[1] Interaction of warm, Atlantic-origin water (AW) and colder, polar origin water (PW) advecting southward in the East Greenland Current (EGC) influences the heat content of water entering Greenland's outlet glacial fjords. Here we use depth and temperature data derived from deep-diving seals to map out water mass variability across the continental shelf and to augment existing bathymetric products. We compare depths derived from the seal dives with the IBCAO Version 3 bathymetric database over the shelf and find differences up to 300 m near several large submarine canyons. In the vertical temperature structure, we find two dominant modes: a cold mode, with the typical AW/PW layering observed in the EGC, and a warm mode, where AW is present throughout the water column. The prevalence of these modes varies seasonally and spatially across the continental shelf, implying distinct AW pathways. In addition, we find that satellite sea surface temperatures (SST) correlate significantly with temperatures in the upper 50 m (R = 0.54), but this correlation decreases with depth (R = 0.22 at 200 m), and becomes insignificant below 250 m. Thus, care must be taken in using SST as a proxy for heat content, as AW mainly resides in these deeper layers. Sample Unit Level Copyright
Recent increases in marine traffic in the Arctic have amplified the demand for reliable ice and marine environmental predictions. This article presents the verification of ice forecast skill from a new system implemented recently at the Canadian Meteorological Centre called the Global Ice Ocean Prediction System (GIOPS). GIOPS provides daily global ice and ocean analyses and 10-day forecasts on a 1/4• -resolution grid. GIOPS includes a multivariate ocean data assimilation system that combines satellite observations of sealevel anomaly and sea-surface temperature (SST) together with in situ observations of temperature and salinity. Ice analyses are produced using a 3D-Var method that assimilates satellite observations from SSM/I and SSMIS together with manual analyses from the Canadian Ice Service. Analyses of total ice concentration are projected onto the thickness categories used in the ice model using spatially and temporally varying weighting functions derived from ice model tendencies. This method may reduce deleterious impacts on the ice thickness distribution when assimilating ice concentration, as it can directly modulate (and reverse) nonlinear processes such as ice deformation. An objective verification of sea ice forecasts is made using two methods: analysis-based error assessment focusing on the marginal ice zone, and a contingency table approach to evaluate ice extent as compared to an independent analysis. Together the methods demonstrate a consistent picture of skilful medium-range forecasts in both the Northern and Southern Hemispheres as compared to persistence. Using the contingency table approach, GIOPS forecasts show a significant open-water bias during spring and summer. However, this bias depends on the choice of threshold used. Ice forecast skill is found to be highly sensitive to the assimilation of SST near the ice edge. Improved observational coverage in these areas (including salinity) would be extremely valuable for further improvement in ice forecast skill.
Abstract. As part of the CONCEPTS (Canadian Operational Network of Coupled Environmental PredicTion Systems) initiative, a high-resolution (1/12 • ) ice-ocean regional model is developed covering the North Atlantic and the Arctic oceans. The long-term objective is to provide Canada with short-term ice-ocean predictions and hazard warnings in ice-infested regions. To evaluate the modelling component (as opposed to the analysis -or data-assimilation -component, which is not covered in this contribution), a series of hindcasts for the period 2003-2009 is carried out, forced at the surface by the Canadian GDPS reforecasts (Smith et al., 2014). These hindcasts test how the model represents upper ocean characteristics and ice cover. Each hindcast implements a new aspect of the modelling or the ice-ocean coupling. Notably, the coupling to the multi-category ice model CICE is tested. The hindcast solutions are then assessed using a verification package under development, including in situ and satellite ice and ocean observations. The conclusions are as follows: (1) the model reproduces reasonably well the time mean, variance and skewness of sea surface height; (2) the model biases in temperature and salinity show that while the mean properties follow expectations, the Pacific Water signature in the Beaufort Sea is weaker than observed; (3) the modelled freshwater content of the Arctic agrees well with observational estimates; (4) the distribution and volume of the sea ice are shown to be improved in the latest hindcast due to modifications to the drag coefficients and to some degree to the ice thickness distribution available in CICE; (5) nonetheless, the model still overestimates the ice drift and ice thickness in the Beaufort Gyre.
As part of the work of the GODAE OceanView Inter-comparison and Validation Task Team (IV-TT), 6 global ocean forecasting systems spread across 5 operational oceanography forecast centres were inter-compared using a common set of observations as a proxy for the truth. The 'Class 4' in the title refers to a set of forecast verification metrics defined in the MERSEA-IP/GODAE internal metrics document (Hernandez 2007), the defining feature of which is that comparisons between forecasts and observations take place in observation space. This approach is seen as a departure from other diagnostic approaches such as analysing model trends or innovation statistics, and is commonly used in the atmospheric community. The physical parameters involved in the comparison are sea surface temperature (SST), sub-surface temperature, sub-surface salinity and sea level anomaly (SLA). SST was measured using in-situ observations obtained from USGODAE, sub-surface conditions were compared to Argo profiles, while sea level anomaly was measured by several satellite altimeters courtesy of AVISO. The 5 forecast centres involved in the project were Met Office, Australian Bureau of Meteorology, Mercator Océan, Environment Canada and NOAA/NWS/NCEP. Combining Met Office, Mercator Océan and Environment Canada forecasts into a mixed resolution multi-model ensemble produces estimates of the ocean state which have better accuracy and associativity properties for SST, SLA and temperature profiles than any individual ensemble component.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.