Articles | Volume 18, issue 2
https://doi.org/10.5194/gmd-18-211-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-211-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
20 Jan 2025
Model description paper |
| 20 Jan 2025
| 20 Jan 2025
HOTSSea v1: a NEMO-based physical Hindcast of the Salish Sea (1980–2018) supporting ecosystem model development
Greig Oldford
CORRESPONDING AUTHOR
Institute for Oceans and Fisheries, University of British Columbia, Vancouver, V6T 1Z4, Canada
Ecosystem Sciences Division, Fisheries and Oceans Canada, Nanaimo, V9T 6N7, Canada
Tereza Jarníková
Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UK
Villy Christensen
Institute for Oceans and Fisheries, University of British Columbia, Vancouver, V6T 1Z4, Canada
Michael Dunphy
Ocean Sciences Division, Institute of Ocean Sciences, Fisheries and Oceans Canada, Sidney, V8L 5T5, Canada
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Tereza Jarníková, Colin Jones, Steven Rumbold, and Corinne Le Quéré
EGUsphere, https://doi.org/10.5194/egusphere-2025-3374, https://doi.org/10.5194/egusphere-2025-3374, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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Southern Ocean winds drive global climate and have strengthened since 1980 due to Antarctic ozone depletion. We assessed which climate reconstructions best capture these changes using sea level pressure observations. We then used an Earth system model to attribute these changes between ozone and greenhouse gas emissions. Ozone depletion dominated past wind acceleration, but greenhouse gases will drive future changes after 2050.
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Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
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The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
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The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
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EGUsphere, https://doi.org/10.5194/egusphere-2023-42, https://doi.org/10.5194/egusphere-2023-42, 2023
Preprint withdrawn
Short summary
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This paper present the Coastal Ice-Ocean Prediction System implemented operationally at Environment and climate change Canada. The objective is to enhance the numerical guidance in coastal areas to support electronic navigation and response to environmental emergencies in the aquatic environment. Model evaluation against observations shows improvements for most surface ocean variables in the coastal system compared to current coarser-resolution operational systems.
Tereza Jarníková, Elise M. Olson, Susan E. Allen, Debby Ianson, and Karyn D. Suchy
Ocean Sci., 18, 1451–1475, https://doi.org/10.5194/os-18-1451-2022, https://doi.org/10.5194/os-18-1451-2022, 2022
Short summary
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Understanding drivers of phytoplankton biomass in dynamic coastal regions is key to predicting present and future ecosystem functioning. Using a clustering-based method, we objectively determined biophysical provinces in a complex estuarine sea. The Salish Sea contains three major distinct provinces where phytoplankton dynamics are controlled by diverse stratification regimes. Our method is simple to implement and broadly applicable for identifying structure in large model-derived datasets.
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Short summary
We developed a 3D ocean model called the Hindcast of the Salish Sea (HOTSSea v1) that recreates physical conditions throughout the Salish Sea from 1980 to 2018. It was not clear that acceptable accuracy could be achieved because of computational and data limitations, but the model's predictions agreed well with observations. When we used the model to examine ocean temperature trends in areas that lack observations, it indicated that some seasons and areas are warming faster than others.
We developed a 3D ocean model called the Hindcast of the Salish Sea (HOTSSea v1) that recreates...
