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122 results for “sea level rise”
Pan‐Arctic Coastal Settlements and Infrastructure Vulnerable to Coastal Erosion, Sea‐Level Rise, and Permafrost Thaw
<p>The datasets are issued from the combination of records of the ESA EO4PAC and Permafrost_cci and HORIZON 2020 Nunataryuk projects. The EO4PAC project aimed to develop a new generation of geospatial products for the observation of permafrost and associated changes from space with a special focus on the coastal Arctic. Four components were considered in the creation of the datasets:</p> <p>(1) Landsat-7/8 for the detection of coastline changes over the 2000-2020 period (Tanguy et al., 2024).</p> <p>(2) Sentinel-1/2 for the detection and mapping of coastal infrastructures (Bartsch et al. 2024), updating Wang et al. (2021).</p> <p>(3) Permafrost_cci timeseries for retrieval of trends of ground temperature and active layer thickness for the 2000-2020 period (Obu et al. 2021a,b), evaluated based on Martin et al (2023) and CALM et al. (2024).</p> <p>(4) Sea level rise by 2100 (Garner et al. 2022).</p> <p>The respective output provides a consistent mapping of settlements along arctic and permafrost-dominated coasts (2), and associated coastline and permafrost conditions changes during the last 20 years (1, 3). Combined together, an assessment of Arctic infrastructures at risk due to permafrost change (GT, ALT) and coastline erosion was possible, the latter with projections for the years 2030, 2050 and 2100.<a name="_heading=h.jkogrw14ymt"></a></p> <p>References</p> <p>Bartsch, Annett, Pointner, Georg, & Nitze, Ingmar. (2023). Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) (Version 2) [Data set]. Zenodo. https://zenodo.org/records/10160636.</p> <p>CALM, GTN-P, Wieczorek, M., Heim, B., Streletskiy, D., Bartsch, A., 2024, GTN-P CALM: 34 years of Active Layer Thickness (ALT) across latitudinal and elevational gradients in the Northern Hemisphere [dataset]. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.972777</p> <p>Garner, G. G., Hermans, T., Kopp, R. E., Slangen, A. B. A., Edwards, T. L., Levermann, A., et al. (2022). IPCC AR6 sea level projections [Dataset]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6382554">https://doi.org/10.5281/zenodo.6382554</a></p> <p>Martin, Julia; Boike, Julia; Chadburn, Sarah; Zwieback, Simon; Anselm, Norbert; Goldau, Maybrit; Hammar, Jennika; Abramova, Ekatarina N; Lisovski, Simeon; Coulombe, Stéphanie; Dakin, Brampton; Wilcox, Evan James; Giamberini, Mariasilvia; Rader, Fieke; Suominen, Otso; Rudd, Daniel Alexander; Mastepanov, Mikhail; Young, Amanda (2023): T-MOSAiC 2021 myThaw data set [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.956039, In: Boike, Julia; Hammar, Jennika; Goldau, Maybrit; Miesner, Frederieke; Anselm, Norbert (2024): Circumarctic seasonal measurements of permafrost parameters (thaw depth, snow depth, vegetation and tree height, water level and soil properties) [dataset publication series]. PANGAEA, https://doi.org/10.1594/PANGAEA.971787</p> <p>Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. <a href="https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85">https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85</a></p> <p>Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. <a href="https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85">https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85</a></p> <p>Tanguy, R., Bartsch, A., Nitze, I., Irrgang, A., Petzold, P., Widhalm, B., von Baeckmann, C., Boike, J., Martin, J., Efimova, A., Vieira, G., Whalen, D., Heim, B., Wieszorek, M., Grosse, G.: Pan‐Arctic Assessment of Coastal Settlements and Infrastructure Vulnerable to Coastal Erosion, Sea‐Level Rise, and Permafrost Thaw, Earth’s Future, 10.1029/2024EF005013.</p> <p>Wang, S., Ramage, J., Bartsch, A., & Efimova, A. (2021). Population in the Arctic Circumpolar Permafrost Region at settlement level (Version 2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4529610" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.4529610</a></p>
Coastal landcover change and the associated biomass trends in the mid-Atlantic sea-level rise hotspot
Climate change is driving worldwide landscape reorganization. In the coastal ecosystem, climate-driven sea level rise is forcing landward marsh migration and forest die-off, with potentially large consequences on coastal carbon balance. Here we used 30 m resolution Landsat images to study coastal landcover change from 1984 to 2020, and analyzed the Normalized Difference Vegetation Index (NDVI, a proxy of plant biomass) trend between 1984 and 2020 in the mid-Atlantic sea level rise hotspot. Our study region stretches across the entire Chesapeake Bay and the Delaware Bay to encompass all areas between 0-5m above sea level (total area ~12,500 km2). Specifically, the data package includes 3 raster datasets derived from the Landsat images. All datasets cover the identical mid-Atlantic region and have identical spatial resolution of 30 m. The two landcover datasets, named as 'Landcover_year1984.tif' and'Landcover_year2020.tif', respectively refer to landcover map in 1984 and 2020. Each of the maps has 7 landcover classes differentiated by different integers, and they are: water (0), farmland (1), urban area(2), upland forest (3), transition forest (4), marsh (5) and sandbar (6). Both landcover maps were generated using a combination of random forest classification and manual delineation, and the resultswere validated with high-resolution aerial photos and satellite images with an overall mapping accuracybeyond 90%. The third raster dataset, named as 'NDVItrend_1984to2020.tif', is the NDVI trend map. The value of each 30 by 30 m pixel in the map represents the slope of the NDVI trendline estimated using annual peak-growing season NDVI images acquired between 1984 and 2020. Negative values in the dataset represent decreases of NDVI (i.e. biomass loss, or ecosystem browning) from 1984 and 2020,whereas positive values correspond to an increase of NDVI (i.e. biomass gain, or ecosystem greening)between 1984 and 2020. The data package is completed.
Lateral and vertical forest retreat rate in the mid-Atlantic sea-level rise hotspot
Ghost forests consisting of dead trees adjacent to marshes are striking indicators of climate change. Here we quantify both the lateral and vertical rate of coastal forest retreat between 1984 and 2020 along the US mid-Atlantic coast. The study region includes areas between 0-5 m above sea level across the Chesapeake Bay and the adjacent Delaware Bay. Specifically, the data package includes 2 shapefile datasets derived from four decades of Landsat satellite observations of coastal treeline dynamics. The two datasets are generated on the same spatial-scale and have the same spatial resolution (0.075 km2), both stored as hexagon grids with a side length 170 m. Here we define "forest retreat" (as shown in the datasets as positive values) as the migration of coastal treeline landwards (lateral retreat) or upslope (vertical retreat), whereas "forest advance" (negative values) refers to treeline migration seawards (lateral advance) or downslope (vertical advance). The number '999999' in both datasets indicates areas of stable coastal treelines (i.e. no change) between 1984 and 2020.
Water levels at tide gauges from: Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution
<p>Data to reproduce the analysis of the Hourly Coastal water levels with Counterfactual (HCC) dataset, presented in the publication "<strong>Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution</strong>" published in Earth System Science Data (ESSD). </p><p>Note that in this repository, water levels are only provided tide gauge locations which were used for the analysis presented in the paper. The full Hourly Coastal water levels with Counterfactual (HCC) dataset is published in the <a href="https://doi.org/10.48364/ISIMIP.749905">ISIMIP repository</a>.</p><h2>File Descriptions</h2><h4>HCC_analysis_and_plots.ipynb</h4><p>This jupyter-notebook contains all scripts to produce the plots presented in the paper. Make sure that all necessary python packages are installed. The script assumes all netCDF files from this repository to be stored in a sub-directory called "data".</p><h3>hcc_gesla3_99pctl_surge_2011_2015.nc</h3><p>Extreme surge levels from 2011-2015 at 999 GESLA-3 tide gauge stations with at least 90 percent of data in the considered period. As astronomical tides are removed from the modeled and observed water levels to yield the surge component. The file also contains monthly relative water levels and monthly geocentric water levels from 1900-2015 from the HCC dataset.</p><h4>Variables:</h4><ul><li><i>observed_99pctl_surge_level_anomaly</i> -- 99th percentile of daily maximum surge level anomalies from 2011-2015</li><li><i>hcc_99pctl_surge_level_anomaly -- </i>HCC surge level anomalies at the same time steps as <i>observed_99pctl_surge_level_anomaly</i></li><li><i>hcc_counterfactual_99pctl_surge_level_anomaly</i> -- HCC counterfactual surge levels at the same time steps as <i>observed_99pctl_surge_level_anomaly</i></li><li><i>hcc_water_level_monthly</i> – Monthly relative water level from 1900-2015</li><li><i>hcc_geocentric_water_level_monthly</i> – Monthly geocentric water level from 1900-2015</li></ul><h3>hcc_hr_psmsl_water_level_monthly_1900_2015.nc</h3><p>Monthly water levels at 663 PSMSL tide gauge stations of at least 20 year length and with at least 30 percent data coverage in the 1993-2012 period. The file contains data from the HCC, HR and PSMSL datasets. To align PSMSL and HR with HCC, the 1993-2012 average from PSMSL and HR is removed from each of those datasets respectively and the 1993-2012 average of HCC is added. The average is calculated only over all time steps where the associated observational record has valid data.</p><h4>Variables:</h4><ul><li><i>hcc_water_level_monthly</i> – Monthly relative water level from the HCC dataset</li><li><i>hr_aligned_water_level_monthly</i> -- Monthly relative water level from the HR dataset, aligned with <i>hcc_water_level_monthly</i></li><li><i>psmsl_aligned_water_level_monthly</i> -- Monthly relative water level from the PSMSL database, aligned with <i>hcc_water_level_monthly</i></li></ul><h3>hcc_codec_hr_gesla3_water_level_hourly_monthly_1979_2015.nc</h3><p>Hourly water levels at 1040 GESLA-3 tide gauge stations which have at least 30 percent of valid observations between 1979 and 2015. The file contains data from the HCC, CoDEC, HR and GESLA-3 datasets. The different records are not vertically aligned.</p><h4>Variables:</h4><ul><li><i>gesla3_water_level_hourly</i> -- Hourly relative water level from the GESLA3 database</li><li><i>hcc_water_level_hourly</i> -- Hourly relative water level from the HCC dataset</li><li><i>codec_water_level_hourly</i> -- Hourly relative water level from the CoDEC dataset</li><li><i>hr_water_level_monthly</i> -- Monthly relative water level from the HR dataset</li></ul><h3> </h3><h3>hcc_gesla3_water_level_hourly_2011_2015.nc</h3><p>Water levels from the HCC and GESLA-3 datasets, only for tide gauge stations with a complete record in the period 2011-2015 and associated HCC grid points.</p><h4>Variables:</h4><ul><li><i>gesla3_water_level_hourly</i> -- Hourly relative water level from the GESLA3 database</li><li><i>hcc_water_level_hourly</i> -- Hourly relative water level from the HCC dataset</li></ul><h3>slr_ds_psmsl_selected.nc</h3><p>Linear estimates of relative sea level rise from 1900 to 2015. Data is provided at 663 PSMSL tide gauge stations of at least 20 year length and with at least 30 percent data coverage in the 1993-2012 period. Estimates are calculated for the HCC, HR and PSMSL datasets.</p><h4>Variables:</h4><ul><li><i>psmsl_rslr, psmsl_rslr_lower, psmsl_rslr_upper</i> -- Relative sea level rise for PSMSL with lower and upper bounds for a 95 percent confidence interval</li><li><i>hcc_long_rslr, hcc_long_rslr_lower, hcc_long_rslr_upper </i>-- Relative sea level rise for HCC with lower and upper bounds for a 95 percent confidence interval</li><li><i>hr_rslr, hr_rslr_lower, hr_rslr_upper</i> -- Relative sea level rise for HR with lower and upper bounds for a 95 percent confidence interval</li></ul><h3>reg_mask_xr.nc</h3><p>Split of the world into 7 ocean basins: Indian Ocean - South Pacific, Northwest Pacific, East Pacific, South Atlantic, Subtropical North Atlantic, Subpolar North Atlantic West and Subpolar North Atlantic East.</p><h4>Variables:</h4><p><i>reg_mask</i> – Float value, representing the ocean basins</p><p> </p>
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.
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>
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>
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>
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>
Supporting data: "RECEIPT D7.3: Future climate scenarios: sea level rise and sea ice extent"
<p>This is the supporting data for deliverable D7.3 from work package 7 ( Sea level rise, infrastructure and coastal flooding ) of the RECEIPT H2020 project (No 820712).</p> <p>Deliverable 7.3 describes the development of future SLR (sea level rise) and sea ice extent scenarios. Each SLR contributor (e.g. thermal expansion, instability of Antarctic and Greenland ice sheets, melting glaciers, ocean circulation and land water storage) are included in the assessment (KNMI, Task 7.4). Sea ice extent is derived from CMIP5/6.</p> <p>This dataset is composed of three compressed files:</p> <p>cmip5_zos_zostoga_v2.zip and cmip6_zos_zostoga_v2.zip: Netcdf files of ocean thermal expansion and ocean dynamics computed from zos and zostoga data from the ESGF nodes.</p> <p>data_RECEIPT_D73.zip: Netcdf files of three sea level scenarios. Data is provided globally but scenarios are designed for the European coast.</p>
Joint series underlying the paper " Compound coastal-riverine flooding of St. Lawrence River coasts under sea level rise conditions"
<p>Compound coastal-riverine flooding, known as flooding events caused by the co-occurrence of high streamflow and coast water levels, can have substantial economic and social implications in low-lying coastal regions. Recent studies over Canada’s coasts have shown that neglecting the interdependency between flood drivers can underestimate the risk of flooding by up to 50%. However, to date, such interdependency and its effect on the frequency of compound riverine-coastal flooding has not been investigated for the coasts of the St. Lawrence River, Estuary, and Gulf system (StL), where Sea Level Rise (SLR), along with intensified river peaks, are already threatening communities. In this study, a copula-based bivariate frequency analysis (AND hazard scenario) was applied to quantify the differences between joint return periods computed under dependent and independent assumptions, for 26 sites along the StL. Furthermore, design pairs for 100-year joint events in the historical period (1986-2020) were compared with the 2100 horizon, where the SLR associated with the RCP8.5 emission scenario was incorporated into the water level time series. Results show that 1) the independence assumption can underestimate the frequency of compound flooding in the Fluvial Section of the StL by up to 30 times and 2) the SLR can increase the frequency of compound flooding by up to 50 times in the Estuary and the Gulf and by up to 5 times in the Fluvial Section of the StL. This study highlights the need for explicit consideration of the dependence between flood drivers and of SLR in the delineation of flood maps along all of the coasts of the St. Lawrence.</p>
Input data for: Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution
<p>This Zenodo archive contains essential input datasets utilized in our <a href="https://doi.org/10.5194/essd-2023-112">research study</a> titled "Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution". </p><p>This archive contains only input data. The Hourly Coastal water levels with Counterfactual (HCC) dataset is published in the <a href="https://doi.org/10.48364/ISIMIP.749905">ISIMIP repository</a>.</p><p><strong>Datasets Included</strong>:</p><p><strong>CoDEC (Coastal Dataset for the Evaluation of Climate Impact)</strong>:</p><ul><li>This dataset is described in<a href="https://doi.org/10.3389/fmars.2020.00263"> Muis et al. (2020)</a></li><li><strong>cf_esl folder</strong>: Contains data representing total CoDEC water levels. Individual NetCDF files store data for each grid point.</li><li><strong>cf_tides folder</strong>: This folder holds data related to tidal elevation.</li><li><strong>coor_coastal.nc</strong>: A NetCDF file featuring the spatial grid utilized in CoDEC. This dataset comprises only coastal grid points.</li></ul><ol><li><strong>HR (Hybrid Reconstructions)</strong>:<ul><li><strong>HybridRec_Upd0422.mat</strong>: This file contains data from the Hybrid Reconstructions dataset (<a href="https://doi.org/10.1038/s41558-019-0531-8">Dangendorf et al 2019</a>), aligned to the CoDEC grid, and includes satellite altimetry integral to producing the Hybrid Reconstructions dataset. Each row corresponds to one grid point on the CoDEC grid. For ease of use in our applications, we offer a preprocessing script in our <a href="https://doi.org/10.5281/zenodo.7771501">source code</a> named split_hr_dataset_to_stations.py.</li></ul></li></ol><p>We here provide the specific versions of HR and CoDEC that are used in our study to ensure accurate replication.</p>
PIE LTER plant biomass associated with marsh sites used in space for time sea level rise study, Rowley, MA.
This dataset contains the mass of dried grasses collected from clip plots in the PIE LTER Space for Time Sea Level Rise study. The space for time study uses an intensive and comprehensive approach to compare low elevation, Spartina alterniflora marsh areas to higher elevation Spartina patens marsh areas. Grasses are sampled in plots from 4 transects per site with five plots per site, arranged from the tidal creek's edge to no more than 150 meters back from the creek. Other related data files include: HTL-RO-ST-MAR-Sites, HTL-RO-ST-MAR-Birds, HTL-RO-ST-MAR-Quads, HTL-RO-ST-MAR-Sediments, HTL-RO-ST-MAR-Bites, HTL-RO-ST-MAR-Sticky, HTL-RO-ST-MAR-Decomp, HTL-RO-ST-MAR-Traps, HTL-RO-ST-MAR-Deep_pitfalls
PIE LTER herbivory measurement associated with marsh sites used in space for time sea level rise study, Rowley, MA.
This dataset contains aggregated observations of predation and herbivory on tethered bait in each quadrat of sites around the Rowley River and the south side of Sawyer Island at the Plum Island LTER. Measurements consist of the consumption status of tethered squid or kelp pieces as a measure of energy transfer between trophic levels. Pieces were left in the field for five days and observers recorded the status of bait over time as either entirely missing, partially consumed, having scrape marks, or fully intact. These measurements can be used to calculate consumption rates (i.e. energy transfer) over time. Notes include fields discussing any additional observations - e.g., if a stick was found missing.
PIE LTER fish and crab trap data associated with marsh sites used in space for time sea level rise study, Rowley, MA.
This dataset consists of assessments of crab and fish abundances using traps placed in creeks adjacent to marsh community survey transects at sites around the Rowley River and the south side of Sawyer Island at the Plum Island LTER. At each site, four sets of crab and fish traps were deployed for one week. Traps were sampled daily and all individuals were identified and then returned to the creek away from the area where traps were placed. See HTL-RO-ST-MAR-Sites for site description.
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.