Abstract. Sea ice is the central component and most sensitive indicator of the Arctic climate system. Both the depletion and areal decline of the Arctic sea ice cover, observed since the 1970s, have accelerated since the millennium. While the relationship of global warming to sea ice reduction is evident and underpinned statistically, it is the connecting mechanisms that are explored in detail in this review.Sea ice erodes both from the top and the bottom. Atmospheric, oceanic and sea ice processes interact in nonlinear ways on various scales. Feedback mechanisms lead to an Arctic amplification of the global warming system: the amplification is both supported by the ice depletion and, at the same time, accelerates ice reduction. Knowledge of the mechanisms of sea ice decline grew during the 1990s and This article reviews recent progress in understanding the sea ice decline. Processes are revisited from atmospheric, oceanic and sea ice perspectives. There is strong evidence that decisive atmospheric changes are the major driver of sea ice change. Feedbacks due to reduced ice concentration, surface albedo, and ice thickness allow for additional local atmospheric and oceanic influences and self-supporting feedbacks. Large-scale ocean influences on Arctic Ocean hydrology and circulation are highly evident. Northward heat fluxes in the ocean are clearly impacting the ice margins, especially in the Atlantic sector of the Arctic. There is little indication of a direct and decisive influence of the warming ocean on the overall sea ice cover, due to an isolating layer of cold and fresh water underneath the sea ice.

The timing of spring snow melt onset (SMO) on Arctic sea ice strongly affects the heat accumulation in snow and ice during the melt season. SMO itself is controlled by surface heat fluxes. Satellite passive microwave (SSM/I) observations show that the apparent melt onset (MO) varies a lot interannually and even over 50–100 km distances. The MO record appeared to be a complex blend of SMO on top of sea ice and opening of leads and polynyas due to divergent sea ice drift. We extracted SMO out of the original MO record using sea ice concentration data. Applying ERA Interim reanalysis, we evaluated the portion of SMO variance explained by radiative and turbulent surface heat fluxes in the period of 1989–2008. The anomaly of the surface net heat flux 1–7 days prior to SMO explained up to 65% of the interannual variance in SMO in the central Arctic. The main term of the net flux was the downward longwave radiation, which explained up to 90% of SMO variance within the western central Arctic. The role of the latent and sensible heat fluxes in earlier/later SMO was not to bring more/less heat to the surface but to reduce/enhance the surface heat loss. Solar radiation was not an important factor alone, but together with other fluxes improved the explained variance of SMO. Local 20‐year SMO trends averaged over the central Arctic Ocean are toward earlier melt by 9 days per decade.

Abstract. The Arctic sea ice is the central and essential component of the Arctic climate system. The depletion and areal decline of the Arctic sea ice cover, observed since the 1970's, have accelerated after the millennium shift. While a relationship to global warming is evident and is underpinned statistically, the mechanisms connected to the sea ice reduction are to be explored in detail. Sea ice erodes both from the top and from the bottom. Atmosphere, sea ice and ocean processes interact in non-linear ways on various scales. Feedback mechanisms lead to an Arctic amplification of the global warming system. The amplification is both supported by the ice depletion and is at the same time accelerating the ice reduction. Knowledge of the mechanisms connected to the sea ice decline has grown during the 1990's and has deepened when the acceleration became clear in the early 2000's. Record summer sea ice extents in 2002, 2005, 2007 and 2012 provided additional information on the mechanisms. This article reviews recent progress in understanding of the sea ice decline. Processes are revisited from an atmospheric, ocean and sea ice perspective. There is strong evidence for decisive atmospheric changes being the major driver of sea ice change. Feedbacks due to reduced ice concentration, surface albedo and thickness allow for additional local atmosphere and ocean influences and self-supporting feedbacks. Large scale ocean influences on the Arctic Ocean hydrology and circulation are highly evident. Northward heat fluxes in the ocean are clearly impacting the ice margins, especially in the Atlantic sector of the Arctic. Only little indication exists for a direct decisive influence of the warming ocean on the overall sea ice cover, due to an isolating layer of cold and fresh water underneath the sea ice.

Summertime atmospheric dynamics in the coastal zone of the industrialized Dunkerque agglomeration in northern France was characterized by a cluster analysis of back trajectories in the context of pollution transport. The MESO-NH atmospheric model was used to simulate the local dynamics at multiple scales with horizontal resolution down to 500 m, and for the online calculation of the Lagrangian backward trajectories with 30-min temporal resolution. Airmass transport was performed along six principal pathways obtained by the weighted k-means clustering technique. Four of these centroids corresponded to a range of wind speeds over the English Channel: two for wind directions from the north-east and two from the south-west. Another pathway corresponded to a south-westerly continental transport. The backward trajectories of the largest and most dispersed sixth cluster contained low wind speeds, including sea-breeze circulations. Based on analyses of meteorological data and pollution measurements, the principal atmospheric pathways were related to local aircontamination events. Continuous air quality and meteorological data were collected during the Benzene-Toluene-Ethylbenzene-Xylene 2006 campaign. The sites of the pollution measurements served as the endpoints for the backward trajectories. Pollutant transport pathways corresponding to the highest air contamination were defined.

The performance of general circulation models (GCMs) varies across regions and periods. When projecting into the future, it is therefore not obvious whether to reject or to prefer a certain GCM. Combining the outputs of several GCMs may enhance results. This paper presents a method to combine multimodel GCM projections by means of a Bayesian model combination (BMC). Here the influence of each GCM is weighted according to its performance in a training period, with regard to observations, as outcome BMC predictive distributions for yet unobserved observations are obtained. Technically, GCM outputs and observations are assumed to vary randomly around common means, which are interpreted as the actual target values under consideration. Posterior parameter distributions of the authors' Bayesian hierarchical model are obtained by a Markov chain Monte Carlo (MCMC) method. Advantageously, all parameters-such as bias and precision of the GCM models-are estimated together. Potential time dependence is accounted for by integrating a Kalman filter. The significance of trend slopes of the common means is evaluated by analyzing the posterior distribution of the parameters. The method is applied to assess the evolution of ice accumulation over the oceanic Arctic region in cold seasons. The observed ice index is created out of NCEP reanalysis data. Outputs of seven GCMs are combined by using the training period 1962-99 and prediction periods 2046-65 and 2082-99 with Special Report on Emissions Scenarios (SRES) A2 and B1. A continuing decrease of ice accumulation is visible for the A2 scenario, whereas the index stabilizes for the B1 scenario in the second prediction period.