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735 results for “Sea level”
Sea-level rise and freshwater management are reshaping coastal ecosystems in the Florida Everglades
Datasets include hydrology (water level and salinity), net ecosystem exchange of CO2, photosynthetically active radiation (PAR), and air temperature for a freshwater marl prairie, brackish marsh ecotone, and saline scrub mangrove forest. Data were derived from multiple sources, including two sites from the South Florida Water Management District (SFWMD) DBhydro web database, two sites from the Florida Coastal Everglades Long Term Ecological Research (FCE-LTER) program and three AmeriFlux sites in the Southeastern Everglades region. Ameriflux sites were co-located with FCE-LTER sites. To understand the effects of sea level rise and freshwater management on landscape carbon exchange (C), we measured the net ecosystem exchange of CO2 (NEE) between subtropical wetland ecosystems and the atmosphere along a dynamic salinity gradient. Ecosystems were representative of freshwater marl prairies, brackish marsh ecotones, and saline scrub mangrove forests. In the southeastern Everglades, the magnitude of environmental change was greatest along the coast, where mangrove scrub forests exhibited a greater capacity to maintain CO2 uptake with changing conditions.
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).
The commitment to global sea level rise over the next 500 years: exploring the threat of the Antarctic Ice Sheet to coastal infrastructure
<p>Within Australia alone, more than A$226 billion of coastal infrastructure is vulnerable to the anticipated rise in sea level by the end of the century. The IPCC Fifth Assessment Report concludes that the likely increase in global mean sea level during the 21st century ranges from 26-55 centimetres (under the low-end RCP2.6 climate scenario) to 45-82 centimetres (under the high-end RCP8.5 climate scenario). However, these projections do not take into account the potential for collapse of the marine-based sectors of the Antarctic Ice Sheet.</p> <p>Recent evidence has indicated that the IPCC projections may be under-estimates, with sea level increases of up to 2.5 metres possible by the end of the 21st century. Modelling studies have also demonstrated the potential for the Antarctic Ice Sheet to undergo irreversible collapse during the coming centuries, leading to dramatic increases in global sea level on time scales relevant to critical coastal infrastructure such as refineries and airports. The most extreme prediction is that Antarctica could contribute 15.65±2.00 metres to global sea level by the year 2500.</p> <p>Here, we combine climate modelling and ice sheet modelling to explore the evolution of the Antarctic Ice Sheet over the next 500 years under a range of climate scenarios. We run the models many times to take into account gaps in our understanding of ice sheet dynamics. This allows us to generate robust projections of the Antarctic contribution to global sea level from the present to the year 2500, complete with quantified confidence intervals. We conclude that the sea level contribution during the 21st century will be modest, consistent with the IPCC Fifth Assessment Report, but that melting of the Antarctic Ice Sheet will accelerate thereafter. By the year 2500, we predict that the Antarctic contribution to global sea level will be at least 5 metres.</p>
Mangrove diversity loss under sea-level rise triggered by bio-morphodynamic feedbacks and anthropogenic pressures
<p>To whom concerned,</p> <p>This dataset is the supplementary dataset for the publication in <em>Environmental Research Letters</em> entitled '<a href="https://dx.doi.org/10.1088/1748-9326/abc122"><em>Mangrove diversity loss under sea-level rise triggered by bio-morphodynamic feedbacks and anthropogenic pressures</em></a>' authored by Danghan Xie, et al. in 2020. The publication can be freely downloaded here: <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abc122">https://iopscience.iop.org/article/10.1088/1748-9326/abc122</a>. The dataset consists of both model results and corresponding codes that one can easily reproduce figures either in the manuscript or the supplementary document. </p> <p>To use the code, one needs to pre-install the Matlab (R2017a) and changes the pre-set route (in the code) to the directory where the dataset is stored. The figure shapes may vary with the size of the user's monitor so output figures may be either squeezed or extended in unpredictable ways, but the window size of the figure can be adjusted to match the shape and the results will not be affected.</p> <p>The author is appreciated that any potential concerns or questions regarding our research from any party or person, so please contact me through the email: <a href="mailto:d.xie@uu.nl">d.xie@uu.nl</a> or <a href="mailto:xiedanghan@gmail.com">xiedanghan@gmail.com</a>. To know more about my research, you can also follow the <a href="https://www.researchgate.net/profile/Danghan_Xie">ResearchGate</a>.</p> <p>With Kind Regards,</p> <p>Danghan</p> <p>11th of November, 2020</p>
Last interglacial (MIS 5e) sea-level proxies in southeastern South America
<p>This dataset is a compilation of published last interglacial sea level indicators for the southeast coast of South America (including Uruguay, Argentina, and one site in southern Chile). We have documented 60 proxies, which includes 48 sea level index points, 11 marine limiting indicators and 1 terrestrial limiting indicator. All of these data have at least some chronological control, with radiocarbon, electron spin resonance, U/Th, amino acid racemization and OSL dating techniques. The vertical uncertainty values were assigned based on information given for measurements, and the type of deposit.</p>
Supplementary Information and data for: "Quaternary and Pliocene sea-level changes at Camarones, central Patagonia, Argentina"
<p>This repository contains the supplementary information and raw data annexed to the manuscript "<em>Quaternary and Pliocene sea-level changes at Camarones, central Patagonia, Argentina</em>", authored by Karla Rubio-Sandoval et al. and submitted for consideration in the journal Quaternary Science Reviews.</p> <p>The folder contains the following items.</p> <p><strong>1. Raw_data.xlsx</strong><br>This is an excel file that includes all survey and analytical data in several sheets, briefly described hereafter.</p> <p>- <em>GNSS data</em>. Data surveyed with differential GNSS in the field.<br>- <em>Sea level index points</em>. Datapoints used as sea-level index points, and associated calculations of paleo Relative Sea Level.<br>- <em>AAR Summary</em>. Table summarising the main results of the AAR analyses.<br>- <em>AAR complete sheet</em>. The complete set of analytical data done for the Amino Acid Racemization dating.<br>- <em>Radiocarbon data</em>. The analytical results of radiocarbon dating.<br>- <em>Literature ages</em>. A compilation of the Electron Spin Resonance and U-series ages published for the Camarones site.<br>- <em>Transects</em>. Topographical transects extracted from the TanDEM-X Digital Elevation model and referred to the GEOIDEAR 16 geoid.<br>- <em>Distance plot</em>. Data for plotting Relative Sea Level vs distance along the coast of the sea-level index points described in the manuscript.</p> <p><strong>2. Holocene (folder)</strong><br>This folder contains two excel files ("Area_Camarones_Accepted.xlsx" and "Area_Camarones_Rejected.xlsx") that include the Holocene data described in the paper compiled following the standard HOLSEA template.</p> <p><strong>3. Runup_modelling</strong><br>This folder contains three folders, each with a Jupyter notebook (.ipynb) and datasets to perform the runup calculations described in the manuscript.</p>
Estimates of Global Coastal Losses Under Multiple Sea Level Rise Scenarios
<p>Results from the Python Coastal Impacts and Adaptation Model (pyCIAM), the inputs and source code necessary to replicate these outputs, and the results presented in Depsky et al. 2023.</p> <p>All zipped Zarr stores can be downloaded and accessed locally or can be directly accessed via code similar to the following:</p> <pre><code>from fsspec.implementations.zip import ZipFileSystem import xarray as xr xr.open_zarr(ZipFileSystem(url_of_file_in_record}}).get_mapper())</code></pre> <p><strong>File Inventory</strong></p> <p><em>Products</em></p> <ul> <li><strong>pyCIAM_outputs.zarr.zip</strong>: Outputs of the pyCIAM model, using the <a href="https://doi.org/10.5281/zenodo.6449230">SLIIDERS</a> dataset to define socioeconomic and extreme sea level characteristics of coastal regions and the 17th, 50th, and 83rd quantiles of local sea level rise as projected by various modeling frameworks (<a href="https://doi.org/10.5281/zenodo.593357">LocalizeSL</a> and <a href="https://doi.org/10.5281/zenodo.6419953">FACTS</a>) and for multiple emissions scenarios and ice sheet models.</li> <li><strong>pyCIAM_outputs_{case}.nc:</strong> A NetCDF version of <code>pyCIAM_outputs</code>, in which the netcdf files are divided up by adaptation "case" to reduce file size.</li> <li><strong>diaz2016_outputs.zarr.zip</strong>: A replication of the results from <a href="https://link.springer.com/article/10.1007/s10584-016-1675-4">Diaz 2016</a> - the model upon which pyCIAM was built, using an identical configuration to that of the original model.</li> <li><strong>suboptimal_capital_by_movefactor.zarr.zip</strong>: An analysis of the observed present-day allocation of capital compared to a "rational" allocation, as a function of the magnitude of non-market costs of relocation assumed in the model. See Depsky et al. 2023 for further details.</li> </ul> <p><em>Inputs</em></p> <ul> <li><strong>ar5-msl-rel-2005-quantiles.zarr.zip</strong>: Quantiles of projected local sea level rise as projected from the LocalizeSL model, using a variety of temperature scenarios and ice sheet models developed in <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2014EF000239">Kopp 2014</a>, <a href="https://www.pnas.org/doi/pdf/10.1073/pnas.1817205116">Bamber 2019</a>, <a href="https://www.geo.umass.edu/climate/papers2/Deconto_Nature_2021.pdf">DeConto 2021</a>, <a href="https://www.ipcc.ch/srocc/">IPCC SROCC</a>. The results contained in <em>pyCIAM_outputs.zarr.zip</em> cover a broader (and newer) range of SLR projections from a more recent projection framework (FACTS); however, these data are more easily obtained from the appropriate Zenodo records and thus are not hosted in this one.</li> <li><strong>diaz2016_inputs_raw.zarr.zip</strong>: The coastal inputs used in <a href="https://link.springer.com/article/10.1007/s10584-016-1675-4">Diaz 2016</a>, obtained from <a href="https://github.com/delavane/CIAM">GitHub</a> and formatted for use in the Python-based pyCIAM. These are based on the <a href="http://diva.globalclimateforum.org">Dynamic Integrated Vulnerability Assessment (DIVA)</a> dataset.</li> <li><strong>surge-lookup-seg(_adm).zarr.zip</strong>: Pre-computed lookup tables estimating average annual losses from extreme sea levels due to mortality and capital stock damage. This is an intermediate output of pyCIAM and is not necessary to replicate the model results. However, it is more time consuming to produce than the rest of the model and is provided for users who may wish to start from the pre-computed dataset. Two versions are provided - the first contains estimates for each unique intersection of ~50km coastal segment and state/province-level administrative unit (admin-1). This is derived from the characteristics in SLIIDERS. The second is simply estimated on a version of SLIIDERS collapsed over administrative units to vary only over coastal segments. Both are used in the process of running pyCIAM.</li> <li><strong>ypk_2000_2100.zarr.zip</strong>: An intermediate output in creating SLIIDERS that contains country-level projections of GDP, capital stock, and population, based on the Shared Socioeconomic Pathways (SSPs). This is only used in normalizing costs estimated in pyCIAM by country and global GDP to report in Depsky et al. 2023. It is not used in the execution of pyCIAM but is provided to replicate results reported in the manuscript.</li> </ul> <p><em>Source Code</em></p> <ul> <li><strong>pyCIAM.zip:</strong> Contains the python-CIAM package as well as a notebook-based workflow to replicate the results presented in Depsky et al. 2023. It also contains two master shell scripts (run_example.sh and run_full_replication.sh) to assist in executing a small sample of the pyCIAM model or in fully executing the workflow of Depsky et al. 2023, respectively. This code is consistent with release 1.2.0 in the <a href="https://github.com/ClimateImpactLab/pyCIAM">pyCIAM GitHub repository</a> and is available as version 1.2.0 of the python-CIAM package on PyPI.</li> </ul> <p> </p> <p><strong>Version history:</strong></p> <p><em><strong>1.2</strong></em></p> <ul> <li>Point `data-acquisition.ipynb` to updated Zenodo deposit that fixes the dtype of `subsets` variable in `diaz2016_inputs_raw.zarr.zip` to be bool rather than int8</li> <li>Variable name bugfix in `data-acquisition.ipynb`</li> <li>Add netcdf versions of SLIIDERS and the pyCIAM results to `upload-zenodo.ipynb`</li> <li>Update results in Zenodo record to use SLIIDERS v1.2</li> <li> </li> </ul> <p><em><strong>1.1.1</strong></em></p> <ul> <li>Bugfix to inputs/diaz2016_inputs_raw.zarr.zip to make the `subsets` variable bool instead of int8.</li> </ul> <p><em><strong>1.1.0</strong></em></p> <ul> <li>Version associated with publication of Depsky et al., 2023</li> </ul>
'Subglacial Water Amplifies Antarctic Contributions to Sea-Level Rise' by Chen Zhao et al.
<p>Code and data used to produce figures in "Subglacial Water Amplifies Antarctic Contributions to Sea-Level Rise" by Chen Zhao et al.</p> <p>We are happy to help with anybody with any problems with this code, please get in touch (chen.zhao@utas.edu.au) or raise an issue.</p>
Data supporting: "Trends in Europe storm surge extremes match the rate of sea-level rise"
<p><strong>Data supporting the paper:</strong></p> <p><strong>Calafat, F. M., T. Wahl, M. G. Tadesse, & S. Sparrow. Trends in Europe storm surge extremes match the rate of sea-level rise. <em>Nature</em> 603, 841-845.</strong></p> <p>Please cite the paper above when using this data set.</p> <p><em>Data description:</em></p> <ul> <li><strong>Bayesian_solutions_historical_total.nc</strong>: Bayesian estimates (posterior draws) of the GEV parameters, including trends in the GEV location parameter, at both tide gauge sites and prediction locations. This file also contains the observed surge annual maxima from tide gauge records on which these estimates are conditioned.</li> <li><strong>Bayesian_solutions_historical_contributions.nc</strong>: Bayesian estimates (posterior draws) of the contributions from external forcing and internal climate variability to the trends in the GEV location parameter.</li> <li><strong>Surge_annual_max_ensemble.nc</strong>: ensemble of surge annual maxima used to extract the pattern of response to external forcing.</li> </ul>
Supplementary data for "Single extreme storm sequence can offset decades of predicted shoreline retreat by sea-level rise"
<p>This dataset comprises topography and bathymetric data at three coastal locations in Australia (Narrabeen), UK (Perranporth) and used for the publication "Single extreme storm sequence can offset decades of predicted shoreline retreat by sea-level rise". Please refer to readme files for metadata</p>
Database of Last Interglacial sea level indicators from the Bahamas, Turks and Caicos, and the east coast of Florida, USA
<p>This is a compilation of stratigraphic constraints and relevant U-series ages from the Last Interglacial period taken from samples in the Bahamas, Turks and Caicos, and the east coast of Florida, USA. It has been exported from the World Atlas of Last Interglacial Shorelines (WALIS) database (https://warmcoasts.eu/world-atlas.html).</p>
First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS
<p>This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI).</p> <p>Abstract:</p> <p>Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.</p>
Sea level between 21000 and 450 BP, from Sathiamurthy and Voris 2006
<p>Sea level extracted and interpreted from Sathiamurthy, Edlic and Harold K. Voris 2006. “Maps of Holocene Sea Level Transgression and Submerged Lakes on the Sunda Shelf,” Tropical Natural History Supplement, 2, p. 1-44.</p> <p> </p>
Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO2 Emissions and Thermosteric Sea Level Rise Projections - Supplementary Materials
<p>Supplementary materials for Gonzalez, A. R., & Lin, T. (2022). Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO<sub>2</sub> Emissions and Thermosteric Sea Level Rise Projections. <em>Earth's Future</em>. In Press.</p> <p><strong>Summary: This study introduces climate science to a broader audience by presenting an accessible research framework and environmental data related to the ongoing COVID-19 pandemic. A series of translated emission pathways (TEPs) were constructed based on the CO<sub>2</sub> emission patterns from the various phases of COVID-19 response. In addition to resembling the forcing scenarios used within climate research, a thermosteric sea level rise analysis was incorporated to further emphasize the environmental benefits that can be obtained from long-term sustainability. As a promising start for including the general public in climate change discussion, this research promotes collective environmental action that mirrors the recommendations of the scientific community.</strong></p>
Data from article "Is the Atlantic a Source for Decadal Predictability of Sea-Level Rise in Venice?"
<p>Data from the article Zanchettin D., et al.: Is the Atlantic a Source for Decadal Predictability of Sea-Level Rise in Venice?, Earth and Space Science, article number 2022EA002494</p> <p>The dataset contains:</p> <p>- annual time series of October-March average of relative sea level in Venice corrected for vertical land movement (VLMcorrectedRSL) with associated standard error of the mean (LMcorrectedRSL_SEM) for the period 1873-2019;</p> <p>- annual time series of estimate of subsidence in Venice (Subsidence) for the period 1873-2019</p> <p>- modeled state of Venice sea level with associated uncertainty, provided as mean (delta_mean), 1st percentile (delta_1_percentile) and 99th percentile (delta_99_percentile), for the period 1873-2019</p> <p>- modeled local stochastic trend of Venice sea level with associated uncertainty, provided as mean (beta_mean), 1st percentile (beta_1_percentile) and 99th percentile (beta_99_percentile), for the period 1873-2019</p>
Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing
<p>The dataset contains supporting data for the paper submitted to The Cryosphere "Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing".<br><br>OGGM_area_projections.nc contains data for Figure 3.<br>OGGM_volume_projections contains data for Figure 4.</p> <p>OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5.</p> <p>OGGM_solid_ice_discharge_regions.nc contains data for Figure 6.</p> <p>OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7.</p> <p>OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.</p>
Supporting data: "Uncertainty in sea level rise projections due to the dependence between contributors"
<p>These files contain the data analyzed in Le Bars 2018. The paper is available on EarthArXiv (https://eartharxiv.org/uvw3s/) and was submitted to Earth's Future.</p> <p>The NetCDF files contain the Probability Density Functions output from the Probabilistic Sea Level Projection (PSLP) model version 1.</p> <p>Simulations are:<br> IPCC1: The control IPCC AR5 simulation<br> IPCC2: The same but assuming independence between sea level contributors<br> IPCC3: The same but assuming correlation of 1 between sea level contributors<br> Prob1: The control simulation from the probabilistic model<br> Prob2: Assuming independence<br> Prob3: Assuming correlation of 1 between sea level contributors<br> Prob4: Low dependence case<br> Prob5: High dependence case<br> Prob6 to Prob9: Sensitivity experiments replacing each contributor by its expected value.</p> <p>The matrices of Spearman correlation for year 2100 for all experiments are called: <br> SpearmanCorr_namelist*_*.txt</p> <p>The Table*.txt files contain the data used to make tables of sea level percentiles in the paper.</p> <p>The pdf files contain the figures used in the paper and additional pannels not included in the paper.</p> <p>Reference:<br> Le Bars, D. (2018, March 8). Uncertainty in sea level rise projections due to the dependence between contributors. http://doi.org/10.17605/OSF.IO/UVW3S</p>
Seasonal cycle in sea level across the coastal zone
<p>Data supplement for manuscript "Seasonal cycle in sea level across the coastal zone" by Rui Ponte, Michael Schindelegger, submitted to <em>Earth and Space Science</em>, 2024.</p> <p> </p> <p><strong>Content:</strong></p> <p>This repository contains amplitude and phase estimates for the mean annual (Sa) and semiannual (Ssa) oscillations in sea level, as observed by satellite altimetry and tide gauges. We also provide related determinations of Sa/Ssa in manometric sea level, steric sea level, and in several secondary phenomena that are typically considered as corrections to altimetric sea levels or tide gauge observations (e.g., inverted barometer effect, vertical land motion):</p> <ul> <li><em>altimetric.tar.gz</em>: Sa/Ssa in gridded DUACS and MEaSUREs altimetry, and the regional X-TRACK-L2P along-track product (<a href="https://www.aviso.altimetry.fr/en/data/products/sea-surface-height-products/regional/x-track-sla/x-track-l2p-sla-version-2022.html" target="_blank" rel="noopener">10.24400/527896/a01-2022.020</a>); all standard altimetry corrections have been applied to these data.</li> <li><em>tgauges_harmonics.da</em>t: Sa/Ssa estimates at 747 globally distributed tide gauges from the GESLA-3 database (<a href="https://gesla787883612.wordpress.com/">https://gesla787883612.wordpress.com/</a>). The harmonics are not corrected for the inverted barometer effect.</li> <li><em>manometric_steric.tar.gz</em>: Gridded Sa/Ssa estimates in manometric sea level, derived from GSFC GRACE 1° mascons, and steric sea level, as deduced from a hydrographic atlas (WOA2023).</li> <li><em>corrections.tar.gz</em>: Sa/Ssa contributions to the oceanic inverted barometer, the astronomical tides, and vertical land motion, along with an estimate for the annual pole tide signal.</li> </ul> <p>The respective files contain a few more relevant specifications such as data sources, underlying grids, and time periods considered for the analysis.</p> <p> </p> <p><strong>Important notes</strong>:</p> <ul> <li>Phases are referred to the vernal equinox, and NOT the beginning of the calendar year. See Ray et al. (2021) for a pertinent discussion.</li> <li>Amplitudes of the corrective fields are in (mm), all other amplitudes are in (cm).</li> </ul> <p> </p> <p><strong>Terms of usage:</strong></p> <ul> <li>If you use the DUACS or MEaSUREs seasonal cycle estimates or the corrective fields, please cite: Ray, R.D., Loomis, B.D. & Zlotnicki, V. (2021). The mean seasonal cycle in relative sea level from satellite altimetry and gravimetry. <em>Journal of Geodesy</em>, 95, 80. <a href="https://doi.org/10.1007/s00190-021-01529-1">https://doi.org/10.1007/s00190-021-01529-1</a>.</li> <li>If you use any of the other datasets (i.e., seasonal cycle in X-TRACK, tide gauges, manometric and steric sea level), please cite: Ponte, R.M. & Schindelegger, M. (2024). Seasonal cycle in sea level across the coastal zone. <em>ESS Open Archive</em> [preprint]. doi: <a href="https://essopenarchive.org/users/534050/articles/1225967-seasonal-cycle-in-sea-level-across-the-coastal-zone" target="_blank" rel="noopener">10.22541/essoar.172736425.52711759/v1</a>.</li> </ul> <p>----</p> <p>Contact: M. Schindelegger (schindelegger@igg.uni-bonn.de)</p> <p> </p>
KNMI'23 sea-level scenarios
<p>Reference data for the KNMI'23 sea-level scenarios for the Netherlands, Bonaire and Saba.</p> <p>The scenarios are height anomalies compared to the reference period 1995-2014. They are not referenced to a vertical reference system.</p> <p>The three files "<a href="https://zenodo.org/api/records/14047707/draft/files/DataKNMI23_SeaLevel_2100_ref_period_1995_2014.csv/content" target="_blank" rel="noopener noreferrer">DataKNMI23_SeaLevel_2100_ref_period_1995_2014.csv</a>", "<a href="https://zenodo.org/api/records/14047707/draft/files/DataKNMI23_Saba_SeaLevel_2100_ref_period_1995_2014.csv/content" target="_blank" rel="noopener noreferrer">DataKNMI23_Saba_SeaLevel_2100_ref_period_1995_2014.csv</a>" and "<a href="https://zenodo.org/api/records/14047707/draft/files/DataKNMI23_Bonaire_SeaLevel_2100_ref_period_1995_2014.csv/content" target="_blank" rel="noopener noreferrer">DataKNMI23_Bonaire_SeaLevel_2100_ref_period_1995_2014.csv</a>" are time series up to 2100 with yearly mean data for the Netherlands, Saba and Bonaire. Multiple percentiles are provided.</p> <p>The three files "<a href="https://zenodo.org/api/records/14047707/draft/files/knmi23_long_term_projections.csv/content" target="_blank" rel="noopener noreferrer">knmi23_long_term_projections.csv</a>", "<span><a href="https://zenodo.org/api/records/14047707/draft/files/knmi23_long_term_projections_Saba.csv/content" target="_blank" rel="noopener noreferrer">knmi23_long_term_projections_Saba.csv</a></span>" and "<span><a href="https://zenodo.org/api/records/14047707/draft/files/knmi23_long_term_projections_Bonaire.csv/content" target="_blank" rel="noopener noreferrer">knmi23_long_term_projections_Bonaire.csv</a></span>" are time series up to 2300 for the Netherlands, Saba and Bonaire.</p> <p>Time series of the low-likelihood high-impact scenarios are also provided for the same three regions (called lphi).</p> <p>The file "<a href="https://zenodo.org/api/records/14047707/draft/files/DataKNMI23_SeaLevel_ref_period_1995_2014_DateToReachHeight.csv/content" target="_blank" rel="noopener noreferrer">DataKNMI23_SeaLevel_ref_period_1995_2014_DateToReachHeight.csv</a>" provides the date to reach a given height rather than the height at a given date.</p>
ScienceDex guides
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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.