CEFASee9b441a-b04d-4013-92fe-56f5eab85b2f English dataset Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory (CEFAS) Cefas Lowestoft Laboratory Pakefield Road Lowestoft Suffolk NR33 0HT UK data.manager@cefas.co.uk pointOfContact 2025-09-25T12:56:06 EPSG Geodetic Parameter Registry 2022-01-07 revision WGS 84 Particle tracking model output simulating floating marine litter in the Bay of Bengal, 2018 to 2019 2024-10-02 publication 2025-09-25 revision 2024-09-25 creation CEFAS22047 https://data.cefas.co.uk These data are particle positions and dates/times which are output from an OceanParcels particle tracking model simulating likely pathways of floating marine macro-litter in the Bay of Bengal between 1st June 208 – 30th September 2019, saved as NetCDF files. The model incorporated advection due to ocean, wind, and Stokes drift velocities, horizontal diffusion, and particle beaching behaviours. Two different hydrodynamic data sets were used to force the particles’ trajectories: a high-resolution ocean velocity hindcast (ROMS – Regional Oceanic Modelling System) and a lower-resolution dataset which included data assimilation (CMEMS - Copernicus Marine Environment Monitoring Service). Sensitivity tests were run to determine whether hourly or daily forcing resulted in significantly different particle end locations and validation simulations were run to compare with undrogued drifter tracks for the same period and region. All model output is stored in NetCDF files. This is data to accompany a manuscript submitted to Ocean Science entitled ‘Monsoonal influence on floating marine litter pathways in the Bay of Bengal’ (Preprint: `EGUsphere - Monsoonal influence on floating marine litter pathways in the Bay of Bengal`_). These experiments aimed to determine source-to-sink connectivity between countries surrounding the Bay of Bengal. .. _`egusphere - monsoonal influence on floating marine litter pathways in the bay of bengal`: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3096/ Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory (CEFAS) Cefas Lowestoft Laboratory Pakefield Road Lowestoft Suffolk NR33 0HT UK data.manager@cefas.co.uk originator Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory (CEFAS) Cefas Lowestoft Laboratory Pakefield Road Lowestoft Suffolk NR33 0HT UK data.manager@cefas.co.uk custodian Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory (CEFAS) Cefas Lowestoft Laboratory Pakefield Road Lowestoft Suffolk NR33 0HT UK data.manager@cefas.co.uk distributor Department for Environment, Food and Rural Affairs (DEFRA) defra.helpline@defra.gov.uk owner notPlanned Modelling Season GEMET, version 1.0 2008-06-01 publication Oceanographic geographical features Habitats and biotopes GEMET - INSPIRE themes, version 1.0 2008-06-01 publication Unknown SeaVoX Vertical Co-ordinate Coverages 2010-05-18 revision Marine Environmental Data and Information Network MEDIN metadata record availability 2012-01-11 publication data.gov.uk MEDIN metadata record availability 2012-01-11 publication otherRestrictions noLimitations otherRestrictions Publicly available data licensed under the Open Government Licence v3.0 ( `https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/)`_ – Please note the applicable attribution statement is as follows: ‘includes Crown copyright, Cefas 2024' .. _`https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/)`: https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/ English environment oceans 70.00 110.00 0.00 30.00 2018-06-01 2019-09-30 Binary Unknown https://data.cefas.co.uk/view/22047 Cefas Data Portal download The Cefas Data Portal contains metadata records and data sets available to download and connect to in support of our commitment to open science. Data is available in the following formats: Binary download. dataset INSPIRE Data Specification on Oceanographic geographical features – Technical Guidelines 2013-12-10 publication false See the referenced specification INSPIRE Data Specification on Habitats and Biotopes – Technical Guidelines 2013-12-10 publication false See the referenced specification Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 publication true See the referenced specification The transport of marine litter was modelled using the OceanParcels v2.3.1** **Lagrangian particle tracking model (Delandmeter and van Sebille, 2019; Lange and van Sebille, 2017). The model includes several processes which influence the movement of floating, buoyant particles around the domain. Advection of particles via surface ocean currents (detailed below) was included using an inbuilt OceanParcels kernel which uses a fourth-order Runge-Kutta advection scheme. Stokes drift velocities were added to surface currents to account for the movement of particles resulting from wave motions. To account for sub-grid scale processes, diffusion is implemented as a random walk, with a diffusion coefficient of 100 m2/s, chosen based on grid cell size (Peliz et al., 2007), as detailed below. Windage is implemented in the model by applying 1% of the wind velocity to the particles’ trajectories, following analysis of observations of the wind’s effect on drifters by Pereiro et al. (2018), which should account for all but very large items of litter. The final process implemented here was beaching. At the end of each timestep, after advancing each particle’s position, ocean velocities were checked at this new position. If the velocity was less than 10-14 m/s, the particle was considered to be beached (after Delandmeter and van Sebille (2019)) and was no longer tracked. There is no resuspension of particles that have beached; the beached location is considered the final sink location. The advection of particles depends on surface ocean currents taken from two different models which were used to evaluate the transport of particles and help quantify uncertainty in the results. The NEMO-based CMEMS Global Ocean Physics Analysis and Forecast hydrodynamic model (E.U. Copernicus Marine Service Information (CMEMS), Marine Data Store (MDS), 2022a) has a resolution of 1/12°, which is roughly 9.2 km at the latitudes of the Bay of Bengal, and includes data assimilation (Lellouche et al., 2018). Also included was the ROMS-based high-resolution model, configured for the North Indian Ocean as a part of the High-Resolution Operational Ocean Forecast and Reanalysis System (known as NIO-HOOFS) by INCOIS for the Indian Ocean (Francis et al., 2020), which has a much higher resolution of 1/48°, corresponding to approximately 2.3 km at these latitudes, but does not include data assimilation. Additional datasets from CMEMS Global Ocean Wave Analysis and Forecasting model (Ardhuin et al., 2010; E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS), 2022b) and ERA5 global atmospheric reanalysis (Hersbach et al., 2023) were used to provide Stokes drift velocities and wind fields at a height of 10 m above land, respectively. Particle release locations were uniformly spaced around all major coastlines in the Bay of Bengal. Particles were released on average 6 km from the coastline, with a maximum distance of 18 km in some locations. A particle was released from each of the 500 coastal locations every day for a year, with 182,500 particles released in total. Model simulations covered 1st June 2018 – 30th September 2019 for each case (CMEMS and ROMS). Following some sensitivity tests detailed below, particles were forced with daily-mean ocean, Stokes drift, and wind velocities. A model time step of 15 minutes was used (following Delandmeter and van Sebille (2019)) and particle positions were output daily. We ran separate simulations for each season, with particles released over a season-specific, four-month period: monsoon = 1st June – 30th September 2018; post-monsoon = 1st October 2018 – 31st January 2019; pre-monsoon = 1st February – 30th May 2019 (Anoop et al., 2015). Regardless of the release period, all particles were tracked until the end of September 2019. To assess model performance, the simulated trajectories of floating litter were compared with paths of drifters which had lost their drogues in the Bay of Bengal between June 2018 – September 2019. Within the Global Drifter Program’s quality-controlled 6-hour interpolated dataset (Lumpkin and Centurioni, 2019), five drifters were identified that met these criteria within the spatial and temporal limits of the model. As the separation between the particles and drifter location is expected to increase with time (Tamtare et al., 2021), each drifter trajectory was separated into week-long segments. CMEMS and ROMS simulations were run, using the same input data and parameters described for the main simulations. Starting at midday on the first full day after each drifter lost its drogue, 100 particles were released at the same location as the drifter. For each subsequent week, a further 100 particles were released from the location of the drifter at that time. Each particle was then followed for one week to compare to the relevant drifter trajectory during that time. To decide the required temporal resolution necessary to simulate particle trajectories across the Bay of Bengal, simulations were run to test the sensitivity of sink locations to temporal forcing. Simulations were forced with either CMEMS or ROMS hydrodynamic forcing at either hourly or daily temporal resolution. All four simulations used the same parameters as well as wind and Stokes drift data as detailed above and were run for the month of July 2020 with particles released for the first two weeks only. This study has been conducted using E.U. Copernicus Marine Service Information: `10.48670/moi-00016`_`10.48670/moi-00017`_ Anoop, T. R., Kumar, V. S., Shanas, P. R., and Johnson, G.: Surface Wave Climatology and Its Variability in the North Indian Ocean Based on ERA-Interim Reanalysis, Journal of Atmospheric and Oceanic Technology, 32, 1372–1385, `https://doi.org/10.1175/JTECH-D-14-00212.1`_, 2015. Ardhuin, F., Rogers, E., Babanin, A. V., Filipot, J.-F., Magne, R., Roland, A., Westhuysen, A. van der, Queffeulou, P., Lefevre, J.-M., Aouf, L., and Collard, F.: Semiempirical Dissipation Source Functions for Ocean Waves. Part I: Definition, Calibration, and Validation, Journal of Physical Oceanography, 40, 1917–1941, `https://doi.org/10.1175/2010JPO4324.1`_, 2010. Delandmeter, P. and van Sebille, E.: The Parcels v2.0 Lagrangian framework: new field interpolation schemes, Geoscientific Model Development, 12, 3571–3584, `https://doi.org/10.5194/gmd-12-3571-2019`_, 2019. Francis, P. A., Jithin, A. K., Effy, J. B., Chatterjee, A., Chakraborty, K., Paul, A., Balaji, B., Shenoi, S. S. C., Biswamoy, P., Mukherjee, A., Singh, P., Deepsankar, B., Reddy, S. S., Vinayachandran, P. N., Kumar, M. S. G., Bhaskar, T. V. S. U., Ravichandran, M., Unnikrishnan, A. S., Shankar, D., Prakash, A., Aparna, S. G., Harikumar, R., Kaviyazhahu, K., Suprit, K., Shesu, R. V., Kumar, N. K., Rao, N. S., Annapurnaiah, K., Venkatesan, R., Rao, A. S., Rajagopal, E. N., Prasad, V. S., Gupta, M. D., Nair, T. M. B., Rao, E. P. R., and Satyanarayana, B. V.: High-Resolution Operational Ocean Forecast and Reanalysis System for the Indian Ocean, Bulletin of the American Meteorological Society, 101, E1340–E1356, `https://doi.org/10.1175/BAMS-D-19-0083.1`_, 2020. Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), `https://doi.org/10.24381/cds.adbb2d47`_, 2023. Lange, M. and van Sebille, E.: Parcels v0.9: prototyping a Lagrangian ocean analysis framework for the petascale age, Geoscientific Model Development, 10, 4175–4186, `https://doi.org/10.5194/gmd-10-4175-2017`_, 2017. Lellouche, J.-M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., Testut, C.-E., Bourdalle-Badie, R., Gasparin, F., Hernandez, O., Levier, B., Drillet, Y., Remy, E., and Le Traon, P.-Y.: Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1∕12° high-resolution system, Ocean Science, 14, 1093–1126, `https://doi.org/10.5194/os-14-1093-2018`_, 2018. Lumpkin, R. and Centurioni, L.: Global Drifter Program quality-controlled 6-hour interpolated data from ocean surface drifting buoys. June 2018-September 2019. NOAA National Centers for Environmental Information. [Dataset.] `https://doi.org/10.25921/7ntx-z961`_, 2019, (Accessed 07 July 2023). Peliz, A., Marchesiello, P., Dubert, J., Marta-Almeida, M., Roy, C., and Queiroga, H.: A study of crab larvae dispersal on the Western Iberian Shelf: Physical processes, Journal of Marine Systems, 68, 215–236, `https://doi.org/10.1016/j.jmarsys.2006.11.007`_, 2007. Pereiro, D., Souto, C., and Gago, J.: Calibration of a marine floating litter transport model, Journal of Operational Oceanography, 11, 125–133, `https://doi.org/10.1080/1755876X.2018.1470892`_, 2018. Tamtare, T., Dumont, D., and Chavanne, C.: Extrapolating Eulerian ocean currents for improving surface drift forecasts, Journal of Operational Oceanography, 14, 71–85, `https://doi.org/10.1080/1755876X.2019.1661564`_, 2021. .. _`10.48670/moi-00016`: https://data.marine.copernicus.eu/product/GLOBAL_ANALYSISFORECAST_PHY_001_024/description .. _`10.48670/moi-00017`: https://data.marine.copernicus.eu/product/GLOBAL_ANALYSISFORECAST_WAV_001_027/description .. _`https://doi.org/10.1175/jtech-d-14-00212.1`: https://journals.ametsoc.org/view/journals/atot/32/7/jtech-d-14-00212_1.xml .. _`https://doi.org/10.1175/2010jpo4324.1`: https://doi.org/10.1175/2010JPO4324.1 .. _`https://doi.org/10.5194/gmd-12-3571-2019`: https://doi.org/10.5194/gmd-12-3571-2019 .. _`https://doi.org/10.1175/bams-d-19-0083.1`: https://doi.org/10.1175/BAMS-D-19-0083.1 .. _`https://doi.org/10.24381/cds.adbb2d47`: https://doi.org/10.24381/cds.adbb2d47 .. _`https://doi.org/10.5194/gmd-10-4175-2017`: https://doi.org/10.5194/gmd-10-4175-2017 .. _`https://doi.org/10.5194/os-14-1093-2018`: https://doi.org/10.5194/os-14-1093-2018 .. _`https://doi.org/10.25921/7ntx-z961`: https://doi.org/10.25921/7ntx-z961 .. _`https://doi.org/10.1016/j.jmarsys.2006.11.007`: https://doi.org/10.1016/j.jmarsys.2006.11.007 .. _`https://doi.org/10.1080/1755876x.2018.1470892`: https://doi.org/10.1080/1755876X.2018.1470892 .. _`https://doi.org/10.1080/1755876x.2019.1661564`: https://doi.org/10.1080/1755876X.2019.1661564 MEDIN 3.1.1