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Sample records for alamos sea ice

  1. Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

    SciTech Connect

    Urrego-Blanco, Jorge Rolando; Urban, Nathan Mark; Hunke, Elizabeth Clare; Turner, Adrian Keith; Jeffery, Nicole

    2016-04-01

    Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. Lastly, it is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.

  2. Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

    DOE PAGES

    Urrego-Blanco, Jorge Rolando; Urban, Nathan Mark; Hunke, Elizabeth Clare; ...

    2016-04-01

    Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual modelmore » parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. Lastly, it is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.« less

  3. Sea Ice

    NASA Technical Reports Server (NTRS)

    Parkinson, Claire L.; Cavalieri, Donald J.

    2005-01-01

    Sea ice covers vast areas of the polar oceans, with ice extent in the Northern Hemisphere ranging from approximately 7 x 10(exp 6) sq km in September to approximately 15 x 10(exp 6) sq km in March and ice extent in the Southern Hemisphere ranging from approximately 3 x 10(exp 6) sq km in February to approximately 18 x 10(exp 6) sq km in September. These ice covers have major impacts on the atmosphere, oceans, and ecosystems of the polar regions, and so as changes occur in them there are potential widespread consequences. Satellite data reveal considerable interannual variability in both polar sea ice covers, and many studies suggest possible connections between the ice and various oscillations within the climate system, such as the Arctic Oscillation, North Atlantic Oscillation, and Antarctic Oscillation, or Southern Annular Mode. Nonetheless, statistically significant long-term trends are also apparent, including overall trends of decreased ice coverage in the Arctic and increased ice coverage in the Antarctic from late 1978 through the end of 2003, with the Antarctic ice increases following marked decreases in the Antarctic ice during the 1970s. For a detailed picture of the seasonally varying ice cover at the start of the 21st century, this chapter includes ice concentration maps for each month of 2001 for both the Arctic and the Antarctic, as well as an overview of what the satellite record has revealed about the two polar ice covers from the 1970s through 2003.

  4. Sea Ice

    NASA Technical Reports Server (NTRS)

    Perovich, D.; Gerland, S.; Hendricks, S.; Meier, Walter N.; Nicolaus, M.; Richter-Menge, J.; Tschudi, M.

    2013-01-01

    During 2013, Arctic sea ice extent remained well below normal, but the September 2013 minimum extent was substantially higher than the record-breaking minimum in 2012. Nonetheless, the minimum was still much lower than normal and the long-term trend Arctic September extent is -13.7 per decade relative to the 1981-2010 average. The less extreme conditions this year compared to 2012 were due to cooler temperatures and wind patterns that favored retention of ice through the summer. Sea ice thickness and volume remained near record-low levels, though indications are of slightly thicker ice compared to the record low of 2012.

  5. Arctic Sea ice model sensitivities.

    SciTech Connect

    Peterson, Kara J.; Bochev, Pavel Blagoveston; Paskaleva, Biliana Stefanova

    2010-12-01

    Arctic sea ice is an important component of the global climate system and, due to feedback effects, the Arctic ice cover is changing rapidly. Predictive mathematical models are of paramount importance for accurate estimates of the future ice trajectory. However, the sea ice components of Global Climate Models (GCMs) vary significantly in their prediction of the future state of Arctic sea ice and have generally underestimated the rate of decline in minimum sea ice extent seen over the past thirty years. One of the contributing factors to this variability is the sensitivity of the sea ice state to internal model parameters. A new sea ice model that holds some promise for improving sea ice predictions incorporates an anisotropic elastic-decohesive rheology and dynamics solved using the material-point method (MPM), which combines Lagrangian particles for advection with a background grid for gradient computations. We evaluate the variability of this MPM sea ice code and compare it with the Los Alamos National Laboratory CICE code for a single year simulation of the Arctic basin using consistent ocean and atmospheric forcing. Sensitivities of ice volume, ice area, ice extent, root mean square (RMS) ice speed, central Arctic ice thickness,and central Arctic ice speed with respect to ten different dynamic and thermodynamic parameters are evaluated both individually and in combination using the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA). We find similar responses for the two codes and some interesting seasonal variability in the strength of the parameters on the solution.

  6. Biogeochemistry in Sea Ice: CICE model developments

    SciTech Connect

    Jeffery, Nicole; Hunke, Elizabeth; Elliott, Scott; Turner, Adrian

    2012-06-18

    Polar primary production unfolds in a dynamic sea ice environment, and the interactions of sea ice with ocean support and mediate this production. In spring, for example, fresh melt water contributes to the shoaling of the mixed layer enhancing ice edge blooms. In contrast, sea ice formation in the fall reduces light penetration to the upper ocean slowing primary production in marine waters. Polar biogeochemical modeling studies typically consider these types of ice-ocean interactions. However, sea ice itself is a biogeochemically active medium, contributing a significant and, possibly, essential source of primary production to polar regions in early spring and fall. Here we present numerical simulations using the Los Alamos Sea Ice Model (CICE) with prognostic salinity and sea ice biogeochemistry. This study investigates the relationship between sea ice multiphase physics and sea ice productivity. Of particular emphasis are the processes of gravity drainage, melt water flushing, and snow loading. During sea ice formation, desalination by gravity drainage facilitates nutrient exchange between ocean and ice maintaining ice algal blooms in early spring. Melt water flushing releases ice algae and nutrients to underlying waters limiting ice production. Finally, snow loading, particularly in the Southern Ocean, forces sea ice below the ocean surface driving an upward flow of nutrient rich water into the ice to the benefit of interior and freeboard communities. Incorporating ice microphysics in CICE has given us an important tool for assessing the importance of these processes for polar algal production at global scales.

  7. Sea ice ecosystems.

    PubMed

    Arrigo, Kevin R

    2014-01-01

    Polar sea ice is one of the largest ecosystems on Earth. The liquid brine fraction of the ice matrix is home to a diverse array of organisms, ranging from tiny archaea to larger fish and invertebrates. These organisms can tolerate high brine salinity and low temperature but do best when conditions are milder. Thriving ice algal communities, generally dominated by diatoms, live at the ice/water interface and in recently flooded surface and interior layers, especially during spring, when temperatures begin to rise. Although protists dominate the sea ice biomass, heterotrophic bacteria are also abundant. The sea ice ecosystem provides food for a host of animals, with crustaceans being the most conspicuous. Uneaten organic matter from the ice sinks through the water column and feeds benthic ecosystems. As sea ice extent declines, ice algae likely contribute a shrinking fraction of the total amount of organic matter produced in polar waters.

  8. Sea Ice Ecosystems

    NASA Astrophysics Data System (ADS)

    Arrigo, Kevin R.

    2014-01-01

    Polar sea ice is one of the largest ecosystems on Earth. The liquid brine fraction of the ice matrix is home to a diverse array of organisms, ranging from tiny archaea to larger fish and invertebrates. These organisms can tolerate high brine salinity and low temperature but do best when conditions are milder. Thriving ice algal communities, generally dominated by diatoms, live at the ice/water interface and in recently flooded surface and interior layers, especially during spring, when temperatures begin to rise. Although protists dominate the sea ice biomass, heterotrophic bacteria are also abundant. The sea ice ecosystem provides food for a host of animals, with crustaceans being the most conspicuous. Uneaten organic matter from the ice sinks through the water column and feeds benthic ecosystems. As sea ice extent declines, ice algae likely contribute a shrinking fraction of the total amount of organic matter produced in polar waters.

  9. Los Alamos Canyon Ice Rink Parking Flood Plain Assessment

    SciTech Connect

    Hathcock, Charles Dean

    2015-02-10

    The project location is in Los Alamos Canyon east of the ice rink facility at the intersection of West and Omega roads (Figure 1). Forty eight parking spaces will be constructed on the north and south side of Omega Road, and a lighted walking path will be constructed to the ice rink. Some trees will be removed during this action. A guardrail of approximately 400 feet will be constructed along the north side of West Road to prevent unsafe parking in that area.

  10. Operation IceBridge: Sea Ice Interlude

    NASA Video Gallery

    Sea ice comes in an array of shapes and sizes and has its own ephemeral beauty. Operation IceBridge studies sea ice at both poles, and also runs across interesting formations en route to other targ...

  11. Quantifying uncertainty and sensitivity in sea ice models

    SciTech Connect

    Urrego Blanco, Jorge Rolando; Hunke, Elizabeth Clare; Urban, Nathan Mark

    2016-07-15

    The Los Alamos Sea Ice model has a number of input parameters for which accurate values are not always well established. We conduct a variance-based sensitivity analysis of hemispheric sea ice properties to 39 input parameters. The method accounts for non-linear and non-additive effects in the model.

  12. Arctic landfast sea ice

    NASA Astrophysics Data System (ADS)

    Konig, Christof S.

    Landfast ice is sea ice which forms and remains fixed along a coast, where it is attached either to the shore, or held between shoals or grounded icebergs. Landfast ice fundamentally modifies the momentum exchange between atmosphere and ocean, as compared to pack ice. It thus affects the heat and freshwater exchange between air and ocean and impacts on the location of ocean upwelling and downwelling zones. Further, the landfast ice edge is essential for numerous Arctic mammals and Inupiat who depend on them for their subsistence. The current generation of sea ice models is not capable of reproducing certain aspects of landfast ice formation, maintenance, and disintegration even when the spatial resolution would be sufficient to resolve such features. In my work I develop a new ice model that permits the existence of landfast sea ice even in the presence of offshore winds, as is observed in mature. Based on viscous-plastic as well as elastic-viscous-plastic ice dynamics I add tensile strength to the ice rheology and re-derive the equations as well as numerical methods to solve them. Through numerical experiments on simplified domains, the effects of those changes are demonstrated. It is found that the modifications enable landfast ice modeling, as desired. The elastic-viscous-plastic rheology leads to initial velocity fluctuations within the landfast ice that weaken the ice sheet and break it up much faster than theoretically predicted. Solving the viscous-plastic rheology using an implicit numerical method avoids those waves and comes much closer to theoretical predictions. Improvements in landfast ice modeling can only verified in comparison to observed data. I have extracted landfast sea ice data of several decades from several sources to create a landfast sea ice climatology that can be used for that purpose. Statistical analysis of the data shows several factors that significantly influence landfast ice distribution: distance from the coastline, ocean depth, as

  13. Arctic Sea Ice Maximum 2011

    NASA Video Gallery

    AMSR-E Arctic Sea Ice: September 2010 to March 2011: Scientists tracking the annual maximum extent of Arctic sea ice said that 2011 was among the lowest ice extents measured since satellites began ...

  14. 2011 Sea Ice Minimum

    NASA Video Gallery

    This video shows Arctic sea ice from March 7, 2011, to Sept. 9, 2011, ending with a comparison of the 30-year average minimum extent, shown in yellow, and the Northwest Passage, in red. (no audio) ...

  15. Record Sea Ice Minimum

    NASA Technical Reports Server (NTRS)

    2007-01-01

    Arctic sea ice reached a record low in September 2007, below the previous record set in 2005 and substantially below the long-term average. This image shows the Arctic as observed by the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) aboard NASA's Aqua satellite on September 16, 2007. In this image, blue indicates open water, white indicates high sea ice concentration, and turquoise indicates loosely packed sea ice. The black circle at the North Pole results from an absence of data as the satellite does not make observations that far north. Three contour lines appear on this image. The red line is the 2007 minimum, as of September 15, about the same time the record low was reached, and it almost exactly fits the sea ice observed by AMSR-E. The green line indicates the 2005 minimum, the previous record low. The yellow line indicates the median minimum from 1979 to 2000.

  16. Sea Ice Minimum 2016

    NASA Video Gallery

    This animation shows the evolution of the Arctic sea ice cover from its wintertime maximum extent, which was reached on Mar. 24, 2016, and was the lowest on record for the second year in a row, to ...

  17. Altimeter Sea Ice Workshop

    DTIC Science & Technology

    1990-09-01

    Science Fax- 164513 Chalmers University of TechnologyI S-41296 Goteborg, Sweden 5 Altimeter Sea Ice Workshop Presentation Summary Hawkins: Present U.S...into the ground. A large tent slides over the top of the pond for solar shading and inclement weather protection. A mobile gantry, which spans the width...tracks can covering the pond to protect the growing ice from weather when necessary. A walkway mounted on the tracks serves as a mobile base on which the

  18. Seafloor Control on Sea Ice

    NASA Technical Reports Server (NTRS)

    Nghiem, S. V.; Clemente-Colon, P.; Rigor, I. G.; Hall, D. K.; Neumann, G.

    2011-01-01

    The seafloor has a profound role in Arctic sea ice formation and seasonal evolution. Ocean bathymetry controls the distribution and mixing of warm and cold waters, which may originate from different sources, thereby dictating the pattern of sea ice on the ocean surface. Sea ice dynamics, forced by surface winds, are also guided by seafloor features in preferential directions. Here, satellite mapping of sea ice together with buoy measurements are used to reveal the bathymetric control on sea ice growth and dynamics. Bathymetric effects on sea ice formation are clearly observed in the conformation between sea ice patterns and bathymetric characteristics in the peripheral seas. Beyond local features, bathymetric control appears over extensive ice-prone regions across the Arctic Ocean. The large-scale conformation between bathymetry and patterns of different synoptic sea ice classes, including seasonal and perennial sea ice, is identified. An implication of the bathymetric influence is that the maximum extent of the total sea ice cover is relatively stable, as observed by scatterometer data in the decade of the 2000s, while the minimum ice extent has decreased drastically. Because of the geologic control, the sea ice cover can expand only as far as it reaches the seashore, the continental shelf break, or other pronounced bathymetric features in the peripheral seas. Since the seafloor does not change significantly for decades or centuries, sea ice patterns can be recurrent around certain bathymetric features, which, once identified, may help improve short-term forecast and seasonal outlook of the sea ice cover. Moreover, the seafloor can indirectly influence cloud cover by its control on sea ice distribution, which differentially modulates the latent heat flux through ice covered and open water areas.

  19. Sea Ice and Oceanographic Conditions.

    ERIC Educational Resources Information Center

    Oceanus, 1986

    1986-01-01

    The coastal waters of the Beaufort Sea are covered with ice three-fourths of the year. These waters (during winter) are discussed by considering: consolidation of coastal ice; under-ice water; brine circulation; biological energy; life under the ice (including kelp and larger animals); food chains; and ice break-up. (JN)

  20. Early Student Support to Investigate the Role of Sea Ice-Albedo Feedback in Sea Ice Predictions

    DTIC Science & Technology

    2013-09-30

    CESM1 in all its versions employs the Los Alamos National Laboratory ( LANL ) sea ice model, known as CICE. The sea ice in CESM1 has been... documented in a series of papers (e.g., Jahn et al, 2012; Kay Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the...collaborator Dr. Elizabeth Hunke from LANL , who is the chief developer of CICE. Dr. Hunke is a partner with the sea ice prediction network and has a

  1. Sea ice/climate studies

    NASA Technical Reports Server (NTRS)

    Parkinson, C. L.

    1988-01-01

    The objectives were to determine and analyze the annual cycle of sea ice extents in the Arctic Ocean and peripheral seas and bays over the period 1973 to 1986, looking in particular for any long term trends; to examine the relationship between local sea ice covers and the surrounding atmosphere and ocean; and to examine sea ice as a potential early indicator of climate change. The work involves creating regional and hemispheric time series of sea ice variables from satellite passive microwave data and analyzing these through various intercomparisons amongst themselves and with oceanographic and atmospheric fields.

  2. 2013 Arctic Sea Ice Minimum

    NASA Video Gallery

    After an unusually cold summer in the northernmost latitudes, Arctic sea ice appears to have reached its annual minimum summer extent for 2013 on Sept. 13, the NASA-supported National Snow and Ice ...

  3. Reducing uncertainty in high-resolution sea ice models.

    SciTech Connect

    Peterson, Kara J.; Bochev, Pavel Blagoveston

    2013-07-01

    Arctic sea ice is an important component of the global climate system, reflecting a significant amount of solar radiation, insulating the ocean from the atmosphere and influencing ocean circulation by modifying the salinity of the upper ocean. The thickness and extent of Arctic sea ice have shown a significant decline in recent decades with implications for global climate as well as regional geopolitics. Increasing interest in exploration as well as climate feedback effects make predictive mathematical modeling of sea ice a task of tremendous practical import. Satellite data obtained over the last few decades have provided a wealth of information on sea ice motion and deformation. The data clearly show that ice deformation is focused along narrow linear features and this type of deformation is not well-represented in existing models. To improve sea ice dynamics we have incorporated an anisotropic rheology into the Los Alamos National Laboratory global sea ice model, CICE. Sensitivity analyses were performed using the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) to determine the impact of material parameters on sea ice response functions. Two material strength parameters that exhibited the most significant impact on responses were further analyzed to evaluate their influence on quantitative comparisons between model output and data. The sensitivity analysis along with ten year model runs indicate that while the anisotropic rheology provides some benefit in velocity predictions, additional improvements are required to make this material model a viable alternative for global sea ice simulations.

  4. Sunlight, Sea Ice, and the Ice Albedo Feedback in a Changing Arctic Sea Ice Cover

    DTIC Science & Technology

    2013-09-30

    1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Sunlight, Sea Ice, and the Ice Albedo Feedback in a...ice age, and iv) onset dates of melt and freezeup. 4. Assess the magnitude of the contribution from ice- albedo feedback to the observed decrease of...COVERED 00-00-2013 to 00-00-2013 4. TITLE AND SUBTITLE Sunlight, Sea Ice, and the Ice Albedo Feedback in a Changing Arctic Sea Ice Cover 5a

  5. Polar Climate: Arctic sea ice

    USGS Publications Warehouse

    Stone, R.S.; Douglas, David C.; Belchansky, G.I.; Drobot, S.D.

    2005-01-01

    Recent decreases in snow and sea ice cover in the high northern latitudes are among the most notable indicators of climate change. Northern Hemisphere sea ice extent for the year as a whole was the third lowest on record dating back to 1973, behind 1995 (lowest) and 1990 (second lowest; Hadley Center–NCEP). September sea ice extent, which is at the end of the summer melt season and is typically the month with the lowest sea ice extent of the year, has decreased by about 19% since the late 1970s (Fig. 5.2), with a record minimum observed in 2002 (Serreze et al. 2003). A record low extent also occurred in spring (Chapman 2005, personal communication), and 2004 marked the third consecutive year of anomalously extreme sea ice retreat in the Arctic (Stroeve et al. 2005). Some model simulations indicate that ice-free summers will occur in the Arctic by the year 2070 (ACIA 2004).

  6. Analysis of sea ice dynamics

    NASA Technical Reports Server (NTRS)

    Zwally, J.

    1988-01-01

    The ongoing work has established the basis for using multiyear sea ice concentrations from SMMR passive microwave for studies of largescale advection and convergence/divergence of the Arctic sea ice pack. Comparisons were made with numerical model simulations and buoy data showing qualitative agreement on daily to interannual time scales. Analysis of the 7-year SMMR data set shows significant interannual variations in the total area of multiyear ice. The scientific objective is to investigate the dynamics, mass balance, and interannual variability of the Arctic sea ice pack. The research emphasizes the direct application of sea ice parameters derived from passive microwave data (SMMR and SSMI) and collaborative studies using a sea ice dynamics model. The possible causes of observed interannual variations in the multiyear ice area are being examined. The relative effects of variations in the large scale advection and convergence/divergence within the ice pack on a regional and seasonal basis are investigated. The effects of anomolous atmospheric forcings are being examined, including the long-lived effects of synoptic events and monthly variations in the mean geostrophic winds. Estimates to be made will include the amount of new ice production within the ice pack during winter and the amount of ice exported from the pack.

  7. Arctic Sea Ice Predictability and the Sea Ice Prediction Network

    NASA Astrophysics Data System (ADS)

    Wiggins, H. V.; Stroeve, J. C.

    2014-12-01

    Drastic reductions in Arctic sea ice cover have increased the demand for Arctic sea ice predictions by a range of stakeholders, including local communities, resource managers, industry and the public. The science of sea-ice prediction has been challenged to keep up with these developments. Efforts such as the SEARCH Sea Ice Outlook (SIO; http://www.arcus.org/sipn/sea-ice-outlook) and the Sea Ice for Walrus Outlook have provided a forum for the international sea-ice prediction and observing community to explore and compare different approaches. The SIO, originally organized by the Study of Environmental Change (SEARCH), is now managed by the new Sea Ice Prediction Network (SIPN), which is building a collaborative network of scientists and stakeholders to improve arctic sea ice prediction. The SIO synthesizes predictions from a variety of methods, including heuristic and from a statistical and/or dynamical model. In a recent study, SIO data from 2008 to 2013 were analyzed. The analysis revealed that in some years the predictions were very successful, in other years they were not. Years that were anomalous compared to the long-term trend have proven more difficult to predict, regardless of which method was employed. This year, in response to feedback from users and contributors to the SIO, several enhancements have been made to the SIO reports. One is to encourage contributors to provide spatial probability maps of sea ice cover in September and the first day each location becomes ice-free; these are an example of subseasonal to seasonal, local-scale predictions. Another enhancement is a separate analysis of the modeling contributions. In the June 2014 SIO report, 10 of 28 outlooks were produced from models that explicitly simulate sea ice from dynamic-thermodynamic sea ice models. Half of the models included fully-coupled (atmosphere, ice, and ocean) models that additionally employ data assimilation. Both of these subsets (models and coupled models with data

  8. Microwave emission characteristics of sea ice

    NASA Technical Reports Server (NTRS)

    Edgerton, A. T.; Poe, G.

    1972-01-01

    A general classification is presented for sea ice brightness temperatures with categories of high and low emission, corresponding to young and weathered sea ice, respectively. A sea ice emission model was developed which allows variations of ice salinity and temperature in directions perpendicular to the ice surface.

  9. Arctic Sea Ice Minimum, 2015

    NASA Video Gallery

    This animation shows the evolution of the Arctic sea ice cover from its wintertime maximum extent, which was reached on Feb. 25, 2015, and was the lowest on record, to its apparent yearly minimum, ...

  10. Development, sensitivity analysis, and uncertainty quantification of high-fidelity arctic sea ice models.

    SciTech Connect

    Peterson, Kara J.; Bochev, Pavel Blagoveston; Paskaleva, Biliana S.

    2010-09-01

    Arctic sea ice is an important component of the global climate system and due to feedback effects the Arctic ice cover is changing rapidly. Predictive mathematical models are of paramount importance for accurate estimates of the future ice trajectory. However, the sea ice components of Global Climate Models (GCMs) vary significantly in their prediction of the future state of Arctic sea ice and have generally underestimated the rate of decline in minimum sea ice extent seen over the past thirty years. One of the contributing factors to this variability is the sensitivity of the sea ice to model physical parameters. A new sea ice model that has the potential to improve sea ice predictions incorporates an anisotropic elastic-decohesive rheology and dynamics solved using the material-point method (MPM), which combines Lagrangian particles for advection with a background grid for gradient computations. We evaluate the variability of the Los Alamos National Laboratory CICE code and the MPM sea ice code for a single year simulation of the Arctic basin using consistent ocean and atmospheric forcing. Sensitivities of ice volume, ice area, ice extent, root mean square (RMS) ice speed, central Arctic ice thickness, and central Arctic ice speed with respect to ten different dynamic and thermodynamic parameters are evaluated both individually and in combination using the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA). We find similar responses for the two codes and some interesting seasonal variability in the strength of the parameters on the solution.

  11. Arctic Sea Ice, Summer 2014

    NASA Video Gallery

    An animation of daily Arctic sea ice extent in summer 2014, from March 21, 2014 to Sept. 17, 2014 – when the ice appeared to reach it’s minimum extent for the year. It’s the sixth lowest minimum se...

  12. Processes controlling surface, bottom and lateral melt of Arctic sea ice in a state of the art sea ice model.

    PubMed

    Tsamados, Michel; Feltham, Daniel; Petty, Alek; Schroeder, David; Flocco, Daniela

    2015-10-13

    We present a modelling study of processes controlling the summer melt of the Arctic sea ice cover. We perform a sensitivity study and focus our interest on the thermodynamics at the ice-atmosphere and ice-ocean interfaces. We use the Los Alamos community sea ice model CICE, and additionally implement and test three new parametrization schemes: (i) a prognostic mixed layer; (ii) a three equation boundary condition for the salt and heat flux at the ice-ocean interface; and (iii) a new lateral melt parametrization. Recent additions to the CICE model are also tested, including explicit melt ponds, a form drag parametrization and a halodynamic brine drainage scheme. The various sea ice parametrizations tested in this sensitivity study introduce a wide spread in the simulated sea ice characteristics. For each simulation, the total melt is decomposed into its surface, bottom and lateral melt components to assess the processes driving melt and how this varies regionally and temporally. Because this study quantifies the relative importance of several processes in driving the summer melt of sea ice, this work can serve as a guide for future research priorities.

  13. Sea ice and the ocean mixed layer over the Antarctic shelf seas

    NASA Astrophysics Data System (ADS)

    Petty, A.; Holland, P.; Feltham, D. L.

    2013-12-01

    An ocean mixed layer model has been incorporated into the Los Alamos sea ice model CICE, to investigate regional variations in the surface-driven formation of Antarctic shelf sea waters. The model captures well the expected sea ice thickness distribution and produces deep (>500 m) mixed layers in the Weddell and Ross shelf seas each winter. By deconstructing the surface power input to the mixed layer, we have shown that the salt/fresh water flux from sea ice growth/melt dominates the evolution of the mixed layer in all shelf sea regions, with a smaller contribution from the mixed layer-surface heat flux. The Weddell and Ross shelf seas have the highest annual ice growth, with a large fraction exported northwards each year, whereas the Bellingshausen shelf sea experiences the highest annual ice melt, driven by the advection of ice from the northeast. Forcing the model with ERA-Interim (1980-2011) and hadGEM2-ES (1980-2099) atmospheric data allows us to look at the impact of atmospheric trends on the sea ice and ocean mixed layer. Both simulations show a shallowing of the wintertime mixed layer in the Amundsen & Bellingshausen seas, potentially increasing the access of warm CDW to ice shelves in both regions. The ERA-I hindcast simulation shows a significant freshening in the Ross and salinification in the Weddell due to surface driven trends (primarily through changes in the sea ice). The Ross freshening is smaller than observed however, highlighting the important role of ice shelf melt in the Amundsen Sea.

  14. Satellite observations of sea ice

    NASA Technical Reports Server (NTRS)

    Cavalieri, D. J.; Zwally, H. J.

    1985-01-01

    An overview is presented of Antarctic and Arctic sea ice studies using data from the Nimbus-5 ESMR and the Nimbus-7 SMMR passive microwave radiometers. Four years (1973-1976) of ESMR data for the Antarctic Ocean define the characteristics of the seasonal cycle including regional contrasts and interannual variations. Major advances include the discovery of the Weddell polynya and the presence of substantial areas of open water in the Antarctic winter pack ice. Regional differences in sea ice extent on time-scales of about a month are shown to be associated with variations in surface-wind fields. In the Arctic, the computation of sea ice concentration is complicated by the presence of multiyear ice, but the amount of multiyear ice becomes an important measurable quantity with dual-polarized, multifrequency passive microwave sensors. Analysis of SMMR data demonstrates its advantage for studying the spatial and temporal variability of the Arctic ice cover. Large observed interannual variations in the distribution of the multiyear pack ice and the presence of significant divergent areas in the central Arctic during winter contrast markedly with the classical view of the Arctic pack ice.

  15. Melting Ice, Rising Seas

    NASA Video Gallery

    Sea level rise is an indicator that our planet is warming. Much of the world's population lives on or near the coast, and rising seas are something worth watching. Sea level can rise for two reason...

  16. Sea Ice Concentration and Extent

    NASA Technical Reports Server (NTRS)

    Comiso, Josefino C.

    2014-01-01

    Among the most seasonal and most dynamic parameters on the surface of the Earth is sea ice which at any one time covers about 3-6% of the planet. In the Northern Hemisphere, sea ice grows in extent from about 6 x 10(exp 6) sq km to 16 x 10(exp 6) sq km, while in the Southern Hemisphere, it grows from about 3 x 10(exp 6) sq km to about 19 x 10(exp 6) sq km (Comiso, 2010; Gloersen et al., 1992). Sea ice is up to about 2-3 m thick in the Northern Hemisphere and about 1 m thick in the Southern Hemisphere (Wadhams, 2002), and compared to the average ocean depth of about 3 km, it is a relatively thin, fragile sheet that can break due to waves and winds or melt due to upwelling of warm water. Being constantly advected by winds, waves, and currents, sea ice is very dynamic and usually follows the directions of the many gyres in the polar regions. Despite its vast expanse, the sea ice cover was previously left largely unstudied and it was only in recent years that we have understood its true impact and significance as related to the Earths climate, the oceans, and marine life.

  17. Sunlight, Sea Ice, and the Ice Albedo Feedback in a Changing Artic Sea Ice Cover

    DTIC Science & Technology

    2015-11-30

    large. The albedo of first year ice is consistently smaller than multiyear ice throughout the remainder of summer. In this example 32% more solar energy ...K. Perovich, M. Nicolaus, T. I. Karlsen, K. Fossan, and M. Bratrein (2014), Autonomous observations of solar energy partitioning in first-year sea...understanding of the partitioning of solar radiation by the Arctic sea ice cover and its impact on the heat and mass balance of the ice and upper ocean

  18. Sea ice thickness and recent Arctic warming

    NASA Astrophysics Data System (ADS)

    Lang, Andreas; Yang, Shuting; Kaas, Eigil

    2017-01-01

    The climatic impact of increased Arctic sea ice loss has received growing attention in the last years. However, little focus has been set on the role of sea ice thickness, although it strongly determines surface heat fluxes. Here ensembles of simulations using the EC-Earth atmospheric model (Integrated Forecast System) are performed and analyzed to quantify the atmospheric impacts of Arctic sea ice thickness change since 1982 as revealed by the sea ice model assimilation Global Ice-Ocean Modeling and Assimilation System. Results show that the recent sea ice thinning has significantly affected the Arctic climate, while remote atmospheric responses are less pronounced owing to a high internal atmospheric variability. Locally, the sea ice thinning results in enhancement of near-surface warming of about 1°C per decade in winter, which is most pronounced over marginal sea ice areas with thin ice. This leads to an increase of the Arctic amplification factor by 37%.

  19. AMSR2 Daily Arctic Sea Ice - 2014

    NASA Video Gallery

    In this animation, the daily Arctic sea ice and seasonal land cover change progress through time, from March 21, 2014 through the 3rd of August, 2014. Over the water, Arctic sea ice changes from da...

  20. Primary Production in Antarctic Sea Ice

    NASA Technical Reports Server (NTRS)

    Arrigo, Kevin R.; Worthen, Denise L.; Lizotte, Michael P.; Dixon, Paul; Dieckmann, Gerhard

    1997-01-01

    A numerical model shows that in Antarctic sea ice, increased flooding in regions with thick snow cover enhances primary production in the infiltration (surface) layer. Productivity in the freeboard (sea level) layer is also determined by sea ice porosity, which varies with temperature. Spatial and temporal variation in snow thickness and the proportion of first-year ice thus determine regional differences in sea ice primary production. Model results show that of the 40 tera-grams of carbon produced annually in the Antarctic ice pack, 75 percent was associated with first-year ice and nearly 50 percent was produced in the Weddell Sea.

  1. Annual sea ice. An air-sea gas exchange moderator

    SciTech Connect

    Gosink, T.A.; Kelley, J.J.

    1982-01-01

    Arctic annual sea ice, particularly when it is relatively warm (> -15/sup 0/C) permits significant gas exchange between the sea and air throughout the entire year. Sea ice, particularly annual sea ice, differs from freshwater ice with respect to its permeability to gases. The presence of brine allows for significant air-sea-ice exchange of CO/sub 2/ throughout the winter, which may significantly affect the global carbon dioxide balance. Other trace gases are also noted to be enriched in sea ice, but less is known about their importance to air-sea-interactions at this time. Both physical and biological factors cause and modify evolution of gases from the surface of sea ice. Quantitative and qualitative descriptions of the nature and physical behavior of sea ice with respect to brine and gases are discussed.

  2. Multi-year Arctic Sea Ice

    NASA Video Gallery

    The most visible change in the Arctic region in recent years has been the rapid decline of the perennial ice cover. The perennial ice is the portion of the sea ice floating on the surface of the oc...

  3. Coupled ice-ocean model of the Baltic Sea - variability of ice cover.

    NASA Astrophysics Data System (ADS)

    Nowicki, A.; Janecki, M.; Jakacki, J.; Dzierzbicka-Glowacka, L.

    2012-04-01

    3D CEMBS (based on CESM/CCSM model) is a fully-coupled global climate model that provides state-of-the-art computer simulations of the Earth's past, present, and future climate states. It has been used to analyze ice cover of the Baltic Sea with a 2 kilometers horizontal resolution. For modeling ice CESM is using CICE4, which is the latest version of the Los Alamos Sea Ice Model, sometimes referred to as the Community Ice CodE. The model was forced by ECMWF atmospheric data (ERA 40 and ERA Interim reanalysis). 50-years hindcast scenario was performed. Anomalies of ice extension, ice thickness and ice area of the whole Baltic Sea are presented. This work was carried out in support of grant (No NN305 111636 - the Polish state Committee of Scientific Research). The partial support for this study was also provided by the project Satellite Monitoring of the Baltic Sea Environment - SatBaltyk founded by European Union through European Regional Development Fund contract no. POIG 01.01.02-22-011/09

  4. Loss of sea ice in the Arctic.

    PubMed

    Perovich, Donald K; Richter-Menge, Jacqueline A

    2009-01-01

    The Arctic sea ice cover is in decline. The areal extent of the ice cover has been decreasing for the past few decades at an accelerating rate. Evidence also points to a decrease in sea ice thickness and a reduction in the amount of thicker perennial sea ice. A general global warming trend has made the ice cover more vulnerable to natural fluctuations in atmospheric and oceanic forcing. The observed reduction in Arctic sea ice is a consequence of both thermodynamic and dynamic processes, including such factors as preconditioning of the ice cover, overall warming trends, changes in cloud coverage, shifts in atmospheric circulation patterns, increased export of older ice out of the Arctic, advection of ocean heat from the Pacific and North Atlantic, enhanced solar heating of the ocean, and the ice-albedo feedback. The diminishing Arctic sea ice is creating social, political, economic, and ecological challenges.

  5. The Sea Ice Board Game

    ERIC Educational Resources Information Center

    Bertram, Kathryn Berry

    2008-01-01

    The National Science Foundation-funded Arctic Climate Modeling Program (ACMP) provides "curriculum resource-based professional development" materials that combine current science information with practical classroom instruction embedded with "best practice" techniques for teaching science to diverse students. The Sea Ice Board…

  6. Ecological consequences of sea-ice decline.

    PubMed

    Post, Eric; Bhatt, Uma S; Bitz, Cecilia M; Brodie, Jedediah F; Fulton, Tara L; Hebblewhite, Mark; Kerby, Jeffrey; Kutz, Susan J; Stirling, Ian; Walker, Donald A

    2013-08-02

    After a decade with nine of the lowest arctic sea-ice minima on record, including the historically low minimum in 2012, we synthesize recent developments in the study of ecological responses to sea-ice decline. Sea-ice loss emerges as an important driver of marine and terrestrial ecological dynamics, influencing productivity, species interactions, population mixing, gene flow, and pathogen and disease transmission. Major challenges in the near future include assigning clearer attribution to sea ice as a primary driver of such dynamics, especially in terrestrial systems, and addressing pressures arising from human use of arctic coastal and near-shore areas as sea ice diminishes.

  7. Sea ice data for all: NSIDC's Arctic Sea Ice News & Analysis

    NASA Astrophysics Data System (ADS)

    Vizcarra, N.; Stroeve, J. C.; Serreze, M. C.; Scambos, T. A.; Meier, W.

    2014-12-01

    Arctic sea ice has long been recognized as a sensitive climate indicator and has undergone a dramatic decline over the past thirty years. The National Snow and Ice Data Center's Arctic Sea Ice News & Analysis blog continues to offer the public a transparent view of sea ice data and analysis. We have expanded our interactive sea ice graph to include Antarctic sea ice in response to increased attention from the public as a result of unexpected behavior of sea ice in the south. This poster explores the blog's new features and how other researchers, the media, and the public are currently using them.

  8. Creating Arctic Sea Ice Protected Areas?

    NASA Astrophysics Data System (ADS)

    Pfirman, S.; Hoff, K.; Temblay, B.; Fowler, C.

    2008-12-01

    As Arctic sea ice retreats and the Northwest Passage and Northern Sea Route open, the Arctic will experience more extensive human activity than it has ever encountered before. New development will put pressure on a system already struggling to adapt to a changing environment. In this analysis, locations are identified within the Arctic that could be protected from resource extraction, transportation and other development in order to create refuges and protect remnants of sea ice habitat, as the Arctic transitions to ice-free summer conditions. Arctic sea ice forms largely along the Siberian and Alaskan coasts and is advected across the North Pole towards Fram Strait, the Canadian Archipelago and the Barents Sea. In addition to the future loss of ice itself, contaminants entrained in sea ice in one part of the ocean can affect other regions as the ice drifts. Using observations and models of sea ice origins, trajectories and ages, we track sea ice from its origins towards marginal ice zones, mapping pathways and termination locations. Critical sea ice source areas and collection regions are identified with the goal of aiding in the protection of the remaining Arctic sea ice habitat for as long as possible.

  9. The role of sea ice dynamics in global climate change

    NASA Technical Reports Server (NTRS)

    Hibler, William D., III

    1992-01-01

    The topics covered include the following: general characteristics of sea ice drift; sea ice rheology; ice thickness distribution; sea ice thermodynamic models; equilibrium thermodynamic models; effect of internal brine pockets and snow cover; model simulations of Arctic Sea ice; and sensitivity of sea ice models to climate change.

  10. Sunlight, Sea Ice, and the Ice Albedo Feedback in a Changing Arctic Sea Ice Cover

    DTIC Science & Technology

    2015-09-30

    solar energy was deposited into first year ice than multiyear ice. 4 Figure 1. Albedo evolution and solar heat input for multiyear (MY) and...S. R. Hudson, D. K. Perovich, M. Nicolaus, T. I. Karlsen, K. Fossan, and M. Bratrein (2014), Autonomous observations of solar energy partitioning in...quantitative understanding of the partitioning of solar radiation by the Arctic sea ice cover and its impact on the heat and mass balance of the ice and upper

  11. Impact of a new anisotropic rheology on simulations of Arctic sea ice

    NASA Astrophysics Data System (ADS)

    Tsamados, M.; Feltham, D. L.; Wilchinsky, A. V.

    2013-01-01

    AbstractA new rheology that explicitly accounts for the subcontinuum anisotropy of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover is implemented into the Los <span class="hlt">Alamos</span> <span class="hlt">sea</span> <span class="hlt">ice</span> model. This is in contrast to all models of <span class="hlt">sea</span> <span class="hlt">ice</span> included in global circulation models that use an isotropic rheology. The model contains one new prognostic variable, the local structure tensor, which quantifies the degree of anisotropy of the <span class="hlt">sea</span> <span class="hlt">ice</span>, and two parameters that set the time scale of the evolution of this tensor. The anisotropic rheology provides a subcontinuum description of the mechanical behavior of <span class="hlt">sea</span> <span class="hlt">ice</span> and accounts for a continuum scale stress with large shear to compression ratio and tensile stress component. Results over the Arctic of a stand-alone version of the model are presented and anisotropic model sensitivity runs are compared with a reference elasto-visco-plastic simulation. Under realistic forcing <span class="hlt">sea</span> <span class="hlt">ice</span> quickly becomes highly anisotropic over large length scales, as is observed from satellite imagery. The influence of the new rheology on the state and dynamics of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover is discussed. Our reference anisotropic run reveals that the new rheology leads to a substantial change of the spatial distribution of <span class="hlt">ice</span> thickness and <span class="hlt">ice</span> drift relative to the reference standard visco-plastic isotropic run, with <span class="hlt">ice</span> thickness regionally increased by more than 1 m, and <span class="hlt">ice</span> speed reduced by up to 50%.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014APS..MARG40001G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014APS..MARG40001G"><span><span class="hlt">Sea</span> <span class="hlt">ice</span>, climate, and multiscale composites</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Golden, Kenneth</p> <p>2014-03-01</p> <p>In September of 2012, the area of the Arctic Ocean covered by <span class="hlt">sea</span> <span class="hlt">ice</span> reached its lowest level ever recorded in more than three decades of satellite measurements. In fact, compared to the 1980's and 1990's, this represents a loss of more than half of the summer Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> pack. While global climate models generally predict <span class="hlt">sea</span> <span class="hlt">ice</span> declines over the 21st century, the precipitous losses observed so far have significantly outpaced most projections. I will discuss how mathematical models of composite materials and statistical physics are being used to study key <span class="hlt">sea</span> <span class="hlt">ice</span> processes and advance how <span class="hlt">sea</span> <span class="hlt">ice</span> is represented in climate models. This work is helping to improve projections of the fate of Earth's <span class="hlt">ice</span> packs, and the response of polar ecosystems. A brief video of a recent Antarctic expedition where <span class="hlt">sea</span> <span class="hlt">ice</span> properties were measured will be shown. Supported by NSF and ONR.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2001RvGeo..39..413M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2001RvGeo..39..413M"><span>Snow on Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Massom, Robert A.; Eicken, Hajo; Hass, Christian; Jeffries, Martin O.; Drinkwater, Mark R.; Sturm, Matthew; Worby, Anthony P.; Wu, Xingren; Lytle, Victoria I.; Ushio, Shuki; Morris, Kim; Reid, Phillip A.; Warren, Stephen G.; Allison, Ian</p> <p>2001-08-01</p> <p>Snow on Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> plays a complex and highly variable role in air-<span class="hlt">sea-ice</span> interaction processes and the Earth's climate system. Using data collected mostly during the past 10 years, this paper reviews the following topics: snow thickness and snow type and their geographical and seasonal variations; snow grain size, density, and salinity; frequency of occurrence of slush; thermal conductivity, snow surface temperature, and temperature gradients within snow; and the effect of snow thickness on albedo. Major findings include large regional and seasonal differences in snow properties and thicknesses; the consequences of thicker snow and thinner <span class="hlt">ice</span> in the Antarctic relative to the Arctic (e.g., the importance of flooding and snow-<span class="hlt">ice</span> formation); the potential impact of increasing snowfall resulting from global climate change; lower observed values of snow thermal conductivity than those typically used in models; periodic large-scale melt in winter; and the contrast in summer melt processes between the Arctic and the Antarctic. Both climate modeling and remote sensing would benefit by taking account of the differences between the two polar regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.C41D0438S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.C41D0438S"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Mapping using Unmanned Aerial Systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Solbø, S.; Storvold, R.</p> <p>2011-12-01</p> <p>Mapping of <span class="hlt">sea</span> <span class="hlt">ice</span> extent and <span class="hlt">sea</span> <span class="hlt">ice</span> features is an important task in climate research. Since the arctic coastal and oceanic areas have a high probability of cloud coverage, aerial platforms are superior to satellite measurements for high-resolution optical measurements. However, routine observations of <span class="hlt">sea</span> <span class="hlt">ice</span> conditions present a variety of problems using conventional piloted aircrafts. Specially, the availability of suitable aircrafts for lease does not cover the demand in major parts of the arctic. With the recent advances in unmanned aerial systems (UAS), there is a high possibility of establishing routine, cost effective aerial observations of <span class="hlt">sea</span> <span class="hlt">ice</span> conditions in the near future. Unmanned aerial systems can carry a wide variety of sensors useful for characterizing <span class="hlt">sea-ice</span> features. For instance, the CryoWing UAS, a system initially designed for measurements of the cryosphere, can be equipped with digital cameras, surface thermometers and laser altimeters for measuring freeboard of <span class="hlt">ice</span> flows. In this work we will present results from recent CryoWing <span class="hlt">sea</span> <span class="hlt">ice</span> flights on Svalbard, Norway. The emphasis will be on data processing for stitching together images acquired with the non-stabilized camera payload, to form high-resolution mosaics covering large spatial areas. These data are being employed to map <span class="hlt">ice</span> conditions; including <span class="hlt">ice</span> and lead features and melt ponds. These high-resolution mosaics are also well suited for <span class="hlt">sea-ice</span> mechanics, classification studies and for validation of satellite <span class="hlt">sea-ice</span> products.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/398057','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/398057"><span>The strength anisotropia of <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Evdokimov, G.N.; Rogachko, S.I.</p> <p>1994-12-31</p> <p>The hydraulic-engineering structure calculations of <span class="hlt">sea</span> <span class="hlt">ice</span> formation force require the <span class="hlt">sea</span> <span class="hlt">ice</span> strength data. The strength characteristics values and the types of <span class="hlt">sea</span> <span class="hlt">ice</span> formations in view of water depth define the type and the design of future structures in each particular region of supposed construction. The most objective information on the <span class="hlt">sea</span> <span class="hlt">ice</span> physical and technical properties can be obtained by field investigations ad the existing methods of their calculations refer to a great number of errors. The accumulated bank of data on studying the <span class="hlt">sea</span> <span class="hlt">ice</span> formation strength properties show one that <span class="hlt">ice</span> as a natural material is of great crystalline structure variety. The level <span class="hlt">ice</span> fields have a number of particularities. The crystal sizes increase in <span class="hlt">ice</span> thickness. The crystals consist of fresh-water thin plates 0.5--0.6 mm in thickness oriented by pickle-water interlayers. Difference in thickness of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover structure is one of the main causes of the changes strength characteristics layer. Besides that the <span class="hlt">sea</span> <span class="hlt">ice</span> strength depends upon the destroying force direction in reference to crystal orientation which characterizes the <span class="hlt">sea</span> <span class="hlt">ice</span> anisotropia as a material.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GeoRL..43.1642G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GeoRL..43.1642G"><span>Predictability of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> edge</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Goessling, H. F.; Tietsche, S.; Day, J. J.; Hawkins, E.; Jung, T.</p> <p>2016-02-01</p> <p>Skillful <span class="hlt">sea</span> <span class="hlt">ice</span> forecasts from days to years ahead are becoming increasingly important for the operation and planning of human activities in the Arctic. Here we analyze the potential predictability of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> edge in six climate models. We introduce the integrated <span class="hlt">ice</span>-edge error (IIEE), a user-relevant verification metric defined as the area where the forecast and the "truth" disagree on the <span class="hlt">ice</span> concentration being above or below 15%. The IIEE lends itself to decomposition into an absolute extent error, corresponding to the common <span class="hlt">sea</span> <span class="hlt">ice</span> extent error, and a misplacement error. We find that the often-neglected misplacement error makes up more than half of the climatological IIEE. In idealized forecast ensembles initialized on 1 July, the IIEE grows faster than the absolute extent error. This means that the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> edge is less predictable than <span class="hlt">sea</span> <span class="hlt">ice</span> extent, particularly in September, with implications for the potential skill of end-user relevant forecasts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.youtube.com/watch?v=nQSyFi3NyEU','SCIGOVIMAGE-NASA'); return false;" href="http://www.youtube.com/watch?v=nQSyFi3NyEU"><span>Arctic Cyclone Breaks Up <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://www.nasa.gov/multimedia/videogallery/index.html">NASA Video Gallery</a></p> <p></p> <p></p> <p>A powerful storm wreaked havoc on the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover in August 2012. This visualization shows the strength and direction of the winds and their impact on the <span class="hlt">ice</span>: the red vectors represent th...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.youtube.com/watch?v=KU6-seRoEf8','SCIGOVIMAGE-NASA'); return false;" href="http://www.youtube.com/watch?v=KU6-seRoEf8"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Changes 2011-2012</span></a></p> <p><a target="_blank" href="http://www.nasa.gov/multimedia/videogallery/index.html">NASA Video Gallery</a></p> <p></p> <p></p> <p>Animation showing changes in monthly Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> volume using data from ESA's CryoSat-2 (red dots) and estimates from the Pan-Arctic <span class="hlt">Ice</span> Ocean Modeling and Assimilation System (PIOMAS) (solid li...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMMR21B2621S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMMR21B2621S"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Friction: The Effect of <span class="hlt">Ice</span> Rubble</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Scourfield, S.; Sammonds, P. R.; Lishman, B.; Riska, K.; Marchenko, A. V.</p> <p>2015-12-01</p> <p><span class="hlt">Ice</span> deformation processes in the Arctic often generate <span class="hlt">ice</span> rubble, and situations arise where <span class="hlt">ice</span> fragments of varying size separate <span class="hlt">sea</span> <span class="hlt">ice</span> floes. While the shear forces between <span class="hlt">sea</span> <span class="hlt">ice</span> floes in direct contact with each other are controlled by <span class="hlt">ice-ice</span> friction, what is not known is how the slip of the floes is affected by the presence of rubble between the sliding surfaces. We present the result of field experiments undertaken on fjord <span class="hlt">ice</span> in Svea, Svalbard, which investigated the velocity and hold time dependence of <span class="hlt">sea</span> <span class="hlt">ice</span> friction involving <span class="hlt">ice</span> gouge. Average air temperature for the duration of time in which experiments were run was -12.4°C, and the thickness of the level fjord <span class="hlt">ice</span> was 70 cm. A double-direct-shear experiment was done on floating <span class="hlt">sea</span> <span class="hlt">ice</span> in the field, with the addition of rubble <span class="hlt">ice</span> between the sliding surfaces. This was achieved by moving a floating <span class="hlt">ice</span> block through a channel of open water whilst subjected to normal loading, which was transferred through regions of <span class="hlt">ice</span> rubble on both sides of the mobile block. The <span class="hlt">ice</span> rubble regions were 30 cm deep and 50 cm wide. The displacement of the block and the force needed to move the block were measured. The rate dependence of friction was investigated for speeds of 10-3 to 10-2 ms-1. To investigate the state dependence of friction, slide-hold-slide (SHS) tests were conducted for hold times ranging from 1 second to 18 hours. When comparing the results from these experiments with a model for <span class="hlt">ice</span> friction presented by Schulson and Fortt (2013), similar behaviour is seen at low hold times, where the peak coefficient of friction has a linear relationship with the logarithm of hold time. This is not the case for long hold times, however, and we attribute this to thermal consolidation of the <span class="hlt">ice</span> rubble region.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GeoRL..43.3889G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GeoRL..43.3889G"><span>Impact of <span class="hlt">sea</span> <span class="hlt">ice</span> initialization on <span class="hlt">sea</span> <span class="hlt">ice</span> and atmosphere prediction skill on seasonal timescales</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Guemas, V.; Chevallier, M.; Déqué, M.; Bellprat, O.; Doblas-Reyes, F.</p> <p>2016-04-01</p> <p>We present a robust assessment of the impact of <span class="hlt">sea</span> <span class="hlt">ice</span> initialization from reconstructions of the real state on the <span class="hlt">sea</span> <span class="hlt">ice</span> and atmosphere prediction skill. We ran two ensemble seasonal prediction experiments from 1979 to 2012 : one using realistic <span class="hlt">sea</span> <span class="hlt">ice</span> initial conditions and another where <span class="hlt">sea</span> <span class="hlt">ice</span> is initialized from a climatology, with two forecast systems. During the melting season in the Arctic Ocean, <span class="hlt">sea</span> <span class="hlt">ice</span> forecasts become skilful with <span class="hlt">sea</span> <span class="hlt">ice</span> initialization until 3-5 months ahead, thanks to the memory held by <span class="hlt">sea</span> <span class="hlt">ice</span> thickness. During the freezing season in both the Arctic and Antarctic Oceans, <span class="hlt">sea</span> <span class="hlt">ice</span> forecasts are skilful for 7 and 2 months, respectively, with negligible differences between the two experiments, the memory being held by the ocean heat content. A weak impact on the atmosphere prediction skill is obtained.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_3 --> <div id="page_4" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="61"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27670112','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27670112"><span>Microbial mercury methylation in Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Gionfriddo, Caitlin M; Tate, Michael T; Wick, Ryan R; Schultz, Mark B; Zemla, Adam; Thelen, Michael P; Schofield, Robyn; Krabbenhoft, David P; Holt, Kathryn E; Moreau, John W</p> <p>2016-08-01</p> <p>Atmospheric deposition of mercury onto <span class="hlt">sea</span> <span class="hlt">ice</span> and circumpolar <span class="hlt">sea</span> water provides mercury for microbial methylation, and contributes to the bioaccumulation of the potent neurotoxin methylmercury in the marine food web. Little is known about the abiotic and biotic controls on microbial mercury methylation in polar marine systems. However, mercury methylation is known to occur alongside photochemical and microbial mercury reduction and subsequent volatilization. Here, we combine mercury speciation measurements of total and methylated mercury with metagenomic analysis of whole-community microbial DNA from Antarctic snow, brine, <span class="hlt">sea</span> <span class="hlt">ice</span> and <span class="hlt">sea</span> water to elucidate potential microbially mediated mercury methylation and volatilization pathways in polar marine environments. Our results identify the marine microaerophilic bacterium Nitrospina as a potential mercury methylator within <span class="hlt">sea</span> <span class="hlt">ice</span>. Anaerobic bacteria known to methylate mercury were notably absent from <span class="hlt">sea-ice</span> metagenomes. We propose that Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> can harbour a microbial source of methylmercury in the Southern Ocean.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26032321','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26032321"><span><span class="hlt">Sea-ice</span> thermodynamics and brine drainage.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Worster, M Grae; Rees Jones, David W</p> <p>2015-07-13</p> <p>Significant changes in the state of the Arctic <span class="hlt">ice</span> cover are occurring. As the summertime extent of <span class="hlt">sea</span> <span class="hlt">ice</span> diminishes, the Arctic is increasingly characterized by first-year rather than multi-year <span class="hlt">ice</span>. It is during the early stages of <span class="hlt">ice</span> growth that most brine is injected into the oceans, contributing to the buoyancy flux that mediates the thermo-haline circulation. Current operational <span class="hlt">sea-ice</span> components of climate models often treat brine rejection between <span class="hlt">sea</span> <span class="hlt">ice</span> and the ocean similarly to a thermodynamic segregation process, assigning a fixed salinity to the <span class="hlt">sea</span> <span class="hlt">ice</span>, typical of multi-year <span class="hlt">ice</span>. However, brine rejection is a dynamical, buoyancy-driven process and the salinity of <span class="hlt">sea</span> <span class="hlt">ice</span> varies significantly during the first growth season. As a result, current operational models may over predict the early brine fluxes from newly formed <span class="hlt">sea</span> <span class="hlt">ice</span>, which may have consequences for coupled simulations of the polar oceans. Improvements both in computational power and our understanding of the processes involved have led to the emergence of a new class of <span class="hlt">sea-ice</span> models that treat brine rejection dynamically and should enhance predictions of the buoyancy forcing of the oceans.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19850017731&hterms=carbon+dioxide+climate+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dcarbon%2Bdioxide%2B%252B%2Bclimate%2Bchange','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19850017731&hterms=carbon+dioxide+climate+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dcarbon%2Bdioxide%2B%252B%2Bclimate%2Bchange"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span>, Climate and Fram Strait</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hunkins, K.</p> <p>1984-01-01</p> <p>When <span class="hlt">sea</span> <span class="hlt">ice</span> is formed the albedo of the ocean surface increases from its open water value of about 0.1 to a value as high as 0.8. This albedo change effects the radiation balance and thus has the potential to alter climate. <span class="hlt">Sea</span> <span class="hlt">ice</span> also partially seals off the ocean from the atmosphere, reducing the exchange of gases such as carbon dioxide. This is another possible mechanism by which climate might be affected. The Marginal <span class="hlt">Ice</span> Zone Experiment (MIZEX 83 to 84) is an international, multidisciplinary study of processes controlling the edge of the <span class="hlt">ice</span> pack in that area including the interactions between <span class="hlt">sea</span>, air and <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20120015900&hterms=exports&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dexports','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20120015900&hterms=exports&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dexports"><span>Variability and Trends in <span class="hlt">Sea</span> <span class="hlt">Ice</span> Extent and <span class="hlt">Ice</span> Production in the Ross <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Comiso, Josefino; Kwok, Ronald; Martin, Seelye; Gordon, Arnold L.</p> <p>2011-01-01</p> <p>Salt release during <span class="hlt">sea</span> <span class="hlt">ice</span> formation in the Ross <span class="hlt">Sea</span> coastal regions is regarded as a primary forcing for the regional generation of Antarctic Bottom Water. Passive microwave data from November 1978 through 2008 are used to examine the detailed seasonal and interannual characteristics of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover of the Ross <span class="hlt">Sea</span> and the adjacent Bellingshausen and Amundsen <span class="hlt">seas</span>. For this period the <span class="hlt">sea</span> <span class="hlt">ice</span> extent in the Ross <span class="hlt">Sea</span> shows the greatest increase of all the Antarctic <span class="hlt">seas</span>. Variability in the <span class="hlt">ice</span> cover in these regions is linked to changes in the Southern Annular Mode and secondarily to the Antarctic Circumpolar Wave. Over the Ross <span class="hlt">Sea</span> shelf, analysis of <span class="hlt">sea</span> <span class="hlt">ice</span> drift data from 1992 to 2008 yields a positive rate of increase in the net <span class="hlt">ice</span> export of about 30,000 sq km/yr. For a characteristic <span class="hlt">ice</span> thickness of 0.6 m, this yields a volume transport of about 20 cu km/yr, which is almost identical, within error bars, to our estimate of the trend in <span class="hlt">ice</span> production. The increase in brine rejection in the Ross Shelf Polynya associated with the estimated increase with the <span class="hlt">ice</span> production, however, is not consistent with the reported Ross <span class="hlt">Sea</span> salinity decrease. The locally generated <span class="hlt">sea</span> <span class="hlt">ice</span> enhancement of Ross <span class="hlt">Sea</span> salinity may be offset by an increase of relatively low salinity of the water advected into the region from the Amundsen <span class="hlt">Sea</span>, a consequence of increased precipitation and regional glacial <span class="hlt">ice</span> melt.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19890018777','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19890018777"><span>Microwave remote sensing of <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Comiso, J. C.</p> <p>1988-01-01</p> <p>The long term objectives are: (1) to understand the physics of the multispectral microwave radiative characteristics of <span class="hlt">sea</span> <span class="hlt">ice</span> as it goes through different phases; (2) to improve characterization of <span class="hlt">sea</span> <span class="hlt">ice</span> cover using satellite microwave sensors; and (3) to study <span class="hlt">ice</span>/ocean physical and biological processes associated with polynya formations and variability of the marginal <span class="hlt">sea</span> <span class="hlt">ice</span> region. Two field experiments were conducted to pursue these objectives. One involved measurements of radiative and physical characteristics of <span class="hlt">sea</span> <span class="hlt">ice</span> from a ship during a 3-month long cruise through the Weddell <span class="hlt">Sea</span> <span class="hlt">ice</span> pack during the Austral winter of 1986. The other involved similar measurements from two aircrafts and a submarine over the Central Arctic and Greenland <span class="hlt">Sea</span> region. Preliminary results have already led to an enhanced understanding of the microwave signatures of pancake <span class="hlt">ice</span>, nilas, first year <span class="hlt">ice</span>, multiyear <span class="hlt">ice</span> and effects of snow cover. Coastal and deep ocean polynyas and their role in bottom water formation and ocean circulation were studied using a time series of <span class="hlt">ice</span> images from SMMR. An unsupervised cluster analysis of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> using SMMR and THIR emissivity and brightness temperature data was implemented. The analysis indicates the existence of several unique and persistent clusters in the Central Arctic region during winter and that the sum of the area of these clusters excluding those of first year <span class="hlt">ice</span> is about 20 percent less than minimum <span class="hlt">ice</span> cover area inferred from a previous summer data. This result is consistent with saline surface for some multiyear <span class="hlt">ice</span> floes as observed during MIZEZ and suggests that a significant fraction of multiyear <span class="hlt">ice</span> floes in the Arctic have first year <span class="hlt">ice</span> signatures.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/1082837','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/1082837"><span>Unlocking a <span class="hlt">Sea</span> <span class="hlt">Ice</span> Secret</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Dr. Rachel Obbard</p> <p>2013-04-22</p> <p>Dr. Rachel Obbard and her research group from Dartmouth College traveled to the Antarctic to collect samples of <span class="hlt">sea</span> <span class="hlt">ice</span>. Next stop: the GeoSoilEnviroCARS x-ray beamline at the Advanced Photon Source at Argonne National Laboratory in Illinois. This U.S. Department of Energy Office of Science synchrotron x-ray research facility gave the Obbard team the frontier scientific tools they needed to study the path bromide takes as it travels from the ocean to the atmosphere.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.youtube.com/watch?v=_qhpxvRt6tY','SCIGOVIMAGE-NASA'); return false;" href="http://www.youtube.com/watch?v=_qhpxvRt6tY"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> from March to August 2016</span></a></p> <p><a target="_blank" href="http://www.nasa.gov/multimedia/videogallery/index.html">NASA Video Gallery</a></p> <p></p> <p></p> <p>In this animation, the daily Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> and seasonal land cover change progress through time, from the prior <span class="hlt">sea</span> <span class="hlt">ice</span> maximum March 24, 2016, through Aug. 13, 2016. The Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover like...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.3174F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.3174F"><span>Validation and Interpretation of a new <span class="hlt">sea</span> <span class="hlt">ice</span> Glob<span class="hlt">Ice</span> dataset using buoys and the CICE <span class="hlt">sea</span> <span class="hlt">ice</span> model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Flocco, D.; Laxon, S. W.; Feltham, D. L.; Haas, C.</p> <p>2012-04-01</p> <p>The Glob<span class="hlt">Ice</span> project has provided high resolution <span class="hlt">sea</span> <span class="hlt">ice</span> product datasets over the Arctic derived from SAR data in the ESA archive. The products are validated <span class="hlt">sea</span> <span class="hlt">ice</span> motion, deformation and fluxes through straits. Glob<span class="hlt">Ice</span> <span class="hlt">sea</span> <span class="hlt">ice</span> velocities, deformation data and <span class="hlt">sea</span> <span class="hlt">ice</span> concentration have been validated using buoy data provided by the International Arctic Buoy Program (IABP). Over 95% of the Glob<span class="hlt">Ice</span> and buoy data analysed fell within 5 km of each other. The Glob<span class="hlt">Ice</span> Eulerian image pair product showed a high correlation with buoy data. The <span class="hlt">sea</span> <span class="hlt">ice</span> concentration product was compared to SSM/I data. An evaluation of the validity of the Glob<span class="hlt">ICE</span> data will be presented in this work. Glob<span class="hlt">ICE</span> <span class="hlt">sea</span> <span class="hlt">ice</span> velocity and deformation were compared with runs of the CICE <span class="hlt">sea</span> <span class="hlt">ice</span> model: in particular the mass fluxes through the straits were used to investigate the correlation between the winter behaviour of <span class="hlt">sea</span> <span class="hlt">ice</span> and the <span class="hlt">sea</span> <span class="hlt">ice</span> state in the following summer.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..11.4193S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.4193S"><span>Trend analysis of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Silva, M. E.; Barbosa, S. M.; Antunes, Luís; Rocha, Conceição</p> <p>2009-04-01</p> <p>The extent of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> is a fundamental parameter of Arctic climate variability. In the context of climate change, the area covered by <span class="hlt">ice</span> in the Arctic is a particularly useful indicator of recent changes in the Arctic environment. Climate models are in near universal agreement that Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent will decline through the 21st century as a consequence of global warming and many studies predict a <span class="hlt">ice</span> free Arctic as soon as 2012. Time series of satellite passive microwave observations allow to assess the temporal changes in the extent of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Much of the analysis of the <span class="hlt">ice</span> extent time series, as in most climate studies from observational data, have been focussed on the computation of deterministic linear trends by ordinary least squares. However, many different processes, including deterministic, unit root and long-range dependent processes can engender trend like features in a time series. Several parametric tests have been developed, mainly in econometrics, to discriminate between stationarity (no trend), deterministic trend and stochastic trends. Here, these tests are applied in the trend analysis of the <span class="hlt">sea</span> <span class="hlt">ice</span> extent time series available at National Snow and <span class="hlt">Ice</span> Data Center. The parametric stationary tests, Augmented Dickey-Fuller (ADF), Phillips-Perron (PP) and the KPSS, do not support an overall deterministic trend in the time series of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent. Therefore, alternative parametrizations such as long-range dependence should be considered for characterising long-term Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> variability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.3331S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.3331S"><span>Fram Strait Spring <span class="hlt">Ice</span> Export and September Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Smedsrud, Lars H.; Halvorsen, Mari H.; Stroeve, Julienne; Zhang, Rong; Kloster, Kjell</p> <p>2016-04-01</p> <p>The Arctic Basin exports between 600 000 - 1 million km² of it's <span class="hlt">sea</span> <span class="hlt">ice</span> cover southwards through Fram Strait each year, comparing to about 10% of the <span class="hlt">ice</span> covered area inside the basin. During winter <span class="hlt">ice</span> export results in growth of new and relatively thin <span class="hlt">ice</span> inside the basin, while during summer or spring export contributes directly to open water further north. A new updated time series from 1935 to 2014 of Fram Strait <span class="hlt">sea</span> <span class="hlt">ice</span> area export shows that the long-term annual mean export is about 880,000 km², with large annual and decadal variability and no long-term trend over the past 80 years. Nevertheless, the last decade has witnessed increased annual <span class="hlt">ice</span> export, with several years having annual <span class="hlt">ice</span> export exceed 1 million km². Evaluating the trend onwards from 1979, when satellite based <span class="hlt">sea</span> <span class="hlt">ice</span> coverage became more readily available, reveals an increase in annual export of about +6% per decade. This increase is caused by higher southward <span class="hlt">ice</span> drift speeds due to stronger southward geostrophic winds, largely explained by increasing surface pressure over Greenland. Spring and summer area export increased more (+11% per decade) than in autumn and winter. Contrary to the last decade the 1950 - 1970 period had low export during spring and summer, and mid-September <span class="hlt">sea</span> <span class="hlt">ice</span> extent was consistently higher than both before and after these decades. We thus find that export anomalies during spring have a clear influence on the following September <span class="hlt">sea</span> <span class="hlt">ice</span> extent in general, and that for the recent decade the export may be partially responsible for the accelerating decline in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013PhDT.......270B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013PhDT.......270B"><span>On the Predictability of <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Blanchard-Wrigglesworth, Edward</p> <p></p> <p>We investigate the persistence and predictability of <span class="hlt">sea</span> <span class="hlt">ice</span> in numerical models and observations. We first use the 3rd generation Community Climate System Model (CCSM3) General Circulation Model (GCM) to investigate the inherent persistence of <span class="hlt">sea-ice</span> area and thickness. We find that <span class="hlt">sea-ice</span> area anomalies have a seasonal decay timescale, exhibiting an initial decorrelation similar to a first order auto-regressive (AR1, or red noise) process. Beyond this initial loss of memory, there is a re-emergence of memory at certain times of the year. There are two distinct modes of re-emergence in the model, one driven by the seasonal coupling of area and thickness anomalies in the summer, the other by the persistence of upper ocean temperature anomalies that originate from <span class="hlt">ice</span> anomalies in the melt season and then influence <span class="hlt">ice</span> anomalies in the growth season. Comparison with satellite observations where available indicate these processes appear in nature. We then use the 4th generation CCSM (CCSM4) to investigate the partition of Arctic <span class="hlt">sea-ice</span> predictability into its initial-value and boundary forced components under present day forcing conditions. We find that initial-value predictability lasts for 1-2 years for <span class="hlt">sea-ice</span> area, and 3-4 years for <span class="hlt">sea-ice</span> volume. Forced predictability arises after just 4-5 years for both area and volume. Initial-value predictability of <span class="hlt">sea-ice</span> area during the summer hinges on the coupling between thickness and area anomalies during that season. We find that the loss of initial-value predictability with time is not uniform --- there is a rapid loss of predictability of <span class="hlt">sea-ice</span> volume during the late spring early summer associated with snow melt and albedo feedbacks. At the same time, loss of predictability is not uniform across different regions. Given the usefulness of <span class="hlt">ice</span> thickness as a predictor of summer <span class="hlt">sea-ice</span> area, we obtain a hindcast of September <span class="hlt">sea-ice</span> area initializing the GCM on May 1with an estimate of observed <span class="hlt">sea-ice</span> thickness</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20040171197','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20040171197"><span>MODIS Snow and <span class="hlt">Sea</span> <span class="hlt">Ice</span> Products</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.</p> <p>2004-01-01</p> <p>In this chapter, we describe the suite of Earth Observing System (EOS) Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow and <span class="hlt">sea</span> <span class="hlt">ice</span> products. Global, daily products, developed at Goddard Space Flight Center, are archived and distributed through the National Snow and <span class="hlt">Ice</span> Data Center at various resolutions and on different grids useful for different communities Snow products include binary snow cover, snow albedo, and in the near future, fraction of snow in a 5OO-m pixel. <span class="hlt">Sea</span> <span class="hlt">ice</span> products include <span class="hlt">ice</span> extent determined with two different algorithms, and <span class="hlt">sea</span> <span class="hlt">ice</span> surface temperature. The algorithms used to develop these products are described. Both the snow and <span class="hlt">sea</span> <span class="hlt">ice</span> products, available since February 24,2000, are useful for modelers. Validation of the products is also discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C51A0683R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C51A0683R"><span>Inter-comparison of isotropic and anisotropic <span class="hlt">sea</span> <span class="hlt">ice</span> rheology in a fully coupled model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Roberts, A.; Cassano, J. J.; Maslowski, W.; Osinski, R.; Seefeldt, M. W.; Hughes, M.; Duvivier, A.; Nijssen, B.; Hamman, J.; Hutchings, J. K.; Hunke, E. C.</p> <p>2015-12-01</p> <p>We present the <span class="hlt">sea</span> <span class="hlt">ice</span> climate of the Regional Arctic System Model (RASM), using a suite of new physics available in the Los <span class="hlt">Alamos</span> <span class="hlt">Sea</span> <span class="hlt">Ice</span> Model (CICE5). RASM is a high-resolution fully coupled pan-Arctic model that also includes the Parallel Ocean Program (POP), the Weather Research and Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) land model. The model domain extends from ~45˚N to the North Pole and is configured to run at ~9km resolution for the <span class="hlt">ice</span> and ocean components, coupled to 50km resolution atmosphere and land models. The baseline <span class="hlt">sea</span> <span class="hlt">ice</span> model configuration includes mushy-layer <span class="hlt">sea</span> <span class="hlt">ice</span> thermodynamics and level-<span class="hlt">ice</span> melt ponds. Using this configuration, we compare the use of isotropic and anisotropic <span class="hlt">sea</span> <span class="hlt">ice</span> mechanics, and evaluate model performance using these two variants against observations including Arctic buoy drift and deformation, satellite-derived drift and deformation, and <span class="hlt">sea</span> <span class="hlt">ice</span> volume estimates from ICESat. We find that the isotropic rheology better approximates spatial patterns of thickness observed across the Arctic, but that both rheologies closely approximate scaling laws observed in the pack using buoys and RGPS data. A fundamental component of both <span class="hlt">ice</span> mechanics variants, the so called Elastic-Viscous-Plastic (EVP) and Anisotropic-Elastic-Plastic (EAP), is that they are highly sensitive to the timestep used for elastic sub-cycling in an inertial-resolving coupled framework, and this has a significant affect on surface fluxes in the fully coupled framework.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22678359','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22678359"><span>Massive phytoplankton blooms under Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Arrigo, Kevin R; Perovich, Donald K; Pickart, Robert S; Brown, Zachary W; van Dijken, Gert L; Lowry, Kate E; Mills, Matthew M; Palmer, Molly A; Balch, William M; Bahr, Frank; Bates, Nicholas R; Benitez-Nelson, Claudia; Bowler, Bruce; Brownlee, Emily; Ehn, Jens K; Frey, Karen E; Garley, Rebecca; Laney, Samuel R; Lubelczyk, Laura; Mathis, Jeremy; Matsuoka, Atsushi; Mitchell, B Greg; Moore, G W K; Ortega-Retuerta, Eva; Pal, Sharmila; Polashenski, Chris M; Reynolds, Rick A; Schieber, Brian; Sosik, Heidi M; Stephens, Michael; Swift, James H</p> <p>2012-06-15</p> <p>Phytoplankton blooms over Arctic Ocean continental shelves are thought to be restricted to waters free of <span class="hlt">sea</span> <span class="hlt">ice</span>. Here, we document a massive phytoplankton bloom beneath fully consolidated pack <span class="hlt">ice</span> far from the <span class="hlt">ice</span> edge in the Chukchi <span class="hlt">Sea</span>, where light transmission has increased in recent decades because of thinning <span class="hlt">ice</span> cover and proliferation of melt ponds. The bloom was characterized by high diatom biomass and rates of growth and primary production. Evidence suggests that under-<span class="hlt">ice</span> phytoplankton blooms may be more widespread over nutrient-rich Arctic continental shelves and that satellite-based estimates of annual primary production in these waters may be underestimated by up to 10-fold.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=10539&hterms=sea+ice&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dsea%2Bice','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=10539&hterms=sea+ice&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dsea%2Bice"><span>Record Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Loss in 2007</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>2007-01-01</p> <p>This image of the Arctic was produced from <span class="hlt">sea</span> <span class="hlt">ice</span> observations collected by the Advanced Microwave Scanning Radiometer (AMSR-E) Instrument on NASA's Aqua satellite on September 16, overlaid on the NASA Blue Marble. The image captures <span class="hlt">ice</span> conditions at the end of the melt season. <span class="hlt">Sea</span> <span class="hlt">ice</span> (white, image center) stretches across the Arctic Ocean from Greenland to Russia, but large areas of open water were apparent as well. In addition to record melt, the summer of 2007 brought an <span class="hlt">ice</span>-free opening though the Northwest Passage that lasted several weeks. The Northeast Passage did not open during the summer of 2007, however, as a substantial tongue of <span class="hlt">ice</span> remained in place north of the Russian coast. According to the National Snow and <span class="hlt">Ice</span> Data Center (NSIDC), on September 16, 2007, <span class="hlt">sea</span> <span class="hlt">ice</span> extent dropped to 4.13 million square kilometers (1.59 million square miles)--38 percent below average and 24 percent below the 2005 record.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940026121','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940026121"><span>A toy model of <span class="hlt">sea</span> <span class="hlt">ice</span> growth</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Thorndike, Alan S.</p> <p>1992-01-01</p> <p>My purpose here is to present a simplified treatment of the growth of <span class="hlt">sea</span> <span class="hlt">ice</span>. By ignoring many details, it is possible to obtain several results that help to clarify the ways in which the <span class="hlt">sea</span> <span class="hlt">ice</span> cover will respond to climate change. Three models are discussed. The first deals with the growth of <span class="hlt">sea</span> <span class="hlt">ice</span> during the cold season. The second describes the cycle of growth and melting for perennial <span class="hlt">ice</span>. The third model extends the second to account for the possibility that the <span class="hlt">ice</span> melts away entirely in the summer. In each case, the objective is to understand what physical processes are most important, what <span class="hlt">ice</span> properties determine the <span class="hlt">ice</span> behavior, and to which climate variables the system is most sensitive.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA617623','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA617623"><span>Multiscale Models of Melting Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2014-09-30</p> <p>represent <span class="hlt">sea</span> <span class="hlt">ice</span> more rigorously in climate models. OBJECTIVES Viewed from high above, the melting <span class="hlt">sea</span> <span class="hlt">ice</span> surface can be thought of as a two phase ...composite of <span class="hlt">ice</span> and melt water. The boundaries between the two phases evolve with increasing complexity and a rapid onset of large scale...connectivity, or percolation of the melt phase . We plan to document this phenomenon with photographic imagery and to develop percolation and other models to</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24586604','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24586604"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> biogeochemistry: a guide for modellers.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tedesco, Letizia; Vichi, Marcello</p> <p>2014-01-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> is a fundamental component of the climate system and plays a key role in polar trophic food webs. Nonetheless <span class="hlt">sea</span> <span class="hlt">ice</span> biogeochemical dynamics at large temporal and spatial scales are still rarely described. Numerical models may potentially contribute integrating among sparse observations, but available models of <span class="hlt">sea</span> <span class="hlt">ice</span> biogeochemistry are still scarce, whether their relevance for properly describing the current and future state of the polar oceans has been recently addressed. A general methodology to develop a <span class="hlt">sea</span> <span class="hlt">ice</span> biogeochemical model is presented, deriving it from an existing validated model application by extension of generic pelagic biogeochemistry model parameterizations. The described methodology is flexible and considers different levels of ecosystem complexity and vertical representation, while adopting a strategy of coupling that ensures mass conservation. We show how to apply this methodology step by step by building an intermediate complexity model from a published realistic application and applying it to analyze theoretically a typical season of first-year <span class="hlt">sea</span> <span class="hlt">ice</span> in the Arctic, the one currently needing the most urgent understanding. The aim is to (1) introduce <span class="hlt">sea</span> <span class="hlt">ice</span> biogeochemistry and address its relevance to ocean modelers of polar regions, supporting them in adding a new <span class="hlt">sea</span> <span class="hlt">ice</span> component to their modelling framework for a more adequate representation of the <span class="hlt">sea</span> <span class="hlt">ice</span>-covered ocean ecosystem as a whole, and (2) extend our knowledge on the relevant controlling factors of <span class="hlt">sea</span> <span class="hlt">ice</span> algal production, showing that beyond the light and nutrient availability, the duration of the <span class="hlt">sea</span> <span class="hlt">ice</span> season may play a key-role shaping the algal production during the on going and upcoming projected changes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3934902','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3934902"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Biogeochemistry: A Guide for Modellers</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Tedesco, Letizia; Vichi, Marcello</p> <p>2014-01-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> is a fundamental component of the climate system and plays a key role in polar trophic food webs. Nonetheless <span class="hlt">sea</span> <span class="hlt">ice</span> biogeochemical dynamics at large temporal and spatial scales are still rarely described. Numerical models may potentially contribute integrating among sparse observations, but available models of <span class="hlt">sea</span> <span class="hlt">ice</span> biogeochemistry are still scarce, whether their relevance for properly describing the current and future state of the polar oceans has been recently addressed. A general methodology to develop a <span class="hlt">sea</span> <span class="hlt">ice</span> biogeochemical model is presented, deriving it from an existing validated model application by extension of generic pelagic biogeochemistry model parameterizations. The described methodology is flexible and considers different levels of ecosystem complexity and vertical representation, while adopting a strategy of coupling that ensures mass conservation. We show how to apply this methodology step by step by building an intermediate complexity model from a published realistic application and applying it to analyze theoretically a typical season of first-year <span class="hlt">sea</span> <span class="hlt">ice</span> in the Arctic, the one currently needing the most urgent understanding. The aim is to (1) introduce <span class="hlt">sea</span> <span class="hlt">ice</span> biogeochemistry and address its relevance to ocean modelers of polar regions, supporting them in adding a new <span class="hlt">sea</span> <span class="hlt">ice</span> component to their modelling framework for a more adequate representation of the <span class="hlt">sea</span> <span class="hlt">ice</span>-covered ocean ecosystem as a whole, and (2) extend our knowledge on the relevant controlling factors of <span class="hlt">sea</span> <span class="hlt">ice</span> algal production, showing that beyond the light and nutrient availability, the duration of the <span class="hlt">sea</span> <span class="hlt">ice</span> season may play a key-role shaping the algal production during the on going and upcoming projected changes. PMID:24586604</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C14B..02H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C14B..02H"><span>The Seasonality of Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Trends</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Holland, P.</p> <p>2014-12-01</p> <p>Unlike the strong decline in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>, Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> is experiencing a weak overall increase in area that is the residual of opposing regional trends. This study considers the seasonal pattern of these trends. In addition to traditional <span class="hlt">ice</span> concentration and <span class="hlt">ice</span> area, temporal rates of change of these quantities are investigated ("intensification" and "expansion," respectively). This is crucial to the attribution of the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> trends, since changes in wind or thermal forcing directly affect <span class="hlt">ice</span> areal change, rather than <span class="hlt">ice</span> area itself. The study shows that diverse regional trends all contribute significantly to the overall Antarctic <span class="hlt">sea-ice</span> increase. In contrast to the widely-held view of a 'south Pacific dipole', trends in the Weddell and Amundsen-Bellingshausen regions are found to best compensate in magnitude and seasonality. Perhaps most importantly, the largest concentration trends, in autumn, are actually caused by intensification trends during spring. Autumn intensification trends directly oppose autumn concentration trends in most places, seemingly as a result of <span class="hlt">ice</span> and ocean feedbacks. Further study of changes during the spring melting season is therefore required to unravel the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> increase.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_4 --> <div id="page_5" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="81"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015OcDyn..65.1353P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015OcDyn..65.1353P"><span>The implementation of <span class="hlt">sea</span> <span class="hlt">ice</span> model on a regional high-resolution scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Prasad, Siva; Zakharov, Igor; Bobby, Pradeep; McGuire, Peter</p> <p>2015-09-01</p> <p>The availability of high-resolution atmospheric/ocean forecast models, satellite data and access to high-performance computing clusters have provided capability to build high-resolution models for regional <span class="hlt">ice</span> condition simulation. The paper describes the implementation of the Los <span class="hlt">Alamos</span> <span class="hlt">sea</span> <span class="hlt">ice</span> model (CICE) on a regional scale at high resolution. The advantage of the model is its ability to include oceanographic parameters (e.g., currents) to provide accurate results. The <span class="hlt">sea</span> <span class="hlt">ice</span> simulation was performed over Baffin Bay and the Labrador <span class="hlt">Sea</span> to retrieve important parameters such as <span class="hlt">ice</span> concentration, thickness, ridging, and drift. Two different forcing models, one with low resolution and another with a high resolution, were used for the estimation of sensitivity of model results. <span class="hlt">Sea</span> <span class="hlt">ice</span> behavior over 7 years was simulated to analyze <span class="hlt">ice</span> formation, melting, and conditions in the region. Validation was based on comparing model results with remote sensing data. The simulated <span class="hlt">ice</span> concentration correlated well with Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and Ocean and <span class="hlt">Sea</span> <span class="hlt">Ice</span> Satellite Application Facility (OSI-SAF) data. Visual comparison of <span class="hlt">ice</span> thickness trends estimated from the Soil Moisture and Ocean Salinity satellite (SMOS) agreed with the simulation for year 2010-2011.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003PhyB..338..274G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003PhyB..338..274G"><span>Critical behavior of transport in <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Golden, K. M.</p> <p>2003-10-01</p> <p>Geophysical materials such as <span class="hlt">sea</span> <span class="hlt">ice</span>, rocks, soils, snow, and glacial <span class="hlt">ice</span> are composite media with complex, random microstructures. The effective fluid, gas, thermal, and electromagnetic transport properties of these materials play an important role in the large-scale dynamics and behavior of many geophysical systems. A striking feature of such media is that subtle changes in microstructural characteristics can induce changes over many orders of magnitude in the transport properties of the materials, which in turn can have significant large-scale geophysical effects. For example, <span class="hlt">sea</span> <span class="hlt">ice</span>, which mediates energy transfer between the ocean and atmosphere, plays a key role in global climate, and serves as an indicator of climatic change, is a porous composite of <span class="hlt">ice</span>, brine and gases. Relevant length scales range from microns and millimeters for individual brine structures, to centimeters and meters for connected brine channels across floes, to hundreds of kilometers across an <span class="hlt">ice</span> pack. <span class="hlt">Sea</span> <span class="hlt">ice</span> is distinguished from many other porous composites, such as sandstones or bone, in that its microstructure and bulk material properties can vary dramatically over a relatively small temperature range. The fluid permeability of <span class="hlt">sea</span> <span class="hlt">ice</span> ranges over six orders of magnitude for temperatures between 0°C and -25°C. Moreover, small changes in brine volume fraction around a threshold value of about 5%, corresponding to variations in temperature around a critical point of about -5°C, control an important transition between low and high fluid permeability regimes. Below this critical temperature, the <span class="hlt">sea</span> <span class="hlt">ice</span> is effectively impermeable, while for higher temperatures the brine phase becomes connected over macroscopic scales, allowing fluid transport through the <span class="hlt">ice</span>. This transition has been observed to impact a wide range of phenomena such as surface flooding and snow-<span class="hlt">ice</span> formation, enhancement of heat transfer due to fluid motion, mixing in the upper ocean, melt pool persistence</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017RSPTA.37550352W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017RSPTA.37550352W"><span>Linking scales in <span class="hlt">sea</span> <span class="hlt">ice</span> mechanics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Weiss, Jérôme; Dansereau, Véronique</p> <p>2017-02-01</p> <p>Mechanics plays a key role in the evolution of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover through its control on drift, on momentum and thermal energy exchanges between the polar oceans and the atmosphere along cracks and faults, and on <span class="hlt">ice</span> thickness distribution through opening and ridging processes. At the local scale, a significant variability of the mechanical strength is associated with the microstructural heterogeneity of saline <span class="hlt">ice</span>, however characterized by a small correlation length, below the <span class="hlt">ice</span> thickness scale. Conversely, the <span class="hlt">sea</span> <span class="hlt">ice</span> mechanical fields (velocity, strain and stress) are characterized by long-ranged (more than 1000 km) and long-lasting (approx. few months) correlations. The associated space and time scaling laws are the signature of the brittle character of <span class="hlt">sea</span> <span class="hlt">ice</span> mechanics, with deformation resulting from a multi-scale accumulation of episodic fracturing and faulting events. To translate the short-range-correlated disorder on strength into long-range-correlated mechanical fields, several key ingredients are identified: long-ranged elastic interactions, slow driving conditions, a slow viscous-like relaxation of elastic stresses and a restoring/healing mechanism. These ingredients constrained the development of a new continuum mechanics modelling framework for the <span class="hlt">sea</span> <span class="hlt">ice</span> cover, called Maxwell-elasto-brittle. Idealized simulations without advection demonstrate that this rheological framework reproduces the main characteristics of <span class="hlt">sea</span> <span class="hlt">ice</span> mechanics, including anisotropy, spatial localization and intermittency, as well as the associated scaling laws. This article is part of the themed issue 'Microdynamics of <span class="hlt">ice</span>'.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28025300','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28025300"><span>Linking scales in <span class="hlt">sea</span> <span class="hlt">ice</span> mechanics.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Weiss, Jérôme; Dansereau, Véronique</p> <p>2017-02-13</p> <p>Mechanics plays a key role in the evolution of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover through its control on drift, on momentum and thermal energy exchanges between the polar oceans and the atmosphere along cracks and faults, and on <span class="hlt">ice</span> thickness distribution through opening and ridging processes. At the local scale, a significant variability of the mechanical strength is associated with the microstructural heterogeneity of saline <span class="hlt">ice</span>, however characterized by a small correlation length, below the <span class="hlt">ice</span> thickness scale. Conversely, the <span class="hlt">sea</span> <span class="hlt">ice</span> mechanical fields (velocity, strain and stress) are characterized by long-ranged (more than 1000 km) and long-lasting (approx. few months) correlations. The associated space and time scaling laws are the signature of the brittle character of <span class="hlt">sea</span> <span class="hlt">ice</span> mechanics, with deformation resulting from a multi-scale accumulation of episodic fracturing and faulting events. To translate the short-range-correlated disorder on strength into long-range-correlated mechanical fields, several key ingredients are identified: long-ranged elastic interactions, slow driving conditions, a slow viscous-like relaxation of elastic stresses and a restoring/healing mechanism. These ingredients constrained the development of a new continuum mechanics modelling framework for the <span class="hlt">sea</span> <span class="hlt">ice</span> cover, called Maxwell-elasto-brittle. Idealized simulations without advection demonstrate that this rheological framework reproduces the main characteristics of <span class="hlt">sea</span> <span class="hlt">ice</span> mechanics, including anisotropy, spatial localization and intermittency, as well as the associated scaling laws.This article is part of the themed issue 'Microdynamics of <span class="hlt">ice</span>'.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19750033049&hterms=Ice+Age&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3DIce%2BAge','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19750033049&hterms=Ice+Age&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3DIce%2BAge"><span>Investigation of radar discrimination of <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Parashar, S. K.; Biggs, A. W.; Fung, A. K.; Moore, R. K.</p> <p>1974-01-01</p> <p>The ability of radar to discriminate <span class="hlt">sea</span> <span class="hlt">ice</span> types and their thickness was studied. Radar backscatter measurements at 400 MHz (multi-polarization) and 13.3 GHz (VV polarization) obtained from NASA Earth Resources Aircraft Program Mission 126 were analyzed in detail. The scatterometer data were separated into seven categories of <span class="hlt">sea</span> <span class="hlt">ice</span> according to age and thickness as interpreted from stereo aerial photographs. The variations of radar backscatter cross-section with <span class="hlt">sea</span> <span class="hlt">ice</span> thickness at various angles are presented at the two frequencies. There is a reversal of angular character of radar return from <span class="hlt">sea</span> <span class="hlt">ice</span> less than 18 cm thick at the two frequencies. Multi-year <span class="hlt">ice</span> (<span class="hlt">sea</span> <span class="hlt">ice</span> greater than 180 cm thick) gives strongest return at 13.3 GHz. First-year <span class="hlt">ice</span> (30 cm to 90 cm thick) gives strongest return at 400 MHz. Open water can be differentiated at both the frequencies. Four-polarization 16.5 GHz radar imagery was also obtained. Open water and three categories of <span class="hlt">sea</span> <span class="hlt">ice</span> can be identified on the images. The results of the imagery analysis are consistent with the radar scatterometer results.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010DSRII..57...86G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010DSRII..57...86G"><span>Arctic <span class="hlt">sea-ice</span> ridges—Safe heavens for <span class="hlt">sea-ice</span> fauna during periods of extreme <span class="hlt">ice</span> melt?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gradinger, Rolf; Bluhm, Bodil; Iken, Katrin</p> <p>2010-01-01</p> <p>The abundances and distribution of metazoan within-<span class="hlt">ice</span> meiofauna (13 stations) and under-<span class="hlt">ice</span> fauna (12 stations) were investigated in level <span class="hlt">sea</span> <span class="hlt">ice</span> and <span class="hlt">sea-ice</span> ridges in the Chukchi/Beaufort <span class="hlt">Seas</span> and Canada Basin in June/July 2005 using a combination of <span class="hlt">ice</span> coring and SCUBA diving. <span class="hlt">Ice</span> meiofauna abundance was estimated based on live counts in the bottom 30 cm of level <span class="hlt">sea</span> <span class="hlt">ice</span> based on triplicate <span class="hlt">ice</span> core sampling at each location, and in individual <span class="hlt">ice</span> chunks from ridges at four locations. Under-<span class="hlt">ice</span> amphipods were counted in situ in replicate ( N=24-65 per station) 0.25 m 2 quadrats using SCUBA to a maximum water depth of 12 m. In level <span class="hlt">sea</span> <span class="hlt">ice</span>, the most abundant <span class="hlt">ice</span> meiofauna groups were Turbellaria (46%), Nematoda (35%), and Harpacticoida (19%), with overall low abundances per station that ranged from 0.0 to 10.9 ind l -1 (median 0.8 ind l -1). In level <span class="hlt">ice</span>, low <span class="hlt">ice</span> algal pigment concentrations (<0.1-15.8 μg Chl a l -1), low brine salinities (1.8-21.7) and flushing from the melting <span class="hlt">sea</span> <span class="hlt">ice</span> likely explain the low <span class="hlt">ice</span> meiofauna concentrations. Higher abundances of Turbellaria, Nematoda and Harpacticoida also were observed in pressure ridges (0-200 ind l -1, median 40 ind l -1), although values were highly variable and only medians of Turbellaria were significantly higher in ridge <span class="hlt">ice</span> than in level <span class="hlt">ice</span>. Median abundances of under-<span class="hlt">ice</span> amphipods at all <span class="hlt">ice</span> types (level <span class="hlt">ice</span>, various <span class="hlt">ice</span> ridge structures) ranged from 8 to 114 ind m -2 per station and mainly consisted of Apherusa glacialis (87%), Onisimus spp. (7%) and Gammarus wilkitzkii (6%). Highest amphipod abundances were observed in pressure ridges at depths >3 m where abundances were up to 42-fold higher compared with level <span class="hlt">ice</span>. We propose that the summer <span class="hlt">ice</span> melt impacted meiofauna and under-<span class="hlt">ice</span> amphipod abundance and distribution through (a) flushing, and (b) enhanced salinity stress at thinner level <span class="hlt">sea</span> <span class="hlt">ice</span> (less than 3 m thickness). We further suggest that pressure ridges, which extend into deeper, high</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010037604','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010037604"><span>Satellite Remote Sensing: Passive-Microwave Measurements of <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Parkinson, Claire L.; Zukor, Dorothy J. (Technical Monitor)</p> <p>2001-01-01</p> <p>Satellite passive-microwave measurements of <span class="hlt">sea</span> <span class="hlt">ice</span> have provided global or near-global <span class="hlt">sea</span> <span class="hlt">ice</span> data for most of the period since the launch of the Nimbus 5 satellite in December 1972, and have done so with horizontal resolutions on the order of 25-50 km and a frequency of every few days. These data have been used to calculate <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations (percent areal coverages), <span class="hlt">sea</span> <span class="hlt">ice</span> extents, the length of the <span class="hlt">sea</span> <span class="hlt">ice</span> season, <span class="hlt">sea</span> <span class="hlt">ice</span> temperatures, and <span class="hlt">sea</span> <span class="hlt">ice</span> velocities, and to determine the timing of the seasonal onset of melt as well as aspects of the <span class="hlt">ice</span>-type composition of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover. In each case, the calculations are based on the microwave emission characteristics of <span class="hlt">sea</span> <span class="hlt">ice</span> and the important contrasts between the microwave emissions of <span class="hlt">sea</span> <span class="hlt">ice</span> and those of the surrounding liquid-water medium.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016PolSc..10..553A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016PolSc..10..553A"><span>Enhancing calculation of thin <span class="hlt">sea</span> <span class="hlt">ice</span> growth</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Appel, Igor</p> <p>2016-12-01</p> <p>The goal of the present study is to develop, generate, and integrate into operational practice a new model of <span class="hlt">ice</span> growth. The development of this <span class="hlt">Sea</span> <span class="hlt">Ice</span> Growth Model for Arctic (SIGMA), a description of the theoretical foundation, the model advantages and analysis of its results are considered in the paper. The enhanced model includes two principal modifications. Surface temperature of snow on <span class="hlt">ice</span> is defined as internal model parameter maintaining rigorous consistency between processes of atmosphere-<span class="hlt">ice</span> thermodynamic interaction and <span class="hlt">ice</span> growth. The snow depth on <span class="hlt">ice</span> is naturally defined as a function of a local snowfall rate and linearly depends on time rather than <span class="hlt">ice</span> thickness. The model was initially outlined in the Visible Infrared Radiometer Suite (VIIRS) <span class="hlt">Sea</span> <span class="hlt">Ice</span> Characterization Algorithm Theoretical Basis Document (Appel et al., 2005) that included two different approaches to retrieve <span class="hlt">sea</span> <span class="hlt">ice</span> age: reflectance analysis for daytime and derivation of <span class="hlt">ice</span> thickness using energy balance for nighttime. Only the latter method is considered in this paper. The improved account for the influence of surface temperature and snow depth increases the reliability of <span class="hlt">ice</span> thickness calculations and is used to develop an analytical Snow Depth/<span class="hlt">Ice</span> Thickness Look up table suitable to the VIIRS observations as well as to other instruments. The applicability of SIGMA to retrieve <span class="hlt">ice</span> thickness from the VIIRS satellite observations and the comparison of its results with the One-dimensional Thermodynamic <span class="hlt">Ice</span> Model (OTIM) are also considered. The comparison of the two models demonstrating the difference between their assessments of heat fluxes and radical distinction between the influences of snow depth uncertainty on errors of <span class="hlt">ice</span> thickness calculations is of great significance to further improve the retrieval of <span class="hlt">ice</span> thickness from satellite observations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=PIA04300&hterms=weather+types&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dweather%2Btypes','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=PIA04300&hterms=weather+types&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dweather%2Btypes"><span><span class="hlt">Ice</span> Types in the Beaufort <span class="hlt">Sea</span>, Alaska</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>2003-01-01</p> <p><p/> Determining the amount and type of <span class="hlt">sea</span> <span class="hlt">ice</span> in the polar oceans is crucial to improving our knowledge and understanding of polar weather and long term climate fluctuations. These views from two satellite remote sensing instruments; the synthetic aperture radar (SAR) on board the RADARSAT satellite and the Multi-angle Imaging SpectroRadiometer (MISR), illustrate different methods that may be used to assess <span class="hlt">sea</span> <span class="hlt">ice</span> type. <span class="hlt">Sea</span> <span class="hlt">ice</span> in the Beaufort <span class="hlt">Sea</span> off the north coast of Alaska was classified and mapped in these concurrent images acquired March 19, 2001 and mapped to the same geographic area.<p/>To identify <span class="hlt">sea</span> <span class="hlt">ice</span> types, the National Oceanic and Atmospheric Administration (NOAA) National <span class="hlt">Ice</span> Center constructs <span class="hlt">ice</span> charts using several data sources including RADARSAT SAR images such as the one shown at left. SAR classifies <span class="hlt">sea</span> <span class="hlt">ice</span> types primarily by how the surface and subsurface roughness influence radar backscatter. In the SAR image, white lines delineate different <span class="hlt">sea</span> <span class="hlt">ice</span> zones as identified by the National <span class="hlt">Ice</span> Center. Regions of mostly multi-year <span class="hlt">ice</span> (A) are separated from regions with large amounts of first year and younger <span class="hlt">ice</span> (B-D), and the dashed white line at bottom marks the coastline. In general, <span class="hlt">sea</span> <span class="hlt">ice</span> types that exhibit increased radar backscatter appear bright in SAR and are identified as rougher, older <span class="hlt">ice</span> types. Younger, smoother <span class="hlt">ice</span> types appear dark to SAR. Near the top of the SAR image, however, red arrows point to bright areas in which large, crystalline 'frost flowers' have formed on young, thin <span class="hlt">ice</span>, causing this young <span class="hlt">ice</span> type to exhibit an increased radar backscatter. Frost flowers are strongly backscattering at radar wavelengths (cm) due to both surface roughness and the high salinity of frost flowers, which causes them to be highly reflective to radar energy.<p/>Surface roughness is also registered by MISR, although the roughness observed is at a different spatial scale. Older, rougher <span class="hlt">ice</span> areas are predominantly backward scattering to</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.5056R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.5056R"><span>Stratospheric Impacts on Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Reichler, Thomas</p> <p>2016-04-01</p> <p>Long-term circulation change in the stratosphere can have substantial effects on the oceans and their circulation. In this study we investigate whether and how <span class="hlt">sea</span> <span class="hlt">ice</span> at the ocean surface responds to intraseasonal stratospheric variability. Our main question is whether the surface impact of stratospheric sudden warmings (SSWs) is strong and long enough to affect <span class="hlt">sea</span> <span class="hlt">ice</span>. A related question is whether the increased frequency of SSWs during the 2000s contributed to the rapid decrease in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> during this time. To this end we analyze observations of <span class="hlt">sea</span> <span class="hlt">ice</span>, NCEP/NCAR reanalysis, and a long control integration with a stratospherically-enhanced version of the GFDL CM2.1 climate model. From both observations and the model we find that stratospheric extreme events have a demonstrable impact on the distribution of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. The areas most affected are near the edge of the climatological <span class="hlt">ice</span> line over the North Atlantic, North Pacific, and the Arctic Ocean. The absolute changes in <span class="hlt">sea</span> <span class="hlt">ice</span> coverage amount to +/-10 %. Areas and magnitudes of increase and decrease are about the same. It is thus unlikely that the increased SSW frequency during the 2000s contributed to the decline of <span class="hlt">sea</span> <span class="hlt">ice</span> during that period. The <span class="hlt">sea</span> <span class="hlt">ice</span> changes are consistent with the impacts of a negative NAO at the surface and can be understood in terms of (1) dynamical change due to altered surface wind stress and (2) thermodynamical change due to altered temperature advection. Both dynamical and thermodynamical change positively reinforce each other in producing <span class="hlt">sea</span> change. A simple advection model is used to demonstrate that most of the <span class="hlt">sea</span> <span class="hlt">ice</span> change can be explained from the <span class="hlt">sea</span> <span class="hlt">ice</span> drift due to the anomalous surface wind stress. Changes in the production or melt of <span class="hlt">sea</span> <span class="hlt">ice</span> by thermodynamical effects are less important. Overall, this study adds to an increasing body of evidence that the stratosphere not only impacts weather and climate of the atmosphere but also the surface and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/AD1012491','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD1012491"><span>Autonomous <span class="hlt">Sea-Ice</span> Thickness Survey</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2016-06-01</p> <p>to tow an electromagnetic induction meter over <span class="hlt">sea</span> <span class="hlt">ice</span> in McMurdo Sound , Antarctica. This proof-of-concept survey aimed to demonstrate improved...Documentation Page ERDC/CRREL SR-16-4 iv Figures and Tables Figures 1 The runway and roads on McMurdo Sound <span class="hlt">sea</span> <span class="hlt">ice</span> in November 2009. (Map data...4 4 The EM31 in the sled towed by Yeti along Pegasus Cut-Off Road on McMurdo Sound <span class="hlt">sea</span> <span class="hlt">ice</span>. The blue box housed the battery</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.9252S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.9252S"><span>Strong coupling among Antarctic <span class="hlt">ice</span> shelves, ocean circulation and <span class="hlt">sea</span> <span class="hlt">ice</span> in a global <span class="hlt">sea-ice</span> - ocean circulation model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sergienko, Olga</p> <p>2016-04-01</p> <p>The thermodynamic effects of Antarctic <span class="hlt">ice</span> shelf interaction with ocean circulation are investigated using a global, high-resolution, isopycnal ocean-circulation model coupled to a <span class="hlt">sea-ice</span> model. The model uses NASA MERRA Reanalysis from 1992 to 2011 as atmospheric forcing. The simulated long-period variability of <span class="hlt">ice</span>-shelf melting/freezing rates differ across geographic locations. The <span class="hlt">ice</span> shelves in Antarctic Peninsula, Amundsen and Bellingshausen <span class="hlt">sea</span> embayments and the Amery <span class="hlt">Ice</span> Shelf experience an increase in melting starting from 2005. This increase in melting is due to an increase in the subsurface (100-500 m) ocean heat content in the embayments of these <span class="hlt">ice</span> shelves, which is caused by an increase in <span class="hlt">sea-ice</span> concentration after 2005, and consequent reduction of the heat loss to the atmosphere. Our simulations provide a strong evidence for a coupling between ocean circulation, <span class="hlt">sea</span> <span class="hlt">ice</span> and <span class="hlt">ice</span> shelves.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=sea+AND+ice&id=EJ827417','ERIC'); return false;" href="http://eric.ed.gov/?q=sea+AND+ice&id=EJ827417"><span>SIPEX--Exploring the Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Zone</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Zicus, Sandra; Dobson, Jane; Worby, Anthony</p> <p>2008-01-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> in the polar regions plays a key role in both regulating global climate and maintaining marine ecosystems. The international <span class="hlt">Sea</span> <span class="hlt">Ice</span> Physics and Ecosystem eXperiment (SIPEX) explored the <span class="hlt">sea</span> <span class="hlt">ice</span> zone around Antarctica in September and October 2007, investigating relationships between the physical <span class="hlt">sea</span> <span class="hlt">ice</span> environment and the structure of…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ChJOL..33..458Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ChJOL..33..458Z"><span>Influences of <span class="hlt">sea</span> <span class="hlt">ice</span> on eastern Bering <span class="hlt">Sea</span> phytoplankton</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhou, Qianqian; Wang, Peng; Chen, Changping; Liang, Junrong; Li, Bingqian; Gao, Yahui</p> <p>2015-03-01</p> <p>The influence of <span class="hlt">sea</span> <span class="hlt">ice</span> on the species composition and cell density of phytoplankton was investigated in the eastern Bering <span class="hlt">Sea</span> in spring 2008. Diatoms, particularly pennate diatoms, dominated the phytoplankton community. The dominant species were Grammonema islandica (Grunow in Van Heurck) Hasle, Fragilariopsis cylindrus (Grunow) Krieger, F. oceanica (Cleve) Hasle, Navicula vanhoeffenii Gran, Thalassiosira antarctica Comber, T. gravida Cleve, T. nordenskiöeldii Cleve, and T. rotula Meunier. Phytoplankton cell densities varied from 0.08×104 to 428.8×104 cells/L, with an average of 30.3×104 cells/L. Using cluster analysis, phytoplankton were grouped into three assemblages defined by <span class="hlt">ice</span>-forming conditions: open water, <span class="hlt">ice</span> edge, and <span class="hlt">sea</span> <span class="hlt">ice</span> assemblages. In spring, when the <span class="hlt">sea</span> <span class="hlt">ice</span> melts, the phytoplankton dispersed from the <span class="hlt">sea</span> <span class="hlt">ice</span> to the <span class="hlt">ice</span> edge and even into open waters. Thus, these phytoplankton in the <span class="hlt">sea</span> <span class="hlt">ice</span> may serve as a "seed bank" for phytoplankton population succession in the subarctic ecosystem. Moreover, historical studies combined with these results suggest that the sizes of diatom species have become smaller, shifting from microplankton to nannoplankton-dominated communities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GeCoA.182...40B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GeCoA.182...40B"><span>Mirabilite solubility in equilibrium <span class="hlt">sea</span> <span class="hlt">ice</span> brines</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Butler, Benjamin Miles; Papadimitriou, Stathys; Santoro, Anna; Kennedy, Hilary</p> <p>2016-06-01</p> <p>The <span class="hlt">sea</span> <span class="hlt">ice</span> microstructure is permeated by brine channels and pockets that contain concentrated seawater-derived brine. Cooling the <span class="hlt">sea</span> <span class="hlt">ice</span> results in further formation of pure <span class="hlt">ice</span> within these pockets as thermal equilibrium is attained, resulting in a smaller volume of increasingly concentrated residual brine. The coupled changes in temperature and ionic composition result in supersaturation of the brine with respect to mirabilite (Na2SO4·10H2O) at temperatures below -6.38 °C, which consequently precipitates within the <span class="hlt">sea</span> <span class="hlt">ice</span> microstructure. Here, mirabilite solubility in natural and synthetic seawater derived brines, representative of <span class="hlt">sea</span> <span class="hlt">ice</span> at thermal equilibrium, has been measured in laboratory experiments between 0.2 and -20.6 °C, and hence we present a detailed examination of mirabilite dynamics within the <span class="hlt">sea</span> <span class="hlt">ice</span> system. Below -6.38 °C mirabilite displays particularly large changes in solubility as the temperature decreases, and by -20.6 °C its precipitation results in 12.90% and 91.97% reductions in the total dissolved Na+ and SO42- concentrations respectively, compared to that of conservative seawater concentration. Such large non-conservative changes in brine composition could potentially impact upon the measurement of <span class="hlt">sea</span> <span class="hlt">ice</span> brine salinity and pH, whilst the altered osmotic conditions may create additional challenges for the sympagic organisms that inhabit the <span class="hlt">sea</span> <span class="hlt">ice</span> system. At temperatures above -6.38 °C, mirabilite again displays large changes in solubility that likely aid in impeding its identification in field samples of <span class="hlt">sea</span> <span class="hlt">ice</span>. Our solubility measurements display excellent agreement with that of the FREZCHEM model, which was therefore used to supplement our measurements to colder temperatures. Measured and modelled solubility data were incorporated into a 1D model for the growth of first-year Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Model results ultimately suggest that mirabilite has a near ubiquitous presence in much of the <span class="hlt">sea</span> <span class="hlt">ice</span> on Earth, and illustrate the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26699509','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26699509"><span>Influence of <span class="hlt">sea</span> <span class="hlt">ice</span> on Arctic precipitation.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kopec, Ben G; Feng, Xiahong; Michel, Fred A; Posmentier, Eric S</p> <p>2016-01-05</p> <p>Global climate is influenced by the Arctic hydrologic cycle, which is, in part, regulated by <span class="hlt">sea</span> <span class="hlt">ice</span> through its control on evaporation and precipitation. However, the quantitative link between precipitation and <span class="hlt">sea</span> <span class="hlt">ice</span> extent is poorly constrained. Here we present observational evidence for the response of precipitation to <span class="hlt">sea</span> <span class="hlt">ice</span> reduction and assess the sensitivity of the response. Changes in the proportion of moisture sourced from the Arctic with <span class="hlt">sea</span> <span class="hlt">ice</span> change in the Canadian Arctic and Greenland <span class="hlt">Sea</span> regions over the past two decades are inferred from annually averaged deuterium excess (d-excess) measurements from six sites. Other influences on the Arctic hydrologic cycle, such as the strength of meridional transport, are assessed using the North Atlantic Oscillation index. We find that the independent, direct effect of <span class="hlt">sea</span> <span class="hlt">ice</span> on the increase of the percentage of Arctic sourced moisture (or Arctic moisture proportion, AMP) is 18.2 ± 4.6% and 10.8 ± 3.6%/100,000 km(2) <span class="hlt">sea</span> <span class="hlt">ice</span> lost for each region, respectively, corresponding to increases of 10.9 ± 2.8% and 2.7 ± 1.1%/1 °C of warming in the vapor source regions. The moisture source changes likely result in increases of precipitation and changes in energy balance, creating significant uncertainty for climate predictions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4711856','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4711856"><span>Influence of <span class="hlt">sea</span> <span class="hlt">ice</span> on Arctic precipitation</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Kopec, Ben G.; Feng, Xiahong; Michel, Fred A.; Posmentier, Eric S.</p> <p>2016-01-01</p> <p>Global climate is influenced by the Arctic hydrologic cycle, which is, in part, regulated by <span class="hlt">sea</span> <span class="hlt">ice</span> through its control on evaporation and precipitation. However, the quantitative link between precipitation and <span class="hlt">sea</span> <span class="hlt">ice</span> extent is poorly constrained. Here we present observational evidence for the response of precipitation to <span class="hlt">sea</span> <span class="hlt">ice</span> reduction and assess the sensitivity of the response. Changes in the proportion of moisture sourced from the Arctic with <span class="hlt">sea</span> <span class="hlt">ice</span> change in the Canadian Arctic and Greenland <span class="hlt">Sea</span> regions over the past two decades are inferred from annually averaged deuterium excess (d-excess) measurements from six sites. Other influences on the Arctic hydrologic cycle, such as the strength of meridional transport, are assessed using the North Atlantic Oscillation index. We find that the independent, direct effect of <span class="hlt">sea</span> <span class="hlt">ice</span> on the increase of the percentage of Arctic sourced moisture (or Arctic moisture proportion, AMP) is 18.2 ± 4.6% and 10.8 ± 3.6%/100,000 km2 <span class="hlt">sea</span> <span class="hlt">ice</span> lost for each region, respectively, corresponding to increases of 10.9 ± 2.8% and 2.7 ± 1.1%/1 °C of warming in the vapor source regions. The moisture source changes likely result in increases of precipitation and changes in energy balance, creating significant uncertainty for climate predictions. PMID:26699509</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24204642','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24204642"><span>Floating <span class="hlt">ice</span>-algal aggregates below melting arctic <span class="hlt">sea</span> <span class="hlt">ice</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Assmy, Philipp; Ehn, Jens K; Fernández-Méndez, Mar; Hop, Haakon; Katlein, Christian; Sundfjord, Arild; Bluhm, Katrin; Daase, Malin; Engel, Anja; Fransson, Agneta; Granskog, Mats A; Hudson, Stephen R; Kristiansen, Svein; Nicolaus, Marcel; Peeken, Ilka; Renner, Angelika H H; Spreen, Gunnar; Tatarek, Agnieszka; Wiktor, Jozef</p> <p>2013-01-01</p> <p>During two consecutive cruises to the Eastern Central Arctic in late summer 2012, we observed floating algal aggregates in the melt-water layer below and between melting <span class="hlt">ice</span> floes of first-year pack <span class="hlt">ice</span>. The macroscopic (1-15 cm in diameter) aggregates had a mucous consistency and were dominated by typical <span class="hlt">ice</span>-associated pennate diatoms embedded within the mucous matrix. Aggregates maintained buoyancy and accumulated just above a strong pycnocline that separated meltwater and seawater layers. We were able, for the first time, to obtain quantitative abundance and biomass estimates of these aggregates. Although their biomass and production on a square metre basis was small compared to <span class="hlt">ice</span>-algal blooms, the floating <span class="hlt">ice</span>-algal aggregates supported high levels of biological activity on the scale of the individual aggregate. In addition they constituted a food source for the <span class="hlt">ice</span>-associated fauna as revealed by pigments indicative of zooplankton grazing, high abundance of naked ciliates, and <span class="hlt">ice</span> amphipods associated with them. During the Arctic melt season, these floating aggregates likely play an important ecological role in an otherwise impoverished near-surface <span class="hlt">sea</span> <span class="hlt">ice</span> environment. Our findings provide important observations and measurements of a unique aggregate-based habitat during the 2012 record <span class="hlt">sea</span> <span class="hlt">ice</span> minimum year.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.youtube.com/watch?v=MKU4rGSLWeM','SCIGOVIMAGE-NASA'); return false;" href="http://www.youtube.com/watch?v=MKU4rGSLWeM"><span>Comparison Graph of <span class="hlt">Sea</span> <span class="hlt">Ice</span> Minimum - 2010</span></a></p> <p><a target="_blank" href="http://www.nasa.gov/multimedia/videogallery/index.html">NASA Video Gallery</a></p> <p></p> <p></p> <p>This animated graph tracks the retreat of <span class="hlt">sea</span> <span class="hlt">ice</span>, measured in millions of square kilometers, averaged from the start of the satellite record in 1979 through 2000 (white). Next, the graph follows t...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.youtube.com/watch?v=LT6OigYy4Hw','SCIGOVIMAGE-NASA'); return false;" href="http://www.youtube.com/watch?v=LT6OigYy4Hw"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Thickness Comparison: 1979 vs 2013</span></a></p> <p><a target="_blank" href="http://www.nasa.gov/multimedia/videogallery/index.html">NASA Video Gallery</a></p> <p></p> <p></p> <p>This animation compares the difference in the area, volume and depth of the average September Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> between 1979, shown in blue, and 2013, shown in orange. The data from these two years ha...</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_5 --> <div id="page_6" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="101"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.youtube.com/watch?v=ACJOxLcjun4','SCIGOVIMAGE-NASA'); return false;" href="http://www.youtube.com/watch?v=ACJOxLcjun4"><span>Approaching the 2015 Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Minimum</span></a></p> <p><a target="_blank" href="http://www.nasa.gov/multimedia/videogallery/index.html">NASA Video Gallery</a></p> <p></p> <p></p> <p>As the sun sets over the Arctic, the end of this year’s melt season is quickly approaching and the <span class="hlt">sea</span> <span class="hlt">ice</span> cover has already shrunk to the fourth lowest in the satellite record. With possibly some ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/6441996','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/6441996"><span>Interaction of oil with arctic <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Thomas, D.R.</p> <p>1983-02-01</p> <p>The purpose of the paper is to summarize relavant knowledge about the interactions between arctic <span class="hlt">sea</span> <span class="hlt">ice</span> and oil. The completion of further experimental oil spill studies, along with recent laboratory studies of the interaction of oil and <span class="hlt">sea</span> <span class="hlt">ice</span> and studies of environmental conditions, makes an updating of those works desirable. An attempt is made to identify the major factors in the interaction between oil and arctic <span class="hlt">sea</span> <span class="hlt">ice</span> and to present them in a way that defines the scope of the problem. Generally, the paper is restricted to factors that can be expected to play a major role in the sequence of events following a large under-<span class="hlt">ice</span> blowout in the Beaufort <span class="hlt">Sea</span> during winter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C11B0368B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C11B0368B"><span>Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> and climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barreira, S.</p> <p>2014-12-01</p> <p>Principal Components Analysis in T-Mode Varimax rotated was performed on Antarctic and Arctic monthly <span class="hlt">sea</span> <span class="hlt">ice</span> concentration anomalies (SICA) fields for the period 1979-2014, in order to investigate which are the main spatial characteristics of <span class="hlt">sea</span> <span class="hlt">ice</span> and its relationship with atmospheric circulation. This analysis provides 5 patterns of <span class="hlt">sea</span> <span class="hlt">ice</span> for inter-spring period and 3 patterns for summer-autumn for Antarctica (69,2% of the total variance) and 3 different patterns for summer-autumn and 3 for winter-spring season for the Arctic Ocean (67,8% of the total variance).Each of these patterns has a positive and negative phase. We used the Monthly Polar Gridded <span class="hlt">Sea</span> <span class="hlt">Ice</span> Concentrations database derived from satellite information generated by NASA Team algorithm. To understand the links between the SICA and climate trends, we extracted the mean pressure and, temperature field patterns for the months with high loadings (positive or negative) of the <span class="hlt">sea</span> <span class="hlt">ice</span> patterns that gave distinct atmospheric structures associated with each one. For Antarctica, the first SICA spatial winter-spring pattern in positive phase shows a negative SICA centre over the Drake Passage and north region of Bellingshausen and Weddell <span class="hlt">Seas</span> together with another negative SICA centre over the East Indian Ocean. Strong positive centres over the rest of the Atlantic and Indian Oceans basins and the Amundsen <span class="hlt">Sea</span> are also presented. A strong negative pressure anomaly covers most of the Antarctic Continent centered over the Bellingshausen <span class="hlt">Sea</span> accompanied by three positive pressure anomalies in middle-latitudes. During recent years, the Arctic showed persistent associations of <span class="hlt">sea-ice</span> and climate patterns principally during summer. Our strongest summer-autumn pattern in negative phase showed a marked reduction on SICA over western Arctic, primarily linked to an overall increase in Arctic atmospheric temperature most pronounced over the Beaufort, Chukchi and East Siberian <span class="hlt">Seas</span>, and a positive anomaly of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C21B0341S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C21B0341S"><span>Using <span class="hlt">Sea</span> <span class="hlt">Ice</span> Age as a Proxy for <span class="hlt">Sea</span> <span class="hlt">Ice</span> Thickness</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stroeve, J. C.; Tschudi, M. A.; Maslanik, J. A.</p> <p>2014-12-01</p> <p>Since the beginning of the modern satellite record starting in October 1978, the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover has been shrinking, with the largest changes observed at the end of the melt season in September. Through 2013, the September <span class="hlt">ice</span> extent has declined at a rate of -14.0% dec-1, or -895,300 km2 dec-1. The seven lowest September extents in the satellite record have all occurred in the past seven years. This reduction in <span class="hlt">ice</span> extent is accompanied by large reductions in winter <span class="hlt">ice</span> thicknesses that are primarily explained by changes in the ocean's coverage of multiyear <span class="hlt">ice</span> (MYI). Using the University of Colorado <span class="hlt">ice</span> age product developed by J. Maslanik and C. Fowler, and currently produced by M. Tschudi we present recent changes in the distribution of <span class="hlt">ice</span> age from the mid 1980s to present. The CU <span class="hlt">ice</span> age product is based on (1) the use of <span class="hlt">ice</span> motion to track areas of <span class="hlt">sea</span> <span class="hlt">ice</span> and thus estimate how long the <span class="hlt">ice</span> survives within the Arctic, and (2) satellite imagery of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration to determine when the <span class="hlt">ice</span> disappears. Age is assigned on a yearly basis, with the age incremented by one year if the <span class="hlt">ice</span> survives summer melt and stays within the Arctic domain. Age is counted from 1 to 10 years, with all <span class="hlt">ice</span> older than 10 years assigned to the "10+" age category. The position of the <span class="hlt">ice</span> is calculated on weekly time steps on NSIDC's 12.5-km EASE-grid. In the mid-1980s, MYI accounted for 70% of total winter <span class="hlt">ice</span> extent, whereas by the end of 2012 it had dropped to less than 20%. This reflects not only a change in <span class="hlt">ice</span> type, but also a general thinning of the <span class="hlt">ice</span> pack, as older <span class="hlt">ice</span> tends to be thicker <span class="hlt">ice</span>. Thus, with older <span class="hlt">ice</span> being replaced by thinner first-year <span class="hlt">ice</span>, the <span class="hlt">ice</span> pack is more susceptible to melting out than it was in 1980's. It has been suggested that <span class="hlt">ice</span> age may be a useful proxy for long-term changes in <span class="hlt">ice</span> thickness. To assess the relationship between <span class="hlt">ice</span> age and thickness, and how this may be changing over time, we compare the <span class="hlt">ice</span> age fields to several</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA617621','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA617621"><span>Wave-<span class="hlt">Ice</span> and Air-<span class="hlt">Ice</span>-Ocean Interaction During the Chukchi <span class="hlt">Sea</span> <span class="hlt">Ice</span> Edge Advance</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2014-09-30</p> <p>Zone Based on a Rheological Parameterization Shen Proving and Improving Wave Models in the Arctic Ocean and its MIZ Wadhams and Doble Wave Climate ...arctic_<span class="hlt">sea</span>_state Ackley, S.F. et al. (6 others), accepted, Surface Flooding of Antarctic summer <span class="hlt">sea</span> <span class="hlt">ice</span>, Annals of Glaciology (publication 2015) Ackley, S.F...E. Murphy and H. Xie (accepted), Ocean heat flux under Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> in the Bellingshausen and Amundsen <span class="hlt">Seas</span>, Annals of Glaciology</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C31A..04B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C31A..04B"><span>Representation of <span class="hlt">Sea</span> <span class="hlt">Ice</span> Processes in State of the Art Earth System Models.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bailey, D. A.; Holland, M. M.</p> <p>2015-12-01</p> <p>The majority of Earth System Models now include thermodynamic-dynamic <span class="hlt">sea</span> <span class="hlt">ice</span> models with a subgridscale representation of <span class="hlt">ice</span> thickness. The current <span class="hlt">sea</span> <span class="hlt">ice</span> component of the Community Earth System Model is the Los <span class="hlt">Alamos</span> <span class="hlt">sea</span> <span class="hlt">ice</span> model (CICE) version 5. This new version of the model includes prognostic salinity in the vertical thermodynamic calculation as well as a representation of melt pond drainage through the <span class="hlt">sea</span> <span class="hlt">ice</span>. The CICE5 also includes a melt pond parameterization that takes into account the deformed and non-deformed <span class="hlt">ice</span> within a model grid cell. Snow on <span class="hlt">sea</span> <span class="hlt">ice</span> processes allow for an evolving effective snow grain radius as a function of temperature, which is used in the shortwave radiative transfer and surface albedo calculation. I will discuss the results from coupled climate model sensitivity simulations that consider the subgridscale representations of some of these processes. This will include analysis of mean state and feedbacks in both the Arctic and Antarctic. Additional discussion will be provided on how we have used observations to guide these efforts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.usgs.gov/of/ofr02232/','USGSPUBS'); return false;" href="https://pubs.usgs.gov/of/ofr02232/"><span>Dissolved pesticides in the <span class="hlt">Alamo</span> River and the Salton <span class="hlt">Sea</span>, California, 1996-97</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Crepeau, Kathryn L.; Kuivila, Kathryn; Bergamaschi, Brian A.</p> <p>2002-01-01</p> <p>Water samples were collected from the <span class="hlt">Alamo</span> River and the Salton <span class="hlt">Sea</span>, California, in autumn 1996 and late winter/early spring 1997 and analyzed for dissolved pesticides. The two seasons chosen for sampling were during pesticide application periods in the Imperial Valley. Pesticide concentrations were measured in filtered water samples using solid-phase extraction and analyzed by gas chromatography/mass spectrometry. Generally, the highest concentrations were measured in the <span class="hlt">Alamo</span> River. The concentrations of carbaryl, chlorpyrifos, cycloate, dacthal, diazinon, and eptam were highest in samples collected in autumn 1996. In contrast, the concentrations of atrazine, carbofuran, and malathion were highest in samples collected in late winter/early spring 1997. The highest concentrations measured of atrazine, carbofuran, dacthal, eptam, and malathion all exceeded 1,000 nanograms per liter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1511437P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1511437P"><span>New Greenland MSA and Na <span class="hlt">ice</span> core records: reliable proxies for Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> changes?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pol, Katy; Wolff, Eric; Abram, Nerilie; McConnell, Joseph R.; Mulvaney, Robert; Fleet, Louise</p> <p>2013-04-01</p> <p>MSA (methanesulfonic acid, derived from marine biogenic emissions) concentrations in coastal Antarctic <span class="hlt">ice</span> cores have been suggested to record changes in <span class="hlt">sea</span> <span class="hlt">ice</span> extent of the previous winter over recent decades. Using post-1979 satellite-derived <span class="hlt">sea</span> <span class="hlt">ice</span> and meteorological data, the reliability of MSA as <span class="hlt">sea</span> <span class="hlt">ice</span> proxy has indeed been demonstrated in the Indian Ocean and Bellinghausen <span class="hlt">Sea</span> sectors, but not in the Weddell <span class="hlt">Sea</span> one. Recently, it has also been argued that the <span class="hlt">sea</span> <span class="hlt">ice</span> surface, not open water, is the dominant source of <span class="hlt">sea</span> salt (including Na) over the Antarctic continent. <span class="hlt">Sea</span> salt <span class="hlt">ice</span> core records may thus provide an alternative to MSA for the reconstruction of past <span class="hlt">sea</span> <span class="hlt">ice</span> changes. Using new MSA and Na <span class="hlt">ice</span> core records from two Greenland sites, we here investigate the potential of those two chemical species as indicators of recent <span class="hlt">sea</span> <span class="hlt">ice</span> changes in the Arctic sector.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26032320','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26032320"><span>Recent changes in Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Turner, John; Hosking, J Scott; Bracegirdle, Thomas J; Marshall, Gareth J; Phillips, Tony</p> <p>2015-07-13</p> <p>In contrast to the Arctic, total <span class="hlt">sea</span> <span class="hlt">ice</span> extent (SIE) across the Southern Ocean has increased since the late 1970s, with the annual mean increasing at a rate of 186×10(3) km(2) per decade (1.5% per decade; p<0.01) for 1979-2013. However, this overall increase masks larger regional variations, most notably an increase (decrease) over the Ross (Amundsen-Bellingshausen) <span class="hlt">Sea</span>. <span class="hlt">Sea</span> <span class="hlt">ice</span> variability results from changes in atmospheric and oceanic conditions, although the former is thought to be more significant, since there is a high correlation between anomalies in the <span class="hlt">ice</span> concentration and the near-surface wind field. The Southern Ocean SIE trend is dominated by the increase in the Ross <span class="hlt">Sea</span> sector, where the SIE is significantly correlated with the depth of the Amundsen <span class="hlt">Sea</span> Low (ASL), which has deepened since 1979. The depth of the ASL is influenced by a number of external factors, including tropical <span class="hlt">sea</span> surface temperatures, but the low also has a large locally driven intrinsic variability, suggesting that SIE in these areas is especially variable. Many of the current generation of coupled climate models have difficulty in simulating <span class="hlt">sea</span> <span class="hlt">ice</span>. However, output from the better-performing IPCC CMIP5 models suggests that the recent increase in Antarctic SIE may be within the bounds of intrinsic/internal variability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015GMDD....810305Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015GMDD....810305Y"><span>Improving the WRF model's simulation over <span class="hlt">sea</span> <span class="hlt">ice</span> surface through coupling with a complex thermodynamic <span class="hlt">sea</span> <span class="hlt">ice</span> model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yao, Y.; Huang, J.; Luo, Y.; Zhao, Z.</p> <p>2015-12-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> plays an important role in the air-<span class="hlt">ice</span>-ocean interaction, but it is often represented simply in many regional atmospheric models. The Noah <span class="hlt">sea</span> <span class="hlt">ice</span> model, which has been widely used in the Weather Research and Forecasting (WRF) model, exhibits cold bias in simulating the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> temperature when validated against the Surface Heat Budget of the Arctic Ocean (SHEBA) in situ observations. According to sensitivity tests, this bias is attributed not only to the simulation of snow depth and turbulent fluxes but also to the heat conduction within snow and <span class="hlt">ice</span>. Compared with the Noah <span class="hlt">sea</span> <span class="hlt">ice</span> model, the high-resolution thermodynamic snow and <span class="hlt">ice</span> model (HIGHTSI) has smaller bias in simulating the <span class="hlt">sea</span> <span class="hlt">ice</span> temperature. HIGHTSI is further coupled with the WRF model to evaluate the possible added value from better resolving the heat transport and solar penetration in <span class="hlt">sea</span> <span class="hlt">ice</span> from a complex thermodynamic <span class="hlt">sea</span> <span class="hlt">ice</span> model. The cold bias in simulating the surface temperature over <span class="hlt">sea</span> <span class="hlt">ice</span> in winter by the original Polar WRF is reduced when HIGHTSI rather than Noah is coupled with the WRF model, and this also leads to a better representation of surface upward longwave radiation and 2 m air temperature. A discussion on the impact of specifying <span class="hlt">sea</span> <span class="hlt">ice</span> thickness in the WRF model is presented. Consistent with previous research, prescribing the <span class="hlt">sea</span> <span class="hlt">ice</span> thickness with observational information would result in the best simulation among the available methods. If no observational information is available, using an empirical method based on the relationship between <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and <span class="hlt">sea</span> <span class="hlt">ice</span> thickness could mimic the large-scale spatial feature of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness. The potential application of a thermodynamic <span class="hlt">sea</span> <span class="hlt">ice</span> model in predicting the change in <span class="hlt">sea</span> <span class="hlt">ice</span> thickness in a RCM is limited by the lack of <span class="hlt">sea</span> <span class="hlt">ice</span> dynamic processes in the model and the coarse assumption on the initial value of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060039967&hterms=sonar&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dsonar','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060039967&hterms=sonar&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dsonar"><span>Fram Strait <span class="hlt">sea</span> <span class="hlt">ice</span> outflow</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kwok, R.; Cunningham, G. F.; Pang, S. S.</p> <p>2004-01-01</p> <p>We summarize 24 years of <span class="hlt">ice</span> export estimates and examine, over a 9-year record, the associated variability in the time-varying upward-looking sonar (ULS) thickness distributions of the Fram Strait. A more thorough assessment of the PMW (passive microwave) <span class="hlt">ice</span> motion with 5 years of synthetic aperture radar (SAR)observations shows the uncertainties to be consistent with that found by Kwok and Rothrock [1999], giving greater confidence to the record of <span class="hlt">ice</span> flux calculations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20080018456&hterms=secret&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dsecret','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20080018456&hterms=secret&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dsecret"><span>The Secret of the Svalbard <span class="hlt">Sea</span> <span class="hlt">Ice</span> Barrier</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Nghiem, Son V.; Van Woert, Michael L.; Neumann, Gregory</p> <p>2004-01-01</p> <p>An elongated <span class="hlt">sea</span> <span class="hlt">ice</span> feature called the Svalbard <span class="hlt">sea</span> <span class="hlt">ice</span> barrier rapidly formed over an area in the Barents <span class="hlt">Sea</span> to the east of Svalbard posing navigation hazards. The secret of its formation lies in the bottom bathymetry that governs the distribution of cold Arctic waters masses, which impacts <span class="hlt">sea</span> <span class="hlt">ice</span> growth on the water surface.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C23D..01R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C23D..01R"><span><span class="hlt">Ice</span> sheet systems and <span class="hlt">sea</span> level change.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rignot, E. J.</p> <p>2015-12-01</p> <p>Modern views of <span class="hlt">ice</span> sheets provided by satellites, airborne surveys, in situ data and paleoclimate records while transformative of glaciology have not fundamentally changed concerns about <span class="hlt">ice</span> sheet stability and collapse that emerged in the 1970's. Motivated by the desire to learn more about <span class="hlt">ice</span> sheets using new technologies, we stumbled on an unexplored field of science and witnessed surprising changes before realizing that most were coming too fast, soon and large. <span class="hlt">Ice</span> sheets are integrant part of the Earth system; they interact vigorously with the atmosphere and the oceans, yet most of this interaction is not part of current global climate models. Since we have never witnessed the collapse of a marine <span class="hlt">ice</span> sheet, observations and exploration remain critical sentinels. At present, these observations suggest that Antarctica and Greenland have been launched into a path of multi-meter <span class="hlt">sea</span> level rise caused by rapid climate warming. While the current loss of <span class="hlt">ice</span> sheet mass to the ocean remains a trickle, every mm of <span class="hlt">sea</span> level change will take centuries of climate reversal to get back, several major marine-terminating sectors have been pushed out of equilibrium, and <span class="hlt">ice</span> shelves are irremediably being lost. As glaciers retreat from their salty, warm, oceanic margins, they will melt away and retreat slower, but concerns remain about <span class="hlt">sea</span> level change from vastly marine-based sectors: 2-m <span class="hlt">sea</span> level equivalent in Greenland and 23-m in Antarctica. Significant changes affect 2/4 marine-based sectors in Greenland - Jakobshavn Isb. and the northeast stream - with Petermann Gl. not far behind. Major changes have affected the Amundsen <span class="hlt">Sea</span> sector of West Antarctica since the 1980s. Smaller yet significant changes affect the marine-based Wilkes Land sector of East Antarctica, a reminder that not all marine-based <span class="hlt">ice</span> is in West Antarctica. Major advances in reducing uncertainties in <span class="hlt">sea</span> level projections will require massive, interdisciplinary efforts that are not currently in place</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19980021232','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19980021232"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> on the Southern Ocean</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jacobs, Stanley S.</p> <p>1998-01-01</p> <p>Year-round satellite records of <span class="hlt">sea</span> <span class="hlt">ice</span> distribution now extend over more than two decades, providing a valuable tool to investigate related characteristics and circulations in the Southern Ocean. We have studied a variety of features indicative of oceanic and atmospheric interactions with Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span>. In the Amundsen & Bellingshausen <span class="hlt">Seas</span>, <span class="hlt">sea</span> <span class="hlt">ice</span> extent was found to have decreased by approximately 20% from 1973 through the early 1990's. This change coincided with and probably contributed to recently warmer surface conditions on the west side of the Antarctic Peninsula, where air temperatures have increased by approximately 0.5 C/decade since the mid-1940's. The <span class="hlt">sea</span> <span class="hlt">ice</span> decline included multiyear cycles of several years in length superimposed on high interannual variability. The retreat was strongest in summer, and would have lowered the regional mean <span class="hlt">ice</span> thickness, with attendant impacts upon vertical heat flux and the formation of snow <span class="hlt">ice</span> and brine. The cause of the regional warming and loss of <span class="hlt">sea</span> <span class="hlt">ice</span> is believed to be linked to large-scale circulation changes in the atmosphere and ocean. At the eastern end of the Weddell Gyre, the Cosmonaut Polyna revealed greater activity since 1986, a recurrence pattern during recent winters and two possible modes of formation. Persistence in polynya location was noted off Cape Ann, where the coastal current can interact more strongly with the Antarctic Circumpolar Current. As a result of vorticity conservation, locally enhanced upwelling brings warmer deep water into the mixed layer, causing divergence and melting. In the Ross <span class="hlt">Sea</span>, <span class="hlt">ice</span> extent fluctuates over periods of several years, with summer minima and winter maxima roughly in phase. This leads to large interannual cycles of <span class="hlt">sea</span> <span class="hlt">ice</span> range, which correlate positively with meridinal winds, regional air temperatures and subsequent shelf water salinities. Deep shelf waters display considerable interannual variability, but have freshened by approximately 0.03/decade</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMNG41B..01M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMNG41B..01M"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> : Trends, Stability and Variability</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moon, W.; Wettlaufer, J. S.</p> <p>2014-12-01</p> <p>A stochastic Arctic <span class="hlt">sea-ice</span> model is derived and analysed in detail to interpret the recent decay and associated variability of Arctic <span class="hlt">sea-ice</span> under changes in radiative forcing. The approach begins from a deterministic model of the heat flux balance through the air/<span class="hlt">sea/ice</span> system, which uses observed monthly-averaged heat fluxesto drive a time evolution of <span class="hlt">sea-ice</span> thickness. This model reproduces the observed seasonal cycle of the <span class="hlt">ice</span> cover and it is to this that stochastic noise--representing high frequency variability--is introduced.The model takes the form of a single periodic non-autonomous stochastic ordinary differential equation. The value of such a model is that it provides a relatively simple framework to examine the role of noise in the basic nonlinear interactions at play as transitions in the state of the <span class="hlt">ice</span> cover (e.g., from perennial to seasonal) are approached. Moreover, the stability and the noise conspire to underlie the inter annual variability and how that variability changes as one approaches the deterministic bifurcations in the system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820009687','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820009687"><span>An optical model for the microwave properties of <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Gloersen, P.; Larabee, J. K.</p> <p>1981-01-01</p> <p>The complex refractive index of <span class="hlt">sea</span> <span class="hlt">ice</span> is modeled and used to predict the microwave signatures of various <span class="hlt">sea</span> <span class="hlt">ice</span> types. Results are shown to correspond well with the observed values of the complex index inferred from dielectic constant and dielectric loss measurements performed in the field, and with observed microwave signatures of <span class="hlt">sea</span> <span class="hlt">ice</span>. The success of this modeling procedure vis a vis modeling of the dielectric properties of <span class="hlt">sea</span> <span class="hlt">ice</span> constituents used earlier by several others is explained. Multiple layer radiative transfer calculations are used to predict the microwave properties of first-year <span class="hlt">sea</span> <span class="hlt">ice</span> with and without snow, and multiyear <span class="hlt">sea</span> <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120010345','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120010345"><span>Laser Altimetry Sampling Strategies over <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Farrell, Sinead L.; Markus, Thorsten; Kwok, Ron; Connor, Laurence</p> <p>2011-01-01</p> <p>With the conclusion of the science phase of the <span class="hlt">Ice</span>, Cloud and land Elevation Satellite (ICESat) mission in late 2009, and the planned launch of ICESat-2 in late 2015, NASA has recently established the <span class="hlt">Ice</span>Bridge program to provide continuity between missions. A major goal of <span class="hlt">Ice</span>Bridge is to obtain a <span class="hlt">sea-ice</span> thickness time series via airborne surveys over the Arctic and Southern Oceans. Typically two laser altimeters, the Airborne Topographic Mapper (ATM) and the Land, Vegetation and <span class="hlt">Ice</span> Sensor (LVIS), are utilized during <span class="hlt">Ice</span>Bridge flights. Using laser altimetry simulations of conventional analogue systems such as ICESat, LVIS and ATM, with the multi-beam system proposed for ICESat-2, we investigate differences in measurements gathered at varying spatial resolutions and the impact on <span class="hlt">sea-ice</span> freeboard. We assess the ability of each system to reproduce the elevation distributions of two seaice models and discuss potential biases in lead detection and <span class="hlt">sea</span>-surface elevation, arising from variable footprint size and spacing. The conventional systems accurately reproduce mean freeboard over 25km length scales, while ICESat-2 offers considerable improvements over its predecessor ICESat. In particular, its dense along-track sampling of the surface will allow flexibility in the algorithmic approaches taken to optimize the signal-to-noise ratio for accurate and precise freeboard retrieval.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUFM.C52A..01C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.C52A..01C"><span>Global <span class="hlt">Sea</span> <span class="hlt">Ice</span> Charting at the National <span class="hlt">Ice</span> Center</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Clemente-Colon, P.</p> <p>2006-12-01</p> <p>The National <span class="hlt">Ice</span> Center (NIC) is a U.S. government tri-agency operational center comprised of components from the United States Navy, the National Oceanic and Atmospheric Administration (NOAA), and the U. S. Coast Guard (USCG). The mission of the NIC is to provide the highest quality strategic and tactical <span class="hlt">ice</span> services tailored to meet operational requirements of U.S. national interests. This includes broad responsibilities to monitor all frozen ocean regions of the world in support of coastal and marine <span class="hlt">sea</span> <span class="hlt">ice</span> operations and research. <span class="hlt">Sea</span> <span class="hlt">ice</span> conditions are routinely monitored and mapped using satellite imagery along with ancillary model and in-situ data. Active microwave images from Synthetic Aperture Radar (SAR) sensors are the data of choice for NIC analysts because of their high spatial resolution (~100 m). SAR is in fact the primary data source for <span class="hlt">ice</span> analysis when available. The high spatial resolution of available SAR data and the reliability shown by the RADARSAT- 1 mission in particular have made the use of these data critical for vessels operating in or near the <span class="hlt">ice</span>. Limited data from the ESA Envisat Advanced SAR (ASAR) are also used in the analyses when available. Preparations for the use of the Phased Array type L-band SAR (PALSAR) aboard the soon to be launched Japanese ALOS satellite are also underway. Scatterometer backscatter imagery from QuikSCAT is also routinely used for basin-scale and circumpolar <span class="hlt">ice</span> edge mapping. Automated algorithms for <span class="hlt">ice</span> type and melt ponds detection as well as the synergy between these observations and the QuikSCAT wind vectors off the marginal <span class="hlt">ice</span> zone (MIZ) are been explored. ESA Envisat Advanced SAR (ASAR) Global Monitoring Mode (GMM) mosaics of the Arctic and Antarctic regions are becoming an important tool for <span class="hlt">sea</span> <span class="hlt">ice</span> edge delineation too. Although SAR observations are the choice for NIC analysts to produce high spatial resolution products gear toward tactical support, passive microwave data such as those from the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC12A..01S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC12A..01S"><span>Towards Improving <span class="hlt">Sea</span> <span class="hlt">Ice</span> Predictabiity: Evaluating Climate Models Against Satellite <span class="hlt">Sea</span> <span class="hlt">Ice</span> Observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stroeve, J. C.</p> <p>2014-12-01</p> <p>The last four decades have seen a remarkable decline in the spatial extent of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover, presenting both challenges and opportunities to Arctic residents, government agencies and industry. After the record low extent in September 2007 effort has increased to improve seasonal, decadal-scale and longer-term predictions of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover. Coupled global climate models (GCMs) consistently project that if greenhouse gas concentrations continue to rise, the eventual outcome will be a complete loss of the multiyear <span class="hlt">ice</span> cover. However, confidence in these projections depends o HoHoweon the models ability to reproduce features of the present-day climate. Comparison between models participating in the World Climate Research Programme Coupled Model Intercomparison Project Phase 5 (CMIP5) and observations of <span class="hlt">sea</span> <span class="hlt">ice</span> extent and thickness show that (1) historical trends from 85% of the model ensemble members remain smaller than observed, and (2) spatial patterns of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness are poorly represented in most models. Part of the explanation lies with a failure of models to represent details of the mean atmospheric circulation pattern that governs the transport and spatial distribution of <span class="hlt">sea</span> <span class="hlt">ice</span>. These results raise concerns regarding the ability of CMIP5 models to realistically represent the processes driving the decline of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> and to project the timing of when a seasonally <span class="hlt">ice</span>-free Arctic may be realized. On shorter time-scales, seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> prediction has been challenged to predict the <span class="hlt">sea</span> <span class="hlt">ice</span> extent from Arctic conditions a few months to a year in advance. Efforts such as the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Outlook (SIO) project, originally organized through the Study of Environmental Change (SEARCH) and now managed by the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Prediction Network project (SIPN) synthesize predictions of the September <span class="hlt">sea</span> <span class="hlt">ice</span> extent based on a variety of approaches, including heuristic, statistical and dynamical modeling. Analysis of SIO contributions reveals that when the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1816267T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1816267T"><span>Constraining the parameters of the EAP <span class="hlt">sea</span> <span class="hlt">ice</span> rheology from satellite observations and discrete element model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tsamados, Michel; Heorton, Harry; Feltham, Daniel; Muir, Alan; Baker, Steven</p> <p>2016-04-01</p> <p>The new elastic-plastic anisotropic (EAP) rheology that explicitly accounts for the sub-continuum anisotropy of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover has been implemented into the latest version of the Los <span class="hlt">Alamos</span> <span class="hlt">sea</span> <span class="hlt">ice</span> model CICE. The EAP rheology is widely used in the climate modeling scientific community (i.e. CPOM stand alone, RASM high resolution regional <span class="hlt">ice</span>-ocean model, MetOffice fully coupled model). Early results from sensitivity studies (Tsamados et al, 2013) have shown the potential for an improved representation of the observed main <span class="hlt">sea</span> <span class="hlt">ice</span> characteristics with a substantial change of the spatial distribution of <span class="hlt">ice</span> thickness and <span class="hlt">ice</span> drift relative to model runs with the reference visco-plastic (VP) rheology. The model contains one new prognostic variable, the local structure tensor, which quantifies the degree of anisotropy of the <span class="hlt">sea</span> <span class="hlt">ice</span>, and two parameters that set the time scale of the evolution of this tensor. Observations from high resolution satellite SAR imagery as well as numerical simulation results from a discrete element model (DEM, see Wilchinsky, 2010) have shown that these individual floes can organize under external wind and thermal forcing to form an emergent isotropic <span class="hlt">sea</span> <span class="hlt">ice</span> state (via thermodynamic healing, thermal cracking) or an anisotropic <span class="hlt">sea</span> <span class="hlt">ice</span> state (via Coulombic failure lines due to shear rupture). In this work we use for the first time in the context of <span class="hlt">sea</span> <span class="hlt">ice</span> research a mathematical metric, the Tensorial Minkowski functionals (Schroeder-Turk, 2010), to measure quantitatively the degree of anisotropy and alignment of the <span class="hlt">sea</span> <span class="hlt">ice</span> at different scales. We apply the methodology on the Glob<span class="hlt">ICE</span> Envisat satellite deformation product (www.globice.info), on a prototype modified version of Glob<span class="hlt">ICE</span> applied on Sentinel-1 Synthetic Aperture Radar (SAR) imagery and on the DEM <span class="hlt">ice</span> floe aggregates. By comparing these independent measurements of the <span class="hlt">sea</span> <span class="hlt">ice</span> anisotropy as well as its temporal evolution against the EAP model we are able to constrain the</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_6 --> <div id="page_7" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="121"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=GL-2002-002288&hterms=hot+ice&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dhot%2Bice','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=GL-2002-002288&hterms=hot+ice&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dhot%2Bice"><span><span class="hlt">Ice</span> in Caspian <span class="hlt">Sea</span> and Aral <span class="hlt">Sea</span>, Kazakhstan</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>2002-01-01</p> <p>In this MODIS image from December 3, 2001, winter <span class="hlt">sea</span> <span class="hlt">ice</span> can be seen forming in the shallow waters of the northern Caspian (left) and Aral (upper right) <span class="hlt">Seas</span>. Despite the inflow of the Volga River (upper left), the northern portion of the Caspian <span class="hlt">Sea</span> averages only 17 ft in depth, and responds to the region's continental climate, which is cold in winter and hot and dry in the summer. The southern part of the <span class="hlt">Sea</span> is deeper and remains <span class="hlt">ice</span>-free throughout the winter. The dirty appearance of the <span class="hlt">ice</span> may be due to sediment in the water, but may also be due to wind-driven dust. The wind in the region can blow at hurricane-force strength and can cause the <span class="hlt">ice</span> to pile up in hummocks that are anchored to the <span class="hlt">sea</span> bottom. The eastern portion of the Aral <span class="hlt">Sea</span> is also beginning to freeze. At least two characteristics of the Aral <span class="hlt">Sea</span> 'compete' in determining whether its waters will freeze. The <span class="hlt">Sea</span> is shallow, which increases the likelihood of freezing, but it is also very salty, which means that lower temperatures are required to freeze it than would be required for fresh water. With average December temperatures of 18o F, it's clearly cold enough to allow <span class="hlt">ice</span> to form. As the waters that feed the Aral <span class="hlt">Sea</span> continue to be diverted for agriculture, the <span class="hlt">Sea</span> becomes shallower and the regional climate becomes even more continental. This is because large bodies of water absorb and retain heat, moderating seasonal changes in temperature. Credit: Jacques Descloitres, MODIS Land Rapid Response Team, NASA/GSFC</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22715789','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22715789"><span>[Spectral features analysis of <span class="hlt">sea</span> <span class="hlt">ice</span> in the Arctic Ocean].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Ke, Chang-qing; Xie, Hong-jie; Lei, Rui-bo; Li, Qun; Sun, Bo</p> <p>2012-04-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> in the Arctic Ocean plays an important role in the global climate change, and its quick change and impact are the scientists' focus all over the world. The spectra of different kinds of <span class="hlt">sea</span> <span class="hlt">ice</span> were measured with portable ASD FieldSpec 3 spectrometer during the long-term <span class="hlt">ice</span> station of the 4th Chinese national Arctic Expedition in 2010, and the spectral features were analyzed systematically. The results indicated that the reflectance of <span class="hlt">sea</span> <span class="hlt">ice</span> covered by snow is the highest one, naked <span class="hlt">sea</span> <span class="hlt">ice</span> the second, and melted <span class="hlt">sea</span> <span class="hlt">ice</span> the lowest. Peak and valley characteristics of spectrum curves of <span class="hlt">sea</span> <span class="hlt">ice</span> covered by thick snow, thin snow, wet snow and snow crystal are very significant, and the reflectance basically decreases with the wavelength increasing. The rules of reflectance change with wavelength of natural <span class="hlt">sea</span> <span class="hlt">ice</span>, white <span class="hlt">ice</span> and blue <span class="hlt">ice</span> are basically same, the reflectance of them is medium, and that of grey <span class="hlt">ice</span> is far lower than natural <span class="hlt">sea</span> <span class="hlt">ice</span>, white <span class="hlt">ice</span> and blue <span class="hlt">ice</span>. It is very significant for scientific research to analyze the spectral features of <span class="hlt">sea</span> <span class="hlt">ice</span> in the Arctic Ocean and to implement the quantitative remote sensing of <span class="hlt">sea</span> <span class="hlt">ice</span>, and to further analyze its response to the global warming.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=STS045-78-087&hterms=Bristol+Bay&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DBristol%2BBay','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=STS045-78-087&hterms=Bristol+Bay&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DBristol%2BBay"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span>, Bristol Bay, Alaska, USA</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>1992-01-01</p> <p>This north looking view shows the coast of Alaska, north of the Aleutians, and the eastern margin of the Bering <span class="hlt">Sea</span> (58.0N, 159.5W). Bristol Bay is apparent in the foreground and Nunivak Island can be seen just below the Earth's horizon, at a distance of about 300 nautical miles. Similar views, photographed during previous missions, when analyzed with these recent views may yield information about regional <span class="hlt">ice</span> drift and breakup of <span class="hlt">ice</span> packs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C54A..08S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C54A..08S"><span>Tropical pacing of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> increase</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schneider, D. P.</p> <p>2015-12-01</p> <p>One reason why coupled climate model simulations generally do not reproduce the observed increase in Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent may be that their internally generated climate variability does not sync with the observed phases of phenomena like the Pacific Decadal Oscillation (PDO) and ENSO. For example, it is unlikely for a free-running coupled model simulation to capture the shift of the PDO from its positive to negative phase during 1998, and the subsequent ~15 year duration of the negative PDO phase. In previously presented work based on atmospheric models forced by observed tropical SSTs and stratospheric ozone, we demonstrated that tropical variability is key to explaining the wind trends over the Southern Ocean during the past ~35 years, particularly in the Ross, Amundsen and Bellingshausen <span class="hlt">Seas</span>, the regions of the largest trends in <span class="hlt">sea</span> <span class="hlt">ice</span> extent and <span class="hlt">ice</span> season duration. Here, we extend this idea to coupled model simulations with the Community Earth System Model (CESM) in which the evolution of SST anomalies in the central and eastern tropical Pacific is constrained to match the observations. This ensemble of 10 "tropical pacemaker" simulations shows a more realistic evolution of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> anomalies than does its unconstrained counterpart, the CESM Large Ensemble (both sets of runs include stratospheric ozone depletion and other time-dependent radiative forcings). In particular, the pacemaker runs show that increased <span class="hlt">sea</span> <span class="hlt">ice</span> in the eastern Ross <span class="hlt">Sea</span> is associated with a deeper Amundsen <span class="hlt">Sea</span> Low (ASL) and stronger westerlies over the south Pacific. These circulation patterns in turn are linked with the negative phase of the PDO, characterized by negative SST anomalies in the central and eastern Pacific. The timing of tropical decadal variability with respect to ozone depletion further suggests a strong role for tropical variability in the recent acceleration of the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> trend, as ozone depletion stabilized by late 1990s, prior to the most</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA131852','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA131852"><span>Mechanical Behavior of <span class="hlt">Sea</span> <span class="hlt">Ice</span>,</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>1983-06-01</p> <p>Schwarz et al . 1981), but these may change as techniques and equipment improve. Uniaxial compression Uniaxial compression is a much-maligned test which...be I to 2 times the <span class="hlt">ice</span> thickness (Schwarz et al . 1981). A beam breaks when the <span class="hlt">ice</span> fails in tension at the con,,ex surface, and the resulting crack...then "effective moduli" cease to have much relevance. However, If the 53 10I A1. Traetteberg et . [(1975) Ids Non-saline <span class="hlt">Ice</span>,-lOCJA2. Kovacs et . al</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C11B0364K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C11B0364K"><span>The variability of <span class="hlt">sea</span> <span class="hlt">ice</span> motion in Antarctic</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kim, C. S.; Kim, T. W.; Kim, H. C.; Lee, S.</p> <p>2014-12-01</p> <p>As well known, <span class="hlt">sea</span> <span class="hlt">ice</span> is a vital component in the marginal <span class="hlt">ice</span> zone as well as the global climate system. Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> is reported to be sensitive to surface wind forcing. We used a simplified linear formula to understand the relationship between the <span class="hlt">ice</span> motion and wind as Kimura (2004). These previous study was evaluated relationship using speed reduction factor and turning angle in the Southern Ocean. We use the two types gridded daily <span class="hlt">sea</span> <span class="hlt">ice</span> products by the National Snow and <span class="hlt">Ice</span> Data Center (NSIDC) ; <span class="hlt">Sea</span> <span class="hlt">Ice</span> Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data and <span class="hlt">sea</span> <span class="hlt">ice</span> motion from Polar Pathfinder Daily 25 km EASE-Grid <span class="hlt">Sea</span> <span class="hlt">Ice</span> Motion Vectors. Satellite-observed <span class="hlt">sea</span> <span class="hlt">ice</span> data was compared with ERA interim reanalysis wind data. In this study, we evaluate the variability of the <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and motion in the Southern Ocean in order to investigate the effects of wind on the spatial and temporal variability of the <span class="hlt">sea</span> <span class="hlt">ice</span> motion. Moreover, we need to know the change in the <span class="hlt">sea</span> <span class="hlt">ice</span> motion in accordance with the <span class="hlt">sea</span> <span class="hlt">ice</span> characteristics in Antarctic.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060039893&hterms=sonar&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dsonar','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060039893&hterms=sonar&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dsonar"><span>Fram Strait <span class="hlt">sea</span> <span class="hlt">ice</span> outflow</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kwok, R.; Cunningham, G. F.; Pang, S. S.</p> <p>2004-01-01</p> <p>We summarize 24 years (1978??2) of <span class="hlt">ice</span> export estimates and examine, over a 9-year record, the associated variability in the time-varying upward-looking sonar (ULS) thickness distributions of the Fram Strait.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GeoRL..4312475K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GeoRL..4312475K"><span>Influence of <span class="hlt">sea</span> <span class="hlt">ice</span> on ocean water vapor isotopes and Greenland <span class="hlt">ice</span> core records</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Klein, Eric S.; Welker, Jeffrey M.</p> <p>2016-12-01</p> <p>A warming climate results in <span class="hlt">sea</span> <span class="hlt">ice</span> loss and impacts to the Arctic water cycle. The water isotope parameter deuterium excess, a moisture source proxy, can serve as a tracer to help understand hydrological changes due to <span class="hlt">sea</span> <span class="hlt">ice</span> loss. However, unlocking the <span class="hlt">sea</span> <span class="hlt">ice</span> change signal of isotopes from <span class="hlt">ice</span> cores requires understanding how <span class="hlt">sea</span> <span class="hlt">ice</span> changes impact deuterium excess, which is unknown. Here we present the first isotope data linking a gradient of <span class="hlt">sea</span> <span class="hlt">ice</span> extents to oceanic water vapor deuterium excess values. Initial loss of <span class="hlt">sea</span> <span class="hlt">ice</span> extent leads to lower deuterium excess moisture sources, and then values progressively increase with further <span class="hlt">ice</span> loss. Our new process-based interpretation suggests that past rapid (1-3 years) Greenland <span class="hlt">ice</span> core changes in deuterium excess during warming might not be the result of abrupt atmospheric circulation shifts, but rather gradual loss of <span class="hlt">sea</span> <span class="hlt">ice</span> extent at northern latitude moisture sources.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.youtube.com/watch?v=DYLbDb8Y8Tw','SCIGOVIMAGE-NASA'); return false;" href="http://www.youtube.com/watch?v=DYLbDb8Y8Tw"><span>2008 Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> from AMSR-E</span></a></p> <p><a target="_blank" href="http://www.nasa.gov/multimedia/videogallery/index.html">NASA Video Gallery</a></p> <p></p> <p></p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> is frozen seawater floating on the surface of the ocean. Some <span class="hlt">sea</span> <span class="hlt">ice</span> is semi-permanent, persisting from year to year, and some is seasonal, melting and refreezing from season to season. Th...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.youtube.com/watch?v=A561WmydceE','SCIGOVIMAGE-NASA'); return false;" href="http://www.youtube.com/watch?v=A561WmydceE"><span>Early 2016 Winter Storm Melts Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://www.nasa.gov/multimedia/videogallery/index.html">NASA Video Gallery</a></p> <p></p> <p></p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> grows during the winter months, reaching its largest extent sometime in March. When something disrupts the cold, dry, winter Arctic atmosphere, <span class="hlt">sea</span> <span class="hlt">ice</span> can feel the effects, and thes...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=PIA02971&hterms=arctic+weather&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Darctic%2Bweather','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=PIA02971&hterms=arctic+weather&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Darctic%2Bweather"><span>Comparative Views of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Growth</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>2000-01-01</p> <p>NASA researchers have new insights into the mysteries of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>, thanks to the unique abilities of Canada's Radarsat satellite. The Arctic is the smallest of the world's four oceans, but it may play a large role in helping scientists monitor Earth's climate shifts.<p/>Using Radarsat's special sensors to take images at night and to peer through clouds, NASA researchers can now see the complete <span class="hlt">ice</span> cover of the Arctic. This allows tracking of any shifts and changes, in unprecedented detail, over the course of an entire winter. The radar-generated, high-resolution images are up to 100 times better than those taken by previous satellites.<p/>The two images above are separated by nine days (earlier image on the left). Both images represent an area (approximately 96 by 128 kilometers; 60 by 80 miles)located in the Baufort <span class="hlt">Sea</span>, north of the Alaskan coast. The brighter features are older thicker <span class="hlt">ice</span> and the darker areas show young, recently formed <span class="hlt">ice</span>. Within the nine-day span, large and extensive cracks in the <span class="hlt">ice</span> cover have formed due to <span class="hlt">ice</span> movement. These cracks expose the open ocean to the cold, frigid atmosphere where <span class="hlt">sea</span> <span class="hlt">ice</span> grows rapidly and thickens.<p/>Using this new information, scientists at NASA's Jet Propulsion Laboratory (JPL), Pasadena, Calif., can generate comprehensive maps of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> thickness for the first time. 'Before we knew only the extent of the <span class="hlt">ice</span> cover,' said Dr. Ronald Kwok, JPL principal investigator of a project called <span class="hlt">Sea</span> <span class="hlt">Ice</span> Thickness Derived From High Resolution Radar Imagery. 'We also knew that the <span class="hlt">sea</span> <span class="hlt">ice</span> extent had decreased over the last 20 years, but we knew very little about <span class="hlt">ice</span> thickness.'<p/>'Since <span class="hlt">sea</span> <span class="hlt">ice</span> is very thin, about 3 meters (10 feet) or less,'Kwok explained, 'it is very sensitive to climate change.'<p/>Until now, observations of polar <span class="hlt">sea</span> <span class="hlt">ice</span> thickness have been available for specific areas, but not for the entire polar region.<p/>The new radar mapping technique has also given scientists a close look at</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GeoRL..43.5309T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GeoRL..43.5309T"><span><span class="hlt">Ice</span> core reconstruction of <span class="hlt">sea</span> <span class="hlt">ice</span> change in the Amundsen-Ross <span class="hlt">Seas</span> since 1702 A.D.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thomas, Elizabeth R.; Abram, Nerilie J.</p> <p>2016-05-01</p> <p>Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> has been increasing in recent decades, but with strong regional differences in the expression of <span class="hlt">sea</span> <span class="hlt">ice</span> change. Declining <span class="hlt">sea</span> <span class="hlt">ice</span> in the Bellingshausen <span class="hlt">Sea</span> since 1979 (the satellite era) has been linked to the observed warming on the Antarctic Peninsula, while the Ross <span class="hlt">Sea</span> sector has seen a marked increase in <span class="hlt">sea</span> <span class="hlt">ice</span> during this period. Here we present a 308 year record of methansulphonic acid from coastal West Antarctica, representing <span class="hlt">sea</span> <span class="hlt">ice</span> conditions in the Amundsen-Ross <span class="hlt">Sea</span>. We demonstrate that the recent increase in <span class="hlt">sea</span> <span class="hlt">ice</span> in this region is part of a longer trend, with an estimated ~1° northward expansion in winter <span class="hlt">sea</span> <span class="hlt">ice</span> extent (SIE) during the twentieth century and a total expansion of ~1.3° since 1702. The greatest reconstructed SIE occurred during the mid-1990s, with five of the past 30 years considered exceptional in the context of the past three centuries.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009JGRC..114.8015G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009JGRC..114.8015G"><span>Evaluation of high-resolution <span class="hlt">sea</span> <span class="hlt">ice</span> models on the basis of statistical and scaling properties of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> drift and deformation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Girard, L.; Weiss, J.; Molines, J. M.; Barnier, B.; Bouillon, S.</p> <p>2009-08-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> drift and deformation from models are evaluated on the basis of statistical and scaling properties. These properties are derived from two observation data sets: the RADARSAT Geophysical Processor System (RGPS) and buoy trajectories from the International Arctic Buoy Program (IABP). Two simulations obtained with the Louvain-la-Neuve <span class="hlt">Ice</span> Model (LIM) coupled to a high-resolution ocean model and a simulation obtained with the Los <span class="hlt">Alamos</span> <span class="hlt">Sea</span> <span class="hlt">Ice</span> Model (CICE) were analyzed. Model <span class="hlt">ice</span> drift compares well with observations in terms of large-scale velocity field and distributions of velocity fluctuations although a significant bias on the mean <span class="hlt">ice</span> speed is noted. On the other hand, the statistical properties of <span class="hlt">ice</span> deformation are not well simulated by the models: (1) The distributions of strain rates are incorrect: RGPS distributions of strain rates are power law tailed, i.e., exhibit "wild randomness," whereas models distributions remain in the Gaussian attraction basin, i.e., exhibit "mild randomness." (2) The models are unable to reproduce the spatial and temporal correlations of the deformation fields: In the observations, <span class="hlt">ice</span> deformation follows spatial and temporal scaling laws that express the heterogeneity and the intermittency of deformation. These relations do not appear in simulated <span class="hlt">ice</span> deformation. Mean deformation in models is almost scale independent. The statistical properties of <span class="hlt">ice</span> deformation are a signature of the <span class="hlt">ice</span> mechanical behavior. The present work therefore suggests that the mechanical framework currently used by models is inappropriate. A different modeling framework based on elastic interactions could improve the representation of the statistical and scaling properties of <span class="hlt">ice</span> deformation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.6827F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.6827F"><span>The ASIBIA <span class="hlt">sea-ice</span> facility: First results from the Atmosphere-<span class="hlt">Sea-Ice</span>-Biogeochemistry in the Arctic chamber</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>France, James L.; Thomas, Max</p> <p>2016-04-01</p> <p>Working in the natural ocean-<span class="hlt">ice</span>-atmosphere system is very difficult, as conducting fieldwork on <span class="hlt">sea-ice</span> presents many challenges <span class="hlt">ice</span> including costs, safety, experimental controls and access. The new ASIBIA (Atmosphere-<span class="hlt">Sea-Ice</span>-Biogeochemistry in the Arctic) coupled Ocean-<span class="hlt">Sea-Ice</span>-(Snow)-Atmosphere chamber facility at the University of East Anglia, UK, we are aiming to perform controlled first-year <span class="hlt">sea-ice</span> investigations in areas such as <span class="hlt">sea-ice</span> physics, physicochemical and biogeochemical processes in <span class="hlt">sea-ice</span> and quantification of the bi-directional flux of gases in various states of first-year <span class="hlt">sea-ice</span> conditions. The facility is a medium sized chamber with programmable temperatures from -55°C to +30°C, allowing a full range of first year <span class="hlt">sea-ice</span> growing conditions in both the Arctic and Antarctic to be simulated. The water depth can be up to 1 m (including up to 25 cm of <span class="hlt">sea-ice</span>) and an optional 1 m tall Teflon film atmosphere on top of the <span class="hlt">sea-ice</span>, thus creating a closed and coupled ocean-<span class="hlt">sea-ice</span>-atmosphere mesocosm. <span class="hlt">Ice</span> growth in the tank is well suited for studying first-year <span class="hlt">sea-ice</span> physical properties, with in-situ <span class="hlt">ice</span>-profile measurements of temperature, salinity, conductivity, pressure and spectral light transmission. Underwater and above <span class="hlt">ice</span> cameras are installed to record the physical development of the <span class="hlt">sea-ice</span>. Here, we present the data from the first suites of experiments in the ASIBIA chamber focussing on <span class="hlt">sea-ice</span> physics and give a brief description of the capabilities of the facility going forward. The ASIBIA chamber was funded as part of an ERC consolidator grant to the late Prof. Roland von Glasow and we hope this work and further development of the facility will act as a lasting legacy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA524685','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA524685"><span>Long-Range Forecasting of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2010-06-01</p> <p>19 1. <span class="hlt">Sea</span> <span class="hlt">Ice</span> Concentrations from Nimbus -7 SMMR and DMSP SSM/I Passive...LEFT BLANK 19 II. DATA AND METHODS A. DATA SETS 1. <span class="hlt">Sea</span> <span class="hlt">Ice</span> Concentrations from Nimbus -7 SMMR and DMSP SSM/I Passive Microwave Data The <span class="hlt">sea</span> <span class="hlt">ice</span>...for Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> research in the past. (e.g., Deser and Teng 2008). The data set is generated from brightness temperature derived from Nimbus -7</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70037527','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70037527"><span>Quaternary <span class="hlt">Sea-ice</span> history in the Arctic Ocean based on a new Ostracode <span class="hlt">sea-ice</span> proxy</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Cronin, T. M.; Gemery, L.; Briggs, W.M.; Jakobsson, M.; Polyak, L.; Brouwers, E.M.</p> <p>2010-01-01</p> <p>Paleo-<span class="hlt">sea-ice</span> history in the Arctic Ocean was reconstructed using the <span class="hlt">sea-ice</span> dwelling ostracode Acetabulastoma arcticum from late Quaternary sediments from the Mendeleyev, Lomonosov, and Gakkel Ridges, the Morris Jesup Rise and the Yermak Plateau. Results suggest intermittently high levels of perennial <span class="hlt">sea</span> <span class="hlt">ice</span> in the central Arctic Ocean during Marine Isotope Stage (MIS) 3 (25-45 ka), minimal <span class="hlt">sea</span> <span class="hlt">ice</span> during the last deglacial (16-11 ka) and early Holocene thermal maximum (11-5 ka) and increasing <span class="hlt">sea</span> <span class="hlt">ice</span> during the mid-to-late Holocene (5-0 ka). Sediment core records from the Iceland and Rockall Plateaus show that perennial <span class="hlt">sea</span> <span class="hlt">ice</span> existed in these regions only during glacial intervals MIS 2, 4, and 6. These results show that <span class="hlt">sea</span> <span class="hlt">ice</span> exhibits complex temporal and spatial variability during different climatic regimes and that the development of modern perennial <span class="hlt">sea</span> <span class="hlt">ice</span> may be a relatively recent phenomenon. ?? 2010.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014GeoRL..41.2411S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014GeoRL..41.2411S"><span>Predicting September <span class="hlt">sea</span> <span class="hlt">ice</span>: Ensemble skill of the SEARCH <span class="hlt">Sea</span> <span class="hlt">Ice</span> Outlook 2008-2013</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stroeve, Julienne; Hamilton, Lawrence C.; Bitz, Cecilia M.; Blanchard-Wrigglesworth, Edward</p> <p>2014-04-01</p> <p>Since 2008, the Study of Environmental Arctic Change <span class="hlt">Sea</span> <span class="hlt">Ice</span> Outlook has solicited predictions of September <span class="hlt">sea-ice</span> extent from the Arctic research community. Individuals and teams employ a variety of modeling, statistical, and heuristic approaches to make these predictions. Viewed as monthly ensembles each with one or two dozen individual predictions, they display a bimodal pattern of success. In years when observed <span class="hlt">ice</span> extent is near its trend, the median predictions tend to be accurate. In years when the observed extent is anomalous, the median and most individual predictions are less accurate. Statistical analysis suggests that year-to-year variability, rather than methods, dominate the variation in ensemble prediction success. Furthermore, ensemble predictions do not improve as the season evolves. We consider the role of initial <span class="hlt">ice</span>, atmosphere and ocean conditions, and summer storms and weather in contributing to the challenge of <span class="hlt">sea-ice</span> prediction.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA480564','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA480564"><span>Navy <span class="hlt">Sea</span> <span class="hlt">Ice</span> Prediction Systems</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2002-01-01</p> <p>ANSI Std Z39-18 45 Oceanography • Vol. 15 • No. 1/2002 part of the International Arctic Buoy Program ( IABP ). These data have been used to support...real-time opera- tions in the Arctic as well as meteorological and oceanographic research of the Arctic basin. More infor- mation on the IABP is...<span class="hlt">ice</span> thickness. Figure 5a represents the observed <span class="hlt">ice</span> motion derived from the available IABP drifting buoys. Figures 5a and 5b show the qualitative</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1993EOSTr..74..121I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1993EOSTr..74..121I"><span>Weddell <span class="hlt">Sea</span> exploration from <span class="hlt">ice</span> station</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ice Station Weddell Group of Principal Investigators; Chief Scientists; Gordon, Arnold L.</p> <p></p> <p>On January 18, 1915, the Endurance and Sir Ernest Shackleton and his crew were stranded in the <span class="hlt">ice</span> of the Weddell <span class="hlt">Sea</span> and began one of the most famous drifts in polar exploration. Shackleton turned a failure into a triumph by leading all of his team to safety [Shackleton, 1919]. The drift track of the Endurance and the <span class="hlt">ice</span> floe occupied by her stranded crew after the ship was lost on November 21, 1915, at 68°38.5‧S and 52°26.5‧W, carried the group along the western rim of the Weddell Gyre, representing a rare human presence in this region of perennial <span class="hlt">sea-ice</span> cover.Seventy-seven years later, in 1992, the first intentional scientific Southern Ocean <span class="hlt">ice</span> drift station, <span class="hlt">Ice</span> Station Weddell-1 (ISW-1), was established in the western Weddell <span class="hlt">Sea</span> by a joint effort of the United States and Russia. ISW-1 followed the track of the Endurance closely (Figure 1) and gathered an impressive array of data in this largely unexplored corner of the Southern Ocean, the western edge of the Weddell Gyre.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1710916P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1710916P"><span>Characterizing <span class="hlt">sea</span> <span class="hlt">ice</span> surface morphology using high-resolution <span class="hlt">Ice</span>Bridge data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Petty, Alek; Farrell, Sinead; Newman, Thomas; Kurtz, Nathan; Richter-Menge, Jacqueline; Tsamados, Michel; Feltham, Daniel</p> <p>2015-04-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> pressure ridges form when <span class="hlt">ice</span> floes collide while drifting under the combined forces of atmospheric drag, oceanic drag and <span class="hlt">ice-ice</span> interaction. <span class="hlt">Sea</span> <span class="hlt">ice</span> ridges, in-turn, affect the resultant form drag on the <span class="hlt">sea</span> <span class="hlt">ice</span> cover and thus impact the fluxes of momentum and heat between the atmosphere and ocean. Here we present initial results of a new <span class="hlt">sea</span> <span class="hlt">ice</span> ridge detection approach that utilizes high resolution, three-dimensional <span class="hlt">ice</span>/snow surface elevation data from the NASA Operation <span class="hlt">Ice</span>Bridge Airborne Topographic Mapper (ATM) laser altimeter merged with coincident high-resolution imagery from the Digital Mapping System (DMS). We derive novel information regarding <span class="hlt">sea</span> <span class="hlt">ice</span> deformation across a variety of <span class="hlt">ice</span> types and regimes. Statistical information regarding <span class="hlt">sea</span> <span class="hlt">ice</span> ridges (height/frequency/orientation) and floe edges (freeboard height) are presented for several <span class="hlt">Ice</span>Bridge flight lines. These novel characterizations of <span class="hlt">sea</span> <span class="hlt">ice</span> surface morphology will be used to validate and inform drag parameterizations in state-of-the-art <span class="hlt">sea</span> <span class="hlt">ice</span> models. Furthermore, they will advance our ability to quantify uncertainties introduced by pressure ridges in the estimation of <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard/thickness from airborne and satellite altimeters.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_7 --> <div id="page_8" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="141"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C21A0302P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C21A0302P"><span>Characterizing <span class="hlt">sea</span> <span class="hlt">ice</span> surface morphology using high-resolution <span class="hlt">Ice</span>Bridge data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Petty, A.; Farrell, S. L.; Newman, T.; Kurtz, N. T.; Richter-Menge, J.; Tsamados, M.; Feltham, D. L.</p> <p>2014-12-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> pressure ridges form when <span class="hlt">ice</span> floes collide while drifting under the combined forces of atmospheric drag, oceanic drag and <span class="hlt">ice-ice</span> interaction. <span class="hlt">Sea</span> <span class="hlt">ice</span> ridges, in-turn, affect the resultant form drag on the <span class="hlt">sea</span> <span class="hlt">ice</span> cover and thus impact the fluxes of momentum and heat between the atmosphere and ocean. Here we present initial results of a new <span class="hlt">sea</span> <span class="hlt">ice</span> ridge detection approach that utilizes high resolution, three-dimensional <span class="hlt">ice</span>/snow surface elevation data from the NASA Operation <span class="hlt">Ice</span>Bridge Airborne Topographic Mapper (ATM) laser altimeter merged with coincident high-resolution imagery from the Digital Mapping System (DMS). We derive novel information regarding <span class="hlt">sea</span> <span class="hlt">ice</span> deformation across a variety of <span class="hlt">ice</span> types and regimes. Statistical information regarding <span class="hlt">sea</span> <span class="hlt">ice</span> ridges (height/frequency/orientation) and floe edges (freeboard height) are presented for several <span class="hlt">Ice</span>Bridge flight lines. These novel characterizations of <span class="hlt">sea</span> <span class="hlt">ice</span> surface morphology will be used to validate and inform drag parameterizations in state-of-the-art <span class="hlt">sea</span> <span class="hlt">ice</span> models. Furthermore, they will advance our ability to quantify uncertainties introduced by pressure ridges in the estimation of <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard/thickness from airborne and satellite altimeters.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA617626','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA617626"><span>Forecasting Future <span class="hlt">Sea</span> <span class="hlt">Ice</span> Conditions: A Lagrangian Approach</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2014-09-30</p> <p>that survives the summer melt season in each of the Arctic peripheral <span class="hlt">seas</span>. The Lagrangian Model is forced with weekly mean satellite-derived <span class="hlt">sea</span>- <span class="hlt">ice</span> ...GCM to drive the Lagrangian code and map the regions for the multi-year <span class="hlt">ice</span> surviving the summer melt in each of the Arctic peripheral <span class="hlt">seas</span> in todays...1995, Emery et al. 1997, Meier et al. 2000, Tschudi et al. 2010) 3- Assess whether the source region of <span class="hlt">sea</span> <span class="hlt">ice</span> melting in peripheral <span class="hlt">seas</span> in the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.C23E0542F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.C23E0542F"><span>Validation and Interpretation of a New <span class="hlt">Sea</span> <span class="hlt">Ice</span> Globice Dataset Using Buoys and the Cice <span class="hlt">Sea</span> <span class="hlt">Ice</span> Model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Flocco, D.; Laxon, S. W.; Feltham, D. L.; Haas, C.</p> <p>2011-12-01</p> <p>The Glob<span class="hlt">Ice</span> project has provided high resolution <span class="hlt">sea</span> <span class="hlt">ice</span> product datasets over the Arctic derived from SAR data in the ESA archive. The products are validated <span class="hlt">sea</span> <span class="hlt">ice</span> motion, deformation and fluxes through straits. Glob<span class="hlt">Ice</span> <span class="hlt">sea</span> <span class="hlt">ice</span> velocities, deformation data and <span class="hlt">sea</span> <span class="hlt">ice</span> concentration have been validated using buoy data provided by the International Arctic Buoy Program (IABP). Over 95% of the Glob<span class="hlt">Ice</span> and buoy data analysed fell within 5 km of each other. The Glob<span class="hlt">Ice</span> Eulerian image pair product showed a high correlation with buoy data. The <span class="hlt">sea</span> <span class="hlt">ice</span> concentration product was compared to SSM/I data. An evaluation of the validity of the Glob<span class="hlt">ICE</span> data will be presented in this work. Glob<span class="hlt">ICE</span> <span class="hlt">sea</span> <span class="hlt">ice</span> velocity and deformation were compared with runs of the CICE <span class="hlt">sea</span> <span class="hlt">ice</span> model: in particular the mass fluxes through the straits were used to investigate the correlation between the winter behaviour of <span class="hlt">sea</span> <span class="hlt">ice</span> and the <span class="hlt">sea</span> <span class="hlt">ice</span> state in the following summer.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1241317','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1241317"><span>Influence of <span class="hlt">Sea</span> <span class="hlt">Ice</span> on Arctic Marine Sulfur Biogeochemistry in the Community Climate System Model</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Deal, Clara; Jin, Meibing</p> <p>2013-06-30</p> <p>Global climate models (GCMs) have not effectively considered how responses of arctic marine ecosystems to a warming climate will influence the global climate system. A key response of arctic marine ecosystems that may substantially influence energy exchange in the Arctic is a change in dimethylsulfide (DMS) emissions, because DMS emissions influence cloud albedo. This response is closely tied to <span class="hlt">sea</span> <span class="hlt">ice</span> through its impacts on marine ecosystem carbon and sulfur cycling, and the <span class="hlt">ice</span>-albedo feedback implicated in accelerated arctic warming. To reduce the uncertainty in predictions from coupled climate simulations, important model components of the climate system, such as feedbacks between arctic marine biogeochemistry and climate, need to be reasonably and realistically modeled. This research first involved model development to improve the representation of marine sulfur biogeochemistry simulations to understand/diagnose the control of <span class="hlt">sea-ice</span>-related processes on the variability of DMS dynamics. This study will help build GCM predictions that quantify the relative current and possible future influences of arctic marine ecosystems on the global climate system. Our overall research objective was to improve arctic marine biogeochemistry in the Community Climate System Model (CCSM, now CESM). Working closely with the Climate Ocean <span class="hlt">Sea</span> <span class="hlt">Ice</span> Model (COSIM) team at Los <span class="hlt">Alamos</span> National Laboratory (LANL), we added 1 <span class="hlt">sea-ice</span> algae and arctic DMS production and related biogeochemistry to the global Parallel Ocean Program model (POP) coupled to the LANL <span class="hlt">sea</span> <span class="hlt">ice</span> model (CICE). Both CICE and POP are core components of CESM. Our specific research objectives were: 1) Develop a state-of-the-art <span class="hlt">ice</span>-ocean DMS model for application in climate models, using observations to constrain the most crucial parameters; 2) Improve the global marine sulfur model used in CESM by including DMS biogeochemistry in the Arctic; and 3) Assess how <span class="hlt">sea</span> <span class="hlt">ice</span> influences DMS dynamics in the arctic marine</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/AD1013697','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD1013697"><span>Early Student Support to Investigate the Role of <span class="hlt">Sea</span> <span class="hlt">Ice</span> Albedo Feedback in <span class="hlt">Sea</span> <span class="hlt">Ice</span> Predictions</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2015-09-30</p> <p>Earth System Model version 1 (CESM1), which can be run in various configurations, such as fully-coupled, with slab-ocean, or <span class="hlt">ice</span>-ocean only. We are...climate feedbacks using ra- diative kernels. J. Climate, 21, 3504–3520. 7 PUBLICATIONS Notz, D. and C.M. Bitz, <span class="hlt">Sea</span> <span class="hlt">Ice</span> in Earth System</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PhDT........69M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PhDT........69M"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span>: Trends, Stability and Variability</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moon, Woosok</p> <p></p> <p>A stochastic Arctic <span class="hlt">sea-ice</span> model is derived and analyzed in detail to interpret the recent decay and associated variability of Arctic <span class="hlt">sea-ice</span> under changes in greenhouse gas forcing widely referred to as global warming. The approach begins from a deterministic model of the heat flux balance through the air/<span class="hlt">sea/ice</span> system, which uses observed monthly-averaged heat fluxes to drive a time evolution of <span class="hlt">sea-ice</span> thickness. This model reproduces the observed seasonal cycle of the <span class="hlt">ice</span> cover and it is to this that stochastic noise---representing high frequency variability---is introduced. The model takes the form of a single periodic non-autonomous stochastic ordinary differential equation. Following an introductory chapter, the two that follow focus principally on the properties of the deterministic model in order to identify the main properties governing the stability of the <span class="hlt">ice</span> cover. In chapter 2 the underlying time-dependent solutions to the deterministic model are analyzed for their stability. It is found that the response time-scale of the system to perturbations is dominated by the destabilizing <span class="hlt">sea-ice</span> albedo feedback, which is operative in the summer, and the stabilizing long wave radiative cooling of the <span class="hlt">ice</span> surface, which is operative in the winter. This basic competition is found throughout the thesis to define the governing dynamics of the system. In particular, as greenhouse gas forcing increases, the <span class="hlt">sea-ice</span> albedo feedback becomes more effective at destabilizing the system. Thus, any projections of the future state of Arctic <span class="hlt">sea-ice</span> will depend sensitively on the treatment of the <span class="hlt">ice</span>-albedo feedback. This in turn implies that the treatment a fractional <span class="hlt">ice</span> cover as the <span class="hlt">ice</span> areal extent changes rapidly, must be handled with the utmost care. In chapter 3, the idea of a two-season model, with just winter and summer, is revisited. By breaking the seasonal cycle up in this manner one can simplify the interpretation of the basic dynamics. Whereas in the fully</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1811195P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1811195P"><span>Recent <span class="hlt">sea-ice</span> reduction and possible causes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Park, Doo-Sun R.</p> <p>2016-04-01</p> <p>Arctic <span class="hlt">sea-ice</span> extent has been rapidly declining since the late 20th century. Given the accelerating rate of the <span class="hlt">sea-ice</span> decline, an <span class="hlt">ice</span>-free Arctic Ocean is expected to occur within this century. This rapid <span class="hlt">sea-ice</span> melting is attributable to various Arctic environmental changes, such as increased downward infrared radiation (IR), <span class="hlt">sea-ice</span> preconditioning, temperate ocean water inflow, and <span class="hlt">sea-ice</span> export. However, their relative contributions are uncertain. Assessing the relative contributions is essential for improving our prediction of the future state of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Most of the previous research had focused on summer <span class="hlt">sea</span> <span class="hlt">ice</span>, which is however sensitive to previous winter <span class="hlt">sea</span> <span class="hlt">ice</span>, suggesting that winter <span class="hlt">sea-ice</span> processes are also important for understanding <span class="hlt">sea-ice</span> variability and its trend. Here we show, for the Arctic winter of 1979-2011, that a positive trend of downward IR accounts for nearly half of the <span class="hlt">sea-ice</span> concentration (SIC) decline. Furthermore, we show that the Arctic downward IR increase is driven by horizontal atmospheric water flux into the Arctic, and not by evaporation from the Arctic Ocean. The rest of the SIC decline likely comes from warm ocean.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28255919','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28255919"><span>Formation of brine channels in <span class="hlt">sea</span> <span class="hlt">ice</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Morawetz, Klaus; Thoms, Silke; Kutschan, Bernd</p> <p>2017-03-01</p> <p>Liquid salty micro-channels (brine) between growing <span class="hlt">ice</span> platelets in <span class="hlt">sea</span> <span class="hlt">ice</span> are an important habitat for CO2-binding microalgaea with great impact on polar ecosystems. The structure formation of <span class="hlt">ice</span> platelets is microscopically described and a phase field model is developed. The pattern formation during solidification of the two-dimensional interstitial liquid is considered by two coupled order parameters, the tetrahedricity as structure of <span class="hlt">ice</span> and the salinity. The coupling and time evolution of these order parameters are described by a consistent set of three model parameters. They determine the velocity of the freezing process and the structure formation, the phase diagram, the super-cooling and super-heating region, and the specific heat. The model is used to calculate the short-time frozen micro-structures. The obtained morphological structure is compared with the vertical brine pore space obtained from X-ray computed tomography.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ClDy..tmp...27C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy..tmp...27C"><span>Intercomparison of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover in global ocean-<span class="hlt">sea</span> <span class="hlt">ice</span> reanalyses from the ORA-IP project</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chevallier, Matthieu; Smith, Gregory C.; Dupont, Frédéric; Lemieux, Jean-François; Forget, Gael; Fujii, Yosuke; Hernandez, Fabrice; Msadek, Rym; Peterson, K. Andrew; Storto, Andrea; Toyoda, Takahiro; Valdivieso, Maria; Vernieres, Guillaume; Zuo, Hao; Balmaseda, Magdalena; Chang, You-Soon; Ferry, Nicolas; Garric, Gilles; Haines, Keith; Keeley, Sarah; Kovach, Robin M.; Kuragano, Tsurane; Masina, Simona; Tang, Yongming; Tsujino, Hiroyuki; Wang, Xiaochun</p> <p>2016-01-01</p> <p>Ocean-<span class="hlt">sea</span> <span class="hlt">ice</span> reanalyses are crucial for assessing the variability and recent trends in the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover. This is especially true for <span class="hlt">sea</span> <span class="hlt">ice</span> volume, as long-term and large scale <span class="hlt">sea</span> <span class="hlt">ice</span> thickness observations are inexistent. Results from the Ocean ReAnalyses Intercomparison Project (ORA-IP) are presented, with a focus on Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> fields reconstructed by state-of-the-art global ocean reanalyses. Differences between the various reanalyses are explored in terms of the effects of data assimilation, model physics and atmospheric forcing on properties of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover, including concentration, thickness, velocity and snow. Amongst the 14 reanalyses studied here, 9 assimilate <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, and none assimilate <span class="hlt">sea</span> <span class="hlt">ice</span> thickness data. The comparison reveals an overall agreement in the reconstructed concentration fields, mainly because of the constraints in surface temperature imposed by direct assimilation of ocean observations, prescribed or assimilated atmospheric forcing and assimilation of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration. However, some spread still exists amongst the reanalyses, due to a variety of factors. In particular, a large spread in <span class="hlt">sea</span> <span class="hlt">ice</span> thickness is found within the ensemble of reanalyses, partially caused by the biases inherited from their <span class="hlt">sea</span> <span class="hlt">ice</span> model components. Biases are also affected by the assimilation of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and the treatment of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness in the data assimilation process. An important outcome of this study is that the spatial distribution of <span class="hlt">ice</span> volume varies widely between products, with no reanalysis standing out as clearly superior as compared to altimetry estimates. The <span class="hlt">ice</span> thickness from systems without assimilation of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration is not worse than that from systems constrained with <span class="hlt">sea</span> <span class="hlt">ice</span> observations. An evaluation of the <span class="hlt">sea</span> <span class="hlt">ice</span> velocity fields reveals that <span class="hlt">ice</span> drifts too fast in most systems. As an ensemble, the ORA-IP reanalyses capture trends in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> area and extent</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/17753773','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/17753773"><span>Ocean circulation: its effects on seasonal <span class="hlt">sea-ice</span> simulations.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hibler, W D; Bryan, K</p> <p>1984-05-04</p> <p>A diagnostic <span class="hlt">ice</span>-ocean model of the Arctic, Greenland, and Norwegian <span class="hlt">seas</span> is constructed and used to examine the role of ocean circulation in seasonal <span class="hlt">sea-ice</span> simulations. The model includes lateral <span class="hlt">ice</span> motion and three-dimensional ocean circulation. The ocean portion of the model is weakly forced by observed temperature and salinity data. Simulation results show that including modeled ocean circulation in seasonal <span class="hlt">sea-ice</span> simulations substantially improves the predicted <span class="hlt">ice</span> drift and <span class="hlt">ice</span> margin location. Simulations that do not include lateral ocean movment predict a much less realistic <span class="hlt">ice</span> edge.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19990064613&hterms=arctic+ice+melt&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Darctic%2Bice%2Bmelt','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990064613&hterms=arctic+ice+melt&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Darctic%2Bice%2Bmelt"><span>Variability of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> as Determined from Satellite Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Parkinson, Claire L.</p> <p>1999-01-01</p> <p>The compiled, quality-controlled satellite multichannel passive-microwave record of polar <span class="hlt">sea</span> <span class="hlt">ice</span> now spans over 18 years, from November 1978 through December 1996, and is revealing considerable information about the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover and its variability. The information includes data on <span class="hlt">ice</span> concentrations (percent areal coverages of <span class="hlt">ice</span>), <span class="hlt">ice</span> extents, <span class="hlt">ice</span> melt, <span class="hlt">ice</span> velocities, the seasonal cycle of the <span class="hlt">ice</span>, the interannual variability of the <span class="hlt">ice</span>, the frequency of <span class="hlt">ice</span> coverage, and the length of the <span class="hlt">sea</span> <span class="hlt">ice</span> season. The data reveal marked regional and interannual variabilities, as well as some statistically significant trends. For the north polar <span class="hlt">ice</span> cover as a whole, maximum <span class="hlt">ice</span> extents varied over a range of 14,700,000 - 15,900,000 sq km, while individual regions experienced much greater percent variations, for instance, with the Greenland <span class="hlt">Sea</span> having a range of 740,000 - 1,110,000 sq km in its yearly maximum <span class="hlt">ice</span> coverage. In spite of the large variations from year to year and region to region, overall the Arctic <span class="hlt">ice</span> extents showed a statistically significant, 2.80% / decade negative trend over the 18.2-year period. <span class="hlt">Ice</span> season lengths, which vary from only a few weeks near the <span class="hlt">ice</span> margins to the full year in the large region of perennial <span class="hlt">ice</span> coverage, also experienced interannual variability, along with spatially coherent overall trends. Linear least squares trends show the <span class="hlt">sea</span> <span class="hlt">ice</span> season to have lengthened in much of the Bering <span class="hlt">Sea</span>, Baffin Bay, the Davis Strait, and the Labrador <span class="hlt">Sea</span>, but to have shortened over a much larger area, including the <span class="hlt">Sea</span> of Okhotsk, the Greenland <span class="hlt">Sea</span>, the Barents <span class="hlt">Sea</span>, and the southeastern Arctic.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010cosp...38..278K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010cosp...38..278K"><span>Observed Nordic <span class="hlt">Sea</span> <span class="hlt">ice</span>-cover variability 1992-2008</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kern, Stefan; Kaleschke, Lars; Spreen, Gunnar</p> <p></p> <p>We examined the <span class="hlt">sea-ice</span> cover of the Arctic peripheral <span class="hlt">seas</span> bordering the Northern North Atlantic: Irminger, Greenland, Barents, Kara, and White/Pechora <span class="hlt">Sea</span> using daily <span class="hlt">sea-ice</span> con-centration data obtained with the ASI algorithm at a grid resolution of 12.5 km × 12.5 km from Special Sensor Microwave/Imager 85 GHz brightness temperature measurements for 1992-2008. The obtained annual cycles of monthly average <span class="hlt">ice</span> area and extent indicate, in agreement with previous studies, an increase of the length of the melt season and reductions in the mean maxi-mum and minimum <span class="hlt">ice</span>-cover in all regions between 1992-1999 and 2000-2008, with wintertime changes of between 5-10% (Greenland <span class="hlt">Sea</span>, White/Pechora <span class="hlt">Sea</span>) and 15-20% (Irminger and Barents <span class="hlt">Sea</span>), and summertime changes between 30% (Kara <span class="hlt">Sea</span>) and up to 55% (Irminger and Barents <span class="hlt">Sea</span>). Monthly mean <span class="hlt">ice</span>-area and -extent anomalies relative to the average annual cycle are calculated and indicate pronounced differences between the Barents <span class="hlt">Sea</span> and the other regions. A lag-correlation analysis between all <span class="hlt">ice</span>-area and -extent anomalies is carried out. The main results are: i) Barents <span class="hlt">Sea</span> <span class="hlt">ice</span>-area and -extent anomalies are significantly auto-correlated for a two-fold longer period of time than respective anomalies in the other regions. ii) Fall/early winter Irminger <span class="hlt">Sea</span> <span class="hlt">ice</span>-area and -extent anomalies are significantly correlated with respective summer/fall Greenland <span class="hlt">Sea</span> anomalies; the average time-lag is 2-3 months, the average (max-imum) duration is 2 (8) months. iii) Barents and Kara <span class="hlt">Sea</span> <span class="hlt">ice</span>-area and -extent anomalies are significantly correlated with each other during summer/fall. We found also a significant correlation between Barents <span class="hlt">Sea</span> Dec. to July and Kara <span class="hlt">Sea</span> July to Sep./Nov. <span class="hlt">ice</span>-area and -extent anomalies with an average duration of 2-3 months. We have investigated the relationship between anomalies in <span class="hlt">ice</span>-area flux between the Arctic Ocean and the considered peripheral <span class="hlt">seas</span> and the <span class="hlt">ice</span>-area and -extent anomalies in these</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.9945J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.9945J"><span>Stochastic <span class="hlt">sea</span> <span class="hlt">ice</span> parameterizations and impacts on polar predictability</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Juricke, Stephan; Goessling, Helge; Jung, Thomas</p> <p>2015-04-01</p> <p>Stochastic <span class="hlt">sea</span> <span class="hlt">ice</span> parameterizations are implemented in a global coupled model to include first estimates of model uncertainty in the assessment of <span class="hlt">sea</span> <span class="hlt">ice</span> predictability. The impact of incorporating estimates of model uncertainty in the <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics is compared to the impact of atmospheric initial condition uncertainty. In this context a set of ensembles with stochastic <span class="hlt">sea</span> <span class="hlt">ice</span> strength perturbations and a set of ensembles with atmospheric initial condition perturbations are investigated. Seasonal integrations show that especially during the first weeks the incorporation of model uncertainty estimates in the <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics leads to a significant increase in ensemble spread of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness in the central Arctic and along coastlines when compared to the ensembles with atmospheric initial perturbations. The latter, in contrast, produce significantly larger variability along the <span class="hlt">ice</span> edge. During the first weeks of the integration, applying the combined perturbations leads to an accumulation of spread from both uncertainties pointing at the importance of including estimates of model uncertainty for subseasonal <span class="hlt">sea</span> <span class="hlt">ice</span> predictions. After the first few weeks, however, the differences between ensemble spreads become mostly insignificant so that estimates of seasonal potential <span class="hlt">sea</span> <span class="hlt">ice</span> predictability for the Arctic remain largely unaffected by uncertainty estimates in the <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics. For the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span>, differences in <span class="hlt">sea</span> <span class="hlt">ice</span> thickness spread between the different ensemble configurations are less pronounced throughout the year. Stochastic perturbations are also applied to the <span class="hlt">sea</span> <span class="hlt">ice</span> thermodynamics, namely the <span class="hlt">sea</span> <span class="hlt">ice</span> albedo parameterization, to investigate the diverse impacts of the incorporation of uncertainty estimates in different parts of the <span class="hlt">sea</span> <span class="hlt">ice</span> model, affecting different regions of the polar regions and at different times during the annual cycle.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014E%26ES...17a2115H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014E%26ES...17a2115H"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> classification using dual polarization SAR data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Huiying, Liu; Huadong, Guo; Lu, Zhang</p> <p>2014-03-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> is an indicator of climate change and also a threat to the navigation security of ships. Polarimetric SAR images are useful in the <span class="hlt">sea</span> <span class="hlt">ice</span> detection and classification. In this paper, backscattering coefficients and texture features derived from dual polarization SAR images are used for <span class="hlt">sea</span> <span class="hlt">ice</span> classification. Firstly, the HH image is recalculated based on the angular dependences of <span class="hlt">sea</span> <span class="hlt">ice</span> types. Then the effective gray level co-occurrence matrix (GLCM) texture features are selected for the support vector machine (SVM) classification. In the end, because <span class="hlt">sea</span> <span class="hlt">ice</span> concentration can provide a better separation of pancake <span class="hlt">ice</span> from old <span class="hlt">ice</span>, it is used to improve the SVM result. This method provides a good classification result, compared with the <span class="hlt">sea</span> <span class="hlt">ice</span> chart from CIS.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040081064&hterms=etl&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Detl','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040081064&hterms=etl&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Detl"><span>EOS Aqua AMSR-E Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Validation Program</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cavalieri, D. J.; Markus, T.; Gasiewski, A.; Klein, M.; Maslanik, J.; Sturm, M.; Stroeve, J.; Heinrichs, J.</p> <p>2004-01-01</p> <p>A coordinated Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> validation field campaign using the NASA Wallops P-3B aircraft was successfully completed in March 2003. This campaign was part of the program for validating the Earth Observing System (EOS) Aqua Advanced Microwave Scanning Radiometer (AMSR-E) <span class="hlt">sea</span> <span class="hlt">ice</span> products. The AMSR-E, designed and built by the Japanese National Space Development Agency for NASA, was launched May 4,2002 on the EOS Aqua spacecraft. The AMSR-E <span class="hlt">sea</span> <span class="hlt">ice</span> products include <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, <span class="hlt">sea</span> <span class="hlt">ice</span> temperature, and snow depth on <span class="hlt">sea</span> <span class="hlt">ice</span>. The primary instrument on the P-3B aircraft was the NOAA ETL Polarimetric Scanning Radiometer (PSR) covering the same frequencies and polarizations as the AMSR-E. This paper describes the objectives of each of the seven flights, the Arctic regions overflown, and the coordination among satellite, aircraft, and surface-based measurements. Two of the seven aircraft flights were coordinated with scientists making surface measurements of snow and <span class="hlt">ice</span> properties including <span class="hlt">sea</span> <span class="hlt">ice</span> temperature and snow depth on <span class="hlt">sea</span> <span class="hlt">ice</span> at a study area near Barrow, AK and at a Navy <span class="hlt">ice</span> camp located in the Beaufort <span class="hlt">Sea</span>. The remaining flights covered portions of the Bering <span class="hlt">Sea</span> <span class="hlt">ice</span> edge, the Chukchi <span class="hlt">Sea</span>, and Norton Sound. Comparisons among the satellite and aircraft PSR data sets are presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19606146','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19606146"><span>Evidence for middle Eocene Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> from diatoms and <span class="hlt">ice</span>-rafted debris.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Stickley, Catherine E; St John, Kristen; Koç, Nalân; Jordan, Richard W; Passchier, Sandra; Pearce, Richard B; Kearns, Lance E</p> <p>2009-07-16</p> <p>Oceanic sediments from long cores drilled on the Lomonosov ridge, in the central Arctic, contain <span class="hlt">ice</span>-rafted debris (IRD) back to the middle Eocene epoch, prompting recent suggestions that <span class="hlt">ice</span> appeared in the Arctic about 46 million years (Myr) ago. However, because IRD can be transported by icebergs (derived from land-based <span class="hlt">ice</span>) and also by <span class="hlt">sea</span> <span class="hlt">ice</span>, IRD records are restricted to providing a history of general <span class="hlt">ice</span>-rafting only. It is critical to differentiate <span class="hlt">sea</span> <span class="hlt">ice</span> from glacial (land-based) <span class="hlt">ice</span> as climate feedback mechanisms vary and global impacts differ between these systems: <span class="hlt">sea</span> <span class="hlt">ice</span> directly affects ocean-atmosphere exchanges, whereas land-based <span class="hlt">ice</span> affects <span class="hlt">sea</span> level and consequently ocean acidity. An earlier report assumed that <span class="hlt">sea</span> <span class="hlt">ice</span> was prevalent in the middle Eocene Arctic on the basis of IRD, and although somewhat preliminary supportive evidence exists, these data are neither comprehensive nor quantified. Here we show the presence of middle Eocene Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> from an extraordinary abundance of a group of <span class="hlt">sea-ice</span>-dependent fossil diatoms (Synedropsis spp.). Analysis of quartz grain textural characteristics further supports <span class="hlt">sea</span> <span class="hlt">ice</span> as the dominant transporter of IRD at this time. Together with new information on cosmopolitan diatoms and existing IRD records, our data strongly suggest a two-phase establishment of <span class="hlt">sea</span> <span class="hlt">ice</span>: initial episodic formation in marginal shelf areas approximately 47.5 Myr ago, followed approximately 0.5 Myr later by the onset of seasonally paced <span class="hlt">sea-ice</span> formation in offshore areas of the central Arctic. Our data establish a 2-Myr record of <span class="hlt">sea</span> <span class="hlt">ice</span>, documenting the transition from a warm, <span class="hlt">ice</span>-free environment to one dominated by winter <span class="hlt">sea</span> <span class="hlt">ice</span> at the start of the middle Eocene climatic cooling phase.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=PIA01786&hterms=weather+radar+new&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dweather%2Bradar%2Bnew','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=PIA01786&hterms=weather+radar+new&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dweather%2Bradar%2Bnew"><span>Space Radar Image of Weddell <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>1994-01-01</p> <p>This is the first calibrated, multi-frequency, multi-polarization spaceborne radar image of the seasonal <span class="hlt">sea-ice</span> cover in the Weddell <span class="hlt">Sea</span>, Antarctica. The multi-channel data provide scientists with details about the <span class="hlt">ice</span> pack they cannot see any other way and indicates that the large expanse of <span class="hlt">sea-ice</span> is, in fact, comprised of many smaller rounded <span class="hlt">ice</span> floes, shown in blue-gray. These data are particularly useful in helping scientists estimate the thickness of the <span class="hlt">ice</span> cover which is often extremely difficult to measure with other remote sensing systems. The extent, and especially thickness, of the polar ocean's <span class="hlt">sea-ice</span> cover together have important implications for global climate by regulating the loss of heat from the ocean to the cold polar atmosphere. The image was acquired on October 3, 1994, by the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR) onboard the space shuttle Endeavour. This image is produced by overlaying three channels of radar data in the following colors: red (C-band, HH-polarization), green (L-band HV-polarization), and blue (L-band, HH-polarization). The image is oriented almost east-west with a center location of 58.2 degrees South and 21.6 degrees East. Image dimensions are 45 kilometers by 18 kilometers (28 miles by 11 miles). Most of the <span class="hlt">ice</span> cover is composed of rounded, undeformed blue-gray floes, about 0.7 meters (2 feet) thick, which are surrounded by a jumble of red-tinged deformed <span class="hlt">ice</span> pieces which are up to 2 meters (7 feet) thick. The winter cycle of <span class="hlt">ice</span> growth and deformation often causes this <span class="hlt">ice</span> cover to split apart, exposing open water or 'leads'. <span class="hlt">Ice</span> growth within these openings is rapid due to the cold, brisk Antarctic atmosphere. Different stages of new-<span class="hlt">ice</span> growth can be seen within the linear leads, resulting from continuous opening and closing. The blue lines within the leads are open water areas in new fractures which are roughened by wind. The bright red lines are an intermediate stage of new-<span class="hlt">ice</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.1074H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.1074H"><span>Mechanical <span class="hlt">sea-ice</span> strength parameterized as a function of <span class="hlt">ice</span> temperature</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hata, Yukie; Tremblay, Bruno</p> <p>2016-04-01</p> <p>Mechanical <span class="hlt">sea-ice</span> strength is key for a better simulation of the timing of landlock <span class="hlt">ice</span> onset and break-up in the Canadian Arctic Archipelago (CAA). We estimate the mechanical strength of <span class="hlt">sea</span> <span class="hlt">ice</span> in the CAA by analyzing the position record measured by the several buoys deployed in the CAA between 2008 and 2013, and wind data from the Canadian Meteorological Centre's Global Deterministic Prediction System (CMC_GDPS) REforecasts (CGRF). First, we calculate the total force acting on the <span class="hlt">ice</span> using the wind data. Next, we estimate upper (lower) bounds on the <span class="hlt">sea-ice</span> strength by identifying cases when the <span class="hlt">sea</span> <span class="hlt">ice</span> deforms (does not deform) under the action of a given total force. Results from this analysis show that the <span class="hlt">ice</span> strength of landlock <span class="hlt">sea</span> <span class="hlt">ice</span> in the CAA is approximately 40 kN/m on the landfast <span class="hlt">ice</span> onset (in <span class="hlt">ice</span> growth season). Additionally, it becomes approximately 10 kN/m on the landfast <span class="hlt">ice</span> break-up (in melting season). The <span class="hlt">ice</span> strength decreases with <span class="hlt">ice</span> temperature increase, which is in accord with results from Johnston [2006]. We also include this new parametrization of <span class="hlt">sea-ice</span> strength as a function of <span class="hlt">ice</span> temperature in a coupled slab ocean <span class="hlt">sea</span> <span class="hlt">ice</span> model. The results from the model with and without the new parametrization are compared with the buoy data from the International Arctic Buoy Program (IABP).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA144448','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA144448"><span>Atmospheric <span class="hlt">Icing</span> on <span class="hlt">Sea</span> Structures,</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>1984-04-01</p> <p>Necessary properties of air for b. Necessary properties of air for the steaming of freshwater, the steaming of saline water (salin- ity 35 0 /0 Figure...b l 0 0110 120 110 00, 90, 80 70 60 50- 40, 30. 20 1 0 - iti f. February. Figure 39 (cont’d). Probability of supercooled fog by month according to...Research Report 123. Laforte, J-L., C.L. Phan, B. Felin and R. Martin (1983a) Adhesion of <span class="hlt">ice</span> on aluminum conductor and crystal size in the surface</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC12A..06R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC12A..06R"><span>Non-linear feedbacks affecting <span class="hlt">sea</span> <span class="hlt">ice</span> deformation in the Regional Arctic System Model (RASM)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Roberts, A.; Maslowski, W.; Mills, T.; Hunke, E. C.; Craig, A.; Osinski, R.; Cassano, J. J.; Duvivier, A.; Hughes, M.; Zeng, X.; Brunke, M.; Gutowski, W. J., Jr.; Fisel, B. J.</p> <p>2014-12-01</p> <p>We present the latest results of high-resolution <span class="hlt">sea</span> <span class="hlt">ice</span> simulations from the fully coupled Regional Arctic System Model (RASM), including explicit melt ponds, form drag and anisotropic <span class="hlt">sea</span> <span class="hlt">ice</span> rheology. RASM is a pan-Arctic model composed of the Parallel Ocean Program (POP) and Los <span class="hlt">Alamos</span> <span class="hlt">Sea</span> <span class="hlt">ice</span> Model (CICE5) at ~9km resolution, coupled to the Weather Research and Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model at 50km resolution using the Community Earth System Model (CESM) coupling framework. Using RASM, we have analyzed coupled feedbacks resulting from different <span class="hlt">sea</span> <span class="hlt">ice</span> mechanics formulations. Strong spatial and temporal scaling of <span class="hlt">sea</span> <span class="hlt">ice</span> deformation has been observed in the Arctic using the Radarsat Geophysical Processing System and Global Positioning System equipped buoys. Whereas previous results from stand-alone <span class="hlt">ice</span>-ocean simulations suggest that the established Elastic Viscous Plastic (EVP) rheology is unable to replicate these features, RASM simulates the observed scaling using EVP, with a spatial scaling fractal dimension of around -0.23, as compared to the observed range of -0.18 to -0.20. Using this metric, we extend our analysis to test for spatial scaling in <span class="hlt">sea</span> <span class="hlt">ice</span> deformation using a recently revised EVP formulation, as well as the new Elastic Plastic Anistropic rheology in CICE5. Our results suggest that a fundamental source of scaling stems from feedbacks associated with frequent coupling between high resolution ocean and atmospheric models, and this result serves as an example of the broader utility of limited-area, fully coupled models in isolating coupled feedbacks and evaluating them using daily in-situ and satellite measurements.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_8 --> <div id="page_9" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="161"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA194980','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA194980"><span>Mechanical Properties of Multi-Year <span class="hlt">Sea</span> <span class="hlt">Ice</span>. Phase 2. <span class="hlt">Ice</span> Structure Analysis,</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>1988-03-01</p> <p>columnar <span class="hlt">ice</span>, the <span class="hlt">ice</span> thin sections were analyzed an undeformed sheet of first-year <span class="hlt">sea</span> <span class="hlt">ice</span> the crys- . on the Rigsby universal stage ( Langway 1958). Us...tals of Rock Mechanics. London: Methuen and ,’- cant decrease in strength with an increase in <span class="hlt">ice</span> Co., Ltd. porosity. Langway , C.C. (1958) <span class="hlt">Ice</span> fabrics</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA213043','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA213043"><span>Mechanical Properties of Multi-Year <span class="hlt">Sea</span> <span class="hlt">Ice</span>. Phase 2. <span class="hlt">Ice</span> Structure Analysis</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>1988-03-01</p> <p>In columnar <span class="hlt">ice</span>, the <span class="hlt">ice</span> thin sections were analyzed an undeformed sheet of first-year <span class="hlt">sea</span> <span class="hlt">ice</span> the crys- on the Rigsby universal stage ( Langway ...signifi- tals of Rock Mechanics. London: Methuen and cant decrease in strength with an increase in <span class="hlt">ice</span> Co., Ltd. porosity. Langway , C.C. (1958) <span class="hlt">Ice</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008GMS...180.....D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008GMS...180.....D"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Decline: Observations, Projections, Mechanisms, and Implications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>DeWeaver, Eric T.; Bitz, Cecilia M.; Tremblay, L.-Bruno</p> <p></p> <p>This volume addresses the rapid decline of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>, placing recent <span class="hlt">sea</span> <span class="hlt">ice</span> decline in the context of past observations, climate model simulations and projections, and simple models of the climate sensitivity of <span class="hlt">sea</span> <span class="hlt">ice</span>. Highlights of the work presented here include • An appraisal of the role played by wind forcing in driving the decline; • A reconstruction of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> conditions prior to human observations, based on proxy data from sediments; • A modeling approach for assessing the impact of <span class="hlt">sea</span> <span class="hlt">ice</span> decline on polar bears, used as input to the U.S. Fish and Wildlife Service's decision to list the polar bear as a threatened species under the Endangered Species Act; • Contrasting studies on the existence of a "tipping point," beyond which Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> decline will become (or has already become) irreversible, including an examination of the role of the small <span class="hlt">ice</span> cap instability in global warming simulations; • A significant summertime atmospheric response to <span class="hlt">sea</span> <span class="hlt">ice</span> reduction in an atmospheric general circulation model, suggesting a positive feedback and the potential for short-term climate prediction. The book will be of interest to researchers attempting to understand the recent behavior of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>, model projections of future <span class="hlt">sea</span> <span class="hlt">ice</span> loss, and the consequences of <span class="hlt">sea</span> <span class="hlt">ice</span> loss for the natural and human systems of the Arctic.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24842027','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24842027"><span>Influence of stochastic <span class="hlt">sea</span> <span class="hlt">ice</span> parametrization on climate and the role of atmosphere-<span class="hlt">sea</span> <span class="hlt">ice</span>-ocean interaction.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Juricke, Stephan; Jung, Thomas</p> <p>2014-06-28</p> <p>The influence of a stochastic <span class="hlt">sea</span> <span class="hlt">ice</span> strength parametrization on the mean climate is investigated in a coupled atmosphere-<span class="hlt">sea</span> <span class="hlt">ice</span>-ocean model. The results are compared with an uncoupled simulation with a prescribed atmosphere. It is found that the stochastic <span class="hlt">sea</span> <span class="hlt">ice</span> parametrization causes an effective weakening of the <span class="hlt">sea</span> <span class="hlt">ice</span>. In the uncoupled model this leads to an Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> volume increase of about 10-20% after an accumulation period of approximately 20-30 years. In the coupled model, no such increase is found. Rather, the stochastic perturbations lead to a spatial redistribution of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> thickness field. A mechanism involving a slightly negative atmospheric feedback is proposed that can explain the different responses in the coupled and uncoupled system. Changes in integrated Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> quantities caused by the stochastic parametrization are generally small, as memory is lost during the melting season because of an almost complete loss of <span class="hlt">sea</span> <span class="hlt">ice</span>. However, stochastic <span class="hlt">sea</span> <span class="hlt">ice</span> perturbations affect regional <span class="hlt">sea</span> <span class="hlt">ice</span> characteristics in the Southern Hemisphere, both in the uncoupled and coupled model. Remote impacts of the stochastic <span class="hlt">sea</span> <span class="hlt">ice</span> parametrization on the mean climate of non-polar regions were found to be small.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016APS..MARF40008A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016APS..MARF40008A"><span>Multifractals, random walks and Arctic <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Agarwal, Sahil; Wettlaufer, John</p> <p></p> <p>We examine the long-term correlations and multifractal properties of daily satellite retrievals of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> albedo, extent, and <span class="hlt">ice</span> velocity for decadal periods. The approach harnesses a recent development called Multifractal Temporally Weighted Detrended Fluctuation Analysis (MF-TWDFA), which exploits the intuition that points closer in time are more likely to be related than distant points. In both data sets we extract multiple crossover times, as characterized by generalized Hurst exponents, ranging from synoptic to decadal. The method goes beyond treatments that assume a single decay scale process, such as a first-order autoregression, which cannot be justifiably fit to these observations. The <span class="hlt">ice</span> extent data exhibits white noise behavior from seasonal to bi-seasonal time scales, whereas the clear fingerprints of the short (weather) and long (~ 7 and 9 year) time scales remain, the latter associated with the recent decay in the <span class="hlt">ice</span> cover. Thus, long term persistence is reentrant beyond the seasonal scale and it is not possible to distinguish whether a given <span class="hlt">ice</span> extent minimum/maximum will be followed by a minimum/maximum that is larger or smaller in magnitude. The <span class="hlt">ice</span> velocity data show long term persistence in auto covariance. NASA Grant NNH13ZDA001N-CRYO and Swedish Research Council Grant No. 638-2013-9243.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120009528','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120009528"><span>Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Variability and Trends, 1979-2010</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Parkinson, C. L.; Cavalieri, D. J.</p> <p>2012-01-01</p> <p>In sharp contrast to the decreasing <span class="hlt">sea</span> <span class="hlt">ice</span> coverage of the Arctic, in the Antarctic the <span class="hlt">sea</span> <span class="hlt">ice</span> cover has, on average, expanded since the late 1970s. More specifically, satellite passive-microwave data for the period November 1978 - December 2010 reveal an overall positive trend in <span class="hlt">ice</span> extents of 17,100 +/- 2,300 square km/yr. Much of the increase, at 13,700 +/- 1,500 square km/yr, has occurred in the region of the Ross <span class="hlt">Sea</span>, with lesser contributions from the Weddell <span class="hlt">Sea</span> and Indian Ocean. One region, that of the Bellingshausen/Amundsen <span class="hlt">Seas</span>, has, like the Arctic, instead experienced significant <span class="hlt">sea</span> <span class="hlt">ice</span> decreases, with an overall <span class="hlt">ice</span> extent trend of -8,200 +/- 1,200 square km/yr. When examined through the annual cycle over the 32-year period 1979-2010, the Southern Hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> cover as a whole experienced positive <span class="hlt">ice</span> extent trends in every month, ranging in magnitude from a low of 9,100 +/- 6,300 square km/yr in February to a high of 24,700 +/- 10,000 square km/yr in May. The Ross <span class="hlt">Sea</span> and Indian Ocean also had positive trends in each month, while the Bellingshausen/Amundsen <span class="hlt">Seas</span> had negative trends in each month, and the Weddell <span class="hlt">Sea</span> and Western Pacific Ocean had a mixture of positive and negative trends. Comparing <span class="hlt">ice</span>-area results to <span class="hlt">ice</span>-extent results, in each case the <span class="hlt">ice</span>-area trend has the same sign as the <span class="hlt">ice</span>-extent trend, but differences in the magnitudes of the two trends identify regions with overall increasing <span class="hlt">ice</span> concentrations and others with overall decreasing <span class="hlt">ice</span> concentrations. The strong pattern of decreasing <span class="hlt">ice</span> coverage in the Bellingshausen/Amundsen <span class="hlt">Seas</span> region and increasing <span class="hlt">ice</span> coverage in the Ross <span class="hlt">Sea</span> region is suggestive of changes in atmospheric circulation. This is a key topic for future research.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA617900','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA617900"><span>Early Student Support to Investigate the Role of <span class="hlt">Sea</span> <span class="hlt">Ice</span>-Albedo Feedback in <span class="hlt">Sea</span> <span class="hlt">Ice</span> Predictions</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2014-09-30</p> <p><span class="hlt">Ice</span>-Albedo Feedback in <span class="hlt">Sea</span> <span class="hlt">Ice</span> Predictions Cecilia M. Bitz Atmospheric Sciences MS351640 University of Washington Seattle, WA 98196-1640 phone...TERM GOALS The overarching goals of this project are to understand the role of <span class="hlt">sea</span> <span class="hlt">ice</span>-albedo feedback on <span class="hlt">sea</span> <span class="hlt">ice</span> predictability, to improve how... feedback in models, and thereby directly relate feedback to predictability. We will use initial conditions from the model itself in idealized</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C53D..01N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C53D..01N"><span>Examining Differences in Arctic and Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Change</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nghiem, S. V.; Rigor, I. G.; Clemente-Colon, P.; Neumann, G.; Li, P.</p> <p>2015-12-01</p> <p>The paradox of the rapid reduction of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> versus the stability (or slight increase) of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> remains a challenge in the cryospheric science research community. Here we start by reviewing a number of explanations that have been suggested by different researchers and authors. One suggestion is that stratospheric ozone depletion may affect atmospheric circulation and wind patterns such as the Southern Annular Mode, and thereby sustaining the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover. The reduction of salinity and density in the near-surface layer may weaken the convective mixing of cold and warmer waters, and thus maintaining regions of no warming around the Antarctic. A decrease in <span class="hlt">sea</span> <span class="hlt">ice</span> growth may reduce salt rejection and upper-ocean density to enhance thermohalocline stratification, and thus supporting Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> production. Melt water from Antarctic <span class="hlt">ice</span> shelves collects in a cool and fresh surface layer to shield the surface ocean from the warmer deeper waters, and thus leading to an expansion of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Also, wind effects may positively contribute to Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> growth. Moreover, Antarctica lacks of additional heat sources such as warm river discharge to melt <span class="hlt">sea</span> <span class="hlt">ice</span> as opposed to the case in the Arctic. Despite of these suggested explanations, factors that can consistently and persistently maintains the stability of <span class="hlt">sea</span> <span class="hlt">ice</span> still need to be identified for the Antarctic, which are opposed to factors that help accelerate <span class="hlt">sea</span> <span class="hlt">ice</span> loss in the Arctic. In this respect, using decadal observations from multiple satellite datasets, we examine differences in <span class="hlt">sea</span> <span class="hlt">ice</span> properties and distributions, together with dynamic and thermodynamic processes and interactions with land, ocean, and atmosphere, causing differences in Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> change to contribute to resolving the Arctic-Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> paradox.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25764550','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25764550"><span><span class="hlt">Ice</span> formation and growth shape bacterial community structure in Baltic <span class="hlt">Sea</span> drift <span class="hlt">ice</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Eronen-Rasimus, Eeva; Lyra, Christina; Rintala, Janne-Markus; Jürgens, Klaus; Ikonen, Vilma; Kaartokallio, Hermanni</p> <p>2015-02-01</p> <p>Drift <span class="hlt">ice</span>, open water and under-<span class="hlt">ice</span> water bacterial communities covering several developmental stages from open water to thick <span class="hlt">ice</span> were studied in the northern Baltic <span class="hlt">Sea</span>. The bacterial communities were assessed with 16S rRNA gene terminal-restriction fragment length polymorphism and cloning, together with bacterial abundance and production measurements. In the early stages, open water and pancake <span class="hlt">ice</span> were dominated by Alphaproteobacteria and Actinobacteria, which are common bacterial groups in Baltic <span class="hlt">Sea</span> wintertime surface waters. The pancake <span class="hlt">ice</span> bacterial communities were similar to the open-water communities, suggesting that the parent water determines the <span class="hlt">sea-ice</span> bacterial community in the early stages of <span class="hlt">sea-ice</span> formation. In consolidated young and thick <span class="hlt">ice</span>, the bacterial communities were significantly different from water bacterial communities as well as from each other, indicating community development in Baltic <span class="hlt">Sea</span> drift <span class="hlt">ice</span> along with <span class="hlt">ice</span>-type changes. The thick <span class="hlt">ice</span> was dominated by typical <span class="hlt">sea-ice</span> genera from classes Flavobacteria and Gammaproteobacteria, similar to those in polar <span class="hlt">sea-ice</span> bacterial communities. Since the thick <span class="hlt">ice</span> bacterial community was remarkably different from that of the parent seawater, results indicate that thick <span class="hlt">ice</span> bacterial communities were recruited from the rarer members of the seawater bacterial community.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27650478','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27650478"><span>Canadian Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> reconstructed from bromine in the Greenland NEEM <span class="hlt">ice</span> core.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Spolaor, Andrea; Vallelonga, Paul; Turetta, Clara; Maffezzoli, Niccolò; Cozzi, Giulio; Gabrieli, Jacopo; Barbante, Carlo; Goto-Azuma, Kumiko; Saiz-Lopez, Alfonso; Cuevas, Carlos A; Dahl-Jensen, Dorthe</p> <p>2016-09-21</p> <p>Reconstructing the past variability of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> provides an essential context for recent multi-year <span class="hlt">sea</span> <span class="hlt">ice</span> decline, although few quantitative reconstructions cover the Holocene period prior to the earliest historical records 1,200 years ago. Photochemical recycling of bromine is observed over first-year, or seasonal, <span class="hlt">sea</span> <span class="hlt">ice</span> in so-called "bromine explosions" and we employ a 1-D chemistry transport model to quantify processes of bromine enrichment over first-year <span class="hlt">sea</span> <span class="hlt">ice</span> and depositional transport over multi-year <span class="hlt">sea</span> <span class="hlt">ice</span> and land <span class="hlt">ice</span>. We report bromine enrichment in the Northwest Greenland Eemian NEEM <span class="hlt">ice</span> core since the end of the Eemian interglacial 120,000 years ago, finding the maximum extension of first-year <span class="hlt">sea</span> <span class="hlt">ice</span> occurred approximately 9,000 years ago during the Holocene climate optimum, when Greenland temperatures were 2 to 3 °C above present values. First-year <span class="hlt">sea</span> <span class="hlt">ice</span> extent was lowest during the glacial stadials suggesting complete coverage of the Arctic Ocean by multi-year <span class="hlt">sea</span> <span class="hlt">ice</span>. These findings demonstrate a clear relationship between temperature and first-year <span class="hlt">sea</span> <span class="hlt">ice</span> extent in the Arctic and suggest multi-year <span class="hlt">sea</span> <span class="hlt">ice</span> will continue to decline as polar amplification drives Arctic temperatures beyond the 2 °C global average warming target of the recent COP21 Paris climate agreement.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016NatSR...633925S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016NatSR...633925S"><span>Canadian Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> reconstructed from bromine in the Greenland NEEM <span class="hlt">ice</span> core</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Spolaor, Andrea; Vallelonga, Paul; Turetta, Clara; Maffezzoli, Niccolò; Cozzi, Giulio; Gabrieli, Jacopo; Barbante, Carlo; Goto-Azuma, Kumiko; Saiz-Lopez, Alfonso; Cuevas, Carlos A.; Dahl-Jensen, Dorthe</p> <p>2016-09-01</p> <p>Reconstructing the past variability of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> provides an essential context for recent multi-year <span class="hlt">sea</span> <span class="hlt">ice</span> decline, although few quantitative reconstructions cover the Holocene period prior to the earliest historical records 1,200 years ago. Photochemical recycling of bromine is observed over first-year, or seasonal, <span class="hlt">sea</span> <span class="hlt">ice</span> in so-called “bromine explosions” and we employ a 1-D chemistry transport model to quantify processes of bromine enrichment over first-year <span class="hlt">sea</span> <span class="hlt">ice</span> and depositional transport over multi-year <span class="hlt">sea</span> <span class="hlt">ice</span> and land <span class="hlt">ice</span>. We report bromine enrichment in the Northwest Greenland Eemian NEEM <span class="hlt">ice</span> core since the end of the Eemian interglacial 120,000 years ago, finding the maximum extension of first-year <span class="hlt">sea</span> <span class="hlt">ice</span> occurred approximately 9,000 years ago during the Holocene climate optimum, when Greenland temperatures were 2 to 3 °C above present values. First-year <span class="hlt">sea</span> <span class="hlt">ice</span> extent was lowest during the glacial stadials suggesting complete coverage of the Arctic Ocean by multi-year <span class="hlt">sea</span> <span class="hlt">ice</span>. These findings demonstrate a clear relationship between temperature and first-year <span class="hlt">sea</span> <span class="hlt">ice</span> extent in the Arctic and suggest multi-year <span class="hlt">sea</span> <span class="hlt">ice</span> will continue to decline as polar amplification drives Arctic temperatures beyond the 2 °C global average warming target of the recent COP21 Paris climate agreement.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5030631','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5030631"><span>Canadian Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> reconstructed from bromine in the Greenland NEEM <span class="hlt">ice</span> core</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Spolaor, Andrea; Vallelonga, Paul; Turetta, Clara; Maffezzoli, Niccolò; Cozzi, Giulio; Gabrieli, Jacopo; Barbante, Carlo; Goto-Azuma, Kumiko; Saiz-Lopez, Alfonso; Cuevas, Carlos A.; Dahl-Jensen, Dorthe</p> <p>2016-01-01</p> <p>Reconstructing the past variability of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> provides an essential context for recent multi-year <span class="hlt">sea</span> <span class="hlt">ice</span> decline, although few quantitative reconstructions cover the Holocene period prior to the earliest historical records 1,200 years ago. Photochemical recycling of bromine is observed over first-year, or seasonal, <span class="hlt">sea</span> <span class="hlt">ice</span> in so-called “bromine explosions” and we employ a 1-D chemistry transport model to quantify processes of bromine enrichment over first-year <span class="hlt">sea</span> <span class="hlt">ice</span> and depositional transport over multi-year <span class="hlt">sea</span> <span class="hlt">ice</span> and land <span class="hlt">ice</span>. We report bromine enrichment in the Northwest Greenland Eemian NEEM <span class="hlt">ice</span> core since the end of the Eemian interglacial 120,000 years ago, finding the maximum extension of first-year <span class="hlt">sea</span> <span class="hlt">ice</span> occurred approximately 9,000 years ago during the Holocene climate optimum, when Greenland temperatures were 2 to 3 °C above present values. First-year <span class="hlt">sea</span> <span class="hlt">ice</span> extent was lowest during the glacial stadials suggesting complete coverage of the Arctic Ocean by multi-year <span class="hlt">sea</span> <span class="hlt">ice</span>. These findings demonstrate a clear relationship between temperature and first-year <span class="hlt">sea</span> <span class="hlt">ice</span> extent in the Arctic and suggest multi-year <span class="hlt">sea</span> <span class="hlt">ice</span> will continue to decline as polar amplification drives Arctic temperatures beyond the 2 °C global average warming target of the recent COP21 Paris climate agreement. PMID:27650478</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3960889','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3960889"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Microorganisms: Environmental Constraints and Extracellular Responses</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Ewert, Marcela; Deming, Jody W.</p> <p>2013-01-01</p> <p>Inherent to <span class="hlt">sea</span> <span class="hlt">ice</span>, like other high latitude environments, is the strong seasonality driven by changes in insolation throughout the year. <span class="hlt">Sea-ice</span> organisms are exposed to shifting, sometimes limiting, conditions of temperature and salinity. An array of adaptations to survive these and other challenges has been acquired by those organisms that inhabit the <span class="hlt">ice</span>. One key adaptive response is the production of extracellular polymeric substances (EPS), which play multiple roles in the entrapment, retention and survival of microorganisms in <span class="hlt">sea</span> <span class="hlt">ice</span>. In this concept paper we consider two main areas of <span class="hlt">sea-ice</span> microbiology: the physico-chemical properties that define <span class="hlt">sea</span> <span class="hlt">ice</span> as a microbial habitat, imparting particular advantages and limits; and extracellular responses elicited in microbial inhabitants as they exploit or survive these conditions. Emphasis is placed on protective strategies used in the face of fluctuating and extreme environmental conditions in <span class="hlt">sea</span> <span class="hlt">ice</span>. Gaps in knowledge and testable hypotheses are identified for future research. PMID:24832800</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C11B0357F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C11B0357F"><span>A New Parameterisation of Frazil and Grease <span class="hlt">Ice</span> Formation in a Climate <span class="hlt">Sea</span> <span class="hlt">Ice</span> Model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Feltham, D. L.; Heorton, H. D.; Wilchinsky, A. V.</p> <p>2014-12-01</p> <p>An idealised model describing frazil <span class="hlt">ice</span> formation in the ocean mixed layer beneath a lead in the <span class="hlt">sea</span> <span class="hlt">ice</span> cover is developed and incorporated into the <span class="hlt">sea</span> <span class="hlt">ice</span> climate model CICE. The frazil <span class="hlt">ice</span> model assumes a steady state formation of single size frazil <span class="hlt">ice</span> crystals. The crystals are uniformly distributed under the lead over the mixed layer depth and the lead width. The basic processes affecting the frazil <span class="hlt">ice</span> mass balance is the rate of frazil <span class="hlt">ice</span> formation due to the heat loss from the open water to the atmosphere, advection of heat and frazil <span class="hlt">ice</span> volume into the lead from the water under <span class="hlt">sea</span> <span class="hlt">ice</span>, and precipitation of frazil <span class="hlt">ice</span> crystals to the ocean surface and formation of grease <span class="hlt">ice</span>. The grease <span class="hlt">ice</span> is pushed against one of the lead edges by wind and water drag keeping the lead open. The frazil <span class="hlt">ice</span> model is incorporated into CICE and used to simulate the <span class="hlt">sea</span> <span class="hlt">ice</span> state in the Arctic Basin and Southern Ocean.In contrast to the original frazil <span class="hlt">ice</span> treatment in CICE which produces <span class="hlt">sea</span> <span class="hlt">ice</span> with only around 10% frazil <span class="hlt">ice</span> fraction, the new model produces of order of 50% of frazil-derived <span class="hlt">sea</span> <span class="hlt">ice</span>, which corresponds better to observations. While the original model can be re-tuned in order to produce a similar average fraction of frazil <span class="hlt">ice</span> by having a frazil collection thickness of 30 cm in the Antarctic and 5 cm in the Arctic, the new model's collection thickness is dynamically calculated, allowing for a larger collection thickness in large leads whereas the old model assumes it to be equal for wide and narrow leads. The new model keeps leads open for a longer period thus increasing the period of frazil <span class="hlt">ice</span> formation. This is particularly important in the central Arctic where the new model's increased frazil <span class="hlt">ice</span> production results in <span class="hlt">sea</span> <span class="hlt">ice</span> 0.5 m thicker than in the old model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.usgs.gov/of/2010/1176/','USGSPUBS'); return false;" href="https://pubs.usgs.gov/of/2010/1176/"><span>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> decline: Projected changes in timing and extent of <span class="hlt">sea</span> <span class="hlt">ice</span> in the Bering and Chukchi <span class="hlt">Seas</span></span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Douglas, D.C.</p> <p>2010-01-01</p> <p>The Arctic region is warming faster than most regions of the world due in part to increasing greenhouse gases and positive feedbacks associated with the loss of snow and <span class="hlt">ice</span> cover. One consequence has been a rapid decline in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> over the past 3 decades?a decline that is projected to continue by state-of-the-art models. Many stakeholders are therefore interested in how global warming may change the timing and extent of <span class="hlt">sea</span> <span class="hlt">ice</span> Arctic-wide, and for specific regions. To inform the public and decision makers of anticipated environmental changes, scientists are striving to better understand how <span class="hlt">sea</span> <span class="hlt">ice</span> influences ecosystem structure, local weather, and global climate. Here, projected changes in the Bering and Chukchi <span class="hlt">Seas</span> are examined because <span class="hlt">sea</span> <span class="hlt">ice</span> influences the presence of, or accessibility to, a variety of local resources of commercial and cultural value. In this study, 21st century <span class="hlt">sea</span> <span class="hlt">ice</span> conditions in the Bering and Chukchi <span class="hlt">Seas</span> are based on projections by 18 general circulation models (GCMs) prepared for the fourth reporting period by the Intergovernmental Panel on Climate Change (IPCC) in 2007. <span class="hlt">Sea</span> <span class="hlt">ice</span> projections are analyzed for each of two IPCC greenhouse gas forcing scenarios: the A1B `business as usual? scenario and the A2 scenario that is somewhat more aggressive in its CO2 emissions during the second half of the century. A large spread of uncertainty among projections by all 18 models was constrained by creating model subsets that excluded GCMs that poorly simulated the 1979-2008 satellite record of <span class="hlt">ice</span> extent and seasonality. At the end of the 21st century (2090-2099), median <span class="hlt">sea</span> <span class="hlt">ice</span> projections among all combinations of model ensemble and forcing scenario were qualitatively similar. June is projected to experience the least amount of <span class="hlt">sea</span> <span class="hlt">ice</span> loss among all months. For the Chukchi <span class="hlt">Sea</span>, projections show extensive <span class="hlt">ice</span> melt during July and <span class="hlt">ice</span>-free conditions during August, September, and October by the end of the century, with high agreement</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.usgs.gov/dds/dds27/','USGSPUBS'); return false;" href="https://pubs.usgs.gov/dds/dds27/"><span>Monthly average polar <span class="hlt">sea-ice</span> concentration</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Schweitzer, Peter N.</p> <p>1995-01-01</p> <p>The data contained in this CD-ROM depict monthly averages of <span class="hlt">sea-ice</span> concentration in the modern polar oceans. These averages were derived from the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I) instruments aboard satellites of the U.S. Air Force Defense Meteorological Satellite Program from 1978 through 1992. The data are provided as 8-bit images using the Hierarchical Data Format (HDF) developed by the National Center for Supercomputing Applications.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19950062297&hterms=desalination&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Ddesalination','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19950062297&hterms=desalination&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Ddesalination"><span>Polarimetric signatures of <span class="hlt">sea</span> <span class="hlt">ice</span>. 1: Theoretical model</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Nghiem, S. V.; Kwok, R.; Yueh, S. H.; Drinkwater, M. R.</p> <p>1995-01-01</p> <p>Physical, structral, and electromagnetic properties and interrelating processes in <span class="hlt">sea</span> <span class="hlt">ice</span> are used to develop a composite model for polarimetric backscattering signatures of <span class="hlt">sea</span> <span class="hlt">ice</span>. Physical properties of <span class="hlt">sea</span> <span class="hlt">ice</span> constituents such as <span class="hlt">ice</span>, brine, air, and salt are presented in terms of their effects on electromagnetic wave interactions. <span class="hlt">Sea</span> <span class="hlt">ice</span> structure and geometry of scatterers are related to wave propagation, attenuation, and scattering. Temperature and salinity, which are determining factors for the thermodynamic phase distribution in <span class="hlt">sea</span> <span class="hlt">ice</span>, are consistently used to derive both effective permittivities and polarimetric scattering coefficients. Polarmetric signatures of <span class="hlt">sea</span> <span class="hlt">ice</span> depend on crystal sizes and brine volumes, which are affected by <span class="hlt">ice</span> growth rates. Desalination by brine expulsion, drainage, or other mechanisms modifies wave penetration and scattering. <span class="hlt">Sea</span> <span class="hlt">ice</span> signatures are further complicated by surface conditions such as rough interfaces, hummocks, snow cover, brine skim, or slush layer. Based on the same set of geophysical parameters characterizing <span class="hlt">sea</span> <span class="hlt">ice</span>, a composite model is developed to calculate effective permittivities and backscattering covariance matrices at microwave frequencies to interpretation of <span class="hlt">sea</span> <span class="hlt">ice</span> polarimetric signatures.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19970009603','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19970009603"><span>Polarimetric Signatures of <span class="hlt">Sea</span> <span class="hlt">Ice</span>. Part 1; Theoretical Model</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Nghiem, S. V.; Kwok, R.; Yueh, S. H.; Drinkwater, M. R.</p> <p>1995-01-01</p> <p>Physical, structural, and electromagnetic properties and interrelating processes in <span class="hlt">sea</span> <span class="hlt">ice</span> are used to develop a composite model for polarimetric backscattering signatures of <span class="hlt">sea</span> <span class="hlt">ice</span>. Physical properties of <span class="hlt">sea</span> <span class="hlt">ice</span> constituents such as <span class="hlt">ice</span>, brine, air, and salt are presented in terms of their effects on electromagnetic wave interactions. <span class="hlt">Sea</span> <span class="hlt">ice</span> structure and geometry of scatterers are related to wave propagation, attenuation, and scattering. Temperature and salinity, which are determining factors for the thermodynamic phase distribution in <span class="hlt">sea</span> <span class="hlt">ice</span>, are consistently used to derive both effective permittivities and polarimetric scattering coefficients. Polarimetric signatures of <span class="hlt">sea</span> <span class="hlt">ice</span> depend on crystal sizes and brine volumes, which are affected by <span class="hlt">ice</span> growth rates. Desalination by brine expulsion, drainage, or other mechanisms modifies wave penetration and scattering. <span class="hlt">Sea</span> <span class="hlt">ice</span> signatures are further complicated by surface conditions such as rough interfaces, hummocks, snow cover, brine skim, or slush layer. Based on the same set of geophysical parameters characterizing <span class="hlt">sea</span> <span class="hlt">ice</span>, a composite model is developed to calculate effective permittivities and backscattering covariance matrices at microwave frequencies for interpretation of <span class="hlt">sea</span> <span class="hlt">ice</span> polarimetric signatures.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015TCry....9.1735P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015TCry....9.1735P"><span>Improving Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> edge forecasts by assimilating high horizontal resolution <span class="hlt">sea</span> <span class="hlt">ice</span> concentration data into the US Navy's <span class="hlt">ice</span> forecast systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Posey, P. G.; Metzger, E. J.; Wallcraft, A. J.; Hebert, D. A.; Allard, R. A.; Smedstad, O. M.; Phelps, M. W.; Fetterer, F.; Stewart, J. S.; Meier, W. N.; Helfrich, S. R.</p> <p>2015-08-01</p> <p>This study presents the improvement in <span class="hlt">ice</span> edge error within the US Navy's operational <span class="hlt">sea</span> <span class="hlt">ice</span> forecast systems gained by assimilating high horizontal resolution satellite-derived <span class="hlt">ice</span> concentration products. Since the late 1980's, the <span class="hlt">ice</span> forecast systems have assimilated near real-time <span class="hlt">sea</span> <span class="hlt">ice</span> concentration derived from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI and then SSMIS). The resolution of the satellite-derived product was approximately the same as the previous operational <span class="hlt">ice</span> forecast system (25 km). As the <span class="hlt">sea</span> <span class="hlt">ice</span> forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy <span class="hlt">sea</span> <span class="hlt">ice</span> forecast system (Arctic Cap Nowcast/Forecast System - ACNFS) went into operations with a horizontal resolution of ~ 3.5 km at the North Pole. A method of blending <span class="hlt">ice</span> concentration observations from the Advanced Microwave Scanning Radiometer (AMSR2) along with a <span class="hlt">sea</span> <span class="hlt">ice</span> mask produced by the National <span class="hlt">Ice</span> Center (NIC) has been developed, resulting in an <span class="hlt">ice</span> concentration product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended <span class="hlt">ice</span> concentration product. The daily <span class="hlt">ice</span> edge locations from model hindcast simulations were compared against independent observed <span class="hlt">ice</span> edge locations. ACNFS initialized using the high resolution blended <span class="hlt">ice</span> concentration data product decreased predicted <span class="hlt">ice</span> edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended <span class="hlt">sea</span> <span class="hlt">ice</span> concentration product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in <span class="hlt">ice</span> edge location over a system using the SSMIS <span class="hlt">sea</span> <span class="hlt">ice</span> concentration product alone. This paper describes the technique used to create the blended <span class="hlt">sea</span> <span class="hlt">ice</span> concentration product and the significant improvements in <span class="hlt">ice</span> edge forecasting in both of the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/484365','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/484365"><span>Modeling of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> in a general circulation model</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Wu, Xingren; Budd, W.F.; Simmonds, I.</p> <p>1997-04-01</p> <p>A dynamic-thermodynamic <span class="hlt">sea</span> <span class="hlt">ice</span> model is developed and coupled with the Melbourne University general circulation model to simulate the seasonal cycle of the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> distributions The model is efficient, rapid to compute, and useful for a range of climate studies. The thermodynamic part of the <span class="hlt">sea</span> <span class="hlt">ice</span> model is similar to that developed by Parkinson and Washington, the dynamics contain a simplified <span class="hlt">ice</span> rheology that resists compression. The thermodynamics is based on energy conservation at the top surface of the <span class="hlt">ice</span>/snow, the <span class="hlt">ice</span>/water interface, and the open water area to determine the <span class="hlt">ice</span> formation, accretion, and ablation. A lead parameterization is introduced with an effective partitioning scheme for freezing between and under the <span class="hlt">ice</span> floes. The dynamic calculation determines the motion of <span class="hlt">ice</span>, which is forced with the atmospheric wind, taking account of <span class="hlt">ice</span> resistance and rafting. The simulated <span class="hlt">sea</span> <span class="hlt">ice</span> distribution compares reasonably well with observations. The seasonal cycle of <span class="hlt">ice</span> extent is well simulated in phase as well as in magnitude. Simulated <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and concentration are also in good agreement with observations over most regions and serve to indicate the importance of advection and ocean drift in the determination of the <span class="hlt">sea</span> <span class="hlt">ice</span> distribution. 64 refs., 15 figs., 2 tabs.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_9 --> <div id="page_10" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="181"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016TCry...10.2191K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016TCry...10.2191K"><span>Mechanism of seasonal Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> evolution and Arctic amplification</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kim, Kwang-Yul; Hamlington, Benjamin D.; Na, Hanna; Kim, Jinju</p> <p>2016-09-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> loss is proposed as a primary reason for the Arctic amplification, although the physical mechanism of the Arctic amplification and its connection with <span class="hlt">sea</span> <span class="hlt">ice</span> melting is still in debate. In the present study, monthly ERA-Interim reanalysis data are analyzed via cyclostationary empirical orthogonal function analysis to understand the seasonal mechanism of <span class="hlt">sea</span> <span class="hlt">ice</span> loss in the Arctic Ocean and the Arctic amplification. While <span class="hlt">sea</span> <span class="hlt">ice</span> loss is widespread over much of the perimeter of the Arctic Ocean in summer, <span class="hlt">sea</span> <span class="hlt">ice</span> remains thin in winter only in the Barents-Kara <span class="hlt">seas</span>. Excessive turbulent heat flux through the <span class="hlt">sea</span> surface exposed to air due to <span class="hlt">sea</span> <span class="hlt">ice</span> reduction warms the atmospheric column. Warmer air increases the downward longwave radiation and subsequently surface air temperature, which facilitates <span class="hlt">sea</span> surface remains to be free of <span class="hlt">ice</span>. This positive feedback mechanism is not clearly observed in the Laptev, East Siberian, Chukchi, and Beaufort <span class="hlt">seas</span>, since <span class="hlt">sea</span> <span class="hlt">ice</span> refreezes in late fall (November) before excessive turbulent heat flux is available for warming the atmospheric column in winter. A detailed seasonal heat budget is presented in order to understand specific differences between the Barents-Kara <span class="hlt">seas</span> and Laptev, East Siberian, Chukchi, and Beaufort <span class="hlt">seas</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19980237551','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19980237551"><span>Modal Behavior of Hemispheric <span class="hlt">Sea</span> <span class="hlt">Ice</span> Covers</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Gloersen, Per; Huang, Norden; Shen, Zheng</p> <p>1998-01-01</p> <p>Recent papers have described 18-year trends and annual oscillations in the Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> extents, areas, and enclosed open water areas based on a newly-formulated 18.2-year <span class="hlt">ice</span> concentration time series. This time series includes data for the entire Arctic and Antarctic <span class="hlt">ice</span> covers, as well as for previously defined subregions consisting of 5 sectors in the Antarctic and 9 regions in the Arctic. It was obtained by fine-tuning the <span class="hlt">sea</span> <span class="hlt">ice</span> algorithm tie points individually for each of the four sensors used to acquire the data. In this paper, we extend these analyses to an examination of the intrinsic modes of these time series, obtained by means of Empirical Mode Decomposition, with emphasis on periodicities greater than the annual cycle. Quasibiennial and quasiquadrennial oscillations observed with a different technique and reported earlier for the first 8.8 years of this time series were also observed in the present series. However, the intrinsic modes were not monochromatic; they feature frequency as well as amplitude modulation within their respective frequency bands. Modal periods of up to 18 years are observed, with important implications for the trend analyses published earlier. These results are compared with the oscillations in the Length-of-Day and North Atlantic Oscillation parameters similarly determined for the same 18.2-year period.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19840008345&hterms=feeling&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dfeeling','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19840008345&hterms=feeling&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dfeeling"><span>Radar image interpretation techniques applied to <span class="hlt">sea</span> <span class="hlt">ice</span> geophysical problems</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Carsey, F. D.</p> <p>1983-01-01</p> <p>The geophysical science problems in the <span class="hlt">sea</span> <span class="hlt">ice</span> area which at present concern understanding the <span class="hlt">ice</span> budget, where <span class="hlt">ice</span> is formed, how thick it grows and where it melts, and the processes which control the interaction of air-<span class="hlt">sea</span> and <span class="hlt">ice</span> at the <span class="hlt">ice</span> margins is discussed. The science problems relate to basic questions of <span class="hlt">sea</span> <span class="hlt">ice</span>: how much is there, thickness, drift rate, production rate, determination of the morphology of the <span class="hlt">ice</span> margin, storms feeling for the <span class="hlt">ice</span>, storms and influence at the margin to alter the pack, and ocean response to a storm at the margin. Some of these questions are descriptive and some require complex modeling of interactions between the <span class="hlt">ice</span>, the ocean, the atmosphere and the radiation fields. All involve measurements of the character of the <span class="hlt">ice</span> pack, and SAR plays a significant role in the measurements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014Natur.509..604K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014Natur.509..604K"><span>Storm-induced <span class="hlt">sea-ice</span> breakup and the implications for <span class="hlt">ice</span> extent</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kohout, A. L.; Williams, M. J. M.; Dean, S. M.; Meylan, M. H.</p> <p>2014-05-01</p> <p>The propagation of large, storm-generated waves through <span class="hlt">sea</span> <span class="hlt">ice</span> has so far not been measured, limiting our understanding of how ocean waves break <span class="hlt">sea</span> <span class="hlt">ice</span>. Without improved knowledge of <span class="hlt">ice</span> breakup, we are unable to understand recent changes, or predict future changes, in Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Here we show that storm-generated ocean waves propagating through Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> are able to transport enough energy to break <span class="hlt">sea</span> <span class="hlt">ice</span> hundreds of kilometres from the <span class="hlt">ice</span> edge. Our results, which are based on concurrent observations at multiple locations, establish that large waves break <span class="hlt">sea</span> <span class="hlt">ice</span> much farther from the <span class="hlt">ice</span> edge than would be predicted by the commonly assumed exponential decay. We observed the wave height decay to be almost linear for large waves--those with a significant wave height greater than three metres--and to be exponential only for small waves. This implies a more prominent role for large ocean waves in <span class="hlt">sea-ice</span> breakup and retreat than previously thought. We examine the wider relevance of this by comparing observed Antarctic <span class="hlt">sea-ice</span> edge positions with changes in modelled significant wave heights for the Southern Ocean between 1997 and 2009, and find that the retreat and expansion of the <span class="hlt">sea-ice</span> edge correlate with mean significant wave height increases and decreases, respectively. This includes capturing the spatial variability in <span class="hlt">sea-ice</span> trends found in the Ross and Amundsen-Bellingshausen <span class="hlt">seas</span>. Climate models fail to capture recent changes in <span class="hlt">sea</span> <span class="hlt">ice</span> in both polar regions. Our results suggest that the incorporation of explicit or parameterized interactions between ocean waves and <span class="hlt">sea</span> <span class="hlt">ice</span> may resolve this problem.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24870546','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24870546"><span>Storm-induced <span class="hlt">sea-ice</span> breakup and the implications for <span class="hlt">ice</span> extent.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kohout, A L; Williams, M J M; Dean, S M; Meylan, M H</p> <p>2014-05-29</p> <p>The propagation of large, storm-generated waves through <span class="hlt">sea</span> <span class="hlt">ice</span> has so far not been measured, limiting our understanding of how ocean waves break <span class="hlt">sea</span> <span class="hlt">ice</span>. Without improved knowledge of <span class="hlt">ice</span> breakup, we are unable to understand recent changes, or predict future changes, in Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Here we show that storm-generated ocean waves propagating through Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> are able to transport enough energy to break <span class="hlt">sea</span> <span class="hlt">ice</span> hundreds of kilometres from the <span class="hlt">ice</span> edge. Our results, which are based on concurrent observations at multiple locations, establish that large waves break <span class="hlt">sea</span> <span class="hlt">ice</span> much farther from the <span class="hlt">ice</span> edge than would be predicted by the commonly assumed exponential decay. We observed the wave height decay to be almost linear for large waves--those with a significant wave height greater than three metres--and to be exponential only for small waves. This implies a more prominent role for large ocean waves in <span class="hlt">sea-ice</span> breakup and retreat than previously thought. We examine the wider relevance of this by comparing observed Antarctic <span class="hlt">sea-ice</span> edge positions with changes in modelled significant wave heights for the Southern Ocean between 1997 and 2009, and find that the retreat and expansion of the <span class="hlt">sea-ice</span> edge correlate with mean significant wave height increases and decreases, respectively. This includes capturing the spatial variability in <span class="hlt">sea-ice</span> trends found in the Ross and Amundsen-Bellingshausen <span class="hlt">seas</span>. Climate models fail to capture recent changes in <span class="hlt">sea</span> <span class="hlt">ice</span> in both polar regions. Our results suggest that the incorporation of explicit or parameterized interactions between ocean waves and <span class="hlt">sea</span> <span class="hlt">ice</span> may resolve this problem.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1610440J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1610440J"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> concentration and <span class="hlt">sea</span> <span class="hlt">ice</span> drift for the Arctic summer using C- and L-band SAR</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Johansson, Malin; Berg, Anders; Eriksson, Leif</p> <p>2014-05-01</p> <p>The decreasing amount of <span class="hlt">sea</span> <span class="hlt">ice</span> and changes from multi-year <span class="hlt">ice</span> to first year <span class="hlt">ice</span> within the Arctic Ocean opens up for increased maritime activities. These activities include transportation, fishing and tourism. One of the major threats for the shipping is the presence of <span class="hlt">sea</span> <span class="hlt">ice</span>. Should an oil spill occur, the search and rescue is heavily dependent on constant updates of <span class="hlt">sea</span> <span class="hlt">ice</span> movements, both to enable a safer working environment and to potentially prevent the oil from reaching the <span class="hlt">sea</span> <span class="hlt">ice</span>. It is therefore necessary to have accurate and updated <span class="hlt">sea</span> <span class="hlt">ice</span> charts for the Arctic Ocean during the entire year. During the melt season that <span class="hlt">ice</span> is subject to melting conditions making satellite observations of <span class="hlt">sea</span> <span class="hlt">ice</span> more difficult. This period coincides with the peak in marine shipping activities and therefore requires highly accurate <span class="hlt">sea</span> <span class="hlt">ice</span> concentration estimates. Synthetic Aperture Radar (SAR) are not hindered by clouds and do not require daylight. The continuous record and high temporal resolution makes C-band data preferable as input data for operational <span class="hlt">sea</span> <span class="hlt">ice</span> mapping. However, with C-band SAR it is sometimes difficult to distinguish between a wet <span class="hlt">sea</span> <span class="hlt">ice</span> surface and surrounding open water. L-band SAR has a larger penetration depth and has been shown to be less sensitive to less sensitive than C-band to the melt season. Inclusion of L-band data into <span class="hlt">sea</span> chart estimates during the melt season in particular could therefore improve <span class="hlt">sea</span> <span class="hlt">ice</span> monitoring. We compare <span class="hlt">sea</span> <span class="hlt">ice</span> concentration melt season observations using Advanced Land Observing Satellite (ALOS) L-band images with Envisat ASAR C-band images. We evaluate if L-band images can be used to improve separation of wet surface <span class="hlt">ice</span> from open water and compare with results for C-band.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060032490&hterms=sonar&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dsonar','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060032490&hterms=sonar&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dsonar"><span>Combined Satellite - and ULS-Derived <span class="hlt">Sea-Ice</span> Flux in the Weddell <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Drinkwater, M.; Liu, X.; Harms, S.</p> <p>2000-01-01</p> <p>Several years of daily microwave satellite <span class="hlt">ice</span>-drift are combined with moored Upward Looking Sonar (ULS) <span class="hlt">ice</span>-drafts into an <span class="hlt">ice</span> volume flux record at points along a flux gate across the Weddell <span class="hlt">Sea</span>, Antarctica.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.8833C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.8833C"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> trends and cyclone activity in the Southern Ocean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Coggins, Jack; McDonald, Adrian; Rack, Wolfgang; Dale, Ethan</p> <p>2015-04-01</p> <p>Significant trends in the extent of Southern Hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> have been noted over the course of the satellite record, with highly variable trends between different seasons and regions. In this presentation, we describe efforts to assess the impact of cyclones on these trends. Employing a maximum cross-correlation method, we derive Southern Ocean <span class="hlt">ice</span>-motion vectors from daily gridded SSMI 85.5 GHz brightness temperatures. We then derive a <span class="hlt">sea</span> <span class="hlt">ice</span> budget from the NASA-Team 25 km square daily <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations. The budget quantifies the total daily change in <span class="hlt">sea</span> <span class="hlt">ice</span> area, and includes terms representing the effects of <span class="hlt">ice</span> advection and divergence. A residual term represents the processes of rafting, ridging, freezing and thawing. We employ a cyclone tracking algorithm developed at the University of Canterbury to determine the timing, location, size and strength of Southern Hemisphere cyclones from mean <span class="hlt">sea</span>-level pressure fields of the ERA-Interim reanalysis. We then form composites of the of <span class="hlt">sea</span> <span class="hlt">ice</span> budget below the location of cyclones. Unsurprisingly, we find that clockwise atmospheric flow around Southern Hemisphere cyclones exerts a strong influence on the movement of <span class="hlt">sea</span> <span class="hlt">ice</span>, an effect which is visible in the advection and divergence terms. Further, we assess the climatological importance of cyclones by comparing seasons of <span class="hlt">sea</span> <span class="hlt">ice</span> advance for periods with varying numbers of cyclones. This analysis is performed independently for each <span class="hlt">sea</span> <span class="hlt">ice</span> concentration pixel, thus affording us insight into the geographical importance of storm systems. We find that Southern Hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> extent is highly sensitive to the presence of cyclones in the periphery of the pack in the advance season. Notably, the sensitivity is particularly high in the northern Ross <span class="hlt">Sea</span>, an area with a marked positive trend in <span class="hlt">sea</span> <span class="hlt">ice</span> extent. We discuss whether trends in cyclone activity in the Southern Ocean may have contributed to <span class="hlt">sea</span> <span class="hlt">ice</span> extent trends in this region.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.C51B0487W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.C51B0487W"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> and Its Changes during the Satellite Period</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, X.; Liu, Y.; Key, J. R.</p> <p>2009-12-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> is a very important indicator and an effective modulator of regional and global climate change. Changes in <span class="hlt">sea</span> <span class="hlt">ice</span> will significantly affect the complex exchanges of momentum, heat, and mass between <span class="hlt">sea</span> and the atmosphere, along with profound socio-economic influences due to its role in transportation, fisheries, hunting, polar animal habitat. Over the last two decades of the 20th century, the Arctic underwent significant changes in <span class="hlt">sea</span> <span class="hlt">ice</span> as part of the accelerated global warming of that period. More accurate, consistent, and detailed <span class="hlt">ice</span> thickness, extent, and volume data are critical for a wide range of applications including climate change detection, climate modeling, and operational applications such as shipping and hazard mitigation. Satellite data provide an unprecedented opportunity to estimate and monitor Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> routinely with relatively high spatial and temporal resolutions. In this study, a One-dimensional Thermodynamic <span class="hlt">Ice</span> Model (OTIM) has been developed to estimate <span class="hlt">sea</span> <span class="hlt">ice</span> thickness based on the surface energy balance at a thermo-equilibrium state, containing all components of the surface energy balance. The OTIM has been extensively validated against submarine Upward-Looking Sonar (ULS) measurements, meteorological station measurements, and comprehensive numerical model simulations. Overall, OTIM-estimated <span class="hlt">sea</span> <span class="hlt">ice</span> thickness is accurate to within about 20% error when compared to submarine ULS <span class="hlt">ice</span> thickness measurements and Canadian meteorological station measurements for <span class="hlt">ice</span> less than 3 m. Along with <span class="hlt">sea</span> <span class="hlt">ice</span> extent information from the SSM/I, the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> volume can be estimated for the satellite period from 1984 to 2004. The OTIM has been used with satellite data from the extended Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder (APP-x) products for the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> thickness, and sequentially <span class="hlt">sea</span> <span class="hlt">ice</span> volume estimations, and following statistical analysis of spatial and temporal distribution and trends in <span class="hlt">sea</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19740014838','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19740014838"><span>The application of ERTS imagery to monitoring Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. [mapping <span class="hlt">ice</span> in Bering <span class="hlt">Sea</span>, Beaufort <span class="hlt">Sea</span>, Canadian Archipelago, and Greenland <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Barnes, J. C. (Principal Investigator); Bowley, C. J.</p> <p>1974-01-01</p> <p>The author has identified the following significant results. Because of the effect of <span class="hlt">sea</span> <span class="hlt">ice</span> on the heat balance of the Arctic and because of the expanding economic interest in arctic oil and minerals, extensive monitoring and further study of <span class="hlt">sea</span> <span class="hlt">ice</span> is required. The application of ERTS data for mapping <span class="hlt">ice</span> is evaluated for several arctic areas, including the Bering <span class="hlt">Sea</span>, the eastern Beaufort <span class="hlt">Sea</span>, parts of the Canadian Archipelago, and the Greenland <span class="hlt">Sea</span>. Interpretive techniques are discussed, and the scales and types of <span class="hlt">ice</span> features that can be detected are described. For the Bering <span class="hlt">Sea</span>, a sample of ERTS-1 imagery is compared with visual <span class="hlt">ice</span> reports and aerial photography from the NASA CV-990 aircraft. The results of the investigation demonstrate that ERTS-1 imagery has substantial practical application for monitoring arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. <span class="hlt">Ice</span> features as small as 80-100 m in width can be detected, and the combined use of the visible and near-IR imagery is a powerful tool for identifying <span class="hlt">ice</span> types. Sequential ERTS-1 observations at high latitudes enable <span class="hlt">ice</span> deformations and movements to be mapped. <span class="hlt">Ice</span> conditions in the Bering <span class="hlt">Sea</span> during early March depicted in ERTS-1 images are in close agreement with aerial <span class="hlt">ice</span> observations and photographs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PolSc...9..185A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PolSc...9..185A"><span>Tundra burning in 2007 - Did <span class="hlt">sea</span> <span class="hlt">ice</span> retreat matter?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alexeev, Vladimir A.; Euskirchen, Eugénie S.; Cherry, Jessica E.; Busey, Robert C.</p> <p>2015-06-01</p> <p>The goal of this study was to assess the importance of the 2007 <span class="hlt">sea</span> <span class="hlt">ice</span> retreat for hydrologic conditions on the Alaskan North Slope, and how this may have influenced the outbreak of tundra fires in this region. This study concentrates on two years, 2007 and 1996, with different arctic <span class="hlt">sea</span> <span class="hlt">ice</span> conditions and tundra fire activity. The year of 2007 is characterized by a low summer <span class="hlt">sea</span> <span class="hlt">ice</span> extent (second lowest) and high tundra fire activity, while 1996 had high <span class="hlt">sea</span> <span class="hlt">ice</span> extent, and few tundra fires. Atmospheric lateral boundary forcing from the NCEP/NCAR Reanalysis drove the Weather Research and Forecast (WRF) model, along with varying <span class="hlt">sea</span> <span class="hlt">ice</span> surface forcing designed to delineate the role of <span class="hlt">sea</span> <span class="hlt">ice</span>. WRF runs successfully reproduced the differences between 1996 and 2007. Surprisingly, replacing <span class="hlt">sea</span> <span class="hlt">ice</span> conditions in 1996 run by those from 2007 and vice versa (2007 run with 1996 <span class="hlt">sea</span> <span class="hlt">ice</span>) did not change the overall picture. The atmospheric circulation in August of 1996 included a significant low-pressure system over the Beaufort and Chukchi <span class="hlt">Seas</span>. However, in 2007, a high-pressure system dominated the circulation over the Beaufort <span class="hlt">Sea</span>. It is argued that this difference in large-scale patterns, rather than retreat of <span class="hlt">sea</span> <span class="hlt">ice</span>, was responsible for anomalously dry and warm atmospheric conditions over the North Slope in summer and autumn 2007, suitable for high tundra fire activity. Circulation in 2012 is contrasted with that in 2007 to further stress its importance for local weather on the North Slope.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017CliPa..13...39M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017CliPa..13...39M"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> and pollution-modulated changes in Greenland <span class="hlt">ice</span> core methanesulfonate and bromine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maselli, Olivia J.; Chellman, Nathan J.; Grieman, Mackenzie; Layman, Lawrence; McConnell, Joseph R.; Pasteris, Daniel; Rhodes, Rachael H.; Saltzman, Eric; Sigl, Michael</p> <p>2017-01-01</p> <p>Reconstruction of past changes in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent may be critical for understanding its future evolution. Methanesulfonate (MSA) and bromine concentrations preserved in <span class="hlt">ice</span> cores have both been proposed as indicators of past <span class="hlt">sea</span> <span class="hlt">ice</span> conditions. In this study, two <span class="hlt">ice</span> cores from central and north-eastern Greenland were analysed at sub-annual resolution for MSA (CH3SO3H) and bromine, covering the time period 1750-2010. We examine correlations between <span class="hlt">ice</span> core MSA and the HadISST1 <span class="hlt">ICE</span> <span class="hlt">sea</span> <span class="hlt">ice</span> dataset and consult back trajectories to infer the likely source regions. A strong correlation between the low-frequency MSA and bromine records during pre-industrial times indicates that both chemical species are likely linked to processes occurring on or near <span class="hlt">sea</span> <span class="hlt">ice</span> in the same source regions. The positive correlation between <span class="hlt">ice</span> core MSA and bromine persists until the mid-20th century, when the acidity of Greenland <span class="hlt">ice</span> begins to increase markedly due to increased fossil fuel emissions. After that time, MSA levels decrease as a result of declining <span class="hlt">sea</span> <span class="hlt">ice</span> extent but bromine levels increase. We consider several possible explanations and ultimately suggest that increased acidity, specifically nitric acid, of snow on <span class="hlt">sea</span> <span class="hlt">ice</span> stimulates the release of reactive Br from <span class="hlt">sea</span> <span class="hlt">ice</span>, resulting in increased transport and deposition on the Greenland <span class="hlt">ice</span> sheet.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19884496','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19884496"><span>The future of <span class="hlt">ice</span> sheets and <span class="hlt">sea</span> <span class="hlt">ice</span>: between reversible retreat and unstoppable loss.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Notz, Dirk</p> <p>2009-12-08</p> <p>We discuss the existence of cryospheric "tipping points" in the Earth's climate system. Such critical thresholds have been suggested to exist for the disappearance of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> and the retreat of <span class="hlt">ice</span> sheets: Once these <span class="hlt">ice</span> masses have shrunk below an anticipated critical extent, the <span class="hlt">ice</span>-albedo feedback might lead to the irreversible and unstoppable loss of the remaining <span class="hlt">ice</span>. We here give an overview of our current understanding of such threshold behavior. By using conceptual arguments, we review the recent findings that such a tipping point probably does not exist for the loss of Arctic summer <span class="hlt">sea</span> <span class="hlt">ice</span>. Hence, in a cooler climate, <span class="hlt">sea</span> <span class="hlt">ice</span> could recover rapidly from the loss it has experienced in recent years. In addition, we discuss why this recent rapid retreat of Arctic summer <span class="hlt">sea</span> <span class="hlt">ice</span> might largely be a consequence of a slow shift in <span class="hlt">ice</span>-thickness distribution, which will lead to strongly increased year-to-year variability of the Arctic summer <span class="hlt">sea-ice</span> extent. This variability will render seasonal forecasts of the Arctic summer <span class="hlt">sea-ice</span> extent increasingly difficult. We also discuss why, in contrast to Arctic summer <span class="hlt">sea</span> <span class="hlt">ice</span>, a tipping point is more likely to exist for the loss of the Greenland <span class="hlt">ice</span> sheet and the West Antarctic <span class="hlt">ice</span> sheet.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC43C1219U','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC43C1219U"><span>Uncertainty Quantification and Sensitivity Analysis in the CICE v5.1 <span class="hlt">Sea</span> <span class="hlt">Ice</span> Model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Urrego-Blanco, J. R.; Urban, N. M.</p> <p>2015-12-01</p> <p>Changes in the high latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with mid latitudes. <span class="hlt">Sea</span> <span class="hlt">ice</span> and climate models used to understand these changes have uncertainties that need to be characterized and quantified. In this work we characterize parametric uncertainty in Los <span class="hlt">Alamos</span> <span class="hlt">Sea</span> <span class="hlt">Ice</span> model (CICE) and quantify the sensitivity of <span class="hlt">sea</span> <span class="hlt">ice</span> area, extent and volume with respect to uncertainty in about 40 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one-at-a-time, this study uses a global variance-based approach in which Sobol sequences are used to efficiently sample the full 40-dimensional parameter space. This approach requires a very large number of model evaluations, which are expensive to run. A more computationally efficient approach is implemented by training and cross-validating a surrogate (emulator) of the <span class="hlt">sea</span> <span class="hlt">ice</span> model with model output from 400 model runs. The emulator is used to make predictions of <span class="hlt">sea</span> <span class="hlt">ice</span> extent, area, and volume at several model configurations, which are then used to compute the Sobol sensitivity indices of the 40 parameters. A ranking based on the sensitivity indices indicates that model output is most sensitive to snow parameters such as conductivity and grain size, and the drainage of melt ponds. The main effects and interactions among the most influential parameters are also estimated by a non-parametric regression technique based on generalized additive models. It is recommended research to be prioritized towards more accurately determining these most influential parameters values by observational studies or by improving existing parameterizations in the <span class="hlt">sea</span> <span class="hlt">ice</span> model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016TCry...10.2173G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016TCry...10.2173G"><span>Estimates of ikaite export from <span class="hlt">sea</span> <span class="hlt">ice</span> to the underlying seawater in a <span class="hlt">sea</span> <span class="hlt">ice</span>-seawater mesocosm</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Geilfus, Nicolas-Xavier; Galley, Ryan J.; Else, Brent G. T.; Campbell, Karley; Papakyriakou, Tim; Crabeck, Odile; Lemes, Marcos; Delille, Bruno; Rysgaard, Søren</p> <p>2016-09-01</p> <p>The precipitation of ikaite and its fate within <span class="hlt">sea</span> <span class="hlt">ice</span> is still poorly understood. We quantify temporal inorganic carbon dynamics in <span class="hlt">sea</span> <span class="hlt">ice</span> from initial formation to its melt in a <span class="hlt">sea</span> <span class="hlt">ice</span>-seawater mesocosm pool from 11 to 29 January 2013. Based on measurements of total alkalinity (TA) and total dissolved inorganic carbon (TCO2), the main processes affecting inorganic carbon dynamics within <span class="hlt">sea</span> <span class="hlt">ice</span> were ikaite precipitation and CO2 exchange with the atmosphere. In the underlying seawater, the dissolution of ikaite was the main process affecting inorganic carbon dynamics. <span class="hlt">Sea</span> <span class="hlt">ice</span> acted as an active layer, releasing CO2 to the atmosphere during the growth phase, taking up CO2 as it melted and exporting both ikaite and TCO2 into the underlying seawater during the whole experiment. Ikaite precipitation of up to 167 µmol kg-1 within <span class="hlt">sea</span> <span class="hlt">ice</span> was estimated, while its export and dissolution into the underlying seawater was responsible for a TA increase of 64-66 µmol kg-1 in the water column. The export of TCO2 from <span class="hlt">sea</span> <span class="hlt">ice</span> to the water column increased the underlying seawater TCO2 by 43.5 µmol kg-1, suggesting that almost all of the TCO2 that left the <span class="hlt">sea</span> <span class="hlt">ice</span> was exported to the underlying seawater. The export of ikaite from the <span class="hlt">ice</span> to the underlying seawater was associated with brine rejection during <span class="hlt">sea</span> <span class="hlt">ice</span> growth, increased vertical connectivity in <span class="hlt">sea</span> <span class="hlt">ice</span> due to the upward percolation of seawater and meltwater flushing during <span class="hlt">sea</span> <span class="hlt">ice</span> melt. Based on the change in TA in the water column around the onset of <span class="hlt">sea</span> <span class="hlt">ice</span> melt, more than half of the total ikaite precipitated in the <span class="hlt">ice</span> during <span class="hlt">sea</span> <span class="hlt">ice</span> growth was still contained in the <span class="hlt">ice</span> when the <span class="hlt">sea</span> <span class="hlt">ice</span> began to melt. Ikaite crystal dissolution in the water column kept the seawater pCO2 undersaturated with respect to the atmosphere in spite of increased salinity, TA and TCO2 associated with <span class="hlt">sea</span> <span class="hlt">ice</span> growth. Results indicate that ikaite export from <span class="hlt">sea</span> <span class="hlt">ice</span> and its dissolution in the underlying seawater can potentially hamper</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C41B0701R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C41B0701R"><span>The Relationship Between Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Albedo and the Geophysical Parameters of the <span class="hlt">Ice</span> Cover</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Riihelä, A.</p> <p>2015-12-01</p> <p>The Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover is thinning and retreating. Remote sensing observations have also shown that the mean albedo of the remaining <span class="hlt">ice</span> cover is decreasing on decadal time scales, albeit with significant annual variability (Riihelä et al., 2013, Pistone et al., 2014). Attribution of the albedo decrease between its different drivers, such as decreasing <span class="hlt">ice</span> concentration and enhanced surface melt of the <span class="hlt">ice</span>, remains an important research question for the forecasting of future conditions of the <span class="hlt">ice</span> cover. A necessary step towards this goal is understanding the relationships between Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> albedo and the geophysical parameters of the <span class="hlt">ice</span> cover. Particularly the question of the relationship between <span class="hlt">sea</span> <span class="hlt">ice</span> albedo and <span class="hlt">ice</span> age is both interesting and not widely studied. The recent changes in the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> zone have led to a substantial decrease of its multi-year <span class="hlt">sea</span> <span class="hlt">ice</span>, as old <span class="hlt">ice</span> melts and is replaced by first-year <span class="hlt">ice</span> during the next freezing season. It is generally known that younger <span class="hlt">sea</span> <span class="hlt">ice</span> tends to have a lower albedo than older <span class="hlt">ice</span> because of several reasons, such as wetter snow cover and enhanced melt ponding. However, the quantitative correlation between <span class="hlt">sea</span> <span class="hlt">ice</span> age and <span class="hlt">sea</span> <span class="hlt">ice</span> albedo has not been extensively studied to date, excepting in-situ measurement based studies which are, by necessity, focused on a limited area of the Arctic Ocean (Perovich and Polashenski, 2012).In this study, I analyze the dependencies of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> albedo relative to the geophysical parameters of the <span class="hlt">ice</span> field. I use remote sensing datasets such as the CM SAF CLARA-A1 (Karlsson et al., 2013) and the NASA MeaSUREs (Anderson et al., 2014) as data sources for the analysis. The studied period is 1982-2009. The datasets are spatiotemporally collocated and analysed. The changes in <span class="hlt">sea</span> <span class="hlt">ice</span> albedo as a function of <span class="hlt">sea</span> <span class="hlt">ice</span> age are presented for the whole Arctic Ocean and for potentially interesting marginal <span class="hlt">sea</span> cases. This allows us to see if the the albedo of the older <span class="hlt">sea</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016TCry...10.2275T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016TCry...10.2275T"><span>The EUMETSAT <span class="hlt">sea</span> <span class="hlt">ice</span> concentration climate data record</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tonboe, Rasmus T.; Eastwood, Steinar; Lavergne, Thomas; Sørensen, Atle M.; Rathmann, Nicholas; Dybkjær, Gorm; Toudal Pedersen, Leif; Høyer, Jacob L.; Kern, Stefan</p> <p>2016-09-01</p> <p>An Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> area and extent dataset has been generated by EUMETSAT's Ocean and <span class="hlt">Sea</span> <span class="hlt">Ice</span> Satellite Application Facility (OSISAF) using the record of microwave radiometer data from NASA's Nimbus 7 Scanning Multichannel Microwave radiometer (SMMR) and the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager and Sounder (SSMIS) satellite sensors. The dataset covers the period from October 1978 to April 2015 and updates and further developments are planned for the next phase of the project. The methodology for computing the <span class="hlt">sea</span> <span class="hlt">ice</span> concentration uses (1) numerical weather prediction (NWP) data input to a radiative transfer model for reduction of the impact of weather conditions on the measured brightness temperatures; (2) dynamical algorithm tie points to mitigate trends in residual atmospheric, <span class="hlt">sea</span> <span class="hlt">ice</span>, and water emission characteristics and inter-sensor differences/biases; and (3) a hybrid <span class="hlt">sea</span> <span class="hlt">ice</span> concentration algorithm using the Bristol algorithm over <span class="hlt">ice</span> and the Bootstrap algorithm in frequency mode over open water. A new <span class="hlt">sea</span> <span class="hlt">ice</span> concentration uncertainty algorithm has been developed to estimate the spatial and temporal variability in <span class="hlt">sea</span> <span class="hlt">ice</span> concentration retrieval accuracy. A comparison to US National <span class="hlt">Ice</span> Center <span class="hlt">sea</span> <span class="hlt">ice</span> charts from the Arctic and the Antarctic shows that <span class="hlt">ice</span> concentrations are higher in the <span class="hlt">ice</span> charts than estimated from the radiometer data at intermediate <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations between open water and 100 % <span class="hlt">ice</span>. The <span class="hlt">sea</span> <span class="hlt">ice</span> concentration climate data record is available for download at <a href=" http://www.osi-saf.org"target="_blank">www.osi-saf.org</a>, including documentation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20030056665&hterms=arctic+ice+loss&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Darctic%2Bice%2Bloss','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20030056665&hterms=arctic+ice+loss&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Darctic%2Bice%2Bloss"><span>30-Year Satellite Record Reveals Accelerated Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Loss, Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Trend Reversal</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cavalieri, Donald J.; Parkinson, C. L.; Vinnikov, K. Y.</p> <p>2003-01-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent decreased by 0.30 plus or minus 0.03 x 10(exp 6) square kilometers per decade from 1972 through 2002, but decreased by 0.36 plus or minus 0.05 x 10(exp 6) square kilometers per decade from 1979 through 2002, indicating an acceleration of 20% in the rate of decrease. In contrast to the Arctic, the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent decreased dramatically over the period 1973-1977, then gradually increased, with an overall 30-year trend of -0.15 plus or minus 0.08 x 10(exp 6) square kilometers per 10yr. The trend reversal is attributed to a large positive anomaly in Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent observed in the early 1970's.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/335308','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/335308"><span>A destabilizing thermohaline circulation-atmosphere-<span class="hlt">sea</span> <span class="hlt">ice</span> feedback</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Jayne, S.R.; Marotzke, J.</p> <p>1999-02-01</p> <p>Some of the interactions and feedbacks between the atmosphere, thermohaline circulation, and <span class="hlt">sea</span> <span class="hlt">ice</span> are illustrated using a simple process model. A simplified version of the annual-mean coupled ocean-atmosphere box model of Nakamura, Stone, and Marotzke is modified to include a parameterization of <span class="hlt">sea</span> <span class="hlt">ice</span>. The model includes the thermodynamic effects of <span class="hlt">sea</span> <span class="hlt">ice</span> and allows for variable coverage. It is found that the addition of <span class="hlt">sea</span> <span class="hlt">ice</span> introduces feedbacks that have a destabilizing influence on the thermohaline circulation: <span class="hlt">Sea</span> <span class="hlt">ice</span> insulates the ocean from the atmosphere, creating colder air temperatures at high latitudes, which cause larger atmospheric eddy heat and moisture transports and weaker oceanic heat transports. These in turn lead to thicker <span class="hlt">ice</span> coverage and hence establish a positive feedback. The results indicate that generally in colder climates, the presence of <span class="hlt">sea</span> <span class="hlt">ice</span> may lead to a significant destabilization of the thermohaline circulation. Brine rejection by <span class="hlt">sea</span> <span class="hlt">ice</span> plays no important role in this model`s dynamics. The net destabilizing effect of <span class="hlt">sea</span> <span class="hlt">ice</span> in this model is the result of two positive feedbacks and one negative feedback and is shown to be model dependent. To date, the destabilizing feedback between atmospheric and oceanic heat fluxes, mediated by <span class="hlt">sea</span> <span class="hlt">ice</span>, has largely been neglected in conceptual studies of thermohaline circulation stability, but it warrants further investigation in more realistic models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19870007754&hterms=dry+snow&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Ddry%2Bsnow','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19870007754&hterms=dry+snow&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Ddry%2Bsnow"><span>Microwave remote sensing of snow-covered <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Borgeaud, M.; Kong, J. A.; Lin, F. C.</p> <p>1986-01-01</p> <p>Snow and <span class="hlt">ice</span> are modeled as random media characterized by different dielectric constants and correlation functions. In order to model the brine inclusions of <span class="hlt">sea</span> <span class="hlt">ice</span>, the random medium is assumed to be anisotropic. A three-layer model is used to simulate a snow-covered <span class="hlt">ice</span> field with the top layer being snow, the middle layer being <span class="hlt">ice</span>, and the bottom layer being <span class="hlt">sea</span> water. The theoretical results are illustrated for thick first-year <span class="hlt">sea</span> <span class="hlt">ice</span> covered by dry snow, and for artificial, thin first-year <span class="hlt">sea</span> <span class="hlt">ice</span> covered by wet snow as measured in controlled model tank experiments. The radar backscattering cross sections are seen to increase with snow cover for snow-covered <span class="hlt">sea</span> <span class="hlt">ice</span> owing to large volume scattering effects of snow.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_10 --> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19890063250&hterms=wegeners&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dwegeners','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19890063250&hterms=wegeners&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dwegeners"><span>Passive microwave in situ observations of winter Weddell <span class="hlt">Sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Comiso, J. C.; Grenfell, T. C.; Bell, D. L.; Lange, M. A.; Ackley, S. F.</p> <p>1989-01-01</p> <p>Results are presented on the microwave radiative characteristics of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> measured during the 1986 Winter Weddell <span class="hlt">Sea</span> Project with a set of portable radiometers. Radiometer measurements at 6, 10, 18, 37, and 90 GHz in vertical and horizontal polarizations were supplemented by near-simultaneous measurements of the <span class="hlt">ice</span> physical characteristics (including <span class="hlt">ice</span> thickness, salinity, temperature, snow cover, and density) made during two cruises, lasting 3 months each. Measurements were obtained on various types of <span class="hlt">sea</span> <span class="hlt">ice</span> over a large portion of the Weddell-<span class="hlt">Sea</span> <span class="hlt">ice</span> cover, including four transects across the entire <span class="hlt">ice</span> pack. Data analysis shows a large variability in the multispectral microwave emissivities of different <span class="hlt">ice</span> types, especially at 90 GHz, demonstrating a strong potential of the use of the 90-GHz channel, in combination with lower-frequency channels, for detailed characterizations of the <span class="hlt">ice</span> cover.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.youtube.com/watch?v=uiSuUe8dhZ0','SCIGOVIMAGE-NASA'); return false;" href="http://www.youtube.com/watch?v=uiSuUe8dhZ0"><span>Summer Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Retreat: May - August 2013</span></a></p> <p><a target="_blank" href="http://www.nasa.gov/multimedia/videogallery/index.html">NASA Video Gallery</a></p> <p></p> <p></p> <p>The melting of <span class="hlt">sea</span> <span class="hlt">ice</span> in the Arctic is well on its way toward its annual "minimum," that time when the floating <span class="hlt">ice</span> cap covers less of the Arctic Ocean than at any other period during the year. 20...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C51A0669K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C51A0669K"><span>Natural climate variabilities and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> trend</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kohyama, T.; Hartmann, D. L.</p> <p>2015-12-01</p> <p>The interannual Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> variability in Indian Ocean shares a large portion of variance with Southern Annular Mode (SAM), and that in Ross <span class="hlt">Sea</span> with El Niño Southern Oscillation (ENSO). If we regress out the influence of these climate modes from the sectorial <span class="hlt">sea</span> <span class="hlt">ice</span> extent time series, the expanding <span class="hlt">sea</span> <span class="hlt">ice</span> trends in the satellite era become insignificant at 95 %. Because SAM has a human-induced trend, the increasing <span class="hlt">sea</span> <span class="hlt">ice</span> extent in Indian Ocean may be explained by superposition of anthropogenic forcing and natural variability. On the other hand, because ENSO does not have a significant trend, the <span class="hlt">sea</span> <span class="hlt">ice</span> trend in Ross <span class="hlt">Sea</span> might be produced purely by natural variability. In addition to SAM and ENSO, some residual <span class="hlt">sea</span> <span class="hlt">ice</span> variances can be explained by other modes, which are not simultaneously-correlated with SAM or ENSO. For instance, a wave-like mode that appears to be Rossby wave trains shares large variance with interannual <span class="hlt">sea</span> <span class="hlt">ice</span> variability in many longitudinal sectors. The spatial trend pattern reconstructed by the Rossby mode exhibits consistent features with the <span class="hlt">ice</span> motion trend pattern shown by Holland and Kwok (2012). These results, based on observational and reanalysis data, suggest that a large portion of expanding trend of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> may be explained by natural climate variability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GeoRL..44.1008L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GeoRL..44.1008L"><span>Hemispheric <span class="hlt">sea</span> <span class="hlt">ice</span> distribution sets the glacial tempo</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, Jung-Eun; Shen, Aaron; Fox-Kemper, Baylor; Ming, Yi</p> <p>2017-01-01</p> <p>The proxy record of global temperature shows that the dominant periodicity of the glacial cycle shifts from 40 kyr (obliquity) to 100 kyr (eccentricity) about a million years ago. Using climate model simulations, here we show that the pace of the glacial cycle depends on the pattern of hemispheric <span class="hlt">sea</span> <span class="hlt">ice</span> growth. In a cold climate the <span class="hlt">sea</span> <span class="hlt">ice</span> grows asymmetrically between two hemispheres under changes to Earth's orbital precession, because <span class="hlt">sea</span> <span class="hlt">ice</span> growth potential outside of the Arctic Circle is limited. This difference in hemispheric <span class="hlt">sea</span> <span class="hlt">ice</span> growth leads to an asymmetry in absorbed solar energy for the two hemispheres, particularly when eccentricity is high, even if the annual average insolation is similar. In a warmer climate, the hemispheric asymmetry of the <span class="hlt">sea</span> <span class="hlt">ice</span> decreases as mean Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> decreases, diminishing the precession and eccentricity signals and explaining the dominant obliquity signal (40 kyr) before the mid-Pleistocene transition.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70017680','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70017680"><span>Contrasts in Arctic shelf <span class="hlt">sea-ice</span> regimes and some implications: Beaufort <span class="hlt">Sea</span> versus Laptev <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Reimnitz, E.; Dethleff, D.; Nurnberg, D.</p> <p>1994-01-01</p> <p>The winter <span class="hlt">ice</span>-regime of the 500 km) from the mainland than in the Beaufort <span class="hlt">Sea</span>. As a result, the annual freeze-up does not incorporate old, deep-draft <span class="hlt">ice</span>, and with a lack of compression, such deep-draft <span class="hlt">ice</span> is not generated in situ, as on the Beaufort <span class="hlt">Sea</span> shelf. The Laptev <span class="hlt">Sea</span> has as much as 1000 km of fetch at the end of summer, when freezing storms move in and large (6 m) waves can form. Also, for the first three winter months, the polynya lies inshore at a water depth of only 10 m. Turbulence and freezing are excellent conditions for sediment entrainment by frazil and anchor <span class="hlt">ice</span>, when compared to conditions in the short-fetched Beaufort <span class="hlt">Sea</span>. We expect entrainment to occur yearly. Different from the intensely <span class="hlt">ice</span>-gouged Beaufort <span class="hlt">Sea</span> shelf, hydraulic bedforms probably dominate in the Laptev <span class="hlt">Sea</span>. Corresponding with the large volume of <span class="hlt">ice</span> produced, more dense water is generated in the Laptev <span class="hlt">Sea</span>, possibly accompanied by downslope sediment transport. Thermohaline convection at the midshelf polynya, together with the reduced rate of bottom disruption by <span class="hlt">ice</span> keels, may enhance benthic productivity and permit establishment of open-shelf benthic communities which in the Beaufort <span class="hlt">Sea</span> can thrive only in the protection of barrier islands. Indirect evidence for high benthic productivity is found in the presence of walrus, who also require year-round open water. By contrast, lack of a suitable environment restricts walrus from the Beaufort <span class="hlt">Sea</span>, although over 700 km farther to the south. We could speculate on other consequences of the different <span class="hlt">ice</span> regimes in the Beaufort and Laptev <span class="hlt">Seas</span>, but these few examples serve to point out the dangers of exptrapolating from knowledge gained in the North American Arctic to other shallow Arctic shelf settings. ?? 1994.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ClDy..tmp..343F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ClDy..tmp..343F"><span>Global coupled <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean state estimation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fenty, Ian; Menemenlis, Dimitris; Zhang, Hong</p> <p>2015-09-01</p> <p>We study the impact of synthesizing ocean and <span class="hlt">sea</span> <span class="hlt">ice</span> concentration data with a global, eddying coupled <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean configuration of the Massachusetts Institute of Technology general circulation model with the goal of reproducing the 2004 three-dimensional time-evolving <span class="hlt">ice</span>-ocean state. This work builds on the state estimation framework developed in the Estimating the Circulation and Climate of the Ocean consortium by seeking a reconstruction of the global <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean system that is simultaneously consistent with (1) a suite of in situ and remotely-sensed ocean and <span class="hlt">ice</span> data and (2) the physics encoded in the numerical model. This dual consistency is successfully achieved here by adjusting only the model's initial hydrographic state and its atmospheric boundary conditions such that misfits between the model and data are minimized in a least-squares sense. We show that synthesizing both ocean and <span class="hlt">sea</span> <span class="hlt">ice</span> concentration data is required for the model to adequately reproduce the observed details of the <span class="hlt">sea</span> <span class="hlt">ice</span> annual cycle in both hemispheres. Surprisingly, only modest adjustments to our first-guess atmospheric state and ocean initial conditions are necessary to achieve model-data consistency, suggesting that atmospheric reanalysis products remain a leading source of errors for <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean model hindcasts and reanalyses. The synthesis of <span class="hlt">sea</span> <span class="hlt">ice</span> data is found to ameliorate misfits in the high latitude ocean, especially with respect to upper ocean stratification, temperature, and salinity. Constraining the model to <span class="hlt">sea</span> <span class="hlt">ice</span> concentration modestly reduces ICESat-derived Arctic <span class="hlt">ice</span> thickness errors by improving the temporal and spatial evolution of seasonal <span class="hlt">ice</span>. Further increases in the accuracy of global <span class="hlt">sea</span> <span class="hlt">ice</span> thickness in the model likely require the direct synthesis of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110012963','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110012963"><span>Interferometric System for Measuring Thickness of <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hussein, Ziad; Jordan, Rolando; McDonald, Kyle; Holt, Benjamin; Huang, John; Kugo, Yasuo; Ishimaru, Akira; Jaruwatanadilok, Semsak; Akins, Torry; Gogineni, Prasad</p> <p>2006-01-01</p> <p>The cryospheric advanced sensor (CAS) is a developmental airborne (and, potentially, spaceborne) radar-based instrumentation system for measuring and mapping the thickness of <span class="hlt">sea</span> <span class="hlt">ice</span>. A planned future version of the system would also provide data on the thickness of snow covering <span class="hlt">sea</span> <span class="hlt">ice</span>. Frequent measurements of the thickness of polar ocean <span class="hlt">sea</span> <span class="hlt">ice</span> and its snow cover on a synoptic scale are critical to understanding global climate change and ocean circulation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016TCry...10.2027S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016TCry...10.2027S"><span><span class="hlt">Sea-ice</span> indicators of polar bear habitat</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stern, Harry L.; Laidre, Kristin L.</p> <p>2016-09-01</p> <p>Nineteen subpopulations of polar bears (Ursus maritimus) are found throughout the circumpolar Arctic, and in all regions they depend on <span class="hlt">sea</span> <span class="hlt">ice</span> as a platform for traveling, hunting, and breeding. Therefore polar bear phenology - the cycle of biological events - is linked to the timing of <span class="hlt">sea-ice</span> retreat in spring and advance in fall. We analyzed the dates of <span class="hlt">sea-ice</span> retreat and advance in all 19 polar bear subpopulation regions from 1979 to 2014, using daily <span class="hlt">sea-ice</span> concentration data from satellite passive microwave instruments. We define the dates of <span class="hlt">sea-ice</span> retreat and advance in a region as the dates when the area of <span class="hlt">sea</span> <span class="hlt">ice</span> drops below a certain threshold (retreat) on its way to the summer minimum or rises above the threshold (advance) on its way to the winter maximum. The threshold is chosen to be halfway between the historical (1979-2014) mean September and mean March <span class="hlt">sea-ice</span> areas. In all 19 regions there is a trend toward earlier <span class="hlt">sea-ice</span> retreat and later <span class="hlt">sea-ice</span> advance. Trends generally range from -3 to -9 days decade-1 in spring and from +3 to +9 days decade-1 in fall, with larger trends in the Barents <span class="hlt">Sea</span> and central Arctic Basin. The trends are not sensitive to the threshold. We also calculated the number of days per year that the <span class="hlt">sea-ice</span> area exceeded the threshold (termed <span class="hlt">ice</span>-covered days) and the average <span class="hlt">sea-ice</span> concentration from 1 June through 31 October. The number of <span class="hlt">ice</span>-covered days is declining in all regions at the rate of -7 to -19 days decade-1, with larger trends in the Barents <span class="hlt">Sea</span> and central Arctic Basin. The June-October <span class="hlt">sea-ice</span> concentration is declining in all regions at rates ranging from -1 to -9 percent decade-1. These <span class="hlt">sea-ice</span> metrics (or indicators of habitat change) were designed to be useful for management agencies and for comparative purposes among subpopulations. We recommend that the National Climate Assessment include the timing of <span class="hlt">sea-ice</span> retreat and advance in future reports.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008PhDT.......143L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008PhDT.......143L"><span>North Pacific climate variability and Arctic <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Linkin, Megan E.</p> <p></p> <p>Boreal winter North Pacific climate variability strongly influences North American hydroclimate and Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> distribution in the marginal Arctic <span class="hlt">seas</span>. Two modes of atmospheric variability explaining 53% of the variance in the Pacific Ocean <span class="hlt">sea</span> level pressure (SLP) field are extracted and identified: the Pacific-North American (PNA) teleconnection and the North Pacific Oscillation/West Pacific (NPO/WP) teleconnection. The NPO/WP, a dipole in North Pacific SLP and geopotential heights, is affiliated with latitudinal displacements of the Asian Pacific jet and an intensification of the Pacific stormtrack. The North American hydroclimate impacts of the NPO/WP are substantial; its impact on Alaska, Pacific Northwest and Great Plains precipitation is more influential than both the PNA and the El Nino-Southern Oscillation (ENSO). The NPO/WP is also strongly associated with a contemporaneous extension of the marginal <span class="hlt">ice</span> zone (MIZ) in the western Bering <span class="hlt">Sea</span> and <span class="hlt">Sea</span> of Okhotsk and MIZ retreat in the eastern Bering <span class="hlt">Sea</span>. Wintertime climate variability also significantly impacts the distribution of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> during the subsequent summer months, due to the hysteretic nature of the <span class="hlt">ice</span> cap. The North Atlantic Oscillation (NAO) is known for its effects on summer <span class="hlt">sea</span> <span class="hlt">ice</span> distribution; this study extends into the Pacific and finds that circulation anomalies related to Pacific <span class="hlt">sea</span> surface temperature (SST) variability also strongly impact summer Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. The NAO and ENSO are related to <span class="hlt">sea</span> <span class="hlt">ice</span> decline in the Eastern Siberian <span class="hlt">Sea</span>, where the linear trend since 1979 is 25% per decade. PDV affects <span class="hlt">sea</span> <span class="hlt">ice</span> in the eastern Arctic, a region which displays no linear trend since 1979. The low frequency of PDV variability and the persistent positive NAO during the 1980s and 1990s results in natural variability being aliased into the total linear trend in summer <span class="hlt">sea</span> <span class="hlt">ice</span> calculated from satellite-based <span class="hlt">sea</span> <span class="hlt">ice</span> concentration. Since 1979, natural variability accounts for 30% of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.C24A..01N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.C24A..01N"><span>Arctic and Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Changes and Impacts (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nghiem, S. V.</p> <p>2013-12-01</p> <p>The extent of springtime Arctic perennial <span class="hlt">sea</span> <span class="hlt">ice</span>, important to preconditioning summer melt and to polar sunrise photochemistry, continues its precipitous reduction in the last decade marked by a record low in 2012, as the Bromine, Ozone, and Mercury Experiment (BROMEX) was conducted around Barrow, Alaska, to investigate impacts of <span class="hlt">sea</span> <span class="hlt">ice</span> reduction on photochemical processes, transport, and distribution in the polar environment. In spring 2013, there was further loss of perennial <span class="hlt">sea</span> <span class="hlt">ice</span>, as it was not observed in the ocean region adjacent to the Alaskan north coast, where there was a stretch of perennial <span class="hlt">sea</span> <span class="hlt">ice</span> in 2012 in the Beaufort <span class="hlt">Sea</span> and Chukchi <span class="hlt">Sea</span>. In contrast to the rapid and extensive loss of <span class="hlt">sea</span> <span class="hlt">ice</span> in the Arctic, Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> has a trend of a slight increase in the past three decades. Given the significant variability in time and in space together with uncertainties in satellite observations, the increasing trend of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> may arguably be considered as having a low confidence level; however, there was no overall reduction of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent anywhere close to the decreasing rate of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. There exist publications presenting various factors driving changes in Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span>. After a short review of these published factors, new observations and atmospheric, oceanic, hydrological, and geological mechanisms contributed to different behaviors of <span class="hlt">sea</span> <span class="hlt">ice</span> changes in the Arctic and Antarctic are presented. The contribution from of hydrologic factors may provide a linkage to and enhance thermal impacts from lower latitudes. While geological factors may affect the sensitivity of <span class="hlt">sea</span> <span class="hlt">ice</span> response to climate change, these factors can serve as the long-term memory in the system that should be exploited to improve future projections or predictions of <span class="hlt">sea</span> <span class="hlt">ice</span> changes. Furthermore, similarities and differences in chemical impacts of Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> changes are discussed. Understanding <span class="hlt">sea</span> <span class="hlt">ice</span> changes and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016TCry...10.1055R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016TCry...10.1055R"><span>neXtSIM: a new Lagrangian <span class="hlt">sea</span> <span class="hlt">ice</span> model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rampal, Pierre; Bouillon, Sylvain; Ólason, Einar; Morlighem, Mathieu</p> <p>2016-05-01</p> <p>The Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover has changed drastically over the last decades. Associated with these changes is a shift in dynamical regime seen by an increase of extreme fracturing events and an acceleration of <span class="hlt">sea</span> <span class="hlt">ice</span> drift. The highly non-linear dynamical response of <span class="hlt">sea</span> <span class="hlt">ice</span> to external forcing makes modelling these changes and the future evolution of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> a challenge for current models. It is, however, increasingly important that this challenge be better met, both because of the important role of <span class="hlt">sea</span> <span class="hlt">ice</span> in the climate system and because of the steady increase of industrial operations in the Arctic. In this paper we present a new dynamical/thermodynamical <span class="hlt">sea</span> <span class="hlt">ice</span> model called neXtSIM that is designed to address this challenge. neXtSIM is a continuous and fully Lagrangian model, whose momentum equation is discretised with the finite-element method. In this model, <span class="hlt">sea</span> <span class="hlt">ice</span> physics are driven by the combination of two core components: a model for <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics built on a mechanical framework using an elasto-brittle rheology, and a model for <span class="hlt">sea</span> <span class="hlt">ice</span> thermodynamics providing damage healing for the mechanical framework. The evaluation of the model performance for the Arctic is presented for the period September 2007 to October 2008 and shows that observed multi-scale statistical properties of <span class="hlt">sea</span> <span class="hlt">ice</span> drift and deformation are well captured as well as the seasonal cycles of <span class="hlt">ice</span> volume, area, and extent. These results show that neXtSIM is an appropriate tool for simulating <span class="hlt">sea</span> <span class="hlt">ice</span> over a wide range of spatial and temporal scales.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19109440','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19109440"><span>Nonlinear threshold behavior during the loss of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Eisenman, I; Wettlaufer, J S</p> <p>2009-01-06</p> <p>In light of the rapid recent retreat of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>, a number of studies have discussed the possibility of a critical threshold (or "tipping point") beyond which the <span class="hlt">ice</span>-albedo feedback causes the <span class="hlt">ice</span> cover to melt away in an irreversible process. The focus has typically been centered on the annual minimum (September) <span class="hlt">ice</span> cover, which is often seen as particularly susceptible to destabilization by the <span class="hlt">ice</span>-albedo feedback. Here, we examine the central physical processes associated with the transition from <span class="hlt">ice</span>-covered to <span class="hlt">ice</span>-free Arctic Ocean conditions. We show that although the <span class="hlt">ice</span>-albedo feedback promotes the existence of multiple <span class="hlt">ice</span>-cover states, the stabilizing thermodynamic effects of <span class="hlt">sea</span> <span class="hlt">ice</span> mitigate this when the Arctic Ocean is <span class="hlt">ice</span> covered during a sufficiently large fraction of the year. These results suggest that critical threshold behavior is unlikely during the approach from current perennial <span class="hlt">sea-ice</span> conditions to seasonally <span class="hlt">ice</span>-free conditions. In a further warmed climate, however, we find that a critical threshold associated with the sudden loss of the remaining wintertime-only <span class="hlt">sea</span> <span class="hlt">ice</span> cover may be likely.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17..406K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17..406K"><span>Influence of <span class="hlt">ice</span> thickness and surface properties on light transmission through Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Katlein, Christian; Arndt, Stefanie; Nicolaus, Marcel; Jakuba, Michael V.; Laney, Samuel; Elliott, Stephen; Whitcomb, Louis L.; McFarland, Christopher J.; Suman, Stefano; Gerdes, Rüdiger; Boetius, Antje; German, Christopher R.</p> <p>2015-04-01</p> <p>The observed changes in physical properties of <span class="hlt">sea</span> <span class="hlt">ice</span> such as decreased thickness and increased melt pond cover severely impact the energy balance of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Increased light transmission leads to increased deposition of solar energy and thus plays a crucial role for <span class="hlt">sea-ice</span>-melt as well as for the amount and timing of under-<span class="hlt">ice</span> primary production. Recent developments in underwater technology provide new opportunities to undertake challenging research at the largely inaccessible underside of <span class="hlt">sea</span> <span class="hlt">ice</span>. We measured spectral under-<span class="hlt">ice</span> radiance and irradiance onboard the new Nereid Under-<span class="hlt">Ice</span> (Nereid-UI) underwater robotic vehicle, during a cruise of the R/V Polarstern to 83°N 6°W in the Arctic Ocean in July 2014. Nereid-UI is a next generation hybrid remotely operated vehicle (H-ROV) designed for both remotely-piloted and autonomous surveys underneath fixed and moving <span class="hlt">sea</span> <span class="hlt">ice</span>. Here we present results from the first comprehensive scientific dive of Nereid-UI employing its interdisciplinary sensor suite. We combine under-<span class="hlt">ice</span> optical measurements with three dimensional under-<span class="hlt">ice</span> topography (multibeam sonar) and aerial images of the surface conditions. We investigate the influence of spatially varying <span class="hlt">ice</span>-thickness and surface properties on the spatial variability of light transmittance on floe scale. Our results indicate that surface properties dominate the spatial distribution of the under-<span class="hlt">ice</span> light field, while <span class="hlt">sea</span> <span class="hlt">ice</span>-thickness and snow-depth are most important for mean light levels.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1338808-cmip6-sea-ice-model-intercomparison-project-simip-understanding-sea-ice-through-climate-model-simulations','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1338808-cmip6-sea-ice-model-intercomparison-project-simip-understanding-sea-ice-through-climate-model-simulations"><span>The CMIP6 <span class="hlt">Sea-Ice</span> Model Intercomparison Project (SIMIP): Understanding <span class="hlt">sea</span> <span class="hlt">ice</span> through climate-model simulations</span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Notz, Dirk; Jahn, Alexandra; Holland, Marika; ...</p> <p>2016-09-23</p> <p>A better understanding of the role of <span class="hlt">sea</span> <span class="hlt">ice</span> for the changing climate of our planet is the central aim of the diagnostic Coupled Model Intercomparison Project 6 (CMIP6)-endorsed <span class="hlt">Sea-Ice</span> Model Intercomparison Project (SIMIP). To reach this aim, SIMIP requests <span class="hlt">sea-ice</span>-related variables from climate-model simulations that allow for a better understanding and, ultimately, improvement of biases and errors in <span class="hlt">sea-ice</span> simulations with large-scale climate models. This then allows us to better understand to what degree CMIP6 model simulations relate to reality, thus improving our confidence in answering <span class="hlt">sea-ice</span>-related questions based on these simulations. Furthermore, the SIMIP protocol provides a standardmore » for <span class="hlt">sea-ice</span> model output that will streamline and hence simplify the analysis of the simulated <span class="hlt">sea-ice</span> evolution in research projects independent of CMIP. To reach its aims, SIMIP provides a structured list of model output that allows for an examination of the three main budgets that govern the evolution of <span class="hlt">sea</span> <span class="hlt">ice</span>, namely the heat budget, the momentum budget, and the mass budget. Furthermore, we explain the aims of SIMIP in more detail and outline how its design allows us to answer some of the most pressing questions that <span class="hlt">sea</span> <span class="hlt">ice</span> still poses to the international climate-research community.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1338808','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1338808"><span>The CMIP6 <span class="hlt">Sea-Ice</span> Model Intercomparison Project (SIMIP): Understanding <span class="hlt">sea</span> <span class="hlt">ice</span> through climate-model simulations</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Notz, Dirk; Jahn, Alexandra; Holland, Marika; Hunke, Elizabeth; Massonnet, François; Stroeve, Julienne; Tremblay, Bruno; Vancoppenolle, Martin</p> <p>2016-09-23</p> <p><p>A better understanding of the role of <span class="hlt">sea</span> <span class="hlt">ice</span> for the changing climate of our planet is the central aim of the diagnostic Coupled Model Intercomparison Project 6 (CMIP6)-endorsed <span class="hlt">Sea-Ice</span> Model Intercomparison Project (SIMIP). To reach this aim, SIMIP requests <span class="hlt">sea-ice</span>-related variables from climate-model simulations that allow for a better understanding and, ultimately, improvement of biases and errors in <span class="hlt">sea-ice</span> simulations with large-scale climate models. This then allows us to better understand to what degree CMIP6 model simulations relate to reality, thus improving our confidence in answering <span class="hlt">sea-ice</span>-related questions based on these simulations. Furthermore, the SIMIP protocol provides a standard for <span class="hlt">sea-ice</span> model output that will streamline and hence simplify the analysis of the simulated <span class="hlt">sea-ice</span> evolution in research projects independent of CMIP. To reach its aims, SIMIP provides a structured list of model output that allows for an examination of the three main budgets that govern the evolution of <span class="hlt">sea</span> <span class="hlt">ice</span>, namely the heat budget, the momentum budget, and the mass budget. Furthermore, we explain the aims of SIMIP in more detail and outline how its design allows us to answer some of the most pressing questions that <span class="hlt">sea</span> <span class="hlt">ice</span> still poses to the international climate-research community.</p></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GMD.....9.3427N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GMD.....9.3427N"><span>The CMIP6 <span class="hlt">Sea-Ice</span> Model Intercomparison Project (SIMIP): understanding <span class="hlt">sea</span> <span class="hlt">ice</span> through climate-model simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Notz, Dirk; Jahn, Alexandra; Holland, Marika; Hunke, Elizabeth; Massonnet, François; Stroeve, Julienne; Tremblay, Bruno; Vancoppenolle, Martin</p> <p>2016-09-01</p> <p>A better understanding of the role of <span class="hlt">sea</span> <span class="hlt">ice</span> for the changing climate of our planet is the central aim of the diagnostic Coupled Model Intercomparison Project 6 (CMIP6)-endorsed <span class="hlt">Sea-Ice</span> Model Intercomparison Project (SIMIP). To reach this aim, SIMIP requests <span class="hlt">sea-ice</span>-related variables from climate-model simulations that allow for a better understanding and, ultimately, improvement of biases and errors in <span class="hlt">sea-ice</span> simulations with large-scale climate models. This then allows us to better understand to what degree CMIP6 model simulations relate to reality, thus improving our confidence in answering <span class="hlt">sea-ice</span>-related questions based on these simulations. Furthermore, the SIMIP protocol provides a standard for <span class="hlt">sea-ice</span> model output that will streamline and hence simplify the analysis of the simulated <span class="hlt">sea-ice</span> evolution in research projects independent of CMIP. To reach its aims, SIMIP provides a structured list of model output that allows for an examination of the three main budgets that govern the evolution of <span class="hlt">sea</span> <span class="hlt">ice</span>, namely the heat budget, the momentum budget, and the mass budget. In this contribution, we explain the aims of SIMIP in more detail and outline how its design allows us to answer some of the most pressing questions that <span class="hlt">sea</span> <span class="hlt">ice</span> still poses to the international climate-research community.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1025348','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1025348"><span>Modeling Abrupt Change in Global <span class="hlt">Sea</span> Level Arising from Ocean - <span class="hlt">Ice</span>-Sheet Interaction</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Holland, David M</p> <p>2011-09-24</p> <p>It is proposed to develop, validate, and apply a coupled ocean <span class="hlt">ice</span>-sheet model to simulate possible, abrupt future change in global <span class="hlt">sea</span> level. This research is to be carried out collaboratively between an academic institute and a Department of Energy Laboratory (DOE), namely, the PI and a graduate student at New York University (NYU) and climate model researchers at the Los <span class="hlt">Alamos</span> National Laboratory (LANL). The NYU contribution is mainly in the area of incorporating new physical processes into the model, while the LANL efforts are focused on improved numerics and overall model development. NYU and LANL will work together on applying the model to a variety of modeling scenarios of recent past and possible near-future abrupt change to the configuration of the periphery of the major <span class="hlt">ice</span> sheets. The project's ultimate goal is to provide a robust, accurate prediction of future global <span class="hlt">sea</span> level change, a feat that no fully-coupled climate model is currently capable of producing. This proposal seeks to advance that ultimate goal by developing, validating, and applying a regional model that can simulate the detailed processes involved in <span class="hlt">sea</span>-level change due to ocean <span class="hlt">ice</span>-sheet interaction. Directly modeling ocean <span class="hlt">ice</span>-sheet processes in a fully-coupled global climate model is not a feasible activity at present given the near-complete absence of development of any such causal mechanism in these models to date.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUFM.C41B0202S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUFM.C41B0202S"><span>A New Look at the Northern Hemisphere <span class="hlt">Sea</span> <span class="hlt">Ice</span> Concentration</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stern, H.; Fowler, C.; Fetterer, F.</p> <p>2004-12-01</p> <p>It is widely recognized that the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover has been shrinking over the past 25 years. Our knowledge of hemispheric <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and extent comes almost entirely from satellite passive microwave (PM) data collected since 1978. In this study, we use a new data set of Northern Hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, derived from weekly operational <span class="hlt">ice</span> charts spanning more than three decades (1972-2003), to re-examine the regional variability and trends in <span class="hlt">sea</span> <span class="hlt">ice</span> area and extent. The <span class="hlt">ice</span> charts from the U.S. National <span class="hlt">Ice</span> Center have been converted to EASE-Grid format. Source data for the charts include visible and infrared satellite imagery, active radar imagery, PM data, aerial reconnaissance, ship and shore observations, buoys, model output, information from foreign <span class="hlt">ice</span> services, and climatology. The PM data are used only when all other forms of data are not available. Thus we have a unique gridded data set that is largely independent of the popular PM products that are widely used by the <span class="hlt">sea</span> <span class="hlt">ice</span> community. We divided the Arctic and sub-Arctic <span class="hlt">seas</span> into regions and created monthly time series of <span class="hlt">sea</span> <span class="hlt">ice</span> area and extent for each region. We also obtained the monthly NASA Team <span class="hlt">sea</span> <span class="hlt">ice</span> concentration products. We re-gridded these to the same EASE-Grid format as the charts, and computed time series of <span class="hlt">sea</span> <span class="hlt">ice</span> area and extent for the same regions. We present comparisons of the regional differences and trends seen in the two data sets. We explain the differences based on the source data used in the charts, and the emissivity of <span class="hlt">sea</span> <span class="hlt">ice</span> as detected by the PM instruments. Future work with the <span class="hlt">ice</span> chart data set includes analysis of multiyear, first-year, and new <span class="hlt">ice</span> concentrations in order to understand the recent record-low summer <span class="hlt">ice</span> minima; duration of the <span class="hlt">ice</span> season, as an indicator of climate change; and analysis of the modes of variability of the <span class="hlt">ice</span> edge, in order to develop a predictive capability for <span class="hlt">sea</span> <span class="hlt">ice</span> extent.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMPA33A2176T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMPA33A2176T"><span>Arctic Summer <span class="hlt">Sea-Ice</span> Extent: How Free is Free?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tremblay, B.; Cullather, R. I.; DeRepentigny, P.; Pfirman, S. L.; Newton, R.</p> <p>2015-12-01</p> <p>As Northern Hemisphere perennial <span class="hlt">sea</span> <span class="hlt">ice</span> cover continues a long-term downward trend, attention has begun to focus on the implications of the changing conditions. A summertime <span class="hlt">ice</span>-free Arctic Ocean is frequently indicated as a signature milestone for these changes, however "<span class="hlt">ice</span>-free" has a substantially different meaning among scientists and interested stakeholders. To climate scientists it may mean when there is so little <span class="hlt">sea</span> <span class="hlt">ice</span> that it plays a minimal role in the climate system. To those interested in development, it may mean a threshold where icebreaker support is not required. To coastal communities it may mean so little <span class="hlt">ice</span> that hunting is not possible. To species dependent on <span class="hlt">sea</span> <span class="hlt">ice</span>, it may mean the point where they cannot find sufficient habitat to survive from spring until fall. In this contribution we document the projected seasonality of the <span class="hlt">sea</span> <span class="hlt">ice</span> retreat and address the following questions. For how long will the Arctic Ocean be <span class="hlt">ice</span> free on average each year? What is the impact of such changes in the seasonality of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover on species that are dependent on <span class="hlt">sea</span> <span class="hlt">ice</span>? To this end, we analyze the seasonal cycle in the <span class="hlt">sea-ice</span> extent simulated by the Community Earth System Model 1 - Large Ensemble (CESM1-LE) output for the 21st century. CESM1-LE simulates a realistic late 20th, early 21st century Arctic climate with a seasonal cycle in <span class="hlt">sea</span> <span class="hlt">ice</span> extent and rate of decline in good agreement with observations. Results from this model show that even by the end of the 21st century, the length of the <span class="hlt">ice</span>-free season is relatively short, with <span class="hlt">ice</span>-free conditions mainly present for 2-3 months between August and October. The result is a much larger amplitude seasonal cycle when compared with the late 20th century climate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.5081M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.5081M"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> variability during the Holocene: evidence from marine and <span class="hlt">ice</span> cores in the Ross <span class="hlt">Sea</span> area, East Antarctica</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mezgec, Karin; Melis, Romana; Crosta, Xavier; Traversi, Rita; Severi, Mirko; Colizza, Ester; Braida, Martina; Stenni, Barbara</p> <p>2013-04-01</p> <p>High latitudes are particularly interesting places to document natural climate variability. <span class="hlt">Sea</span> <span class="hlt">ice</span> is an important element in the climate system because it influences bottom water formation and ocean circulation and regulates the ocean-atmosphere heat exchange. Understanding climate and environmental changes through the reconstruction of past <span class="hlt">sea</span> <span class="hlt">ice</span> variability, atmospheric circulation and oceanographic conditions in the Southern Ocean could represent one of the most important keys to predict with confidence future climate changes on global scale. In fact, the oceanic area surrounding Antarctica represents the main source of bottom water formation affecting the global climate through the oceanic circulation. In this study, we present an interdisciplinary proxies analysis considering marine and <span class="hlt">ice</span> core records, as part of the ESF PolarCLIMATE HOLOCLIP (Holocene climate variability at high-southern latitudes: an integrated perspective) project, to document <span class="hlt">sea</span> <span class="hlt">ice</span> variability in the Ross <span class="hlt">Sea</span> continental shelf area. Diatom assemblages from three sediment cores located in the north-western Ross <span class="hlt">Sea</span> (Joides Basin, Cape Hallett and Wood Bay) have been studied and the <span class="hlt">sea</span> salt Na+ (a potential proxy of <span class="hlt">sea</span> <span class="hlt">ice</span>) records from two <span class="hlt">ice</span> core sites (Taylor Dome and Talos Dome) facing the Ross <span class="hlt">Sea</span> area have been considered. The significant positive correlations among the <span class="hlt">sea</span> <span class="hlt">ice</span> diatom Fragilariopsis curta relative abundance and <span class="hlt">sea</span> salt Na+ records from Talos Dome and Taylor Dome <span class="hlt">ice</span> cores, suggest that <span class="hlt">sea</span> salt Na+ could be used as a proxy for <span class="hlt">sea</span> <span class="hlt">ice</span> extent and/or duration in the Ross <span class="hlt">Sea</span> area. These preliminary results look as a positive premise in view of integrating proxies from different realms (marine and glacial) in order to achieve a more complete view of the climate and environmental changes occurring during the Holocene. The combination of geological and glacial records will greatly improve our knowledge on paleo <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_11 --> <div id="page_12" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="221"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002EGSGA..27..285H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002EGSGA..27..285H"><span>Revisiting Observations of Arctic <span class="hlt">Sea-ice</span> Motion and Deformation To Investigate Bounds of <span class="hlt">Sea-ice</span> Variability.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Heil, P.</p> <p></p> <p>The Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> is an important part of the northern polar climate system. Individual records of <span class="hlt">sea-ice</span> drift in the Arctic were obtained through the last century. Observa- tions with high spatial or temporal coverage start in the 1970's when remote observa- tions of <span class="hlt">sea-ice</span> motion are available from satellite-based instruments and in situ from drifting buoys. In the climatology of Arctic <span class="hlt">sea-ice</span> drift the clockwise Beaufort Gyre, a northward motion off the Siberian Coast, and a south-eastward drift from the North Pole towards Fram Strait, have been identified. During recent decades this picture of Arctic <span class="hlt">ice</span> motion has shifted away from the baseline state in response to changed at- mospheric conditions. Here we investigate high-resolution time-series of <span class="hlt">ice</span> motion derived from IABP drifting buoys to identify further patterns of preferred <span class="hlt">ice</span> motion in the Arctic. These can be associated with atmospheric regimes by correlating the two- dimensional variance of the buoy-derived <span class="hlt">ice</span> velocities with the horizontal gradient of the mean <span class="hlt">sea</span>-level pressure. Changes in the spatial pattern of regional meander coeffi- cients for the <span class="hlt">sea-ice</span> motion provide further evidence for repeated transitions between patterns of <span class="hlt">ice</span> motion in dependence to the atmospheric forcing. This is supported by dynamic frequency spectra of <span class="hlt">sea-ice</span> divergence, which suggest damping of subdaily deformation cycles during low AO years. For regions with persistent buoy coverage (e.g., the western Arctic) time-series of <span class="hlt">sea-ice</span> motion and deformation characteris- tics have been constructed. The identification of a dominant spatial pattern of <span class="hlt">sea-ice</span> motion and deformation in response to changing atmospheric forcing is then used to explain the variability seen in Arctic <span class="hlt">ice</span> extent and volume.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JGRC..119.5395W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JGRC..119.5395W"><span>Interdecadal changes in snow depth on Arctic <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Webster, Melinda A.; Rigor, Ignatius G.; Nghiem, Son V.; Kurtz, Nathan T.; Farrell, Sinead L.; Perovich, Donald K.; Sturm, Matthew</p> <p>2014-08-01</p> <p>Snow plays a key role in the growth and decay of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. In winter, it insulates <span class="hlt">sea</span> <span class="hlt">ice</span> from cold air temperatures, slowing <span class="hlt">sea</span> <span class="hlt">ice</span> growth. From spring to summer, the albedo of snow determines how much insolation is absorbed by the <span class="hlt">sea</span> <span class="hlt">ice</span> and underlying ocean, impacting <span class="hlt">ice</span> melt processes. Knowledge of the contemporary snow depth distribution is essential for estimating <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and volume, and for understanding and modeling <span class="hlt">sea</span> <span class="hlt">ice</span> thermodynamics in the changing Arctic. This study assesses spring snow depth distribution on Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> using airborne radar observations from Operation <span class="hlt">Ice</span>Bridge for 2009-2013. Data were validated using coordinated in situ measurements taken in March 2012 during the Bromine, Ozone, and Mercury Experiment (BROMEX) field campaign. We find a correlation of 0.59 and root-mean-square error of 5.8 cm between the airborne and in situ data. Using this relationship and <span class="hlt">Ice</span>Bridge snow thickness products, we compared the recent results with data from the 1937, 1954-1991 Soviet drifting <span class="hlt">ice</span> stations. The comparison shows thinning of the snowpack, from 35.1 ± 9.4 to 22.2 ± 1.9 cm in the western Arctic, and from 32.8 ± 9.4 to 14.5 ± 1.9 cm in the Beaufort and Chukchi <span class="hlt">seas</span>. These changes suggest a snow depth decline of 37 ± 29% in the western Arctic and 56 ± 33% in the Beaufort and Chukchi <span class="hlt">seas</span>. Thinning is negatively correlated with the delayed onset of <span class="hlt">sea</span> <span class="hlt">ice</span> freezeup during autumn.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA617624','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA617624"><span>Mass Balance of Multiyear <span class="hlt">Sea</span> <span class="hlt">Ice</span> in the Southern Beaufort <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2014-09-30</p> <p>seaice.alaska.edu/gi/ LONG-TERM GOALS 1) Determination of the net growth and melt of multiyear (MY) <span class="hlt">sea</span> <span class="hlt">ice</span> during its transit through the southern Beaufort...states of the Arctic <span class="hlt">ice</span> pack OBJECTIVES We have four main scientific objectives: I) Estimation of MY <span class="hlt">ice</span> volume entrained into the Beaufort <span class="hlt">Sea</span> from...north of Canada The region north of the Canadian Archipelago contains some of the oldest and thickest <span class="hlt">ice</span> in the Arctic and the amount of this <span class="hlt">ice</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19870057750&hterms=weather+types&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dweather%2Btypes','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19870057750&hterms=weather+types&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dweather%2Btypes"><span>A microwave radiometer weather-correcting <span class="hlt">sea</span> <span class="hlt">ice</span> algorithm</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Walters, J. M.; Ruf, C.; Swift, C. T.</p> <p>1987-01-01</p> <p>A new algorithm for estimating the proportions of the multiyear and first-year <span class="hlt">sea</span> <span class="hlt">ice</span> types under variable atmospheric and <span class="hlt">sea</span> surface conditions is presented, which uses all six channels of the SMMR. The algorithm is specifically tuned to derive <span class="hlt">sea</span> <span class="hlt">ice</span> parameters while accepting error in the auxiliary parameters of surface temperature, ocean surface wind speed, atmospheric water vapor, and cloud liquid water content. Not only does the algorithm naturally correct for changes in these weather conditions, but it retrieves <span class="hlt">sea</span> <span class="hlt">ice</span> parameters to the extent that gross errors in atmospheric conditions propagate only small errors into the <span class="hlt">sea</span> <span class="hlt">ice</span> retrievals. A preliminary evaluation indicates that the weather-correcting algorithm provides a better data product than the 'UMass-AES' algorithm, whose quality has been cross checked with independent surface observations. The algorithm performs best when the <span class="hlt">sea</span> <span class="hlt">ice</span> concentration is less than 20 percent.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C41D0761S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C41D0761S"><span>NWS Alaska <span class="hlt">Sea</span> <span class="hlt">Ice</span> Program: Operations and Decision Support Services</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schreck, M. B.; Nelson, J. A., Jr.; Heim, R.</p> <p>2015-12-01</p> <p>The National Weather Service's Alaska <span class="hlt">Sea</span> <span class="hlt">Ice</span> Program is designed to service customers and partners operating and planning operations within Alaska waters. The Alaska <span class="hlt">Sea</span> <span class="hlt">Ice</span> Program offers daily <span class="hlt">sea</span> <span class="hlt">ice</span> and <span class="hlt">sea</span> surface temperature analysis products. The program also delivers a five day <span class="hlt">sea</span> <span class="hlt">ice</span> forecast 3 times each week, provides a 3 month <span class="hlt">sea</span> <span class="hlt">ice</span> outlook at the end of each month, and has staff available to respond to <span class="hlt">sea</span> <span class="hlt">ice</span> related information inquiries. These analysis and forecast products are utilized by many entities around the state of Alaska and nationally for safety of navigation and community strategic planning. The list of current customers stem from academia and research institutions, to local state and federal agencies, to resupply barges, to coastal subsistence hunters, to gold dredgers, to fisheries, to the general public. Due to a longer <span class="hlt">sea</span> <span class="hlt">ice</span> free season over recent years, activity in the waters around Alaska has increased. This has led to a rise in decision support services from the Alaska <span class="hlt">Sea</span> <span class="hlt">Ice</span> Program. The ASIP is in constant contact with the National <span class="hlt">Ice</span> Center as well as the United States Coast Guard (USCG) for safety of navigation. In the past, the ASIP provided briefings to the USCG when in support of search and rescue efforts. Currently, not only does that support remain, but our team is also briefing on <span class="hlt">sea</span> <span class="hlt">ice</span> outlooks into the next few months. As traffic in the Arctic increases, the ASIP will be called upon to provide more and more services on varying time scales to meet customer needs. This talk will address the many facets of the current Alaska <span class="hlt">Sea</span> <span class="hlt">Ice</span> Program as well as delve into what we see as the future of the ASIP.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C13C0831F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C13C0831F"><span>First Results from the ASIBIA (Arctic <span class="hlt">Sea-Ice</span>, snow, Biogeochemistry and Impacts on the Atmosphere) <span class="hlt">Sea-Ice</span> Chamber</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Frey, M. M.; France, J.; von Glasow, R.; Thomas, M.</p> <p>2015-12-01</p> <p>The ocean-<span class="hlt">ice</span>-atmosphere system is very complex, and there are numerous challenges with conducting fieldwork on <span class="hlt">sea-ice</span> including costs, safety, experimental controls and access. By creating a new coupled Ocean-<span class="hlt">Sea-Ice</span>-(Snow)-Atmosphere facility at the University of East Anglia, UK, we are able to perform controlled investigations in areas such as <span class="hlt">sea-ice</span> physics, physicochemical and biogeochemical processes in <span class="hlt">sea-ice</span>, and to quantify the bi-directional flux of gases in established, freezing and melting <span class="hlt">sea-ice</span>. The environmental chamber is capable of controlled programmable temperatures from -55°C to +30°C, allowing a full range of first year <span class="hlt">sea-ice</span> growing conditions in both the Arctic and Antarctic to be simulated. The <span class="hlt">sea-ice</span> tank within the chamber measures 2.4 m x 1.4 m x 1 m water depth, with an identically sized Teflon film atmosphere on top of the tank. The tank and atmosphere forms a coupled, isolated mesocosm. Above the atmosphere is a light bank with dimmable solar simulation LEDs, and UVA and UVB broadband fluorescent battens, providing light for a range of experiments such as under <span class="hlt">ice</span> biogeochemistry and photochemistry. <span class="hlt">Ice</span> growth in the tank will be ideally suited for studying first-year <span class="hlt">sea-ice</span> physical properties, with in-situ <span class="hlt">ice</span>-profile measurements of temperature, salinity, conductivity, pressure and spectral light transmission. Under water and above <span class="hlt">ice</span> cameras are installed to observe the physical development of the <span class="hlt">sea-ice</span>. The ASIBIA facility is also well equipped for gas exchange and diffusion studies through <span class="hlt">sea-ice</span> with a suite of climate relevant gas measuring instruments (CH4, CO2, O3, NOx, NOy permanently installed, further instruments available) able to measure either directly in the atmospheric component, or via a membrane for water side dissolved gases. Here, we present the first results from the ASIBIA <span class="hlt">sea-ice</span> chamber, focussing on the physical development of first-year <span class="hlt">sea-ice</span> and show the future plans for the facility over</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C31A..01G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C31A..01G"><span>Seasonal Changes of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Physical Properties Observed During N-<span class="hlt">ICE</span>2015: An Overview</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gerland, S.; Spreen, G.; Granskog, M. A.; Divine, D.; Ehn, J. K.; Eltoft, T.; Gallet, J. C.; Haapala, J. J.; Hudson, S. R.; Hughes, N. E.; Itkin, P.; King, J.; Krumpen, T.; Kustov, V. Y.; Liston, G. E.; Mundy, C. J.; Nicolaus, M.; Pavlov, A.; Polashenski, C.; Provost, C.; Richter-Menge, J.; Rösel, A.; Sennechael, N.; Shestov, A.; Taskjelle, T.; Wilkinson, J.; Steen, H.</p> <p>2015-12-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> is changing, and for improving the understanding of the cryosphere, data is needed to describe the status and processes controlling current seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> growth, change and decay. We present preliminary results from in-situ observations on <span class="hlt">sea</span> <span class="hlt">ice</span> in the Arctic Basin north of Svalbard from January to June 2015. Over that time, the Norwegian research vessel «Lance» was moored to in total four <span class="hlt">ice</span> floes, drifting with the <span class="hlt">sea</span> <span class="hlt">ice</span> and allowing an international group of scientists to conduct detailed research. Each drift lasted until the ship reached the marginal <span class="hlt">ice</span> zone and <span class="hlt">ice</span> started to break up, before moving further north and starting the next drift. The ship stayed within the area approximately 80°-83° N and 5°-25° E. While the expedition covered measurements in the atmosphere, the snow and <span class="hlt">sea</span> <span class="hlt">ice</span> system, and in the ocean, as well as biological studies, in this presentation we focus on physics of snow and <span class="hlt">sea</span> <span class="hlt">ice</span>. Different <span class="hlt">ice</span> types could be investigated: young <span class="hlt">ice</span> in refrozen leads, first year <span class="hlt">ice</span>, and old <span class="hlt">ice</span>. Snow surveys included regular snow pits with standardized measurements of physical properties and sampling. Snow and <span class="hlt">ice</span> thickness were measured at stake fields, along transects with electromagnetics, and in drillholes. For quantifying <span class="hlt">ice</span> physical properties and texture, <span class="hlt">ice</span> cores were obtained regularly and analyzed. Optical properties of snow and <span class="hlt">ice</span> were measured both with fixed installed radiometers, and from mobile systems, a sledge and an ROV. For six weeks, the surface topography was scanned with a ground LIDAR system. Spatial scales of surveys ranged from spot measurements to regional surveys from helicopter (<span class="hlt">ice</span> thickness, photography) during two months of the expedition, and by means of an array of autonomous buoys in the region. Other regional information was obtained from SAR satellite imagery and from satellite based radar altimetry. The analysis of the data collected has started, and first results will be</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.8637L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.8637L"><span>Amplification of European Little <span class="hlt">Ice</span> Age by <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean-atmosphere feedbacks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lehner, Flavio; Born, Andreas; Raible, Christoph C.; Stocker, Thomas F.</p> <p>2013-04-01</p> <p>The transition from the Medieval Climate Anomaly (~950-1250 AD) to the Little <span class="hlt">Ice</span> Age (~1400-1700 AD) is believed to have been driven by an interplay of external forcing and climate system-internal variability. While the hemispheric signal seems to have been dominated by solar irradiance and volcanic eruptions, the understanding of mechanisms shaping the climate on continental scale is less robust. Examining an ensemble of transient model simulations as well as a new type of sensitivity experiments with artificial <span class="hlt">sea</span> <span class="hlt">ice</span> growth, we identify a <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean-atmosphere feedback mechanism that amplifies the Little <span class="hlt">Ice</span> Age cooling in the North Atlantic-European region and produces the temperature pattern expected from reconstructions. Initiated by increasing negative forcing, the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> substantially expands at the beginning of the Little <span class="hlt">Ice</span> Age. The excess of <span class="hlt">sea</span> <span class="hlt">ice</span> is exported to the subpolar North Atlantic, where it melts, thereby weakening convection of the ocean. As a consequence, northward ocean heat transport is reduced, reinforcing the expansion of the <span class="hlt">sea</span> <span class="hlt">ice</span> and the cooling of the Northern Hemisphere. In the Nordic <span class="hlt">Seas</span>, <span class="hlt">sea</span> surface height anomalies cause the oceanic recirculation to strengthen at the expense of the warm Barents <span class="hlt">Sea</span> inflow, thereby further reinforcing <span class="hlt">sea</span> <span class="hlt">ice</span> growth in the Barents <span class="hlt">Sea</span>. The absent ocean-atmosphere heat flux in the Barents <span class="hlt">Sea</span> results in an amplified cooling over Northern Europe. The positive nature of this feedback mechanism enables <span class="hlt">sea</span> <span class="hlt">ice</span> to remain in an expanded state for decades to centuries and explain sustained cold periods over Europe such as the Little <span class="hlt">Ice</span> Age. Support for the feedback mechanism comes from recent proxy reconstructions around the Nordic <span class="hlt">Seas</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFM.C33B1132S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFM.C33B1132S"><span>A New Look at the Northern Hemisphere <span class="hlt">Sea</span> <span class="hlt">Ice</span> Concentration</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stern, H.; Fetterer, F.; Fowler, C.</p> <p>2005-12-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent has decreased over the past 25 years by 7% in winter and 17% in summer, with record or near-record summer lows since 2002. These estimates come from satellite passive microwave (PM) data collected since 1979. In this study we use a new data set of Northern Hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, derived from weekly operational <span class="hlt">ice</span> charts (1972-2004) produced by the U.S. National <span class="hlt">Ice</span> Center (NIC), to re-examine regional variability and trends in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> area and extent. The <span class="hlt">ice</span> charts through 1994 were quality-checked and converted to EASE-Grid format by the Arctic Climatology Project (2000). Charts for 1995-2004 were converted to EASE-Grid by us. Source data used by NIC to create the charts included visible and infrared satellite imagery, active radar imagery, PM data, aerial reconnaissance, ship and shore observations, buoys, model output, information from foreign <span class="hlt">ice</span> services, and climatology. The PM data were incorporated into the charts only when all other forms of data were not available. We divide the Arctic and sub-Arctic <span class="hlt">seas</span> into regions and compare chart-derived monthly <span class="hlt">sea</span> <span class="hlt">ice</span> concentration in each region to that derived from PM data. We find that the <span class="hlt">ice</span> charts give a more realistic depiction of the <span class="hlt">ice</span> edge, the marginal <span class="hlt">ice</span> zone, and coastal areas. The PM data have the advantage of being available as a daily product rather than weekly or biweekly. The <span class="hlt">ice</span> charts for 1995-2004 also include concentrations of multiyear <span class="hlt">ice</span>, first-year <span class="hlt">ice</span>, and new <span class="hlt">ice</span>. We present results of our analysis of these data, as well as calculations of the duration of the <span class="hlt">ice</span> season in each region, and the variability of the <span class="hlt">ice</span> edge.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUFM.C21A1122S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.C21A1122S"><span>A New Look at the Northern Hemisphere <span class="hlt">Sea</span> <span class="hlt">Ice</span> Concentration</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stern, H.; Fetterer, F.; Fowler, C.</p> <p>2006-12-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent has decreased over the past 25 years by 7% in winter and 17% in summer, with record or near-record summer lows since 2002. These estimates come from satellite passive microwave (PM) data collected since 1979. In this study we use a new data set of Northern Hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, derived from weekly operational <span class="hlt">ice</span> charts (1972-2004) produced by the U.S. National <span class="hlt">Ice</span> Center (NIC), to re- examine regional variability and trends in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> area and extent. The <span class="hlt">ice</span> charts through 1994 were quality-checked and converted to EASE-Grid format by the Arctic Climatology Project (2000). Charts for 1995-2004 were converted to EASE-Grid by us. Source data used by NIC to create the charts included visible and infrared satellite imagery, active radar imagery, PM data, aerial reconnaissance, ship and shore observations, buoys, model output, information from foreign <span class="hlt">ice</span> services, and climatology. The PM data were incorporated into the charts only when all other forms of data were not available. We divide the Arctic and sub-Arctic <span class="hlt">seas</span> into regions and compare chart-derived monthly <span class="hlt">sea</span> <span class="hlt">ice</span> concentration in each region to that derived from PM data. We find that the <span class="hlt">ice</span> charts give a more realistic depiction of the <span class="hlt">ice</span> edge, the marginal <span class="hlt">ice</span> zone, and coastal areas. The PM data have the advantage of being available as a daily product rather than weekly or biweekly. The <span class="hlt">ice</span> charts for 1995-2004 also include concentrations of multiyear <span class="hlt">ice</span>, first-year <span class="hlt">ice</span>, and new <span class="hlt">ice</span>. We present results of our analysis of these data, as well as calculations of the duration of the <span class="hlt">ice</span> season in each region, and the variability of the <span class="hlt">ice</span> edge.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19910030932&hterms=discrimination&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Ddiscrimination','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19910030932&hterms=discrimination&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Ddiscrimination"><span>The discrimination of <span class="hlt">sea</span> <span class="hlt">ice</span> types using SAR backscatter statistics</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shuchman, Robert A.; Wackerman, Christopher C.; Maffett, Andrew L.; Onstott, Robert G.; Sutherland, Laura L.</p> <p>1989-01-01</p> <p>X-band (HH) synthetic aperture radar (SAR) data of <span class="hlt">sea</span> <span class="hlt">ice</span> collected during the Marginal <span class="hlt">Ice</span> Zone Experiment in March and April of 1987 was statistically analyzed with respect to discriminating open water, first-year <span class="hlt">ice</span>, multiyear <span class="hlt">ice</span>, and Odden. Odden are large expanses of nilas <span class="hlt">ice</span> that rapidly form in the Greenland <span class="hlt">Sea</span> and transform into pancake <span class="hlt">ice</span>. A first-order statistical analysis indicated that mean versus variance can segment out open water and first-year <span class="hlt">ice</span>, and skewness versus modified skewness can segment the Odden and multilayer categories. In additions to first-order statistics, a model has been generated for the distribution function of the SAR <span class="hlt">ice</span> data. Segmentation of <span class="hlt">ice</span> types was also attempted using textural measurements. In this case, the general co-occurency matrix was evaluated. The textural method did not generate better results than the first-order statistical approach.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/961004','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/961004"><span>Age characteristics in a multidecadal Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> simulation</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Hunke, Elizabeth C; Bitz, Cecllia M</p> <p>2008-01-01</p> <p>Results from adding a tracer for age of <span class="hlt">sea</span> <span class="hlt">ice</span> to a sophisticated <span class="hlt">sea</span> <span class="hlt">ice</span> model that is widely used for climate studies are presented. The consistent simulation of <span class="hlt">ice</span> age, dynamics, and thermodynamics in the model shows explicitly that the loss of Arctic perennial <span class="hlt">ice</span> has accelerated in the past three decades, as has been seen in satellite-derived observations. Our model shows that the September <span class="hlt">ice</span> age average across the Northern Hemisphere varies from about 5 to 8 years, and the <span class="hlt">ice</span> is much younger (about 2--3 years) in late winter because of the expansion of first-year <span class="hlt">ice</span>. We find seasonal <span class="hlt">ice</span> on average comprises about 5% of the total <span class="hlt">ice</span> area in September, but as much as 1.34 x 10{sup 6} km{sup 2} survives in some years. Our simulated <span class="hlt">ice</span> age in the late 1980s and early 1990s declined markedly in agreement with other studies. After this period of decline, the <span class="hlt">ice</span> age began to recover, but in the final years of the simulation very little young <span class="hlt">ice</span> remains after the melt season, a strong indication that the age of the pack will again decline in the future as older <span class="hlt">ice</span> classes fail to be replenished. The Arctic <span class="hlt">ice</span> pack has fluctuated between older and younger <span class="hlt">ice</span> types over the past 30 years, while <span class="hlt">ice</span> area, thickness, and volume all declined over the same period, with an apparent acceleration in the last decade.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PhDT.........4W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PhDT.........4W"><span>Relationships in Areal Variability: The Ross <span class="hlt">Sea</span> Polynya and <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ward, Jason Michael</p> <p></p> <p>General increases in Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> coverage occur primarily in the Ross <span class="hlt">Sea</span>. This study investigates the Ross <span class="hlt">Sea</span> Polynya's relationship with the Ross <span class="hlt">Sea</span> <span class="hlt">ice</span> areal coverage. A unique, relatively long term Ross <span class="hlt">Sea</span> Polynya area dataset was created through the application of the Polynya Signature Simulation Method (PSSM) onto Special Sensor Microwave Imager (SSM/I) data inputs. Bivariate regression analyses were used to determine the relationships, at the 95% confidence level, between Ross <span class="hlt">Sea</span> Polynya and <span class="hlt">ice</span> areal trends, annual seasonalities, and anomalies at the full temporal scale as well as the monthly level. Polynya and <span class="hlt">sea</span> <span class="hlt">ice</span> have significant positive relationships in the late austral summer and early spring (February to March), and a significant negative relationship in the late austral winter (August). The areal anomalies only had a significant relationship in February, while the trends were not correlated at any time.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010026440','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010026440"><span>Observation of <span class="hlt">Sea</span> <span class="hlt">Ice</span> Surface Thermal States Under Cloud Cover</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Nghiem, S. V.; Perovich, D. K.; Gow, A. J.; Kwok, R.; Barber, D. G.; Comiso, J. C.; Zukor, Dorothy J. (Technical Monitor)</p> <p>2001-01-01</p> <p>Clouds interfere with the distribution of short-wave and long-wave radiations over <span class="hlt">sea</span> <span class="hlt">ice</span>, and thereby strongly affect the surface energy balance in polar regions. To evaluate the overall effects of clouds on climatic feedback processes in the atmosphere-<span class="hlt">ice</span>-ocean system, the challenge is to observe <span class="hlt">sea</span> <span class="hlt">ice</span> surface thermal states under both clear sky and cloudy conditions. From laboratory experiments, we show that C-band radar (transparent to clouds) backscatter is very sensitive to the surface temperature of first-year <span class="hlt">sea</span> <span class="hlt">ice</span>. The effect of <span class="hlt">sea</span> <span class="hlt">ice</span> surface temperature on the magnitude of backscatter change depends on the thermal regimes of <span class="hlt">sea</span> <span class="hlt">ice</span> thermodynamic states. For the temperature range above the mirabilite (Na2SO4.10H20) crystallization point (-8.2 C), C-band data show <span class="hlt">sea</span> <span class="hlt">ice</span> backscatter changes by 8-10 dB for incident angles from 20 to 35 deg at both horizontal and vertical polarizations. For temperatures below the mirabilite point but above the crystallization point of MgCl2.8H2O (-18.0 C), relatively strong backwater changes between 4-6 dB are observed. These backscatter changes correspond to approximately 8 C change in temperature for both cases. The backscattering mechanism is related to the temperature which determines the thermodynamic distribution of brine volume in the <span class="hlt">sea</span> <span class="hlt">ice</span> surface layer. The backscatter is positively correlated to temperature and the process is reversible with thermodynamic variations such as diurnal insolation effects. From two different dates in May 1993 with clear and overcast conditions determined by the Advanced Very High Resolution Radiometer (AVHRR), concurrent Earth Resources Satellite 1 (ERS-1) C-band <span class="hlt">ice</span> observed with increases in backscatter over first-year <span class="hlt">sea</span> <span class="hlt">ice</span>, and verified by increases in in-situ <span class="hlt">sea</span> <span class="hlt">ice</span> surface temperatures measured at the Collaborative-Interdisciplinary Cryosphere Experiment (C-<span class="hlt">ICE</span>) site.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/263523','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/263523"><span>Radiative transfer in atmosphere-<span class="hlt">sea</span> <span class="hlt">ice</span>-ocean system</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Jin, Z.; Stamnes, K.; Weeks, W.F.; Tsay, S.C.</p> <p>1996-04-01</p> <p>Radiative energy is critical in controlling the heat and mass balance of <span class="hlt">sea</span> <span class="hlt">ice</span>, which significantly affects the polar climate. In the polar oceans, light transmission through the atmosphere and <span class="hlt">sea</span> <span class="hlt">ice</span> is essential to the growth of plankton and algae and, consequently, to the microbial community both in the <span class="hlt">ice</span> and in the ocean. Therefore, the study of radiative transfer in the polar atmosphere, <span class="hlt">sea</span> <span class="hlt">ice</span>, and ocean system is of particular importance. Lacking a properly coupled radiative transfer model for the atmosphere-<span class="hlt">sea</span> <span class="hlt">ice</span>-ocean system, a consistent study of the radiative transfer in the polar atmosphere, snow, <span class="hlt">sea</span> <span class="hlt">ice</span>, and ocean system has not been undertaken before. The radiative transfer processes in the atmosphere and in the <span class="hlt">ice</span> and ocean have been treated separately. Because the radiation processes in the atmosphere, <span class="hlt">sea</span> <span class="hlt">ice</span>, and ocean depend on each other, this separate treatment is inconsistent. To study the radiative interaction between the atmosphere, clouds, snow, <span class="hlt">sea</span> <span class="hlt">ice</span>, and ocean, a radiative transfer model with consistent treatment of radiation in the coupled system is needed and is under development.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.3538V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.3538V"><span>What can bromine in <span class="hlt">ice</span> cores tell us about Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> in the past?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vallelonga, Paul; Spolaor, Andrea; Maffazzoli, Niccolo; Kjær, Helle; Barbante, Carlo; Saiz-Lopez, Alfonso</p> <p>2016-04-01</p> <p>Bromine is of interest as a potential <span class="hlt">sea</span> <span class="hlt">ice</span> proxy due to its role in polar atmospheric chemistry, particularly the photochemical "bromine explosion" events which occur over the seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> surface. A growing body of literature has demonstrated that bromine is reliably deposited and preserved in polar <span class="hlt">ice</span> caps and can be used to investigate variability over timescales varying from seasonal to multimillenial. For <span class="hlt">sea</span> <span class="hlt">ice</span> reconstructions, bromine and sodium are usually evaluated with respect to their relative abundances in seawater. Competing processes of bromine enrichment due to the bromine explosion, and bromine depletion due to scavenging and deposition, must be taken into account when comparing results from coastal and inland sampling sites. We will review existing bromine-based <span class="hlt">sea</span> <span class="hlt">ice</span> reconstructions and present new data for locations from Svalbard, Severnaya Zemlya, Northwest Greenland (NEEM <span class="hlt">ice</span> core) and central East Greenland (Renland <span class="hlt">ice</span> core).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70021023','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70021023"><span>Physical characteristics of summer <span class="hlt">sea</span> <span class="hlt">ice</span> across the Arctic Ocean</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Tucker, W. B.; Gow, A.J.; Meese, D.A.; Bosworth, H.W.; Reimnitz, E.</p> <p>1999-01-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> characteristics were investigated during July and August on the 1994 transect across the Arctic Ocean. Properties examined from <span class="hlt">ice</span> cores included salinity, temperature, and <span class="hlt">ice</span> structure. Salinities measured near zero at the surface, increasing to 3-4??? at the <span class="hlt">ice</span>-water interface. <span class="hlt">Ice</span> crystal texture was dominated by columnar <span class="hlt">ice</span>, comprising 90% of the <span class="hlt">ice</span> sampled. Surface albedos of various <span class="hlt">ice</span> types, measured with radiometers, showed integrated shortwave albedos of 0.1 to 0.3 for melt ponds, 0.5 for bare, discolored <span class="hlt">ice</span>, and 0.6 to 0.8 for a deteriorated surface or snow-covered <span class="hlt">ice</span>. Aerial photography was utilized to document the distribution of open melt ponds, which decreased from 12% coverage of the <span class="hlt">ice</span> surface in late July at 76??N to almost none in mid-August at 88??N. Most melt ponds were shallow, and depth bore no relationship to size. Sediment was pervasive from the southern Chukchi <span class="hlt">Sea</span> to the north pole, occurring in bands or patches. It was absent in the Eurasian Arctic, where it had been observed on earlier expeditions. Calculations of reverse trajectories of the sediment-bearing floes suggest that the southernmost sediment was entrained during <span class="hlt">ice</span> formation in the Beaufort <span class="hlt">Sea</span> while more northerly samples probably originated in the East Siberian <span class="hlt">Sea</span>, some as far west as the New Siberian Islands.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.2702B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.2702B"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> thickness in the Weddell <span class="hlt">Sea</span>, inferred from upward looking sonar measurements</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Behrendt, Axel; Dierking, Wolfgang; Witte, Hannelore</p> <p>2014-05-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> has been routinely monitored by satellites since 1979. However, thickness measurements of <span class="hlt">sea</span> <span class="hlt">ice</span> are still very sparse, especially in the Southern Hemisphere. Satellite altimetry still provides relatively uncertain estimates of <span class="hlt">ice</span> thickness. Today, the only tool for monitoring <span class="hlt">sea</span> <span class="hlt">ice</span> thickness over long time periods with highest accuracy (5-10 cm) are moored upward looking sonars (ULS). The instruments measure the subsurface portion (draft) of the <span class="hlt">ice</span>, which can be converted into total <span class="hlt">ice</span> thickness. We present a data set of ULS time series from 13 positions in the Atlantic sector of the Southern Ocean (Weddell <span class="hlt">Sea</span>), which were made in different years between 1990 and 2010. Monthly mean <span class="hlt">sea</span> <span class="hlt">ice</span> draft shows high interannual variability and can reach more than 3 m in the dynamic coastal regions of the eastern and western Weddell <span class="hlt">Sea</span>. The thinnest <span class="hlt">ice</span> is found away from the coast in the eastern Weddell <span class="hlt">Sea</span> and rarely exceeds 1 m in the monthly mean. In single years the ULS data allow for a clear discrimination between thermodynamic <span class="hlt">ice</span> growth and dynamic <span class="hlt">ice</span> growth due to rafting and ridging of the floes. We demonstrate that the thermodynamic <span class="hlt">ice</span> thickness can reach its theoretical maximum value of 1 m in the central Weddell basin. Despite significant gaps, the presented data set provides an important validation tool for satellite algorithms and <span class="hlt">sea</span> <span class="hlt">ice</span> models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70012715','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70012715"><span>Time-dependence of <span class="hlt">sea-ice</span> concentration and multiyear <span class="hlt">ice</span> fraction in the Arctic Basin</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Gloersen, P.; Zwally, H.J.; Chang, A.T.C.; Hall, D.K.; Campbell, W.J.; Ramseier, R.O.</p> <p>1978-01-01</p> <p>The time variation of the <span class="hlt">sea-ice</span> concentration and multiyear <span class="hlt">ice</span> fraction within the pack <span class="hlt">ice</span> in the Arctic Basin is examined, using microwave images of <span class="hlt">sea</span> <span class="hlt">ice</span> recently acquired by the Nimbus-5 spacecraft and the NASA CV-990 airborne laboratory. The images used for these studies were constructed from data acquired from the Electrically Scanned Microwave Radiometer (ESMR) which records radiation from earth and its atmosphere at a wavelength of 1.55 cm. Data are analyzed for four seasons during 1973-1975 to illustrate some basic differences in the properties of the <span class="hlt">sea</span> <span class="hlt">ice</span> during those times. Spacecraft data are compared with corresponding NASA CV-990 airborne laboratory data obtained over wide areas in the Arctic Basin during the Main Arctic <span class="hlt">Ice</span> Dynamics Joint Experiment (1975) to illustrate the applicability of passive-microwave remote sensing for monitoring the time dependence of <span class="hlt">sea-ice</span> concentration (divergence). These observations indicate significant variations in the <span class="hlt">sea-ice</span> concentration in the spring, late fall and early winter. In addition, deep in the interior of the Arctic polar <span class="hlt">sea-ice</span> pack, heretofore unobserved large areas, several hundred kilometers in extent, of <span class="hlt">sea-ice</span> concentrations as low as 50% are indicated. ?? 1978 D. Reidel Publishing Company.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140010420','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140010420"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Thickness, Freeboard, and Snow Depth products from Operation <span class="hlt">Ice</span>Bridge Airborne Data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kurtz, N. T.; Farrell, S. L.; Studinger, M.; Galin, N.; Harbeck, J. P.; Lindsay, R.; Onana, V. D.; Panzer, B.; Sonntag, J. G.</p> <p>2013-01-01</p> <p>The study of <span class="hlt">sea</span> <span class="hlt">ice</span> using airborne remote sensing platforms provides unique capabilities to measure a wide variety of <span class="hlt">sea</span> <span class="hlt">ice</span> properties. These measurements are useful for a variety of topics including model evaluation and improvement, assessment of satellite retrievals, and incorporation into climate data records for analysis of interannual variability and long-term trends in <span class="hlt">sea</span> <span class="hlt">ice</span> properties. In this paper we describe methods for the retrieval of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness, freeboard, and snow depth using data from a multisensor suite of instruments on NASA's Operation <span class="hlt">Ice</span>Bridge airborne campaign. We assess the consistency of the results through comparison with independent data sets that demonstrate that the <span class="hlt">Ice</span>Bridge products are capable of providing a reliable record of snow depth and <span class="hlt">sea</span> <span class="hlt">ice</span> thickness. We explore the impact of inter-campaign instrument changes and associated algorithm adaptations as well as the applicability of the adapted algorithms to the ongoing <span class="hlt">Ice</span>Bridge mission. The uncertainties associated with the retrieval methods are determined and placed in the context of their impact on the retrieved <span class="hlt">sea</span> <span class="hlt">ice</span> thickness. Lastly, we present results for the 2009 and 2010 <span class="hlt">Ice</span>Bridge campaigns, which are currently available in product form via the National Snow and <span class="hlt">Ice</span> Data Center</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_12 --> <div id="page_13" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="241"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA123712','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA123712"><span>Physical Properties of the <span class="hlt">Ice</span> Cover of the Greenland <span class="hlt">Sea</span>.</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>1982-11-01</p> <p>DA-A13 i PHYSICAL PROPERTIES OF THE <span class="hlt">ICE</span> COVER OF THE GREENLAND 1/1 I SEAMU COLD REGIONS RESEARCH AND ENGINEERING LAB USI FE HANOVER NH N F REEKS NOV...I PERIOD COVERED PHYSICAL PROPERTIES OF THE <span class="hlt">ICE</span> COVER OF THE GREENLAND <span class="hlt">SEA</span> S. PERFORMING ORG. REPORT NUMBER 7. AUTHOR(e) S. CONTRACT OR GRANT NUMBER...NOTES 19. KEY WORDS (Continue on revere aide if neceaary and identify by block number) Greenland <span class="hlt">Ice</span> <span class="hlt">Ice</span> properties <span class="hlt">Sea</span> <span class="hlt">ice</span> SABSTRACT (Vntmm em reverse</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1172858','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1172858"><span>Land-<span class="hlt">ice</span> modeling for <span class="hlt">sea</span>-level prediction</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Lipscomb, William H</p> <p>2010-06-11</p> <p>There has been major progress in <span class="hlt">ice</span> sheet modeling since IPCC AR4. We will soon have efficient higherorder <span class="hlt">ice</span> sheet models that can run at ",1 km resolution for entire <span class="hlt">ice</span> sheets, either standalone or coupled to GeMs. These models should significantly reduce uncertainties in <span class="hlt">sea</span>-level predictions. However, the least certain and potentially greatest contributions to 21st century <span class="hlt">sea</span>-level rise may come from <span class="hlt">ice</span>-ocean interactions, especially in West Antarctica. This is a coupled modeling problem that requires collaboration among <span class="hlt">ice</span>, ocean and atmosphere modelers.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70004576','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70004576"><span>Quantifying the influence of <span class="hlt">sea</span> <span class="hlt">ice</span> on ocean microseism using observations from the Bering <span class="hlt">Sea</span>, Alaska</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Tsai, Victor C.; McNamara, Daniel E.</p> <p>2011-01-01</p> <p>Microseism is potentially affected by all processes that alter ocean wave heights. Because strong <span class="hlt">sea</span> <span class="hlt">ice</span> prevents large ocean waves from forming, <span class="hlt">sea</span> <span class="hlt">ice</span> can therefore significantly affect microseism amplitudes. Here we show that this link between <span class="hlt">sea</span> <span class="hlt">ice</span> and microseism is not only a robust one but can be quantified. In particular, we show that 75–90% of the variability in microseism power in the Bering <span class="hlt">Sea</span> can be predicted using a fairly crude model of microseism damping by <span class="hlt">sea</span> <span class="hlt">ice</span>. The success of this simple parameterization suggests that an even stronger link can be established between the mechanical strength of <span class="hlt">sea</span> <span class="hlt">ice</span> and microseism power, and that microseism can eventually be used to monitor the strength of <span class="hlt">sea</span> <span class="hlt">ice</span>, a quantity that is not as easily observed through other means.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70032589','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70032589"><span>Quantifying the influence of <span class="hlt">sea</span> <span class="hlt">ice</span> on ocean microseism using observations from the Bering <span class="hlt">Sea</span>, Alaska</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Tsai, V.C.; McNamara, D.E.</p> <p>2011-01-01</p> <p>Microseism is potentially affected by all processes that alter ocean wave heights. Because strong <span class="hlt">sea</span> <span class="hlt">ice</span> prevents large ocean waves from forming, <span class="hlt">sea</span> <span class="hlt">ice</span> can therefore significantly affect microseism amplitudes. Here we show that this link between <span class="hlt">sea</span> <span class="hlt">ice</span> and microseism is not only a robust one but can be quantified. In particular, we show that 75-90% of the variability in microseism power in the Bering <span class="hlt">Sea</span> can be predicted using a fairly crude model of microseism damping by <span class="hlt">sea</span> <span class="hlt">ice</span>. The success of this simple parameterization suggests that an even stronger link can be established between the mechanical strength of <span class="hlt">sea</span> <span class="hlt">ice</span> and microseism power, and that microseism can eventually be used to monitor the strength of <span class="hlt">sea</span> <span class="hlt">ice</span>, a quantity that is not as easily observed through other means. Copyright 2011 by the American Geophysical Union.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/AD1013710','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD1013710"><span>Mass Balance of Multiyear <span class="hlt">Sea</span> <span class="hlt">Ice</span> in the Southern Beaufort <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2015-09-30</p> <p>interpret airborne electromagnetic induction and <span class="hlt">ice</span>-profiling sonar from the region. The project also contributes <span class="hlt">ice</span> thickness and <span class="hlt">ice</span> core data sets...However, we will make use of airborne electromagnetic (AEM) data from the Tuktoyaktuk and Barrow regions to examine differences in the thickness of <span class="hlt">ice</span>...calculated back- and forward-trajectories of <span class="hlt">sea</span> <span class="hlt">ice</span> at different points along the flight tracks of airborne electromagnetic (AEM) <span class="hlt">ice</span> thickness</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70175240','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70175240"><span>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> decline contributes to thinning lake <span class="hlt">ice</span> trend in northern Alaska</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Alexeev, Vladimir; Arp, Christopher D.; Jones, Benjamin M.; Cai, Lei</p> <p>2016-01-01</p> <p>Field measurements, satellite observations, and models document a thinning trend in seasonal Arctic lake <span class="hlt">ice</span> growth, causing a shift from bedfast to floating <span class="hlt">ice</span> conditions. September <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations in the Arctic Ocean since 1991 correlate well (r = +0.69,p < 0.001) to this lake regime shift. To understand how and to what extent <span class="hlt">sea</span> <span class="hlt">ice</span> affects lakes, we conducted model experiments to simulate winters with years of high (1991/92) and low (2007/08) <span class="hlt">sea</span> <span class="hlt">ice</span> extent for which we also had field measurements and satellite imagery characterizing lake <span class="hlt">ice</span> conditions. A lake <span class="hlt">ice</span> growth model forced with Weather Research and Forecasting model output produced a 7% decrease in lake <span class="hlt">ice</span> growth when 2007/08 <span class="hlt">sea</span> <span class="hlt">ice</span> was imposed on 1991/92 climatology and a 9% increase in lake <span class="hlt">ice</span> growth for the opposing experiment. Here, we clearly link early winter 'ocean-effect' snowfall and warming to reduced lake <span class="hlt">ice</span> growth. Future reductions in <span class="hlt">sea</span> <span class="hlt">ice</span> extent will alter hydrological, biogeochemical, and habitat functioning of Arctic lakes and cause sub-lake permafrost thaw.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ERL....11g4022A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ERL....11g4022A"><span>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> decline contributes to thinning lake <span class="hlt">ice</span> trend in northern Alaska</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alexeev, Vladimir A.; Arp, Christopher D.; Jones, Benjamin M.; Cai, Lei</p> <p>2016-07-01</p> <p>Field measurements, satellite observations, and models document a thinning trend in seasonal Arctic lake <span class="hlt">ice</span> growth, causing a shift from bedfast to floating <span class="hlt">ice</span> conditions. September <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations in the Arctic Ocean since 1991 correlate well (r = +0.69, p < 0.001) to this lake regime shift. To understand how and to what extent <span class="hlt">sea</span> <span class="hlt">ice</span> affects lakes, we conducted model experiments to simulate winters with years of high (1991/92) and low (2007/08) <span class="hlt">sea</span> <span class="hlt">ice</span> extent for which we also had field measurements and satellite imagery characterizing lake <span class="hlt">ice</span> conditions. A lake <span class="hlt">ice</span> growth model forced with Weather Research and Forecasting model output produced a 7% decrease in lake <span class="hlt">ice</span> growth when 2007/08 <span class="hlt">sea</span> <span class="hlt">ice</span> was imposed on 1991/92 climatology and a 9% increase in lake <span class="hlt">ice</span> growth for the opposing experiment. Here, we clearly link early winter ‘ocean-effect’ snowfall and warming to reduced lake <span class="hlt">ice</span> growth. Future reductions in <span class="hlt">sea</span> <span class="hlt">ice</span> extent will alter hydrological, biogeochemical, and habitat functioning of Arctic lakes and cause sub-lake permafrost thaw.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C23B0795F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C23B0795F"><span>Physical Characteristics and Geobiology of 'Rotten' Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Frantz, C. M.; Light, B.; Orellana, M. V.; Carpenter, S.; Junge, K.</p> <p>2015-12-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> in its final stage of demise, "rotten <span class="hlt">ice</span>", is characterized by seriously compromised structural integrity, making it difficult to collect and study. Consequently, little is known about the physical, chemical and biological properties of this <span class="hlt">ice</span> type. Yet, as the Arctic melt season lengthens, this <span class="hlt">ice</span> type will likely appear sooner and become more prevalent in the Arctic Ocean and its occurrence may be more common than satellite mapping and <span class="hlt">ice</span> charts suggest (e.g., Barber et al., 2009). Here we present physical, chemical, biological, and optical measurements of first-year <span class="hlt">ice</span> near Barrow, Alaska during the spring and summer of 2015. Samples represent a progression from solid, "springtime" shorefast <span class="hlt">ice</span> (May); through melting, heavily melt-ponded, "summertime" shorefast <span class="hlt">ice</span> (June); to the final stage of barely-intact, "rotten" <span class="hlt">ice</span> collected from small floes Beaufort <span class="hlt">Sea</span> (July). Results indicate that rotten <span class="hlt">ice</span> exhibits low salinity, is well drained and has a lower density than its springtime counterpart. X-ray tomography of dimethyl phthalate-casted <span class="hlt">sea</span> <span class="hlt">ice</span> samples indicates differences in porosity and relative permeability in rotten <span class="hlt">ice</span> vs. spring- and summertime <span class="hlt">ice</span>. We also present a preliminary characterization of rotten <span class="hlt">sea</span> <span class="hlt">ice</span> as a microbial habitat using preliminary results of chemical measurements (nutrients, dissolved organic and inorganic carbon), and microbiological characterizations (concentrations and16S/18S rDNA-based identifications) from seawater vs. <span class="hlt">sea</span> <span class="hlt">ice</span> vs. <span class="hlt">sea</span> <span class="hlt">ice</span> brines. Optical measurements show that while decreased <span class="hlt">ice</span> thickness and increased melt pond coverage cause an overall increase in solar radiation to the ocean as <span class="hlt">sea</span> <span class="hlt">ice</span> warms, rotten <span class="hlt">ice</span> is actually less transparent to solar radiation than its spring- and summertime counterparts. These factors determine solar heating in the ocean and, ultimately, the potential for accelerated <span class="hlt">ice</span> melting (e.g., Light et al., 2008). This work provides a foundation for understanding</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/14558902','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/14558902"><span><span class="hlt">Sea-ice</span> switches and abrupt climate change.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Gildor, Hezi; Tziperman, Eli</p> <p>2003-09-15</p> <p>We propose that past abrupt climate changes were probably a result of rapid and extensive variations in <span class="hlt">sea-ice</span> cover. We explain why this seems a perhaps more likely explanation than a purely thermohaline circulation mechanism. We emphasize that because of the significant influence of <span class="hlt">sea</span> <span class="hlt">ice</span> on the climate system, it seems that high priority should be given to developing ways for reconstructing high-resolution (in space and time) <span class="hlt">sea-ice</span> extent for past climate-change events. If proxy data can confirm that <span class="hlt">sea</span> <span class="hlt">ice</span> was indeed the major player in past abrupt climate-change events, it seems less likely that such dramatic abrupt changes will occur due to global warming, when extensive <span class="hlt">sea-ice</span> cover will not be present.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20050185661','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20050185661"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Kinematics and Thickness from RGPS: Observations and Theory</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Stern, Harry; Lindsay, Ron; Yu, Yan-Ling; Moritz, Richard; Rothrock, Drew</p> <p>2005-01-01</p> <p>The RADARSAT Geophysical Processor System (RGPS) has produced a wealth of data on Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> motion, deformation, and thickness with broad geographical coverage and good temporal resolution. These data provide unprecedented spatial detail of the structure and evolution of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover. The broad purpose of this study was to take advantage of the strengths of the RGPS data set to investigate <span class="hlt">sea</span> <span class="hlt">ice</span> kinematics and thickness, which affect the climate through their influence on <span class="hlt">ice</span> production, ridging, and transport (i.e. mass balance); heat flux to the atmosphere; and structure of the upper ocean mixed layer. The objectives of this study were to: (1) Explain the relationship between the discontinuous motion of the <span class="hlt">ice</span> cover and the large-scale, smooth wind field that drives the <span class="hlt">ice</span>; (2) Characterize the <span class="hlt">sea</span> <span class="hlt">ice</span> deformation in the Arctic at different temporal and spatial scales, and compare it with deformation predicted by a state-of-theart <span class="hlt">ice</span>/ocean model; and (3) Compare RGPS-derived <span class="hlt">sea</span> <span class="hlt">ice</span> thickness with other data, and investigate the thinning of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover as seen in ULS data obtained by U.S. Navy submarines. We briefly review the results of our work below, separated into the topics of <span class="hlt">sea</span> <span class="hlt">ice</span> deformation and <span class="hlt">sea</span> <span class="hlt">ice</span> thickness. This is followed by a list of publications, meetings and presentations, and other activities supported under this grant. We are attaching to this report copies of all the listed publications. Finally, we would like to point out our community service to NASA through our involvement with the ASF User Working Group and the RGPS Science Working Group, as evidenced in the list of meetings and presentations below.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/AD1013712','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD1013712"><span>The Seasonal Evolution of <span class="hlt">Sea</span> <span class="hlt">Ice</span> Floe Size Distribution</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2015-09-30</p> <p>1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. “The Seasonal Evolution of <span class="hlt">Sea</span> <span class="hlt">Ice</span> Floe Size Distribution...occur in the appearance and morphology of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover over and annual cycle. These photos were taken over the pack <span class="hlt">ice</span> near SHEBA in May...element model [Hopkins et al., 2004], using morphological conditions derived from the analyzed satellite imagery, confirms that breaking occurs along</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008QuRes..70....1B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008QuRes..70....1B"><span>The Younger Dryas and the <span class="hlt">Sea</span> of Ancient <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bradley, Raymond S.; England, John H.</p> <p>2008-07-01</p> <p>We propose that prior to the Younger Dryas period, the Arctic Ocean supported extremely thick multi-year fast <span class="hlt">ice</span> overlain by superimposed <span class="hlt">ice</span> and firn. We re-introduce the historical term paleocrystic <span class="hlt">ice</span> to describe this. The <span class="hlt">ice</span> was independent of continental (glacier) <span class="hlt">ice</span> and formed a massive floating body trapped within the almost closed Arctic Basin, when <span class="hlt">sea</span>-level was lower during the last glacial maximum. As <span class="hlt">sea</span>-level rose and the Barents <span class="hlt">Sea</span> Shelf became deglaciated, the volume of warm Atlantic water entering the Arctic Ocean increased, as did the corresponding egress, driving the paleocrystic <span class="hlt">ice</span> towards Fram Strait. New evidence shows that Bering Strait was resubmerged around the same time, providing further dynamical forcing of the <span class="hlt">ice</span> as the Transpolar Drift became established. Additional freshwater entered the Arctic Basin from Siberia and North America, from proglacial lakes and meltwater derived from the Laurentide <span class="hlt">Ice</span> Sheet. Collectively, these forces drove large volumes of thick paleocrystic <span class="hlt">ice</span> and relatively fresh water from the Arctic Ocean into the Greenland <span class="hlt">Sea</span>, shutting down deepwater formation and creating conditions conducive for extensive <span class="hlt">sea-ice</span> to form and persist as far south as 60°N. We propose that the forcing responsible for the Younger Dryas cold episode was thus the result of extremely thick <span class="hlt">sea-ice</span> being driven from the Arctic Ocean, dampening or shutting off the thermohaline circulation, as <span class="hlt">sea</span>-level rose and Atlantic and Pacific waters entered the Arctic Basin. This hypothesis focuses attention on the potential role of Arctic <span class="hlt">sea-ice</span> in causing the Younger Dryas episode, but does not preclude other factors that may also have played a role.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004JGRC..109.6005A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004JGRC..109.6005A"><span>Geometrical constraints on the evolution of ridged <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Amundrud, Trisha L.; Melling, Humfrey; Ingram, R. Grant</p> <p>2004-06-01</p> <p>A numerical model of the evolving draft distribution of seasonal pack <span class="hlt">ice</span> is driven by freezing and <span class="hlt">ice</span> field compression in one dimension. Spatial transects of <span class="hlt">sea</span> <span class="hlt">ice</span> draft acquired during winter in the Beaufort <span class="hlt">Sea</span> are used to evaluate the model. Histograms obtained by <span class="hlt">ice</span>-profiling sonar on subsea moorings reveal changes in the draft distribution, while observations of <span class="hlt">ice</span> velocity by Doppler sonar allow calculation of the strain to which the draft distribution is responding. Numerical diffusion in thermal <span class="hlt">ice</span> growth is controlled using a remapping algorithm. Mechanical redistribution algorithms in common use generate much more deep ridged <span class="hlt">ice</span> than is observed. Geometric constraints on ridge-keel development that reflect the finite extent of level floes available for ridge building and the true average shape of keels produce more realistic results. In the seasonal pack <span class="hlt">ice</span> of the Beaufort <span class="hlt">Sea</span>, 75% of all floes are too small to provide a volume of <span class="hlt">ice</span> sufficient to construct a keel of draft equal to that commonly assumed in <span class="hlt">ice</span> dynamics modeling. On average, the distribution of draft within keels has a negative exponential form, implying a cusped keel shape with more area on the thinner flanks than at the crest; models commonly assume a uniform redistribution of <span class="hlt">ice</span> into a keel of triangular shape. Clearly, the spatial organization of <span class="hlt">ice</span> within seasonal pack or, equivalently, the existence of ridges and floes should be an acknowledged factor in redistribution theory for pack <span class="hlt">ice</span> thickness.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040015192&hterms=climate+change+impact+ocean&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dclimate%2Bchange%2Bimpact%2Bocean','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040015192&hterms=climate+change+impact+ocean&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dclimate%2Bchange%2Bimpact%2Bocean"><span>Observed and Modeled Trends in Southern Ocean <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Parkinson, Claire L.</p> <p>2003-01-01</p> <p>Conceptual models and global climate model (GCM) simulations have both indicated the likelihood of an enhanced sensitivity to climate change in the polar regions, derived from the positive feedbacks brought about by the polar abundance of snow and <span class="hlt">ice</span> surfaces. Some models further indicate that the changes in the polar regions can have a significant impact globally. For instance, 37% of the temperature sensitivity to a doubling of atmospheric CO2 in simulations with the GCM of the Goddard Institute for Space Studies (GISS) is attributable exclusively to inclusion of <span class="hlt">sea</span> <span class="hlt">ice</span> variations in the model calculations. Both <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and <span class="hlt">sea</span> <span class="hlt">ice</span> extent decrease markedly in the doubled CO, case, thereby allowing the <span class="hlt">ice</span> feedbacks to occur. Stand-alone <span class="hlt">sea</span> <span class="hlt">ice</span> models have shown Southern Ocean hemispherically averaged winter <span class="hlt">ice</span>-edge retreats of 1.4 deg latitude for each 1 K increase in atmospheric temperatures. Observations, however, show a much more varied Southern Ocean <span class="hlt">ice</span> cover, both spatially and temporally, than many of the modeled expectations. In fact, the satellite passive-microwave record of Southern Ocean <span class="hlt">sea</span> <span class="hlt">ice</span> since late 1978 has revealed overall increases rather than decreases in <span class="hlt">ice</span> extents, with <span class="hlt">ice</span> extent trends on the order of 11,000 sq km/year. When broken down spatially, the positive trends are strongest in the Ross <span class="hlt">Sea</span>, while the trends are negative in the Bellingshausen/Amundsen <span class="hlt">Seas</span>. Greater spatial detail can be obtained by examining trends in the length of the <span class="hlt">sea</span> <span class="hlt">ice</span> season, and those trends show a coherent picture of shortening <span class="hlt">sea</span> <span class="hlt">ice</span> seasons throughout almost the entire Bellingshausen and Amundsen <span class="hlt">Seas</span> to the west of the Antarctic Peninsula and in the far western Weddell <span class="hlt">Sea</span> immediately to the east of the Peninsula, with lengthening <span class="hlt">sea</span> <span class="hlt">ice</span> seasons around much of the rest of the continent. This pattern corresponds well with the spatial pattern of temperature trends, as the Peninsula region is the one region in the Antarctic with a strong</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017TCry...11...65S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017TCry...11...65S"><span>Fram Strait <span class="hlt">sea</span> <span class="hlt">ice</span> export variability and September Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent over the last 80 years</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Smedsrud, Lars H.; Halvorsen, Mari H.; Stroeve, Julienne C.; Zhang, Rong; Kloster, Kjell</p> <p>2017-01-01</p> <p>A new long-term data record of Fram Strait <span class="hlt">sea</span> <span class="hlt">ice</span> area export from 1935 to 2014 is developed using a combination of satellite radar images and station observations of surface pressure across Fram Strait. This data record shows that the long-term annual mean export is about 880 000 km2, representing 10 % of the <span class="hlt">sea-ice</span>-covered area inside the basin. The time series has large interannual and multi-decadal variability but no long-term trend. However, during the last decades, the amount of <span class="hlt">ice</span> exported has increased, with several years having annual <span class="hlt">ice</span> exports that exceeded 1 million km2. This increase is a result of faster southward <span class="hlt">ice</span> drift speeds due to stronger southward geostrophic winds, largely explained by increasing surface pressure over Greenland. Evaluating the trend onwards from 1979 reveals an increase in annual <span class="hlt">ice</span> export of about +6 % per decade, with spring and summer showing larger changes in <span class="hlt">ice</span> export (+11 % per decade) compared to autumn and winter (+2.6 % per decade). Increased <span class="hlt">ice</span> export during winter will generally result in new <span class="hlt">ice</span> growth and contributes to thinning inside the Arctic Basin. Increased <span class="hlt">ice</span> export during summer or spring will, in contrast, contribute directly to open water further north and a reduced summer <span class="hlt">sea</span> <span class="hlt">ice</span> extent through the <span class="hlt">ice</span>-albedo feedback. Relatively low spring and summer export from 1950 to 1970 is thus consistent with a higher mid-September <span class="hlt">sea</span> <span class="hlt">ice</span> extent for these years. Our results are not sensitive to long-term change in Fram Strait <span class="hlt">sea</span> <span class="hlt">ice</span> concentration. We find a general moderate influence between export anomalies and the following September <span class="hlt">sea</span> <span class="hlt">ice</span> extent, explaining 18 % of the variance between 1935 and 2014, but with higher values since 2004.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EaFut...2..315O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EaFut...2..315O"><span>Global warming releases microplastic legacy frozen in Arctic <span class="hlt">Sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Obbard, Rachel W.; Sadri, Saeed; Wong, Ying Qi; Khitun, Alexandra A.; Baker, Ian; Thompson, Richard C.</p> <p>2014-06-01</p> <p>When <span class="hlt">sea</span> <span class="hlt">ice</span> forms it scavenges and concentrates particulates from the water column, which then become trapped until the <span class="hlt">ice</span> melts. In recent years, melting has led to record lows in Arctic <span class="hlt">Sea</span> <span class="hlt">ice</span> extent, the most recent in September 2012. Global climate models, such as that of Gregory et al. (2002), suggest that the decline in Arctic <span class="hlt">Sea</span> <span class="hlt">ice</span> volume (3.4% per decade) will actually exceed the decline in <span class="hlt">sea</span> <span class="hlt">ice</span> extent, something that Laxon et al. (2013) have shown supported by satellite data. The extent to which melting <span class="hlt">ice</span> could release anthropogenic particulates back to the open ocean has not yet been examined. Here we show that Arctic <span class="hlt">Sea</span> <span class="hlt">ice</span> from remote locations contains concentrations of microplastics at least two orders of magnitude greater than those that have been previously reported in highly contaminated surface waters, such as those of the Pacific Gyre. Our findings indicate that microplastics have accumulated far from population centers and that polar <span class="hlt">sea</span> <span class="hlt">ice</span> represents a major historic global sink of man-made particulates. The potential for substantial quantities of legacy microplastic contamination to be released to the ocean as the <span class="hlt">ice</span> melts therefore needs to be evaluated, as do the physical and toxicological effects of plastics on marine life.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011JGRC..11612005G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011JGRC..11612005G"><span>Smoluchowski coagulation models of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness distribution dynamics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Godlovitch, D.; Illner, R.; Monahan, A.</p> <p>2011-12-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> thickness distributions display a ubiquitous exponential decrease with thickness. This tail characterizes the range of <span class="hlt">ice</span> thickness produced by mechanical redistribution of <span class="hlt">ice</span> through the process of ridging, rafting, and shearing. We investigate how well the thickness distribution can be simulated by representing mechanical redistribution as a generalized stacking process. Such processes are naturally described by a well-studied class of models known as Smoluchowski Coagulation Models (SCMs), which describe the dynamics of a population of fixed-mass "particles" which combine in pairs to form a "particle" with the combined mass of the constituent pair at a rate which depends on the mass of the interacting particles. Like observed <span class="hlt">sea</span> <span class="hlt">ice</span> thickness distributions, the mass distribution of the populations generated by SCMs has an exponential or quasi-exponential form. We use SCMs to model <span class="hlt">sea</span> <span class="hlt">ice</span>, identifying mass-increasing particle combinations with thickness-increasing <span class="hlt">ice</span> redistribution processes. Our model couples an SCM component with a thermodynamic component and generates qualitatively accurate thickness distributions with a variety of rate kernels. Our results suggest that the exponential tail of the <span class="hlt">sea</span> <span class="hlt">ice</span> thickness distribution arises from the nature of the ridging process, rather than specific physical properties of <span class="hlt">sea</span> <span class="hlt">ice</span> or the spatial arrangement of floes, and that the relative strengths of the dynamic and thermodynamic processes are key in accurately simulating the rate at which the <span class="hlt">sea</span> <span class="hlt">ice</span> thickness tail drops off with thickness.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1812003O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1812003O"><span>On the Role of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Deformations: An Evaluation of the Regional Arctic System Model Results with Observations.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Osinski, Robert; Maslowski, Wieslaw; Roberts, Andrew</p> <p>2016-04-01</p> <p>The atmosphere - <span class="hlt">sea</span> <span class="hlt">ice</span> - ocean fluxes and their contribution to rapid changes in the Arctic system are not well understood and generally are not resolved by global climate models (GCMs). While many significant model refinements have been made in the recent past, including the representation of <span class="hlt">sea</span> <span class="hlt">ice</span> rheology, surface albedo and <span class="hlt">ice</span>-albedo feedback, other processes such as <span class="hlt">sea</span> <span class="hlt">ice</span> deformations, still require further studies and model advancements. Of particular potential interest here are linear kinematic features (LKFs), which control winter air-<span class="hlt">sea</span> heat exchange and affect buoyancy forces in the ocean. Their importance in Arctic climate change, especially under an increasing first-year <span class="hlt">ice</span> cover, is yet to be determined and their simulation requires representation of processes currently at sub-grid scale of most GCMs. To address some of the GCM limitations and to better understand the role of LKFs in air-<span class="hlt">sea</span> exchange we use the Regional Arctic System Model (RASM), which allows high spatio-temporal resolution and regional focus on the Arctic. RASM is a fully coupled regional climate model, developed to study dynamic and thermodynamic processes and their coupling across the atmosphere-<span class="hlt">sea</span> <span class="hlt">ice</span>-ocean interface. It consists of the Weather Research and Forecasting (WRF) atmospheric model, the Parallel Ocean Program (POP), the Community <span class="hlt">Ice</span> Model (CICE) and the Variable Infiltration Capacity (VIC) land hydrology model. The <span class="hlt">sea</span> <span class="hlt">ice</span> component has been upgraded to the Los <span class="hlt">Alamos</span> Community <span class="hlt">Ice</span> Model version 5.1 (CICE5.1), which allows either Elastic-Viscous-Plastic (EVP) or a new anisotropic (EPA) rheology. RASM's domain is pan-Arctic, with the ocean and <span class="hlt">sea</span> <span class="hlt">ice</span> components configured at an eddy-permitting horizontal resolution of 1/12-degree as well as 1/48-degree, for limited simulations. The atmosphere and land model components are configured at 50-km grids. All the components are coupled at a 20-minute time step. Results from multiple RASM simulations are analyzed and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19850017730&hterms=deep+sea&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Ddeep%2Bsea','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19850017730&hterms=deep+sea&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Ddeep%2Bsea"><span>Possible <span class="hlt">Sea</span> <span class="hlt">Ice</span> Impacts on Oceanic Deep Convection</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Parkinson, C. L.</p> <p>1984-01-01</p> <p>Many regions of the world ocean known or suspected to have deep convection are <span class="hlt">sea-ice</span> covered for at least a portion of the annual cycle. As this suggests that <span class="hlt">sea</span> <span class="hlt">ice</span> might have some impact on generating or maintaining this phenomenon, several mechanisms by which <span class="hlt">sea</span> <span class="hlt">ice</span> could exert an influence are presented in the following paragraphs. <span class="hlt">Sea</span> <span class="hlt">ice</span> formation could be a direct causal factor in deep convection by providing the surface density increase necessary to initiate the convective overturning. As <span class="hlt">sea</span> <span class="hlt">ice</span> forms, either by <span class="hlt">ice</span> accretion or by in situ <span class="hlt">ice</span> formation in open water or in lead areas between <span class="hlt">ice</span> floes, salt is rejected to the underlying water. This increases the water salinity, thereby increasing water density in the mixed layer under the <span class="hlt">ice</span>. A sufficient increase in density will lead to mixing with deeper waters, and perhaps to deep convection or even bottom water formation. Observations are needed to establish whether this process is actually occurring; it is most likely in regions with extensive <span class="hlt">ice</span> formation and a relatively unstable oceanic density structure.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JGRC..121.3278P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JGRC..121.3278P"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> circulation around the Beaufort Gyre: The changing role of wind forcing and the <span class="hlt">sea</span> <span class="hlt">ice</span> state</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Petty, Alek A.; Hutchings, Jennifer K.; Richter-Menge, Jacqueline A.; Tschudi, Mark A.</p> <p>2016-05-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> drift estimates from feature tracking of satellite passive microwave data are used to investigate seasonal trends and variability in the <span class="hlt">ice</span> circulation around the Beaufort Gyre, over the multidecadal period 1980-2013. Our results suggest an amplified response of the Beaufort Gyre <span class="hlt">ice</span> circulation to wind forcing, especially during the late 2000s. We find increasing anticyclonic <span class="hlt">ice</span> drift across all seasons, with the strongest trend in autumn, associated with increased <span class="hlt">ice</span> export out of the southern Beaufort <span class="hlt">Sea</span> (into the Chukchi <span class="hlt">Sea</span>). A flux gate analysis highlights consistency across a suite of drift products. Despite these seasonal anticyclonic <span class="hlt">ice</span> drift trends, a significant anticyclonic wind trend occurs in summer only, driven, in-part, by anomalously anticyclonic winds in 2007. Across all seasons, the <span class="hlt">ice</span> drift curl is more anticyclonic than predicted from a linear relationship to the wind curl in the 2000s, compared to the 1980s/1990s. The strength of this anticyclonic <span class="hlt">ice</span> drift curl amplification is strongest in autumn and appears to have increased since the 1980s (up to 2010). In spring and summer, the <span class="hlt">ice</span> drift curl amplification occurs mainly between 2007 and 2010. These results suggest nonlinear <span class="hlt">ice</span> interaction feedbacks (e.g., a weaker, more mobile <span class="hlt">sea</span> <span class="hlt">ice</span> pack), enhanced atmospheric drag, and/or an increased role of the ocean. The results also show a weakening of the anticyclonic wind and <span class="hlt">ice</span> circulation since 2010.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_13 --> <div id="page_14" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="261"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015OcMod..93...22B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015OcMod..93...22B"><span>Assimilation of <span class="hlt">sea</span> surface temperature, <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and <span class="hlt">sea</span> <span class="hlt">ice</span> drift in a model of the Southern Ocean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barth, Alexander; Canter, Martin; Van Schaeybroeck, Bert; Vannitsem, Stéphane; Massonnet, François; Zunz, Violette; Mathiot, Pierre; Alvera-Azcárate, Aida; Beckers, Jean-Marie</p> <p>2015-09-01</p> <p>Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating <span class="hlt">sea</span> surface temperature, <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and <span class="hlt">sea</span> <span class="hlt">ice</span> drift. In the following it is also shown how surface winds in the Southern Ocean can be improved using <span class="hlt">sea</span> <span class="hlt">ice</span> drift estimated from infrared radiometers. Such satellite observations are available since the late seventies and have the potential to improve the wind forcing before more direct measurements of winds over the ocean are available using scatterometry in the late nineties. The model results are compared to the assimilated data and to independent measurements (the World Ocean Database 2009 and the mean dynamic topography based on observations). The overall improvement of the assimilation is quantified, in particular the impact of the assimilation on the representation of the polar front is discussed. Finally a method to identify model errors in the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> area is proposed based on Model Output Statistics techniques using a series of potential predictors. This approach provides new directions for model improvements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.5746B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5746B"><span>Assimilation of <span class="hlt">sea</span> surface temperature, <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and <span class="hlt">sea</span> <span class="hlt">ice</span> drift in a model of the Southern Ocean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barth, Alexander; Canter, Martin; Van Schaeybroeck, Bert; Vannitsem, Stéphane; Massonnet, François; Zunz, Violette; Mathiot, Pierre; Alvera-Azcárate, Aida; Beckers, Jean-Marie</p> <p>2015-04-01</p> <p>Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating <span class="hlt">sea</span> surface temperature, <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and <span class="hlt">sea</span> <span class="hlt">ice</span> drift. In the following it is also shown how surface winds in the Southern Ocean can be improved using <span class="hlt">sea</span> <span class="hlt">ice</span> drift estimated from infrared radiometers. Such satellite observations are available since the late seventies and have the potential to improve the wind forcing before more direct measurements of winds over the ocean are available using scatterometry in the late nineties. The model results are compared to the assimilated data and to independent measurements (the World Ocean Database 2009 and the mean dynamic topography based on observations). The overall improvement of the assimilation is quantified, in particular the impact of the assimilation on the representation of the polar front is discussed. Finally a method to identify model errors in the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> area is proposed based on Model Output Statistics techniques using a series of potential predictors. This approach provides new directions for model improvements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C11D..02B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C11D..02B"><span>Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Patterns and Its Relationship with Climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barreira, S.</p> <p>2015-12-01</p> <p>Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> concentration fields show a strong seasonal and interannual variation closely tied to changes in climate patterns. The Ross, Amundsen, Bellingshausen, and Weddell <span class="hlt">Seas</span> during Summer-Autumn and the Southern Ocean regions north of these areas during Winter-Spring have the greatest <span class="hlt">sea</span> <span class="hlt">ice</span> variability. Principal components analysis in T- mode, Varimax-rotated applied on Antarctic monthly <span class="hlt">sea</span> <span class="hlt">ice</span> concentration anomaly (SICA) fields for 1979-2015 (NASA Team algorithm data sets available at nsidc.org) revealed the main spatial characteristics of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> patterns and their relationship with atmospheric circulation. This analysis yielded five patterns of <span class="hlt">sea</span> <span class="hlt">ice</span> for winter-spring and three patterns for summer-autumn, each of which has a positive and negative phase. To understand the links between the SICA patterns and climate, we extracted the mean pressure and temperature fields for the months with high loadings (positive or negative) of the <span class="hlt">sea</span> <span class="hlt">ice</span> patterns. The first pattern of winter-spring <span class="hlt">sea</span> <span class="hlt">ice</span> concentration is a dipole structure between the Drake Passage and northern regions of the Bellingshausen and Weddell <span class="hlt">Seas</span> and, the South Atlantic Ocean. The negative phase shows a strong negative SICA over the Atlantic basin. This pattern can be associated with to the atmospheric structures related to a positive SAM index and a wave-3 arrangement around the continent. That is, a strong negative pressure anomaly centered over the Bellingshausen <span class="hlt">Sea</span> accompanied by three positive pressure anomalies in middle-latitudes. For summer-autumn, the first pattern shows two strong positive SICA areas, in the eastern Weddell <span class="hlt">Sea</span> and the northwestern Ross <span class="hlt">Sea</span>. A negative SICA covers the Amundsen-Bellingshausen <span class="hlt">Seas</span> and northwest of the Antarctic Peninsula. This pattern, frequently seen in summers since 2008, is associated with cool conditions over the Weddell <span class="hlt">Sea</span> but warmer temperatures and high surface air pressure west, north and northwest of the Peninsula.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.C33F..01P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.C33F..01P"><span>Contributing factors to an enhanced <span class="hlt">ice</span> albedo feedback in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Perovich, D. K.; Jones, K. F.; Light, B.; Holland, M. M.</p> <p>2012-12-01</p> <p>The Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover is in decline. In recent years there has been a decrease in summer <span class="hlt">ice</span> area; a thinning of the <span class="hlt">ice</span> cover; an increase in the amount of seasonal <span class="hlt">ice</span>; an earlier onset of summer melt; and a later start of fall freeze up. Decreases in <span class="hlt">ice</span> concentration substantially increase solar heat input to the ocean. Earlier dates of melt onset reduce <span class="hlt">ice</span> albedo during a period when incident solar irradiance is large increasing solar heat input to the <span class="hlt">ice</span>. Seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> typically has a smaller albedo than perennial <span class="hlt">ice</span> throughout the melt season. Thus, the observed shift to a seasonal <span class="hlt">ice</span> cover causes greater solar heat input to the <span class="hlt">ice</span> and more melting thereby accelerating <span class="hlt">ice</span> decay. Thinner <span class="hlt">ice</span> results in greater transmission of solar heat to the upper ocean, where it contributes to bottom melting, lateral melting, and warming of the water. All of these changes enhance the amount of solar energy deposited in the <span class="hlt">ice</span> ocean system, and increasing <span class="hlt">ice</span> melt. We will examine the relative magnitude of each of these changes individually as well as their collective contribution to the <span class="hlt">ice</span> albedo feedback.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007ChJOL..25..132L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007ChJOL..25..132L"><span>Fine-resolution simulation of surface current and <span class="hlt">sea</span> <span class="hlt">ice</span> in the Arctic Mediterranean <span class="hlt">Seas</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, Xiying; Zhang, Xuehong; Yu, Rucong; Liu, Hailong; Li, Wei</p> <p>2007-04-01</p> <p>A fine-resolution model is developed for ocean circulation simulation in the National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Chinese Academy of Sciences, and is applied to simulate surface current and <span class="hlt">sea</span> <span class="hlt">ice</span> variations in the Arctic Mediterranean <span class="hlt">Seas</span>. A dynamic <span class="hlt">sea</span> <span class="hlt">ice</span> model in elastic-viscous-plastic rheology and a thermodynamic <span class="hlt">sea</span> <span class="hlt">ice</span> model are employed. A 200-year simulation is performed and a dimatological average of a 10-year period (141st 150th) is presented with focus on <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and surface current variations in the Arctic Mediterranean <span class="hlt">Seas</span>. The model is able to simulate well the East Greenland Current, Beaufort Gyre and the Transpolar Drift, but the simulated West Spitsbergen Current is small and weak. In the March climatology, the <span class="hlt">sea</span> <span class="hlt">ice</span> coverage can be simulated well except for a bit more <span class="hlt">ice</span> in east of Spitsbergen Island. The result is also good for the September scenario except for less <span class="hlt">ice</span> concentration east of Greenland and greater <span class="hlt">ice</span> concentration near the <span class="hlt">ice</span> margin. The extra <span class="hlt">ice</span> east of Spitsbergen Island is caused by <span class="hlt">sea</span> <span class="hlt">ice</span> current convergence forced by atmospheric wind stress.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=GL-2002-002280&hterms=Ross&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DRoss','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=GL-2002-002280&hterms=Ross&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DRoss"><span>Victoria Land, Ross <span class="hlt">Sea</span>, and Ross <span class="hlt">Ice</span> Shelf, Antarctica</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>2002-01-01</p> <p>On December 19, 2001, MODIS acquired data that produced this image of Antarctica's Victoria Land, Ross <span class="hlt">Ice</span> Shelf, and the Ross <span class="hlt">Sea</span>. The coastline that runs up and down along the left side of the image denotes where Victoria Land (left) meets the Ross <span class="hlt">Ice</span> Shelf (right). The Ross <span class="hlt">Ice</span> Shelf is the world's largest floating body of <span class="hlt">ice</span>, approximately the same size as France. Credit: Jacques Descloitres, MODIS Land Rapid Response Team, NASA/GSFC</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27660738','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27660738"><span>Influence of <span class="hlt">ice</span> thickness and surface properties on light transmission through Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Katlein, Christian; Arndt, Stefanie; Nicolaus, Marcel; Perovich, Donald K; Jakuba, Michael V; Suman, Stefano; Elliott, Stephen; Whitcomb, Louis L; McFarland, Christopher J; Gerdes, Rüdiger; Boetius, Antje; German, Christopher R</p> <p>2015-09-01</p> <p>The observed changes in physical properties of <span class="hlt">sea</span> <span class="hlt">ice</span> such as decreased thickness and increased melt pond cover severely impact the energy budget of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Increased light transmission leads to increased deposition of solar energy in the upper ocean and thus plays a crucial role for amount and timing of <span class="hlt">sea-ice</span>-melt and under-<span class="hlt">ice</span> primary production. Recent developments in underwater technology provide new opportunities to study light transmission below the largely inaccessible underside of <span class="hlt">sea</span> <span class="hlt">ice</span>. We measured spectral under-<span class="hlt">ice</span> radiance and irradiance using the new Nereid Under-<span class="hlt">Ice</span> (NUI) underwater robotic vehicle, during a cruise of the R/V Polarstern to 83°N 6°W in the Arctic Ocean in July 2014. NUI is a next generation hybrid remotely operated vehicle (H-ROV) designed for both remotely piloted and autonomous surveys underneath land-fast and moving <span class="hlt">sea</span> <span class="hlt">ice</span>. Here we present results from one of the first comprehensive scientific dives of NUI employing its interdisciplinary sensor suite. We combine under-<span class="hlt">ice</span> optical measurements with three dimensional under-<span class="hlt">ice</span> topography (multibeam sonar) and aerial images of the surface conditions. We investigate the influence of spatially varying <span class="hlt">ice</span>-thickness and surface properties on the spatial variability of light transmittance during summer. Our results show that surface properties such as melt ponds dominate the spatial distribution of the under-<span class="hlt">ice</span> light field on small scales (<1000 m(2)), while <span class="hlt">sea</span> <span class="hlt">ice</span>-thickness is the most important predictor for light transmission on larger scales. In addition, we propose the use of an algorithm to obtain histograms of light transmission from distributions of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and surface albedo.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C23B0785K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C23B0785K"><span>Influence of <span class="hlt">ice</span> thickness and surface properties on light transmission through Arctic <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Katlein, C.; Arndt, S.; Nicolaus, M.; Perovich, D. K.; Jakuba, M.; Suman, S.; Elliott, S.; Whitcomb, L. L.; McFarland, C.; Gerdes, R.; Boetius, A.</p> <p>2015-12-01</p> <p>The changes in physical properties of <span class="hlt">sea</span> <span class="hlt">ice</span> such as decreased thickness and increased melt pond cover observed over the last decades severely impact the energy budget of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Increased light transmission leads to increased deposition of solar energy in the upper ocean and thus plays a crucial role in the amount and timing of <span class="hlt">sea-ice</span>-melt and under-<span class="hlt">ice</span> primary production. Recent developments in underwater technology provide new opportunities to undertake challenging research at the largely inaccessible underside of <span class="hlt">sea</span> <span class="hlt">ice</span>. We measured spectral under-<span class="hlt">ice</span> radiance and irradiance onboard the new Nereid Under-<span class="hlt">Ice</span> (NUI) underwater robotic vehicle, during a cruise of the R/V Polarstern to 83°N 6°W in the Arctic Ocean in July 2014. NUI is a next generation hybrid remotely operated vehicle (H-ROV) designed for both remotely-piloted and autonomous surveys underneath land-fast and moving <span class="hlt">sea</span> <span class="hlt">ice</span>. Here we present results from one of the first comprehensive scientific dives of NUI employing its interdisciplinary sensor suite. We combine under-<span class="hlt">ice</span> optical measurements with three-dimensional under-<span class="hlt">ice</span> topography and aerial images of the surface conditions. We investigate the influence of spatially varying <span class="hlt">ice</span>-thickness and surface properties during summer on the spatial variability of light transmittance. Results show that surface properties dominate the spatial distribution of the under-<span class="hlt">ice</span> light field on small scales (<1000m²), while <span class="hlt">sea</span> <span class="hlt">ice</span>-thickness is the most important predictor for light transmission on larger scales. In addition, we suggest an algorithm to obtain histograms of light transmission from distributions of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and surface albedo.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JGRC..120.5932K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRC..120.5932K"><span>Influence of <span class="hlt">ice</span> thickness and surface properties on light transmission through Arctic <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Katlein, Christian; Arndt, Stefanie; Nicolaus, Marcel; Perovich, Donald K.; Jakuba, Michael V.; Suman, Stefano; Elliott, Stephen; Whitcomb, Louis L.; McFarland, Christopher J.; Gerdes, Rüdiger; Boetius, Antje; German, Christopher R.</p> <p>2015-09-01</p> <p>The observed changes in physical properties of <span class="hlt">sea</span> <span class="hlt">ice</span> such as decreased thickness and increased melt pond cover severely impact the energy budget of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Increased light transmission leads to increased deposition of solar energy in the upper ocean and thus plays a crucial role for amount and timing of <span class="hlt">sea-ice</span>-melt and under-<span class="hlt">ice</span> primary production. Recent developments in underwater technology provide new opportunities to study light transmission below the largely inaccessible underside of <span class="hlt">sea</span> <span class="hlt">ice</span>. We measured spectral under-<span class="hlt">ice</span> radiance and irradiance using the new Nereid Under-<span class="hlt">Ice</span> (NUI) underwater robotic vehicle, during a cruise of the R/V Polarstern to 83°N 6°W in the Arctic Ocean in July 2014. NUI is a next generation hybrid remotely operated vehicle (H-ROV) designed for both remotely piloted and autonomous surveys underneath land-fast and moving <span class="hlt">sea</span> <span class="hlt">ice</span>. Here we present results from one of the first comprehensive scientific dives of NUI employing its interdisciplinary sensor suite. We combine under-<span class="hlt">ice</span> optical measurements with three dimensional under-<span class="hlt">ice</span> topography (multibeam sonar) and aerial images of the surface conditions. We investigate the influence of spatially varying <span class="hlt">ice</span>-thickness and surface properties on the spatial variability of light transmittance during summer. Our results show that surface properties such as melt ponds dominate the spatial distribution of the under-<span class="hlt">ice</span> light field on small scales (<1000 m2), while <span class="hlt">sea</span> <span class="hlt">ice</span>-thickness is the most important predictor for light transmission on larger scales. In addition, we propose the use of an algorithm to obtain histograms of light transmission from distributions of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and surface albedo.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C14B..01R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C14B..01R"><span>NASA <span class="hlt">Ice</span>Bridge: Airborne surveys of the polar <span class="hlt">sea</span> <span class="hlt">ice</span> covers</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Richter-Menge, J.; Farrell, S. L.</p> <p>2014-12-01</p> <p>The NASA Operation <span class="hlt">Ice</span>Bridge (OIB) airborne <span class="hlt">sea</span> <span class="hlt">ice</span> surveys are designed to continue a valuable series of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness measurements by bridging the gap between NASA's <span class="hlt">Ice</span>, Cloud and Land Elevation Satellite (ICESat), which operated from 2003 to 2009, and ICESat-2, which is scheduled for launch in 2017. Initiated in 2009, OIB has conducted campaigns over the western Arctic Ocean (March/April) and Southern Oceans (October/November) on an annual basis. Primary OIB sensors being used for <span class="hlt">sea</span> <span class="hlt">ice</span> observations include the Airborne Topographic Mapper laser altimeter, the Digital Mapping System digital camera, a Ku-band radar altimeter, a frequency-modulated continuous-wave (FMCW) snow radar, and a KT-19 infrared radiation pyrometer. Data from the campaigns are available to the research community at: http://nsidc.org/data/icebridge/. This presentation will summarize the spatial and temporal extent of the campaigns and highlight key scientific accomplishments, which include: • Documented changes in the Arctic marine cryosphere since the dramatic <span class="hlt">sea</span> <span class="hlt">ice</span> loss of 2007 • Novel snow depth measurements over <span class="hlt">sea</span> <span class="hlt">ice</span> in the Arctic • Improved skill of April-to-September <span class="hlt">sea</span> <span class="hlt">ice</span> predictions via numerical <span class="hlt">ice</span>/ocean models • Validation of satellite altimetry measurements (ICESat, CryoSat-2, and <span class="hlt">Ice</span>Sat-2/MABEL)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.usgs.gov/fs/2008/3041/','USGSPUBS'); return false;" href="https://pubs.usgs.gov/fs/2008/3041/"><span>Pacific Walrus Response to Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Losses</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Jay, Chadwick V.; Fischbach, Anthony S.</p> <p>2008-01-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> plays an important role in the life of the Pacific walrus (Odobenus rosmarus divergens). U.S. Geological Survey (USGS) scientists are seeking to understand how losses of <span class="hlt">sea</span> <span class="hlt">ice</span> during summer over important foraging grounds in the Chukchi <span class="hlt">Sea</span> will affect walruses. USGS scientists recently modified a remotely deployed satellite radio-tag that will aid in studying walrus foraging habitats and behaviors. Information from the tags will help USGS understand how walruses are responding to their changing environment.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2000JGR...10511299K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2000JGR...10511299K"><span>Results of the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Model Intercomparison Project: Evaluation of <span class="hlt">sea</span> <span class="hlt">ice</span> rheology schemes for use in climate simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kreyscher, Martin; Harder, Markus; Lemke, Peter; Flato, Gregory M.</p> <p>2000-05-01</p> <p>A hierarchy of <span class="hlt">sea</span> <span class="hlt">ice</span> rheologies is evaluated on the basis of a comprehensive set of observational data. The investigations are part of the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Model Intercomparison Project (SIMIP). Four different <span class="hlt">sea</span> <span class="hlt">ice</span> rheology schemes are compared: a viscous-plastic rheology, a cavitating-fluid model, a compressible Newtonian fluid, and a simple free drift approach with velocity correction. The same grid, land boundaries, and forcing fields are applied to all models. As verification data, there are (1) <span class="hlt">ice</span> thickness data from upward looking sonars (ULS), (2) <span class="hlt">ice</span> concentration data from the passive microwave radiometers SMMR and SSM/I, (3) daily buoy drift data obtained by the International Arctic Buoy Program (IABP), and (4) satellite-derived <span class="hlt">ice</span> drift fields based on the 85 GHz channel of SSM/I. All models are optimized individually with respect to mean drift speed and daily drift speed statistics. The impact of <span class="hlt">ice</span> strength on the <span class="hlt">ice</span> cover is best revealed by the spatial pattern of <span class="hlt">ice</span> thickness, <span class="hlt">ice</span> drift on different timescales, daily drift speed statistics, and the drift velocities in Fram Strait. Overall, the viscous-plastic rheology yields the most realistic simulation. In contrast, the results of the very simple free-drift model with velocity correction clearly show large errors in simulated <span class="hlt">ice</span> drift as well as in <span class="hlt">ice</span> thicknesses and <span class="hlt">ice</span> export through Fram Strait compared to observation. The compressible Newtonian fluid cannot prevent excessive <span class="hlt">ice</span> thickness buildup in the central Arctic and overestimates the internal forces in Fram Strait. Because of the lack of shear strength, the cavitating-fluid model shows marked differences to the statistics of observed <span class="hlt">ice</span> drift and the observed spatial pattern of <span class="hlt">ice</span> thickness. Comparison of required computer resources demonstrates that the additional cost for the viscous-plastic <span class="hlt">sea</span> <span class="hlt">ice</span> rheology is minor compared with the atmospheric and oceanic model components in global climate simulations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010100393','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010100393"><span>Variability of Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> 1979-1998</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Zwally, H. Jay; Comiso, Josefino C.; Parkinson, Claire L.; Cavalieri, Donald J.; Gloersen, Per; Koblinsky, Chester J. (Technical Monitor)</p> <p>2001-01-01</p> <p>The principal characteristics of the variability of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover as previously described from satellite passive-microwave observations are also evident in a systematically-calibrated and analyzed data set for 20.2 years (1979-1998). The total Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent (concentration > 15 %) increased by 13,440 +/- 4180 sq km/year (+1.18 +/- 0.37%/decade). The area of <span class="hlt">sea</span> <span class="hlt">ice</span> within the extent boundary increased by 16,960 +/- 3,840 sq km/year (+1.96 +/- 0.44%/decade). Regionally, the trends in extent are positive in the Weddell <span class="hlt">Sea</span> (1.5 +/- 0.9%/decade), Pacific Ocean (2.4 +/- 1.4%/decade), and Ross (6.9 +/- 1.1 %/decade) sectors, slightly negative in the Indian Ocean (-1.5 +/- 1.8%/decade, and strongly negative in the Bellingshausen-Amundsen <span class="hlt">Seas</span> sector (-9.5 +/- 1.5%/decade). For the entire <span class="hlt">ice</span> pack, small <span class="hlt">ice</span> increases occur in all seasons with the largest increase during autumn. On a regional basis, the trends differ season to season. During summer and fall, the trends are positive or near zero in all sectors except the Bellingshausen-Amundsen <span class="hlt">Seas</span> sector. During winter and spring, the trends are negative or near zero in all sectors except the Ross <span class="hlt">Sea</span>, which has positive trends in all seasons. Components of interannual variability with periods of about 3 to 5 years are regionally large, but tend to counterbalance each other in the total <span class="hlt">ice</span> pack. The interannual variability of the annual mean <span class="hlt">sea-ice</span> extent is only 1.6% overall, compared to 5% to 9% in each of five regional sectors. Analysis of the relation between regional <span class="hlt">sea</span> <span class="hlt">ice</span> extents and spatially-averaged surface temperatures over the <span class="hlt">ice</span> pack gives an overall sensitivity between winter <span class="hlt">ice</span> cover and temperature of -0.7% change in <span class="hlt">sea</span> <span class="hlt">ice</span> extent per K. For summer, some regional <span class="hlt">ice</span> extents vary positively with temperature and others negatively. The observed increase in Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover is counter to the observed decreases in the Arctic. It is also qualitatively consistent with the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA617029','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA617029"><span>Radar Remote Sensing of <span class="hlt">Ice</span> and <span class="hlt">Sea</span> State and Air-<span class="hlt">Sea</span> Interaction in the Marginal <span class="hlt">Ice</span> Zone</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2014-09-30</p> <p>1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Radar Remote Sensing of <span class="hlt">Ice</span> and <span class="hlt">Sea</span> State and Air-<span class="hlt">Sea</span>...Interaction in the Marginal <span class="hlt">Ice</span> Zone Hans C. Graber RSMAS – Department of Ocean Sciences Center for Southeastern Tropical Advanced Remote Sensing...scattering and attenuation process of ocean waves interacting with <span class="hlt">ice</span> . A nautical X-band radar on a vessel dedicated to science would be used to follow the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1711972M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1711972M"><span>Measurements of <span class="hlt">sea</span> <span class="hlt">ice</span> proxies from Antarctic coastal shallow cores</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maffezzoli, Niccolò; Vallelonga, Paul; Spolaor, Andrea; Barbante, Carlo; Frezzotti, Massimo</p> <p>2015-04-01</p> <p>Despite its close relationship with climate, the climatic impact of <span class="hlt">sea</span> <span class="hlt">ice</span> remains only partially understood: an indication of this is the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> which is declining at a faster rate than models predict. Thus, the need for reliable <span class="hlt">sea</span> <span class="hlt">ice</span> proxies is of crucial importance. Among the <span class="hlt">sea</span> <span class="hlt">ice</span> proxies that can be extracted from <span class="hlt">ice</span> cores, interest has recently been shown in the halogens Iodine (I) and Bromine (Br) (Spolaor, A., et al., 2013a, 2013b). The production of <span class="hlt">sea</span> <span class="hlt">ice</span> is a source of Sodium and Bromine aerosols through frost flower crystal formation and sublimation of salty blowing snow, while Iodine is emitted by the algae living underneath <span class="hlt">sea</span> <span class="hlt">ice</span>. We present here the results of Na, Br and I measurements in Antarctic shallow cores, drilled during a traverse made in late 2013 - early 2014 from Talos Dome (72° 00'S, 159°12'E) to GV7 (70° 41'S, 158° 51'E) seeking for <span class="hlt">sea</span> <span class="hlt">ice</span> signature. The samples were kept frozen until the analyses, that were carried out by Sector Field Mass Spectroscopy Inductive Coupled Plasma (SFMS-ICP): special precautions and experimental steps were adopted for the detection of such elements. The coastal location of the cores allows a clear signal from the nearby <span class="hlt">sea</span> <span class="hlt">ice</span> masses. The multiple cores are located about 50 km from each other and can help us to infer the provenance of the <span class="hlt">sea</span> <span class="hlt">ice</span> that contributed to the proxy signature. Moreover, by simultaneously determining other chemical elements and compounds in the snow, it is possible to determine the relative timing of their deposition, thus helping us to understand their processes of emission and deposition.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ACP....15.9731S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ACP....15.9731S"><span>A mechanism for biologically induced iodine emissions from <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Saiz-Lopez, A.; Blaszczak-Boxe, C. S.; Carpenter, L. J.</p> <p>2015-09-01</p> <p>Ground- and satellite-based measurements have reported high concentrations of iodine monoxide (IO) in coastal Antarctica. The sources of such a large iodine burden in the coastal Antarctic atmosphere remain unknown. We propose a mechanism for iodine release from <span class="hlt">sea</span> <span class="hlt">ice</span> based on the premise that micro-algae are the primary source of iodine emissions in this environment. The emissions are triggered by the biological production of iodide (I-) and hypoiodous acid (HOI) from micro-algae (contained within and underneath <span class="hlt">sea</span> <span class="hlt">ice</span>) and their diffusion through <span class="hlt">sea-ice</span> brine channels, ultimately accumulating in a thin brine layer (BL) on the surface of <span class="hlt">sea</span> <span class="hlt">ice</span>. Prior to reaching the BL, the diffusion timescale of iodine within <span class="hlt">sea</span> <span class="hlt">ice</span> is depth-dependent. The BL is also a vital component of the proposed mechanism as it enhances the chemical kinetics of iodine-related reactions, which allows for the efficient release of iodine to the polar boundary layer. We suggest that iodine is released to the atmosphere via three possible pathways: (1) emitted from the BL and then transported throughout snow atop <span class="hlt">sea</span> <span class="hlt">ice</span>, from where it is released to the atmosphere; (2) released directly from the BL to the atmosphere in regions of <span class="hlt">sea</span> <span class="hlt">ice</span> that are not covered with snowpack; or (3) emitted to the atmosphere directly through fractures in the <span class="hlt">sea-ice</span> pack. To investigate the proposed biology-<span class="hlt">ice</span>-atmosphere coupling at coastal Antarctica we use a multiphase model that incorporates the transport of iodine species, via diffusion, at variable depths, within brine channels of <span class="hlt">sea</span> <span class="hlt">ice</span>. Model simulations were conducted to interpret observations of elevated springtime IO in the coastal Antarctic, around the Weddell <span class="hlt">Sea</span>. While a lack of experimental and observational data adds uncertainty to the model predictions, the results nevertheless show that the levels of inorganic iodine (i.e. I2, IBr, ICl) released from <span class="hlt">sea</span> <span class="hlt">ice</span> through this mechanism could account for the observed IO concentrations during</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19990064090&hterms=arctic+ice+melt&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Darctic%2Bice%2Bmelt','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990064090&hterms=arctic+ice+melt&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Darctic%2Bice%2Bmelt"><span>Variability of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> as Viewed from Space</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Parkinson, Claire L.</p> <p>1998-01-01</p> <p>Over the past 20 years, satellite passive-microwave radiometry has provided a marvelous means for obtaining information about the variability of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover and particularly about <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations (% areal coverages) and from them <span class="hlt">ice</span> extents and the lengths of the <span class="hlt">sea</span> <span class="hlt">ice</span> season. This ability derives from the sharp contrast between the microwave emissions of <span class="hlt">sea</span> <span class="hlt">ice</span> versus liquid water and allows routine monitoring of the vast Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover, which typically varies in extent from a minimum of about 8,000,000 sq km in September to a maximum of about 15,000,000 sq km in March, the latter value being over 1.5 times the area of either the United States or Canada. The vast Arctic <span class="hlt">ice</span> cover has many impacts, including hindering heat, mass, and y momentum exchanges between the oceans and the atmosphere, reducing the amount of solar radiation absorbed at the Earth's surface, affecting freshwater transports and ocean circulation, and serving as a vital surface for many species of polar animals. These direct impacts also lead to indirect impacts, including effects on local and perhaps global atmospheric temperatures, effects that are being examined in general circulation modeling studies, where preliminary results indicate that changes on the order of a few percent <span class="hlt">sea</span> <span class="hlt">ice</span> concentration can lead to temperature changes of 1 K or greater even in local areas outside of the <span class="hlt">sea</span> <span class="hlt">ice</span> region. Satellite passive-microwave data for November 1978 through December 1996 reveal marked regional and interannual variabilities in both the <span class="hlt">ice</span> extents and the lengths of the <span class="hlt">sea</span> <span class="hlt">ice</span> season, as well as some statistically significant trends. For the north polar <span class="hlt">ice</span> cover as a whole, maximum <span class="hlt">ice</span> extents varied over a range of 14,700,000 - 15,900,000 km(2), while individual regions showed much greater percentage variations, e.g., with the Greenland <span class="hlt">Sea</span> experiencing a range of 740,000 - 1,1110,000 km(2) in its yearly maximum <span class="hlt">ice</span> coverage. Although variations from year to</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.C11B..03P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.C11B..03P"><span>Airborne radar surveys of snow depth over Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> during Operation <span class="hlt">Ice</span>Bridge</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Panzer, B.; Gomez-Garcia, D.; Leuschen, C.; Paden, J. D.; Gogineni, P. S.</p> <p>2012-12-01</p> <p>Over the last decade, multiple satellite-based laser and radar altimeters, optimized for polar observations, have been launched with one of the major objectives being the determination of global <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and distribution [5, 6]. Estimation of <span class="hlt">sea-ice</span> thickness from these altimeters relies on freeboard measurements and the presence of snow cover on <span class="hlt">sea</span> <span class="hlt">ice</span> affects this estimate. Current means of estimating the snow depth rely on daily precipitation products and/or data from passive microwave sensors [2, 7]. Even a small uncertainty in the snow depth leads to a large uncertainty in the <span class="hlt">sea-ice</span> thickness estimate. To improve the accuracy of the <span class="hlt">sea-ice</span> thickness estimates and provide validation for measurements from satellite-based sensors, the Center for Remote Sensing of <span class="hlt">Ice</span> Sheets deploys the Snow Radar as a part of NASA Operation <span class="hlt">Ice</span>Bridge. The Snow Radar is an ultra-wideband, frequency-modulated, continuous-wave radar capable of resolving snow depth on <span class="hlt">sea</span> <span class="hlt">ice</span> from 5 cm to more than 2 meters from long-range, airborne platforms [4]. This paper will discuss the algorithm used to directly extract snow depth estimates exclusively using the Snow Radar data set by tracking both the air-snow and snow-<span class="hlt">ice</span> interfaces. Prior work in this regard used data from a laser altimeter for tracking the air-snow interface or worked under the assumption that the return from the snow-<span class="hlt">ice</span> interface was greater than that from the air-snow interface due to a larger dielectric contrast, which is not true for thick or higher loss snow cover [1, 3]. This paper will also present snow depth estimates from Snow Radar data during the NASA Operation <span class="hlt">Ice</span>Bridge 2010-2011 Antarctic campaigns. In 2010, three <span class="hlt">sea</span> <span class="hlt">ice</span> flights were flown, two in the Weddell <span class="hlt">Sea</span> and one in the Amundsen and Bellingshausen <span class="hlt">Seas</span>. All three flight lines were repeated in 2011, allowing an annual comparison of snow depth. In 2011, a repeat pass of an earlier flight in the Weddell <span class="hlt">Sea</span> was flown, allowing for a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..11.1959A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.1959A"><span>Validation of simulated <span class="hlt">sea-ice</span> concentrations from <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean models and polynya classification methods in the Laptev <span class="hlt">Sea</span> area using satellite data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Adams, S.; Willmes, S.; Heinemann, G.</p> <p>2009-04-01</p> <p>The Laptev <span class="hlt">Sea</span> represents one of the most significant areas of net <span class="hlt">ice</span> production in the Arctic. Most of the <span class="hlt">ice</span> production takes place in a polynya forming at the fast <span class="hlt">ice</span> edge during strong offshore wind conditions. The simulation of these polynya events is a challenge for current <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean models, and validation of simulated <span class="hlt">sea-ice</span> concentrations is necessary for model improvements. High-quality data sets of <span class="hlt">sea-ice</span> concentration from remote sensing data are covering the period from 1978 to the present. These data sets are well suited for the validation of model results of <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean models. Based on the brightness temperature observations obtained from the Advanced Microwave Scanning Radiometer (AMSR-E), the ARTIST (Arctic Radiation and Turbulence Interaction Study) <span class="hlt">Sea</span> <span class="hlt">Ice</span> (ASI) algorithm is used to calculate mean daily <span class="hlt">sea-ice</span> concentrations. Here we use AMSR-E data for the validation of <span class="hlt">sea-ice</span> concentrations in the Laptev <span class="hlt">Sea</span>, which are simulated by the coupled <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean models North Atlantic - Arctic Ocean - <span class="hlt">Sea-Ice</span> Model (NAOSIM) and Finite Element <span class="hlt">Sea</span> <span class="hlt">Ice</span> Ocean Model (FESOM). The general distribution of the <span class="hlt">sea-ice</span> concentrations, the simulation of the polynya events and the position of polynyas are examined for the period October 2007 to April 2008. In addition, the polynya signature simulation method (PSSM) was applied to classify open water, thin <span class="hlt">ice</span> and thick <span class="hlt">ice</span>. The results of the validation show that the simulated distributions of the <span class="hlt">sea-ice</span> fields show similar structures, but an underestimation of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration. The simulation of the polynya-events from the two models agrees reasonably well with satellite data. However, because of the absent fast <span class="hlt">ice</span> edge in both models, the position of the polynyas is shifted to the coast line. Therefore it would be necessary to include the fast <span class="hlt">ice</span> edge for simulating polynyas at the right position. Further investigations about the position of the polynyas will be performed with simulation</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.C51A0504Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.C51A0504Y"><span>Photon counting altimetry for Operation <span class="hlt">Ice</span> Bridge over East Antarctica: Comparison of ICECAP's <span class="hlt">ALAMO</span> dataset to GLAS altimetry</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Young, D. A.; Lindzey, L. E.; Blankenship, D. D.; Greenbaum, J. S.; Kempf, S. D.; Fisher, J. R.</p> <p>2013-12-01</p> <p>The ICECAP portion of NASA's Operation <span class="hlt">Ice</span> Bridge collected over 200,000 line kilometers of low elevation swath photon counting lidar data over both East Antarctic and Greenland between 2010 and 2012, as part of a comprehensive multi-instrumented campaign of aerogeophysical mapping. A primary goal of this project was to refly GLAS satellite altimetry tracks collected between 2003 and 2009, to reduce the cross track uncertainty inherent in the GLAS dataset and obtain a longer time series of <span class="hlt">ice</span> sheet change. We combined data from our well understood nadir pointing laser altimeter and a subset version of the photon counting lidar to obtain the hybrid Airborne Laser Altimeter with Mapping Optics (<span class="hlt">ALAMO</span>) product, which provides accurate cross track slope information. We compare this with the GLAH12 Release 33 product with the new gaussian-centroid correction applied. Over the Wilkes land margin, we find this comparison yields more stable dhdt results for the 2003-2009 record than GLAS alone approaches, and continue the time series for an additional three years in places.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_14 --> <div id="page_15" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="281"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006GeoRL..3315810W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006GeoRL..3315810W"><span>Coincident vortices in Antarctic wind fields and <span class="hlt">sea</span> <span class="hlt">ice</span> motion</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wassermann, S.; Schmitt, C.; Kottmeier, C.; Simmonds, I.</p> <p>2006-08-01</p> <p>This study introduces a method to examine the coincidence of rotational <span class="hlt">ice</span> drift and winds caused by the forcing of <span class="hlt">ice</span> motion by Antarctic cyclones. Vortices are automatically detected using the algorithm of Murray and Simmonds (1991) from both ECMWF surface pressures and SSM/I <span class="hlt">sea</span> <span class="hlt">ice</span> motions. For compatibility with this algorithm <span class="hlt">sea</span> <span class="hlt">ice</span> motion vectors are transformed to a scalar stream function. During a seven-day test period positions of pressure minima and stream function maxima (SFM) of <span class="hlt">ice</span> drift are within 300 km in 96% of the cases. Lowest pressure minima are related to highest stream function maxima. The results promise the method to provide a complementary tool of detecting and localizing low-pressure systems over <span class="hlt">sea</span> <span class="hlt">ice</span>, adding to numerical pressure analyses.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20150014250&hterms=sea+ice&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dsea%2Bice','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20150014250&hterms=sea+ice&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dsea%2Bice"><span>Aquarius Retrieval of <span class="hlt">Sea</span> <span class="hlt">Ice</span> Thickness: Initial Results</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>De Matthaeis, Paolo; Utku, C.; Le Vine, David M.; Moyer, A.</p> <p>2014-01-01</p> <p>Aquarius brightness temperature data are used to calculate <span class="hlt">sea</span> <span class="hlt">ice</span> thickness in the Arctic region. The method is based on the inversion of a radiative transfer model for icecovered <span class="hlt">sea</span>. Using this technique, the initial <span class="hlt">sea</span> <span class="hlt">ice</span> thickness values retrieved from Aquarius data are compared to the SMOSIce Data as well as to estimates from NASA's Operation <span class="hlt">Ice</span>Bridge. The results show similar trends between the SMOS- and Aquarius-derived <span class="hlt">sea</span> <span class="hlt">ice</span> thickness, however the Aquarius estimates tend to be higher and noisier than the corresponding SMOS values. The accuracy of retrieved Aquarius <span class="hlt">ice</span> thickness is possibly influenced by uncertainties in the ancillary input parameters and by the coarser resolutions of Aquarius.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011EOSTr..92....1W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011EOSTr..92....1W"><span>Tradition and Technology: <span class="hlt">Sea</span> <span class="hlt">Ice</span> Science on Inuit Sleds</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wilkinson, Jeremy P.; Hanson, Susanne; Hughes, Nick E.; James, Alistair; Jones, Bryn; MacKinnon, Rory; Rysgaard, Søren; Toudal, Leif</p> <p>2011-01-01</p> <p>The Arctic is home to a circumpolar community of native people whose culture and traditions have enabled them to thrive in what most would perceive as a totally inhospitable and untenable environment. In many ways, <span class="hlt">sea</span> <span class="hlt">ice</span> can be viewed as the glue that binds these northern communities together; it is utilized in all aspects of their daily life. <span class="hlt">Sea</span> <span class="hlt">ice</span> acts as highways of the north; indeed, one can travel on these highways with dogsleds and snowmobiles. These travels over the frozen ocean occur at all periods of the <span class="hlt">sea</span> <span class="hlt">ice</span> cycle and over different <span class="hlt">ice</span> types and ages. Excursions may be hunting trips to remote regions or social visits to nearby villages. Furthermore, hunting on the <span class="hlt">sea</span> <span class="hlt">ice</span> contributes to the health, culture, and commercial income of a community.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C11B0375S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C11B0375S"><span>A Lagrangian analysis of <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics in the Arctic</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Szanyi, S.; Lukovich, J. V.; Haller, G.; Barber, D. G.</p> <p>2014-12-01</p> <p>Recent studies have highlighted acceleration in <span class="hlt">sea</span> <span class="hlt">ice</span> drift and deformation in the Arctic over the last several decades, underlining the need for improved understanding of <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics and dispersion. In this study we present Lagrangian diagnostics to quantify changes in the dynamical characteristics of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover from 1979 to 2012 during the transition from a predominantly multi-year to a first-year <span class="hlt">ice</span> regime. Examined in particular is the evolution in finite-time Lyapunov exponents (FTLEs), which monitor the rate at which neighboring particle trajectories diverge, and stretching rates throughout the Arctic. In this analysis we compute FTLEs for the Arctic <span class="hlt">ice</span> drift field using National Snow and <span class="hlt">Ice</span> Data Centre (NSIDC) Polar Pathfinder Daily 25 km EASE-Grid weekly <span class="hlt">sea</span> <span class="hlt">ice</span> motion vectors for the annual cycle beginning both from the <span class="hlt">sea</span> <span class="hlt">ice</span> minimum in September, and maximum in March. Sensitivity analyses show that maximal FTLEs, or ridges, are robust even with the introduction of significant noise. Probability density functions and mean values of FTLEs show a trend towards higher FTLE values characteristic of increased mixing in the Arctic in the last decade, in keeping with a transition to a weaker, thinner <span class="hlt">ice</span> cover.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110015207','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110015207"><span>Regional Changes in the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Cover and <span class="hlt">Ice</span> Production in the Antarctic</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Comiso, Josefino C.</p> <p>2011-01-01</p> <p>Coastal polynyas around the Antarctic continent have been regarded as <span class="hlt">sea</span> <span class="hlt">ice</span> factories because of high <span class="hlt">ice</span> production rates in these regions. The observation of a positive trend in the extent of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> during the satellite era has been intriguing in light of the observed rapid decline of the <span class="hlt">ice</span> extent in the Arctic. The results of analysis of the time series of passive microwave data indicate large regional variability with the trends being strongly positive in the Ross <span class="hlt">Sea</span>, strongly negative in the Bellingshausen/Amundsen <span class="hlt">Seas</span> and close to zero in the other regions. The atmospheric circulation in the Antarctic is controlled mainly by the Southern Annular Mode (SAM) and the marginal <span class="hlt">ice</span> zone around the continent shows an alternating pattern of advance and retreat suggesting the presence of a propagating wave (called Antarctic Circumpolar Wave) around the circumpolar region. The results of analysis of the passive microwave data suggest that the positive trend in the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover could be caused primarily by enhanced <span class="hlt">ice</span> production in the Ross <span class="hlt">Sea</span> that may be associated with more persistent and larger coastal polynyas in the region. Over the Ross <span class="hlt">Sea</span> shelf, analysis of <span class="hlt">sea</span> <span class="hlt">ice</span> drift data from 1992 to 2008 yields a positive rate-of-increase in the net <span class="hlt">ice</span> export of about 30,000 km2 per year. For a characteristic <span class="hlt">ice</span> thickness of 0.6 m, this yields a volume transport of about 20 km3/year, which is almost identical, within error bars, to our estimate of the trend in <span class="hlt">ice</span> production. In addition to the possibility of changes in SAM, modeling studies have also indicated that the ozone hole may have a role in that it causes the deepening of the lows in the western Antarctic region thereby causing strong winds to occur offthe Ross-<span class="hlt">ice</span> shelf.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19840002650','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19840002650"><span>Antartic <span class="hlt">sea</span> <span class="hlt">ice</span>, 1973 - 1976: Satellite passive-microwave observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Zwally, H. J.; Comiso, J. C.; Parkinson, C. L.; Campbell, W. J.; Carsey, F. D.; Gloersen, P.</p> <p>1983-01-01</p> <p>Data from the Electrically Scanning Microwave Radiometer (ESMR) on the Nimbus 5 satellite are used to determine the extent and distribution of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span>. The characteristics of the southern ocean, the mathematical formulas used to obtain quantitative <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations, the general characteristics of the seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> growth/decay cycle and regional differences, and the observed seasonal growth/decay cycle for individual years and interannual variations of the <span class="hlt">ice</span> cover are discussed. The <span class="hlt">sea</span> <span class="hlt">ice</span> data from the ESMR are presented in the form of color-coded maps of the Antarctic and the southern oceans. The maps show brightness temperatures and concentrations of pack <span class="hlt">ice</span> averaged for each month, 4-year monthly averages, and month-to-month changes. Graphs summarizing the results, such as areas of <span class="hlt">sea</span> <span class="hlt">ice</span> as a function of time in the various sectors of the southern ocean are included. The images demonstrate that satellite microwave data provide unique information on large-scale <span class="hlt">sea</span> <span class="hlt">ice</span> conditions for determining climatic conditions in polar regions and possible global climatic changes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003PhDT........37L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003PhDT........37L"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> climatology, variations and teleconnections: Observational and modeling studies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, Jiping</p> <p></p> <p>Hypotheses, models and observations suggest that <span class="hlt">sea</span> <span class="hlt">ice</span> plays an important role in the local, regional and global climate through a variety of processes across a full range of scales. However, our documentation and understanding of the nature of the polar-extrapolar climate teleconnections and their underlying causal and mechanistic links are still rudimentary, and the largest disagreements among model simulations of present and future climate are in the polar regions. In an effort to address these issues, we evaluated the simulated Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> variability and its climate teleconnections in three coupled global climate models (GISS, NCAR and GFDL) as compared to the observations. All the models capture the El Nino-Southern Oscillation (ENSO)-like phenomenon to some degree, although almost all the models miss some observed linkages. The GISS and NCAR models also capture the observed Antarctic Dipole and meridional banding structure through the Pacific. The Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> regions showing the strongest sensitivity to global teleconnections differ among the models and between the models and observations. We then proposed that the changes of the regional mean meridional atmospheric circulation (the regional Ferrel Cell) are one such mechanism leading to the covariability of the ENSO and Antarctic Dipole by modulating the mean meridional heat flux using the observational data. To more accurately represent <span class="hlt">sea</span> <span class="hlt">ice</span> simulations and associated feedbacks with the atmosphere and the ocean, the GISS coupled model was used to investigate the sensitivity of <span class="hlt">sea</span> <span class="hlt">ice</span> to the following physical parameterizations: (a) two <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics (cavitating fluid and viscous-plastic), (b) the specification of oceanic isopyncal mixing coefficients in the Gent and McWillams isopyncal mixing, (c) the Wajsowicz viscosity diffusion, (d) surface albedo, (e) the penetration of solar radiation in <span class="hlt">sea</span> <span class="hlt">ice</span>, (f) effects of including a <span class="hlt">sea</span> <span class="hlt">ice</span> salinity budget, and (g) the <span class="hlt">ice</span>-ocean boundary</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC54A..04W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC54A..04W"><span>Spring Snow Depth on Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> using the <span class="hlt">Ice</span>Bridge Snow Depth Product (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Webster, M.; Rigor, I. G.; Nghiem, S. V.; Kurtz, N. T.; Farrell, S. L.</p> <p>2013-12-01</p> <p>Snow has dual roles in the growth and decay of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. In winter, it insulates <span class="hlt">sea</span> <span class="hlt">ice</span> from colder air temperatures, slowing its growth. From spring into summer, the albedo of snow determines how much insolation is transmitted through the <span class="hlt">sea</span> <span class="hlt">ice</span> and into the underlying ocean, ultimately impacting the progression of the summer <span class="hlt">ice</span> melt. Knowing the snow thickness and distribution are essential for understanding and modeling <span class="hlt">sea</span> <span class="hlt">ice</span> thermodynamics and the surface heat budget. Therefore, an accurate assessment of the snow cover is necessary for identifying its impacts in the changing Arctic. This study assesses springtime snow conditions on Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> using airborne snow thickness measurements from Operation <span class="hlt">Ice</span>Bridge (2009-2012). The 2012 data were validated with coordinated in situ measurements taken in March 2012 during the BRomine, Ozone, and Mercury EXperiment field campaign. We find a statistically significant correlation coefficient of 0.59 and RMS error of 5.8 cm. The comparison between the <span class="hlt">Ice</span>Bridge snow thickness product and the 1937, 1954-1991 Soviet drifting <span class="hlt">ice</span> station data suggests that the snow cover has thinned by 33% in the western Arctic and 44% in the Beaufort and Chukchi <span class="hlt">Seas</span>. A rudimentary estimation shows that a thinner snow cover in the Beaufort and Chukchi <span class="hlt">Seas</span> translates to a mid-December surface heat flux as high as 81 W/m2 compared to 32 W/m2. The relationship between the 2009-2012 thinner snow depth distribution and later <span class="hlt">sea</span> <span class="hlt">ice</span> freeze-up is statistically significant, with a correlation coefficient of 0.59. These results may help us better understand the surface energy budget in the changing Arctic, and may improve our ability to predict the future state of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060044035&hterms=Norwegian+Sea&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DNorwegian%2BSea','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060044035&hterms=Norwegian+Sea&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DNorwegian%2BSea"><span>On large outflows of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> into the Barents <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kwok, Ron; Maslowski, Wieslaw; Laxon, Seymour W.</p> <p>2005-01-01</p> <p>Winter outflows of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> into the Barents <span class="hlt">Sea</span> are estimated using a 10-year record of satellite <span class="hlt">ice</span> motion and thickness. The mean winter volume export through the Svalbard/Franz Josef Land passage is 40 km3, and ranges from -280 km3 to 340 km3. A large outflow in 2003 is preconditioned by an unusually high concentration of thick perennial <span class="hlt">ice</span> over the Nansen Basin at the end of the 2002 summer. With a deep atmospheric low situated over the eastern Barents <span class="hlt">Sea</span> in winter, the result is an increased export of Arctic <span class="hlt">ice</span>. The Oct-Mar <span class="hlt">ice</span> area flux, at 110 x 10 to the third power km3, is not only unusual in magnitude but also remarkable in that >70% of the area is multiyear <span class="hlt">ice</span>; the <span class="hlt">ice</span> volume flux at340 km3 is almost one-fifth of the <span class="hlt">ice</span> flux through the Fram Strait. Another large outflow of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> through this passage, comparable to that in 2003, is found in 1996. This southward flux of <span class="hlt">sea</span> <span class="hlt">ice</span> represents one of two major sources of freshwater in the Barents <span class="hlt">Sea</span>; the other is the eastward flux of water via the Norwegian Coastal Current. The possible consequences of variable freshwater input on the Barents <span class="hlt">Sea</span> hydrography and its impact on transformation of Atlantic Water en route to the Arctic Ocean are examined with a 25-year coupled <span class="hlt">ice</span>-ocean model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C23B0771B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C23B0771B"><span>The Importance of Snow Distribution on <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Butler, B.; Polashenski, C.; Divine, D.; King, J.; Liston, G. E.; Nicolaus, M.; Rösel, A.</p> <p>2015-12-01</p> <p>Snow's insulating and reflective properties substantially influence Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> growth and decay. A particularly important, but under-appreciated, aspect of snow on <span class="hlt">sea</span> <span class="hlt">ice</span> is its fine-scale spatial distribution. Snow redistribution into dunes and drifts controls the effective thermal conductivity of a snowpack and dictates the locations of melt pond formation, exerting considerable control over <span class="hlt">ice</span> mass balance. The effective thermal conductivity of snow distributions created on <span class="hlt">sea</span> <span class="hlt">ice</span>, for example, is often considerably greater than a uniform snowpack of equivalent mean thickness. During the N-<span class="hlt">ICE</span> 2015 campaign north of Svalbard, we studied snow distributions across multiple <span class="hlt">ice</span> types and the impacts these have on thermal fluxes and <span class="hlt">ice</span> mass balance. We used terrestrial LiDAR to observe the snow surface topography over km2 areas, conducted many thousands of manual snow depth measurements, and collected hundreds of observations of the snow physical properties in snow pits. We find that the wind driven redistribution of snow can alter the net effect of a constant snow cover volume on <span class="hlt">ice</span> mass balance as strongly as inter-annual variability in the amount and timing of snowfall. Further comparison with snow depth distributions from field campaigns in other parts of the Arctic highlights regional and inter-annual differences in snow distribution. We quantify the impact of this variability on <span class="hlt">ice</span> mass balance and demonstrate the need for considering snow distributions and redistribution processes in <span class="hlt">sea</span> <span class="hlt">ice</span> models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70015240','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70015240"><span><span class="hlt">SEA-ICE</span> INFLUENCE ON ARCTIC COASTAL RETREAT.</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Reimnitz, Erk; Barnes, P.W.</p> <p>1987-01-01</p> <p>Recent studies document the effectiveness of <span class="hlt">sea</span> <span class="hlt">ice</span> in reshaping the seafloor of the inner shelf into sharp-relief features, including <span class="hlt">ice</span> gouges with jagged flanking ridges, <span class="hlt">ice</span>-wallow relief, and 2- to 6-m-deep strudel-scour craters. These <span class="hlt">ice</span>-related relief forms are in disequilibrium with classic open-water hydraulic processes and thus are smoothed over by waves and currents in one to two years. Such alternate reworking of the shelf by <span class="hlt">ice</span> and currents - two diverse types of processes, which in the case of <span class="hlt">ice</span> wallow act in unison-contributes to sediment mobility and, thus, to sediment loss from the coast and inner shelf. The bulldozing action by <span class="hlt">ice</span> results in coast-parallel sediment displacement. Additionally, suspension of sediment by frazil and anchor <span class="hlt">ice</span>, followed by <span class="hlt">ice</span> rafting, can move large amounts of bottom-derived materials. Our understanding of all these processes is insufficient to model Arctic coastal processes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20070018253&hterms=klein&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAuthor-Name%26N%3D0%26No%3D60%26Ntt%3Dklein','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20070018253&hterms=klein&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAuthor-Name%26N%3D0%26No%3D60%26Ntt%3Dklein"><span>Microwave Signatures of Snow on <span class="hlt">Sea</span> <span class="hlt">Ice</span>: Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Markus, Thorsten; Cavalieri, Donald J.; Gasiewski, Albin J.; Klein, Marian; Maslanik, James A.; Powell, Dylan C.; Stankov, B. Boba; Stroeve, Julienne C.; Sturm, Matthew</p> <p>2006-01-01</p> <p>Part of the Earth Observing System Aqua Advanced Microwave Scanning Radiometer (AMSR-E) Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> validation campaign in March 2003 was dedicated to the validation of snow depth on <span class="hlt">sea</span> <span class="hlt">ice</span> and <span class="hlt">ice</span> temperature products. The difficulty with validating these two variables is that neither can currently be measured other than in situ. For this reason, two aircraft flights on March 13 and 19,2003, were dedicated to these products, and flight lines were coordinated with in situ measurements of snow and <span class="hlt">sea</span> <span class="hlt">ice</span> physical properties. One flight was in the vicinity of Barrow, AK, covering Elson Lagoon and the adjacent Chukchi and Beaufort <span class="hlt">Seas</span>. The other flight was farther north in the Beaufort <span class="hlt">Sea</span> (about 73 N, 147.5 W) and was coordinated with a Navy <span class="hlt">ice</span> camp. The results confirm the AMSR-E snow depth algorithm and its coefficients for first-year <span class="hlt">ice</span> when it is relatively smooth. For rough first-year <span class="hlt">ice</span> and for multiyear <span class="hlt">ice</span>, there is still a relationship between the spectral gradient ratio of 19 and 37 GHz, but a different set of algorithm coefficients is necessary. Comparisons using other AMSR-E channels did not provide a clear signature of <span class="hlt">sea</span> <span class="hlt">ice</span> characteristics and, hence, could not provide guidance for the choice of algorithm coefficients. The limited comparison of in situ snow-<span class="hlt">ice</span> interface and surface temperatures with 6-GHz brightness temperatures, which are used for the retrieval of <span class="hlt">ice</span> temperature, shows that the 6-GHz temperature is correlated with the snow-<span class="hlt">ice</span> interface temperature to only a limited extent. For strong temperature gradients within the snow layer, it is clear that the 6-GHz temperature is a weighted average of the entire snow layer.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19730015654','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19730015654"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> and surface water circulation, Alaskan Continental Shelf</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wright, F. F. (Principal Investigator); Sharma, G. D.; Burn, J. J.</p> <p>1973-01-01</p> <p>The author has identified the following significant results. The boundaries of land-fast <span class="hlt">ice</span>, distribution of pack <span class="hlt">ice</span>, and major polynya were studied in the vicinity of the Bering Strait. Movement of pack <span class="hlt">ice</span> during 24 hours was determined by plotting the distinctly identifiable <span class="hlt">ice</span> floes on ERTS-1 imagery obtained from two consecutive passes. Considerably large shallow area along the western Seward Peninsula just north of the Bering Strait is covered by land fast <span class="hlt">ice</span>. This <span class="hlt">ice</span> hinders the movement of <span class="hlt">ice</span> formed in eastern Chukchi <span class="hlt">Sea</span> southward through the Bering Strait. The movement of <span class="hlt">ice</span> along the Russian coast is relatively faster. Plotting of some of the <span class="hlt">ice</span> floes indicated movement of <span class="hlt">ice</span> in excess of 30 km in and south of the Bering Strait between 6 and 7 March, 1973. North of the Bering Strait the movement approached 18 km. The movement of <span class="hlt">ice</span> observed during March 6 and 7 considerably altered the distribution and extent of polynya. These features when continually plotted should be of considerable aid in navigation of <span class="hlt">ice</span> breakers. The movement of <span class="hlt">ice</span> will also help delineate the migration and distribution of <span class="hlt">sea</span> mammals.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19900033480&hterms=Continental+shelf&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DContinental%2Bshelf','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19900033480&hterms=Continental+shelf&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DContinental%2Bshelf"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> and oceanic processes on the Ross <span class="hlt">Sea</span> continental shelf</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jacobs, S. S.; Comiso, J. C.</p> <p>1989-01-01</p> <p>The spatial and temporal variability of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations on the Ross <span class="hlt">Sea</span> continental shelf have been investigated in relation to oceanic and atmospheric forcing. <span class="hlt">Sea</span> <span class="hlt">ice</span> data were derived from Nimbus 7 scanning multichannel microwave radiometer (SMMR) brightness temperatures from 1979-1986. <span class="hlt">Ice</span> cover over the shelf was persistently lower than above the adjacent deep ocean, averaging 86 percent during winter with little month-to-month of interannual variability. The large spring Ross <span class="hlt">Sea</span> polynya on the western shelf results in a longer period of summer insolation, greater surface layer heat storage, and later <span class="hlt">ice</span> formation in that region the following autumn.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.C43D0577F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.C43D0577F"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> and Hydrographic Variability in the Northwest North Atlantic</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fenty, I. G.; Heimbach, P.; Wunsch, C. I.</p> <p>2010-12-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> anomalies in the Northwest North Atlantic's Labrador <span class="hlt">Sea</span> are of climatic interest because of known and hypothesized feedbacks with hydrographic anomalies, deep convection/mode water formation, and Northern Hemisphere atmospheric patterns. As greenhouse gas concentrations increase, hydrographic anomalies formed in the Arctic Ocean associated with warming will propagate into the Labrador <span class="hlt">Sea</span> via the Fram Strait/West Greenland Current and the Canadian Archipelago/Baffin Island Current. Therefore, understanding the dynamical response of <span class="hlt">sea</span> <span class="hlt">ice</span> in the basin to hydrographic anomalies is essential for the prediction and interpretation of future high-latitude climate change. Historically, efforts to quantify the link between the observed <span class="hlt">sea</span> <span class="hlt">ice</span> and hydrographic variability in the region has been limited due to in situ observation paucity and technical challenges associated with synthesizing ocean and <span class="hlt">sea</span> <span class="hlt">ice</span> observations with numerical models. To elaborate the relationship between <span class="hlt">sea</span> <span class="hlt">ice</span> and ocean variability, we create three one-year (1992-1993, 1996-1997, 2003-2004) three-dimensional time-varying reconstructions of the ocean and <span class="hlt">sea</span> <span class="hlt">ice</span> state in Labrador <span class="hlt">Sea</span> and Baffin Bay. The reconstructions are syntheses of a regional coupled 32 km ocean-<span class="hlt">sea</span> <span class="hlt">ice</span> model with a suite of contemporary in situ and satellite hydrographic and <span class="hlt">ice</span> data using the adjoint method. The model and data are made consistent, in a least-squares sense, by iteratively adjusting several model control variables (e.g., ocean initial and lateral boundary conditions and the atmospheric state) to minimize an uncertainty-weighted model-data misfit cost function. The reconstructions reveal that the <span class="hlt">ice</span> pack attains a state of quasi-equilibrium in mid-March (the annual <span class="hlt">sea</span> <span class="hlt">ice</span> maximum) in which the total <span class="hlt">ice</span>-covered area reaches a steady state -<span class="hlt">ice</span> production and dynamical divergence along the coasts balances dynamical convergence and melt along the pack’s seaward edge. <span class="hlt">Sea</span> <span class="hlt">ice</span> advected to the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014GeoRL..41..880T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014GeoRL..41..880T"><span>Can regional climate engineering save the summer Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tilmes, S.; Jahn, Alexandra; Kay, Jennifer E.; Holland, Marika; Lamarque, Jean-Francois</p> <p>2014-02-01</p> <p>Rapid declines in summer Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent are projected under high-forcing future climate scenarios. Regional Arctic climate engineering has been suggested as an emergency strategy to save the <span class="hlt">sea</span> <span class="hlt">ice</span>. Model simulations of idealized regional dimming experiments compared to a business-as-usual greenhouse gas emission simulation demonstrate the importance of both local and remote feedback mechanisms to the surface energy budget in high latitudes. With increasing artificial reduction in incoming shortwave radiation, the positive surface albedo feedback from Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss is reduced. However, changes in Arctic clouds and the strongly increasing northward heat transport both counteract the direct dimming effects. A 4 times stronger local reduction in solar radiation compared to a global experiment is required to preserve summer Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> area. Even with regional Arctic dimming, a reduction in the strength of the oceanic meridional overturning circulation and a shut down of Labrador <span class="hlt">Sea</span> deep convection are possible.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GeoRL..43.5124K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GeoRL..43.5124K"><span>Skill improvement of dynamical seasonal Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Krikken, Folmer; Schmeits, Maurice; Vlot, Willem; Guemas, Virginie; Hazeleger, Wilco</p> <p>2016-05-01</p> <p>We explore the error and improve the skill of the outcome from dynamical seasonal Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> reforecasts using different bias correction and ensemble calibration methods. These reforecasts consist of a five-member ensemble from 1979 to 2012 using the general circulation model EC-Earth. The raw model reforecasts show large biases in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> area, mainly due to a differently simulated seasonal cycle and long term trend compared to observations. This translates very quickly (1-3 months) into large biases. We find that (heteroscedastic) extended logistic regressions are viable ensemble calibration methods, as the forecast skill is improved compared to standard bias correction methods. Analysis of regional skill of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> shows that the Northeast Passage and the Kara and Barents <span class="hlt">Sea</span> are most predictable. These results show the importance of reducing model error and the potential for ensemble calibration in improving skill of seasonal forecasts of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70010308','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70010308"><span>Aircraft measurements of microwave emission from Arctic <span class="hlt">Sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Wilheit, T.; Nordberg, W.; Blinn, J.; Campbell, W.; Edgerton, A.</p> <p>1971-01-01</p> <p>Measurements of the microwave emission from Arctic <span class="hlt">Sea</span> <span class="hlt">ice</span> were made with aircraft at 8 wavelengths ranging from 0.510 to 2.81 cm. The expected contrast in emissivities between <span class="hlt">ice</span> and water was observed at all wavelengths. Distributions of <span class="hlt">sea</span> <span class="hlt">ice</span> and open water were mapped from altitudes up to 11 km in the presence of dense cloud cover. Different forms of <span class="hlt">ice</span> also exhibited strong contrasts in emissivity. Emissivity differences of up to 0.2 were observed between two types of <span class="hlt">ice</span> at the 0.811-cm wavelength. The higher emissivity <span class="hlt">ice</span> type is tentatively identified as having been formed more recently than the lower emissivity <span class="hlt">ice</span>. ?? 1971.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19730020484','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19730020484"><span>Use of ERTS data for mapping Arctic <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Barnes, J. C. (Principal Investigator); Bowley, C. J.</p> <p>1973-01-01</p> <p>The author has identified the following significant results. Data from ERTS passes crossing the Bering <span class="hlt">Sea</span> in early March have been correlated with <span class="hlt">ice</span> observations collected in the Bering <span class="hlt">Sea</span> Experiment (BESEX). On two flights of the NASA CV-990 aircraft, the <span class="hlt">ice</span> conditions in the vicinity of St. Lawrence Island reported by the onboard observer are in close agreement with the <span class="hlt">ice</span> conditions mapped from the corresponding ERTS imagery. The <span class="hlt">ice</span> features identified in ERTS imagery and substantiated by the aerial observer include the locations of boundaries between areas consisting of mostly grey <span class="hlt">ice</span> and of mostly first and multi-year <span class="hlt">ice</span>, the existence of shearing leads, and the occurrence of open water with the associated development of stratus cloud streaks. The BESEX correlative <span class="hlt">ice</span> formation verifies the potential of practical applications of ERTS data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25229453','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25229453"><span>Environmental predictors of <span class="hlt">ice</span> seal presence in the Bering <span class="hlt">Sea</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Miksis-Olds, Jennifer L; Madden, Laura E</p> <p>2014-01-01</p> <p><span class="hlt">Ice</span> seals overwintering in the Bering <span class="hlt">Sea</span> are challenged with foraging, finding mates, and maintaining breathing holes in a dark and <span class="hlt">ice</span> covered environment. Due to the difficulty of studying these species in their natural environment, very little is known about how the seals navigate under <span class="hlt">ice</span>. Here we identify specific environmental parameters, including components of the ambient background sound, that are predictive of <span class="hlt">ice</span> seal presence in the Bering <span class="hlt">Sea</span>. Multi-year mooring deployments provided synoptic time series of acoustic and oceanographic parameters from which environmental parameters predictive of species presence were identified through a series of mixed models. <span class="hlt">Ice</span> cover and 10 kHz sound level were significant predictors of seal presence, with 40 kHz sound and prey presence (combined with <span class="hlt">ice</span> cover) as potential predictors as well. <span class="hlt">Ice</span> seal presence showed a strong positive correlation with <span class="hlt">ice</span> cover and a negative association with 10 kHz environmental sound. On average, there was a 20-30 dB difference between sound levels during solid <span class="hlt">ice</span> conditions compared to open water or melting conditions, providing a salient acoustic gradient between open water and solid <span class="hlt">ice</span> conditions by which <span class="hlt">ice</span> seals could orient. By constantly assessing the acoustic environment associated with the seasonal <span class="hlt">ice</span> movement in the Bering <span class="hlt">Sea</span>, it is possible that <span class="hlt">ice</span> seals could utilize aspects of the soundscape to gauge their safe distance to open water or the <span class="hlt">ice</span> edge by orienting in the direction of higher sound levels indicative of open water, especially in the frequency range above 1 kHz. In rapidly changing Arctic and sub-Arctic environments, the seasonal <span class="hlt">ice</span> conditions and soundscapes are likely to change which may impact the ability of animals using <span class="hlt">ice</span> presence and cues to successfully function during the winter breeding season.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_15 --> <div id="page_16" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="301"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4167550','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4167550"><span>Environmental Predictors of <span class="hlt">Ice</span> Seal Presence in the Bering <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Miksis-Olds, Jennifer L.</p> <p>2014-01-01</p> <p><span class="hlt">Ice</span> seals overwintering in the Bering <span class="hlt">Sea</span> are challenged with foraging, finding mates, and maintaining breathing holes in a dark and <span class="hlt">ice</span> covered environment. Due to the difficulty of studying these species in their natural environment, very little is known about how the seals navigate under <span class="hlt">ice</span>. Here we identify specific environmental parameters, including components of the ambient background sound, that are predictive of <span class="hlt">ice</span> seal presence in the Bering <span class="hlt">Sea</span>. Multi-year mooring deployments provided synoptic time series of acoustic and oceanographic parameters from which environmental parameters predictive of species presence were identified through a series of mixed models. <span class="hlt">Ice</span> cover and 10 kHz sound level were significant predictors of seal presence, with 40 kHz sound and prey presence (combined with <span class="hlt">ice</span> cover) as potential predictors as well. <span class="hlt">Ice</span> seal presence showed a strong positive correlation with <span class="hlt">ice</span> cover and a negative association with 10 kHz environmental sound. On average, there was a 20–30 dB difference between sound levels during solid <span class="hlt">ice</span> conditions compared to open water or melting conditions, providing a salient acoustic gradient between open water and solid <span class="hlt">ice</span> conditions by which <span class="hlt">ice</span> seals could orient. By constantly assessing the acoustic environment associated with the seasonal <span class="hlt">ice</span> movement in the Bering <span class="hlt">Sea</span>, it is possible that <span class="hlt">ice</span> seals could utilize aspects of the soundscape to gauge their safe distance to open water or the <span class="hlt">ice</span> edge by orienting in the direction of higher sound levels indicative of open water, especially in the frequency range above 1 kHz. In rapidly changing Arctic and sub-Arctic environments, the seasonal <span class="hlt">ice</span> conditions and soundscapes are likely to change which may impact the ability of animals using <span class="hlt">ice</span> presence and cues to successfully function during the winter breeding season. PMID:25229453</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA609978','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA609978"><span>Level-<span class="hlt">Ice</span> Melt Ponds in the Los <span class="hlt">Alamos</span> <span class="hlt">Sea</span> <span class="hlt">Ice</span> Model, CICE</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2012-12-06</p> <p>assumed to infiltrate the snow. If there is enough water to fill the air spaces within the snowpack , then the pond becomes visible above the snow...h eff pnd ¼ 0. Otherwise, we assume that the snowpack is saturated with liquid water. Liquid water percolates down very quickly into the snow. Here...Comparing Fig. 14 with Fig. 11, it is clear that effective pond area in July 1998–2007 is larger in central areas (where the snowpack has not melted) for</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060043223&hterms=Ice+Age&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3DIce%2BAge','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060043223&hterms=Ice+Age&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3DIce%2BAge"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> investigations from Seasat to the present</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Holt, Benjamin; Kwok, Ron</p> <p>2003-01-01</p> <p>In this paper, we provide a short review of <span class="hlt">sea</span> <span class="hlt">ice</span> investigations starting from Seasat. We focus particularly on the detailed and quantitative measurements of the <span class="hlt">sea</span> <span class="hlt">ice</span> motion field, which were some of the earliest results from Seasat and have sugsequently been shown to be of critical value to the derivation of several key climatically important variables. Other key investigations discussed include examination of the seasonal melt cycle, <span class="hlt">ice</span> extent and concentration, and estimates of thickness from the proxy measurements of <span class="hlt">ice</span> type and age and more directly from freeboard. We end with a brief discussion on how thse measurements might be improved in the future.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.5573L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.5573L"><span>Temporal variatiions of <span class="hlt">Sea</span> <span class="hlt">ice</span> cover in the Baltic <span class="hlt">Sea</span> derived from operational <span class="hlt">sea</span> <span class="hlt">ice</span> products used in NWP.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lange, Martin; Paul, Gerhard; Potthast, Roland</p> <p>2014-05-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> cover is a crucial parameter for surface fluxes of heat and moisture over water areas. The isolating effect and the much higher albedo strongly reduces the turbulent exchange of heat and moisture from the surface to the atmosphere and allows for cold and dry air mass flow with strong impact on the stability of the whole boundary layer and consequently cloud formation as well as precipitation in the downstream regions. Numerical weather centers as, ECMWF, MetoFrance or DWD use external products to initialize SST and <span class="hlt">sea</span> <span class="hlt">ice</span> cover in their NWP models. To the knowledge of the author there are mainly two global <span class="hlt">sea</span> <span class="hlt">ice</span> products well established with operational availability, one from NOAA NCEP that combines measurements with satellite data, and the other from OSI-SAF derived from SSMI/S sensors. The latter one is used in the Ostia product. DWD additionally uses a regional product for the Baltic <span class="hlt">Sea</span> provided by the national center for shipping and hydrografie which combines observations from ships (and icebreakers) for the German part of the Baltic <span class="hlt">Sea</span> and model analysis from the hydrodynamic HIROMB model of the Swedish meteorological service for the rest of the domain. The temporal evolution of the three different products are compared for a cold period in Februar 2012. Goods and bads will be presented and suggestions for a harmonization of strong day to day jumps over large areas are suggested.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.1005B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.1005B"><span>Coupled model of INM-IO global ocean model, CICE <span class="hlt">sea</span> <span class="hlt">ice</span> model and SCM OIAS framework</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bayburin, Ruslan; Rashit, Ibrayev; Konstantin, Ushakov; Vladimir, Kalmykov; Gleb, Dyakonov</p> <p>2015-04-01</p> <p>Status of coupled Arctic model of ocean and <span class="hlt">sea</span> <span class="hlt">ice</span> is presented. Model consists of INM IO global ocean component of high resolution, Los <span class="hlt">Alamos</span> National Laboratory CICE <span class="hlt">sea</span> <span class="hlt">ice</span> model and a framework SCM OIAS for the ocean-<span class="hlt">ice</span>-atmosphere-land coupled modeling on massively-parallel architectures. Model is currently under development at the Institute of Numerical Mathematics (INM), Hydrometeorological Center (HMC) and P.P. Shirshov Institute of Oceanology (IO). Model is aimed at modeling of intra-annual variability of hydrodynamics in Arctic and. The computational characteristics of the world ocean-<span class="hlt">sea</span> <span class="hlt">ice</span> coupled model governed by SCM OIAS are presented. The model is parallelized using MPI technologies and currently can use efficiently up to 5000 cores. Details of programming implementation, computational configuration and physical phenomena parametrization are analyzed in terms of intercoupling complex. Results of five year computational experiment of <span class="hlt">sea</span> <span class="hlt">ice</span>, snow and ocean state evolution in Arctic region on tripole grid with horizontal resolution of 3-5 kilometers, closed by atmospheric forcing field from repeating "normal" annual course taken from CORE1 experiment data base are presented and analyzed in terms of the state of vorticity and warm Atlantic water expansion.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20040171250','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20040171250"><span>ICESat Observations of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span>: A First Look</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kwok, Ron; Zwally, H. Jay; Yi, Dong-Hui</p> <p>2004-01-01</p> <p>Analysis of near-coincident ICESat and RADARSAT imagery shows that the retrieved elevations from the laser altimeter are sensitive to new openings (containing thin <span class="hlt">ice</span> or open water) in the <span class="hlt">sea</span> <span class="hlt">ice</span> cover as well as to surface relief of old and first-year <span class="hlt">ice</span>. The precision of the elevation estimates, measured over relatively flat <span class="hlt">sea</span> <span class="hlt">ice</span>, is approx. 2 cm Using the thickness of thin-<span class="hlt">ice</span> in recent openings to estimate <span class="hlt">sea</span> level references, we obtain the <span class="hlt">sea-ice</span> free-board along the altimeter tracks. This step is necessitated by the large uncertainties in the time-varying <span class="hlt">sea</span> surface topography compared to that required for accurate determination of free-board. Unknown snow depth introduces the largest uncertainty in the conversion of free-board to <span class="hlt">ice</span> thickness. Surface roughness is also derived, for the first time, from the variability of successive elevation estimates along the altimeter track Overall, these ICESat measurements provide an unprecedented view of the Arctic Ocean <span class="hlt">ice</span> cover at length scales at and above the spatial dimension of the altimeter footprint.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20070025099&hterms=arctic+ocean&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Darctic%2Bocean','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20070025099&hterms=arctic+ocean&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Darctic%2Bocean"><span>ICESat Observations of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span>: A First Look</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kwok, Ron; Zwally, H. Jay; Yi, Donghui</p> <p>2004-01-01</p> <p>Analysis of near-coincident ICESat and RADARSAT imagery shows that the retrieved elevations from the laser altimeter are sensitive to new openings (containing thin <span class="hlt">ice</span> or open water) in the <span class="hlt">sea</span> <span class="hlt">ice</span> cover as well as to surface relief of old and first-year <span class="hlt">ice</span>. The precision of the elevation estimates, measured over relatively flat <span class="hlt">sea</span> <span class="hlt">ice</span>, is approx. 2 cm. Using the thickness of thin-<span class="hlt">ice</span> in recent openings to estimate <span class="hlt">sea</span> level references, we obtain the <span class="hlt">sea-ice</span> freeboard along the altimeter tracks. This step is necessitated by the large uncertainties in the <span class="hlt">sea</span> surface topography compared to that required for accurate determination of freeboard. Unknown snow depth introduces the largest uncertainty in the conversion of freeboard to <span class="hlt">ice</span> thickness. Surface roughness is also derived, for the first time, from the variability of successive elevation estimates along the altimeter track. Overall, these ICESat measurements provide an unprecedented view of the Arctic Ocean <span class="hlt">ice</span> cover at length scales at and above the spatial dimension of the altimeter footprint of approx. 70 m.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMNG33B1507G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMNG33B1507G"><span>Smoluchowski Coagulation Models Of <span class="hlt">Sea</span> <span class="hlt">Ice</span> Thickness Distribution Dynamics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Godlovitch, D.; Illner, R.; Monahan, A. H.</p> <p>2011-12-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> thickness distributions display a ubiquitous exponential decrease with thickness. This tail characterises the range of <span class="hlt">ice</span> thickness produced by mechanical redistribution of <span class="hlt">ice</span> through the process of ridging, rafting, and shearing. It is possible to simulate thickness distribution dynamics by representing mechanical redistribution as a generalized stacking process. Stacking processes may be described by a class of models known as Smoluchowski Coagulation models, which originated in Statistical Mechanics and describe the dynamics of a population of fixed-mass "particles" which combine in pairs to form a "particle" with the combined mass of the constituent pair at a rate which depends on the mass of the interacting particles. We use SCMs to model <span class="hlt">sea</span> <span class="hlt">ice</span>, identifying mass-increasing particle combinations with thickness-increasing <span class="hlt">ice</span> redistribution processes. Our model couples an SCM component with a thermodynamic component and generates qualitatively accurate thickness distributions. The model behaviour suggests that the exponential tail of the <span class="hlt">sea</span> <span class="hlt">ice</span> thickness distribution arises from the nature of the ridging process, rather than specific physical properties of <span class="hlt">sea</span> <span class="hlt">ice</span> or the spatial arrangement of floes, and that the relative strengths of the dynamic and thermodynamic processes are key in accurately simulating the rate at which the <span class="hlt">sea</span> <span class="hlt">ice</span> thickness tail drops off with thickness.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AAS...20920905N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AAS...20920905N"><span>Applying Archimedes' Law to <span class="hlt">Ice</span> Melting in <span class="hlt">Sea</span> Water</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Noerdlinger, Peter D.; Brower, K. R.</p> <p>2006-12-01</p> <p>Archimedes stated that a floating body displaces its own weight of liquid, but his law has been widely misapplied to <span class="hlt">ice</span> floating in the oceans by scientists who assumed that equal weights correspond to equal liquid volumes. It is often said that when floating <span class="hlt">ice</span> melts, the <span class="hlt">sea</span> level does not rise "because of Archimedes' law." True when <span class="hlt">ice</span> floats in fresh water, but a myth for <span class="hlt">ice</span> in oceans! Most <span class="hlt">ice</span> floating in the oceans is nearly pure water. When it melts, the pure water produced has about 2.6% more volume than the salt water that was displaced, and the ocean slightly rises. It is often suggested that students demonstrate the "fact" of no rise in the <span class="hlt">sea</span> surface by melting <span class="hlt">ice</span> cubes floating in a glass of water; such a demonstration even appears in the movie "An Inconvenient Truth." Let's teach students to spot such errors. We highlight a couple more "surprise issues." First, the density of the floating <span class="hlt">ice</span>, if it is free of salt and dirt, is irrelevant, so long as it floats. Next, when "grounded" <span class="hlt">ice</span> (resting on land), enters the <span class="hlt">sea</span>, it initially displaces less water than its melted form will eventually add to the <span class="hlt">sea</span>. Thus, an event of that kind, such as formation of an iceberg, produces a rise of the <span class="hlt">sea</span> level in two stages. We conclude with a series of thought-experiments that could help teachers and students discern the correct result, and a photo of a demonstration.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2001AGUFM.A32A0032B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2001AGUFM.A32A0032B"><span>Atmospheric Response to Variations in Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Conditions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bhatt, U.; Alexander, M.; Walsh, J.; Timlin, M.; Miller, J.</p> <p>2001-12-01</p> <p>While it is generally accepted that changes in air temperature and circulation determine <span class="hlt">sea</span> <span class="hlt">ice</span> conditions, it is not understood how the atmosphere is influenced by changes in <span class="hlt">sea</span> <span class="hlt">ice</span>. We employ the NCAR CCM 3.6 with specified <span class="hlt">ice</span> extent and <span class="hlt">sea</span> surface temperatures (sst). The overarching question addressed in this study is: how do variations in <span class="hlt">sea</span> <span class="hlt">ice</span> influence the atmosphere? We are particularly interested in the summer time response to highlight this unique aspect of this research. A control experiment has been integrated for 55 years by repeating the mean annual cycle of observed <span class="hlt">sea</span> <span class="hlt">ice</span> extent (either 0% or 100% <span class="hlt">ice</span> cover) and sst, based on the period 1979-99. Sets of 50 member ensemble experiments were constructed by integrating the CCM from October to April using climatological sst (same as control) and observed <span class="hlt">sea</span> <span class="hlt">ice</span> extent from the winters of 1982-83 (<span class="hlt">ice</span> maximum) and 1995-96 (<span class="hlt">ice</span> minimum). Similar summertime sensitivity experiments were performed using <span class="hlt">ice</span> extent conditions from April to October during 1982 (maximum) and 1995 (minimum). While responses were found both in winter and summer, the results described below refer to the summer of 1995. A set of 50 ensembles was also integrated for the summer of 1995 using <span class="hlt">sea</span> <span class="hlt">ice</span> concentration instead of extent. During the summer of 1995, negative <span class="hlt">sea</span> <span class="hlt">ice</span> anomalies were particularly large in the Siberian Arctic. <span class="hlt">Sea</span> <span class="hlt">ice</span> reductions result in increased surface and air temperatures and enhanced latent, sensible, and longwave fluxes out of the ocean. However, the net heat flux out of the ocean decreases because the changes are dominated by increased absorption of solar radiation over the low-albedo ocean. Cloud feedbacks are important in the Arctic and the downwelling solar at the surface decreases. The total cloud amount decreases due to reductions in low level clouds, however, convective cloud amounts increased. The net cloud radiative (shortwave and longwave) forcing is smaller in the experiment than the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22798610','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22798610"><span><span class="hlt">Ice</span> volume and <span class="hlt">sea</span> level during the last interglacial.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Dutton, A; Lambeck, K</p> <p>2012-07-13</p> <p>During the last interglacial period, ~125,000 years ago, <span class="hlt">sea</span> level was at least several meters higher than at present, with substantial variability observed for peak <span class="hlt">sea</span> level at geographically diverse sites. Speculation that the West Antarctic <span class="hlt">ice</span> sheet collapsed during the last interglacial period has drawn particular interest to understanding climate and <span class="hlt">ice</span>-sheet dynamics during this time interval. We provide an internally consistent database of coral U-Th ages to assess last interglacial <span class="hlt">sea</span>-level observations in the context of isostatic modeling and stratigraphic evidence. These data indicate that global (eustatic) <span class="hlt">sea</span> level peaked 5.5 to 9 meters above present <span class="hlt">sea</span> level, requiring smaller <span class="hlt">ice</span> sheets in both Greenland and Antarctica relative to today and indicating strong <span class="hlt">sea</span>-level sensitivity to small changes in radiative forcing.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1815732H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1815732H"><span>Solidification and convective instability during early <span class="hlt">sea</span> <span class="hlt">ice</span> growth</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hitchen, Joseph; Wells, Andrew</p> <p>2016-04-01</p> <p>Growing <span class="hlt">sea</span> <span class="hlt">ice</span> rejects large amounts of cold, salty water into the underlying ocean which contributes to the formation of Antarctic Bottom Water, North Atlantic Deep Water, and maintaining the cold halocline in the Arctic ocean. This cold, salty water is formed by the partial solidification of <span class="hlt">sea</span> water to form porous <span class="hlt">sea</span> <span class="hlt">ice</span>, which is an example of a mushy layer. Convection within the porous <span class="hlt">ice</span> interior drives the drainage of dense brine into the underlying ocean. We consider how realistic surface cooling and variations in physical properties affect the time-dependent development of early <span class="hlt">sea</span> <span class="hlt">ice</span> growth, and the impact on solidification and convective instability within the <span class="hlt">ice</span>. Whilst many previous studies of mushy layers have focussed on growth at a steady rate, we here model geophysically-motivated settings where the growth rate evolves with time. We quantify how the onset of convection in <span class="hlt">sea</span> <span class="hlt">ice</span> depends on the initial salinity of the <span class="hlt">sea</span> water and the rate of heat loss to the overlying atmosphere, and show that slower cooling rates can promote the formation of larger convection cells within the <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012NPGeo..19...81M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012NPGeo..19...81M"><span>Albedo parametrization and reversibility of <span class="hlt">sea</span> <span class="hlt">ice</span> decay</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Müller-Stoffels, M.; Wackerbauer, R.</p> <p>2012-02-01</p> <p>The Arctic's <span class="hlt">sea</span> <span class="hlt">ice</span> cover has been receding rapidly in recent years, and global climate models typically predict a further decline over the next century. It is an open question whether a possible loss of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> is reversible. We study the stability of Arctic model <span class="hlt">sea</span> <span class="hlt">ice</span> in a conceptual, two-dimensional energy-based regular network model of the <span class="hlt">ice</span>-ocean layer that considers ARM's longwave radiative budget data and SHEBA albedo measurements. Seasonal <span class="hlt">ice</span> cover, perennial <span class="hlt">ice</span> and perennial open water are asymptotic states accessible by the model. We show that the shape of albedo parameterization near the melting temperature differentiates between reversible continuous <span class="hlt">sea</span> <span class="hlt">ice</span> decrease under atmospheric forcing and hysteresis behavior. Fixed points induced solely by the surface energy budget are essential for understanding the interaction of surface energy with the radiative forcing and the underlying body of <span class="hlt">ice</span>/water, particularly close to a bifurcation point. Future studies will explore <span class="hlt">ice</span> edge stability and reversibility in this lattice model, generalized to a latitudinal transect with spatiotemporal lateral atmospheric heat transfer and high spatial resolution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1711010P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1711010P"><span>Study of <span class="hlt">sea</span> <span class="hlt">ice</span> regions using AltiKa measurements</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Poisson, Jean-Christophe; Thibaut, Pierre; Hoang, Duc; Boy, François; Guillot, Amandine; Picot, Nicolas</p> <p>2015-04-01</p> <p>Since the launch of the SARAL/AltiKa mission on February 25th, 2013, altimeter measurements of excellent quality are acquired all over the globe for the first time in Ka-band. One of the main benefits of the Ka-band is to have a very low penetration length in the <span class="hlt">ice</span> (unlike the Ku-band historically used by previous altimetry missions), which allows to significantly reduce measurements uncertainties of the <span class="hlt">sea</span> <span class="hlt">ice</span> topography. Flying on the Envisat orbit and providing measurements at 40 Hz, the exploitation of AltiKa waveforms on <span class="hlt">sea</span> <span class="hlt">ice</span> is of great interest. <span class="hlt">Sea</span> <span class="hlt">ice</span> covered regions are characterized by a large number of different surfaces with a multitude of backscattering properties rapidly evolving with time. Thanks to the high resolution and precision of the AltiKa measurements, backscattering properties from each of these surfaces (first year <span class="hlt">ice</span>, multiyear <span class="hlt">ice</span>, fast <span class="hlt">ice</span>, leads, polynyas, etc. …) can be observed through rapid changes of the returned echo shape. In the framework of the PEACHI project (Prototype for Expertise on AltiKa, for Coastal, Hydrology and <span class="hlt">Ice</span> funded by CNES) which aims at analyzing and improving AltiKa measurements, a waveform processing based on an altimeter echo classification is developed and performed on all available AltiKa data in the Arctic ocean. Through this processing a study is conducted on the the evolution of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover observed in Ka-band.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20070021406&hterms=snowpack&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dsnowpack','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20070021406&hterms=snowpack&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dsnowpack"><span>Microwave Signatures of Snow on <span class="hlt">Sea</span> <span class="hlt">Ice</span>: Modeling</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Powell, D. C.; Markus, T.; Cavalieri, D. J.; Gasiewski, A. J.; Klein, M.; Maslanik, J. A.; Stroeve, J. C.; Sturm, M.</p> <p>2006-01-01</p> <p>Accurate knowledge of snow-depth distribution over <span class="hlt">sea</span> <span class="hlt">ice</span> is critical for polar climate studies. Current snow-depth-over-<span class="hlt">sea-ice</span> retrieval algorithms do not sufficiently account for variations in snow and <span class="hlt">ice</span> physical properties that can affect the accuracy of retrievals. For this reason, airborne microwave observations were coordinated with ground-based measurements of snow depth and snow properties in the vicinity of Barrow, AK, in March 2003. In this paper, the effects of snowpack properties and <span class="hlt">ice</span> conditions on microwave signatures are examined using detailed surface-based measurements and airborne observations in conjunction with a thermal microwave-emission model. A comparison of the Microwave Emission Model of Layered Snowpacks (MEMLS) simulations with detailed snowpack and <span class="hlt">ice</span> data from stakes along the Elson Lagoon and the Beaufort <span class="hlt">Sea</span> and ra- 'diometer data taken from low-level flights using a Polarimetric Scanning Radiometer (PSR-A) shows that MEMLS can be used to simulate snow on <span class="hlt">sea</span> <span class="hlt">ice</span> and is a useful tool for understanding the limitations of the snow-depth algorithm. Analysis of radiance data taken over the Elson Lagoon and the Beaufort <span class="hlt">Sea</span> using MEMLS suggests that the radiometric differences between the two locations are due to the differences in <span class="hlt">sea-ice</span> emissivity. Furthermore, measured brightness temperatures suggest that the current snow-depth retrieval algorithm is sufficient for areas of smooth first-year <span class="hlt">sea</span> <span class="hlt">ice</span>, whereas new algorithm coefficients are needed for rough first-year <span class="hlt">sea</span> <span class="hlt">ice</span>. Snowpack grain size and density remain an unresolved issue for snow-depth retrievals using passive-microwave radiances.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140013006','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140013006"><span>Effects of Mackenzie River Discharge and Bathymetry on <span class="hlt">Sea</span> <span class="hlt">Ice</span> in the Beaufort <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Nghiem, S. V.; Hall, D. K.; Rigor, I. G; Li, P.; Neumann, G.</p> <p>2014-01-01</p> <p>Mackenzie River discharge and bathymetry effects on <span class="hlt">sea</span> <span class="hlt">ice</span> in the Beaufort <span class="hlt">Sea</span> are examined in 2012 when Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent hit a record low. Satellite-derived <span class="hlt">sea</span> surface temperature revealed warmer waters closer to river mouths. By 5 July 2012, Mackenzie warm waters occupied most of an open water area about 316,000 sq km. Surface temperature in a common open water area increased by 6.5 C between 14 June and 5 July 2012, before and after the river waters broke through a recurrent landfast <span class="hlt">ice</span> barrier formed over the shallow seafloor offshore the Mackenzie Delta. In 2012, melting by warm river waters was especially effective when the strong Beaufort Gyre fragmented <span class="hlt">sea</span> <span class="hlt">ice</span> into unconsolidated floes. The Mackenzie and other large rivers can transport an enormous amount of heat across immense continental watersheds into the Arctic Ocean, constituting a stark contrast to the Antarctic that has no such rivers to affect <span class="hlt">sea</span> <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140011036','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140011036"><span>Improving Surface Mass Balance Over <span class="hlt">Ice</span> Sheets and Snow Depth on <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Koenig, Lora Suzanne; Box, Jason; Kurtz, Nathan</p> <p>2013-01-01</p> <p>Surface mass balance (SMB) over <span class="hlt">ice</span> sheets and snow on <span class="hlt">sea</span> <span class="hlt">ice</span> (SOSI) are important components of the cryosphere. Large knowledge gaps remain in scientists' abilities to monitor SMB and SOSI, including insufficient measurements and difficulties with satellite retrievals. On <span class="hlt">ice</span> sheets, snow accumulation is the sole mass gain to SMB, and meltwater runoff can be the dominant single loss factor in extremely warm years such as 2012. SOSI affects the growth and melt cycle of the Earth's polar <span class="hlt">sea</span> <span class="hlt">ice</span> cover. The summer of 2012 saw the largest satellite-recorded melt area over the Greenland <span class="hlt">ice</span> sheet and the smallest satellite-recorded Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent, making this meeting both timely and relevant.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ESASP.740E.382V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ESASP.740E.382V"><span>Estimation of Snow Thickness on <span class="hlt">Sea</span> <span class="hlt">Ice</span> and Lake <span class="hlt">Ice</span> Using Airborne SnowSAR Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Veijola, Katriina; Makynen, Marko; Lemmetyinen, Juha; Praks, Jaan</p> <p>2016-08-01</p> <p>Currently, snow thickness on <span class="hlt">sea</span> <span class="hlt">ice</span> is operationally estimated using microwave radiometer data. However, the estimates are hampered by the inherent coarse spatial resolution of passive microwave sensors. Successful application of SAR imagery for snow thickness estimation has the potential of providing estimates of snow thickness with much finer spatial resolution.In this study, we concentrate on assessing the capability of X- and Ku-band SAR backscattering to estimate snow thickness on <span class="hlt">sea</span> and lake <span class="hlt">ice</span>. Co- and cross -polarized X- and Ku-band SAR backscattering data, acquired with the ESA airborne SnowSAR sensor, are applied. The SAR data acquisition and co-incident in-situ measurements were conducted in Finland in the winter of 2012 over <span class="hlt">sea</span> <span class="hlt">ice</span> and lake <span class="hlt">ice</span> test sites.Our analysis shows which frequency and polarization combinations have best sensitivity to snow thickness on <span class="hlt">sea</span> and lake <span class="hlt">ice</span> and in deep discussion provides plausible ways to improve the results.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.C43A0584Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.C43A0584Y"><span>Surface roughness of <span class="hlt">sea</span> <span class="hlt">ice</span> in Fram Strait - A characteristic of the <span class="hlt">ice</span>-atmosphere interface</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yearsley, W. A.; Herzfeld, U. C.; McDonald, B.; Wallin, B. F.; Maslanik, J. A.; Fladeland, M. M.; Long, D. G.; Crocker, R. I.</p> <p>2012-12-01</p> <p>Surface roughness is an important characteristic of the interface between the lower atmosphere and the <span class="hlt">sea</span> <span class="hlt">ice</span>. In this paper, we present observational and mathematical methods that yield surface roughness length at centimeter to kilometer scales along transects of several hundred kilometers in Fram Strait. During the Characterization of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Experiment (CASIE, July-August 2009), centimeter-scale laser profilometer data and microASAR data were collected from unmanned aircraft, the SIERRA of NASA's Ames Research Center. After correction for altitude using GPS data, aerodynamic roughness length is derived using patial classification parameters and geometric surface properties. Statistical distributions of ridges in <span class="hlt">sea-ice</span> are calculated. The roughness-based parameters have several uses in modeling energy flux between ocean, <span class="hlt">ice</span> and boundary layer and in modeling ridging processes in <span class="hlt">sea</span> <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4990695','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4990695"><span>Antarctic last interglacial isotope peak in response to <span class="hlt">sea</span> <span class="hlt">ice</span> retreat not <span class="hlt">ice</span>-sheet collapse</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Holloway, Max D.; Sime, Louise C.; Singarayer, Joy S.; Tindall, Julia C.; Bunch, Pete; Valdes, Paul J.</p> <p>2016-01-01</p> <p>Several studies have suggested that <span class="hlt">sea</span>-level rise during the last interglacial implies retreat of the West Antarctic <span class="hlt">Ice</span> Sheet (WAIS). The prevalent hypothesis is that the retreat coincided with the peak Antarctic temperature and stable water isotope values from 128,000 years ago (128 ka); very early in the last interglacial. Here, by analysing climate model simulations of last interglacial WAIS loss featuring water isotopes, we show instead that the isotopic response to WAIS loss is in opposition to the isotopic evidence at 128 ka. Instead, a reduction in winter <span class="hlt">sea</span> <span class="hlt">ice</span> area of 65±7% fully explains the 128 ka <span class="hlt">ice</span> core evidence. Our finding of a marked retreat of the <span class="hlt">sea</span> <span class="hlt">ice</span> at 128 ka demonstrates the sensitivity of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent to climate warming. PMID:27526639</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_16 --> <div id="page_17" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="321"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27526639','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27526639"><span>Antarctic last interglacial isotope peak in response to <span class="hlt">sea</span> <span class="hlt">ice</span> retreat not <span class="hlt">ice</span>-sheet collapse.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Holloway, Max D; Sime, Louise C; Singarayer, Joy S; Tindall, Julia C; Bunch, Pete; Valdes, Paul J</p> <p>2016-08-16</p> <p>Several studies have suggested that <span class="hlt">sea</span>-level rise during the last interglacial implies retreat of the West Antarctic <span class="hlt">Ice</span> Sheet (WAIS). The prevalent hypothesis is that the retreat coincided with the peak Antarctic temperature and stable water isotope values from 128,000 years ago (128 ka); very early in the last interglacial. Here, by analysing climate model simulations of last interglacial WAIS loss featuring water isotopes, we show instead that the isotopic response to WAIS loss is in opposition to the isotopic evidence at 128 ka. Instead, a reduction in winter <span class="hlt">sea</span> <span class="hlt">ice</span> area of 65±7% fully explains the 128 ka <span class="hlt">ice</span> core evidence. Our finding of a marked retreat of the <span class="hlt">sea</span> <span class="hlt">ice</span> at 128 ka demonstrates the sensitivity of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent to climate warming.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMOS11B1654B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMOS11B1654B"><span>Skin Temperature Processes in the Presence of <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Brumer, S. E.; Zappa, C. J.; Brown, S.; McGillis, W. R.; Loose, B.</p> <p>2013-12-01</p> <p>Monitoring the <span class="hlt">sea-ice</span> margins of polar oceans and understanding the physical processes at play at the <span class="hlt">ice</span>-ocean-air interface is essential in the perspective of a changing climate in which we face an accelerated decline of <span class="hlt">ice</span> caps and <span class="hlt">sea</span> <span class="hlt">ice</span>. Remote sensing and in particular InfraRed (IR) imaging offer a unique opportunity not only to observe physical processes at <span class="hlt">sea-ice</span> margins, but also to measure air-<span class="hlt">sea</span> exchanges near <span class="hlt">ice</span>. It permits monitoring <span class="hlt">ice</span> and ocean temperature variability, and can be used for derivation of surface flow field allowing investigating turbulence and shearing at the <span class="hlt">ice</span>-ocean interface as well as ocean-atmosphere gas transfer. Here we present experiments conducted with the aim of gaining an insight on how the presence of <span class="hlt">sea</span> <span class="hlt">ice</span> affects the momentum exchange between the atmosphere and ocean and investigate turbulence production in the interplay of <span class="hlt">ice</span>-water shear, convection, waves and wind. A set of over 200 high resolution IR imagery records was taken at the US Army Cold Regions Research and Engineering Laboratory (CRREL, Hanover NH) under varying <span class="hlt">ice</span> coverage, fan and pump settings. In situ instruments provided air and water temperature, salinity, subsurface currents and wave height. Air side profiling provided environmental parameters such as wind speed, humidity and heat fluxes. The study aims to investigate what can be gained from small-scale high-resolution IR imaging of the <span class="hlt">ice</span>-ocean-air interface; in particular how <span class="hlt">sea</span> <span class="hlt">ice</span> modulates local physics and gas transfer. The relationship between water and <span class="hlt">ice</span> temperatures with current and wind will be addressed looking at the ocean and <span class="hlt">ice</span> temperature variance. Various skin temperature and gas transfer parameterizations will be evaluated at <span class="hlt">ice</span> margins under varying environmental conditions. Furthermore the accuracy of various techniques used to determine surface flow will be assessed from which turbulence statistics will be determined. This will give an insight on how <span class="hlt">ice</span> presence</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.3675K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.3675K"><span>Applicability of highly branched isoprenoids as a <span class="hlt">sea</span> <span class="hlt">ice</span> proxy in the Ross <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kim, Jung-Hyun; Lee, Jae Il; Belt, Simon T.; Gal, Jong-Ku; Smik, Lukas; Shin, Kyung-Hoon</p> <p>2016-04-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> is an integral component of the polar climate system, constraining the effect of changing surface albedo, ocean-atmosphere heat exchanges, the formation of deep and intermediate waters that participate in driving the meridional overturning circulation and thus global climate. In recent years, a mono-unsaturated highly branched isoprenoid (HBI) alkene which is biosynthesised by certain <span class="hlt">sea</span> <span class="hlt">ice</span> diatoms during the spring bloom and, upon <span class="hlt">ice</span> melt, deposited into underlying sediments, has been uniquely observed in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> and in Arctic sediments. Hence, the term IP25 (<span class="hlt">ice</span> proxy with 25 carbon atoms) was proposed to distinguish this compound from other HBI isomers and has become an established proxy for the reconstruction of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. In contrast, a monounsaturated HBI alkene, i.e. IP25, has not been observed in <span class="hlt">sea</span> <span class="hlt">ice</span> or sediments from the Antarctic. Hence, the application of diene and triene HBI concentrations and the resulting diene/triene (D/T) ratio was alternatively introduced as <span class="hlt">sea</span> <span class="hlt">ice</span>/open water indicators in the Southern Ocean. However, there is still lack of data covering the wide areas around the Antarctic, especially from the Ross <span class="hlt">Sea</span>. Hence, we investigated surface sediment samples from the Ross <span class="hlt">Sea</span> (n=14) collected during the R/V ARAON cruise in 2015 as well as from the Antarctic Peninsula (n=17) collected during several R/V ARAON cruises between 2001 and 2013. We will present our preliminary results and will discuss the applicability of the HBI in the Ross <span class="hlt">Sea</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/467679','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/467679"><span>An analysis of space scales for <span class="hlt">sea</span> <span class="hlt">ice</span> drift</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Carrieres, T.</p> <p>1994-12-31</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> presents a hazard to navigation off Canada`s east coast from January to June. The <span class="hlt">Ice</span> Centre Environment Canada (ICEC) which is part of the Atmospheric Environment Service monitors <span class="hlt">ice</span> conditions in order to assist safe and efficient operations through or around the <span class="hlt">ice</span>. The <span class="hlt">ice</span> program depends on an advanced data acquisition, analysis and forecasting effort. Support for the latter is provided by kinematic models as well as a fairly simple dynamic <span class="hlt">sea</span> <span class="hlt">ice</span> model. In order to improve ICEC`s forecasting capabilities, the Department of Fisheries and Oceans (DFO) conducts <span class="hlt">ice</span> modelling research and regular field experiments. The experiments provide a better understanding of the <span class="hlt">ice</span> and also allow models to be validated and refined. The Bedford Institute of Oceanography (BIO, part of DFO) regularly deploys beacons on <span class="hlt">ice</span> floes off the Labrador and Newfoundland coasts. These beacons provide environmental as well as location information through Service ARGOS. Documentation on the accuracy and information of the sensors is documented in Prinsenberg, 1993. The beacon locations are used here to infer an relatively unbiased representation of <span class="hlt">sea</span> <span class="hlt">ice</span> drift.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1811971I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1811971I"><span>Relating Regional Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> and climate extremes over Europe</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ionita-Scholz, Monica; Grosfeld, Klaus; Lohmann, Gerrit; Scholz, Patrick</p> <p>2016-04-01</p> <p>The potential increase of temperature extremes under climate change is a major threat to society, as temperature extremes have a deep impact on environment, hydrology, agriculture, society and economy. Hence, the analysis of the mechanisms underlying their occurrence, including their relationships with the large-scale atmospheric circulation and <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, is of major importance. At the same time, the decline in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover during the last 30 years has been widely documented and it is clear that this change is having profound impacts at regional as well as planetary scale. As such, this study aims to investigate the relation between the autumn regional <span class="hlt">sea</span> <span class="hlt">ice</span> concentration variability and cold winters in Europe, as identified by the numbers of cold nights (TN10p), cold days (TX10p), <span class="hlt">ice</span> days (ID) and consecutive frost days (CFD). We analyze the relationship between Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> variation in autumn (September-October-November) averaged over eight different Arctic regions (Barents/Kara <span class="hlt">Seas</span>, Beaufort <span class="hlt">Sea</span>, Chukchi/Bering <span class="hlt">Seas</span>, Central Arctic, Greenland <span class="hlt">Sea</span>, Labrador <span class="hlt">Sea</span>/Baffin Bay, Laptev/East Siberian <span class="hlt">Seas</span> and Northern Hemisphere) and variations in atmospheric circulation and climate extreme indices in the following winter season over Europe using composite map analysis. Based on the composite map analysis it is shown that the response of the winter extreme temperatures over Europe is highly correlated/connected to changes in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> variability. However, this signal is not symmetrical for the case of high and low <span class="hlt">sea</span> <span class="hlt">ice</span> years. Moreover, the response of temperatures extreme over Europe to <span class="hlt">sea</span> <span class="hlt">ice</span> variability over the different Arctic regions differs substantially. The regions which have the strongest impact on the extreme winter temperature over Europe are: Barents/Kara <span class="hlt">Seas</span>, Beaufort <span class="hlt">Sea</span>, Central Arctic and the Northern Hemisphere. For the years of high <span class="hlt">sea</span> <span class="hlt">ice</span> concentration in the Barents/Kara <span class="hlt">Seas</span> there is a reduction in the number</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ESASP.740E.346P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ESASP.740E.346P"><span>Remote Oil Spill Detection and Monitoring Beneath <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Polak, Adam; Marshall, Stephen; Ren, Jinchang; Hwang, Byongjun (Phil); Hagan, Bernard; Stothard, David J. M.</p> <p>2016-08-01</p> <p>The spillage of oil in Polar Regions is particularly serious due to the threat to the environment and the difficulties in detecting and tracking the full extent of the oil seepage beneath the <span class="hlt">sea</span> <span class="hlt">ice</span>. Development of fast and reliable sensing techniques is highly desirable. In this paper hyperspectral imaging combined with signal processing and classification techniques are proposed as a potential tool to detect the presence of oil beneath the <span class="hlt">sea</span> <span class="hlt">ice</span>. A small sample, lab based experiment, serving as a proof of concept, resulted in the successful identification of oil presence beneath the thin <span class="hlt">ice</span> layer as opposed to the other sample with <span class="hlt">ice</span> only. The paper demonstrates the results of this experiment that granted a financial support to execute full feasibility study of this technology for oil spill detection beneath the <span class="hlt">sea</span> <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/6662140','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/6662140"><span><span class="hlt">Sea-ice</span> interaction with the thermohaline circulation</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Jiayan Yang; Neelin, J.D. )</p> <p>1993-02-05</p> <p>Linkages have been suggested between observed interdecadal variability of <span class="hlt">sea-ice</span> and salinity in the North Atlantic. A plausible mechanism for generating such variability through the interaction of <span class="hlt">sea-ice</span> and the thermohaline circulation (THC) is examined in a zonally-averaged THC model coupled to a thermodynamic <span class="hlt">ice</span> model. A self-sustaining interdecadal oscillation arises through the feedbacks between salinity anomalies induced by the <span class="hlt">sea-ice</span> melting-freezing process and anomalous meridional heat transport associated with the THC. The oscillation time scale is not associated with any oceanic time scales but fundamentally depends on <span class="hlt">ice</span>-THC coupling. The period is quite robust to stochastic forcing, although the regularity is strongly affected. 25 refs., 7 figs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ThApC.127..753K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ThApC.127..753K"><span>Temporal evolution of Hudson Bay <span class="hlt">Sea</span> <span class="hlt">Ice</span> (1971-2011)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kowal, Slawomir; Gough, William A.; Butler, Ken</p> <p>2017-02-01</p> <p>Previous work has found Hudson Bay seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> particularly sensitive to climate change with a strong signal of earlier breakup dates. This work extends the previous analysis by including eight additional years of recent <span class="hlt">sea</span> <span class="hlt">ice</span> data. The expanded <span class="hlt">sea</span> <span class="hlt">ice</span> record, 1971 to 2011, revealed stronger and more statistically significant trends than the earlier work, most strikingly for the later freeze up. The average magnitude of the temporal trend for all 36 locations studied is 0.50 days/year for earlier breakup, 0.46 days/year for the later freeze-up, and 0.91 days/year for longer <span class="hlt">ice</span>-free period. Of the 36 points, 12 points for the breakup period, 30 points for the freeze-up period, and 22 points for the <span class="hlt">ice</span>-free season have accelerating temporal trends during the past decade.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GeoRL..4310303O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GeoRL..4310303O"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> drift and deformation in the coastal boundary zone</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Oikkonen, Annu; Haapala, Jari; Lensu, Mikko; Karvonen, Juha</p> <p>2016-10-01</p> <p>Small-scale <span class="hlt">sea</span> <span class="hlt">ice</span> deformation was studied in the coastal boundary zone (CBZ). Sequences of coastal radar images from the northern Baltic <span class="hlt">Sea</span> (13 February to 13 May 2011) were used and trajectories of identifiable objects calculated. Average drift velocities in CBZ are small (<0.01 m/s), and events of high drift speeds are short and local. Deformations follow power law scaling but with an exponent of greater magnitude than in the Arctic. We discovered a connection between air temperature and <span class="hlt">sea</span> <span class="hlt">ice</span> deformation on a short time scale. During warm days, the mean deformation rate was significantly higher in all length scales than during cold days. This cannot be explained by changes in <span class="hlt">ice</span> thickness or concentration, which suggests that the <span class="hlt">ice</span> pack strength responds to air temperature faster than previously assumed. However, we cannot quantify how much this response is enhanced by lower <span class="hlt">ice</span> thickness compared to the Arctic.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20120016582&hterms=sea+ice&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dsea%2Bice','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20120016582&hterms=sea+ice&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dsea%2Bice"><span>Large-Scale Surveys of Snow Depth on Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> from Operation <span class="hlt">Ice</span>Bridge</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kurtz, Nathan T.; Farrell, Sinead L.</p> <p>2011-01-01</p> <p>We show the first results of a large ]scale survey of snow depth on Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> from NASA fs Operation <span class="hlt">Ice</span>Bridge snow radar system for the 2009 season and compare the data to climatological snow depth values established over the 1954.1991 time period. For multiyear <span class="hlt">ice</span>, the mean radar derived snow depth is 33.1 cm and the corresponding mean climatological snow depth is 33.4 cm. The small mean difference suggests consistency between contemporary estimates of snow depth with the historical climatology for the multiyear <span class="hlt">ice</span> region of the Arctic. A 16.5 cm mean difference (climatology minus radar) is observed for first year <span class="hlt">ice</span> areas suggesting that the increasingly seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> cover of the Arctic Ocean has led to an overall loss of snow as the region has transitioned away from a dominantly multiyear <span class="hlt">ice</span> cover.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140017422','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140017422"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Prediction Has Easy and Difficult Years</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hamilton, Lawrence C.; Bitz, Cecilia M.; Blanchard-Wrigglesworth, Edward; Cutler, Matthew; Kay, Jennifer; Meier, Walter N.; Stroeve, Julienne; Wiggins, Helen</p> <p>2014-01-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> follows an annual cycle, reaching its low point in September each year. The extent of <span class="hlt">sea</span> <span class="hlt">ice</span> remaining at this low point has been trending downwards for decades as the Arctic warms. Around the long-term downward trend, however, there is significant variation in the minimum extent from one year to the next. Accurate forecasts of yearly conditions would have great value to Arctic residents, shipping companies, and other stakeholders and are the subject of much current research. Since 2008 the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Outlook (SIO) (http://www.arcus.org/search-program/seaiceoutlook) organized by the Study of Environmental Arctic Change (SEARCH) (http://www.arcus.org/search-program) has invited predictions of the September Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> minimum extent, which are contributed from the Arctic research community. Individual predictions, based on a variety of approaches, are solicited in three cycles each year in early June, July, and August. (SEARCH 2013).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.youtube.com/watch?v=IKJhvT2KROg','SCIGOVIMAGE-NASA'); return false;" href="http://www.youtube.com/watch?v=IKJhvT2KROg"><span>Arctic Daily <span class="hlt">Sea</span> <span class="hlt">Ice</span>, March 2012 to Feb. 2013</span></a></p> <p><a target="_blank" href="http://www.nasa.gov/multimedia/videogallery/index.html">NASA Video Gallery</a></p> <p></p> <p></p> <p>This animation shows the seasonal change in the extent of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> between March 1, 2012 and February 28, 2013. The annual cycle starts with the maximum extent reached on March 15, 2012. ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014NatCo...5E4197B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014NatCo...5E4197B"><span>Source identification of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> proxy IP25</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Brown, T. A.; Belt, S. T.; Tatarek, A.; Mundy, C. J.</p> <p>2014-06-01</p> <p>Analysis of the organic geochemical biomarker IP25 in marine sediments is an established method for carrying out palaeo <span class="hlt">sea</span> <span class="hlt">ice</span> reconstructions for the Arctic. Such reconstructions cover timescales from decades back to the early Pleistocene, and are critical for understanding past climate conditions on Earth and for informing climate prediction models. Key attributes of IP25 include its strict association with Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> together with its ubiquity and stability in underlying marine sediments; however, the sources of IP25 have remained undetermined. Here we report the identification of IP25 in three (or four) relatively minor (<5%) <span class="hlt">sea</span> <span class="hlt">ice</span> diatoms isolated from mixed assemblages collected from the Canadian Arctic. In contrast, IP25 was absent in the dominant taxa. Chemical and taxonomical investigations suggest that the IP25-containing taxa represent the majority of producers and are distributed pan-Arctic, thus establishing the widespread applicability of the IP25 proxy for palaeo Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> reconstruction.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C43C0817C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C43C0817C"><span>An attempt at multibeam imaging of laboratory <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chayes, D. N.; Schmidt, V. E.</p> <p>2015-12-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> was grown in a wave tank at the Hamburgische Schiffbau-Versuchsanstalt GmbH (HSVA) in Hamburg, Germany from December 12-20, 2013 as part of an EU-funded effort to understand the behavior of crude oil under <span class="hlt">sea</span> <span class="hlt">ice</span>. As an add-on to that experiment, we borrowed a Teledyne ODOM MB1 multibeam sonar that works in the frequency range from 170 to 220 kHz, mounted it on a moveable trolly, and collected beamformed and time series data with it looking upward at <span class="hlt">sea</span> <span class="hlt">ice</span> grown under various conditions.The water depth between the sonar transducer and the bottom of the <span class="hlt">sea</span> <span class="hlt">ice</span> was shallower than expected so the sonar was operating in the vicinity of the near field boundary. The experimental setup, data processing methods, and results will be presented in this poster.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C31D0341K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C31D0341K"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Characteristics and the Open-Linked Data World</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Khalsa, S. J. S.; McGuinness, D. L.; Duerr, R.; Pulsifer, P. L.; Fox, P. A.; Thompson, C.; Yan, R.</p> <p>2014-12-01</p> <p>The audience for <span class="hlt">sea</span> <span class="hlt">ice</span> data sets has broadened dramatically over the past several decades. Initially the National Snow and <span class="hlt">Ice</span> Data Center (NSIDC) <span class="hlt">sea</span> <span class="hlt">ice</span> products were used primarily by <span class="hlt">sea</span> <span class="hlt">ice</span> specialists. However, now they are in demand by researchers in many different domains and some are used by the public. This growth in the number and type of users has presented challenges to content providers aimed particularly at supporting interdisciplinary and multidisciplinary data use. In our experience, it is generally insufficient to simply make the data available as originally formatted. New audiences typically need data in different forms; forms that meet their needs, that work with their specific tools. Moreover, simple data reformatting is rarely enough. The data needs to be aggregated, transformed or otherwise converted into forms that better serve the needs of the new audience. The Semantic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Interoperability Initiative (SSIII) is an NSF-funded research project aimed at making <span class="hlt">sea</span> <span class="hlt">ice</span> data more useful to more people using semantic technologies. The team includes domain and science data experts as well as knowledge representation and linked data experts. Beginning with a series of workshops involving members of the operations, <span class="hlt">sea</span> <span class="hlt">ice</span> research and modeling communities, as well as members of local communities in Alaska, a suite of ontologies describing the physical characteristics of <span class="hlt">sea</span> <span class="hlt">ice</span> have been developed and used to provide one of NSIDC's data sets, the operational Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> charts obtained from the Canadian <span class="hlt">Ice</span> Center, as open-linked data. These data extend nearly a decade into the past and can now be queried either directly through a publicly available SPARQL end point (for those who are familiar with open-linked data) or through a simple Open Geospatial Consortium (OGC) standards map-based query tool. Questions like "What were the characteristics (i.e., <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, form and stage of development) of the <span class="hlt">sea</span> <span class="hlt">ice</span> in the region</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JMS...166....4S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JMS...166....4S"><span>Modelling <span class="hlt">sea</span> <span class="hlt">ice</span> formation in the Terra Nova Bay polynya</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sansiviero, M.; Morales Maqueda, M. Á.; Fusco, G.; Aulicino, G.; Flocco, D.; Budillon, G.</p> <p>2017-02-01</p> <p>Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> is constantly exported from the shore by strong near surface winds that open leads and large polynyas in the pack <span class="hlt">ice</span>. The latter, known as wind-driven polynyas, are responsible for significant water mass modification due to the high salt flux into the ocean associated with enhanced <span class="hlt">ice</span> growth. In this article, we focus on the wind-driven Terra Nova Bay (TNB) polynya, in the western Ross <span class="hlt">Sea</span>. Brine rejected during <span class="hlt">sea</span> <span class="hlt">ice</span> formation processes that occur in the TNB polynya densifies the water column leading to the formation of the most characteristic water mass of the Ross <span class="hlt">Sea</span>, the High Salinity Shelf Water (HSSW). This water mass, in turn, takes part in the formation of Antarctic Bottom Water (AABW), the densest water mass of the world ocean, which plays a major role in the global meridional overturning circulation, thus affecting the global climate system. A simple coupled <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean model has been developed to simulate the seasonal cycle of <span class="hlt">sea</span> <span class="hlt">ice</span> formation and export within a polynya. The <span class="hlt">sea</span> <span class="hlt">ice</span> model accounts for both thermal and mechanical <span class="hlt">ice</span> processes. The oceanic circulation is described by a one-and-a-half layer, reduced gravity model. The domain resolution is 1 km × 1 km, which is sufficient to represent the salient features of the coastline geometry, notably the Drygalski <span class="hlt">Ice</span> Tongue. The model is forced by a combination of Era Interim reanalysis and in-situ data from automatic weather stations, and also by a climatological oceanic dataset developed from in situ hydrographic observations. The sensitivity of the polynya to the atmospheric forcing is well reproduced by the model when atmospheric in situ measurements are combined with reanalysis data. Merging the two datasets allows us to capture in detail the strength and the spatial distribution of the katabatic winds that often drive the opening of the polynya. The model resolves fairly accurately the <span class="hlt">sea</span> <span class="hlt">ice</span> drift and <span class="hlt">sea</span> <span class="hlt">ice</span> production rates in the TNB polynya, leading to</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100033640','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100033640"><span>The Satellite Passive-Microwave Record of <span class="hlt">Sea</span> <span class="hlt">Ice</span> in the Ross <span class="hlt">Sea</span> Since Late 1978</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Parkinson, Claire L.</p> <p>2009-01-01</p> <p>Satellites have provided us with a remarkable ability to monitor many aspects of the globe day-in and day-out and <span class="hlt">sea</span> <span class="hlt">ice</span> is one of numerous variables that by now have quite substantial satellite records. Passive-microwave data have been particularly valuable in <span class="hlt">sea</span> <span class="hlt">ice</span> monitoring, with a record that extends back to August 1987 on daily basis (for most of the period), to November 1970 on a less complete basis (again for most of the period), and to December 1972 on a less complete basis. For the period since November 1970, Ross <span class="hlt">Sea</span> <span class="hlt">sea</span> <span class="hlt">ice</span> imagery is available at spatial resolution of approximately 25 km. This allows good depictions of the seasonal advance and retreat of the <span class="hlt">ice</span> cover each year, along with its marked interannual variability. The Ross <span class="hlt">Sea</span> <span class="hlt">ice</span> extent typically reaches a minimum of approximately 0.7 x 10(exp 6) square kilometers in February, rising to a maximum of approximately 4.0 x 10(exp 6) square kilometers in September, with much variability among years for both those numbers. The Ross <span class="hlt">Sea</span> images show clearly the day-by-day activity greatly from year to year. Animations of the data help to highlight the dynamic nature of the Ross <span class="hlt">Sea</span> <span class="hlt">ice</span> cover. The satellite data also allow calculation of trends in the <span class="hlt">ice</span> cover over the period of the satellite record. Using linear least-squares fits, the Ross <span class="hlt">Sea</span> <span class="hlt">ice</span> extent increased at an average rate of 12,600 plus or minus 1,800 square kilometers per year between November 1978 and December 2007, with every month exhibiting increased <span class="hlt">ice</span> extent and the rates of increase ranging from a low of 7,500 plus or minus 5,000 square kilometers per year for the February <span class="hlt">ice</span> extents to a high of 20,300 plus or minus 6,100 kilometers per year for the October <span class="hlt">ice</span> extents. On a yearly average basis, for 1979-2007 the Ross <span class="hlt">Sea</span> <span class="hlt">ice</span> extent increased at a rate of 4.8 plus or minus 1.6 % per decade. Placing the Ross <span class="hlt">Sea</span> in the context of the Southern Ocean as a whole, over the November 1978-December 2007 period the Ross <span class="hlt">Sea</span> had</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19990079779&hterms=arctic+ocean&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Darctic%2Bocean','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990079779&hterms=arctic+ocean&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Darctic%2Bocean"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Motion from Wavelet Analysis of Satellite Data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Liu, Antony K.; Zhao, Yunhe</p> <p>1998-01-01</p> <p>Wavelet analysis of DMSP SSM/I (Special Sensor Microwave/Imager) 85 GHz and 37 GHz radiance data, SMMR (Scanning Multichannel Microwave Radiometer) 37 GHz, and NSCAT (NASA Scatterometer) 13.9 GHZ data can be used to obtain daily <span class="hlt">sea</span> <span class="hlt">ice</span> drift information for both the northern and southern polar regions. The derived maps of <span class="hlt">sea</span> <span class="hlt">ice</span> drift provide both improved spatial coverage over the existing array of Arctic Ocean buoys and better temporal resolution over techniques utilizing data from satellite synthetic aperture radars (SAR). Examples of derived <span class="hlt">ice</span>-drift maps in the Arctic illustrate large-scale circulation reversals within a period of a couple weeks. Comparisons with <span class="hlt">ice</span> displacements derived from buoys show good quantitative agreement. NASA Scatterometer (NSCAT) 13.9 GHZ data have been also used for wavelet analysis to derive <span class="hlt">sea-ice</span> drift. First, the 40' incidence-angle, sigma-zero (surface roughness) daily map of whole Arctic region with 25 km of pixel size from satellite's 600 km swath has been constructed. Then, the similar wavelet transform procedure to SSM/I data can be applied. Various scales of wavelet transform and threshold have been tested. By overlaying , neighbor filtering, and block-averaging the results of multiscale wavelet transforms, the final <span class="hlt">sea</span> <span class="hlt">ice</span> drift vectors are much smooth and representative to the <span class="hlt">sea</span> <span class="hlt">ice</span> motion. This wavelet analysis procedure is robust and can make a major contribution to the understanding of <span class="hlt">ice</span> motion over large areas at relatively high temporal resolutions. The results of wavelet analysis of SSM/I and NSCAT images and buoy data can be merged by some data fusion techniques and will help to improve our current knowledge of <span class="hlt">sea</span> <span class="hlt">ice</span> drift and related processes through the data assimilation of ocean-<span class="hlt">ice</span> numerical model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002Sci...295..641T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002Sci...295..641T"><span>Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span>-a Habitat for Extremophiles</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thomas, D. N.; Dieckmann, G. S.</p> <p>2002-01-01</p> <p>The pack <span class="hlt">ice</span> of Earth's polar oceans appears to be frozen white desert, devoid of life. However, beneath the snow lies a unique habitat for a group of bacteria and microscopic plants and animals that are encased in an <span class="hlt">ice</span> matrix at low temperatures and light levels, with the only liquid being pockets of concentrated brines. Survival in these conditions requires a complex suite of physiological and metabolic adaptations, but <span class="hlt">sea-ice</span> organisms thrive in the <span class="hlt">ice</span>, and their prolific growth ensures they play a fundamental role in polar ecosystems. Apart from their ecological importance, the bacterial and algae species found in <span class="hlt">sea</span> <span class="hlt">ice</span> have become the focus for novel biotechnology, as well as being considered proxies for possible life forms on <span class="hlt">ice</span>-covered extraterrestrial bodies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70014136','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70014136"><span><span class="hlt">Ice</span> gouge processes in the Alaskan Beaufort <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Rearic, Douglas M.; Ticken, Edward J.</p> <p>1988-01-01</p> <p>A generalized picture of <span class="hlt">ice</span> gouge characteristics from shallow inshore depths to the outer shelf at about 60 m of water is presented. Data from recent studies show that the size and quantity of gouging increases in an offshore direction to depths of about 45 m where this trend then reverses and the features decrease in size and quantity as the shelf break is approached. <span class="hlt">Ice</span> gouges are oriented east-west and this suggests that most gouging is caused by <span class="hlt">ice</span> approaching from the east, possibly driven by the Beaufort <span class="hlt">Sea</span> gyre. The most intense gouging occurs in the stamukhi zone, between 20 and 40 m of water, and is caused by a high rate of <span class="hlt">ice</span> keel production owing to shearing forces between mobile and stable <span class="hlt">sea</span> <span class="hlt">ice</span>. Inshore of the stamukhi zone, <span class="hlt">ice</span> gouging still presents a significant hazard but their greatly decreased size and number make it possible to design against this hazard.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_17 --> <div id="page_18" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="341"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/1123376','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/1123376"><span>CLIVAR Exchanges No. 62: <span class="hlt">Sea</span> Level Rise, Ocean/<span class="hlt">Ice</span> Shelf Interactions and <span class="hlt">Ice</span> Sheets</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Pirani, Anna; Danabasoglu, Gokhan; Griffies, Stephen; Marsland, Simon</p> <p>2013-08-01</p> <p>This special issue of CLIVAR Exchanges is devoted to presenting a selection of the science contributed by both speakers and poster presenters at the CLIVAR Workshop on <span class="hlt">Sea</span> Level Rise, Ocean/<span class="hlt">Ice</span> Shelf Interactions and <span class="hlt">Ice</span> Sheets at CSIRO Marine and Atmospheric Research in Hobart, Australia, on 18-20 February 2013. The workshop brought together leading international scientists and early-career researchers from the ocean, <span class="hlt">ice</span>-sheet, <span class="hlt">ice</span>-shelf, and <span class="hlt">sea</span>-level rise modelling and observational communities to explore the state-of-science and emerging pathways for development of the next generation of coupled climate models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/7063861','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/7063861"><span>Sensitivity study of a dynamic thermodynamic <span class="hlt">sea</span> <span class="hlt">ice</span> model</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Holland, D.M.; Mysak, L.A.; Manak, D.K. )</p> <p>1993-02-15</p> <p>A numerical simulation of the seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> cover in the Arctic Ocean and the Greenland, Iceland, and Norwegian <span class="hlt">seas</span> is presented. The <span class="hlt">sea</span> <span class="hlt">ice</span> model is extracted from Oberhuber's (1990) coupled <span class="hlt">sea</span> <span class="hlt">ice</span>-mixed layer-isopycnal general circulation model and is written in spherical coordinates. The advantage of such a model over previous <span class="hlt">sea</span> <span class="hlt">ice</span> models is that it can be easily coupled to either global atmospheric or ocean general circulation models written in spherical coordinates. In this model, the thermodynamics are a modification of that of Parkinson and Washington, while the dynamics use the full Hibler viscous-plastic rheology. Monthly thermodynamic and dynamic forcing fields for the atmosphere and ocean are specified. The simulations of the seasonal cycle of <span class="hlt">ice</span> thickness, compactness, and velocity, for a control set of parameters, compare favorably with the known seasonal characteristics of these fields. A sensitivity study of the control simulation of the seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> cover is presented. The sensitivity runs are carried out under three different themes, namely, numerical conditions, parameter values, and physical processes. This last theme refers to experiments in which physical processes are either newly added or completely removed from the model. Approximately 80 sensitivity runs have been performed in which a change from the control run environment has been implemented. Comparisons have been made between the control run and a particular sensitivity run based on time series of the seasonal cycle of the domain-averaged <span class="hlt">ice</span> thickness, compactness, areal coverage, and kinetic energy. In addition, spatially varying fields of <span class="hlt">ice</span> thickness, compactness, velocity, and surface temperature for each season are presented for selected experiments. A brief description and discussion of the more interesting experiments are presented. The simulation of the seasonal cycle of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover is shown to be robust. 31 refs., 20 figs., 5 tabs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26882269','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26882269"><span>Bacterial communities from Arctic seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> are more compositionally variable than those from multi-year <span class="hlt">sea</span> <span class="hlt">ice</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hatam, Ido; Lange, Benjamin; Beckers, Justin; Haas, Christian; Lanoil, Brian</p> <p>2016-10-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> can be classified into two types: seasonal <span class="hlt">ice</span> (first-year <span class="hlt">ice</span>, FYI) and multi-year <span class="hlt">ice</span> (MYI). Despite striking differences in the physical and chemical characteristics of FYI and MYI, and the key role <span class="hlt">sea</span> <span class="hlt">ice</span> bacteria play in biogeochemical cycles of the Arctic Ocean, there are a limited number of studies comparing the bacterial communities from these two <span class="hlt">ice</span> types. Here, we compare the membership and composition of bacterial communities from FYI and MYI sampled north of Ellesmere Island, Canada. Our results show that communities from both <span class="hlt">ice</span> types were dominated by similar class-level phylogenetic groups. However, at the operational taxonomic unit (OTU) level, communities from MYI and FYI differed in both membership and composition. Communities from MYI sites had consistent structure, with similar membership (presence/absence) and composition (OTU abundance) independent of location and year of sample. By contrast, communities from FYI were more variable. Although FYI bacterial communities from different locations and different years shared similar membership, they varied significantly in composition. Should these findings apply to <span class="hlt">sea</span> <span class="hlt">ice</span> across the Arctic, we predict increased compositional variability in <span class="hlt">sea</span> <span class="hlt">ice</span> bacterial communities resulting from the ongoing transition from predominantly MYI to FYI, which may impact nutrient dynamics in the Arctic Ocean.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C11B0362S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C11B0362S"><span>Sub-Regional <span class="hlt">Sea</span> <span class="hlt">Ice</span> Preferences of Pacific Walrus in the Bering <span class="hlt">Sea</span> Using SAR Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sacco, A.; Mahoney, A. R.; Eicken, H.; Johnson, M. A.; Ray, C.</p> <p>2014-12-01</p> <p>The Pacific walrus (O. r. divergens) uses winter <span class="hlt">sea</span> <span class="hlt">ice</span> in the Bering <span class="hlt">Sea</span> for numerous parts of its natural history including courtship, foraging, and migration. Recent and predicted loss of <span class="hlt">sea</span> <span class="hlt">ice</span> has caused the Pacific walrus to be considered for an elevated status under the Endangered Species Act. Study of the <span class="hlt">ice</span> conditions during this period is required to investigate changes in the Bering <span class="hlt">Sea</span> <span class="hlt">ice</span> pack and its effects on walrus sustainability. Using Radarsat-1 data and second-order texture statistics, a classification system was devised to separate <span class="hlt">sea</span> <span class="hlt">ice</span> into three distinguishable classes based on walrus needs of open water availability in the pack <span class="hlt">ice</span>: discontinuous pack <span class="hlt">ice</span>, continuous pack <span class="hlt">ice</span>, and open water. Classifications are performed on sub-regional image areas to facilitate classification of heterogeneous seascapes which are thought to be distinguishable by walrus. Spatial, as well as temporal, changes in the seascape cover, based on the classification, are achieved. These results are then combined with ship-based observations of walrus to quantify walrus habitat preference. The three-class algorithm has a success rate of 94% for the discontinuous <span class="hlt">ice</span> and continuous pack <span class="hlt">ice</span>. Radarsat-1 images from 2004 - 2008 were analyzed for changes in seasonal and annual discontinuous <span class="hlt">ice</span> extent. After classification, the spatial extent of discontinuous <span class="hlt">ice</span> was found to vary throughout 2004 - 2008 in the Bering <span class="hlt">Sea</span> shelf. Walrus are also shown to prefer discontinuous pack far from the southernmost <span class="hlt">ice</span> edge. Maps of walrus habitat preference and persistent areas of <span class="hlt">sea</span> <span class="hlt">ice</span> seascapes are created and then can be used for the walrus' status consideration under the Endangered Species Act in addition to general species management issues.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.int-res.com/abstracts/meps/v407/p293-302/','USGSPUBS'); return false;" href="http://www.int-res.com/abstracts/meps/v407/p293-302/"><span>Divergent movements of walrus and <span class="hlt">sea</span> <span class="hlt">ice</span> in the Nothern Bering <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Jay, Chadwick V.; Udevitz, Mark S.; Kwok, Ron; Fischbach, Anthony S.; Douglas, David C.</p> <p>2010-01-01</p> <p>The Pacific walrus Odobenus rosmarus divergens is a large Arctic pinniped of the Chukchi and Bering <span class="hlt">Seas</span>. Reductions of <span class="hlt">sea</span> <span class="hlt">ice</span> projected to occur in the Arctic by mid-century raise concerns for conservation of the Pacific walrus. To understand the significance of <span class="hlt">sea</span> <span class="hlt">ice</span> loss to the viability of walruses, it would be useful to better understand the spatial associations between the movements of <span class="hlt">sea</span> <span class="hlt">ice</span> and walruses. We investigated whether local-scale (~1 to 100 km) walrus movements correspond to movements of <span class="hlt">sea</span> <span class="hlt">ice</span> in the Bering <span class="hlt">Sea</span> in early spring, using locations from radio-tracked walruses and measures of <span class="hlt">ice</span> floe movements from processed synthetic aperture radar satellite imagery. We used generalized linear mixed-effects models to analyze the angle between walrus and <span class="hlt">ice</span> floe movement vectors and the distance between the final geographic position of walruses and their associated <span class="hlt">ice</span> floes (displacement), as functions of observation duration, proportion of time the walrus was in water, and geographic region. Analyses were based on 121 walrus-<span class="hlt">ice</span> vector pairs and observations lasting 12 to 36 h. Angles and displacements increased with observation duration, proportion of time the walrus spent in the water, and varied among regions (regional mean angles ranged from 40° to 81° and mean displacements ranged from 15 to 35 km). Our results indicated a lack of correspondence between walruses and their initially associated <span class="hlt">ice</span> floes, suggesting that local areas of walrus activities were independent of the movement of <span class="hlt">ice</span> floes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12.2443J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.2443J"><span>Influence of ocean - <span class="hlt">sea</span> <span class="hlt">ice</span> - atmosphere feedbacks in Antarctica</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jourdain, Nicolas C.; Mathiot, Pierre; Gallée, Hubert; Barnier, Bernard</p> <p>2010-05-01</p> <p>The Ross <span class="hlt">Sea</span> sector is a major place of dense water formation. A significant amount of dense water is formed in polynyas and results from air - <span class="hlt">sea</span> <span class="hlt">ice</span> - ocean interactions. However, the representation of physical processes specific to polar regions is generally poor within climate models. Our aim is to quantify the effects of physical feedbacks, in particular those in which <span class="hlt">sea</span> <span class="hlt">ice</span> is involved. We choose limited area modeling in order to use parametrizations specific to polar regions at a relatively high resolution (40 km). Physical feedbacks are involved in air - <span class="hlt">sea</span> <span class="hlt">ice</span> - ocean interactions, and some atmospheric regional models have therefore been coupled to a <span class="hlt">sea</span> <span class="hlt">ice</span> model or a 1-layer ocean model. However, none of these models have been coupled to a 3-dimensional ocean model in Antarctica, although this is needed to represent dense water formation. We therefore describe and evaluate the new coupled atmosphere - <span class="hlt">sea</span> <span class="hlt">ice</span> - ocean regional model TANGO (Jourdain et al., 2010). This is a coupling of the regional atmospheric model MAR (Gallée et al., 2005) and the ocean - <span class="hlt">sea</span> <span class="hlt">ice</span> model NEMO (Madec et al., 2008). This study is motivated by previous studies that have emphasized the improvement of ocean - <span class="hlt">sea</span> <span class="hlt">ice</span> simulations (using the model NEMO) when it is forced by the atmospheric regional model MAR (Mathiot et al., 2008, 2010). Stand alone atmosphere or ocean - <span class="hlt">sea</span> <span class="hlt">ice</span> experiments are performed to evaluate the skills of MAR and NEMO in the Ross <span class="hlt">Sea</span> sector, Antarctica. A methodology is described to isolate physical feedbacks as captured by TANGO. Our methodology provides an estimation of the effects of physical feedbacks. It is shown that they significantly affect the <span class="hlt">sea</span> <span class="hlt">ice</span> properties, the atmospheric boundary layer, and the first 700~m of the ocean, even after a few months of model-integration. The dense water formation in polynyas is affected by coupling, although the turbulent heat flux parametrization has a larger impact. Finally, TANGO is evaluated using</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1811086D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1811086D"><span>Atmospheric forcing of <span class="hlt">sea</span> <span class="hlt">ice</span> anomalies in the Ross <span class="hlt">Sea</span> Polynya region</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dale, Ethan; McDonald, Adrian; Rack, Wolfgang</p> <p>2016-04-01</p> <p>Despite warming trends in global temperatures, <span class="hlt">sea</span> <span class="hlt">ice</span> extent in the southern hemisphere has shown an increasing trend over recent decades. Wind-driven <span class="hlt">sea</span> <span class="hlt">ice</span> export from coastal polynyas is an important source of <span class="hlt">sea</span> <span class="hlt">ice</span> production. Areas of major polynyas in the Ross <span class="hlt">Sea</span>, the region with largest increase in <span class="hlt">sea</span> <span class="hlt">ice</span> extent, have been suggested to produce the vast amount of the <span class="hlt">sea</span> <span class="hlt">ice</span> in the region. We investigate the impacts of strong wind events on polynyas and the subsequent <span class="hlt">sea</span> <span class="hlt">ice</span> production. We utilize Bootstrap <span class="hlt">sea</span> <span class="hlt">ice</span> concentration (SIC) measurements derived from satellite based, Special Sensor Microwave Imager (SSM/I) brightness temperature images. These are compared with surface wind measurements made by automatic weather stations of the University of Wisconsin-Madison Antarctic Meteorology Program. Our analysis focusses on the winter period defined as 1st April to 1st November in this study. Wind data was used to classify each day into characteristic regimes based on the change of wind speed. For each regime, a composite of SIC anomaly was formed for the Ross <span class="hlt">Sea</span> region. We found that persistent weak winds near the edge of the Ross <span class="hlt">Ice</span> Shelf are generally associated with positive SIC anomalies in the Ross <span class="hlt">Sea</span> polynya area (RSP). Conversely we found negative SIC anomalies in this area during persistent strong winds. By analyzing <span class="hlt">sea</span> <span class="hlt">ice</span> motion vectors derived from SSM/I brightness temperatures, we find significant <span class="hlt">sea</span> <span class="hlt">ice</span> motion anomalies throughout the Ross <span class="hlt">Sea</span> during strong wind events. These anomalies persist for several days after the strong wing event. Strong, negative correlations are found between SIC within the RSP and wind speed indicating that strong winds cause significant advection of <span class="hlt">sea</span> <span class="hlt">ice</span> in the RSP. This rapid decrease in SIC is followed by a more gradual recovery in SIC. This increase occurs on a time scale greater than the average persistence of strong wind events and the resulting <span class="hlt">Sea</span> <span class="hlt">ice</span> motion anomalies, highlighting the production</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20010023033&hterms=Okhotsk+Sea&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DOkhotsk%252C%2BSea','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20010023033&hterms=Okhotsk+Sea&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DOkhotsk%252C%2BSea"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Variability in the <span class="hlt">Sea</span> of Okhotsk from Passive Microwave Satellite Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cavalieri, Donald J.; Zukor, Dorothy (Technical Monitor)</p> <p>2000-01-01</p> <p>The <span class="hlt">Sea</span> of Okhotsk, located between 50 and 60 N, is bounded by the Kamchatka Peninsula, Siberia, Sakhalin Island, and the Kuril Island chain and is the largest midlatitude seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> zone in the Northern Hemisphere. The winter <span class="hlt">sea</span> <span class="hlt">ice</span> cover begins to form in November and expands to cover most of the <span class="hlt">sea</span> by March. Over the following three months, the <span class="hlt">ice</span> retreats with only small <span class="hlt">ice</span>-covered areas remaining by the beginning of June. The <span class="hlt">sea</span> is <span class="hlt">ice</span> free or nearly <span class="hlt">ice</span> free on average for six months of the year, from June through November. The recent compilation of a consistent, long-term record of Northern Hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> extents based on passive microwave satellite observations from the Nimbus 7 Scanning Multichannel Microwave Radiometer and from four Defense Meteorological Satellite Program Special Sensor Microwave Imagers provides the basis for assessing long-term <span class="hlt">sea</span> <span class="hlt">ice</span> extent variability in the <span class="hlt">Sea</span> of Okhotsk. Analysis of this 20-year data record (1979-1998) shows that based on yearly averages the overall extent of the <span class="hlt">Sea</span> of Okhotsk <span class="hlt">ice</span> cover is decreasing at the rate of -8.1+/-2.1x10(exp 3) sq km/yr (-17.2%/decade), in contrast to the rate of decrease of -33.3+/-0.7x10(exp 3) sq km/yr (-2.7%/decade) for the Northern Hemisphere as a whole. There is large regional <span class="hlt">sea</span> <span class="hlt">ice</span> extent variability of the Arctic <span class="hlt">ice</span> cover. Two of the nine Arctic regions analyzed, the Bering <span class="hlt">Sea</span> and the Gulf of St. Lawrence, show increases of 0.8+/-1.4xl0(exp 3) sq km/yr (2.7%/decade) and 1.2+/-0.5xl0(exp 3) sq km/yr (17.1%/decade), respectively. Interestingly, the <span class="hlt">Sea</span> of Okhotsk and the Gulf of St. Lawrence show about equal percentage changes, but of opposite sign. The <span class="hlt">Sea</span> of Okhotsk exhibits its greatest percent decrease (-24.3%/decade) during spring (April-June). The year of maximum winter <span class="hlt">sea</span> <span class="hlt">ice</span> extent for the <span class="hlt">Sea</span> of Okhotsk was 1979, whereas the minimum winter <span class="hlt">sea</span> <span class="hlt">ice</span> extent occurred in 1984.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C31B..07K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C31B..07K"><span>New advances in remote sensing of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> thickness from Operation <span class="hlt">Ice</span>Bridge and beyond</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kurtz, N. T.; Studinger, M.; Sonntag, J. G.; Yungel, J.; Yi, D.; Harbeck, J.; Onana, V.; Petty, A.</p> <p>2015-12-01</p> <p>Since 2009, NASA's Operation <span class="hlt">Ice</span>Bridge mission has undertaken an annual campaign of measuring <span class="hlt">sea</span> <span class="hlt">ice</span> thickness in the western Arctic. The combined suite of laser, radar, visible, and infrared instruments provides an incredibly rich set of data for accurately measuring <span class="hlt">sea</span> <span class="hlt">ice</span> thickness at a variety of scales, and has broad implications for extension beyond the <span class="hlt">Ice</span>Bridge mission. The traditional approach to measuring <span class="hlt">sea</span> <span class="hlt">ice</span> thickness is taken from hydrostatic balance assumptions and measurements of the <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard and snow thickness. In going beyond the traditional methods previously employed, this work will present new techniques for the remote sensing of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness, which will allow for a more accurate record focusing on retrieval of the full thickness distribution of <span class="hlt">sea</span> <span class="hlt">ice</span>. The retrieval of thin <span class="hlt">sea</span> <span class="hlt">ice</span> thickness is shown through the use of a simple thermodynamic calculation and surface temperature retrievals from a new high resolution thermal infrared camera installed on the <span class="hlt">Ice</span>Bridge mission in 2015. The retrieval of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness from ridged and deformed <span class="hlt">sea</span> <span class="hlt">ice</span> is shown through use of the high resolution of the <span class="hlt">Ice</span>Bridge ATM laser altimeter. Together with surface topography measurements, the high spatial resolution ultra-wideband radar suite on the <span class="hlt">Ice</span>Bridge mission is currently being used to provide information on the thickness of snow on <span class="hlt">sea</span> <span class="hlt">ice</span>. We will discuss how the extension of the <span class="hlt">Ice</span>Bridge radar and topography results has wide applicability to improving the retrieval of <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard and thickness from CryoSat-2 since the physical mechanisms governing the scattering of radar returns are very similar. Lastly, we will demonstrate a new approach which synthesizes the combined knowledge generated by the <span class="hlt">Ice</span>Bridge airborne data set along with satellite and model data sources to produce a record of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness change in the Arctic.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19970006690','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19970006690"><span><span class="hlt">Sea-Ice</span> Feature Mapping using JERS-1 Imagery</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Maslanik, James; Heinrichs, John</p> <p>1994-01-01</p> <p>JERS-1 SAR and OPS imagery are examined in combination with other data sets to investigate the utility of the JERS-1 sensors for mapping fine-scale <span class="hlt">sea</span> <span class="hlt">ice</span> conditions. Combining ERS-1 C band and JERS-1 L band SAR aids in discriminating multiyear and first-year <span class="hlt">ice</span>. Analysis of OPS imagery for a field site in the Canadian Archipelago highlights the advantages of OPS's high spatial and spectral resolution for mapping <span class="hlt">ice</span> structure, melt pond distribution, and surface albedo.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA123762','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA123762"><span>The Growth, Structure, and Properties of <span class="hlt">Sea</span> <span class="hlt">Ice</span>,</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>1982-11-01</p> <p>111 94. Simulated returned power/incident power vs depth 112 95. Scattering albedo vs scatterer size to wavelength ratio for <span class="hlt">ice</span> overlying...fresh water ----------------------- 116 96. Changes in brightness temperature plotted against scat- tering albedo with the layer thickness to... Antarctica ; and finally Zotikov et al. (1980) obtained a0 values of 5 mm from <span class="hlt">sea</span> <span class="hlt">ice</span> formed on the base of the 416-m-thick Ross <span class="hlt">Ice</span> Shelf. Figure 40 shows a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19990078517&hterms=Grh&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DGrh','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990078517&hterms=Grh&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DGrh"><span>A Modified NASA Team <span class="hlt">Sea</span> <span class="hlt">Ice</span> Algorithm for the Antarctic</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cavalieri, Donald J.; Markus, Thorsten</p> <p>1998-01-01</p> <p>A recent comparative study of the NASA Team and Bootstrap passive microwave <span class="hlt">sea</span> <span class="hlt">ice</span> algorithms revealed significantly different <span class="hlt">sea</span> <span class="hlt">ice</span> concentration retrievals in some parts of the Antarctic. The study identified potential reasons for the discrepancies including the influence of <span class="hlt">sea</span> <span class="hlt">ice</span> temperature variability on the Bootstrap retrievals and the influence of <span class="hlt">ice</span> surface reflectivity on the horizontally polarized emissivity in the NASA Team retrievals. In this study, we present a modified version of the NASA Team algorithm which reduces the error associated with the use of horizontally polarized radiance data, while retaining the relative insensitivity to <span class="hlt">ice</span> temperature variations provided by radiance ratios. By retaining the 19 GHz polarization as an independent variable, we also maintain a relatively large dynamic range in <span class="hlt">sea</span> <span class="hlt">ice</span> concentration. The modified algorithm utilizes the 19 GHz polarization (PR19) and both gradient ratios, GRV and GRH defined by (37V-19V)/(37V+19V) and (37H-19H)/(37H+19H), respectively, rather than just GRV used in the current NASA Team algorithm. A plot of GRV versus GRH shows that the preponderance of points lie along a quadratic curve, whereas those points affected by surface reflectivity anomalies deviate from this curve. This serves as a method of identifying the problems points. The 19H brightness temperature of these problem points is increased so they too fall along quadratic curve. <span class="hlt">Sea</span> <span class="hlt">ice</span> concentrations derived from AVHRR imagery illustrate the extent to which this method reduces the error associated with surface layering.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.usgs.gov/of/1995/0070/report.pdf','USGSPUBS'); return false;" href="https://pubs.usgs.gov/of/1995/0070/report.pdf"><span>Methane in coastal <span class="hlt">sea</span> water, <span class="hlt">sea</span> <span class="hlt">ice</span>, and bottom sediments, Beaufort <span class="hlt">Sea</span>, Alaska</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Lorenson, T.D.; Kvenvolden, Keith A.</p> <p>1995-01-01</p> <p>This report summarizes data acquired from 1990 to 1994 for the gas-hydrate portion of the USGS project 'Permafrost and gas hydrate as possible sources of methane' of the USGS Global Change and Climate History program. The objective of this project has been to test the hypothesis that gas hydrate deposits of the Beaufort <span class="hlt">Sea</span> continental shelf are destabilized by the ~10?C temperature increase that has resulted from the Holocene transgression of the Arctic Ocean. To test this idea we have selected an area off the north coast of Alaska centered on Harrison Bay. We have measured the concentration of methane in surficial sediments, in the water column when <span class="hlt">ice</span> is present and absent, and in seasonal <span class="hlt">sea</span> <span class="hlt">ice</span>. Our results show that more methane is present in the water when <span class="hlt">ice</span> is present than when <span class="hlt">ice</span> is absent, and that methane is also present within the <span class="hlt">ice</span> itself, often at higher concentrations than in the water. Thus the Beaufort <span class="hlt">Sea</span> shelf of Alaska is a seasonal source of methane. The primary source of this methane has not yet been defined, but gas hydrate is a reasonable candidate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19950045752&hterms=climate+change+impact+ocean&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dclimate%2Bchange%2Bimpact%2Bocean','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19950045752&hterms=climate+change+impact+ocean&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dclimate%2Bchange%2Bimpact%2Bocean"><span>The role of <span class="hlt">sea</span> <span class="hlt">ice</span> in 2 x CO2 climate model sensitivity. Part 1: The total influence of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and extent</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Rind, D.; Healy, R.; Parkinson, C.; Martinson, D.</p> <p>1995-01-01</p> <p>As a first step in investigating the effects of <span class="hlt">sea</span> <span class="hlt">ice</span> changes on the climate sensitivity to doubled atmospheric CO2, the authors use a standard simple <span class="hlt">sea</span> <span class="hlt">ice</span> model while varying the <span class="hlt">sea</span> <span class="hlt">ice</span> distributions and thicknesses in the control run. Thinner <span class="hlt">ice</span> amplifies the atmospheric temperature senstivity in these experiments by about 15% (to a warming of 4.8 C), because it is easier for the thinner <span class="hlt">ice</span> to be removed as the climate warms. Thus, its impact on sensitivity is similar to that of greater <span class="hlt">sea</span> <span class="hlt">ice</span> extent in the control run, which provides more opportunity for <span class="hlt">sea</span> <span class="hlt">ice</span> reduction. An experiment with <span class="hlt">sea</span> <span class="hlt">ice</span> not allowed to change between the control and doubled CO2 simulations illustrates that the total effect of <span class="hlt">sea</span> <span class="hlt">ice</span> on surface air temperature changes, including cloud cover and water vapor feedbacks that arise in response to <span class="hlt">sea</span> <span class="hlt">ice</span> variations, amounts to 37% of the temperature sensitivity to the CO2 doubling, accounting for 1.56 C of the 4.17 C global warming. This is about four times larger than the <span class="hlt">sea</span> <span class="hlt">ice</span> impact when no feedbacks are allowed. The different experiments produce a range of results for southern high latitudes with the hydrologic budget over Antarctica implying <span class="hlt">sea</span> level increases of varying magnitude or no change. These results highlight the importance of properly constraining the <span class="hlt">sea</span> <span class="hlt">ice</span> response to climate perturbations, necessitating the use of more realistic <span class="hlt">sea</span> <span class="hlt">ice</span> and ocean models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/57457','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/57457"><span>The role of <span class="hlt">sea</span> <span class="hlt">ice</span> in 2 x CO2 climate model sensitivity. Part 1: The total influence of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and extent</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Rind, D.; Healy, R.; Parkinson, C.; Martinson, D. ||</p> <p>1995-03-01</p> <p>As a first step in investigating the effects of <span class="hlt">sea</span> <span class="hlt">ice</span> changes on the climate sensitivity to doubled atmospheric CO2, the authors use a standard simple <span class="hlt">sea</span> <span class="hlt">ice</span> model while varying the <span class="hlt">sea</span> <span class="hlt">ice</span> distributions and thicknesses in the control run. Thinner <span class="hlt">ice</span> amplifies the atmospheric temperature senstivity in these experiments by about 15% (to a warming of 4.8 C), because it is easier for the thinner <span class="hlt">ice</span> to be removed as the climate warms. Thus, its impact on sensitivity is similar to that of greater <span class="hlt">sea</span> <span class="hlt">ice</span> extent in the control run, which provides more opportunity for <span class="hlt">sea</span> <span class="hlt">ice</span> reduction. An experiment with <span class="hlt">sea</span> <span class="hlt">ice</span> not allowed to change between the control and doubled CO2 simulations illustrates that the total effect of <span class="hlt">sea</span> <span class="hlt">ice</span> on surface air temperature changes, including cloud cover and water vapor feedbacks that arise in response to <span class="hlt">sea</span> <span class="hlt">ice</span> variations, amounts to 37% of the temperature sensitivity to the CO2 doubling, accounting for 1.56 C of the 4.17 C global warming. This is about four times larger than the <span class="hlt">sea</span> <span class="hlt">ice</span> impact when no feedbacks are allowed. The different experiments produce a range of results for southern high latitudes with the hydrologic budget over Antarctica implying <span class="hlt">sea</span> level increases of varying magnitude or no change. These results highlight the importance of properly constraining the <span class="hlt">sea</span> <span class="hlt">ice</span> response to climate perturbations, necessitating the use of more realistic <span class="hlt">sea</span> <span class="hlt">ice</span> and ocean models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/20434194','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/20434194"><span>Arctic Ocean <span class="hlt">sea</span> <span class="hlt">ice</span> drift origin derived from artificial radionuclides.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Cámara-Mor, P; Masqué, P; Garcia-Orellana, J; Cochran, J K; Mas, J L; Chamizo, E; Hanfland, C</p> <p>2010-07-15</p> <p>Since the 1950s, nuclear weapon testing and releases from the nuclear industry have introduced anthropogenic radionuclides into the <span class="hlt">sea</span>, and in many instances their ultimate fate are the bottom sediments. The Arctic Ocean is one of the most polluted in this respect, because, in addition to global fallout, it is impacted by regional fallout from nuclear weapon testing, and indirectly by releases from nuclear reprocessing facilities and nuclear accidents. <span class="hlt">Sea-ice</span> formed in the shallow continental shelves incorporate sediments with variable concentrations of anthropogenic radionuclides that are transported through the Arctic Ocean and are finally released in the melting areas. In this work, we present the results of anthropogenic radionuclide analyses of <span class="hlt">sea-ice</span> sediments (SIS) collected on five cruises from different Arctic regions and combine them with a database including prior measurements of these radionuclides in SIS. The distribution of (137)Cs and (239,240)Pu activities and the (240)Pu/(239)Pu atom ratio in SIS showed geographical differences, in agreement with the two main <span class="hlt">sea</span> <span class="hlt">ice</span> drift patterns derived from the mean field of <span class="hlt">sea-ice</span> motion, the Transpolar Drift and Beaufort Gyre, with the Fram Strait as the main ablation area. A direct comparison of data measured in SIS samples against those reported for the potential source regions permits identification of the regions from which <span class="hlt">sea</span> <span class="hlt">ice</span> incorporates sediments. The (240)Pu/(239)Pu atom ratio in SIS may be used to discern the origin of <span class="hlt">sea</span> <span class="hlt">ice</span> from the Kara-Laptev <span class="hlt">Sea</span> and the Alaskan shelf. However, if the (240)Pu/(239)Pu atom ratio is similar to global fallout, it does not provide a unique diagnostic indicator of the source area, and in such cases, the source of SIS can be constrained with a combination of the (137)Cs and (239,240)Pu activities. Therefore, these anthropogenic radionuclides can be used in many instances to determine the geographical source area of <span class="hlt">sea-ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JGRD..12113886P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JGRD..12113886P"><span>Bellingshausen <span class="hlt">Sea</span> <span class="hlt">ice</span> extent recorded in an Antarctic Peninsula <span class="hlt">ice</span> core</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Porter, Stacy E.; Parkinson, Claire L.; Mosley-Thompson, Ellen</p> <p>2016-12-01</p> <p>Annual net accumulation (An) from the Bruce Plateau (BP) <span class="hlt">ice</span> core retrieved from the Antarctic Peninsula exhibits a notable relationship with <span class="hlt">sea</span> <span class="hlt">ice</span> extent (SIE) in the Bellingshausen <span class="hlt">Sea</span>. Over the satellite era, both BP An and Bellingshausen SIE are influenced by large-scale climatic factors such as the Amundsen <span class="hlt">Sea</span> Low, Southern Annular Mode, and Southern Oscillation. In addition to the direct response of BP An to Bellingshausen SIE (e.g., more open water as a moisture source), these large-scale climate phenomena also link the BP and the Bellingshausen <span class="hlt">Sea</span> indirectly such that they exhibit similar responses (e.g., northerly wind anomalies advect warm, moist air to the Antarctic Peninsula and neighboring Bellingshausen <span class="hlt">Sea</span>, which reduces SIE and increases An). Comparison with a time series of fast <span class="hlt">ice</span> at South Orkney Islands reveals a relationship between BP An and <span class="hlt">sea</span> <span class="hlt">ice</span> in the northern Weddell <span class="hlt">Sea</span> that is relatively consistent over the twentieth century, except when it is modulated by atmospheric wave patterns described by the Trans-Polar Index. The trend of increasing accumulation on the Bruce Plateau since 1970 agrees with other climate records and reconstructions in the region and suggests that the current rate of <span class="hlt">sea</span> <span class="hlt">ice</span> loss in the Bellingshausen <span class="hlt">Sea</span> is unrivaled in the twentieth century.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/237970','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/237970"><span>Observations of <span class="hlt">sea</span> <span class="hlt">ice</span> and icebergs in the western Barents <span class="hlt">Sea</span> during the winter of 1987</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Loeset, S.; Carstens, T.</p> <p>1995-12-31</p> <p>A multisensor <span class="hlt">ice</span> data acquisition program for the western Barents <span class="hlt">Sea</span> was carried out during three field campaigns in the mid winter and fall of 1987. The main purpose of the program was to obtain comprehensive information about the <span class="hlt">ice</span> in the area at that time. The reasoning was that prior to any oil/gas exploration and production in the Barents <span class="hlt">Sea</span>, the physical environment has to be quantitatively surveyed in order to ensure safe operations related to human safety, the regular operability and safety of the structure and protection of the environment. Prior to this field investigation program in 1987 data on <span class="hlt">sea</span> <span class="hlt">ice</span> and icebergs for engineering purposes for the western Barents <span class="hlt">Sea</span> were meager. The present paper highlights some of the findings with emphasis on <span class="hlt">ice</span> edge speeds, <span class="hlt">ice</span> edge displacement and <span class="hlt">ice</span> drift. For icebergs, the paper focuses on population, size distributions and geometric parameters.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhDT.........5D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhDT.........5D"><span>On using numerical <span class="hlt">sea-ice</span> prediction and indigenous observations to improve operational <span class="hlt">sea-ice</span> forecasts during spring in the bering <span class="hlt">sea</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Deemer, Gregory Joseph</p> <p></p> <p>Impacts of a rapidly changing climate are amplified in the Arctic. The most notorious change has come in the form of record-breaking summertime <span class="hlt">sea-ice</span> retreat. Larger areas of open water and a prolonged <span class="hlt">ice</span>-free season create opportunity for some industries, but bring new challenges to indigenous populations that rely on <span class="hlt">sea-ice</span> cover for subsistence. Observed and projected increases in maritime activities require accurate <span class="hlt">sea-ice</span> forecasts on the weather timescale, which are currently lacking. Motivated by this need, this study explores how new modeling developments and local-scale observations can contribute to improving <span class="hlt">sea-ice</span> forecasts. The Arctic Cap Nowcast/Forecast System, a research <span class="hlt">sea-ice</span> forecast model developed by the U.S. Navy, is evaluated for forecast skill. Forecasts of <span class="hlt">ice</span> concentration, thickness, and drift speed produced by the model from April through June 2011 in the Bering <span class="hlt">Sea</span> were investigated to determine how the model performs relative to persistence and climatology. Results show that model forecasts can outperform forecasts based on climatology or persistence. However, predictive skill is less consistent during powerful, synoptic-scale events and near the Bering Slope. Forecast case studies in Western Alaska were presented. Community-based observations from recognized indigenous <span class="hlt">sea-ice</span> experts have been analyzed to gauge the prospect of using local observations in the operational <span class="hlt">sea-ice</span> monitoring and prediction process. Local observations were discussed in the context of cross-validating model guidance, data sources used in operational <span class="hlt">ice</span> monitoring, and public <span class="hlt">sea-ice</span> information products issued by the U.S. National Weather Service. Instrumentation for observing <span class="hlt">sea-ice</span> and weather at the local scale was supplied to key observers. The instrumentation shows utility in the field and may help translate the context of indigenous observations and provide ground-truth data for use by forecasters.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5016760','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5016760"><span>Influence of <span class="hlt">ice</span> thickness and surface properties on light transmission through Arctic <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Arndt, Stefanie; Nicolaus, Marcel; Perovich, Donald K.; Jakuba, Michael V.; Suman, Stefano; Elliott, Stephen; Whitcomb, Louis L.; McFarland, Christopher J.; Gerdes, Rüdiger; Boetius, Antje; German, Christopher R.</p> <p>2015-01-01</p> <p>Abstract The observed changes in physical properties of <span class="hlt">sea</span> <span class="hlt">ice</span> such as decreased thickness and increased melt pond cover severely impact the energy budget of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Increased light transmission leads to increased deposition of solar energy in the upper ocean and thus plays a crucial role for amount and timing of sea‐ice‐melt and under‐<span class="hlt">ice</span> primary production. Recent developments in underwater technology provide new opportunities to study light transmission below the largely inaccessible underside of <span class="hlt">sea</span> <span class="hlt">ice</span>. We measured spectral under‐<span class="hlt">ice</span> radiance and irradiance using the new Nereid Under‐<span class="hlt">Ice</span> (NUI) underwater robotic vehicle, during a cruise of the R/V Polarstern to 83°N 6°W in the Arctic Ocean in July 2014. NUI is a next generation hybrid remotely operated vehicle (H‐ROV) designed for both remotely piloted and autonomous surveys underneath land‐fast and moving <span class="hlt">sea</span> <span class="hlt">ice</span>. Here we present results from one of the first comprehensive scientific dives of NUI employing its interdisciplinary sensor suite. We combine under‐<span class="hlt">ice</span> optical measurements with three dimensional under‐<span class="hlt">ice</span> topography (multibeam sonar) and aerial images of the surface conditions. We investigate the influence of spatially varying ice‐thickness and surface properties on the spatial variability of light transmittance during summer. Our results show that surface properties such as melt ponds dominate the spatial distribution of the under‐<span class="hlt">ice</span> light field on small scales (<1000 m2), while <span class="hlt">sea</span> ice‐thickness is the most important predictor for light transmission on larger scales. In addition, we propose the use of an algorithm to obtain histograms of light transmission from distributions of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and surface albedo. PMID:27660738</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_18 --> <div id="page_19" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="361"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C31A..03A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C31A..03A"><span>Interactions Between <span class="hlt">Ice</span> Thickness, Bottom <span class="hlt">Ice</span> Algae, and Transmitted Spectral Irradiance in the Chukchi <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Arntsen, A. E.; Perovich, D. K.; Polashenski, C.; Stwertka, C.</p> <p>2015-12-01</p> <p>The amount of light that penetrates the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover impacts <span class="hlt">sea-ice</span> mass balance as well as ecological processes in the upper ocean. The seasonally evolving macro and micro spatial variability of transmitted spectral irradiance observed in the Chukchi <span class="hlt">Sea</span> from May 18 to June 17, 2014 can be primarily attributed to variations in snow depth, <span class="hlt">ice</span> thickness, and bottom <span class="hlt">ice</span> algae concentrations. This study characterizes the interactions among these dominant variables using observed optical properties at each sampling site. We employ a normalized difference index to compute estimates of Chlorophyll a concentrations and analyze the increased attenuation of incident irradiance due to absorption by biomass. On a kilometer spatial scale, the presence of bottom <span class="hlt">ice</span> algae reduced the maximum transmitted irradiance by about 1.5 orders of magnitude when comparing floes of similar snow and <span class="hlt">ice</span> thicknesses. On a meter spatial scale, the combined effects of disparities in the depth and distribution of the overlying snow cover along with algae concentrations caused maximum transmittances to vary between 0.0577 and 0.282 at a single site. Temporal variability was also observed as the average integrated transmitted photosynthetically active radiation increased by one order of magnitude to 3.4% for the last eight measurement days compared to the first nine. Results provide insight on how interrelated physical and ecological parameters of <span class="hlt">sea</span> <span class="hlt">ice</span> in varying time and space may impact new trends in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent and the progression of melt.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C43D..01R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C43D..01R"><span>NASA <span class="hlt">Ice</span>Bridge: Scientific Insights from Airborne Surveys of the Polar <span class="hlt">Sea</span> <span class="hlt">Ice</span> Covers</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Richter-Menge, J.; Farrell, S. L.</p> <p>2015-12-01</p> <p>The NASA Operation <span class="hlt">Ice</span>Bridge (OIB) airborne <span class="hlt">sea</span> <span class="hlt">ice</span> surveys are designed to continue a valuable series of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness measurements by bridging the gap between NASA's <span class="hlt">Ice</span>, Cloud and Land Elevation Satellite (ICESat), which operated from 2003 to 2009, and ICESat-2, which is scheduled for launch in 2017. Initiated in 2009, OIB has conducted campaigns over the western Arctic Ocean (March/April) and Southern Oceans (October/November) on an annual basis when the thickness of <span class="hlt">sea</span> <span class="hlt">ice</span> cover is nearing its maximum. More recently, a series of Arctic surveys have also collected observations in the late summer, at the end of the melt season. The Airborne Topographic Mapper (ATM) laser altimeter is one of OIB's primary sensors, in combination with the Digital Mapping System digital camera, a Ku-band radar altimeter, a frequency-modulated continuous-wave (FMCW) snow radar, and a KT-19 infrared radiation pyrometer. Data from the campaigns are available to the research community at: http://nsidc.org/data/icebridge/. This presentation will summarize the spatial and temporal extent of the OIB campaigns and their complementary role in linking in situ and satellite measurements, advancing observations of <span class="hlt">sea</span> <span class="hlt">ice</span> processes across all length scales. Key scientific insights gained on the state of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover will be highlighted, including snow depth, <span class="hlt">ice</span> thickness, surface roughness and morphology, and melt pond evolution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/17868292','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/17868292"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> occurrence predicts genetic isolation in the Arctic fox.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Geffen, Eli; Waidyaratne, Sitara; Dalén, Love; Angerbjörn, Anders; Vila, Carles; Hersteinsson, Pall; Fuglei, Eva; White, Paula A; Goltsman, Michael; Kapel, Christian M O; Wayne, Robert K</p> <p>2007-10-01</p> <p>Unlike Oceanic islands, the islands of the Arctic <span class="hlt">Sea</span> are not completely isolated from migration by terrestrial vertebrates. The pack <span class="hlt">ice</span> connects many Arctic <span class="hlt">Sea</span> islands to the mainland during winter months. The Arctic fox (Alopex lagopus), which has a circumpolar distribution, populates numerous islands in the Arctic <span class="hlt">Sea</span>. In this study, we used genetic data from 20 different populations, spanning the entire distribution of the Arctic fox, to identify barriers to dispersal. Specifically, we considered geographical distance, occurrence of <span class="hlt">sea</span> <span class="hlt">ice</span>, winter temperature, ecotype, and the presence of red fox and polar bear as nonexclusive factors that influence the dispersal behaviour of individuals. Using distance-based redundancy analysis and the BIOENV procedure, we showed that occurrence of <span class="hlt">sea</span> <span class="hlt">ice</span> is the key predictor and explained 40-60% of the genetic distance among populations. In addition, our analysis identified the Commander and Pribilof Islands Arctic populations as genetically unique suggesting they deserve special attention from a conservation perspective.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007JGlac..53..490B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007JGlac..53..490B"><span>Sensitivity of ocean circulation and <span class="hlt">sea-ice</span> conditions to loss of West Antarctic <span class="hlt">ice</span> shelves and <span class="hlt">ice</span> sheet</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bougamont, Marion; Hunke, Elizabeth C.; Tulaczyk, Slawek</p> <p></p> <p>We use a global coupled ocean-<span class="hlt">sea</span> <span class="hlt">ice</span> model to test the hypothesis that the disintegration of the West Antarctic <span class="hlt">ice</span> sheet (WAIS), or just its <span class="hlt">ice</span> shelves, may modify ocean circulation and <span class="hlt">sea-ice</span> conditions in the Southern Ocean. We compare the results of three model runs: (1) a control run with a standard (modern) configuration of landmask in West Antarctica, (2) a no-shelves run with West Antarctic <span class="hlt">ice</span> shelves removed and (3) a no-WAIS run. In the latter two runs, up to a few million square kilometres of new <span class="hlt">sea</span> surface area opens to <span class="hlt">sea-ice</span> formation, causing the volume and extent of Antarctic <span class="hlt">sea-ice</span> cover to increase compared with the control run. In general, near-surface waters are cooler around Antarctica in the no-shelves and no-WAIS model runs than in the control run, while warm intermediate and deep waters penetrate further south, increasing poleward heat transport. Varying regional responses to the imposed changes in landmask configuration are determined by the fact that Antarctic polynyas and fast <span class="hlt">ice</span> develop in different parts of the model domain in each run. Model results suggest that changes in the extent of WAIS may modify oceanographic conditions in the Southern Ocean.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014SPIE.9240E..03B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014SPIE.9240E..03B"><span><span class="hlt">Sea-ice</span> distribution and variability in the East Greenland <span class="hlt">Sea</span>, 2003-13</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Boccolari, Mauro; Guerrieri, Lorenzo; Parmiggiani, Fiorigi</p> <p>2014-10-01</p> <p>This study presents an analysis of the <span class="hlt">sea-ice</span> area time series for the East Greenland <span class="hlt">Sea</span> for the period January 2003 - December 2013. The data used are a subset of the Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Concentration data set derived from the observations of the passive microwave sensors AMSR-E and AMSR-2 and produced, on a daily basis, by the Inst. of Environ. Physics of the University of Bremen. The area of interest goes, approximately, from 57°N to 84°N and from 53°W to 15°E. On the basis of previous studies, the parameter <span class="hlt">Sea</span> <span class="hlt">Ice</span> Area as the sum of all pixels whose <span class="hlt">sea</span> <span class="hlt">ice</span> concentration is above 70%, was introduced for measuring <span class="hlt">sea-ice</span> extent. A first survey of the Greenland <span class="hlt">Sea</span> data set showed a large anomaly in year 2012; this anomaly, clearly linked with the transition period from AMSR-E to AMSR-2 when re-sampled SSM/I data were used, was partially corrected with a linear regression procedure. The correlation between monthly mean <span class="hlt">Sea</span> <span class="hlt">Ice</span> Area and other geophysical parameters, like air temperature, surface wind and cloud cover, was further investigated. High anti-correlation coefficients between air temperature, at <span class="hlt">sea</span> level and in five different tropospheric layers, and observed <span class="hlt">ice</span> cover is confirmed. Our analysis shows that the strong decline of Arctic <span class="hlt">sea-ice</span> area in the last 10 years is not observed in the East Greenland <span class="hlt">Sea</span>; this implies that large reductions have occurred in the Canadian and Russian Arctic. This result confirms a hypothesis recently postulated to explain the different <span class="hlt">sea-ice</span> decline in the Arctic and Antarctic regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/9241880','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/9241880"><span><span class="hlt">Sea-ice</span> production and transport of pollutants in the Laptev <span class="hlt">Sea</span>, 1979-1993.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Rigor, I; Colony, R</p> <p>1997-08-25</p> <p>Pollutants such as radionuclides can be incorporated into <span class="hlt">ice</span> formed in shallow waters of the marginal <span class="hlt">seas</span>, by suspension freezing, including frazil- and anchor-<span class="hlt">ice</span> formation. This <span class="hlt">ice</span> thickens through the winter and can survive the summer melt to eventually be pushed into the perennial <span class="hlt">ice</span> zone and thus be transported long distances. After a few years, when the <span class="hlt">ice</span> finally melts, these radionuclides can be re-released in biologically rich waters. We estimate that a mean of 256,000 km2 of <span class="hlt">sea</span> <span class="hlt">ice</span> is produced annually in the shallow water area of the Laptev <span class="hlt">Sea</span> during the October freeze-up and in the flaw lead during winter, accounting for approx. 20% of the total <span class="hlt">ice</span> area fluxing through the Fram Strait per year.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.C44A..06M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.C44A..06M"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> deformation and the <span class="hlt">ice</span> thickness distribution: How novel observations can help to improve models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Martin, T.</p> <p>2012-12-01</p> <p>Several decades of Arctic-wide observational records of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and drift offer great opportunities to validate the <span class="hlt">sea</span> <span class="hlt">ice</span> component of global climate models (GCMs). Recent advancements in air- and space-borne <span class="hlt">ice</span> thickness retrieval add the long missed "3rd dimension" to these observations. However, in order to understand why a model diverges from observations, why the model physics may be inadequate and how to improve these, often observations with great detail rather than great coverage are desired. From a modeler's perspective an ideal set of observations offers the opportunity to build parameterizations directly upon prognostic model variables, such as <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, thickness and drift. However, the dependency on prognostic variables is not a guaranty yet that a parameterization is flexible enough to cope with the rapid changes currently observed in the Arctic. In particular in <span class="hlt">sea</span> <span class="hlt">ice</span> modeling there is a need to revisit parameters and parameterizations dating back to the 1970s to make sure they are still valid. Examples relating to <span class="hlt">sea</span> <span class="hlt">ice</span> mechanics are drag coefficients, <span class="hlt">sea</span> <span class="hlt">ice</span> strength, and deformation. The latter importantly shapes the <span class="hlt">ice</span> thickness distribution (ITD) by ridging thin into thick <span class="hlt">ice</span> thereby compacting the <span class="hlt">ice</span> cover, which opens up leads. Leads in the pack <span class="hlt">ice</span> act like windows in a solid wall: they enable a direct, enhanced exchange between ocean and atmosphere. Most <span class="hlt">sea</span> <span class="hlt">ice</span> models used in GCMs consider an ITD for the computation of the conductive heat flux only in an idealized statistical manner. Currently, few models consider the effect of <span class="hlt">sea</span> <span class="hlt">ice</span> mechanics on the ITD, i.e. advection and deformation. The ITD has lately gained greater attention because advances in both models and observations enable higher spatial resolution furthering the perception that changes in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> characteristics go along with changing <span class="hlt">ice</span> types. In this presentation simulations with a GCM are used to demonstrate the impact of a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014GeoRL..41.3510S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014GeoRL..41.3510S"><span>Twentieth century <span class="hlt">sea-ice</span> trends in the Ross <span class="hlt">Sea</span> from a high-resolution, coastal <span class="hlt">ice</span>-core record</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sinclair, Kate E.; Bertler, Nancy A. N.; Bowen, Melissa M.; Arrigo, Kevin R.</p> <p>2014-05-01</p> <p>We present the first proxy record of <span class="hlt">sea-ice</span> area (SIA) in the Ross <span class="hlt">Sea</span>, Antarctica, from a 130 year coastal <span class="hlt">ice</span>-core record. High-resolution deuterium excess data show prevailing stable SIA from the 1880s until the 1950s, a 2-5% reduction from the mid-1950s to the early-1990s, and a 5% increase after 1993. Additional support for this reconstruction is derived from <span class="hlt">ice</span>-core methanesulphonic acid concentrations and whaling records. While SIA has continued to decline around much of the West Antarctic coastline since the 1950s, concurrent with increasing air and ocean temperatures, the underlying trend is masked in the Ross <span class="hlt">Sea</span> by a switch to positive SIA anomalies since the early-1990s. This increase is associated with a strengthening of southerly winds and the enhanced northward advection of <span class="hlt">sea</span> <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ESASP.740E..46J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ESASP.740E..46J"><span>Newly Formed <span class="hlt">Sea</span> <span class="hlt">Ice</span> in Arctic Leads Monitored by C- and L-Band SAR</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Johansson, A. Malin; Brekke, Camilla; Spreen, Gunnar; King, Jennifer A.; Gerland, Sebastian</p> <p>2016-08-01</p> <p>We investigate the scattering entropy and co-polarization ratio for Arctic lead <span class="hlt">ice</span> using C- and L-band synthetic aperture radar (SAR) satellite scenes. During the Norwegian Young <span class="hlt">sea</span> <span class="hlt">ICE</span> (N-<span class="hlt">ICE</span>2015) cruise campaign overlapping SAR scenes, helicopter borne <span class="hlt">sea</span> <span class="hlt">ice</span> thickness measurements and photographs were collected. We can therefore relate the SAR signal to <span class="hlt">sea</span> <span class="hlt">ice</span> thickness measurements as well as photographs taken of the <span class="hlt">sea</span> <span class="hlt">ice</span>. We show that a combination of scattering and co-polarization ratio values can be used to distinguish young <span class="hlt">ice</span> from open water and surrounding <span class="hlt">sea</span> <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.C41C0477K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.C41C0477K"><span>Coupling a Thermodynamic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Model with WRF</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Krieger, J. R.; Zhang, J.</p> <p>2009-12-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> plays a significant role in shaping the atmospheric dynamics of the Arctic and surrounding regions through the modification of surface characteristics such as surface roughness, heat conductivity, and albedo. These in turn have both thermodynamic impacts on the surface heat budget and direct dynamic impacts on the low-level winds. In numerical atmospheric models, the accurate treatment of <span class="hlt">sea</span> <span class="hlt">ice</span> is therefore of critical importance in producing realistic simulations, not only on the global scale but at local and regional scales as well. However, <span class="hlt">sea</span> <span class="hlt">ice</span> is an often-neglected component of mesoscale meteorological models, many times being treated as just another land cover type without the sufficient complexity necessary to properly characterize its thermodynamic effects. To address this deficiency, we have recently coupled a thermodynamic <span class="hlt">sea</span> <span class="hlt">ice</span> model with the latest version of the Weather Research and Forecasting (WRF) model in order to improve the latter's simulation of <span class="hlt">sea</span> <span class="hlt">ice</span> surface temperatures, and by extension its simulation of Arctic conditions as a whole. A series of case studies was performed in which results from the coupled and unmodified versions of WRF were compared to determine the efficacy of this approach in improving weather simulations along the Beaufort and Chukchi <span class="hlt">Sea</span> coasts in northern Alaska. In addition to surface station data, observations made as part of the SHEBA and SEDNA field campaigns and by two buoys recently deployed in the Beaufort <span class="hlt">Sea</span> were used to verify the model output.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70186594','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70186594"><span>Diminishing <span class="hlt">sea</span> <span class="hlt">ice</span> in the western Arctic Ocean</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Stone, R.S.; Belchansky, G.I.; Drobot, Sheldon; Douglas, D.C.; Levinson, D.H.; Waple, A.M.</p> <p>2004-01-01</p> <p>Since the advent of satellite passive microwave radiometry (1978), variations in <span class="hlt">sea</span> <span class="hlt">ice</span> extent and concentration have been carefully monitored from space. An estimated 7.4% decrease in <span class="hlt">sea</span> <span class="hlt">ice</span> extent has occurred in the last 25 yr (Johannessen et al. 2004), with recent record minima (e.g., Maslanik et al. 1999; Serreze et al. 2003) accounting for much of the decline. Comparisons between the time series of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> melt dynamics and snowmelt dates at the NOAA–CMDL Barrow Observatory (BRW) reveal intriguing correlations.Melt-onset dates over <span class="hlt">sea</span> <span class="hlt">ice</span> (Drobot and Anderson 2001) were cross correlated with the melt-date time series from BRW, and a prominent region of high correlation between snowmelt onset over <span class="hlt">sea</span> <span class="hlt">ice</span> and the BRW record of melt dates was approximately aligned with the climatological center of the Beaufort <span class="hlt">Sea</span> Anticyclone (BSA). The BSA induces anticyclonic <span class="hlt">ice</span> motion in the region, effectively forcing the Beaufort gyre. A weak gyre caused by a breakdown of the BSA diminishes transport of multiyear <span class="hlt">ice</span> into this region (Drobot and Maslanik 2003). Similarly, the annual snow cycle at BRW varies with the position and intensity of the BSA (Stone et al. 2002, their Fig. 6). Thus, variations in the BSA appear to have far-reaching effects on the annual accumulation and subsequent melt of snow over a large region of the western Arctic.A dramatic increase in melt season duration (Belchansky et al. 2004) was also observed within the same region of high correlation between onset of melt over the <span class="hlt">ice</span> pack and snowmelt at BRW (Fig. 5.7). By inference, this suggests linkages between factors that modulate the annual cycle of snow on land and processes that influence melting of snow and <span class="hlt">ice</span> in the western Arctic Ocean.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000101021&hterms=springer&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26Nf%3DPublication-Date%257CBTWN%2B19970101%2B20031231%26N%3D0%26No%3D50%26Ntt%3Dspringer','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000101021&hterms=springer&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26Nf%3DPublication-Date%257CBTWN%2B19970101%2B20031231%26N%3D0%26No%3D50%26Ntt%3Dspringer"><span>Satellite Microwave Radar Observations of Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span>. Chapter 8</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Drinkwater, Mark R.</p> <p>1998-01-01</p> <p>Historical data on Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> extent and concentration have traditionally been derived from visible and near-infrared images acquired by the polar-orbiting National Oceanic and Atmospheric Agency's (NOAA) meteorological satellites, using the Advanced Very High Resolution Radiometer (AVHRR), and more recently by the Defense Meteorological Satellite Program's Operational Linescan System (OLS). The limitation of these systems is that the majority of energy imparted to the Antarctic <span class="hlt">sea-ice</span> system is transferred during b6y fast-moving low pressure systems. Since the Southern Ocean <span class="hlt">sea-ice</span> cover is completely bounded at its lower latitude limit by open ocean, these "polar lows" transport large amounts of moisture (contained in warm air masses) over the outer <span class="hlt">ice</span> cover. The result is that most, if not all, noteworthy periods of wind- and temperature-driven dynamic changes in the <span class="hlt">ice</span> cover are accompanied by periods where the region is blanketed by cloud, and when the atmosphere is inherently more electromagnetically opaque. During storms, the probability with which the area is cloud covered is extremely high, thereby ruling out use of visible or near-infrared images as a practical method of monitoring the associated changes in <span class="hlt">ice</span> conditions. Instead, Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and DMSP Special Sensor Microwave/imager (SSM/I) have been the primary workhorses to build up a microwave record of Antarctic <span class="hlt">sea-ice</span> characteristics. Similar problems, however, occur in passive microwave retrievals of <span class="hlt">sea-ice</span> concentration, and the algorithms are called into question during these periods of change. The influence of water vapor in the atmosphere alone can modify the <span class="hlt">ice</span> concentration retrievals by fractions exceeding 105, and that retrievals of <span class="hlt">ice</span> concentration must compensate for the atmospheric water vapor and liquid water contents.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PolSc...8..385Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PolSc...8..385Y"><span>Photosynthetic characteristics of sinking microalgae under the <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yamamoto, Shinya; Michel, Christine; Gosselin, Michel; Demers, Serge; Fukuchi, Mitsuo; Taguchi, Satoru</p> <p>2014-12-01</p> <p>The photosynthetic characteristics of sinking a microalgal community were studied to compare with the <span class="hlt">ice</span> algal community in the <span class="hlt">sea</span> <span class="hlt">ice</span> and the phytoplankton community in the water column under the <span class="hlt">sea</span> <span class="hlt">ice</span> at the beginning of the light season in the first-year <span class="hlt">sea</span> <span class="hlt">ice</span> ecosystem on the Mackenzie Shelf, in the western Canadian Arctic. The phytoplankton community was collected using a water bottle, whereas the sinking algal community was collected using particle collectors, and the <span class="hlt">ice</span> algal community was obtained by using an <span class="hlt">ice</span>-core sampler from the bottom portion of <span class="hlt">ice</span> core. Photosynthesis versus irradiance (P-E) incubation experiments were conducted on deck to obtain the initial slope (αB) and the maximum photosynthetic rate (PmB) of the three algal communities. The αB and the PmB of the light saturation curve, and chlorophyll a (Chl a) specific absorption coefficient (āph*) between the sinking microalgal community and the <span class="hlt">ice</span> algal community were similar and were distinctly different from the phytoplankton community. The significant linear relationship between αB and PmB, which was obtained among the three groups, may suggest that a photo-acclimation strategy is common for all algal communities under the low light regime of the early season. Although the sinking algal community could be held for the entire duration of deployment at maximum, this community remained photosynthetically active once exposed to light. This response suggests that sinking algal communities can be the seed population, which results in a subsequent phytoplankton bloom under the <span class="hlt">sea</span> <span class="hlt">ice</span> or in a surface layer, as well as representing food for the higher trophic level consumers in the Arctic Ocean even before the receding of the <span class="hlt">sea</span> <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060044030&hterms=Ross&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DRoss','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060044030&hterms=Ross&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DRoss"><span>Ross <span class="hlt">sea</span> <span class="hlt">ice</span> motion, area flux, and deformation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>kwok, Ron</p> <p>2005-01-01</p> <p>The <span class="hlt">sea</span> <span class="hlt">ice</span> motion, area export, and deformation of the Ross <span class="hlt">Sea</span> <span class="hlt">ice</span> cover are examined with satellite passive microwave and RADARSAT observations. The record of high-resolution synthetic aperture radar (SAR) data, from 1998 and 2000, allows the estimation of the variability of <span class="hlt">ice</span> deformation at the small scale (10 km) and to assess the quality of the longer record of passive microwave <span class="hlt">ice</span> motion. Daily and subdaily deformation fields and RADARSAT imagery highlight the variability of motion and deformation in the Ross <span class="hlt">Sea</span>. With the passive microwave <span class="hlt">ice</span> motion, the area export at a flux gate positioned between Cape Adare and Land Bay is estimated. Between 1992 and 2003, a positive trend can be seen in the winter (March-November) <span class="hlt">ice</span> area flux that has a mean of 990 x 103 km2 and ranges from a low of 600 x 103 km2 in 1992 to a peak of 1600 x 103 km2 in 2001. In the mean, the southern Ross <span class="hlt">Sea</span> produces almost twice its own area of <span class="hlt">sea</span> <span class="hlt">ice</span> during the winter. Cross-gate <span class="hlt">sea</span> level pressure (SLP) gradients explain 60% of the variance in the <span class="hlt">ice</span> area flux. A positive trend in this gradient, from reanalysis products, suggests a 'spinup' of the Ross <span class="hlt">Sea</span> Gyre over the past 12 yr. In both the NCEP-NCAR and ERA-40 surface pressure fields, longer-term trends in this gradient and mean SLP between 1979 and 2002 are explored along with positive anomalies in the monthly cross-gate SLP gradient associated with the positive phase of the Southern Hemisphere annular mode and the extrapolar Southern Oscillation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016SPIE.9877E..2GS','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016SPIE.9877E..2GS"><span>Satellite information of <span class="hlt">sea</span> <span class="hlt">ice</span> for model validation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Saheed, P. P.; Mitra, Ashis K.; Momin, Imranali M.; Mahapatra, Debasis K.; Rajagopal, E. N.</p> <p>2016-05-01</p> <p>Emergence of extensively large computational facilities have enabled the scientific world to use earth system models for understating the prevailing dynamics of the earth's atmosphere, ocean and cryosphere and their inter relations. The <span class="hlt">sea</span> <span class="hlt">ice</span> in the arctic and the Antarctic has been identified as one of the main proxies to study the climate changes. The rapid <span class="hlt">sea-ice</span> melting in the Arctic and disappearance of multi-year <span class="hlt">sea</span> <span class="hlt">ice</span> has become a matter of concern. The earth system models couple the ocean, atmosphere and <span class="hlt">sea-ice</span> in order to bring out the possible inter connections between these three very important components and their role in the changing climate. The Indian monsoon is seen to be subjected to nonlinear changes in the recent years. The rapid <span class="hlt">ice</span> melt in the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> is apparently linked to the changes in the weather and climate of the Indian subcontinent. The recent findings reveal the relation between the high events occurs in the Indian subcontinent and the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> melt episodes. The coupled models are being used in order to study the depth of these relations. However, the models have to be validated extensively by using measured parameters. The satellite measurements of <span class="hlt">sea-ice</span> starts from way back in 1979. There have been many data sets available since then. Here in this study, an evaluation of the existing data sets is conducted. There are some uncertainties in these data sets. It could be associated with the absence of a single sensor for a long period of time and also the absence of accurate in-situ measurements in order to validate the satellite measurements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/12686207','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/12686207"><span><span class="hlt">Ice</span> binding, recrystallization inhibition, and cryoprotective properties of <span class="hlt">ice</span>-active substances associated with Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> diatoms.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Raymond, James A; Knight, Charles A</p> <p>2003-04-01</p> <p>Extracellular macromolecules associated with Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> diatoms were previously shown to have <span class="hlt">ice</span>-binding activities. The function of these <span class="hlt">ice</span>-active substances (IASs) has not been identified. Here we show that two of the IASs have a strong ability to inhibit the recrystallization of <span class="hlt">ice</span>, possibly signifying a cryoprotectant function. To test this possibility, two species of marine diatom (one Antarctic and one temperate) were subjected to a single freeze-thaw cycle (approximately 20h at -4 or -5 degrees C) in the presence or absence of IAS. Viability, based on a double staining technique, was 15-29% higher in the presence of IAS. Etching of single crystal <span class="hlt">ice</span> hemispheres grown from dilute IAS solutions indicated that the IASs bind to specific faces of <span class="hlt">ice</span> and are incorporated into the <span class="hlt">ice</span> lattice. Together, these results suggest that the IASs acts as a cryoprotectant, probably through some <span class="hlt">ice</span>-binding mechanism.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JGRC..121.2659H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JGRC..121.2659H"><span>Recent changes in <span class="hlt">sea</span> <span class="hlt">ice</span> area flux through the Beaufort <span class="hlt">Sea</span> during the summer</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Howell, Stephen E. L.; Brady, Michael; Derksen, Chris; Kelly, Richard E. J.</p> <p>2016-04-01</p> <p>Over the annual cycle, <span class="hlt">sea</span> <span class="hlt">ice</span> is sequestered from the Canadian Basin and transported through the Beaufort <span class="hlt">Sea</span> toward the Chukchi <span class="hlt">Sea</span>. In recent years, the Beaufort <span class="hlt">Sea</span> has experienced considerable <span class="hlt">ice</span> loss during the summer, which may be indicative of recent changes to this process. In order to investigate this, we quantify the <span class="hlt">sea</span> <span class="hlt">ice</span> area flux using RADARSAT from 1997 to 2014 at three gates in Beaufort <span class="hlt">Sea</span>: the Canadian Basin (entrance), mid-Beaufort (midpoint), and Chukchi (exit). There was a mean annual flux of 42 ± 56 × 103 km2 at the Canadian Basin gate, 94 ± 92 × 103 km2 at the mid-Beaufort gate and -83 ± 68 × 103 km2 at the Chukchi gate (positive and negative flux signs correspond to <span class="hlt">ice</span> inflow and outflow, respectively). The majority of <span class="hlt">ice</span> transport in Beaufort <span class="hlt">Sea</span> was found to occur from October to May providing replenishment for <span class="hlt">ice</span> lost during the summer months. The cross-strait gradient in <span class="hlt">sea</span> level pressure explains ˜40% of the variance in the <span class="hlt">ice</span> area flux at the gates. Remarkably, the mean July-October net <span class="hlt">sea</span> <span class="hlt">ice</span> area flux at the Chukchi gate decreased by ˜80% from 2008 to 2014 relative to 1997-2007 and became virtually <span class="hlt">ice</span>-free every year since 2008. This reduction was associated with younger (thinner) <span class="hlt">ice</span> that was unable to survive the summer melt season when either being sequestered from the Canadian Basin and transported through Beaufort <span class="hlt">Sea</span> during the melt season (2008-2011) or remaining immobile and present in the vicinity of the Chukchi gate during the melt season (2012-2014).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/107838','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/107838"><span>Eastern-western Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> analysis, 1993</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p></p> <p>1993-12-31</p> <p>This publication is the 20th edition of the annual Arctic <span class="hlt">sea-ice</span> atlases prepared by the JIC. The atlas contains weekly charts depicting Northern Hemisphere <span class="hlt">ice</span> conditions and extent. The significant use of high resolution satellite imagery, combined with valuable <span class="hlt">ice</span> reconnaissance data from various sources, has greatly improved the accuracy of these analyses. The purpose of this atlas is to provide the user with reliable weekly hemispheric <span class="hlt">ice</span> analyses. These charts are prepared by experienced Navy and NOAA <span class="hlt">ice</span> analysts who plot and evaluate numerous data sources: (a) Conventional shore station, ship, and aerial reconnaissance observations; and (b) Satellite data from various sensors. Table I, located on the inside back cover, lists these sensors and their availability. A final product is synthesized from the inputs described above. When insufficient data is available, estimated boundaries are plotted, using meteorological data and computer generated <span class="hlt">ice</span> drift vectors to determine estimated <span class="hlt">ice</span> position.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19950051821&hterms=microwaves+work&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dmicrowaves%2Bwork','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19950051821&hterms=microwaves+work&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dmicrowaves%2Bwork"><span>A microwave technique for mapping thin <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cavalieri, Donald J.</p> <p>1994-01-01</p> <p>A technique is presented for mapping the distribution of new, young and first-year <span class="hlt">sea</span> <span class="hlt">ice</span> in seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> zones that utilizes microwave spectral and polarization information from the Defense Meteorological Satellite Program Special Sensor Microwave/Imager (DMSP SSM/I). The motivation for this work stems from the need for accurate estimates of open water and thin <span class="hlt">ice</span> within the Arctic <span class="hlt">ice</span> pack. The technique utilizes the microwave polarization and spectral characteristics of these three <span class="hlt">ice</span> types through two microwave radiance ratios: the 19.4 GHz polarization and the spectral gradient ratio, which is a measure of the spectral difference between the 19.4-GHz and the 37.0-GHz vertically polarized radiance components. The combined use of the spectral gradient ratio and polarization reduces the low <span class="hlt">ice</span> concentration bias generally associated with the presence of thin <span class="hlt">ice</span> types. The microwave polarization, which is sensitive to changes in <span class="hlt">ice</span> thickness and <span class="hlt">ice</span> surface characteristics, is used to classify new, young, and first-year <span class="hlt">ice</span> types.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000101018&hterms=Continental+Drift&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DContinental%2BDrift','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000101018&hterms=Continental+Drift&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DContinental%2BDrift"><span>Active Microwave Remote Sensing Observations of Weddell <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Drinkwater, Mark R.</p> <p>1997-01-01</p> <p>Since July 1991, the European Space Agency's ERS-1 and ERS-2 satellites have acquired radar data of the Weddell <span class="hlt">Sea</span>, Antarctica. The Active Microwave Instrument on board ERS has two modes; SAR and Scatterometer. Two receiving stations enable direct downlink and recording of high bit-rate, high resolution SAR image data of this region. When not in an imaging mode, when direct SAR downlink is not possible, or when a receiving station is inoperable, the latter mode allows normalized radar cross-section data to be acquired. These low bit-rate ERS scatterometer data are tape recorded, downlinked and processed off-line. Recent advances in image generation from Scatterometer backscatter measurements enable complementary medium-scale resolution images to be made during periods when SAR images cannot be acquired. Together, these combined C-band microwave image data have for the first time enabled uninterrupted night and day coverage of the Weddell <span class="hlt">Sea</span> region at both high (25 m) and medium-scale (-20 km) resolutions. C-band ERS-1 radar data are analyzed in conjunction with field data from two simultaneous field experiments in 1992. Satellite radar signature data are compared with shipborne radar data to extract a regional and seasonal signature database for recognition of <span class="hlt">ice</span> types in the images. Performance of automated <span class="hlt">sea-ice</span> tracking algorithms is tested on Antarctic data to evaluate their success. Examples demonstrate that both winter and summer <span class="hlt">ice</span> can be effectively tracked. The kinematics of the main <span class="hlt">ice</span> zones within the Weddell <span class="hlt">Sea</span> are illustrated, together with the complementary time-dependencies in their radar signatures. Time-series of satellite images are used to illustrate the development of the Weddell <span class="hlt">Sea</span> <span class="hlt">ice</span> cover from its austral summer minimum (February) to its winter maximum (September). The combination of time-dependent microwave signatures and <span class="hlt">ice</span> dynamics tracking enable various drift regimes to be defined which relate closely to the circulation of the</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_19 --> <div id="page_20" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="381"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27435531','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27435531"><span>Biopolymers form a gelatinous microlayer at the air-<span class="hlt">sea</span> interface when Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> melts.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Galgani, Luisa; Piontek, Judith; Engel, Anja</p> <p>2016-07-20</p> <p>The interface layer between ocean and atmosphere is only a couple of micrometers thick but plays a critical role in climate relevant processes, including the air-<span class="hlt">sea</span> exchange of gas and heat and the emission of primary organic aerosols (POA). Recent findings suggest that low-level cloud formation above the Arctic Ocean may be linked to organic polymers produced by marine microorganisms. <span class="hlt">Sea</span> <span class="hlt">ice</span> harbors high amounts of polymeric substances that are produced by cells growing within the <span class="hlt">sea-ice</span> brine. Here, we report from a research cruise to the central Arctic Ocean in 2012. Our study shows that microbial polymers accumulate at the air-<span class="hlt">sea</span> interface when the <span class="hlt">sea</span> <span class="hlt">ice</span> melts. Proteinaceous compounds represented the major fraction of polymers supporting the formation of a gelatinous interface microlayer and providing a hitherto unrecognized potential source of marine POA. Our study indicates a novel link between <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean and atmosphere that may be sensitive to climate change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016NatSR...629465G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016NatSR...629465G"><span>Biopolymers form a gelatinous microlayer at the air-<span class="hlt">sea</span> interface when Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> melts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Galgani, Luisa; Piontek, Judith; Engel, Anja</p> <p>2016-07-01</p> <p>The interface layer between ocean and atmosphere is only a couple of micrometers thick but plays a critical role in climate relevant processes, including the air-<span class="hlt">sea</span> exchange of gas and heat and the emission of primary organic aerosols (POA). Recent findings suggest that low-level cloud formation above the Arctic Ocean may be linked to organic polymers produced by marine microorganisms. <span class="hlt">Sea</span> <span class="hlt">ice</span> harbors high amounts of polymeric substances that are produced by cells growing within the <span class="hlt">sea-ice</span> brine. Here, we report from a research cruise to the central Arctic Ocean in 2012. Our study shows that microbial polymers accumulate at the air-<span class="hlt">sea</span> interface when the <span class="hlt">sea</span> <span class="hlt">ice</span> melts. Proteinaceous compounds represented the major fraction of polymers supporting the formation of a gelatinous interface microlayer and providing a hitherto unrecognized potential source of marine POA. Our study indicates a novel link between <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean and atmosphere that may be sensitive to climate change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20080036090&hterms=sea+boundaries&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dsea%2Bboundaries','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20080036090&hterms=sea+boundaries&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dsea%2Bboundaries"><span>Ross <span class="hlt">Sea</span> Polynyas: Response of <span class="hlt">Ice</span> Concentration Retrievals to Large Areas of Thin <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kwok, R.; Comiso, J. C.; Martin, S.; Drucker, R.</p> <p>2007-01-01</p> <p>For a 3-month period between May and July of 2005, we examine the response of the Advanced Microwave Scanning Radiometer (AMSR-E) Enhanced NASA Team 2 (NT2) and AMSR-E Bootstrap (ABA) <span class="hlt">ice</span> concentration algorithms to large areas of thin <span class="hlt">ice</span> of the Ross <span class="hlt">Sea</span> polynyas. Coincident Envisat Synthetic Aperture Radar (SAR) coverage of the region during this period offers a detailed look at the development of the polynyas within several hundred kilometers of the <span class="hlt">ice</span> front. The high-resolution imagery and derived <span class="hlt">ice</span> motion fields show bands of polynya <span class="hlt">ice</span>, covering up to approximately 105 km(sup 2) of the Ross <span class="hlt">Sea</span>, that are associated with wind-forced advection. In this study, <span class="hlt">ice</span> thickness from AMSR-E 36 GHz polarization information serves as the basis for examination of the response. The quality of the thickness of newly formed <span class="hlt">sea</span> <span class="hlt">ice</span> (<10 cm) from AMSR-E is first assessed with thickness estimates derived from <span class="hlt">ice</span> surface temperatures from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. The effect of large areas of thin <span class="hlt">ice</span> in lowering the <span class="hlt">ice</span> concentration estimates from both NT2/ABA approaches is clearly demonstrated. Results show relatively robust relationships between retrieved <span class="hlt">ice</span> concentrations and thin <span class="hlt">ice</span> thickness estimates that differ between the two algorithms. These relationships define the approximate spatial coincidence of <span class="hlt">ice</span> concentration and thickness isopleths. Using the 83% (ABA) and 91% (NT2) isopleths as polynya boundaries, we show that the computed coverage compares well with that using the estimated 10-cm thickness contour. The thin <span class="hlt">ice</span> response characterized here suggests that in regions with polynyas, the retrieval results could be used to provide useful geophysical information, namely thickness and coverage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C13C..05M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C13C..05M"><span>Surface and basal <span class="hlt">sea</span> <span class="hlt">ice</span> melt from autonomous buoy arrays during the 2014 <span class="hlt">sea</span> <span class="hlt">ice</span> retreat in the Beaufort/Chukchi <span class="hlt">Seas</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maksym, T. L.; Wilkinson, J.; Hwang, P. B.</p> <p>2014-12-01</p> <p>As the Arctic continues its transition to a seasonal <span class="hlt">ice</span> cover, the nature and role of the processes driving <span class="hlt">sea</span> <span class="hlt">ice</span> retreat are expected to change. Key questions revolve around how the coupling between dynamics and thermodynamic processes and potential changes in the role of melt ponds contribute to an accelerated seasonal <span class="hlt">ice</span> retreat. To address these issues, 44 autonomous platforms were deployed in four arrays in the Beaufort <span class="hlt">Sea</span> in March, 2014, with an additional array deployed in August in the Chukchi <span class="hlt">Sea</span> to monitor the evolution of <span class="hlt">ice</span> conditions during the seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> retreat. Each "5-dice" array included four or five co-sited <span class="hlt">ice</span> mass balance buoys (IMB) and wave buoys with digital cameras, and one automatic weather station (AWS) at the array center. The sensors on these buoys, combined with satellite imagery monitoring the large-scale evolution of the <span class="hlt">ice</span> cover, provide a near-complete history of the processes involved in the seasonal melt of <span class="hlt">sea</span> <span class="hlt">ice</span>. We present a preliminary analysis of the contributions of several key processes to the seasonal <span class="hlt">ice</span> decay. The evolution of surface ponding was observed at several sites with differing <span class="hlt">ice</span> types and surface morphologies. The records of surface melt and <span class="hlt">ice</span> thickness demonstrate a key role of <span class="hlt">ice</span> type in driving the evolution of the <span class="hlt">ice</span> cover. Analysis of the surface forcing and estimates of solar energy partitioning between the surface and upper ocean is compared to the surface and basal mass balance from the IMBs. The role of <span class="hlt">ice</span> divergence and deformation in driving <span class="hlt">sea</span> <span class="hlt">ice</span> decay - in particular its role in accelerating thermodynamic melt processes - is discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/400792','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/400792"><span>Self-excited oscillations in <span class="hlt">sea</span> <span class="hlt">ice</span> and evaluation of the <span class="hlt">ice</span> forces</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Smirnov, V.N.</p> <p>1996-12-01</p> <p>The processes of the dynamical interactions of the <span class="hlt">ice</span> are described. Special attention is given to the self-excited oscillating processes when the <span class="hlt">ice</span> goes through periodical deformations similar to these which appear in the structures in the <span class="hlt">sea</span> <span class="hlt">ice</span>. The range of the self-excited oscillations periods is from 0.1 s to 20 s. The jump-like processes transform themselves into the quasi-harmonical ones up to sound frequency range. For comparison purposes the spectra of the frictional self-excited oscillations in the glaciers are presented. An iceberg interacting with the drifting <span class="hlt">ice</span> also forms self-excited oscillating system with period of up to 10 s. An example of numerical evaluation of the forces of interaction between a drifting <span class="hlt">ice</span> island and the <span class="hlt">sea</span> <span class="hlt">ice</span> is given.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19425351','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19425351"><span>[Comparative analysis of <span class="hlt">sea-ice</span> diatom species composition in the <span class="hlt">seas</span> of Russian Arctic].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Il'iash, L V; Zhitina, L S</p> <p>2009-01-01</p> <p>Comparative analysis of species composition of <span class="hlt">ice</span> diatom algae (IDA) of the White, Barents, Kara, Laptev, East Siberian, Chukchi <span class="hlt">Seas</span> and the Basin of the Arctic Ocean was conducted on the basis of both original and published data. Species composition of IDA counts 567 taxa including 122 centric and 446 pennate diatoms. The freshwater algae composed about 18% of the total species number. In the White <span class="hlt">Sea</span>, IDA were the most numerous (272 taxa), in the Kara <span class="hlt">Sea</span> they are the least numerous (57 taxa). The species compositions in different <span class="hlt">seas</span> differ significantly from each other. Similarity of IDA was consistent with the Arctic Ocean circulation and <span class="hlt">ice</span> drift. IDA of Chukchi, East Siberian and Laptev <span class="hlt">Seas</span> are the most similar, as are IDA of White and Kara <span class="hlt">Seas</span>. Similarity of IDA of Chukchi <span class="hlt">Sea</span> to those of other <span class="hlt">seas</span> decrease in the west direction. IDA species differences between regions within one <span class="hlt">sea</span> could be greater than those between different <span class="hlt">seas</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C23B0403B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C23B0403B"><span><span class="hlt">Ice</span> loss from West Antarctica to the Bellingshausen <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bingham, R. G.; Smith, A.; King, E. C.; Gudmundsson, G. H.; Thomas, E. R.</p> <p>2014-12-01</p> <p>Determination of Antarctica's <span class="hlt">ice</span>-sheet mass balance (more correctly, mass imbalance) is of paramount concern due to its impact on global <span class="hlt">sea</span> levels. Monitoring with satellite remote sensing since the early 1990s has demonstrated that the imbalance has become progressively more negative, with losses dominated by the ocean-forced drawdown of <span class="hlt">ice</span> from West Antarctica into the Amundsen and Bellingshausen <span class="hlt">Seas</span>. Recent years have hosted unprecedented study and increased understanding of the <span class="hlt">ice</span>-ocean processes contributing to Amundsen-<span class="hlt">Sea</span> losses, leaving ocean-forced <span class="hlt">ice</span>-dynamical losses to the Bellingshausen <span class="hlt">Sea</span> relatively neglected. We therefore present here, with the aid of dedicated field data in austral season 2009/2010, a detailed assessment of the mass imbalance of Ferrigno <span class="hlt">Ice</span> Stream (FIS), the dominant contributor of mass directly to the Bellingshausen <span class="hlt">Sea</span>. We assess mass imbalance using the input-output method for (i) 1992, and (ii) 2010; the temporal markers being defined by the acquisition of the first comprehensive satellite-velocity coverage and the acquisition of the field measurements respectively. Input by snowfall is estimated using existing maps of Antarctic snow accumulation calibrated with 2010-acquired field data in the form of a 20-m <span class="hlt">ice</span> core recovered at the upper FIS <span class="hlt">ice</span> divide and englacial layering across the catchment imaged with 500 MHz over-snow radar. Output by discharge across the grounding line requires measurements of <span class="hlt">ice</span> velocity and depth across a "flux gate." In 2010, we obtained flux gate measurements directly from the field using DGPS and 2 MHz over-snow radar, and we also refer to satellite-acquired <span class="hlt">ice</span>-velocity data (MeASUREs) and airborne-acquired <span class="hlt">ice</span> depths (Operation <span class="hlt">Ice</span>Bridge) acquired at a similar time. Output from 1992 is calculated using 1992-acquired satellite <span class="hlt">ice</span>-velocities (Rignot, 2006) and <span class="hlt">ice</span> depth retroactively inferred from the 2010-acquired <span class="hlt">ice</span> depth corrected for 1992-2010 surface elevation loss. We calculate</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19830043095&hterms=cinematography&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dcinematography','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19830043095&hterms=cinematography&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dcinematography"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> motion measurements from Seasat SAR images</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Leberl, F.; Raggam, J.; Elachi, C.; Campbell, W. J.</p> <p>1983-01-01</p> <p>Data from the Seasat synthetic aperture radar (SAR) experiment are analyzed in order to determine the accuracy of this information for mapping the distribution of <span class="hlt">sea</span> <span class="hlt">ice</span> and its motion. Data from observations of <span class="hlt">sea</span> <span class="hlt">ice</span> in the Beaufort <span class="hlt">Sea</span> from seven sequential orbits of the satellite were selected to study the capabilities and limitations of spaceborne radar application to <span class="hlt">sea-ice</span> mapping. Results show that there is no difficulty in identifying homologue <span class="hlt">ice</span> features on sequential radar images and the accuracy is entirely controlled by the accuracy of the orbit data and the geometric calibration of the sensor. Conventional radargrammetric methods are found to serve well for satellite radar <span class="hlt">ice</span> mapping, while ground control points can be used to calibrate the <span class="hlt">ice</span> location and motion measurements in the cases where orbit data and sensor calibration are lacking. The <span class="hlt">ice</span> motion was determined to be approximately 6.4 + or - 0.5 km/day. In addition, the accuracy of pixel location was found over land areas. The use of one control point in 10,000 sq km produced an accuracy of about + or 150 m, while with a higher density of control points (7 in 1000 sq km) the location accuracy improves to the image resolution of + or - 25 m. This is found to be applicable for both optical and digital data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19850053020&hterms=arctic+territories&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Darctic%2Bterritories','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19850053020&hterms=arctic+territories&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Darctic%2Bterritories"><span>Active microwave measurements of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> under summer conditions</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Onstott, R. G.; Gogineni, S. P.</p> <p>1985-01-01</p> <p>Radar provides a valuable tool in the study of <span class="hlt">sea-ice</span> conditions and the solution of <span class="hlt">sea-ice</span> operational problems. For this reason, the U.S. and Canada have conducted studies to define a bilateral synthetic aperture radar (SAR) satellite program. The present paper is concerned with work which has been performed to explore the needs associated with the study of <span class="hlt">sea-ice</span>-covered waters. The design of a suitable research or operational spaceborne SAR or real aperture radar must be based on an adequate knowledge of the backscatter coefficients of the <span class="hlt">ice</span> features which are of interest. In order to obtain the needed information, studies involving the use of a helicopter were conducted. In these studies L-C-X-Ku-band calibrated radar data were acquired over areas of Arctic first-year and multiyear <span class="hlt">ice</span> during the first half of the summer of 1982. The results show that the microwave response in the case of <span class="hlt">sea</span> <span class="hlt">ice</span> is greatly influenced by summer melt, which produces significant changes in the properties of the snowpack and <span class="hlt">ice</span> sheet.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26032323','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26032323"><span>Regional variability in <span class="hlt">sea</span> <span class="hlt">ice</span> melt in a changing Arctic.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Perovich, Donald K; Richter-Menge, Jacqueline A</p> <p>2015-07-13</p> <p>In recent years, the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover has undergone a precipitous decline in summer extent. The <span class="hlt">sea</span> <span class="hlt">ice</span> mass balance integrates heat and provides insight on atmospheric and oceanic forcing. The amount of surface melt and bottom melt that occurs during the summer melt season was measured at 41 sites over the time period 1957 to 2014. There are large regional and temporal variations in both surface and bottom melting. Combined surface and bottom melt ranged from 16 to 294 cm, with a mean of 101 cm. The mean <span class="hlt">ice</span> equivalent surface melt was 48 cm and the mean bottom melt was 53 cm. On average, surface melting decreases moving northward from the Beaufort <span class="hlt">Sea</span> towards the North Pole; however interannual differences in atmospheric forcing can overwhelm the influence of latitude. Substantial increases in bottom melting are a major contributor to <span class="hlt">ice</span> losses in the Beaufort <span class="hlt">Sea</span>, due to decreases in <span class="hlt">ice</span> concentration. In the central Arctic, surface and bottom melting demonstrate interannual variability, but show no strong temporal trends from 2000 to 2014. This suggests that under current conditions, summer melting in the central Arctic is not large enough to completely remove the <span class="hlt">sea</span> <span class="hlt">ice</span> cover.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20160004954&hterms=sea+ice&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dsea%2Bice','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20160004954&hterms=sea+ice&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dsea%2Bice"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Simulation in the PlioMIP Ensemble</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Howell, Fergus W.; Haywood, Alan M.; Otto-Bliesner, Bette L.; Bragg, Fran; Chan, Wing-Le; Chandler, Mark A.; Contoux, Camille; Kamae, Youichi; Abe-Ouchi, Ayako; Rosenbloom, Nan A.; Stepanek, Christian; Zhang, Zhongshi</p> <p>2016-01-01</p> <p>Eight general circulation models have simulated the mid-Pliocene warm period (mid-Pliocene, 3.264 to 3.025 Ma) as part of the Pliocene Modelling Intercomparison Project (PlioMIP). Here, we analyse and compare their simulation of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> for both the pre-industrial period and the mid-Pliocene. Mid-Pliocene <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and extent is reduced, and the model spread of extent is more than twice the pre-industrial spread in some summer months. Half of the PlioMIP models simulate <span class="hlt">ice</span>-free conditions in the mid-Pliocene. This spread amongst the ensemble is in line with the uncertainties amongst proxy reconstructions for mid-Pliocene <span class="hlt">sea</span> <span class="hlt">ice</span> extent. Correlations between mid-Pliocene Arctic temperatures and <span class="hlt">sea</span> <span class="hlt">ice</span> extents are almost twice as strong as the equivalent correlations for the pre-industrial simulations. The need for more comprehensive <span class="hlt">sea</span> <span class="hlt">ice</span> proxy data is highlighted, in order to better compare model performances.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120010403','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120010403"><span>Satellite Observations of Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Thickness and Volume</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kurtz, Nathan; Markus, Thorsten</p> <p>2012-01-01</p> <p>We utilize satellite laser altimetry data from ICESat combined with passive microwave measurements to analyze basin-wide changes in Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and volume over a 5 year period from 2003-2008. <span class="hlt">Sea</span> <span class="hlt">ice</span> thickness exhibits a small negative trend while area increases in the summer and fall balanced losses in thickness leading to small overall volume changes. Using a five year time-series, we show that only small <span class="hlt">ice</span> thickness changes of less than -0.03 m/yr and volume changes of -266 cu km/yr and 160 cu km/yr occurred for the spring and summer periods, respectively. The calculated thickness and volume trends are small compared to the observational time period and interannual variability which masks the determination of long-term trend or cyclical variability in the <span class="hlt">sea</span> <span class="hlt">ice</span> cover. These results are in stark contrast to the much greater observed losses in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> volume and illustrate the different hemispheric changes of the polar <span class="hlt">sea</span> <span class="hlt">ice</span> covers in recent years.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4455714','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4455714"><span>Regional variability in <span class="hlt">sea</span> <span class="hlt">ice</span> melt in a changing Arctic</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Perovich, Donald K.; Richter-Menge, Jacqueline A.</p> <p>2015-01-01</p> <p>In recent years, the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover has undergone a precipitous decline in summer extent. The <span class="hlt">sea</span> <span class="hlt">ice</span> mass balance integrates heat and provides insight on atmospheric and oceanic forcing. The amount of surface melt and bottom melt that occurs during the summer melt season was measured at 41 sites over the time period 1957 to 2014. There are large regional and temporal variations in both surface and bottom melting. Combined surface and bottom melt ranged from 16 to 294 cm, with a mean of 101 cm. The mean <span class="hlt">ice</span> equivalent surface melt was 48 cm and the mean bottom melt was 53 cm. On average, surface melting decreases moving northward from the Beaufort <span class="hlt">Sea</span> towards the North Pole; however interannual differences in atmospheric forcing can overwhelm the influence of latitude. Substantial increases in bottom melting are a major contributor to <span class="hlt">ice</span> losses in the Beaufort <span class="hlt">Sea</span>, due to decreases in <span class="hlt">ice</span> concentration. In the central Arctic, surface and bottom melting demonstrate interannual variability, but show no strong temporal trends from 2000 to 2014. This suggests that under current conditions, summer melting in the central Arctic is not large enough to completely remove the <span class="hlt">sea</span> <span class="hlt">ice</span> cover. PMID:26032323</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016CliPa..12..749H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016CliPa..12..749H"><span>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> simulation in the PlioMIP ensemble</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Howell, Fergus W.; Haywood, Alan M.; Otto-Bliesner, Bette L.; Bragg, Fran; Chan, Wing-Le; Chandler, Mark A.; Contoux, Camille; Kamae, Youichi; Abe-Ouchi, Ayako; Rosenbloom, Nan A.; Stepanek, Christian; Zhang, Zhongshi</p> <p>2016-03-01</p> <p>Eight general circulation models have simulated the mid-Pliocene warm period (mid-Pliocene, 3.264 to 3.025 Ma) as part of the Pliocene Modelling Intercomparison Project (PlioMIP). Here, we analyse and compare their simulation of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> for both the pre-industrial period and the mid-Pliocene. Mid-Pliocene <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and extent is reduced, and the model spread of extent is more than twice the pre-industrial spread in some summer months. Half of the PlioMIP models simulate <span class="hlt">ice</span>-free conditions in the mid-Pliocene. This spread amongst the ensemble is in line with the uncertainties amongst proxy reconstructions for mid-Pliocene <span class="hlt">sea</span> <span class="hlt">ice</span> extent. Correlations between mid-Pliocene Arctic temperatures and <span class="hlt">sea</span> <span class="hlt">ice</span> extents are almost twice as strong as the equivalent correlations for the pre-industrial simulations. The need for more comprehensive <span class="hlt">sea</span> <span class="hlt">ice</span> proxy data is highlighted, in order to better compare model performances.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C41A0691C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C41A0691C"><span>Decadal variability of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> in the Canada Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Connor, L. N.</p> <p>2015-12-01</p> <p>A series of spring Arctic flight campaigns surveying a region over the Canada Basin, from 2006 to 2015, has resulted in unique observations that reveal new details of <span class="hlt">sea</span> <span class="hlt">ice</span> leads and freeboard evolution, during a decade of significant interannual variability in the Arctic <span class="hlt">ice</span> cover. The series began in 2006 with a joint NASA/NOAA airborne altimetry campaign over a 1300 km survey line northwest of the Canadian Archipelago extending into the northern Beaufort <span class="hlt">Sea</span>. Operation <span class="hlt">Ice</span>Bridge (OIB) took up this flight line again in 2009 and repeated it annually through 2012. Additional observations have been collected along a 1000+ km flight line, in the southern Canada Basin and eastern Beaufort <span class="hlt">Sea</span>, between 2009 and 2015. Here we examine laser altimetry, snow radar data, and high-resolution visible imagery to better understand the frequency and distribution of leads and <span class="hlt">ice</span> floes, the characteristics of first- and multi-year <span class="hlt">ice</span> types in the survey region, and their impact on the derivation and accuracy of <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard. We demonstrate a novel lead detection methodology that depends only upon laser altimeter measurements, and we quantify the impact of low lead frequencies on estimates of instantaneous <span class="hlt">sea</span> surface height. The analysis reveals a variable springtime freeboard north of 78° N, significantly reduced after 2006, and a notable lead outbreak over the Canada Basin during 2010.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70040743','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70040743"><span>Walrus areas of use in the Chukchi <span class="hlt">Sea</span> during sparse <span class="hlt">sea</span> <span class="hlt">ice</span> cover</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Jay, Chadwick V.; Fischbach, Anthony S.; Kochnev, Anatoly A.</p> <p>2012-01-01</p> <p>The Pacific walrus Odobenus rosmarus divergens feeds on benthic invertebrates on the continental shelf of the Chukchi and Bering <span class="hlt">Seas</span> and rests on <span class="hlt">sea</span> <span class="hlt">ice</span> between foraging trips. With climate warming, <span class="hlt">ice</span>-free periods in the Chukchi <span class="hlt">Sea</span> have increased and are projected to increase further in frequency and duration. We radio-tracked walruses to estimate areas of walrus foraging and occupancy in the Chukchi <span class="hlt">Sea</span> from June to November of 2008 to 2011, years when <span class="hlt">sea</span> <span class="hlt">ice</span> was sparse over the continental shelf in comparison to historical records. The earlier and more extensive <span class="hlt">sea</span> <span class="hlt">ice</span> retreat in June to September, and delayed freeze-up of <span class="hlt">sea</span> <span class="hlt">ice</span> in October to November, created conditions for walruses to arrive earlier and stay later in the Chukchi <span class="hlt">Sea</span> than in the past. The lack of <span class="hlt">sea</span> <span class="hlt">ice</span> over the continental shelf from September to October caused walruses to forage in nearshore areas instead of offshore areas as in the past. Walruses did not frequent the deep waters of the Arctic Basin when <span class="hlt">sea</span> <span class="hlt">ice</span> retreated off the shelf. Walruses foraged in most areas they occupied, and areas of concentrated foraging generally corresponded to regions of high benthic biomass, such as in the northeastern (Hanna Shoal) and southwestern Chukchi <span class="hlt">Sea</span>. A notable exception was the occurrence of concentrated foraging in a nearshore area of northwestern Alaska that is apparently depauperate in walrus prey. With increasing <span class="hlt">sea</span> <span class="hlt">ice</span> loss, it is likely that walruses will increase their use of coastal haul-outs and nearshore foraging areas, with consequences to the population that are yet to be understood.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70159860','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70159860"><span>Increased land use by Chukchi <span class="hlt">Sea</span> polar bears in relation to changing <span class="hlt">sea</span> <span class="hlt">ice</span> conditions</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Rode, Karyn D.; Wilson, Ryan R.; Regehr, Eric V.; St. Martin, Michelle; Douglas, David; Olson, Jay</p> <p>2015-01-01</p> <p>Recent observations suggest that polar bears (Ursus maritimus) are increasingly using land habitats in some parts of their range, where they have minimal access to their preferred prey, likely in response to loss of their <span class="hlt">sea</span> <span class="hlt">ice</span> habitat associated with climatic warming. We used location data from female polar bears fit with satellite radio collars to compare land use patterns in the Chukchi <span class="hlt">Sea</span> between two periods (1986–1995 and 2008–2013) when substantial summer <span class="hlt">sea-ice</span> loss occurred. In both time periods, polar bears predominantly occupied <span class="hlt">sea-ice</span>, although land was used during the summer <span class="hlt">sea-ice</span> retreat and during the winter for maternal denning. However, the proportion of bears on land for > 7 days between August and October increased between the two periods from 20.0% to 38.9%, and the average duration on land increased by 30 days. The majority of bears that used land in the summer and for denning came to Wrangel and Herald Islands (Russia), highlighting the importance of these northernmost land habitats to Chukchi <span class="hlt">Sea</span> polar bears. Where bears summered and denned, and how long they spent there, was related to the timing and duration of <span class="hlt">sea</span> <span class="hlt">ice</span> retreat. Our results are consistent with other studies supporting increased land use as a common response of polar bears to <span class="hlt">sea-ice</span> loss. Implications of increased land use for Chukchi <span class="hlt">Sea</span> polar bears are unclear, because a recent study observed no change in body condition or reproductive indices between the two periods considered here. This result suggests that the ecology of this region may provide a degree of resilience to <span class="hlt">sea</span> <span class="hlt">ice</span> loss. However, projections of continued <span class="hlt">sea</span> <span class="hlt">ice</span> loss suggest that polar bears in the Chukchi <span class="hlt">Sea</span> and other parts of the Arctic may increasingly use land habitats in the future, which has the potential to increase nutritional stress and human-polar bear interactions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4651550','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4651550"><span>Increased Land Use by Chukchi <span class="hlt">Sea</span> Polar Bears in Relation to Changing <span class="hlt">Sea</span> <span class="hlt">Ice</span> Conditions</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Rode, Karyn D.; Wilson, Ryan R.; Regehr, Eric V.; St. Martin, Michelle; Douglas, David C.; Olson, Jay</p> <p>2015-01-01</p> <p>Recent observations suggest that polar bears (Ursus maritimus) are increasingly using land habitats in some parts of their range, where they have minimal access to their preferred prey, likely in response to loss of their <span class="hlt">sea</span> <span class="hlt">ice</span> habitat associated with climatic warming. We used location data from female polar bears fit with satellite radio collars to compare land use patterns in the Chukchi <span class="hlt">Sea</span> between two periods (1986–1995 and 2008–2013) when substantial summer <span class="hlt">sea-ice</span> loss occurred. In both time periods, polar bears predominantly occupied <span class="hlt">sea-ice</span>, although land was used during the summer <span class="hlt">sea-ice</span> retreat and during the winter for maternal denning. However, the proportion of bears on land for > 7 days between August and October increased between the two periods from 20.0% to 38.9%, and the average duration on land increased by 30 days. The majority of bears that used land in the summer and for denning came to Wrangel and Herald Islands (Russia), highlighting the importance of these northernmost land habitats to Chukchi <span class="hlt">Sea</span> polar bears. Where bears summered and denned, and how long they spent there, was related to the timing and duration of <span class="hlt">sea</span> <span class="hlt">ice</span> retreat. Our results are consistent with other studies supporting increased land use as a common response of polar bears to <span class="hlt">sea-ice</span> loss. Implications of increased land use for Chukchi <span class="hlt">Sea</span> polar bears are unclear, because a recent study observed no change in body condition or reproductive indices between the two periods considered here. This result suggests that the ecology of this region may provide a degree of resilience to <span class="hlt">sea</span> <span class="hlt">ice</span> loss. However, projections of continued <span class="hlt">sea</span> <span class="hlt">ice</span> loss suggest that polar bears in the Chukchi <span class="hlt">Sea</span> and other parts of the Arctic may increasingly use land habitats in the future, which has the potential to increase nutritional stress and human-polar bear interactions. PMID:26580809</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26580809','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26580809"><span>Increased Land Use by Chukchi <span class="hlt">Sea</span> Polar Bears in Relation to Changing <span class="hlt">Sea</span> <span class="hlt">Ice</span> Conditions.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Rode, Karyn D; Wilson, Ryan R; Regehr, Eric V; St Martin, Michelle; Douglas, David C; Olson, Jay</p> <p>2015-01-01</p> <p>Recent observations suggest that polar bears (Ursus maritimus) are increasingly using land habitats in some parts of their range, where they have minimal access to their preferred prey, likely in response to loss of their <span class="hlt">sea</span> <span class="hlt">ice</span> habitat associated with climatic warming. We used location data from female polar bears fit with satellite radio collars to compare land use patterns in the Chukchi <span class="hlt">Sea</span> between two periods (1986-1995 and 2008-2013) when substantial summer <span class="hlt">sea-ice</span> loss occurred. In both time periods, polar bears predominantly occupied <span class="hlt">sea-ice</span>, although land was used during the summer <span class="hlt">sea-ice</span> retreat and during the winter for maternal denning. However, the proportion of bears on land for > 7 days between August and October increased between the two periods from 20.0% to 38.9%, and the average duration on land increased by 30 days. The majority of bears that used land in the summer and for denning came to Wrangel and Herald Islands (Russia), highlighting the importance of these northernmost land habitats to Chukchi <span class="hlt">Sea</span> polar bears. Where bears summered and denned, and how long they spent there, was related to the timing and duration of <span class="hlt">sea</span> <span class="hlt">ice</span> retreat. Our results are consistent with other studies supporting increased land use as a common response of polar bears to <span class="hlt">sea-ice</span> loss. Implications of increased land use for Chukchi <span class="hlt">Sea</span> polar bears are unclear, because a recent study observed no change in body condition or reproductive indices between the two periods considered here. This result suggests that the ecology of this region may provide a degree of resilience to <span class="hlt">sea</span> <span class="hlt">ice</span> loss. However, projections of continued <span class="hlt">sea</span> <span class="hlt">ice</span> loss suggest that polar bears in the Chukchi <span class="hlt">Sea</span> and other parts of the Arctic may increasingly use land habitats in the future, which has the potential to increase nutritional stress and human-polar bear interactions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27130467','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27130467"><span>Modeling oil weathering and transport in <span class="hlt">sea</span> <span class="hlt">ice</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Afenyo, Mawuli; Khan, Faisal; Veitch, Brian; Yang, Ming</p> <p>2016-06-15</p> <p>This paper presents a model of oil weathering and transport in <span class="hlt">sea</span> <span class="hlt">ice</span>. It contains a model formulation and scenario simulation to test the proposed model. The model formulation is based on state-of-the-art models for individual weathering and transport processes. The approach incorporates the dependency of weathering and transport processes on each other, as well as their simultaneous occurrence after an oil spill in <span class="hlt">sea</span> <span class="hlt">ice</span>. The model is calibrated with available experimental data. The experimental data and model prediction show close agreement. A sensitivity analysis is conducted to determine the most sensitive parameters in the model. The model is useful for contingency planning of a potential oil spill in <span class="hlt">sea</span> <span class="hlt">ice</span>. It is suitable for coupling with a level IV fugacity model, to estimate the concentration and persistence of hydrocarbons in air, <span class="hlt">ice</span>, water and sediments for risk assessment purposes.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_20 --> <div id="page_21" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="401"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/1011619','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/1011619"><span>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> modeling with the material-point method.</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Peterson, Kara J.; Bochev, Pavel Blagoveston</p> <p>2010-04-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> plays an important role in global climate by reflecting solar radiation and insulating the ocean from the atmosphere. Due to feedback effects, the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover is changing rapidly. To accurately model this change, high-resolution calculations must incorporate: (1) annual cycle of growth and melt due to radiative forcing; (2) mechanical deformation due to surface winds, ocean currents and Coriolis forces; and (3) localized effects of leads and ridges. We have demonstrated a new mathematical algorithm for solving the <span class="hlt">sea</span> <span class="hlt">ice</span> governing equations using the material-point method with an elastic-decohesive constitutive model. An initial comparison with the LANL CICE code indicates that the <span class="hlt">ice</span> edge is sharper using Materials-Point Method (MPM), but that many of the overall features are similar.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19850013448','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19850013448"><span>Passive microwave remote sensing for <span class="hlt">sea</span> <span class="hlt">ice</span> research</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>1984-01-01</p> <p>Techniques for gathering data by remote sensors on satellites utilized for <span class="hlt">sea</span> <span class="hlt">ice</span> research are summarized. Measurement of brightness temperatures by a passive microwave imager converted to maps of total <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and to the areal fractions covered by first year and multiyear <span class="hlt">ice</span> are described. Several ancillary observations, especially by means of automatic data buoys and submarines equipped with upward looking sonars, are needed to improve the validation and interpretation of satellite data. The design and performance characteristics of the Navy's Special Sensor Microwave Imager, expected to be in orbit in late 1985, are described. It is recommended that data from that instrument be processed to a form suitable for research applications and archived in a readily accessible form. The <span class="hlt">sea</span> <span class="hlt">ice</span> data products required for research purposes are described and recommendations for their archival and distribution to the scientific community are presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20040040106','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20040040106"><span>The Effects of Snow Depth Forcing on Southern Ocean <span class="hlt">Sea</span> <span class="hlt">Ice</span> Simulations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Powel, Dylan C.; Markus, Thorsten; Stoessel, Achim</p> <p>2003-01-01</p> <p>The spatial and temporal distribution of snow on <span class="hlt">sea</span> <span class="hlt">ice</span> is an important factor for <span class="hlt">sea</span> <span class="hlt">ice</span> and climate models. First, it acts as an efficient insulator between the ocean and the atmosphere, and second, snow is a source of fresh water for altering the already weak Southern Ocean stratification. For the Antarctic, where the <span class="hlt">ice</span> thickness is relatively thin, snow can impact the <span class="hlt">ice</span> thickness in two ways: a) As mentioned above snow on <span class="hlt">sea</span> <span class="hlt">ice</span> reduces the ocean-atmosphere heat flux and thus reduces freezing at the base of the <span class="hlt">ice</span> flows; b) a heavy snow load can suppress the <span class="hlt">ice</span> below <span class="hlt">sea</span> level which causes flooding and, with subsequent freezing, a thickening of the <span class="hlt">sea</span> <span class="hlt">ice</span> (snow-to-<span class="hlt">ice</span> conversion). In this paper, we compare different snow fall paramterizations (incl. the incorporation of satellite-derived snow depth) and study the effect on the <span class="hlt">sea</span> <span class="hlt">ice</span> using a <span class="hlt">sea</span> <span class="hlt">ice</span> model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1811757R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1811757R"><span>Melt ponds and marginal <span class="hlt">ice</span> zone from new algorithm of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration retrieval</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Repina, Irina; Tikhonov, Vasiliy; Komarova, Nataliia; Raev, Mikhail; Sharkov, Evgeniy</p> <p>2016-04-01</p> <p>Studies of spatial and temporal properties of <span class="hlt">sea</span> <span class="hlt">ice</span> distribution in polar regions help to monitor global environmental changes and reveal their natural and anthropogenic factors, as well as make forecasts of weather, marine transportation and fishing conditions, assess perspectives of mineral mining on the continental shelf, etc. Contact methods of observation are often insufficient to meet the goals, very complicated technically and organizationally and not always safe for people involved. Remote sensing techniques are believed to be the best alternative. Its include monitoring of polar regions by means of passive microwave sensing with the aim to determine spatial distribution, types, thickness and snow cover of <span class="hlt">ice</span>. However, the algorithms employed today to retrieve <span class="hlt">sea</span> <span class="hlt">ice</span> characteristics from passive microwave sensing data for different reasons give significant errors, especially in summer period and also near <span class="hlt">ice</span> edges and in cases of open <span class="hlt">ice</span>. A new algorithm of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration retrieval in polar regions from satellite microwave radiometry data is discussed. Beside estimating <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, the algorithm makes it possible to indicate <span class="hlt">ice</span> areas with melting snow and melt ponds. Melt ponds are an important element of the Arctic climate system. Covering up to 50% of the surface of drifting <span class="hlt">ice</span> in summer, they are characterized by low albedo values and absorb several times more incident shortwave radiation than the rest of the snow and <span class="hlt">ice</span> cover. The change of melt ponds area in summer period 1987-2015 is investigated. The marginal <span class="hlt">ice</span> zone (MIZ) is defined as the area where open ocean processes, including specifically ocean waves, alter significantly the dynamical properties of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover. Ocean wave fields comprise short waves generated locally and swell propagating from the large ocean basins. Depending on factors like wind direction and ocean currents, it may consist of anything from isolated, small and large <span class="hlt">ice</span> floes drifting over a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.C11C..04K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.C11C..04K"><span>River-<span class="hlt">ice</span> and <span class="hlt">sea-ice</span> velocity fields from near-simultaneous satellite imagery</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kaeaeb, A.; Leprince, S.; Prowse, T. D.; Beltaos, S.; Lamare, M.; Abrams, M.</p> <p>2013-12-01</p> <p>Satellite stereo and satellites that follow each other on similar orbits within short time periods produce near-simultaneous space imagery, a kind of data that is little exploited. In this study, we track river-<span class="hlt">ice</span> and <span class="hlt">sea-ice</span> motion over time periods of tens of seconds to several minutes, which is the typical time lag between the two or more images of such near-simultaneous acquisition constellations. Using this novel approach, we measure and visualize for the first time the almost complete two-dimensional minute-scale velocity fields over several thousand square-kilometers of <span class="hlt">sea</span> <span class="hlt">ice</span> cover or over up to several hundred kilometers long river reaches. We present the types of near-simultaneous imagery and constellations suitable for the measurements and discuss application examples, using a range of high and medium resolution imagery such as from ASTER, ALOS PRISM, Ikonos, WorldView-2, Landsat and EO-1. The river <span class="hlt">ice</span> velocities obtained provide new insights into <span class="hlt">ice</span> dynamics, river flow and river morphology, in particular during <span class="hlt">ice</span> breakup. River-<span class="hlt">ice</span> breakup and the associated downstream transport of <span class="hlt">ice</span> debris is often the most important hydrological event of the year, producing flood levels that commonly exceed those for the open-water period and dramatic consequences for river infrastructure and ecology. We also estimate river discharge from <span class="hlt">ice</span>/water surface velocities using near-simultaneous satellite imagery. Our results for <span class="hlt">sea</span> <span class="hlt">ice</span> complement velocity fields typically obtained over time-scales of days and can thus contribute to better understanding of a number of processes involved in <span class="hlt">sea</span> <span class="hlt">ice</span> drift, such as wind impact, tidal currents and interaction of <span class="hlt">ice</span> floes with each other and with obstacles.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUFM.C21C1184F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.C21C1184F"><span>National <span class="hlt">Ice</span> Center Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Charts and Climatologies In Gridded and GIS Format</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fetterer, F.; Fowler, C.; Ballagh, L. M.; Street, T.; Meier, W. N.; Clemente-Colon, P.</p> <p>2006-12-01</p> <p>The U.S. National <span class="hlt">Ice</span> Center (NIC) is a joint Navy, NOAA, and Coast Guard <span class="hlt">sea</span> <span class="hlt">ice</span> analysis and forecasting center. Since 1972, NIC has produced weekly Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> charts for operational uses including mission planning and safety of navigation. Arctic charts include information on <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and edge position as well as (since about 1995) information on <span class="hlt">ice</span> type. The charts are constructed by analysts using available in situ, remotely sensed, and model data sources. Data sources and methods of chart construction have evolved since 1972 resulting in inconsistencies in the data record; a characteristic shared with most operational products. However the arctic-wide charts are the product of manual interpretation and data fusion, informed by the analyst's expertise and by ancillary products such as climatologies and <span class="hlt">ice</span> information shared by foreign operational <span class="hlt">ice</span> services. They are therefore often more accurate, especially since the addition of synthetic aperture radar to data sources in the mid 1990s, than are the passive microwave derived <span class="hlt">sea</span> <span class="hlt">ice</span> data sets commonly used by researchers. This is especially true for <span class="hlt">ice</span> edge location because of its operational importance. NIC provides charts free of charge on their Web site. These charts are not easy for most researchers to use, however, because they are in a proprietary GIS format and the <span class="hlt">ice</span> concentration and type information is encoded in polygon attributes that follow World Meteorological Organization coding conventions. We converted the charts to a gridded raster format (Equal Area Scalable Earth, or EASE-Grid) and created monthly climatology products (median, maximum, minimum, first quartile, and third quartile concentrations as well as frequency of occurrence of <span class="hlt">ice</span> at any concentration for 33 year, 10 year, and 5 year periods.) Charts and climatologies are available at the National Snow and <span class="hlt">Ice</span> Data Center. The products cover 1972-2004, and we plan to update the collection yearly.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12.8769F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.8769F"><span>Peopling of the high Arctic - induced by <span class="hlt">sea</span> <span class="hlt">ice</span>?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Funder, Svend</p> <p>2010-05-01</p> <p>'We travelled in the winter after the return of daylight and did not go into fixed camp until spring, when the <span class="hlt">ice</span> broke up. There was good hunting on the way, seals, beluga, walrus, bear.' (From Old Merkrusârk's account of his childhood's trek from Baffin Island to Northwest Greenland, told to Knud Rasmussen on Saunders Island in 1904) Five thousand years ago people moving eastwards from Beringia spread over the barrens of the Canadian high Arctic. This was the first of three waves of prehistoric Arctic 'cultures', which eventually reached Greenland. The passage into Greenland has to go through the northernmost and most hostile part of the country with a 5 month Polar night, and to understand this extraordinary example of human behaviour and endurance, it has been customary to invoke a more favourable (warmer) climate. This presentation suggests that land-fast <span class="hlt">sea</span> <span class="hlt">ice</span>, i.e. stationary <span class="hlt">sea</span> <span class="hlt">ice</span> anchored to the coast, is among the most important environmental factors behind the spread of prehistoric polar cultures. The <span class="hlt">ice</span> provides the road for travelling and social communion - and access to the most important source of food, the ocean. In the LongTerm Project (2006 and 2007) we attempted to establish a Holocene record for <span class="hlt">sea</span> <span class="hlt">ice</span> variations along oceanic coasts in northernmost Greenland. Presently the coasts north of 80° N are beleaguered by year-round <span class="hlt">sea</span> <span class="hlt">ice</span> - for ten months this is land-fast <span class="hlt">ice</span>, and only for a period in the stormy autumn months are the coasts exposed to pack-<span class="hlt">ice</span>. This presentation Land-fast <span class="hlt">ice</span> - as opposed to pack-<span class="hlt">ice</span> - is a product of local temperatures, but its duration over the year, and especially into the daylight season, is also conditioned by other factors, notably wind strength. In the geological record we recognize long lasting land-fast <span class="hlt">ice</span> by two absences: absence of traces of wave action (no beach formation), which, however, can also be a result of pack-<span class="hlt">ice</span> along the coast; - and absence of driftwood on the shore (land-fast <span class="hlt">ice</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23413190','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23413190"><span>Export of algal biomass from the melting Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Boetius, Antje; Albrecht, Sebastian; Bakker, Karel; Bienhold, Christina; Felden, Janine; Fernández-Méndez, Mar; Hendricks, Stefan; Katlein, Christian; Lalande, Catherine; Krumpen, Thomas; Nicolaus, Marcel; Peeken, Ilka; Rabe, Benjamin; Rogacheva, Antonina; Rybakova, Elena; Somavilla, Raquel; Wenzhöfer, Frank</p> <p>2013-03-22</p> <p>In the Arctic, under-<span class="hlt">ice</span> primary production is limited to summer months and is restricted not only by <span class="hlt">ice</span> thickness and snow cover but also by the stratification of the water column, which constrains nutrient supply for algal growth. Research Vessel Polarstern visited the <span class="hlt">ice</span>-covered eastern-central basins between 82° to 89°N and 30° to 130°E in summer 2012, when Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> declined to a record minimum. During this cruise, we observed a widespread deposition of <span class="hlt">ice</span> algal biomass of on average 9 grams of carbon per square meter to the deep-<span class="hlt">sea</span> floor of the central Arctic basins. Data from this cruise will contribute to assessing the effect of current climate change on Arctic productivity, biodiversity, and ecological function.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C43A0776P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C43A0776P"><span>High resolution Holocene <span class="hlt">sea</span> <span class="hlt">ice</span> records from Herald Canyon, East Siberian <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pearce, C.; Rattray, J.; Jakobsson, M.; Barrientos, N.; Muschitiello, F.; Smittenberg, R.; O'Regan, M.; Coxall, H.</p> <p>2015-12-01</p> <p>Arctic Ocean <span class="hlt">sea</span> <span class="hlt">ice</span> plays a critical role in the Earth's climate system because of the positive <span class="hlt">ice</span>-albedo feedback mechanisms as well as its control on ocean-atmospheric heat exchange and potential influence on the thermohaline circulation. Key to improving our understanding of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover and its reaction to external forcing is the reconstruction of past variability through paleo-records such as marine sediment cores. Although the observed recent <span class="hlt">sea</span> <span class="hlt">ice</span> loss seems to be the strongest of the last millennia, it is still uncertain whether the shift from perennial to seasonal <span class="hlt">ice</span> cover expected for the near future was unprecedented during the current interglacial. High resolution <span class="hlt">sea</span> <span class="hlt">ice</span> reconstructions from the Arctic Ocean are rare, and specifically records from the Russian Arctic are underrepresented. In this study, we present results from marine sediment cores from the Herald Canyon in the East Siberian <span class="hlt">Sea</span>. The area is one of the major conduits of Pacific water entering the Arctic Ocean basin from the Bering Strait and is thus an ideal place to study past variability of the inflow of these nutrient rich waters. Radiocarbon dating of mollusks indicates very high sedimentation rates at the coring sites which allowed for analyses at centennial resolution up to decadal resolution in the late Holocene. Core samples were analyzed for the biomarker IP25, which is produced by diatoms living in <span class="hlt">sea</span> <span class="hlt">ice</span> and is used as a proxy of past seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations. Preliminary results indicate the presence of seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> during the entire Late Holocene and show a significant increase of <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations during the last millennia.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.9702P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.9702P"><span>High resolution Holocene <span class="hlt">sea</span> <span class="hlt">ice</span> records from Herald Canyon, Chukchi <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pearce, Christof; Jakobsson, Martin; O'Regan, Matt; Rattray, Jayne; Barrientos, Natalia; Muchitiello, Francesco; Smittenburg, Rienk; Cronin, Tom; Coxall, Helen; Semiletov, Igor</p> <p>2016-04-01</p> <p>Arctic Ocean <span class="hlt">sea</span> <span class="hlt">ice</span> plays a critical role in the Earth's climate system because of the positive <span class="hlt">ice</span>-albedo feedback mechanisms as well as its control on ocean-atmospheric heat exchange and potential influence on the thermohaline circulation. Key to improving our understanding of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover and its reaction to external forcing is the reconstruction of past variability through paleo-records such as marine sediment cores. Although the observed recent <span class="hlt">sea</span> <span class="hlt">ice</span> loss seems to be the strongest of the last millennia, it is still uncertain whether the shift from perennial to seasonal <span class="hlt">ice</span> cover expected for the near future was unprecedented during the current interglacial. High resolution <span class="hlt">sea</span> <span class="hlt">ice</span> reconstructions from the Arctic Ocean are rare, and specifically records from the Russian Arctic are underrepresented. In this study, we present results from marine sediment cores from the Herald Canyon in the East Siberian <span class="hlt">Sea</span>. The area is one of the major conduits of Pacific water entering the Arctic Ocean basin from the Bering Strait and is thus an ideal place to study past variability of the inflow of these nutrient rich waters. Radiocarbon dating of mollusks indicates very high sedimentation rates at the coring sites which allowed for analyses at centennial resolution up to decadal resolution in the late Holocene. Core samples were analyzed for the biomarker IP25, which is produced by diatoms living in <span class="hlt">sea</span> <span class="hlt">ice</span> and is used as a proxy of past seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations. Preliminary results indicate the presence of seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> during the entire Late Holocene and show a significant increase of <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations during the last millennia.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19730020518','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19730020518"><span><span class="hlt">Sea-ice</span> and surface water circulation, Alaskan continental shelf</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wright, F. F.; Sharma, G. D.; Burns, J. J. (Principal Investigator)</p> <p>1973-01-01</p> <p>The author has identified the following significant results. Over 1500 water samples from surface and from standard hydrographic depths were collected during June and July 1973 from Bering <span class="hlt">Sea</span> and Gulf of Alaska. The measurement of temperature, salinity, and productivity indicated that various distinct water masses cover the Bering <span class="hlt">Sea</span> Shelf. The suspended load in surface waters will be correlated with the ERTS-1 imagery as it becomes available to delineate the surface water circulation. The movement of <span class="hlt">ice</span> floes in the Bering Strait and Bering <span class="hlt">Sea</span> indicated that movement of <span class="hlt">ice</span> varies considerably and may depend on wind stress as well as ocean currents.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110011892','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110011892"><span>Observations of Recent Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Volume Loss and Its Impact on Ocean-Atmosphere Energy Exchange and <span class="hlt">Ice</span> Production</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kurtz, N. T.; Markus, T.; Farrell, S. L.; Worthen, D. L.; Boisvert, L. N.</p> <p>2011-01-01</p> <p>Using recently developed techniques we estimate snow and <span class="hlt">sea</span> <span class="hlt">ice</span> thickness distributions for the Arctic basin through the combination of freeboard data from the <span class="hlt">Ice</span>, Cloud, and land Elevation Satellite (ICESat) and a snow depth model. These data are used with meteorological data and a thermodynamic <span class="hlt">sea</span> <span class="hlt">ice</span> model to calculate ocean-atmosphere heat exchange and <span class="hlt">ice</span> volume production during the 2003-2008 fall and winter seasons. The calculated heat fluxes and <span class="hlt">ice</span> growth rates are in agreement with previous observations over multiyear <span class="hlt">ice</span>. In this study, we calculate heat fluxes and <span class="hlt">ice</span> growth rates for the full distribution of <span class="hlt">ice</span> thicknesses covering the Arctic basin and determine the impact of <span class="hlt">ice</span> thickness change on the calculated values. Thinning of the <span class="hlt">sea</span> <span class="hlt">ice</span> is observed which greatly increases the 2005-2007 fall period ocean-atmosphere heat fluxes compared to those observed in 2003. Although there was also a decline in <span class="hlt">sea</span> <span class="hlt">ice</span> thickness for the winter periods, the winter time heat flux was found to be less impacted by the observed changes in <span class="hlt">ice</span> thickness. A large increase in the net Arctic ocean-atmosphere heat output is also observed in the fall periods due to changes in the areal coverage of <span class="hlt">sea</span> <span class="hlt">ice</span>. The anomalously low <span class="hlt">sea</span> <span class="hlt">ice</span> coverage in 2007 led to a net ocean-atmosphere heat output approximately 3 times greater than was observed in previous years and suggests that <span class="hlt">sea</span> <span class="hlt">ice</span> losses are now playing a role in increasing surface air temperatures in the Arctic.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20070016598&hterms=feedback&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dfeedback','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20070016598&hterms=feedback&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dfeedback"><span>Observational Evidence of a Hemispheric-wide <span class="hlt">Ice</span>-ocean Albedo Feedback Effect on Antarctic <span class="hlt">Sea-ice</span> Decay</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Nihashi, Sohey; Cavalieri, Donald J.</p> <p>2007-01-01</p> <p>The effect of <span class="hlt">ice</span>-ocean albedo feedback (a kind of <span class="hlt">ice</span>-albedo feedback) on <span class="hlt">sea-ice</span> decay is demonstrated over the Antarctic <span class="hlt">sea-ice</span> zone from an analysis of satellite-derived hemispheric <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and European Centre for Medium-Range Weather Forecasts (ERA-40) atmospheric data for the period 1979-2001. <span class="hlt">Sea</span> <span class="hlt">ice</span> concentration in December (time of most active melt) correlates better with the meridional component of the wind-forced <span class="hlt">ice</span> drift (MID) in November (beginning of the melt season) than the MID in December. This 1 month lagged correlation is observed in most of the Antarctic <span class="hlt">sea-ice</span> covered ocean. Daily time series of <span class="hlt">ice</span> , concentration show that the <span class="hlt">ice</span> concentration anomaly increases toward the time of maximum <span class="hlt">sea-ice</span> melt. These findings can be explained by the following positive feedback effect: once <span class="hlt">ice</span> concentration decreases (increases) at the beginning of the melt season, solar heating of the upper ocean through the increased (decreased) open water fraction is enhanced (reduced), leading to (suppressing) a further decrease in <span class="hlt">ice</span> concentration by the oceanic heat. Results obtained fi-om a simple <span class="hlt">ice</span>-ocean coupled model also support our interpretation of the observational results. This positive feedback mechanism explains in part the large interannual variability of the <span class="hlt">sea-ice</span> cover in summer.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A42C..05D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A42C..05D"><span>Arctic spring ozone reduction associated with projected <span class="hlt">sea</span> <span class="hlt">ice</span> loss</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Deser, C.; Sun, L.; Tomas, R. A.; Polvani, L. M.</p> <p>2013-12-01</p> <p>The impact of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss on the stratosphere is investigated using the Whole-Atmosphere Community Climate Model (WACCM), by prescribing the <span class="hlt">sea</span> <span class="hlt">ice</span> in the late 20th century and late 21st century, respectively. The localized <span class="hlt">Sea</span> Surface Temperature (SST) change associated with <span class="hlt">sea</span> <span class="hlt">ice</span> melt is also included in the future run. Overall, the model simulates a negative annular-mode response in the winter and spring. In the stratosphere, polar vortex strengthens from February to April, peaking in March. Consistent with it, there is an anomalous cooling in the high-latitude stratosphere, and polar cap ozone reduction is up to 20 DU. Since the difference between these two runs lies only in the <span class="hlt">sea</span> <span class="hlt">ice</span> and localized SST in the Arctic, the stratospheric circulation and ozone changes can be attributed to the surface forcing. Eliassen-Palm analysis reveals that the upward propagation of planetary waves is suppressed in the spring as a consequence of <span class="hlt">sea</span> <span class="hlt">ice</span> loss. The reduction in propagation causes less wave dissipation and thus less zonal wind deceleration in the extratropical stratosphere.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015E%26PSL.431..127B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015E%26PSL.431..127B"><span>Identification of paleo Arctic winter <span class="hlt">sea</span> <span class="hlt">ice</span> limits and the marginal <span class="hlt">ice</span> zone: Optimised biomarker-based reconstructions of late Quaternary Arctic <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Belt, Simon T.; Cabedo-Sanz, Patricia; Smik, Lukas; Navarro-Rodriguez, Alba; Berben, Sarah M. P.; Knies, Jochen; Husum, Katrine</p> <p>2015-12-01</p> <p>Analysis of >100 surface sediments from across the Barents <span class="hlt">Sea</span> has shown that the relative abundances of the mono-unsaturated <span class="hlt">sea</span> <span class="hlt">ice</span> diatom-derived biomarker IP25 and a tri-unsaturated highly branched isoprenoid (HBI) lipid (HBI III) are characteristic of the overlying surface oceanographic conditions, most notably, the location of the seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> edge. Thus, while IP25 is generally limited to locations experiencing seasonal <span class="hlt">sea</span> <span class="hlt">ice</span>, with higher abundances found for locations with longer periods of <span class="hlt">ice</span> cover, HBI III is found in sediments from all sampling locations, but is significantly enhanced in sediments within the vicinity of the retreating <span class="hlt">sea</span> <span class="hlt">ice</span> edge or marginal <span class="hlt">ice</span> zone (MIZ). The response of HBI III to this well-defined <span class="hlt">sea</span> <span class="hlt">ice</span> scenario also appears to be more selective than that of the more generic phytoplankton biomarker, brassicasterol. The potential for the combined analysis of IP25 and HBI III to provide more detailed assessments of past <span class="hlt">sea</span> <span class="hlt">ice</span> conditions than IP25 alone has been investigated by quantifying both biomarkers in three marine downcore records from locations with contrasting modern <span class="hlt">sea</span> <span class="hlt">ice</span> settings. For sediment cores from the western Barents <span class="hlt">Sea</span> (intermittent seasonal <span class="hlt">sea</span> <span class="hlt">ice</span>) and the northern Norwegian <span class="hlt">Sea</span> (<span class="hlt">ice</span>-free), high IP25 and low HBI III during the Younger Dryas (ca. 12.9-11.9 cal. kyr BP) is consistent with extensive <span class="hlt">sea</span> cover, with relatively short periods of <span class="hlt">ice</span>-free conditions resulting from late summer retreat. Towards the end of the YD (ca. 11.9-11.5 cal. kyr BP), a general amelioration of conditions resulted in a near winter maximum <span class="hlt">ice</span> edge scenario for both locations, although this was somewhat variable, and the eventual transition to predominantly <span class="hlt">ice</span>-free conditions was later for the western Barents <span class="hlt">Sea</span> site (ca. 9.9 cal. kyr BP) compared to NW Norway (ca. 11.5 cal. kyr BP). For both locations, coeval elevated HBI III (but absent IP25) potentially provides further evidence for increased Atlantic Water inflow</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFM.C11B0434G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.C11B0434G"><span>Making <span class="hlt">sea</span> <span class="hlt">ice</span> Motion Data From RGPS More Accessible</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gens, R.; Barker, E.; Backstrom, L.</p> <p>2007-12-01</p> <p>The Radarsat Geophysical Processing System (RGPS) was designed to generate <span class="hlt">sea</span> <span class="hlt">ice</span> products providing information about <span class="hlt">sea</span> <span class="hlt">ice</span> motion, deformation and <span class="hlt">sea</span> <span class="hlt">ice</span> thickness. Radarsat-1 ScanSAR Wide B (SWB) imagery has been acquired over more than a decade for the Arctic Ocean with a spatial resolution of 100 m. At the beginning of each winter season a regular grid is initialized and the grid points are tracked over the season to monitor the <span class="hlt">sea</span> <span class="hlt">ice</span> motion. With the changing <span class="hlt">ice</span> conditions the regular grid becomes distorted in shape and location. The distorted Lagrangian grid is used to generate the RGPS data products which reflect the <span class="hlt">ice</span> condition for a three-day snapshot. These products are currently distributed in a custom designed binary format. They are only used for the long-term monitoring of <span class="hlt">sea</span> <span class="hlt">ice</span> on the Arctic basin scale, hence the data is vastly underutilized. The resolution also allows long-term monitoring studies on the regional scale as well as on a local scale. The goal of this prototype development is to make the RGPS data more accessible to allow the data to be used at the regional and local scale, e.g. to develop lead typologies or verify <span class="hlt">ice</span> charting forecasts. A prototype has been developed that makes the RGPS data more accessible to the research community. A number of raster and vector products are generated for the nominal three-day snapshot. The image mosaics for the part of the Arctic basin that is covered in the snapshot have a 500 m spatial resolution. Basic metadata are provided that allow the user to identify features of interest in the mosaics and their corresponding image within an image data coverage layer. With this metadata the imagery of interest can be directly ordered. Additionally, a weather data layer is derived from model data. For RGPS data that has already been processed the <span class="hlt">sea</span> <span class="hlt">ice</span> monitoring information a <span class="hlt">sea</span> <span class="hlt">ice</span> layer is created that include all the relevant information from the RGPS database. These vector layers are now</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C53B0781Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C53B0781Z"><span>CryoSat-2 Estimates of <span class="hlt">Sea</span> <span class="hlt">Ice</span> Freeboard in the Greenland <span class="hlt">Sea</span> of Arctic</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, S.</p> <p>2015-12-01</p> <p>Arctic region is one of the most important parts that contribute to the global climate system. As an important climatic indicator, <span class="hlt">sea</span> <span class="hlt">ice</span> has also undergone dramatic changes. Due to the limitations of poor geographical conditions and a lack of in-situ observations, knowledge about Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> has not been explored well for a long time, furthermore it is especial difficult to get a high quality of Arctic <span class="hlt">Sea</span> <span class="hlt">ice</span> thickness information.Equipped with a Ku-band SIRAL, CryoSat-2 has been launched in 2010 as an important European Space Agency Earth Explorer Opportunity mission. CryoSat-2 has the advantage of measuring the thickness of polar <span class="hlt">sea</span> <span class="hlt">ice</span> and monitoring changes in the <span class="hlt">ice</span> sheets that blanket Greenland and Antarctica with high precision. In this paper, the CryoSat-2/SIRAL radar altimeter data were used to retrieve the <span class="hlt">sea</span> <span class="hlt">ice</span> thickness in the Greenland <span class="hlt">Sea</span>, Arctic, validated with the <span class="hlt">Ice</span>, Cloud, and Land Elevation (ICESat) laser altimeter measurements from National Aeronautics and Space Administration (NASA) and Beaufort Gyre Experiment Program (BGEP) Upward Looking Sonar (ULS) measurements. Results show that the <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard in Greenland <span class="hlt">Sea</span> has a remarkable seasonal variation and presents an evident regional characteristics. As it show below, during the frozen season in autumn and winter the <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard concentrated in around 0.23m , the average freeboard in Greenland <span class="hlt">Sea</span> in June decreased to around 0.18m, the minimum freeboard 0.12m appeared in September. In the Western Greenland <span class="hlt">Sea</span> near the Greenland and the Fram Strait with higher-latitude where multi-year <span class="hlt">ice</span> occupy most has a larger freeboard around 0.3m in winter. In the south-eastern Greenland <span class="hlt">Sea</span> with lower-latitude and shallow <span class="hlt">sea</span> water, the freeboard composed by first-year <span class="hlt">ice</span> concentrated in around 0.1m in winter. At the same time, the <span class="hlt">sea</span> <span class="hlt">ice</span> area also had seasonal variations, its maximum was in January and March, and minimum was in September.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70012473','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70012473"><span>Arctic continental shelf morphology related to <span class="hlt">sea-ice</span> zonation, Beaufort <span class="hlt">Sea</span>, Alaska</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Reimnitz, E.; Toimil, L.; Barnes, P.</p> <p>1978-01-01</p> <p>Landsat-1 and NOAA satellite imagery for the winter 1972-1973, and a variety of <span class="hlt">ice</span> and <span class="hlt">sea</span>-floor data were used to study <span class="hlt">sea-ice</span> zonation and dynamics and their relation to bottom morphology and geology on the Beaufort <span class="hlt">Sea</span> continental shelf of arctic Alaska. In early winter the location of the boundary between undeformed fast <span class="hlt">ice</span> and westward-drifting pack <span class="hlt">ice</span> of the Pacific Gyre is controlled by major coastal promontories. Pronounced linear pressure- and shear-ridges, as well as hummock fields, form along this boundary and are stabilized by grounding, generally between the 10- and 20-m isobaths. Slippage along this boundary occurs intermittently at or seaward of the grounded ridges, forming new grounded ridges in a widening zone, the stamukhi zone, which by late winter extends out to the 40-m isobath. Between intermittent events along the stamukhi zone, pack-<span class="hlt">ice</span> drift and slippage is continuous along the shelf edge, at average rates of 3-10 km/day. Whether slippage occurs along the stamukhi zone or along the shelf edge, it is restricted to a zone several hundred meters wide, and <span class="hlt">ice</span> seaward of the slip face moves at uniform rates without discernible drag effects. A causal relationship is seen between the spatial distribution of major <span class="hlt">ice</span>-ridge systems and offshore shoals downdrift of major coastal promontories. The shoals appear to have migrated shoreward under the influence of <span class="hlt">ice</span> up to 400 m in the last 25 years. The <span class="hlt">sea</span> floor seaward of these shoals within the stamukhi zone shows high <span class="hlt">ice</span>-gouge density, large incision depths, and a high degree of disruption of internal sedimentary structures. The concentration of large <span class="hlt">ice</span> ridges and our <span class="hlt">sea</span> floor data in the stamukhi zone indicate that much of the available marine energy is expended here, while the inner shelf and coast, where the relatively undeformed fast <span class="hlt">ice</span> grows, are sheltered. There is evidence that anomalies in the overall arctic shelf profile are related to <span class="hlt">sea-ice</span> zonation, <span class="hlt">ice</span> dynamics, and bottom</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20070034034&hterms=arctic+ocean&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Darctic%2Bocean','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20070034034&hterms=arctic+ocean&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Darctic%2Bocean"><span>Contrasts in <span class="hlt">Sea</span> <span class="hlt">Ice</span> Deformation and Production in the Arctic Seasonal and Perennial <span class="hlt">Ice</span> Zones</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kwok, K.</p> <p>2006-01-01</p> <p>Four years (1997-2000) of RADARSAT Geophysical Processor System (RGPS) data are used to contrast the <span class="hlt">sea</span> <span class="hlt">ice</span> deformation and production regionally, and in the seasonal (SIZ) and perennial (PIZ) <span class="hlt">ice</span> zones. <span class="hlt">Ice</span> production is of seasonal <span class="hlt">ice</span> in openings during the winter. Three-day estimates of these quantities are provided within Lagrangian elements initially 10 km on a side. A distinct seasonal cycle is seen in both zones with these estimates highest in the late fall and with seasonal minimums in the midwinter. Regional divergence over the winter could be up to 30%. Spatially, the highest deformation is seen in the SIZ north of coastal Alaska. Both <span class="hlt">ice</span> deformation and production are higher in the SIZ: deformation-related <span class="hlt">ice</span> production in the SIZ (approx.0.5 m) is 1.5-2.3 times that of the PIZ (approx.0.3 m): this is connected to <span class="hlt">ice</span> strength and thickness. Atmospheric forcing and boundary layer structure contribute to only the seasonal and interannual variability. Seasonal <span class="hlt">ice</span> growth in <span class="hlt">ice</span> fractures accounts for approx.25-40% of the total <span class="hlt">ice</span> production of the Arctic Ocean. Uncertainties in these estimates are discussed. By itself, this deformation-<span class="hlt">ice</span> production relationship could be considered a negative feedback when thickness is perturbed. However, the overall effect on <span class="hlt">ice</span> production in the face of increasing seasonal and thinner/weaker <span class="hlt">ice</span> coverage could be modified by local destabilization of the water column promoting overturning of warmer water due to increased brine rejection; and the upwelling of the pynocline associated with increased occurrence of large shear motion in <span class="hlt">sea</span> <span class="hlt">ice</span>. Divergence is shown to be negligibly correlated to cyclonic motion in summer and winter in both <span class="hlt">ice</span> zones.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMNG51E1684M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMNG51E1684M"><span>Multi-fractal structure in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> satellite data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moon, W.; Wettlaufer, J. S.</p> <p>2011-12-01</p> <p>Since 21th century, Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> has shown significant decrease especially during summer season. These decline has been considered by climate scientists as a critical sign of the effect of global warming. Especially, IPCC global climate models confirmed the recent decline of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> and predict the more decline in the near future. Even with rather significant amount of inter-model variability in climate models, satellite data during around 30 years support the results of climate models. However, the decline of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> is confirmed based on monthly averaged data and linear regression after erasing seasonal cycle. Even with strong conjecture of the real decline of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>, we cannot exclude one part of natural oscillations affecting Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> physics due to strong non-stationarity. It is also possible not to make any conclusive statements using the monthly averaged data due to a lack of data or multi-fractal structure of data excluded in the data. Here, we will use MF-DFA (Multi-Fractal Detrended Fluctuation Analysis) to determine the multi-scale structure of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> area and albedo using AVHRR Polar Pathfinder Twice-Daily 5km EASE-grid composites, which is expected to decide whether the decline shown in the linear regression of the monthly averaged data can be considered as the real decline caused by global warming or not. Also, the multi-scale structure information drawn from this analysis is expected to give us the guideline for selecting significant physics for low-order Arctic <span class="hlt">sea</span> <span class="hlt">ice</span></p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_21 --> <div id="page_22" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="421"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015GeoRL..4210704Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015GeoRL..4210704Y"><span>Predicted slowdown in the rate of Atlantic <span class="hlt">sea</span> <span class="hlt">ice</span> loss</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yeager, Stephen G.; Karspeck, Alicia R.; Danabasoglu, Gokhan</p> <p>2015-12-01</p> <p>Coupled climate models initialized from historical climate states and subject to anthropogenic forcings can produce skillful decadal predictions of <span class="hlt">sea</span> surface temperature change in the subpolar North Atlantic. The skill derives largely from initialization, which improves the representation of slow changes in ocean circulation and associated poleward heat transport. We show that skillful predictions of decadal trends in Arctic winter <span class="hlt">sea</span> <span class="hlt">ice</span> extent are also possible, particularly in the Atlantic sector. External radiative forcing contributes to the skill of retrospective decadal <span class="hlt">sea</span> <span class="hlt">ice</span> predictions, but the spatial and temporal accuracy is greatly enhanced by the more realistic representation of ocean heat transport anomalies afforded by initialization. Recent forecasts indicate that a spin-down of the thermohaline circulation that began near the turn of the century will continue, and this will result in near-neutral decadal trends in Atlantic winter <span class="hlt">sea</span> <span class="hlt">ice</span> extent in the coming years, with decadal growth in select regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/981847','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/981847"><span>Controls on Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> from first-year and multi-year survival rates</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Hunke, Jes</p> <p>2009-01-01</p> <p>The recent decrease in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover has transpired with a significant loss of multi year <span class="hlt">ice</span>. The transition to an Arctic that is populated by thinner first year <span class="hlt">sea</span> <span class="hlt">ice</span> has important implications for future trends in area and volume. Here we develop a reduced model for Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> with which we investigate how the survivability of first year and multi year <span class="hlt">ice</span> control the mean state, variability, and trends in <span class="hlt">ice</span> area and volume.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060036455&hterms=ice+melt&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dice%2Bmelt','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060036455&hterms=ice+melt&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dice%2Bmelt"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Floe Size Distribution in the Beaufort <span class="hlt">Sea</span> Measured by ERS-1 SAR (abstract)</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Holt, B.; Martin, S.</p> <p>1996-01-01</p> <p>Model results indicate that understanding summer heat balance and freshwater balance in the polar oceans requires knowledge of how much goes into vertical and lateral <span class="hlt">sea</span> <span class="hlt">ice</span> melt. In addition to thickness, two of the key <span class="hlt">ice</span> parameters that affect melt rate are <span class="hlt">ice</span> concentration and floe size. Smaller <span class="hlt">ice</span> floes and more open water enables more heat to go into lateral melt preferentially to vertical melt, thereby enhancing warming up the upper ocean and increasing stratification. Using ERS-1 SAR imagery along two areas, one in the Beaufort <span class="hlt">Sea</span> and another in the Chukchi <span class="hlt">Sea</span>, floe size distributions were obtained during the summer period in 1992. Comparisons will be made of floe distributions, together with meteorological and buoy measurements, to examine the differences between an <span class="hlt">ice</span> sink region (Chukchi) and a multiyear <span class="hlt">ice</span> region (Beaufort) in the summer melt process.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19810011207','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19810011207"><span>Oceanographic influences on the <span class="hlt">sea</span> <span class="hlt">ice</span> cover in the <span class="hlt">Sea</span> of Okhotsk</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Gratz, A. J.; Parkinson, C. L.</p> <p>1981-01-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> conditions in the <span class="hlt">Sea</span> of Okhotsk, as determined by satellite images from the electrically scanning microwave radiometer on board Nimbus 5, were analyzed in conjunction with the known oceanography. In particular, the <span class="hlt">sea</span> <span class="hlt">ice</span> coverage was compared with the bottom bathymetry and the surface currents, water temperatures, and salinity. It is found that <span class="hlt">ice</span> forms first in cold, shallow, low salinity waters. Once formed, the <span class="hlt">ice</span> seems to drift in a direction approximating the Okhotsk-Kuril current system. Two basic patterns of <span class="hlt">ice</span> edge positioning which persist for significant periods were identified as a rectangular structure and a wedge structure. Each of these is strongly correlated with the bathymetry of the region and with the known current system, suggesting that convective depth and ocean currents play an important role in determining <span class="hlt">ice</span> patterns.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GeoRL..4311688C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GeoRL..4311688C"><span>Diagnostic <span class="hlt">sea</span> <span class="hlt">ice</span> predictability in the pan-Arctic and U.S. Arctic regional <span class="hlt">seas</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cheng, Wei; Blanchard-Wrigglesworth, Edward; Bitz, Cecilia M.; Ladd, Carol; Stabeno, Phyllis J.</p> <p>2016-11-01</p> <p>This study assesses <span class="hlt">sea</span> <span class="hlt">ice</span> predictability in the pan-Arctic and U.S. Arctic regional (Bering, Chukchi, and Beaufort) <span class="hlt">seas</span> with a purpose of understanding regional differences from the pan-Arctic perspective and how predictability might change under changing climate. Lagged correlation is derived using existing output from the Community Earth System Model Large Ensemble (CESM-LE), Pan-Arctic <span class="hlt">Ice</span>-Ocean Modeling and Assimilation System, and NOAA Coupled Forecast System Reanalysis models. While qualitatively similar, quantitative differences exist in Arctic <span class="hlt">ice</span> area lagged correlation in models with or without data assimilation. On regional scales, modeled <span class="hlt">ice</span> area lagged correlations are strongly location and season dependent. A robust feature in the CESM-LE is that the pan-Arctic melt-to-freeze season <span class="hlt">ice</span> area memory intensifies, whereas the freeze-to-melt season memory weakens as climate warms, but there are across-region variations in the <span class="hlt">sea</span> <span class="hlt">ice</span> predictability changes with changing climate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1248935','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1248935"><span>Norwegian Young <span class="hlt">Sea</span> <span class="hlt">Ice</span> Experiment (N-<span class="hlt">ICE</span>) Field Campaign Report</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Walden, V. P.; Hudson, S. R.; Cohen, L.</p> <p>2016-03-01</p> <p>The Norwegian Young <span class="hlt">Sea</span> <span class="hlt">Ice</span> (N-<span class="hlt">ICE</span>) experiment was conducted aboard the R/V Lance research vessel from January through June 2015. The primary purpose of the experiment was to better understand thin, first-year <span class="hlt">sea</span> <span class="hlt">ice</span>. This includes understanding of how different components of the Arctic system affect <span class="hlt">sea</span> <span class="hlt">ice</span>, but also how changing <span class="hlt">sea</span> <span class="hlt">ice</span> affects the system. A major part of this effort is to characterize the atmospheric conditions throughout the experiment. A micropulse lidar (MPL) (S/N: 108) was deployed from the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility as part of the atmospheric suite of instruments. The MPL operated successfully throughout the entire experiment, acquiring data from 21 January 2015 through 23 June 2015. The MPL was the essential instrument for determining the phase (water, <span class="hlt">ice</span> or mixed) of the lower-level clouds over the <span class="hlt">sea</span> <span class="hlt">ice</span>. Data obtained from the MPL during the N-<span class="hlt">ICE</span> experiment show large cloud fractions over young, thin Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> from January through June 2015 (north of Svalbard). The winter season was characterized by frequent synoptic storms and large fluctuations in the near-surface temperature. There was much less synoptic activity in spring and summer as the near-surface temperature rose to 0 C. The cloud fraction was lower in winter (60%) than in the spring and summer (80%). Supercooled liquid clouds were observed for most of the deployment, appearing first in mid-February. Spring and summer clouds were characterized by low, thick, uniform clouds.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1811112T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1811112T"><span>Greenland <span class="hlt">ice</span> sheet initiation and Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> coincide with Eocene and Oligocene CO2 changes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tripati, Aradhna; Darby, Dennis</p> <p>2016-04-01</p> <p>Earth's modern ocean-climate system is largely defined by the presence of glacial <span class="hlt">ice</span> on landmasses in both hemispheres. Northern Hemisphere <span class="hlt">ice</span> was previously thought to have formed no earlier than the Miocene or Oligocene, about 20-30 million years after the widespread onset of Antarctic glaciation at the Eocene-Oligocene boundary. Controversially, the episodic presence of seasonal Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> and glacial <span class="hlt">ice</span> in the Northern Hemisphere beginning in the early Oligocene to Middle Eocene has been inferred from multiple observations. Here we use precise source determinations based on geochemical measurements of <span class="hlt">ice</span>-rafted debris (IRD) from an ODP core in the Greenland <span class="hlt">Sea</span> (75° N) to constrain glacial <span class="hlt">ice</span> and <span class="hlt">sea</span> <span class="hlt">ice</span>-rafting in the Northern Hemisphere during the middle Eocene through early Oligocene. The chemical fingerprint of 2,334 detrital Fe oxide grains indicates most of these grains are from Greenland with >98% certainty. Thus the coarse IRD in the Greenland <span class="hlt">Sea</span> originates from widespread areas of east Greenland as far south as the Denmark Strait area (~68° N), with additional IRD sources from the circum-Arctic Ocean. This is the first definitive evidence that mid-Eocene IRD in the Greenland <span class="hlt">Sea</span> is from Greenland. Episodic glaciation of different source regions on Greenland is synchronous with times of <span class="hlt">ice</span>-rafting in the western Arctic and ephemeral perennial Arctic <span class="hlt">ice</span> cover. Intervals of bipolar glacial <span class="hlt">ice</span> storage in the middle Eocene through early Oligocene coincide with evidence for periods of reduced CO2, associated with carbon cycle perturbations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.C52B..02S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.C52B..02S"><span>Comparing Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Kinematics from Satellite Remote Sensing Data to Coupled <span class="hlt">Sea</span> <span class="hlt">Ice</span>-Ocean Model Results</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Spreen, G.; Menemenlis, D.; Kwok, R.; Nguyen, A. T.</p> <p>2009-12-01</p> <p>The Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> in many respects is an important component of the Earth's climate system, e.g., <span class="hlt">sea</span> <span class="hlt">ice</span> governs the ocean to atmosphere heat flux, freezing and melting influences the upper ocean salinity and density, and <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics act as a latent energy transport. During recent years substantial changes of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover have been observed. Many of these aspects of <span class="hlt">sea</span> <span class="hlt">ice</span> and its recent changes can be reproduced by coupled <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean models. In part this can be attributed to the fact that model parameters are tuned to produce observed <span class="hlt">ice</span> concentration (extent) and drift distributions. Detailed comparisons between satellite remote sensing data with model results, however, reveal big differences in certain aspects of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover, e.g., for fracture zones and for small scale dynamic processes. It remains unclear whether the model physics are suited to reproduce these observed <span class="hlt">sea</span> <span class="hlt">ice</span> features. Accurate modeling of leads and fracture zones is important for realistic (1) new <span class="hlt">ice</span> production estimates, (2) ocean to air heat flux, and (3) brine rejection into the ocean. In this study we use satellite remote sensing data to compare with and to improve results of the MIT general circulation model (MITgcm) as used in the framework of the ECCO2 project (http://ecco2.org). Model integrations in an Arctic domain at horizontal grid spacing of 18, 9, and 4.5 km using two different atmospheric forcing datasets (ERA40 and JRA-25) were carried out. <span class="hlt">Sea</span> <span class="hlt">ice</span> motion, deformation, and estimates of <span class="hlt">ice</span> production are obtained from Synthetic Aperture Radar (SAR) using the RADARSAT Geophysical Processor System (RGPS). Even though the viscous-plastic dynamic <span class="hlt">sea</span> <span class="hlt">ice</span> model with elliptical yield curve is able to produce what appears to be linear kinematic features (LKFs), the orientation and spatial density are far from that which is observed. In addition the LKFs occur less frequently in the simulations. Figure 1 shows an example of the fractional number each</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26585690','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26585690"><span>Additional Arctic observations improve weather and <span class="hlt">sea-ice</span> forecasts for the Northern <span class="hlt">Sea</span> Route.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Inoue, Jun; Yamazaki, Akira; Ono, Jun; Dethloff, Klaus; Maturilli, Marion; Neuber, Roland; Edwards, Patti; Yamaguchi, Hajime</p> <p>2015-11-20</p> <p>During <span class="hlt">ice</span>-free periods, the Northern <span class="hlt">Sea</span> Route (NSR) could be an attractive shipping route. The decline in Arctic <span class="hlt">sea-ice</span> extent, however, could be associated with an increase in the frequency of the causes of severe weather phenomena, and high wind-driven waves and the advection of <span class="hlt">sea</span> <span class="hlt">ice</span> could make ship navigation along the NSR difficult. Accurate forecasts of weather and <span class="hlt">sea</span> <span class="hlt">ice</span> are desirable for safe navigation, but large uncertainties exist in current forecasts, partly owing to the sparse observational network over the Arctic Ocean. Here, we show that the incorporation of additional Arctic observations improves the initial analysis and enhances the skill of weather and <span class="hlt">sea-ice</span> forecasts, the application of which has socioeconomic benefits. Comparison of 63-member ensemble atmospheric forecasts, using different initial data sets, revealed that additional Arctic radiosonde observations were useful for predicting a persistent strong wind event. The <span class="hlt">sea-ice</span> forecast, initialised by the wind fields that included the effects of the observations, skilfully predicted rapid wind-driven <span class="hlt">sea-ice</span> advection along the NSR.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4653624','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4653624"><span>Additional Arctic observations improve weather and <span class="hlt">sea-ice</span> forecasts for the Northern <span class="hlt">Sea</span> Route</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Inoue, Jun; Yamazaki, Akira; Ono, Jun; Dethloff, Klaus; Maturilli, Marion; Neuber, Roland; Edwards, Patti; Yamaguchi, Hajime</p> <p>2015-01-01</p> <p>During <span class="hlt">ice</span>-free periods, the Northern <span class="hlt">Sea</span> Route (NSR) could be an attractive shipping route. The decline in Arctic <span class="hlt">sea-ice</span> extent, however, could be associated with an increase in the frequency of the causes of severe weather phenomena, and high wind-driven waves and the advection of <span class="hlt">sea</span> <span class="hlt">ice</span> could make ship navigation along the NSR difficult. Accurate forecasts of weather and <span class="hlt">sea</span> <span class="hlt">ice</span> are desirable for safe navigation, but large uncertainties exist in current forecasts, partly owing to the sparse observational network over the Arctic Ocean. Here, we show that the incorporation of additional Arctic observations improves the initial analysis and enhances the skill of weather and <span class="hlt">sea-ice</span> forecasts, the application of which has socioeconomic benefits. Comparison of 63-member ensemble atmospheric forecasts, using different initial data sets, revealed that additional Arctic radiosonde observations were useful for predicting a persistent strong wind event. The <span class="hlt">sea-ice</span> forecast, initialised by the wind fields that included the effects of the observations, skilfully predicted rapid wind-driven <span class="hlt">sea-ice</span> advection along the NSR. PMID:26585690</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1113752H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1113752H"><span>L-band radiometry for <span class="hlt">sea</span> <span class="hlt">ice</span> applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Heygster, G.; Hedricks, S.; Mills, P.; Kaleschke, L.; Stammer, D.; Tonboe, R.</p> <p>2009-04-01</p> <p>Although <span class="hlt">sea</span> <span class="hlt">ice</span> remote sensing has reached the level of operational exploitation with well established retrieval methods, several important tasks are still unsolved. In particular during freezing and melting periods with mixed <span class="hlt">ice</span> and water surfaces, estimates of <span class="hlt">ice</span> concentration with passive and active microwave sensors remain challenging. Newly formed thin <span class="hlt">ice</span> is also hard to distinguish from open water with radiometers for frequencies above 8 GHz. The SMOS configuration (planned launch 2009) with a radiometer at 1.4 GHz is a promising technique to complement observations at higher microwave frequencies. ESA has initiated a project to investigate the possibilities for an additional Level-2 <span class="hlt">sea</span> <span class="hlt">ice</span> data product based on SMOS. In detail, the project objectives are (1) to model the L band emission of <span class="hlt">sea</span> <span class="hlt">ice</span>, and to assess the potential (2) to retrieve <span class="hlt">sea</span> <span class="hlt">ice</span> parameters, especially concentration and thickness, and (3) to use cold water regions for an external calibration of SMOS. Modelling of L band emission: Several models have are investigated. All of them work on the same basic principles and have a vertically-layered, plane-parallel geometry. They are comprised of three basic components: (1) effective permittivities are calculated for each layer based on <span class="hlt">ice</span> bulk and micro-structural properties; (2) these are integrated across the total depth to derive emitted brightness temperature; (3) scattering terms can also be added because of the granular structure of <span class="hlt">ice</span> and snow. MEMLS (Microwave Emission Model of Layered Snowpacks (Wiesmann and Matzler 1999)) is one such model that contains all three elements in a single Matlab program. In the absence of knowledge about the internal structure of the <span class="hlt">sea</span> <span class="hlt">ice</span>, three-layer (air, <span class="hlt">ice</span> and water) dielectric slab models which take as input a single effective permittivity for the <span class="hlt">ice</span> layer are appropriate. By ignoring scattering effects one can derive a simple analytic expression for a dielectric slab as shown by Apinis and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA242491','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA242491"><span>SSM/I <span class="hlt">Sea</span> <span class="hlt">Ice</span> Products Validation</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>1991-06-01</p> <p>4.0 presents both a coarse resolution <span class="hlt">ice</span> classification algorithm designed to separate different <span class="hlt">ice</span> types from highly multilooked SAR imagery, along...algorithms. The coarse resolution algorithm uses SAR data which is highly multilooked to reduce the effects of the multiplicative noise inherent to all...data. This paper will present comparisons between these SSM/I derived <span class="hlt">ice</span> concentration estimates and estimates derived from high resolution SAR data</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040081367&hterms=arctic+temperature&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Darctic%2Btemperature','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040081367&hterms=arctic+temperature&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Darctic%2Btemperature"><span>EOS Aqua AMSR-E Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Validation Program: Intercomparison Between Modeled and Measured <span class="hlt">Sea</span> <span class="hlt">Ice</span> Brightness Temperatures</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Stroeve, J.; Markus, T.; Cavalieri, D. J.; Maslanik, J.; Sturm, M.; Henrichs, J.; Gasiewski, A.; Klein, M.</p> <p>2004-01-01</p> <p>During March 2003, an extensive field campaign was conducted near Barrow, Alaska to validate AQUA Advanced Microwave Scanning Radiometer (AMSR) <span class="hlt">sea</span> <span class="hlt">ice</span> products. Field, airborne and satellite data were collected over three different types of <span class="hlt">sea</span> <span class="hlt">ice</span>: 1) first year <span class="hlt">ice</span> with little deformation, 2) first year <span class="hlt">ice</span> with various amounts of deformation and 3) mixed first year <span class="hlt">ice</span> and multi-year <span class="hlt">ice</span> with various degrees of deformation. The validation plan relies primarily on comparisons between satellite, aircraft flights and ground-based measurements. Although these efforts are important, key aspects such as the effects of atmospheric conditions, snow properties, surface roughness, melt processes, etc on the <span class="hlt">sea</span> <span class="hlt">ice</span> algorithms are not sufficiently well understood or documented. To improve our understanding of these effects, we combined the detailed, in-situ data collection from the 2003 field campaign with radiance modeling using a radiative transfer model to simulate the top of the atmosphere AMSR brightness temperatures. This study reports on the results of the simulations for a variety of snow and <span class="hlt">ice</span> types and compares the results with the National Oceanographic and Atmospheric Administration Environmental Technology Laboratory Polarimetric Scanning Radiometer (NOAA) (ETL) (PSR) microwave radiometer that was flown on the NASA P-3.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011PhDT.......110D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011PhDT.......110D"><span>Alaska shorefast <span class="hlt">ice</span>: Interfacing geophysics with local <span class="hlt">sea</span> <span class="hlt">ice</span> knowledge and use</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Druckenmiller, Matthew L.</p> <p></p> <p>This thesis interfaces geophysical techniques with local and traditional knowledge (LTK) of indigenous <span class="hlt">ice</span> experts to track and evaluate coastal <span class="hlt">sea</span> <span class="hlt">ice</span> conditions over annual and inter-annual timescales. A novel approach is presented for consulting LTK alongside a systematic study of where, when, and how the community of Barrow, Alaska uses the <span class="hlt">ice</span> cover. The goal of this research is to improve our understanding of and abilities to monitor the processes that govern the state and dynamics of shorefast <span class="hlt">sea</span> <span class="hlt">ice</span> in the Chukchi <span class="hlt">Sea</span> and use of <span class="hlt">ice</span> by the community. Shorefast <span class="hlt">ice</span> stability and community strategies for safe hunting provide a framework for data collection and knowledge sharing that reveals how nuanced observations by Inupiat <span class="hlt">ice</span> experts relate to identifying hazards. In particular, shorefast <span class="hlt">ice</span> break-out events represent a significant threat to the lives of hunters. Fault tree analysis (FTA) is used to combine local and time-specific observations of <span class="hlt">ice</span> conditions by both geophysical instruments and local experts, and to evaluate how <span class="hlt">ice</span> features, atmospheric and oceanic forces, and local to regional processes interact to cause break-out events. Each year, the Barrow community builds trails across shorefast <span class="hlt">ice</span> for use during the spring whaling season. In collaboration with hunters, a systematic multi-year survey (2007--2011) was performed to map these trails and measure <span class="hlt">ice</span> thickness along them. Relationships between <span class="hlt">ice</span> conditions and hunter strategies that guide trail placement and risk assessment are explored. In addition, trail surveys provide a meaningful and consistent approach to monitoring the thickness distribution of shorefast <span class="hlt">ice</span>, while establishing a baseline for assessing future environmental change and potential impacts to the community. Coastal communities in the region have proven highly adaptive in their ability to safely and successfully hunt from <span class="hlt">sea</span> <span class="hlt">ice</span> over the last 30 years as significant changes have been observed in the <span class="hlt">ice</span> zone</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/17363664','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/17363664"><span>Perspectives on the Arctic's shrinking <span class="hlt">sea-ice</span> cover.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Serreze, Mark C; Holland, Marika M; Stroeve, Julienne</p> <p>2007-03-16</p> <p>Linear trends in arctic <span class="hlt">sea-ice</span> extent over the period 1979 to 2006 are negative in every month. This <span class="hlt">ice</span> loss is best viewed as a combination of strong natural variability in the coupled <span class="hlt">ice</span>-ocean-atmosphere system and a growing radiative forcing associated with rising concentrations of atmospheric greenhouse gases, the latter supported by evidence of qualitative consistency between observed trends and those simulated by climate models over the same period. Although the large scatter between individual model simulations leads to much uncertainty as to when a seasonally <span class="hlt">ice</span>-free Arctic Ocean might be realized, this transition to a new arctic state may be rapid once the <span class="hlt">ice</span> thins to a more vulnerable state. Loss of the <span class="hlt">ice</span> cover is expected to affect the Arctic's freshwater system and surface energy budget and could be manifested in middle latitudes as altered patterns of atmospheric circulation and precipitation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020060091','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020060091"><span>A Rapidly Declining Arctic Perennial <span class="hlt">Sea</span> <span class="hlt">Ice</span> Cover</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Comiso, Josefino C.; Koblinsky, Chester J.