Gas diffusion through columnar laboratory sea ice: implications for mixed-layer ventilation of CO₂ in the seasonal ice zone

Gas diffusion through the porous microstructure of sea ice represents a pathway for ocean–atmosphere exchange and for transport of biogenic gases produced within sea ice. We report on the experimental determination of the bulk gas diffusion coefficients, D, for oxygen (O2) and sulphur hexafluoride (SF6) through columnar sea ice under constant ice thickness conditions for ice surface temperatures between -4 and -12 °C. Profiles of SF6 through the ice indicate decreasing gas concentration from the ice/water interface to the ice/air interface, with evidence for solubility partitioning between gas-filled and liquid-filled pore spaces. On average, DSF6 inline image was 1.3 × 10-4 cm2 s-1 (±40%) and DO2 was 3.9 × 10-5 cm2 s-1 (±41%). The preferential partitioning of SF6 to the gas phase, which is the dominant diffusion pathway produced the greater rate of SF6 diffusion. Comparing these estimates of D with an existing estimate of the air–sea gas transfer through leads indicates that ventilation of the mixed layer by diffusion through sea ice may be negligible, compared to air–sea gas exchange through fractures in the ice pack, even when the fraction of open water is less than 1%

Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air–snow (a–s) and snow–ice (s–i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two ground-based campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice

Implications of surface flooding on airborne estimates of snow depth on sea ice

Snow depth observations from airborne snow radars, such as the NASA's Operation IceBridge (OIB) mission, have recently been used in altimeter-derived sea ice thickness estimates, as well as for model parameterization. A number of validation studies comparing airborne and in situ snow depth measurements have been conducted in the western Arctic Ocean, demonstrating the utility of the airborne data. However, there have been no validation studies in the Atlantic sector of the Arctic. Recent observations in this region suggest a significant and predominant shift towards a snow-ice regime caused by deep snow on thin sea ice. During the Norwegian young sea Ice, Climate and Ecosystems (ICE) expedition (N-ICE2015) in the area north of Svalbard, a validation study was conducted on 19 March 2015. This study collected ground truth data during an OIB overflight. Snow and ice thickness measurements were obtained across a two-dimensional (2-D) 400 m × 60 m grid. Additional snow and ice thickness measurements collected in situ from adjacent ice floes helped to place the measurements obtained at the gridded survey field site into a more regional context. Widespread negative freeboards and flooding of the snowpack were observed during the N-ICE2015 expedition due to the general situation of thick snow on relatively thin sea ice. These conditions caused brine wicking into and saturation of the basal snow layers. This causes the airborne radar signal to undergo more diffuse scattering, resulting in the location of the radar main scattering horizon being detected well above the snow–ice interface. This leads to a subsequent underestimation of snow depth; if only radar-based information is used, the average airborne snow depth was 0.16 m thinner than that measured in situ at the 2-D survey field. Regional data within 10 km of the 2-D survey field suggested however a smaller deviation between average airborne and in situ snow depth, a 0.06 m underestimate in snow depth by the airborne radar, which is close to the resolution limit of the OIB snow radar system. Our results also show a broad snow depth distribution, indicating a large spatial variability in snow across the region. Differences between the airborne snow radar and in situ measurements fell within the standard deviation of the in situ data (0.15–0.18 m). Our results suggest that seawater flooding of the snow–ice interface leads to underestimations of snow depth or overestimations of sea ice freeboard measured from radar altimetry, in turn impacting the accuracy of sea ice thickness estimates.</p

Ice and ocean velocity in the Arctic marginal ice zone: Ice roughness and momentum transfer

The interplay between sea ice concentration, sea ice roughness, ocean stratification, and momentum transfer to the ice and ocean is subject to seasonal and decadal variations that are crucial to understanding the present and future air-ice-ocean system in the Arctic. In this study, continuous observations in the Canada Basin from March through December 2014 were used to investigate spatial differences and temporal changes in under-ice roughness and momentum transfer as the ice cover evolved seasonally. Observations of wind, ice, and ocean properties from four clusters of drifting instrument systems were complemented by direct drill-hole measurements and instrumented overhead flights by NASA operation IceBridge in March, as well as satellite remote sensing imagery about the instrument clusters. Spatially, directly estimated ice-ocean drag coefficients varied by a factor of three with rougher ice associated with smaller multi-year ice floe sizes embedded within the first-year-ice/multi-year-ice conglomerate. Temporal differences in the ice-ocean drag coefficient of 20–30% were observed prior to the mixed layer shoaling in summer and were associated with ice concentrations falling below 100%. The ice-ocean drag coefficient parameterization was found to be invalid in September with low ice concentrations and small ice floe sizes. Maximum momentum transfer to the ice occurred for moderate ice concentrations, and transfer to the ocean for the lowest ice concentrations and shallowest stratification. Wind work and ocean work on the ice were the dominant terms in the kinetic energy budget of the ice throughout the melt season, consistent with free drift conditions. Overall, ice topography, ice concentration, and the shallow summer mixed layer all influenced mixed layer currents and the transfer of momentum within the air-ice-ocean system. The observed changes in momentum transfer show that care must be taken to determine appropriate parameterizations of momentum transfer, and imply that the future Arctic system could become increasingly seasonal

The atmospheric role in the Arctic water cycle: A review on processes, past and future changes, and their impacts

This is the final version of the article. Available from the publisher via the DOI in this record.Atmospheric humidity, clouds, precipitation, and evapotranspiration are essential components of the Arctic climate system. During recent decades, specific humidity and precipitation have generally increased in the Arctic, but changes in evapotranspiration are poorly known. Trends in clouds vary depending on the region and season. Climate model experiments suggest that increases in precipitation are related to global warming. In turn, feedbacks associated with the increase in atmospheric moisture and decrease in sea ice and snow cover have contributed to the Arctic amplification of global warming. Climate models have captured the overall wetting trend but have limited success in reproducing regional details. For the rest of the 21st century, climate models project strong warming and increasing precipitation, but different models yield different results for changes in cloud cover. The model differences are largest in months of minimum sea ice cover. Evapotranspiration is projected to increase in winter but in summer to decrease over the oceans and increase over land. Increasing net precipitation increases river discharge to the Arctic Ocean. Over sea ice in summer, projected increase in rain and decrease in snowfall decrease the surface albedo and, hence, further amplify snow/ice surface melt. With reducing sea ice, wind forcing on the Arctic Ocean increases with impacts on ocean currents and freshwater transport out of the Arctic. Improvements in observations, process understanding, and modeling capabilities are needed to better quantify the atmospheric role in the Arctic water cycle and its changes.We thank all colleagues involved in the Arctic Freshwater Synthesis (AFS) for fruitful discussions. In particular, John Walsh is acknowledged for his constructive comments on the manuscript. AFS has been sponsored by the World Climate Research Programme’s Climate and the Cryosphere project (WCRP-CliC), the International Arctic Science Committee (IASC), and the Arctic Monitoring and Assessment Programme (AMAP). The work for this paper has been supported by the Academy of Finland (contracts 259537 and 283101), the UK Natural Environment Research Council (grant NE/J019585/1), the US National Science Foundation grant ARC-1023592 and the Program “Arctic” and the Basic Research Program of the Presidium Russian Academy of Sciences. NCAR is supported by the U.S. National Science Foundation. We gratefully acknowledge the project coordination and meeting support of Jenny Baeseman and Gwenaelle Hamon at the CliC International Project Office. No new data were applied in the manuscript. Data applied for Figures 2 and 3 are available from the JRA-55 archive at http://jra. kishou.go.jp/JRA-55/index_en. html#usage

“A very orderly retreat”: Democratic transition in East Germany, 1989-90

East Germany's 1989-90 democratisation is among the best known of East European transitions, but does not lend itself to comparative analysis, due to the singular way in which political reform and democratic consolidation were subsumed by Germany's unification process. Yet aspects of East Germany's democratisation have proved amenable to comparative approaches. This article reviews the comparative literature that refers to East Germany, and finds a schism between those who designate East Germany's transition “regime collapse” and others who contend that it exemplifies “transition through extrication”. It inquires into the merits of each position and finds in favour of the latter. Drawing on primary and secondary literature, as well as archival and interview sources, it portrays a communist elite that was, to a large extent, prepared to adapt to changing circumstances and capable of learning from “reference states” such as Poland. Although East Germany was the Soviet state in which the positions of existing elites were most threatened by democratic transition, here too a surprising number succeeded in maintaining their position while filing across the bridge to market society. A concluding section outlines the alchemy through which their bureaucratic power was transmuted into property and influence in the “new Germany”

Climate change threatens polar bear populations : a stochastic demographic analysis

Author Posting. © Ecological Society of America, 2010. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecology 91 (2010): 2883–2897, doi:10.1890/09-1641.1.The polar bear (Ursus maritimus) depends on sea ice for feeding, breeding, and movement. Significant reductions in Arctic sea ice are forecast to continue because of climate warming. We evaluated the impacts of climate change on polar bears in the southern Beaufort Sea by means of a demographic analysis, combining deterministic, stochastic, environment-dependent matrix population models with forecasts of future sea ice conditions from IPCC general circulation models (GCMs). The matrix population models classified individuals by age and breeding status; mothers and dependent cubs were treated as units. Parameter estimates were obtained from a capture–recapture study conducted from 2001 to 2006. Candidate statistical models allowed vital rates to vary with time and as functions of a sea ice covariate. Model averaging was used to produce the vital rate estimates, and a parametric bootstrap procedure was used to quantify model selection and parameter estimation uncertainty. Deterministic models projected population growth in years with more extensive ice coverage (2001–2003) and population decline in years with less ice coverage (2004–2005). LTRE (life table response experiment) analysis showed that the reduction in λ in years with low sea ice was due primarily to reduced adult female survival, and secondarily to reduced breeding. A stochastic model with two environmental states, good and poor sea ice conditions, projected a declining stochastic growth rate, log λs, as the frequency of poor ice years increased. The observed frequency of poor ice years since 1979 would imply log λs ≈ − 0.01, which agrees with available (albeit crude) observations of population size. The stochastic model was linked to a set of 10 GCMs compiled by the IPCC; the models were chosen for their ability to reproduce historical observations of sea ice and were forced with “business as usual” (A1B) greenhouse gas emissions. The resulting stochastic population projections showed drastic declines in the polar bear population by the end of the 21st century. These projections were instrumental in the decision to list the polar bear as a threatened species under the U.S. Endangered Species Act.We acknowledge primary funding for model development and analysis from the U.S. Geological Survey and additional funding from the National Science Foundation (DEB-0343820 and DEB-0816514), NOAA, the Ocean Life Institute and the Arctic Research Initiative at WHOI, and the Institute of Arctic Biology at the University of Alaska–Fairbanks. Funding for the capture–recapture effort in 2001–2006 was provided by the U.S. Geological Survey, the Canadian Wildlife Service, the Department of Environment and Natural Resources of the Government of the Northwest Territories, and the Polar Continental Shelf Project, Ottawa, Canada