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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.dsr.2009.09.008 Byline: Christian B. Rodehacke (a)(b), Wolfgang Roether (a), Hartmut H. Hellmer (c), Timothy Hall (d) Abstract: The knowledge of chlorofluorocarbon (CFC11, CFC12) concentrations in ocean surface waters is a prerequisite for deriving formation rates of, and water mass ages in, deep and bottom waters on the basis of CFC data. In the Antarctic coastal region, surface-layer data are sparse in time and space, primarily due to the limited accessibility of the region. To help filling this gap, we carried out CFC simulations using a regional ocean general circulation model (OGCM) for the Southern Ocean, which includes the ocean-ice shelf interaction. The simulated surface layer saturations, i.e. the actual surface concentrations relative to solubility-equilibrium values, are verified against available observations. The CFC surface saturations driven by concentration gradients between atmosphere and ocean are controlled mainly by the sea ice cover, sea surface temperature, and salinity. However, no uniform explanation exists for the controlling mechanisms. Here, we present simulated long-term trends and seasonal variations of surface-layer saturation at Southern Ocean deep and bottom water formation sites and other key regions, and we discuss differences between these regions. The amplitudes of the seasonal saturation cycle vary from 22% to 66% and their long-term trends range from 0.1%/year to 0.9%/year. The seasonal surface saturation maximum lags the ice cover minimum by two months. By utilizing observed bottle data the full seasonal CFC saturation cycle can be determined offering the possibility to predict long-term trends in the future. We show that ignoring the trends and using instead the saturations actually observed can lead to systematic errors in deduced inventory-based formation rates by up to 10% and suggest an erroneous decline with time. Author Affiliation: (a) Universitat Bremen, Institut fur Umweltphysik (IUP), Abteilung Ozeanographie, Otto-Hahn-Allee, D-28359 Bremen, Germany (b) Columbia University, Department for Applied Physics and Applied Mathematics, 2880 Broadway, New York, NY 10025, USA (c) Alfred Wegener Institut, Department Climate Science, BussestraAe 24, D-27570 Bremerhaven, Germany (d) NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025, USA Article History: Received 20 March 2008; Revised 29 August 2009; Accepted 22 October 2009

We assess the response of pack ice penguins, Emperor (Aptenodytes forsteri) and Adelie (Pygoscelis adeliae), to habitat variability and, then, by modeling habitat alterations, the qualitative changes to their populations, size and distribution, as Earth's average tropospheric temperature reaches 2[degrees]C above preindustrial levels (ca. 1860), the benchmark set by the European Union in efforts to reduce greenhouse gases. First, we assessed models used in the Intergovernmental Panel on Climate Change Fourth Assessment Report (AR4) on penguin performance duplicating existing conditions in the Southern Ocean. We chose four models appropriate for gauging changes to penguin habitat: GFDL-CM2.1, GFDL-CM2.0, MIROC3.2(hi-res), and MRI-CGCM2.3.2a. Second, we analyzed the composited model ENSEMBLE to estimate the point of 2[degrees]C warming (2025-2052) and the projected changes to sea ice coverage (extent, persistence, and concentration), sea ice thickness, wind speeds, precipitation, and air temperatures. Third, we considered studies of ancient colonies and sediment cores and some recent modeling, which indicate the (space/time) large/centennial-scale penguin response to habitat limits of all ice or no ice. Then we considered results of statistical modeling at the temporal interannual-decadal scale in regard to penguin response over a continuum of rather complex, meso-to large-scale habitat conditions, some of which have opposing and others interacting effects. The ENSEMBLE meso/decadal-scale output projects a marked narrowing of penguins' zoogeographic range at the 2[degrees]C point. Colonies north of 70[degrees] S are projected to decrease or disappear: ~50% of Emperor colonies (40% of breeding population) and ~75% of Adelie colonies (70% of breeding population), but limited growth might occur south of 73[degrees] S. Net change would result largely from positive responses to increase in polynya persistence at high latitudes, overcome by decreases in pack ice cover at lower latitudes and, particularly for Emperors, ice thickness. Adelie Penguins might colonize new breeding habitat where concentrated pack ice diverges and/or disintegrating ice shelves expose coastline. Limiting increase will be decreased persistence of pack ice north of the Antarctic Circle, as this species requires daylight in its wintering areas. Adelies would be affected negatively by increasing snowfall, predicted to increase in certain areas owing to intrusions of warm, moist marine air due to changes in the Polar Jet Stream. Key words: Adelie Penguin; Antarctica; climate change; climate modeling; Emperor Penguin; habitat optimum; sea ice; 2[degrees]C warming.

Projections of polar bear (Ursus maritimus) sea ice habitat distribution in the polar basin during the 21st century were developed to understand the consequences of anticipated sea ice reductions on polar bear populations. We used location data from satellite-collared polar bears and environmental data (e.g., bathymetry, distance to coastlines, and sea ice) collected from 1985 to 1995 to build resource selection functions (RSFs). RSFs described habitats that polar bears preferred in summer, autumn, winter, and spring. When applied to independent data from 1996 to 2006, the RSFs consistently identified habitats most frequently used by polar bears. We applied the RSFs to monthly maps of 21st-century sea ice concentration projected by 10 general circulation models (GCMs) used in the Intergovernmental Panel of Climate Change Fourth Assessment Report, under the A1B greenhouse gas forcing scenario. Despite variation in their projections, all GCMs indicated habitat losses in the polar basin during the 21st century. Losses in the highest-valued RSF habitat (optimal habitat) were greatest in the southern seas of the polar basin, especially the Chukchi and Barents seas, and least along the Arctic Ocean shores of Banks Island to northern Greenland. Mean loss of optimal polar bear habitat was greatest during summer; from an observed 1.0 million [km.sup.2] in 1985-1995 (baseline) to a projected multi-model mean of 0.32 million [km.sup.2] in 2090-2099 (-68% change). Projected winter losses of polar bear habitat were less: from 1.7 million [km.sup.2] in 1985 1995 to 1.4 million [km.sup.2] in 2090-2099 (-17% change). Habitat losses based on GCM multi-model means may be conservative; simulated rates of habitat loss during 1985-2006 from many GCMs were less than the actual observed rates of loss. Although a reduction in the total amount of optimal habitat will likely reduce polar bear populations, exact relationships between habitat losses and population demographics remain unknown. Density and energetic effects may become important as polar bears make long-distance annual migrations from traditional winter ranges to remnant high-latitude summer sea ice. These impacts will likely affect specific sex and age groups differently and may ultimately preclude bears from seasonally returning to their traditional ranges. Key words: climate change; general circulation model; IPCC; passive microwave; polar basin; polar bear; radio telemetry; resource selection function; sea ice; SMMR; SSM/I; Ursus maritimus.

In light of the rapid recent retreat of Arctic sea ice, a number of studies have discussed the possibility of a critical threshold (or 'tipping point') beyond which the ice--albedo feedback causes the ice cover to melt away in an irreversible process. The focus has typically been centered on the annual minimum (September) ice cover, which is often seen as particularly susceptible to destabilization by the ice--albedo feedback. Here, we examine the central physical processes associated with the transition from ice-covered to ice-free Arctic Ocean conditions. We show that although the ice--albedo feedback promotes the existence of multiple ice-cover states, the stabilizing thermodynamic effects of sea ice mitigate this when the Arctic Ocean is ice covered during a sufficiently large fraction of the year. These results suggest that critical threshold behavior is unlikely during the approach from current perennial sea-ice conditions to seasonally ice-free conditions. In a further warmed climate, however, we find that a critical threshold associated with the sudden loss of the remaining wintertime-only sea ice cover may be likely. Arctic climate | bifurcation | climate change | tipping point

Full-scale tests of ice-structure interaction were conducted on three multileg jacket platforms in Bohai Sea. Two of them were installed with ice-breaking cones on each of their four legs to reduce ice force and mitigate ice-induced vibration. The other is a three-leg platform with cylindrical piles without ice-breaking cones. Dynamic responses of these structures were recorded continuously, the variations in ice force were measured by load panels, and the failure processes of ice acting on cones and cylindrical pile were observed by video cameras. For the structure with ice-breaking cones, the ice sheet fails in bending, but the breaking frequency could coincide with the natural frequency of the structure at a fast ice speed and significant dynamic amplifying could be induced. For the cylindrical structure, two types of vibration are classed. At a high ice speed, the amplitude of structure response is uneven and ice fails with the brittle flanking model. At a medium ice speed, the event of steady-state vibration could take place. The explanation of ice-induced steady-state vibration is presented based on failure processes of ice within the ductile-brittle transition zone. Field observations reveal that sea ice can induce the offshore jacket structure to vibrate in resonance regardless of the fact that ice-breaking cones have been installed.

The sea ice model for simulating ice growth, decay, and drift in the Bohai Sea was developed based on the ice conditions in the Sea and on existing sea ice models. Both dynamic and thermodynamic processes of sea ice are incorporated into the model. The viscous-plastic constitutive law is applied to estimate the internal ice stress. The thermodynamic growth rates are determined from the effect of thermodynamic forcing from atmosphere and ocean, which are parameterized according to the heat exchanges at the air/ice, air/water, and ice/water interfaces. This model is coupled with an oceanic model and is linked to a numerical weather prediction model for forecasting ice conditions in the Bohai Sea and the northern Yellow Sea. Statistical verification was used to make an objective assessment of the model. This paper summarizes the development of the sea ice model and its application in Bohai Sea ice forecast.

Extensive work in recent years has been carried out on the calculation of global ice loads on a probabilistic basis. An analysis method is presented for local ice pressures, which yields values of pressure for specific values of exceedance probability. In developing this method, particular attention has been paid to problems of exposure (length, position and number of impacts), as well as the area of exposure (area within area versus nominal contact area). The solution has been formulated for a series of discrete impacts, e.g., rams by a vessel, or a series of periods of continuous interactions. Data for the MV CANMAR Kigoriak and USCGC Polar Sea were ranked and curves were fitted through the tail of probability plots for three panel sizes. This allowed determination of exceedance probabilities of the design coefficients for pressure as a function of area. The method developed was then applied to an example for a ship based on the data and expected number of rams per year. Formulas useful in the design of structures in ice are presented.

A morphological, model-based method for the automatic identification of ice leads is presented. The morphological model is based on skeletons, a tool from mathematical morphology that provides a structural description of ice lead shape. The model provides a significant reduction in the space needed to store an image with virtually no loss of morphological information. To illustrate the use of skeletons, various lead statistics, such as length and area, are calculated from lead skeletons and used to assess the effect of image smoothing and thresholding.

The modeling of the energetics of the ridging process using a particle simulation model to describe ridging formation is evaluated. It was mentioned that continuum models have been previously used to determine the strength needed to describe ice behavior. The evaluation describes the various modifications of the AIDJEX model including its comparison with the particle simulation model.