Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1445-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-14-1445-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
15 Mar 2021
Development and technical paper |
| 15 Mar 2021
| 15 Mar 2021
The Regional Ice Ocean Prediction System v2: a pan-Canadian ocean analysis system using an online tidal harmonic analysis
Gregory C. Smith
CORRESPONDING AUTHOR
Meteorological Research Division, Environment and Climate Change
Canada (ECCC), Dorval, H9P1J3, Canada
Yimin Liu
Meteorological Research Division, Environment and Climate Change
Canada (ECCC), Dorval, H9P1J3, Canada
Mounir Benkiran
Mercator Océan International, Toulouse, France
Kamel Chikhar
Meteorological Service of Canada, ECCC, Dorval, H9P1J3, Canada
Dorina Surcel Colan
Meteorological Service of Canada, ECCC, Dorval, H9P1J3, Canada
Audrey-Anne Gauthier
Meteorological Service of Canada, ECCC, Dorval, H9P1J3, Canada
Charles-Emmanuel Testut
Mercator Océan International, Toulouse, France
Frederic Dupont
Meteorological Service of Canada, ECCC, Dorval, H9P1J3, Canada
Ji Lei
Meteorological Service of Canada, ECCC, Dorval, H9P1J3, Canada
François Roy
Meteorological Research Division, Environment and Climate Change
Canada (ECCC), Dorval, H9P1J3, Canada
Jean-François Lemieux
Meteorological Research Division, Environment and Climate Change
Canada (ECCC), Dorval, H9P1J3, Canada
Fraser Davidson
Northwest Atlantic Fisheries Centre, Fisheries and Ocean Canada, St.
John's, Newfoundland, Canada
Related authors
Christopher Subich, Pierre Pellerin, Gregory Smith, and Frederic Dupont
Geosci. Model Dev., 13, 4379–4398, https://doi.org/10.5194/gmd-13-4379-2020, https://doi.org/10.5194/gmd-13-4379-2020, 2020
Short summary
Short summary
This work presents a semi-Lagrangian advection module for the NEMO (OPA) ocean model. Semi-Lagrangian advection transports fluid properties (temperature, salinity, velocity) between time steps by following fluid motion and interpolating from upstream locations of fluid parcels.
This method is commonly used in atmospheric models to extend time step size, but it has not previously been applied to operational ocean models. Overcoming this required a new approach for solid boundaries (coastlines).
Jean-Francois Lemieux, Mathieu Plante, Nils Hutter, Damien Ringeisen, Bruno Tremblay, Francois Roy, and Philippe Blain
EGUsphere, https://doi.org/10.5194/egusphere-2024-3831, https://doi.org/10.5194/egusphere-2024-3831, 2025
Short summary
Short summary
Sea ice models simulate angles between cracks that are too wide compared to observations. Ringeisen et al. argue that this is due to the flow rule which defines the fracture deformations. We implemented a non-normal flow rule. This flow rule also leads to angles that are too wide. This is a consequence of deformations that tend to align with the grid. Nevertheless, this flow rule could be used to optimize deformations while other parameters could be used to modify landfast ice and ice drift.
Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Amélie Bouchat, Damien Ringeisen, Philippe Blain, Stephen Howell, Mike Brady, Alexander S. Komarov, Béatrice Duval, Lekima Yakuden, and Frédérique Labelle
Earth Syst. Sci. Data, 17, 423–434, https://doi.org/10.5194/essd-17-423-2025, https://doi.org/10.5194/essd-17-423-2025, 2025
Short summary
Short summary
Sea ice forms a thin boundary between the ocean and the atmosphere, with complex, crust-like dynamics and ever-changing networks of sea ice leads and ridges. Statistics of these dynamical features are often used to evaluate sea ice models. Here, we present a new pan-Arctic dataset of sea ice deformations derived from satellite imagery, from 1 September 2017 to 31 August 2023. We discuss the dataset coverage and some limitations associated with uncertainties in the computed values.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
Short summary
Short summary
We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Adrienne Tivy, Joey Angnatok, François Roy, Gregory Smith, Frédéric Dupont, and Adrian K. Turner
The Cryosphere, 18, 1685–1708, https://doi.org/10.5194/tc-18-1685-2024, https://doi.org/10.5194/tc-18-1685-2024, 2024
Short summary
Short summary
We use a sea ice model to reproduce ice growth observations from two buoys deployed on coastal sea ice and analyze the improvements brought by new physics that represent the presence of saline liquid water in the ice interior. We find that the new physics with default parameters degrade the model performance, with overly rapid ice growth and overly early snow flooding on top of the ice. The performance is largely improved by simple modifications to the ice growth and snow-flooding algorithms.
Oreste Marquis, Bruno Tremblay, Jean-François Lemieux, and Mohammed Islam
The Cryosphere, 18, 1013–1032, https://doi.org/10.5194/tc-18-1013-2024, https://doi.org/10.5194/tc-18-1013-2024, 2024
Short summary
Short summary
We developed a standard viscous–plastic sea-ice model based on the numerical framework called smoothed particle hydrodynamics. The model conforms to the theory within an error of 1 % in an idealized ridging experiment, and it is able to simulate stable ice arches. However, the method creates a dispersive plastic wave speed. The framework is efficient to simulate fractures and can take full advantage of parallelization, making it a good candidate to investigate sea-ice material properties.
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023, https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
Jean-Philippe Paquin, François Roy, Gregory C. Smith, Sarah MacDermid, Ji Lei, Frédéric Dupont, Youyu Lu, Stephanne Taylor, Simon St-Onge-Drouin, Hauke Blanken, Michael Dunphy, and Nancy Soontiens
EGUsphere, https://doi.org/10.5194/egusphere-2023-42, https://doi.org/10.5194/egusphere-2023-42, 2023
Preprint withdrawn
Short summary
Short summary
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.
Frédéric Dupont, Dany Dumont, Jean-François Lemieux, Elie Dumas-Lefebvre, and Alain Caya
The Cryosphere, 16, 1963–1977, https://doi.org/10.5194/tc-16-1963-2022, https://doi.org/10.5194/tc-16-1963-2022, 2022
Short summary
Short summary
In some shallow seas, grounded ice ridges contribute to stabilizing and maintaining a landfast ice cover. A scheme has already proposed where the keel thickness varies linearly with the mean thickness. Here, we extend the approach by taking into account the ice thickness and bathymetry distributions. The probabilistic approach shows a reasonably good agreement with observations and previous grounding scheme while potentially offering more physical insights into the formation of landfast ice.
Jean-François Lemieux, L. Bruno Tremblay, and Mathieu Plante
The Cryosphere, 14, 3465–3478, https://doi.org/10.5194/tc-14-3465-2020, https://doi.org/10.5194/tc-14-3465-2020, 2020
Short summary
Short summary
Sea ice pressure poses great risk for navigation; it can lead to ship besetting and damages. Sea ice forecasting systems can predict the evolution of pressure. However, these systems have low spatial resolution (a few km) compared to the dimensions of ships. We study the downscaling of pressure from the km-scale to scales relevant for navigation. We find that the pressure applied on a ship beset in heavy ice conditions can be markedly larger than the pressure predicted by the forecasting system.
Christopher Subich, Pierre Pellerin, Gregory Smith, and Frederic Dupont
Geosci. Model Dev., 13, 4379–4398, https://doi.org/10.5194/gmd-13-4379-2020, https://doi.org/10.5194/gmd-13-4379-2020, 2020
Short summary
Short summary
This work presents a semi-Lagrangian advection module for the NEMO (OPA) ocean model. Semi-Lagrangian advection transports fluid properties (temperature, salinity, velocity) between time steps by following fluid motion and interpolating from upstream locations of fluid parcels.
This method is commonly used in atmospheric models to extend time step size, but it has not previously been applied to operational ocean models. Overcoming this required a new approach for solid boundaries (coastlines).
Cited articles
Amante, C. and Eakins, B. W.: ETOPO1 1-Arc-Minute Global Relief Model:
Procedures, Data Sources and Analysis, Tech. rep., NOAA Technical Memorandum
NESDIS NGDC-24, National Geophysical Data Center, NOAA,
https://doi.org/10.7289/V5C8276M, 2009.
Arbic, B. K., St-Laurent, P., Sutherland, G., and Garrett, C.: On the resonance and influence of the tides in Ungava Bay and Hudson Strait,
Geophys. Res. Lett., 34, L17606, https://doi.org/10.1029/2007GL030845, 2007.
Bell, M. J., Schiller, A., Le Traon, P.-Y., Smith, N. R., Dombrowsky, E., and Wilmer-Becker, K.: An introduction to GODAE OceanView, J. Oper. Oceanogr., 8, s2–s11, https://doi.org/10.1080/1755876X.2015.1022041, 2015.
Benkiran, M. and Greiner, E.: Impact of the Incremental Analysis Updates on a Real-Time System of the North Atlantic Ocean, J. Atmos. Ocean. Tech., 25, 2055–2073, 2008.
Blanke, B. and Delecluse, P.: Variability of the Tropical Atlantic Ocean Simulated by a General Circulation Model with Two Different Mixed-Layer Physics, J. Phys. Oceanogr., 23, 1363–1388, 1993.
Bloom, S. C., Takacs, L. L., Da Silva, A. M., and Ledvina, D.: Data assimilation using incremental analysis updates, Mon. Weather Rev., 124, 1256–1271, 1996.
Boutin, G., Lique, C., Ardhuin, F., Rousset, C., Talandier, C., Accensi, M., and Girard-Ardhuin, F.: Towards a coupled model to investigate wave–sea ice interactions in the Arctic marginal ice zone, The Cryosphere, 14, 709–735, https://doi.org/10.5194/tc-14-709-2020, 2020.
Boyer, T. P., Antonov, J. I., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A., Johnson, D. R., Locarnini, R. A., Mishonov, A. V., O'Brien, T. D., Paver, C. R., Reagan, J. R., Seidov, D., Smolyar, I. V., and Zweng, M. M.: World Ocean Database 2013, NOAA Atlas NESDIS 72, edited by: Levitus, S. and Mishonov, A., Silver Spring, MD, 209 pp.,
https://doi.org/10.7289/V5NZ85MT, 2013.
Brasnett, B. and Colan, D. S.: Assimilating retrievals of sea surface temperature from VIIRS and AMSR2, J. Atmos. Ocean. Tech., 33, 361–375, https://doi.org/10.1175/JTECH-D-15-0093.1, 2016.
Browne, P. A., de Rosnay, P., Zuo, H., Bennett, A. and Dawson, A.: Weakly coupled ocean–atmosphere data assimilation in the ECMWF NWP system, Remote Sens., 11, 234, https://doi.org/10.3390/rs11030234, 2019.
Buehner, M., Caya, A., Pogson, L., Carrieres, T., and Pestieau, P.: A New Environment Canada Regional Ice Analysis System, Atmos. Ocean, 51, 18–34, 2013.
Buehner, M., Caya, A., Carrieres, T., and Pogson, L.: Assimilation of SSMIS and ASCAT data and the replacement of highly uncertain estimates in the
Environment Canada Regional Ice Prediction System, Q. J. Roy. Meteor. Soc.,
142, 562–573, 2016.
Carrère, L. and Lyard, F.: Modelling the barotropic response of the global ocean to atmospheric wind and pressure forcing -comparisons with observations, Geophys. Res. Lett., 30, 1275, https://doi.org/10.1029/2002GL016473, 2003.
Carrère, L., Lyard, F., Cancet, M., Roblou, L., and Guillot, A.: ES 2012: a new global tidal model taking advantage of nearly 20 years of altimetry measurements, in: Proc. 20 Years of Progress in Radar Altimetry Symp., Venice-Lido, Italy, 22–29, 2012.
Chao, Y., Farrara, J. D., Zhang, H., Armenta, K. J., Centurioni, L., Chavez, F., Girton, J. B., Rudnick, D., and Walter, R. K.: Development, implementation, and validation of a California coastal ocean modeling, data
assimilation, and forecasting system, Deep-Sea Res. Pt. II, 151, 49–63, 2018.
Chikhar, K., Lemieux, J. F., Dupont, F., Roy, F., Smith, G. C., Brady, M., Howell, S. E., and Beaini, R.: Sensitivity of Ice Drift to Form Drag and Ice Strength Parameterization in a Coupled Ice–Ocean Model, Atmos. Ocean, 57, 329–349, 2019.
Cho, K.-H., Li, Y., Wang, H., Park, K.-S., Choi, J.-Y., Shin, K.-I., and Kwon, J.-I.: Development and Validation of an Operational Searchand Rescue Modelling System for the Yellow Sea and the East and South China Seas, J. Atmos. Ocean Tech., 31, 197–215, 2014.
Desroziers, G., Berre, L., Chapnik, B., and Polli, P.: Diagnosis of observation, background and analysis-error statistics in observation space, Q. J. Roy. Meteor. Soc., 131, 3385–3396,
https://doi.org/10.1256/qj.05.108, 2005.
Dibarboure, G., Pujol, M.-I., Briol, F., Le Traon, P. Y., Larnicol, G., Picot, N., Mertz, F., and Ablain, M.: Jason-2 in DUACS: Updated system description, first tandem results and impact on processing and products, Mar. Geod., 34, 214–241, 2011.
Divakaran, P., Brassington, G. B., Ryan, A. G., Regnier, C., Spindler, T., Mehra, A., Hernandez, F., Smith, G., Liu, Y., and Davidson, F.: GODAE OceanView Class-4 Inter-comparison for the Australian Region, J. Oper. Oceanogr., 8, s112–s126, https://doi.org/10.1080/1755876X.2015.1022333, 2015.
Dupont, F., Higginson, S., Bourdallé-Badie, R., Lu, Y., Roy, F., Smith, G. C., Lemieux, J.-F., Garric, G., and Davidson, F.: A high-resolution ocean and sea-ice modelling system for the Arctic and North Atlantic oceans, Geosci. Model Dev., 8, 1577–1594, https://doi.org/10.5194/gmd-8-1577-2015, 2015.
Dupont F., Smith, G., Liu, Y., Chikhar, K., Surcel Colan, D., Lei, J., Roy, F., Pellerin, P.: Changes from version 1.3.0 to version 2.0.0 of the Regional Ice Ocean Prediction System (RIOPS), CMC Tech Doc., 39 pp., available at:
https://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/tech_notes/technote_riops-200_e.pdf (lats access: 25 February 2021), 2019.
Egbert, G. D. and Erofeeva, S. Y.: Efficient inverse modeling of barotropic ocean tides, J. Atmos. Ocean. Tech., 19, 183–204, 2002.
Gaspar, P., Grégoris, Y., and Lefevre, J. M.: A simple eddy kinetic energy model for simulations of the oceanic vertical mixing: Tests at station Papa and Long-Term Upper Ocean Study site, J. Geophys. Res.-Oceans, 95, 16179–16193, 1990.
Hirose, N., Usui, N., Sakamoto, K., Tsujino, H., Yamanaka, G., Nakano, H., Urakawa, S., Toyoda, T., Fujii, Y., and Kohno, N.: Development of a new operational system for monitoring and forecasting coastal and open-ocean states around Japan, Ocean Dynam., 69, 1333–1357, 2019.
Hunke, E. C.: Viscous-plastic sea ice dynamics with the EVP model:
linearization issues, J. Comput. Phys., 170, 18–38, 2001.
Hunke, E. C. and Lipscomb, W. H.: CICE: The Los Alamos sea ice model, Documentation and software user's manual version 4.0 (Tech. Rep.
LA-CC-06-012), Los Alamos National Laboratory, Los Alamos, NM, 2008.
Jacobs, G. A., D'Addezio, J. M., Bartels, B., and Spence, P. L.: Constrained scales in ocean forecasting, Adv. Space Res., https://doi.org/10.1016/j.asr.2019.09.018, online first, 2019.
Jung, T., Gordon, N. D., Bauer, P., Bromwich, D. H., Chevallier, M., Day, J.
J., Dawson, J., Doblas-Reyes, F., Fairall, C., Goessling, H. F., Holland,
M., Inoue, J., Iversen, T., Klebe, S., Lemke, P., Losch, M., Makshtas. A.,
Mills, B., Nurmi, P., Perovich, D., Reid, P., Renfrew, I. A., Smith, G.,
Svensson, G., Tolstykh, M., and Yang, Q.: Advancing Polar Prediction
Capabilities on Daily to Seasonal Time Scales, B. Am. Meteorol. Soc.,
https://doi.org/10.1175/BAMS-D-14-00246.1, 2016.
King, R. R., While, J., Martin, M. J., Lea, D. J., Lemieux-Dudon, B., Waters, J., and O'Dea, E.: Improving the initialisation of the Met Office operational shelf-seas model, Ocean Model., 130, 1–14, 2018.
Kleptsova, O. and Pietrzak, J. D.: High resolution tidal model of Canadian Arctic Archipelago, Baffin and Hudson Bay, Ocean Model., 128, 15–47, 2018.
Kourafalou, V. H., De Mey, P., Staneva, J., Ayoub, N., Barth, A., Chao, Y., Cirano M., Fiechter, J., Herzfeld, M., Kurapov, A., and Moore, A. M.: Coastal Ocean Forecasting: science foundation and user benefits, J Oper. Oceanogr., 8, s147–s167, 2015.
Large, W. G. and Yeager, S. G.: Diurnal to decadal global forcing for ocean and seaice models, NCAR Technical Note, 1–22, 2004.
Lellouche, J.-M., Le Galloudec, O., Drévillon, M., Régnier, C., Greiner, E., Garric, G., Ferry, N., Desportes, C., Testut, C.-E., Bricaud, C., Bourdallé-Badie, R., Tranchant, B., Benkiran, M., Drillet, Y., Daudin, A., and De Nicola, C.: Evaluation of global monitoring and forecasting systems at Mercator Océan, Ocean Sci., 9, 57–81, https://doi.org/10.5194/os-9-57-2013, 2013.
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 Sci., 14, 1093–1126, https://doi.org/10.5194/os-14-1093-2018, 2018.
Lemieux, J.-F., Tremblay, L. B., Dupont, F., Plante, M., Smith, G. C., and Dumont, D.: A basal stress parameterization for modeling landfast ice, J. Geophys. Res., 120, 3157–3173, https://doi.org/10.1002/2014JC010678, 2015.
Lemieux, J.-F., Beaudoin, C., Dupont, F., Roy, F., Smith, G. C., Shlyaeva, A., Buehner, M., Caya, A., Chen, J., Carrieres, T., Pogson, L.,
DeRepentigny, P., Plante, A., Pestieau, P., Pellerin, P., Ritchie, H.,
Garric, G., and Ferry, N.: The Regional Ice Prediction System (RIPS):
verification of forecast sea ice concentration, Q. J.
Roy. Meteor. Soc., 142, 632–643, https://doi.org/10.1002/qj.2526, 2016a.
Lemieux, J.-F., Dupont, F., Blain, P., Roy, F., Smith, G. C., and Flato, G. M.: Improving the simulation of landfast ice by combining tensile strength and a parameterization for grounded ridges, J. Geophys. Res., 121, 7354–7368, 2016b.
Lemieux, J.-F., Lei, J., Dupont, F., Roy, F., Losch, M., Lique, C. and Laliberté, F.: The Impact of Tides on Simulated Landfast Ice in a
Pan-Arctic Ice-Ocean Model, J. Geophys. Res.-Oceans, 123, 7747–7762,
2018.
Lin, H., Merryfield, W. J., Muncaster, R., Smith, G. C., Markovic, M., Dupont, F., Roy, F., Lemieux, J.-F., Dirkson, A., Kharin, V. V., and Lee, W. S.: The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2), Weather Forecast., 35, 1317–1343, 2020.
Lipscomb, W. H., Hunke, E. C., Maslowski, W., and Jakacki, J.: Ridging, strength, and stability in high-resolution sea ice models, J. Geophys. Res., 112, C03S91, https://doi.org/10.1029/2005JC003355, 2007.
Madec, G.: NEMO reference manual, ocean dynamics component: NEMO-OPA. Preliminary version, Note du Pole de modélisation, Institut Pierre-Simon Laplace (IPSL), France, 27, 1288–1619, 2008.
Madec, G. and Imbard, M.: A global ocean mesh to overcome the North Pole singularity, Clim. Dynam., 12, 381–388, 1996.
Madec, G., Delecluse, P., Imbard, M., and Levy, C.: OPA 8.1 general
circulation model reference manual, Notes de l'IPSL, University P. et M.
Curie, B102 T15-E5, Paris, No. 11, 91 pp., 1998.
Masson, D. and Cummins, P. F.: Temperature trends and interannual
variability in the Strait of Georgia, British Columbia, Continental Shelf
Res., 27, 634–649, 2007.
McTaggart-Cowan, R., Vaillancourt, P. A., Zadra, A., Chamberland, S., Charron, M., Corvec, S., Milbrandt, J. A., Paquin-Ricard, D., Patoine, A., Roch, M., Separovic, L., and Yang J.: Modernization of atmospheric physics parameterization in Canadian NWP, J. Adv. Model. Earth Sy., 11, 3593–3635, https://doi.org/10.1029/2019MS001781, 2019.
Mehra, A. and Rivin, I.: A real time ocean forecast system for the North Atlantic Ocean, Terr. Atmos. Ocean. Sci., 21, 211–228, 2010.
Moore, A. M., Arango, H. G., Broquet, G., Powell, B. S., Zavala-Garay, J., and Weaver, A. T.: The Regional Ocean Modelling System (ROMS) 4-dimensional variational data assimilation systems, Part I: System overview and formulation, Prog. Oceanogr., 91, 34–49, 2011.
Nudds, S., Lu, Y., Higginson, S., Haigh, S., Paquin, J.-P.,
O'Flaherty-Sproul, M., Taylor, S., Blanken, H., Marcotte, G., Smith, G. C.,
Bernier, N., MacAulay, P., Wu, Y., Zhai, L., Hu, X., Chanut, J., Dunphy, M.,
Dupont, F., Greenberg, D., Davidson, F., and Page, F.: Evaluation of Structured and Unstructured Models for Application in Operational Ocean Forecasting in Nearshore Waters, J. Mar. Sci. Eng., 8, 484, https://doi.org/10.3390/jmse8070484, 2020.
O'Reilly, C. T., Solvason, R., and Solomon, C.: Where are the world's largest tides?, in: BIO Annual Report “2004 in Review,”, edited by: Ryan, J., Biotechnol. Ind. Org., Washington D. C., 44–46, 2005.
Park, K. S., Heo, K. Y., Jun, K., Kwon, J. I., Kim, J., Choi, J. Y., Cho, K. H., Choi, B. J., Seo, S. N., Kim, Y. H., and Kim, S. D.: Development of the operational oceanographic system of Korea, Ocean Sci. J., 50, 353–369, https://doi.org/10.1007/s12601-015-0033-1, 2015.
Pawlowicz, R., Beardsley, B., and Lentz, S.: Classical tidal harmonic analysis including error estimates in MATLAB using T_TIDE,
Comput. Geosci., 28, 929–937, 2002.
Pellerin, P., Ritchie, H., Saucier, S. J., Roy, F., Desjardins, S., Valin, M., and Lee, V.: Impact of a Two-Way Coupling between an Atmospheric and an Ocean-ice Model over the Gulf of St. Lawrence, Mon. Weather Rev., 132, 1379–1398, 2004.
Pham, D., Verron, J., and Roubaud, M.: A Singular Evolutive Extended Kalman filter for data assimilation in oceanography, J. Marine Syst., 16, 323–340, 1998.
Rigor, I. and Ortmeyer, M.: The International Arctic Buoy
Programme–monitoring the Arctic Ocean for forecasting and research, Arctic
Research of the United States, 18, 1–21, 2004.
Rio, M.-H., Mulet, S., and Picot, N.: Beyond GOCE for the ocean circulation estimate: Synergetic use of altimetry, gravimetry, and in situ data provides new insight into geostrophic and Ekman currents, Geophys. Res. Lett., 41, 8918–8925, https://doi.org/10.1002/2014GL061773, 2014.
Roy, F., Chevallier, M., Smith, G. C., Dupont, F., Garric, G., Lemieux, J.-F., Lu, Y., and Davidson, F.: Arctic sea ice and freshwater sensitivity to the treatment of the atmosphere-ice-ocean surface layer. J. Geophys. Res.-Oceans, 120, 4392–4417, 2015.
Ryan, A. G., Regnier, C., Divakaran, P., Spindler, T., Mehra, A., Hernandez, F., Smith, G. C., Liu, Y., and Davidson, F.: GODAE OceanView Class 4 forecast verification framework: Global ocean inter-comparison, J. Oper. Oceanogr., 8, s98–s111, https://doi.org/10.1080/1755876X.2015.1022330, 2015.
Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656, https://doi.org/10.5194/os-8-633-2012, 2012.
Saucier, F. J. and Chassé, J.: Tidal circulation and buoyancy effects in the St. Lawrence Estuary, Atmos. Ocean, 38, 505–556, 2000.
Saucier, F. J., Roy, F., Gilbert, D., Pellerin, P., and Ritchie, H.: The formation of water masses and sea ice in the Gulf of St. Lawrence, J. Geophys. Res., 108, 3269–3289, 2003.
Saucier, F. J., Senneville, S., Prinsenberg, S., Roy, F., Smith, G., Gachon, P., Caya, D., and Laprise, R.: Modelling the Sea Ice-Ocean Seasonal Cycle in Hudson Bay, Foxe Basin and Hudson Strait, Canada. Clim. Dyn., 23, 303–326, 2004.
Smith, G. C., Roy, F., and Brasnett, B.: Evaluation of an Operational Ice-Ocean Analysis and Forecasting System for the Gulf of St. Lawrence, Q. J. Roy. Meteor. Soc., 139, 419–433, https://doi.org/10.1002/qj.1982, 2012.
Smith, G. C., Davidson, F., and Lu, Y.: The CONCEPTS Initiative: Canadian Operational Network of Coupled Environmental Prediction Systems, J. Ocean Technol., 8, 80–81, 2013.
Smith, G. C., Roy, F., Reszka, M., Surcel Colan, D., He, Z., Deacu, D., Belanger, J. M., Skachko, S., Liu, Y., Dupont, F., and Lemieux, J.-F.: Sea ice forecast verification in the Canadian global ice ocean prediction system, Q. J. Roy. Meteor. Soc., 142, 659–671, https://doi.org/10.1002/qj.2555, 2016.
Smith, G. C., Bélanger, J. M., Roy, F., Pellerin, P., Ritchie, H., Onu, K., Roch, M., Zadra, A., Surcel Colan, D., Winter, B., and Fontecilla, J. S.: Impact of Coupling with an Ice-Ocean Model on Global Medium-Range NWP Forecast Skill, Mon. Weather Rev., 146, 1157–1180, https://doi.org/10.1175/MWR-D-17-0157.1, 2018.
Smith, G. C., Surcel Colan, D., Chikhar, K., and Liu, Y.: Global Ice Ocean Prediction System (GIOPS): Update from version 2.3.1 to version 3.0.0. Technical Documentation for GIOPSv3.0, CMC Tech Doc., 49 pp., available at: https://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/tech_notes/technote_giops-300_e.pdf (last access: 25 February 2021), 2019a.
Smith, G. C., Allard, R., Babin, M., Bertino, L., Chevallier, M., Corlett, G., Crout, J., Davidson, F., Delille, B., Gille, S. T., Hebert, D., Hyder, P., Intrieri, J., Lagunas, J., Larnicol, G., Kaminski, T., Kater, B., Kauker, F., Marec, C., Mazloff, M., Metzger, E. J., Mordy, C., O'Carroll, A., Olsen, S. M., Phelps, M., Posey, P., Prandi, P., Rehm, E., Reid, P., Rigor, I., Sandven, S., Shupe, M., Swart, S., Smedstad, O. M., Solomon, A., Storto, A., Thibaut, P., Toole, J., Wood, K., Xie, J., Yang, Q., and the WWRP PPP Steering Group: Polar Ocean Observations: A Critical Gap in the Observing System and its effect on Environmental Predictions from Hours to a Season, Front. Mar. Sci., 6, 429, doi.org/10.3389/fmars.2019.00429, 2019b.
Soontiens, N., Allen, S. E., Latornell, D., Le Souëf, K., Machuca, I., Paquin, J.-P., Lu, Y., Thompson, K., and Korabel, V.: Storm surges in the Strait of Georgia simulated with a regional model, Atmos.-Ocean, 54, 1–21, 2016.
Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Reffray, G., Amo-Baladrón, A., Benkiran, M., and Alvarez Fanjul, E.: The MyOcean IBI Ocean Forecast and Reanalysis Systems: operational
products and roadmap to the future Copernicus Service, J. Oper. Oceanogr.,
8, 63–79, https://doi.org/10.1080/1755876X.2015.1014663, 2015.
Sutherland, G., Soontiens, N., Davidson, F., Smith, G. C., Bernier, N., Blanken, H., Shillinger, D., Marcotte, G., Röhrs, J., Dagestad, K.-F., Christensen, K. H., and Breivik, Ø.: Evaluating the leeway coefficient of ocean drifters using operational marine environmental prediction systems, J. Atmos. Ocean. Tech., 37, 1943–1954, https://doi.org/10.1175/JTECH-D-20-0013.1, 2020.
Talagrand, O.: A posteriori evaluation and verification of analysis and assimilation algorithms, in: Proc. of ECMWF Workshop on Diagnosis of Data Assimilation System, 2–4 November, 1998, Reading, UK, 17–28, 1998.
Tang, C. L., Yao, T., Perrie, W., Detracey, B.M., Toulany, B., Dunlap E., and Wu, Y.: BIO ice-ocean and wave forecasting models and systems for eastern Canadian waters, Canadian Technical Report of Hydrography and Ocean Sciences, 261, 61 pp., 2008.
Tonani, M., Pinardi, N., Dobricic, S., Pujol, I., and Fratianni, C.: A high-resolution free-surface model of the Mediterranean Sea, Ocean Sci., 4, 1–14, https://doi.org/10.5194/os-4-1-2008, 2008.
Tonani, M., Balmaseda, M., Bertino, L., Blockley, E., Brassington, G. B., Davidson, F., Drillet, Y., Hogan, P. J., Kuragano, T., Lee, T., Mehra, A., Paranathara, F., Tanajura, C. A. S., and Wang, H.:
Status and future of global and regional ocean prediction systems, J. Oper. Oceanogr., 8, 201–220, https://doi.org/10.1080/1755876X.2015.1049892, 2015.
Toole, J. M., Krishfield, R. A., Timmermans, M.-L., and Proshutinsky, A.: The Ice-Tethered Profiler: Argo of the Arctic, Oceanography, 24, 126–135, https://doi.org/10.5670/oceanog.2011.64, 2011.
Tschudi, M., Meier, W. N., Stewart, J. S., Fowler, C., and Maslanik, J.: Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 4, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center https://doi.org/10.5067/INAWUWO7QH7B, 2019.
Umlauf, L. and Burchard, H.: A generic length-scale equation for geophysical turbulence models, J. Mar. Res., 61, 235–265, 2003.
Ungermann, M., Tremblay, L. B., Martin, T., and Losch, M.: Impact of the ice strength formulation on the performance of a sea ice thickness distribution model in the A rctic, J. Geophys. Res.-Oceans, 122, 2090–2107, 2017.
Wood, K. R., Jayne, S. R., Mordy, C. W., Bond, N., Overland, J. E., Ladd, C., Stabeno, P. J., Ekholm, A. K., Robbins, P. E., Schreck, M. B., and Heim, R.: Results of the First Arctic Heat Open Science Experiment, B. Am. Meteorol. Soc., 99, 513–520, 2018.
Xie, J., Counillon, F., Zhu, J., and Bertino, L.: An eddy resolving tidal-driven model of the South China Sea assimilating along-track SLA data using the EnOI, Ocean Sci., 7, 609–627, https://doi.org/10.5194/os-7-609-2011, 2011.
Xue, H., Shi, L., Cousins, S., and Pettigrew, N. R.: The GoMOOS
nowcast/forecast system, Continental Shelf Res., 25, 2122–2146,
2005.
Zhang, W. G., Wilkin, J. L., and Arango, H. G.: Towards an integrated observation and modeling system in the New York Bight using variational methods. Part I: 4DVAR data assimilation, Ocean Model., 35, 119–133, 2010.
Zedel, L., Wang, Y., Davidson, F., and Xu, J.: Comparing ADCP data collected during a seismic survey off the coast of Newfoundland with analysis data from the CONCEPTS operational ocean model, J. Oper. Oceanogr., 11, 100–111, 2018.
Zhuang, S. Y., Fu, W. W., and She, J.: A pre-operational three Dimensional variational data assimilation system in the North/Baltic Sea, Ocean Sci., 7, 771–781, https://doi.org/10.5194/os-7-771-2011, 2011.
Short summary
Canada's coastlines include diverse ocean environments. In response to the strong need to support marine activities and security, we present the first pan-Canadian operational regional ocean analysis system. A novel online tidal harmonic analysis method is introduced that uses a sliding-window approach. Innovations are compared to those from the Canadian global analysis system. Particular improvements are found near the Gulf Stream due to the higher model grid resolution.
Canada's coastlines include diverse ocean environments. In response to the strong need to...
Special issue