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41 results for “Sea state”
Data archive for the peer-reviewed journal article "Links between atmospheric aerosols and sea state in the Arctic Ocean"
<p>This dataset accompanies the peer-reviewed journal article titled "Links between atmospheric aerosols and sea state in the Arctic Ocean" which was accepted for publication in the Journal of Atmospheric Environment in September 2024, https://doi.org/10.1016/j.atmosenv.2024.120844. </p> <p>This dataset contains information on sea surface properties, meteorology, and aerosol data from measurements conducted during the Arctic Century Expedition which was carried out in August and September of 2021 in the Russian Arctic region. The dataset contains the following information:</p> <p><br>1) aerosol_size_distributions.csv: The hourly averaged time-series of aerosol size distribution measurements from an aerodynamic particle sizer. Further information for this data file is provided in Meta_data_for_aerosol_size_distributions.txt.</p> <p><br>2) aerosol_composition_and_volume.csv: Time series of mass concentrations of Na+Mg (SSA proxy) and Al+Si+Ca (dust proxy) in aerosol particles collected on filters. The time-series also contains aerosol volume concentration information for the coarse and fine aerosol categories, i.e., samples with count median diameters larger than 0.99 µm and smaller than 0.99 µm, respectively. Further information for this data file is provided in Meta_data_for_aerosol_composition_and_volume.txt. </p> <p><br>3) sea_surface_elevation_time_series.pkl: a pickle file containing the sea surface elevation time-series. The sea surface elevation data was extracted from 3D-reconstructed sea surface data. The 3D reconstruction of the sea surface was achieved by processing stereoscopic images of the sea surface using the Waves Acquisition Stereo System (WASS) software (Bergamasco et al., 2017). Further information for this data file is provided in Metadata_for_sea_surface_elevation_time_series.txt.</p> <p><br>4) aerosol_meteo_wave_merged_data.csv: This file contains the time-series of merged hourly averages of aerosol number concentrations, meteorological data, environmental data, and sea surface properties. The dataset also contains the average coordinate of the research vessel and its distance to land masses throughout the expedition. The meteorological data were measured during the expedition and the original unmerged data are available in Thurnherr et al. (2024). Other environmental data, such as sea surface temperature, are obtained from the fifth generation ECMWF reanalysis for the global climate and weather (ERA5, Hersbach et al., 2023), and sea ice concentration was obtained from AMSR-2 daily satellite measurements (Copernicus Climate Change Service (C3S), 2020). Sea surface properties are extracted from time series of sea surface elevation. Further information for this data file is provided in Metadata_for_aerosol_meteo_wave_merged_data.txt.</p>
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.
Рис. 1. Карта-схема литоральных Экспедиций Института биологии морЯ (Национального научного центра морской биологии им. А.В. Жирмунского ДВО РАН) и Дальневосточного государственого университета (Дальневосточного федерального университета) (по: Ivanova, Tsurpalo [2012], с дополнениЯми). Fig. 1. A schematic map of intertidal expeditions performed by the Institute of Marine Biology (A.V. Zhirmunsky National Scientific Centre of Marine Biology FEB RAS) and Far Eastern State University (Far Eastern Federal University) (after Ivanova, Tsurpalo [2012], with additions). in Bivalve mollusks of the intertidal zone of the Far Eastern seas of Russia
Рис. 1. Карта-схема литоральных Экспедиций Института биологии морЯ (Национального научного центра морской биологии им. А.В. Жирмунского ДВО РАН) и Дальневосточного государственого университета (Дальневосточного федерального университета) (по: Ivanova, Tsurpalo [2012], с дополнениЯми). Fig. 1. A schematic map of intertidal expeditions performed by the Institute of Marine Biology (A.V. Zhirmunsky National Scientific Centre of Marine Biology FEB RAS) and Far Eastern State University (Far Eastern Federal University) (after Ivanova, Tsurpalo [2012], with additions).
Figure 1 in Shallow-water polychaete assemblages in the northwestern Mediterranean Sea and its possible use in the evaluation of good environmental state
Figure 1. (Upper left graph) Map of the studied zone. Blue circles represent sampled stations from the Gulf of Lions and red circles from the Northern Mediterranean Spanish coast. (Lower graph) Schematic diagram showing the distribution of the four studied communities in the mesoscale studied area
Figure 1 in Ostracods in the plankton of the Sivash Bay (the Sea of Azov) during its transformation from brackish to hypersaline state
Figure 1. Bay Sivash. Occurrence of the five ostracod species (only "alive" specimens) at the sampling stations in 2004, 2014 and 2015. Red icons – Cyprideis torosa, green – Loxoconcha bulgarica, violet – Loxoconcha aestuarii, blue – Cytherois cepa, yellow – Leptocythere devexa.
Рис. 3. Platarctia ornata: 1–4 — имаго, виΑ сверху (1, 2 — самцы; 3, 4 — самки); 5–10 — гусеницы сеΑьмого возраста (5, 6 — форма с черными и рыжими воΛосками; 7, 8 — форма с рыжими воΛосками; 9, 10 — форма с черными воΛосками); 11 — кокон; 12–14 — кукоΛка; 15 — неΑавно отроΑившийся самец. 5, 7, 9, 13 — виΑ сбоку; 6, 8, 10, 14 — виΑ сверху; 12 — виΑ снизу. Δанные сбора имаго: 1 — Буреинский заповеΑник, 4 км В корΑона «Новый МеΑвежий», 1400 м наΑ ур. м., 24.06.2014; 2, 3 — Буреинский заповеΑник, корΑон «Новый МеΑвежий», ex pupa 12–13.09.2018; 4 — там же, 4.07.2018 Fig. 3. Platarctia ornata: 1–4 — imago, dorsal view (1, 2 — males; 3, 4 — females); 5–10 — seventh instar larvae (5, 6 — with black and red hairs; 7, 8 — with red hairs; 9, 10 — with black hairs only); 11 — cocoon; 12–14 — pupа; 15 — newly emerged male. 5, 7, 9, 13 — lateral view; 6, 8, 10, 14 — dorsal view; 12 — ventral view. Data labels for imago insects: 1 — Bureinsky State Nature Reserve, 4 km E Novyi Medvezhii cordon, 1400 m above sea level, 24.06.2014; 2, 3 — Bureinsky State Nature Reserve, Novyi Medvezhii cordon, ex pupa 12–13.09.2018; 4 — same location, 4.07.2018 in Moths (Lepidoptera, Macroheterocera, Excluding Geometridae And Noctuidae S.L.) Of The Bureinsky State Nature Reserve And Adjacent Territories (Khabarovsk Krai, Russia)
Рис. 3. Platarctia ornata: 1–4 — имаго, виΑ сверху (1, 2 — самцы; 3, 4 — самки); 5–10 — гусеницы сеΑьмого возраста (5, 6 — форма с черными и рыжими воΛосками; 7, 8 — форма с рыжими воΛосками; 9, 10 — форма с черными воΛосками); 11 — кокон; 12–14 — кукоΛка; 15 — неΑавно отроΑившийся самец. 5, 7, 9, 13 — виΑ сбоку; 6, 8, 10, 14 — виΑ сверху; 12 — виΑ снизу. Δанные сбора имаго: 1 — Буреинский заповеΑник, 4 км В корΑона «Новый МеΑвежий», 1400 м наΑ ур. м., 24.06.2014; 2, 3 — Буреинский заповеΑник, корΑон «Новый МеΑвежий», ex pupa 12–13.09.2018; 4 — там же, 4.07.2018 Fig. 3. Platarctia ornata: 1–4 — imago, dorsal view (1, 2 — males; 3, 4 — females); 5–10 — seventh instar larvae (5, 6 — with black and red hairs; 7, 8 — with red hairs; 9, 10 — with black hairs only); 11 — cocoon; 12–14 — pupа; 15 — newly emerged male. 5, 7, 9, 13 — lateral view; 6, 8, 10, 14 — dorsal view; 12 — ventral view. Data labels for imago insects: 1 — Bureinsky State Nature Reserve, 4 km E Novyi Medvezhii cordon, 1400 m above sea level, 24.06.2014; 2, 3 — Bureinsky State Nature Reserve, Novyi Medvezhii cordon, ex pupa 12–13.09.2018; 4 — same location, 4.07.2018
The Current State and 125 Kyr History of Permafrost in the Kara Sea Shelf: Modeling Constraints
<p>The database for modeling the evolution of permafrost in the Kara shelf for the past 125 kyr presented in the manuscript <a href="https://www.the-cryosphere-discuss.net/tc-2019-112/">https://www.the-cryosphere-discuss.net/tc-2019-112/</a> </p>
Data for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation
<h2>Overview</h2> <p>This dataset supports the draft manuscript "Simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation" which describes a way to infer the daily maps of the sea ice concentration and empirical properties of the sea ice (relating to its snow cover and its physical properties, such as air inclusions) along with the creation of a new empirical model for the sea ice surface emissivity. This is done using knowledge of the atmosphere state, skin temperature and ocean water emissivity from the European Centre for Medium-range Weather Forecasts (ECMWF) weather forecasting model and the observed radiances at microwave frequencies from the Advanced Microwave Scanning Radiometer 2 (AMSR2). The inverse modelling and state estimation is achieved by combining empirical machine learning elements in a Bayesian-inspired network along with a number of physical components. The work also introduces the idea of an "empirical state", in this case describing the aspects of the sea ice physical state which affect the observations, and which is defined by the inputs to the new empirical model component (in machine learning terms, it is defined by the latent input state of a neural network). This dataset includes the data used in training the model and inferring the sea ice parameters, as well as the outputs from that training process. The software used to perform the training is in Python and uses the Keras and Tensorflow software. See the draft manuscript for full details of this data.</p> <p>The code used in the draft manuscript is archived at <a href="https://doi.org/10.5281/zenodo.10013542">https://doi.org/10.5281/zenodo.10013542</a></p> <p>The data used in the draft manuscript is archived at <a href="https://doi.org/10.5281/zenodo.10033377">https://doi.org/10.5281/zenodo.10033377</a></p> <h2>Training data </h2> <h3>Observation space training and ancillary data</h3> <p>Training is done at the location of AMSR2 superobservations (superobs) over ocean with less than 1% land contamination and polewards of 45 degrees latitude, between 1st July 2020 and 30th June 2021. There are 64,184,021 superobs used. A superob is the average of all raw JAXA level 1B observations from one orbit falling into a grid box on an approximately constant area (reduced Gaussian) grid at approximately 40 km by 40 km resolution (noting that polar regions can thus have up to around 7 superobs per day). The superobs have been computed using the field of view central locations for each channel as derived from the JAXA level 1B data. A subset of 10 of the AMSR2 channels is used, from 10 GHz, V polarised, to 89 GHz, H polarised.</p> <p>At each superob location, the relevant fields from the ECMWF 12 hour 'background' forecast are interpolated to the observation time and location. The atmosphere is represented indirectly by the relevant radiative transfer terms from a scattering radiative transfer model. The sea ice concentration from the ECMWF OCEAN5 analysis is included as a validation reference but is not used in the training itself, except to provide a monthly mean first guess to speed up the training. Each field is provided in a separate netCDF file:</p> <ul> <li>field_v2_JULIAN_DAY.nc - superob time in days since 12 UTC on Nov 24th 4714 BC on the proleptic Gregorian calendar</li> <li>field_v2_LAT.nc - superob central latitude in degrees</li> <li>field_v2_LON.nc - superob central longitude in degrees</li> <li>field_v2_IGRID.nc - corresponding grid number on the map grid used in this work (see below)</li> <li>field_v2_OBSVALUE.nc - observed superob brightness temperature at each of 10 AMSR2 channels.</li> <li>field_v2_TSFC.nc - skin temperature computed by the ECMWF forecast model</li> <li>field_v2_WINDSPEED10M.nc - 10m wind speed computed by the ECMWF forecast model</li> <li>field_v2_EMIS_WATER.nc - Ocean water surface emissivity at 10 AMSR2 channels, simulated from the ECMWF forecast fields using the FASTEM-6 model</li> <li>field_v2_CLOUD_FRACTION.nc - Effective cloud fraction used in the atmospheric radiative transfer model at each of 10 AMSR2 channels</li> <li>field_v2_TAUSFC_CLD.nc - Surface to space transmittance in the cloudy column at each of 10 AMSR2 channels</li> <li>field_v2_TUP_CLD.nc - Upwelling brightness temperature from the atmosphere in the cloudy column at each of 10 AMSR2 channels</li> <li>field_v2_TDOWN_CLD.nc - Downwelling brightness temperature from the atmosphere in the cloudy column at each of 10 AMSR2 channels</li> <li>field_v2_TAUSFC.nc - Equivalently for the clear column</li> <li>field_v2_TUP.nc - Equivalently for the clear column</li> <li>field_v2_TDOWN.nc - Equivalently for the clear column</li> <li>field_v2_SEAICE.nc - Sea ice concentration from the ECMWF OCEAN5 analysis, for validation only (not used in training)</li> </ul> <h3>Grid space data: initial data for training; validation sea ice data</h3> <p>A number of properties are provided to the hybrid physical-empirical model that is being trained, on a special map grid defined in this project, including all 62,499 of the reduced Gaussian 40km grid points that have at least one superob at some point during the year of training data. These are:</p> <ul> <li>ifs_seaice_initials_year.nc - sea ice concentration from OCEAN5, monthly averaged on the grid, and then provided on all days of the relevant month as initial conditions (technically, first guess) for the training. This includes an additional day before the beginning of the training, used for time-lagging (see draft paper).</li> <li>ifs_tsfc_year_dailyx.nc - skin temperature from ECMWF forecast fields at observation locations, averaged onto the daily grid, to help provide constraints on the likelihood of sea ice as part of a sea ice loss function.</li> </ul> <p>For diagnostic and validation purposes, the ECMWF OCEAN5 analysis is also provided on the grid:</p> <ul> <li>ifs_seaice_year.nc - sea ice concentration from OCEAN5 at observation locations, averaged onto the daily grid</li> </ul> <p>All these fields are provided on the following dimensions:</p> <ul> <li>LON - the longitude of the grid point in degrees</li> <li>DAY - the day through the training year (0-364, 1st July 2020 to 30th June 2021) or through the training year extended forward by one day (30th June 2020) for the sea ice (0-365). In practice the days are offset by 3 hours from the UTC day to match the ECMWF data assimilation windows, which start at 21 UTC the day before.</li> </ul> <p>The latitude is also provided</p> <ul> <li>LAT - the latitude of the grid point in degrees</li> </ul> <p>Note that the observation location IGRID is on the custom grid of the ML model that is defined implicitly in these gridded files. The LON and LAT vectors in these files are the longitude and latitude points of the grid and are of 62499 in length. The IGRID number for an observation is the index into these arrays from 0-62498.</p> <h2>Outputs from training</h2> <p>The following files are the output and diagnostics from the year-long training. The python code and the draft paper are the primary documentation for these:</p> <ul> <li>models_year.nc - settings of the model are recorded here, along with the trained values of the smaller empirical components/layers within the hybrid model. For example, the layer weights of the wind speed bias correction, the observation space bias correction, and the empirical surface emissivity model are recorded here. The values of the loss function at each epoch are also recorded here.</li> <li>properties_year.nc - trained values of each of 3 empirical properties of sea ice on the map grid (3 properties by 62499 locations by 365 days from 1st July 2020)</li> <li>seaice_year.nc - inferred values of sea ice fraction on the map grid (62499 locations by 365 days from 1st July 2020, discarding the additional day at the start)</li> <li>tbsim_year.nc - simulated AMSR2 brightness temperatures from the trained network</li> <li>tbsim_initial_year.nc - simulated AMSR2 brightness temperatures using the untrained network</li> </ul> <p>The longitude and latitude of the map grid is found in any of the initial data files described in the previous section. The days are 0-364 corresponding to 1st July 2020 to 30th June 2021.</p> <h3>Sea ice surface emissivity at grid locations</h3> <p>A packaged version of the sea ice surface emissivity is provided at grid locations, alongside the surface emissivity model, the sea ice concentration and the four inputs to the model, i.e. the normalised skin temperature and the three empirical variables:</p> <ul> <li>emissivity_grid_year.nc</li> </ul> <p>Note that in the training, the surface emissivity is computed at observation locations and has not been stored due to memory limitations. For easier comparison to other datasets, the surface emissivity has been recomputed on grid locations in this package, using the year-long trained emissivity model and its trained inputs. The sea ice surface emissivity is only physically meaningful for sea ice concentrations above around 0.25. Also be aware of the "hole at the pole" which is the small region of the Arctic ocean that is sometimes not covered by an AMSR2 overpass, and which is found from 88 degrees N. On days where the hole or part of the hole exists, the sea ice emissivity on the grid is not valid at these locations. These locations can be identified by having all values of the empirical properties zero (because the empirical properties were never constrained by any observations on that day, and remain at their initial values before training).</p> <h2>Sensitivity tests</h2> <p>Extensive sensitivity tests were carried out, as described in the appendices of the draft paper and as documented in the Python code, using the month of August 2020 as an example. These required equivalent month-long training and initial data similar to those described above, but all observation space fields are contained within the same file in this case. Output files follow similar principles to those described above. The full package is provided as a tar file:</p> <ul> <li>sensitivity.tar</li> </ul> <p>This contains the training and initial files:</p> <ul> <li>amsr2_v2_202008.nc</li> <li>ifs_tsfc_dailyx_202008.nc</li> <li>ifs_seaice_202008.nc</li> </ul> <p>as well as directories containing the trained model outputs and diagnostics at each of the sensitivity tests, using the same formats as described for the yearly training, with these names:</p> <ul> <li>nprop - number of empirical properties</li> <li>epoch - number of epochs</li> <li>deep - configuration of the empirical sea ice emissivity model, including multiple layers of nonlinear dense neural network</li> <li>bseaice - background error for the sea ice physical bounds background error (loss) term</li> <li>bemis - background error for the sea ice emissivity background error (loss) term</li> <li>bbias - background error for the bias correction background error (loss) term</li> <li>batchsize - batch size used in training</li> <li>bbatchsize - extended epochs testing of batch size used in training</li> </ul> <h2>Licensing</h2> <p>This data product is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). To view a copy of this licence, visit <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p> <p>You are free to:</p> <ul> <li>Share — copy and redistribute the material in any medium or format</li> <li>Adapt — remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <p>Under the following terms:</p> <ul> <li>You must give appropriate credit (attribution) to ECMWF as outlined below, provide a link to the licence, and indicate if changes were made.</li> <li>No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the licence permits.</li> </ul> <p>The following wording shall be attached to the use of this ECMWF data product: </p> <ol> <li>Copyright statement: Copyright "© 2023 European Centre for Medium-Range Weather Forecasts (ECMWF)".</li> <li>Source <a href="http://www.ecmwf.int/">www.ecmwf.int </a>and <a href="https://doi.org/10.5281/zenodo.10009497">https://doi.org/10.5281/zenodo.10009497</a></li> <li>Licence Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></li> <li>Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.</li> <li>Where applicable, an indication if the material has been modified and an indication of previous modifications.</li> <li>DOI: 10.5281/zenodo.10009498</li> </ol> <p>Original data for this value-added product was provided by Japan Aerospace Exploitation Agency (JAXA). Specifically, this dataset builds on the Advanced Microwave Scanning Radiometer 2 (AMSR2) level 1B data available from the JAXA G-Portal, https://gportal.jaxa.jp/gpr/, which has the following attribution and licensing:</p> <ol> <li>Give credit for the original data to JAXA, i.e. "Original data for this value added data product was provided by Japan Aerospace Exploration Agency"</li> <li>DOI for original JAXA data is L1B-Brightness temperature (TB) GCOM-W/AMSR2 L1B Brightness Temperature: <a href="https://doi.org/10.57746/EO.01gs73ans548qghaknzdjyxd2h">https://doi.org/10.57746/EO.01gs73ans548qghaknzdjyxd2h</a></li> <li>Original terms of data service from JAXA, with highlighted extracts: <ul> <li><a href="https://gportal.jaxa.jp/gpr/index/eula?lang=en">https://gportal.jaxa.jp/gpr/index/eula</a> <ol> <li>The user is entitled to use G-Portal data free of charge without any restrictions (including commercial use) except for the condition about acknowledgement of data credit as stipulated in Article 7.(2). (see above)</li> <li>JAXA is collecting results (papers, theses, reports, etc.) using G-Portal data. If you have any results using G-Portal data, please mail/e-mail a copy of the result to G-Portal Support Desk (Contact Information written at the end of the Terms of Use). We appreciate your cooperation very much.</li> </ol> </li> </ul> </li> </ol>
Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines
<p><strong>Code and data for Section 2 of the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines</strong></p> <p><strong>Versions:</strong></p> <p>Version 1.1 This one:</p> <ul> <li>updated region names</li> </ul> <p>Version 1.0 <a href="https://doi.org/10.5281/zenodo.5951626">https://doi.org/10.5281/zenodo.5951626</a></p> <p>This repository contains the code and data needed to produce the trajectories, projections, and observations for the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines.</p> <p>The report can be found on <a href="https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html">https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html</a></p> <p>An interactive tool to study the observations, trajectories, and scenarios can be accessed from <a href="https://sealevel.nasa.gov/task-force-scenario-tool">https://sealevel.nasa.gov/task-force-scenario-tool</a></p> <p>Frequently-asked questions: <a href="https://sealevel.nasa.gov/faq/16/">https://sealevel.nasa.gov/faq/16/</a></p> <p><strong>Authors</strong></p> <ul> <li>William V. Sweet, NOAA National Ocean Service</li> <li>Benjamin D. Hamlington, NASA Jet Propulsion Laboratory</li> <li>Robert E. Kopp, Rutgers University</li> <li>Christopher P. Weaver, U.S. Environmental Protection Agency</li> <li>Patrick L. Barnard, U.S. Geological Survey</li> <li>Michael Craghan, U.S. Environmental Protection Agency</li> <li>Gregory Dusek, NOAA National Ocean Service</li> <li>Thomas Frederikse, NASA Jet Propulsion Laboratory</li> <li>Gregory Garner, Rutgers University</li> <li>Ayesha S. Genz, University of Hawai‘i at Mānoa, Cooperative Institute for Marine and Atmospheric Research</li> <li>John P. Krasting, NOAA Geophysical Fluid Dynamics Laboratory</li> <li>Eric Larour, NASA Jet Propulsion Laboratory</li> <li>Doug Marcy, NOAA National Ocean Service</li> <li>John J. Marra, NOAA National Centers for Environmental Information</li> <li>Jayantha Obeysekera, Florida International University</li> <li>Mark Osler, NOAA National Ocean Service</li> <li>Matthew Pendleton, Lynker</li> <li>Daniel Roman, NOAA National Ocean Service</li> <li>Lauren Schmied, FEMA Risk Management Directorate</li> <li>William C. Veatch, U.S. Army Corps of Engineers</li> <li>Kathleen D. White, U.S. Department of Defense</li> <li>Casey Zuzak, FEMA Risk Management Directorate</li> </ul> <p><strong>Contents</strong></p> <p>This data and code set contains the following directories:</p> <p><em>Results</em></p> <p>The <code>Results</code> folder contains the resulting projections, trajectories and observations from the report.</p> <ul> <li><code>TR_global_projections.nc</code>: GMSL projections, trajectory, and observations</li> <li><code>TR_regional_projections.nc</code>: Regional observations, projections and trajectories</li> <li><code>TR_local_projections.nc</code>: Local observations, projections and trajectories</li> <li><code>TR_gridded_projections.nc</code>: Gridded projections</li> </ul> <p>These files are in the NetCDF forrmat. To read the NetCDF files, many free software packages are available, including <a href="http://meteora.ucsd.edu/~pierce/ncview_home_page.html">ncview</a> and <a href="https://www.giss.nasa.gov/tools/panoply/">Panoply</a>. Free NetCDF packages are available to directly import the data into <a href="https://github.com/Alexander-Barth/NCDatasets.jl">Julia</a> and <a href="https://unidata.github.io/netcdf4-python/">Python</a> code.</p> <p><em>Code</em></p> <p>The <code>Code</code> folder contains all the computer code used to read and analyze the observations and the projections, and to generate the trajectories.</p> <p>To run this code, you need <a href="https://julialang.org/">Julia</a>. The code requires the Julia packages <code>CSV</code>, <code>Interpolations</code>, <code>JSON</code>, <code>LoopVectorization</code>, <code>MAT</code>, <code>NCDatasets</code>, <code>NetCDF</code>, <code>Plots</code>, <code>XLSX</code>, <code>LinearAlgebra</code>, and <code>Statistics</code>. They can be installed by pressing <code>]</code> at the Julia REPL and typing:</p> <pre><code>add CSV Interpolations JSON LoopVectorization MAT NCDatasets NetCDF Plots XLSX LinearAlgebra Statistics </code></pre> <p>This program also requires <a href="http://segal.ubi.pt/hector/">Hector</a>. Hector needs to be installed or compiled. In the file <code>Hector.jl</code> update the path to the Hector executable on lines 30 and 104.</p> <p>Run <code>Run_TR.jl</code> in the REPL or run <code>julia Run_TR.jl</code> from the command line to run the projections. The projections are then written to the <code>.\Data</code> directory.</p> <p>The folder contains the following files:</p> <ul> <li><code>Run_TR.jl</code>: This is the main routine that (eventually) calls all the functions to compute the projections.</li> <li><code>ConvertNCA5ToGrid.jl</code>: Converts the original NCA5 projections to a set of netCDF files that's used throughout this code</li> <li><code>ProcessObservations.jl</code>: Reads and processes the tide-gauge and altimetry observations</li> <li><code>GlobalProjections.jl</code>: Reads and processes the GMSL observations and projections, and computes the trajectory</li> <li><code>RegionalProjections.jl</code>: Reads and processes the regional projections and computes the trajectories</li> <li><code>LocalProjections.jl</code>: Reads and processes the local projections at the tide-gauge locations and computes the trajectories</li> <li><code>GriddedProjections.jl</code>: Reads the gridded NCA5 projections and add a GMSL baseline correction for the 2005 vs 2000 baseline</li> <li><code>SaveFigureData.jl</code>: Reads the results and writes text files for GMT</li> <li><code>Hector.jl</code>: Wrapper for <a href="http://segal.ubi.pt/hector/">Hector</a>, used to compute trends and uncertainties.</li> <li><code>Masks.jl</code>: Defines the region masks for each region.</li> </ul> <p><em>Data</em></p> <p>The <code>Data</code> directory contains the input data sets used during the computations. Please appropriately cite the input data if you use it. It contains the following:</p> <p>Directories:</p> <ul> <li><code>ClimIdx</code>: Map with climate indices (NAO, PDO, MEI) used to remove internal variability. All the indices come from NOAA <a href="https://psl.noaa.gov/data/climateindices/">Physical Sciences Laboratory (PSL)</a> and <a href="https://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml">NOAA Climate Prediction Centre (CPC)</a></li> <li><code>NCA5_projections</code> Contains the NCA5 projections for each scenario (Low, IntLow, Int, IntHigh, and High). For each scenario, the GMSL projections, projections at tide-gauge locations and on a 1-degree grid are provided.</li> </ul> <p>Files:</p> <ul> <li><code>basin_codes.nc</code>: Map with basin codes. from Eric Leuliette/NOAA. Data provided by the NOAA Laboratory for Satellite Altimetry.</li> <li><code>CDS_monthly_1993_2020.nc</code>: Monthly-mean sea level (1993-2020) from gridded altimetry. Obtained from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global">Copernicus Climate Data Store</a>. This dataset contains modified Copernicus Climate Change Service information [2020]</li> <li><code>enso_correction.mat</code>: GMSL correction for ENSO/PDO from Hamlington, B. D., Frederikse, T., Nerem, R. S., Fasullo, J. T., & Adhikari, S. (2020). Investigating the Acceleration of Regional Sea‐level Rise During the Satellite Altimeter Era. Geophysical Research Letters. <a href="https://doi.org/10.1029/2019GL086528">https://doi.org/10.1029/2019GL086528</a></li> <li><code>filelist_psmsl.txt</code>: List with PSMSL file names and PSMSL IDs. Obtained from the Permanent Service for Mean Sea Level (<a href="http://www.psmsl.org/">PSMSL</a>), 2021, Retrieved 29 Nov 2021. Simon J. Holgate, Andrew Matthews, Philip L. Woodworth, Lesley J. Rickards, Mark E. Tamisiea, Elizabeth Bradshaw, Peter R. Foden, Kathleen M. Gordon, Svetlana Jevrejeva, and Jeff Pugh (2013) New Data Systems and Products at the Permanent Service for Mean Sea Level. Journal of Coastal Research: Volume 29, Issue 3: pp. 493 – 504. <a href="https://doi.org/:10.2112/JCOASTRES-D-12-00175.1">https://doi.org/:10.2112/JCOASTRES-D-12-00175.1</a>.</li> <li><code>GEBCO_bathymetry_05.nc</code>: Bathymetry map of the global oceans from the General Bathymetric Chart of the Oceans (<a href="https://www.gebco.net/">GEBCO</a>). Source: GEBCO Compilation Group (2021) GEBCO 2021 Grid (<code>doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f</code>) The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>GIA_Caron_stats_05.nc</code>: Glacial Isostatic Adjustment estimates from Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., & Blewitt, G. (2018). GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science. Geophysical Research Letters, 45(5), 2203–2212. <a href="https://doi.org/10.1002/2017GL076644">https://doi.org/10.1002/2017GL076644</a>. The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>global_timeseries_measures.nc</code>: Time series of estimated 20th-century GMSL and its components, based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_ensembles.nc</code>: Ensemble GMSL reconstruction from tide-gauges based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_TPJAOS_5.0_199209_202106.txt</code>: Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1 [Data set]. NASA Physical Oceanography DAAC. <a href="https://doi.org/10.5067/GMSLM-TJ151">https://doi.org/10.5067/GMSLM-TJ151</a>. This altimetry dataset uses the methods as described in Beckley, B. D., Callahan, P. S., Hancock, D. W., Mitchum, G. T., & Ray, R. D. (2017). On the “Cal-Mode” Correction to TOPEX Satellite Altimetry and Its Effect on the Global Mean Sea Level Time Series. Journal of Geophysical Research: Oceans, 122(11), 8371–8384. <a href="https://doi.org/10.1002/2017JC013090">https://doi.org/10.1002/2017JC013090</a></li> <li><code>grd_1992_2020.nc</code>: Seafloor deformation due to contemporary GRD effects based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>region_mask.nc</code>: Mask with the definition of all regions.</li> <li><code>US_tg_monthly.xlsx</code>: Tide gauge observations from the NOAA tide gauge network</li> </ul> <p><em>GMT</em></p> <p>This directory contains the <a href="https://www.generic-mapping-tools.org/">GMT</a> scripts to make Figures 1.2, 2.1, 2.2, 2.6, and A.1.2 from the report. To generate the figures, make sure GMT is installed and run the Shell script in each directory.</p>
Scoping survey on the state of play of Open Science at SEA-EU universities
<p>Survey data that sought to collect information about the state of play on Open Research Data Management practices amongst the SEA-EU Alliance.</p>
Sea turtle relative abundance in nearshore waters adjacent to the Mississippi River delta, Gulf of Mexico, United States
<p><span>We measured the relative abundance of sea turtles using standardized transect surveys conducted during the summer and fall of 2013 in neritic waters surrounding the Mississippi River delta in Louisiana, USA. Data comprise sea turtle locations, observation circumstances, and environmental covariates recorded at the beginning of each transect and at the time of each turtle observation. Turtles were recorded by species and size class, as well as location in the water column and the distance the turtle was from the transect line. Transects were performed on an 8.2-meter vessel with two observers atop a 4.5-meter elevated platform, with vessel speed standardized at ~15 km/hr. These data are the first to describe relative abundance of sea turtles observed from small vessels in this region. Detection of turtles <45 cm SSCL and data detail are greater than aerial surveys. The data serve to inform resource managers and researchers regarding these protected marine species. </span></p>
Sea turtle relative abundance in nearshore waters adjacent to the Mississippi River delta, Gulf of Mexico, United States
Open the record for dataset details and reuse information.
Strandings of marine mammals, sea turtles and seabirds along the South Santa Catarina state coastline, from 2015 to 2018.
Open the record for dataset details and reuse information.
Sea level rise, groundwater rise, and contaminated sites in the San Francisco Bay Area, and Superfund Sites in the contiguous United States
Open the record for dataset details and reuse information.
Behavioral responses across a mosaic of ecosystem states restructure a sea otter-urchin trophic cascade
Open the record for dataset details and reuse information.
FIGURE 2 in Kudoa ajurutellus n. sp. (Multivalvulida: Kudoidae), a parasite of the skeletal musculature of the Bressou sea catfish, Aspistor quadriscutis, in northeastern of the State of Pará
FIGURE 2. Light photomicrography: Fresh pseudocysts and spores of Kudoa ajurutellus n. sp. from the fiber muscular skeletal of A. quadriscutis. A. Fresh pseudocysts of Kudoa ajurutellus n. sp. (P) from the musculares skeletal. Scale bar: 10 µm. B. Spores (S) in muscle fiber (F). In detail: Spores of Kudoa ajurutellus n. sp., showing the four polar capsules (PC) under DIC. Scale bar: 20 µm
FIGURE 4 in Kudoa ajurutellus n. sp. (Multivalvulida: Kudoidae), a parasite of the skeletal musculature of the Bressou sea catfish, Aspistor quadriscutis, in northeastern of the State of Pará
FIGURE 4. Phylogenetic tree generated by the Bayesian Inference (BI) analysis of the aligned partial 18S rDNA gene sequences of Kudoa ajurutellus n. sp. and related Kudoa spp.. The GenBank accession numbers are shown adjacent to the species names. The numbers at the nodes are posterior probability values calculated for BI. The new species is shown in bold type.
Distribution. SE Colombia and NW Brazil (Amazonas State), N of the Rio Solimoes between the rios Japura-Caqueta and Ica-Putumayo, W of the Rio Yari to the N of the Rio Caquetd, through the basin of the Rio Caguan, and lower parts of the Rio Orteguaza, W to the Andean foothills to 500 m above sea level. in Callitrichiade
Distribution. SE Colombia and NW Brazil (Amazonas State), N of the Rio Solimoes between the rios Japura-Caqueta and Ica-Putumayo, W of the Rio Yari to the N of the Rio Caquetd, through the basin of the Rio Caguan, and lower parts of the Rio Orteguaza, W to the Andean foothills to 500 m above sea level.
Subspecies and Distribution. L.e.europaeusPallas,1778—WesternEurope. L. e. caspicus Hemprich & Ehrenberg, 1832 — Lower Volga, Kalmykia (Russia) and W Kazakhstan. JR e. connor Robinson, 1918 — NW Iran. e. creticus Barrett-Hamilton, 1903 — Crete (Greece). a e. cyprius Barrett-Hamilton, 1903 — Cyprus. e. cyrensis Satunin, 1905 — Azerbaijan, Transcaucasia. a e. hybridus Desmarest, 1822 — Baltic States, Belarus, Ukraine, Finland, W & C Russia. Sl e. judeae Gray, 1867 — Palestine. aE e. karpathorum Hilzheimer, 1906 — Carpathian Mts. all e. medius Nilsson, 1820 — Denmark. al e. occidentalis de Winton, 1898 — Great Britain. ul e. parnassius Miller, 1903 — C Greece. el. e. ponticus Ognev, 1929 — Black Sea coast (Russia). ul. e. rhodius Festa, 1914 — Rhodes (Greece). Bl e. syriacus Hemprich & Ehrenberg, 1832 — Syria. ab. e. transsylvanicus Matschie, 1901 — E & SE Europe. in Leporidae
Subspecies and Distribution. L.e.europaeusPallas,1778—WesternEurope. L. e. caspicus Hemprich & Ehrenberg, 1832 — Lower Volga, Kalmykia (Russia) and W Kazakhstan. JR e. connor Robinson, 1918 — NW Iran. e. creticus Barrett-Hamilton, 1903 — Crete (Greece). a e. cyprius Barrett-Hamilton, 1903 — Cyprus. e. cyrensis Satunin, 1905 — Azerbaijan, Transcaucasia. a e. hybridus Desmarest, 1822 — Baltic States, Belarus, Ukraine, Finland, W & C Russia. Sl e. judeae Gray, 1867 — Palestine. aE e. karpathorum Hilzheimer, 1906 — Carpathian Mts. all e. medius Nilsson, 1820 — Denmark. al e. occidentalis de Winton, 1898 — Great Britain. ul e. parnassius Miller, 1903 — C Greece. el. e. ponticus Ognev, 1929 — Black Sea coast (Russia). ul. e. rhodius Festa, 1914 — Rhodes (Greece). Bl e. syriacus Hemprich & Ehrenberg, 1832 — Syria. ab. e. transsylvanicus Matschie, 1901 — E & SE Europe.
Subspecies and Distribution. 1. p. pileatus Blyth, 1843 — NE India highlands S and E of the Brahmaputra River, in the states of Arunachal Pradesh, Assam, Meghalaya, and Nagaland (Karbi Anglong Plateau, Barail Range, and Khasi, Garo, Naga, and Jaintia Hills), and in NW Myanmar (W of the Chindwin River, S to Chin Hills Mts and Mt Victoria); the elevational range is 600-3000 m. 1. p. brahma Wroughton, 1916 — NE India, known only from the Dafla Hills, N of the Brahmaputra River, in Arunachal Pradesh State. 1: p. durga Wroughton, 1916 — E Bangladesh and NE India in the states of Assam, Mizoram, and Tripura (Naga Hills, Lakhimpur, Golaghat, Cachar Hills, Samaguting, and Sibsagar), adjoining the distribution of 7. p. pileatus to the N, but at lower elevations (i.e. from nearly sea level up to 600 m). 1. p. tenebricus Hinton, 1923 — NE India (Assam State) and Bhutan, in the Manas region N of the Brahmaputra River, with an elevational range of 100-2000 m. in Cercopithecidae
Subspecies and Distribution. 1. p. pileatus Blyth, 1843 — NE India highlands S and E of the Brahmaputra River, in the states of Arunachal Pradesh, Assam, Meghalaya, and Nagaland (Karbi Anglong Plateau, Barail Range, and Khasi, Garo, Naga, and Jaintia Hills), and in NW Myanmar (W of the Chindwin River, S to Chin Hills Mts and Mt Victoria); the elevational range is 600-3000 m. 1. p. brahma Wroughton, 1916 — NE India, known only from the Dafla Hills, N of the Brahmaputra River, in Arunachal Pradesh State. 1: p. durga Wroughton, 1916 — E Bangladesh and NE India in the states of Assam, Mizoram, and Tripura (Naga Hills, Lakhimpur, Golaghat, Cachar Hills, Samaguting, and Sibsagar), adjoining the distribution of 7. p. pileatus to the N, but at lower elevations (i.e. from nearly sea level up to 600 m). 1. p. tenebricus Hinton, 1923 — NE India (Assam State) and Bhutan, in the Manas region N of the Brahmaputra River, with an elevational range of 100-2000 m.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.