The Regional Ice Ocean Prediction System v2: a pan-Canadian ocean analysis system using an online tidal harmonic analysis
Authors: Smith Gregory C. , Liu Yimin , Benkiran Mounir , Chikhar Kamel , Surcel Colan Dorina , Gauthier Audrey-Anne , Testut Charles-Emmanuel , Dupont Frederic , Lei Ji , Roy François , Lemieux Jean-François , Davidson Fraser 
Published: March 2021

Abstract. Canada has the longest coastline in the world and includes diverse
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, and
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 multivariate data assimilation system based on a reduced-order
extended Kalman filter together with a 3D-Var 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 multivariate
error covariances. A novel online tidal harmonic analysis method is
introduced that uses a sliding-window approach to reduce numerical costs and allow for the time-varying harmonic constants necessary in seasonally
ice-infested waters. 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 (SST). In
addition to the tidal harmonic analysis, the observation operator for sea
level anomaly (SLA) 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 near 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 mesoscale 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 compared to 66 km). Observations made as part of the Year of Polar
Prediction (2017–2019) provide a rare glimpse at errors in Arctic water mass
properties and show average salinity biases over the upper 500 m of 0.3–0.4 psu in the eastern Beaufort Sea in RIOPSv2.