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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-255
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-255
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 26 Aug 2020

Submitted as: development and technical paper | 26 Aug 2020

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This preprint is currently under review for the journal GMD.

The Regional Ice Ocean Prediction System v2: a pan-Canadian ocean analysis system

Gregory C. Smith1, Yimin Liu1, Mounir Benkiran3, Kamel Chikhar2, Dorina Surcel Colan2, Charles-Emmanuel Testut3, Frederic Dupont2, Ji Lei2, François Roy1, Jean-Francois Lemieux1, and Fraser Davidson4 Gregory C. Smith et al.
  • 1Meteorological Research Division, Environment and Climate Change Canada (ECCC), Dorval, H9P1J3, Canada
  • 2Meteorological Service of Canada, ECCC, Dorval, H9P1J3, Canada
  • 3Mercator Océan International, Toulouse, France
  • 4Northwest Atlantic Fisheries Centre, Fisheries and Ocean Canada, St. John's, Newfoundland, Canada

Abstract. Canada has the longest coastline in the world and includes a diversity of ocean environments, from the frozen waters of the Canadian Arctic Archipelago to the confluence region of Labrador and Gulf Stream waters on the East Coast. There is a strong need for a pan-Canadian operational regional ocean prediction capacity covering all Canadian coastal areas, in support of marine activities including emergency response, search and rescue as well as safe navigation in ice-infested waters. Here we present the first pan-Canadian operational regional ocean analysis system developed as part of the Regional Ice Ocean Prediction System version 2 (RIOPSv2) running in operations at the Canadian Centre for Meteorological and Environmental Prediction (CCMEP). The RIOPSv2 domain extends from 26° N in the Atlantic Ocean through the Arctic Ocean to 44° N in the Pacific Ocean, with a model grid-resolution that varies between 3 and 8 km. RIOPSv2 includes a multi-variate data assimilation system based on a reduced-order extended Kalman filter together with a 3DVar bias correction system for water mass properties. The analysis system assimilates satellite observations of sea level anomaly and sea surface temperature, as well as in situ temperature and salinity measurements. Background model error is specified in terms of seasonally varying model anomalies from a 10-year forced model integration allowing inhomogeneous anisotropic multi-variate error covariances. A novel online tidal harmonic analysis method is introduced that uses a sliding-window approach to reduce numerical costs and to allow time-varying harmonic constants, necessary in seasonally ice-infested waters. As compared to the Global Ice Ocean Prediction System (GIOPS) running at CCMEP, RIOPSv2 also includes a spatial filtering of model fields as part of the observation operator for sea surface temperature. In addition to the tidal harmonic analysis, the observation operator for sea level anomaly is also modified to remove the inverse barometer effect due to the application of atmospheric pressure forcing fields. RIOPSv2 is compared to GIOPS and shown to provide similar innovation statistics over a 3-year evaluation period. Specific improvements are found in the vicinity of the Gulf Stream for all model fields due to the higher model grid-resolution, with smaller root-mean-squared (RMS) innovations for RIOPSv2 of about 5 cm for SLA and 0.5 °C for SST. Verification against along-track satellite observations demonstrates the improved representation of meso-scale features in RIOPSv2 compared to GIOPS, with increased correlations of SLA (0.83 compared to 0.73) and reduced RMS differences (12 cm compared to 14 cm). While the RIOPSv2 grid resolution is 3 times higher than GIOPS, the power spectral density of surface kinetic energy provides an indication that the effective resolution of RIOPSv2 is roughly double that of the global system (35 km as compared to 66 km). Observations made as part of the Year of Polar Prediction (2017–19) provide a rare glimpse at errors in Arctic water mass properties and show salinity biases of 0.3–0.4 psu in the eastern Beaufort Sea in RIOPSv2.

Gregory C. Smith et al.

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Gregory C. Smith et al.

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The Regional Ice Ocean Prediction System v2 Gregory C. Smith https://doi.org/10.5281/zenodo.3978269

Gregory C. Smith et al.

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Latest update: 01 Dec 2020
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Short summary
Canada's coastlines include a diversity of 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 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 a diversity of ocean environments. In response to the strong need to...
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