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111 results for “ECCO”
ECCO Iter22 Global Ocean State Estimate - 1 January 2004 to 30 April 2005
<p>Time series of global ocean temperature, salinity, and sound speed derived from the “Estimating the Circulation and Climate of the Ocean" (ECCO) program Iter22 state estimates. The sound speed fields were computed for simulation of acoustic propagation over basin scales or longer in a realistic oceanic environment. These estimates were computed in 2010 by the JPL-MIT-SIO ECCO program. <br>Original link: http://ecco2.jpl.nasa.gov/data9/cube/iter22/lat_lon/quart_80S_80N/THETA/ , now defunct.</p> <p>The solution is mesoscale permitting. The solution was obtained on a cube sphere grid between 80S and 80N with 18-km horizontal grid spacing and 50 vertical levels (Menemenlis et al., 2005, NASA supercomputer improves prospects for ocean <br>climate research, Eos Trans., AGU 86, 89, 95–96.). State estimates were averaged over a 3-day interval. File 003 is averaged over 2004/1/1 -- 2004/1/3. Three-day-mean temperature and salinity profiles from the iter22 solution were provided on 1/4 degree <br>grid for the period 1 January 2004 to 30 April 2005. There are 162 snapshots at 3-day intervals. </p> <p>Depths were decimated to the standard 33 depths of the World Ocean Atlas to 5500 m. YearDay 1 is 1 January 1992. The number of the filename indicates the yearday in 2004. In situ temperature was computed from model potential temperature. Sound speed was computed using the Del Grosso sound speed equation. Original model profiles descended only to the model sea floor. Temperature, salinity and sound speed were filled in on a uniform grid using nearest neighbor to 5500 m depth. Values on a regular grid make life easier. Product documented in Dushaw and Menemenlis, 2014, Antipodal acoustic thermometry: 1960, 2004, Deep Sea Research Part I: Oceanographic Research Papers, 86, 1–20, https://doi.org/10.1016/j.dsr.824 2013.12.008.</p> <p>Each snapshot is stored as a netcdf 4 file. Latitude, Longitude, Depth, and YearDay variables given separately in sspgrid.nc . <br>N.B.: Values in the files are stored as 32-bit or 16-bit integers to save disk space:</p> <p>Sound Speed: saved as "round( (c-1000)*1000 )", so to get actual c: c=1000. + double(c)/1000. <br>Sound speed is stored to 3 decimal places as a 32-bit integer.</p> <p>Temperature: saved as "round( (T-10)*1000 )", so to get actual T: T=10. + double(T)/1000. <br>Temperature is stored to 3 decimal places as a 16-bit integer. Note that abyssal temperature can sometimes be negative.</p> <p>Salinity: saved as "round( (S-10)*1000 )", so to get actual S: S=10. + double(S)/1000. <br>Salinity is stored to 3 decimal places as a 16-bit integer.</p> <p>Data directory also has two matlab routines: get_section.m and dist.m. get_section.m shows how to load the files, compute the physical variable from the stored value, and compute a section of ssp, T, or S. dist.m is a utility for computing geodesics; it relies on R. Pawlowitz's m_map package which can be downloaded freely from his University of Vancouver web page.</p> <p>$ md5sum *tgz <br>53ca3621f422b89c599c92fbab71d2fc S_ecco_iter22.tgz (2.99 GB)<br>a9e9109b6dae7355bf2fa0926d436d9a ssp_ecco_iter22.tgz (6.09 GB)<br>0cb8d1c2cdee4c7377353dff722ece34 T_ecco_iter22.tgz (4.33 GB)</p>
Martini Ecco (e1049)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Martini Ecco<br><u>musiXplora-ID</u>: e1049<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/e1049">https://musixplora.de/mxp/e1049</a><br><u>Gender</u>: m<br><u>First Mentioned</u>: 1608<br><u>Sectors</u>: Instrumentenbau<br><u>Professions (Musical)</u>: Lautenmacher<br><u>Other Places of Activity</u>: Rom<br><br><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Kirsch 2023</td><td>The Long Lives of Old Lutes. The Cultural and Material History of the Veneration of Old Musical Instruments. Dissertation der Universität Leipzig 2022. . Erschienen als Enhanced Publication. LM-enc Band 2. Schriftenreihe der Forschungsstelle DIGITAL ORGANOLOGY am Musikinstrumentenmuseum der Universität Leipzig. herausgegeben von Josef Focht</td><td><a href="https://musixplora.de/mxp/5034090">5034090</a></td></tr></tbody></table><br><u>Ereignisse:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Hersteller</td><td></td><td>Herstellung</td><td><a href="https://musixplora.de/mxp/6017203">6017203</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Exploring the Relationship Between Upper Ocean States and the Falling Ice Radiative Effects using ECCO Product and Global Climate Models
<p><strong><span>Sensitivity test using CESM1-CAM5 following CMIP5 protocool from 1980-2005</span></strong></p> <p><strong><span>NOS: no falling ice radiative effects (FIREs), four data sets</span></strong></p> <p><strong><span>SON: with FIREs, for data sets</span></strong></p> <p><strong><span> Xsize = 362 Ysize = 182 Zsize = 18</span></strong></p> <p><strong><span>Format: netcdf</span></strong></p> <p><strong><span>Upper 200 meter ocean variables</span></strong></p> <p><strong><span>Annual mean (ANN)</span></strong></p> <p><strong><span>CESM2-var-NOS (or SON)-ANN.nc, var = (UO, VO, WO, TO) = (zonal velocity, meridional velocity, ascending velocity, potential temperature) : (cm/s, cm/s, cm/s, K)</span></strong></p>
AI4ER GTC: ECCO Streamfunction, MOC Strength, and Surface Variables
<p><strong>Overview</strong></p> <p>This repository contains all <a href="https://ecco-group.org/" target="_blank" rel="noopener">NASA ECCO</a> data used for our AI4ER Guided Team Challenge project. Data was downloaded from <a href="https://podaac.jpl.nasa.gov/" target="_blank" rel="noopener">PO.DAAC</a>. Download and preprocessing scripts can be found in the corresponding GitHub repository (see the <a href="https://github.com/ai4er-cdt/OTP/tree/main" target="_blank" rel="noopener">repository</a> or the <a href="../doi/10.5281/zenodo.10817670" target="_blank" rel="noopener">archived version</a>).</p> <p>Use and redistribution of this data is in line with <a href="https://www.earthdata.nasa.gov/engage/open-data-services-and-software/data-and-information-policy" target="_blank" rel="noopener">NASA EarthData's Data and Information Policy</a> and <a href="https://podaac.jpl.nasa.gov/CitingPODAAC" target="_blank" rel="noopener">NASA PO.DAAC's Data Use and Citation Policy</a>.</p> <p><strong>Data Structure</strong></p> <p>Please see the included <code>README.md</code> file for full details on the dataset.</p> <p><strong>Authors</strong></p> <ul> <li><a href="mailto:el590@cam.ac.uk">Emilio Luz-Ricca</a></li> <li><a href="mailto:sm2783@cam.ac.uk">Sharan Maiya</a></li> <li><a href="mailto:av656@cam.ac.uk">Aline Van Driessche</a></li> <li><a href="mailto:ngb34@cam.ac.uk">Nina Baranduin</a></li> <li><a href="mailto:tc656@cam.ac.uk">Thomas Cowperthwaite</a></li> </ul>
ECCO v4 standard analysis sample (v4r3)
<p>Generated from the ECCO output (version 4, release 3) available to download from <a href="https://ecco-group.org/products.htm">ecco-group.org</a> using a Julia workflow available from <a href="https://github.com/gaelforget/OceanStateEstimation.jl">OceanStateEstimation.jl</a> (see <a href="https://gaelforget.github.io/OceanStateEstimation.jl/dev/">the docs</a>).</p>
ECCO v4 standard analysis sample
<p>Generated from the ECCO output (version 4, release 2) available to download from <a href="https://ecco-group.org/products.htm">ecco-group.org</a> using a Julia workflow available from <a href="https://github.com/gaelforget/OceanStateEstimation.jl">OceanStateEstimation.jl</a> (see <a href="https://gaelforget.github.io/OceanStateEstimation.jl/dev/">the docs</a>).</p> <p>The ECCO output (version 4, release 2) used here is archived @ <a href="https://doi.org/10.7910/DVN/NXYKDW">https://doi.org/10.7910/DVN/NXYKDW</a></p>
ECCO v4 standard analysis sample (v4r4)
<p>Generated from the ECCO output (version 4, release 4) available to download from <a href="https://ecco-group.org/products.htm">ecco-group.org</a> using a Julia workflow available from <a href="https://github.com/gaelforget/OceanStateEstimation.jl">OceanStateEstimation.jl</a> (see <a href="https://gaelforget.github.io/OceanStateEstimation.jl/dev/">the docs</a>).</p> <p> </p>
Mean and standard deviation climatologies for T & S (for use in e.g. MITprof & ECCO)
<p>All variables are gridded on the 50 levels and lat-lon-cap (LLC90) grid defined in in the ECCO Version 4 paper (https://doi.org/10.5194/gmd-8-3071-2015)</p> <p>1. Monthly climatologies of potential temperature and salinity, blended as described in https://doi.org/10.5194/gmd-8-3071-2015 :</p> <p>- T_OWPv1_M_eccollc_90x50.bin<br> - S_OWPv1_M_eccollc_90x50.bin</p> <p>2. Ocean basin masks : basin_masks_eccollc_90x50.bin</p> <p>3. Non-seasonal standard deviation climatologies, which is used in MITprof as an estimate of representation error for data sets that do not resolve the mess-scale and below (see https://doi.org/10.1175/JPO3072.1 ) e.g. in ECCO v4 for the MITprof insitu data constraints (see https://doi.org/10.5194/gmd-8-3071-2015). </p> <p>These global, time-mean, maps are updated versions of the Forget and Wunsch estimate (https://doi.org/10.1175/JPO3072.1). The latest ones are based on median absolute deviations computed at different resolutions and rescaled according to theory for normal distributions.</p> <p>Non-seasonal temperature standard deviation :</p> <p>- sigma_T_eccollc.bin<br> - sigma_T_mad_feb2013.bin<br> - sigma_T_nov2015.bin (latest, used in MITprof))</p> <p>Non-seasonal salinity standard deviation :</p> <p>- sigma_S_eccollc.bin<br> - sigma_S_mad_feb2013.bin<br> - sigma_S_nov2015.bin (latest, used in MITprof)</p> <p> </p>
ECCO-Darwin ED-SBS model and running instructions for "Biogeochemical river runoff drives intense coastal Arctic Ocean outgassing"
<p>Here is the model code and instructions file to run the ED-SBS simulations of "Biogeochemical river runoff drives intense Arctic Ocean outgassing" article.</p> <p>For more informations follow the instructions in the README.md file.</p>
GRID used in ECCO version 4 release 5
<p>There are slight differences in the land mask as compare with previous ECCO version 4 solutions (release 1,2,3,4; https://zenodo.org/doi/10.5281/zenodo.3966419)</p>
Implementing Riverine Biogeochemical Inputs in ECCO-Darwin: A Sensitivity Analysis of Terrestrial Fluxes in a Data-Assimilative Global Ocean Biogeochemistry Model
<p>Resolving riverine biogeochemical inputs in ocean biogeochemistry models is pivotal for capturing the spatiotemporal variability of nutrients and carbon in coastal regions and in the global ocean. ECCO-Darwin is a pioneering data-assimilative global-ocean biogeochemistry model, which, to date, has focused on the pelagic zone. In this work, we use an optimized version of ECCO-Darwin to perform a sensitivity analysis to quantify the response of the open ocean and coastal margins to lateral inputs of carbon and nutrients. We generate riverine inputs by combining point-source freshwater discharge from JRA55-do with the Global NEWS 2 watershed model, accounting for lateral inputs from 5171 watersheds worldwide. While adding carbon and nutrients along with freshwater improves biogeochemical skill in river plume regions and coastal waters, the open-ocean response may be overestimated due to an excess of carbon and nutrients advected offshore. This highlights the need for a more nuanced representation of land-to-ocean and nearshore processes for quantifying how global ocean primary production and carbon cycling respond to land-to-ocean inputs.</p>
ECCO v4 standard analysis sample (v4r5-rc2)
<p>Generated from the ECCO output (version 4, release 5, release candidate 2) available to download from ecco-group.org using a Julia workflow available from OceanStateEstimation.jl (see the docs).</p>
Exercise, Cancer and Cognition: The ECCO-Study
ClinicalTrials.gov study NCT04789187. IPD Sharing: NO. Countries: 1. Publications: 1.
VIIRS data and Ulmo model for comparison to the LLC4320 ECCO ocean general circulation model
Open the record for dataset details and reuse information.
ECCO Ocean Velocity - Daily Mean 0.5 Degree (Version 4 Release 4)
This dataset contains daily-averaged ocean velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Sea Surface Height - Monthly Mean 0.5 Degree (Version 4 Release 4b)
This dataset contains monthly-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4b revision 4 (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Sea-Ice and Snow Concentration and Thickness - Daily Mean 0.5 Degree (Version 4 Release 4)
This dataset contains daily-averaged sea-ice and snow concentration and thickness interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Ocean and Sea-Ice Surface Freshwater Fluxes - Monthly Mean 0.5 Degree (Version 4 Release 4)
This dataset contains monthly-averaged ocean and sea-ice surface freshwater fluxes interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Sea-Ice and Snow Concentration and Thickness - Daily Mean llc90 Grid (Version 4 Release 4)
This dataset provides daily-averaged sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Gent-McWilliams Bolus Transport Streamfunction - Monthly Mean llc90 Grid (Version 4 Release 4)
This dataset provides monthly-averaged Gent-McWilliams ocean bolus transport streamfunction on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
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International Brain Laboratory public data
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OpenNeuro
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