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Understanding and predicting Arctic change and its impacts on global climate requires broad, sustained observations of the atmosphere-ice-ocean system, yet technological and logistical challenges severely restrict the temporal and spatial scope of observing efforts. Satellite remote sensing provides unprecedented, pan-Arctic measurements of the surface, but complementary in situ observations are required to complete the picture. Over the past few decades, a diverse range of autonomous platforms have been developed to make broad, sustained observations of the ice-free ocean, often with near-real-time data delivery. Though these technologies are well suited to the difficult environmental conditions and remote logistics that complicate Arctic observing, they face a suite of additional challenges, such as limited access to satellite services that make geolocation and communication possible. This paper reviews new platform and sensor developments, adaptations of mature technologies, and approaches for their use, placed within the framework of Arctic Ocean observing needs.

Understanding and predicting Arctic change and its impacts on global climate requires broad, sustained observations of the atmosphere-ice-ocean system, yet technological and logistical challenges severely restrict the temporal and spatial scope of observing efforts. Satellite remote sensing provides unprecedented, pan-Arctic measurements of the surface, but complementary in situ observations are required to complete the picture. Over the past few decades, a diverse range of autonomous platforms have been developed to make broad, sustained observations of the ice-free ocean, often with near-real-time data delivery. Though these technologies are well suited to the difficult environmental conditions and remote logistics that complicate Arctic observing, they face a suite of additional challenges, such as limited access to satellite services that make geolocation and communication possible. This paper reviews new platform and sensor developments, adaptations of mature technologies, and approaches for their use, placed within the framework of Arctic Ocean observing needs.

icrowave radiometry has provided valuable spaceborne observations of Earth’s geophysical properties for decades. The recent SMOS, Aquarius, and SMAP satellites have demonstrated the value of measurements at 1400 MHz for observ- ing surface soil moisture, sea surface salinity, sea ice thickness, soil freeze/thaw state, and other geophysical variables. However, the information obtained is limited by penetration through the subsur- face at 1400 MHz and by a reduced sensitivity to surface salinity in cold or wind-roughened waters. Recent airborne experiments have shown the potential of brightness temperature measurements from 500–1400 MHz to address these limitations by enabling sensing of soil moisture and sea ice thickness to greater depths, sensing of temperature deep within ice sheets, improved sensing of sea salinity in cold waters, and enhanced sensitivity to soil moisture under veg- etation canopies. However, the absence of significant spectrum re- served for passive microwave measurements in the 500–1400 MHz band requires both an opportunistic sensing strategy and systems for reducing the impact of radio-frequency interference. Here, we summarize the potential advantages and applications of 500–1400 MHz microwave radiometry for Earth observation and review recent experiments and demonstrations of these concepts. We also describe the remaining questions and challenges to be addressed in advancing to future spaceborne operation of this technology along with recommendations for future research activities.

The changing Arctic freshwater content and distribution have significant implications for ocean circulation, climate, and water and biogeochemical cycles. The paucity of in‐situ salinity measurements in the Arctic Ocean has limited our ability to study Arctic‐Ocean freshwater variability. Although satellite‐derived sea surface height (SSH) and ocean bottom pressure (OBP) have been used together to infer depth‐integrated freshwater content changes, these measurements are limited in sampling and resolution. Motivated by the recent development of sea surface salinity (SSS) remote sensing, we explore the use of SSS as a proxy for Arctic freshwater changes. As a first step, here we conduct a proof‐of‐concept study by analyzing the output of an ocean‐ice state estimation product. We find that SSS variations are coherent with those of SSH‐minus‐OBP across the Arctic basin (with R ~ ‐0.8) except for in the center of the Beaufort Gyre and the region affected by the subpolar North Atlantic inflow. On Arctic shelves, the linear regression coefficient between SSS and SSH‐minus‐OBP is ‐0.3 pss/cm or larger in magnitude. The results suggest that SSS is a good proxy for Arctic freshwater changes, and satellite SSH‐minus‐OBP can be used to evaluate satellite SSS quality.

The changing Arctic freshwater content and distribution have significant implications for ocean circulation, climate, and water and biogeochemical cycles. The paucity of in‐situ salinity measurements in the Arctic Ocean has limited our ability to study Arctic‐Ocean freshwater variability. Although satellite‐derived sea surface height (SSH) and ocean bottom pressure (OBP) have been used together to infer depth‐integrated freshwater content changes, these measurements are limited in sampling and resolution. Motivated by the recent development of sea surface salinity (SSS) remote sensing, we explore the use of SSS as a proxy for Arctic freshwater changes. As a first step, here we conduct a proof‐of‐concept study by analyzing the output of an ocean‐ice state estimation product. We find that SSS variations are coherent with those of SSH‐minus‐OBP across the Arctic basin (with R ~ ‐0.8) except for in the center of the Beaufort Gyre and the region affected by the subpolar North Atlantic inflow. On Arctic shelves, the linear regression coefficient between SSS and SSH‐minus‐OBP is ‐0.3 pss/cm or larger in magnitude. The results suggest that SSS is a good proxy for Arctic freshwater changes, and satellite SSH‐minus‐OBP can be used to evaluate satellite SSS quality.

Continental slopes – steep regions between the shelf break and abyssal ocean – play key roles in the climatology and ecology of the Arctic Ocean. Here, through review and synthesis, we find that the narrow slope regions contribute to ecosystem functioning disproportionately to the size of the habitat area (∼6% of total Arctic Ocean area). Driven by inflows of sub-Arctic waters and steered by topography, boundary currents transport boreal properties and particle loads from the Atlantic and Pacific Oceans along-slope, thus creating both along and cross-slope connectivity gradients in water mass properties and biomass. Drainage of dense, saline shelf water and material within these, and contributions of river and meltwater also shape the characteristics of the slope domain. These and other properties led us to distinguish upper and lower slope domains; the upper slope (shelf break to ∼800 m) is characterized by stronger currents, warmer sub-surface temperatures, and higher biomass across several trophic levels (especially near inflow areas). In contrast, the lower slope has slower-moving currents, is cooler, and exhibits lower vertical carbon flux and biomass. Distinct zonation of zooplankton, benthic and fish communities result from these differences. Slopes display varying levels of system connectivity: (1) along-slope through property and material transport in boundary currents, (2) cross-slope through upwelling of warm and nutrient rich water and down-welling of dense water and organic rich matter, and (3) vertically through shear and mixing. Slope dynamics also generate separating functions through (1) along-slope and across-slope fronts concentrating biological activity, and (2) vertical gradients in the water column and at the seafloor that maintain distinct physical structure and community turnover. At the upper slope, climatic change is manifested in sea-ice retreat, increased heat and mass transport by sub-Arctic inflows, surface warming, and altered vertical

The changing Arctic freshwater content and distribution have significant implications for ocean circulation, climate, and water and biogeochemical cycles. The paucity of in‐situ salinity measurements in the Arctic Ocean has limited our ability to study Arctic‐Ocean freshwater variability. Although satellite‐derived sea surface height (SSH) and ocean bottom pressure (OBP) have been used together to infer depth‐integrated freshwater content changes, these measurements are limited in sampling and resolution. Motivated by the recent development of sea surface salinity (SSS) remote sensing, we explore the use of SSS as a proxy for Arctic freshwater changes. As a first step, here we conduct a proof‐of‐concept study by analyzing the output of an ocean‐ice state estimation product. We find that SSS variations are coherent with those of SSH‐minus‐OBP across the Arctic basin (with R ~ ‐0.8) except for in the center of the Beaufort Gyre and the region affected by the subpolar North Atlantic inflow. On Arctic shelves, the linear regression coefficient between SSS and SSH‐minus‐OBP is ‐0.3 pss/cm or larger in magnitude. The results suggest that SSS is a good proxy for Arctic freshwater changes, and satellite SSH‐minus‐OBP can be used to evaluate satellite SSS quality.

Continental slopes – steep regions between the shelf break and abyssal ocean – play key roles in the climatology and ecology of the Arctic Ocean. Here, through review and synthesis, we find that the narrow slope regions contribute to ecosystem functioning disproportionately to the size of the habitat area (∼6% of total Arctic Ocean area). Driven by inflows of sub-Arctic waters and steered by topography, boundary currents transport boreal properties and particle loads from the Atlantic and Pacific Oceans along-slope, thus creating both along and cross-slope connectivity gradients in water mass properties and biomass. Drainage of dense, saline shelf water and material within these, and contributions of river and meltwater also shape the characteristics of the slope domain. These and other properties led us to distinguish upper and lower slope domains; the upper slope (shelf break to ∼800 m) is characterized by stronger currents, warmer sub-surface temperatures, and higher biomass across several trophic levels (especially near inflow areas). In contrast, the lower slope has slower-moving currents, is cooler, and exhibits lower vertical carbon flux and biomass. Distinct zonation of zooplankton, benthic and fish communities result from these differences. Slopes display varying levels of system connectivity: (1) along-slope through property and material transport in boundary currents, (2) cross-slope through upwelling of warm and nutrient rich water and down-welling of dense water and organic rich matter, and (3) vertically through shear and mixing. Slope dynamics also generate separating functions through (1) along-slope and across-slope fronts concentrating biological activity, and (2) vertical gradients in the water column and at the seafloor that maintain distinct physical structure and community turnover. At the upper slope, climatic change is manifested in sea-ice retreat, increased heat and mass transport by sub-Arctic inflows, surface warming, and altered vertical

Continental slopes – steep regions between the shelf break and abyssal ocean – play key roles in the climatology and ecology of the Arctic Ocean. Here, through review and synthesis, we find that the narrow slope regions contribute to ecosystem functioning disproportionately to the size of the habitat area (∼6% of total Arctic Ocean area). Driven by inflows of sub-Arctic waters and steered by topography, boundary currents transport boreal properties and particle loads from the Atlantic and Pacific Oceans along-slope, thus creating both along and cross-slope connectivity gradients in water mass properties and biomass. Drainage of dense, saline shelf water and material within these, and contributions of river and meltwater also shape the characteristics of the slope domain. These and other properties led us to distinguish upper and lower slope domains; the upper slope (shelf break to ∼800 m) is characterized by stronger currents, warmer sub-surface temperatures, and higher biomass across several trophic levels (especially near inflow areas). In contrast, the lower slope has slower-moving currents, is cooler, and exhibits lower vertical carbon flux and biomass. Distinct zonation of zooplankton, benthic and fish communities result from these differences. Slopes display varying levels of system connectivity: (1) along-slope through property and material transport in boundary currents, (2) cross-slope through upwelling of warm and nutrient rich water and down-welling of dense water and organic rich matter, and (3) vertically through shear and mixing. Slope dynamics also generate separating functions through (1) along-slope and across-slope fronts concentrating biological activity, and (2) vertical gradients in the water column and at the seafloor that maintain distinct physical structure and community turnover. At the upper slope, climatic change is manifested in sea-ice retreat, increased heat and mass transport by sub-Arctic inflows, surface warming, and altered vertical

Snow is the most reflective, and also the most insulative, natural material on Earth. Consequently, it is an integral part of the sea-ice and climate systems. However, the spatial and temporal heterogeneities of snow pose challenges for observing, understanding and modelling those systems under anthropogenic warming. Here, we survey the snow–ice system, then provide recommendations for overcoming present challenges. These include: collecting process-oriented observations for model diagnostics and understanding snow–ice feedbacks, and improving our remote sensing capabilities of snow for monitoring large-scale changes in snow on sea ice. These efforts could be achieved through stronger coordination between the observational, remote sensing and modelling communities, and would pay dividends through distinct improvements in predictions of polar environments.

The Arctic Oscillation (AO) is the leading mode of extratropical northern hemisphere atmospheric variability, affecting surface pressure, winds, temperature, and precipitation. Here we use an altimeter sea level record spanning 2003–2014, covering the ice‐covered and ice‐free ocean, to examine the influence of the AO on Arctic sea level and surface geostrophic circulation. AO‐driven alongshore wind anomalies drive cross‐shelf Ekman transport and opposing barotropic sea level anomalies between the shelf seas and deep basins of the Arctic Ocean, with maximum sea level anomaly differences across the shelf‐break of ~3 cm per unit AO index. This pattern of sea level variability generates topographically steered (generally along‐shelf) current anomalies of around 0.5 cm/s per unit AO index. AO‐driven wind variability modulates surface currents associated with Atlantic and Pacific water inflow, with opposing inflow anomalies between the Barents Sea Opening and Bering Strait. Plain Language Summary The Arctic Oscillation dominates large‐scale changes in atmospheric pressure, wind, temperature, and precipitation north of 20°N. This study uses satellite measurements to investigate the impact the Arctic Oscillation has on sea level and upper ocean currents in the Arctic Ocean. Winds associated with the Arctic Oscillation cause sea levels to rise in the shallow coastal shelf seas, and drop in the deeper ocean, or vice versa, depending if the Arctic Oscillation is in a positive or negative phase. These sea level patterns drive currents which generally follow the edge of the continental shelf. The Arctic Oscillation also influences the strength of the currents associated with the inflow of Atlantic Water through the Barents Sea and the inflow Pacific Water through the Bering Strait.

The Arctic Oscillation (AO) is the leading mode of extratropical northern hemisphere atmospheric variability, affecting surface pressure, winds, temperature, and precipitation. Here we use an altimeter sea level record spanning 2003–2014, covering the ice‐covered and ice‐free ocean, to examine the influence of the AO on Arctic sea level and surface geostrophic circulation. AO‐driven alongshore wind anomalies drive cross‐shelf Ekman transport and opposing barotropic sea level anomalies between the shelf seas and deep basins of the Arctic Ocean, with maximum sea level anomaly differences across the shelf‐break of ~3 cm per unit AO index. This pattern of sea level variability generates topographically steered (generally along‐shelf) current anomalies of around 0.5 cm/s per unit AO index. AO‐driven wind variability modulates surface currents associated with Atlantic and Pacific water inflow, with opposing inflow anomalies between the Barents Sea Opening and Bering Strait. Plain Language Summary The Arctic Oscillation dominates large‐scale changes in atmospheric pressure, wind, temperature, and precipitation north of 20°N. This study uses satellite measurements to investigate the impact the Arctic Oscillation has on sea level and upper ocean currents in the Arctic Ocean. Winds associated with the Arctic Oscillation cause sea levels to rise in the shallow coastal shelf seas, and drop in the deeper ocean, or vice versa, depending if the Arctic Oscillation is in a positive or negative phase. These sea level patterns drive currents which generally follow the edge of the continental shelf. The Arctic Oscillation also influences the strength of the currents associated with the inflow of Atlantic Water through the Barents Sea and the inflow Pacific Water through the Bering Strait.

Author Posting. © The Author(s), 2017. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Science 356 (2017): 285-291, doi:10.1126/science.aai8204., Arctic sea-ice loss is a leading indicator of climate change and can be attributed, in large part, to atmospheric forcing. Here we show that recent ice reductions, weakening of the halocline, and shoaling of intermediate-depth Atlantic Water layer in the eastern Eurasian Basin have increased winter ventilation in the ocean interior, making this region structurally similar to that of the western Eurasian Basin. The associated enhanced release of oceanic heat has reduced winter sea-ice formation at a rate now comparable to losses from atmospheric thermodynamic forcing, thus explaining the recent reduction in sea-ice cover in the eastern Eurasian Basin. This encroaching “atlantification” of the Eurasian Basin represents an essential step toward a new Arctic climate state, with a substantially greater role for Atlantic inflows., This study was supported by NSF grants #1203473 and #1249133 (AP, IP, MA, RR, VI), NOAA grant # NA15OAR4310155 (AP, IP, MA, RR, TB, VI) and by the A-TWAIN project, funded by the Arctic Ocean program at the FRAM-High North Research Centre for Climate and the Environment.

Author Posting. © The Author(s), 2017. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Science 356 (2017): 285-291, doi:10.1126/science.aai8204., Arctic sea-ice loss is a leading indicator of climate change and can be attributed, in large part, to atmospheric forcing. Here we show that recent ice reductions, weakening of the halocline, and shoaling of intermediate-depth Atlantic Water layer in the eastern Eurasian Basin have increased winter ventilation in the ocean interior, making this region structurally similar to that of the western Eurasian Basin. The associated enhanced release of oceanic heat has reduced winter sea-ice formation at a rate now comparable to losses from atmospheric thermodynamic forcing, thus explaining the recent reduction in sea-ice cover in the eastern Eurasian Basin. This encroaching “atlantification” of the Eurasian Basin represents an essential step toward a new Arctic climate state, with a substantially greater role for Atlantic inflows., This study was supported by NSF grants #1203473 and #1249133 (AP, IP, MA, RR, VI), NOAA grant # NA15OAR4310155 (AP, IP, MA, RR, TB, VI) and by the A-TWAIN project, funded by the Arctic Ocean program at the FRAM-High North Research Centre for Climate and the Environment.

Author Posting. © American Meteorological Society, 2015. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 96 (2015): 2079–2105, doi:10.1175/BAMS-D-13-00177.1., The loss of Arctic sea ice has emerged as a leading signal of global warming. This, together with acknowledged impacts on other components of the Earth system, has led to the term “the new Arctic.” Global coupled climate models predict that ice loss will continue through the twenty-first century, with implications for governance, economics, security, and global weather. A wide range in model projections reflects the complex, highly coupled interactions between the polar atmosphere, ocean, and cryosphere, including teleconnections to lower latitudes. This paper summarizes our present understanding of how heat reaches the ice base from the original sources—inflows of Atlantic and Pacific Water, river discharge, and summer sensible heat and shortwave radiative fluxes at the ocean/ice surface—and speculates on how such processes may change in the new Arctic. The complexity of the coupled Arctic system, and the logistic and technological challenges of working in the Arctic Ocean, require a coordinated interdisciplinary and international program that will not only improve understanding of this critical component of global climate but will also provide opportunities to develop human resources with the skills required to tackle related problems in complex climate systems. We propose a research strategy with components that include 1) improved mapping of the upper- and middepth Arctic Ocean, 2) enhanced quantification of important process, 3) expanded long-term monitoring at key heat-flux locations, and 4) development of numerical capabilities that focus on parameterization of heat-flux mechanisms and their interactions., 2016-06-01

Author Posting. © American Meteorological Society, 2015. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 96 (2015): 2079–2105, doi:10.1175/BAMS-D-13-00177.1., The loss of Arctic sea ice has emerged as a leading signal of global warming. This, together with acknowledged impacts on other components of the Earth system, has led to the term “the new Arctic.” Global coupled climate models predict that ice loss will continue through the twenty-first century, with implications for governance, economics, security, and global weather. A wide range in model projections reflects the complex, highly coupled interactions between the polar atmosphere, ocean, and cryosphere, including teleconnections to lower latitudes. This paper summarizes our present understanding of how heat reaches the ice base from the original sources—inflows of Atlantic and Pacific Water, river discharge, and summer sensible heat and shortwave radiative fluxes at the ocean/ice surface—and speculates on how such processes may change in the new Arctic. The complexity of the coupled Arctic system, and the logistic and technological challenges of working in the Arctic Ocean, require a coordinated interdisciplinary and international program that will not only improve understanding of this critical component of global climate but will also provide opportunities to develop human resources with the skills required to tackle related problems in complex climate systems. We propose a research strategy with components that include 1) improved mapping of the upper- and middepth Arctic Ocean, 2) enhanced quantification of important process, 3) expanded long-term monitoring at key heat-flux locations, and 4) development of numerical capabilities that focus on parameterization of heat-flux mechanisms and their interactions., 2016-06-01

This article takes stock of the current state of research on knowledge processes in virtual teams (VTs) and consolidates the extent research findings. Virtual teams, on the one hand, constitute important organisational entities that facilitate the integration of diverse and distributed knowledge resources. On the other hand, collaborating in a virtual environment creates particular challenges for the knowledge processes. The article seeks to consolidate the diverse evidence on knowledge processes in VTs with a specific focus on identifying the factors that influence the effectiveness of these knowledge processes. The article draws on the four basic knowledge processes outlined by Alavi and Leidner (2001) (i.e. creation, transferring, storage/retrieval and application) to frame the investigation and discuss the extent research. The consolidation of the existing research findings allows us to recognise the gaps in the understanding of knowledge processes in VTs and identify the important avenues for future research.

This article takes stock of the current state of research on knowledge processes in virtual teams (VTs) and consolidates the extent research findings. Virtual teams, on the one hand, constitute important organisational entities that facilitate the integration of diverse and distributed knowledge resources. On the other hand, collaborating in a virtual environment creates particular challenges for the knowledge processes. The article seeks to consolidate the diverse evidence on knowledge processes in VTs with a specific focus on identifying the factors that influence the effectiveness of these knowledge processes. The article draws on the four basic knowledge processes outlined by Alavi and Leidner (2001) (i.e. creation, transferring, storage/retrieval and application) to frame the investigation and discuss the extent research. The consolidation of the existing research findings allows us to recognise the gaps in the understanding of knowledge processes in VTs and identify the important avenues for future research.

Author Posting. © American Geophysical Union, 2014. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 119 (2014): 4141–4167, doi:10.1002/2014JC009943., We examine the snow radar data from the Weddell and Bellingshausen Seas acquired by eight IceBridge (OIB) flightlines in October of 2010 and 2011. In snow depth retrieval, the sidelobes from the stronger scattering snow-ice (s-i) interfaces could be misidentified as returns from the weaker air-snow (a-s) interfaces. In this paper, we first introduce a retrieval procedure that accounts for the structure of the radar system impulse response followed by a survey of the snow depths in the Weddell and Bellingshausen Seas. Limitations and potential biases in our approach are discussed. Differences between snow depth estimates from a repeat survey of one Weddell Sea track separated by 12 days, without accounting for variability due to ice motion, is −0.7 ± 13.6 cm. Average snow depth is thicker in coastal northwestern Weddell and thins toward Cape Norvegia, a decrease of >30 cm. In the Bellingshausen, the thickest snow is found nearshore in both Octobers and is thickest next to the Abbot Ice Shelf. Snow depth is linearly related to freeboard when freeboards are low but diverge as the freeboard increases especially in the thicker/rougher ice of the western Weddell. We find correlations of 0.71–0.84 between snow depth and surface roughness suggesting preferential accumulation over deformed ice. Retrievals also seem to be related to radar backscatter through surface roughness. Snow depths reported here, generally higher than those from in situ records, suggest dissimilarities in sample populations. Implications of these differences on Antarctic sea ice thickness are discussed., R. Kwok carried out this work at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. T. Maksym carried out this work at the Woods Hole Oceanographic Institution, under contract with the National Aeronautics and Space Administration., 2015-01-08

Author Posting. © American Geophysical Union, 2014. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 119 (2014): 4141–4167, doi:10.1002/2014JC009943., We examine the snow radar data from the Weddell and Bellingshausen Seas acquired by eight IceBridge (OIB) flightlines in October of 2010 and 2011. In snow depth retrieval, the sidelobes from the stronger scattering snow-ice (s-i) interfaces could be misidentified as returns from the weaker air-snow (a-s) interfaces. In this paper, we first introduce a retrieval procedure that accounts for the structure of the radar system impulse response followed by a survey of the snow depths in the Weddell and Bellingshausen Seas. Limitations and potential biases in our approach are discussed. Differences between snow depth estimates from a repeat survey of one Weddell Sea track separated by 12 days, without accounting for variability due to ice motion, is −0.7 ± 13.6 cm. Average snow depth is thicker in coastal northwestern Weddell and thins toward Cape Norvegia, a decrease of >30 cm. In the Bellingshausen, the thickest snow is found nearshore in both Octobers and is thickest next to the Abbot Ice Shelf. Snow depth is linearly related to freeboard when freeboards are low but diverge as the freeboard increases especially in the thicker/rougher ice of the western Weddell. We find correlations of 0.71–0.84 between snow depth and surface roughness suggesting preferential accumulation over deformed ice. Retrievals also seem to be related to radar backscatter through surface roughness. Snow depths reported here, generally higher than those from in situ records, suggest dissimilarities in sample populations. Implications of these differences on Antarctic sea ice thickness are discussed., R. Kwok carried out this work at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. T. Maksym carried out this work at the Woods Hole Oceanographic Institution, under contract with the National Aeronautics and Space Administration., 2015-01-08

Unlike the rapid sea-ice losses reported in the Arctic, satellite observations show an overall increase in Antarctic sea ice concentration over recent decades. However, observations of decadal trends in Antarctic ice thickness, and hence ice volume, do not currently exist. In this study a model of the Southern Ocean and its sea ice, forced by atmospheric reanalyses, is used to assess 1992—2010 trends in ice thickness and volume. The model successfully reproduces observations of mean ice concentration, thickness, and drift, and decadal trends in ice concentration and drift, imparting some confidence in the hindcasted trends in ice thickness. The model suggests that overall Antarctic sea ice volume has increased by approximately 30km3/y (0.4%/y) as an equal result of areal expansion (20×103km2/y, or 0.2%/y) and thickening (1.5mm/y, or 0.2%/y). This ice volume increase is an order of magnitude smaller than the Arctic decrease, and about half the size of the increased freshwater supply from the Antarctic Ice Sheet. Similarly to the observed ice concentration trends, the small overall increase in modelled ice volume is actually the residual of much larger opposing regional trends. Thickness changes near the ice edge follow observed concentration changes, with increasing concentration corresponding to increased thickness. Ice thickness increases are also found in the inner pack in the Amundsen and Weddell seas, where the model suggests that observed ice-drift trends directed towards the coast have caused dynamical thickening in autumn and winter. Modelled changes are predominantly dynamic in origin in the Pacific sector and thermodynamic elsewhere.

Unlike the rapid sea ice losses reported in the Arctic, satellite observations show an overall increase in Antarctic sea ice concentration over recent decades. However, observations of decadal trends in Antarctic ice thickness, and hence ice volume, do not currently exist. In this study a model of the Southern Ocean and its sea ice, forced by atmospheric reanalyses, is used to assess 1992–2010 trends in ice thickness and volume. The model successfully reproduces observations of mean ice concentration, thickness, and drift, and decadal trends in ice concentration and drift, imparting some confidence in the hindcasted trends in ice thickness. The model suggests that overall Antarctic sea ice volume has increased by approximately 30 km3 yr−1 (0.4% yr−1) as an equal result of areal expansion (20 × 103 km2 yr−1 or 0.2% yr−1) and thickening (1.5 mm yr−1 or 0.2% yr−1). This ice volume increase is an order of magnitude smaller than the Arctic decrease, and about half the size of the increased freshwater supply from the Antarctic Ice Sheet. Similarly to the observed ice concentration trends, the small overall increase in modeled ice volume is actually the residual of much larger opposing regional trends. Thickness changes near the ice edge follow observed concentration changes, with increasing concentration corresponding to increased thickness. Ice thickness increases are also found in the inner pack in the Amundsen and Weddell Seas, where the model suggests that observed ice-drift trends directed toward the coast have caused dynamical thickening in autumn and winter. Modeled changes are predominantly dynamic in origin in the Pacific sector and thermodynamic elsewhere.

Unlike the rapid sea ice losses reported in the Arctic, satellite observations show an overall increase in Antarctic sea ice concentration over recent decades. However, observations of decadal trends in Antarctic ice thickness, and hence ice volume, do not currently exist. In this study a model of the Southern Ocean and its sea ice, forced by atmospheric reanalyses, is used to assess 1992–2010 trends in ice thickness and volume. The model successfully reproduces observations of mean ice concentration, thickness, and drift, and decadal trends in ice concentration and drift, imparting some confidence in the hindcasted trends in ice thickness. The model suggests that overall Antarctic sea ice volume has increased by approximately 30 km3 yr−1 (0.4% yr−1) as an equal result of areal expansion (20 × 103 km2 yr−1 or 0.2% yr−1) and thickening (1.5 mm yr−1 or 0.2% yr−1). This ice volume increase is an order of magnitude smaller than the Arctic decrease, and about half the size of the increased freshwater supply from the Antarctic Ice Sheet. Similarly to the observed ice concentration trends, the small overall increase in modeled ice volume is actually the residual of much larger opposing regional trends. Thickness changes near the ice edge follow observed concentration changes, with increasing concentration corresponding to increased thickness. Ice thickness increases are also found in the inner pack in the Amundsen and Weddell Seas, where the model suggests that observed ice-drift trends directed toward the coast have caused dynamical thickening in autumn and winter. Modeled changes are predominantly dynamic in origin in the Pacific sector and thermodynamic elsewhere.

This article takes stock of the current state of research on knowledge processes in virtual teams (VTs) and consolidates the extent research findings. Virtual teams, on the one hand, constitute important organisational entities that facilitate the integration of diverse and distributed knowledge resources. On the other hand, collaborating in a virtual environment creates particular challenges for the knowledge processes. The article seeks to consolidate the diverse evidence on knowledge processes in VTs with a specific focus on identifying the factors that influence the effectiveness of these knowledge processes. The article draws on the four basic knowledge processes outlined by Alavi and Leidner (2001) (i.e. creation, transferring, storage/retrieval and application) to frame the investigation and discuss the extent research. The consolidation of the existing research findings allows us to recognise the gaps in the understanding of knowledge processes in VTs and identify the important avenues for future research.

This article takes stock of the current state of research on knowledge processes in virtual teams (VTs) and consolidates the extent research findings. Virtual teams, on the one hand, constitute important organisational entities that facilitate the integration of diverse and distributed knowledge resources. On the other hand, collaborating in a virtual environment creates particular challenges for the knowledge processes. The article seeks to consolidate the diverse evidence on knowledge processes in VTs with a specific focus on identifying the factors that influence the effectiveness of these knowledge processes. The article draws on the four basic knowledge processes outlined by Alavi and Leidner (2001) (i.e. creation, transferring, storage/retrieval and application) to frame the investigation and discuss the extent research. The consolidation of the existing research findings allows us to recognise the gaps in the understanding of knowledge processes in VTs and identify the important avenues for future research.

This article takes stock of the current state of research on knowledge processes in virtual teams (VTs) and consolidates the extent research findings. Virtual teams, on the one hand, constitute important organisational entities that facilitate the integration of diverse and distributed knowledge resources. On the other hand, collaborating in a virtual environment creates particular challenges for the knowledge processes. The article seeks to consolidate the diverse evidence on knowledge processes in VTs with a specific focus on identifying the factors that influence the effectiveness of these knowledge processes. The article draws on the four basic knowledge processes outlined by Alavi and Leidner (2001) (i.e. creation, transferring, storage/retrieval and application) to frame the investigation and discuss the extent research. The consolidation of the existing research findings allows us to recognise the gaps in the understanding of knowledge processes in VTs and identify the important avenues for future research.

Unlike the rapid sea-ice losses reported in the Arctic, satellite observations show an overall increase in Antarctic sea ice concentration over recent decades. However, observations of decadal trends in Antarctic ice thickness, and hence ice volume, do not currently exist. In this study a model of the Southern Ocean and its sea ice, forced by atmospheric reanalyses, is used to assess 1992—2010 trends in ice thickness and volume. The model successfully reproduces observations of mean ice concentration, thickness, and drift, and decadal trends in ice concentration and drift, imparting some confidence in the hindcasted trends in ice thickness. The model suggests that overall Antarctic sea ice volume has increased by approximately 30km3/y (0.4%/y) as an equal result of areal expansion (20×103km2/y, or 0.2%/y) and thickening (1.5mm/y, or 0.2%/y). This ice volume increase is an order of magnitude smaller than the Arctic decrease, and about half the size of the increased freshwater supply from the Antarctic Ice Sheet. Similarly to the observed ice concentration trends, the small overall increase in modelled ice volume is actually the residual of much larger opposing regional trends. Thickness changes near the ice edge follow observed concentration changes, with increasing concentration corresponding to increased thickness. Ice thickness increases are also found in the inner pack in the Amundsen and Weddell seas, where the model suggests that observed ice-drift trends directed towards the coast have caused dynamical thickening in autumn and winter. Modelled changes are predominantly dynamic in origin in the Pacific sector and thermodynamic elsewhere.

Author Posting. © American Geophysical Union, 2013. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 40 (2013): 732–737, doi:10.1002/grl.50193., Satellite records show a decline in ice extent over more than three decades, with a record minimum in September 2012. Results from the Pan-Arctic Ice-Ocean Modelling and Assimilation system (PIOMAS) suggest that the decline in extent has been accompanied by a decline in volume, but this has not been confirmed by data. Using new data from the European Space Agency CryoSat-2 (CS-2) mission, validated with in situ data, we generate estimates of ice volume for the winters of 2010/11 and 2011/12. We compare these data with current estimates from PIOMAS and earlier (2003–8) estimates from the National Aeronautics and Space Administration ICESat mission. Between the ICESat and CryoSat-2 periods, the autumn volume declined by 4291 km3 and the winter volume by 1479 km3. This exceeds the decline in ice volume in the central Arctic from the PIOMAS model of 2644 km3 in the autumn, but is less than the 2091 km3 in winter, between the two time periods., This work was funded by the UK’s Natural Environment Research Council, the European Space Agency, the German Aerospace Center (DLR), Alberta Ingenuity, National Science Foundation (NSF)., 2013-08-28

Author Posting. © American Geophysical Union, 2013. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 40 (2013): 732–737, doi:10.1002/grl.50193., Satellite records show a decline in ice extent over more than three decades, with a record minimum in September 2012. Results from the Pan-Arctic Ice-Ocean Modelling and Assimilation system (PIOMAS) suggest that the decline in extent has been accompanied by a decline in volume, but this has not been confirmed by data. Using new data from the European Space Agency CryoSat-2 (CS-2) mission, validated with in situ data, we generate estimates of ice volume for the winters of 2010/11 and 2011/12. We compare these data with current estimates from PIOMAS and earlier (2003–8) estimates from the National Aeronautics and Space Administration ICESat mission. Between the ICESat and CryoSat-2 periods, the autumn volume declined by 4291 km3 and the winter volume by 1479 km3. This exceeds the decline in ice volume in the central Arctic from the PIOMAS model of 2644 km3 in the autumn, but is less than the 2091 km3 in winter, between the two time periods., This work was funded by the UK’s Natural Environment Research Council, the European Space Agency, the German Aerospace Center (DLR), Alberta Ingenuity, National Science Foundation (NSF)., 2013-08-28

Satellite records show a decline in ice extent over more than three decades, with a record minimum in September 2012. Results from the Pan-Arctic Ice-Ocean Modelling and Assimilation system (PIOMAS) suggest that the decline in extent has been accompanied by a decline in volume, but this has not been confirmed by data. Using new data from the European Space Agency CryoSat-2 (CS-2) mission, validated with in situ data, we generate estimates of ice volume for the winters of 2010/11 and 2011/12. We compare these data with current estimates from PIOMAS and earlier (2003–8) estimates from the National Aeronautics and Space Administration ICESat mission. Between the ICESat and CryoSat-2 periods, the autumn volume declined by 4291 km3 and the winter volume by 1479 km3. This exceeds the decline in ice volume in the central Arctic from the PIOMAS model of 2644 km3 in the autumn, but is less than the 2091 km3 in winter, between the two time periods.

Satellite records show a decline in ice extent over more than three decades, with a record minimum in September 2012. Results from the Pan-Arctic Ice-Ocean Modelling and Assimilation system (PIOMAS) suggest that the decline in extent has been accompanied by a decline in volume, but this has not been confirmed by data. Using new data from the European Space Agency CryoSat-2 (CS-2) mission, validated with in situ data, we generate estimates of ice volume for the winters of 2010/11 and 2011/12. We compare these data with current estimates from PIOMAS and earlier (2003–8) estimates from the National Aeronautics and Space Administration ICESat mission. Between the ICESat and CryoSat-2 periods, the autumn volume declined by 4291 km3 and the winter volume by 1479 km3. This exceeds the decline in ice volume in the central Arctic from the PIOMAS model of 2644 km3 in the autumn, but is less than the 2091 km3 in winter, between the two time periods.

Author Posting. © American Geophysical Union, 2012. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 117 (2012): C00D13, doi:10.1029/2011JC007257., Six Arctic Ocean Model Intercomparison Project model simulations are compared with estimates of sea ice thickness derived from pan-Arctic satellite freeboard measurements (2004–2008); airborne electromagnetic measurements (2001–2009); ice draft data from moored instruments in Fram Strait, the Greenland Sea, and the Beaufort Sea (1992–2008) and from submarines (1975–2000); and drill hole data from the Arctic basin, Laptev, and East Siberian marginal seas (1982–1986) and coastal stations (1998–2009). Despite an assessment of six models that differ in numerical methods, resolution, domain, forcing, and boundary conditions, the models generally overestimate the thickness of measured ice thinner than ∼2 m and underestimate the thickness of ice measured thicker than about ∼2 m. In the regions of flat immobile landfast ice (shallow Siberian Seas with depths less than 25–30 m), the models generally overestimate both the total observed sea ice thickness and rates of September and October ice growth from observations by more than 4 times and more than one standard deviation, respectively. The models do not reproduce conditions of fast ice formation and growth. Instead, the modeled fast ice is replaced with pack ice which drifts, generating ridges of increasing ice thickness, in addition to thermodynamic ice growth. Considering all observational data sets, the better correlations and smaller differences from observations are from the Estimating the Circulation and Climate of the Ocean, Phase II and Pan-Arctic Ice Ocean Modeling and Assimilation System models., This research is supported by the National Science Foundation Office of Polar Programs covering awards of AOMIP collaborative research projects: ARC-0804180 (M.J.), ARC-0804010 (A.P.), ARC-0805141 (W.M.), ARC080789, and ARC0908769 (J.Z.). This research is also supported by the Russian Foundation of Basic Research, projects 09-05-00266 and 09-05-01231. At the National Oceanography Centre Southampton, this study was funded by the UK Natural Environment Research Council as a contribution to the Marine Centres’ Strategic Research Programme Oceans 2025., 2012-09-15

Sea ice area and thickness have been on the decline in the Arctic over the past several decades. Understanding the role of ice motion, deformation, and export is important to determining if the Arctic will continue toward seasonal ice coverage or if natural variability is capable of reversing this trend. We have analyzed sea ice model output and satellite data to advance the understanding of potentially critical physical processes and feedbacks in the region. In particular, comparisons of RGPS data and sea ice results from ice-ocean and fully coupled regional climate models have been made to evaluate model skill in representing ice kinematics. Both sea ice model configurations maintain a 1/12o (~9km) horizontal spacing and multiple thickness categories in each grid cell. Advanced model representation of sea ice deformations, combined with high spatial resolution, allow direct comparison with satellite data for resolving small-scale linear kinematic features, which contribute to changes in sea ice thickness distribution. These results offer an improved insight into what forces determine the survivability of sea ice in the Arctic.

Author Posting. © American Geophysical Union, 2012. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 117 (2012): C00D13, doi:10.1029/2011JC007257., Six Arctic Ocean Model Intercomparison Project model simulations are compared with estimates of sea ice thickness derived from pan-Arctic satellite freeboard measurements (2004–2008); airborne electromagnetic measurements (2001–2009); ice draft data from moored instruments in Fram Strait, the Greenland Sea, and the Beaufort Sea (1992–2008) and from submarines (1975–2000); and drill hole data from the Arctic basin, Laptev, and East Siberian marginal seas (1982–1986) and coastal stations (1998–2009). Despite an assessment of six models that differ in numerical methods, resolution, domain, forcing, and boundary conditions, the models generally overestimate the thickness of measured ice thinner than ∼2 m and underestimate the thickness of ice measured thicker than about ∼2 m. In the regions of flat immobile landfast ice (shallow Siberian Seas with depths less than 25–30 m), the models generally overestimate both the total observed sea ice thickness and rates of September and October ice growth from observations by more than 4 times and more than one standard deviation, respectively. The models do not reproduce conditions of fast ice formation and growth. Instead, the modeled fast ice is replaced with pack ice which drifts, generating ridges of increasing ice thickness, in addition to thermodynamic ice growth. Considering all observational data sets, the better correlations and smaller differences from observations are from the Estimating the Circulation and Climate of the Ocean, Phase II and Pan-Arctic Ice Ocean Modeling and Assimilation System models., This research is supported by the National Science Foundation Office of Polar Programs covering awards of AOMIP collaborative research projects: ARC-0804180 (M.J.), ARC-0804010 (A.P.), ARC-0805141 (W.M.), ARC080789, and ARC0908769 (J.Z.). This research is also supported by the Russian Foundation of Basic Research, projects 09-05-00266 and 09-05-01231. At the National Oceanography Centre Southampton, this study was funded by the UK Natural Environment Research Council as a contribution to the Marine Centres’ Strategic Research Programme Oceans 2025., 2012-09-15

Six AOMIP model simulations are compared with estimates of sea ice thickness derived from pan-arctic satellite freeboard measurements (2004-2008), airborne electromagnetic measurements (2001-2009), ice-draft data from moored instruments in Fram Strait, the Greenland Sea and the Beaufort Sea (1992- 2008) and from submarines (1975-2000), drill hole data from the Arctic basin, Laptev and East Siberian marginal seas (1982-1986) and coastal stations (1998-2009). Despite an assessment of six models that differ in numerical methods, resolution, domain, forcing, and boundary conditions, the models generally overestimate the thickness of measured ice thinner than ~2 m and underestimate the thickness of ice measured thicker than about ~2 m. In the regions of flat immobile land-fast ice (shallow Siberian Seas with depths less than 25-30 m), the models generally overestimate both the total observed sea ice thickness and rates of September and October ice growth from observations by more than four times and more than one standard deviation, respectively. The models do not reproduce conditions of fast-ice formation and growth. Instead, the modeled fast-ice is replaced with pack ice which drifts, generates ridges of increasing ice thickness, in addition to thermodynamic ice growth. Considering all observational data sets, the better correlations and smaller differences from observations are from the ECCO2 and UW models.

The sea-ice cover around Antarctica has experienced a slight expansion in area over the past decades1, 2. This small overall increase is the sum of much larger opposing trends in different sectors that have been proposed to result from changes in atmospheric temperature or wind stress3, 4, 5, precipitation6, 7, ocean temperature8, and atmosphere or ocean feedbacks9, 10. However, climate models have failed to reproduce the overall increase in sea ice11. Here we present a data set of satellite-tracked sea-ice motion for the period of 1992–2010 that reveals large and statistically significant trends in Antarctic ice drift, which, in most sectors, can be linked to local winds. We quantify dynamic and thermodynamic processes in the internal ice pack and show that wind-driven changes in ice advection are the dominant driver of ice-concentration trends around much of West Antarctica, whereas wind-driven thermodynamic changes dominate elsewhere. The ice-drift trends also imply large changes in the surface stress that drives the Antarctic ocean gyres, and in the fluxes of heat and salt responsible for the production of Antarctic bottom and intermediate waters.

Six Arctic Ocean Model Intercomparison Project model simulations are compared with estimates of sea ice thickness derived from pan-Arctic satellite freeboard measurements (2004-2008); airborne electromagnetic measurements (2001-2009); ice draft data from moored instruments in Fram Strait, the Greenland Sea, and the Beaufort Sea (1992-2008) and from submarines (1975-2000); and drill hole data from the Arctic basin, Laptev, and East Siberian marginal seas (1982-1986) and coastal stations (1998-2009). Despite an assessment of six models that differ in numerical methods, resolution, domain, forcing, and boundary conditions, the models generally overestimate the thickness of measured ice thinner than approximately 2 mand underestimate the thickness of ice measured thicker than about approximately 2m. In the regions of flat immobile landfast ice (shallow Siberian Seas with depths less than 25-30 m), the models generally overestimate both the total observed sea ice thickness and rates of September and October ice growth from observations by more than 4 times and more than one standard deviation, respectively. The models do not reproduce conditions of fast ice formation and growth. Instead, the modeled fast ice is replaced with pack ice which drifts, generating ridges of increasing ice thickness, in addition to thermodynamic ice growth. Considering all observational data sets, the better correlations and smaller differences from observations are from the Estimating the Circulation and Climate of the Ocean, Phase II and Pan-Arctic Ice Ocean Modeling and Assimilation System models.

Six AOMIP model simulations are compared with estimates of sea ice thickness derived from pan-arctic satellite freeboard measurements (2004-2008), airborne electromagnetic measurements (2001-2009), ice-draft data from moored instruments in Fram Strait, the Greenland Sea and the Beaufort Sea (1992- 2008) and from submarines (1975-2000), drill hole data from the Arctic basin, Laptev and East Siberian marginal seas (1982-1986) and coastal stations (1998-2009). Despite an assessment of six models that differ in numerical methods, resolution, domain, forcing, and boundary conditions, the models generally overestimate the thickness of measured ice thinner than ~2 m and underestimate the thickness of ice measured thicker than about ~2 m. In the regions of flat immobile land-fast ice (shallow Siberian Seas with depths less than 25-30 m), the models generally overestimate both the total observed sea ice thickness and rates of September and October ice growth from observations by more than four times and more than one standard deviation, respectively. The models do not reproduce conditions of fast-ice formation and growth. Instead, the modeled fast-ice is replaced with pack ice which drifts, generates ridges of increasing ice thickness, in addition to thermodynamic ice growth. Considering all observational data sets, the better correlations and smaller differences from observations are from the ECCO2 and UW models.

The sea-ice cover around Antarctica has experienced a slight expansion in area over the past decades1, 2. This small overall increase is the sum of much larger opposing trends in different sectors that have been proposed to result from changes in atmospheric temperature or wind stress3, 4, 5, precipitation6, 7, ocean temperature8, and atmosphere or ocean feedbacks9, 10. However, climate models have failed to reproduce the overall increase in sea ice11. Here we present a data set of satellite-tracked sea-ice motion for the period of 1992–2010 that reveals large and statistically significant trends in Antarctic ice drift, which, in most sectors, can be linked to local winds. We quantify dynamic and thermodynamic processes in the internal ice pack and show that wind-driven changes in ice advection are the dominant driver of ice-concentration trends around much of West Antarctica, whereas wind-driven thermodynamic changes dominate elsewhere. The ice-drift trends also imply large changes in the surface stress that drives the Antarctic ocean gyres, and in the fluxes of heat and salt responsible for the production of Antarctic bottom and intermediate waters.

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

We compare results from six AOMIP model simulations with estimates of sea ice thickness obtained from ICESat, moored and submarine-based upward looking sensors, airborne electromagnetic measurements and drill holes. Our goal is to find patterns of model performance to guide model improvement. The satellite data is pan-arctic from 2004-2008, ice-draft data is from moored instruments in Fram Strait, the Greenland Sea and the Beaufort Sea from 1992-2008 and from submarines from 1975-2000. The drill hole data are from the Laptev and East Siberian marginal seas from 1982-1986 and from coastal stations from 1998-2009. While there are important caveats when comparing modeled results with measurements from different platforms and time periods such as these, the models agree well with moored ULS data. In general, the AOMIP models underestimate the thickness of measured ice thicker than about 2 m and overestimate thickness of ice thinner than 2 m. The simulated results are poor over the fast ice and marginal seas of the Siberian shelves. Averaging over all observational data sets, the better correlations and smaller differences from observed thickness are from the ECCO2 and UW models.

Our long-term goals are to understand how the physical oceanography, sea-ice dynamics, and marine mammal utilization of arctic shelves will change in response to a diminishing ice cover. We thus seek to understand better the wind-forced response of the shelf and shelfbreak and the cross-shelf exchange of mass, materials, and momentum. These responses will likely affect the use of arctic shelves by marine mammals. We are applying several recently developed technologies to an arctic shelf in synergistic ways, including passive acoustic recorders, moored profiling CTDs, autonomous underwater vehicles, shore-based current mapping radars, and geophysical processing tools to determine ice displacement and deformation. These bear on another long-term goal which is to demonstrate the applicability of these technologies to other arctic shelves., Prepared in collaboration with NASA Jet Propulsion laboratory, Pasadena, CA., Woods Hole Oceanographic Institution, Woods Hole, MA, Washington University, Seattle, WA.

Our long-term goals are to understand how the physical oceanography, sea-ice dynamics, and marine mammal utilization of arctic shelves will change in response to a diminishing ice cover. We thus seek to understand better the wind-forced response of the shelf and shelfbreak and the cross-shelf exchange of mass, materials, and momentum. These responses will likely affect the use of arctic shelves by marine mammals. We are applying several recently developed technologies to an arctic shelf in synergistic ways, including passive acoustic recorders, moored profiling CTDs, autonomous underwater vehicles, shore-based current mapping radars, and geophysical processing tools to determine ice displacement and deformation. These bear on another long-term goal which is to demonstrate the applicability of these technologies to other arctic shelves.

Intelligent software agents are promising in improving the effectiveness of e-marketplaces for e-commerce. Although a large amount of research has been conducted to develop negotiation protocols and mechanisms for e-marketplaces, existing negotiation mechanisms are weak in dealing with complex and dynamic negotiation spaces often found in e-commerce. This paper illustrates a novel knowledge discovery method and a probabilistic negotiation decision making mechanism to improve the performance of negotiation agents. Our preliminary experiments show that the probabilistic negotiation agents empowered by knowledge discovery mechanisms are more effective and efficient than the Pareto optimal negotiation agents in simulated e-marketplaces.

Our long-term goal is to understand how the circulation, stratification, sea-ice dynamics, and marine mammal utilization of arctic shelves will change in response to a diminishing ice cover. We thus seek to understand better the wind-forced response of the shelf and the shelfbreak, and the cross-shelf exchange of mass, materials, and momentum. These responses will likely affect the use of arctic shelves by marine mammals. Our study is applying a variety of recently developed technologies in a synergistic manner. These include passive acoustic recorders, moored profiling temperature/salinity sensors, autonomous underwater vehicles, shore-based current mapping radars, and geophysical processing tools to determine ice displacement and deformation. An additional long-term goal is to demonstrate the applicability of these technologies and their synergistic usage to other arctic shelves., Prepared in cooperation with NASA Jet Propulsion Laboratory, Pasadena, CA; Woods Hole Oceanographic Institution, Department of Physical Oceanography MS 21, Woods Hole, MA; and University of Washington, Applied Physics Lab, Seattle, WA. A National Oceanographic Partnership Program Award. The original document contains color images.

In preparation for the first European Earth Remote Sensing (ERS-1) mission, a series of multitemporal, multifrequency, multipolarization aircraft synthetic aperture radar (SAR) data sets were acquired over the Bonanza Creek Experimental Forest near Fairbanks, Alaska in March 1988. Significant change in radar backscatter was observed over the two-week experimental period due to changing environmental conditions. These preliminary results are presented to illustrate the opportunity afforded by the ERS-1 SAR to monitor temporal change in forest ecosystems.

A geophysical processor for deriving sea ice type and ice motion information from sequential SAR image data has been designed and is in implementation phase for use with ERS-1 SAR data at the Alaska SAR Facility (ASF). This SAR ice data processor, called the ASF Geophysical Processing System, or ASF-GPS, will be in place for launch in May 1991. Descriptions of the salient aspects of ASF-GPS and its current status are, presented. The next step in the evolution of processors for geophysical descriptions of sea ice is now in design phase; it involves the utilization of data from other sensors and sources and the generation of higher-level products. The augmented data are environmental, e.g., weather agency analyses, satellite-derived surface temperatures and drifting buoy data. These data serve to (1) improve the performance of the basic data product generation, the ice type and motion data sets, by increasing accuracy and shortening processing time, and (2) extend the level of the data products by computation of key geophysical fluxes. Geophysical quantities required from the sea ice processor include the surface heat, momentum, brine and freshwater fluxes, radiation balance, snow cover, melt pond cover and thermodynamic state. The estimation of two of these fluxes, brine and freshwater, is discussed, and the requirements for suitable environmental data are also presented. Finally, the system design of the ASF-GPS and the follow-on processor, designed initially to utilize SAR data from RADARSAT with weather and other inputs, e.g., AVHRR, and, after upgrade, from the suite of EOS instruments, will be presented.

Sea ice area and thickness have been on the decline in the Arctic over the past several decades. Understanding the role of ice motion, deformation, and export is important to determining if the Arctic will continue toward seasonal ice coverage or if natural variability is capable of reversing this trend. We have analyzed sea ice model output and satellite data to advance the understanding of potentially critical physical processes and feedbacks in the region. In particular, comparisons of RGPS data and sea ice results from ice-ocean and fully coupled regional climate models have been made to evaluate model skill in representing ice kinematics. Both sea ice model configurations maintain a 1/12o (~9km) horizontal spacing and multiple thickness categories in each grid cell. Advanced model representation of sea ice deformations, combined with high spatial resolution, allow direct comparison with satellite data for resolving small-scale linear kinematic features, which contribute to changes in sea ice thickness distribution. These results offer an improved insight into what forces determine the survivability of sea ice in the Arctic., http://archive.org/details/evaluationofseic109456838, Lieutenant Commander, United States Navy