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129 results for “lagrangian”
Data from: Satellite-based Lagrangian model reveals how upwelling and oceanic circulation shape krill hotspots in the California Current System [updated]
<p><strong>Abstract</strong></p> <p>In the California Current System, wind-driven nutrient supply and primary production, computed from satellite data, provide a synoptic view of how phytoplankton production is coupled to upwelling. In contrast, linking upwelling to zooplankton populations is difficult due to relatively scarce observations and the inherent patchiness of zooplankton. While phytoplankton respond quickly to environmental forcing, zooplankton grow slower and tend to aggregate into mesoscale “hotspot” regions spatially decoupled from upwelling centers. To better understand mechanisms controlling the formation of zooplankton hotspots, we use a satellite-based Lagrangian method where variables from a plankton model, forced by wind-driven nutrient supply, are advected by near-surface currents following upwelling events. Modeled zooplankton distribution reproduces published accounts of euphausiid (krill) hotspots, including the location of major hotspots and their interannual variability. This satellite-based modeling tool is used to analyze the variability and drivers of krill hotspots in the California Current System, and to investigate how water masses of different origin and history converge to form predictable biological hotspots. The Lagrangian framework suggests that two conditions are necessary for a hotspot to form: a convergence of coastal water masses, and above average nutrient supply where these water masses originated from. The results highlight the role of upwelling, oceanic circulation, and plankton temporal dynamics in shaping krill mesoscale distribution, seasonal northward propagation, and interannual variability.</p> <p><strong>Data set description</strong></p> <p>This data set includes 2 files:</p> <ul> <li>a satellite-based 1993-2023 monthly retrospective of krill concentrations (Zbig) modeled using the growth-advection method in the California Current upwelling system. Inputs include the nitrate supply product described below and GlobCurrent 15 m oceanic currents. This dataset is updated monthly (using NRT data) at https://www.mbari.org/science/upper-ocean-systems/biological-oceanography/krill-hotspots-in-the-california-current/.</li> <li>a satellite-based 1993-2023 monthly retrospective of wind-driven nitrate supply estimated in a 150 km coastal band at 0.125° latitudinal resolution. Nitrate supply was calculated based primarily on CCMP v3.1 winds, AVISO geostrophic currents, and a climatology of in situ nitrate at 60m. This dataset is updated monthly (using NRT data) at https://www.mbari.org/science/upper-ocean-systems/biological-oceanography/nitrate-supply-estimates-in-upwelling-systems/.</li> </ul> <p>See details regarding data sources and calculations in <a href="https://doi.org/10.3389/fmars.2022.835813">Messié et al. (2022)</a>.</p> <p>[IMPORTANT NOTE:] There is an error in the Ekman pumping fields (trans_pump, Nsupply_pump, Nsupply_total) that will be corrected soon (those fields are not used in publications where only coastal transport was considered). Please contact me if you need Ekman pumping fields before this is fixed.</p>
Calculated moisture sources for the Yangtse River Valley for past, present and future climate using a Lagrangian moisture source diagnostic
<p>This dataset contains calculated moisture sources for the Yangtse River Valley (110–122°E and 27–33°N, eastern China) for past, present and future climate using a Lagrangian moisture source diagnostic. The dataset comprises gridded monthly moisture source data files and monthly time series files for a Last Glacial Maximum (LGM) simulation and a Pre-Industrial reference simulation (PRE) with CAM5.1 using prescribed sea surface temperatures, and a control simulation (CTL, 2001-2010) and a climate scenario run with representative concentration pathway 6 (RCP, 2061-2070) with the coupled NorESM-1M model. Each file covers a 10-year time period, computed with the Lagrangian moisture source diagnostic WaterSip (Sodemann et al., 2008).</p>
A Lagrangian study of the contribution of the Canary coastal upwelling to the nitrogen budget of the open North Atlantic
<p>The attached datasets constitute the particle trajectory data produced in the experiment for Hailegeorgis et al..</p> <p>The "traj_upwell_1d_70m_1d-variables.nc" contains variables that describe different aspects of each upwelled particle (mostly regarding a particle's release or its initial or final conditions).</p> <p>The rest of the files with the format "traj_upwell_1d_70m_XXX-traj.nc" describe an attribute XXX (location or nutrient concentration) along the trajectory of upwelled particles tracked as part of the experiment.</p> <p>With ARIANE, particles are released and tracked in a ROMS simulation of the Canary coastal upwelling region. Out of the ~10M particles, the trajectories of the ~353K (~3.6%) that upwell are included. The variable "index_in_full_exp" in file "traj_upwell_1d_70m_1d-variables.nc" shows the index of each of these upwelling particles in the larger pool of released particles. For each upwelled particle, out of the 720-day trajectories starting from its release into the coast, the values from its upwelling step to its exit from the experiment are included, with the values outside this range being filled with a generic value (1.e20). An upwelled particle exits the experiment when it leaves the regional ROMS simulation altogether or when it leaves the coast and returns to the coast to re-upwell (more details in the paper).</p> <p>Be mindful of the different values of time. In "traj_upwell_1d_70m_1d-variables.nc", the variable "release_time" tells each particle's release time, in days since onset of the ROMS simulation, while variable "coast_exit_time" tells each particle's day of exiting coast, in days since its release. In each particle's trajectory (in traj_lon, traj_lat, etc), the first and last steps with valid values are the same as the days of its upwelling and its exit, respectively, since its release.</p> <p>The files contain the name and description of each variable. Along with the details in the publication, the descriptions here should be enough to fully interpret the information and replicate our analysis.</p>
Lagrangian Decomposition of the Meridional Heat Transport at 26.5N - Water Parcel Crossings of the RAPID 26.5N Array
<p>This dataset contains the initial and final positions and properties of Lagrangian trajectories evaluated using 5-day mean velocity and tracer fields output from the ORCA0083-N06 ocean sea-ice model hindcast (1958-2015). Numerical water parcels are initialised to sample the full-depth southward transport across the RAPID 26.5N array every month during 2004-2015. Water parcels are advected backwards-in-time using a bespoke version of TRACMASS v7.1 Lagrangian particle tracking tool which enables users to specify a custom domain using a mask netCDF file.</p><p>Particles are initialised on the first-available day of each month (based on the centre of the model 5-day mean field windows) between 2004 and 2015 (inclusive) before being advected backwards-in-time within the North Atlantic Ocean until any one of four termination conditions are met: (1) water parcels reach the RAPID 26.5N array, (2) water parcels reach the OSNAP (West or East) arrays in the subpolar North Atlantic, (3) water parcels reach either the English Channel or Gibraltar Strait, or (4) particles reach the maximum advection time of 25-years. The 25-year maximum advection time ensures that we adequately resolve the subtropical gyre circulation north to the RAPID 26.5N array. The pathway transporting dense North Atlantic Deep Water from the OSNAP arrays to RAPID at 26.5N is not fully resolved in this Lagrangian experiment since these water parcels transit on multi-decadal timescales.</p><p>The number of water parcels initialised in each model-grid cell scales with the total northward transport through that cell, such that the maximum possible transport conveyed by any single particle is 5.0 mSv (mSv == 10-3 Sv), enabling the calculation of robust Lagrangian statistics. In reality, the average. water parcel has an associated volume transport of 3.3 mSv which is conserved throughout its circulation.</p><p>Water parcel locations (converted to geographical coordinates) and properties (conservative temperature, absolute salinity, potential density [TEOS-10]) are output on every model-grid cell crossing. TRACMASS determines particle properties on grid-cell crossings by taking the average of the properties stored at the nearest two T-grid points. Here, we provide the initial and final locations and properties of all water parcels initialised from RAPID 26.5N.</p><p>All Lagrangian experiments were completed using the JASMIN High-Performance Computing facility (<a href="https://jasmin.ac.uk">https://jasmin.ac.uk</a>).</p><p><strong>For a complete description of the ORCA0083-N06 hindcast configuration see:</strong> Moat et al. (2016).</p><p><strong>For a complete description of TRACMASS v7.1 see</strong>: <a href="https://www.tracmass.org">https://www.tracmass.org</a></p>
Datasets for "A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models"
<p>This repository contains the post-processed model outputs from HAMSTER v1.2.0 as used in the following paper: </p> <p>Keune, J., Schumacher, D. L., and Miralles, D. G.: A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models, Geosci. Model Dev., 15, 1875–1898, https://doi.org/10.5194/gmd-15-1875-2022, 2022.<br> <br> The data set contains (1) global validation statistics for the three fluxes (evaporation, precipitation, sensible heat), and (2) the climatological source regions of precipitation and heat for Denver, Beijing and Windhoek. The former are found in the directory 'validation/global', and the latter are found in the directories '1001' (Denver), '3001' (Beijing) and '5002' (Windhoek). Multiple experiments were performed to assess the uncertainty of the source regions. Thus, multiple files exist, that show the same variables but for multiple experiments (indicated by the names "ALLPBL", "RH-10-20", "SOD08-SCH19", "SCH20", "FAS19" in the file name). For the moisture source regions, the uncertainty of the attribution methodology was assessed; these are indicated by the different folders, i.e. 'linear_upscaled' and 'random2_upscaled'. For each city and each experiment, the climatologically averaged source regions ('_mean.nc') and the climatologically averaged individual backward day contributions ('_bwmean.nc') are provided. Data sets are in the netCDF format and contain metadata following the CF convention.</p>
Lagrangian overturning in the eastern subpolar North Atlantic Ocean - ORCA025-GJM189 Particle Trajectory Dataset
<p>This dataset contains the output of Lagrangian particle tracking experiments using 5-day mean velocity and hydrographic fields from the ORCA025-GJM189 ocean sea-ice model hindcast configured during the Drakkar project in which numerical particles are initialised along the northward inflows across the Overturning in the Subpolar North Atlantic Program (OSNAP) East section. Particles are advected using a bespoke version of TRACMASS v7.1 Lagrangian particle tracking tool using the regular step-wise stationary advection scheme and an adapted implementation of the vertical turbulent mixing parameterisation created by Paris et al. (2013) for the Connectivity Modelling System Lagrangian particle tracking tool. This vertical turbulent mixing scheme only acts on particles found within the surface mixed layer (as evaluated along particle trajectories) and randomly reshuffles them according to a maximum vertical velocity of 10 cm/s - characteristic of vertical convective plumes. Note, particles cannot be artificially subducted across the base of the mixed layer into the ocean interior using this scheme.</p> <p>Particles are initialised on the first-available day of each month (based on the centre of the model fields 5-day mean windows) between 1976 and 2008 (inclusive) before being advected within the Iceland and Irminger Basins until any one of three termination conditions are met: 1) particles return southward across OSNAP East, 2) particles flow northward across the Greenland-Scotland Ridge, or 3) particles reach the maximum advection time of 7-years. The 7-year maximum advection time ensures >99.1% of all initialised particles meet one of conditions 1) or 2), hence only 0.9% of all particles are terminated between OSNAP East and the Greenland-Scotland Ridge.</p> <p>The number of particles initialised in each model-grid cell scales with the total northward transport through that cell, such that the maximum possible transport conveyed by any single particle is 2.5 mSv (mSv == 1E-3 Sv), enabling the calculation of robust Lagrangian statistics.</p> <p>Particle locations (referenced to the original ORCA025 model grid) and properties (potential temperature, salinity, potential density and local mixed layer depth) are stored in the output files on every model-grid cell crossing. TRACMASS determines particle properties on grid-cell crossings by taking the average of the properties stored at the nearest two T-grid points.</p> <p>In total, four Lagrangian experiments were conducted at the Department of Earth Sciences, University of Oxford. Please see README.md for a full description of all Lagrangian experiments and the accompanying output files.</p> <p><strong>For a complete description of the ORCA025-GJM189 hindcast configuration see:</strong> https://github.com/meom-configurations/ORCA025.L75-GJM189.</p> <p><strong>For a complete description of TRACMASS v7.1 see</strong>: https://github.com/TRACMASS/tracmass</p>
Lagrangian statistics in turbulent channel flow
<p>A set of Lagrangian statistics of passive tracers in a turbulent channel flow. The particle trajectories are obtained by means of integration of a simulated flow field, computed via Direct Numerical Simulation, at four different Reynolds numbers. The Reynolds numbers here employed are <span class="math-tex">\(\mathrm{Re}_{\tau} = \frac{u_{\tau}\delta}{\nu} = 180,\,395,\,590,\,950\)</span>, where <span class="math-tex">\(u_{\tau}\)</span>is the frictional velocity, <span class="math-tex">\(\delta\)</span> is the channel half height and <span class="math-tex">\(\nu\)</span> is the kinematic viscosity.</p> <p>Additional information about the database is provided in the included documentation.</p> <p>v1.0 -> Added statistics at ReT = 950</p> <p>v1.1 -> Added statistics at ReT = [180, 395, 590]</p> <p>v1.2 -> Added statistics at ReT = 265</p>
Deformation composite of the RADARSAT Geophysical Processor System (RGPS) Lagrangian motion data
<p>Deformation composite constructed from the Lagrangian RADARSAT Geophysical Processor System (RGPS) Lagrangian motion data for January-February-March, 1997 to 2008. The nominal temporal and spatial scales for the composite data are T<sup>*</sup> = 3 days, and L<sup>*</sup> = 10 km. This data is analyzed and compared with model deformation statistics in Bouchat et al., Sea Ice Rheology Experiment (SIREx), Part I: Scaling and statistical properties of sea-ice deformation fields, Journal of Geophysical Research: Oceans (2022).</p> <p>The original RGPS Lagrangian motion data set consists in lists of trajectories (time and positions records) for points that are tracked in sequential synthetic aperture radar (SAR) images. The trajectories are organized in different “streams”, corresponding to different initial satellite passes over which a set of tracked points were initialized. For all streams, the trajectories are initialized on a uniform 10 km x 10 km grid at the beginning of the winter in November. Each tracked point can therefore be assigned to <em>(i,j)</em> indices corresponding to its initialization location on the grid. As time increases and the position records are updated, the tracked points are no longer uniformly separated, but their assigned <em>(i,j)</em> indices do not change. The trajectory records are updated when the tracking algorithm detects the tracked points in a new SAR image. The update interval is therefore not always the same for all points, nor is it always on the same time/day within a given stream as the tracking algorithm may be unsuccessful for certain images/points. Moreover, the multiple streams can overlap spatially, such that more than one trajectory can be assigned to the same<em> (i,j)</em> indices. Computing strain rates directly from the original RGPS Lagrangian motion product therefore results in deformation estimates that can span a wide range of spatio-temporal scales, that are not temporally coherent across all streams, and that can also be spatially redundant. The goal of constructing a deformation composite from the original RGPS Lagrangian motion product is to generate a coherent set of non-overlapping Lagrangian deformation estimates at fixed time intervals and with a uniform spatial scale that can be used for statistical analysis.</p> <p>The RGPS Lagrangian deformation composite is constructed using the weighted-average pre-processing method described in Bouchat & Tremblay (2020) and Hutter et al. (2020) and summarized here. For each stream separately, we first define quadrilateral Lagrangian cells assigned to the <em>(i,j)</em> indices by combining records from the <em>(i,j),</em> <em>(i+1,j)</em>, <em>(i,j+1)</em>, and <em>(i+1, j+1)</em> available Lagrangian trajectories. For each <em>(i,j) </em>cell, we then compute the Lagrangian strain rates if, between any two update times, the cell's records have: (i) simultaneous (plus or minus 3 hours) start and end times for all fours corners, (ii) an average time interval for all corners that corresponds to the nominal temporal resolution of T<sup>*</sup>= 3 days, and (iii) an area at the start time that corresponds to the nominal spatial resolution of L<sup>*</sup>= 10 km. The strain rates, the cell area, and the start and end times used to compute the cell's strain rates are also assigned to the <em>(i,j) </em>indices. Then, to create the composite deformation estimates at the same fixed start and end dates for all cells, we average the strain rate and area records at each <em>(i,j)</em> indices in fixed 3-day periods starting on January 1st, using the overlapping time between their start/end date interval with the fixed 3-day periods as weight. For visualization purposes only, we also average the cells' corners' starting positions from all records overlapping with the fixed 3-day interval and use these averaged positions as approximate coordinates for the composite deformation cells. Finally, all streams are spatially combined into a single strain rate composite. In the case of spatial overlap between two or more streams, we keep the cells that have the longest time coverage and discard the other ones.</p> <p> </p> <p>There is one netCDF file per year. Data are organized in matrices where the <em>(i,j)</em> indices are the Lagrangian cells identifier. This allows us to keep track of neighbouring cells for the scaling analysis. See below for more information on what variables are included in the files and their structure. </p> <p> </p> <p><strong>1. Variables included</strong></p> <ul> <li><em>(x1,y1), (x1,y2), (x3,y3), (x4,y4)</em>: Average positions of the composite cells' corners. Used for visualization only (deformations should not be computed using these positions) - (meters);</li> <li><em>A</em>: Composite cells' area - (meters squared);</li> <li><em>dudx, dudy, dvdx, dvdy</em>: Composite cell's velocity derivatives (strain rates/deformation) - (1/seconds);</li> <li><em>d_dudx, d_dudy, d_dvdx, d_dvdy</em>: Trajectory error on the composite cells' velocity derivatives - (1/seconds);</li> <li><em>time</em>: Day of year.</li> </ul> <p><strong>*Note:</strong> The composite cells were removed if their average position was within 100 km from land. Before comparing the deformation statistics with sea-ice models, one should only keep cells available in both the model and the RGPS composite.</p> <p> </p> <p><strong>2. Variable structure</strong></p> <p>All variables (except <em>time</em>) are matrices with axes (<em>it, i, j </em>), where <em>it</em> is the time stamp/iteration and<em> i,j </em>are the cells identifiers. See below for how the cells are defined: </p> <p> |--------------------------------------------------------------><sub> <strong>j-axis</strong> </sub> <br> | <br> | <strong>(</strong><strong>x1_ij,y1_ij</strong><strong>)</strong> <strong>o</strong> --------------------<strong>o</strong> <strong>(</strong><strong>x2_ij,y2_ij</strong><strong>)</strong> <br> | | | <br> | | <strong>A_ij or dudx_ij</strong> | <strong> </strong><br> | | | <br> | <strong>(</strong><strong>x4_ij,y4_ij</strong><strong>) </strong><strong>o</strong> ------------------- <strong>o</strong> <strong>(</strong><strong>x3_ij,y3_ij</strong><strong>)</strong> <br> | <br> |<br> V<sub><strong>i-axis</strong></sub> </p> <p> </p> <p> </p> <p> </p> <p><strong>References:</strong><br> Bouchat, A., & Tremblay, B. (2020). Reassessing the Quality of Sea-Ice Deformation Estimates Derived From the RADARSAT Geophysical Processor System and Its Impact on the Spatiotemporal Scaling Statistics. Journal of Geophysical Research: Oceans, 125(8), https://doi.org/10.1029/2019JC015944</p> <p>Hutter, N. and Losch, M.: Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations, The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, 2020.</p> <p>The original RGPS Lagrangian Motion data set can be accessed here: https://asf.alaska.edu/data-sets/derived-data-sets/seaice-measures/sea-ice-measures-data-products/</p>
Model Lagrangian trajectories and deformation data analyzed in the Sea Ice Rheology Experiment - Part I
<p>Model Lagrangian trajectories and deformation estimates for sea-ice models participating in the Sea Ice Rheology Experiment (SIREx) - Part I. Model Lagrangian trajectories are integrated offline, starting on January 1st with all available raw RGPS cells positions (interpolated to January 1st 00:00:00 UTC). The trajectories are advected at an hourly time step with the models daily velocity output until March 31st. The trajectories are then sampled at a 3-day interval to match the RGPS composite time stamps, and the velocity derivatives (deformation) are calculated using the line integral approximations on the cells' contour. All model trajectories and Lagrangian deformation data therefore have nominal temporal and spatial scales of 3-days and 10-km (same as the RGPS composite), regardless of the original resolution of the model output. The model Lagrangian deformation estimates form the basis quantity for the statistical and spatio-temporal scaling analysis presented in Bouchat et al., Sea Ice Rheology Experiment (SIREx), Part I: Scaling and statistical properties of sea-ice deformation fields, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the model trajectory integration and deformation calculation.</p> <p>There is one netCDF file per model, per year (1997 and/or 2008). Data are organized in matrices where the (i,j) indices are the Lagrangian cells identifier. This allows us to keep track of neighbouring cells for the scaling analysis. See below for more information on what variables are included in the files, their structure, and how to cite. </p> <p> </p> <p><strong>1. File naming convention</strong></p> <p>"< Model simulation label >" + _ + "deformation" + _ + "< year >" </p> <p> </p> <p><strong>2. Variables included</strong></p> <ul> <li><em>(x1,y1), (x1,y2), (x3,y3), (x4,y4)</em>: Position of the cells' corners (Lagrangian trajectories) - (meters);</li> <li><em>A</em>: Cells' area - (meters squared);</li> <li><em>dudx, dudy, dvdx, dvdy</em>: Cell's velocity derivatives (strain rates/deformation) - (1/seconds);</li> <li><em>d_dudx, d_dudy, d_dvdx, d_dvdy</em>: Trajectory error on cells' velocity derivatives - (1/seconds);</li> <li><em>time</em>: Day of year.</li> </ul> <p><strong>*Note:</strong> the model trajectories are terminated if they move within 100 km from land. Before computing deformation statistics to compare with RGPS composite data, one should mask both deformation sets to only keep cells available in both the model and RGPS data sets.</p> <p> </p> <p><strong>3. Variable structure</strong></p> <p>All variables (except <em>time</em>) are matrices with axes (<em>it, i, j </em>), where <em>it</em> is the time stamp/iteration and<em> i,j </em>are the cells identifiers. See below for how the cells are defined: </p> <p> |--------------------------------------------------------------><sub> <strong>j-axis</strong> </sub> <br> | <br> | <strong>(</strong><strong>x1_ij,y1_ij</strong><strong>)</strong> <strong>o</strong> -------------------<strong> o</strong> <strong>(</strong><strong>x2_ij,y2_ij</strong><strong>)</strong> <br> | | | <br> | | <strong>A_ij or dudx_ij</strong> | <strong> </strong><br> | | | <br> | <strong>(</strong><strong>x4_ij,y4_ij</strong><strong>) </strong><strong>o</strong> ------------------- <strong>o</strong> <strong>(</strong><strong>x3_ij,y3_ij</strong><strong>)</strong> <br> | <br> |<br> V<sub><strong>i-axis</strong></sub> </p> <p> </p> <p>Hence, coordinates are repeated between neighbouring cells, for example: (x2_ij,y2_ij) = (x1_ij+1,y1_ij+1) and (x4_ij,y4_ij) = (x1_i+1j,y1_i+1j)</p> <p> </p> <p><strong>4. Recommended citation usage</strong></p> <p>If <em>all</em> simulations included in the current archive are used in a future study, we ask to cite this archive and the SIREx paper (Bouchat et al., 2022). If only <em>selected </em>simulations are used, we ask to cite both this archive and the reference paper(s) applying to the selected simulation(s) (as stated indicated in Table 1 of the SIREx papers).</p>
Global Lagrangian dataset of Marine litter
<p><strong>Global Lagrangian dataset of Marine litter</strong></p> <p>This dataset regroups 12 yearly files (<em>global-marine-litter-[2010–2021].nc</em>) combining monthly releases of 32,300 particles initially distributed across the globe following global Mismanaged Plastic Waste (MPW) inputs. The particles are advected with OceanParcels (<a href="https://doi.org/10.5194/gmd-12-3571-2019">Delandmeter, P and E van Sebille, 2019</a>) using ocean surface velocity, a wind drag coefficient of 1%, and a small random walk component with a uniform horizontal turbulent diffusion coefficient of K<sub>h</sub> = 1m<sup>2</sup>s<sup>-1</sup> representing unresolved turbulent motions in the ocean (see <a href="https://doi.org/10.3389/fmars.2021.667591">Chassignet et al. 2021</a> for more details).</p> <p><strong>Global oceanic current and atmospheric wind</strong></p> <p>Ocean surface velocities are obtained from GOFS3.1, a global ocean reanalysis based on the HYbrid Coordinate Ocean Model (HYCOM) and the Navy Coupled Ocean Data Assimilation (NCODA; <a href="https://www.frontiersin.org/articles/10.3389/fmars.2021.667591/full#B7">Chassignet et al., 2009</a>; <a href="https://www.jstor.org/stable/24862187?seq=1#metadata_info_tab_contents">Metzger et al., 2014</a>). NCODA uses a three-dimensional (3D) variational scheme and assimilates satellite and altimeter observations as well as in-situ temperature and salinity measurements from moored buoys, Expendable Bathythermographs (XBTs), Argo floats (<a href="https://link.springer.com/chapter/10.1007/978-3-642-35088-7_13">Cummings and Smedstad, 2013</a>). Surface information is projected downward into the water column using Improved Synthetic Ocean Profiles (<a href="https://apps.dtic.mil/sti/citations/ADA585251">Helber et al., 2013</a>). The horizontal resolution and the temporal frequency for the GOF3.1 outputs are 1/12° (8 km at the equator, 6 km at mid-latitudes) and 3-hourly, respectively. Details on the validation of the ocean circulation model are available in <a href="https://apps.dtic.mil/sti/citations/AD1034517">Metzger et al. (2017)</a>.</p> <p>Wind velocities are obtained from JRA55, the Japanese 55-year atmospheric reanalysis. The JRA55, which spans from 1958 to the present, is the longest third-generation reanalysis that uses the full observing system and a 4D advanced data assimilation variational scheme. The horizontal resolution of JRA55 is about 55 km and the temporal frequency is 3-hourly (see <a href="https://www.sciencedirect.com/science/article/pii/S146350031830235X?via%3Dihub">Tsujino et al. (2018)</a> for more details).</p> <p><strong>Marine Litter Sources</strong></p> <p>The marine litter sources are obtained by combining MPW direct inputs from coastal regions, which are defined as areas within 50 km of the coastline (<a href="https://doi.org/10.1057/s41599-018-0212-7">Lebreton and Andrady 2019</a>), and indirect inputs from inland regions via rivers (<a href="https://doi.org/10.1038/ncomms15611">Lebreton et al. 2017</a>). </p> <p><strong>File Format</strong></p> <p>The locations (<em>lon</em>, <em>lat</em>), the corresponding weight (<em>tons</em>), and the source (<em>1</em>: land, <em>0</em>: river) associated with the 32,300 particles are described in the file <em>initial-location-global.csv</em>. The particle trajectories are regrouped into yearly files (<em>marine-litter-[2010–2021].nc</em>) which contain 12 monthly releases, resulting in a total of 387,600 trajectories per file. More precisely, in each of the yearly files, the first 32,300 lines contain the trajectories of particles released on January 1st, then lines 32,301–64,600 contain the trajectories of particles released on February 1st, and so on. The trajectories are recorded daily and are advected from their release until 2021-12-31, resulting in longer time series for earlier years of the dataset. </p> <p><strong>References</strong></p> <p>Chassignet, E. P., Hurlburt, H. E., Metzger, E. J., Smedstad, O. M., Cummings, J., Halliwell, G. R., et al. (2009). U.S. GODAE: global ocean prediction with the hybrid coordinate ocean model (HYCOM). Oceanography 22, 64–75. doi: <a href="https://doi.org/10.5670/oceanog.2009.39">10.5670/oceanog.2009.39</a></p> <p>Chassignet, E. P., Xu, X., and Zavala-Romero, O. (2021). Tracking Marine Litter With a Global Ocean Model: Where Does It Go? Where Does It Come From?. <em>Frontiers in Marine Science</em>, <em>8</em>, 414, doi: <a href="https://doi.org/10.3389/fmars.2021.667591">10.3389/fmars.2021.667591</a></p> <p>Cummings, J. A., and Smedstad, O. M. (2013). “Chapter 13: variational data assimilation for the global ocean”, in Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, Vol. II, eds S. Park and L. Xu (Berlin: Springer), 303–343. doi: <a href="https://doi.org/10.1007/978-3-642-35088-7_13">10.1007/978-3-642-35088-7_13</a></p> <p>Delandmeter, P., and van Sebille, E. (2019). The Parcels v2.0 Lagrangian framework: new field interpolation schemes. Geosci. Model Dev. 12, 3571–3584. doi: <a href="https://doi.org/10.5194/gmd-12-3571-2019">10.5194/gmd-12-3571-2019</a></p> <p>Helber, R. W., Townsend, T. L., Barron, C. N., Dastugue, J. M., and Carnes, M. R. (2013). Validation Test Report for the Improved Synthetic Ocean Profile (ISOP) System, Part I: Synthetic Profile Methods and Algorithm. NRL Memo. Report, NRL/MR/7320—13-9364 Hancock, MS: Stennis Space Center.</p> <p>Metzger, E. J., Smedstad, O. M., Thoppil, P. G., Hurlburt, H. E., Cummings, J. A., Wallcraft, A. J., et al. (2014). US Navy operational global ocean and Arctic ice prediction systems. Oceanography 27, 32–43, doi: <a href="https://doi.org/10.5670/oceanog.2014.66">10.5670/oceanog.2014.66</a>.</p> <p>Metzger, E., Helber, R. W., Hogan, P. J., Posey, P. G., Thoppil, P. G., Townsend, T. L., et al. (2017). Global Ocean Forecast System 3.1 validation test. Technical Report. NRL/MR/7320–17-9722. Hancock, MS: Stennis Space Center, 61.</p> <p>Lebreton, L., and Andrady, A. (2019). Future scenarios of global plastic waste generation and disposal. Palgrave Commun. 5:6, doi: <a href="https://doi.org/10.1057/s41599-018-0212-7">10.1057/s41599-018-0212-7</a>.</p> <p>Lebreton, L., van der Zwet, J., Damsteeg, J. W., Slat, B., Andrady, A., and Reisser, J. (2017). River plastic emissions to the world’s oceans. Nat. Commun. 8:15611, doi: <a href="https://doi.org/10.1038/ncomms15611">10.1038/ncomms15611</a>.</p> <p>Tsujino H., S. Urakawa, H. Nakano, R.J. Small, W.M. Kim, S.G. Yeager, G. Danabasoglu, T. Suzuki, J.L. Bamber, M. Bentsen, C. Böning, A. Bozec, E.P. Chassignet, E. Curchitser, F. Boeira Dias, P.J. Durack, S.M. Griffies, Y. Harada, M. Ilicak, S.A. Josey, C. Kobayashi, S. Kobayashi, Y. Komuro, W.G. Large, J. Le Sommer, S.J. Marsland, S. Masina, M. Scheinert, H. Tomita, M. Valdivieso, and D. Yamazaki, 2018. JRA-55 based surface dataset for driving ocean-sea-ice models (JRA55-do).<em> Ocean Modelling</em>, <strong>130</strong>, 79-139, doi: <a href="https://doi.org/10.1016/j.ocemod.2018.07.002">10.1016/j.ocemod.2018.07.002</a>.</p>
Eulerian and Lagrangian diagnostics of the dynamical properties of the water masses sampled during the Tara Pacific Expedition 2016-2018
<p>In order to provide a description of the dynamical properties of the water masses sampled, different Eulerian and Lagrangian diagnostics were calculated. </p> <p>For each of the 246 stations sampled, we proceeded as follows.</p> <p>We identified the water mass sampled at the given station. This was considered as a stadium shape with the two semi-circles centered on the starting and ending points of the transect, respectively. The radius of the stadium semi-circles was considered 0.1°, which is in accordance with previous studies25,29,30. The stadium was filled with virtual particles separated by 0.01°.</p> <p>For each virtual particle inside the stadium shape, we calculated an Eulerian or Lagrangian diagnostic (described above). The Eulerian diagnostics were extracted directly from the velocity field of the day of sampling. Concerning the Lagrangian diagnostics, these were obtained by advecting the virtual particle backward in time for an amount of time 𝞽 from the day of sampling day_S. For the Lagrangian betweenness, the advection was performed between day_S+𝞽/2 and day_S-𝞽/2, so that the advective time window was centered on the sampling day (details in25).</p> <p>For the Lagrangian diagnostics, we used the following advective times 𝞽: 5, 10, 15, 20, 30, and 60 days. The only exception is the retention time, which, by construction, was calculated only with the largest advective time, namely 𝞽=60 days.</p> <p>Once that, a given diagnostic (Eulerian or Lagrangian) was calculated for all the virtual particles filling the stadium shape, we calculated the mean value, and the 25, 50, and 75 percentiles. The percentiles were calculated in order to quantify the spatial variation of the diagnostic inside the stadium shape. Therefore, we associated each station with four values (mean, 25, 50, and 75 percentiles) of a given diagnostic.</p> <p> Furthermore, two different velocity fields were used, which are described as follows. </p> <p>Both the velocity fields were downloaded from E.U. Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). The first velocity field used was MULTIOBS_GLO_PHY_REP_015_004 [GlobEkmanDt]. This was produced by combining the altimetry derived geostrophic velocities and modeled Ekman surface currents. It had a spatial resolution of 0.25° and a temporal resolution of one day. The second velocity field was GLOBAL_REANALYSIS_PHY_001_030 [GloryS12]. It was obtained by a NEMO model assimilating altimetry and other observations. It had a spatial resolution of 1/12° and a temporal resolution of 1 day.</p> <p>The following Eulerian diagnostics were calculated:</p> <ul> <li> <p>Absolute velocity ([Uabs], m s-1): sqrt(u2+v2), where u and v are the zonal and meridional components of the horizontal velocity field used (described below)</p> </li> <li> <p>Kinetic energy ([Ekin], m2 .s-2): 0.5*(u2+v2)</p> </li> <li> <p>Divergence ([EulerDiverg], d-1): du/dx + dv/dy</p> </li> <li> <p>Vorticity ([Vorticity], d-1): dv/dx - du/dy</p> </li> <li> <p>Okubo-Weiss ([OW], d-2): s2-vorticity2, where s2 is (du/dx-dv/dy)2 + (dv/dx+du/dy)2. If negative, it indicates that the station sampled was inside an eddy.</p> </li> </ul> <p>The following Lagrangian diagnostics were calculated:</p> <ul> <li> <p>Finite-Time Lyapunov Exponents ([Ftle], d-1): it indicates the rate of horizontal stirring, and it is a means to quantify the intensity of turbulence in a given region. FTLE are commonly used to identify Lagrangian Coherent Structures, i.e. barriers to transport. In this case, a strong FTLE value indicates a region separating water masses which were far away backward in time.</p> </li> <li> <p>Lagrangian betweenness ([betw], adimensional): this diagnostic draws inspiration from Lagrangian Flow Network Theory26. It can identify regions which act as bottlenecks for the circulation, in that they receive waters coming from different origins, and that are then spread over several different destinations. These can represent possible hotspots driving biodiversity25.</p> </li> <li> <p>Lagrangian Divergence ([LagrDiverg], d-1). This diagnostic was calculated by integrating the Eulerian divergence along the backward trajectories. If positive, it indicates a water mass that, during the previous days, was subjected to a strong divergence, thus to a possible upwelling. If negative, it indicates a strong convergence, thus possible downwelling.</p> </li> <li> <p>Retention Time ([RetentionTime], d). This diagnostic indicates how many days a water mass has spent inside an eddy in the previous period. If the water mass is outside an eddy, then its retention time is set to zero.</p> </li> </ul>
Ocean surface currents, SSH and SST from LLC4320, before and after Lagrangian filtering
<p>This dataset comprises daily snapshots of horizontal velocity, sea surface height and sea surface temperature from LLC4320, a high resolution setup of the MITgcm, in the Agulhas region. We provide the unfiltered data, and the data after Lagrangian filtering as described in Jones, CS, Xiao, Q, Abernathey, RP and Smith, KS <em>Separating balanced and unbalanced flow at the surface of the Agulhas region using Lagrangian filtering (preprint: </em><a href="https://doi.org/10.31223/X5D352">https://doi.org/10.31223/X5D352</a> ). Lagrangian filtering is not applied to the sea surface temperature.</p> <p>This dataset is not the dataset that was used to make the figures in Jones et al. (see <a href="https://doi.org/10.5281/zenodo.6574163">https://doi.org/10.5281/zenodo.6574163</a>), but a separate dataset that is meant to be used in future study. We have decided to make this dataset publicly available because it may be useful for machine learning, or for studies that investigate the dynamical equations that govern the sea surface height and horizontal velocity field.</p> <p>unfilt_u_v_ssh_sst.nc contains unfiltered horizontal velocity, sea surface height and sea surface temperature</p> <p>filt_u_v_ssh.nc contains horizontal velocity and sea surface height after Lagrangian filtering</p> <p>This work was supported by NASA award 80NSSC20K1142.</p>
BetaEddyOne: A long-lived 1.5 layer quasigeostrophic eddy on a beta plane, with Lagrangian particles
<p>BetaEddyOne is a yearlong simulation of a large, nonlinear oceanic eddy under 1.5 layer quasigeostrophic dynamics on a beta plane, initially located at 24˚N. The simulation is run at 512 x 256 resolution and is seeded with Lagrangian particles with one particle per grid point. </p> <p>This simulation is indended for use as a common test case for eddy-related diagnostics and analysis methods. It approximately replicates the eddy analyzed in detail in Early, Samelson, and Chelton (2011), <a href="https://doi.org/10.1175/2011JPO4601.1">https://doi.org/10.1175/2011JPO4601.1</a>. The simulation was created using the WaveVortexModel (Early, Lelong, and Sundermeyer, 2021, <a href="https://doi.org/10.1017/jfm.2020.995">https://doi.org/10.1017/jfm.2020.995</a>), the code for which is available on GitHub at <a href="https://github.com/Energy-Pathways-Group/GLOceanKit">https://github.com/Energy-Pathways-Group/GLOceanKit</a>. This particular simulation was created for use in the paper</p> <p>Lilly, J. M., J. Feske, B. Fox-Kemper, and J. J. Early (2024). Integral theorems for the gradient of a vector field, with a fluid dynamical application. <em>Proceedings of the Royal Society of London, Series A</em>. <strong>480</strong> (2293): 20230550, 1–30. <a href="https://doi.org/10.1098/rspa.2023.0550">doi 10.1098/rspa.2023.0550</a>.</p> <p>The figure shows a snapshot of the model's vertical vorticity. </p> <p> </p>
Larval dispersal histogram data used for ATLAS deliverable D1.6: Biologically realistic Lagrangian dispersal and connectivity
<p>Larval dispersal histogram data for ATLAS deliverable D1.6 "Biologically realistic Lagrangian connectivity" (https://www.eu-atlas.org/resources/atlas-partners-document-area/atlas-deliverables/455-d1-6-biologically-realistic-lagrangian-connectivity/file). Tar archive files are ordered by ATLAS case study source region and with folders by larval behaviour type. The numbered behaviour types are described in deliverable D1.6. Each netcdf histogram file, e.g. hists_age_21.nc, contains the histogram for larvae of a single age in 5-day steps, from 00 (0 days) to 37 (185 days).</p> <p>Within each file histogram file, particle counts in each Viking20 model grid-cell are contained in a 4-d array with dimensions (launch month, lauch year, model gridsquare y index, model gridsquare x index). The Viking20 grid in the North Atlantic is the ORCA tripolar grid. Details of the model mesh are in the included file viking20_mesh_mask.tgz</p> <p>Histograms are in netcdf files:</p> <p>============================</p> <p>$ ncdump -h hists_age_00.nc<br> netcdf hists_age_00 {<br> dimensions:<br> coordinate = 4 ;<br> coordinate_1 = 50 ;<br> coordinate_2 = 1719 ;<br> coordinate_3 = 1784 ;<br> variables:<br> int64 coordinate(coordinate) ;<br> coordinate:units = "month" ;<br> coordinate:long_name = "Launch month" ;<br> int64 coordinate_1(coordinate_1) ;<br> coordinate_1:units = "year" ;<br> coordinate_1:long_name = "Launch year" ;<br> int64 coordinate_2(coordinate_2) ;<br> coordinate_2:units = "index" ;<br> coordinate_2:long_name = "J index" ;<br> int64 coordinate_3(coordinate_3) ;<br> coordinate_3:units = "index" ;<br> coordinate_3:long_name = "I index" ;<br> int64 data(coordinate, coordinate_1, coordinate_2, coordinate_3) ;<br> data :long_name = "particle count" ;</p> <p>// global attributes:<br> :Conventions = "CF-1.6" ;<br> }</p> <p>==========================================</p> <p> </p> <p> </p>
Supporting Data for "Identifying the Origins of Nanoplastics in the Abyssal South Atlantic Using Backtracking Lagrangian Simulations with Fragmentation"
<p>Supporting Data for "Identifying the Origins of Nanoplastics in the Abyssal South Atlantic Using Backtracking Lagrangian Simulations with Fragmentation", published in the Ocean and Coastal Research Journal. The repository consists of the code used for the simulations and analysis, and the supplementary information document associated with the main manuscript, the Lagrangian simulation outputs and aditional data used for the analysis.</p>
A global Lagrangian eddy dataset based on satellite altimetry (GLED v1.0)
<p>Mesoscale eddies, defined as rotating structures ranging typically from tens to hundreds of kilometers and lasting for several weeks to months, are ubiquitous in the global ocean. Isolated mesoscale eddies are generally considered as coherent structures with a material barrier that can trap the fluid within the eddy interior. Methods employed to identify coherent eddies can be classified into Eulerian and Lagrangian frameworks. Eddy datasets based on Eulerian methods, especially the eddy census of Chelton et al. (2011), have been used in a huge range of applications, from physics to biology. However, recent works have shown that Eulerian eddies are not necessarily coherent because there is strong and persistent water exchange across the Eulerian eddy boundary. In this study, millions of Lagrangian particles are advected by satellite-derived surface geostrophic velocities over a period of 1993-2019. Using the method of Lagrangian-averaged vorticity deviation by Haller et al. (2016), we present a global Lagrangian eddies dataset (GLED v1.0). This open-source dataset contains not only general features (eddy center position, equivalent radius, rotation property, etc.) of eddies with lifespans of 30, 90, and 180 days, but also the trajectory of particles trapped by coherent eddy boundaries over the lifetime. The greatest strength of GLED v1.0 is that the identified eddies are all material objects by construction. Our eddy dataset provides an additional option for oceanographers in studying the interaction between coherent eddies and other physical or biochemical processes in the Earth system.</p>
A Lagrangian analysis of the sources of rainfall over the Horn of Africa Drylands
<p>This repository contains datasets required to reproduce the figures in the paper titled "A Lagrangian analysis of the sources of rainfall over the Horn of Africa Drylands"</p> <p>The relevant codes for generating the figures can be found in the following repository: https://github.com/akashkoppa/HAD-Moisture-Source</p>
Lagrangian Water Age trajectories initiated from the coastal 500m isobath and derived from surface velocities obtained from satellite observations
We conduct a Lagrangian particle trajectory analysis of surface velocities. We define an “offshore water age” as the time taken by a water parcel to be advected backward in time from its current position along its trajectory until it crosses the 500 m isobath. The rationale of this diagnostic is to detect filaments of coastal water advected offshore by horizontal transport and to estimate the time for water parcels in the filament o leave the coastal area. For example, a value of “20 days” assigned to a pixel means that the water parcel in that area was in the coastal area approximately 20 days before, where it was likely enriched in nutrients.
Gut Fluorescence measurements of mesozooplankton grazing on autotrophic prey. Samples collected in the CCE-LTER region on Process Cruises from 2006 to the present. Summaries for each Lagrangian Cycle.
Mesozooplankton are collected with plankton nets (typically a 71-cm diameter, 202-um mesh Bongo net) and samples flash frozen at sea in liquid N2 for subsequent shore-based measurements of ingested phytoplankton chlorophyll-a. Measurements of mesozooplankton gut fluorescence are done by fluorometric analysis on a Turner Designs fluorometer of gut pigments extracted in 90% acetone. Analyses are done on mesozooplankton size-fractionated into 5 different categories on Nitex mesh (> 0.2 mm, 0.5 mm, 1.0 mm, 2.0 mm, 5.0 mm). The pigment content (as Chl-a and phaeopigments) is then expressed as mass of pigment ingested per m3 of water filtered, or divided by the dry weight biomass of the mesozooplankton in the same sample in order to obtain mass-specific ingestion per m3 of water. Application of published values of the temperature-dependent gut passage time are used to estimate the mesozooplankton grazing rate, as pigments ingested per m3 per unit time, or the corresponding mass-specific rate of ingestion. Samples for gut fluorescence assays have been collected on CCE-LTER Process Cruises since 2006 and these collections are ongoing.
Dry weight biomass measurements of net-collected mesozooplankton. Samples collected in the CCE-LTER region on Process Cruises from 2006 to the present. Summaries for each Lagrangian Cycle.
Mesozooplankton are collected with plankton nets (typically a 71-cm diameter, 202-um mesh Bongo net) and samples flash frozen at sea in liquid N2 for subsequent shore-based measurements of dry weight biomass. Measurements are made by weighing pre-tared Nitex mesh on an analytical balance, for mesozooplankton size-fractionated into 5 different categories (> 0.2 mm, 0.5 mm, 1.0 mm, 2.0 mm, 5.0 mm). Biomass is expressed as dry mass of zooplankton per m3 of water filtered, or when multiplied by the maximum depth of the tow, as integrated dry mass of zooplankton per m2 of sea surface. Samples for dry weight biomass have been collected on CCE-LTER Process Cruises since 2006 and these collections are ongoing.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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