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Simulation output for Beutel & Allen (2024) - Seasonal and interannual Salish Sea inflow origins using Lagrangian tracking

Description: This dataset includes setup files and simulation results for quantitative Lagrangian analysis using Ariane, an offline Lagrangian tool capable of calculating 3D parcel trajectories and volume transport, used in Beutel & Allen (2024). The simulations are based on the output from two regional ocean models: CIOPS-BC12, an atmosphere-ocean-ice forecasting system for the west coast of Canada, and SalishSeaCast, a 3D physical-biological-chemical ocean model of the Salish Sea. The simulation results were utilized to analyze the water masses entering the Salish Sea and to assess the seasonal and interannual variability in their contributions to inflow. This knowledge is crucial for understanding the vulnerability of the Salish Sea to anthropogenic impacts.
Notes: Item exited embargo and became publicly available on 2024-05-24
Authors: Beutel, Becca; University of British Columbia; ORCID iD 0000-0002-0653-2177
Allen, Susan; University of British Columbia; ORCID iD 0000-0003-2079-6520
Keywords: Lagrangian tracking
water masses
coastal
Field of Research: 
Earth and related environmental sciences
>
Oceanography
>
Physical oceanography
Publication Date: 2024-05-24
Publisher: Federated Research Data Repository / dépôt fédéré de données de recherche
Funder: Natural Sciences and Engineering Research Council; Canadian Graduate Scholarship
Natural Sciences and Engineering Research Council; Discovery Grant
URI: https://doi.org/10.20383/103.0765
Related Identifiers: 
This dataset is cited by

Geographic Coverage: 
Place Name
Salish Sea
Province /
Province / Territory
British Columbia
Territory
 
Country
Canada

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Access to this dataset is subject to the following terms:
Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
Citation
Beutel, B., Allen, S. (2024). Simulation output for Beutel & Allen (2024) - Seasonal and interannual Salish Sea inflow origins using Lagrangian tracking. Federated Research Data Repository. https://doi.org/10.20383/103.0765