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735 results for “Sea level”
HOLSEA-NL: Holocene water level and sea-level indicator dataset for the Netherlands
<p>This dataset contains an assembly of geological water-level indicators, relevant for studying relative sea level rise (RSLR), regional subsidence quantification and causal breakdown, coastal prism accommodation and Holocene aggradation chronology of the Holocene Netherlands. It gives a sources-referenced, uniform overview of 658 basal geological water-level indicators collected from original research of various type and application (140 primary references). From the indicators, 59% was collected in 1950-2000, mainly in academic studies and survey mapping campaigns; 37% was collected in 2000-2020 in academic studies and archaeological surveying projects, 4% was newly collected (this study), the latter mainly in previously under sampled central and northern Netherlands regions. 117 are true sea-level indicators (so-called SLIPs), the majority of datapoints (536) are inland water level indicators that are upper limiting to sea-level. The total number of entries is 712, because we included some literature mentioned rejected samples and deep positioned intercalated water level indicators.</p> <p>The dataset is compiled in the so-called HOLSEA workbook format. It covers measured, calculated and classification fields defining the geological observational data and its uncertainties, allowing to document and assess indicative meaning adapted to specific use variants. Hereto, the workbook contains expansions to the original format. See Related Works (ESSD paper: De Wit et al. 2024).</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>
Regional Sea-level Budget from 1993-2016
<p>This repository contains supporting data for Camargo et al.: 'Regionalizing Sea-level Budget with Machine Learning Techniques', Ocean Sciences (2022), https://egusphere.copernicus.org/preprints/2022/egusphere-2022-876/.</p> <p>**<em><strong>Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. In order to avoid creating a new DOI for this dataset, we have added just a warning, instead of updating the file. </strong></em>** This has no impact on the results of the manuscript, as the 'axis error' occurred only when organising the files to be published. </p> <p>** Please cite the appropriate papers when using this data **<br> Please cite 'Regionalizing Sea-level Budget with Machine Learning Techniques' when using this data set. However, <strong>most of the data heavily relies on previous work and data sets by many authors,</strong> so please acknowledge that work by citing the original sources of the data (which can be found in the main text of 'Regionalizing Sea-level Budget with Machine Learning Techniques').<br> ** please check this carefully!**</p> <p>This repository contains the following files:</p> <p><strong>budget_components_ENS.nc</strong><br> Regional (1x1 degree) trend, uncertainty and time series of the ensemble mean of each of the budget components: total sea-level change (from altimetry) and the drivers (steric, GRD and dynamic). <em><strong>Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. In order to avoid creating a new DOI for this dataset, we have added just a warning, instead of updating the file. </strong></em>If required the individual data sets used for the ensemble, please contact the author. </p> <p><strong>masks.nc</strong><br> netcdf containing land-ocean mask, as well as the domains maps (SOM and delta-MAPS). We refer to the manuscript for more information of how the regional domains were acquired.</p> <p><strong>dmaps_trend.pkl (and .xlsx)</strong><br> Trend and uncertainties of each of the budget components for each delta-MAPS domains. Available as an excel table (.xlsx) and as pickle file (.pkl)</p> <p><strong>som_trend.pkl (and .xlsx)</strong><br> Trend and uncertainties of each of the budget components for each SOM domains. Available as an excel table (.xlsx) and as pickle file (.pkl)</p> <p>The code to generate this data and the manuscript figures can be found at https://github.com/carocamargo/SLB</p> <p><br> Corresponding author: carolina.camargo@nioz.nl</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>
Supplementary data for: "The influence of reef isostasy, dynamic topography, and glacial isostatic adjustment on the Last Interglacial sea-level record of Northeastern Australia"
<p>This repository contains supplementary information and data for the paper: ""The influence of reef isostasy, dynamic topography, and glacial isostatic adjustment on the Last Interglacial sea-level record of Northeastern Australia", submitted to Communications Earth & Environment.</p> <p>This version (1.1) was produced to answer comments from reviewers.</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>
Mean sea level fields used within the GTSMip simulations
<ul> <li><em>TotalSeaLevel_MapsSROCC_rcp85_Perc50_zero1986to2005dflow_extrap.nc </em>provides mean sea level fields using as reference period. Sea level fields are computed from the sum of different contributors, including dynamic changes, thermal expansion, changes in gravitational fields, and contribution from glaciers and ice sheets. The different contributions are computed and combined using the probabilistic model described in Le Bars (2018). For the period 1950-2016, we use products based on observations for the Antarctic and Greenland ice sheets (Mouginot et al., 2019; Rignot et al., 2019), the glaciers (Marzeion et al., 2015), thermal expansion between 0 and 2000 m depth (Levitus et al., 2012), and climate-driven water storage (Humphrey & Gudmundsson, 2019). The ice sheets are assumed to be in equilibrium before 1979 for Antarctica and 1972 for Greenland because no data are available before these dates. For the period 2016-2050 we use sea-level rise projections based on the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) for the RCP8.5 scenario (Church et al., 2013), very similar to the SSP585 scenario used by the models as above. The redistribution of water in the ocean due to wind changes and local steric effects is taken from the CMIP5 models (i.e. ‘zos’ field for the entire period). The fingerprints for the ice sheets, glaciers and land water storage are from the AR5 assessment, and include the gravitational, rotational and Earth elastic response. For the dynamics of the Antarctic contribution we use the re-evaluation presented in the IPCC’s Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) (Oppenheimer et al., 2019). Additionally, we add the glacial isostatic adjustment from the ICE-6G model (Peltier et al., 2015) but do not consider other processes of vertical land motion, such as subsidence or tectonics. The uncertainty in mean sea level is removed by selecting the median of the sea level observations and projections distributions. Note that at the time the GTSM simulation were carried out the SLR projections based on CMIP6 were not yet available. To serve as input to GTSM, the files are converted from a water level to a pressure.</li> <li><em>ERAInterim_average_msl_neg_19491215_19510101.nc </em>provides a vertical reference based on the mean sea-level pressure field (MSLP) over 1986–2005 as calculated with GTSMv3.0 forced by ERA-Interim. This corrections is used to make the definition of MSL in GTSM more consistent with the vertical reference used in the SLR field .</li> </ul>
Updated gridded reconstruction of sea level pressure, temperature, and precipitation during winter in the North Atlantic region covering 1241-1970 CE
<ul> <li>This dataset is an updated version of the gridded climate reconstruction by Sjolte et al. 2018 (SEA18): Solar and volcanic forcing of North Atlantic climate inferred from a process-based reconstruction, <em>Climate of the Past,</em> 14, 1179–1194, https://doi.org/10.5194/cp-14-1179-2018. </li> </ul> <p> </p> <ul> <li>Relevant results of this new version (SEA18v2) are available in our recent paper: Tao, Q. , Sjolte, J. , & Muscheler, R. (2023). Persistent model biases in the spatial variability of winter North Atlantic atmospheric circulation. Geophysical Research Letters, 50, e2023GL105231. https://doi.org/10.1029/2023GL105231</li> </ul> <p> </p> <ul> <li>This dataset contains the gridded reconstruction of winter sea level pressure (slp), 2m temperature (t2m) and precipitation (precip) for the North Atlantic region over 1241-1970.</li> </ul> <p> </p> <ul> <li><strong>Methodology:</strong> The new reconstruction (SEA18v2), has been optimized for a better representation of the variability of the main modes of sea level pressure. The original reconstruction, SEA18, was an ensemble of 39 model analogues for each year and the reconstruction comprised of the mean of the analogues. For the new version, SEA18v2, a different approach to calculating the ensemble mean of the analogues has been applied. While the overall evaluation and ranking of model analogues are the same as for SEA18, we now apply a weighting function so that poor-fitting model analogues receive less weight and good-fitting analogues receive more weight. Furthermore, we evaluate the main modes of the reconstructed SLP and test the minimum number of ensemble members that can be used and still retain skill for the temporal and spatial variability of the first three modes. Retaining 16 ensemble members gives better performance for the spatial patterns for the first three EOFs of SLP compared to SEA18 and good skill for the temporal variability of the NAO.</li> </ul>
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>
Data supporting manuscript "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble"
<p>Data supporting the results presented in the article Milovac et al: "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble".</p> <p>1. data_raw.tar contains annual and seasonal, global and regional (i.e. over ocean IPCC regions and ocean biomes), mean sea surface and near surface temperatures, calculated for the selected 26 CMIP6 global climate models (GCMs) at low resolution (listed in the file models_low_res.txt) and 1 GCM at high resolution (listed in the file models_high_res.txt). The original files, downloaded from one of the ESGF data centers, were all interpolated onto a common grid with the 1-degree resolution for low-resolution output and the 0.25-degree resolution for high-resolution output. The output was generated using the cdo tool (<a href="https://zenodo.org/record/7112925">https://zenodo.org/record/7112925</a>).</p> <p>2. data_txt.tar contains the results used to obtain all the figures given in the article.</p>
Monthly average of sea level measurements from San Diego Harbor, the sea level average seasonal cycle, and the long term trend are presented, 1906 - 2024
Monthly average sea level measurements, and derived sea-level anomolies from San Diego Harbor's station (32°42.80N, 117°10.40W) is obtained from the University of Hawai’i Sea Level Center. The sea level average seasonal cycle and the long term trend are calculated (mm) from the monthly averages obtained from the GLOSS/CLIVAR (formerly known as the WOCE) "fast" sea level database - University of Hawaii Sea Level Center. The resulting anomalies of de-trended sea level are used as a mid-latitude index of El Niño.
Monthly sea-level summary data for the Fort Pulaski, Georgia, water level station (NOAA/NOS CO-OPS ID 8670870) from 01-Jul-1935 to 30-Jun-2006
Monthly mean water levels based on MLLW (mean lower low water) datum in meters were acquired from the NOAA/NOS Center for Operational Oceanographic Products and Services web site (http://tidesandcurrents.noaa.gov/) for station ID 8670870 (Fort Pulaski, Georgia). Selected date/time and data columns were extracted from the CO-OPS web pages, standardized and documented using GCE-LTER metadata templates. This data set covers the period from 01-Jul-1935 to 30-Jun-2006
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.
PIE LTER quadrat percent cover associated with marsh sites used in space for time sea level rise study, Rowley, MA.
To assess community structure, first each 1 square meter is first examined by moving grass around to scan the substrate for mussels, crab burrows, amphipod burrows, and Littorina littorea snails. Researchers then estimates the percent live cover of a variety of plant species, checking against a standard list of species. Species not on the list are also recorded, and, if unknown, for identification in the lab after sampling. Each species has its cover recorded individually, which could lead to greater than 100% cover. Percent cover of bare space, detritus, and wrack are also recorded. Last, Melampus bidentata snails are recorded in 10 cm x 10 cm at each corner of the quadrat.
Coastal SEES Collaborative Research: Coastal Sustainability: A cross-site comparison of salt marsh persistence in response to sea-level rise and feedbacks from social adaptations
Coastal ecosystems are often valued for decision-making purposes based on monetized market and non-market values of goods and services, and associated economic impacts. Examples include values of fishery landings, price changes for waterfront homes, and tourism revenues. Monetized quantities such as these do not provide a comprehensive characterization of the values provided by these ecosystems. Human reliance on the goods and services provided by ecosystems and the global decline in the health of many of these ecosystems suggests the need for ecosystem valuation to help inform decision-making and conservation policy. However, traditionally employed economic valuation methods are rarely able to capture the full scope of the benefits ecosystems provide, including benefits provided by "cultural" ecosystem services. Qualitative methods such as focus groups can provide insight on these values not available through quantitative methods alone. This research explores public perceptions of salt marsh value through the use of semi-structured focus groups in marsh-adjacent communities in Massachusetts, Virginia, and Georgia. The data include de-identified focus group transcripts from three 90-minute focus groups held in each state. Initial questions were drawn from the same semi-structured question list in each focus group, with exploratory follow-up questions based on participant responses. Results of text analysis suggest that in case study communities, outdoor experiences in salt marshes inspire serenity in Massachusetts, influence shore identities in Virginia, and promote stewardship cultivation in Georgia. Perceived threats to these benefits, such as the threat of residential development, industrial pollution, and increasing flood risk, together constitute the context for various community responses related to marsh protection. Results supplement information from extant economic valuations and show the importance of utilizing diverse methods to elicit information on soci
Synthesis of Sea level rise and carbon accumulation rates in United States tidal wetlands
Coastal wetlands accumulate soil carbon more efficiently than terrestrial systems, but sea level rise potentially threatens the persistence of this prominent carbon sink. Here, we combine a published dataset of 372 soil carbon accumulation rates from across the United States with new analysis of 131 sites in coastal Louisiana. The combined database featured 503 measurements of carbon accumulation, spanning broad gradients in mean annual temperature, tide range, and dominant vegetation.
Output data for manuscript "Tidal analysis of GNSS reflectometry applied for coastal sea level sensing in Antarctica and Greenland"
<p>We retrieve sea levels in polar regions via GNSS reflectometry (GNSS-R), using signal-to-noise ratio (SNR) observations from eight POLENET GNSS stations. Although geodetic-quality antennas are designed to boost the direct reception from GNSS satellites and to suppress indirect reflections from natural surfaces, the latter can still be used to estimate the sea level in a stable terrestrial reference frame. Here, typical GNSS-R retrieval methodology is improved in two ways, 1) constraining phase-shifts to yield more precise reflector heights and 2) employing an extended dynamic filter to account for the second-order height rate of change (vertical acceleration). We validate retrievals over a 4-year period at Palmer Station (Antarctica), where there is a co-located tide gauge (TG). Because ice contaminates the long-period tidal constituents, we focus on the main tidal species (daily and subdaily), by employing a deseasonalization filter. The difference between sub-hourly GNSS-R retrievals of the ocean surface and TG records has a root-mean-square error (RMSE) of 15.4 cm and a correlation of 0.903, while the tidal prediction has a RMSE of 1.9 cm and a correlation of 0.998. There is excellent millimetric agreement between the two sensors for most eight major tidal constituents, with the exception of luni-solar diurnal (<em>K<sub>1</sub></em>), principal solar (<em>S<sub>2</sub></em>), and luni-solar semidiurnal (<em>K</em><sub>2</sub>) components, which are biased in GNSS-R due to the leakage of the GPS orbital period. We also compare the GNSS-R tidal constituents from seven additional POLENET sites, without co-located TG, to global and local ocean tide models. We find that the root-sum-square-error (RSSE) of eight major constituents varies between 26.0 cm and 56.9 cm for different models. Given that the agreement in tidal constituents between the TG and GNSS-R was better at Palmer Station, we conclude that assimilating the GNSS-R retrievals into tidal models would improve their accuracy in Antarctica and Greenland, provided that care is exercised to avoid the orbital period overtones and also sea ice.</p>
Dataset for "Brief communication: On calculating the sea-level contribution in marine ice-sheet models"
<p>This archive provides the data in Figures 3 and S1 of the following publication:</p> <p>Goelzer, H., Coulon, V., Pattyn, F., de Boer, B., and van de Wal, R.: Brief communication: On calculating the sea-level contribution in marine ice-sheet models , The Cryosphere, 14, 833–840, https://doi.org/10.5194/tc-14-833-2020, 2020.</p>
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OpenNeuro
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