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&ndash;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_h = 1m²s^-1 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&deg; (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>).&nbsp;</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&ndash;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&ndash;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.&nbsp;</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&ndash;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). &ldquo;Chapter 13: variational data assimilation for the global ocean&rdquo;, in Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, Vol. II, eds S. Park and L. Xu (Berlin: Springer), 303&ndash;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&ndash;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&mdash;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&ndash;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&ndash;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&rsquo;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. B&ouml;ning, 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>