Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. It is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.

This study examines the main physical processes affecting the interannual variability of circulation over the eastern Canadian continental shelf (ECS) based on numerical circulation results for the period [1988][1989][1990][1991][1992][1993][1994][1995][1996][1997][1998][1999][2000][2001][2002][2003][2004] produced by a regional ocean-ice model. The regional circulation model is applied to the northwest Atlantic and uses a horizontal curvilinear grid with a horizontal resolution of approximately 1/4°. The model is forced by atmospheric reanalysis fields produced by Large and Yeager (2004) and boundary forcing based on the ocean reanalysis data produced by Smith et al. (2010). In comparison with previous observational and numerical results in the literature, the regional circulation model has reasonable skill in simulating the large-scale circulation and associated seasonal and interannual variability over the ECS. Complex Empirical Orthogonal Function (CEOF) analysis is used to examine the interannual variability in the monthly mean temperature and salinity anomalies in the model. The CEOF analysis of model results demonstrates that the interannual variability over the Labrador and northern Newfoundland shelves is significantly affected by the variability at high latitudes which propagates onto these shelves through the northern open boundary. Over the eastern Newfoundland Shelf, the interannual variability is significantly affected by the non-linear interaction of the Labrador Current with the North Atlantic Current and by the variability propagating from the southern Labrador Shelf. The interannual variability of circulation and hydrography over the Slope Water region off the Scotian Shelf is significantly affected by anomalies advected by the Gulf Stream and by the non-linear dynamics taking place in the deep waters to the south of the Tail of the Grand Banks. (2010). Comparativement aux résultats observationnels et numériques précédents que l'on trouve dans la littérature, le modèle de circulation régional possède une habileté raisonnable pour la simulation de la circulation à grande échelle et de la variabilité saisonnière et interannuelle associée sur l'ECS. Nous utilisons l'analyse par fonctions orthogonales empiriques complexes (CEOF) pour examiner la variabilité interannuelle des anomalies mensuelles moyennes de température et de salinité dans le modèle. L'analyse par CEOF des résultats du modèle montre que la variabilité interannuelle sur les plateaux du Labrador et du nord de Terre-Neuve est significativement influencée par la variabilité aux hautes latitudes qui se propage sur ces plateaux à travers la limite nord ouverte. Sur le plateau de l'est de Terre-Neuve, la variabilité interannuelle est significativement influencée par l'interaction non linéaire du courant du Labrador avec le courant de l'Atlantique Nord et par la variabilité se propageant depuis le plateau du sud du Labrador. La variabilité interannuelle de la circulation et de l'hydrographie dans la région du talus continental au ...

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Using a perturbed parameter ensemble of a coupled climate model, emerging relationships are identified between sea ice area, net surface longwave radiation, and the atmospheric circulation over the Beaufort gyre. There is a strong positive correlation between sea ice area and the net longwave radiation over the ocean-ice surface during the melting season and a negative correlation during the freezing season. The mechanisms responsible for the longwave radiation balance at the surface are mainly driven by sea ice variations in the freezing season and by clouds in the melting season. A strong positive (negative) correlation is also found between the fall (summer) total sea ice area in the Arctic and the sea level pressure over the Beaufort High region. It is argued that as sea ice coverage is lost, static stability losses are severe in fall, resulting in enhanced evaporation, vertical motions, and weakening of the general large-scale anticyclonic circulation of the Beaufort High. Key Points:• sea ice area and sea level pressure over the Beaufort High have positive correlation in fall and negative in summer • sea ice area and net longwave radiation over the ocean-ice have positive correlation in melting season and negative in freezing season • sea ice area and net longwave radiation correlations are driven by sea ice in the freezing season and by clouds in the melting season Supporting Information:• Supporting Information S1• Figure S1 • Figure S2 • Figure S3 • Figure S4