losses of transcriptions factors important for environmental response. These data provide key insights into the biology of <em>E</em>. sp. EZ as well as the diversification of <em>Echinometra</em> more widely and will serve as a useful tool for the community to explore questions in this taxonomic group and beyond.</p>
IPCC AR6 Sea Level Milestones
<p><strong>Description</strong></p> <p>This data set contains elements of the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains files indicating the likelihood of when sea level milestones are crossed over time. For global mean sea level, it includes probability distributions for all the workflows described in AR6 WG1 9.6.3.2, as well as p-boxes derived from these distributions; for regional projections, with and without vertical land motion, it includes the p-boxes associated corresponding to those shown in the milestone excedance timing figures.</p> <p>Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool.</p> <p><strong>Required Acknowledgements and Citation </strong></p> <p>In order to document the impact of these sea-level rise projections, users of the projections are obligated to cite chapter 9 of Working Group 1 contribution to the the IPCC Sixth Assessment Report, the Framework for Assessment of Changes To Sea-level (FACTS) model description paper, and the version of the data set used:</p> <ul> <li>Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, <a href="https://doi.org/10.1017/9781009157896.011" rel="nofollow">doi:10.1017/9781009157896.011</a>.</li> <li>Kopp, R. E., Garner, G. G., Hermans, T. H. J., Jha, S., Kumar, P., Reedy, A., Slangen, A. B. A., Turilli, M., Edwards, T. L., Gregory, J. M., Koubbe, G., Levermann, A., Merzky, A., Nowicki, S., Palmer, M. D., & Smith, C. (2023). The Framework for Assessing Changes To Sea-Level (FACTS) v1.0: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. Geoscientific Model Development, 16, 7461–7489. <a href="https://doi.org/10.5194/gmd-16-7461-2023" rel="nofollow">https://doi.org/10.5194/gmd-16-7461-2023</a></li> <li>Garner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Sea Level Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709" rel="nofollow">https://doi.org/10.5281/zenodo.5914709</a>.</li> </ul> <p><em>Please also include in the acknowledgements of works citing these projections:</em></p> <blockquote> <p>We thank the projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea-Level Change Team for developing and hosting the IPCC AR6 Sea-Level Projection Tool.</p> </blockquote> <p><strong>IPCC AR6 Licensing</strong></p> <p>The IPCC AR6 Sea-Level Rise Projections are licensed by the authors under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</p>
Input and Output files for "Contributions to Streamflow and Sea Level Rise in High Mountain Asia from 2003-2009 Glacier Recession"
<p>All files below were prepared by Collin B. Lawrence. </p> <p>All ARCIDs correspond to the HydroSHEDS Dataset for Asia (Lehner et al., 2008). (http://www.hydrosheds.org/)</p> <p>GLDAS data are from the Global Land Data Assimilation System (Rodell et al., 2004). (https://ldas.gsfc.nasa.gov/gldas/)</p> <p>Q_JJA_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m<sup>3</sup> s<sup>-1</sup>, and are averaged for the months of June, July, and August from the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).</p> <p>Q_annual_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m<sup>3</sup> s<sup>-1</sup>, and are yearly averages for the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).</p> <p>phi_i_JJA is the accumulated subsurface and surface runoff for the CLM, MOSAIC, NOAH, and VIC model average. The June, July, and August output was averaged over the years 2003 – 2009. Column 1 is ARCID and the accumulated runoff is expressed in m<sup>3</sup> s<sup>-1</sup>.</p> <p>phi_i_JJA_err is the standard error of the model mean in phi_i_JJA.</p> <p>phi_g_JJA contains the accumulated glacier recession flow in m<sup>3</sup> s<sup>-1 </sup>for the months of June, July, August from 2003 - 2009.</p> <p>phi_g_JJA_err is the standard error of the model mean in phi_g_JJA.</p> <p>phi_g_annual contains the annually averaged accumulated glacier recession flow in m<sup>3</sup> s<sup>-1 </sup>for 2003 – 2009.</p> <p>lambda.csv contains the fraction of streamflow from glacier recession.</p>
Data supplementing article "Tidal response to sea-level rise in different types of estuaries: the importance of length, bathymetry, and geometry" submitted to Geophysical Research Letters
<p>These data supplement the article "Tidal response to sea-level rise in different types of estuaries: the importance of length, bathymetry, and geometry". </p> <p>contact: Jiabi Du, jiabi.du@gmail.com</p> <p>Below are descriptions of the data files included here:</p> <p>1. ReadMe.txt</p> <p>- the description of each model configuration is listed in this file, inlcuding the estuarine geometry type, length, and bathymetry type. </p> <p>2. model grid and surface elevation output</p> <p>- In each directory named after the grid_name, a grid file with .gr3 format is included. <br> The .gr3 file contains two parts: the location of each node (first part), and the node number for each triangle grid (second part).</p> <p>- Two sub_directory name 'base' and 'slr' contains the time series of water level. </p> <p>3. MatlaScript ExtractWL</p> <p>- a function named "f_extract_wl.m" are used to extract the station output</p> <p> </p>
Wave climate simulations for Denmark - for paper 'Coinciding storm surge and wave setup: A regional assessment of sea level rise impact'
<p>This wave climate dataset are the results for the paper titled 'Coinciding storm surge and wave setup: A regional assessment of sea level rise impact'.</p> <p>The operational wave forecasting service provided by DMI-WAM uses the WAM Cycle version 4.5.4, a third-generation spectral wave model. DMI-WAM is used for the wave climate simulations. The meteorological forcing used in this study was obtained from the regional climate model DMI-HIRHAM, developed by the Danish Meteorological Institute (DMI). It is a component of the CORDEX (Coordinated Regional Climate Downscaling Experiment) ensemble in Europe. Regarding the selection of the time frame and IPCC scenarios in our study, we adhered to the recommendations provided by municipalities. Municipalities are keenly interested in obtaining near-future wind wave data for the specific purpose of using them for risk management. Therefore, the examination of forthcoming weather extremes in the near future within the context of the high greenhouse gas emission scenario (RCP8.5 scenario) is of significance within this investigation. We conduct simulations that encompass two distinct time periods: the historical period spanning from 1976 to 2005, and the near-future period from 2041 to 2070. We analyse the WAM model results for wave climate under both present climate conditions (1976-2005) and future climate scenarios (2041-2070) under the RCP8.5 scenario. Furthermore, note that while our wave climate simulations provide valuable insights into the dynamics of wind-induced waves, the mean SLR is not explicitly taken into account. The mean SLR component is considered in the storm surge simulations.</p> <p>Description of files:</p> <p><a href="../api/records/11052226/draft/files/sla.swh.slope.hist.final.max.nc/content" target="_blank" rel="noopener noreferrer">sla.swh.slope.hist.final.max.nc</a> - Maximum sea level, significant wave height, wave length and slope for the historical period.</p> <p><a href="../api/records/11052226/draft/files/sla.swh.slope.rcp85.final.max.MSLR35.nc/content" target="_blank" rel="noopener noreferrer">sla.swh.slope.rcp85.final.max.MSLR35.nc</a> - Maximum sea level, significant wave height, wave length and slope for the RCP8.5 period.</p> <p><a href="../api/records/11052226/draft/files/wavesetup.hist.final.max.nc/content" target="_blank" rel="noopener noreferrer">wavesetup.hist.final.max.nc</a> - Maximum wave setup for the historical period.</p> <p><a href="../api/records/11052226/draft/files/wavesetup.rcp85.final.max.MSLR35.nc/content" target="_blank" rel="noopener noreferrer">wavesetup.rcp85.final.max.MSLR35.nc</a> - Maximum wave setup for the RCP8.5 period.</p> <p><a href="../api/records/11052226/draft/files/wam.grib.his.swh.98p.nc/content" target="_blank" rel="noopener noreferrer">wam.grib.his.swh.98p.nc</a> - 2% exceedence of significant wave height for the historical period.</p> <p><a href="../api/records/11052226/draft/files/wam.grib.rcp8.swh.98p.nc/content" target="_blank" rel="noopener noreferrer">wam.grib.rcp8.swh.98p.nc</a> - 2% exceedence of significant wave height for the RCP8.5 period.</p>
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.