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Roy F.,Direction de la Recherche Meteorologique | Chevallier M.,French National Center of Weather Research | Smith G.C.,Direction de la Recherche Meteorologique | Dupont F.,Service Meteorologique du Canada | And 4 more authors.
Journal of Geophysical Research C: Oceans | Year: 2015

Global simulations are presented focusing on the atmosphere-ice-ocean (AIO) surface layer (SL) in the Arctic. Results are produced using an ocean model (NEMO) coupled to two different sea ice models: the Louvain-La-Neuve single-category model (LIM2) and the Los Alamos multicategory model (CICE4). A more objective way to adjust the sea ice-ocean drag is proposed compared to a coefficient tuning approach. The air-ice drag is also adjusted to be more consistent with the atmospheric forcing data set. Improving the AIO SL treatment leads to more realistic results, having a significant impact on the sea ice volume trend, sea ice thickness, and the Arctic freshwater (FW) budget. The physical mechanisms explaining this sensitivity are studied. Improved sea ice drift speeds result in less sea ice accumulation in the Beaufort Sea, correcting a typical ice thickness bias. Sea ice thickness and drag parameters affect how atmospheric stress is transferred to the ocean, thereby influencing Ekman transport and FW retention in the Beaufort Gyre (BG). Increasing sea ice-ocean roughness reduces sea ice growth in winter by reducing ice deformation and lead fractions in the BG. It also increases the total Arctic FW content by reducing sea ice export through Fram Strait. Similarly, increasing air-ice roughness increases the total Arctic FW content by increasing FW retention in the BG. Key Points: Improved ice-ocean surface layer leads to more realistic Arctic simulations A more objective approach is proposed to specify the ice-ocean drag Drag parameters and ice thickness have significant impact on freshwater budget © 2015. American Geophysical Union. All Rights Reserved. Source

Dupont F.,Service Meteorologique du Canada | Higginson S.,Bedford Institute of Oceanography | Bourdalle-Badie R.,Mercator Ocean | Lu Y.,Bedford Institute of Oceanography | And 5 more authors.
Geoscientific Model Development | Year: 2015

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. © Author(s) 2015. Source

Rapaic M.,Service Meteorologique du Canada | Brown R.,Environment Canada | Markovic M.,Canadian Center for Meteorological and Environmental Prediction | Chaumont D.,Ouranos Consortium
Atmosphere - Ocean | Year: 2015

The spatial and temporal consistency of seasonal air temperature and precipitation in eight widely used gridded observation-based climate datasets (CANGRD, CRU-TS3.1, CRUTEM4.1, GISTEMP, GPCC, GPCP, HadCRUT3, and UDEL) and eight reanalyses (20CR, CFSR, ERA-40, ERA-Interim, JRA25, MERRA, NARR, and NCEP2) was evaluated over the Canadian Arctic for the 1950-2010 period. The evaluation used the CANGRD dataset, which is based on homogenized temperature and adjusted precipitation from climate stations, as a reference. Dataset agreement and bias were observed to exhibit important spatial, seasonal, and temporal variability over the Canadian Arctic with the largest spread occurring between datasets over mountain and coastal regions and over the Canadian Arctic Archipelago. Reanalysis datasets were typically warmer and wetter than surface observation-based datasets, with CFSR and 20CR exhibiting biases in total annual precipitation on the order of 300 mm. Warm bias in 20CR exceeded 12°C in winter over the western Arctic. Analysis of the temporal consistency of datasets over the 1950-2010 period showed evidence of discontinuities in several datasets as well as a noticeable increase in dataset spread in the period after approximately 2000. Declining station networks, increased automation, and the inclusion of new satellite data streams in reanalyses are potential contributing factors to this phenomenon. Evaluation of trends over the 1950-2010 period showed a relatively consistent picture of warming and increased precipitation over the Canadian Arctic from all datasets, with CANGRD giving moistening trends two times larger than the multi-dataset average related to the adjustment of the station precipitation data. The study results indicate that considerable care is needed when using gridded climate datasets in local or regional scale applications in the Canadian Arctic. © 2015 Taylor & Francis. Source

Shlyaeva A.,Environment Canada | Buehner M.,Environment Canada | Caya A.,Environment Canada | Lemieux J.-F.,Recherche en Prevision Numerique Environnementale | And 4 more authors.
Quarterly Journal of the Royal Meteorological Society | Year: 2016

A short-range high-resolution sea ice prediction system has been developed at Environment Canada. This study describes the first steps towards transitioning this system from a simple deterministic data assimilation system based on the three-dimensional variational (3D-Var) approach into a data assimilation system based on an ensemble of ensemble-variational (EnVar) analyses. First, an ensemble of 3D-Var analyses using static background-error covariances is implemented and used to evaluate different strategies for simulating model uncertainties during the ensemble forecast step; these range from perturbing parameters within the sea ice model to completely disabling the sea ice dynamics or thermodynamics in some of the ensemble members. The experiments show a good ensemble spread-error relationship in areas with low or high ice concentration, though more work is needed to better simulate uncertainties in areas with intermediate ice concentration. Second, results from idealized experiments with EnVar analyses using ensemble covariances are presented. They demonstrate the potential improvement of sea ice analyses from using state-dependent multivariate ensemble covariances when assimilating ice concentration observations to correct both ice concentration and unobserved variables such as ice thickness and ocean temperature. © 2016 Royal Meteorological Society. Source

Chevallier M.,French National Center of Weather Research | Smith G.C.,Recherche en Prevision Numerique Environnementale | Dupont F.,Service Meteorologique du Canada | Lemieux J.-F.,Recherche en Prevision Numerique Environnementale | And 27 more authors.
Climate Dynamics | Year: 2016

Ocean–sea ice reanalyses are crucial for assessing the variability and recent trends in the Arctic sea ice cover. This is especially true for sea ice volume, as long-term and large scale sea ice thickness observations are inexistent. Results from the Ocean ReAnalyses Intercomparison Project (ORA-IP) are presented, with a focus on Arctic sea ice fields reconstructed by state-of-the-art global ocean reanalyses. Differences between the various reanalyses are explored in terms of the effects of data assimilation, model physics and atmospheric forcing on properties of the sea ice cover, including concentration, thickness, velocity and snow.Amongst the 14 reanalyses studied here, 9 assimilate sea ice concentration, and none assimilate sea ice thickness data. The comparison reveals an overall agreement in the reconstructed concentration fields, mainly because of the constraints in surface temperature imposed by direct assimilation of ocean observations, prescribed or assimilated atmospheric forcing and assimilation of sea ice concentration. However, some spread still exists amongst the reanalyses, due to a variety of factors. In particular, a large spread in sea ice thickness is found within the ensemble of reanalyses, partially caused by the biases inherited from their sea ice model components. Biases are also affected by the assimilation of sea ice concentration and the treatment of sea ice thickness in the data assimilation process. An important outcome of this study is that the spatial distribution of ice volume varies widely between products, with no reanalysis standing out as clearly superior as compared to altimetry estimates. The ice thickness from systems without assimilation of sea ice concentration is not worse than that from systems constrained with sea ice observations. An evaluation of the sea ice velocity fields reveals that ice drifts too fast in most systems. As an ensemble, the ORA-IP reanalyses capture trends in Arctic sea ice area and extent relatively well. However, the ensemble can not be used to get a robust estimate of recent trends in the Arctic sea ice volume. Biases in the reanalyses certainly impact the simulated air–sea fluxes in the polar regions, and questions the suitability of current sea ice reanalyses to initialize seasonal forecasts. © 2016 Springer-Verlag Berlin Heidelberg Source

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