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62 results for “Sea level change”
Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux -- Supplemental Data Set: Sea Level Sensitivity Kernels
<p><strong>Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux<br> SUPPLEMENTAL DATA SET: SEA LEVEL SENSITIVITY KERNELS</strong></p> <p>To accompany</p> <p> Jerry X. Mitrovica, Carling C. Hay, Robert E. Kopp, Christopher Harig, and<br> Konstantin Laytchev (2018). Quantifying the Sensitivity of Sea Level Change<br> in Coastal Localities to the Geometry of Polar Ice Mass Flux. Journal of<br> Climate. doi: 10.1175/JCLI-D-17-0465.1.</p> <p>We provide sea level kernels for ~740 tide gauge sites in the Permanent Service for Mean Sea Level (PSMSL) database (Holgate et al., 2013). Kernels associated with sensitivities to Greenland and Alaskan glacier melt are given on a spatial grid covering the globe, with 512 latitude rows (i=1,512) and 1024 longitude (j=1,1024) columns.</p> <p>Longitude values are evenly spaced moving eastward from Greenwich (the jth grid point has an east longitude value of (j-1)×360°/1024). Latitude values are Gauss-Legendre points beginning close to the North Pole and ending near the South Pole. Kernels associated with sensitivities to Antarctic melt are given on a spatial grid covering the globe, with 256 (Gauss-Legendre) latitude rows (i=1,256) and 512 longitude (j=1,512) columns. Longitude values are evenly spaced moving eastward from Greenwich.</p> <p>The format of the files is: </p> <p> grid_sitenumber_region.txt</p> <p>where “region” is either “green” (Greenland), “ant” (Antarctic) or “Alaska” (Alaska). The list of sites (and site numbers) is provided in the sites.txt file. The first 8 sites in this list were test sites and can be ignored.</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.
Dataset for "Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections"
<p>This dataset is used to reproduce the results presented in the following publication:</p> <p>Goelzer, H., Noel, B. P. Y., Edwards, T. L., Fettweis, X., Gregory, J. M., Lipscomb, W. H., van de Wal, R. S. W., and van den Broeke, M. R.: Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-188, in review, 2019.</p> <p> </p>
A Spatially Variable Time Series of Sea Level Change Due to Artificial Water Impoundment
<p>This database contains a series of gravitational, rotational, and deformational (GRD) "fingerprints"—the spatial response of sea level—corresponding to redistribution of water mass because of impoundment of water in artificial reservoirs, as reported in Hawley <em>et al</em>. (2020). Fingerprints for the GRanD database (Lehner <em>et al</em>.; 2011) are for individual years, noted in the file name.</p> <p>Three additional files come from the dataset provided by Zarfl <em>et al</em>. (2015), as described in Hawley <em>et al.</em> (2020). "Const" includes the fingerprint for all reservoirs under construction in their database; "Plan" includes the fingerprint for all reservoirs in the planning phase. "Zarfl" includes the fingerprint for all reservoirs in "Const," with 15 years of seepage, as well as all reservoirs for "Plan" with 5 years of seepage, as described in Hawley <em>et al</em>. (2020).</p> <p>Each fingerprint has 525,825 points, which fill out a global grid of 513 x 1025 [lat x lon] points. Each node in latitude and longitude is evenly spaced. The first point represents the northernmost point at 0 [deg] longitude, and increase first to the east, then to the south.</p>
Reconstruction of Mediterranean sea-level changes and contributions for 1960-2018
<p><strong>Data supporting the paper:</strong></p> <p><strong>Calafat, F. M., Frederikse, T., and Horsburgh, K. (2022). The Sources of Sea-Level Changes in the Mediterranean Sea since 1960, Journal of Geophysical Research Oceans, under review.</strong></p> <p>Please cite the paper above when using this data set.</p> <p>This new version of the data set has been published to support the paper above and includes more data than the previous version as well as several improvements and refinements. Version 2.3 has been created to include regional estimates of rates due to the inverse barometer effect.</p> <p><em>Data description:</em></p> <ul> <li><strong>Bayesian_estimates_Mediterranean_sea_level.nc:</strong> this file contains gridded estimates of relative sea-level changes and their instantaneous rates for 1960-2018 in the Mediterranean Sea, separated into the individual contributions of: <ol> <li>Sterodynamic changes (i.e., ocean dynamics and thermal expansion).</li> <li>Contemporary GRD (i.e., changes in Earth gravity, Earth rotation, and solid-earth deformation due to land-mass changes).</li> <li>GIA (i.e., glacial isostatic adjustment).</li> <li>Short-term variability (interannual to decadal). </li> <li>Inverse barometer effect.</li> </ol> </li> <li><strong>data_input.mat:</strong> this file contains all of the data needed to run the Bayesian hierarchical model. This includes the observational data from tide gauges and satellite altimetry as well as the ensemble-mean and ensemble covariance matrices for the sea-level fingerprints associated with contemporary GRD effects and GIA.</li> </ul> <p>The Bayesian estimates have been obtained using a spatiotemporal Bayesian hierarchical model (see paper). This work has been carried out within the framework of the EuroSea project funded by European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626.</p>
Supplementary files for Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach
<p>Model input and output files associated with the manuscript entitled "Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach" that will be submitted to Journal of Geophysical Research.</p>
Data supplement to 'Vertical land motion reconstruction unveils non-linear effects on relative sea level changes from 1900-2150'
<p>This is a data supplement to <strong>'Vertical land motion reconstruction unveils non-linear effects on relative sea level changes from 1900-2150</strong>'. It presents a global-scale Vertical Land Motion (VLM) reconstruction that resolves height changes in the period 1995-2020. It is based on the joint probabilistic analysis of an extensive network of more than 11,000 GNSS stations, tide gauges, and satellite altimetry. The approach used to derive this reconstruction is described in the paper. The dataset variables are explained in the .pdf file.</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>
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>
Framework for Assessing Changes To Sea-level (FACTS) Module Data - Part 2
<p>Additional input module data sets from the Framework for Assessing Changes To Sea-level. These files should be installed in the modules-data/ directory. See https://github.com/radical-collaboration/facts for more information.</p>
Text-fig. 6. Shallowing pattern during the Middle Miocene to Late Miocene/Pliocene due to increasing magmatic activity as an external parameter. a: palaeobathymetry map during the Middle Miocene to Pliocene; b: sea level change curve indicating a shallowing pattern; c: relative changes of sea level and magmatic activity curve (Haq et al. 1987, Soeria-Atmadja et al. 1998, Muljana 2012). in Lithofacies And Ichnofacies Of Turbidite Deposits, West Java, Indonesia
Text-fig. 6. Shallowing pattern during the Middle Miocene to Late Miocene/Pliocene due to increasing magmatic activity as an external parameter. a: palaeobathymetry map during the Middle Miocene to Pliocene; b: sea level change curve indicating a shallowing pattern; c: relative changes of sea level and magmatic activity curve (Haq et al. 1987, Soeria-Atmadja et al. 1998, Muljana 2012).
FIGURE 10 in Palaeoecology and sea level changes: Decline of mammal species richness during late Quaternary island formation in the Montebello Islands, north-western Australia
FIGURE 10. Number of species identified from each spit in Morgan's Cave, illustrating the increasing loss of species from spits four to one.
FIGURE 9 in Palaeoecology and sea level changes: Decline of mammal species richness during late Quaternary island formation in the Montebello Islands, north-western Australia
FIGURE 9. Species-area plot for islands on the north-west continental shelf (filled circles) (data from Abbott and Burbidge, 1995), and spits one to seven in Morgan's Cave (open circles).
FIGURE 8 in Palaeoecology and sea level changes: Decline of mammal species richness during late Quaternary island formation in the Montebello Islands, north-western Australia
FIGURE 8. Log non-volant species vs log area plot for the north-west islands (filled circles) and the super-island at sea level 10 m below present (open circle).
FIGURE 6 in Palaeoecology and sea level changes: Decline of mammal species richness during late Quaternary island formation in the Montebello Islands, north-western Australia
FIGURE 6. Species accumulation curve for Morgan's Cave, showing increasing species with increased sampling effort (cumulative NISP). Further sampling effort could have yielded more species.
Figure 4 in Climate Changes of the Temperature of the Surface and Level of the Black Sea by the Data of Remote Sensing at the Coast of the Krasnodar Krai and the Republic of Abkhazia
Figure 4. Spatial variability of the climatic rate of the Black Sea level change (cm/yr) for period from 1993 to 2015.
STEHME files & HOLSEA spreadsheet for "Creel et al. 2022: Postglacial Relative Sea Level Change in Norway" and Balascio et al. 2023: "Refining Holocene sea-level dynamics for the Lofoten and Vesterålen archipelagos, northern Norway: Implications for prehistoric human-environment interactions"
<p><strong>Data Files for Creel et al. 2022: "Postglacial Relative Sea Level Change in Norway" and Balascio et al. 2023: "</strong><strong>Refining Holocene sea-level dynamics for the Lofoten and Vesterålen archipelagos, northern Norway: Implications for prehistoric human-environment interactions"</strong></p> <p>This repository contains the following files:</p> <p>1. Netcdf and csv files for the mean (stehme_mean.nc, stehme_mean_ts.csv) and standard deviation (stehme_std.nc, stehme_std_ts.csv) of the spatiotemporal empirical hierarchical model ensemble (STEHME) produced for Creel et al. 2022. The 'ts' suffix denotes time series for each unique lat/lon site. The netcdf files contain spatial maps at 100 yr resolution.</p> <p>2. mmc1.xlsx, the HOLSEA format Norway data compilation produced for Creel et al. 2022.</p> <p>3. Netcdf and csv files for the mean (stehme_mean_230721.nc, stehme_mean_ts_230721.csv) and standard deviation (stehme_std_230721.nc, stehme_std_ts_230721.csv) of the spatiotemporal empirical hierarchical model ensemble (STEHME) produced for Balascio et al. 2023. The 'ts' suffix denotes time series for each unique lat/lon site. The netcdf files contain spatial maps at 100 yr resolution. </p>
Direct comparison of the tsunami-generated magnetic field with sea level change for the 2009 Samoa and 2010 Chile tsunamis
<p>This is the dataset for the paper of</p> <p>“<strong>Direct comparison of the tsunami-generated magnetic field with sea level change for the 2009 Samoa and 2010 Chile tsunamis</strong>”</p> <p>in Journal of Geophysical Research: Solid Earth.</p> <p> </p> <p>This dataset includes three zips:</p> <p><strong>1. Processed Observation Tsunami Data</strong></p> <p>-- In this zip, there have the observation tsunami magnetic field and sea level change data of 2009 Samoa and 2010 Chile earthquakes which extracted from the data of the TIARES experiment (Suetsugu et al., 2012).</p> <p><strong>2. Simulated Tsunami Data</strong></p> <p>-- This is the simulated tsunami sea level change and magnetic field of 2009 Samoa and 2010 Chile earthquakes. The tsunami sea level was simulated by JAGURSv5.2 (Baba et al., 2017) and the tsunami magnetic field was simulated by TMTGEMv1.1 (Minami et al., 2017).</p> <p><strong>3. Converted Tsunami Sea Level Change</strong></p> <p>-- The converted sea level changes were calculated by the 2-D analytical solution of tsunami magnetic field (Minami et al., 2021) using the filtered tsunami magnetic vertical component Bz.</p>
Framework for Assessing Changes To Sea-level (FACTS) Module Data
<p>Input module data sets from the Framework for Assessing Changes To Sea-level. These files should be installed in the modules-data/ directory. See https://github.com/radical-collaboration/facts for more information.</p>
Probabilistic projections of mean sea level change in Finland by 2100
<p><strong>Paper describing the methods used to calculate these projections: Pellikka, H., Johansson, M. M., Nordman, M., and Ruosteenoja, K.: Probabilistic projections and past trends of sea level rise in Finland, Nat. Hazards Earth Syst. Sci., <a href="https://doi.org/10.5194/nhess-2022-230">https://doi.org/10.5194/nhess-2022-230</a>, 2023.</strong></p> <p>This dataset includes probability distributions of projected mean sea level in Finland in 2030, 2040, ... 2100, as well as time series of projected mean sea level 2005-2100. Data is provided for 13 tide gauge locations and 3 emission scenarios: low (RCP2.6 / SSP1-2.6), medium (RCP4.5 / SSP2-4.5), and high (RCP8.5 / SSP5-8.5).</p> <p>There are two data packages, <em>distributions.zip</em> and <em>timeseries.zip</em>. The data files included in these packages are tab- or space-delimited text files with the file extension .dat.</p> <p>All filenames start with a three-character code xxx that determines the tide gauge (1-letter symbol) and the emission scenario (2 digits). For example:</p> <p>v26 means Vaasa, low emission scenario (RCP2.6 / SSP1-2.6)<br> e45 means Helsinki, medium emission scenario (RCP4.5 / SSP2-4.5)<br> t85 means Turku, high emission scenario (RCP8.5 / SSP5-8.5)</p> <p>The letter symbols and locations of the tide gauges are, from north to south along the coast:</p> <p>a - Kemi (65.67 N, 24.52 E)<br> o - Oulu (65.04 N, 25.42 E)<br> b - Raahe (64.67 N, 24.41 E)<br> p - Pietarsaari (63.71 N, 22.69 E)<br> v - Vaasa (63.08 N, 21.57 E)<br> s - Kaskinen (62.34 N, 21.21 E)<br> m - Mäntyluoto (61.59 N, 21.46 E)<br> r - Rauma (61.13 N, 21.44 E)<br> t - Turku (60.43 N, 22.1 E)<br> d - Degerby (60.03 N, 20.38 E)<br> h - Hanko (59.82 N, 22.98 E)<br> e - Helsinki (60.15 N, 24.96 E)<br> f - Hamina (60.56 N, 27.18 E)</p> <p>1) <em>distributions.zip > xxx_fitdistr_yyyy.dat</em><br> These files include the probability distribution (probability density function) of projected mean sea level in year yyyy (2030, 2040, ... 2100). There are two columns: sea level and probability. Sea level values are millimetres in the Finnish N2000 height system.</p> <p>2)<em> timeseries.zip > xxx_timeseries.dat</em><br> These files include the time series of projected mean sea level in 2005-2100. The files have 8 columns: year and 7 sea level values representing different percentiles of the probability distribution. The percentiles are 1%, 5%, 17%, 50% (median), 83%, 95%, 99%. Sea level values are centimetres in the Finnish N2000 height system.</p> <p><strong>Please note that all projections for years other than 2100 are indicative and based on a simple 2nd order fit made to the current rate of mean sea level change and the projected mean sea level in 2100. In other words, the projections for intermediate years are based on the 2100 projections assuming constant acceleration in mean sea level change rates.</strong></p> <p>Example figures <em>distributions.png</em> and <em>timeseries.png</em> are included to illustrate the data. The Matlab script <em>slrfinland_figures.m</em> used to produce these figures is also included.</p>
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