Search

The ocean circulation is typically constrained in operational analysis and forecasting systems through the assimilation of sea level anomaly (SLA) retrievals from satellite altimetry. This approach has limited benefits in the Arctic Ocean and surrounding seas due to data gaps caused by sea ice coverage. Moreover, assimilation of SLA in seasonally ice-free regions may be negatively affected by the quality of the Mean Sea Surface (MSS) used to derive the SLA. Here, we use the Regional Ice Ocean Prediction System (RIOPS) to investigate the impact of assimilating Absolute Dynamic Topography (ADT) fields on the circulation in the Arctic Ocean. This approach avoids the use of a MSS and additionally provides information on sea level in ice covered regions using measurements across leads (openings) in the sea ice. RIOPS uses a coupled ice-ocean model on a 3-4 km grid-resolution pan-Arctic domain together with a multi-variate reduced-order Kalman Filter. The system assimilates satellite altimetry and sea surface temperature together with in situ profile observations. The background error is modified to match the spectral characteristics of the ADT fields, which contain less energy at small scales than traditional SLA due to filtering applied to reduce noise originating in the geoid product used. A series of four-year reanalyses demonstrate significant reductions in innovation statistics with important impacts across the Arctic Ocean. Results suggest that the assimilation of ADT can improve circulation and sea ice drift in the Arctic Ocean, and intensify volume transports through key Arctic gateways and resulting exchanges with the Atlantic Ocean. A reanalysis with a modified Mean Dynamic Topography (MDT) is able to reproduce many of the benefits of the ADT but does not capture the enhanced transports. Assimilation of SLA observations from leads in the sea ice appears to degrade several circulation features; however, these results may be sensitive to errors in MDT. This study highlights the large uncertainties that exist in present operational ocean forecasting systems for the Arctic Ocean due to the relative paucity and reduced quality of observations compared to icefree areas of the Global Ocean. Moreover, this underscores the need for dedicated and focused efforts to address this critical gap in the Global Ocean Observing System. [ABSTRACT FROM AUTHOR]

Icepack (v1.1.0) – the column thermodynamics model of the Community Ice CodE (CICE) version 6 – is used to assess how changing the thermodynamics from the physics (hereafter BL99) to the mushy-layer physics impacts the model performance in reproducing in situ landfast ice observations from two ice mass balance (IMB) buoys co-deployed in the landfast ice close to Nain (Labrador) in February 2017. To this end, a new automated surface retrieval algorithm is used to determine the in situ ice thickness, snow depth, basal ice congelation and snow-ice formation from the measured vertical temperature profiles. Icepack simulations are run to reproduce these observations using each thermodynamics scheme, with a particular interest in how the different physics influence the representation of snow-ice formation and ice congelation. Results show that the BL99 parameterization represents well the ice congelation but underrepresents the snow-ice contribution to the ice mass balance. In particular, defining snow-ice formation based on the hydrostatic balance alone does not reproduce the negative freeboards observed for several days in the IMB observations, resulting in an earlier snow-flooding onset, a positive ice thickness bias and reduced snow depth variations. We find that the mushy-layer thermodynamics with default parameters significantly degrades the model performance, overestimating both the congelation growth and snow-ice formation. The simulated thermodynamics response to flooding, however, better represents the observations, and the best results are obtained when allowing for negative freeboards in the mushy-layer physics. We find that the mushy-layer thermodynamics produces a larger variability in congelation rates at the ice bottom interface, alternating between periods of exceedingly fast growth and periods of unrealistic basal melt. This pattern is related to persistent brine dilution in the lowest ice layer by the congelation and brine drainage parameterizations. We also show that the mushy-layer congelation parameterization produces significant frazil formation, which is not expected in a landfast ice context. This behavior is attributed to the congelation parameterization not fully accounting for the conductive heat flux imbalance at the ice–ocean boundary. We propose a modification of the mushy-layer congelation scheme that largely reduces the frazil formation and allows for better tuning of the congelation rates to match the observations. Our results demonstrate that the mushy-layer physics and its parameters can be tuned to closely match the in situ observations, although more observations are needed to better constrain them. [ABSTRACT FROM AUTHOR]

The Bay of Islands complex, Newfoundland, Canada, represents a fossil subduction system that was obducted onto the Laurentian margin during the Taconic Orogeny. We present whole-rock geochemistry and geochronology for the Bay of Islands metamorphic sole from the Table Mountain and North Arm Mountain massifs. The Bay of Islands metamorphic sole can be subdivided into three units: a high metamorphic-grade Grt-Cpx amphibolite and Grt-Cpx granulite unit, a medium metamorphic-grade common amphibolite unit, and a medium metamorphic- grade metasedimentary-dominated unit. Based on their major and trace element contents, two types of protoliths can be inferred for the Bay of Islands sole metabasites: cumulate gabbroic protoliths with high Mg numbers and depleted trace element contents, and basaltic protoliths with trace element contents defining a normal mid-oceanic-ridge basalt (N-MORB) affinity. Titanite in situ U-Pb analyses of two common amphibolite samples yield dates of 486 ± 7 Ma and 484 ± 4 Ma, which are interpreted as a metamorphic age (ca. 485 Ma). One sample from the metasedimentary unit yields detrital zircon dates that range from Archean to Ordovician, with the youngest dates defining the most dominant peak at ca. 490 Ma. This youngest peak is consistent with an arc-related source that may be linked with arc magmatism of the upper plate during the Taconic Orogeny. These observations suggest that (1) the subduction responsible for the formation of the Bay of Islands complex nucleated in an oceanic domain before ca. 485 Ma; and (2) the sediments that form the metasedimentary unit were partly sourced from the upper plate and were deposited on the lower plate while subduction was active. [ABSTRACT FROM AUTHOR]

This paper describes the Coastal Ice Ocean Prediction System for the East Coast of Canada (CIOPS-E) running operationally at Environment and Climate Change Canada (ECCC). CIOPS-E uses a one-way downscaling technique on a 1/36° horizontal grid (~2 km) to simulate high-resolution ice and ocean conditions over the northwest Atlantic Ocean and the Gulf of St. Lawrence (GSL). CIOPS-E is forced at its lateral boundaries with ECCC's Regional Ice-Ocean Prediction System (RIOPS) and tidal conditions from the Oregon State University TPXO model. The three-dimensional temperature and salinity fields are spectrally nudged towards the RIOPS solution offshore of the 1500 m isobath to, effectively constrain mesoscale features in the Gulf Stream area. Over the continental shelf and the GSL, the CIOPS-E solution is free to develop fully according to model dynamics. CIOPS-E is evaluated over one year from March 2019 to February 2020. Overall, the CIOPS-E improves the representation of tides compared to ECCC's lower resolution systems: RIOPS (1/12°) and the Regional Marine Prediction System – Gulf of St. Lawrence (RMPS-GSL, 5 km). The accuracy of the tides are comparable to the TPXO at most coastal tide gauges. Sub-tidal water levels from CIOPS-E agree well with the observed seasonal variability and show improved errors statistics at all stations compared to RIOPS and RMPS-GSL. Improvements are especially noted for the GSL. Sea surface temperatures (SSTs) from CIOPS-E are lower (higher) in spring (fall) over most of the GSL compared to satellite-derived analyses and RIOPS. Comparison with in-situ observations of SST show significant improvement in CIOPS-E with respect to the RMPS-GSL. Lastly, sea ice conditions in the GSL are compared with the Canadian Ice Service (CIS) charts and the RMPS-GSL model. The sea ice cover and thickness from the pseudo-analysis component (without data assimilation) shows an overestimation compared to the CIS estimates, which is subsequently corrected in the forecast phase through the direct insertion of a Radarsat image analysis product. [ABSTRACT FROM AUTHOR]

The column thermodynamics package (Icepack v1.1.0) of the Community Ice Code (CICE) version 6 is used to reproduce observations from two Ice Mass Balance (IMB) buoys co-deployed in the landfast ice close to Nain (Labrador) in February 2017. A new automated surface retrieval algorithm is used to determine the ice thickness and snow depths from the measured vertical temperature profiles. The buoys recorded heavy snow precipitation over relatively thin ice, negative ice freeboards and delayed snow flooding. Icepack simulations are produced to evaluate the performance of the Bitz and Lipscomb (1999) thermodynamics used in the Environment and Climate Change Canada (ECCC) ice-ocean systems and to investigate the improvements associated with the use of mushy layer physics. Results show that the Bitz and Lipscomb (1999) scheme produces a smooth thermodynamics growth that fails to capture the observed variability in bottom sea ice congelation rates. The mushy layer physics produces similar temperature profiles but better captures the variability in congelation rates at the ice bottom interface, with periods of rapid ice growth that coincide with IMB observations. Large differences are also found associated with the snow-ice parameterization: the volume of snow-ice formed during flooding is largely underestimated when using a mass conserving snow-formation scheme, but largely improved when using the mushy layer parameterization in which sea-water is filling the porosity of the snow layer. Both schemes are however unable to reproduce the delayed snow-ice formation, as they rely on the hydrostatic balance and do not allow for negative freeboards. This calls for added brine fraction or ice porosity dependencies in the snow-ice parameterizations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The second version of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2) was implemented operationally at Environment and Climate Change Canada (ECCC) in July 2019. Like its predecessors, CanSIPSv2 applies a multimodel ensemble approach with two coupled atmosphere–ocean models, CanCM4i and GEM-NEMO. While CanCM4i is a climate model, which is upgraded from CanCM4 of the previous CanSIPSv1 with improved sea ice initialization, GEM-NEMO is a newly developed numerical weather prediction (NWP)-based global atmosphere–ocean coupled model. In this paper, CanSIPSv2 is introduced, and its performance is assessed based on the reforecast of 30 years from 1981 to 2010, with 10 ensemble members of 12-month integrations for each model. Ensemble seasonal forecast skill of 2-m air temperature, 500-hPa geopotential height, precipitation rate, sea surface temperature, and sea ice concentration is assessed. Verification is also performed for the Niño-3.4, the Pacific–North American pattern (PNA), the North Atlantic Oscillation (NAO), and the Madden–Julian oscillation (MJO) indices. It is found that CanSIPSv2 outperforms the previous CanSIPSv1 system in many aspects. Atmospheric teleconnections associated with the El Niño–Southern Oscillation (ENSO) are reasonably well captured by the two CanSIPSv2 models, and a large part of the seasonal forecast skill in boreal winter can be attributed to the ENSO impact. The two models are also able to simulate the Northern Hemisphere teleconnection associated with the tropical MJO, which likely provides another source of skill on the subseasonal to seasonal time scale. [ABSTRACT FROM AUTHOR]