Arctic climate system change has accelerated tremendously since the beginning of this century, and a strikingly extreme sea‐ice loss occurred in summer 2007. However, the greenhouse‐gas‐emissions forcing has only increased gradually and the driving role in Arctic climate change of the positively‐polarized Arctic/North Atlantic Oscillation (AO/NAO) trend has substantially weakened. Although various contributing factors have been examined, the fundamental physical process, which orchestrates these contributors to drive the acceleration and the latest extreme event, remains unknown. We report on drastic, systematic spatial changes in atmospheric circulations, showing a sudden jump from the conventional tri‐polar AO/NAO to an unprecedented dipolar leading pattern, following accelerated northeastward shifts of the AO/NAO centers of action. These shifts provide an accelerating impetus for the recent rapid Arctic climate system changes, perhaps shedding light on recent arguments about a tipping point of global‐warming‐forced climate change in the Arctic. The radical spatial shift is a precursor to the observed extreme change event, demonstrating skilful information for future prediction.

We use surface air temperature to evaluate the decadal forecast skill of the fully coupled Max Planck Institut Earth System Model (MPI‐ESM) initialized using only surface wind stress applied to the ocean component of the model (Modini: Model initialization by partially coupled spin‐up). Our analysis shows that the greenhouse gas forcing alone results in a significant forecast skill on the 2–5 and 6–9 year range even for uninitialized hindcasts. For the first forecast year, the forecast skill of Modini is generally comparable with previous initialization procedures applied to MPI‐ESM. But only Modini is able to generate a significant skill (correlation) in the tropical Pacific for a 2–5 year (and to a lesser extent for a 6–9 year) hindcast. Modini is also better able to capture the observed hiatus in global warming in hindcast mode than the other methods. Finally, we present forecasts for 2015 and the average of years 2016–2019 and 2020–2024, predicting an end to the hiatus.

[1] Sea ice concentration is a fundamental property of the Arctic ice-ocean-atmosphere system reflecting both dynamics and thermodynamics. Concentration integrates across space and time and is useful for characterizing both observed and numerically simulated systems. Concentration is reasonably well measured by remote sensing, and several high-quality sea ice concentration data sets exist beginning with the satellite era. In this paper we examine the simulated sea ice concentration from nine ice-ocean numerical models that are part of the coordinated experiments of the Arctic Ocean Model Intercomparison Project (AOMIP). Spatial patterns of means and differences between models and observations, and among models, are compared for a multiyear record and for the September sea ice minimum. Interannual variations are assessed on data with monthly climatology removed. As a proxy for the annual cycle of open water for each model, the total areas with concentration less than 10% are compared among models. Mean ice statistics are computed for grid points with greater than 1% and greater than 10% concentrations. The results show that the models have similar characteristics for the winter months when 100% cover is produced, and most models reproduce an observed minimum in sea ice concentration for 1990. The compared observational data sets use the NASA Team algorithm (Goddard Space Flight Center data, the adjusted or Walsh data, and the Hadley Centre data) and the Bootstrap algorithm. Variability in sea ice concentration is less among the four observational records than among models.

The Arctic Ocean is an important component of the global climate system. The processes occurring in the Arctic Ocean affect the rate of deep and bottom water formation in the convective regions of the high North Atlantic and influence ocean circulation across the globe. This fact is highlighted by global climate modeling studies that consistently show the Arctic to be one of the most sensitive regions to climate change. But an identification of the differences among models and model systematic errors in the Arctic Ocean remains unchecked, despite being essential to interpreting the simulation results and their implications for climate variability. For this reason, the Arctic Ocean Model Intercomparison Project (AOMIP), an international effort, was recently established to carry out a thorough analysis of model differences and errors. The geographical focus of this effort is shown in Figure 1.

In Antarctica, ice crystals emerge from ice shelf cavities and accumulate in unconsolidated layers beneath nearby sea ice. Such sub‐ice platelet layers form a unique habitat and serve as an indicator for the state of an ice shelf. However, the lack of a suitable methodology impedes an efficient quantification of this phenomenon on scales beyond point measurements. In this study, we inverted multifrequency electromagnetic (EM) induction soundings, obtained on fast ice with an underlying platelet layer along profiles of 100 km length in the eastern Weddell Sea. EM‐derived platelet layer thickness and conductivity are consistent with other field observations. Our results suggest that platelet layer volume is higher than previously thought in this region and that platelet layer ice volume fraction is proportional to its thickness. We conclude that multifrequency EM is a suitable tool to determine platelet layer volume, with the potential to obtain crucial knowledge of associated processes in otherwise inaccessible ice shelf cavities.