Sample records for ice extent sea

  1. Trend analysis of Arctic sea ice extent

    NASA Astrophysics Data System (ADS)

    Silva, M. E.; Barbosa, S. M.; Antunes, Luís; Rocha, Conceição

    2009-04-01

    The extent of Arctic sea ice is a fundamental parameter of Arctic climate variability. In the context of climate change, the area covered by ice in the Arctic is a particularly useful indicator of recent changes in the Arctic environment. Climate models are in near universal agreement that Arctic sea ice extent will decline through the 21st century as a consequence of global warming and many studies predict a ice free Arctic as soon as 2012. Time series of satellite passive microwave observations allow to assess the temporal changes in the extent of Arctic sea ice. Much of the analysis of the ice 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 sea ice extent time series available at National Snow and Ice 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 sea ice extent. Therefore, alternative parametrizations such as long-range dependence should be considered for characterising long-term Arctic sea ice variability.

  2. Probabilistic Forecasting of Arctic Sea Ice Extent

    NASA Astrophysics Data System (ADS)

    Slater, A. G.

    2013-12-01

    Sea ice in the Arctic is changing rapidly. Most noticeable has been the series of record, or near-record, annual minimums in sea ice extent in the past six years. The changing regime of sea ice has prompted much interest in seasonal prediction of sea ice extent, particularly as opportunities for Arctic shipping and resource exploration or extraction increase. This study presents a daily sea ice extent probabilistic forecast method with a 50-day lead time. A base projection is made from historical data and near-real-time sea ice concentration is assimilated on the issue date of the forecast. When considering the September mean ice extent for the period 1995-2012, the performance of the 50-day lead time forecast is very good: correlation=0.94, Bias = 0.14 ×106 km^2 and RMSE = 0.36 ×106 km^2. Forecasts for the daily minimum contains equal skill levels. The system is highly competitive with any of the SEARCH Sea Ice Outlook estimates. The primary finding of this study is that large amounts of forecast skill can be gained from knowledge of the initial conditions of concentration (perhaps more than previously thought). Given the simplicity of the forecast model, improved skill should be available from system refinement and with suitable proxies for large scale atmosphere and ocean circulation.

  3. Global Warming and Northern Hemisphere Sea Ice Extent.

    PubMed

    Vinnikov; Robock; Stouffer; Walsh; Parkinson; Cavalieri; Mitchell; Garrett; Zakharov

    1999-12-03

    Surface and satellite-based observations show a decrease in Northern Hemisphere sea ice extent during the past 46 years. A comparison of these trends to control and transient integrations (forced by observed greenhouse gases and tropospheric sulfate aerosols) from the Geophysical Fluid Dynamics Laboratory and Hadley Centre climate models reveals that the observed decrease in Northern Hemisphere sea ice extent agrees with the transient simulations, and both trends are much larger than would be expected from natural climate variations. From long-term control runs of climate models, it was found that the probability of the observed trends resulting from natural climate variability, assuming that the models' natural variability is similar to that found in nature, is less than 2 percent for the 1978-98 sea ice trends and less than 0.1 percent for the 1953-98 sea ice trends. Both models used here project continued decreases in sea ice thickness and extent throughout the next century.

  4. A Model Assessment of Satellite Observed Trends in Polar Sea Ice Extents

    NASA Technical Reports Server (NTRS)

    Vinnikov, Konstantin Y.; Cavalieri, Donald J.; Parkinson, Claire L.

    2005-01-01

    For more than three decades now, satellite passive microwave observations have been used to monitor polar sea ice. Here we utilize sea ice extent trends determined from primarily satellite data for both the Northern and Southern Hemispheres for the period 1972(73)-2004 and compare them with results from simulations by eleven climate models. In the Northern Hemisphere, observations show a statistically significant decrease of sea ice extent and an acceleration of sea ice retreat during the past three decades. However, from the modeled natural variability of sea ice extents in control simulations, we conclude that the acceleration is not statistically significant and should not be extrapolated into the future. Observations and model simulations show that the time scale of climate variability in sea ice extent in the Southern Hemisphere is much larger than in the Northern Hemisphere and that the Southern Hemisphere sea ice extent trends are not statistically significant.

  5. Variability and Trends in Sea Ice Extent and Ice Production in the Ross Sea

    NASA Technical Reports Server (NTRS)

    Comiso, Josefino; Kwok, Ronald; Martin, Seelye; Gordon, Arnold L.

    2011-01-01

    Salt release during sea ice formation in the Ross Sea 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 sea ice cover of the Ross Sea and the adjacent Bellingshausen and Amundsen seas. For this period the sea ice extent in the Ross Sea shows the greatest increase of all the Antarctic seas. Variability in the ice cover in these regions is linked to changes in the Southern Annular Mode and secondarily to the Antarctic Circumpolar Wave. Over the Ross Sea shelf, analysis of sea ice drift data from 1992 to 2008 yields a positive rate of increase in the net ice export of about 30,000 sq km/yr. For a characteristic ice 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 ice production. The increase in brine rejection in the Ross Shelf Polynya associated with the estimated increase with the ice production, however, is not consistent with the reported Ross Sea salinity decrease. The locally generated sea ice enhancement of Ross Sea salinity may be offset by an increase of relatively low salinity of the water advected into the region from the Amundsen Sea, a consequence of increased precipitation and regional glacial ice melt.

  6. Holocene sea surface temperature and sea ice extent in the Okhotsk and Bering Seas

    USGS Publications Warehouse

    Harada, Naomi; Katsuki, Kota; Nakagawa, Mitsuhiro; Matsumoto, Akiko; Seki, Osamu; Addison, Jason A.; Finney, Bruce P.; Sato, Miyako

    2014-01-01

    Accurate prediction of future climate requires an understanding of the mechanisms of the Holocene climate; however, the driving forces, mechanisms, and processes of climate change in the Holocene associated with different time scales remain unclear. We investigated the drivers of Holocene sea surface temperature (SST) and sea ice extent in the North Pacific Ocean, and the Okhotsk and Bering Seas, as inferred from sediment core records, by using the alkenone unsaturation index as a biomarker of SST and abundances of sea ice-related diatoms (F. cylindrus and F. oceanica) as an indicator of sea ice extent to explore controlling mechanisms in the high-latitude Pacific. Temporal changes in alkenone content suggest that alkenone production was relatively high during the middle Holocene in the Okhotsk Sea and the western North Pacific, but highest in the late Holocene in the eastern Bering Sea and the eastern North Pacific. The Holocene variations of alkenone-SSTs at sites near Kamchatka in the Northwest Pacific, as well as in the western and eastern regions of the Bering Sea, and in the eastern North Pacific track the changes of Holocene summer insolation at 50°N, but at other sites in the western North Pacific, in the southern Okhotsk Sea, and the eastern Bering Sea they do not. In addition to insolation, other atmosphere and ocean climate drivers, such as sea ice distribution and changes in the position and activity of the Aleutian Low, may have systematically influenced the timing and magnitude of warming and cooling during the Holocene within the subarctic North Pacific. Periods of high sea ice extent in both the Okhotsk and Bering Seas may correspond to some periods of frequent or strong winter–spring dust storms in the Mongolian Gobi Desert, particularly one centered at ∼4–3 thousand years before present (kyr BP). Variation in storm activity in the Mongolian Gobi Desert region may reflect changes in the strength and positions of the Aleutian Low and Siberian

  7. Springtime atmospheric transport controls Arctic summer sea-ice extent

    NASA Astrophysics Data System (ADS)

    Kapsch, Marie; Graversen, Rune; Tjernström, Michael

    2013-04-01

    The sea-ice extent in the Arctic has been steadily decreasing during the satellite remote sensing era, 1979 to present, with the highest rate of retreat found in September. Contributing factors causing the ice retreat are among others: changes in surface air temperature (SAT; Lindsay and Zhang, 2005), ice circulation in response to winds/pressure patterns (Overland et al., 2008) and ocean currents (Comiso et al., 2008), as well as changes in radiative fluxes (e.g. due to changes in cloud cover; Francis and Hunter, 2006; Maksimovich and Vihma, 2012) and ocean conditions. However, large interannual variability is superimposed onto the declining trend - the ice extent by the end of the summer varies by several million square kilometer between successive years (Serreze et al., 2007). But what are the processes causing the year-to-year ice variability? A comparison of years with an anomalously large September sea-ice extent (HIYs - high ice years) with years showing an anomalously small ice extent (LIYs - low ice years) reveals that the ice variability is most pronounced in the Arctic Ocean north of Siberia (which became almost entirely ice free in September of 2007 and 2012). Significant ice-concentration anomalies of up to 30% are observed for LIYs and HIYs in this area. Focusing on this area we find that the greenhouse effect associated with clouds and water-vapor in spring is crucial for the development of the sea ice during the subsequent months. In years where the end-of-summer sea-ice extent is well below normal, a significantly enhanced transport of humid air is evident during spring into the region where the ice retreat is encountered. The anomalous convergence of humidity increases the cloudiness, resulting in an enhancement of the greenhouse effect. As a result, downward longwave radiation at the surface is larger than usual. In mid May, when the ice anomaly begins to appear and the surface albedo therefore becomes anomalously low, the net shortwave radiation

  8. Arctic sea ice decline: Projected changes in timing and extent of sea ice in the Bering and Chukchi Seas

    USGS Publications Warehouse

    Douglas, David C.

    2010-01-01

    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 ice cover. One consequence has been a rapid decline in Arctic sea ice 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 sea ice Arctic-wide, and for specific regions. To inform the public and decision makers of anticipated environmental changes, scientists are striving to better understand how sea ice influences ecosystem structure, local weather, and global climate. Here, projected changes in the Bering and Chukchi Seas are examined because sea ice influences the presence of, or accessibility to, a variety of local resources of commercial and cultural value. In this study, 21st century sea ice conditions in the Bering and Chukchi Seas 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. Sea ice 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 ice extent and seasonality. At the end of the 21st century (2090-2099), median sea ice projections among all combinations of model ensemble and forcing scenario were qualitatively similar. June is projected to experience the least amount of sea ice loss among all months. For the Chukchi Sea, projections show extensive ice melt during July and ice-free conditions during August, September, and October by the end of the century, with high agreement

  9. Moving beyond the total sea ice extent in gauging model biases

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Ivanova, Detelina P.; Gleckler, Peter J.; Taylor, Karl E.

    Here, reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. This study describes several approaches to improve how model biases in total sea ice distribution are quantified, and applies them to historically forced simulations contributed to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent, is often used to evaluate model performance. A new approach is introduced that investigates additional details about the structure of model errors, with an aim to reduce the potential impact of compensating errors when gauging differencesmore » between simulated and observed sea ice. Using multiple observational datasets, several new methods are applied to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. It is shown that in some models, error compensation can be substantial, for example resulting from too much sea ice in one region and too little in another. Error compensation tends to be larger in models that agree more closely with the observed total sea ice area, which may result from model tuning. The results herein suggest that consideration of only the total hemispheric sea ice area or extent can be misleading when quantitatively comparing how well models agree with observations. Further work is needed to fully develop robust methods to holistically evaluate the ability of models to capture the finescale structure of sea ice characteristics; however, the “sector scale” metric used here aids in reducing the impact of compensating errors in hemispheric integrals.« less

  10. Moving beyond the total sea ice extent in gauging model biases

    DOE PAGES

    Ivanova, Detelina P.; Gleckler, Peter J.; Taylor, Karl E.; ...

    2016-11-29

    Here, reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. This study describes several approaches to improve how model biases in total sea ice distribution are quantified, and applies them to historically forced simulations contributed to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent, is often used to evaluate model performance. A new approach is introduced that investigates additional details about the structure of model errors, with an aim to reduce the potential impact of compensating errors when gauging differencesmore » between simulated and observed sea ice. Using multiple observational datasets, several new methods are applied to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. It is shown that in some models, error compensation can be substantial, for example resulting from too much sea ice in one region and too little in another. Error compensation tends to be larger in models that agree more closely with the observed total sea ice area, which may result from model tuning. The results herein suggest that consideration of only the total hemispheric sea ice area or extent can be misleading when quantitatively comparing how well models agree with observations. Further work is needed to fully develop robust methods to holistically evaluate the ability of models to capture the finescale structure of sea ice characteristics; however, the “sector scale” metric used here aids in reducing the impact of compensating errors in hemispheric integrals.« less

  11. Bellingshausen Sea Ice Extent Recorded in an Antarctic Peninsula Ice Core

    NASA Technical Reports Server (NTRS)

    Porter, Stacy E.; Parkinson, Claire L.; Mosley-Thompson, Ellen

    2016-01-01

    Annual net accumulation (A(sub n)) from the Bruce Plateau (BP) ice core retrieved from the Antarctic Peninsula exhibits a notable relationship with sea ice extent (SIE) in the Bellingshausen Sea. Over the satellite era, both BP A(sub n) and Bellingshausen SIE are influenced by large-scale climatic factors such as the Amundsen Sea Low, Southern Annular Mode, and Southern Oscillation. In addition to the direct response of BP A(sub n) to Bellingshausen SIE (e.g., more open water as a moisture source), these large-scale climate phenomena also link the BP and the Bellingshausen Sea indirectly such that they exhibit similar responses (e.g., northerly wind anomalies advect warm, moist air to the Antarctic Peninsula and neighboring Bellingshausen Sea, which reduces SIE and increases A(sub n)). Comparison with a time series of fast ice at South Orkney Islands reveals a relationship between BP A(sub n) and sea ice in the northern Weddell Sea 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 approximately 1970 agrees with other climate records and reconstructions in the region and suggests that the current rate of sea ice loss in the Bellingshausen Sea is unrivaled in the twentieth century.

  12. Changes in the Areal Extent of Arctic Sea Ice: Observations from Satellites

    NASA Technical Reports Server (NTRS)

    Parkinson, Claire L.

    2000-01-01

    Wintertime sea ice covers 15 million square kilometers of the north polar region, an area exceeding one and a half times the area of the U. S. Even at the end of the summer melt season, sea ice still covers 7 million square kilometers. This vast ice cover is an integral component of the climate system, being moved around by winds and waves, restricting heat and other exchanges between the ocean and atmosphere, reflecting most of the solar radiation incident on it, transporting cold, relatively fresh water equatorward, and affecting the overturning of ocean waters underneath, with impacts that can be felt worldwide. Sea ice also is a major factor in the Arctic ecosystem, affecting life forms ranging from minute organisms living within the ice, sometimes to the tune of millions in a single ice floe, to large marine mammals like walruses that rely on sea ice as a platform for resting, foraging, social interaction, and breeding. Since 1978, satellite technology has allowed the monitoring of the vast Arctic sea ice cover on a routine basis. The satellite observations reveal that, overall, the areal extent of Arctic sea ice has been decreasing since 1978, at an average rate of 2.7% per decade through the end of 1998. Through 1998, the greatest rates of decrease occurred in the Seas of Okhotsk and Japan and the Kara and Barents Seas, with most other regions of the Arctic also experiencing ice extent decreases. The two regions experiencing ice extent increases over this time period were the Bering Sea and the Gulf of St. Lawrence. Furthermore, the satellite data reveal that the sea ice season shortened by over 25 days per decade in the central Sea of Okhotsk and the eastern Barents Sea, and by lesser amounts throughout much of the rest of the Arctic seasonal sea ice region, although not in the Bering Sea or the Gulf of St. Lawrence. Concern has been raised that if the trends toward shortened sea ice seasons and lesser sea ice coverage continue, this could entail major

  13. Modulation of the Seasonal Cycle of Antarctic Sea Ice Extent Related to the Southern Annular Mode

    NASA Astrophysics Data System (ADS)

    Doddridge, Edward W.; Marshall, John

    2017-10-01

    Through analysis of remotely sensed sea surface temperature (SST) and sea ice concentration data, we investigate the impact of winds related to the Southern Annular Mode (SAM) on sea ice extent around Antarctica. We show that positive SAM anomalies in the austral summer are associated with anomalously cold SSTs that persist and lead to anomalous ice growth in the following autumn, while negative SAM anomalies precede warm SSTs and a reduction in sea ice extent during autumn. The largest effect occurs in April, when a unit change in the detrended summertime SAM is followed by a 1.8±0.6 ×105 km2 change in detrended sea ice extent. We find no evidence that sea ice extent anomalies related to the summertime SAM affect the wintertime sea ice extent maximum. Our analysis shows that the wind anomalies related to the negative SAM during the 2016/2017 austral summer contributed to the record minimum Antarctic sea ice extent observed in March 2017.

  14. The impact of lower sea-ice extent on Arctic greenhouse-gas exchange

    USGS Publications Warehouse

    Parmentier, Frans-Jan W.; Christensen, Torben R.; Sørensen, Lise Lotte; Rysgaard, Søren; McGuire, A. David; Miller, Paul A.; Walker, Donald A.

    2013-01-01

    In September 2012, Arctic sea-ice extent plummeted to a new record low: two times lower than the 1979–2000 average. Often, record lows in sea-ice cover are hailed as an example of climate change impacts in the Arctic. Less apparent, however, are the implications of reduced sea-ice cover in the Arctic Ocean for marine–atmosphere CO2 exchange. Sea-ice decline has been connected to increasing air temperatures at high latitudes. Temperature is a key controlling factor in the terrestrial exchange of CO2 and methane, and therefore the greenhouse-gas balance of the Arctic. Despite the large potential for feedbacks, many studies do not connect the diminishing sea-ice extent with changes in the interaction of the marine and terrestrial Arctic with the atmosphere. In this Review, we assess how current understanding of the Arctic Ocean and high-latitude ecosystems can be used to predict the impact of a lower sea-ice cover on Arctic greenhouse-gas exchange.

  15. Dynamic and thermodynamic impacts of the winter Arctic Oscillation on summer sea ice extent.

    NASA Astrophysics Data System (ADS)

    Park, H. S.; Stewart, A.

    2017-12-01

    Arctic summer sea ice extent exhibits substantial interannual variability, as is highlighted by the remarkable recovery in sea ice extent in 2013 following the record minimum in the summer of 2012. Here, we explore the mechanism via which Arctic Oscillation (AO)-induced ice thickness changes impact summer sea ice, using observations and reanalysis data. A positive AO weakens the basin-scale anticyclonic sea ice drift and decreases the winter ice thickness by 15cm and 10cm in the Eurasian and the Pacific sectors of the Arctic respectively. Three reanalysis datasets show that the (upward) surface heat fluxes are reduced over wide areas of the Arctic, suppressing the ice growth during the positive AO winters. The winter dynamic and thermodynamic thinning preconditions the ice for enhanced radiative forcing via the ice-albedo feedback in late spring-summer, leading to an additional 8-10 cm of thinning over the Pacific sector of the Arctic. Because of these winter AO-induced dynamic and thermodynamics effects, the winter AO explains about 22% (r = -0.48) of the interannual variance of September sea ice extent from year 1980 to 2015.

  16. Sea salt sodium record from Talos Dome (East Antarctica) as a potential proxy of the Antarctic past sea ice extent.

    PubMed

    Severi, M; Becagli, S; Caiazzo, L; Ciardini, V; Colizza, E; Giardi, F; Mezgec, K; Scarchilli, C; Stenni, B; Thomas, E R; Traversi, R; Udisti, R

    2017-06-01

    Antarctic sea ice has shown an increasing trend in recent decades, but with strong regional differences from one sector to another of the Southern Ocean. The Ross Sea and the Indian sectors have seen an increase in sea ice during the satellite era (1979 onwards). Here we present a record of ssNa + flux in the Talos Dome region during a 25-year period spanning from 1979 to 2003, showing that this marker could be used as a potential proxy for reconstructing the sea ice extent in the Ross Sea and Western Pacific Ocean at least for recent decades. After finding a positive relationship between the maxima in sea ice extent for a 25-year period, we used this relationship in the TALDICE record in order to reconstruct the sea ice conditions over the 20th century. Our tentative reconstruction highlighted a decline in the sea ice extent (SIE) starting in the 1950s and pointed out a higher variability of SIE starting from the 1960s and that the largest sea ice extents of the last century occurred during the 1990s. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Large-scale variations in observed Antarctic Sea ice extent and associated atmospheric circulation

    NASA Technical Reports Server (NTRS)

    Cavalieri, D. J.; Parkinson, C. L.

    1981-01-01

    The 1974 Antarctic large scale sea ice extent is studied from data from Nimbus 2 and 5 and temperature and sea level pressure fields from the Australian Meteorological Data Set. Electrically Scanning Microwave Radiometer data were three-day averaged and compared with 1000 mbar atmospheric pressure and sea level pressure data, also in three-day averages. Each three-day period was subjected to a Fourier analysis and included the mean latitude of the ice extent and the phases and percent variances in terms of the first six Fourier harmonics. Centers of low pressure were found to be generally east of regions which displayed rapid ice growth, and winds acted to extend the ice equatorward. An atmospheric response was also noted as caused by the changing ice cover.

  18. The Impact of a Lower Sea Ice Extent on Arctic Greenhouse Gas Exchange

    NASA Astrophysics Data System (ADS)

    Parmentier, Frans-Jan W.; Christensen, Torben R.; Lotte Sørensen, Lise; Rysgaard, Søren; McGuire, A. David; Miller, Paul A.; Walker, Donald A.

    2013-04-01

    Arctic sea ice extent hit a new record low in September 2012, when it fell to a level about two times lower than the 1979-2000 average. Record low sea ice extents such as these are often hailed as an obvious example of the impact of climate change on the Arctic. Less obvious, however, are the further implications of a lower sea ice extent on Arctic greenhouse gas exchange. For example, a reduction in sea ice, in consort with a lower snow cover, has been connected to higher surface temperatures in the terrestrial part of the Arctic (Screen et al., 2012). These higher temperatures and longer growing seasons have the potential to alter the CO2 balance of Arctic tundra through enhanced photosynthesis and respiration, as well as the magnitude of methane emissions. In fact, large changes are already observed in terrestrial ecosystems (Post et al., 2009), and concerns have been raised of large releases of carbon through permafrost thaw (Schuur et al., 2011). While these changes in the greenhouse gas balance of the terrestrial Arctic are described in numerous studies, a connection with a decline in sea ice extent is nonetheless seldom made. In addition to these changes on land, a lower sea ice extent also has a direct effect on the exchange of greenhouse gases between the ocean and the atmosphere. For example, due to sea ice retreat, more ocean surface remains in contact with the atmosphere, and this has been suggested to increase the oceanic uptake of CO2 (Bates et al., 2006). However, the sustainability of this increased uptake is uncertain (Cai et al., 2010), and carbon fluxes related directly to the sea ice itself add much uncertainty to the oceanic uptake of CO2 (Nomura et al., 2006; Rysgaard et al., 2007). Furthermore, significant emissions of methane from the Arctic Ocean have been observed (Kort et al., 2012; Shakhova et al., 2010), but the consequence of a lower sea ice extent thereon is still unclear. Overall, the decline in sea ice that has been seen in recent

  19. Response of Arctic Snow and Sea Ice Extents to Melt Season Atmospheric Forcing Across the Land-Ocean Boundary

    NASA Astrophysics Data System (ADS)

    Bliss, A. C.; Anderson, M. R.

    2011-12-01

    Little research has gone into studying the concurrent variations in the annual loss of continental snow cover and sea ice extent across the land-ocean boundary, however, the analysis of these data averaged spatially over three study regions located in North America and Eastern and Western Russia, reveals a distinct difference in the response of anomalous snow and sea ice conditions to the atmospheric forcing. This study compares the monthly continental snow cover and sea ice extent loss in the Arctic, during the melt season months (May-August) for the period 1979-2007, with regional atmospheric conditions known to influence summer melt including: mean sea level pressures, 925 hPa air temperatures, and mean 2 m U and V wind vectors from NCEP/DOE Reanalysis 2. The monthly hemispheric snow cover extent data used are from the Rutgers University Global Snow Lab and sea ice extents for this study are derived from the monthly passive microwave satellite Bootstrap algorithm sea ice concentrations available from the National Snow and Ice Data Center. Three case study years (1985, 1996, and 2007) are used to compare the direct response of monthly anomalous sea ice and snow cover areal extents to monthly mean atmospheric forcing averaged spatially over the extent of each study region. This comparison is then expanded for all summer months over the 29 year study period where the monthly persistence of sea ice and snow cover extent anomalies and changes in the sea ice and snow conditions under differing atmospheric conditions are explored further. The monthly anomalous atmospheric conditions are classified into four categories including: warmer temperatures with higher pressures, warmer temperatures with lower pressures, cooler temperatures with higher pressures, and cooler temperatures with lower pressures. Analysis of the atmospheric conditions surrounding anomalous loss of snow and ice cover over the independent study regions indicates that conditions of warmer temperatures

  20. Critical Mechanisms for the Formation of Extreme Arctic Sea-Ice Extent in the Summers of 2007 and 1996

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Dong, Xiquan; Zib, Benjamin J.; Xi, Baike

    A warming Arctic climate is undergoing significant e 21 nvironmental change, most evidenced by the reduction of Arctic sea-ice extent during the summer. In this study, we examine two extreme anomalies of September sea-ice extent in 2007 and 1996, and investigate the impacts of cloud fraction (CF), atmospheric precipitable water vapor (PWV), downwelling longwave flux (DLF), surface air temperature (SAT), pressure and winds on the sea-ice variation in 2007 and 1996 using both satellite-derived sea-ice products and MERRA reanalysis. The area of the Laptev, East Siberian and West Chukchi seas (70-90oN, 90-180oE) has experienced the largest variation in sea-ice extentmore » from year-to-year and defined here as the Area Of Focus (AOF). The record low September sea-ice extent in 2007 was associated with positive anomalies 30 of CF, PWV, DLF, and SAT over the AOF. Persistent anti-cyclone positioned over the Beaufort Sea coupled with low pressure over Eurasia induced easterly zonal and southerly meridional winds. In contrast, negative CF, PWV, DLF and SAT anomalies, as well as opposite wind patterns to those in 2007, characterized the 1996 high September sea-ice extent. Through this study, we hypothesize the following positive feedbacks of clouds, water vapor, radiation and atmospheric variables on the sea-ice retreat during the summer 2007. The record low sea-ice extent during the summer 2007 is initially triggered by the atmospheric circulation anomaly. The southerly winds across the Chukchi and East Siberian seas transport warm, moist air from the north Pacific, which is not only enhancing sea-ice melt across the AOF, but also increasing clouds. The positive cloud feedback results in higher SAT and more sea-ice melt. Therefore, 40 more water vapor could be evaporated from open seas and higher SAT to form more clouds, which will enhance positive cloud feedback. This enhanced positive cloud feedback will then further increase SAT and accelerate the sea-ice retreat

  1. Conditions leading to the unprecedented low Antarctic sea ice extent during the 2016 austral spring season

    NASA Astrophysics Data System (ADS)

    Stuecker, Malte F.; Bitz, Cecilia M.; Armour, Kyle C.

    2017-09-01

    The 2016 austral spring was characterized by the lowest Southern Hemisphere (SH) sea ice extent seen in the satellite record (1979 to present) and coincided with anomalously warm surface waters surrounding most of Antarctica. We show that two distinct processes contributed to this event: First, the extreme El Niño event peaking in December-February 2015/2016 contributed to pronounced extratropical SH sea surface temperature and sea ice extent anomalies in the eastern Ross, Amundsen, and Bellingshausen Seas that persisted in part until the following 2016 austral spring. Second, internal unforced atmospheric variability of the Southern Annular Mode promoted the exceptional low sea ice extent in November-December 2016. These results suggest that a combination of tropically forced and internal SH atmospheric variability contributed to the unprecedented sea ice decline during the 2016 austral spring, on top of a background of slow changes expected from greenhouse gas and ozone forcing.

  2. Data-adaptive Harmonic Decomposition and Real-time Prediction of Arctic Sea Ice Extent

    NASA Astrophysics Data System (ADS)

    Kondrashov, Dmitri; Chekroun, Mickael; Ghil, Michael

    2017-04-01

    Decline in the Arctic sea ice extent (SIE) has profound socio-economic implications and is a focus of active scientific research. Of particular interest is prediction of SIE on subseasonal time scales, i.e. from early summer into fall, when sea ice coverage in Arctic reaches its minimum. However, subseasonal forecasting of SIE is very challenging due to the high variability of ocean and atmosphere over Arctic in summer, as well as shortness of observational data and inadequacies of the physics-based models to simulate sea-ice dynamics. The Sea Ice Outlook (SIO) by Sea Ice Prediction Network (SIPN, http://www.arcus.org/sipn) is a collaborative effort to facilitate and improve subseasonal prediction of September SIE by physics-based and data-driven statistical models. Data-adaptive Harmonic Decomposition (DAH) and Multilayer Stuart-Landau Models (MSLM) techniques [Chekroun and Kondrashov, 2017], have been successfully applied to the nonlinear stochastic modeling, as well as retrospective and real-time forecasting of Multisensor Analyzed Sea Ice Extent (MASIE) dataset in key four Arctic regions. In particular, DAH-MSLM predictions outperformed most statistical models and physics-based models in real-time 2016 SIO submissions. The key success factors are associated with DAH ability to disentangle complex regional dynamics of MASIE by data-adaptive harmonic spatio-temporal patterns that reduce the data-driven modeling effort to elemental MSLMs stacked per frequency with fixed and small number of model coefficients to estimate.

  3. Recalculated Areas for Maximum Ice Extents of the Baltic Sea During Winters 1971-2008

    NASA Astrophysics Data System (ADS)

    Niskanen, T.; Vainio, J.; Eriksson, P.; Heiler, I.

    2009-04-01

    Publication of operational ice charts in Finland was started from the Baltic Sea in a year 1915. Until year 1993 all ice charts were hand drawn paper copies but in the year 1993 ice charting software IceMap was introduced. Since then all ice charts were produced digitally. Since the year 1996 IceMap has had an option that user can calculate areas of single ice area polygons in the chart. Using this option the area of the maximum ice extent can be easily solved fully automatically. Before this option was introduced (and in full operation) all maximum extent areas were calculated manually by a planimeter. During recent years it has become clear that some areas calculated before 1996 don't give the same result as IceMap. Differences can come from for example inaccuracy of old coastlines, map projections, the calibration of the planimeter or interpretation of old ice area symbols. Old ice charts since winter 1970-71 have now been scanned, rectified and re-drawn. New maximum ice extent areas for Baltic Sea have now been re-calculated. By these new technological tools it can be concluded that in some cases clear differences can be found.

  4. Seasonal regional forecast of the minimum sea ice extent in the LapteV Sea

    NASA Astrophysics Data System (ADS)

    Tremblay, B.; Brunette, C.; Newton, R.

    2017-12-01

    Late winter anomaly of sea ice export from the peripheral seas of the Atctic Ocean was found to be a useful predictor for the minimum sea ice extent (SIE) in the Arctic Ocean (Williams et al., 2017). In the following, we present a proof of concept for a regional seasonal forecast of the min SIE for the Laptev Sea based on late winter coastal divergence quantified using a Lagrangian Ice Tracking System (LITS) forced with satellite derived sea-ice drifts from the Polar Pathfinder. Following Nikolaeva and Sesterikov (1970), we track an imaginary line just offshore of coastal polynyas in the Laptev Sea from December of the previous year to May 1 of the following year using LITS. Results show that coastal divergence in the Laptev Sea between February 1st and May 1st is best correlated (r = -0.61) with the following September minimum SIE in accord with previous results from Krumpen et al. (2013, for the Laptev Sea) and Williams et a. (2017, for the pan-Arctic). This gives a maximum seasonal predictability of Laptev Sea min SIE anomalies from observations of approximately 40%. Coastal ice divergence leads to formation of thinner ice that melts earlier in early summer, hence creating areas of open water that have a lower albedo and trigger an ice-albedo feedback. In the Laptev Sea, we find that anomalies of coastal divergence in late winter are amplified threefold to result in the September SIE. We also find a correlation coefficient r = 0.49 between February-March-April (FMA) anomalies of coastal divergence with the FMA averaged AO index. Interestingly, the correlation is stronger, r = 0.61, when comparing the FMA coastal divergence anomalies to the DJFMA averaged AO index. It is hypothesized that the AO index at the beginning of the winter (and the associated anomalous sea ice export) also contains information that impact the magnitude of coastal divergence opening later in the winter. Our approach differs from previous approaches (e.g. Krumpen et al and Williams et al

  5. Seasonal and interannual variability of fast ice extent in the southeastern Laptev Sea between 1999 and 2013

    NASA Astrophysics Data System (ADS)

    Selyuzhenok, V.; Krumpen, T.; Mahoney, A.; Janout, M.; Gerdes, R.

    2015-12-01

    Along with changes in sea ice extent, thickness, and drift speed, Arctic sea ice regime is characterized by a decrease of fast ice season and reduction of fast ice extent. The most extensive fast ice cover in the Arctic develops in the southeastern Laptev Sea. Using weekly operational sea ice charts produced by Arctic and Antarctic Research Institute (AARI, Russia) from 1999 to 2013, we identified five main key events that characterize the annual evolution of fast ice in the southeastern Laptev Sea. Linking the occurrence of the key events with the atmospheric forcing, bathymetry, freezeup, and melt onset, we examined the processes driving annual fast ice cycle. The analysis revealed that fast ice in the region is sensitive to thermodynamic processes throughout a season, while the wind has a strong influence only on the first stages of fast ice development. The maximal fast ice extent is closely linked to the bathymetry and local topography and is primarily defined by the location of shoals, where fast ice is likely grounded. The annual fast ice cycle shows significant changes over the period of investigation, with tendencies toward later fast ice formation and earlier breakup. These tendencies result in an overall decrease of the fast ice season by 2.8 d/yr, which is significantly higher than previously reported trends.

  6. A 21-Year Record of Arctic Sea Ice Extents and Their Regional, Seasonal, and Monthly Variability and Trends

    NASA Technical Reports Server (NTRS)

    Parkinson, Claire L.; Cavalieri, Donald J.; Zukor, Dorothy J. (Technical Monitor)

    2001-01-01

    Satellite passive-microwave data have been used to calculate sea ice extents over the period 1979-1999 for the north polar sea ice cover as a whole and for each of nine regions. Over this 21-year time period, the trend in yearly average ice extents for the ice cover as a whole is -32,900 +/- 6,100 sq km/yr (-2.7 +/- 0.5 %/decade), indicating a reduction in sea ice coverage that has decelerated from the earlier reported value of -34,000 +/- 8,300 sq km/yr (-2.8 +/- 0.7 %/decade) for the period 1979-1996. Regionally, the reductions are greatest in the Arctic Ocean, the Kara and Barents Seas, and the Seas of Okhotsk and Japan, whereas seasonally, the reductions are greatest in summer, for which season the 1979-1999 trend in ice extents is -41,600 +/- 12,900 sq km/ yr (-4.9 +/- 1.5 %/decade). On a monthly basis, the reductions are greatest in July and September for the north polar ice cover as a whole, in September for the Arctic Ocean, in June and July for the Kara and Barents Seas, and in April for the Seas of Okhotsk and Japan. Only two of the nine regions show overall ice extent increases, those being the Bering Sea and the Gulf of St. Lawrence.For neither of these two regions is the increase statistically significant, whereas the 1079 - 1999 ice extent decreases are statistically significant at the 99% confidence level for the north polar region as a whole, the Arctic Ocean, the Seas of Okhotsk and Japan, and Hudson Bay.

  7. The role of sea ice in 2 x CO2 climate model sensitivity. Part 1: The total influence of sea ice thickness and extent

    NASA Technical Reports Server (NTRS)

    Rind, D.; Healy, R.; Parkinson, C.; Martinson, D.

    1995-01-01

    As a first step in investigating the effects of sea ice changes on the climate sensitivity to doubled atmospheric CO2, the authors use a standard simple sea ice model while varying the sea ice distributions and thicknesses in the control run. Thinner ice 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 ice to be removed as the climate warms. Thus, its impact on sensitivity is similar to that of greater sea ice extent in the control run, which provides more opportunity for sea ice reduction. An experiment with sea ice not allowed to change between the control and doubled CO2 simulations illustrates that the total effect of sea ice on surface air temperature changes, including cloud cover and water vapor feedbacks that arise in response to sea ice 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 sea ice 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 sea level increases of varying magnitude or no change. These results highlight the importance of properly constraining the sea ice response to climate perturbations, necessitating the use of more realistic sea ice and ocean models.

  8. Predicting the Extent of Summer Sea Ice in the Arctic

    NASA Astrophysics Data System (ADS)

    Rigor, I. G.; Wallace, J. M.

    2003-12-01

    The summers of 1998 and 2002 had the least sea ice extent (SIE) in the Arctic. These observations seem to agree with the trends noted by Parkinson, et al. (1999, hereafter P99) for the period 1979-1997, but the spatial pattern of these recent decreases in summer SIE were different. The summer trends shown by P99, exhibit large decreases in SIE primarily in the East Siberian Sea (ESS), while the decreases observed during 1998 and 2002 were much larger in the Beaufort and Chukchi seas (BCS). We now show that the trends for the period 1979 - 2002 are much smaller in the ESS than the trends shown by P99, and the largest decreasing trends have shifted from the ESS to the BCS. Rigor, et al. (2002) showed that the changes in SIE that P99 noted were driven by changes in atmospheric circulation related to the phase of the prior winter Arctic Oscillation (AO, Thompson and Wallace, 1998) index. Given that the latest trends in SIE are different than those shown by P99, one could ask whether the affect of the AO on sea ice noted by Rigor, et al. (2002) has also changed, and whether some large scale climate modes other than the AO has influenced the climate of the Arctic Ocean more? To answer these questions, we applied Empirical Orthogonal Function (EOF) analysis on the September SIE data from microwave satellites, and found that the first two modes SIE were most highly correlated to the prior winter AO, and the AO index of the summer months just prior to each September. These modes explain more than 45% of the variance in SIE, and show that the influence of the winter and summer AO dominates Arctic climate from 1979 - 2002. Using data from the International Arctic Buoy Programme and the National Centers for Environmental Prediction, we will show that the changes in sea ice extent are primarily driven by dynamic changes in sea ice thickness and discuss the implications for predicting summer SIE.

  9. On the relationship between atmospheric circulation and the fluctuations in the sea ice extents of the Bering and Okhotsk Seas

    NASA Technical Reports Server (NTRS)

    Cavalieri, D. J.; Parkinson, C. L.

    1987-01-01

    The influence of the hemispheric atmospheric circulation on the sea ice covers of the Bering Sea and the Sea of Okhotsk is examined using data obtained with the Nimbus 5 electrically scanning microwave radiometer for the four winters of the 1973-1976 period. The 3-day averaged sea ice extent data were used to establish periods for which there is an out-of-phase relationship between fluctuations of the two ice covers. A comparison of the sea-level atmospheric pressure field with the seasonal, interannual, and short-term sea ice fluctuations reveal an association between changes in the phase and the amplitude of the long waves in the atmosphere and advance and retreat of Arctic ice covers.

  10. Towards Improving Sea Ice Predictabiity: Evaluating Climate Models Against Satellite Sea Ice Observations

    NASA Astrophysics Data System (ADS)

    Stroeve, J. C.

    2014-12-01

    The last four decades have seen a remarkable decline in the spatial extent of the Arctic sea ice 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 sea ice 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 ice 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 sea ice extent and thickness show that (1) historical trends from 85% of the model ensemble members remain smaller than observed, and (2) spatial patterns of sea ice 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 sea ice. These results raise concerns regarding the ability of CMIP5 models to realistically represent the processes driving the decline of Arctic sea ice and to project the timing of when a seasonally ice-free Arctic may be realized. On shorter time-scales, seasonal sea ice prediction has been challenged to predict the sea ice extent from Arctic conditions a few months to a year in advance. Efforts such as the Sea Ice Outlook (SIO) project, originally organized through the Study of Environmental Change (SEARCH) and now managed by the Sea Ice Prediction Network project (SIPN) synthesize predictions of the September sea ice extent based on a variety of approaches, including heuristic, statistical and dynamical modeling. Analysis of SIO contributions reveals that when the

  11. 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.

  12. The Impact of Stratospheric Circulation Extremes on Minimum Arctic Sea Ice Extent

    NASA Astrophysics Data System (ADS)

    Smith, K. L.; Polvani, L. M.; Tremblay, B.

    2017-12-01

    The interannual variability of summertime Arctic sea ice extent (SIE) is anti-correlated with the leading mode of extratropical atmospheric variability in preceding winter, the Arctic Oscillation (AO). Given this relationship and the need for better seasonal predictions of Arctic SIE, we here examine the role of stratospheric circulation extremes and stratosphere-troposphere coupling in linking the AO and Arctic SIE variability. We show that extremes in the stratospheric circulation during the winter season, namely stratospheric sudden warming (SSW) and strong polar vortex (SPV) events, are associated with significant anomalies in sea ice concentration in the Bering Straight and the Sea of Okhotsk in winter, the Barents Sea in spring and along the Eurasian coastline in summer in both observations and a fully-coupled, stratosphere-resolving general circulation model. The accompanying figure shows the composite mean sea ice concentration anomalies from the Whole Atmosphere Community Climate Model (WACCM) for SSWs (N = 126, top row) and SPVs (N = 99, bottom row) for winter (a,d), spring (b,e) and summer (c,f). Consistent with previous work on the AO, we find that SSWs, which are followed by the negative phase of the AO at the surface, result in sea ice growth, whereas SPVs, which are followed by the positive phase of the AO at the surface, result in sea ice loss, although the dynamic and thermodynamic processes driving these sea ice anomalies in the three Arctic regions, noted above, are different. Our analysis suggests that the presence or absence of stratospheric circulation extremes in winter may play a non-trivial role in determining total September Arctic SIE when combined with other factors.

  13. Estimating the extent of Antarctic summer sea ice during the Heroic Age of Antarctic Exploration

    NASA Astrophysics Data System (ADS)

    Edinburgh, Tom; Day, Jonathan J.

    2016-11-01

    In stark contrast to the sharp decline in Arctic sea ice, there has been a steady increase in ice extent around Antarctica during the last three decades, especially in the Weddell and Ross seas. In general, climate models do not to capture this trend and a lack of information about sea ice coverage in the pre-satellite period limits our ability to quantify the sensitivity of sea ice to climate change and robustly validate climate models. However, evidence of the presence and nature of sea ice was often recorded during early Antarctic exploration, though these sources have not previously been explored or exploited until now. We have analysed observations of the summer sea ice edge from the ship logbooks of explorers such as Robert Falcon Scott, Ernest Shackleton and their contemporaries during the Heroic Age of Antarctic Exploration (1897-1917), and in this study we compare these to satellite observations from the period 1989-2014, offering insight into the ice conditions of this period, from direct observations, for the first time. This comparison shows that the summer sea ice edge was between 1.0 and 1.7° further north in the Weddell Sea during this period but that ice conditions were surprisingly comparable to the present day in other sectors.

  14. Influence of Arctic Sea Ice Extent on Polar Cloud Fraction and Vertical Structure and Implications for Regional Climate

    NASA Technical Reports Server (NTRS)

    Palm, Stephen P.; Strey, Sara T.; Spinhirne, James; Markus, Thorsten

    2010-01-01

    Recent satellite lidar measurements of cloud properties spanning a period of 5 years are used to examine a possible connection between Arctic sea ice amount and polar cloud fraction and vertical distribution. We find an anticorrelation between sea ice extent and cloud fraction with maximum cloudiness occurring over areas with little or no sea ice. We also find that over ice!free regions, there is greater low cloud frequency and average optical depth. Most of the optical depth increase is due to the presence of geometrically thicker clouds over water. In addition, our analysis indicates that over the last 5 years, October and March average polar cloud fraction has increased by about 7% and 10%, respectively, as year average sea ice extent has decreased by 5% 7%. The observed cloud changes are likely due to a number of effects including, but not limited to, the observed decrease in sea ice extent and thickness. Increasing cloud amount and changes in vertical distribution and optical properties have the potential to affect the radiative balance of the Arctic region by decreasing both the upwelling terrestrial longwave radiation and the downward shortwave solar radiation. Because longwave radiation dominates in the long polar winter, the overall effect of increasing low cloud cover is likely a warming of the Arctic and thus a positive climate feedback, possibly accelerating the melting of Arctic sea ice.

  15. Anomalous Variability in Antarctic Sea Ice Extents During the 1960s With the Use of Nimbus Data

    NASA Technical Reports Server (NTRS)

    Gallaher, David W.; Campbell, G. Garrett; Meier, Walter N.

    2014-01-01

    The Nimbus I, II, and III satellites provide a new opportunity for climate studies in the 1960s. The rescue of the visible and infrared imager data resulted in the utilization of the early Nimbus data to determine sea ice extent. A qualitative analysis of the early NASA Nimbus missions has revealed Antarctic sea ice extents that are signicant larger and smaller than the historic 1979-2012 passive microwave record. The September 1964 ice mean area is 19.7x10 km +/- 0.3x10 km. This is more the 250,000 km greater than the 19.44x10 km seen in the new 2012 historic maximum. However, in August 1966 the maximum sea ice extent fell to 15.9x10 km +/- 0.3x10 km. This is more than 1.5x10 km below the passive microwave record of 17.5x10 km set in September of 1986. This variation between 1964 and 1966 represents a change of maximum sea ice of over 3x10 km in just two years. These inter-annual variations while large, are small when compared to the Antarctic seasonal cycle.

  16. Skillful Spring Forecasts of September Arctic Sea Ice Extent Using Passive Microwave Data

    NASA Technical Reports Server (NTRS)

    Petty, A. A.; Schroder, D.; Stroeve, J. C.; Markus, Thorsten; Miller, Jeffrey A.; Kurtz, Nathan Timothy; Feltham, D. L.; Flocco, D.

    2017-01-01

    In this study, we demonstrate skillful spring forecasts of detrended September Arctic sea ice extent using passive microwave observations of sea ice concentration (SIC) and melt onset (MO). We compare these to forecasts produced using data from a sophisticated melt pond model, and find similar to higher skill values, where the forecast skill is calculated relative to linear trend persistence. The MO forecasts shows the highest skill in March-May, while the SIC forecasts produce the highest skill in June-August, especially when the forecasts are evaluated over recent years (since 2008). The high MO forecast skill in early spring appears to be driven primarily by the presence and timing of open water anomalies, while the high SIC forecast skill appears to be driven by both open water and surface melt processes. Spatial maps of detrended anomalies highlight the drivers of the different forecasts, and enable us to understand regions of predictive importance. Correctly capturing sea ice state anomalies, along with changes in open water coverage appear to be key processes in skillfully forecasting summer Arctic sea ice.

  17. 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).

  18. The Influence of Arctic Sea Ice Extent on Polar Cloud Fraction and Vertical Structure and Implications for Regional Climate

    NASA Technical Reports Server (NTRS)

    Palm, Stephen P.; Strey, Sara T.; Spinhirne, James; Markus, Thorsten

    2010-01-01

    Recent satellite lidar measurements of cloud properties spanning a period of five years are used to examine a possible connection between Arctic sea ice amount and polar cloud fraction and vertical distribution. We find an anti-correlation between sea ice extent and cloud fraction with maximum cloudiness occurring over areas with little or no sea ice. We also find that over ice free regions, there is greater low cloud frequency and average optical depth. Most of the optical depth increase is due to the presence of geometrically thicker clouds over water. In addition, our analysis indicates that over the last 5 years, October and March average polar cloud fraction has increased by about 7 and 10 percent, respectively, as year average sea ice extent has decreased by 5 to 7 percent. The observed cloud changes are likely due to a number of effects including, but not limited to, the observed decrease in sea ice extent and thickness. Increasing cloud amount and changes in vertical distribution and optical properties have the potential to affect the radiative balance of the Arctic region by decreasing both the upwelling terrestrial longwave radiation and the downward shortwave solar radiation. Since longwave radiation dominates in the long polar winter, the overall effect of increasing low cloud cover is likely a warming of the Arctic and thus a positive climate feedback, possibly accelerating the melting of Arctic sea ice.

  19. Anomalous Variability in Antarctic Sea Ice Extents During the 1960s With the Use of Nimbus Data

    NASA Technical Reports Server (NTRS)

    Gallaher, David W.; Campbell, G. Garrett; Meier, Walter N.

    2013-01-01

    The Nimbus I, II, and III satellites provide a new opportunity for climate studies in the 1960s. The rescue of the visible and infrared imager data resulted in the utilization of the early Nimbus data to determine sea ice extent. A qualitative analysis of the early NASA Nimbus missions has revealed Antarctic sea ice extents that are significant larger and smaller than the historic 1979-2012 passive microwave record. The September 1964 ice mean area is 19.7x10(exp 6) sq. km +/- 0.3x10(exp 6) sq. km. This is more the 250,000 sq. km greater than the 19.44x10(exp 6) sq. km seen in the new 2012 historic maximum. However, in August 1966 the maximum sea ice extent fell to 15.9x10(exp 6) sq. km +/- 0.3x10(exp 6) sq. km. This is more than 1.5x10(exp 6) sq. km below the passive microwave record of 17.5x10(exp 6) sq. km set in September of 1986. This variation between 1964 and 1966 represents a change of maximum sea ice of over 3x10(exp 6) sq. km in just two years. These inter-annual variations while large, are small when compared to the Antarctic seasonal cycle.

  20. New Tools for Sea Ice Data Analysis and Visualization: NSIDC's Arctic Sea Ice News and Analysis

    NASA Astrophysics Data System (ADS)

    Vizcarra, N.; Stroeve, J.; Beam, K.; Beitler, J.; Brandt, M.; Kovarik, J.; Savoie, M. H.; Skaug, M.; Stafford, T.

    2017-12-01

    Arctic sea ice has long been recognized as a sensitive climate indicator and has undergone a dramatic decline over the past thirty years. Antarctic sea ice continues to be an intriguing and active field of research. The National Snow and Ice Data Center's Arctic Sea Ice News & Analysis (ASINA) offers researchers and the public a transparent view of sea ice data and analysis. We have released a new set of tools for sea ice analysis and visualization. In addition to Charctic, our interactive sea ice extent graph, the new Sea Ice Data and Analysis Tools page provides access to Arctic and Antarctic sea ice data organized in seven different data workbooks, updated daily or monthly. An interactive tool lets scientists, or the public, quickly compare changes in ice extent and location. Another tool allows users to map trends, anomalies, and means for user-defined time periods. Animations of September Arctic and Antarctic monthly average sea ice extent and concentration may also be accessed from this page. Our tools help the NSIDC scientists monitor and understand sea ice conditions in near real time. They also allow the public to easily interact with and explore sea ice data. Technical innovations in our data center helped NSIDC quickly build these tools and more easily maintain them. The tools were made publicly accessible to meet the desire from the public and members of the media to access the numbers and calculations that power our visualizations and analysis. This poster explores these tools and how other researchers, the media, and the general public are using them.

  1. 2015 Arctic Sea Ice Maximum Annual Extent Is Lowest On Record

    NASA Image and Video Library

    2015-03-19

    The sea ice cap of the Arctic appeared to reach its annual maximum winter extent on Feb. 25, according to data from the NASA-supported National Snow and Ice Data Center (NSIDC) at the University of Colorado, Boulder. At 5.61 million square miles (14.54 million square kilometers), this year’s maximum extent was the smallest on the satellite record and also one of the earliest. Read more: 1.usa.gov/1Eyvelz Credit: NASA's Goddard Space Flight Center NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

  2. Variability of Antarctic Sea Ice 1979-1998

    NASA Technical Reports Server (NTRS)

    Zwally, H. Jay; Comiso, Josefino C.; Parkinson, Claire L.; Cavalieri, Donald J.; Gloersen, Per; Koblinsky, Chester J. (Technical Monitor)

    2001-01-01

    The principal characteristics of the variability of Antarctic sea ice 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 sea ice extent (concentration > 15 %) increased by 13,440 +/- 4180 sq km/year (+1.18 +/- 0.37%/decade). The area of sea ice 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 Sea (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 Seas sector (-9.5 +/- 1.5%/decade). For the entire ice pack, small ice 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 Seas sector. During winter and spring, the trends are negative or near zero in all sectors except the Ross Sea, 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 ice pack. The interannual variability of the annual mean sea-ice extent is only 1.6% overall, compared to 5% to 9% in each of five regional sectors. Analysis of the relation between regional sea ice extents and spatially-averaged surface temperatures over the ice pack gives an overall sensitivity between winter ice cover and temperature of -0.7% change in sea ice extent per K. For summer, some regional ice extents vary positively with temperature and others negatively. The observed increase in Antarctic sea ice cover is counter to the observed decreases in the Arctic. It is also qualitatively consistent with the

  3. 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.

  4. The role of declining summer sea ice extent in increasing Arctic winter precipitation

    NASA Astrophysics Data System (ADS)

    Hamman, J.; Roberts, A.; Cassano, J. J.; Nijssen, B.

    2016-12-01

    In the past three decades, the Arctic has experienced large declines in summer sea ice cover, permafrost extent, and spring snow cover, and increases in winter precipitation. This study explores the relationship between declining Arctic sea ice extent (IE) and winter precipitation (WP) across the Arctic land masses. The first part of this presentation presents the observed relationship between IE and WP. Using satellite estimates of IE and WP data based on a combination of in-situ observations and global reanalyses, we show that WP is negatively correlated with summer IE and that this relationship is strongest before the year 2000. After 2000, around the time IE minima began to decline most rapidly, the relationship between IE and WP degenerates. This indicates that other processes are driving changes in IE and WP. We hypothesize that positive anomalies in poleward moisture transport have historically driven anomalously low IE and high WP, and that since the significant decline in IE, moisture divergence from the central Arctic has been a larger contributor to WP over land. To better understand the physical mechanisms driving the observed changes in the Arctic climate system and the sensitivity of the Arctic climate system to declining sea ice, we have used the fully-coupled Regional Arctic System Model (RASM) to simulate two distinct sea ice climates. The first climate represents normal IE, while the second includes reduced summer IE. The second portion of this presentation analyzes these two RASM simulations, in conjunction with our observation-based analysis, to understand the coupled relationship between poleward moisture transport, IE, evaporation from the Arctic Ocean, and precipitation. We will present the RASM-simulated Arctic water budget and demonstrate the role of IE in driving WP anomalies. Finally, a spatial correlation analysis identifies characteristic patterns in IE, ocean evaporation, and polar cap convergence that contribute to anomalies in WP.

  5. Determining the ice seasons severity during 1982-2015 using the ice extents sum as a new characteristic

    NASA Astrophysics Data System (ADS)

    Rjazin, Jevgeni; Pärn, Ove

    2016-04-01

    Sea ice is a key climate factor and it restricts considerably the winter navigation in sever seasons on the Baltic Sea. So determining ice conditions severity and describing ice cover behaviour at severe seasons interests scientists, engineers and navigation managers. The present study is carried out to determine the ice seasons severity degree basing on the ice seasons 1982 to 2015. A new integrative characteristic is introduced to describe the ice season severity. It is the sum of ice extents of the ice season id est the daily ice extents of the season are summed. The commonly used procedure to determine the ice season severity degree by the maximal ice extent is in this research compared to the new characteristic values. The remote sensing data on the ice concentrations on the Baltic Sea published in the European Copernicus Programme are used to obtain the severity characteristic values. The ice extents are calculated on these ice concentration data. Both the maximal ice extent of the season and a newly introduced characteristic - the ice extents sum are used to classify the winters with respect of severity. The most severe winter of the reviewed period is 1986/87. Also the ice seasons 1981/82, 1984/85, 1985/86, 1995/96 and 2002/03 are classified as severe. Only three seasons of this list are severe by both the criteria. They are 1984/85, 1985/86 and 1986/87. We interpret this coincidence as the evidence of enough-during extensive ice cover in these three seasons. In several winters, for example 2010/11 ice cover extended enough for some time, but did not endure. At few other ice seasons as 2002/03 the Baltic Sea was ice-covered in moderate extent, but the ice cover stayed long time. At 11 winters the ice extents sum differed considerably (> 10%) from the maximal ice extent. These winters yield one third of the studied ice seasons. The maximal ice extent of the season is simple to use and enables to reconstruct the ice cover history and to predict maximal ice

  6. Reconstruction of the extent and variability of late Quaternary ice sheets and Arctic sea ice: Insights from new mineralogical and geochemical proxy records

    NASA Astrophysics Data System (ADS)

    Stein, R. H.; Niessen, F.; Fahl, K.; Forwick, M.; Kudriavtseva, A.; Ponomarenko, E.; Prim, A. K.; Quatmann-Hense, A.; Spielhagen, R. F.; Zou, H.

    2016-12-01

    The Arctic Ocean and surrounding continents are key areas within the Earth system and very sensitive to present and past climate change. In this context, the timing and extent of circum-Arctic ice sheets and its interaction with oceanic and sea-ice dynamics are major interest and focus of international research. New sediment cores recovered during the Polarstern Expeditions PS87 (Lomonosov Ridge/2014) and PS93.1 (Fram Strait/2015) together with several sediment cores available from previous Polarstern expeditions allow to carry out a detailed sedimentological and geochemical study that may help to unravel the changes in Arctic sea ice and circum-Arctic ice sheets during late Quaternary times. Our new data include biomarkers indicative for past sea-ice extent, phytoplankton productivity and terrigenous input as well as grain size, physical property, XRD and XRF data indicative for sources and pathways of terrigenous sediments (ice-rafted debris/IRD) related to glaciations in Eurasia, East Siberia, Canada and Greenland. Here, we present examples from selected sediment cores that give new insights into the timing and extent of sea ice and glaciations during MIS 6 to MIS 2. To highlight one example: SE-NW oriented, streamlined landforms have been mapped on top of the southern Lomonosov Ridge (LR) at water depths between 800 and 1000 m over long distances during Polarstern Expedition PS87, interpreted to be glacial lineations that formed beneath grounded ice sheets and ice streams. The orientations of the lineations identified are similar to those on the East Siberian continental margin, suggesting an East Siberian Chukchi Ice Sheet extended far to the north on LR during times of extreme Quaternary glaciations. Based on our new biomarker records from Core PS2757 (located on LR near 81°N) indicating a MIS 6 ice-edge situation with some open-water phytoplankton productivity, the glacial erosional event should have been older than MIS 6 (e.g., MIS 12?).

  7. Predictability of the Arctic sea ice edge

    NASA Astrophysics Data System (ADS)

    Goessling, H. F.; Tietsche, S.; Day, J. J.; Hawkins, E.; Jung, T.

    2016-02-01

    Skillful sea ice 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 sea ice edge in six climate models. We introduce the integrated ice-edge error (IIEE), a user-relevant verification metric defined as the area where the forecast and the "truth" disagree on the ice concentration being above or below 15%. The IIEE lends itself to decomposition into an absolute extent error, corresponding to the common sea ice 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 sea ice edge is less predictable than sea ice extent, particularly in September, with implications for the potential skill of end-user relevant forecasts.

  8. 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.

  9. Antarctic Sea Ice Variability and Trends, 1979-2010

    NASA Technical Reports Server (NTRS)

    Parkinson, C. L.; Cavalieri, D. J.

    2012-01-01

    In sharp contrast to the decreasing sea ice coverage of the Arctic, in the Antarctic the sea ice 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 ice 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 Sea, with lesser contributions from the Weddell Sea and Indian Ocean. One region, that of the Bellingshausen/Amundsen Seas, has, like the Arctic, instead experienced significant sea ice decreases, with an overall ice 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 sea ice cover as a whole experienced positive ice 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 Sea and Indian Ocean also had positive trends in each month, while the Bellingshausen/Amundsen Seas had negative trends in each month, and the Weddell Sea and Western Pacific Ocean had a mixture of positive and negative trends. Comparing ice-area results to ice-extent results, in each case the ice-area trend has the same sign as the ice-extent trend, but differences in the magnitudes of the two trends identify regions with overall increasing ice concentrations and others with overall decreasing ice concentrations. The strong pattern of decreasing ice coverage in the Bellingshausen/Amundsen Seas region and increasing ice coverage in the Ross Sea region is suggestive of changes in atmospheric circulation. This is a key topic for future research.

  10. Sea ice dynamics across the Mid-Pleistocene transition in the Bering Sea.

    PubMed

    Detlef, H; Belt, S T; Sosdian, S M; Smik, L; Lear, C H; Hall, I R; Cabedo-Sanz, P; Husum, K; Kender, S

    2018-03-05

    Sea ice and associated feedback mechanisms play an important role for both long- and short-term climate change. Our ability to predict future sea ice extent, however, hinges on a greater understanding of past sea ice dynamics. Here we investigate sea ice changes in the eastern Bering Sea prior to, across, and after the Mid-Pleistocene transition (MPT). The sea ice record, based on the Arctic sea ice biomarker IP 25 and related open water proxies from the International Ocean Discovery Program Site U1343, shows a substantial increase in sea ice extent across the MPT. The occurrence of late-glacial/deglacial sea ice maxima are consistent with sea ice/land ice hysteresis and land-glacier retreat via the temperature-precipitation feedback. We also identify interactions of sea ice with phytoplankton growth and ocean circulation patterns, which have important implications for glacial North Pacific Intermediate Water formation and potentially North Pacific abyssal carbon storage.

  11. Predicting September sea ice: Ensemble skill of the SEARCH Sea Ice Outlook 2008-2013

    NASA Astrophysics Data System (ADS)

    Stroeve, Julienne; Hamilton, Lawrence C.; Bitz, Cecilia M.; Blanchard-Wrigglesworth, Edward

    2014-04-01

    Since 2008, the Study of Environmental Arctic Change Sea Ice Outlook has solicited predictions of September sea-ice 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 ice 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 ice, atmosphere and ocean conditions, and summer storms and weather in contributing to the challenge of sea-ice prediction.

  12. Revisiting the Potential of Melt Pond Fraction as a Predictor for the Seasonal Arctic Sea Ice Extent Minimum

    NASA Technical Reports Server (NTRS)

    Liu, Jiping; Song, Mirong; Horton, Radley M.; Hu, Yongyun

    2015-01-01

    The rapid change in Arctic sea ice in recent decades has led to a rising demand for seasonal sea ice prediction. A recent modeling study that employed a prognostic melt pond model in a stand-alone sea ice model found that September Arctic sea ice extent can be accurately predicted from the melt pond fraction in May. Here we show that satellite observations show no evidence of predictive skill in May. However, we find that a significantly strong relationship (high predictability) first emerges as the melt pond fraction is integrated from early May to late June, with a persistent strong relationship only occurring after late July. Our results highlight that late spring to mid summer melt pond information is required to improve the prediction skill of the seasonal sea ice minimum. Furthermore, satellite observations indicate a much higher percentage of melt pond formation in May than does the aforementioned model simulation, which points to the need to reconcile model simulations and observations, in order to better understand key mechanisms of melt pond formation and evolution and their influence on sea ice state.

  13. Sea-ice evaluation of NEMO-Nordic 1.0: a NEMO-LIM3.6-based ocean-sea-ice model setup for the North Sea and Baltic Sea

    NASA Astrophysics Data System (ADS)

    Pemberton, Per; Löptien, Ulrike; Hordoir, Robinson; Höglund, Anders; Schimanke, Semjon; Axell, Lars; Haapala, Jari

    2017-08-01

    The Baltic Sea is a seasonally ice-covered marginal sea in northern Europe with intense wintertime ship traffic and a sensitive ecosystem. Understanding and modeling the evolution of the sea-ice pack is important for climate effect studies and forecasting purposes. Here we present and evaluate the sea-ice component of a new NEMO-LIM3.6-based ocean-sea-ice setup for the North Sea and Baltic Sea region (NEMO-Nordic). The setup includes a new depth-based fast-ice parametrization for the Baltic Sea. The evaluation focuses on long-term statistics, from a 45-year long hindcast, although short-term daily performance is also briefly evaluated. We show that NEMO-Nordic is well suited for simulating the mean sea-ice extent, concentration, and thickness as compared to the best available observational data set. The variability of the annual maximum Baltic Sea ice extent is well in line with the observations, but the 1961-2006 trend is underestimated. Capturing the correct ice thickness distribution is more challenging. Based on the simulated ice thickness distribution we estimate the undeformed and deformed ice thickness and concentration in the Baltic Sea, which compares reasonably well with observations.

  14. Arctic sea ice decline contributes to thinning lake ice trend in northern Alaska

    USGS Publications Warehouse

    Alexeev, Vladimir; Arp, Christopher D.; Jones, Benjamin M.; Cai, Lei

    2016-01-01

    Field measurements, satellite observations, and models document a thinning trend in seasonal Arctic lake ice growth, causing a shift from bedfast to floating ice conditions. September sea ice 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 sea ice affects lakes, we conducted model experiments to simulate winters with years of high (1991/92) and low (2007/08) sea ice extent for which we also had field measurements and satellite imagery characterizing lake ice conditions. A lake ice growth model forced with Weather Research and Forecasting model output produced a 7% decrease in lake ice growth when 2007/08 sea ice was imposed on 1991/92 climatology and a 9% increase in lake ice growth for the opposing experiment. Here, we clearly link early winter 'ocean-effect' snowfall and warming to reduced lake ice growth. Future reductions in sea ice extent will alter hydrological, biogeochemical, and habitat functioning of Arctic lakes and cause sub-lake permafrost thaw.

  15. ARCTIC SEA ICE EXTENT AND DRIFT, MODELED AS A VISCOUS FLUID.

    USGS Publications Warehouse

    Ling, Chi-Hai; Parkinson, Claire L.

    1986-01-01

    A dynamic/thermodynamic numerical model of sea ice has been used to calculate the yearly cycle of sea ice thicknesses, concentrations, and velocities in the Arctic Ocean and surrounding seas. The model combines the formulations of two previous models, taking the thermodynamics and momentum equations from the model of Parkinson and Washington and adding the constitutive equation and equation of state from the model of Ling, Rasmussen, and Campbell. Simulated annually averaged ice drift vectors compare well with observed ice drift from the Arctic Ocean Buoy Program.

  16. Sea-level records from the U.S. mid-Atlantic constrain Laurentide Ice Sheet extent during Marine Isotope Stage 3

    PubMed Central

    Pico, T; Creveling, J. R.; Mitrovica, J. X.

    2017-01-01

    The U.S. mid-Atlantic sea-level record is sensitive to the history of the Laurentide Ice Sheet as the coastline lies along the ice sheet's peripheral bulge. However, paleo sea-level markers on the present-day shoreline of Virginia and North Carolina dated to Marine Isotope Stage (MIS) 3, from 50 to 35 ka, are surprisingly high for this glacial interval, and remain unexplained by previous models of ice age adjustment or other local (for example, tectonic) effects. Here, we reconcile this sea-level record using a revised model of glacial isostatic adjustment characterized by a peak global mean sea level during MIS 3 of approximately −40 m, and far less ice volume within the eastern sector of the Laurentide Ice Sheet than traditional reconstructions for this interval. We conclude that the Laurentide Ice Sheet experienced a phase of very rapid growth in the 15 kyr leading into the Last Glacial Maximum, thus highlighting the potential of mid-field sea-level records to constrain areal extent of ice cover during glacial intervals with sparse geological observables. PMID:28555637

  17. A review of sea ice proxy information from polar ice cores

    NASA Astrophysics Data System (ADS)

    Abram, Nerilie J.; Wolff, Eric W.; Curran, Mark A. J.

    2013-11-01

    Sea ice plays an important role in Earth's climate system. The lack of direct indications of past sea ice coverage, however, means that there is limited knowledge of the sensitivity and rate at which sea ice dynamics are involved in amplifying climate changes. As such, there is a need to develop new proxy records for reconstructing past sea ice conditions. Here we review the advances that have been made in using chemical tracers preserved in ice cores to determine past changes in sea ice cover around Antarctica. Ice core records of sea salt concentration show promise for revealing patterns of sea ice extent particularly over glacial-interglacial time scales. In the coldest climates, however, the sea salt signal appears to lose sensitivity and further work is required to determine how this proxy can be developed into a quantitative sea ice indicator. Methane sulphonic acid (MSA) in near-coastal ice cores has been used to reconstruct quantified changes and interannual variability in sea ice extent over shorter time scales spanning the last ˜160 years, and has potential to be extended to produce records of Antarctic sea ice changes throughout the Holocene. However the MSA ice core proxy also requires careful site assessment and interpretation alongside other palaeoclimate indicators to ensure reconstructions are not biased by non-sea ice factors, and we summarise some recommended strategies for the further development of sea ice histories from ice core MSA. For both proxies the limited information about the production and transfer of chemical markers from the sea ice zone to the Antarctic ice sheets remains an issue that requires further multidisciplinary study. Despite some exploratory and statistical work, the application of either proxy as an indicator of sea ice change in the Arctic also remains largely unknown. As information about these new ice core proxies builds, so too does the potential to develop a more comprehensive understanding of past changes in sea

  18. 30-Year Satellite Record Reveals Accelerated Arctic Sea Ice Loss, Antarctic Sea Ice Trend Reversal

    NASA Technical Reports Server (NTRS)

    Cavalieri, Donald J.; Parkinson, C. L.; Vinnikov, K. Y.

    2003-01-01

    Arctic sea ice 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 sea ice 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 sea ice extent observed in the early 1970's.

  19. The future of ice sheets and sea ice: between reversible retreat and unstoppable loss.

    PubMed

    Notz, Dirk

    2009-12-08

    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 sea ice and the retreat of ice sheets: Once these ice masses have shrunk below an anticipated critical extent, the ice-albedo feedback might lead to the irreversible and unstoppable loss of the remaining ice. 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 sea ice. Hence, in a cooler climate, sea ice could recover rapidly from the loss it has experienced in recent years. In addition, we discuss why this recent rapid retreat of Arctic summer sea ice might largely be a consequence of a slow shift in ice-thickness distribution, which will lead to strongly increased year-to-year variability of the Arctic summer sea-ice extent. This variability will render seasonal forecasts of the Arctic summer sea-ice extent increasingly difficult. We also discuss why, in contrast to Arctic summer sea ice, a tipping point is more likely to exist for the loss of the Greenland ice sheet and the West Antarctic ice sheet.

  20. The future of ice sheets and sea ice: Between reversible retreat and unstoppable loss

    PubMed Central

    Notz, Dirk

    2009-01-01

    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 sea ice and the retreat of ice sheets: Once these ice masses have shrunk below an anticipated critical extent, the ice–albedo feedback might lead to the irreversible and unstoppable loss of the remaining ice. 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 sea ice. Hence, in a cooler climate, sea ice could recover rapidly from the loss it has experienced in recent years. In addition, we discuss why this recent rapid retreat of Arctic summer sea ice might largely be a consequence of a slow shift in ice-thickness distribution, which will lead to strongly increased year-to-year variability of the Arctic summer sea-ice extent. This variability will render seasonal forecasts of the Arctic summer sea-ice extent increasingly difficult. We also discuss why, in contrast to Arctic summer sea ice, a tipping point is more likely to exist for the loss of the Greenland ice sheet and the West Antarctic ice sheet. PMID:19884496

  1. 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.

  2. Current Status and Future Plan of Arctic Sea Ice monitoring in South Korea

    NASA Astrophysics Data System (ADS)

    Shin, J.; Park, J.

    2016-12-01

    Arctic sea ice is one of the most important parameters in climate. For monitoring of sea ice changes, the National Meteorological Satellite Center (NMSC) of Korea Metrological Administration has developed the "Arctic sea ice monitoring system" to retrieve the sea ice extent and surface roughness using microwave sensor data, and statistical prediction model for Arctic sea ice extent. This system has been implemented to the web site for real-time public service. The sea ice information can be retrieved using the spaceborne microwave sensor-Special Sensor Microwave Imager/Sounder (SSMI/S). The sea ice information like sea ice extent, sea ice surface roughness, and predictive sea ice extent are produced weekly base since 2007. We also publish the "Analysis report of the Arctic sea ice" twice a year. We are trying to add more sea ice information into this system. Details of current status and future plan of Arctic sea ice monitoring and the methodology of the sea ice information retrievals will be presented in the meeting.

  3. The Last Arctic Sea Ice Refuge

    NASA Astrophysics Data System (ADS)

    Pfirman, S. L.; Tremblay, B.; Newton, R.; Fowler, C.

    2010-12-01

    Summer sea ice may persist along the northern flank of Canada and Greenland for decades longer than the rest of the Arctic, raising the possibility of a naturally formed refugium for ice-associated species. Observations and models indicate that some ice in this region forms locally, while some is transported to the area by winds and ocean currents. Depending on future changes in melt patterns and sea ice transport rates, both the central Arctic and Siberian shelf seas may be sources of ice to the region. An international system of monitoring and management of the sea ice refuge, along with the ice source regions, has the potential to maintain viable habitat for ice-associated species, including polar bears, for decades into the future. Issues to consider in developing a strategy include: + the likely duration and extent of summer sea ice in this region based on observations, models and paleoenvironmental information + the extent and characteristics of the “ice shed” contributing sea ice to the refuge, including its dynamics, physical and biological characteristics as well as potential for contamination from local or long-range sources + likely assemblages of ice-associated species and their habitats + potential stressors such as transportation, tourism, resource extraction, contamination + policy, governance, and development issues including management strategies that could maintain the viability of the refuge.

  4. NASA Science Flights Target Melting Arctic Sea Ice

    NASA Image and Video Library

    2017-12-08

    This summer, with sea ice across the Arctic Ocean shrinking to below-average levels, a NASA airborne survey of polar ice just completed its first flights. Its target: aquamarine pools of melt water on the ice surface that may be accelerating the overall sea ice retreat. NASA’s Operation IceBridge completed the first research flight of its new 2016 Arctic summer campaign on July 13. The science flights, which continue through July 25, are collecting data on sea ice in a year following a record-warm winter in the Arctic. Read more: go.nasa.gov/29T6mxc Caption: A large pool of melt water over sea ice, as seen from an Operation IceBridge flight over the Beaufort Sea on July 14, 2016. During this summer campaign, IceBridge will map the extent, frequency and depth of melt ponds like these to help scientists forecast the Arctic sea ice yearly minimum extent in September. Credit: NASA/Operation IceBridge

  5. The Effect of Recent Decreases in Sea Ice Extent and Increases in SST on the Seasonal Availability of Arctic Cod (Boreogadus saida) to Seabirds in the Beaufort Sea

    NASA Astrophysics Data System (ADS)

    Divoky, G.; Druckenmiller, M. L.

    2016-02-01

    With major decreases in pan-Arctic summer sea ice extent steadily underway, the Beaufort Sea has been nearly ice-free in five of the last eight summers. This loss of a critical arctic marine habitat and the concurrent warming of the recently ice-free waters could potentially cause major changes in the biological oceanography of the Beaufort Sea and alter the distribution, abundance and condition of the region's upper trophic level predators that formerly relied on prey associated with sea ice or cold (<2°C) surface waters. Arctic cod (Boreogadus saida), the primary forage fish for seabirds in the Beaufort Sea, is part of the cryopelagic fauna associated with sea ice and is also found in adjacent ice-free waters. In the extreme western Beaufort Sea near Cooper Island, Arctic cod availability to breeding Black Guillemots (Cepphus grylle), a diving seabird, has declined since 2002. Guillemots are a good indicator of Arctic cod availability in surface waters and the upper water column as they feed at depths of 1-20m. Currently, when sea ice is absent from the nearshore and SST exceeds 4°C, guillemots are observed to seasonally shift from Arctic cod to nearshore demersal prey, with a resulting decrease in nestling survival and quality. Arctic cod is the primary prey for many of the seabirds utilizing the Beaufort Sea as a post-breeding staging area and migratory corridor in late summer and early fall. The loss of approximately 200-300 thousand sq km of summer sea ice habitat in recent years could be expected to affect the distribution, abundance, and movements of these species as there are few alternative fish resources in the region. We examine temporal and spatial variation in August sea ice extent and SST in the Beaufort Sea to determine the regions, periods and bird species that are potentially most affected as the Beaufort Sea transitions to becoming regularly ice-free in late summer.

  6. Record low lake ice thickness and bedfast ice extent on Alaska's Arctic Coastal Plain in 2017 exemplify the value of monitoring freshwater ice to understand sea-ice forcing and predict permafrost dynamics

    NASA Astrophysics Data System (ADS)

    Arp, C. D.; Alexeev, V. A.; Bondurant, A. C.; Creighton, A.; Engram, M. J.; Jones, B. M.; Parsekian, A.

    2017-12-01

    The winter of 2016/2017 was exceptionally warm and snowy along the coast of Arctic Alaska partly due to low fall sea ice extent. Based on several decades of field measurements, we documented a new record low maximum ice thickness (MIT) for lakes on the Barrow Peninsula, averaging 1.2 m. This is in comparison to a long-term average MIT of 1.7 m stretching back to 1962 with a maximum of 2.1 m in 1970 and previous minimum of 1.3 m in 2014. The relevance of thinner lake ice in arctic coastal lowlands, where thermokarst lakes cover greater than 20% of the land area, is that permafrost below lakes with bedfast ice is typically preserved. Lakes deeper than the MIT warm and thaw sub-lake permafrost forming taliks. Remote sensing analysis using synthetic aperture radar (SAR) is a valuable tool for scaling the field observations of MIT to the entire freshwater landscape to map bedfast ice. A new, long-term time-series of late winter multi-platform SAR from 1992 to 2016 shows a large dynamic range of bedfast ice extent, 29% of lake area or 6% of the total land area over this period, and adding 2017 to this record is expected to extend this range further. Empirical models of lake mean annual bed temperature suggest that permafrost begins to thaw at depths less than 60% of MIT. Based on this information and knowledge of average lake ice growth trajectories, we suggest that future SAR analysis of lake ice should focus on mid-winter (January) to evaluate the extent of bedfast ice and corresponding zones of sub-lake permafrost thaw. Tracking changes in these areas from year to year in mid-winter may provide the best landscape-scale evaluation of changing permafrost conditions in lake-rich arctic lowlands. Because observed changes in MIT coupled with mid-winter bedfast ice extent provide much information on permafrost stability, we suggest that these measurements can serve as Essential Climate Variables (EVCs) to indicate past and future changes in lake-rich arctic regions. The

  7. Arctic Sea Ice Simulation in the PlioMIP Ensemble

    NASA Technical Reports Server (NTRS)

    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.; hide

    2016-01-01

    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 sea ice for both the pre-industrial period and the mid-Pliocene. Mid-Pliocene sea ice 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 ice-free conditions in the mid-Pliocene. This spread amongst the ensemble is in line with the uncertainties amongst proxy reconstructions for mid-Pliocene sea ice extent. Correlations between mid-Pliocene Arctic temperatures and sea ice extents are almost twice as strong as the equivalent correlations for the pre-industrial simulations. The need for more comprehensive sea ice proxy data is highlighted, in order to better compare model performances.

  8. Canadian Arctic sea ice reconstructed from bromine in the Greenland NEEM ice core.

    PubMed

    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

    2016-09-21

    Reconstructing the past variability of Arctic sea ice provides an essential context for recent multi-year sea ice 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, sea ice in so-called "bromine explosions" and we employ a 1-D chemistry transport model to quantify processes of bromine enrichment over first-year sea ice and depositional transport over multi-year sea ice and land ice. We report bromine enrichment in the Northwest Greenland Eemian NEEM ice core since the end of the Eemian interglacial 120,000 years ago, finding the maximum extension of first-year sea ice occurred approximately 9,000 years ago during the Holocene climate optimum, when Greenland temperatures were 2 to 3 °C above present values. First-year sea ice extent was lowest during the glacial stadials suggesting complete coverage of the Arctic Ocean by multi-year sea ice. These findings demonstrate a clear relationship between temperature and first-year sea ice extent in the Arctic and suggest multi-year sea ice 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.

  9. The Satellite Passive-Microwave Record of Sea Ice in the Ross Sea Since Late 1978

    NASA Technical Reports Server (NTRS)

    Parkinson, Claire L.

    2009-01-01

    Satellites have provided us with a remarkable ability to monitor many aspects of the globe day-in and day-out and sea ice is one of numerous variables that by now have quite substantial satellite records. Passive-microwave data have been particularly valuable in sea ice 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 Sea sea ice imagery is available at spatial resolution of approximately 25 km. This allows good depictions of the seasonal advance and retreat of the ice cover each year, along with its marked interannual variability. The Ross Sea ice 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 Sea 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 Sea ice cover. The satellite data also allow calculation of trends in the ice cover over the period of the satellite record. Using linear least-squares fits, the Ross Sea ice 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 ice 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 ice extents to a high of 20,300 plus or minus 6,100 kilometers per year for the October ice extents. On a yearly average basis, for 1979-2007 the Ross Sea ice extent increased at a rate of 4.8 plus or minus 1.6 % per decade. Placing the Ross Sea in the context of the Southern Ocean as a whole, over the November 1978-December 2007 period the Ross Sea had

  10. Mechanisms influencing seasonal to inter-annual prediction skill of sea ice extent in the Arctic Ocean in MIROC

    NASA Astrophysics Data System (ADS)

    Ono, Jun; Tatebe, Hiroaki; Komuro, Yoshiki; Nodzu, Masato I.; Ishii, Masayoshi

    2018-02-01

    To assess the skill of seasonal to inter-annual predictions of the detrended sea ice extent in the Arctic Ocean (SIEAO) and to clarify the underlying physical processes, we conducted ensemble hindcasts, started on 1 January, 1 April, 1 July and 1 October for each year from 1980 to 2011, for lead times up to three years, using the Model for Interdisciplinary Research on Climate (MIROC) version 5 initialised with the observed atmosphere and ocean anomalies and sea ice concentration. Significant skill is found for the winter months: the December SIEAO can be predicted up to 11 months ahead (anomaly correlation coefficient is 0.42). This skill might be attributed to the subsurface ocean heat content originating in the North Atlantic. A plausible mechanism is as follows: the subsurface water flows into the Barents Sea from spring to fall and emerges at the surface in winter by vertical mixing, and eventually affects the sea ice variability there. Meanwhile, the September SIEAO predictions are skillful for lead times of up to two months, due to the persistence of sea ice in the Beaufort, Chukchi, and East Siberian seas initialised in July, as suggested by previous studies.

  11. Improved method for sea ice age computation based on combination of sea ice drift and concentration

    NASA Astrophysics Data System (ADS)

    Korosov, Anton; Rampal, Pierre; Lavergne, Thomas; Aaboe, Signe

    2017-04-01

    Sea Ice Age is one of the components of the Sea Ice ECV as defined by the Global Climate Observing System (GCOS) [WMO, 2015]. It is an important climate indicator describing the sea ice state in addition to sea ice concentration (SIC) and thickness (SIT). The amount of old/thick ice in the Arctic Ocean has been decreasing dramatically [Perovich et al. 2015]. Kwok et al. [2009] reported significant decline in the MYI share and consequent loss of thickness and therefore volume. Today, there is only one acknowledged sea ice age climate data record [Tschudi, et al. 2015], based on Maslanik et al. [2011] provided by National Snow and Ice Data Center (NSIDC) [http://nsidc.org/data/docs/daac/nsidc0611-sea-ice-age/]. The sea ice age algorithm [Fowler et al., 2004] is using satellite-derived ice drift for Lagrangian tracking of individual ice parcels (12-km grid cells) defined by areas of sea ice concentration > 15% [Maslanik et al., 2011], i.e. sea ice extent, according to the NASA Team algorithm [Cavalieri et al., 1984]. This approach has several drawbacks. (1) Using sea ice extent instead of sea ice concentration leads to overestimation of the amount of older ice. (2) The individual ice parcels are not advected uniformly over (long) time. This leads to undersampling in areas of consistent ice divergence. (3) The end product grid cells are assigned the age of the oldest ice parcel within that cell, and the frequency distribution of the ice age is not taken into account. In addition, the base sea ice drift product (https://nsidc.org/data/docs/daac/nsidc0116_icemotion.gd.html) is known to exhibit greatly reduced accuracy during the summer season [Sumata et al 2014, Szanyi, 2016] as it only relies on a combination of sea ice drifter trajectories and wind-driven "free-drift" motion during summer. This results in a significant overestimate of old-ice content, incorrect shape of the old-ice pack, and lack of information about the ice age distribution within the grid cells. We

  12. Sea ice in the Greenland Sea

    NASA Image and Video Library

    2017-12-08

    As the northern hemisphere experiences the heat of summer, ice moves and melts in the Arctic waters and the far northern lands surrounding it. The Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Aqua satellite captured this true-color image of sea ice off Greenland on July 16, 2015. Large chunks of melting sea ice can be seen in the sea ice off the coast, and to the south spirals of ice have been shaped by the winds and currents that move across the Greenland Sea. Along the Greenland coast, cold, fresh melt water from the glaciers flows out to the sea, as do newly calved icebergs. Frigid air from interior Greenland pushes the ice away from the shoreline, and the mixing of cold water and air allows some sea ice to be sustained even at the height of summer. According to observations from satellites, 2015 is on track to be another low year for arctic summer sea ice cover. The past ten years have included nine of the lowest ice extents on record. The annual minimum typically occurs in late August or early September. The amount of Arctic sea ice cover has been dropping as global temperatures rise. The Arctic is two to three times more sensitive to temperature changes as the Earth as a whole. Credit: NASA/GSFC/Jeff Schmaltz/MODIS Land Rapid Response Team NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

  13. Sea ice and pollution-modulated changes in Greenland ice core methanesulfonate and bromine

    NASA Astrophysics Data System (ADS)

    Maselli, Olivia J.; Chellman, Nathan J.; Grieman, Mackenzie; Layman, Lawrence; McConnell, Joseph R.; Pasteris, Daniel; Rhodes, Rachael H.; Saltzman, Eric; Sigl, Michael

    2017-01-01

    Reconstruction of past changes in Arctic sea ice extent may be critical for understanding its future evolution. Methanesulfonate (MSA) and bromine concentrations preserved in ice cores have both been proposed as indicators of past sea ice conditions. In this study, two ice 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 ice core MSA and the HadISST1 ICE sea ice 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 sea ice in the same source regions. The positive correlation between ice core MSA and bromine persists until the mid-20th century, when the acidity of Greenland ice begins to increase markedly due to increased fossil fuel emissions. After that time, MSA levels decrease as a result of declining sea ice extent but bromine levels increase. We consider several possible explanations and ultimately suggest that increased acidity, specifically nitric acid, of snow on sea ice stimulates the release of reactive Br from sea ice, resulting in increased transport and deposition on the Greenland ice sheet.

  14. There goes the sea ice: following Arctic sea ice parcels and their properties.

    NASA Astrophysics Data System (ADS)

    Tschudi, M. A.; Tooth, M.; Meier, W.; Stewart, S.

    2017-12-01

    Arctic sea ice distribution has changed considerably over the last couple of decades. Sea ice extent record minimums have been observed in recent years, the distribution of ice age now heavily favors younger ice, and sea ice is likely thinning. This new state of the Arctic sea ice cover has several impacts, including effects on marine life, feedback on the warming of the ocean and atmosphere, and on the future evolution of the ice pack. The shift in the state of the ice cover, from a pack dominated by older ice, to the current state of a pack with mostly young ice, impacts specific properties of the ice pack, and consequently the pack's response to the changing Arctic climate. For example, younger ice typically contains more numerous melt ponds during the melt season, resulting in a lower albedo. First-year ice is typically thinner and more fragile than multi-year ice, making it more susceptible to dynamic and thermodynamic forcing. To investigate the response of the ice pack to climate forcing during summertime melt, we have developed a database that tracks individual Arctic sea ice parcels along with associated properties as these parcels advect during the summer. Our database tracks parcels in the Beaufort Sea, from 1985 - present, along with variables such as ice surface temperature, albedo, ice concentration, and convergence. We are using this database to deduce how these thousands of tracked parcels fare during summer melt, i.e. what fraction of the parcels advect through the Beaufort, and what fraction melts out? The tracked variables describe the thermodynamic and dynamic forcing on these parcels during their journey. This database will also be made available to all interested investigators, after it is published in the near future. The attached image shows the ice surface temperature of all parcels (right) that advected through the Beaufort Sea region (left) in 2014.

  15. Aircraft Surveys of the Beaufort Sea Seasonal Ice Zone

    NASA Astrophysics Data System (ADS)

    Morison, J.

    2016-02-01

    The Seasonal Ice Zone Reconnaissance Surveys (SIZRS) is a program of repeated ocean, ice, and atmospheric measurements across the Beaufort-Chukchi sea seasonal sea ice zone (SIZ) utilizing US Coast Guard Arctic Domain Awareness (ADA) flights of opportunity. The SIZ is the region between maximum winter sea ice extent and minimum summer sea ice extent. As such, it contains the full range of positions of the marginal ice zone (MIZ) where sea ice interacts with open water. The increasing size and changing air-ice-ocean properties of the SIZ are central to recent reductions in Arctic sea ice extent. The changes in the interplay among the atmosphere, ice, and ocean require a systematic SIZ observational effort of coordinated atmosphere, ice, and ocean observations covering up to interannual time-scales, Therefore, every year beginning in late Spring and continuing to early Fall, SIZRS makes monthly flights across the Beaufort Sea SIZ aboard Coast Guard C-130H aircraft from USCG Air Station Kodiak dropping Aircraft eXpendable CTDs (AXCTD) and Aircraft eXpendable Current Profilers (AXCP) for profiles of ocean temperature, salinity and shear, dropsondes for atmospheric temperature, humidity, and velocity profiles, and buoys for atmosphere and upper ocean time series. Enroute measurements include IR imaging, radiometer and lidar measurements of the sea surface and cloud tops. SIZRS also cooperates with the International Arctic Buoy Program for buoy deployments and with the NOAA Earth System Research Laboratory atmospheric chemistry sampling program on board the aircraft. Since 2012, SIZRS has found that even as SIZ extent, ice character, and atmospheric forcing varies year-to-year, the pattern of ocean freshening and radiative warming south of the ice edge is consistent. The experimental approach, observations and extensions to other projects will be discussed.

  16. Visualizing Glaciers and Sea Ice via Google Earth

    NASA Astrophysics Data System (ADS)

    Ballagh, L. M.; Fetterer, F.; Haran, T. M.; Pharris, K.

    2006-12-01

    The NOAA team at NSIDC manages over 60 distinct cryospheric and related data products. With an emphasis on data rescue and in situ data, these products hold value for both the scientific and non-scientific user communities. The overarching goal of this presentation is to promote products from two components of the cryosphere (glaciers and sea ice). Our Online Glacier Photograph Database contains approximately 3,000 photographs taken over many decades, exemplifying change in the glacier terminus over time. The sea ice product shows sea ice extent and concentration along with anomalies and trends. This Sea Ice Index product, which starts in 1979 and is updated monthly, provides visuals of the current state of sea ice in both hemispheres with trends and anomalies. The long time period covered by the data set means that many of the trends in ice extent and concentration shown in this product are statistically significant despite the large natural variability in sea ice. The minimum arctic sea ice extent has been a record low in September 2002 and 2005, contributing to an accelerated trend in sea ice reduction. With increasing world-wide interest in indicators of global climate change, and the upcoming International Polar Year, these data products are of interest to a broad audience. To further extend the impact of these data, we have made them viewable through Google Earth via the Keyhole Markup Language (KML). This presents an opportunity to branch out to a more diverse audience by using a new and innovative tool that allows spatial representation of data of significant scientific and educational interest.

  17. Variability of Arctic Sea Ice as Determined from Satellite Observations

    NASA Technical Reports Server (NTRS)

    Parkinson, Claire L.

    1999-01-01

    The compiled, quality-controlled satellite multichannel passive-microwave record of polar sea ice now spans over 18 years, from November 1978 through December 1996, and is revealing considerable information about the Arctic sea ice cover and its variability. The information includes data on ice concentrations (percent areal coverages of ice), ice extents, ice melt, ice velocities, the seasonal cycle of the ice, the interannual variability of the ice, the frequency of ice coverage, and the length of the sea ice season. The data reveal marked regional and interannual variabilities, as well as some statistically significant trends. For the north polar ice cover as a whole, maximum ice 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 Sea having a range of 740,000 - 1,110,000 sq km in its yearly maximum ice coverage. In spite of the large variations from year to year and region to region, overall the Arctic ice extents showed a statistically significant, 2.80% / decade negative trend over the 18.2-year period. Ice season lengths, which vary from only a few weeks near the ice margins to the full year in the large region of perennial ice coverage, also experienced interannual variability, along with spatially coherent overall trends. Linear least squares trends show the sea ice season to have lengthened in much of the Bering Sea, Baffin Bay, the Davis Strait, and the Labrador Sea, but to have shortened over a much larger area, including the Sea of Okhotsk, the Greenland Sea, the Barents Sea, and the southeastern Arctic.

  18. Will Arctic sea ice thickness initialization improve seasonal forecast skill?

    NASA Astrophysics Data System (ADS)

    Day, J. J.; Hawkins, E.; Tietsche, S.

    2014-11-01

    Arctic sea ice thickness is thought to be an important predictor of Arctic sea ice extent. However, coupled seasonal forecast systems do not generally use sea ice thickness observations in their initialization and are therefore missing a potentially important source of additional skill. To investigate how large this source is, a set of ensemble potential predictability experiments with a global climate model, initialized with and without knowledge of the sea ice thickness initial state, have been run. These experiments show that accurate knowledge of the sea ice thickness field is crucially important for sea ice concentration and extent forecasts up to 8 months ahead, especially in summer. Perturbing sea ice thickness also has a significant impact on the forecast error in Arctic 2 m temperature a few months ahead. These results suggest that advancing capabilities to observe and assimilate sea ice thickness into coupled forecast systems could significantly increase skill.

  19. MODIS Snow and Sea Ice Products

    NASA Technical Reports Server (NTRS)

    Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.

    2004-01-01

    In this chapter, we describe the suite of Earth Observing System (EOS) Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow and sea ice products. Global, daily products, developed at Goddard Space Flight Center, are archived and distributed through the National Snow and Ice 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. Sea ice products include ice extent determined with two different algorithms, and sea ice surface temperature. The algorithms used to develop these products are described. Both the snow and sea ice products, available since February 24,2000, are useful for modelers. Validation of the products is also discussed.

  20. Atmospheric forcing of sea ice anomalies in the Ross Sea Polynya region

    NASA Astrophysics Data System (ADS)

    Dale, Ethan; McDonald, Adrian; Rack, Wolfgang

    2016-04-01

    Despite warming trends in global temperatures, sea ice extent in the southern hemisphere has shown an increasing trend over recent decades. Wind-driven sea ice export from coastal polynyas is an important source of sea ice production. Areas of major polynyas in the Ross Sea, the region with largest increase in sea ice extent, have been suggested to produce the vast amount of the sea ice in the region. We investigate the impacts of strong wind events on polynyas and the subsequent sea ice production. We utilize Bootstrap sea ice 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 Sea region. We found that persistent weak winds near the edge of the Ross Ice Shelf are generally associated with positive SIC anomalies in the Ross Sea polynya area (RSP). Conversely we found negative SIC anomalies in this area during persistent strong winds. By analyzing sea ice motion vectors derived from SSM/I brightness temperatures, we find significant sea ice motion anomalies throughout the Ross Sea 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 sea ice 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 Sea ice motion anomalies, highlighting the production

  1. Variability of Arctic Sea Ice as Viewed from Space

    NASA Technical Reports Server (NTRS)

    Parkinson, Claire L.

    1998-01-01

    Over the past 20 years, satellite passive-microwave radiometry has provided a marvelous means for obtaining information about the variability of the Arctic sea ice cover and particularly about sea ice concentrations (% areal coverages) and from them ice extents and the lengths of the sea ice season. This ability derives from the sharp contrast between the microwave emissions of sea ice versus liquid water and allows routine monitoring of the vast Arctic sea ice 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 ice 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 sea ice concentration can lead to temperature changes of 1 K or greater even in local areas outside of the sea ice region. Satellite passive-microwave data for November 1978 through December 1996 reveal marked regional and interannual variabilities in both the ice extents and the lengths of the sea ice season, as well as some statistically significant trends. For the north polar ice cover as a whole, maximum ice 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 Sea experiencing a range of 740,000 - 1,1110,000 km(2) in its yearly maximum ice coverage. Although variations from year to

  2. Operationally Monitoring Sea Ice at the Canadian Ice Service

    NASA Astrophysics Data System (ADS)

    de Abreu, R.; Flett, D.; Carrieres, T.; Falkingham, J.

    2004-05-01

    The Canadian Ice Service (CIS) of the Meteorological Service of Canada promotes safe and efficient maritime operations and protects Canada's environment by providing reliable and timely information about ice and iceberg conditions in Canadian waters. Daily and seasonal charts describing the extent, type and concentration of sea ice and icebergs are provided to support navigation and other activities (e.g. oil and gas) in coastal waters. The CIS relies on a suite of spaceborne visible, infrared and microwave sensors to operationally monitor ice conditions in Canadian coastal and inland waterways. These efforts are complemented by operational sea ice models that are customized and run at the CIS. The archive of these data represent a 35 year archive of ice conditions and have proven to be a valuable dataset for historical sea ice analysis. This presentation will describe the daily integration of remote sensing observations and modelled ice conditions used to produce ice and iceberg products. A review of the decadal evolution of this process will be presented, as well as a glimpse into the future of ice and iceberg monitoring. Examples of the utility of the CIS digital sea ice archive for climate studies will also be presented.

  3. 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.

  4. Satellite Remote Sensing: Passive-Microwave Measurements of Sea Ice

    NASA Technical Reports Server (NTRS)

    Parkinson, Claire L.; Zukor, Dorothy J. (Technical Monitor)

    2001-01-01

    Satellite passive-microwave measurements of sea ice have provided global or near-global sea ice 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 sea ice concentrations (percent areal coverages), sea ice extents, the length of the sea ice season, sea ice temperatures, and sea ice velocities, and to determine the timing of the seasonal onset of melt as well as aspects of the ice-type composition of the sea ice cover. In each case, the calculations are based on the microwave emission characteristics of sea ice and the important contrasts between the microwave emissions of sea ice and those of the surrounding liquid-water medium.

  5. Sea Ice Prediction Has Easy and Difficult Years

    NASA Technical Reports Server (NTRS)

    Hamilton, Lawrence C.; Bitz, Cecilia M.; Blanchard-Wrigglesworth, Edward; Cutler, Matthew; Kay, Jennifer; Meier, Walter N.; Stroeve, Julienne; Wiggins, Helen

    2014-01-01

    Arctic sea ice follows an annual cycle, reaching its low point in September each year. The extent of sea ice 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 Sea Ice 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 sea ice 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).

  6. Global warming releases microplastic legacy frozen in Arctic Sea ice

    NASA Astrophysics Data System (ADS)

    Obbard, Rachel W.; Sadri, Saeed; Wong, Ying Qi; Khitun, Alexandra A.; Baker, Ian; Thompson, Richard C.

    2014-06-01

    When sea ice forms it scavenges and concentrates particulates from the water column, which then become trapped until the ice melts. In recent years, melting has led to record lows in Arctic Sea ice extent, the most recent in September 2012. Global climate models, such as that of Gregory et al. (2002), suggest that the decline in Arctic Sea ice volume (3.4% per decade) will actually exceed the decline in sea ice extent, something that Laxon et al. (2013) have shown supported by satellite data. The extent to which melting ice could release anthropogenic particulates back to the open ocean has not yet been examined. Here we show that Arctic Sea ice 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 sea ice 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 ice melts therefore needs to be evaluated, as do the physical and toxicological effects of plastics on marine life.

  7. Sea Ice on the Southern Ocean

    NASA Technical Reports Server (NTRS)

    Jacobs, Stanley S.

    1998-01-01

    Year-round satellite records of sea ice 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 sea ice. In the Amundsen & Bellingshausen Seas, sea ice 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 sea ice 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 ice thickness, with attendant impacts upon vertical heat flux and the formation of snow ice and brine. The cause of the regional warming and loss of sea ice 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 Sea, ice extent fluctuates over periods of several years, with summer minima and winter maxima roughly in phase. This leads to large interannual cycles of sea ice 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

  8. Observed and Modeled Trends in Southern Ocean Sea Ice

    NASA Technical Reports Server (NTRS)

    Parkinson, Claire L.

    2003-01-01

    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 ice 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 sea ice variations in the model calculations. Both sea ice thickness and sea ice extent decrease markedly in the doubled CO, case, thereby allowing the ice feedbacks to occur. Stand-alone sea ice models have shown Southern Ocean hemispherically averaged winter ice-edge retreats of 1.4 deg latitude for each 1 K increase in atmospheric temperatures. Observations, however, show a much more varied Southern Ocean ice cover, both spatially and temporally, than many of the modeled expectations. In fact, the satellite passive-microwave record of Southern Ocean sea ice since late 1978 has revealed overall increases rather than decreases in ice extents, with ice extent trends on the order of 11,000 sq km/year. When broken down spatially, the positive trends are strongest in the Ross Sea, while the trends are negative in the Bellingshausen/Amundsen Seas. Greater spatial detail can be obtained by examining trends in the length of the sea ice season, and those trends show a coherent picture of shortening sea ice seasons throughout almost the entire Bellingshausen and Amundsen Seas to the west of the Antarctic Peninsula and in the far western Weddell Sea immediately to the east of the Peninsula, with lengthening sea ice 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

  9. Precipitation Impacts of a Shrinking Arctic Sea Ice Cover

    NASA Astrophysics Data System (ADS)

    Stroeve, J. C.; Frei, A.; Gong, G.; Ghatak, D.; Robinson, D. A.; Kindig, D.

    2009-12-01

    Since the beginning of the modern satellite record in October 1978, the extent of Arctic sea ice has declined in all months, with the strongest downward trend at the end of the melt season in September. Recently the September trends have accelerated. Through 2001, the extent of September sea ice was decreasing at a rate of -7 per cent per decade. By 2006, the rate of decrease had risen to -8.9 per cent per decade. In September 2007, Arctic sea ice extent fell to its lowest level recorded, 23 per cent below the previous record set in 2005, boosting the downward trend to -10.7 per cent per decade. Ice extent in September 2008 was the second lowest in the satellite record. Including 2008, the trend in September sea ice extent stands at -11.8 percent per decade. Compared to the 1970s, September ice extent has retreated by 40 per cent. Summer 2009 looks to repeat the anomalously low ice conditions that characterized the last couple of years. Scientists have long expected that a shrinking Arctic sea ice cover will lead to strong warming of the overlying atmosphere, and as a result, affect atmospheric circulation and precipitation patterns. Recent results show clear evidence of Arctic warming linked to declining ice extent, yet observational evidence for responses of atmospheric circulation and precipitation patterns is just beginning to emerge. Rising air temperatures should lead to an increase in the moisture holding capacity of the atmosphere, with the potential to impact autumn precipitation. Although climate models predict a hemispheric wide decrease in snow cover as atmospheric concentrations of GHGs increase, increased precipitation, particular in autumn and winter may result as the Arctic transitions towards a seasonally ice free state. In this study we use atmospheric reanalysis data and a cyclone tracking algorithm to investigate the influence of recent extreme ice loss years on precipitation patterns in the Arctic and the Northern Hemisphere. Results show

  10. Collaborative, International Efforts at Estimating Arctic Sea Ice Processes During IPY (Invited)

    NASA Astrophysics Data System (ADS)

    Overland, J. E.; Eicken, H.; Wiggins, H. V.

    2009-12-01

    Planning for the fourth IPY was conducted during a time of moderate decadal change in the Arctic. However, after this initial planning was completed, further rapid changes were seen, including a 39 % reduction in summer sea ice extent in 2007 and 2008 relative to the 1980s-1990s, loss of multi-year sea ice, and increased sea ice mobility. The SEARCH and DAMOCLES Programs endeavored to increase communication within the research community to promote observations and understanding of rapidly changing Arctic sea ice conditions during IPY. In May 2008 a web-based Sea Ice Outlook was initiated, an international collaborative effort that synthesizes, on a monthly basis throughout the summer, the community’s projections for September arctic sea ice extent. Each month, participating investigators provided a projection for the mean September sea ice extent based on spring and early summer data, along with a rationale for their estimates. The Outlook continued in summer of 2009. The Outlook is a method of rapidly synthesizing a broad range of remote sensing and field observations collected at the peak of the IPY, with analysis methods ranging from heuristic to statistical to ice-ocean model ensemble runs. The 2008 Outlook was a success with 20 groups participating and providing a median sea ice extent projection from June 2008 data of 4.4 million square kilometers (MSQK)—near the observed extent in September 2008 of 4.7 MSQK, and well below the 1979-2007 climatological extent of 6.7 MSQK. More importantly, the contrast of sea ice conditions and atmospheric forcing in 2008 compared to 2007 provided clues to the future fate of arctic sea ice. The question was whether the previous loss of multi-year ice and delay in autumn freeze-up in 2007 would allow sufficient winter thickening of sea ice to last through the summer 2008, promoting recovery from the 2007 minimum, or whether most first-year sea ice would melt out as in 2005 and 2007, resulting in a new record minimum extent

  11. A 100-year Reconstruction of Regional Sea Ice Extent in the Ross and Amundsen-Bellingshausen Seas as Derived from the RICE Ice Core, Coastal West Antarctica

    NASA Astrophysics Data System (ADS)

    Emanuelsson, D. B.; Bertler, N. A. N.; Baisden, W. T.; Keller, E. D.

    2014-12-01

    Antarctic sea ice increased over the past decades. This increase is the result of an increase in the Ross Sea (RS) and along the coast of East Antarctica, whereas the Amundsen-Bellingshausen Seas (ABS) and the Antarctic Peninsula has seen a general decline. Several mechanisms have been suggested as drivers for the regional, complex sea ice pattern, which include changes in ocean currents, wind pattern, as well as ocean and atmospheric temperature. As part of the Roosevelt Island Climate Evolution (RICE) project, a 763 m deep ice core was retrieved from Roosevelt Island (RI; W161° 21', S79°41', 560 m a.s.l.), West Antarctica. The new record provides a unique opportunity to investigate mechanism driving sea ice variability in the RS and ABS sectors. Here we present the water stable isotope record (δD) from the upper part of the RICE core 0-40 m, spanning the time period from 1894 to 2011 (Fig. 1a). Annual δD are correlated with Sea Ice Concentration (SIC). A significant negative (r= -0.45, p≤ 0.05) correlation was found between annual δD and SIC in the eastern RS sector (boxed region in Fig. 1b) for the following months NDJFMA (austral summer and fall). During NDJFMA, RI receives local moisture input from the RS, while during the rest of the year a large extent of this local moisture source area will be covered with sea ice with the exception of the RS Polynya. Concurrently, we observe positive δD and SIC correlations in the ABS, showing a dipole pattern with the eastern RS. For this reason, we suggest that the RICE δD might be used as a proxy for past SIC for the RS and ABS region. There is no overall trend in δD over 100 years (r= -0.08 ‰ dec-1, p= 0.81, 1894-2011). However, we observe a strong increase from 2000-2011 of 17.7 ‰ dec-1(p≤ 0.1), yet the recent δD values and trend of the last decade are not unprecedented (Fig. 1a). We investigate changes in sea surface temperature, atmospheric temperature, inferred surface ocean currents and

  12. Regional Changes in the Sea Ice Cover and Ice Production in the Antarctic

    NASA Technical Reports Server (NTRS)

    Comiso, Josefino C.

    2011-01-01

    Coastal polynyas around the Antarctic continent have been regarded as sea ice factories because of high ice production rates in these regions. The observation of a positive trend in the extent of Antarctic sea ice during the satellite era has been intriguing in light of the observed rapid decline of the ice 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 Sea, strongly negative in the Bellingshausen/Amundsen Seas 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 ice 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 sea ice cover could be caused primarily by enhanced ice production in the Ross Sea that may be associated with more persistent and larger coastal polynyas in the region. Over the Ross Sea shelf, analysis of sea ice drift data from 1992 to 2008 yields a positive rate-of-increase in the net ice export of about 30,000 km2 per year. For a characteristic ice 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 ice 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-ice shelf.

  13. Air-Sea Interactions in the Marginal Ice Zone

    DTIC Science & Technology

    2016-03-31

    Arctic Ocean has increased with the significant retreat of the seasonal sea-ice extent. Here, we use wind, wave, turbulence, and ice measurements to...which has experienced a significant retreat of the seasonal ice extent (Comiso and Nishio, 2008; Comiso et al., 2008). Thomson and Rogers (2014) showed

  14. Statistical Analysis of SSMIS Sea Ice Concentration Threshold at the Arctic Sea Ice Edge during Summer Based on MODIS and Ship-Based Observational Data.

    PubMed

    Ji, Qing; Li, Fei; Pang, Xiaoping; Luo, Cong

    2018-04-05

    The threshold of sea ice concentration (SIC) is the basis for accurately calculating sea ice extent based on passive microwave (PM) remote sensing data. However, the PM SIC threshold at the sea ice edge used in previous studies and released sea ice products has not always been consistent. To explore the representable value of the PM SIC threshold corresponding on average to the position of the Arctic sea ice edge during summer in recent years, we extracted sea ice edge boundaries from the Moderate-resolution Imaging Spectroradiometer (MODIS) sea ice product (MOD29 with a spatial resolution of 1 km), MODIS images (250 m), and sea ice ship-based observation points (1 km) during the fifth (CHINARE-2012) and sixth (CHINARE-2014) Chinese National Arctic Research Expeditions, and made an overlay and comparison analysis with PM SIC derived from Special Sensor Microwave Imager Sounder (SSMIS, with a spatial resolution of 25 km) in the summer of 2012 and 2014. Results showed that the average SSMIS SIC threshold at the Arctic sea ice edge based on ice-water boundary lines extracted from MOD29 was 33%, which was higher than that of the commonly used 15% discriminant threshold. The average SIC threshold at sea ice edge based on ice-water boundary lines extracted by visual interpretation from four scenes of the MODIS image was 35% when compared to the average value of 36% from the MOD29 extracted ice edge pixels for the same days. The average SIC of 31% at the sea ice edge points extracted from ship-based observations also confirmed that choosing around 30% as the SIC threshold during summer is recommended for sea ice extent calculations based on SSMIS PM data. These results can provide a reference for further studying the variation of sea ice under the rapidly changing Arctic.

  15. New aerogeophysical data reveal the extent of the Weddell Sea Rift beneath the Institute and Möller ice streams

    NASA Astrophysics Data System (ADS)

    Jordan, T. A.; Ferraccioli, F.; Siegert, M. J.; Ross, N.; Corr, H.; Bingham, R. G.; Rippin, D. M.; Le Brocq, A. M.

    2011-12-01

    Significant continental rifting associated with Gondwana breakup has been widely recognised in the Weddell Sea region. However, plate reconstructions and the extent of this rift system onshore beneath the West Antarctic Ice Sheet (WAIS) are ambiguous, due to the paucity of modern geophysical data across the Institute and Möller ice stream catchments. Understanding this region is key to unravelling Gondwana breakup and the possible kinematic links between the Weddell Sea and the West Antarctic Rift System. The nature of the underlying tectonic structure is also critical, as it provides the template for ice-flow draining ~20% of the West Antarctic Ice Sheet (WAIS). During the 2010/11 Antarctic field season ~25,000 km of new airborne radar, aerogravity and aeromagnetic data were collected to help unveil the crustal structure and geological boundary conditions beneath the Institute and Möller ice streams. Our new potential field maps delineate varied subglacial geology beneath the glacial catchments, including Jurassic intrusive rocks, sedimentary basins, and Precambrian basement rocks of the Ellsworth Mountains. Inversion of airborne gravity data reveal significant crustal thinning directly beneath the faster flowing coastal parts of the Institute and Möller ice streams. We suggest that continental rifting focussed along the Weddell Sea margin of the Ellsworth-Whitmore Mountains block, providing geological controls for the fast flowing ice streams of the Weddell Sea Embayment. Further to the south we suggest that strike-slip motion between the East Antarctica and the Ellsworth-Whitmore Mountains block may provide a kinematic link between Cretaceous-Cenozoic extension in the West Antarctic Rift System and deformation in the Weddell Sea Embayment.

  16. Will sea ice thickness initialisation improve Arctic seasonal-to-interannual forecast skill?

    NASA Astrophysics Data System (ADS)

    Day, J. J.; Hawkins, E.; Tietsche, S.

    2014-12-01

    A number of recent studies have suggested that Arctic sea ice thickness is an important predictor of Arctic sea ice extent. However, coupled forecast systems do not currently use sea ice thickness observations in their initialization and are therefore missing a potentially important source of additional skill. A set of ensemble potential predictability experiments, with a global climate model, initialized with and without knowledge of the sea ice thickness initial state, have been run to investigate this. These experiments show that accurate knowledge of the sea ice thickness field is crucially important for sea ice concentration and extent forecasts up to eight months ahead. Perturbing sea ice thickness also has a significant impact on the forecast error in the 2m temperature and surface pressure fields a few months ahead. These results show that advancing capabilities to observe and assimilate sea ice thickness into coupled forecast systems could significantly increase skill.

  17. Sea Ice in the Bellingshausen Sea

    NASA Image and Video Library

    2017-12-08

    Antarctica—the continent at the southernmost reach of the planet—is fringed by cold, often frozen waters of the Southern Ocean. The extent of sea ice around the continent typically reaches a peak in September and a minimum in February. The photograph above shows Antarctic sea ice on November 5, 2014, during the annual cycle of melt. The image was acquired by the Digital Mapping System (DMS), a digital camera installed in the belly of research aircraft to capture images of terrain below. In this case, the system flew on the DC-8 during a flight as part of NASA’s Operation IceBridge. Most of the view shows first-year sea ice in the Bellingshausen Sea, as it appeared from an altitude of 328 meters (1,076 feet). The block of ice on the right side of the image is older, thicker, and was once attached to the Antarctic Ice Sheet. By the time this image was acquired, however, the ice had broken away to form an iceberg. Given its close proximity to the ice sheet, this could have been a relatively new berg. Read more: earthobservatory.nasa.gov/IOTD/view.php?id=86721 Credit: NASA/Goddard/IceBridge DMS L0 Raw Imagery courtesy of the Digital Mapping System (DMS) team and the NASA DAAC at the National Snow and Ice Data Center Credit: NASA Earth Observatory NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

  18. Changes in sea ice cover and ice sheet extent at the Yermak Plateau during the last 160 ka - Reconstructions from biomarker records

    NASA Astrophysics Data System (ADS)

    Kremer, A.; Stein, R.; Fahl, K.; Ji, Z.; Yang, Z.; Wiers, S.; Matthiessen, J.; Forwick, M.; Löwemark, L.; O'Regan, M.; Chen, J.; Snowball, I.

    2018-02-01

    The Yermak Plateau is located north of Svalbard at the entrance to the Arctic Ocean, i.e. in an area highly sensitive to climate change. A multi proxy approach was carried out on Core PS92/039-2 to study glacial-interglacial environmental changes at the northern Barents Sea margin during the last 160 ka. The main emphasis was on the reconstruction of sea ice cover, based on the sea ice proxy IP25 and the related phytoplankton - sea ice index PIP25. Sea ice was present most of the time but showed significant temporal variability decisively affected by movements of the Svalbard Barents Sea Ice Sheet. For the first time, we prove the occurrence of seasonal sea ice at the eastern Yermak Plateau during glacial intervals, probably steered by a major northward advance of the ice sheet and the formation of a coastal polynya in front of it. Maximum accumulation of terrigenous organic carbon, IP25 and the phytoplankton biomarkers (brassicasterol, dinosterol, HBI III) can be correlated to distinct deglaciation events. More severe, but variable sea ice cover prevailed at the Yermak Plateau during interglacials. The general proximity to the sea ice margin is further indicated by biomarker (GDGT) - based sea surface temperatures below 2.5 °C.

  19. Arctic and Antarctic Sea Ice Changes and Impacts (Invited)

    NASA Astrophysics Data System (ADS)

    Nghiem, S. V.

    2013-12-01

    The extent of springtime Arctic perennial sea ice, 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 sea ice reduction on photochemical processes, transport, and distribution in the polar environment. In spring 2013, there was further loss of perennial sea ice, as it was not observed in the ocean region adjacent to the Alaskan north coast, where there was a stretch of perennial sea ice in 2012 in the Beaufort Sea and Chukchi Sea. In contrast to the rapid and extensive loss of sea ice in the Arctic, Antarctic sea ice 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 sea ice may arguably be considered as having a low confidence level; however, there was no overall reduction of Antarctic sea ice extent anywhere close to the decreasing rate of Arctic sea ice. There exist publications presenting various factors driving changes in Arctic and Antarctic sea ice. After a short review of these published factors, new observations and atmospheric, oceanic, hydrological, and geological mechanisms contributed to different behaviors of sea ice 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 sea ice 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 sea ice changes. Furthermore, similarities and differences in chemical impacts of Arctic and Antarctic sea ice changes are discussed. Understanding sea ice changes and

  20. Approaching the 2015 Arctic Sea Ice Minimum

    NASA Image and Video Library

    2017-12-08

    As the sun sets over the Arctic, the end of this year’s melt season is quickly approaching and the sea ice cover has already shrunk to the fourth lowest in the satellite record. With possibly some days of melting left, the sea ice extent could still drop to the second or third lowest on record. Arctic sea ice, which regulates the planet’s temperature by bouncing solar energy back to space, has been on a steep decline for the last two decades. This animation shows the evolution of Arctic sea ice in 2015, from its annual maximum wintertime extent, reached on February 25, to September 6. Credit: NASA Scientific Visualization Studio DOWNLOAD THIS VIDEO HERE: svs.gsfc.nasa.gov/cgi-bin/details.cgi?aid=11999 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

  1. Sea Ice off the Princess Astrid Coast

    NASA Image and Video Library

    2015-04-08

    On April 5, 2015, the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite acquired this natural-color image of sea ice off the coast of East Antarctica’s Princess Astrid Coast. White areas close to the continent are sea ice, while white areas in the northeast corner of the image are clouds. One way to better distinguish ice from clouds is with false-color imagery. In the false-color view of the scene here, ice is blue and clouds are white. The image was acquired after Antarctic sea ice had passed its annual minimum extent (reached on February 20, 2015), and had resumed expansion toward its maximum extent (usually reached in September). Credit: NASA image by Jeff Schmaltz, LANCE/EOSDIS Rapid Response. Caption by Kathryn Hansen via NASA's Earth Observatory Read more: www.nasa.gov/content/sea-ice-off-east-antarcticas-princes... NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

  2. Satellite altimetry in sea ice regions - detecting open water for estimating sea surface heights

    NASA Astrophysics Data System (ADS)

    Müller, Felix L.; Dettmering, Denise; Bosch, Wolfgang

    2017-04-01

    The Greenland Sea and the Farm Strait are transporting sea ice from the central Arctic ocean southwards. They are covered by a dynamic changing sea ice layer with significant influences on the Earth climate system. Between the sea ice there exist various sized open water areas known as leads, straight lined open water areas, and polynyas exhibiting a circular shape. Identifying these leads by satellite altimetry enables the extraction of sea surface height information. Analyzing the radar echoes, also called waveforms, provides information on the surface backscatter characteristics. For example waveforms reflected by calm water have a very narrow and single-peaked shape. Waveforms reflected by sea ice show more variability due to diffuse scattering. Here we analyze altimeter waveforms from different conventional pulse-limited satellite altimeters to separate open water and sea ice waveforms. An unsupervised classification approach employing partitional clustering algorithms such as K-medoids and memory-based classification methods such as K-nearest neighbor is used. The classification is based on six parameters derived from the waveform's shape, for example the maximum power or the peak's width. The open-water detection is quantitatively compared to SAR images processed while accounting for sea ice motion. The classification results are used to derive information about the temporal evolution of sea ice extent and sea surface heights. They allow to provide evidence on climate change relevant influences as for example Arctic sea level rise due to enhanced melting rates of Greenland's glaciers and an increasing fresh water influx into the Arctic ocean. Additionally, the sea ice cover extent analyzed over a long-time period provides an important indicator for a globally changing climate system.

  3. Improving Surface Mass Balance Over Ice Sheets and Snow Depth on Sea Ice

    NASA Technical Reports Server (NTRS)

    Koenig, Lora Suzanne; Box, Jason; Kurtz, Nathan

    2013-01-01

    Surface mass balance (SMB) over ice sheets and snow on sea ice (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 ice 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 sea ice cover. The summer of 2012 saw the largest satellite-recorded melt area over the Greenland ice sheet and the smallest satellite-recorded Arctic sea ice extent, making this meeting both timely and relevant.

  4. Variability and Anomalous Trends in the Global Sea Ice Cover

    NASA Technical Reports Server (NTRS)

    Comiso, Josefino C.

    2012-01-01

    The advent of satellite data came fortuitously at a time when the global sea ice cover has been changing rapidly and new techniques are needed to accurately assess the true state and characteristics of the global sea ice cover. The extent of the sea ice in the Northern Hemisphere has been declining by about -4% per decade for the period 1979 to 2011 but for the period from 1996 to 2010, the rate of decline became even more negative at -8% per decade, indicating an acceleration in the decline. More intriguing is the drastically declining perennial sea ice area, which is the ice that survives the summer melt and observed to be retreating at the rate of -14% per decade during the 1979 to 2012 period. Although a slight recovery occurred in the last three years from an abrupt decline in 2007, the perennial ice extent was almost as low as in 2007 in 2011. The multiyear ice, which is the thick component of the perennial ice and regarded as the mainstay of the Arctic sea ice cover is declining at an even higher rate of -19% per decade. The more rapid decline of the extent of this thicker ice type means that the volume of the ice is also declining making the survival of the Arctic ice in summer highly questionable. The slight recovery in 2008, 2009 and 2010 for the perennial ice in summer was likely associated with an apparent cycle in the time series with a period of about 8 years. Results of analysis of concurrent MODIS and AMSR-E data in summer also provide some evidence of more extensive summer melt and meltponding in 2007 and 2011 than in other years. Meanwhile, the Antarctic sea ice cover, as observed by the same set of satellite data, is showing an unexpected and counter intuitive increase of about 1 % per decade over the same period. Although a strong decline in ice extent is apparent in the Bellingshausen/ Amundsen Seas region, such decline is more than compensated by increases in the extent of the sea ice cover in the Ross Sea region. The results of analysis of

  5. Estimation of Arctic Sea Ice Freeboard and Thickness Using CryoSat-2

    NASA Astrophysics Data System (ADS)

    Lee, S.; Im, J.; Kim, J. W.; Kim, M.; Shin, M.

    2014-12-01

    Arctic sea ice is one of the significant components of the global climate system as it plays a significant role in driving global ocean circulation. Sea ice extent has constantly declined since 1980s. Arctic sea ice thickness has also been diminishing along with the decreasing sea ice extent. Because extent and thickness, two main characteristics of sea ice, are important indicators of the polar response to on-going climate change. Sea ice thickness has been measured with numerous field techniques such as surface drilling and deploying buoys. These techniques provide sparse and discontinuous data in spatiotemporal domain. Spaceborne radar and laser altimeters can overcome these limitations and have been used to estimate sea ice thickness. Ice Cloud and land Elevation Satellite (ICEsat), a laser altimeter provided data to detect polar area elevation change between 2003 and 2009. CryoSat-2 launched with Synthetic Aperture Radar (SAR)/Interferometric Radar Altimeter (SIRAL) in April 2010 can provide data to estimate time-series of Arctic sea ice thickness. In this study, Arctic sea ice freeboard and thickness between 2011 and 2014 were estimated using CryoSat-2 SAR and SARIn mode data that have sea ice surface height relative to the reference ellipsoid WGS84. In order to estimate sea ice thickness, freeboard, i.e., elevation difference between the top of sea ice surface should be calculated. Freeboard can be estimated through detecting leads. We proposed a novel lead detection approach. CryoSat-2 profiles such as pulse peakiness, backscatter sigma-0, stack standard deviation, skewness and kurtosis were examined to distinguish leads from sea ice. Near-real time cloud-free MODIS images corresponding to CryoSat-2 data measured were used to visually identify leads. Rule-based machine learning approaches such as See5.0 and random forest were used to identify leads. The proposed lead detection approach better distinguished leads from sea ice than the existing approaches

  6. Biologically-Oriented Processes in the Coastal Sea Ice Zone of the White Sea

    NASA Astrophysics Data System (ADS)

    Melnikov, I. A.

    2002-12-01

    The annual advance and retreat of sea ice is a major physical determinant of spatial and temporal changes in the structure and function of marine coastal biological communities. Sea ice biological data obtained in the tidal zone of Kandalaksha Gulf (White Sea) during 1996-2001 period will be presented. Previous observations in this area were mainly conducted during the ice-free summer season. However, there is little information on the ice-covered winter season (6-7 months duration), and, especially, on the sea-ice biology in the coastal zone within tidal regimes. During the January-May period time-series observations were conducted on transects along shorelines with coastal and fast ice. Trends in the annual extent of sea ice showed significant impacts on ice-associated biological communities. Three types of sea ice impact on kelps, balanoides, littorinas and amphipods are distinguished: (i) positive, when sea ice protects these populations from grinding (ii) negative, when ice grinds both fauna and flora, and (iii) a combined effect, when fast ice protects, but anchored ice grinds plant and animals. To understand the full spectrum of ecological problems caused by pollution on the coastal zone, as well as the problems of sea ice melting caused by global warming, an integrated, long-term study of the physical, chemical, and biological processes is needed.

  7. Integrating Observations and Models to Better Understand a Changing Arctic Sea Ice Cover

    NASA Astrophysics Data System (ADS)

    Stroeve, J. C.

    2017-12-01

    TThe loss of the Arctic sea ice cover has captured the world's attention. While much attention has been paid to the summer ice loss, changes are not limited to summer. The last few winters have seen record low sea ice extents, with 2017 marking the 3rdyear in a row with a new record low for the winter maximum extent. More surprising is the number of consecutive months between January 2016 through April 2017 with ice extent anomalies more than 2 standard deviations below the 1981-2010 mean. Additionally, October 2016 through April 2017 saw 7 consecutive months with record low extents, something that had not happened before in the last 4 decades of satellite observations. As larger parts of the Arctic Ocean become ice-free in summer, regional seas gradually transition from a perennial to a seasonal ice cover. The Barents Sea is already only seasonally ice covered, whereas the Kara Sea has recently lost most of its summer ice and is thereby starting to become a seasonally ice covered region. These changes serve as harbinger for what's to come for other Arctic seas. Given the rapid pace of change, there is an urgent need to improve our understanding of the drivers behind Arctic sea ice loss, the implications of this ice loss and to predict future changes to better inform policy makers. Climate models play a fundamental role in helping us synthesize the complex elements of the Arctic sea ice system yet generally fail to simulate key features of the sea ice system and the pace of sea ice loss. Nevertheless, modeling advances continue to provide better means of diagnosing sea ice change, and new insights are likely to be gained with model output from the 6th phase of the Coupled Model Intercomparison Project (CMIP6). The CMIP6 Sea-Ice Model Intercomparison Project (SIMIP) aim is to better understand biases and errors in sea ice simulations so that we can improve our understanding of the likely future evolution of the sea ice cover and its impacts on global climate. To

  8. The Arctic sea ice cover of 2016: a year of record-low highs and higher-than-expected lows

    NASA Astrophysics Data System (ADS)

    Petty, Alek A.; Stroeve, Julienne C.; Holland, Paul R.; Boisvert, Linette N.; Bliss, Angela C.; Kimura, Noriaki; Meier, Walter N.

    2018-02-01

    The Arctic sea ice cover of 2016 was highly noteworthy, as it featured record low monthly sea ice extents at the start of the year but a summer (September) extent that was higher than expected by most seasonal forecasts. Here we explore the 2016 Arctic sea ice state in terms of its monthly sea ice cover, placing this in the context of the sea ice conditions observed since 2000. We demonstrate the sensitivity of monthly Arctic sea ice extent and area estimates, in terms of their magnitude and annual rankings, to the ice concentration input data (using two widely used datasets) and to the averaging methodology used to convert concentration to extent (daily or monthly extent calculations). We use estimates of sea ice area over sea ice extent to analyse the relative "compactness" of the Arctic sea ice cover, highlighting anomalously low compactness in the summer of 2016 which contributed to the higher-than-expected September ice extent. Two cyclones that entered the Arctic Ocean during August appear to have driven this low-concentration/compactness ice cover but were not sufficient to cause more widespread melt-out and a new record-low September ice extent. We use concentration budgets to explore the regions and processes (thermodynamics/dynamics) contributing to the monthly 2016 extent/area estimates highlighting, amongst other things, rapid ice intensification across the central eastern Arctic through September. Two different products show significant early melt onset across the Arctic Ocean in 2016, including record-early melt onset in the North Atlantic sector of the Arctic. Our results also show record-late 2016 freeze-up in the central Arctic, North Atlantic and the Alaskan Arctic sector in particular, associated with strong sea surface temperature anomalies that appeared shortly after the 2016 minimum (October onwards). We explore the implications of this low summer ice compactness for seasonal forecasting, suggesting that sea ice area could be a more reliable

  9. The EUMETSAT sea ice concentration climate data record

    NASA Astrophysics Data System (ADS)

    Tonboe, Rasmus T.; Eastwood, Steinar; Lavergne, Thomas; Sørensen, Atle M.; Rathmann, Nicholas; Dybkjær, Gorm; Toudal Pedersen, Leif; Høyer, Jacob L.; Kern, Stefan

    2016-09-01

    An Arctic and Antarctic sea ice area and extent dataset has been generated by EUMETSAT's Ocean and Sea Ice 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 sea ice 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, sea ice, and water emission characteristics and inter-sensor differences/biases; and (3) a hybrid sea ice concentration algorithm using the Bristol algorithm over ice and the Bootstrap algorithm in frequency mode over open water. A new sea ice concentration uncertainty algorithm has been developed to estimate the spatial and temporal variability in sea ice concentration retrieval accuracy. A comparison to US National Ice Center sea ice charts from the Arctic and the Antarctic shows that ice concentrations are higher in the ice charts than estimated from the radiometer data at intermediate sea ice concentrations between open water and 100 % ice. The sea ice concentration climate data record is available for download at www.osi-saf.org, including documentation.

  10. 30-Year Satellite Record Reveals Contrasting Arctic and Antarctic Decadal Sea Ice Variability

    NASA Technical Reports Server (NTRS)

    Cavalieri, D. J.; Parkinson, C. L.; Vinnikov, K. Y.

    2003-01-01

    A 30-year satellite record of sea ice extents derived mostly from satellite microwave radiometer observations reveals that the Arctic sea ice extent decreased by 0.30+0.03 x 10(exp 6) square kilometers per 10 yr from 1972 through 2002, but by 0.36 plus or minus 0.05 x 10(exp 6) square kilometers per 10yr from 1979 through 2002, indicating an acceleration of 20% in the rate of decrease. In contrast, the Antarctic sea ice extent decreased dramatically over the period 1973-1977, then gradually increased. Over the full 30-year period, the Antarctic ice extent decreased by 0.15 plus or minus 0.08 x 10(exp 6) square kilometers per 10 yr. The trend reversal is attributed to a large positive anomaly in Antarctic sea ice extent in the early 1970's, an anomaly that apparently began in the late 1960's, as observed in early visible and infrared satellite images.

  11. A New Normal for the Sea Ice Index

    NASA Technical Reports Server (NTRS)

    Fetterer, Florence; Windnagel, Ann; Meier, Walter N.

    2014-01-01

    The NSIDC Sea Ice Index is a popular data product that shows users how ice extent and concentration have changed since the beginning of the passive microwave satellite record in 1978. It shows time series of monthly ice extent anomalies rather than actual extent values, in order to emphasize the information the data are carrying. Along with the time series, an image of average extent for the previous month is shown as a white field, with a pink line showing the median extent for that month. These are updated monthly; corresponding daily products are updated daily.

  12. Nudging the Arctic Ocean to quantify Arctic sea ice feedbacks

    NASA Astrophysics Data System (ADS)

    Dekker, Evelien; Severijns, Camiel; Bintanja, Richard

    2017-04-01

    It is well-established that the Arctic is warming 2 to 3 time faster than rest of the planet. One of the great uncertainties in climate research is related to what extent sea ice feedbacks amplify this (seasonally varying) Arctic warming. Earlier studies have analyzed existing climate model output using correlations and energy budget considerations in order to quantify sea ice feedbacks through indirect methods. From these analyses it is regularly inferred that sea ice likely plays an important role, but details remain obscure. Here we will take a different and a more direct approach: we will keep the sea ice constant in a sensitivity simulation, using a state-of -the-art climate model (EC-Earth), applying a technique that has never been attempted before. This experimental technique involves nudging the temperature and salinity of the ocean surface (and possibly some layers below to maintain the vertical structure and mixing) to a predefined prescribed state. When strongly nudged to existing (seasonally-varying) sea surface temperatures, ocean salinity and temperature, we force the sea ice to remain in regions/seasons where it is located in the prescribed state, despite the changing climate. Once we obtain fixed' sea ice, we will run a future scenario, for instance 2 x CO2 with and without prescribed sea ice, with the difference between these runs providing a measure as to what extent sea ice contributes to Arctic warming, including the seasonal and geographical imprint of the effects.

  13. Variability and trends in the Arctic Sea ice cover: Results from different techniques

    NASA Astrophysics Data System (ADS)

    Comiso, Josefino C.; Meier, Walter N.; Gersten, Robert

    2017-08-01

    Variability and trend studies of sea ice in the Arctic have been conducted using products derived from the same raw passive microwave data but by different groups using different algorithms. This study provides consistency assessment of four of the leading products, namely, Goddard Bootstrap (SB2), Goddard NASA Team (NT1), EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF 1.2), and Hadley HadISST 2.2 data in evaluating variability and trends in the Arctic sea ice cover. All four provide generally similar ice patterns but significant disagreements in ice concentration distributions especially in the marginal ice zone and adjacent regions in winter and meltponded areas in summer. The discrepancies are primarily due to different ways the four techniques account for occurrences of new ice and meltponding. However, results show that the different products generally provide consistent and similar representation of the state of the Arctic sea ice cover. Hadley and NT1 data usually provide the highest and lowest monthly ice extents, respectively. The Hadley data also show the lowest trends in ice extent and ice area at -3.88%/decade and -4.37%/decade, respectively, compared to an average of -4.36%/decade and -4.57%/decade for all four. Trend maps also show similar spatial distribution for all four with the largest negative trends occurring at the Kara/Barents Sea and Beaufort Sea regions, where sea ice has been retreating the fastest. The good agreement of the trends especially with updated data provides strong confidence in the quantification of the rate of decline in the Arctic sea ice cover.Plain Language SummaryThe declining Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover, especially in the summer, has been the center of attention in recent years. Reports on the <span class="hlt">sea</span> <span class="hlt">ice</span> cover have been provided by different institutions using basically the same set of satellite data but different techniques for estimating key parameters such as <span class="hlt">ice</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C43B0754M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C43B0754M"><span>Coordinated Mapping of <span class="hlt">Sea</span> <span class="hlt">Ice</span> Deformation Features with Autonomous Vehicles</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.; Williams, G. D.; Singh, H.; Weissling, B.; Anderson, J.; Maki, T.; Ackley, S. F.</p> <p>2016-12-01</p> <p>Decreases in summer <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> in the Beaufort and Chukchi <span class="hlt">Seas</span> has lead to a transition from a largely perennial <span class="hlt">ice</span> cover, to a seasonal <span class="hlt">ice</span> cover. This drives shifts in <span class="hlt">sea</span> <span class="hlt">ice</span> production, dynamics, <span class="hlt">ice</span> types, and thickness distribution. To examine how the processes driving <span class="hlt">ice</span> advance might also impact the morphology of the <span class="hlt">ice</span> cover, a coordinated <span class="hlt">ice</span> mapping effort was undertaken during a field campaign in the Beaufort <span class="hlt">Sea</span> in October, 2015. Here, we present observations of <span class="hlt">sea</span> <span class="hlt">ice</span> draft topography from six missions of an Autonomous Underwater Vehicle run under different <span class="hlt">ice</span> types and deformation features observed during autumn freeze-up. <span class="hlt">Ice</span> surface features were also mapped during coordinated drone photogrammetric missions over each site. We present preliminary results of a comparison between <span class="hlt">sea</span> <span class="hlt">ice</span> surface topography and <span class="hlt">ice</span> underside morphology for a range of sample <span class="hlt">ice</span> types, including hummocked multiyear <span class="hlt">ice</span>, rubble fields, young <span class="hlt">ice</span> ridges and rafts, and consolidated pancake <span class="hlt">ice</span>. These data are compared to prior observations of <span class="hlt">ice</span> morphological features from deformed Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Such data will be useful for improving parameterizations of <span class="hlt">sea</span> <span class="hlt">ice</span> redistribution during deformation, and for better constraining estimates of airborne or satellite <span class="hlt">sea</span> <span class="hlt">ice</span> thickness.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.5885L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.5885L"><span>Estimation of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Freeboard and Thickness Using CryoSat-2</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, Sanggyun; Im, Jungho; yoon, Hyeonjin; Shin, Minso; Kim, Miae</p> <p>2014-05-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> is one of the significant components of the global climate system as it plays a significant role in driving global ocean circulation, provides a continuous insulating layer at air-<span class="hlt">sea</span> interface, and reflects a large portion of the incoming solar radiation in Polar Regions. <span class="hlt">Sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> has constantly declined since 1980s. Its area was the lowest ever recorded on 16 September 2012 since the satellite record began in 1979. Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> thickness has also been diminishing along with the decreasing <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>. Because <span class="hlt">extent</span> and thickness, two main characteristics of <span class="hlt">sea</span> <span class="hlt">ice</span>, are important indicators of the polar response to on-going climate change, there has been a great effort to quantify them using various approaches. <span class="hlt">Sea</span> <span class="hlt">ice</span> thickness has been measured with numerous field techniques such as surface drilling and deploying buoys. These techniques provide sparse and discontinuous data in spatiotemporal domain. Spaceborne radar and laser altimeters can overcome these limitations and have been used to estimate <span class="hlt">sea</span> <span class="hlt">ice</span> thickness. <span class="hlt">Ice</span> Cloud and land Elevation Satellite (ICEsat), a laser altimeter from National Aeronautics and Space Administration (NASA), provided data to detect polar area elevation change between 2003 and 2009. CryoSat-2 launched with Synthetic Aperture Radar (SAR)/Interferometric Radar Altimeter (SIRAL) on April 2010 can provide data to estimate time-series of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> thickness. In this study, Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard and thickness in 2012 and 2013 were estimated using CryoSat-2 SAR mode data that has <span class="hlt">sea</span> <span class="hlt">ice</span> surface height relative to the reference ellipsoid WGS84. In order to estimate <span class="hlt">sea</span> <span class="hlt">ice</span> thickness, freeboard height, elevation difference between the top of <span class="hlt">sea</span> <span class="hlt">ice</span> surface and leads should be calculated. CryoSat-2 profiles such as pulse peakiness, backscatter sigma-0, number of echoes, and significant wave height were examined to distinguish leads from <span class="hlt">sea</span> <span class="hlt">ice</span>. Several near-real time cloud-free MODIS images as CryoSat-2</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1919277B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1919277B"><span>Quantifying model uncertainty in seasonal Arctic <span class="hlt">sea-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>Blanchard-Wrigglesworth, Edward; Barthélemy, Antoine; Chevallier, Matthieu; Cullather, Richard; Fučkar, Neven; Massonnet, François; Posey, Pamela; Wang, Wanqiu; Zhang, Jinlun; Ardilouze, Constantin; Bitz, Cecilia; Vernieres, Guillaume; Wallcraft, Alan; Wang, Muyin</p> <p>2017-04-01</p> <p>Dynamical model forecasts in the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Outlook (SIO) of September Arctic <span class="hlt">sea-ice</span> <span class="hlt">extent</span> over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or post-processing techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer <span class="hlt">sea</span> <span class="hlt">ice</span> using SIO dynamical models initialized with identical <span class="hlt">sea-ice</span> thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September <span class="hlt">sea-ice</span> volume and <span class="hlt">extent</span>, this is not the case for <span class="hlt">sea-ice</span> concentration. Additionally, forecast uncertainty of <span class="hlt">sea-ice</span> thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040035786&hterms=ships+location&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dships%2Blocation','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040035786&hterms=ships+location&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dships%2Blocation"><span>Studies of the Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Edges and <span class="hlt">Ice</span> <span class="hlt">Extents</span> from Satellite and Ship Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Worby, Anthony P.; Comiso, Josefino C.</p> <p>2003-01-01</p> <p>Passive-microwave derived <span class="hlt">ice</span> edge locations in Antarctica are assessed against other satellite data as well as in situ observations of <span class="hlt">ice</span> edge location made between 1989 and 2000. The passive microwave data generally agree with satellite and ship data but the <span class="hlt">ice</span> concentration at the observed <span class="hlt">ice</span> edge varies greatly with averages of 14% for the TEAM algorithm and 19% for the Bootstrap algorithm. The comparisons of passive microwave with the field data show that in the <span class="hlt">ice</span> growth season (March - October) the agreement is extremely good, with r(sup 2) values of 0.9967 and 0.9797 for the Bootstrap and TEAM algorithms respectively. In the melt season however (November - February) the passive microwave <span class="hlt">ice</span> edge is typically 1-2 degrees south of the observations due to the low concentration and saturated nature of the <span class="hlt">ice</span>. Sensitivity studies show that these results can have significant impact on trend and mass balance studies of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover in the Southern Ocean.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C11C0929S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C11C0929S"><span>Collaborations for Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Information and Tools</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sheffield Guy, L.; Wiggins, H. V.; Turner-Bogren, E. J.; Rich, R. H.</p> <p>2017-12-01</p> <p>The dramatic and rapid changes in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> require collaboration across boundaries, including between disciplines, sectors, institutions, and between scientists and decision-makers. This poster will highlight several projects that provide knowledge to advance the development and use of <span class="hlt">sea</span> <span class="hlt">ice</span> knowledge. <span class="hlt">Sea</span> <span class="hlt">Ice</span> for Walrus Outlook (SIWO: https://www.arcus.org/search-program/siwo) - SIWO is a resource for Alaskan Native subsistence hunters and other interested stakeholders. SIWO provides weekly reports, during April-June, of <span class="hlt">sea</span> <span class="hlt">ice</span> conditions relevant to walrus in the northern Bering and southern Chukchi <span class="hlt">seas</span>. Collaboration among scientists, Alaskan Native <span class="hlt">sea-ice</span> experts, and the Eskimo Walrus Commission is fundamental to this project's success. <span class="hlt">Sea</span> <span class="hlt">Ice</span> Prediction Network (SIPN: https://www.arcus.org/sipn) - A collaborative, multi-agency-funded project focused on seasonal Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> predictions. The goals of SIPN include: coordinate and evaluate Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> predictions; integrate, assess, and guide observations; synthesize predictions and observations; and disseminate predictions and engage key stakeholders. The <span class="hlt">Sea</span> <span class="hlt">Ice</span> Outlook—a key activity of SIPN—is an open process to share and synthesize predictions of the September minimum Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and other variables. Other SIPN activities include workshops, webinars, and communications across the network. Directory of <span class="hlt">Sea</span> <span class="hlt">Ice</span> Experts (https://www.arcus.org/researchers) - ARCUS has undertaken a pilot project to develop a web-based directory of <span class="hlt">sea</span> <span class="hlt">ice</span> experts across institutions, countries, and sectors. The goal of the project is to catalyze networking between individual investigators, institutions, funding agencies, and other stakeholders interested in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Study of Environmental Arctic Change (SEARCH: https://www.arcus.org/search-program) - SEARCH is a collaborative program that advances research, synthesizes research findings, and broadly communicates the results to support</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-200910220008HQ.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-200910220008HQ.html"><span><span class="hlt">Ice</span> Bridge Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2009-10-21</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> is seen out the window of NASA's DC-8 research aircraft as it flies 2,000 feet above the Bellingshausen <span class="hlt">Sea</span> in West Antarctica on Wednesday, Oct., 21, 2009. This was the fourth science flight of NASA’s Operation <span class="hlt">Ice</span> Bridge airborne Earth science mission to study Antarctic <span class="hlt">ice</span> sheets, <span class="hlt">sea</span> <span class="hlt">ice</span>, and <span class="hlt">ice</span> shelves. Photo Credit: (NASA/Jane Peterson)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C32B..01T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C32B..01T"><span>Some Results on <span class="hlt">Sea</span> <span class="hlt">Ice</span> Rheology for the Seasonal <span class="hlt">Ice</span> Zone, Obtained from the Deformation Field of <span class="hlt">Sea</span> <span class="hlt">Ice</span> Drift Pattern</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Toyota, T.; Kimura, N.</p> <p>2017-12-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> rheology which relates <span class="hlt">sea</span> <span class="hlt">ice</span> stress to the large-scale deformation of the <span class="hlt">ice</span> cover has been a big issue to numerical <span class="hlt">sea</span> <span class="hlt">ice</span> modelling. At present the treatment of internal stress within <span class="hlt">sea</span> <span class="hlt">ice</span> area is based mostly on the rheology formulated by Hibler (1979), where the whole <span class="hlt">sea</span> <span class="hlt">ice</span> area behaves like an isotropic and plastic matter under the ordinary stress with the yield curve given by an ellipse with an aspect ratio (e) of 2, irrespective of <span class="hlt">sea</span> <span class="hlt">ice</span> area and horizontal resolution of the model. However, this formulation was initially developed to reproduce the seasonal variation of the perennial <span class="hlt">ice</span> in the Arctic Ocean. As for its applicability to the seasonal <span class="hlt">ice</span> zones (SIZ), where various types of <span class="hlt">sea</span> <span class="hlt">ice</span> are present, it still needs validation from observational data. In this study, the validity of this rheology was examined for the <span class="hlt">Sea</span> of Okhotsk <span class="hlt">ice</span>, typical of the SIZ, based on the AMSR-derived <span class="hlt">ice</span> drift pattern in comparison with the result obtained for the Beaufort <span class="hlt">Sea</span>. To examine the dependence on a horizontal scale, the coastal radar data operated near the Hokkaido coast, Japan, were also used. <span class="hlt">Ice</span> drift pattern was obtained by a maximum cross-correlation method with grid spacings of 37.5 km from the 89 GHz brightness temperature of AMSR-E for the entire <span class="hlt">Sea</span> of Okhotsk and the Beaufort <span class="hlt">Sea</span> and 1.3 km from the coastal radar for the near-shore <span class="hlt">Sea</span> of Okhotsk. The validity of this rheology was investigated from a standpoint of work rate done by deformation field, following the theory of Rothrock (1975). In analysis, the relative rates of convergence were compared between theory and observation to check the shape of yield curve, and the strain ellipse at each grid cell was estimated to see the horizontal variation of deformation field. The result shows that the ellipse of e=1.7-2.0 as the yield curve represents the observed relative conversion rates well for all the <span class="hlt">ice</span> areas. Since this result corresponds with the yield criterion by Tresca and</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('http://adsabs.harvard.edu/abs/2017EGUGA..1912428I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1912428I"><span>September Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> minimum prediction - a new skillful statistical approach</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; Scholz, Patrick; Treffeisen, Renate; Lohmann, Gerrit</p> <p>2017-04-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> in both Polar Regions is an important indicator for the expression of global climate change and its polar amplification. Consequently, a broad interest exists on <span class="hlt">sea</span> <span class="hlt">ice</span>, its coverage, variability and long term change. Knowledge on <span class="hlt">sea</span> <span class="hlt">ice</span> requires high quality data on <span class="hlt">ice</span> <span class="hlt">extent</span>, thickness and its dynamics. However, its predictability is complex and it depends on various climate and oceanic parameters and conditions. In order to provide insights into the potential development of a monthly/seasonal signal of <span class="hlt">sea</span> <span class="hlt">ice</span> evolution, we developed a robust statistical model based on ocean heat content, <span class="hlt">sea</span> surface temperature and different atmospheric variables to calculate an estimate of the September <span class="hlt">Sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> (SSIE) on monthly time scale. Although previous statistical attempts at monthly/seasonal forecasts of SSIE show a relatively reduced skill, we show here that more than 92% (r = 0.96) of the September <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> can be predicted at the end of May by using previous months' climate and oceanic conditions. The skill of the model increases with a decrease in the time lag used for the forecast. At the end of August, our predictions are even able to explain 99% of the SSIE. Our statistical model captures both the general trend as well as the interannual variability of the SSIE. Moreover, it is able to properly forecast the years with extreme high/low SSIE (e.g. 1996/ 2007, 2012, 2013). Besides its forecast skill for SSIE, the model could provide a valuable tool for identifying relevant regions and climate parameters that are important for the <span class="hlt">sea</span> <span class="hlt">ice</span> development in the Arctic and for detecting sensitive and critical regions in global coupled climate models with focus on <span class="hlt">sea</span> <span class="hlt">ice</span> formation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A21Q..08F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A21Q..08F"><span>Response of Antarctic <span class="hlt">sea</span> surface temperature and <span class="hlt">sea</span> <span class="hlt">ice</span> to ozone depletion</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ferreira, D.; Gnanadesikan, A.; Kostov, Y.; Marshall, J.; Seviour, W.; Waugh, D.</p> <p>2017-12-01</p> <p>The influence of the Antarctic ozone hole extends all the way from the stratosphere through the troposphere down to the surface, with clear signatures on surface winds, and SST during summer. In this talk we discuss the impact of these changes on the ocean circulation and <span class="hlt">sea</span> <span class="hlt">ice</span> state. We are notably motivated by the observed cooling of the surface Southern Ocean and associated increase in Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> since the 1970s. These trends are not reproduced by CMIP5 climate models, and the underlying mechanism at work in nature and the models remain unexplained. Did the ozone hole contribute to the observed trends?Here, we review recent advances toward answering these issues using "abrupt ozone depletion" experiments. The ocean and <span class="hlt">sea</span> <span class="hlt">ice</span> response is rather complex, comprising two timescales: a fast ( 1-2y) cooling of the surface ocean and <span class="hlt">sea</span> <span class="hlt">ice</span> cover increase, followed by a slower warming trend, which, depending on models, flip the sign of the SST and <span class="hlt">sea</span> <span class="hlt">ice</span> responses on decadal timescale. Although the basic mechanism seems robust, comparison across climate models reveal large uncertainties in the timescales and amplitude of the response to the <span class="hlt">extent</span> that even the sign of the ocean and <span class="hlt">sea</span> <span class="hlt">ice</span> response to ozone hole and recovery remains unconstrained. After briefly describing the dynamics and thermodynamics behind the two-timescale response, we will discuss the main sources of uncertainties in the modeled response, namely cloud effects and air-<span class="hlt">sea</span> heat exchanges, surface wind stress response and ocean eddy transports. Finally, we will consider the implications of our results on the ability of coupled climate models to reproduce observed Southern Ocean changes.</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> marginal <span class="hlt">ice</span> zone is mainly ablated via large sustained turbulent ocean enthalpy fluxes. The sensible heat required for these sustained fluxes is drawn from a reservoir of warm subsurface waters of subtropical origin entrained into the mixed layer via convective mixing. Analysis of ocean surface buoyancy fluxes during the period preceding quasi-equilibrium reveals that low-salinity upper ocean anomalies are required for <span class="hlt">ice</span> to advance seaward of the Arctic Water/Irminger Water thermohaline front in the northern Labrador <span class="hlt">Sea</span>. Anomalous low-salinity waters inhibit mixed layer deepening, shielding the advancing <span class="hlt">ice</span> pack from the subsurface heat reservoir, and are conducive to a positive surface stratification enhancement feedback from <span class="hlt">ice</span> meltwater release. Interestingly, the climatological location of the front coincides with the minimum observed wintertime <span class="hlt">ice</span> <span class="hlt">extent</span>; positive <span class="hlt">ice</span> <span class="hlt">extent</span> anomalies may require hydrographic preconditioning. If true, the export of low-salinity anomalies from melting Arctic <span class="hlt">ice</span> associated with future warming may be predicted to lead positive <span class="hlt">ice</span> <span class="hlt">extent</span> anomalies in Labrador <span class="hlt">Sea</span> via the positive surface stratification enhancement mechanism feedback outlined above.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018GeoRL..45.4114P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018GeoRL..45.4114P"><span>Bedrock Erosion Surfaces Record Former East Antarctic <span class="hlt">Ice</span> Sheet <span class="hlt">Extent</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Paxman, Guy J. G.; Jamieson, Stewart S. R.; Ferraccioli, Fausto; Bentley, Michael J.; Ross, Neil; Armadillo, Egidio; Gasson, Edward G. W.; Leitchenkov, German; DeConto, Robert M.</p> <p>2018-05-01</p> <p>East Antarctica hosts large subglacial basins into which the East Antarctic <span class="hlt">Ice</span> Sheet (EAIS) likely retreated during past warmer climates. However, the <span class="hlt">extent</span> of retreat remains poorly constrained, making quantifying past and predicted future contributions to global <span class="hlt">sea</span> level rise from these marine basins challenging. Geomorphological analysis and flexural modeling within the Wilkes Subglacial Basin are used to reconstruct the <span class="hlt">ice</span> margin during warm intervals of the Oligocene-Miocene. Flat-lying bedrock plateaus are indicative of an <span class="hlt">ice</span> sheet margin positioned >400-500 km inland of the modern grounding zone for extended periods of the Oligocene-Miocene, equivalent to a 2-m rise in global <span class="hlt">sea</span> level. Our findings imply that if major EAIS retreat occurs in the future, isostatic rebound will enable the plateau surfaces to act as seeding points for extensive <span class="hlt">ice</span> rises, thus limiting extensive <span class="hlt">ice</span> margin retreat of the scale seen during the early EAIS.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70012715','USGSPUBS'); return false;" href="https://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 <span class="hlt">extent</span>, 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://adsabs.harvard.edu/abs/2012GeoRL..39.8502N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012GeoRL..39.8502N"><span>Observations reveal external driver for Arctic <span class="hlt">sea-ice</span> retreat</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; Marotzke, Jochem</p> <p>2012-04-01</p> <p>The very low summer <span class="hlt">extent</span> of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> that has been observed in recent years is often casually interpreted as an early-warning sign of anthropogenic global warming. For examining the validity of this claim, previously IPCC model simulations have been used. Here, we focus on the available observational record to examine if this record allows us to identify either internal variability, self-acceleration, or a specific external forcing as the main driver for the observed <span class="hlt">sea-ice</span> retreat. We find that the available observations are sufficient to virtually exclude internal variability and self-acceleration as an explanation for the observed long-term trend, clustering, and magnitude of recent <span class="hlt">sea-ice</span> minima. Instead, the recent retreat is well described by the superposition of an externally forced linear trend and internal variability. For the externally forced trend, we find a physically plausible strong correlation only with increasing atmospheric CO2 concentration. Our results hence show that the observed evolution of Arctic <span class="hlt">sea-ice</span> <span class="hlt">extent</span> is consistent with the claim that virtually certainly the impact of an anthropogenic climate change is observable in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> already today.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C23E..01R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C23E..01R"><span>Variational Ridging in <span class="hlt">Sea</span> <span class="hlt">Ice</span> Models</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.; Hunke, E. C.; Lipscomb, W. H.; Maslowski, W.; Kamal, S.</p> <p>2017-12-01</p> <p>This work presents the results of a new development to make basin-scale <span class="hlt">sea</span> <span class="hlt">ice</span> models aware of the shape, porosity and <span class="hlt">extent</span> of individual ridges within the pack. We have derived an analytic solution for the Euler-Lagrange equation of individual ridges that accounts for non-conservative forces, and therefore the compressive strength of individual ridges. Because a region of the pack is simply a collection of paths of individual ridges, we are able to solve the Euler-Lagrange equation for a large-scale <span class="hlt">sea</span> <span class="hlt">ice</span> field also, and therefore the compressive strength of a region of the pack that explicitly accounts for the macro-porosity of ridged debris. We make a number of assumptions that have simplified the problem, such as treating <span class="hlt">sea</span> <span class="hlt">ice</span> as a granular material in ridges, and assuming that bending moments associated with ridging are perturbations around an isostatic state. Regardless of these simplifications, the ridge model is remarkably predictive of macro-porosity and ridge shape, and, because our equations are analytic, they do not require costly computations to solve the Euler-Lagrange equation of ridges on the large scale. The new ridge model is therefore applicable to large-scale <span class="hlt">sea</span> <span class="hlt">ice</span> models. We present results from this theoretical development, as well as plans to apply it to the Regional Arctic System Model and a community <span class="hlt">sea</span> <span class="hlt">ice</span> code. Most importantly, the new ridging model is particularly useful for pinpointing gaps in our observational record of <span class="hlt">sea</span> <span class="hlt">ice</span> ridges, and points to the need for improved measurements of the evolution of porosity of deformed <span class="hlt">ice</span> in the Arctic and Antarctic. Such knowledge is not only useful for improving models, but also for improving estimates of <span class="hlt">sea</span> <span class="hlt">ice</span> volume derived from altimetric measurements of <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27812435','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27812435"><span>Loitering of the retreating <span class="hlt">sea</span> <span class="hlt">ice</span> edge in the Arctic <span class="hlt">Seas</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Steele, Michael; Ermold, Wendy</p> <p>2015-12-01</p> <p>Each year, the arctic <span class="hlt">sea</span> <span class="hlt">ice</span> edge retreats from its winter maximum <span class="hlt">extent</span> through the Seasonal <span class="hlt">Ice</span> Zone (SIZ) to its summer minimum <span class="hlt">extent</span>. On some days, this retreat happens at a rapid pace, while on other days, parts of the pan-arctic <span class="hlt">ice</span> edge hardly move for periods of days up to 1.5 weeks. We term this stationary behavior "<span class="hlt">ice</span> edge loitering," and identify areas that are more prone to loitering than others. Generally, about 20-25% of the SIZ area experiences loitering, most often only one time at any one location during the retreat season, but sometimes two or more times. The main mechanism controlling loitering is an interaction between surface winds and warm <span class="hlt">sea</span> surface temperatures in areas from which the <span class="hlt">ice</span> has already retreated. When retreat happens early enough to allow atmospheric warming of this open water, winds that force <span class="hlt">ice</span> floes into this water cause melting. Thus, while individual <span class="hlt">ice</span> floes are moving, the <span class="hlt">ice</span> edge as a whole appears to loiter. The time scale of loitering is then naturally tied to the synoptic time scale of wind forcing. Perhaps surprisingly, the area of loitering in the arctic <span class="hlt">seas</span> has not changed over the past 25 years, even as the SIZ area has grown. This is because rapid <span class="hlt">ice</span> retreat happens most commonly late in the summer, when atmospheric warming of open water is weak. We speculate that loitering may have profound effects on both physical and biological conditions at the <span class="hlt">ice</span> edge during the retreat season.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70186594','USGSPUBS'); return false;" href="https://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, David 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> <span class="hlt">extent</span> and concentration have been carefully monitored from space. An estimated 7.4% decrease in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> 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('http://adsabs.harvard.edu/abs/2008AGUFM.U24B..02O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.U24B..02O"><span>Summer 2007 and 2008 Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Loss in Context: OUTLOOK 2008</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Overland, J. E.; Eicken, H.; Wiggins, H. V.</p> <p>2008-12-01</p> <p>The Arctic is changing faster than the publication cycle for new information. In response, the SEARCH and DAMOCLES Programs initiated an OUTLOOK 2008 to provide broad-based communication and assessment within the arctic science community on the causes of rapid summer <span class="hlt">sea</span> <span class="hlt">ice</span> loss, synthesizing information from Arctic observing networks and model simulations. The question for summer 2008 was whether the previous loss of multi-year <span class="hlt">sea</span> <span class="hlt">ice</span> and delay in <span class="hlt">sea</span> <span class="hlt">ice</span> formation in autumn 2007 would still allow sufficient winter growth of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness to last through the summer 2008, potentially allowing for recovery from the 2007 minimum. The answer is no; summer 2008 was a second sequential year of extremely low minimum <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>. To organize OUTLOOK 2008, respondents were asked in May, June and July to provide a rationale and semi-quantitative assessment of arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> anticipated for September 2008. OUTLOOK 2008 supplemented information maintained by <span class="hlt">ice</span> centers, universities and other data providers. Using a range of methods, all of the approximately 20 groups responded that summer <span class="hlt">sea</span> <span class="hlt">ice</span> would not return to climatological mean conditions, with a median response near the 2007 <span class="hlt">extent</span>. The range of responses depended on the relative weight given to "initial conditions," e.g., age and thickness of <span class="hlt">sea</span> <span class="hlt">ice</span> at the end of spring, versus whether summer winds in 2008 would be as supportive for <span class="hlt">ice</span> loss as in 2007. Initial conditions turned out to be a primary factor for summer 2008, with implications for continued <span class="hlt">sea</span> <span class="hlt">ice</span> loss in future years. OUTLOOK 2008 highlighted aspects of the observation and modeling efforts that require further attention such as interpretation of summer microwave signatures, in situ buoy measurements, and data assimilation in models. We appreciate the contributions from respondents and reviewers who made OUTLOOK 2008 a success.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C33C1210S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C33C1210S"><span>Towards development of an operational snow on <span class="hlt">sea</span> <span class="hlt">ice</span> product</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.; Liston, G. E.; Barrett, A. P.; Tschudi, M. A.; Stewart, S.</p> <p>2017-12-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> has been visibly changing over the past couple of decades; most notably the annual minimum <span class="hlt">extent</span> which has shown a distinct downward, and recently accelerating, trend. September mean <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> was over 7×106 km2 in the 1980's, but has averaged less than 5×106 km2 in the last decade. Should this loss continue, there will be wide-ranging impacts on marine ecosystems, coastal communities, prospects for resource extraction and marine activity, and weather conditions in the Arctic and beyond. While changes in the spatial <span class="hlt">extent</span> of <span class="hlt">sea</span> <span class="hlt">ice</span> have been routinely monitored since the 1970s, less is known about how the thickness of the <span class="hlt">ice</span> cover has changed. While estimates of <span class="hlt">ice</span> thickness across the Arctic Ocean have become available over the past 20 years based on data from ERS-1/2, Envisat, ICESat, CryoSat-2 satellites and Operation <span class="hlt">Ice</span>Bridge aircraft campaigns, the variety of these different measurement approaches, sensor technologies and spatial coverage present formidable challenges. Key among these is that measurement techniques do not measure <span class="hlt">ice</span> thickness directly - retrievals also require snow depth and density. Towards that end, a sophisticated snow accumulation model is tested in a Lagrangian framework to map daily snow depths across the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover using atmospheric reanalysis data as input. Accuracy of the snow accumulation is assessed through comparison with Operation <span class="hlt">Ice</span>Bridge data and <span class="hlt">ice</span> mass balance buoys (IMBs). Impacts on <span class="hlt">ice</span> thickness retrievals are further discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70190395','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70190395"><span>Polar bears and <span class="hlt">sea</span> <span class="hlt">ice</span> habitat change</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Durner, George M.; Atwood, Todd C.; Butterworth, Andy</p> <p>2017-01-01</p> <p>The polar bear (Ursus maritimus) is an obligate apex predator of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> and as such can be affected by climate warming-induced changes in the <span class="hlt">extent</span> and composition of pack <span class="hlt">ice</span> and its impacts on their seal prey. <span class="hlt">Sea</span> <span class="hlt">ice</span> declines have negatively impacted some polar bear subpopulations through reduced energy input because of loss of hunting habitats, higher energy costs due to greater <span class="hlt">ice</span> drift, <span class="hlt">ice</span> fracturing and open water, and ultimately greater challenges to recruit young. Projections made from the output of global climate models suggest that polar bears in peripheral Arctic and sub-Arctic <span class="hlt">seas</span> will be reduced in numbers or become extirpated by the end of the twenty-first century if the rate of climate warming continues on its present trajectory. The same projections also suggest that polar bears may persist in the high-latitude Arctic where heavy multiyear <span class="hlt">sea</span> <span class="hlt">ice</span> that has been typical in that region is being replaced by thinner annual <span class="hlt">ice</span>. Underlying physical and biological oceanography provides clues as to why polar bear in some regions are negatively impacted, while bears in other regions have shown no apparent changes. However, continued declines in <span class="hlt">sea</span> <span class="hlt">ice</span> will eventually challenge the survival of polar bears and efforts to conserve them in all regions of the Arctic.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA601068','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA601068"><span>Sunlight, <span class="hlt">Sea</span> <span class="hlt">Ice</span>, and the <span class="hlt">Ice</span> Albedo Feedback in a Changing Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Cover</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2013-09-30</p> <p><span class="hlt">Sea</span> <span class="hlt">Ice</span> , and the <span class="hlt">Ice</span> Albedo Feedback in a...COVERED 00-00-2013 to 00-00-2013 4. TITLE AND SUBTITLE Sunlight, <span class="hlt">Sea</span> <span class="hlt">Ice</span> , and the <span class="hlt">Ice</span> Albedo Feedback in a Changing Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Cover 5a...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</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="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2002-01-01</p> <p>for the IABP drifting buoys (red), the model (green), and the model with assimilation (black). 55 Oceanography • Vol. 15 • No. 1/2002 trate the need...SPECIAL ISSUE – NAVY OPERATIONAL MODELS : TEN YEARS LATER Oceanography • Vol. 15 • No. 1/2002 44 <span class="hlt">ice</span> <span class="hlt">extent</span> and/or <span class="hlt">ice</span> thickness. A general trend...most often based on a combination of models and data. Modeling <span class="hlt">sea</span> <span class="hlt">ice</span> can be a difficult problem, as it exists in many different forms (Figure 1). It</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> <span class="hlt">extent</span>, 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/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> <span class="hlt">extent</span> 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> <span class="hlt">extent</span> 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://adsabs.harvard.edu/abs/2016AGUFMGC23A1220I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC23A1220I"><span>Statistical prediction of September Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> minimum based on stable teleconnections with global climate and oceanic patterns</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ionita, M.; Grosfeld, K.; Scholz, P.; Lohmann, G.</p> <p>2016-12-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> in both Polar Regions is an important indicator for the expression of global climate change and its polar amplification. Consequently, a broad information interest exists on <span class="hlt">sea</span> <span class="hlt">ice</span>, its coverage, variability and long term change. Knowledge on <span class="hlt">sea</span> <span class="hlt">ice</span> requires high quality data on <span class="hlt">ice</span> <span class="hlt">extent</span>, thickness and its dynamics. However, its predictability depends on various climate parameters and conditions. In order to provide insights into the potential development of a monthly/seasonal signal, we developed a robust statistical model based on ocean heat content, <span class="hlt">sea</span> surface temperature and atmospheric variables to calculate an estimate of the September minimum <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> for every year. Although previous statistical attempts at monthly/seasonal forecasts of September <span class="hlt">sea</span> <span class="hlt">ice</span> minimum show a relatively reduced skill, here it is shown that more than 97% (r = 0.98) of the September <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> can predicted three months in advance by using previous months conditions via a multiple linear regression model based on global <span class="hlt">sea</span> surface temperature (SST), mean <span class="hlt">sea</span> level pressure (SLP), air temperature at 850hPa (TT850), surface winds and <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> persistence. The statistical model is based on the identification of regions with stable teleconnections between the predictors (climatological parameters) and the predictand (here <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>). The results based on our statistical model contribute to the <span class="hlt">sea</span> <span class="hlt">ice</span> prediction network for the <span class="hlt">sea</span> <span class="hlt">ice</span> outlook report (https://www.arcus.org/sipn) and could provide a tool for identifying relevant regions and climate parameters that are important for the <span class="hlt">sea</span> <span class="hlt">ice</span> development in the Arctic and for detecting sensitive and critical regions in global coupled climate models with focus on <span class="hlt">sea</span> <span class="hlt">ice</span> formation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016DSRII.131....7H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016DSRII.131....7H"><span>SIPEX 2012: Extreme <span class="hlt">sea-ice</span> and atmospheric conditions off 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>Heil, P.; Stammerjohn, S.; Reid, P.; Massom, R. A.; Hutchings, J. K.</p> <p>2016-09-01</p> <p>In 2012, Antarctic <span class="hlt">sea-ice</span> coverage was marked by weak annual-mean climate anomalies that consisted of opposing anomalies early and late in the year (some setting new records) which were interspersed by near-average conditions for most of the austral autumn and winter. Here, we investigate the ocean-<span class="hlt">ice</span>-atmosphere system off East Antarctica, prior to and during the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Physics and Ecosystems eXperiment [SIPEX] 2012, by exploring relationships between atmospheric and oceanic forcing together with the <span class="hlt">sea-ice</span> and snow characteristics. During August and September 2012, just prior to SIPEX 2012, atmospheric circulation over the Southern Ocean was near-average, setting up the ocean-<span class="hlt">ice</span>-atmosphere system for near-average conditions. However, below-average surface pressure and temperature as well as strengthened circumpolar winds prevailed during June and July 2012. This led to a new record (19.48×106 km2) in maximum Antarctic <span class="hlt">sea-ice</span> <span class="hlt">extent</span> recorded in late September. In contrast to the weak circum-Antarctic conditions, the East Antarctic sector (including the SIPEX 2012 region) experienced positive <span class="hlt">sea-ice</span> <span class="hlt">extent</span> and concentration anomalies during most of 2012, coincident with negative atmospheric pressure and <span class="hlt">sea</span>-surface temperature anomalies. Heavily deformed <span class="hlt">sea</span> <span class="hlt">ice</span> appeared to be associated with intensified wind stress due to increased cyclonicity as well as an increased influx of <span class="hlt">sea</span> <span class="hlt">ice</span> from the east. This increased westward <span class="hlt">ice</span> flux is likely linked to the break-up of nearly 80% of the Mertz Glacier Tongue in 2010, which strongly modified the coastal configuration and hence the width of the westward coastal current. Combined with favourable atmospheric conditions the associated changed coastal configuration allowed more <span class="hlt">sea</span> <span class="hlt">ice</span> to remain within the coastal current at the expense of a reduced northward flow in the region around 141°-145°E. In addition a westward propagating positive anomaly of <span class="hlt">sea-ice</span> <span class="hlt">extent</span> from the western Ross <span class="hlt">Sea</span> during austral winter</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> realistic polynya <span class="hlt">extent</span> estimates. The model-derived polynya <span class="hlt">extent</span> has been validated by comparing the modelled <span class="hlt">sea</span> <span class="hlt">ice</span> concentration against MODIS high resolution satellite images, confirming that the model is able to reproduce reasonably well the TNB polynya evolution in terms of both shape and <span class="hlt">extent</span>.</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 <span class="hlt">extent</span> 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> </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/2018E%26PSL.488...36L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018E%26PSL.488...36L"><span>Precession and atmospheric CO2 modulated variability of <span class="hlt">sea</span> <span class="hlt">ice</span> in the central Okhotsk <span class="hlt">Sea</span> since 130,000 years ago</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lo, Li; Belt, Simon T.; Lattaud, Julie; Friedrich, Tobias; Zeeden, Christian; Schouten, Stefan; Smik, Lukas; Timmermann, Axel; Cabedo-Sanz, Patricia; Huang, Jyh-Jaan; Zhou, Liping; Ou, Tsong-Hua; Chang, Yuan-Pin; Wang, Liang-Chi; Chou, Yu-Min; Shen, Chuan-Chou; Chen, Min-Te; Wei, Kuo-Yen; Song, Sheng-Rong; Fang, Tien-Hsi; Gorbarenko, Sergey A.; Wang, Wei-Lung; Lee, Teh-Quei; Elderfield, Henry; Hodell, David A.</p> <p>2018-04-01</p> <p>Recent reduction in high-latitude <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> demonstrates that <span class="hlt">sea</span> <span class="hlt">ice</span> is highly sensitive to external and internal radiative forcings. In order to better understand <span class="hlt">sea</span> <span class="hlt">ice</span> system responses to external orbital forcing and internal oscillations on orbital timescales, here we reconstruct changes in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and summer <span class="hlt">sea</span> surface temperature (SSST) over the past 130,000 yrs in the central Okhotsk <span class="hlt">Sea</span>. We applied novel organic geochemical proxies of <span class="hlt">sea</span> <span class="hlt">ice</span> (IP25), SSST (TEX86L) and open water marine productivity (a tri-unsaturated highly branched isoprenoid and biogenic opal) to marine sediment core MD01-2414 (53°11.77‧N, 149°34.80‧E, water depth 1123 m). To complement the proxy data, we also carried out transient Earth system model simulations and sensitivity tests to identify contributions of different climatic forcing factors. Our results show that the central Okhotsk <span class="hlt">Sea</span> was <span class="hlt">ice</span>-free during Marine Isotope Stage (MIS) 5e and the early-mid Holocene, but experienced variable <span class="hlt">sea</span> <span class="hlt">ice</span> cover during MIS 2-4, consistent with intervals of relatively high and low SSST, respectively. Our data also show that the <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> was governed by precession-dominated insolation changes during intervals of atmospheric CO2 concentrations ranging from 190 to 260 ppm. However, the proxy record and the model simulation data show that the central Okhotsk <span class="hlt">Sea</span> was near <span class="hlt">ice</span>-free regardless of insolation forcing throughout the penultimate interglacial, and during the Holocene, when atmospheric CO2 was above ∼260 ppm. Past <span class="hlt">sea</span> <span class="hlt">ice</span> conditions in the central Okhotsk <span class="hlt">Sea</span> were therefore strongly modulated by both orbital-driven insolation and CO2-induced radiative forcing during the past glacial/interglacial cycle.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMPP13C..01S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMPP13C..01S"><span>Coherent <span class="hlt">Sea</span> <span class="hlt">Ice</span> Variations in the Nordic <span class="hlt">Seas</span> and Abrupt Greenland Climate Changes over Dansgaard-Oeschger Cycles</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sadatzki, H.; Berben, S.; Dokken, T.; Stein, R.; Fahl, K.; Jansen, E.</p> <p>2016-12-01</p> <p>Rapid changes in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> in the Nordic <span class="hlt">Seas</span> may have played a crucial role in controlling the abruptness of ocean circulation and climate changes associated with Dansgaard-Oeschger (D-O) cycles during the last glacial (Li et al., 2010; Dokken et al., 2013). To investigate the role of <span class="hlt">sea</span> <span class="hlt">ice</span> for abrupt climate changes, we produced a <span class="hlt">sea</span> <span class="hlt">ice</span> record from the Norwegian <span class="hlt">Sea</span> Core MD99-2284 at a temporal resolution approaching that of <span class="hlt">ice</span> core records, covering four D-O cycles at ca. 32-41 ka. This record is based on the <span class="hlt">sea</span> <span class="hlt">ice</span> diatom biomarker IP25, open-water phytoplankton biomarker dinosterol and semi-quantitative phytoplankton-IP25 (PIP25) estimates. A detailed tephrochronology of MD99-2284 corroborates the tuning-based age model and independently constrains the GS9/GIS8 transition, allowing for direct comparison between our sediment and <span class="hlt">ice</span> core records. For cold stadials we find extremely low fluxes of total organic carbon, dinosterol and IP25, which points to a general absence of open-water phytoplankton and <span class="hlt">ice</span> algae production under a near-permanent <span class="hlt">sea</span> <span class="hlt">ice</span> cover. For the interstadials, in turn, all biomarker fluxes are strongly enhanced, reflecting a highly productive <span class="hlt">sea</span> <span class="hlt">ice</span> edge situation and implying largely open ocean conditions for the eastern Nordic <span class="hlt">Seas</span>. As constrained by three tephra layers, we observe that the stadial-interstadial <span class="hlt">sea</span> <span class="hlt">ice</span> decline was rapid and may have induced a coeval abrupt northward shift in the Greenland precipitation moisture source as recorded in <span class="hlt">ice</span> cores. The <span class="hlt">sea</span> <span class="hlt">ice</span> retreat also facilitated a massive heat release through deep convection in the previously stratified Nordic <span class="hlt">Seas</span>, generating atmospheric warming of the D-O events. We thus conclude that rapid changes in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> in the Nordic <span class="hlt">Seas</span> amplified oceanic reorganizations and were a key factor in controlling abrupt Greenland climate changes over D-O cycles. Dokken, T.M. et al., 2013. Paleoceanography 28, 491-502 Li, C. et al., 2010. Journ. Clim. 23, 5457-5475</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C31B0742N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C31B0742N"><span>A Quantitative Proxy for <span class="hlt">Sea-Ice</span> Based on Diatoms: A Cautionary Tale.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nesterovich, A.; Caissie, B.</p> <p>2016-12-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> in the Polar Regions supports unique and productive ecosystems, but the current decline in the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> prompts questions about previous <span class="hlt">sea</span> <span class="hlt">ice</span> declines and the response of <span class="hlt">ice</span> related ecosystems. Since satellite data only extend back to 1978, the study of <span class="hlt">sea</span> <span class="hlt">ice</span> before this time requires a proxy. Being one of the most productive, diatom-dominated regions in the world and having a wide range of <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations, the Bering and Chukchi <span class="hlt">seas</span> are a perfect place to find a relationship between the presence of <span class="hlt">sea</span> <span class="hlt">ice</span> and diatom community composition. The aim of this work is to develop a diatom-based proxy for the <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>. A total of 473 species have been identified in 104 sediment samples, most of which were collected on board the US Coast Guard Cutter Healy <span class="hlt">ice</span> breaker (2006, 2007) and the Norseman II (2008). The study also included some of the archived diatom smear slides made from sediments collected in 1969. The assemblages were compared to satellite-derived <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> data averaged over the 10 years preceding the sampling. Previous studies in the Arctic and Antarctic regions demonstrated that the Generalized Additive Model (GAM) is one of the best choices for proxy construction. It has the advantage of using only several species instead of the whole assemblage, thus including only <span class="hlt">sea</span> <span class="hlt">ice</span>-associated species and minimizing the noise created by species responding to other environmental factors. Our GAM on three species (Connia compita, Fragilariopsis reginae-jahniae, and Neodenticula seminae) has low standard deviation, high level of explained variation, and holds under the ten-fold cross-validation; the standard residual analysis is acceptable. However, a spatial residual analysis revealed that the model consistently over predicts in the Chukchi <span class="hlt">Sea</span> and under predicts in the Bering <span class="hlt">Sea</span>. Including a spatial model into the GAM didn't improve the situation. This has led us to test other methods, including a non-parametric model</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GML....36..101M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GML....36..101M"><span>High-resolution IP25-based reconstruction of <span class="hlt">sea-ice</span> variability in the western North Pacific and Bering <span class="hlt">Sea</span> during the past 18,000 years</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Méheust, Marie; Stein, Ruediger; Fahl, Kirsten; Max, Lars; Riethdorf, Jan-Rainer</p> <p>2016-04-01</p> <p>Due to its strong influence on heat and moisture exchange between the ocean and the atmosphere, <span class="hlt">sea</span> <span class="hlt">ice</span> is an essential component of the global climate system. In the context of its alarming decrease in terms of concentration, thickness and duration, understanding the processes controlling <span class="hlt">sea-ice</span> variability and reconstructing paleo-<span class="hlt">sea-ice</span> <span class="hlt">extent</span> in polar regions have become of great interest for the scientific community. In this study, for the first time, IP25, a recently developed biomarker <span class="hlt">sea-ice</span> proxy, was used for a high-resolution reconstruction of the <span class="hlt">sea-ice</span> <span class="hlt">extent</span> and its variability in the western North Pacific and western Bering <span class="hlt">Sea</span> during the past 18,000 years. To identify mechanisms controlling the <span class="hlt">sea-ice</span> variability, IP25 data were associated with published <span class="hlt">sea</span>-surface temperature as well as diatom and biogenic opal data. The results indicate that a seasonal <span class="hlt">sea-ice</span> cover existed during cold periods (Heinrich Stadial 1 and Younger Dryas), whereas during warmer intervals (Bølling-Allerød and Holocene) reduced <span class="hlt">sea</span> <span class="hlt">ice</span> or <span class="hlt">ice</span>-free conditions prevailed in the study area. The variability in <span class="hlt">sea-ice</span> <span class="hlt">extent</span> seems to be linked to climate anomalies and <span class="hlt">sea</span>-level changes controlling the oceanographic circulation between the subarctic Pacific and the Bering <span class="hlt">Sea</span>, especially the Alaskan Stream injection though the Aleutian passes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19930013511','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19930013511"><span>The influence of the hydrologic cycle on the <span class="hlt">extent</span> of <span class="hlt">sea</span> <span class="hlt">ice</span> with climatic implications</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Dean, Kenneson G.; Stringer, William J.; Searcy, Craig</p> <p>1993-01-01</p> <p>Multi-temporal satellite images, field observations, and field measurements were used to investigate the mechanisms by which <span class="hlt">sea</span> <span class="hlt">ice</span> melts offshore from the Mackenzie River delta. Advanced Very High Resolution Radiometer (AVHRR) satellite data recorded in 1986 were analyzed. The satellite data were geometrically corrected and radiometrically calibrated so that albedo and temperature values could be extracted. The investigation revealed that <span class="hlt">sea</span> <span class="hlt">ice</span> melted approximately 2 weeks earlier offshore from the Mackenzie River delta than along coasts where river discharge is minimal or non-existent. There is significant intra-delta variability in the timing and patterns of <span class="hlt">ice</span> melt. An estimation of energy flux indicates that 30 percent more of the visible wavelength energy and 25 percent more of the near-infrared wavelength energy is absorbed by water offshore of the delta compared to coastal areas with minimal river discharge. The analysis also revealed that the removal of <span class="hlt">sea</span> <span class="hlt">ice</span> involves the following: over-<span class="hlt">ice</span>-flooding along the coast offshore from river delta channels; under-<span class="hlt">ice</span> flow of 'warm' river water; melting and calving of the fast <span class="hlt">ice</span>; and, the formation of a bight in the pack <span class="hlt">ice</span> edge. Two stages in the melting of <span class="hlt">sea</span> <span class="hlt">ice</span> were identified: (1) an early stage where heat is supplied to overflows largely by solar radiation, and (2) a later stage where heat is supplied by river discharge in addition to solar radiation. A simple thermodynamic model of the thaw process in the fast <span class="hlt">ice</span> zone was developed and parameterized based on events recorded by the satellite images. The model treats river discharge as the source of sensible heat at the base of the <span class="hlt">ice</span> cover. The results of a series of sensitivity tests to assess the influence of river discharge on the near shore <span class="hlt">ice</span> are presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C43B0393W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C43B0393W"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Predictability and the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Prediction Network</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wiggins, H. V.; Stroeve, J. C.</p> <p>2014-12-01</p> <p>Drastic reductions in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover have increased the demand for Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> predictions by a range of stakeholders, including local communities, resource managers, industry and the public. The science of <span class="hlt">sea-ice</span> prediction has been challenged to keep up with these developments. Efforts such as the SEARCH <span class="hlt">Sea</span> <span class="hlt">Ice</span> Outlook (SIO; http://www.arcus.org/sipn/<span class="hlt">sea-ice</span>-outlook) and the <span class="hlt">Sea</span> <span class="hlt">Ice</span> for Walrus Outlook have provided a forum for the international <span class="hlt">sea-ice</span> 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 <span class="hlt">Sea</span> <span class="hlt">Ice</span> Prediction Network (SIPN), which is building a collaborative network of scientists and stakeholders to improve arctic <span class="hlt">sea</span> <span class="hlt">ice</span> 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 <span class="hlt">sea</span> <span class="hlt">ice</span> cover in September and the first day each location becomes <span class="hlt">ice</span>-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 <span class="hlt">sea</span> <span class="hlt">ice</span> from dynamic-thermodynamic <span class="hlt">sea</span> <span class="hlt">ice</span> models. Half of the models included fully-coupled (atmosphere, <span class="hlt">ice</span>, and ocean) models that additionally employ data assimilation. Both of these subsets (models and coupled models with data</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28835469','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28835469"><span><span class="hlt">Sea-ice</span> induced growth decline in Arctic shrubs.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Forchhammer, Mads</p> <p>2017-08-01</p> <p>Measures of increased tundra plant productivity have been associated with the accelerating retreat of the Arctic <span class="hlt">sea-ice</span>. Emerging studies document opposite effects, advocating for a more complex relationship between the shrinking <span class="hlt">sea-ice</span> and terrestrial plant productivity. I introduce an autoregressive plant growth model integrating effects of biological and climatic conditions for analysing individual ring-width growth time series. Using 128 specimens of Salix arctica , S. glauca and Betula nana sampled across Greenland to Svalbard, an overall negative effect of the retreating June <span class="hlt">sea-ice</span> <span class="hlt">extent</span> was found on the annual growth. The negative effect of the retreating June <span class="hlt">sea-ice</span> was observed for younger individuals with large annual growth allocations and with little or no trade-off between previous and current year's growth. © 2017 The Author(s).</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> <span class="hlt">extent</span> 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('http://hdl.handle.net/2060/20110015436','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110015436"><span>Intersensor Calibration Between F13 SSMI and F17 SSMIS for Global <span class="hlt">Sea</span> <span class="hlt">Ice</span> Data Records</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, Claire L.; DiGirolamo, Nicolo; Ivanoff, Alvaro</p> <p>2011-01-01</p> <p>An intercalibration between F13 Special Sensor Microwave Imager (SSMI) and F17 Special Sensor Microwave Imager Sounder (SSMIS) <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> and areas for a full year of overlap was undertaken preparatory to extending the 1979-2007 NASA Goddard Space Flight Center (GSFC) NASA Team algorithm time series of global <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> and areas. The 1979- 2007 time series was created from Scanning Multichannel Microwave Radiometer (SMMR) and SSMI data. After intercalibration, the yearly mean F17 and F13 difference in Northern Hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> is -0.0156%, with a standard deviation of the differences of 0.6204%, and the yearly mean difference in Northern Hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> areas is 0.5433%, with a standard deviation of 0.3519%. For the Southern Hemisphere, the yearly mean difference in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> is 0.0304% +/- 0.4880%, and the mean difference in <span class="hlt">sea</span> <span class="hlt">ice</span> areas is 0.1550% +/- 0.3753%. This F13/F17 intercalibration enables the extension of the 28-year 1979-2007 SMMR/SSMI <span class="hlt">sea</span> <span class="hlt">ice</span> time series for as long as there are stable F17 SSMIS brightness temperatures available.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120009376','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120009376"><span>Intersensor Calibration Between F13 SSMI and F17 SSMIS for Global <span class="hlt">Sea</span> <span class="hlt">Ice</span> Data Records</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, Claire L.; DiGirolamo, Nicolo; Ivanoff, Alvaro</p> <p>2012-01-01</p> <p>An intercalibration between F13 Special Sensor Microwave Imager (SSMI) and F17 Special Sensor Microwave Imager Sounder (SSMIS) <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> and areas for a full year of overlap was undertaken preparatory to extending the 1979-2007 NASA Goddard Space Flight Center (GSFC) NASA Team algorithm time series of global <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> and areas. The 1979- 2007 time series was created from Scanning Multichannel Microwave Radiometer (SMMR) and SSMI data. After intercalibration, the yearly mean F17 and F13 difference in Northern Hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> is -0.0156%, with a standard deviation of the differences of 0.6204%, and the yearly mean difference in Northern Hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> areas is 0.5433%, with a standard deviation of 0.3519%. For the Southern Hemisphere, the yearly mean difference in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> is 0.0304% 0.4880%, and the mean difference in <span class="hlt">sea</span> <span class="hlt">ice</span> areas is 0.1550% 0.3753%. This F13/F17 intercalibration enables the extension of the 28-year 1979-2007 SMMR/SSMI <span class="hlt">sea</span> <span class="hlt">ice</span> time series for as long as there are stable F17 SSMIS brightness temperatures available.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1346837','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1346837"><span>A New Discrete Element <span class="hlt">Sea-Ice</span> Model for Earth System Modeling</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Turner, Adrian Keith</p> <p></p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> forms a frozen crust of <span class="hlt">sea</span> water oating in high-latitude oceans. It is a critical component of the Earth system because its formation helps to drive the global thermohaline circulation, and its seasonal waxing and waning in the high north and Southern Ocean signi cantly affects planetary albedo. Usually 4{6% of Earth's marine surface is covered by <span class="hlt">sea</span> <span class="hlt">ice</span> at any one time, which limits the exchange of heat, momentum, and mass between the atmosphere and ocean in the polar realms. Snow accumulates on <span class="hlt">sea</span> <span class="hlt">ice</span> and inhibits its vertical growth, increases its albedo, and contributes to pooledmore » water in melt ponds that darken the Arctic <span class="hlt">ice</span> surface in the spring. <span class="hlt">Ice</span> <span class="hlt">extent</span> and volume are subject to strong seasonal, inter-annual and hemispheric variations, and climatic trends, which Earth System Models (ESMs) are challenged to simulate accurately (Stroeve et al., 2012; Stocker et al., 2013). This is because there are strong coupled feedbacks across the atmosphere-<span class="hlt">ice</span>-ocean boundary layers, including the <span class="hlt">ice</span>-albedo feedback, whereby a reduced <span class="hlt">ice</span> cover leads to increased upper ocean heating, further enhancing <span class="hlt">sea-ice</span> melt and reducing incident solar radiation re ected back into the atmosphere (Perovich et al., 2008). A reduction in perennial Arctic <span class="hlt">sea-ice</span> during the satellite era has been implicated in mid-latitude weather changes, including over North America (Overland et al., 2015). Meanwhile, most ESMs have been unable to simulate observed inter-annual variability and trends in Antarctic <span class="hlt">sea-ice</span> <span class="hlt">extent</span> during the same period (Gagne et al., 2014).« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFM.A12B..01M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFM.A12B..01M"><span>Overview of <span class="hlt">Sea-Ice</span> Properties, Distribution and Temporal Variations, for Application to <span class="hlt">Ice</span>-Atmosphere Chemical Processes.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moritz, R. E.</p> <p>2005-12-01</p> <p>The properties, distribution and temporal variation of <span class="hlt">sea-ice</span> are reviewed for application to problems of <span class="hlt">ice</span>-atmosphere chemical processes. Typical vertical structure of <span class="hlt">sea-ice</span> is presented for different <span class="hlt">ice</span> types, including young <span class="hlt">ice</span>, first-year <span class="hlt">ice</span> and multi-year <span class="hlt">ice</span>, emphasizing factors relevant to surface chemistry and gas exchange. Time average annual cycles of large scale variables are presented, including <span class="hlt">ice</span> concentration, <span class="hlt">ice</span> <span class="hlt">extent</span>, <span class="hlt">ice</span> thickness and <span class="hlt">ice</span> age. Spatial and temporal variability of these large scale quantities is considered on time scales of 1-50 years, emphasizing recent and projected changes in the Arctic pack <span class="hlt">ice</span>. The amount and time evolution of open water and thin <span class="hlt">ice</span> are important factors that influence ocean-<span class="hlt">ice</span>-atmosphere chemical processes. Observations and modeling of the <span class="hlt">sea-ice</span> thickness distribution function are presented to characterize the range of variability in open water and thin <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.C31A0435M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.C31A0435M"><span>Help, I don’t know which <span class="hlt">sea</span> <span class="hlt">ice</span> algorithm to use?!: Developing an authoritative <span class="hlt">sea</span> <span class="hlt">ice</span> 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>Meier, W.; Stroeve, J.; Duerr, R. E.; Fetterer, F. M.</p> <p>2009-12-01</p> <p>The declining Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> is one of the most dramatic indicators of climate change and is being recognized as a key factor in future climate impacts on biology, human activities, and global climate change. As such, the audience for <span class="hlt">sea</span> <span class="hlt">ice</span> data is expanding well beyond the <span class="hlt">sea</span> <span class="hlt">ice</span> community. The most comprehensive <span class="hlt">sea</span> <span class="hlt">ice</span> data are from a series of satellite-borne passive microwave sensors. They provide a near-complete daily timeseries of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and <span class="hlt">extent</span> since late-1978. However, there are many complicating issues in using such data, particularly for novice users. First, there is not one single, definitive algorithm, but several. And even for a given algorithm, different processing and quality-control methods may be used, depending on the source. Second, for all algorithms, there are uncertainties in any retrieved value. In general, these limitations are well-known: low spatial-resolution results in an imprecise <span class="hlt">ice</span> edge determination and lack of small-scale detail (e.g., lead detection) within the <span class="hlt">ice</span> pack; surface melt depresses concentration values during summer; thin <span class="hlt">ice</span> is underestimated in some algorithms; some algorithms are sensitive to physical surface temperature; other surface features (e.g., snow) can influence retrieved data. While general error estimates are available for concentration values, currently the products do not carry grid-cell level or even granule level data quality information. Finally, metadata and data provenance information are limited, both of which are essential for future reprocessing. Here we describe the progress to date toward development of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration products and outline the future steps needed to complete a <span class="hlt">sea</span> <span class="hlt">ice</span> climate data record.</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> <span class="hlt">extent</span> 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://adsabs.harvard.edu/abs/2016AGUFM.C32B..05B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C32B..05B"><span>Expanding Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span>: Anthropogenic or Natural Variability?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bitz, C. M.</p> <p>2016-12-01</p> <p>Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> has increased over the last 36 years according to the satellite record. Concurrent with Antarctic <span class="hlt">sea-ice</span> expansion has been broad cooling of the Southern Ocean <span class="hlt">sea</span>-surface temperature. Not only are Southern Ocean <span class="hlt">sea</span> <span class="hlt">ice</span> and SST trends at odds with expectations from greenhouse gas-induced warming, the trend patterns are not reproduced in historical simulations with comprehensive global climate models. While a variety of different factors may have contributed to the observed trends in recent decades, we propose that it is atmospheric circulation changes - and the changes in ocean circulation they induce - that have emerged as the most likely cause of the observed Southern Ocean <span class="hlt">sea</span> <span class="hlt">ice</span> and SST trends. I will discuss deficiencies in models that could explain their incorrect response. In addition, I will present results from a series of experiments where the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> and ocean are forced by atmospheric perturbations imposed within a coupled climate model. Figure caption: Linear trends of annual-mean SST (left) and annual-mean <span class="hlt">sea-ice</span> concentration (right) over 1980-2014. SST is from NOAA's Optimum Interpolation SST dataset (version 2; Reynolds et al. 2002). <span class="hlt">Sea-ice</span> concentration is from passive microwave observations using the NASA Team algorithm. Only the annual means are shown here for brevity and because the signal to noise is greater than in the seasonal means. Figure from Armour and Bitz (2015).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JGRC..119.2327A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JGRC..119.2327A"><span>Implications of fractured Arctic perennial <span class="hlt">ice</span> cover on thermodynamic and dynamic <span class="hlt">sea</span> <span class="hlt">ice</span> processes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Asplin, Matthew G.; Scharien, Randall; Else, Brent; Howell, Stephen; Barber, David G.; Papakyriakou, Tim; Prinsenberg, Simon</p> <p>2014-04-01</p> <p>Decline of the Arctic summer minimum <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> is characterized by large expanses of open water in the Siberian, Laptev, Chukchi, and Beaufort <span class="hlt">Seas</span>, and introduces large fetch distances in the Arctic Ocean. Long waves can propagate deep into the pack <span class="hlt">ice</span>, thereby causing flexural swell and failure of the <span class="hlt">sea</span> <span class="hlt">ice</span>. This process shifts the floe size diameter distribution smaller, increases floe surface area, and thereby affects <span class="hlt">sea</span> <span class="hlt">ice</span> dynamic and thermodynamic processes. The results of Radarsat-2 imagery analysis show that a flexural fracture event which occurred in the Beaufort <span class="hlt">Sea</span> region on 6 September 2009 affected ˜40,000 km2. Open water fractional area in the area affected initially decreased from 3.7% to 2.7%, but later increased to ˜20% following wind-forced divergence of the <span class="hlt">ice</span> pack. Energy available for lateral melting was assessed by estimating the change in energy entrainment from longwave and shortwave radiation in the mixed-layer of the ocean following flexural fracture. 11.54 MJ m-2 of additional energy for lateral melting of <span class="hlt">ice</span> floes was identified in affected areas. The impact of this process in future Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> melt seasons was assessed using estimations of earlier occurrences of fracture during the melt season, and is discussed in context with ocean heat fluxes, atmospheric mixing of the ocean mixed layer, and declining <span class="hlt">sea</span> <span class="hlt">ice</span> cover. We conclude that this process is an important positive feedback to Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss, and timing of initiation is critical in how it affects <span class="hlt">sea</span> <span class="hlt">ice</span> thermodynamic and dynamic processes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1815826M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1815826M"><span>Evaluating Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> predictability at seasonal to interannual timescales in global climate models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Marchi, Sylvain; Fichefet, Thierry; Goosse, Hugues; Zunz, Violette; Tietsche, Steffen; Day, Jonny; Hawkins, Ed</p> <p>2016-04-01</p> <p>Unlike the rapid <span class="hlt">sea</span> <span class="hlt">ice</span> losses reported in the Arctic, satellite observations show an overall increase in Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> over recent decades. Although many processes have already been suggested to explain this positive trend, it remains the subject of current investigations. Understanding the evolution of the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> turns out to be more complicated than for the Arctic for two reasons: the lack of observations and the well-known biases of climate models in the Southern Ocean. Irrespective of those issues, another one is to determine whether the positive trend in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> would have been predictable if adequate observations and models were available some decades ago. This study of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> predictability is carried out using 6 global climate models (HadGEM1.2, MPI-ESM-LR, GFDL CM3, EC-Earth V2, MIROC 5.2 and ECHAM 6-FESOM) which are all part of the APPOSITE project. These models are used to perform hindcast simulations in a perfect model approach. The predictive skill is estimated thanks to the PPP (Potential Prognostic Predictability) and the ACC (Anomaly Correlation Coefficient). The former is a measure of the uncertainty of the ensemble while the latter assesses the accuracy of the prediction. These two indicators are applied to different variables related to <span class="hlt">sea</span> <span class="hlt">ice</span>, in particular the total <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and the <span class="hlt">ice</span> edge location. This first model intercomparison study about <span class="hlt">sea</span> <span class="hlt">ice</span> predictability in the Southern Ocean aims at giving a general overview of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> predictability in current global climate models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20160013301&hterms=sea&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dsea','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20160013301&hterms=sea&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dsea"><span>Assessment of Arctic and Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Predictability in CMIP5 Decadal Hindcasts</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Yang, Chao-Yuan; Liu, Jiping (Inventor); Hu, Yongyun; Horton, Radley M.; Chen, Liqi; Cheng, Xiao</p> <p>2016-01-01</p> <p>This paper examines the ability of coupled global climate models to predict decadal variability of Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span>. We analyze decadal hindcasts/predictions of 11 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Decadal hindcasts exhibit a large multimodel spread in the simulated <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, with some models deviating significantly from the observations as the predicted <span class="hlt">ice</span> <span class="hlt">extent</span> quickly drifts away from the initial constraint. The anomaly correlation analysis between the decadal hindcast and observed <span class="hlt">sea</span> <span class="hlt">ice</span> suggests that in the Arctic, for most models, the areas showing significant predictive skill become broader associated with increasing lead times. This area expansion is largely because nearly all the models are capable of predicting the observed decreasing Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover. <span class="hlt">Sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> in the North Pacific has better predictive skill than that in the North Atlantic (particularly at a lead time of 3-7 years), but there is a reemerging predictive skill in the North Atlantic at a lead time of 6-8 years. In contrast to the Arctic, Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> decadal hindcasts do not show broad predictive skill at any timescales, and there is no obvious improvement linking the areal <span class="hlt">extent</span> of significant predictive skill to lead time increase. This might be because nearly all the models predict a retreating Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover, opposite to the observations. For the Arctic, the predictive skill of the multi-model ensemble mean outperforms most models and the persistence prediction at longer timescales, which is not the case for the Antarctic. Overall, for the Arctic, initialized decadal hindcasts show improved predictive skill compared to uninitialized simulations, although this improvement is not present in the Antarctic.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012PhDT.......190H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012PhDT.......190H"><span>The influence of <span class="hlt">sea</span> <span class="hlt">ice</span> on Antarctic <span class="hlt">ice</span> core sulfur chemistry and on the future evolution of Arctic snow depth: Investigations using global models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hezel, Paul J.</p> <p></p> <p>Observational studies have examined the relationship between methanesulfonic acid (MSA) measured in Antarctic <span class="hlt">ice</span> cores and <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> measured by satellites with the aim of producing a proxy for past <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>. MSA is an oxidation product of dimethylsulfide (DMS) and is potentially linked to <span class="hlt">sea</span> <span class="hlt">ice</span> based on observations of very high surface seawater DMS in the <span class="hlt">sea</span> <span class="hlt">ice</span> zone. Using a global chemical transport model, we present the first modeling study that specifically examines this relationship on interannual and on glacial-interglacial time scales. On interannual time scales, the model shows no robust relationship between MSA deposited in Antarctica and <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>. We show that lifetimes of MSA and DMS are longer in the high latitudes than in the global mean, interannual variability of <span class="hlt">sea</span> <span class="hlt">ice</span> is small (<25%) as a fraction of <span class="hlt">sea</span> <span class="hlt">ice</span> area, and <span class="hlt">sea</span> <span class="hlt">ice</span> determines only a fraction of the variability (<30%) of DMS emissions from the ocean surface. A potentially larger fraction of the variability in DMS emissions is determined by surface wind speed (up to 46%) via the parameterization for ocean-to-atmosphere gas exchange. Furthermore, we find that a significant fraction (up to 74%) of MSA deposited in Antarctica originates from north of 60°S, north of the seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> zone. We then examine the deposition of MSA and non-<span class="hlt">sea</span>-salt sulfate (nss SO2-4 ) on glacial-interglacial time scales. <span class="hlt">Ice</span> core observations on the East Antarctic Plateau suggest that MSA increases much more than nss SO2-4 during the last glacial maximum (LGM) compared to the modern period. It has been suggested that high MSA during the LGM is indicative of higher primary productivity and DMS emissions in the LGM compared to the modern day. Studies have also shown that MSA is subject to post-depositional volatilization, especially during the modern period. Using the same chemical transport model driven by meteorology from a global climate model, we examine the sensitivity of MSA and nss</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C21E..08S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C21E..08S"><span>Rate and state dependent processes in <span class="hlt">sea</span> <span class="hlt">ice</span> deformation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sammonds, P. R.; Scourfield, S.; Lishman, B.</p> <p>2014-12-01</p> <p>Realistic models of <span class="hlt">sea</span> <span class="hlt">ice</span> processes and properties are needed to assess <span class="hlt">sea</span> <span class="hlt">ice</span> thickness, <span class="hlt">extent</span> and concentration and, when run within GCMs, provide prediction of climate change. The deformation of <span class="hlt">sea</span> <span class="hlt">ice</span> is a key control on the Arctic Ocean dynamics. But the deformation of <span class="hlt">sea</span> <span class="hlt">ice</span> is dependent not only on the rate of the processes involved but also the state of the <span class="hlt">sea</span> <span class="hlt">ice</span> and particular in terms of its evolution with time and temperature. Shear deformation is a dominant mechanism from the scale of basin-scale shear lineaments, through floe-floe interaction to block sliding in <span class="hlt">ice</span> ridges. The shear deformation will not only depend on the speed of movement of <span class="hlt">ice</span> surfaces but also the degree that the surfaces have bonded during thermal consolidation and compaction. Frictional resistance to sliding can vary by more than two orders of magnitude depending on the state of the interface. But this in turn is dependent upon both imposed conditions and <span class="hlt">sea</span> <span class="hlt">ice</span> properties such as size distribution of interfacial broken <span class="hlt">ice</span>, angularity, porosity, salinity, etc. We review experimental results in <span class="hlt">sea</span> <span class="hlt">ice</span> mechanics from mid-scale experiments, conducted in the Hamburg model ship <span class="hlt">ice</span> tank, simulating <span class="hlt">sea</span> <span class="hlt">ice</span> floe motion and interaction and compare these with laboratory experiments on <span class="hlt">ice</span> friction done in direct shear from which a rate and state constitutive relation for shear deformation is derived. Finally we apply this to field measurement of <span class="hlt">sea</span> <span class="hlt">ice</span> friction made during experiments in the Barents <span class="hlt">Sea</span> to assess the other environmental factors, the state terms, that need to be modelled in order to up-scale to Arctic Ocean-scale 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_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://adsabs.harvard.edu/abs/2018E%26PSL.481...61C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018E%26PSL.481...61C"><span>Seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> cover during the warm Pliocene: Evidence from the Iceland <span class="hlt">Sea</span> (ODP Site 907)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Clotten, Caroline; Stein, Ruediger; Fahl, Kirsten; De Schepper, Stijn</p> <p>2018-01-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> is a critical component in the Arctic and global climate system, yet little is known about its <span class="hlt">extent</span> and variability during past warm intervals, such as the Pliocene (5.33-2.58 Ma). Here, we present the first multi-proxy (IP25, sterols, alkenones, palynology) <span class="hlt">sea</span> <span class="hlt">ice</span> reconstructions for the Late Pliocene Iceland <span class="hlt">Sea</span> (ODP Site 907). Our interpretation of a seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> cover with occasional <span class="hlt">ice</span>-free intervals between 3.50-3.00 Ma is supported by reconstructed alkenone-based summer <span class="hlt">sea</span> surface temperatures. As evidenced from brassicasterol and dinosterol, primary productivity was low between 3.50 and 3.00 Ma and the site experienced generally oligotrophic conditions. The East Greenland Current (and East Icelandic Current) may have transported <span class="hlt">sea</span> <span class="hlt">ice</span> into the Iceland <span class="hlt">Sea</span> and/or brought cooler and fresher waters favoring local <span class="hlt">sea</span> <span class="hlt">ice</span> formation. Between 3.00 and 2.40 Ma, the Iceland <span class="hlt">Sea</span> is mainly <span class="hlt">sea</span> <span class="hlt">ice</span>-free, but seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> occurred between 2.81 and 2.74 Ma. <span class="hlt">Sea</span> <span class="hlt">ice</span> extending into the Iceland <span class="hlt">Sea</span> at this time may have acted as a positive feedback for the build-up of the Greenland <span class="hlt">Ice</span> Sheet (GIS), which underwent a major expansion ∼2.75 Ma. Thereafter, most likely a stable <span class="hlt">sea</span> <span class="hlt">ice</span> edge developed close to Greenland, possibly changing together with the expansion and retreat of the GIS and affecting the productivity in the Iceland <span class="hlt">Sea</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA257132','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA257132"><span>Investigation of Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Concentration by Means of Selected Algorithms</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1992-05-08</p> <p>Changes in areal <span class="hlt">extent</span> and concentration of <span class="hlt">sea</span> <span class="hlt">ice</span> around Antarctica may serve as sensitive indicators of global warming . A comparison study was...occurred from July, 1987 through June, 1990. Antarctic Ocean, Antarctic regions, Global warming , <span class="hlt">Sea</span> <span class="hlt">ice</span>-Antarctic regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19990109666','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19990109666"><span>The Formation each Winter of the Circumpolar Wave in the <span class="hlt">Sea</span> <span class="hlt">Ice</span> around Antarctica</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Gloersen, Per; White, Warren B.</p> <p>1999-01-01</p> <p>Seeking to improve upon the visualization of the Antarctic Circumpolar Wave (ACW) , we compare a 16-year sequence of 6-month winter averages of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> and concentrations with those of adjacent <span class="hlt">sea</span> surface temperatures (SSTs). Here we follow SSTs around the globe along the maximum <span class="hlt">sea</span> <span class="hlt">ice</span> edge rather than in a zonal band equatorward of it. The results are similar to the earlier ones, but the ACWs do not propagate with equal amplitude or speed. Additionally in a sequence of 4 polar stereographic plots of these SSTs and <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations, we find a remarkable correlation between SST minima and <span class="hlt">sea</span> <span class="hlt">ice</span> concentration maxima, even to the <span class="hlt">extent</span> of matching contours across the <span class="hlt">ice-sea</span> boundary, in the sector between 900E and the Palmer Peninsula. Based on these observations, we suggest that the memory of the ACW in the <span class="hlt">sea</span> <span class="hlt">ice</span> is carried from one Austral winter to the next by the neighboring SSTS, since the <span class="hlt">sea</span> <span class="hlt">ice</span> is nearly absent in the Austral summer.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-GSFC_20171208_Archive_e000837.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-GSFC_20171208_Archive_e000837.html"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> around Ostrov Sakhalin, eastern Russia</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2017-12-08</p> <p>Located off the east coast of Russia, the <span class="hlt">Sea</span> of Okhotsk stretches down to 45 degrees North latitude, and <span class="hlt">sea</span> <span class="hlt">ice</span> forms regularly in the basin. In fact, it is the lowest latitude for seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> formation in the world. On January 4, 2015, the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite captured this true-color image of the <span class="hlt">ice</span>-covered <span class="hlt">Sea</span> of Okhotsk. Every winter, winds from East Siberia, frigid air temperatures, and a large amount of freshwater flowing out from rivers promote the formation of <span class="hlt">sea</span> <span class="hlt">ice</span> in the region. Much of the freshwater comes from the Amur River, one of the ten longest rivers in the world. From year to year, variations in temperature and wind speed can cause large fluctuations in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>. The <span class="hlt">sea</span> spans more than 1,500,000 square kilometers (600,000 square miles), and <span class="hlt">ice</span> cover can spread across 50 to 90 percent of it at its annual peak. On average, that <span class="hlt">ice</span> persists for 180 days. According to research published in 2014, the region's <span class="hlt">sea</span> <span class="hlt">ice</span> has been decreasing over a 34-year period. Annual <span class="hlt">ice</span> production in the <span class="hlt">Sea</span> of Okhotsk dropped by more than 11 percent from 1974 to 2008. The researchers suggest that this decline has, at least in part, "led to weakening of the overturning in the North Pacific." Water with less <span class="hlt">sea</span> <span class="hlt">ice</span> is fresher, less dense, and unable to sink and circulate as well as salty, dense water. A weakened circulation in the North Pacific has implications for the supply of nutrients, such as iron, that affect biological productivity. Credit: NASA/GSFC/Jeff Schmaltz/MODIS Land Rapid Response Team NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JGRF..118.1533D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JGRF..118.1533D"><span>The Greenland <span class="hlt">Ice</span> Sheet's surface mass balance in a seasonally <span class="hlt">sea</span> <span class="hlt">ice</span>-free Arctic</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Day, J. J.; Bamber, J. L.; Valdes, P. J.</p> <p>2013-09-01</p> <p>General circulation models predict a rapid decrease in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> with concurrent increases in near-surface air temperature and precipitation in the Arctic over the 21st century. This has led to suggestions that some Arctic land <span class="hlt">ice</span> masses may experience an increase in accumulation due to enhanced evaporation from a seasonally <span class="hlt">sea</span> <span class="hlt">ice</span>-free Arctic Ocean. To investigate the impact of this phenomenon on Greenland <span class="hlt">Ice</span> Sheet climate and surface mass balance (SMB), a regional climate model, HadRM3, was used to force an insolation-temperature melt SMB model. A set of experiments designed to investigate the role of <span class="hlt">sea</span> <span class="hlt">ice</span> independently from <span class="hlt">sea</span> surface temperature (SST) forcing are described. In the warmer and wetter SI + SST simulation, Greenland experiences a 23% increase in winter SMB but 65% reduced summer SMB, resulting in a net decrease in the annual value. This study shows that <span class="hlt">sea</span> <span class="hlt">ice</span> decline contributes to the increased winter balance, causing 25% of the increase in winter accumulation; this is largest in eastern Greenland as the result of increased evaporation in the Greenland <span class="hlt">Sea</span>. These results indicate that the seasonal cycle of Greenland's SMB will increase dramatically as global temperatures increase, with the largest changes in temperature and precipitation occurring in winter. This demonstrates that the accurate prediction of changes in <span class="hlt">sea</span> <span class="hlt">ice</span> cover is important for predicting Greenland SMB and <span class="hlt">ice</span> sheet evolution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.C21C0622M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.C21C0622M"><span>Meteorological conditions influencing the formation of level <span class="hlt">ice</span> within the Baltic <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>Mazur, A. K.; Krezel, A.</p> <p>2012-12-01</p> <p>The Baltic <span class="hlt">Sea</span> is covered by <span class="hlt">ice</span> every winter and on average, the <span class="hlt">ice</span>-covered area is 45% of the total area of the Baltic <span class="hlt">Sea</span>. The beginning of <span class="hlt">ice</span> season usually starts in the end of November, <span class="hlt">ice</span> <span class="hlt">extent</span> is the largest between mid-February and mid-March and <span class="hlt">sea</span> <span class="hlt">ice</span> disappears completely in May. The <span class="hlt">ice</span> covered areas during a typical winter are the Gulf of Bothnia, the Gulf of Finland and the Gulf of Riga. The studies of <span class="hlt">sea</span> <span class="hlt">ice</span> in the Baltic <span class="hlt">Sea</span> are related to two aspects: climate and marine transport. Depending on the local weather conditions during the winter different types of <span class="hlt">sea</span> <span class="hlt">ice</span> can be formed. From the point of winter shipping it is important to locate level and deformed <span class="hlt">ice</span> areas (rafted <span class="hlt">ice</span>, ridged <span class="hlt">ice</span>, and hummocked <span class="hlt">ice</span>). Because of cloud and daylight independency as well as good spatial resolution, SAR data seems to be the most suitable source of data for <span class="hlt">sea</span> <span class="hlt">ice</span> observation in the comparatively small area of the Baltic <span class="hlt">Sea</span>. We used ASAR Wide Swath Mode data with spatial resolution 150 m. We analyzed data from the three winter seasons which were examples of severe, typical and mild winters. To remove the speckle effect the data were resampled to 250 m pixel size and filtred using Frost filter 5x5. To detect edges we used Sobel filter. The data were also converted into grayscale. <span class="hlt">Sea</span> <span class="hlt">ice</span> classification was based on Object-Based Image Analysis (OBIA). Object-based methods are not a common tool in <span class="hlt">sea</span> <span class="hlt">ice</span> studies but they seem to accurately separate level <span class="hlt">ice</span> within the <span class="hlt">ice</span> pack. The data were segmented and classified using eCognition Developer software. Level <span class="hlt">ice</span> were classified based on texture features defined by Haralick (Grey Level Co-Occurrence Matrix homogeneity, GLCM contrast, GLCM entropy and GLCM correlation). The long-term changes of the Baltic <span class="hlt">Sea</span> <span class="hlt">ice</span> conditions have been already studied. They include date of freezing, date of break-up, <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and some of work also <span class="hlt">ice</span> thickness. There is a little knowledge about the relationship of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JGRC..123.1406T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JGRC..123.1406T"><span>An Examination of the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Rheology for Seasonal <span class="hlt">Ice</span> Zones Based on <span class="hlt">Ice</span> Drift and Thickness Observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Toyota, Takenobu; Kimura, Noriaki</p> <p>2018-02-01</p> <p>The validity of the <span class="hlt">sea</span> <span class="hlt">ice</span> rheological model formulated by Hibler (1979), which is widely used in present numerical <span class="hlt">sea</span> <span class="hlt">ice</span> models, is examined for the <span class="hlt">Sea</span> of Okhotsk as an example of the seasonal <span class="hlt">ice</span> zone (SIZ), based on satellite-derived <span class="hlt">sea</span> <span class="hlt">ice</span> velocity, concentration and thickness. Our focus was the formulation of the yield curve, the shape of which can be estimated from <span class="hlt">ice</span> drift pattern based on the energy equation of deformation, while the strength of the <span class="hlt">ice</span> cover that determines its magnitude was evaluated using <span class="hlt">ice</span> concentration and thickness data. <span class="hlt">Ice</span> drift was obtained with a grid spacing of 37.5 km from the AMSR-E 89 GHz brightness temperature using a maximum cross-correlation method. The <span class="hlt">ice</span> thickness was obtained with a spatial resolution of 100 m from a regression of the PALSAR backscatter coefficients with <span class="hlt">ice</span> thickness. To assess scale dependence, the <span class="hlt">ice</span> drift data derived from a coastal radar covering a 70 km range in the southernmost <span class="hlt">Sea</span> of Okhotsk were similarly analyzed. The results obtained were mostly consistent with Hibler's formulation that was based on the Arctic Ocean on both scales with no dependence on a time scale, and justify the treatment of <span class="hlt">sea</span> <span class="hlt">ice</span> as a plastic material, with an elliptical shaped yield curve to some <span class="hlt">extent</span>. However, it also highlights the difficulty in parameterizing sub-grid scale ridging in the model because grid scale <span class="hlt">ice</span> velocities reduce the deformation magnitude by half due to the large variation of the deformation field in the SIZ.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ClDy..tmp.2399R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ClDy..tmp.2399R"><span>Links between the Amundsen <span class="hlt">Sea</span> Low and <span class="hlt">sea</span> <span class="hlt">ice</span> in the Ross <span class="hlt">Sea</span>: seasonal and interannual relationships</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Raphael, Marilyn N.; Holland, Marika M.; Landrum, Laura; Hobbs, William R.</p> <p>2018-05-01</p> <p>Previous studies have shown that <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> in the Southern Ocean is influenced by the intensity and location of the Amundsen <span class="hlt">Sea</span> Low (ASL), through their effect on the meridional winds. However, the inhomogeneous nature of the influence of the ASL on <span class="hlt">sea</span> <span class="hlt">ice</span> as well as its influence during critical periods of the <span class="hlt">sea</span> <span class="hlt">ice</span> annual cycle is not clear. In this study, we do a spatio-temporal analysis of links between the ASL and the <span class="hlt">sea</span> <span class="hlt">ice</span> during the advance and retreat periods of the <span class="hlt">ice</span> over the period 1979-2013 focusing on the role of the meridional and zonal winds. We use the ERA-Interim monthly-averaged 500 mb geopotential height and 10 m wind data along with monthly Passive Microwave <span class="hlt">Sea</span> <span class="hlt">Ice</span> Concentrations (SIC) to examine the seasonal and interannual relationships between the ASL and SIC in the Ross-Amundsen <span class="hlt">sea</span> <span class="hlt">ice</span> sector. To characterize the state of the ASL we use indices that describe its location and its intensity. We show that the ASL has preferred locations and intensities during <span class="hlt">ice</span> advance and retreat seasons. The strength and direction of the influence of the ASL are not spatially homogeneous and can change from advance to retreat season and there are strong significant relationships between the characteristics of the ASL and SIC, within and across seasons and interannually.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1997JCli...10..593W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1997JCli...10..593W"><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://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, Xingren; Simmonds, Ian; Budd, W. F.</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> distribution. 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> <span class="hlt">extent</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/484365-modeling-antarctic-sea-ice-general-circulation-model','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/484365-modeling-antarctic-sea-ice-general-circulation-model"><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/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</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. Amore » 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> <span class="hlt">extent</span> 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.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150000779','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150000779"><span>Global <span class="hlt">Sea</span> <span class="hlt">Ice</span> Coverage from Satellite Data: Annual Cycle and 35-Year Trends</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>2014-01-01</p> <p>Well-established satellite-derived Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> are combined to create the global picture of <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> and their changes over the 35-yr period 1979-2013. Results yield a global annual <span class="hlt">sea</span> <span class="hlt">ice</span> cycle more in line with the high-amplitude Antarctic annual cycle than the lower-amplitude Arctic annual cycle but trends more in line with the high-magnitude negative Arctic trends than the lower-magnitude positive Antarctic trends. Globally, monthly <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> reaches a minimum in February and a maximum generally in October or November. All 12 months show negative trends over the 35-yr period, with the largest magnitude monthly trend being the September trend, at -68,200 +/- 10,500 sq km/yr (-2.62% 6 +/- 0.40%/decade), and the yearly average trend being -35,000 +/- 5900 sq km/yr (-1.47% +/- 0.25%/decade).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150021289','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150021289"><span>Global <span class="hlt">Sea</span> <span class="hlt">Ice</span> Coverage from Satellite Data: Annual Cycle and 35-Yr Trends</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>2014-01-01</p> <p>Well-established satellite-derived Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> are combined to create the global picture of <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> and their changes over the 35-yr period 1979-2013. Results yield a global annual <span class="hlt">sea</span> <span class="hlt">ice</span> cycle more in line with the high-amplitude Antarctic annual cycle than the lower-amplitude Arctic annual cycle but trends more in line with the high-magnitude negative Arctic trends than the lower-magnitude positive Antarctic trends. Globally, monthly <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> reaches a minimum in February and a maximum generally in October or November. All 12 months show negative trends over the 35-yr period, with the largest magnitude monthly trend being the September trend, at -68200 +/- 10500 km sq yr(exp -1) (-2.62% +/- 0.40%decade(exp -1)), and the yearly average trend being -35000 +/-5900 km sq yr(exp -1) (-1.47% +/- 0.25%decade(exp -1)).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMNG31A1833A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMNG31A1833A"><span>The statistical properties of <span class="hlt">sea</span> <span class="hlt">ice</span> velocity fields</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Agarwal, S.; Wettlaufer, J. S.</p> <p>2016-12-01</p> <p>Thorndike and Colony (1982) showed that more than 70% of the variance of the <span class="hlt">ice</span> motion can be explained by the geostrophic winds. This conclusion was reached by analyzing only 2 years of data. Due to the importance of <span class="hlt">ice</span> motion in Arctic climate we ask how persistent is such a prediction. In so doing, we study and develop a stochastic model for the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> velocity fields based on the observed <span class="hlt">sea</span> <span class="hlt">ice</span> velocity fields from satellites and buoys for the period 1978 - 2012. Having previously found that the Arctic <span class="hlt">Sea</span> Equivalent <span class="hlt">Ice</span> <span class="hlt">Extent</span> (EIE) has a white noise structure on annual to bi-annual time scales (Agarwal et. al. 2012), we assess the connection to <span class="hlt">ice</span> motion. We divide the Arctic into dynamic and thermodynamic components, with focus on the dynamic part i.e. the velocity fields of <span class="hlt">sea</span> <span class="hlt">ice</span> driven by the geostrophic winds over the Arctic. We show (1) the stationarity of the spatial correlation structure of the velocity fields, and (2) the robustness of white noise structure present in the velocity fields on annual to bi-annual time scales, which combine to explain the white noise characteristics of the EIE on these time scales. S. Agarwal, W. Moon and J.S. Wettlaufer, Trends, noise and reentrant long-term persistence in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>, Proc. R. Soc. A, 468, 2416 (2012). A.S. Thorndike and R. Colony, <span class="hlt">Sea</span> <span class="hlt">ice</span> motion in response to geostrophic winds, J. Geophys. Res. 87, 5845 (1982).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20070035024','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20070035024"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Parameters from AMSR-E Data using Two Techniques, and Comparisons with <span class="hlt">Sea</span> <span class="hlt">Ice</span> from SSM</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.; Parkinson, Claire L.</p> <p>2007-01-01</p> <p>We use two algorithms to process AMSR-E data in order to determine algorithm dependence, if any, on the estimates of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, <span class="hlt">ice</span> <span class="hlt">extent</span> and area, and trends and to evaluate how AMSR-E data compare with historical SSM/I data. The monthly <span class="hlt">ice</span> concentrations derived from the two algorithms from AMSR-E data (the AMSR-E Bootstrap Algorithm, or ABA, and the enhanced NASA Team algorithm, or NT2) differ on average by about 1 to 3%, with data from the consolidated <span class="hlt">ice</span> region being generally comparable for ABA and NT2 retrievals while data in the marginal <span class="hlt">ice</span> zones and thin <span class="hlt">ice</span> regions show higher values when the NT2 algorithm is used. The <span class="hlt">ice</span> <span class="hlt">extents</span> and areas derived separately from AMSR-E using these two algorithms are, however, in good agreement, with the differences (ABA-NT2) being about 6.6 x 10(exp 4) square kilometers on average for <span class="hlt">ice</span> <span class="hlt">extents</span> and -6.6 x 10(exp 4) square kilometers for <span class="hlt">ice</span> area which are small compared to mean seasonal values of 10.5 x 10(exp 6) and 9.8 x 10(exp 6) for <span class="hlt">ice</span> <span class="hlt">extent</span> and area: respectively. Likewise, <span class="hlt">extents</span> and areas derived from the same algorithm but from AMSR-E and SSM/I data are consistent but differ by about -24.4 x 10(exp 4) square kilometers and -13.9 x 10(exp 4) square kilometers, respectively. The discrepancies are larger with the estimates of <span class="hlt">extents</span> than area mainly because of differences in channel selection and sensor resolutions. Trends in <span class="hlt">extent</span> during the AMSR-E era were also estimated and results from all three data sets are shown to be in good agreement (within errors).</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/2009EOSTr..90R.169P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EOSTr..90R.169P"><span>Developing and Implementing Protocols for Arctic <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>Perovich, Donald K.; Gerland, Sebastian</p> <p>2009-05-01</p> <p>Arctic Surface-Based <span class="hlt">Sea</span> <span class="hlt">Ice</span> Observations: Integrated Protocols and Coordinated Data Acquisition; Tromsø, Norway, 26-27 January 2009; The Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover is diminishing. Over the past several years, not only has <span class="hlt">ice</span> thinned but the <span class="hlt">extent</span> of <span class="hlt">ice</span> at the end of summer, and hence perennial <span class="hlt">ice</span>, has declined markedly. These changes affect a wide range of issues and are important for a varied group of stakeholders, including Arctic coastal communities, policy makers, industry, the scientific community, and the public. Concerns range from the role of <span class="hlt">sea</span> <span class="hlt">ice</span> cover as an indicator and amplifier of climate change to marine transportation, resource extraction, and coastal erosion. To understand and respond to these ongoing changes, it is imperative to develop and implement consistent and robust observational protocols that can be used to describe the current state of the <span class="hlt">ice</span> cover as well as future changes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-200910220009HQ.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-200910220009HQ.html"><span><span class="hlt">Ice</span> Bridge Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2009-10-21</p> <p>An iceberg is seen out the window of NASA's DC-8 research aircraft as it flies 2,000 feet above the Amundsen <span class="hlt">Sea</span> in West Antarctica on Wednesday, Oct., 21, 2009. This was the fourth science flight of NASA‚Äôs Operation <span class="hlt">Ice</span> Bridge airborne Earth science mission to study Antarctic <span class="hlt">ice</span> sheets, <span class="hlt">sea</span> <span class="hlt">ice</span>, and <span class="hlt">ice</span> shelves. Photo Credit: (NASA/Jane Peterson)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-GSFC_20171208_Archive_e002001.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-GSFC_20171208_Archive_e002001.html"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> in McClure Strait</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2017-12-08</p> <p>NASA image acquired August 17, 2010 In mid-August 2010, the Northwest Passage was almost—but not quite—free of <span class="hlt">ice</span>. The <span class="hlt">ice</span> content in the northern route through the passage (through the Western Parry Channel) was very light, but <span class="hlt">ice</span> remained in McClure (or M’Clure) Strait. The Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite captured this natural-color image on August 17, 2010. Although most of McClure Strait looks perfectly <span class="hlt">ice</span>-free, immediately west of Prince Patrick Island, a band of <span class="hlt">sea</span> <span class="hlt">ice</span> stretches southward across the strait (left edge of the image). The National Snow and <span class="hlt">Ice</span> Data Center <span class="hlt">Sea</span> <span class="hlt">Ice</span> News and Analysis blog reported that even more <span class="hlt">ice</span> remained in the southern route (through Amundsen’s Passage) of the Northwest Passage in mid-August 2010. Nevertheless, the <span class="hlt">ice</span> content in the northern route was not only well below the 1968–2000 average, but also nearly a month ahead of the clearing observed in 2007, when Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> set a record low. As of mid-August 2010, however, overall <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> was higher than it had been at the same time of year in 2007. Credit: NASA/GSFC/Jeff Schmaltz/MODIS Land Rapid Response Team Caption by Michon Scott. To learn more go to: earthobservatory.nasa.gov/NaturalHazards/view.php?id=45333 Instrument: Terra - MODIS NASA Goddard Space Flight Center is home to the nation's largest organization of combined scientists, engineers and technologists that build spacecraft, instruments and new technology to study the Earth, the sun, our solar system, and the universe. Follow us on Twitter Join us on Facebook Click here to see more images from NASA Goddard’s Earth Observatory</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C33E..08N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C33E..08N"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Classification and Mapping for Surface Albedo Parameterization in <span class="hlt">Sea</span> <span class="hlt">Ice</span> Modeling</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.; Clemente-Colón, P.; Perovich, D. K.; Polashenski, C.; Simpson, W. R.; Rigor, I. G.; Woods, J. E.; Nguyen, D. T.; Neumann, G.</p> <p>2016-12-01</p> <p>A regime shift of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> from predominantly perennial <span class="hlt">sea</span> <span class="hlt">ice</span> (multi-year <span class="hlt">ice</span> or MYI) to seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> (first-year <span class="hlt">ice</span> or FYI) has occurred in recent decades. This shift has profoundly altered the proportional composition of different <span class="hlt">sea</span> <span class="hlt">ice</span> classes and the surface albedo distribution pertaining to each <span class="hlt">sea</span> <span class="hlt">ice</span> class. Such changes impacts physical, chemical, and biological processes in the Arctic atmosphere-<span class="hlt">ice</span>-ocean system. The drastic changes upset the traditional geophysical representation of surface albedo of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover in current models. A critical science issue is that these profound changes must be rigorously and systematically observed and characterized to enable a transformative re-parameterization of key model inputs, such as <span class="hlt">ice</span> surface albedo, to <span class="hlt">ice</span>-ocean-atmosphere climate modeling in order to obtain re-analyses that accurately reproduce Arctic changes and also to improve <span class="hlt">sea</span> <span class="hlt">ice</span> and weather forecast models. Addressing this challenge is a strategy identified by the National Research Council study on "Seasonal to Decadal Predictions of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> - Challenges and Strategies" to replicate the new Arctic reality. We review results of albedo characteristics associated with different <span class="hlt">sea</span> <span class="hlt">ice</span> classes such as FYI and MYI. Then we demonstrate the capability for <span class="hlt">sea</span> <span class="hlt">ice</span> classification and mapping using algorithms developed by the Jet Propulsion Laboratory and by the U.S. National <span class="hlt">Ice</span> Center for use with multi-sourced satellite radar data at L, C, and Ku bands. Results obtained with independent algorithms for different radar frequencies consistently identify <span class="hlt">sea</span> <span class="hlt">ice</span> classes and thereby cross-verify the <span class="hlt">sea</span> <span class="hlt">ice</span> classification methods. Moreover, field observations obtained from buoy webcams and along an extensive trek across Elson Lagoon and a sector of the Beaufort <span class="hlt">Sea</span> during the BRomine, Ozone, and Mercury EXperiment (BROMEX) in March 2012 are used to validate satellite products of <span class="hlt">sea</span> <span class="hlt">ice</span> classes. This research enables the mapping</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A31D0050O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A31D0050O"><span>The role of summer surface wind anomalies in the summer Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> in 2010 and 2011</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ogi, M.; Wallace, J. M.</p> <p>2012-12-01</p> <p>Masayo Ogi 1 and John M. Wallace 2 masayo.ogi@jamstec.go.jp wallace@atmos.washington.edu 1Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan 2 Department of Atmospheric Sciences, University of Washington, Seattle, Washington The seasonal evolutions of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> (SIE) during the summers of 2010 and 2011 are contrasted with that in 2007. The June SIE in 2010 was lower than that in 2007 and was the lowest for that calendar month in the 32-year (1979-2010) record. The September SIE in 2010 would have set a new record low had it not been for the fact that the <span class="hlt">ice</span> retreated more slowly during the summer months in that year than it did in 2007. Hence from early July onward, the SIE in 2010 remained at levels above those observed in 2007. The SIE minimum in September 2010 proved to be the third lowest on record, eclipsed by values in both 2007 and 2008. In spring and summer of 2011, the Arctic SIE was as low as it was in 2007, but the SIE in September 2011 did not reach record low levels. The SIE minimum in 2011 proved to be the second lowest on record for the period of 1979-2011. Summertime atmospheric conditions play an important role in controlling the variations in Arctic SIE. In a previous study based on statistical analysis of data collected prior to 2007, we showed that anticyclonic summertime circulation anomalies over the Arctic Ocean during the summer months favor low September SIE. We also found that the record-low <span class="hlt">ice</span> summer year 2007 was characterized by a strong anticyclonic circulation anomaly, accompanied by an Ekman drift of <span class="hlt">ice</span> out of the marginal <span class="hlt">seas</span> toward the central Arctic and eventually toward the Fram Strait, as evidenced by the tracks of drifting buoys. Here we assess the <span class="hlt">extent</span> to which year-to-year differences in summer winds over the Arctic might have contributed to the differing rates of retreat of <span class="hlt">ice</span> during the summers of 2007, 2010, and 2011. Our results show that the May-June (MJ) pattern in 2010 is</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/2015GeoRL..42.8481G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015GeoRL..42.8481G"><span>Impact of aerosol emission controls on future Arctic <span class="hlt">sea</span> <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>Gagné, M.-Ã..; Gillett, N. P.; Fyfe, J. C.</p> <p>2015-10-01</p> <p>We examine the response of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> to projected aerosol and aerosol precursor emission changes under the Representative Concentration Pathway (RCP) scenarios in simulations of the Canadian Earth System Model. The overall decrease in aerosol loading causes a warming, largest over the Arctic, which leads to an annual mean reduction in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> of approximately 1 million km2 over the 21st century in all RCP scenarios. This accounts for approximately 25% of the simulated reduction in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> in RCP 4.5, and 40% of the reduction in RCP 2.5. In RCP 4.5, the Arctic ocean is projected to become <span class="hlt">ice</span>-free during summertime in 2045, but it does not become <span class="hlt">ice</span>-free until 2057 in simulations with aerosol precursor emissions held fixed at 2000 values. Thus, while reductions in aerosol emissions have significant health and environmental benefits, their substantial contribution to projected Arctic climate change should not be overlooked.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170003226','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170003226"><span>Does a Relationship Between Arctic Low Clouds and <span class="hlt">Sea</span> <span class="hlt">Ice</span> Matter?</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Taylor, Patrick C.</p> <p>2016-01-01</p> <p>Arctic low clouds strongly affect the Arctic surface energy budget. Through this impact Arctic low clouds influence important aspects of the Arctic climate system, namely surface and atmospheric temperature, <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and thickness, and atmospheric circulation. Arctic clouds are in turn influenced by these elements of the Arctic climate system, and these interactions create the potential for Arctic cloud-climate feedbacks. To further our understanding of potential Arctic cloudclimate feedbacks, the goal of this paper is to quantify the influence of atmospheric state on the surface cloud radiative effect (CRE) and its covariation with <span class="hlt">sea</span> <span class="hlt">ice</span> concentration (SIC). We build on previous research using instantaneous, active remote sensing satellite footprint data from the NASA A-Train. First, the results indicate significant differences in the surface CRE when stratified by atmospheric state. Second, there is a weak covariation between CRE and SIC for most atmospheric conditions. Third, the results show statistically significant differences in the average surface CRE under different SIC values in fall indicating a 3-5 W m(exp -2) larger LW CRE in 0% versus 100% SIC footprints. Because systematic changes on the order of 1 W m(exp -2) are sufficient to explain the observed long-term reductions in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, our results indicate a potentially significant amplifying <span class="hlt">sea</span> <span class="hlt">ice</span>-cloud feedback, under certain meteorological conditions, that could delay the fall freeze-up and influence the variability in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and volume. Lastly, a small change in the frequency of occurrence of atmosphere states may yield a larger Arctic cloud feedback than any cloud response to <span class="hlt">sea</span> <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017QSRv..169...13D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017QSRv..169...13D"><span>Current state and future perspectives on coupled <span class="hlt">ice</span>-sheet - <span class="hlt">sea</span>-level modelling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>de Boer, Bas; Stocchi, Paolo; Whitehouse, Pippa L.; van de Wal, Roderik S. W.</p> <p>2017-08-01</p> <p>The interaction between <span class="hlt">ice</span>-sheet growth and retreat and <span class="hlt">sea</span>-level change has been an established field of research for many years. However, recent advances in numerical modelling have shed new light on the precise interaction of marine <span class="hlt">ice</span> sheets with the change in near-field <span class="hlt">sea</span> level, and the related stability of the grounding line position. Studies using fully coupled <span class="hlt">ice</span>-sheet - <span class="hlt">sea</span>-level models have shown that accounting for gravitationally self-consistent <span class="hlt">sea</span>-level change will act to slow down the retreat and advance of marine <span class="hlt">ice</span>-sheet grounding lines. Moreover, by simultaneously solving the '<span class="hlt">sea</span>-level equation' and modelling <span class="hlt">ice</span>-sheet flow, coupled models provide a global field of relative <span class="hlt">sea</span>-level change that is consistent with dynamic changes in <span class="hlt">ice</span>-sheet <span class="hlt">extent</span>. In this paper we present an overview of recent advances, possible caveats, methodologies and challenges involved in coupled <span class="hlt">ice</span>-sheet - <span class="hlt">sea</span>-level modelling. We conclude by presenting a first-order comparison between a suite of relative <span class="hlt">sea</span>-level data and output from a coupled <span class="hlt">ice</span>-sheet - <span class="hlt">sea</span>-level model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20080023287','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080023287"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Variability and Trends, 1979-2006</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.; Cavalieri, Donald J.</p> <p>2008-01-01</p> <p>Analysis of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> derived from satellite passive-microwave data for the 28 years, 1979-2006 yields an overall negative trend of -45,100 +/- 4,600 km2/yr (-3.7 +/- 0.4%/decade) in the yearly averages, with negative <span class="hlt">ice-extent</span> trends also occurring for each of the four seasons and each of the 12 months. For the yearly averages the largest decreases occur in the Kara and Barents <span class="hlt">Seas</span> and the Arctic Ocean, with linear least squares slopes of -10,600 +/- 2,800 km2/yr (-7.4 +/- 2.0%/decade) and -10,100 +/- 2,200 km2/yr (-1.5 +/- 0.3%/decade), respectively, followed by Baffin Bay/Labrador <span class="hlt">Sea</span>, with a slope of -8,000 +/- 2,000 km2/yr) -9.0 +/- 2.3%/decade), the Greenland <span class="hlt">Sea</span>, with a slope of -7,000 +/- 1,400 km2/yr (-9.3 +/- 1.9%/decade), and Hudson Bay, with a slope of -4,500 +/- 900 km2/yr (-5.3 +/- 1.1%/decade). These are all statistically significant decreases at a 99% confidence level. The <span class="hlt">Seas</span> of Okhotsk and Japan also have a statistically significant <span class="hlt">ice</span> decrease, although at a 95% confidence level, and the three remaining regions, the Bering <span class="hlt">Sea</span>, Canadian Archipelago, and Gulf of St. Lawrence, have negative slopes that are not statistically significant. The 28-year trends in <span class="hlt">ice</span> areas for the Northern Hemisphere total are also statistically significant and negative in each season, each month, and for the yearly averages.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20170009008&hterms=sea&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dsea','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20170009008&hterms=sea&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dsea"><span>Variability and Trends in the Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Cover: Results from Different Techniques</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.; Meier, Walter N.; Gersten, Robert</p> <p>2017-01-01</p> <p>Variability and trend studies of <span class="hlt">sea</span> <span class="hlt">ice</span> in the Arctic have been conducted using products derived from the same raw passive microwave data but by different groups using different algorithms. This study provides consistency assessment of four of the leading products, namely, Goddard Bootstrap (SB2), Goddard NASA Team (NT1), EUMETSAT Ocean and <span class="hlt">Sea</span> <span class="hlt">Ice</span> Satellite Application Facility (OSI-SAF 1.2), and Hadley HadISST 2.2 data in evaluating variability and trends in the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover. All four provide generally similar <span class="hlt">ice</span> patterns but significant disagreements in <span class="hlt">ice</span> concentration distributions especially in the marginal <span class="hlt">ice</span> zone and adjacent regions in winter and meltponded areas in summer. The discrepancies are primarily due to different ways the four techniques account for occurrences of new <span class="hlt">ice</span> and meltponding. However, results show that the different products generally provide consistent and similar representation of the state of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover. Hadley and NT1 data usually provide the highest and lowest monthly <span class="hlt">ice</span> <span class="hlt">extents</span>, respectively. The Hadley data also show the lowest trends in <span class="hlt">ice</span> <span class="hlt">extent</span> and <span class="hlt">ice</span> area at negative 3.88 percent decade and negative 4.37 percent decade, respectively, compared to an average of negative 4.36 percent decade and negative 4.57 percent decade for all four. Trend maps also show similar spatial distribution for all four with the largest negative trends occurring at the Kara/Barents <span class="hlt">Sea</span> and Beaufort <span class="hlt">Sea</span> regions, where <span class="hlt">sea</span> <span class="hlt">ice</span> has been retreating the fastest. The good agreement of the trends especially with updated data provides strong confidence in the quantification of the rate of decline in the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C31D..01S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C31D..01S"><span>The <span class="hlt">Sea-Ice</span> Floe Size Distribution</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. L., III; Schweiger, A. J. B.; Zhang, J.; Steele, M.</p> <p>2017-12-01</p> <p>The size distribution of <span class="hlt">ice</span> floes in the polar <span class="hlt">seas</span> affects the dynamics and thermodynamics of the <span class="hlt">ice</span> cover and its interaction with the ocean and atmosphere. <span class="hlt">Ice</span>-ocean models are now beginning to include the floe size distribution (FSD) in their simulations. In order to characterize seasonal changes of the FSD and provide validation data for our <span class="hlt">ice</span>-ocean model, we calculated the FSD in the Beaufort and Chukchi <span class="hlt">seas</span> over two spring-summer-fall seasons (2013 and 2014) using more than 250 cloud-free visible-band scenes from the MODIS sensors on NASA's Terra and Aqua satellites, identifying nearly 250,000 <span class="hlt">ice</span> floes between 2 and 30 km in diameter. We found that the FSD follows a power-law distribution at all locations, with a seasonally varying exponent that reflects floe break-up in spring, loss of smaller floes in summer, and the return of larger floes after fall freeze-up. We extended the results to floe sizes from 10 m to 2 km at selected time/space locations using more than 50 high-resolution radar and visible-band satellite images. Our analysis used more data and applied greater statistical rigor than any previous study of the FSD. The incorporation of the FSD into our <span class="hlt">ice</span>-ocean model resulted in reduced <span class="hlt">sea-ice</span> thickness, mainly in the marginal <span class="hlt">ice</span> zone, which improved the simulation of <span class="hlt">sea-ice</span> <span class="hlt">extent</span> and yielded an earlier <span class="hlt">ice</span> retreat. We also examined results from 17 previous studies of the FSD, most of which report power-law FSDs but with widely varying exponents. It is difficult to reconcile the range of results due to different study areas, seasons, and methods of analysis. We review the power-law representation of the FSD in these studies and discuss some mathematical details that are important to consider in any future analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1918039N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1918039N"><span>ICESat-2, its retrievals of <span class="hlt">ice</span> sheet elevation change and <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard, and potential synergies with CryoSat-2</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Neumann, Thomas; Markus, Thorsten; Smith, Benjamin; Kwok, Ron</p> <p>2017-04-01</p> <p>Understanding the causes and magnitudes of changes in the cryosphere remains a priority for Earth science research. Over the past decade, NASA's and ESA's Earth-observing satellites have documented a decrease in both the areal <span class="hlt">extent</span> and thickness of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>, and an ongoing loss of grounded <span class="hlt">ice</span> from the Greenland and Antarctic <span class="hlt">ice</span> sheets. Understanding the pace and mechanisms of these changes requires long-term observations of <span class="hlt">ice</span>-sheet mass, <span class="hlt">sea-ice</span> thickness, and <span class="hlt">sea-ice</span> <span class="hlt">extent</span>. NASA's ICESat-2 mission is the next-generation space-borne laser altimeter mission and will use three pairs of beams, each pair separated by about 3 km across-track with a pair spacing of 90 m. The spot size is 17 m with an along-track sampling interval of 0.7 m. This measurement concept is a result of the lessons learned from the original ICESat mission. The multi-beam approach is critical for removing the effects of <span class="hlt">ice</span> sheet surface slope from the elevation change measurements of most interest. For <span class="hlt">sea</span> <span class="hlt">ice</span>, the dense spatial sampling (eliminating along-track gaps) and the small footprint size are especially useful for <span class="hlt">sea</span> surface height measurements in the, often narrow, leads needed for <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard and <span class="hlt">ice</span> thickness retrievals. Currently, algorithms are being developed to calculate <span class="hlt">ice</span> sheet elevation change and <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard from ICESat-2 data. The orbits of ICESat-2 and Cryosat-2 both converge at 88 degrees of latitude, though the orbit altitude differences result in different ground track patterns between the two missions. This presentation will present an overview of algorithm approaches and how ICESat-2 and Cryosat-2 data may augment each other.</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 <span class="hlt">extent</span> 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('http://adsabs.harvard.edu/abs/2017AGUFM.C33B1185F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C33B1185F"><span>The role of feedbacks in 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>Feltham, D. L.; Frew, R. C.; Holland, P.</p> <p>2017-12-01</p> <p>The changes in Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> over the last thirty years have a strong seasonal dependence, and the way these changes grow in spring and decay in autumn suggests that feedbacks are strongly involved. The changes may ultimately be caused by atmospheric warming, the winds, snowfall changes, etc., but we cannot understand these forcings without first untangling the feedbacks. A highly simplified coupled <span class="hlt">sea</span> <span class="hlt">ice</span> -mixed layer model has been developed to investigate the importance of feedbacks on the evolution of <span class="hlt">sea</span> <span class="hlt">ice</span> in two contrasting regions in the Southern Ocean; the Amundsen <span class="hlt">Sea</span> where <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> has been decreasing, and the Weddell <span class="hlt">Sea</span> where it has been expanding. The change in mixed layer depth in response to changes in the atmosphere to ocean energy flux is implicit in a strong negative feedback on <span class="hlt">ice</span> cover changes in the Amundsen <span class="hlt">Sea</span>, with atmospheric cooling leading to a deeper mixed layer resulting in greater entrainment of warm Circumpolar Deep Water, causing increased basal melting of <span class="hlt">sea</span> <span class="hlt">ice</span>. This strong negative feedback produces counter intuitive responses to changes in forcings in the Amundsen <span class="hlt">Sea</span>. This feedback is absent in the Weddell due to the complete destratification and strong water column cooling that occurs each winter in simulations. The impact of other feedbacks, including the albedo feedback, changes in insulation due to <span class="hlt">ice</span> thickness and changes in the freezing temperature of the mixed layer, were found to be of secondary importance compared to changes in the mixed layer depth.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=PIA02971&hterms=sea+world&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dsea%2Bworld','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=PIA02971&hterms=sea+world&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dsea%2Bworld"><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 <span class="hlt">extent</span> 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> <span class="hlt">extent</span> 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/1990ClDy....5..111M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1990ClDy....5..111M"><span><span class="hlt">Sea-ice</span> anomalies observed in the Greenland and Labrador <span class="hlt">seas</span> during 1901 1984 and their relation to an interdecadal Arctic climate cycle</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mysak, L. A.; Manak, D. K.; Marsden, R. F.</p> <p>1990-12-01</p> <p>Two independent <span class="hlt">ice</span> data sets from the Greenland and Labrador <span class="hlt">Seas</span> have been analyzed for the purpose of characterizing interannual and decadal time scale <span class="hlt">sea-ice</span> <span class="hlt">extent</span> anomalies during this century. <span class="hlt">Sea-ice</span> concentration data for the 1953 1984 period revealed the presence of a large positive anomaly in the Greenland <span class="hlt">Sea</span> during the 1960s which coincided with the “great salinity anomaly”, an upper-ocean low-salinity water mass that was observed to travel cyclonically around the northern North Atlantic during 1968 1982. This <span class="hlt">ice</span> anomaly as well as several smaller ones propagated into the Labrador <span class="hlt">Sea</span> and then across to the Labrador and east Newfoundland coast, over a period of 3 to 5 years. A complex empirical orthogonal function analysis of the same data also confirmed this propagation phenomenon. An inverse relation between <span class="hlt">sea-ice</span> and salinity anomalies in the Greenland-Labrador <span class="hlt">Sea</span> region was also generally found. An analysis of spring and summer <span class="hlt">ice</span>-limit data obtained from Danish Meteorological Institute charts for the period 1901 1956 indicated the presence of heavy <span class="hlt">ice</span> conditions (i.e., positive <span class="hlt">ice</span> anomalies) in the Greenland <span class="hlt">Sea</span> during 1902 1920 and in the late 1940s, and generally negative <span class="hlt">ice</span> anomalies during the 1920s and 1930s. Only limited evidence of the propagation of Greenland <span class="hlt">Sea</span> <span class="hlt">ice</span> anomalies into the Labrador <span class="hlt">Sea</span> was observed, however, probably because the data were from the <span class="hlt">ice</span>-melt seasons. On the other hand, several large <span class="hlt">ice</span> anomalies in the Greenland <span class="hlt">Sea</span> occurred 2 3 years after large runoffs (in the early 1930s and the late 1940s) from northern Canada into the western Arctic Ocean. Similarly, a large runoff into the Arctic during 1964 1966 preceded the large Greenland <span class="hlt">Sea</span> <span class="hlt">ice</span> anomaly of the 1960s. These facts, together with recent evidence of ‘climatic jumps’ in the Northern Hemisphere tropospheric circulation, suggest the existence of an interdecadal self-sustained climate cycle in the Arctic. In the Greenland <span class="hlt">Sea</span>, this cycle is</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMOS14A..04Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMOS14A..04Z"><span>Local Effects of <span class="hlt">Ice</span> Floes on Skin <span class="hlt">Sea</span> Surface Temperature in the Marginal <span class="hlt">Ice</span> Zone from UAVs</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zappa, C. J.; Brown, S.; Emery, W. J.; Adler, J.; Wick, G. A.; Steele, M.; Palo, S. E.; Walker, G.; Maslanik, J. A.</p> <p>2013-12-01</p> <p>Recent years have seen extreme changes in the Arctic. Particularly striking are changes within the Pacific sector of the Arctic Ocean, and especially in the <span class="hlt">seas</span> north of the Alaskan coast. These areas have experienced record warming, reduced <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, and loss of <span class="hlt">ice</span> in areas that had been <span class="hlt">ice</span>-covered throughout human memory. Even the oldest and thickest <span class="hlt">ice</span> types have failed to survive through the summer melt period in areas such as the Beaufort <span class="hlt">Sea</span> and Canada Basin, and fundamental changes in ocean conditions such as earlier phytoplankton blooms may be underway. Marginal <span class="hlt">ice</span> zones (MIZ), or areas where the "<span class="hlt">ice</span>-albedo feedback" driven by solar warming is highest and <span class="hlt">ice</span> melt is extensive, may provide insights into the <span class="hlt">extent</span> of these changes. Airborne remote sensing, in particular InfraRed (IR), offers a unique opportunity to observe physical processes at <span class="hlt">sea-ice</span> margins. It permits monitoring the <span class="hlt">ice</span> <span class="hlt">extent</span> and coverage, as well as the <span class="hlt">ice</span> and ocean temperature variability. It can also be used for derivation of surface flow field allowing investigation of turbulence and mixing at the <span class="hlt">ice</span>-ocean interface. Here, we present measurements of visible and IR imagery of melting <span class="hlt">ice</span> floes in the marginal <span class="hlt">ice</span> zone north of Oliktok Point AK in the Beaufort <span class="hlt">Sea</span> made during the Marginal <span class="hlt">Ice</span> Zone Ocean and <span class="hlt">Ice</span> Observations and Processes EXperiment (MIZOPEX) in July-August 2013. The visible and IR imagery were taken from the unmanned airborne vehicle (UAV) ScanEagle. The visible imagery clearly defines the scale of the <span class="hlt">ice</span> floes. The IR imagery show distinct cooling of the skin <span class="hlt">sea</span> surface temperature (SST) as well as a intricate circulation and mixing pattern that depends on the surface current, wind speed, and near-surface vertical temperature/salinity structure. Individual <span class="hlt">ice</span> floes develop turbulent wakes as they drift and cause transient mixing of an influx of colder surface (fresh) melt water. The upstream side of the <span class="hlt">ice</span> floe shows the coldest skin SST, and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19980237537','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19980237537"><span>Spatial Distribution of Trends and Seasonality in the 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, P.; Parkinson, C. L.; Cavalieri, D. J.; Cosmiso, J. C.; Zwally, H. J.</p> <p>1998-01-01</p> <p>We extend earlier analyses of a 9-year <span class="hlt">sea</span> <span class="hlt">ice</span> data set that described the local seasonal and trend variations in each of the hemispheric <span class="hlt">sea</span> <span class="hlt">ice</span> covers to the recently merged 18.2-year <span class="hlt">sea</span> <span class="hlt">ice</span> record from four satellite instruments. The seasonal cycle characteristics remain essentially the same as for the shorter time series, but the local trends are markedly different, in some cases reversing sign. The sign reversal reflects the lack of a consistent long-term trend and could be the result of localized long-term oscillations in the hemispheric <span class="hlt">sea</span> <span class="hlt">ice</span> covers. By combining the separate hemispheric <span class="hlt">sea</span> <span class="hlt">ice</span> records into a global one, we have shown that there are statistically significant net decreases in the <span class="hlt">sea</span> <span class="hlt">ice</span> coverage on a global scale. The change in the global <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, is -0.01 +/- 0.003 x 10(exp 6) sq km per decade. The decrease in the areal coverage of the <span class="hlt">sea</span> <span class="hlt">ice</span> is only slightly smaller, so that the difference in the two, the open water within the packs, has no statistically significant change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C41A0639L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C41A0639L"><span>Upper Ocean Evolution Across the Beaufort <span class="hlt">Sea</span> Marginal <span class="hlt">Ice</span> Zone</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, C.; Rainville, L.; Gobat, J. I.; Perry, M. J.; Freitag, L. E.; Webster, S.</p> <p>2016-12-01</p> <p>The observed reduction of Arctic summertime <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and expansion of the marginal <span class="hlt">ice</span> zone (MIZ) have profound impacts on the balance of processes controlling <span class="hlt">sea</span> <span class="hlt">ice</span> evolution, including the introduction of several positive feedback mechanisms that may act to accelerate melting. Examples of such feedbacks include increased upper ocean warming though absorption of solar radiation, elevated internal wave energy and mixing that may entrain heat stored in subsurface watermasses (e.g., the relatively warm Pacific Summer and Atlantic waters), and elevated surface wave energy that acts to deform and fracture <span class="hlt">sea</span> <span class="hlt">ice</span>. Spatial and temporal variability in <span class="hlt">ice</span> properties and open water fraction impact these processes. To investigate how upper ocean structure varies with changing <span class="hlt">ice</span> cover, how the balance of processes shift as a function of <span class="hlt">ice</span> fraction and distance from open water, and how these processes impact <span class="hlt">sea</span> <span class="hlt">ice</span> evolution, a network of autonomous platforms sampled the atmosphere-<span class="hlt">ice</span>-ocean system in the Beaufort, beginning in spring, well before the start of melt, and ending with the autumn freeze-up. Four long-endurance autonomous Seagliders occupied sections that extended from open water, through the marginal <span class="hlt">ice</span> zone, deep into the pack during summer 2014 in the Beaufort <span class="hlt">Sea</span>. Gliders penetrated up to 200 km into the <span class="hlt">ice</span> pack, under complete <span class="hlt">ice</span> cover for up to 10 consecutive days. Sections reveal strong fronts where cold, <span class="hlt">ice</span>-covered waters meet waters that have been exposed to solar warming, and O(10 km) scale eddies near the <span class="hlt">ice</span> edge. In the pack, Pacific Summer Water and a deep chlorophyll maximum form distinct layers at roughly 60 m and 80 m, respectively, which become increasingly diffuse late in the season as they progress through the MIZ and into open water. Stratification just above the Pacific Summer Water rapidly weakens near the <span class="hlt">ice</span> edge and temperature variance increases, likely due to mixing or energetic vertical exchange associated with strong</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 <span class="hlt">extent</span> 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('http://hdl.handle.net/2060/20010028707','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010028707"><span>Southern Ocean Climate and <span class="hlt">Sea</span> <span class="hlt">Ice</span> Anomalies Associated with the Southern Oscillation</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.</p> <p>2001-01-01</p> <p>The anomalies in the climate and <span class="hlt">sea</span> <span class="hlt">ice</span> cover of the Southern Ocean and their relationships with the Southern Oscillation (SO) are investigated using a 17-year of data set from 1982 through 1998. We correlate the polar climate anomalies with the Southern Oscillation index (SOI) and examine the composites of these anomalies under the positive (SOI > 0), neutral (0 > SOI > -1), and negative (SOI < -1) phases of SOL The climate data set consists of <span class="hlt">sea</span>-level pressure, wind, surface air temperature, and <span class="hlt">sea</span> surface temperature fields, while the <span class="hlt">sea</span> <span class="hlt">ice</span> data set describes its <span class="hlt">extent</span>, concentration, motion, and surface temperature. The analysis depicts, for the first time, the spatial variability in the relationship of the above variables and the SOL The strongest correlation between the SOI and the polar climate anomalies are found in the Bellingshausen, Amundsen and Ross <span class="hlt">sea</span> sectors. The composite fields reveal anomalies that are organized in distinct large-scale spatial patterns with opposing polarities at the two extremes of SOI, and suggest oscillating climate anomalies that are closely linked to the SO. Within these sectors, positive (negative) phases of the SOI are generally associated with lower (higher) <span class="hlt">sea</span>-level pressure, cooler (warmer) surface air temperature, and cooler (warmer) <span class="hlt">sea</span> surface temperature in these sectors. Associations between these climate anomalies and the behavior of the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover are clearly evident. Recent anomalies in the <span class="hlt">sea</span> <span class="hlt">ice</span> cover that are apparently associated with the SOI include: the record decrease in the <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> in the Bellingshausen <span class="hlt">Sea</span> from mid- 1988 through early 199 1; the relationship between Ross <span class="hlt">Sea</span> SST and ENSO signal, and reduced <span class="hlt">sea</span> <span class="hlt">ice</span> concentration in the Ross <span class="hlt">Sea</span>; and, the shortening of the <span class="hlt">ice</span> season in the eastern Ross <span class="hlt">Sea</span>, Amundsen <span class="hlt">Sea</span>, far western Weddell <span class="hlt">Sea</span>, and the lengthening of the <span class="hlt">ice</span> season in the western Ross <span class="hlt">Sea</span>, Bellingshausen <span class="hlt">Sea</span> and central Weddell <span class="hlt">Sea</span> gyre over the period 1988</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19910021293','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19910021293"><span>The influence of the hydrologic cycle on the <span class="hlt">extent</span> of <span class="hlt">sea</span> <span class="hlt">ice</span> with climatic implications</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Dean, Ken; Gosink, Joan</p> <p>1991-01-01</p> <p>The role was analyzed of the hydrologic cycle on the distribution of <span class="hlt">sea</span> <span class="hlt">ice</span>, and its influence on forcings and fluxes between the marine environment and the atmosphere. River discharge plays a significant role in degrading the <span class="hlt">sea</span> <span class="hlt">ice</span> before any melting occurs elsewhere along the coast. The influence is considered of river discharge on the albedo, thermal balance, and distribution of <span class="hlt">sea</span> <span class="hlt">ice</span>. Quantitative atmospheric-hydrologic models are being developed to describe these processes in the coastal zone. Input for the models will come from satellite images, hydrologic data, and field observations. The resulting analysis provides a basis for the study of the significance of the hydrologic cycle throughout the Arctic Basin and its influence on the regional climate as a result of possible climatic scenarios. The area offshore from the Mackenzie River delta was selected as the study area.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002JCli...15..487K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002JCli...15..487K"><span>Southern Ocean Climate and <span class="hlt">Sea</span> <span class="hlt">Ice</span> Anomalies Associated with the Southern Oscillation.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kwok, R.; Comiso, J. C.</p> <p>2002-03-01</p> <p>The anomalies in the climate and <span class="hlt">sea</span> <span class="hlt">ice</span> cover of the Southern Ocean and their relationships with the Southern Oscillation (SO) are investigated using a 17-yr dataset from 1982 to 1998. The polar climate anomalies are correlated with the Southern Oscillation index (SOI) and the composites of these anomalies are examined under the positive (SOI > 0), neutral (0 > SOI > 1), and negative (SOI < 1) phases of SOI. The climate dataset consists of <span class="hlt">sea</span> level pressure, wind, surface air temperature, and <span class="hlt">sea</span> surface temperature fields, while the <span class="hlt">sea</span> <span class="hlt">ice</span> dataset describes its <span class="hlt">extent</span>, concentration, motion, and surface temperature. The analysis depicts, for the first time, the spatial variability in the relationship of the above variables with the SOI. The strongest correlation between the SOI and the polar climate anomalies are found in the Bellingshausen, Amundsen, and Ross <span class="hlt">Seas</span>. The composite fields reveal anomalies that are organized in distinct large-scale spatial patterns with opposing polarities at the two extremes of SOI, and suggest oscillations that are closely linked to the SO. Within these sectors, positive (negative) phases of the SOI are generally associated with lower (higher) <span class="hlt">sea</span> level pressure, cooler (warmer) surface air temperature, and cooler (warmer) <span class="hlt">sea</span> surface temperature in these sectors. Associations between these climate anomalies and the behavior of the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover are evident. Recent anomalies in the <span class="hlt">sea</span> <span class="hlt">ice</span> cover that are clearly associated with the SOI include the following: the record decrease in the <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> in the Bellingshausen <span class="hlt">Sea</span> from mid-1988 to early 1991; the relationship between Ross <span class="hlt">Sea</span> SST and the ENSO signal, and reduced <span class="hlt">sea</span> <span class="hlt">ice</span> concentration in the Ross <span class="hlt">Sea</span>; and the shortening of the <span class="hlt">ice</span> season in the eastern Ross <span class="hlt">Sea</span>, Amundsen <span class="hlt">Sea</span>, far western Weddell <span class="hlt">Sea</span> and lengthening of the <span class="hlt">ice</span> season in the western Ross <span class="hlt">Sea</span>, Bellinghausen <span class="hlt">Sea</span>, and central Weddell <span class="hlt">Sea</span> gyre during the period 1988-94. Four ENSO episodes over the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GeoRL..44.4953B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GeoRL..44.4953B"><span>Skillful regional prediction of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> 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>Bushuk, Mitchell; Msadek, Rym; Winton, Michael; Vecchi, Gabriel A.; Gudgel, Rich; Rosati, Anthony; Yang, Xiaosong</p> <p>2017-05-01</p> <p>Recent Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> seasonal prediction efforts and forecast skill assessments have primarily focused on pan-Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> (SIE). In this work, we move toward stakeholder-relevant spatial scales, investigating the regional forecast skill of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> in a Geophysical Fluid Dynamics Laboratory (GFDL) seasonal prediction system. Using a suite of retrospective initialized forecasts spanning 1981-2015 made with a coupled atmosphere-ocean-<span class="hlt">sea</span> <span class="hlt">ice</span>-land model, we show that predictions of detrended regional SIE are skillful at lead times up to 11 months. Regional prediction skill is highly region and target month dependent and generically exceeds the skill of an anomaly persistence forecast. We show for the first time that initializing the ocean subsurface in a seasonal prediction system can yield significant regional skill for winter SIE. Similarly, as suggested by previous work, we find that <span class="hlt">sea</span> <span class="hlt">ice</span> thickness initial conditions provide a crucial source of skill for regional summer SIE.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C43B0750J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C43B0750J"><span>Landfast <span class="hlt">Sea</span> <span class="hlt">Ice</span> Breakouts: Stabilizing <span class="hlt">Ice</span> Features, Oceanic and Atmospheric Forcing at Barrow, Alaska</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jones, J.; Eicken, H.; Mahoney, A. R.; MV, R.; Kambhamettu, C.; Fukamachi, Y.; Ohshima, K. I.; George, C.</p> <p>2016-12-01</p> <p>Landfast <span class="hlt">sea</span> <span class="hlt">ice</span> is an important seasonal feature along most Arctic coastlines, such as that of the Chukchi <span class="hlt">Sea</span> near Barrow, Alaska. Its stability throughout the <span class="hlt">ice</span> season is determined by many factors but grounded pressure ridges are the primary stabilizing component. Landfast <span class="hlt">ice</span> breakouts occur when these grounded ridges fail or unground, and previously stationary <span class="hlt">ice</span> detaches from the coast and drifts away. Using ground-based radar imagery from a coastal <span class="hlt">ice</span> and ocean observatory at Barrow, we have developed a method to estimate the <span class="hlt">extent</span> of grounded ridges by tracking <span class="hlt">ice</span> motion and deformation over the course of winter and have derived <span class="hlt">ice</span> keel depth and potential for grounding from cumulative convergent <span class="hlt">ice</span> motion. Estimates of landfast <span class="hlt">ice</span> grounding strength have been compared to the atmospheric and oceanic stresses acting on the landfast <span class="hlt">ice</span> before and during breakout events to determine prevailing causes for the failure of stabilizing features. Applying this approach to two case studies in 2008 and 2010, we conclude that a combination of atmospheric and oceanic stresses may have caused the breakouts analyzed in this study, with the latter as the dominant force. Preconditioning (as weakening) of grounded ridges by <span class="hlt">sea</span> level variations may facilitate failure of the <span class="hlt">ice</span> sheet leading to breakout events.</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('http://adsabs.harvard.edu/abs/2016CSR...126...50J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016CSR...126...50J"><span>Landfast <span class="hlt">sea</span> <span class="hlt">ice</span> breakouts: Stabilizing <span class="hlt">ice</span> features, oceanic and atmospheric forcing at Barrow, Alaska</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jones, Joshua; Eicken, Hajo; Mahoney, Andrew; MV, Rohith; Kambhamettu, Chandra; Fukamachi, Yasushi; Ohshima, Kay I.; George, J. Craig</p> <p>2016-09-01</p> <p>Landfast <span class="hlt">sea</span> <span class="hlt">ice</span> is an important seasonal feature along most Arctic coastlines, such as that of the Chukchi <span class="hlt">Sea</span> near Barrow, Alaska. Its stability throughout the <span class="hlt">ice</span> season is determined by many factors but grounded pressure ridges are the primary stabilizing component. Landfast <span class="hlt">ice</span> breakouts occur when these grounded ridges fail or unground, and previously stationary <span class="hlt">ice</span> detaches from the coast and drifts away. Using ground-based radar imagery from a coastal <span class="hlt">ice</span> and ocean observatory at Barrow, we have developed a method to estimate the <span class="hlt">extent</span> of grounded ridges by tracking <span class="hlt">ice</span> motion and deformation over the course of winter and have derived <span class="hlt">ice</span> keel depth and potential for grounding from cumulative convergent <span class="hlt">ice</span> motion. Estimates of landfast <span class="hlt">ice</span> grounding strength have been compared to the atmospheric and oceanic stresses acting on the landfast <span class="hlt">ice</span> before and during breakout events to determine prevailing causes for the failure of stabilizing features. Applying this approach to two case studies in 2008 and 2010, we conclude that a combination of atmospheric and oceanic stresses may have caused the breakouts analyzed in this study, with the latter as the dominant force. Preconditioning (as weakening) of grounded ridges by <span class="hlt">sea</span> level variations may facilitate failure of the <span class="hlt">ice</span> sheet leading to breakout events.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1208882-development-global-sea-ice-cice-configuration-met-office-global-coupled-model','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1208882-development-global-sea-ice-cice-configuration-met-office-global-coupled-model"><span>Development of global <span class="hlt">sea</span> <span class="hlt">ice</span> 6.0 CICE configuration for the Met Office global coupled model</span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Rae, J. . G. L; Hewitt, H. T.; Keen, A. B.; ...</p> <p>2015-03-05</p> <p>The new <span class="hlt">sea</span> <span class="hlt">ice</span> configuration GSI6.0, used in the Met Office global coupled configuration GC2.0, is described and the <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, thickness and volume are compared with the previous configuration and with observationally-based datasets. In the Arctic, the <span class="hlt">sea</span> <span class="hlt">ice</span> is thicker in all seasons than in the previous configuration, and there is now better agreement of the modelled concentration and <span class="hlt">extent</span> with the HadISST dataset. In the Antarctic, a warm bias in the ocean model has been exacerbated at the higher resolution of GC2.0, leading to a large reduction in <span class="hlt">ice</span> <span class="hlt">extent</span> and volume; further work is requiredmore » to rectify this in future configurations.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C21A0650P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C21A0650P"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Summer Camp: Bringing Together Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Modelers and Observers</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.; Holland, M. M.</p> <p>2016-12-01</p> <p>The Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> has undergone dramatic change and numerical models project this to continue for the foreseeable future. Understanding the mechanisms behind <span class="hlt">sea</span> <span class="hlt">ice</span> loss and its consequences for the larger Arctic and global systems is of critical importance if we are to anticipate and plan for the future. One impediment to progress is a disconnect between the observational and modeling communities. A <span class="hlt">sea</span> <span class="hlt">ice</span> summer camp was held in Barrow Alaska from 26 May to 1 June 2016 to overcome this impediment and better integrate the <span class="hlt">sea</span> <span class="hlt">ice</span> community. The 25 participants were a mix of modelers and observers from 13 different institutions at career stages from graduate student to senior scientist. The summer camp provided an accelerated program on <span class="hlt">sea</span> <span class="hlt">ice</span> observations and models and also fostered future collaborative interdisciplinary activities. Each morning was spent in the classroom with a daily lecture on an aspect of modeling or remote sensing followed by practical exercises. Topics included using models to assess sensitivity, to test hypotheses and to explore sources of uncertainty in future Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss. The afternoons were spent on the <span class="hlt">ice</span> making observations. There were four observational activities; albedo observations, <span class="hlt">ice</span> thickness measurements, <span class="hlt">ice</span> coring and physical properties, and <span class="hlt">ice</span> morphology surveys. The last field day consisted of a grand challenge where the group formulated a hypothesis, developed an observational and modeling strategy to test the hypothesis, and then integrated the observations and model results. The impacts of changing <span class="hlt">sea</span> <span class="hlt">ice</span> are being felt today in Barrow Alaska. We opened a dialog with Barrow community members to further understand these changes. This included an evening discussion with two Barrow <span class="hlt">sea</span> <span class="hlt">ice</span> experts and a community presentation of our work in a public lecture at the Inupiat Heritage Center.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PrOce.136..151D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PrOce.136..151D"><span>Effects of recent decreases in arctic <span class="hlt">sea</span> <span class="hlt">ice</span> on an <span class="hlt">ice</span>-associated marine bird</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Divoky, George J.; Lukacs, Paul M.; Druckenmiller, Matthew L.</p> <p>2015-08-01</p> <p>Recent major reductions in summer arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> could be expected to be affecting the distributions and life histories of arctic marine biota adapted to living adjacent to <span class="hlt">sea</span> <span class="hlt">ice</span>. Of major concern are the effects of <span class="hlt">ice</span> reductions, and associated increasing SST, on the most abundant forage fish in the Arctic, Arctic cod (Boreogadus saida), the primary prey for the region's upper trophic level marine predators. The black guillemot (Cepphus grylle mandtii) is an <span class="hlt">ice</span>-obligate diving seabird specializing in feeding on Arctic cod and has been studied annually since 1975 at a breeding colony in the western Beaufort <span class="hlt">Sea</span>. The data set is one of the few allowing assessment of the response of an upper trophic marine predator to recent decadal changes in the region's cryosphere. Analysis of oceanographic conditions north of the colony from 1975 to 2012 for the annual period when parents provision young (mid-July to early September), found no major regime shifts in <span class="hlt">ice</span> <span class="hlt">extent</span> or SST until the late 1990s with major decreases in <span class="hlt">ice</span> and increases in SST in the first decade of the 21st Century. We examined decadal variation in late summer oceanographic conditions, nestling diet and success, and overwinter adult survival, comparing a historical period (1975-1984) with a recent (2003-2012) one. In the historical period <span class="hlt">sea</span> <span class="hlt">ice</span> retreated an average of 1.8 km per day from 15 July to 1 September to an average distance of 95.8 km from the colony, while in the recent period <span class="hlt">ice</span> retreat averaged 9.8 km per day to an average distance of 506.9 km for the same time period. SST adjacent to the island increased an average of 2.9 °C between the two periods. While Arctic cod comprised over 95% of the prey provided to nestlings in the historical period, in the recent period 80% of the years had seasonal decreases, with Arctic cod decreasing to <5% of the nestling diet, and nearshore demersals, primarily sculpin (Cottidae), comprising the majority of the diet. A five-fold increase in</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.8163M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.8163M"><span>How <span class="hlt">sea</span> <span class="hlt">ice</span> could be the cold beating heart of European weather</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Margrethe Ringgaard, Ida; Yang, Shuting; Hesselbjerg Christensen, Jens; Kaas, Eigil</p> <p>2017-04-01</p> <p>The possibility that the ongoing rapid demise of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> may instigate abrupt changes is, however, not tackled by current research in general. <span class="hlt">Ice</span> cores from the Greenland <span class="hlt">Ice</span> Sheet (GIS) show clear evidence of past abrupt warm events with up to 15 degrees warming in less than a decade, most likely triggered by rapid disappearance of Nordic <span class="hlt">Seas</span> <span class="hlt">sea</span> <span class="hlt">ice</span>. At present, both Arctic <span class="hlt">Sea</span> <span class="hlt">ice</span> and the GIS are in strong transformation: Arctic <span class="hlt">sea-ice</span> cover has been retreating during most of the satellite era and in recent years, Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> experienced a dramatic reduction and the summer <span class="hlt">extent</span> was in 2012 and 2016 only half of the 1979-2000 average. With such dramatic change in the current <span class="hlt">sea</span> <span class="hlt">ice</span> coverage as a point of departure, several studies have linked reduction in wintertime <span class="hlt">sea</span> <span class="hlt">ice</span> in the Barents-Kara <span class="hlt">seas</span> to cold weather anomalies over Europe and through large scale tele-connections to regional warming elsewhere. Here we aim to investigate if, and how, Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> impacts European weather, i.e. if the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> works as the 'cold heart' of European weather. To understand the effects of the <span class="hlt">sea</span> <span class="hlt">ice</span> reduction on the full climate system, a fully-coupled global climate model, EC-Earth, is used. A new energy-conserving method for assimilating <span class="hlt">sea</span> <span class="hlt">ice</span> using the sensible heat flux is implemented in the coupled climate model and compared to the traditional, non-conserving, method of assimilating <span class="hlt">sea</span> <span class="hlt">ice</span>. Using this new method, experiments are performed with reduced <span class="hlt">sea</span> <span class="hlt">ice</span> cover in the Barents-Kara <span class="hlt">seas</span> under both warm and cold conditions in Europe. These experiments are used to evaluate how the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> modulates European winter weather under present climate conditions with a view towards favouring both relatively cold and warm conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JGRC..123.1586G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JGRC..123.1586G"><span>Atmosphere-<span class="hlt">Ice</span>-Ocean-Ecosystem Processes in a Thinner Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Regime: The Norwegian Young <span class="hlt">Sea</span> <span class="hlt">ICE</span> (N-<span class="hlt">ICE</span>2015) Expedition</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Granskog, Mats A.; Fer, Ilker; Rinke, Annette; Steen, Harald</p> <p>2018-03-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> has been in rapid decline the last decade and the Norwegian young <span class="hlt">sea</span> <span class="hlt">ICE</span> (N-<span class="hlt">ICE</span>2015) expedition sought to investigate key processes in a thin Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> regime, with emphasis on atmosphere-snow-<span class="hlt">ice</span>-ocean dynamics and <span class="hlt">sea</span> <span class="hlt">ice</span> associated ecosystem. The main findings from a half-year long campaign are collected into this special section spanning the Journal of Geophysical Research: Atmospheres, Journal of Geophysical Research: Oceans, and Journal of Geophysical Research: Biogeosciences and provide a basis for a better understanding of processes in a thin <span class="hlt">sea</span> <span class="hlt">ice</span> regime in the high Arctic. All data from the campaign are made freely available to the research community.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150021053','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150021053"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Outlook for September 2015 June Report - NASA Global Modeling and Assimilation Office</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cullather, Richard I.; Keppenne, Christian L.; Marshak, Jelena; Pawson, Steven; Schubert, Siegfried D.; Suarez, Max J.; Vernieres, Guillaume; Zhao, Bin</p> <p>2015-01-01</p> <p>The recent decline in perennial <span class="hlt">sea</span> <span class="hlt">ice</span> cover in Arctic Ocean is a topic of enormous scientific interest and has relevance to a broad variety of scientific disciplines and human endeavors including biological and physical oceanography, atmospheric circulation, high latitude ecology, the sustainability of indigenous communities, commerce, and resource exploration. A credible seasonal prediction of <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> would be of substantial use to many of the stakeholders in these fields and may also reveal details on the physical processes that result in the current trends in the <span class="hlt">ice</span> cover. Forecasts are challenging due in part to limitations in the polar observing network, the large variability in the climate system, and an incomplete knowledge of the significant processes. Nevertheless it is a useful to understand the current capabilities of high latitude seasonal forecasting and identify areas where such forecasts may be improved. Since 2008 the Arctic Research Consortium of the United States (ARCUS) has conducted a seasonal forecasting contest in which the average Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> for the month of September (the month of the annual <span class="hlt">extent</span> minimum) is predicted from available forecasts in early June, July, and August. The competition is known as the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Outlook (SIO) but recently came under the auspices of the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Prediction Network (SIPN), and multi-agency funded project to evaluate the SIO. The forecasts are submitted based on modeling, statistical, and heuristic methods. Forecasts of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> from the GMAO are derived from seasonal prediction system of the NASA Goddard Earth Observing System model, version 5 (GEOS 5) coupled atmosphere and ocean general circulation model (AOGCM). The projections are made in order to understand the relative skill of the forecasting system and to determine the effects of future improvements to the system. This years prediction is for a September average Arctic <span class="hlt">ice</span> <span class="hlt">extent</span> of 5.030.41 million km2.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014OcMod..84...51L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014OcMod..84...51L"><span>Processes driving <span class="hlt">sea</span> <span class="hlt">ice</span> variability in the Bering <span class="hlt">Sea</span> in an eddying ocean/<span class="hlt">sea</span> <span class="hlt">ice</span> model: Mean seasonal cycle</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Linghan; McClean, Julie L.; Miller, Arthur J.; Eisenman, Ian; Hendershott, Myrl C.; Papadopoulos, Caroline A.</p> <p>2014-12-01</p> <p>The seasonal cycle of <span class="hlt">sea</span> <span class="hlt">ice</span> variability in the Bering <span class="hlt">Sea</span>, together with the thermodynamic and dynamic processes that control it, are examined in a fine resolution (1/10°) global coupled ocean/<span class="hlt">sea-ice</span> model configured in the Community Earth System Model (CESM) framework. The ocean/<span class="hlt">sea-ice</span> model consists of the Los Alamos National Laboratory Parallel Ocean Program (POP) and the Los Alamos <span class="hlt">Sea</span> <span class="hlt">Ice</span> Model (CICE). The model was forced with time-varying reanalysis atmospheric forcing for the time period 1970-1989. This study focuses on the time period 1980-1989. The simulated seasonal-mean fields of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration strongly resemble satellite-derived observations, as quantified by root-mean-square errors and pattern correlation coefficients. The <span class="hlt">sea</span> <span class="hlt">ice</span> energy budget reveals that the seasonal thermodynamic <span class="hlt">ice</span> volume changes are dominated by the surface energy flux between the atmosphere and the <span class="hlt">ice</span> in the northern region and by heat flux from the ocean to the <span class="hlt">ice</span> along the southern <span class="hlt">ice</span> edge, especially on the western side. The <span class="hlt">sea</span> <span class="hlt">ice</span> force balance analysis shows that <span class="hlt">sea</span> <span class="hlt">ice</span> motion is largely associated with wind stress. The force due to divergence of the internal <span class="hlt">ice</span> stress tensor is large near the land boundaries in the north, and it is small in the central and southern <span class="hlt">ice</span>-covered region. During winter, which dominates the annual mean, it is found that the simulated <span class="hlt">sea</span> <span class="hlt">ice</span> was mainly formed in the northern Bering <span class="hlt">Sea</span>, with the maximum <span class="hlt">ice</span> growth rate occurring along the coast due to cold air from northerly winds and <span class="hlt">ice</span> motion away from the coast. South of St Lawrence Island, winds drive the model <span class="hlt">sea</span> <span class="hlt">ice</span> southwestward from the north to the southwestern part of the <span class="hlt">ice</span>-covered region. Along the <span class="hlt">ice</span> edge in the western Bering <span class="hlt">Sea</span>, model <span class="hlt">sea</span> <span class="hlt">ice</span> is melted by warm ocean water, which is carried by the simulated Bering Slope Current flowing to the northwest, resulting in the S-shaped asymmetric <span class="hlt">ice</span> edge. In spring and fall, similar thermodynamic and dynamic</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120009093','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120009093"><span>The Antarctic <span class="hlt">Ice</span> Sheet, <span class="hlt">Sea</span> <span class="hlt">Ice</span>, and the Ozone Hole: Satellite Observations of how they are Changing</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>2012-01-01</p> <p>Antarctica is the Earth's coldest and highest continent and has major impacts on the climate and life of the south polar vicinity. It is covered almost entirely by the Earth's largest <span class="hlt">ice</span> sheet by far, with a volume of <span class="hlt">ice</span> so great that if all the Antarctic <span class="hlt">ice</span> were to go into the ocean (as <span class="hlt">ice</span> or liquid water), this would produce a global <span class="hlt">sea</span> level rise of about 60 meters (197 feet). The continent is surrounded by <span class="hlt">sea</span> <span class="hlt">ice</span> that in the wintertime is even more expansive than the continent itself and in the summertime reduces to only about a sixth of its wintertime <span class="hlt">extent</span>. Like the continent, the expansive <span class="hlt">sea</span> <span class="hlt">ice</span> cover has major impacts, reflecting the sun's radiation back to space, blocking exchanges between the ocean and the atmosphere, and providing a platform for some animal species while impeding other species. Far above the continent, the Antarctic ozone hole is a major atmospheric phenomenon recognized as human-caused and potentially quite serious to many different life forms. Satellites are providing us with remarkable information about the <span class="hlt">ice</span> sheet, the <span class="hlt">sea</span> <span class="hlt">ice</span>, and the ozone hole. Satellite visible and radar imagery are providing views of the large scale structure of the <span class="hlt">ice</span> sheet never seen before; satellite laser altimetry has produced detailed maps of the topography of the <span class="hlt">ice</span> sheet; and an innovative gravity-measuring two-part satellite has allowed mapping of regions of mass loss and mass gain on the <span class="hlt">ice</span> sheet. The surrounding <span class="hlt">sea</span> <span class="hlt">ice</span> cover has a satellite record that goes back to the 1970s, allowing trend studies that show a decreasing <span class="hlt">sea</span> <span class="hlt">ice</span> presence in the region of the Bellingshausen and Amundsen <span class="hlt">seas</span>, to the west of the prominent Antarctic Peninsula, but increasing <span class="hlt">sea</span> <span class="hlt">ice</span> presence around much of the rest of the continent. Overall, <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> around Antarctica has increased at an average rate of about 17,000 square kilometers per year since the late 1970s, as determined from satellite microwave data that can be collected under both light and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA624416','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA624416"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Sensitivities in the 0.72 deg and 0.08 deg Arctic Cap Coupled HYCOM/CICE Models</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2014-09-30</p> <p>1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. <span class="hlt">Sea</span> <span class="hlt">Ice</span> Sensitivities in the 0.72°and 0.08° Arctic Cap...Arctic <span class="hlt">ice</span> <span class="hlt">extent</span>, which corresponds to the <span class="hlt">sea</span> <span class="hlt">ice</span> that remains during the summer minimum, has decreased over the years 1979–2007 by more than 10% per...Goosse et al. 2009) with the lowest observed <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> in the satellite record (1979-present) occurring in September 2012 (Perovich et al. 2012</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70020441','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70020441"><span>Greenland <span class="hlt">Sea</span> Odden <span class="hlt">sea</span> <span class="hlt">ice</span> feature: Intra-annual and interannual variability</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Shuchman, R.A.; Josberger, E.G.; Russel, C.A.; Fischer, K.W.; Johannessen, O.M.; Johannessen, J.; Gloersen, P.</p> <p>1998-01-01</p> <p>The "Odden" is a large <span class="hlt">sea</span> <span class="hlt">ice</span> feature that forms in the east Greenland <span class="hlt">Sea</span> that may protrude eastward to 5??E from the main <span class="hlt">sea</span> <span class="hlt">ice</span> pack (at about 8??W) between 73?? and 77??N. It generally forms at the beginning of the winter season and can cover 300,000 km2. Throughout the winter the outer edge of the Odden may advance and retreat by several hundred kilometers on timescales of a few days to weeks. Satellite passive microwave observations from 1978 through 1995 provide a continuous record of the spatial and temporal variations of this extremely dynamic phenomenon. Aircraft synthetic aperture radar, satellite passive microwave, and ship observations in the Odden show that the Odden consists of new <span class="hlt">ice</span> types, rather than older <span class="hlt">ice</span> types advected eastward from the main pack. The 17-year record shows both strong interannual and intra-annual variations in Odden <span class="hlt">extent</span> and temporal behavior. For example, in 1983 the Odden was weak, in 1984 the Odden did not occur, and in 1985 the Odden returned late in the season. An analysis of the <span class="hlt">ice</span> area and <span class="hlt">extent</span> time series derived from the satellite passive microwave observations along with meteorological data from the International Arctic Buoy Program (IABP) determined the meteorological forcing associated with Odden growth, maintenance, and decay. The key meteorological parameters that are related to the rapid <span class="hlt">ice</span> formation and decay associated with the Odden are, in order of importance, air temperature, wind speed, and wind direction. Oceanographic parameters must play an important role in controlling Odden formation, but it is not yet possible to quantify this role because of a lack of long-term oceanographic observations. Copyright 1998 by the American Geophysical Union.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-GSFC_20171208_Archive_e000190.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-GSFC_20171208_Archive_e000190.html"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Is Losing Its Bulwark Against Warming Summers</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2017-12-08</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>, the vast sheath of frozen seawater floating on the Arctic Ocean and its neighboring <span class="hlt">seas</span>, has been hit with a double whammy over the past decades: as its <span class="hlt">extent</span> shrunk, the oldest and thickest <span class="hlt">ice</span> has either thinned or melted away, leaving the <span class="hlt">sea</span> <span class="hlt">ice</span> cap more vulnerable to the warming ocean and atmosphere. “What we’ve seen over the years is that the older <span class="hlt">ice</span> is disappearing,” said Walt Meier, a <span class="hlt">sea</span> <span class="hlt">ice</span> researcher at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. “This older, thicker <span class="hlt">ice</span> is like the bulwark of <span class="hlt">sea</span> <span class="hlt">ice</span>: a warm summer will melt all the young, thin <span class="hlt">ice</span> away but it can’t completely get rid of the older <span class="hlt">ice</span>. But this older <span class="hlt">ice</span> is becoming weaker because there’s less of it and the remaining old <span class="hlt">ice</span> is more broken up and thinner, so that bulwark is not as good as it used to be.” Read more: go.nasa.gov/2dPJ9zT NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010037608','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010037608"><span>Trends in the Length of the Southern Ocean <span class="hlt">Sea</span> <span class="hlt">Ice</span> Season: 1979-1999</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 data can be used to observe the <span class="hlt">sea</span> <span class="hlt">ice</span> distribution around the continent of Antarctica on a daily basis and hence to determine how many days a year have <span class="hlt">sea</span> <span class="hlt">ice</span> at each location. This has been done for each of the 21 years 1979-1999. Mapping the trends in these data over the 21-year period reveals a detailed pattern of changes in the length of the <span class="hlt">sea</span> <span class="hlt">ice</span> season around Antarctica. Most of the Ross <span class="hlt">Sea</span> <span class="hlt">ice</span> cover has undergone a lengthening of the <span class="hlt">sea</span> <span class="hlt">ice</span> season, whereas most of the Amundsen <span class="hlt">Sea</span> <span class="hlt">ice</span> cover and almost the entire Bellingshausen <span class="hlt">Sea</span> <span class="hlt">ice</span> cover have undergone a shortening of the <span class="hlt">sea</span> <span class="hlt">ice</span> season. Results around the rest of the continent, including in the Weddell <span class="hlt">Sea</span>, are more mixed, but overall, more of the Southern Ocean experienced a lengthening of the <span class="hlt">sea</span> <span class="hlt">ice</span> season than a shortening. For instance, the area experiencing a lengthening of the <span class="hlt">sea</span> <span class="hlt">ice</span> season by at least 1 day per year is 5.8 x 10(exp 6) sq km, whereas the area experiencing a shortening of the <span class="hlt">sea</span> <span class="hlt">ice</span> season by at least 1 day per year is less than half that, at 2.8 x 10(exp 6) sq km. This contrasts sharply with what is happened over the same period in the Arctic, where, overall, there has been some depletion of the <span class="hlt">ice</span> cover, including shortened <span class="hlt">sea</span> <span class="hlt">ice</span> seasons and decreased <span class="hlt">ice</span> <span class="hlt">extents</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19890025240&hterms=wind+monitor&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dwind%2Bmonitor','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19890025240&hterms=wind+monitor&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dwind%2Bmonitor"><span>Wind, current and swell influences on the <span class="hlt">ice</span> <span class="hlt">extent</span> and flux in the Grand Banks-Labrador <span class="hlt">sea</span> area as observed in the LIMEX '87 experiment</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Argus, Susan Digby; Carsey, Frank; Holt, Benjamin</p> <p>1988-01-01</p> <p>This paper presents data collected by airborne and satellite instruments during the Labrador <span class="hlt">Ice</span> Margin Experiment, that demonstrate the effects of oceanic and atmospheric processes on the <span class="hlt">ice</span> conditions in the Grand Banks-Labrador <span class="hlt">sea</span> area. Special consideration is given to the development of algorithms for extracting information from SAR data. It is shown that SAR data can be used to monitor <span class="hlt">ice</span> <span class="hlt">extent</span>, determine <span class="hlt">ice</span> motion, locate shear zones, monitor the penetration of swell into the <span class="hlt">ice</span>, estimate floe sizes, and establish the dimensions of the <span class="hlt">ice</span> velocity zones. It is also shown that the complex interaction of the <span class="hlt">ice</span> cover with winds, currents, swell, and coastlines is similar to the dynamics established for a number of sites in both polar regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C31D..06T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C31D..06T"><span>Submesoscale <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean interactions in marginal <span class="hlt">ice</span> zones</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thompson, A. F.; Manucharyan, G.</p> <p>2017-12-01</p> <p>Signatures of ocean eddies, fronts and filaments are commonly observed within the marginal <span class="hlt">ice</span> zones (MIZ) from satellite images of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, in situ observations via <span class="hlt">ice</span>-tethered profilers or under-<span class="hlt">ice</span> gliders. Localized and intermittent <span class="hlt">sea</span> <span class="hlt">ice</span> heating and advection by ocean eddies are currently not accounted for in climate models and may contribute to their biases and errors in <span class="hlt">sea</span> <span class="hlt">ice</span> forecasts. Here, we explore mechanical <span class="hlt">sea</span> <span class="hlt">ice</span> interactions with underlying submesoscale ocean turbulence via a suite of numerical simulations. We demonstrate that the release of potential energy stored in meltwater fronts can lead to energetic submesoscale motions along MIZs with sizes O(10 km) and Rossby numbers O(1). In low-wind conditions, cyclonic eddies and filaments efficiently trap the <span class="hlt">sea</span> <span class="hlt">ice</span> and advect it over warmer surface ocean waters where it can effectively melt. The horizontal eddy diffusivity of <span class="hlt">sea</span> <span class="hlt">ice</span> mass and heat across the MIZ can reach O(200 m2 s-1). Submesoscale ocean variability also induces large vertical velocities (order of 10 m day-1) that can bring relatively warm subsurface waters into the mixed layer. The ocean-<span class="hlt">sea</span> <span class="hlt">ice</span> heat fluxes are localized over cyclonic eddies and filaments reaching about 100 W m-2. We speculate that these submesoscale-driven intermittent fluxes of heat and <span class="hlt">sea</span> <span class="hlt">ice</span> can potentially contribute to the seasonal evolution of MIZs. With continuing global warming and <span class="hlt">sea</span> <span class="hlt">ice</span> thickness reduction in the Arctic Ocean, as well as the large expanse of thin <span class="hlt">sea</span> <span class="hlt">ice</span> in the Southern Ocean, submesoscale <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean processes are expected to play a significant role in the climate system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018TCry...12..365R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018TCry...12..365R"><span>Consistent biases in Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> concentration simulated by climate models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Roach, Lettie A.; Dean, Samuel M.; Renwick, James A.</p> <p>2018-01-01</p> <p>The simulation of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> in global climate models often does not agree with observations. In this study, we examine the compactness of <span class="hlt">sea</span> <span class="hlt">ice</span>, as well as the regional distribution of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, in climate models from the latest Coupled Model Intercomparison Project (CMIP5) and in satellite observations. We find substantial differences in concentration values between different sets of satellite observations, particularly at high concentrations, requiring careful treatment when comparing to models. As a fraction of total <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, models simulate too much loose, low-concentration <span class="hlt">sea</span> <span class="hlt">ice</span> cover throughout the year, and too little compact, high-concentration cover in the summer. In spite of the differences in physics between models, these tendencies are broadly consistent across the population of 40 CMIP5 simulations, a result not previously highlighted. Separating models with and without an explicit lateral melt term, we find that inclusion of lateral melt may account for overestimation of low-concentration cover. Targeted model experiments with a coupled ocean-<span class="hlt">sea</span> <span class="hlt">ice</span> model show that choice of constant floe diameter in the lateral melt scheme can also impact representation of loose <span class="hlt">ice</span>. This suggests that current <span class="hlt">sea</span> <span class="hlt">ice</span> thermodynamics contribute to the inadequate simulation of the low-concentration regime in many models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19920051541&hterms=Parkinsons&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3DParkinsons','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19920051541&hterms=Parkinsons&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3DParkinsons"><span>Interannual variability of monthly Southern Ocean <span class="hlt">sea</span> <span class="hlt">ice</span> distributions</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>1992-01-01</p> <p>The interannual variability of the Southern-Ocean <span class="hlt">sea-ice</span> distributions was mapped and analyzed using data from Nimbus-5 ESMR and Nimbus-7 SMMR, collected from 1973 to 1987. The set of 12 monthly maps obtained reveals many details on spatial variability that are unobtainable from time series of <span class="hlt">ice</span> <span class="hlt">extents</span>. These maps can be used as baseline maps for comparisons against future Southern Ocean <span class="hlt">sea</span> <span class="hlt">ice</span> distributions. The maps are supplemented by more detailed maps of the frequency of <span class="hlt">ice</span> coverage, presented in this paper for one month within each of the four seasons, and by the breakdown of these results to the periods covered individually by each of the two passive-microwave imagers.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JGRC..122.9455M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JGRC..122.9455M"><span>Submesoscale <span class="hlt">Sea</span> <span class="hlt">Ice</span>-Ocean Interactions in Marginal <span class="hlt">Ice</span> Zones</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Manucharyan, Georgy E.; Thompson, Andrew F.</p> <p>2017-12-01</p> <p>Signatures of ocean eddies, fronts, and filaments are commonly observed within marginal <span class="hlt">ice</span> zones (MIZs) from satellite images of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, and in situ observations via <span class="hlt">ice</span>-tethered profilers or underice gliders. However, localized and intermittent <span class="hlt">sea</span> <span class="hlt">ice</span> heating and advection by ocean eddies are currently not accounted for in climate models and may contribute to their biases and errors in <span class="hlt">sea</span> <span class="hlt">ice</span> forecasts. Here, we explore mechanical <span class="hlt">sea</span> <span class="hlt">ice</span> interactions with underlying submesoscale ocean turbulence. We demonstrate that the release of potential energy stored in meltwater fronts can lead to energetic submesoscale motions along MIZs with spatial scales O(10 km) and Rossby numbers O(1). In low-wind conditions, cyclonic eddies and filaments efficiently trap the <span class="hlt">sea</span> <span class="hlt">ice</span> and advect it over warmer surface ocean waters where it can effectively melt. The horizontal eddy diffusivity of <span class="hlt">sea</span> <span class="hlt">ice</span> mass and heat across the MIZ can reach O(200 m2 s-1). Submesoscale ocean variability also induces large vertical velocities (order 10 m d-1) that can bring relatively warm subsurface waters into the mixed layer. The ocean-<span class="hlt">sea</span> <span class="hlt">ice</span> heat fluxes are localized over cyclonic eddies and filaments reaching about 100 W m-2. We speculate that these submesoscale-driven intermittent fluxes of heat and <span class="hlt">sea</span> <span class="hlt">ice</span> can contribute to the seasonal evolution of MIZs. With the continuing global warming and <span class="hlt">sea</span> <span class="hlt">ice</span> thickness reduction in the Arctic Ocean, submesoscale <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean processes are expected to become increasingly prominent.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC32B..02P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC32B..02P"><span>Contrasting Trends in Arctic and Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Coverage Since the Late 1970s</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Parkinson, C. L.</p> <p>2016-12-01</p> <p>Satellite observations have allowed a near-continuous record of Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> coverage since late 1978. This record has revealed considerable interannual variability in both polar regions but also significant long-term trends, with the Arctic losing, the Antarctic gaining, and the Earth as a whole losing <span class="hlt">sea</span> <span class="hlt">ice</span> coverage. Over the period 1979-2015, the trend in yearly average <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> in the Arctic is -53,100 km2/yr (-4.3 %/decade) and in the Antarctic is 23,800 km2/yr (2.1 %/decade). For all 12 months, trends are negative in the Arctic and positive in the Antarctic, with the highest magnitude monthly trend being for September in the Arctic, at -85,300 km2/yr (-10.9 %/decade). The decreases in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> have been so dominant that not a single month since 1986 registered a new monthly record high, whereas 75 months registered new monthly record lows between 1987 and 2015 and several additional record lows were registered in 2016. The Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> record highs and lows are also out of balance, in the opposite direction, although not in such dramatic fashion. Geographic details on the changing <span class="hlt">ice</span> covers, down to the level of individual pixels, can be seen by examining changes in the length of the <span class="hlt">sea</span> <span class="hlt">ice</span> season. Results reveal (and quantify) shortening <span class="hlt">ice</span> seasons throughout the bulk of the Arctic marginal <span class="hlt">ice</span> zone, the main exception being within the Bering <span class="hlt">Sea</span>, and lengthening <span class="hlt">sea</span> <span class="hlt">ice</span> seasons through much of the Southern Ocean but shortening seasons in the Bellingshausen <span class="hlt">Sea</span>, southern Amundsen <span class="hlt">Sea</span>, and northwestern Weddell <span class="hlt">Sea</span>. The decreasing Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> coverage was widely anticipated and fits well with a large array of environmental changes in the Arctic, whereas the increasing Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> coverage was not widely anticipated and explaining it remains an area of active research by many scientists exploring a variety of potential explanations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JGRC..121..267B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JGRC..121..267B"><span>Physical processes contributing to an <span class="hlt">ice</span> free Beaufort <span class="hlt">Sea</span> during September 2012</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Babb, D. G.; Galley, R. J.; Barber, D. G.; Rysgaard, S.</p> <p>2016-01-01</p> <p>During the record September 2012 <span class="hlt">sea</span> <span class="hlt">ice</span> minimum, the Beaufort <span class="hlt">Sea</span> became <span class="hlt">ice</span> free for the first time during the observational record. Increased dynamic activity during late winter enabled increased open water and seasonal <span class="hlt">ice</span> coverage that contributed to negative <span class="hlt">sea</span> <span class="hlt">ice</span> anomalies and positive solar absorption anomalies which drove rapid bottom melt and <span class="hlt">sea</span> <span class="hlt">ice</span> loss. As had happened in the Beaufort <span class="hlt">Sea</span> during previous years of exceptionally low September <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, anomalous solar absorption developed during May, increased during June, peaked during July, and persisted into October. However in situ observations from a single floe reveal less than 78% of the energy required for bottom melt during 2012 was available from solar absorption. We show that the 2012 <span class="hlt">sea</span> <span class="hlt">ice</span> minimum in the Beaufort was the result of anomalously large solar absorption that was compounded by an arctic cyclone and other sources of heat such as solar transmission, oceanic upwelling, and riverine inputs, but was ultimately made possible through years of preconditioning toward a younger, thinner <span class="hlt">ice</span> pack. Significant negative trends in <span class="hlt">sea</span> <span class="hlt">ice</span> concentration between 1979 and 2012 from June to October, coupled with a tendency toward earlier <span class="hlt">sea</span> <span class="hlt">ice</span> reductions have fostered a significant trend of +12.9 MJ m-2 yr-1 in cumulative solar absorption, sufficient to melt an additional 4.3 cm m-2 yr-1. Overall through preconditioning toward a younger, thinner <span class="hlt">ice</span> pack the Beaufort <span class="hlt">Sea</span> has become increasingly susceptible to increased <span class="hlt">sea</span> <span class="hlt">ice</span> loss that may render it <span class="hlt">ice</span> free more frequently in coming years.</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/2016AGUFM.C43B0748B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C43B0748B"><span>Physical Processes contributing to an <span class="hlt">ice</span> free Beaufort <span class="hlt">Sea</span> during September 2012</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Babb, D.; Galley, R.; Barber, D. G.; Rysgaard, S.</p> <p>2016-12-01</p> <p>During the record September 2012 <span class="hlt">sea</span> <span class="hlt">ice</span> minimum the Beaufort <span class="hlt">Sea</span> became <span class="hlt">ice</span> free for the first time during the observational record. Increased dynamic activity during late winter enabled increased open water and seasonal <span class="hlt">ice</span> coverage that contributed to negative <span class="hlt">sea</span> <span class="hlt">ice</span> anomalies and positive solar absorption anomalies which drove rapid bottom melt and <span class="hlt">sea</span> <span class="hlt">ice</span> loss. As had happened in the Beaufort <span class="hlt">Sea</span> during previous years of exceptionally low September <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, anomalous solar absorption developed during May, increased during June, peaked during July and persisted into October. However in situ observations from a single floe reveal less than 78% of the energy required for bottom melt during 2012 was available from solar absorption. We show that the 2012 <span class="hlt">sea</span> <span class="hlt">ice</span> minimum in the Beaufort was the result of anomalously large solar absorption that was compounded by an arctic cyclone and other sources of heat such as solar transmission, oceanic upwelling and riverine inputs, but was ultimately made possible through years of preconditioning towards a younger, thinner <span class="hlt">ice</span> pack. Significant negative trends in <span class="hlt">sea</span> <span class="hlt">ice</span> concentration between 1979 and 2012 from June to October, coupled with a tendency towards earlier <span class="hlt">sea</span> <span class="hlt">ice</span> reductions have fostered a significant trend of +12.9 MJ m-2 year-1 in cumulative solar absorption, sufficient to melt an additional 4.3 cm m-2 year-1. Overall through preconditioning towards a younger, thinner <span class="hlt">ice</span> pack the Beaufort <span class="hlt">Sea</span> has become increasingly susceptible to increased <span class="hlt">sea</span> <span class="hlt">ice</span> loss that may render it <span class="hlt">ice</span> free more frequently in coming years.</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 <span class="hlt">extent</span> 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/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> <span class="hlt">extent</span> 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/2016AGUFM.C42B..03D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C42B..03D"><span>Assessing deformation and morphology of Arctic landfast <span class="hlt">sea</span> <span class="hlt">ice</span> using InSAR to support use and management of coastal <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>Dammann, D. O.; Eicken, H.; Meyer, F. J.; Mahoney, A. R.</p> <p>2016-12-01</p> <p>Arctic landfast <span class="hlt">sea</span> <span class="hlt">ice</span> provides important services to people, including coastal communities and industry, as well as key marine biota. In many regions of the Arctic, the use of landfast <span class="hlt">sea</span> <span class="hlt">ice</span> by all stakeholders is increasingly limited by reduced stability of the <span class="hlt">ice</span> cover, which results in more deformation and rougher <span class="hlt">ice</span> conditions as well as reduced <span class="hlt">extent</span> and an increased likelihood of detachment from the shore. Here, we use Synthetic Aperture Radar Interferometry (InSAR) to provide stakeholder-relevant data on key constraints for <span class="hlt">sea</span> <span class="hlt">ice</span> use, in particular <span class="hlt">ice</span> stability and morphology, which are difficult to assess using conventional SAR. InSAR has the capability to detect small-scale landfast <span class="hlt">ice</span> displacements, which are linked to important coastal hazards, including the formation of cracks, ungrounding of <span class="hlt">ice</span> pressure ridges, and catastrophic breakout events. While InSAR has previously been used to identify the <span class="hlt">extent</span> of landfast <span class="hlt">ice</span> and regions of deformation within, quantitative analysis of small-scale <span class="hlt">ice</span> motion has yet to be thoroughly validated and its potential remains largely underutilized in <span class="hlt">sea</span> <span class="hlt">ice</span> science. Using TanDEM-X interferometry, we derive surface displacements of landfast <span class="hlt">ice</span> within Elson Lagoon near Barrow, Alaska, which we validate using in-situ DGPS data. We then apply an inverse model to estimate rates and patterns of shorefast <span class="hlt">ice</span> deformation in other regions of landfast <span class="hlt">ice</span> using interferograms generated with long-temporal baseline L-band ALOS-1 PALSAR-1 data. The model is able to correctly identify deformation modes and proxies for the associated relative internal elastic stress. The derived potential for fractures corresponds well with large-scale <span class="hlt">sea</span> <span class="hlt">ice</span> patterns and local in-situ observations. The utility of InSAR to quantify <span class="hlt">sea</span> <span class="hlt">ice</span> roughness has also been explored using TanDEM-X bistatic interferometry, which eliminates the effects of temporal changes in the <span class="hlt">ice</span> cover. The InSAR-derived DEM shows good correlation with a high</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1914888H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1914888H"><span>Stress and deformation characteristics of <span class="hlt">sea</span> <span class="hlt">ice</span> in a high resolution numerical <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>Heorton, Harry; Feltham, Daniel; Tsamados, Michel</p> <p>2017-04-01</p> <p>The drift and deformation of <span class="hlt">sea</span> <span class="hlt">ice</span> floating on the polar oceans is due to the applied wind and ocean currents. The deformations of <span class="hlt">sea</span> <span class="hlt">ice</span> over ocean basin length scales have observable patterns; cracks and leads in satellite images and within the velocity fields generated from floe tracking. In a climate <span class="hlt">sea</span> <span class="hlt">ice</span> model the deformation of <span class="hlt">sea</span> <span class="hlt">ice</span> over ocean basin length scales is modelled using a rheology that represents the relationship between stresses and deformation within the <span class="hlt">sea</span> <span class="hlt">ice</span> cover. Here we investigate the link between observable deformation characteristics and the underlying internal <span class="hlt">sea</span> <span class="hlt">ice</span> stresses and force balance using the Los Alamos numerical <span class="hlt">sea</span> <span class="hlt">ice</span> climate model. In order to mimic laboratory experiments on the deformation of small cubes of <span class="hlt">sea</span> <span class="hlt">ice</span> we have developed an idealised square domain that tests the model response at spatial resolutions of up to 500m. We use the Elastic Anisotropic Plastic and Elastic Viscous Plastic rheologies, comparing their stability over varying resolutions and time scales. <span class="hlt">Sea</span> <span class="hlt">ice</span> within the domain is forced by idealised winds in order to compare the confinement of wind stresses and internal <span class="hlt">sea</span> <span class="hlt">ice</span> stresses. We document the characteristic deformation patterns of convergent, divergent and rotating stress states.</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="http://www.dtic.mil/">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... <span class="hlt">sea</span> - <span class="hlt">ice</span> albedo is modeled and how <span class="hlt">sea</span> <span class="hlt">ice</span> predictions are initialized, and then to evaluate how these improvements</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012CliPa...8.2079V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012CliPa...8.2079V"><span><span class="hlt">Sea-ice</span> dynamics strongly promote Snowball Earth initiation and destabilize tropical <span class="hlt">sea-ice</span> margins</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Voigt, A.; Abbot, D. S.</p> <p>2012-12-01</p> <p>The Snowball Earth bifurcation, or runaway <span class="hlt">ice</span>-albedo feedback, is defined for particular boundary conditions by a critical CO2 and a critical <span class="hlt">sea-ice</span> cover (SI), both of which are essential for evaluating hypotheses related to Neoproterozoic glaciations. Previous work has shown that the Snowball Earth bifurcation, denoted as (CO2, SI)*, differs greatly among climate models. Here, we study the effect of bare <span class="hlt">sea-ice</span> albedo, <span class="hlt">sea-ice</span> dynamics and ocean heat transport on (CO2, SI)* in the atmosphere-ocean general circulation model ECHAM5/MPI-OM with Marinoan (~ 635 Ma) continents and solar insolation (94% of modern). In its standard setup, ECHAM5/MPI-OM initiates a~Snowball Earth much more easily than other climate models at (CO2, SI)* ≈ (500 ppm, 55%). Replacing the model's standard bare <span class="hlt">sea-ice</span> albedo of 0.75 by a much lower value of 0.45, we find (CO2, SI)* ≈ (204 ppm, 70%). This is consistent with previous work and results from net evaporation and local melting near the <span class="hlt">sea-ice</span> margin. When we additionally disable <span class="hlt">sea-ice</span> dynamics, we find that the Snowball Earth bifurcation can be pushed even closer to the equator and occurs at a hundred times lower CO2: (CO2, SI)* ≈ (2 ppm, 85%). Therefore, the simulation of <span class="hlt">sea-ice</span> dynamics in ECHAM5/MPI-OM is a dominant determinant of its high critical CO2 for Snowball initiation relative to other models. Ocean heat transport has no effect on the critical <span class="hlt">sea-ice</span> cover and only slightly decreases the critical CO2. For disabled <span class="hlt">sea-ice</span> dynamics, the state with 85% <span class="hlt">sea-ice</span> cover is stabilized by the Jormungand mechanism and shares characteristics with the Jormungand climate states. However, there is no indication of the Jormungand bifurcation and hysteresis in ECHAM5/MPI-OM. The state with 85% <span class="hlt">sea-ice</span> cover therefore is a soft Snowball state rather than a true Jormungand state. Overall, our results demonstrate that differences in <span class="hlt">sea-ice</span> dynamics schemes can be at least as important as differences in <span class="hlt">sea-ice</span> albedo for</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMPP41C2260G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMPP41C2260G"><span>The Role of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> in Last Millennium Climate Variability: Model-Proxy Comparisons Using Ensemble Members and Novel Model Experiments.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gertler, C. G.; Monier, E.; Prinn, R. G.</p> <p>2016-12-01</p> <p>Variability in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> is a prominent feature of forced simulations of the last millennium and reconstructions of paleoclimate using proxy records. The rapid 20th century decline in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> is most likely due to greenhouse gas forcing, but the accuracy of future projections depend on the characterization of natural variability. Declining <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> affects regional climate and society, but also plays a large role in Arctic amplification, with implications for mid-latitude circulation and even large-scale climate oscillations. To characterize the effects of natural and anthropogenic climate forcing on <span class="hlt">sea</span> <span class="hlt">ice</span> and the related changes in large-scale atmospheric circulation, a combination of instrumental record, paleoclimate reconstructions, and general circulation models can be employed to recreate <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> and the corresponding atmosphere-ocean states. Model output from the last millennium ensemble (LME) is compared to a proxy-based <span class="hlt">sea</span> <span class="hlt">ice</span> reconstruction and a global proxy network using a variety of statistical and data assimilation techniques. Further model runs using the Community Earth Systems Model (CESM) are performed with the same inputs as LME but forced with experimental <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span>, and results are contextualized within the larger ensemble by a variety of metrics.</p> </li> <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> <span class="hlt">extent</span> and the progression of melt.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ClDy...50.4599S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ClDy...50.4599S"><span>Tropically driven and externally forced patterns of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> change: reconciling observed and modeled trends</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schneider, David P.; Deser, Clara</p> <p>2018-06-01</p> <p>Recent work suggests that natural variability has played a significant role in the increase of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> during 1979-2013. The <span class="hlt">ice</span> <span class="hlt">extent</span> has responded strongly to atmospheric circulation changes, including a deepened Amundsen <span class="hlt">Sea</span> Low (ASL), which in part has been driven by tropical variability. Nonetheless, this increase has occurred in the context of externally forced climate change, and it has been difficult to reconcile observed and modeled Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> trends. To understand observed-model disparities, this work defines the internally driven and radiatively forced patterns of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> change and exposes potential model biases using results from two sets of historical experiments of a coupled climate model compared with observations. One ensemble is constrained only by external factors such as greenhouse gases and stratospheric ozone, while the other explicitly accounts for the influence of tropical variability by specifying observed SST anomalies in the eastern tropical Pacific. The latter experiment reproduces the deepening of the ASL, which drives an increase in regional <span class="hlt">ice</span> <span class="hlt">extent</span> due to enhanced <span class="hlt">ice</span> motion and <span class="hlt">sea</span> surface cooling. However, the overall <span class="hlt">sea</span> <span class="hlt">ice</span> trend in every ensemble member of both experiments is characterized by <span class="hlt">ice</span> loss and is dominated by the forced pattern, as given by the ensemble-mean of the first experiment. This pervasive <span class="hlt">ice</span> loss is associated with a strong warming of the ocean mixed layer, suggesting that the ocean model does not locally store or export anomalous heat efficiently enough to maintain a surface environment conducive to <span class="hlt">sea</span> <span class="hlt">ice</span> expansion. The pervasive upper-ocean warming, not seen in observations, likely reflects ocean mean-state biases.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ClDy..tmp..676S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ClDy..tmp..676S"><span>Tropically driven and externally forced patterns of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> change: reconciling observed and modeled trends</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schneider, David P.; Deser, Clara</p> <p>2017-09-01</p> <p>Recent work suggests that natural variability has played a significant role in the increase of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> during 1979-2013. The <span class="hlt">ice</span> <span class="hlt">extent</span> has responded strongly to atmospheric circulation changes, including a deepened Amundsen <span class="hlt">Sea</span> Low (ASL), which in part has been driven by tropical variability. Nonetheless, this increase has occurred in the context of externally forced climate change, and it has been difficult to reconcile observed and modeled Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> trends. To understand observed-model disparities, this work defines the internally driven and radiatively forced patterns of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> change and exposes potential model biases using results from two sets of historical experiments of a coupled climate model compared with observations. One ensemble is constrained only by external factors such as greenhouse gases and stratospheric ozone, while the other explicitly accounts for the influence of tropical variability by specifying observed SST anomalies in the eastern tropical Pacific. The latter experiment reproduces the deepening of the ASL, which drives an increase in regional <span class="hlt">ice</span> <span class="hlt">extent</span> due to enhanced <span class="hlt">ice</span> motion and <span class="hlt">sea</span> surface cooling. However, the overall <span class="hlt">sea</span> <span class="hlt">ice</span> trend in every ensemble member of both experiments is characterized by <span class="hlt">ice</span> loss and is dominated by the forced pattern, as given by the ensemble-mean of the first experiment. This pervasive <span class="hlt">ice</span> loss is associated with a strong warming of the ocean mixed layer, suggesting that the ocean model does not locally store or export anomalous heat efficiently enough to maintain a surface environment conducive to <span class="hlt">sea</span> <span class="hlt">ice</span> expansion. The pervasive upper-ocean warming, not seen in observations, likely reflects ocean mean-state biases.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMGC13C1092S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMGC13C1092S"><span>Impacts of projected <span class="hlt">sea</span> <span class="hlt">ice</span> changes on trans-Arctic navigation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stephenson, S. R.; Smith, L. C.</p> <p>2012-12-01</p> <p>Reduced Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> continues to be a palpable signal of global change. Record lows in September <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> from 2007 - 2011 have fueled speculation that trans-Arctic navigation routes may become physically viable in the 21st century. General Circulation Models project a nearly <span class="hlt">ice</span>-free Arctic Ocean in summer by mid-century; however, how reduced <span class="hlt">sea</span> <span class="hlt">ice</span> will realistically impact navigation is not well understood. Using the ATAM (Arctic Transportation Accessibility Model) we present simulations of 21st-century trans-Arctic voyages as a function of climatic (<span class="hlt">ice</span>) conditions and vessel class. Simulations are based on <span class="hlt">sea</span> <span class="hlt">ice</span> projections for three climatic forcing scenarios (RCP 4.5, 6.0, and 8.5 W/m^2) representing present-day and mid-century conditions, assuming Polar Class 6 (PC6) and open-water vessels (OW) with medium and no <span class="hlt">ice</span>-breaking capability, respectively. Optimal least-cost routes (minimizing travel time while avoiding <span class="hlt">ice</span> impassible to a given vessel class) between the North Atlantic and the Bering Strait were calculated for summer months of each time window. While Arctic navigation depends on other factors besides <span class="hlt">sea</span> <span class="hlt">ice</span> including economics, infrastructure, bathymetry, current, and weather, these projections should be useful for strategic planning by governments, regulatory and environmental agencies, and the global maritime industry to assess potential changes in the spatial and temporal ranges of Arctic marine operations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4455712','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4455712"><span>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> trends, variability and implications for seasonal <span class="hlt">ice</span> forecasting</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Serreze, Mark C.; Stroeve, Julienne</p> <p>2015-01-01</p> <p>September Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> over the period of satellite observations has a strong downward trend, accompanied by pronounced interannual variability with a detrended 1 year lag autocorrelation of essentially zero. We argue that through a combination of thinning and associated processes related to a warming climate (a stronger albedo feedback, a longer melt season, the lack of especially cold winters) the downward trend itself is steepening. The lack of autocorrelation manifests both the inherent large variability in summer atmospheric circulation patterns and that oceanic heat loss in winter acts as a negative (stabilizing) feedback, albeit insufficient to counter the steepening trend. These findings have implications for seasonal <span class="hlt">ice</span> forecasting. In particular, while advances in observing <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and assimilating thickness into coupled forecast systems have improved forecast skill, there remains an inherent limit to predictability owing to the largely chaotic nature of atmospheric variability. PMID:26032315</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20160007571&hterms=information&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dinformation','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20160007571&hterms=information&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dinformation"><span>New Visualizations Highlight New Information on the Contrasting Arctic and Antarctic <span class="hlt">Sea-Ice</span> Trends Since the Late 1970s</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.; DiGirolamo, Nicolo E.</p> <p>2016-01-01</p> <p>Month-by-month ranking of 37 years (1979-2015) of satellite-derived <span class="hlt">sea-ice</span> <span class="hlt">extents</span> in the Arctic and Antarctic reveals interesting new details in the overall trends toward decreasing <span class="hlt">sea-ice</span> coverage in the Arctic and increasing <span class="hlt">sea-ice</span> coverage in the Antarctic. The Arctic decreases are so definitive that there has not been a monthly record high in Arctic <span class="hlt">sea-ice</span> <span class="hlt">extents</span> in any month since 1986, a time period during which there have been 75 monthly record lows. The Antarctic, with the opposite but weaker trend toward increased <span class="hlt">ice</span> <span class="hlt">extents</span>, experienced monthly record lows in 5 months of 1986, then 6 later monthly record lows scattered through the dataset, with the last two occurring in 2006, versus 45 record highs since 1986. However, in the last three years of the 1979-2015 dataset, the downward trends in Arctic <span class="hlt">sea-ice</span> <span class="hlt">extents</span> eased up, with no new record lows in any month of 2013 or 2014 and only one record low in 2015,while the upward trends in Antarctic <span class="hlt">ice</span> <span class="hlt">extents</span> notably strengthened, with new record high <span class="hlt">ice</span> <span class="hlt">extents</span> in 4 months (August-November) of 2013, in 6 months (April- September) of 2014, and in 3 months (January, April, and May) of 2015. Globally, there have been only 3 monthly record highs since 1986 (only one since 1988), whereas there have been 43 record lows, although the last record lows (in the 1979-2015 dataset) occurred in 2012.</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/2010AGUFM.C43E0586E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.C43E0586E"><span>Carbon Dioxide Transfer Through <span class="hlt">Sea</span> <span class="hlt">Ice</span>: Modelling Flux in Brine Channels</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Edwards, L.; Mitchelson-Jacob, G.; Hardman-Mountford, N.</p> <p>2010-12-01</p> <p>For many years <span class="hlt">sea</span> <span class="hlt">ice</span> was thought to act as a barrier to the flux of CO2 between the ocean and atmosphere. However, laboratory-based and in-situ observations suggest that while <span class="hlt">sea</span> <span class="hlt">ice</span> may in some circumstances reduce or prevent transfer (e.g. in regions of thick, superimposed multi-year <span class="hlt">ice</span>), it may also be highly permeable (e.g. thin, first year <span class="hlt">ice</span>) with some studies observing significant fluxes of CO2. <span class="hlt">Sea</span> <span class="hlt">ice</span> covered regions have been observed to act both as a sink and a source of atmospheric CO2 with the permeability of <span class="hlt">sea</span> <span class="hlt">ice</span> and direction of flux related to <span class="hlt">sea</span> <span class="hlt">ice</span> temperature and the presence of brine channels in the <span class="hlt">ice</span>, as well as seasonal processes such as whether the <span class="hlt">ice</span> is freezing or thawing. Brine channels concentrate dissolved inorganic carbon (DIC) as well as salinity and as these dense waters descend through both the <span class="hlt">sea</span> <span class="hlt">ice</span> and the surface ocean waters, they create a sink for CO2. Calcium carbonate (ikaite) precipitation in the <span class="hlt">sea</span> <span class="hlt">ice</span> is thought to enhance this process. Micro-organisms present within the <span class="hlt">sea</span> <span class="hlt">ice</span> will also contribute to the CO2 flux dynamics. Recent evidence of decreasing <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and the associated change from a multi-year <span class="hlt">ice</span> to first-year <span class="hlt">ice</span> dominated system suggest the potential for increased CO2 flux through regions of thinner, more porous <span class="hlt">sea</span> <span class="hlt">ice</span>. A full understanding of the processes and feedbacks controlling the flux in these regions is needed to determine their possible contribution to global CO2 levels in a future warming climate scenario. Despite the significance of these regions, the air-<span class="hlt">sea</span> CO2 flux in <span class="hlt">sea</span> <span class="hlt">ice</span> covered regions is not currently included in global climate models. Incorporating this carbon flux system into Earth System models requires the development of a well-parameterised <span class="hlt">sea</span> <span class="hlt">ice</span>-air flux model. In our work we use the Los Alamos <span class="hlt">sea</span> <span class="hlt">ice</span> model, CICE, with a modification to incorporate the movement of CO2 through brine channels including the addition of DIC processes and <span class="hlt">ice</span> algae production to</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GeoRL..44.7955M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GeoRL..44.7955M"><span>Remarkable separability of circulation response to Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss and greenhouse gas forcing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McCusker, K. E.; Kushner, P. J.; Fyfe, J. C.; Sigmond, M.; Kharin, V. V.; Bitz, C. M.</p> <p>2017-08-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss may influence midlatitude climate by changing large-scale circulation. The <span class="hlt">extent</span> to which climate change can be understood as greenhouse gas-induced changes that are modulated by this loss depends on how additive the responses to the separate influences are. A novel <span class="hlt">sea</span> <span class="hlt">ice</span> nudging methodology in a fully coupled climate model reveals that the separate effects of doubled atmospheric carbon dioxide (CO2) concentrations and associated Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss are remarkably additive and insensitive to the mean climate state. This separability is evident in several fields throughout most of the year, from hemispheric to synoptic scales. The <span class="hlt">extent</span> to which the regional response to <span class="hlt">sea</span> <span class="hlt">ice</span> loss sometimes agrees with and sometimes cancels the response to CO2 is quantified. The separability of the responses might provide a means to better interpret the diverse array of modeling and observational studies of Arctic change and influence.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1325643','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1325643"><span>Uncertainty quantification and global sensitivity analysis of the Los Alamos <span class="hlt">sea</span> <span class="hlt">ice</span> model</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Urrego-Blanco, Jorge Rolando; Urban, Nathan Mark; Hunke, Elizabeth Clare</p> <p></p> <p>Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. <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. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of <span class="hlt">sea</span> <span class="hlt">ice</span> models. We characterize parametric uncertainty in the Los Alamos <span class="hlt">sea</span> <span class="hlt">ice</span> model (CICE) in a standalone configuration and quantify the sensitivity of <span class="hlt">sea</span> <span class="hlt">ice</span> area, <span class="hlt">extent</span>, 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 <span class="hlt">sea</span> <span class="hlt">ice</span> model whose predictions of <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, 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 <span class="hlt">sea</span> <span class="hlt">ice</span> model.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1325643-uncertainty-quantification-global-sensitivity-analysis-los-alamos-sea-ice-model','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1325643-uncertainty-quantification-global-sensitivity-analysis-los-alamos-sea-ice-model"><span>Uncertainty quantification and global sensitivity analysis of the Los Alamos <span class="hlt">sea</span> <span class="hlt">ice</span> model</span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Urrego-Blanco, Jorge Rolando; Urban, Nathan Mark; Hunke, Elizabeth Clare; ...</p> <p>2016-04-01</p> <p>Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. <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. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of <span class="hlt">sea</span> <span class="hlt">ice</span> models. We characterize parametric uncertainty in the Los Alamos <span class="hlt">sea</span> <span class="hlt">ice</span> model (CICE) in a standalone configuration and quantify the sensitivity of <span class="hlt">sea</span> <span class="hlt">ice</span> area, <span class="hlt">extent</span>, 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 <span class="hlt">sea</span> <span class="hlt">ice</span> model whose predictions of <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, 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 <span class="hlt">sea</span> <span class="hlt">ice</span> model.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JGRC..121.2709U','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JGRC..121.2709U"><span>Uncertainty quantification and global sensitivity analysis of the Los Alamos <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, Jorge R.; Urban, Nathan M.; Hunke, Elizabeth C.; Turner, Adrian K.; Jeffery, Nicole</p> <p>2016-04-01</p> <p>Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. <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. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of <span class="hlt">sea</span> <span class="hlt">ice</span> models. We characterize parametric uncertainty in the Los Alamos <span class="hlt">sea</span> <span class="hlt">ice</span> model (CICE) in a standalone configuration and quantify the sensitivity of <span class="hlt">sea</span> <span class="hlt">ice</span> area, <span class="hlt">extent</span>, 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 <span class="hlt">sea</span> <span class="hlt">ice</span> model whose predictions of <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, 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. 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 <span class="hlt">sea</span> <span class="hlt">ice</span> model.</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('https://www.ncbi.nlm.nih.gov/pubmed/29080010','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29080010"><span>Future <span class="hlt">sea</span> <span class="hlt">ice</span> conditions and weather forecasts in the Arctic: Implications for Arctic shipping.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Gascard, Jean-Claude; Riemann-Campe, Kathrin; Gerdes, Rüdiger; Schyberg, Harald; Randriamampianina, Roger; Karcher, Michael; Zhang, Jinlun; Rafizadeh, Mehrad</p> <p>2017-12-01</p> <p>The ability to forecast <span class="hlt">sea</span> <span class="hlt">ice</span> (both <span class="hlt">extent</span> and thickness) and weather conditions are the major factors when it comes to safe marine transportation in the Arctic Ocean. This paper presents findings focusing on <span class="hlt">sea</span> <span class="hlt">ice</span> and weather prediction in the Arctic Ocean for navigation purposes, in particular along the Northeast Passage. Based on comparison with the observed <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations for validation, the best performing Earth system models from the Intergovernmental Panel on Climate Change (IPCC) program (CMIP5-Coupled Model Intercomparison Project phase 5) were selected to provide ranges of potential future <span class="hlt">sea</span> <span class="hlt">ice</span> conditions. Our results showed that, despite a general tendency toward less <span class="hlt">sea</span> <span class="hlt">ice</span> cover in summer, internal variability will still be large and shipping along the Northeast Passage might still be hampered by <span class="hlt">sea</span> <span class="hlt">ice</span> blocking narrow passages. This will make <span class="hlt">sea</span> <span class="hlt">ice</span> forecasts on shorter time and space scales and Arctic weather prediction even more important.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/16905428','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/16905428"><span>Crustacea in Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span>: distribution, diet and life history strategies.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Arndt, Carolin E; Swadling, Kerrie M</p> <p>2006-01-01</p> <p>This review concerns crustaceans that associate with <span class="hlt">sea</span> <span class="hlt">ice</span>. Particular emphasis is placed on comparing and contrasting the Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> habitats, and the subsequent influence of these environments on the life history strategies of the crustacean fauna. <span class="hlt">Sea</span> <span class="hlt">ice</span> is the dominant feature of both polar marine ecosystems, playing a central role in physical processes and providing an essential habitat for organisms ranging in size from viruses to whales. Similarities between the Arctic and Antarctic marine ecosystems include variable cover of <span class="hlt">sea</span> <span class="hlt">ice</span> over an annual cycle, a light regimen that can extend from months of total darkness to months of continuous light and a pronounced seasonality in primary production. Although there are many similarities, there are also major differences between the two regions: The Antarctic experiences greater seasonal change in its <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, much of the <span class="hlt">ice</span> is over very deep water and more than 80% breaks out each year. In contrast, Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> often covers comparatively shallow water, doubles in its <span class="hlt">extent</span> on an annual cycle and the <span class="hlt">ice</span> may persist for several decades. Crustaceans, particularly copepods and amphipods, are abundant in the <span class="hlt">sea</span> <span class="hlt">ice</span> zone at both poles, either living within the brine channel system of the <span class="hlt">ice</span>-crystal matrix or inhabiting the <span class="hlt">ice</span>-water interface. Many species associate with <span class="hlt">ice</span> for only a part of their life cycle, while others appear entirely dependent upon it for reproduction and development. Although similarities exist between the two faunas, many differences are emerging. Most notable are the much higher abundance and biomass of Antarctic copepods, the dominance of the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> copepod fauna by calanoids, the high euphausiid biomass in Southern Ocean waters and the lack of any species that appear fully dependent on the <span class="hlt">ice</span>. In the Arctic, the <span class="hlt">ice</span>-associated fauna is dominated by amphipods. Calanoid copepods are not tightly associated with the <span class="hlt">ice</span>, while harpacticoids and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C33C1203F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C33C1203F"><span>Fragmentation and melting of the seasonal <span class="hlt">sea</span> <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>Feltham, D. L.; Bateson, A.; Schroeder, D.; Ridley, J. K.; Aksenov, Y.</p> <p>2017-12-01</p> <p>Recent years have seen a rapid reduction in the summer <span class="hlt">extent</span> of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. This trend has implications for navigation, oil exploration, wildlife, and local communities. Furthermore the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover impacts the exchange of heat and momentum between the ocean and atmosphere with significant teleconnections across the climate system, particularly mid to low latitudes in the Northern Hemisphere. The treatment of melting and break-up processes of the seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> cover within climate models is currently limited. In particular floes are assumed to have a uniform size which does not evolve with time. Observations suggest however that floe sizes can be modelled as truncated power law distributions, with different exponents for smaller and larger floes. This study aims to examine factors controlling the floe size distribution in the seasonal and marginal <span class="hlt">ice</span> zone. This includes lateral melting, wave induced break-up of floes, and the feedback between floe size and the mixed ocean layer. These results are then used to quantify the proximate mechanisms of seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> reduction in a <span class="hlt">sea</span> ice—ocean mixed layer model. Observations are used to assess and calibrate the model. The impacts of introducing these processes to the model will be discussed and the preliminary results of sensitivity and feedback studies will also be presented.</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://adsabs.harvard.edu/abs/2014GeoRL..41.1035T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014GeoRL..41.1035T"><span>Seasonal to interannual Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> predictability in current global climate models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tietsche, S.; Day, J. J.; Guemas, V.; Hurlin, W. J.; Keeley, S. P. E.; Matei, D.; Msadek, R.; Collins, M.; Hawkins, E.</p> <p>2014-02-01</p> <p>We establish the first intermodel comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and volume, there is potential predictive skill for lead times of up to 3 years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. <span class="hlt">Sea</span> <span class="hlt">ice</span> concentration errors are largest in the marginal <span class="hlt">ice</span> zone, and in winter they are almost zero away from the <span class="hlt">ice</span> edge. <span class="hlt">Sea</span> <span class="hlt">ice</span> thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by <span class="hlt">sea</span> <span class="hlt">ice</span> advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.A43D2493M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.A43D2493M"><span>Remarkable separability of the circulation response to Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss and greenhouse gas forcing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McCusker, K. E.; Kushner, P. J.; Fyfe, J. C.; Sigmond, M.; Kharin, V. V.; Bitz, C. M.</p> <p>2017-12-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss has an important effect on local climate through increases in ocean to atmosphere heat flux and associated feedbacks, and may influence midlatitude climate by changing large-scale circulation that can enhance or counter changes that are due to greenhouse gases. The <span class="hlt">extent</span> to which climate change in a warming world can be understood as greenhouse gas-induced changes that are modulated by Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss depends on how additive the responses to the separate influences are. Here we use a novel <span class="hlt">sea</span> <span class="hlt">ice</span> nudging methodology in the Canadian Earth System Model, which has a fully coupled ocean, to isolate the effects of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss and doubled atmospheric carbon dioxide (CO2) to determine their additivity and sensitivity to mean state. We find that the separate effects of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss and doubled CO2 are remarkably additive and relatively insensitive to mean climate state. This separability is evident in several thermodynamic and dynamic fields throughout most of the year, from hemispheric to synoptic scales. The <span class="hlt">extent</span> to which the regional response to <span class="hlt">sea</span> <span class="hlt">ice</span> loss sometimes agrees with and sometimes cancels the response to CO2 is quantified. In this model, Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss enhances the CO2-induced surface air temperature changes nearly everywhere and zonal wind changes over the Pacific sector, whereas <span class="hlt">sea</span> <span class="hlt">ice</span> loss counters CO2-induced <span class="hlt">sea</span> level pressure changes nearly everywhere over land and zonal wind changes over the Atlantic sector. This separability of the response to Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss from the response to CO2 doubling gives credence to the body of work in which Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss is isolated from the forcing that modified it, and might provide a means to better interpret the diverse array of modeling and observational studies of Arctic change and influence.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C21E1171K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C21E1171K"><span>New details about the LGM <span class="hlt">extent</span> and subsequent retreat of the West Antarctic <span class="hlt">Ice</span> Sheet from the easternmost Amundsen <span class="hlt">Sea</span> Embayment shelf</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Klages, J. P.; Hillenbrand, C. D.; Kuhn, G.; Smith, J. A.; Graham, A. G. C.; Nitsche, F. O.; Frederichs, T.; Arndt, J. E.; Gebhardt, C.; Robin, Z.; Uenzelmann-Neben, G.; Gohl, K.; Jernas, P.; Wacker, L.</p> <p>2017-12-01</p> <p>In recent years several previously undiscovered grounding-zone wedges (GZWs) have been described within the Abbot-Cosgrove palaeo-<span class="hlt">ice</span> stream trough on the easternmost Amundsen <span class="hlt">Sea</span> Embayment shelf. These GZWs document both the Last Glacial Maximum (LGM; 26.5-19 cal. ka BP) grounding-line <span class="hlt">extent</span> and the subsequent episodic retreat within this trough that neighbors the larger Pine Island-Thwaites trough to the west. Here we combine bathymetric, seismic, and geologic data showing that 1) the grounding line in Abbot Trough did not reach the continental shelf break at any time during the last glacial period, and 2) a prominent stacked GZW constructed from six individual wedges lying upon another was deposited 100 km upstream from the LGM grounding-line position. The available data allow for calculating volumes for most of these individual GZWs and for the entire stack. Sediment cores were recovered seawards from the outermost GZW in the trough, and from the individual wedges of the stacked GZW in order to define the LGM grounding-line <span class="hlt">extent</span>, and provide minimum grounding-line retreat ages for the respective positions on the stacked GZW. We present implications of a grounded-<span class="hlt">ice</span> free outer shelf throughout the last glacial period. Furthermore, we assess the significance of the grounding-line stillstand period recorded by the stacked GZW in Abbot Trough for the timing of post-LGM retreat of the West Antarctic <span class="hlt">Ice</span> Sheet from the Amundsen <span class="hlt">Sea</span> Embayment shelf.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70037527','USGSPUBS'); return false;" href="https://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/2014EGUGA..16.1690J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.1690J"><span>Antarctic Climate Variability: Covariance of Ozone and <span class="hlt">Sea</span> <span class="hlt">Ice</span> in Atmosphere - Ocean Coupled 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>Jrrar, Amna; Abraham, N. Luke; Pyle, John A.; Holland, David</p> <p>2014-05-01</p> <p>Changes in <span class="hlt">sea</span> <span class="hlt">ice</span> significantly modulate climate change because of its high reflective and insulating nature. While Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> <span class="hlt">Extent</span> (SIE) shows a negative trend. Antarctic SIE shows a weak but positive trend, estimated at 0.127 x 106 km2 per decade. The trend results from large regional cancellations, more <span class="hlt">ice</span> in the Weddell and the Ross <span class="hlt">seas</span>, and less <span class="hlt">ice</span> in the Amundsen - Bellingshausen <span class="hlt">seas</span>. A number of studies had demonstrated that stratospheric ozone depletion has had a major impact on the atmospheric circulation, causing a positive trend in the Southern Annular Mode (SAM), which has been linked to the observed positive trend in autumn <span class="hlt">sea</span> <span class="hlt">ice</span> in the Ross <span class="hlt">Sea</span>. However, other modelling studies show that models forced with prescribed ozone hole simulate decreased <span class="hlt">sea</span> <span class="hlt">ice</span> in all regions comparative to a control run. A recent study has also shown that stratospheric ozone recovery will mitigate Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss. To verify this assumed relationship, it is important first to investigate the covariance between ozone's natural (dynamical) variability and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> distribution in pre-industrial climate, to estimate the trend due to natural variability. We investigate the relationship between anomalous Antarctic ozone years and the subsequent changes in Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> distribution in a multidecadal control simulation using the AO-UMUKCA model. The model has a horizontal resolution of 3.75 X 2.5 degrees in longitude and latitude; and 60 hybrid height levels in the vertical, from the surface up to a height of 84 km. The ocean component is the NEMO ocean model on the ORCA2 tripolar grid, and the <span class="hlt">sea</span> <span class="hlt">ice</span> model is CICE. We evaluate the model's performance in terms of <span class="hlt">sea</span> <span class="hlt">ice</span> distribution, and we calculate <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> trends for composites of anomalously low versus anomalously high SH polar ozone column. We apply EOF analysis to the seasonal anomalies of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, MSLP, and Z 500, and identify the leading climate modes controlling the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMPP44C..03D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMPP44C..03D"><span>Biogeochemical Cycling and <span class="hlt">Sea</span> <span class="hlt">Ice</span> Dynamics in the Bering <span class="hlt">Sea</span> across the Mid-Pleistocene Transition</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Detlef, H.; Sosdian, S. M.; Belt, S. T.; Smik, L.; Lear, C. H.; Hall, I. R.; Kender, S.; Leng, M. J.; Husum, K.; Cabedo-Sanz, P.</p> <p>2017-12-01</p> <p>Today the Bering <span class="hlt">Sea</span> is characterized by high primary productivity (PP) along the eastern shelf, maintained by CO2 and nutrient rich upwelled deep waters and nutrient release during spring <span class="hlt">sea</span> <span class="hlt">ice</span> melting. As such, low oxygen concentrations are pervasive in mid-depth waters. Changes in ventilation and export productivity in the past have been shown to impact this oxygen minimum zone. On glacial/interglacial (G/IG) timescales <span class="hlt">sea</span> <span class="hlt">ice</span> formation plays a pivotal role on intermediate water ventilation with evidence pointing to the formation of North Pacific Intermediate Water (NPIW) in the Bering <span class="hlt">Sea</span> during Pleistocene glacial intervals. In addition, <span class="hlt">sea</span> <span class="hlt">ice</span> plays a significant role in both long- and short-term climate change via associated feedback mechanisms. Thus, records of <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics and biogeochemical cycling in the Bering <span class="hlt">Sea</span> are necessary to fully understand the interaction between PP, circulation patterns, and past G/IG climates with potential implications for the North Pacific carbon cycle. Here we use a multi-proxy approach to study <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics and bottom water oxygenation, across three intervals prior to, across, and after the Mid-Pleistocene Transition (MPT, 1.2-0.7 Ma) from International Ocean Discovery Program Site U1343. The MPT, most likely driven by internal climate mechanisms, is ideal to study changes in <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics and sedimentary redox conditions on orbital timescales and to investigate the implications for associated feedback mechanisms. The <span class="hlt">sea</span> <span class="hlt">ice</span> record, based on various biomarkers, including IP25, shows substantial increase in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> across the MPT and the occurrence of a late-glacial/deglacial <span class="hlt">sea</span> <span class="hlt">ice</span> spike, with consequences for glacial NPIW formation and land glacier retreat via the temperature-precipitation feedback. U/Mn of foraminiferal authigenic coatings, a novel proxy for bottom water oxygenation, also shows distinct variability on G/IG timescales across the MPT, most likely a result of PP and water mass</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.C33A0669O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.C33A0669O"><span>Recent Increases in Snow Accumulation and Decreases in <span class="hlt">Sea-Ice</span> Concentration Recorded in a Coastal NW Greenland <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>Osterberg, E. C.; Thompson, J. T.; Wong, G. J.; Hawley, R. L.; Kelly, M. A.; Lutz, E.; Howley, J.; Ferris, D. G.</p> <p>2013-12-01</p> <p> correlation represents a significant Na contribution from frost flowers growing on fall frazil <span class="hlt">ice</span>. Ongoing analyses will evaluate the relationship between MSA concentrations and <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>. Our results show that a deep <span class="hlt">ice</span> core collected from this dynamic and climate-sensitive region of NW Greenland would produce a valuable record of late Holocene climate and <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018TCry...12.1681P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018TCry...12.1681P"><span>Variability of <span class="hlt">sea</span> salts in <span class="hlt">ice</span> and firn cores from Fimbul <span class="hlt">Ice</span> Shelf, Dronning Maud Land, Antarctica</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Paulina Vega, Carmen; Isaksson, Elisabeth; Schlosser, Elisabeth; Divine, Dmitry; Martma, Tõnu; Mulvaney, Robert; Eichler, Anja; Schwikowski-Gigar, Margit</p> <p>2018-05-01</p> <p>Major ions were analysed in firn and <span class="hlt">ice</span> cores located at Fimbul <span class="hlt">Ice</span> Shelf (FIS), Dronning Maud Land - DML, Antarctica. FIS is the largest <span class="hlt">ice</span> shelf in the Haakon VII <span class="hlt">Sea</span>, with an <span class="hlt">extent</span> of approximately 36 500 km2. Three shallow firn cores (about 20 m deep) were retrieved in different <span class="hlt">ice</span> rises, Kupol Ciolkovskogo (KC), Kupol Moskovskij (KM), and Blåskimen Island (BI), while a 100 m long core (S100) was drilled near the FIS edge. These sites are distributed over the entire FIS area so that they provide a variety of elevation (50-400 m a.s.l.) and distance (3-42 km) to the <span class="hlt">sea</span>. <span class="hlt">Sea</span>-salt species (mainly Na+ and Cl-) generally dominate the precipitation chemistry in the study region. We associate a significant sixfold increase in median <span class="hlt">sea</span>-salt concentrations, observed in the S100 core after the 1950s, to an enhanced exposure of the S100 site to primary <span class="hlt">sea</span>-salt aerosol due to a shorter distance from the S100 site to the <span class="hlt">ice</span> front, and to enhanced <span class="hlt">sea</span>-salt aerosol production from blowing salty snow over <span class="hlt">sea</span> <span class="hlt">ice</span>, most likely related to the calving of Trolltunga occurred during the 1960s. This increase in <span class="hlt">sea</span>-salt concentrations is synchronous with a shift in non-<span class="hlt">sea</span>-salt sulfate (nssSO42-) toward negative values, suggesting a possible contribution of fractionated aerosol to the <span class="hlt">sea</span>-salt load in the S100 core most likely originating from salty snow found on <span class="hlt">sea</span> <span class="hlt">ice</span>. In contrast, there is no evidence of a significant contribution of fractionated <span class="hlt">sea</span> salt to the <span class="hlt">ice</span>-rises sites, where the signal would be most likely masked by the large inputs of biogenic sulfate estimated for these sites. In summary, these results suggest that the S100 core contains a <span class="hlt">sea</span>-salt record dominated by the proximity of the site to the ocean, and processes of <span class="hlt">sea</span> <span class="hlt">ice</span> formation in the neighbouring waters. In contrast, the <span class="hlt">ice</span>-rises firn cores register a larger-scale signal of atmospheric flow conditions and a less efficient transport of <span class="hlt">sea</span>-salt aerosols to these sites. These findings are a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26032315','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26032315"><span>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> trends, variability and implications for seasonal <span class="hlt">ice</span> forecasting.</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; Stroeve, Julienne</p> <p>2015-07-13</p> <p>September Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> over the period of satellite observations has a strong downward trend, accompanied by pronounced interannual variability with a detrended 1 year lag autocorrelation of essentially zero. We argue that through a combination of thinning and associated processes related to a warming climate (a stronger albedo feedback, a longer melt season, the lack of especially cold winters) the downward trend itself is steepening. The lack of autocorrelation manifests both the inherent large variability in summer atmospheric circulation patterns and that oceanic heat loss in winter acts as a negative (stabilizing) feedback, albeit insufficient to counter the steepening trend. These findings have implications for seasonal <span class="hlt">ice</span> forecasting. In particular, while advances in observing <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and assimilating thickness into coupled forecast systems have improved forecast skill, there remains an inherent limit to predictability owing to the largely chaotic nature of atmospheric variability. © 2015 The Author(s) Published by the Royal Society. All rights reserved.</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 <span class="hlt">extent</span>. 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('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 <span class="hlt">extent</span>. 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. © 2015 The Author(s) Published by the Royal Society. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC23D1175M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC23D1175M"><span><span class="hlt">Sea</span> <span class="hlt">ice</span>-induced cold air advection as a mechanism controlling tundra primary productivity</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Macias-Fauria, M.; Karlsen, S. R.</p> <p>2015-12-01</p> <p>The recent sharp decline in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, concentration, and volume leaves urgent questions regarding its effects on ecological processes. Changes in tundra productivity have been associated with <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics on the basis that most tundra ecosystems lay close to the <span class="hlt">sea</span>. Although some studies have addressed the potential effect of <span class="hlt">sea</span> <span class="hlt">ice</span> decline on the primary productivity of terrestrial arctic ecosystems (Bhatt et al., 2010), a clear picture of the mechanisms and patterns linking both processes remains elusive. We hypothesised that <span class="hlt">sea</span> <span class="hlt">ice</span> might influence tundra productivity through 1) cold air advection during the growing season (direct/weather effect) or 2) changes in regional climate induced by changes in <span class="hlt">sea</span> <span class="hlt">ice</span> (indirect/climate effect). We present a test on the direct/weather effect hypothesis: that is, tundra productivity is coupled with <span class="hlt">sea</span> <span class="hlt">ice</span> when <span class="hlt">sea</span> <span class="hlt">ice</span> remains close enough from land vegetation during the growing season for cold air advection to limit temperatures locally. We employed weekly MODIS-derived Normalised Difference Vegetation Index (as a proxy for primary productivity) and <span class="hlt">sea</span> <span class="hlt">ice</span> data at a spatial resolution of 232m for the period 2000-2014 (included), covering the Svalbard Archipelago. Our results suggest that <span class="hlt">sea</span> <span class="hlt">ice</span>-induced cold air advection is a likely mechanism to explain patterns of NDVI trends and heterogeneous spatial dynamics in the Svalbard archipelago. The mechanism offers the potential to explain <span class="hlt">sea</span> <span class="hlt">ice</span>/tundra productivity dynamics in other Arctic areas.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.7084M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.7084M"><span>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss and recent extreme cold winter in Eurasia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mori, Masato; Watanabe, Masahiro; Ishii, Masayoshi; Kimoto, Masahide</p> <p>2014-05-01</p> <p>Extreme cold winter over the Eurasia has occurred more frequently in recent years. Observational evidence in recent studies shows that the wintertime cold anomalies over the Eurasia are associated with decline of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> in preceding autumn to winter season. However, the tropical and/or mid-latitude <span class="hlt">sea</span> surface temperature (SST) anomalies have great influence on the mid- and high-latitude atmospheric variability, it is difficult to isolate completely the impacts of <span class="hlt">sea</span> <span class="hlt">ice</span> change from observational data. In this study, we examine possible linkage between the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss and the extreme cold winter over the Eurasia using a state-of-the-art MIROC4 (T106L56) atmospheric general circulation model (AGCM) to assess the pure atmospheric responses to <span class="hlt">sea</span> <span class="hlt">ice</span> reduction. We perform two sets of experiments with different realistic <span class="hlt">sea</span> <span class="hlt">ice</span> boundary conditions calculated by composite of observed <span class="hlt">sea</span> <span class="hlt">ice</span> concentration; one is reduced <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> case (referred to as LICE run) and another is enhanced case (HICE run). In both experiments, the model is integrated 6-month from September to February with 100-member ensemble under the climatological SST boundary condition. The difference in ensemble mean of each experiment (LICE minus HICE) shows cold anomalies over the Eurasia in winter and its spatial pattern is very similar to corresponding observation, though the magnitude is smaller than observation. This result indicates that a part of observed cold anomaly can be attributed to the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> loss. We would like to introduce more important results and mechanisms in detail in my presentation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140005669','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140005669"><span>Computing and Representing <span class="hlt">Sea</span> <span class="hlt">Ice</span> Trends: Toward a Community Consensus</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wohlleben, T.; Tivy, A.; Stroeve, J.; Meier, Walter N.; Fetterer, F.; Wang, J.; Assel, R.</p> <p>2013-01-01</p> <p>Estimates of the recent decline in Arctic Ocean summer <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> can vary due to differences in <span class="hlt">sea</span> <span class="hlt">ice</span> data sources, in the number of years used to compute the trend, and in the start and end years used in the trend computation. Compounding such differences, estimates of the relative decline in <span class="hlt">sea</span> <span class="hlt">ice</span> cover (given in percent change per decade) can further vary due to the choice of reference value (the initial point of the trend line, a climatological baseline, etc.). Further adding to the confusion, very often when relative trends are reported in research papers, the reference values used are not specified or made clear. This can lead to confusion when trend studies are cited in the press and public reports.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1814695S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1814695S"><span>N-<span class="hlt">ICE</span>2015: Multi-disciplinary study of the young <span class="hlt">sea</span> <span class="hlt">ice</span> system north of Svalbard from winter to summer.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Steen, Harald; Granskog, Mats; Assmy, Philipp; Duarte, Pedro; Hudson, Stephen; Gerland, Sebastian; Spreen, Gunnar; Smedsrud, Lars H.</p> <p>2016-04-01</p> <p>The Arctic Ocean is shifting to a new regime with a thinner and smaller <span class="hlt">sea-ice</span> area cover. Until now, winter <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> has changed less than during summer, as the heat loss to the atmosphere during autumn and winter is large enough form an <span class="hlt">ice</span> cover in most regions. The insulating snow cover also heavily influences the winter <span class="hlt">ice</span> growth. Consequently, the older, thicker multi-year <span class="hlt">sea</span> <span class="hlt">ice</span> has been replace by a younger and thinner <span class="hlt">sea</span>. These large changes in the <span class="hlt">sea</span> <span class="hlt">ice</span> cover may have dramatic consequences for ecosystems, energy fluxes and ultimately atmospheric circulation and the Northern Hemisphere climate. To study the effects of the changing Arctic the Norwegian Polar Institute, together with national and international partners, launched from January 11 to June 24, 2015 the Norwegian Young <span class="hlt">Sea</span> <span class="hlt">ICE</span> cruise 2015 (N-<span class="hlt">ICE</span>2015). N-<span class="hlt">ICE</span>2015 was a multi-disciplinary cruise aimed at simultaneously studying the effect of the Arctic Ocean changes in the <span class="hlt">sea</span> <span class="hlt">ice</span>, the atmosphere, in radiation, in ecosystems. as well as water chemistry. R/V Lance was frozen into the drift <span class="hlt">ice</span> north of Svalbard at about N83 E25 and drifted passively southwards with the <span class="hlt">ice</span> until she was broken loose. When she was loose, R/V Lance was brought back north to a similar starting position. While fast in the <span class="hlt">ice</span>, she served as a living and working platform for 100 scientist and engineers from 11 countries. One aim of N-<span class="hlt">ICE</span>2015 is to present a comprehensive data-set on the first year <span class="hlt">ice</span> dominated system available for the scientific community describing the state and changes of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> system from freezing to melt. Analyzing the data is progressing and some first results will be presented.</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 <span class="hlt">extent</span> 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> </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://polar.ncep.noaa.gov/seaice','SCIGOVWS'); return false;" href="http://polar.ncep.noaa.gov/seaice"><span>NCEP MMAB <span class="hlt">Sea</span> <span class="hlt">Ice</span> Home Page</span></a></p> <p><a target="_blank" href="http://www.science.gov/aboutsearch.html">Science.gov Websites</a></p> <p></p> <p></p> <p>NCEP MMAB <em><span class="hlt">Sea</span></em> <span class="hlt">Ice</span> Home Page The Polar and Great Lakes <span class="hlt">Ice</span> group works on <em><span class="hlt">sea</span></em> <span class="hlt">ice</span> analysis from satellite, <em><span class="hlt">sea</span></em> <span class="hlt">ice</span> modeling, and <span class="hlt">ice</span>-atmosphere-ocean coupling. Our work supports the Alaska Region of the @noaa.gov Last Modified 2 July 2012 Pages of Interest Analysis Daily <em><span class="hlt">Sea</span></em> <span class="hlt">Ice</span> Analyses Animations of the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JGRC..119.4168M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JGRC..119.4168M"><span>Calibration of <span class="hlt">sea</span> <span class="hlt">ice</span> dynamic parameters in an ocean-<span class="hlt">sea</span> <span class="hlt">ice</span> model using an ensemble Kalman filter</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Massonnet, F.; Goosse, H.; Fichefet, T.; Counillon, F.</p> <p>2014-07-01</p> <p>The choice of parameter values is crucial in the course of <span class="hlt">sea</span> <span class="hlt">ice</span> model development, since parameters largely affect the modeled mean <span class="hlt">sea</span> <span class="hlt">ice</span> state. Manual tuning of parameters will soon become impractical, as <span class="hlt">sea</span> <span class="hlt">ice</span> models will likely include more parameters to calibrate, leading to an exponential increase of the number of possible combinations to test. Objective and automatic methods for parameter calibration are thus progressively called on to replace the traditional heuristic, "trial-and-error" recipes. Here a method for calibration of parameters based on the ensemble Kalman filter is implemented, tested and validated in the ocean-<span class="hlt">sea</span> <span class="hlt">ice</span> model NEMO-LIM3. Three dynamic parameters are calibrated: the <span class="hlt">ice</span> strength parameter P*, the ocean-<span class="hlt">sea</span> <span class="hlt">ice</span> drag parameter Cw, and the atmosphere-<span class="hlt">sea</span> <span class="hlt">ice</span> drag parameter Ca. In twin, perfect-model experiments, the default parameter values are retrieved within 1 year of simulation. Using 2007-2012 real <span class="hlt">sea</span> <span class="hlt">ice</span> drift data, the calibration of the <span class="hlt">ice</span> strength parameter P* and the oceanic drag parameter Cw improves clearly the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> drift properties. It is found that the estimation of the atmospheric drag Ca is not necessary if P* and Cw are already estimated. The large reduction in the <span class="hlt">sea</span> <span class="hlt">ice</span> speed bias with calibrated parameters comes with a slight overestimation of the winter <span class="hlt">sea</span> <span class="hlt">ice</span> areal export through Fram Strait and a slight improvement in the <span class="hlt">sea</span> <span class="hlt">ice</span> thickness distribution. Overall, the estimation of parameters with the ensemble Kalman filter represents an encouraging alternative to manual tuning for ocean-<span class="hlt">sea</span> <span class="hlt">ice</span> models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018BGeo...15.1987S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018BGeo...15.1987S"><span>Do pelagic grazers benefit from <span class="hlt">sea</span> <span class="hlt">ice</span>? Insights from the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> proxy IPSO25</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schmidt, Katrin; Brown, Thomas A.; Belt, Simon T.; Ireland, Louise C.; Taylor, Kyle W. R.; Thorpe, Sally E.; Ward, Peter; Atkinson, Angus</p> <p>2018-04-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> affects primary production in polar regions in multiple ways. It can dampen water column productivity by reducing light or nutrient supply, provide a habitat for <span class="hlt">ice</span> algae and condition the marginal <span class="hlt">ice</span> zone (MIZ) for phytoplankton blooms on its seasonal retreat. The relative importance of three different carbon sources (<span class="hlt">sea</span> <span class="hlt">ice</span> derived, <span class="hlt">sea</span> <span class="hlt">ice</span> conditioned, non-<span class="hlt">sea-ice</span> associated) for the polar food web is not well understood, partly due to the lack of methods that enable their unambiguous distinction. Here we analysed two highly branched isoprenoid (HBI) biomarkers to trace <span class="hlt">sea-ice</span>-derived and <span class="hlt">sea-ice</span>-conditioned carbon in Antarctic krill (Euphausia superba) and relate their concentrations to the grazers' body reserves, growth and recruitment. During our sampling in January-February 2003, the proxy for <span class="hlt">sea</span> <span class="hlt">ice</span> diatoms (a di-unsaturated HBI termed IPSO25, δ13C = -12.5 ± 3.3 ‰) occurred in open waters of the western Scotia <span class="hlt">Sea</span>, where seasonal <span class="hlt">ice</span> retreat was slow. In suspended matter from surface waters, IPSO25 was present at a few stations close to the <span class="hlt">ice</span> edge, but in krill the marker was widespread. Even at stations that had been <span class="hlt">ice</span>-free for several weeks, IPSO25 was found in krill stomachs, suggesting that they gathered the <span class="hlt">ice</span>-derived algae from below the upper mixed layer. Peak abundances of the proxy for MIZ diatoms (a tri-unsaturated HBI termed HBI III, δ13C = -42.2 ± 2.4 ‰) occurred in regions of fast <span class="hlt">sea</span> <span class="hlt">ice</span> retreat and persistent salinity-driven stratification in the eastern Scotia <span class="hlt">Sea</span>. Krill sampled in the area defined by the <span class="hlt">ice</span> edge bloom likewise contained high amounts of HBI III. As indicators for the grazer's performance we used the mass-length ratio, size of digestive gland and growth rate for krill, and recruitment for the biomass-dominant calanoid copepods Calanoides acutus and Calanus propinquus. These indices consistently point to blooms in the MIZ as an important feeding ground for pelagic grazers. Even though <span class="hlt">ice</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1997Natur.387..897L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1997Natur.387..897L"><span>Effects of <span class="hlt">sea-ice</span> <span class="hlt">extent</span> and krill or salp dominance on the Antarctic food web</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Loeb, V.; Siegel, V.; Holm-Hansen, O.; Hewitt, R.; Fraser, W.; Trivelpiece, W.; Trivelpiece, S.</p> <p>1997-06-01</p> <p>Krill (Euphausia superba) provide a direct link between primary producers and higher trophic levels in the Antarctic marine food web. The pelagic tunicate Salpa thompsoni can also be important during spring and summer through the formation of extensive and dense blooms. Although salps are not a major dietary item for Antarctic vertebrate predators,, their blooms can affect adult krill reproduction and survival of krill larvae. Here we provide data from 1995 and 1996 that support hypothesized relationships between krill, salps and region-wide <span class="hlt">sea-ice</span> conditions,. We have assessed salp consumption as a proportion of net primary production, and found correlations between herbivore densities and integrated chlorophyll-a that indicate that there is a degree of competition between krill and salps. Our analysis of the relationship between annual <span class="hlt">sea-ice</span> cover and a longer time series of air temperature measurements, indicates a decreased frequency of winters with extensive <span class="hlt">sea-ice</span> development over the last five decades. Our data suggest that decreased krill availability may affect the levels of their vertebrate predators. Regional warming and reduced krill abundance therefore affect the marine food web and krill resource management.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PhDT........48D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PhDT........48D"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Trafficability - New Strategies for a Changing Icescape</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dammann, Dyre Oliver</p> <p></p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> is an important part of the Arctic social-environmental system, in part because it provides a platform for human transportation and for marine flora and fauna that use the <span class="hlt">ice</span> as a habitat. <span class="hlt">Sea</span> <span class="hlt">ice</span> loss projected for coming decades is expected to change <span class="hlt">ice</span> conditions throughout the Arctic, but little is known about the nature and <span class="hlt">extent</span> of anticipated changes and in particular potential implications for over-<span class="hlt">ice</span> travel and <span class="hlt">ice</span> use as a platform. This question has been addressed here through an extensive effort to link <span class="hlt">sea</span> <span class="hlt">ice</span> use and key geophysical properties of <span class="hlt">sea</span> <span class="hlt">ice</span>, drawing upon extensive field surveys around on-<span class="hlt">ice</span> operations and local and Indigenous knowledge for the widely different <span class="hlt">ice</span> uses and <span class="hlt">ice</span> regimes of Utqiagvik, Kotzebue, and Nome, Alaska.. A set of nine parameters that constrain landfast <span class="hlt">sea</span> <span class="hlt">ice</span> use has been derived, including spatial <span class="hlt">extent</span>, stability, and timing and persistence of landfast <span class="hlt">ice</span>. This work lays the foundation for a framework to assess and monitor key <span class="hlt">ice</span>-parameters relevant in the context of <span class="hlt">ice</span>-use feasibility, safety, and efficiency, drawing on different remote-sensing techniques. The framework outlines the steps necessary to further evaluate relevant parameters in the context of user objectives and key stakeholder needs for a given <span class="hlt">ice</span> regime and <span class="hlt">ice</span> use scenario. I have utilized this framework in case studies for three different <span class="hlt">ice</span> regimes, where I find uses to be constrained by <span class="hlt">ice</span> thickness, roughness, and fracture potential and develop assessment strategies with accuracy at the relevant spatial scales. In response to the widely reported importance of high-confidence <span class="hlt">ice</span> thickness measurements, I have developed a new strategy to estimate appropriate thickness compensation factors. Compensation factors have the potential to reduce risk of misrepresenting areas of thin <span class="hlt">ice</span> when using point-based in-situ assessment methods along a particular route. This approach was tested on an <span class="hlt">ice</span> road near Kotzebue, Alaska, where</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.C54A..04T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.C54A..04T"><span>Trends in Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Volume 2010-2013 from CryoSat-2</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tilling, R.; Ridout, A.; Wingham, D.; Shepherd, A.; Haas, C.; Farrell, S. L.; Schweiger, A. J.; Zhang, J.; Giles, K.; Laxon, S.</p> <p>2013-12-01</p> <p>Satellite records show a decline in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> over the past three decades with a record minimum in September 2012, and results from the Pan-Arctic <span class="hlt">Ice</span>-Ocean Modelling and Assimilation System (PIOMAS) suggest that this has been accompanied by a reduction in volume. We use three years of measurements recorded by the European Space Agency CryoSat-2 (CS-2) mission, validated with in situ data, to generate estimates of seasonal variations and inter-annual trends in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> volume between 2010 and 2013. The CS-2 estimates of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness agree with in situ estimates derived from upward looking sonar measurements of <span class="hlt">ice</span> draught and airborne measurements of <span class="hlt">ice</span> thickness and freeboard to within 0.1 metres. Prior to the record minimum in summer 2012, autumn and winter Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> volume had fallen by ~1300 km3 relative to the previous year. Using the full 3-year period of CS-2 observations, we estimate that winter Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> volume has decreased by ~700 km3/yr since 2010, approximately twice the average rate since 1980 as predicted by the PIOMAS.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24276772','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24276772"><span>Fatty acid and stable isotope characteristics of <span class="hlt">sea</span> <span class="hlt">ice</span> and pelagic particulate organic matter in the Bering <span class="hlt">Sea</span>: tools for estimating <span class="hlt">sea</span> <span class="hlt">ice</span> algal contribution to Arctic food web production.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wang, Shiway W; Budge, Suzanne M; Gradinger, Rolf R; Iken, Katrin; Wooller, Matthew J</p> <p>2014-03-01</p> <p>We determined fatty acid (FA) profiles and carbon stable isotopic composition of individual FAs (δ(13)CFA values) from <span class="hlt">sea</span> <span class="hlt">ice</span> particulate organic matter (i-POM) and pelagic POM (p-POM) in the Bering <span class="hlt">Sea</span> during maximum <span class="hlt">ice</span> <span class="hlt">extent</span>, <span class="hlt">ice</span> melt, and <span class="hlt">ice</span>-free conditions in 2010. Based on FA biomarkers, differences in relative composition of diatoms, dinoflagellates, and bacteria were inferred for i-POM versus p-POM and for seasonal succession stages in p-POM. Proportions of diatom markers were higher in i-POM (16:4n-1, 6.6-8.7%; 20:5n-3, 19.6-25.9%) than in p-POM (16:4n-1, 1.2-4.0%; 20:5n-3, 5.5-14.0%). The dinoflagellate marker 22:6n-3/20:5n-3 was highest in p-POM. Bacterial FA concentration was higher in the bottom 1 cm of <span class="hlt">sea</span> <span class="hlt">ice</span> (14-245 μg L(-1)) than in the water column (0.6-1.7 μg L(-1)). Many i-POM δ(13)C(FA) values were higher (up to ~10‰) than those of p-POM, and i-POM δ(13)C(FA) values increased with day length. The higher i-POM δ(13)C(FA) values are most likely related to the reduced dissolved inorganic carbon (DIC) availability within the semi-closed <span class="hlt">sea</span> <span class="hlt">ice</span> brine channel system. Based on a modified Rayleigh equation, the fraction of <span class="hlt">sea</span> <span class="hlt">ice</span> DIC fixed in i-POM ranged from 12 to 73%, implying that carbon was not limiting for primary productivity in the sympagic habitat. These differences in FA composition and δ(13)C(FA) values between i-POM and p-POM will aid efforts to track the proportional contribution of <span class="hlt">sea</span> <span class="hlt">ice</span> algal carbon to higher trophic levels in the Bering <span class="hlt">Sea</span> and likely other Arctic <span class="hlt">seas</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C51A0955L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C51A0955L"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> roughness: the key for predicting Arctic summer <span class="hlt">ice</span> albedo</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Landy, J.; Ehn, J. K.; Tsamados, M.; Stroeve, J.; Barber, D. G.</p> <p>2017-12-01</p> <p>Although melt ponds on Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> evolve in stages, <span class="hlt">ice</span> with smoother surface topography typically allows the pond water to spread over a wider area, reducing the <span class="hlt">ice</span>-albedo and accelerating further melt. Building on this theory, we simulated the distribution of meltwater on a range of statistically-derived topographies to develop a quantitative relationship between premelt <span class="hlt">sea</span> <span class="hlt">ice</span> surface roughness and summer <span class="hlt">ice</span> albedo. Our method, previously applied to ICESat observations of the end-of-winter <span class="hlt">sea</span> <span class="hlt">ice</span> roughness, could account for 85% of the variance in AVHRR observations of the summer <span class="hlt">ice</span>-albedo [Landy et al., 2015]. Consequently, an Arctic-wide reduction in <span class="hlt">sea</span> <span class="hlt">ice</span> roughness over the ICESat operational period (from 2003 to 2008) explained a drop in <span class="hlt">ice</span>-albedo that resulted in a 16% increase in solar heat input to the <span class="hlt">sea</span> <span class="hlt">ice</span> cover. Here we will review this work and present new research linking pre-melt <span class="hlt">sea</span> <span class="hlt">ice</span> surface roughness observations from Cryosat-2 to summer <span class="hlt">sea</span> <span class="hlt">ice</span> albedo over the past six years, examining the potential of winter roughness as a significant new source of <span class="hlt">sea</span> <span class="hlt">ice</span> predictability. We will further evaluate the possibility for high-resolution (kilometre-scale) forecasts of summer <span class="hlt">sea</span> <span class="hlt">ice</span> albedo from waveform-level Cryosat-2 roughness data in the landfast <span class="hlt">sea</span> <span class="hlt">ice</span> zone of the Canadian Arctic. Landy, J. C., J. K. Ehn, and D. G. Barber (2015), Albedo feedback enhanced by smoother Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>, Geophys. Res. Lett., 42, 10,714-10,720, doi:10.1002/2015GL066712.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMGC24A..05K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMGC24A..05K"><span>Identifying Climate Model Teleconnection Mechanisms Between Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Loss and Mid-Latitude Winter Storms</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kravitz, B.; Mills, C.; Rasch, P. J.; Wang, H.; Yoon, J. H.</p> <p>2016-12-01</p> <p>The role of Arctic amplification, including observed decreases in <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, thickness, and <span class="hlt">extent</span>, with potential for exciting downstream atmospheric responses in the mid-latitudes, is a timely issue. We identify the role of the regionality of autumn <span class="hlt">sea</span> <span class="hlt">ice</span> loss on downstream mid-latitude responses using engineering methodologies adapted to climate modeling, which allow for multiple Arctic <span class="hlt">sea</span> regions to be perturbed simultaneously. We evaluate downstream responses in various climate fields (e.g., temperature, precipitation, cloud cover) associated with perturbations in the Beaufort/Chukchi <span class="hlt">Seas</span> and the Kara/Barents <span class="hlt">Seas</span>. Simulations suggest that the United States response is primarily linked to <span class="hlt">sea</span> <span class="hlt">ice</span> changes in the Beaufort/Chukchi <span class="hlt">Seas</span>, whereas Eurasian response is primarily due to Kara/Barents <span class="hlt">sea</span> <span class="hlt">ice</span> coverage changes. Downstream effects are most prominent approximately 6-10 weeks after the initial perturbation (<span class="hlt">sea</span> <span class="hlt">ice</span> loss). Our findings suggest that winter mid-latitude storms (connected to the so-called "Polar Vortex") are linked to <span class="hlt">sea</span> <span class="hlt">ice</span> loss in particular areas, implying that further <span class="hlt">sea</span> <span class="hlt">ice</span> loss associated with climate change will exacerbate these types of extreme events.</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('https://pubs.er.usgs.gov/publication/70020035','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70020035"><span><span class="hlt">Sea-ice</span> processes in the Laptev <span class="hlt">Sea</span> and their importance for sediment export</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Eicken, H.; Reimnitz, E.; Alexandrov, V.; Martin, T.; Kassens, H.; Viehoff, T.</p> <p>1997-01-01</p> <p>Based on remote-sensing data and an expedition during August-September 1993, the importance of the Laptev <span class="hlt">Sea</span> as a source area for sediment-laden <span class="hlt">sea</span> <span class="hlt">ice</span> was studied. <span class="hlt">Ice</span>-core analysis demonstrated the importance of dynamic <span class="hlt">ice</span>-growth mechanisms as compared to the multi-year cover of the Arctic Basin. <span class="hlt">Ice</span>-rafted sediment (IRS) was mostly associated with congealed frazil <span class="hlt">ice</span>, although evidence for other entrainment mechanisms (anchor <span class="hlt">ice</span>, entrainment into freshwater <span class="hlt">ice</span>) was also found. Concentrations of suspended particulate matter (SPM) in patches of dirty <span class="hlt">ice</span> averaged at 156 g m-3 (standard deviation ?? = 140 g m-3), with a background concentration of 5 g m-3. The potential for sediment entrainment over the broad, shallow Laptev <span class="hlt">Sea</span> shelf during fall freeze-up was studied through analysis of remote-sensing data and weather-station records for the period 1979-1994. Freeze-up commences on 26 September (?? = 7 d) and is completed after 19 days (?? = 6 d). Meteorological conditions as well as <span class="hlt">ice</span> <span class="hlt">extent</span> prior to and during freeze-up vary considerably, the open-water area ranging between 107 x 103 and 447 x 103 km2. <span class="hlt">Ice</span> motion and transport of IRS were derived from satellite imagery and drifting buoys for the period during and after the expedition (mean <span class="hlt">ice</span> velocities of 0.04 and 0.05 m s-1, respectively). With a best-estimate sediment load of 16 t km-2 (ranging between 9 and 46 t km-2), sediment export from the eastern Laptev <span class="hlt">Sea</span> amounts to 4 x 10-6 t yr-1, with extremes of 2 x 10-6 and 11 x 106 t yr-1. Implications for the sediment budget of the Laptev shelf, in particular with respect to riverine input of SPM, which may be of the same order of magnitude, are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFMED11D1122R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFMED11D1122R"><span>What About <span class="hlt">Sea</span> <span class="hlt">Ice</span>? People, animals, and climate change in the polar regions: An online resource for the International Polar Year 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>Renfrow, S.; Meier, W. N.; Wolfe, J.; Scott, D.; Leon, A.; Weaver, R.</p> <p>2005-12-01</p> <p>Decreasing Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> has been one of the most noticeable changes on Earth over the past quarter-century. The years 2002 through 2005 have had much lower summer <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> than the long-term (1979-2000). Reduced <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> has a direct impact on Arctic wildlife and people, as well as ramifications for regional and global climate. Students, educators, and the general public want and need to have a better understanding of <span class="hlt">sea</span> <span class="hlt">ice</span>. Most of us are unfamiliar with <span class="hlt">sea</span> <span class="hlt">ice</span>: what it is, where it occurs, and how it affects global climate. The upcoming International Polar Year will provide an opportunity for the public to learn about <span class="hlt">sea</span> <span class="hlt">ice</span>. Here, we provide an overview of <span class="hlt">sea</span> <span class="hlt">ice</span>, the changes that the <span class="hlt">sea</span> <span class="hlt">ice</span> is undergoing, and information about the relation between <span class="hlt">sea</span> <span class="hlt">ice</span> and climate. The information presented here is condensed from the National Snow and <span class="hlt">Ice</span> Data Center's new 'All About <span class="hlt">Sea</span> <span class="hlt">Ice</span>' Web site (http://www.nsidc.org/seaice/), a comprehensive resource of information for <span class="hlt">sea</span> <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.9227L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.9227L"><span>Upper Ocean Evolution Across the Beaufort <span class="hlt">Sea</span> Marginal <span class="hlt">Ice</span> Zone from Autonomous Gliders</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, Craig; Rainville, Luc; Perry, Mary Jane</p> <p>2016-04-01</p> <p>The observed reduction of Arctic summertime <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and expansion of the marginal <span class="hlt">ice</span> zone (MIZ) have profound impacts on the balance of processes controlling <span class="hlt">sea</span> <span class="hlt">ice</span> evolution, including the introduction of several positive feedback mechanisms that may act to accelerate melting. Examples of such feedbacks include increased upper ocean warming though absorption of solar radiation, elevated internal wave energy and mixing that may entrain heat stored in subsurface watermasses (e.g., the relatively warm Pacific Summer (PSW) and Atlantic (AW) waters), and elevated surface wave energy that acts to deform and fracture <span class="hlt">sea</span> <span class="hlt">ice</span>. Spatial and temporal variability in <span class="hlt">ice</span> properties and open water fraction impact these processes. To investigate how upper ocean structure varies with changing <span class="hlt">ice</span> cover, and how the balance of processes shift as a function of <span class="hlt">ice</span> fraction and distance from open water, four long-endurance autonomous Seagliders occupied sections that extended from open water, through the marginal <span class="hlt">ice</span> zone, deep into the pack during summer 2014 in the Beaufort <span class="hlt">Sea</span>. Sections reveal strong fronts where cold, <span class="hlt">ice</span>-covered waters meet waters that have been exposed to solar warming, and O(10 km) scale eddies near the <span class="hlt">ice</span> edge. In the pack, Pacific Summer Water and a deep chlorophyll maximum form distinct layers at roughly 60 m and 80 m, respectively, which become increasingly diffuse as they progress through the MIZ and into open water. The isopynal layer between 1023 and 1024 kgm-3, just above the PSW, consistently thickens near the <span class="hlt">ice</span> edge, likely due to mixing or energetic vertical exchange associated with strong lateral gradients in this region. This presentation will discuss the upper ocean variability, its relationship to <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, and evolution over the summer to the start of freeze up.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUOSHE21A..06L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUOSHE21A..06L"><span>Upper Ocean Evolution Across the Beaufort <span class="hlt">Sea</span> Marginal <span class="hlt">Ice</span> Zone from Autonomous Gliders</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, C.; Rainville, L.; Perry, M. J.</p> <p>2016-02-01</p> <p>The observed reduction of Arctic summertime <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and expansion of the marginal <span class="hlt">ice</span> zone (MIZ) have profound impacts on the balance of processes controlling <span class="hlt">sea</span> <span class="hlt">ice</span> evolution, including the introduction of several positive feedback mechanisms that may act to accelerate melting. Examples of such feedbacks include increased upper ocean warming though absorption of solar radiation, elevated internal wave energy and mixing that may entrain heat stored in subsurface watermasses (e.g., the relatively warm Pacific Summer (PSW) and Atlantic (AW) waters), and elevated surface wave energy that acts to deform and fracture <span class="hlt">sea</span> <span class="hlt">ice</span>. Spatial and temporal variability in <span class="hlt">ice</span> properties and open water fraction impact these processes. To investigate how upper ocean structure varies with changing <span class="hlt">ice</span> cover, and how the balance of processes shift as a function of <span class="hlt">ice</span> fraction and distance from open water, four long-endurance autonomous Seagliders occupied sections that extended from open water, through the marginal <span class="hlt">ice</span> zone, deep into the pack during summer 2014 in the Beaufort <span class="hlt">Sea</span>. Sections reveal strong fronts where cold, <span class="hlt">ice</span>-covered waters meet waters that have been exposed to solar warming, and O(10 km) scale eddies near the <span class="hlt">ice</span> edge. In the pack, Pacific Summer Water and a deep chlorophyll maximum form distinct layers at roughly 60 m and 80 m, respectively, which become increasingly diffuse as they progress through the MIZ and into open water. The isopynal layer between 1023 and 1024 kg m-3, just above the PSW, consistently thickens near the <span class="hlt">ice</span> edge, likely due to mixing or energetic vertical exchange associated with strong lateral gradients in this region. This presentation will discuss the upper ocean variability, its relationship to <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, and evolution over the summer to the start of freeze up.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C11C0930W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C11C0930W"><span>The <span class="hlt">Sea</span> <span class="hlt">Ice</span> Index: A Resource for Cryospheric Knowledge Mobilization</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Windnagel, A. K.; Fetterer, F. M.</p> <p>2017-12-01</p> <p>The <span class="hlt">Sea</span> <span class="hlt">Ice</span> Index is a popular source of information about Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> data and trends created at the National Snow and <span class="hlt">Ice</span> Data Center (NSIDC) in 2002. It has been used by cryospheric scientists, cross-discipline scientists, the press, policy makers, and the public for the past 15 years. The Index started as a prototype <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> product in 2001 and was envisioned as a website that would meet a need for readily accessible, easy-to-use information on <span class="hlt">sea</span> <span class="hlt">ice</span> trends and anomalies, with products that would assist in monitoring and diagnosing the <span class="hlt">ice</span> <span class="hlt">extent</span> minima that were gaining increasing attention in the research community in the late 1990s. The goal was to easily share these valuable data with everyone that needed them, which is the essence of knowledge mobilization. As time has progressed, we have found new ways of disseminating the information carried by the data by providing simple pictures on a website, animating those images, creating Google Earth animations that show the data on a globe, providing simple text files of data values that do not require special software to read, writing a monthly blog about the data that has over 1.7 million readers annually, providing the data to NOAA's Science on Sphere to be seen in museums and classrooms across 23 countries, and creating geo-registered images for use in geospatial software. The Index helps to bridge the gap between <span class="hlt">sea</span> <span class="hlt">ice</span> science and the public. Through NSIDC's User Services Office, we receive feedback on the Index and have endeavored to meet the changing needs of our stakeholder communities to best mobilize this knowledge in their direction. We have learned through trial-by-fire the best practices for delivering these data and data services. This tells the tale of managing an unassuming data set as it has journeyed from a simple product consisting of images of <span class="hlt">sea</span> <span class="hlt">ice</span> to one that is robust enough to be used in the IPCC Climate Change Report but easy enough to be understood by K-12</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000037971&hterms=round&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dround','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000037971&hterms=round&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dround"><span>Year-Round Pack <span class="hlt">Ice</span> in the Weddell <span class="hlt">Sea</span>, Antarctica: Response and Sensitivity to Atmospheric and Oceanic Forcing</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Geiger, Cathleen A.; Ackley, Stephen F.; Hibler, William D., III</p> <p>1997-01-01</p> <p>Using a dynamic-thermodynamic numerical <span class="hlt">sea-ice</span> model, external oceanic and atmospheric forcings on <span class="hlt">sea</span> <span class="hlt">ice</span> in the Weddell <span class="hlt">Sea</span> are examined to identify physical processes associated with the seasonal cycle of pack <span class="hlt">ice</span>, and to identify further the parameters that coupled models need to consider in predicting the response of the pack <span class="hlt">ice</span> to climate and ocean-circulation changes. In agreement with earlier studies, the primary influence on the winter <span class="hlt">ice</span>-edge maximum <span class="hlt">extent</span> is air temperature. Ocean heat flux has more impact on the minimum-<span class="hlt">ice</span>-edge <span class="hlt">extent</span> and in reducing pack-<span class="hlt">ice</span> thickness, especially in the eastern-Weddell <span class="hlt">Sea</span>. Low relative humidity enhances <span class="hlt">ice</span> growth in thin <span class="hlt">ice</span> and open-water regions, producing a more realistic <span class="hlt">ice</span> edge along the coastal areas of the western-Weddell <span class="hlt">Sea</span> where dry continental air has an impact. The modeled <span class="hlt">extent</span> of the Weddell summer pack is equally sensitive to ocean heat flux and atmospheric relative humidity variations with the more dynamic responses being from the atmosphere. Since the atmospheric regime in the eastern Weddell is dominated by marine intrusions from lower latitudes, with high humidity already, it is unlikely that either the moisture trans- port could be further raised or that it could be significantly lowered because of its distance from the continent (the lower humidity source). Ocean heat-transport variability is shown to lead to overall <span class="hlt">ice</span> thinning in the model response and is a known feature of the actual system, as evidenced by the occurrence of the Weddell Polynya in the mid 1970s.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C31D..03C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C31D..03C"><span>Modulation of <span class="hlt">Sea</span> <span class="hlt">Ice</span> Melt Onset and Retreat in the Laptev <span class="hlt">Sea</span> by the Timing of Snow Retreat in the West Siberian Plain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Crawford, A. D.; Stroeve, J.; Serreze, M. C.; Rajagopalan, B.; Horvath, S.</p> <p>2017-12-01</p> <p>As much of the Arctic Ocean transitions to <span class="hlt">ice</span>-free conditions in summer, efforts have increased to improve seasonal forecasts of not only <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, but also the timing of melt onset and retreat. This research investigates the potential of regional terrestrial snow retreat in spring as a predictor for subsequent <span class="hlt">sea</span> <span class="hlt">ice</span> melt onset and retreat in Arctic <span class="hlt">seas</span>. One pathway involves earlier snow retreat enhancing atmospheric moisture content, which increases downwelling longwave radiation over <span class="hlt">sea</span> <span class="hlt">ice</span> cover downstream. Another pathway involves manipulation of jet stream behavior, which may affect the <span class="hlt">sea</span> <span class="hlt">ice</span> pack via both dynamic and thermodynamic processes. Although several possible connections between snow and <span class="hlt">sea</span> <span class="hlt">ice</span> regions are identified using a mutual information criterion, the physical mechanisms linking snow retreat and <span class="hlt">sea</span> <span class="hlt">ice</span> phenology are most clearly exemplified by variability of snow retreat in the West Siberian Plain impacting melt onset and <span class="hlt">sea</span> <span class="hlt">ice</span> retreat in the Laptev <span class="hlt">Sea</span>. The detrended time series of snow retreat in the West Siberian Plain explains 26% of the detrended variance in Laptev <span class="hlt">Sea</span> melt onset (29% for <span class="hlt">sea</span> <span class="hlt">ice</span> retreat). With modest predictive skill and an average time lag of 53 (88) days between snow retreat and <span class="hlt">sea</span> <span class="hlt">ice</span> melt onset (retreat), West Siberian Plains snow retreat is useful for refining seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> predictions in the Laptev <span class="hlt">Sea</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25845501','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25845501"><span>Physicochemical control of bacterial and protist community composition and diversity 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>Torstensson, Anders; Dinasquet, Julie; Chierici, Melissa; Fransson, Agneta; Riemann, Lasse; Wulff, Angela</p> <p>2015-10-01</p> <p>Due to climate change, <span class="hlt">sea</span> <span class="hlt">ice</span> experiences changes in terms of <span class="hlt">extent</span> and physical properties. In order to understand how <span class="hlt">sea</span> <span class="hlt">ice</span> microbial communities are affected by changes in physicochemical properties of the <span class="hlt">ice</span>, we used 454-sequencing of 16S and 18S rRNA genes to examine environmental control of microbial diversity and composition in Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span>. We observed a high diversity and richness of bacteria, which were strongly negatively correlated with temperature and positively with brine salinity. We suggest that bacterial diversity in <span class="hlt">sea</span> <span class="hlt">ice</span> is mainly controlled by physicochemical properties of the <span class="hlt">ice</span>, such as temperature and salinity, and that <span class="hlt">sea</span> <span class="hlt">ice</span> bacterial communities are sensitive to seasonal and environmental changes. For the first time in Antarctic interior <span class="hlt">sea</span> <span class="hlt">ice</span>, we observed a strong eukaryotic dominance of the dinoflagellate phylotype SL163A10, comprising 63% of the total sequences. This phylotype is known to be kleptoplastic and could be a significant primary producer in <span class="hlt">sea</span> <span class="hlt">ice</span>. We conclude that mixotrophic flagellates may play a greater role in the <span class="hlt">sea</span> <span class="hlt">ice</span> microbial ecosystem than previously believed, and not only during the polar night but also during summer when potential food sources are abundant. © 2015 Society for Applied Microbiology and John Wiley & Sons Ltd.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110005552','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110005552"><span>ICESat Observations of Seasonal and Interannual Variations of <span class="hlt">Sea-Ice</span> Freeboard and Estimated Thickness in the Weddell <span class="hlt">Sea</span>, Antarctica (2003-2009)</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Yi, Donghui; Robbins, John W.</p> <p>2010-01-01</p> <p><span class="hlt">Sea-ice</span> freeboard heights for 17 ICESat campaign periods from 2003 to 2009 are derived from ICESat data. Freeboard is combined with snow depth from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) data and nominal densities of snow, water and <span class="hlt">sea</span> <span class="hlt">ice</span>, to estimate <span class="hlt">sea-ice</span> thickness. <span class="hlt">Sea-ice</span> freeboard and thickness distributions show clear seasonal variations that reflect the yearly cycle of growth and decay of the Weddell <span class="hlt">Sea</span> (Antarctica) pack <span class="hlt">ice</span>. During October-November, <span class="hlt">sea</span> <span class="hlt">ice</span> grows to its seasonal maximum both in area and thickness; the mean freeboards are 0.33-0.41 m and the mean thicknesses are 2.10-2.59 m. During February-March, thinner <span class="hlt">sea</span> <span class="hlt">ice</span> melts away and the <span class="hlt">sea-ice</span> pack is mainly distributed in the west Weddell <span class="hlt">Sea</span>; the mean freeboards are 0.35-0.46 m and the mean thicknesses are 1.48-1.94 m. During May-June, the mean freeboards and thicknesses are 0.26-0.29 m and 1.32-1.37 m, respectively. The 6 year trends in <span class="hlt">sea-ice</span> <span class="hlt">extent</span> and volume are (0.023+/-0.051) x 10(exp 6)sq km/a (0.45%/a) and (0.007+/-1.0.092) x 10(exp 3)cu km/a (0.08%/a); however, the large standard deviations indicate that these positive trends are not statistically significant.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19720021734','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19720021734"><span>Microwave emission characteristics 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>Edgerton, A. T.; Poe, G.</p> <p>1972-01-01</p> <p>A general classification is presented for <span class="hlt">sea</span> <span class="hlt">ice</span> brightness temperatures with categories of high and low emission, corresponding to young and weathered <span class="hlt">sea</span> <span class="hlt">ice</span>, respectively. A <span class="hlt">sea</span> <span class="hlt">ice</span> emission model was developed which allows variations of <span class="hlt">ice</span> salinity and temperature in directions perpendicular to the <span class="hlt">ice</span> surface.</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/2017ClDy..tmp..916E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ClDy..tmp..916E"><span>Diagnosing <span class="hlt">sea</span> <span class="hlt">ice</span> from the north american multi model ensemble and implications on mid-latitude winter climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Elders, Akiko; Pegion, Kathy</p> <p>2017-12-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> plays an important role in the climate system, moderating the exchange of energy and moisture between the ocean and the atmosphere. An emerging area of research investigates how changes, particularly declines, in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> (SIE) impact climate in regions local to and remote from the Arctic. Therefore, both observations and model estimates of <span class="hlt">sea</span> <span class="hlt">ice</span> become important. This study investigates the skill of <span class="hlt">sea</span> <span class="hlt">ice</span> predictions from models participating in the North American Multi-Model Ensemble (NMME) project. Three of the models in this project provide <span class="hlt">sea-ice</span> predictions. The ensemble average of these models is used to determine seasonal climate impacts on surface air temperature (SAT) and <span class="hlt">sea</span> level pressure (SLP) in remote regions such as the mid-latitudes. It is found that declines in fall SIE are associated with cold temperatures in the mid-latitudes and pressure patterns across the Arctic and mid-latitudes similar to the negative phase of the Arctic Oscillation (AO). These findings are consistent with other studies that have investigated the relationship between declines in SIE and mid-latitude weather and climate. In an attempt to include additional NMME models for <span class="hlt">sea-ice</span> predictions, a proxy for SIE is used to estimate <span class="hlt">ice</span> <span class="hlt">extent</span> in the remaining models, using <span class="hlt">sea</span> surface temperature (SST). It is found that SST is a reasonable proxy for SIE estimation when compared to model SIE forecasts and observations. The proxy <span class="hlt">sea-ice</span> estimates also show similar relationships to mid-latitude temperature and pressure as the actual <span class="hlt">sea-ice</span> predictions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1338808','SCIGOV-STC'); return false;" href="https://www.osti.gov/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/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Notz, Dirk; Jahn, Alexandra; Holland, Marika</p> <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 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('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://adsabs.harvard.edu/abs/2013AGUFMIN11C1538S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMIN11C1538S"><span>The Timing of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Advance and Retreat as an Indicator of <span class="hlt">Ice</span>-Dependent Marine Mammal 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, H. L.; Laidre, K. L.</p> <p>2013-12-01</p> <p>The Arctic is widely recognized as the front line of climate change. Arctic air temperature is rising at twice the global average rate, and the <span class="hlt">sea-ice</span> cover is shrinking and thinning, with total disappearance of summer <span class="hlt">sea</span> <span class="hlt">ice</span> projected to occur in a matter of decades. Arctic marine mammals such as polar bears, seals, walruses, belugas, narwhals, and bowhead whales depend on the <span class="hlt">sea-ice</span> cover as an integral part of their existence. While the downward trend in <span class="hlt">sea-ice</span> <span class="hlt">extent</span> in a given month is an often-used metric for quantifying physical changes in the <span class="hlt">ice</span> cover, it is not the most relevant measure for characterizing changes in the <span class="hlt">sea-ice</span> habitat of marine mammals. Species that depend on <span class="hlt">sea</span> <span class="hlt">ice</span> are behaviorally tied to the annual retreat of <span class="hlt">sea</span> <span class="hlt">ice</span> in the spring and advance in the fall. Changes in the timing of the spring retreat and the fall advance are more relevant to Arctic marine species than changes in the areal <span class="hlt">sea-ice</span> coverage in a particular month of the year. Many ecologically important regions of the Arctic are essentially <span class="hlt">ice</span>-covered in winter and <span class="hlt">ice</span>-free in summer, and will probably remain so for a long time into the future. But the dates of <span class="hlt">sea-ice</span> retreat in spring and advance in fall are key indicators of climate change for <span class="hlt">ice</span>-dependent marine mammals. We use daily <span class="hlt">sea-ice</span> concentration data derived from satellite passive microwave sensors to calculate the dates of <span class="hlt">sea-ice</span> retreat in spring and advance in fall in 12 regions of the Arctic for each year from 1979 through 2013. The regions include the peripheral <span class="hlt">seas</span> around the Arctic Ocean (Beaufort, Chukchi, East Siberian, Laptev, Kara, Barents), the Canadian Arctic Archipelago, and the marginal <span class="hlt">seas</span> (Okhotsk, Bering, East Greenland, Baffin Bay, Hudson Bay). We find that in 11 of the 12 regions (all except the Bering <span class="hlt">Sea</span>), <span class="hlt">sea</span> <span class="hlt">ice</span> is retreating earlier in spring and advancing later in fall. Rates of spring retreat range from -5 to -8 days/decade, and rates of fall advance range from +5 to +9</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.A51G0147C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.A51G0147C"><span>In situ observations of Arctic cloud properties across the Beaufort <span class="hlt">Sea</span> marginal <span class="hlt">ice</span> zone</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Corr, C.; Moore, R.; Winstead, E.; Thornhill, K. L., II; Crosbie, E.; Ziemba, L. D.; Beyersdorf, A. J.; Chen, G.; Martin, R.; Shook, M.; Corbett, J.; Smith, W. L., Jr.; Anderson, B. E.</p> <p>2016-12-01</p> <p>Clouds play an important role in Arctic climate. This is particularly true over the Arctic Ocean where feedbacks between clouds and <span class="hlt">sea-ice</span> impact the surface radiation budget through modifications of <span class="hlt">sea-ice</span> <span class="hlt">extent</span>, <span class="hlt">ice</span> thickness, cloud base height, and cloud cover. This work summarizes measurements of Arctic cloud properties made aboard the NASA C-130 aircraft over the Beaufort <span class="hlt">Sea</span> during ARISE (Arctic Radiation - <span class="hlt">Ice</span>Bridge <span class="hlt">Sea&Ice</span> Experiment) in September 2014. The influence of surface-type on cloud properties is also investigated. Specifically, liquid water content (LWC), droplet concentrations, and droplet size distributions are compared for clouds sampled over three distinct regimes in the Beaufort <span class="hlt">Sea</span>: 1) open water, 2) the marginal <span class="hlt">ice</span> zone, and 3) <span class="hlt">sea-ice</span>. Regardless of surface type, nearly all clouds intercepted during ARISE were liquid-phase clouds. However, differences in droplet size distributions and concentrations were evident for the surface types; clouds over the MIZ and <span class="hlt">sea-ice</span> generally had fewer and larger droplets compared to those over open water. The potential implication these results have for understanding cloud-surface albedo climate feedbacks in Arctic are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19890018778','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19890018778"><span>Analysis of <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Zwally, J.</p> <p>1988-01-01</p> <p>The ongoing work has established the basis for using multiyear <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations from SMMR passive microwave for studies of largescale advection and convergence/divergence of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> 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 <span class="hlt">ice</span>. The scientific objective is to investigate the dynamics, mass balance, and interannual variability of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> pack. The research emphasizes the direct application of <span class="hlt">sea</span> <span class="hlt">ice</span> parameters derived from passive microwave data (SMMR and SSMI) and collaborative studies using a <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics model. The possible causes of observed interannual variations in the multiyear <span class="hlt">ice</span> area are being examined. The relative effects of variations in the large scale advection and convergence/divergence within the <span class="hlt">ice</span> 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 <span class="hlt">ice</span> production within the <span class="hlt">ice</span> pack during winter and the amount of <span class="hlt">ice</span> exported from the pack.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C33C1202F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C33C1202F"><span>Determination of a Critical <span class="hlt">Sea</span> <span class="hlt">Ice</span> Thickness Threshold for the Central Arctic Ocean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ford, V.; Frauenfeld, O. W.; Nowotarski, C. J.</p> <p>2017-12-01</p> <p>While <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> is readily measurable from satellite observations and can be used to assess the overall survivability of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> pack, determining the spatial variability of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness remains a challenge. Turbulent and conductive heat fluxes are extremely sensitive to <span class="hlt">ice</span> thickness but are dominated by the sensible heat flux, with energy exchange expected to increase with thinner <span class="hlt">ice</span> cover. Fluxes over open water are strongest and have the greatest influence on the atmosphere, while fluxes over thick <span class="hlt">sea</span> <span class="hlt">ice</span> are minimal as heat conduction from the ocean through thick <span class="hlt">ice</span> cannot reach the atmosphere. We know that turbulent energy fluxes are strongest over open ocean, but is there a "critical thickness of <span class="hlt">ice</span>" where fluxes are considered non-negligible? Through polar-optimized Weather Research and Forecasting model simulations, this study assesses how the wintertime Arctic surface boundary layer, via sensible heat flux exchange and surface air temperature, responds to <span class="hlt">sea</span> <span class="hlt">ice</span> thinning. The region immediately north of Franz Josef Land is characterized by a thickness gradient where <span class="hlt">sea</span> <span class="hlt">ice</span> transitions from the thickest multi-year <span class="hlt">ice</span> to the very thin marginal <span class="hlt">ice</span> <span class="hlt">seas</span>. This provides an ideal location to simulate how the diminishing Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> interacts with a warming atmosphere. Scenarios include both fixed <span class="hlt">sea</span> surface temperature domains for idealized thickness variability, and fixed <span class="hlt">ice</span> fields to detect changes in the ocean-<span class="hlt">ice</span>-atmosphere energy exchange. Results indicate that a critical thickness threshold exists below 1 meter. The threshold is between 0.4-1 meters thinner than the critical thickness for melt season survival - the difference between first year and multi-year <span class="hlt">ice</span>. Turbulent heat fluxes and surface air temperature increase as <span class="hlt">sea</span> <span class="hlt">ice</span> thickness transitions from perennial <span class="hlt">ice</span> to seasonal <span class="hlt">ice</span>. While models predict a <span class="hlt">sea</span> <span class="hlt">ice</span> free Arctic at the end of the warm season in future decades, <span class="hlt">sea</span> <span class="hlt">ice</span> will continue to transform</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC53E0936K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC53E0936K"><span>Toward Sub-seasonal to Seasonal Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Forecasting Using 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>Kamal, S.; Maslowski, W.; Roberts, A.; Osinski, R.; Cassano, J. J.; Seefeldt, M. W.</p> <p>2017-12-01</p> <p>The Regional Arctic system model has been developed and used to advance the current state of Arctic modeling and increase the skill of <span class="hlt">sea</span> <span class="hlt">ice</span> forecast. RASM is a fully coupled, limited-area model that includes the atmosphere, ocean, <span class="hlt">sea</span> <span class="hlt">ice</span>, land hydrology and runoff routing components and the flux coupler to exchange information among them. Boundary conditions are derived from NCEP Climate Forecasting System Reanalyses (CFSR) or Era Iterim (ERA-I) for hindcast simulations or from NCEP Coupled Forecast System Model version 2 (CFSv2) for seasonal forecasts. We have used RASM to produce <span class="hlt">sea</span> <span class="hlt">ice</span> forecasts for September 2016 and 2017, in contribution to the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Outlook (SIO) of the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Prediction Network (SIPN). Each year, we produced three SIOs for the September minimum, initialized on June 1, July 1 and August 1. In 2016, predictions used a simple linear regression model to correct for systematic biases and included the mean September <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, the daily minimum and the week of the minimum. In 2017, we produced a 12-member ensemble on June 1 and July 1, and 28-member ensemble August 1. The predictions of September 2017 included the pan-Arctic and regional Alaskan <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, daily and monthly mean pan-Arctic maps of <span class="hlt">sea</span> <span class="hlt">ice</span> probability, concentration and thickness. No bias correction was applied to the 2017 forecasts. Finally, we will also discuss future plans for RASM forecasts, which include increased resolution for model components, ecosystem predictions with marine biogeochemistry extensions (mBGC) to the ocean and <span class="hlt">sea</span> <span class="hlt">ice</span> components, and feasibility of optional boundary conditions using the Navy Global Environmental Model (NAVGEM).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007PhDT........29K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007PhDT........29K"><span>Arctic landfast <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>Konig, Christof S.</p> <p></p> <p>Landfast <span class="hlt">ice</span> is <span class="hlt">sea</span> <span class="hlt">ice</span> which forms and remains fixed along a coast, where it is attached either to the shore, or held between shoals or grounded icebergs. Landfast <span class="hlt">ice</span> fundamentally modifies the momentum exchange between atmosphere and ocean, as compared to pack <span class="hlt">ice</span>. 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 <span class="hlt">ice</span> edge is essential for numerous Arctic mammals and Inupiat who depend on them for their subsistence. The current generation of <span class="hlt">sea</span> <span class="hlt">ice</span> models is not capable of reproducing certain aspects of landfast <span class="hlt">ice</span> formation, maintenance, and disintegration even when the spatial resolution would be sufficient to resolve such features. In my work I develop a new <span class="hlt">ice</span> model that permits the existence of landfast <span class="hlt">sea</span> <span class="hlt">ice</span> even in the presence of offshore winds, as is observed in mature. Based on viscous-plastic as well as elastic-viscous-plastic <span class="hlt">ice</span> dynamics I add tensile strength to the <span class="hlt">ice</span> 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 <span class="hlt">ice</span> modeling, as desired. The elastic-viscous-plastic rheology leads to initial velocity fluctuations within the landfast <span class="hlt">ice</span> that weaken the <span class="hlt">ice</span> 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 <span class="hlt">ice</span> modeling can only verified in comparison to observed data. I have extracted landfast <span class="hlt">sea</span> <span class="hlt">ice</span> data of several decades from several sources to create a landfast <span class="hlt">sea</span> <span class="hlt">ice</span> climatology that can be used for that purpose. Statistical analysis of the data shows several factors that significantly influence landfast <span class="hlt">ice</span> distribution: distance from the coastline, ocean depth, as</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('https://ntrs.nasa.gov/search.jsp?R=20000038180&hterms=dependency&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Ddependency','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000038180&hterms=dependency&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Ddependency"><span>The Role of <span class="hlt">Sea</span> <span class="hlt">Ice</span> in 2 x CO2 Climate Model Sensitivity. Part 2; Hemispheric Dependencies</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>1997-01-01</p> <p>How sensitive are doubled CO2 simulations to GCM control-run <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and <span class="hlt">extent</span>? This issue is examined in a series of 10 control-run simulations with different <span class="hlt">sea</span> <span class="hlt">ice</span> and corresponding doubled CO2 simulations. Results show that with increased control-run <span class="hlt">sea</span> <span class="hlt">ice</span> coverage in the Southern Hemisphere, temperature sensitivity with climate change is enhanced, while there is little effect on temperature sensitivity of (reasonable) variations in control-run <span class="hlt">sea</span> <span class="hlt">ice</span> thickness. In the Northern Hemisphere the situation is reversed: <span class="hlt">sea</span> <span class="hlt">ice</span> thickness is the key parameter, while (reasonable) variations in control-run <span class="hlt">sea</span> <span class="hlt">ice</span> coverage are of less importance. In both cases, the quantity of <span class="hlt">sea</span> <span class="hlt">ice</span> that can be removed in the warmer climate is the determining factor. Overall, the Southern Hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> coverage change had a larger impact on global temperature, because Northern Hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> was sufficiently thick to limit its response to doubled CO2, and <span class="hlt">sea</span> <span class="hlt">ice</span> changes generally occurred at higher latitudes, reducing the <span class="hlt">sea</span> <span class="hlt">ice</span>-albedo feedback. In both these experiments and earlier ones in which <span class="hlt">sea</span> <span class="hlt">ice</span> was not allowed to change, the model displayed a sensitivity of -0.02 C global warming per percent change in Southern Hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> coverage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-GSFC_20171208_Archive_e000769.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-GSFC_20171208_Archive_e000769.html"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> off western Alaska</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-02-20</p> <p>On February 4, 2014 the Moderate Resolution Imaging Spectroradiometer (MODIS) flying aboard NASA’s Aqua satellite captured a true-color image of <span class="hlt">sea</span> <span class="hlt">ice</span> off of western Alaska. In this true-color image, the snow and <span class="hlt">ice</span> covered land appears bright white while the floating <span class="hlt">sea</span> <span class="hlt">ice</span> appears a duller grayish-white. Snow over the land is drier, and reflects more light back to the instrument, accounting for the very bright color. <span class="hlt">Ice</span> overlying oceans contains more water, and increasing water decreases reflectivity of <span class="hlt">ice</span>, resulting in duller colors. Thinner <span class="hlt">ice</span> is also duller. The ocean waters are tinted with green, likely due to a combination of sediment and phytoplankton. Alaska lies to the east in this image, and Russia to the west. The Bering Strait, covered with <span class="hlt">ice</span>, lies between to two. South of the Bering Strait, the waters are known as the Bering <span class="hlt">Sea</span>. To the north lies the Chukchi <span class="hlt">Sea</span>. The bright white island south of the Bering Strait is St. Lawrence Island. Home to just over 1200 people, the windswept island belongs to the United States, but sits closer to Russia than to Alaska. To the southeast of the island a dark area, loosely covered with floating <span class="hlt">sea</span> <span class="hlt">ice</span>, marks a persistent polynya – an area of open water surrounded by more frozen <span class="hlt">sea</span> <span class="hlt">ice</span>. Due to the prevailing winds, which blow the <span class="hlt">sea</span> <span class="hlt">ice</span> away from the coast in this location, the area rarely completely freezes. The <span class="hlt">ice</span>-covered areas in this image, as well as the Beaufort <span class="hlt">Sea</span>, to the north, are critical areas for the survival of the ringed seal, a threatened species. The seals use the <span class="hlt">sea</span> <span class="hlt">ice</span>, including <span class="hlt">ice</span> caves, to rear their young, and use the free-floating <span class="hlt">sea</span> <span class="hlt">ice</span> for molting, raising the young and breeding. In December 2014, the National Oceanic and Atmospheric Administration (NOAA) proposed that much of this region be set aside as critical, protected habitat for the ringed seal. Credit: NASA/GSFC/Jeff Schmaltz/MODIS Land Rapid Response Team NASA image use policy. NASA Goddard Space Flight Center</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ClDy...50...51R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ClDy...50...51R"><span>Automated parameter tuning applied to <span class="hlt">sea</span> <span class="hlt">ice</span> in a global climate model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Roach, Lettie A.; Tett, Simon F. B.; Mineter, Michael J.; Yamazaki, Kuniko; Rae, Cameron D.</p> <p>2018-01-01</p> <p>This study investigates the hypothesis that a significant portion of spread in climate model projections of <span class="hlt">sea</span> <span class="hlt">ice</span> is due to poorly-constrained model parameters. New automated methods for optimization are applied to historical <span class="hlt">sea</span> <span class="hlt">ice</span> in a global coupled climate model (HadCM3) in order to calculate the combination of parameters required to reduce the difference between simulation and observations to within the range of model noise. The optimized parameters result in a simulated <span class="hlt">sea-ice</span> time series which is more consistent with Arctic observations throughout the satellite record (1980-present), particularly in the September minimum, than the standard configuration of HadCM3. Divergence from observed Antarctic trends and mean regional <span class="hlt">sea</span> <span class="hlt">ice</span> distribution reflects broader structural uncertainty in the climate model. We also find that the optimized parameters do not cause adverse effects on the model climatology. This simple approach provides evidence for the contribution of parameter uncertainty to spread in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> trends and could be customized to investigate uncertainties in other climate variables.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.C54A..08M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.C54A..08M"><span>Object-based Image Classification of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> and Melt Ponds through Aerial Photos</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Miao, X.; Xie, H.; Li, Z.; Lei, R.</p> <p>2013-12-01</p> <p>The last six years have marked the lowest Arctic summer <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> in the modern era, with a new record summer minimum (3.4 million km2) set on 13 September 2012. It has been predicted that the Arctic could be free of summer <span class="hlt">ice</span> within the next 25-30. The loss of Arctic summer <span class="hlt">ice</span> could have serious consequences, such as higher water temperature due to the positive feedback of albedo, more powerful and frequent storms, rising <span class="hlt">sea</span> levels, diminished habitats for polar animals, and more pollution due to fossil fuel exploitation and/ or increased traffic through the Northwest/ Northeast Passage. In these processes, melt ponds play an important role in Earth's radiation balance since they strongly absorb solar radiation rather than reflecting it as snow and <span class="hlt">ice</span> do. Therefore, it is necessary to develop the ability of predicting the <span class="hlt">sea</span> <span class="hlt">ice</span>/ melt pond <span class="hlt">extents</span> and space-time evolution, which is pivotal to prepare for the variation and uncertainty of the future environment, political, economic, and military needs. A lot of efforts have been put into Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> modeling to simulate <span class="hlt">sea</span> <span class="hlt">ice</span> processes. However, these <span class="hlt">sea</span> <span class="hlt">ice</span> models were initiated and developed based on limited field surveys, aircraft or satellite image data. Therefore, it is necessary to collect high resolution <span class="hlt">sea</span> <span class="hlt">ice</span> aerial photo in a systematic way to tune up, validate, and improve models. Currently there are many <span class="hlt">sea</span> <span class="hlt">ice</span> aerial photos available, such as Chinese Arctic Exploration (CHINARE 2008, 2010, 2012), SHEBA 1998 and HOTRAX 2005. However, manually delineating of <span class="hlt">sea</span> <span class="hlt">ice</span> and melt pond from these images is time-consuming and labor-intensive. In this study, we use the object-based remote sensing classification scheme to extract <span class="hlt">sea</span> <span class="hlt">ice</span> and melt ponds efficiently from 1,727 aerial photos taken during the CHINARE 2010. The algorithm includes three major steps as follows. (1) Image segmentation groups the neighboring pixels into objects according to the similarity of spectral and texture</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017TCry...11.1553S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017TCry...11.1553S"><span><span class="hlt">Sea-ice</span> deformation in a coupled ocean-<span class="hlt">sea-ice</span> model and in satellite remote sensing data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Spreen, Gunnar; Kwok, Ron; Menemenlis, Dimitris; Nguyen, An T.</p> <p>2017-07-01</p> <p>A realistic representation of <span class="hlt">sea-ice</span> deformation in models is important for accurate simulation of the <span class="hlt">sea-ice</span> mass balance. Simulated <span class="hlt">sea-ice</span> deformation from numerical simulations with 4.5, 9, and 18 km horizontal grid spacing and a viscous-plastic (VP) <span class="hlt">sea-ice</span> rheology are compared with synthetic aperture radar (SAR) satellite observations (RGPS, RADARSAT Geophysical Processor System) for the time period 1996-2008. All three simulations can reproduce the large-scale <span class="hlt">ice</span> deformation patterns, but small-scale <span class="hlt">sea-ice</span> deformations and linear kinematic features (LKFs) are not adequately reproduced. The mean <span class="hlt">sea-ice</span> total deformation rate is about 40 % lower in all model solutions than in the satellite observations, especially in the seasonal <span class="hlt">sea-ice</span> zone. A decrease in model grid spacing, however, produces a higher density and more localized <span class="hlt">ice</span> deformation features. The 4.5 km simulation produces some linear kinematic features, but not with the right frequency. The dependence on length scale and probability density functions (PDFs) of absolute divergence and shear for all three model solutions show a power-law scaling behavior similar to RGPS observations, contrary to what was found in some previous studies. Overall, the 4.5 km simulation produces the most realistic divergence, vorticity, and shear when compared with RGPS data. This study provides an evaluation of high and coarse-resolution viscous-plastic <span class="hlt">sea-ice</span> simulations based on spatial distribution, time series, and power-law scaling metrics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.P31A2081H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.P31A2081H"><span>Monitoring Subsurface <span class="hlt">Ice</span>-Ocean Processes Using Underwater Acoustics 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>Haxel, J. H.; Dziak, R. P.; Matsumoto, H.; Lee, W. S.; Yun, S.</p> <p>2016-12-01</p> <p>The Ross <span class="hlt">Sea</span> is a dynamic area of <span class="hlt">ice</span>-ocean interaction, where a large component of the Southern Ocean's <span class="hlt">sea</span> <span class="hlt">ice</span> formation occurs within regional polynyas in addition to the destructive processes happening at the seaward boundary of the Ross <span class="hlt">Ice</span> Shelf. Recent studies show the <span class="hlt">sea-ice</span> season has been lengthening and the <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> has been growing with more persistent and larger regional polynyas. These trends have important implications for the Ross <span class="hlt">Sea</span> ecosystem with polynyas supporting high rates of primary productivity in the area. Monitoring trends in <span class="hlt">sea</span> <span class="hlt">ice</span> and <span class="hlt">ice</span> shelf dynamics in the Southern Ocean has relied heavily on satellite imagery and remote sensing methods despite a significant portion of these physical processes occurring beneath the ocean surface. In January 2014, an ocean bottom hydrophone (OBH) was moored on the seafloor in the polynya area of Terra Nova Bay in the northwest region of the Ross <span class="hlt">Sea</span>, north of the Drygalski <span class="hlt">Ice</span> Tongue. The OBH recorded a year long record of the underwater low frequency acoustic spectrum up to 500 Hz from January 29 until it was recovered the following December 17, 2014. The acoustic records reveal a complex annual history of <span class="hlt">ice</span> generated signals with over 50,000 detected events. These <span class="hlt">ice</span> generated events related to collisions and cracking provide important insight for the timing and intensity of the <span class="hlt">ice</span>-ocean dynamics happening below the <span class="hlt">sea</span> surface as the polynya grows and expands and the nearby Drygalski <span class="hlt">ice</span> tongue flows into Terra Nova Bay. Additionally, high concentrations of baleen whale vocalizations in frequencies ranging from 200-400 Hz from September - December suggest a strong seasonal presence of whales in this ecologically important polynya region.</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('https://www.ncbi.nlm.nih.gov/pubmed/29784779','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29784779"><span>Strong and highly variable push of ocean waves on Southern Ocean <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>Stopa, Justin E; Sutherland, Peter; Ardhuin, Fabrice</p> <p>2018-06-05</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> in the Southern Ocean has expanded over most of the past 20 y, but the decline in <span class="hlt">sea</span> <span class="hlt">ice</span> since 2016 has taken experts by surprise. This recent evolution highlights the poor performance of numerical models for predicting <span class="hlt">extent</span> and thickness, which is due to our poor understanding of <span class="hlt">ice</span> dynamics. Ocean waves are known to play an important role in <span class="hlt">ice</span> break-up and formation. In addition, as ocean waves decay, they cause a stress that pushes the <span class="hlt">ice</span> in the direction of wave propagation. This wave stress could not previously be quantified due to insufficient observations at large scales. Sentinel-1 synthetic aperture radars (SARs) provide high-resolution imagery from which wave height is measured year round encompassing Antarctica since 2014. Our estimates give an average wave stress that is comparable to the average wind stress acting over 50 km of <span class="hlt">sea</span> <span class="hlt">ice</span>. We further reveal highly variable half-decay distances ranging from 400 m to 700 km, and wave stresses from 0.01 to 1 Pa. We expect that this variability is related to <span class="hlt">ice</span> properties and possibly different floe sizes and <span class="hlt">ice</span> thicknesses. A strong feedback of waves on <span class="hlt">sea</span> <span class="hlt">ice</span>, via break-up and rafting, may be the cause of highly variable <span class="hlt">sea-ice</span> properties.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100032968','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100032968"><span>CBSIT 2009: Airborne Validation of Envisat Radar Altimetry and In Situ <span class="hlt">Ice</span> Camp Measurements Over 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>Connor, Laurence; Farrell, Sinead; McAdoo, David; Krabill, William; Laxon, Seymour; Richter-Menge, Jacqueline; Markus, Thorsten</p> <p>2010-01-01</p> <p>The past few years have seen the emergence of satellite altimetry as valuable tool for taking quantitative <span class="hlt">sea</span> <span class="hlt">ice</span> monitoring beyond the traditional surface <span class="hlt">extent</span> measurements and into estimates of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness and volume, parameters that arc fundamental to improved understanding of polar dynamics and climate modeling. Several studies have now demonstrated the use of both microwave (ERS, Envisat/RA-2) and laser (ICESat/GLAS) satellite altimeters for determining <span class="hlt">sea</span> <span class="hlt">ice</span> thickness. The complexity of polar environments, however, continues to make <span class="hlt">sea</span> <span class="hlt">ice</span> thickness determination a complicated remote sensing task and validation studies remain essential for successful monitoring of <span class="hlt">sea</span> <span class="hlt">ice</span> hy satellites. One such validation effort, the Arctic Aircraft Altimeter (AAA) campaign of2006. included underflights of Envisat and ICESat north of the Canadian Archipelago using NASA's P-3 aircraft. This campaign compared Envisat and ICESat <span class="hlt">sea</span> <span class="hlt">ice</span> elevation measurements with high-resolution airborne elevation measurements, revealing the impact of refrozen leads on radar altimetry and <span class="hlt">ice</span> drift on laser altimetry. Continuing this research and validation effort, the Canada Basin <span class="hlt">Sea</span> <span class="hlt">Ice</span> Thickness (CBSIT) experiment was completed in April 2009. CBSIT was conducted by NOAA. and NASA as part of NASA's Operation <span class="hlt">Ice</span> Bridge, a gap-filling mission intended to supplement <span class="hlt">sea</span> and land <span class="hlt">ice</span> monitoring until the launch of NASA's ICESat-2 mission. CBIST was flown on the NASA P-3, which was equipped with a scanning laser altimeter, a Ku-band snow radar, and un updated nadir looking photo-imaging system. The CB5IT campaign consisted of two flights: an under flight of Envisat along a 1000 km track similar to that flown in 2006, and a flight through the Nares Strait up to the Lincoln <span class="hlt">Sea</span> that included an overflight of the Danish GreenArc <span class="hlt">Ice</span> Camp off the coast of northern Greenland. We present an examination of data collected during this campaign, comparing airborne laser altimeter measurements</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> </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('http://adsabs.harvard.edu/abs/2014TCry....8.2409L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014TCry....8.2409L"><span><span class="hlt">Ice</span> and AIS: ship speed data and <span class="hlt">sea</span> <span class="hlt">ice</span> forecasts in the Baltic <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>Löptien, U.; Axell, L.</p> <p>2014-12-01</p> <p>The Baltic <span class="hlt">Sea</span> is a seasonally <span class="hlt">ice</span>-covered marginal <span class="hlt">sea</span> located in a densely populated area in northern Europe. Severe <span class="hlt">sea</span> <span class="hlt">ice</span> conditions have the potential to hinder the intense ship traffic considerably. Thus, <span class="hlt">sea</span> <span class="hlt">ice</span> fore- and nowcasts are regularly provided by the national weather services. Typically, the forecast comprises several <span class="hlt">ice</span> properties that are distributed as prognostic variables, but their actual usefulness is difficult to measure, and the ship captains must determine their relative importance and relevance for optimal ship speed and safety ad hoc. The present study provides a more objective approach by comparing the ship speeds, obtained by the automatic identification system (AIS), with the respective forecasted <span class="hlt">ice</span> conditions. We find that, despite an unavoidable random component, this information is useful to constrain and rate fore- and nowcasts. More precisely, 62-67% of ship speed variations can be explained by the forecasted <span class="hlt">ice</span> properties when fitting a mixed-effect model. This statistical fit is based on a test region in the Bothnian <span class="hlt">Sea</span> during the severe winter 2011 and employs 15 to 25 min averages of ship speed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.7407K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.7407K"><span><span class="hlt">Sea</span>-level and solid-Earth deformation feedbacks in <span class="hlt">ice</span> sheet modelling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Konrad, Hannes; Sasgen, Ingo; Klemann, Volker; Thoma, Malte; Grosfeld, Klaus; Martinec, Zdeněk</p> <p>2014-05-01</p> <p>The interactions of <span class="hlt">ice</span> sheets with the <span class="hlt">sea</span> level and the solid Earth are important factors for the stability of the <span class="hlt">ice</span> shelves and the tributary inland <span class="hlt">ice</span> (e.g. Thomas and Bentley, 1978; Gomez et al, 2012). First, changes in <span class="hlt">ice</span> <span class="hlt">extent</span> and <span class="hlt">ice</span> thickness induce viscoelastic deformation of the Earth surface and Earth's gravity field. In turn, global and local changes in <span class="hlt">sea</span> level and bathymetry affect the grounding line and, subsequently, alter the <span class="hlt">ice</span> dynamic behaviour. Here, we investigate these feedbacks for a synthetic <span class="hlt">ice</span> sheet configuration as well as for the Antarctic <span class="hlt">ice</span> sheet using a three-dimensional thermomechanical <span class="hlt">ice</span> sheet and shelf model, coupled to a viscoelastic solid-Earth and gravitationally self-consistent <span class="hlt">sea</span>-level model. The respective <span class="hlt">ice</span> sheet undergoes a forcing from rising <span class="hlt">sea</span> level, warming ocean, and/or changing surface mass balance. The coupling is realized by exchanging <span class="hlt">ice</span> thickness, Earth surface deformation and <span class="hlt">sea</span> level periodically. We apply several sets of viscoelastic Earth parameters to our coupled model, e.g. simulating a low-viscous upper mantle present at the Antarctic Peninsula (Ivins et al., 2011). Special focus of our study lies on the evolution of Earth surface deformation and local <span class="hlt">sea</span> level changes, as well as on the accompanying grounding line evolution. N. Gomez, D. Pollard, J. X. Mitrovica, P. Huybers, and P. U. Clark 2012. Evolution of a coupled marine <span class="hlt">ice</span> sheet-<span class="hlt">sea</span> level model, J. Geophys. Res., 117, F01013, doi:10.1029/2011JF002128. E. R. Ivins, M. M. Watkins, D.-N. Yuan, R. Dietrich, G. Casassa, and A. Rülke 2011. On-land <span class="hlt">ice</span> loss and glacial isostatic adjustment at the Drake Passage: 2003-2009, J. Geophys. Res. 116, B02403, doi: 10.1029/2010JB007607 R. H. Thomas and C. R. Bentley 1978. A model for Holocene retreat of the West Antarctic <span class="hlt">Ice</span> Sheet, Quaternary Research, 10 (2), pages 150-170, doi: 10.1016/0033-5894(78)90098-4.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-PIA01786.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-PIA01786.html"><span>Space Radar Image of Weddell <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>1999-04-15</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 <span class="hlt">extent</span>, 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('https://www.ncbi.nlm.nih.gov/pubmed/26214910','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26214910"><span>Polar bear population dynamics in the southern Beaufort <span class="hlt">Sea</span> during a period of <span class="hlt">sea</span> <span class="hlt">ice</span> decline.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bromaghin, Jeffrey F; Mcdonald, Trent L; Stirling, Ian; Derocher, Andrew E; Richardson, Evan S; Regehr, Eric V; Douglas, David C; Durner, George M; Atwood, Todd; Amstrup, Steven C</p> <p>2015-04-01</p> <p>In the southern Beaufort <span class="hlt">Sea</span> of the United States and Canada, prior investigations have linked declines in summer <span class="hlt">sea</span> <span class="hlt">ice</span> to reduced physical condition, growth, and survival of polar bears (Ursus maritimus). Combined with projections of population decline due to continued climate warming and the ensuing loss of <span class="hlt">sea</span> <span class="hlt">ice</span> habitat, those findings contributed to the 2008 decision to list the species as threatened under the U.S. Endangered Species Act. Here, we used mark-recapture models to investigate the population dynamics of polar bears in the southern Beaufort <span class="hlt">Sea</span> from 2001 to 2010, years during which the spatial and temporal <span class="hlt">extent</span> of summer <span class="hlt">sea</span> <span class="hlt">ice</span> generally declined. Low survival from 2004 through 2006 led to a 25-50% decline in abundance. We hypothesize that low survival during this period resulted from (1) unfavorable <span class="hlt">ice</span> conditions that limited access to prey during multiple seasons; and possibly, (2) low prey abundance. For reasons that are not clear, survival of adults and cubs began to improve in 2007 and abundance was comparatively stable from 2008 to 2010, with ~900 bears in 2010 (90% CI 606-1212). However, survival of subadult bears declined throughout the entire period. Reduced spatial and temporal availability of <span class="hlt">sea</span> <span class="hlt">ice</span> is expected to increasingly force population dynamics of polar bears as the climate continues to warm. However, in the short term, our findings suggest that factors other than <span class="hlt">sea</span> <span class="hlt">ice</span> can influence survival. A refined understanding of the ecological mechanisms underlying polar bear population dynamics is necessary to improve projections of their future status and facilitate development of management strategies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C21G1187P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C21G1187P"><span>Spatial and Temporal Means and Variability of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Climate Indicators from 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>Peng, G.; Meier, W.; Bliss, A. C.; Steele, M.; Dickinson, S.</p> <p>2017-12-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> has been undergoing rapid and accelerated loss since satellite-based measurements became available in late 1970s, especially the summer <span class="hlt">ice</span> coverage. For the Arctic as a whole, the long-term trend for the annual <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> (SIE) minimum is about -13.5±2.93 % per decade change relative to the 1979-2015 climate average, while the trends of the annual SIE minimum for the local regions can range from 0 to up to -42 % per decade. This presentation aims to examine and baseline spatial and temporal means and variability of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> climate indicators, such as the annual SIE minimum and maximum, snow/<span class="hlt">ice</span> melt onset, etc., from a consistent, inter-calibrated, long-term time series of remote sensing <span class="hlt">sea</span> <span class="hlt">ice</span> data for understanding regional vulnerability and monitoring <span class="hlt">ice</span> state for climate adaptation and risk mitigation.</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://hdl.handle.net/2060/20070034151','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20070034151"><span>Thin <span class="hlt">Sea-Ice</span> Thickness as Inferred from Passive Microwave and In Situ Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Naoki, Kazuhiro; Ukita, Jinro; Nishio, Fumihiko; Nakayama, Masashige; Comiso, Josefino C.; Gasiewski, Al</p> <p>2007-01-01</p> <p>Since microwave radiometric signals from <span class="hlt">sea-ice</span> strongly reflect physical conditions of a layer near the <span class="hlt">ice</span> surface, a relationship of brightness temperature with thickness is possible especially during the early stages of <span class="hlt">ice</span> growth. <span class="hlt">Sea</span> <span class="hlt">ice</span> is most saline during formation stage and as the salinity decreases with time while at the same time the thickness of the <span class="hlt">sea</span> <span class="hlt">ice</span> increases, a corresponding change in the dielectric properties and hence the brightness temperature may occur. This study examines the <span class="hlt">extent</span> to which the relationships of thickness with brightness temperature (and with emissivity) hold for thin <span class="hlt">sea-ice</span>, approximately less than 0.2 -0.3 m, using near concurrent measurements of <span class="hlt">sea-ice</span> thickness in the <span class="hlt">Sea</span> of Okhotsk from a ship and passive microwave brightness temperature data from an over-flying aircraft. The results show that the brightness temperature and emissivity increase with <span class="hlt">ice</span> thickness for the frequency range of 10-37 GHz. The relationship is more pronounced at lower frequencies and at the horizontal polarization. We also established an empirical relationship between <span class="hlt">ice</span> thickness and salinity in the layer near the <span class="hlt">ice</span> surface from a field experiment, which qualitatively support the idea that changes in the near-surface brine characteristics contribute to the observed thickness-brightness temperature/emissivity relationship. Our results suggest that for thin <span class="hlt">ice</span>, passive microwave radiometric signals contain, <span class="hlt">ice</span> thickness information which can be utilized in polar process studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1235897-development-global-sea-ice-cice-configuration-met-office-global-coupled-model','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1235897-development-global-sea-ice-cice-configuration-met-office-global-coupled-model"><span>Development of the global <span class="hlt">sea</span> <span class="hlt">ice</span> 6.0 CICE configuration for the Met Office global coupled model</span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Rae, J. G. L.; Hewitt, H. T.; Keen, A. B.; ...</p> <p>2015-07-24</p> <p>The new <span class="hlt">sea</span> <span class="hlt">ice</span> configuration GSI6.0, used in the Met Office global coupled configuration GC2.0, is described and the <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, thickness and volume are compared with the previous configuration and with observationally based data sets. In the Arctic, the <span class="hlt">sea</span> <span class="hlt">ice</span> is thicker in all seasons than in the previous configuration, and there is now better agreement of the modelled concentration and <span class="hlt">extent</span> with the HadISST data set. As a result, in the Antarctic, a warm bias in the ocean model has been exacerbated at the higher resolution of GC2.0, leading to a large reduction in <span class="hlt">ice</span> extentmore » and volume; further work is required to rectify this in future configurations.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1235897','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1235897"><span>Development of the global <span class="hlt">sea</span> <span class="hlt">ice</span> 6.0 CICE configuration for the Met Office global coupled model</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Rae, J. G. L.; Hewitt, H. T.; Keen, A. B.</p> <p></p> <p>The new <span class="hlt">sea</span> <span class="hlt">ice</span> configuration GSI6.0, used in the Met Office global coupled configuration GC2.0, is described and the <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, thickness and volume are compared with the previous configuration and with observationally based data sets. In the Arctic, the <span class="hlt">sea</span> <span class="hlt">ice</span> is thicker in all seasons than in the previous configuration, and there is now better agreement of the modelled concentration and <span class="hlt">extent</span> with the HadISST data set. As a result, in the Antarctic, a warm bias in the ocean model has been exacerbated at the higher resolution of GC2.0, leading to a large reduction in <span class="hlt">ice</span> extentmore » and volume; further work is required to rectify this in future configurations.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ClDy...49.1399B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ClDy...49.1399B"><span>Multi-model seasonal forecast of Arctic <span class="hlt">sea-ice</span>: forecast uncertainty at pan-Arctic and regional scales</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, E.; Barthélemy, A.; Chevallier, M.; Cullather, R.; Fučkar, N.; Massonnet, F.; Posey, P.; Wang, W.; Zhang, J.; Ardilouze, C.; Bitz, C. M.; Vernieres, G.; Wallcraft, A.; Wang, M.</p> <p>2017-08-01</p> <p>Dynamical model forecasts in the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Outlook (SIO) of September Arctic <span class="hlt">sea-ice</span> <span class="hlt">extent</span> over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer <span class="hlt">sea</span> <span class="hlt">ice</span> using SIO dynamical models initialized with identical <span class="hlt">sea-ice</span> thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September <span class="hlt">sea-ice</span> volume and <span class="hlt">extent</span>, this is not the case for <span class="hlt">sea-ice</span> concentration. Additionally, forecast uncertainty of <span class="hlt">sea-ice</span> thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-GSFC_20171208_Archive_e001527.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-GSFC_20171208_Archive_e001527.html"><span>Blue Beaufort <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="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2017-12-08</p> <p>Mosaic image of <span class="hlt">sea</span> <span class="hlt">ice</span> in the Beaufort <span class="hlt">Sea</span> created by the Digital Mapping System (DMS) instrument aboard the <span class="hlt">Ice</span>Bridge P-3B. The dark area in the middle of the image is open water seen through a lead, or opening, in the <span class="hlt">ice</span>. Light blue areas are thick <span class="hlt">sea</span> <span class="hlt">ice</span> and dark blue areas are thinner <span class="hlt">ice</span> formed as water in the lead refreezes. Leads are formed when cracks develop in <span class="hlt">sea</span> <span class="hlt">ice</span> as it moves in response to wind and ocean currents. DMS uses a modified digital SLR camera that points down through a window in the underside of the plane, capturing roughly one frame per second. These images are then combined into an image mosaic using specialized computer software. Credit: NASA/DMS NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFMED11A0111M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFMED11A0111M"><span>Whither Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span>? - An Earth Exploration Toolbook chapter on the climate's canary in a coal mine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Meier, W. N.; Youngman, E.; Dahlman, L.</p> <p>2007-12-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> is declining rapidly. Since 2002, summer Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> have been at record or near-record lows; winter <span class="hlt">extents</span> have also showed a marked decline. Even in comparison to the previous five extreme low years, the 2007 summer melt season has been stunning, with dramatically less <span class="hlt">ice</span> than the previous record in 2005. This is further evidence that the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> may have already passed a tipping point toward a state without <span class="hlt">ice</span> during the summer by 2050 or before. Such a change will have profound impacts on climate as well as human and wildlife activities in the region. The "Whither Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span>?" Earth Exploration Toolbook chapter (http://serc.carleton.edu/eet/seaice/index.html) exposes students to satellite-derived <span class="hlt">sea</span> <span class="hlt">ice</span> data and allows them to process and interpret the data to "discover" these <span class="hlt">sea</span> <span class="hlt">ice</span> changes for themselves. A sample case study in Hudson Bay has been developed that relates the physical changes occurring on the <span class="hlt">sea</span> <span class="hlt">ice</span> to peoples and wildlife that depend on the <span class="hlt">ice</span> for their livelihood. This approach provides a personal connection for students and allows them to relate to the impacts of the changes. Suggestions are made for further case studies that can be developed using the same data relating to topical events in the Arctic. The EET chapter exposes students to climate change, scientific data, statistical concepts, and image processing software providing an avenue for the communication of IPY data and science to teachers and students.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003AGUFM.C41C0990P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003AGUFM.C41C0990P"><span>Assessing, understanding, and conveying the state of the Arctic <span class="hlt">sea</span> <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>Perovich, D. K.; Richter-Menge, J. A.; Rigor, I.; Parkinson, C. L.; Weatherly, J. W.; Nghiem, S. V.; Proshutinsky, A.; Overland, J. E.</p> <p>2003-12-01</p> <p>Recent studies indicate that the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover is undergoing significant climate-induced changes, affecting both its <span class="hlt">extent</span> and thickness. Satellite-derived estimates of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> suggest a reduction of about 3% per decade since 1978. <span class="hlt">Ice</span> thickness data from submarines suggest a net thinning of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover since 1958. Changes (including oscillatory changes) in atmospheric circulation and the thermohaline properties of the upper ocean have also been observed. These changes impact not only the Arctic, but the global climate system and are likely accelerated by such processes as the <span class="hlt">ice</span>-albedo feedback. It is important to continue and expand long-term observations of these changes to (a) improve the fundamental understanding of the role of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover in the global climate system and (b) use the changes in the <span class="hlt">sea</span> <span class="hlt">ice</span> cover as an early indicator of climate change. This is a formidable task that spans a range of temporal and spatial scales. Fortunately, there are numerous tools that can be brought to bear on this task, including satellite remote sensing, autonomous buoys, ocean moorings, field campaigns and numerical models. We suggest the integrated and coordinated use of these tools during the International Polar Year to monitor the state of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover and investigate its governing processes. For example, satellite remote sensing provides the large-scale snapshots of such basic parameters as <span class="hlt">ice</span> distribution, melt zone, and cloud fraction at intervals of half a day to a week. Buoys and moorings can contribute high temporal resolution and can measure parameters currently unavailable from space including <span class="hlt">ice</span> thickness, internal <span class="hlt">ice</span> temperature, and ocean temperature and salinity. Field campaigns can be used to explore, in detail, the processes that govern the <span class="hlt">ice</span> cover. Numerical models can be used to assess the character of the changes in the <span class="hlt">ice</span> cover and predict their impacts on the rest of the climate system. This work</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018AdAtS..35..106Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018AdAtS..35..106Z"><span>Record low <span class="hlt">sea-ice</span> concentration in the central Arctic during summer 2010</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhao, Jinping; Barber, David; Zhang, Shugang; Yang, Qinghua; Wang, Xiaoyu; Xie, Hongjie</p> <p>2018-01-01</p> <p>The Arctic <span class="hlt">sea-ice</span> <span class="hlt">extent</span> has shown a declining trend over the past 30 years. <span class="hlt">Ice</span> coverage reached historic minima in 2007 and again in 2012. This trend has recently been assessed to be unique over at least the last 1450 years. In the summer of 2010, a very low <span class="hlt">sea-ice</span> concentration (SIC) appeared at high Arctic latitudes—even lower than that of surrounding pack <span class="hlt">ice</span> at lower latitudes. This striking low <span class="hlt">ice</span> concentration—referred to here as a record low <span class="hlt">ice</span> concentration in the central Arctic (CARLIC)—is unique in our analysis period of 2003-15, and has not been previously reported in the literature. The CARLIC was not the result of <span class="hlt">ice</span> melt, because <span class="hlt">sea</span> <span class="hlt">ice</span> was still quite thick based on in-situ <span class="hlt">ice</span> thickness measurements. Instead, divergent <span class="hlt">ice</span> drift appears to have been responsible for the CARLIC. A high correlation between SIC and wind stress curl suggests that the <span class="hlt">sea</span> <span class="hlt">ice</span> drift during the summer of 2010 responded strongly to the regional wind forcing. The drift trajectories of <span class="hlt">ice</span> buoys exhibited a transpolar drift in the Atlantic sector and an eastward drift in the Pacific sector, which appeared to benefit the CARLIC in 2010. Under these conditions, more solar energy can penetrate into the open water, increasing melt through increased heat flux to the ocean. We speculate that this divergence of <span class="hlt">sea</span> <span class="hlt">ice</span> could occur more often in the coming decades, and impact on hemispheric SIC and feed back to the climate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1351197','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1351197"><span>Validation of <span class="hlt">sea</span> <span class="hlt">ice</span> models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR <span class="hlt">SEA</span> <span class="hlt">ICE</span> MODEL VALIDATION</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Urrego-Blanco, Jorge R.; Hunke, Elizabeth C.; Urban, Nathan M.</p> <p></p> <p>Here, we implement a variance-based distance metric (D n) to objectively assess skill of <span class="hlt">sea</span> <span class="hlt">ice</span> models when multiple output variables or uncertainties in both model predictions and observations need to be considered. The metric compares observations and model data pairs on common spatial and temporal grids improving upon highly aggregated metrics (e.g., total <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> or volume) by capturing the spatial character of model skill. The D n metric is a gamma-distributed statistic that is more general than the χ 2 statistic commonly used to assess model fit, which requires the assumption that the model is unbiased andmore » can only incorporate observational error in the analysis. The D n statistic does not assume that the model is unbiased, and allows the incorporation of multiple observational data sets for the same variable and simultaneously for different variables, along with different types of variances that can characterize uncertainties in both observations and the model. This approach represents a step to establish a systematic framework for probabilistic validation of <span class="hlt">sea</span> <span class="hlt">ice</span> models. The methodology is also useful for model tuning by using the D n metric as a cost function and incorporating model parametric uncertainty as part of a scheme to optimize model functionality. We apply this approach to evaluate different configurations of the standalone Los Alamos <span class="hlt">sea</span> <span class="hlt">ice</span> model (CICE) encompassing the parametric uncertainty in the model, and to find new sets of model configurations that produce better agreement than previous configurations between model and observational estimates of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and thickness.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1351197-validation-sea-ice-models-using-uncertainty-based-distance-metric-multiple-model-variables-new-metric-sea-ice-model-validation','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1351197-validation-sea-ice-models-using-uncertainty-based-distance-metric-multiple-model-variables-new-metric-sea-ice-model-validation"><span>Validation of <span class="hlt">sea</span> <span class="hlt">ice</span> models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR <span class="hlt">SEA</span> <span class="hlt">ICE</span> MODEL VALIDATION</span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Urrego-Blanco, Jorge R.; Hunke, Elizabeth C.; Urban, Nathan M.; ...</p> <p>2017-04-01</p> <p>Here, we implement a variance-based distance metric (D n) to objectively assess skill of <span class="hlt">sea</span> <span class="hlt">ice</span> models when multiple output variables or uncertainties in both model predictions and observations need to be considered. The metric compares observations and model data pairs on common spatial and temporal grids improving upon highly aggregated metrics (e.g., total <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> or volume) by capturing the spatial character of model skill. The D n metric is a gamma-distributed statistic that is more general than the χ 2 statistic commonly used to assess model fit, which requires the assumption that the model is unbiased andmore » can only incorporate observational error in the analysis. The D n statistic does not assume that the model is unbiased, and allows the incorporation of multiple observational data sets for the same variable and simultaneously for different variables, along with different types of variances that can characterize uncertainties in both observations and the model. This approach represents a step to establish a systematic framework for probabilistic validation of <span class="hlt">sea</span> <span class="hlt">ice</span> models. The methodology is also useful for model tuning by using the D n metric as a cost function and incorporating model parametric uncertainty as part of a scheme to optimize model functionality. We apply this approach to evaluate different configurations of the standalone Los Alamos <span class="hlt">sea</span> <span class="hlt">ice</span> model (CICE) encompassing the parametric uncertainty in the model, and to find new sets of model configurations that produce better agreement than previous configurations between model and observational estimates of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and thickness.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/20827996','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/20827996"><span>[Reflectance of <span class="hlt">sea</span> <span class="hlt">ice</span> in Liaodong Bay].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Xu, Zhan-tang; Yang, Yue-zhong; Wang, Gui-fen; Cao, Wen-xi; Kong, Xiang-peng</p> <p>2010-07-01</p> <p>In the present study, the relationships between <span class="hlt">sea</span> <span class="hlt">ice</span> albedo and the bidirectional reflectance distribution in Liaodong Bay were investigated. The results indicate that: (1) <span class="hlt">sea</span> <span class="hlt">ice</span> albedo alpha(lambda) is closely related to the components of <span class="hlt">sea</span> <span class="hlt">ice</span>, the higher the particulate concentration in <span class="hlt">sea</span> <span class="hlt">ice</span> surface is, the lower the <span class="hlt">sea</span> <span class="hlt">ice</span> albedo alpha(lambda) is. On the contrary, the higher the bubble concentration in <span class="hlt">sea</span> <span class="hlt">ice</span> is, the higher <span class="hlt">sea</span> <span class="hlt">ice</span> albedo alpha(lambda) is. (2) <span class="hlt">Sea</span> <span class="hlt">ice</span> albedo alpha(lambda) is similar to the bidirectional reflectance factor R(f) when the probe locates at nadir. The R(f) would increase with the increase in detector zenith theta, and the correlation between R(f) and the detector azimuth would gradually increase. When the theta is located at solar zenith 63 degrees, the R(f) would reach the maximum, and the strongest correlation is also shown between the R(f) and the detector azimuth. (3) Different types of <span class="hlt">sea</span> <span class="hlt">ice</span> would have the different anisotropic reflectance factors.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1511292F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1511292F"><span><span class="hlt">Ice</span>2<span class="hlt">sea</span> - Estimating the future contribution of continental <span class="hlt">ice</span> to <span class="hlt">sea</span>-level rise - project summary</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ford, Elaina; Vaughan, David</p> <p>2013-04-01</p> <p><span class="hlt">Ice</span>2<span class="hlt">sea</span> brings together the EU's scientific and operational expertise from 24 leading institutions across Europe and beyond. Improved projections of the contribution of <span class="hlt">ice</span> to <span class="hlt">sea</span>-level rise produced by this major European-funded programme will inform the fifth IPCC report (due in September 2013). In 2007, the fourth Intergovernmental Panel on Climate Change (IPCC) report highlighted <span class="hlt">ice</span>-sheets as the most significant remaining uncertainty in projections of <span class="hlt">sea</span>-level rise. Understanding about the crucial <span class="hlt">ice</span>-sheet effects was "too limited to assess their likelihood or provide a best estimate of an upper bound for <span class="hlt">sea</span>-level rise". <span class="hlt">Ice</span>2<span class="hlt">sea</span> was created to address these issues - the project started in 2009 and is now drawing to a close, with our final symposium in May 2013, and final publicity activities around the IPCC report release in autumn 2013. Here we present a summary of the overall and key outputs of the <span class="hlt">ice</span>2<span class="hlt">sea</span> project.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23908231','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23908231"><span>Ecological consequences of <span class="hlt">sea-ice</span> decline.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>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</p> <p>2013-08-02</p> <p>After a decade with nine of the lowest arctic <span class="hlt">sea-ice</span> minima on record, including the historically low minimum in 2012, we synthesize recent developments in the study of ecological responses to <span class="hlt">sea-ice</span> decline. <span class="hlt">Sea-ice</span> 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 <span class="hlt">sea</span> <span class="hlt">ice</span> 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 <span class="hlt">sea</span> <span class="hlt">ice</span> diminishes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29806697','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29806697"><span>The Arctic's <span class="hlt">sea</span> <span class="hlt">ice</span> cover: trends, variability, predictability, and comparisons to the Antarctic.</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; Meier, Walter N</p> <p>2018-05-28</p> <p>As assessed over the period of satellite observations, October 1978 to present, there are downward linear trends in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> for all months, largest at the end of the melt season in September. The <span class="hlt">ice</span> cover is also thinning. Downward trends in <span class="hlt">extent</span> and thickness have been accompanied by pronounced interannual and multiyear variability, forced by both the atmosphere and ocean. As the <span class="hlt">ice</span> thins, its response to atmospheric and oceanic forcing may be changing. In support of a busier Arctic, there is a growing need to predict <span class="hlt">ice</span> conditions on a variety of time and space scales. A major challenge to providing seasonal scale predictions is the 7-10 days limit of numerical weather prediction. While a seasonally <span class="hlt">ice</span>-free Arctic Ocean is likely well within this century, there is much uncertainty in the timing. This reflects differences in climate model structure, the unknown evolution of anthropogenic forcing, and natural climate variability. In sharp contrast to the Arctic, Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, while highly variable, has increased slightly over the period of satellite observations. The reasons for this different behavior remain to be resolved, but responses to changing atmospheric circulation patterns appear to play a strong role. © 2018 New York Academy of Sciences.</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('http://adsabs.harvard.edu/abs/2016AGUFM.C13E..04H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C13E..04H"><span>Towards decadal time series of Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> thickness from radar altimetry</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hendricks, S.; Rinne, E. J.; Paul, S.; Ricker, R.; Skourup, H.; Kern, S.; Sandven, S.</p> <p>2016-12-01</p> <p>The CryoSat-2 mission has demonstrated the value of radar altimetry to assess the interannual variability and short-term trends of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> over the existing observational record of 6 winter seasons. CryoSat-2 is a particular successful mission for <span class="hlt">sea</span> <span class="hlt">ice</span> mass balance assessment due to its novel radar altimeter concept and orbit configuration, but radar altimetry data is available since 1993 from the ERS-1/2 and Envisat missions. Combining these datasets promises a decadal climate data record of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness, but inter-mission biases must be taken into account due to the evolution of radar altimeters and the impact of changing <span class="hlt">sea</span> <span class="hlt">ice</span> conditions on retrieval algorithm parametrizations. The ESA Climate Change Initiative on <span class="hlt">Sea</span> <span class="hlt">Ice</span> aims to <span class="hlt">extent</span> the list of data records for Essential Climate Variables (ECV's) with a consistent time series of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness from available radar altimeter data. We report on the progress of the algorithm development and choices for auxiliary data sets for <span class="hlt">sea</span> <span class="hlt">ice</span> thickness retrieval in the Arctic and Antarctic Oceans. Particular challenges are the classification of surface types and freeboard retrieval based on radar waveforms with significantly varying footprint sizes. In addition, auxiliary data sets, e.g. for snow depth, are far less developed in the Antarctic and we will discuss the expected skill of the <span class="hlt">sea</span> <span class="hlt">ice</span> thickness ECV's in both hemispheres.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC44B..04H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC44B..04H"><span>The Arctic-Subarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> System is Entering a Seasonal Regime: Implications for Future Arctic Amplication</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Haine, T. W. N.; Martin, T.</p> <p>2017-12-01</p> <p>The loss of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> is a conspicuous example of climate change. Climate models project <span class="hlt">ice</span>-free conditions during summer this century under realistic emission scenarios, reflecting the increase in seasonality in <span class="hlt">ice</span> cover. To quantify the increased seasonality in the Arctic-Subarctic <span class="hlt">sea</span> <span class="hlt">ice</span> system, we define a non-dimensional seasonality number for <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, area, and volume from satellite data and realistic coupled climate models. We show that the Arctic-Subarctic, i.e. the northern hemisphere, <span class="hlt">sea</span> <span class="hlt">ice</span> now exhibits similar levels of seasonality to the Antarctic, which is in a seasonal regime without significant change since satellite observations began in 1979. Realistic climate models suggest that this transition to the seasonal regime is being accompanied by a maximum in Arctic amplification, which is the faster warming of Arctic latitudes compared to the global mean, in the 2010s. The strong link points to a peak in <span class="hlt">sea-ice</span>-related feedbacks that occurs long before the Arctic becomes <span class="hlt">ice</span>-free in summer.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JGRC..122.1497K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JGRC..122.1497K"><span><span class="hlt">Sea-ice</span> thickness from field measurements in the northwestern Barents <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>King, Jennifer; Spreen, Gunnar; Gerland, Sebastian; Haas, Christian; Hendricks, Stefan; Kaleschke, Lars; Wang, Caixin</p> <p>2017-02-01</p> <p>The Barents <span class="hlt">Sea</span> is one of the fastest changing regions of the Arctic, and has experienced the strongest decline in winter-time <span class="hlt">sea-ice</span> area in the Arctic, at -23±4% decade-1. <span class="hlt">Sea-ice</span> thickness in the Barents <span class="hlt">Sea</span> is not well studied. We present two previously unpublished helicopter-borne electromagnetic (HEM) <span class="hlt">ice</span> thickness measurements from the northwestern Barents <span class="hlt">Sea</span> acquired in March 2003 and 2014. The HEM data are compared to <span class="hlt">ice</span> thickness calculated from <span class="hlt">ice</span> draft measured by ULS deployed between 1994 and 1996. These data show that <span class="hlt">ice</span> thickness varies greatly from year to year; influenced by the thermodynamic and dynamic processes that govern local formation vs long-range advection. In a year with a large inflow of <span class="hlt">sea-ice</span> from the Arctic Basin, the Barents <span class="hlt">Sea</span> <span class="hlt">ice</span> cover is dominated by thick multiyear <span class="hlt">ice</span>; as was the case in 2003 and 1995. In a year with an <span class="hlt">ice</span> cover that was mainly grown in situ, the <span class="hlt">ice</span> will be thin and mechanically unstable; as was the case in 2014. The HEM data allow us to explore the spatial and temporal variability in <span class="hlt">ice</span> thickness. In 2003 the dominant <span class="hlt">ice</span> class was more than 2 years old; and modal <span class="hlt">sea-ice</span> thickness varied regionally from 0.6 to 1.4 m, with the thinner <span class="hlt">ice</span> being either first-year <span class="hlt">ice</span>, or multiyear <span class="hlt">ice</span> which had come into contact with warm Atlantic water. In 2014 the <span class="hlt">ice</span> cover was predominantly locally grown <span class="hlt">ice</span> less than 1 month old (regional modes of 0.5-0.8 m). These two situations represent two extremes of a range of possible <span class="hlt">ice</span> thickness distributions that can present very different conditions for shipping traffic; or have a different impact on heat transport from ocean to atmosphere.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C11C0923F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C11C0923F"><span>Improving Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Observations and Data Access to Support Advances in <span class="hlt">Sea</span> <span class="hlt">Ice</span> Forecasting</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Farrell, S. L.</p> <p>2017-12-01</p> <p>The economic and strategic importance of the Arctic region is becoming apparent. One of the most striking and widely publicized changes underway is the declining <span class="hlt">sea</span> <span class="hlt">ice</span> cover. Since <span class="hlt">sea</span> <span class="hlt">ice</span> is a key component of the climate system, its ongoing loss has serious, and wide-ranging, socio-economic implications. Increasing year-to-year variability in the geographic location, concentration, and thickness of the Arctic <span class="hlt">ice</span> cover will pose both challenges and opportunities. The <span class="hlt">sea</span> <span class="hlt">ice</span> research community must be engaged in sustained Arctic Observing Network (AON) initiatives so as to deliver fit-for-purpose remote sensing data products to a variety of stakeholders including Arctic communities, the weather forecasting and climate modeling communities, industry, local, regional and national governments, and policy makers. An example of engagement is the work currently underway to improve research collaborations between scientists engaged in obtaining and assessing <span class="hlt">sea</span> <span class="hlt">ice</span> observational data and those conducting numerical modeling studies and forecasting <span class="hlt">ice</span> conditions. As part of the US AON, in collaboration with the Interagency Arctic Research Policy Committee (IARPC), we are developing a strategic framework within which observers and modelers can work towards the common goal of improved <span class="hlt">sea</span> <span class="hlt">ice</span> forecasting. Here, we focus on <span class="hlt">sea</span> <span class="hlt">ice</span> thickness, a key varaible of the Arctic <span class="hlt">ice</span> cover. We describe multi-sensor, and blended, <span class="hlt">sea</span> <span class="hlt">ice</span> thickness data products under development that can be leveraged to improve model initialization and validation, as well as support data assimilation exercises. We will also present the new PolarWatch initiative (polarwatch.noaa.gov) and discuss efforts to advance access to remote sensing satellite observations and improve communication with Arctic stakeholders, so as to deliver data products that best address societal needs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007JGRC..11211013D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007JGRC..11211013D"><span>Influence of <span class="hlt">sea</span> <span class="hlt">ice</span> cover and icebergs on circulation and water mass formation in a numerical circulation model of the Ross <span class="hlt">Sea</span>, Antarctica</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dinniman, Michael S.; Klinck, John M.; Smith, Walker O.</p> <p>2007-11-01</p> <p>Satellite imagery shows that there was substantial variability in the <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> in the Ross <span class="hlt">Sea</span> during 2001-2003. Much of this variability is thought to be due to several large icebergs that moved through the area during that period. The effects of these changes in <span class="hlt">sea</span> <span class="hlt">ice</span> on circulation and water mass distributions are investigated with a numerical general circulation model. It would be difficult to simulate the highly variable <span class="hlt">sea</span> <span class="hlt">ice</span> from 2001 to 2003 with a dynamic <span class="hlt">sea</span> <span class="hlt">ice</span> model since much of the variability was due to the floating icebergs. Here, <span class="hlt">sea</span> <span class="hlt">ice</span> concentration is specified from satellite observations. To examine the effects of changes in <span class="hlt">sea</span> <span class="hlt">ice</span> due to iceberg C-19, simulations were performed using either climatological <span class="hlt">ice</span> concentrations or the observed <span class="hlt">ice</span> for that period. The heat balance around the Ross <span class="hlt">Sea</span> Polynya (RSP) shows that the dominant term in the surface heat budget is the net exchange with the atmosphere, but advection of oceanic warm water is also important. The area average annual basal melt rate beneath the Ross <span class="hlt">Ice</span> Shelf is reduced by 12% in the observed <span class="hlt">sea</span> <span class="hlt">ice</span> simulation. The observed <span class="hlt">sea</span> <span class="hlt">ice</span> simulation also creates more High-Salinity Shelf Water. Another simulation was performed with observed <span class="hlt">sea</span> <span class="hlt">ice</span> and a fixed iceberg representing B-15A. There is reduced advection of warm surface water during summer from the RSP into McMurdo Sound due to B-15A, but a much stronger reduction is due to the late opening of the RSP in early 2003 because of C-19.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC44B..03T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC44B..03T"><span>Multi-decadal Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> roughness.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tsamados, M.; Stroeve, J.; Kharbouche, S.; Muller, J. P., , Prof; Nolin, A. W.; Petty, A.; Haas, C.; Girard-Ardhuin, F.; Landy, J.</p> <p>2017-12-01</p> <p>The transformation of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> from mainly perennial, multi-year <span class="hlt">ice</span> to a seasonal, first-year <span class="hlt">ice</span> is believed to have been accompanied by a reduction of the roughness of the <span class="hlt">ice</span> cover surface. This smoothening effect has been shown to (i) modify the momentum and heat transfer between the atmosphere and ocean, (ii) to alter the <span class="hlt">ice</span> thickness distribution which in turn controls the snow and melt pond repartition over the <span class="hlt">ice</span> cover, and (iii) to bias airborne and satellite remote sensing measurements that depend on the scattering and reflective characteristics over the <span class="hlt">sea</span> <span class="hlt">ice</span> surface topography. We will review existing and novel remote sensing methodologies proposed to estimate <span class="hlt">sea</span> <span class="hlt">ice</span> roughness, ranging from airborne LIDAR measurement (ie Operation <span class="hlt">Ice</span>Bridge), to backscatter coefficients from scatterometers (ASCAT, QUICKSCAT), to multi angle maging spectroradiometer (MISR), and to laser (Icesat) and radar altimeters (Envisat, Cryosat, Altika, Sentinel-3). We will show that by comparing and cross-calibrating these different products we can offer a consistent multi-mission, multi-decadal view of the declining <span class="hlt">sea</span> <span class="hlt">ice</span> roughness. Implications for <span class="hlt">sea</span> <span class="hlt">ice</span> physics, climate and remote sensing will also be discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140017491','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140017491"><span>NASA Team 2 <span class="hlt">Sea</span> <span class="hlt">Ice</span> Concentration Algorithm Retrieval Uncertainty</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Brucker, Ludovic; Cavalieri, Donald J.; Markus, Thorsten; Ivanoff, Alvaro</p> <p>2014-01-01</p> <p>Satellite microwave radiometers are widely used to estimate <span class="hlt">sea</span> <span class="hlt">ice</span> cover properties (concentration, <span class="hlt">extent</span>, and area) through the use of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration (IC) algorithms. Rare are the algorithms providing associated IC uncertainty estimates. Algorithm uncertainty estimates are needed to assess accurately global and regional trends in IC (and thus <span class="hlt">extent</span> and area), and to improve <span class="hlt">sea</span> <span class="hlt">ice</span> predictions on seasonal to interannual timescales using data assimilation approaches. This paper presents a method to provide relative IC uncertainty estimates using the enhanced NASA Team (NT2) IC algorithm. The proposed approach takes advantage of the NT2 calculations and solely relies on the brightness temperatures (TBs) used as input. NT2 IC and its associated relative uncertainty are obtained for both the Northern and Southern Hemispheres using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) TB. NT2 IC relative uncertainties estimated on a footprint-by-footprint swath-by-swath basis were averaged daily over each 12.5-km grid cell of the polar stereographic grid. For both hemispheres and throughout the year, the NT2 relative uncertainty is less than 5%. In the Southern Hemisphere, it is low in the interior <span class="hlt">ice</span> pack, and it increases in the marginal <span class="hlt">ice</span> zone up to 5%. In the Northern Hemisphere, areas with high uncertainties are also found in the high IC area of the Central Arctic. Retrieval uncertainties are greater in areas corresponding to NT2 <span class="hlt">ice</span> types associated with deep snow and new <span class="hlt">ice</span>. Seasonal variations in uncertainty show larger values in summer as a result of melt conditions and greater atmospheric contributions. Our analysis also includes an evaluation of the NT2 algorithm sensitivity to AMSR-E sensor noise. There is a 60% probability that the IC does not change (to within the computed retrieval precision of 1%) due to sensor noise, and the cumulated probability shows that there is a 90% chance that the IC varies by less than</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/20020695-arctic-sea-ice-variability-context-recent-atmospheric-circulation-trends','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/20020695-arctic-sea-ice-variability-context-recent-atmospheric-circulation-trends"><span>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> variability in the context of recent atmospheric circulation trends</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Deser, C.; Walsh, J.E.; Timlin, M.S.</p> <p></p> <p>, there appears to be a local response of the atmospheric circulation to the changing <span class="hlt">sea</span> <span class="hlt">ice</span> cover east of Greenland. Specifically, cyclone frequencies have increased and mean SLPs have decreased over the retracted <span class="hlt">ice</span> margin in the Greenland <span class="hlt">Sea</span>, and these changes differ from those associated directly with the North Atlantic oscillation. The dominant mode of <span class="hlt">sea</span> <span class="hlt">ice</span> variability in summer (July-September) is more spatially uniform than that in winter. Summer <span class="hlt">ice</span> <span class="hlt">extent</span> for the Arctic as a whole has exhibited a nearly monotonic decline (-4% decade{sup {minus}1}) during the past 40 yr. Summer <span class="hlt">sea</span> <span class="hlt">ice</span> variations appear to be initiated by atmospheric circulation anomalies over the high Arctic in late spring. Positive <span class="hlt">ice</span>-albedo feedback may account for the relatively long delay (2--3 months) between the time of atmospheric forcing and the maximum <span class="hlt">ice</span> response, and it may have served to amplify the summer <span class="hlt">ice</span> retreat.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.C52B..05L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.C52B..05L"><span>Tracking <span class="hlt">sea</span> <span class="hlt">ice</span> floes from the Lincoln <span class="hlt">Sea</span> to Nares Strait and deriving large scale melt from coincident spring and summer (2009) aerial EM thickness surveys</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lange, B. A.; Haas, C.; Beckers, J.; Hendricks, S.</p> <p>2011-12-01</p> <p>Satellite observations demonstrate a decreasing summer Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> over the past ~40 years, as well as a smaller perennial <span class="hlt">sea</span> <span class="hlt">ice</span> zone, with a significantly accelerated decline in the last decade. Recent <span class="hlt">ice</span> <span class="hlt">extent</span> observations are significantly lower than predicted by any model employed by the Intergovernmental Panel on Climate Change. The disagreement of the modeled and observed results, along with the large variability of model results, can be in part attributed to a lack of consistent and long term <span class="hlt">sea</span> <span class="hlt">ice</span> mass balance observations for the High Arctic. This study presents the derivation of large scale (individual floe) seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> mass balance in the Lincoln <span class="hlt">Sea</span> and Nares Strait. Large scale melt estimates are derived by comparing aerial borne electromagnetic induction thickness surveys conducted in spring with surveys conducted in summer 2009. The comparison of coincident floes is ensured by tracking <span class="hlt">sea</span> <span class="hlt">ice</span> using ENIVSAT ASAR and MODIS satellite imagery. Only EM thickness survey sections of floes that were surveyed in both spring and summer are analyzed and the resulting modal thicknesses of the distributions, which represent the most abundant <span class="hlt">ice</span> type, are compared to determine the difference in thickness and therefore total melt (snow+basal <span class="hlt">ice</span>+surface <span class="hlt">ice</span> melt). Preliminary analyses demonstrate a bulk (regional <span class="hlt">ice</span> tracking) seasonal total thickness variability of 1.1m, Lincoln <span class="hlt">Sea</span> modal thickness 3.7m (April, 2009) and Nares Strait modal thickness 2.6m (August 2009)(Fig1). More detailed floe tracking, in depth analysis of EM surveys and removal of deformed ridged/rafted <span class="hlt">sea</span> <span class="hlt">ice</span> (due to inaccuracies over deformed <span class="hlt">ice</span>) will result in more accurate melt estimates for this region and will be presented. The physical structure of deformed <span class="hlt">sea</span> <span class="hlt">ice</span> and the footprint of the EM instrument typically underestimate the total thicknesses observed. Seasonal variations of <span class="hlt">sea</span> <span class="hlt">ice</span> properties can add additional uncertainty to the response of the EM</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JMS...165..124H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JMS...165..124H"><span>The importance of <span class="hlt">sea</span> <span class="hlt">ice</span> for exchange of habitat-specific protist communities in the Central Arctic Ocean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hardge, Kristin; Peeken, Ilka; Neuhaus, Stefan; Lange, Benjamin A.; Stock, Alexandra; Stoeck, Thorsten; Weinisch, Lea; Metfies, Katja</p> <p>2017-01-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> is one of the main features influencing the Arctic marine protist community composition and diversity in <span class="hlt">sea</span> <span class="hlt">ice</span> and <span class="hlt">sea</span> water. We analyzed protist communities within <span class="hlt">sea</span> <span class="hlt">ice</span>, melt pond water, under-<span class="hlt">ice</span> water and deep-chlorophyll maximum water at eight <span class="hlt">sea</span> <span class="hlt">ice</span> stations sampled during summer of the 2012 record <span class="hlt">sea</span> <span class="hlt">ice</span> minimum year. Using Illumina sequencing, we identified characteristic communities associated with specific habitats and investigated protist exchange between these habitats. The highest abundance and diversity of unique taxa were found in <span class="hlt">sea</span> <span class="hlt">ice</span>, particularly in multi-year <span class="hlt">ice</span> (MYI), highlighting the importance of <span class="hlt">sea</span> <span class="hlt">ice</span> as a unique habitat for <span class="hlt">sea</span> <span class="hlt">ice</span> protists. Melting of <span class="hlt">sea</span> <span class="hlt">ice</span> was associated with increased exchange of communities between <span class="hlt">sea</span> <span class="hlt">ice</span> and the underlying water column. In contrast, <span class="hlt">sea</span> <span class="hlt">ice</span> formation was associated with increased exchange between all four habitats, suggesting that brine rejection from the <span class="hlt">ice</span> is an important factor for species redistribution in the Central Arctic. Ubiquitous taxa (e.g. Gymnodinium) that occurred in all habitats still had habitat-preferences. This demonstrates a limited ability to survive in adjacent but different environments. Our results suggest that the continued reduction of <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, and particularly of MYI, will likely lead to diminished protist exchange and subsequently, could reduce species diversity in all habitats of the Central Arctic Ocean. An important component of the unique <span class="hlt">sea</span> <span class="hlt">ice</span> protist community could be endangered because specialized taxa restricted to this habitat may not be able to adapt to rapid environmental changes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70144117','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70144117"><span>Polar bear population dynamics in the southern Beaufort <span class="hlt">Sea</span> during a period of <span class="hlt">sea</span> <span class="hlt">ice</span> decline</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Bromaghin, Jeffrey F.; McDonald, Trent L.; Stirling, Ian; Derocher, Andrew E.; Richardson, Evan S.; Regehr, Eric V.; Douglas, David C.; Durner, George M.; Atwood, Todd C.; Amstrup, Steven C.</p> <p>2015-01-01</p> <p>In the southern Beaufort <span class="hlt">Sea</span> of the United States and Canada, prior investigations have linked declines in summer <span class="hlt">sea</span> <span class="hlt">ice</span> to reduced physical condition, growth, and survival of polar bears (Ursus maritimus). Combined with projections of population decline due to continued climate warming and the ensuing loss of <span class="hlt">sea</span> <span class="hlt">ice</span> habitat, those findings contributed to the 2008 decision to list the species as threatened under the U.S. Endangered Species Act. Here, we used mark–recapture models to investigate the population dynamics of polar bears in the southern Beaufort <span class="hlt">Sea</span> from 2001 to 2010, years during which the spatial and temporal <span class="hlt">extent</span> of summer <span class="hlt">sea</span> <span class="hlt">ice</span> generally declined. Low survival from 2004 through 2006 led to a 25–50% decline in abundance. We hypothesize that low survival during this period resulted from (1) unfavorable <span class="hlt">ice</span> conditions that limited access to prey during multiple seasons; and possibly, (2) low prey abundance. For reasons that are not clear, survival of adults and cubs began to improve in 2007 and abundance was comparatively stable from 2008 to 2010, with ~900 bears in 2010 (90% CI 606–1212). However, survival of subadult bears declined throughout the entire period. Reduced spatial and temporal availability of <span class="hlt">sea</span> <span class="hlt">ice</span> is expected to increasingly force population dynamics of polar bears as the climate continues to warm. However, in the short term, our findings suggest that factors other than <span class="hlt">sea</span> <span class="hlt">ice</span> can influence survival. A refined understanding of the ecological mechanisms underlying polar bear population dynamics is necessary to improve projections of their future status and facilitate development of management strategies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25901605','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25901605"><span>Comparing springtime <span class="hlt">ice</span>-algal chlorophyll a and physical properties of multi-year and first-year <span class="hlt">sea</span> <span class="hlt">ice</span> from the Lincoln <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>Lange, Benjamin A; Michel, Christine; Beckers, Justin F; Casey, J Alec; Flores, Hauke; Hatam, Ido; Meisterhans, Guillaume; Niemi, Andrea; Haas, Christian</p> <p>2015-01-01</p> <p>With near-complete replacement of Arctic multi-year <span class="hlt">ice</span> (MYI) by first-year <span class="hlt">ice</span> (FYI) predicted to occur within this century, it remains uncertain how the loss of MYI will impact the abundance and distribution of <span class="hlt">sea</span> <span class="hlt">ice</span> associated algae. In this study we compare the chlorophyll a (chl a) concentrations and physical properties of MYI and FYI from the Lincoln <span class="hlt">Sea</span> during 3 spring seasons (2010-2012). Cores were analysed for texture, salinity, and chl a. We identified annual growth layers for 7 of 11 MYI cores and found no significant differences in chl a concentration between the bottom first-year-<span class="hlt">ice</span> portions of MYI, upper old-<span class="hlt">ice</span> portions of MYI, and FYI cores. Overall, the maximum chl a concentrations were observed at the bottom of young FYI. However, there were no significant differences in chl a concentrations between MYI and FYI. This suggests little or no change in algal biomass with a shift from MYI to FYI and that the spatial <span class="hlt">extent</span> and regional variability of refrozen leads and younger FYI will likely be key factors governing future changes in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> algal biomass. Bottom-integrated chl a concentrations showed negative logistic relationships with snow depth and bulk (snow plus <span class="hlt">ice</span>) integrated extinction coefficients; indicating a strong influence of snow cover in controlling bottom <span class="hlt">ice</span> algal biomass. The maximum bottom MYI chl a concentration was observed in a hummock, representing the thickest <span class="hlt">ice</span> with lowest snow depth of this study. Hence, in this and other studies MYI chl a biomass may be under-estimated due to an under-representation of thick MYI (e.g., hummocks), which typically have a relatively thin snowpack allowing for increased light transmission. Therefore, we suggest the on-going loss of MYI in the Arctic Ocean may have a larger impact on <span class="hlt">ice</span>-associated production than generally assumed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1986QuRes..26....3D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1986QuRes..26....3D"><span>Global <span class="hlt">ice</span>-sheet system interlocked by <span class="hlt">sea</span> level</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Denton, George H.; Hughes, Terence J.; Karlén, Wibjörn</p> <p>1986-07-01</p> <p>Denton and Hughes (1983, Quaternary Research20, 125-144) postulated that <span class="hlt">sea</span> level linked a global <span class="hlt">ice</span>-sheet system with both terrestrial and grounded marine components during late Quaternary <span class="hlt">ice</span> ages. Summer temperature changes near Northern Hemisphere melting margins initiated <span class="hlt">sea</span>-level fluctuations that controlled marine components in both polar hemispheres. It was further proposed that variations of this <span class="hlt">ice</span>-sheet system amplified and transmitted Milankovitch summer half-year insolation changes between 45 and 75°N into global climatic changes. New tests of this hypothesis implicate <span class="hlt">sea</span> level as a major control of the areal <span class="hlt">extent</span> of grounded portions of the Antarctic <span class="hlt">Ice</span> Sheet, thus fitting the concept of a globally interlocked <span class="hlt">ice</span>-sheet system. But recent atmospheric modeling results ( Manabe and Broccoli, 1985, Journal of Geophysical Research90, 2167-2190) suggest that factors other than areal changes of the grounded Antarctic <span class="hlt">Ice</span> Sheet strongly influenced Southern Hemisphere climate and terminated the last <span class="hlt">ice</span> age simultaneously in both polar hemispheres. Atmospheric carbon dioxide linked to high-latitude oceans is the most likely candidate ( Shackleton and Pisias, 1985, Atmospheric carbon dioxide, orbital forcing, and climate. In "The Carbon Cycle and Atmospheric CO 2: Natural Variations Archean to Present" (E. T. Sundquest and W. S. Broecker, Eds.), pp. 303-318. Geophysical Monograph 32, American Geophysical Union, Washington, D.C.), but another potential influence was high-frequency climatic oscillations (2500 yr). It is postulated that variations in atmospheric carbon dioxide acted through an Antarctic <span class="hlt">ice</span> shelf linked to the grounded <span class="hlt">ice</span> sheet to produce and terminate Southern Hemisphere <span class="hlt">ice</span>-age climate. It is further postulated that Milankovitch summer insolation combined with a warm high-frequency oscillation caused marked recession of Northern Hemisphere <span class="hlt">ice</span>-sheet melting margins and the North Atlantic polar front about 14,000 14C yr B.P. This</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19740022688&hterms=oil+monitoring&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Doil%2Bmonitoring','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19740022688&hterms=oil+monitoring&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Doil%2Bmonitoring"><span>Monitoring Arctic <span class="hlt">Sea</span> <span class="hlt">ice</span> using ERTS imagery. [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.; Bowley, C. J.</p> <p>1974-01-01</p> <p>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 other 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 imagery is compared with visual <span class="hlt">ice</span> reports and aerial photography from the NASA CV-990 aircraft.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014TCD.....8.3811L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014TCD.....8.3811L"><span><span class="hlt">Ice</span> and AIS: ship speed data and <span class="hlt">sea</span> <span class="hlt">ice</span> forecasts in the Baltic <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>Löptien, U.; Axell, L.</p> <p>2014-07-01</p> <p>The Baltic <span class="hlt">Sea</span> is a seasonally <span class="hlt">ice</span> covered marginal <span class="hlt">sea</span> located in a densely populated area in northern Europe. Severe <span class="hlt">sea</span> <span class="hlt">ice</span> conditions have the potential to hinder the intense ship traffic considerably. Thus, <span class="hlt">sea</span> <span class="hlt">ice</span> fore- and nowcasts are regularly provided by the national weather services. Typically, several <span class="hlt">ice</span> properties are allocated, but their actual usefulness is difficult to measure and the ship captains must determine their relative importance and relevance for optimal ship speed and safety ad hoc. The present study provides a more objective approach by comparing the ship speeds, obtained by the Automatic Identification System (AIS), with the respective forecasted <span class="hlt">ice</span> conditions. We find that, despite an unavoidable random component, this information is useful to constrain and rate fore- and nowcasts. More precisely, 62-67% of ship speed variations can be explained by the forecasted <span class="hlt">ice</span> properties when fitting a mixed effect model. This statistical fit is based on a test region in the Bothnian Bay during the severe winter 2011 and employes 15 to 25 min averages of ship speed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://eric.ed.gov/?q=sea&pg=5&id=EJ827417','ERIC'); return false;" href="https://eric.ed.gov/?q=sea&pg=5&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/2017AGUFM.C33B1187W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C33B1187W"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> in the NCEP Seasonal Forecast System</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, X.; Saha, S.; Grumbine, R. W.; Bailey, D. A.; Carton, J.; Penny, S. G.</p> <p>2017-12-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> is known to play a significant role in the global climate system. For a weather or climate forecast system (CFS), it is important that the realistic distribution of <span class="hlt">sea</span> <span class="hlt">ice</span> is represented. <span class="hlt">Sea</span> <span class="hlt">ice</span> prediction is challenging; <span class="hlt">sea</span> <span class="hlt">ice</span> can form or melt, it can move with wind and/or ocean current; <span class="hlt">sea</span> <span class="hlt">ice</span> interacts with both the air above and ocean underneath, it influences by, and has impact on the air and ocean conditions. NCEP has developed coupled CFS (version 2, CFSv2) and also carried out CFS reanalysis (CFSR), which includes a coupled model with the NCEP global forecast system, a land model, an ocean model (GFDL MOM4), and a <span class="hlt">sea</span> <span class="hlt">ice</span> model. In this work, we present the NCEP coupled model, the CFSv2 <span class="hlt">sea</span> <span class="hlt">ice</span> component that includes a dynamic thermodynamic <span class="hlt">sea</span> <span class="hlt">ice</span> model and a simple "assimilation" scheme, how <span class="hlt">sea</span> <span class="hlt">ice</span> has been assimilated in CFSR, the characteristics of the <span class="hlt">sea</span> <span class="hlt">ice</span> from CFSR and CFSv2, and the improvements of <span class="hlt">sea</span> <span class="hlt">ice</span> needed for future seasonal prediction system, part of the Unified Global Coupled System (UGCS), which is being developed and under testing, including <span class="hlt">sea</span> <span class="hlt">ice</span> data assimilation with the Local Ensemble Transform Kalman Filter (LETKF). Preliminary results from the UGCS testing will also be presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19920052554&hterms=AES&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DAES','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19920052554&hterms=AES&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DAES"><span>NASA, Navy, and AES/York <span class="hlt">sea</span> <span class="hlt">ice</span> concentration comparison of SSM/I algorithms with SAR derived values</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jentz, R. R.; Wackerman, C. C.; Shuchman, R. A.; Onstott, R. G.; Gloersen, Per; Cavalieri, Don; Ramseier, Rene; Rubinstein, Irene; Comiso, Joey; Hollinger, James</p> <p>1991-01-01</p> <p>Previous research studies have focused on producing algorithms for extracting geophysical information from passive microwave data regarding <span class="hlt">ice</span> floe size, <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, open water lead locations, and <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>. These studies have resulted in four separate algorithms for extracting these geophysical parameters. <span class="hlt">Sea</span> <span class="hlt">ice</span> concentration estimates generated from each of these algorithms (i.e., NASA/Team, NASA/Comiso, AES/York, and Navy) are compared to <span class="hlt">ice</span> concentration estimates produced from coincident high-resolution synthetic aperture radar (SAR) data. The SAR concentration estimates are produced from data collected in both the Beaufort <span class="hlt">Sea</span> and the Greenland <span class="hlt">Sea</span> in March 1988 and March 1989, respectively. The SAR data are coincident to the passive microwave data generated by the Special Sensor Microwave/Imager (SSM/I).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C21E1165W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C21E1165W"><span>A Detailed Geophysical Investigation of the Grounding of Henry <span class="hlt">Ice</span> Rise, with Implications for Holocene <span class="hlt">Ice</span>-Sheet <span class="hlt">Extent</span>.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wearing, M.; Kingslake, J.</p> <p>2017-12-01</p> <p>It is generally assumed that since the Last Glacial Maximum the West Antarctic <span class="hlt">Ice</span> Sheet (WAIS) has experienced monotonic retreat of the grounding line (GL). However, recent studies have cast doubt on this assumption, suggesting that the retreat of the WAIS grounding line may have been followed by a significant advance during the Holocene in the Weddell and Ross <span class="hlt">Sea</span> sectors. Constraining this evolution is important as reconstructions of past <span class="hlt">ice</span>-sheet <span class="hlt">extent</span> are used to spin-up predictive <span class="hlt">ice</span>-sheet models and correct mass-balance observations for glacial isostatic adjustment. Here we examine in detail the formation of the Henry <span class="hlt">Ice</span> Rise (HIR), which <span class="hlt">ice</span>-sheet model simulations suggest played a key role in Holocene <span class="hlt">ice</span>-mass changes in the Weddell <span class="hlt">Sea</span> sector. Observations from a high-resolution ground-based, <span class="hlt">ice</span>-penetrating radar survey are best explained if the <span class="hlt">ice</span> rise formed when the Ronne <span class="hlt">Ice</span> Shelf grounded on a submarine high, underwent a period of <span class="hlt">ice</span>-rumple flow, before the GL migrated outwards to form the present-day <span class="hlt">ice</span> rise. We constrain the relative chronology of this evolution by comparing the alignment and intersection of isochronal internal layers, relic crevasses, surface features and investigating the dynamic processes leading to their complex structure. We also draw analogies between HIR and the neighbouring Doake <span class="hlt">Ice</span> Rumples. The date of formation is estimated using vertical velocities derived with a phase-sensitive radio-echo sounder (pRES). <span class="hlt">Ice</span>-sheet models suggest that the formation of the HIR and other <span class="hlt">ice</span> rises may have halted and reversed large-scale GL retreat. Hence the small-scale dynamics of these crucial regions could have wide-reaching consequences for future <span class="hlt">ice</span>-sheet mass changes and constraining their formation and evolution further would be beneficial. One stringent test of our geophysics-based conclusions would be to drill to the bed of HIR to sample the <span class="hlt">ice</span> for isotopic analysis and the bed for radiocarbon analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-GSFC_20171208_Archive_e001605.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-GSFC_20171208_Archive_e001605.html"><span>Iceberg in <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2017-12-08</p> <p>An iceberg embedded in <span class="hlt">sea</span> <span class="hlt">ice</span> as seen from the <span class="hlt">Ice</span>Bridge DC-8 over the Bellingshausen <span class="hlt">Sea</span> on Oct. 19, 2012. Credit: NASA / James Yungel NASA's Operation <span class="hlt">Ice</span>Bridge is an airborne science mission to study Earth's polar <span class="hlt">ice</span>. For more information about <span class="hlt">Ice</span>Bridge, visit: www.nasa.gov/icebridge NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram</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('http://adsabs.harvard.edu/abs/2016AGUFMPA13A1975T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMPA13A1975T"><span>Guide to <span class="hlt">Sea</span> <span class="hlt">Ice</span> Information and <span class="hlt">Sea</span> <span class="hlt">Ice</span> Data Online - the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Knowledge and Data Platform www.meereisportal.de and www.seaiceportal.de</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Treffeisen, R. E.; Nicolaus, M.; Bartsch, A.; Fritzsch, B.; Grosfeld, K.; Haas, C.; Hendricks, S.; Heygster, G.; Hiller, W.; Krumpen, T.; Melsheimer, C.; Ricker, R.; Weigelt, M.</p> <p>2016-12-01</p> <p>The combination of multi-disciplinary <span class="hlt">sea</span> <span class="hlt">ice</span> science and the rising demand of society for up-to-date information and user customized products places emphasis on creating new ways of communication between science and society. The new knowledge platform is a contribution to the cross-linking of scientifically qualified information on climate change, and focuses on the theme: `<span class="hlt">sea</span> <span class="hlt">ice</span>' in both Polar Regions. With this platform, the science opens to these changing societal demands. It is the first comprehensive German speaking knowledge platform on <span class="hlt">sea</span> <span class="hlt">ice</span>; the platform went online in 2013. The web site delivers popularized information for the general public as well as scientific data meant primarily for the more expert readers and scientists. It also provides various tools allowing for visitor interaction. The demand for the web site indicates a high level of interest from both the general public and experts. It communicates science-based information to improve awareness and understanding of <span class="hlt">sea</span> <span class="hlt">ice</span> related research. The principle concept of the new knowledge platform is based on three pillars: (1) <span class="hlt">sea</span> <span class="hlt">ice</span> knowledge and background information, (2) data portal with visualizations, and (3) expert knowledge, latest research results and press releases. Since then, the content and selection of data sets increased and the data portal received increasing attention, also from the international science community. Meanwhile, we are providing near-real time and archived data of many key parameters of <span class="hlt">sea</span> <span class="hlt">ice</span> and its snow cover. The data sets result from measurements acquired by various platforms as well as numerical simulations. Satellite observations (e.g., AMSR2, CryoSat-2 and SMOS) of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, freeboard, thickness and drift are available as gridded data sets. <span class="hlt">Sea</span> <span class="hlt">ice</span> and snow temperatures and thickness as well as atmospheric parameters are available from autonomous <span class="hlt">ice</span>-tethered platforms (buoys). Additional ship observations, <span class="hlt">ice</span> station measurements, and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.C11B0499S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.C11B0499S"><span>Expanding research capabilities with <span class="hlt">sea</span> <span class="hlt">ice</span> climate records for analysis of long-term climate change and short-term variability</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Scott, D. J.; Meier, W. N.</p> <p>2008-12-01</p> <p>Recent <span class="hlt">sea</span> <span class="hlt">ice</span> analysis is leading to predictions of a <span class="hlt">sea</span> <span class="hlt">ice</span>-free summertime in the Arctic within 20 years, or even sooner. <span class="hlt">Sea</span> <span class="hlt">ice</span> topics, such as concentration, <span class="hlt">extent</span>, motion, and age, are predominately studied using satellite data. At the National Snow and <span class="hlt">Ice</span> Data Center (NSIDC), passive microwave <span class="hlt">sea</span> <span class="hlt">ice</span> data sets provide timely assessments of seasonal-scale variability as well as consistent long-term climate data records. Such data sets are crucial to understanding changes and assessing their impacts. Noticeable impacts of changing <span class="hlt">sea</span> <span class="hlt">ice</span> conditions on native cultures and wildlife in the Arctic region are now being documented. With continued deterioration in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>, global economic impacts will be seen as new shipping routes open. NSIDC is at the forefront of making climate data records available to address the changes in <span class="hlt">sea</span> <span class="hlt">ice</span> and its global impacts. By focusing on integrated data sets, NSIDC leads the way by broadening the studies of <span class="hlt">sea</span> <span class="hlt">ice</span> beyond the traditional cryospheric community.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013QSRv...79..122D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013QSRv...79..122D"><span>Reconstructing past <span class="hlt">sea</span> <span class="hlt">ice</span> cover of the Northern Hemisphere from dinocyst assemblages: status of the approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>de Vernal, Anne; Rochon, André; Fréchette, Bianca; Henry, Maryse; Radi, Taoufik; Solignac, Sandrine</p> <p>2013-11-01</p> <p>Dinocysts occur in a wide range of environmental conditions, including polar areas. We review here their use for the reconstruction of paleo <span class="hlt">sea</span> <span class="hlt">ice</span> cover in such environments. In the Arctic Ocean and subarctic <span class="hlt">seas</span> characterized by dense <span class="hlt">sea</span> <span class="hlt">ice</span> cover, Islandinium minutum, Islandinium? cezare, Echinidinium karaense, Polykrikos sp. var. Arctic, Spiniferites elongatus-frigidus and Impagidinium pallidum are common and often occur with more cosmopolitan taxa such as Operculodinium centrocarpum sensu Wall & Dale, cyst of Pentapharsodinium dalei and Brigantedinium spp. Canonical correspondence analyses conducted on dinocyst assemblages illustrate relationships with <span class="hlt">sea</span> surface parameters such as salinity, temperature, and <span class="hlt">sea</span> <span class="hlt">ice</span> cover. The application of the modern analogue technique permits quantitative reconstruction of past <span class="hlt">sea</span> <span class="hlt">ice</span> cover, which is expressed in terms of seasonal <span class="hlt">extent</span> of <span class="hlt">sea</span> <span class="hlt">ice</span> cover (months per year with more than 50% of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration) or mean annual <span class="hlt">sea</span> <span class="hlt">ice</span> concentration (in tenths). The accuracy of reconstructions or root mean square error of prediction (RMSEP) is ±1.1 over 10, which corresponds to perennial <span class="hlt">sea</span> <span class="hlt">ice</span>. Such an error is close to the interannual variability (standard deviation) of observed <span class="hlt">sea</span> <span class="hlt">ice</span> cover. Mismatch between the time interval of instrumental data used as reference (1953-2000) and the time interval represented by dinocyst populations in surface sediment samples, which may cover decades if not centuries, is another source of error. Despite uncertainties, dinocyst assemblages are useful for making quantitative reconstruction of seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> cover.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018TCry...12.1103S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018TCry...12.1103S"><span>Atmospheric influences on the anomalous 2016 Antarctic <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>Schlosser, Elisabeth; Haumann, F. Alexander; Raphael, Marilyn N.</p> <p>2018-03-01</p> <p>In contrast to the Arctic, where total <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> (SIE) has been decreasing for the last three decades, Antarctic SIE has shown a small, but significant, increase during the same time period. However, in 2016, an unusually early onset of the melt season was observed; the maximum Antarctic SIE was already reached as early as August rather than the end of September, and was followed by a rapid decrease. The decay was particularly strong in November, when Antarctic SIE exhibited a negative anomaly (compared to the 1979-2015 average) of approximately 2 million km2. ECMWF Interim reanalysis data showed that the early onset of the melt and the rapid decrease in <span class="hlt">sea</span> <span class="hlt">ice</span> area (SIA) and SIE were associated with atmospheric flow patterns related to a positive zonal wave number three (ZW3) index, i.e., synoptic situations leading to strong meridional flow and anomalously strong southward heat advection in the regions of strongest <span class="hlt">sea</span> <span class="hlt">ice</span> decline. A persistently positive ZW3 index from May to August suggests that SIE decrease was preconditioned by SIA decrease. In particular, in the first third of November northerly flow conditions in the Weddell <span class="hlt">Sea</span> and the Western Pacific triggered accelerated <span class="hlt">sea</span> <span class="hlt">ice</span> decay, which was continued in the following weeks due to positive feedback effects, leading to the unusually low November SIE. In 2016, the monthly mean Southern Annular Mode (SAM) index reached its second lowest November value since the beginning of the satellite observations. A better spatial and temporal coverage of reliable <span class="hlt">ice</span> thickness data is needed to assess the change in <span class="hlt">ice</span> mass rather than <span class="hlt">ice</span> area.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18..693S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18..693S"><span>Development of source specific diatom lipids biomarkers as Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> proxies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Smik, Lukas; Belt, Simon T.; Brown, Thomas A.; Lieser, Jan L.; Armand, Leanne K.; Leventer, Amy; Allen, Claire S.</p> <p>2016-04-01</p> <p>C25 highly branched isoprenoid (HBI) are lipid biomarkers biosynthesised by a relatively small number of diatom genera, but are, nonetheless, common constituents of global marine sediments. The occurrence and variable abundance of certain C25 highly branched isoprenoid (HBI) biomarkers in Antarctic marine sediments has previously been proposed as a proxy measure of paleo <span class="hlt">sea-ice</span> <span class="hlt">extent</span> in the Southern Ocean and a small number of paleo <span class="hlt">sea-ice</span> reconstructions based on the variable abundances of these HBIs have appeared in recent years. However, the development of HBIs as proxies for Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> is much less advanced than that for IP25 (another HBI) in the Arctic and has been based on relatively small number of analyses in <span class="hlt">sea</span> <span class="hlt">ice</span>, water column and sediment samples. To provide further insights into the use of these HBIs as proxies for Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span>, we here describe an assessment of their distributions in surface water, surface sediment and <span class="hlt">sea</span> <span class="hlt">ice</span> samples collected from a number of Antarctic locations experiencing contrasting <span class="hlt">sea</span> <span class="hlt">ice</span> conditions in recent years. Our study shows that distributions of a di-unsaturated HBI (diene II) and tri-unsaturated HBI (triene III) in surface water samples were found to be extremely sensitive to the local <span class="hlt">sea-ice</span> conditions, with diene II detected for sampling sites that experienced seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> and highest concentrations found in coastal locations with longer-lasting <span class="hlt">ice</span> cover and a recurrent polynya. In contrast, triene III was observed in all of the samples analysed, but with highest concentrations within the region of the retreating <span class="hlt">sea</span> <span class="hlt">ice</span> edge, an observation consistent with significant environmental control over the biosynthesis of diene II and triene III by <span class="hlt">sea</span> <span class="hlt">ice</span> diatoms and open water phytoplankton, respectively. However, additional local factors, such as those associated with polynya formation, may also exert some control over the distribution of triene III and the relative concentrations of diene II and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C21E..02I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C21E..02I"><span>Measurements of <span class="hlt">sea</span> <span class="hlt">ice</span> mass redistribution during <span class="hlt">ice</span> deformation event in Arctic winter</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Itkin, P.; Spreen, G.; King, J.; Rösel, A.; Skourup, H.; Munk Hvidegaard, S.; Wilkinson, J.; Oikkonen, A.; Granskog, M. A.; Gerland, S.</p> <p>2016-12-01</p> <p><span class="hlt">Sea-ice</span> growth during high winter is governed by <span class="hlt">ice</span> dynamics. The highest growth rates are found in leads that open under divergent conditions, where exposure to the cold atmosphere promotes thermodynamic growth. Additionally <span class="hlt">ice</span> thickens dynamically, where convergence causes rafting and ridging. We present a local study of <span class="hlt">sea-ice</span> growth and mass redistribution between two consecutive airborne measurements, on 19 and 24 April 2015, during the N-<span class="hlt">ICE</span>2015 expedition in the area north of Svalbard. Between the two overflights an <span class="hlt">ice</span> deformation event was observed. Airborne laser scanner (ALS) measurements revisited the same <span class="hlt">sea-ice</span> area of approximately 3x3 km. By identifying the <span class="hlt">sea</span> surface within the ALS measurements as a reference the <span class="hlt">sea</span> <span class="hlt">ice</span> plus snow freeboard was obtained with a spatial resolution of 5 m. By assuming isostatic equilibrium of level floes, the freeboard heights can be converted to <span class="hlt">ice</span> thickness. The snow depth is estimated from in-situ measurements. <span class="hlt">Sea</span> <span class="hlt">ice</span> thickness measurements were made in the same area as the ALS measurements by electromagnetic sounding from a helicopter (HEM), and with a ground-based device (EM31), which allows for cross-validation of the <span class="hlt">sea-ice</span> thickness estimated from all 3 procedures. Comparison of the ALS snow freeboard distributions between the first and second overflight shows a decrease in the thin <span class="hlt">ice</span> classes and an increase of the thick <span class="hlt">ice</span> classes. While there was no observable snowfall and a very low <span class="hlt">sea-ice</span> growth of older level <span class="hlt">ice</span> during this period, an autonomous buoy array deployed in the surroundings of the area measured by the ALS shows first divergence followed by convergence associated with shear. To quantify and link the <span class="hlt">sea</span> <span class="hlt">ice</span> deformation with the associated <span class="hlt">sea-ice</span> thickness change and mass redistribution we identify over 100 virtual buoys in the ALS data from both overflights. We triangulate the area between the buoys and calculate the strain rates and freeboard change for each individual triangle</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C41B0700O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C41B0700O"><span>Light Absorption in Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> - Black Carbon vs Chlorophyll</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ogunro, O. O.; Wingenter, O. W.; Elliott, S.; Hunke, E. C.; Flanner, M.; Wang, H.; Dubey, M. K.; Jeffery, N.</p> <p>2015-12-01</p> <p>The fingerprint of climate change is more obvious in the Arctic than any other place on Earth. This is not only because the surface temperature there has increased at twice the rate of global mean temperature but also because Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> has reached a record low of 49% reduction relative to the 1979-2000 climatology. Radiation absorption through black carbon (BC) deposited on Arctic snow and <span class="hlt">sea</span> <span class="hlt">ice</span> surface is one of the major hypothesized contributors to the decline. However, we note that chlorophyll-a absorption owing to increasing biology activity in this region could be a major competitor during boreal spring. Modeling of <span class="hlt">sea-ice</span> physical and biological processes together with experiments and field observations promise rapid progress in the quality of Arctic <span class="hlt">ice</span> predictions. Here we develop a dynamic <span class="hlt">ice</span> system module to investigate discrete absorption of both BC and chlorophyll in the Arctic, using BC deposition fields from version 5 of Community Atmosphere Model (CAM5) and vertically distributed layers of chlorophyll concentrations from <span class="hlt">Sea</span> <span class="hlt">Ice</span> Model (CICE). To this point, our black carbon mixing ratios compare well with available in situ data. Both results are in the same order of magnitude. Estimates from our calculations show that <span class="hlt">sea</span> <span class="hlt">ice</span> and snow around the Canadian Arctic Archipelago and Baffin Bay has the least black carbon absorption while values at the <span class="hlt">ice</span>-ocean perimeter in the region of the Barents <span class="hlt">Sea</span> peak significantly. With regard to pigment concentrations, high amounts of chlorophyll are produced in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> by the bottom microbial community, and also within the columnar pack wherever substantial biological activity takes place in the presence of moderate light. We show that the percentage of photons absorbed by chlorophyll in the spring is comparable to the amount attributed to BC, especially in areas where the total deposition rates are decreasing with time on interannual timescale. We expect a continuous increase in</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012TCD.....6..505F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012TCD.....6..505F"><span>Quantification of ikaite in 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>Fischer, M.; Thomas, D. N.; Krell, A.; Nehrke, G.; Göttlicher, J.; Norman, L.; Riaux-Gobin, C.; Dieckmann, G. S.</p> <p>2012-02-01</p> <p>Calcium carbonate precipitation in <span class="hlt">sea</span> <span class="hlt">ice</span> can increase pCO2 during precipitation in winter and decrease pCO2 during dissolution in spring. CaCO3 precipitation in <span class="hlt">sea</span> <span class="hlt">ice</span> is thought to potentially drive significant CO2 uptake by the ocean. However, little is known about the quantitative spatial and temporal distribution of CaCO3 within <span class="hlt">sea</span> <span class="hlt">ice</span>. This is the first quantitative study of hydrous calcium carbonate, as ikaite, in <span class="hlt">sea</span> <span class="hlt">ice</span> and discusses its potential significance for the carbon cycle in polar oceans. <span class="hlt">Ice</span> cores and brine samples were collected from pack and land fast <span class="hlt">sea</span> <span class="hlt">ice</span> between September and December 2007 during an expedition in the East Antarctic and another off Terre Adélie, Antarctica. Samples were analysed for CaCO3, Salinity, DOC, DON, Phosphate, and total alkalinity. A relationship between the measured parameters and CaCO3 precipitation could not be observed. We found calcium carbonate, as ikaite, mostly in the top layer of <span class="hlt">sea</span> <span class="hlt">ice</span> with values up to 126 mg ikaite per liter melted <span class="hlt">sea</span> <span class="hlt">ice</span>. This potentially represents a contribution between 0.12 and 9 Tg C to the annual carbon flux in polar oceans. The horizontal distribution of ikaite in <span class="hlt">sea</span> <span class="hlt">ice</span> was heterogenous. We also found the precipitate in the snow on top of the <span class="hlt">sea</span> <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ERL....13c4008Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ERL....13c4008Z"><span>Wind-<span class="hlt">sea</span> surface temperature-<span class="hlt">sea</span> <span class="hlt">ice</span> relationship in the Chukchi-Beaufort <span class="hlt">Seas</span> during autumn</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Jing; Stegall, Steve T.; Zhang, Xiangdong</p> <p>2018-03-01</p> <p>Dramatic climate changes, especially the largest <span class="hlt">sea</span> <span class="hlt">ice</span> retreat during September and October, in the Chukchi-Beaufort <span class="hlt">Seas</span> could be a consequence of, and further enhance, complex air-<span class="hlt">ice-sea</span> interactions. To detect these interaction signals, statistical relationships between surface wind speed, <span class="hlt">sea</span> surface temperature (SST), and <span class="hlt">sea</span> <span class="hlt">ice</span> concentration (SIC) were analyzed. The results show a negative correlation between wind speed and SIC. The relationships between wind speed and SST are complicated by the presence of <span class="hlt">sea</span> <span class="hlt">ice</span>, with a negative correlation over open water but a positive correlation in <span class="hlt">sea</span> <span class="hlt">ice</span> dominated areas. The examination of spatial structures indicates that wind speed tends to increase when approaching the <span class="hlt">ice</span> edge from open water and the area fully covered by <span class="hlt">sea</span> <span class="hlt">ice</span>. The anomalous downward radiation and thermal advection, as well as their regional distribution, play important roles in shaping these relationships, though wind-driven sub-grid scale boundary layer processes may also have contributions. Considering the feedback loop involved in the wind-SST-SIC relationships, climate model experiments would be required to further untangle the underlying complex physical processes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4406449','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4406449"><span>Comparing Springtime <span class="hlt">Ice</span>-Algal Chlorophyll a and Physical Properties of Multi-Year and First-Year <span class="hlt">Sea</span> <span class="hlt">Ice</span> from the Lincoln <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>Lange, Benjamin A.; Michel, Christine; Beckers, Justin F.; Casey, J. Alec; Flores, Hauke; Hatam, Ido; Meisterhans, Guillaume; Niemi, Andrea; Haas, Christian</p> <p>2015-01-01</p> <p>With near-complete replacement of Arctic multi-year <span class="hlt">ice</span> (MYI) by first-year <span class="hlt">ice</span> (FYI) predicted to occur within this century, it remains uncertain how the loss of MYI will impact the abundance and distribution of <span class="hlt">sea</span> <span class="hlt">ice</span> associated algae. In this study we compare the chlorophyll a (chl a) concentrations and physical properties of MYI and FYI from the Lincoln <span class="hlt">Sea</span> during 3 spring seasons (2010-2012). Cores were analysed for texture, salinity, and chl a. We identified annual growth layers for 7 of 11 MYI cores and found no significant differences in chl a concentration between the bottom first-year-<span class="hlt">ice</span> portions of MYI, upper old-<span class="hlt">ice</span> portions of MYI, and FYI cores. Overall, the maximum chl a concentrations were observed at the bottom of young FYI. However, there were no significant differences in chl a concentrations between MYI and FYI. This suggests little or no change in algal biomass with a shift from MYI to FYI and that the spatial <span class="hlt">extent</span> and regional variability of refrozen leads and younger FYI will likely be key factors governing future changes in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> algal biomass. Bottom-integrated chl a concentrations showed negative logistic relationships with snow depth and bulk (snow plus <span class="hlt">ice</span>) integrated extinction coefficients; indicating a strong influence of snow cover in controlling bottom <span class="hlt">ice</span> algal biomass. The maximum bottom MYI chl a concentration was observed in a hummock, representing the thickest <span class="hlt">ice</span> with lowest snow depth of this study. Hence, in this and other studies MYI chl a biomass may be under-estimated due to an under-representation of thick MYI (e.g., hummocks), which typically have a relatively thin snowpack allowing for increased light transmission. Therefore, we suggest the on-going loss of MYI in the Arctic Ocean may have a larger impact on ice–associated production than generally assumed. PMID:25901605</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 Alamos <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, <span class="hlt">extent</span> 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> <span class="hlt">extent</span>, 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/2009EGUGA..11.4734V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.4734V"><span><span class="hlt">Ice</span>2<span class="hlt">sea</span> - the future glacial contribution to <span class="hlt">sea</span>-level rise</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vaughan, D. G.; Ice2sea Consortium</p> <p>2009-04-01</p> <p>The melting of continental <span class="hlt">ice</span> (glaciers, <span class="hlt">ice</span> caps and <span class="hlt">ice</span> sheets) is a substantial source of current <span class="hlt">sea</span>-level rise, and one that is accelerating more rapidly than was predicted even a few years ago. Indeed, the most recent report from Intergovernmental Panel on Climate Change highlighted that the uncertainty in projections of future <span class="hlt">sea</span>-level rise is dominated by uncertainty concerning continental <span class="hlt">ice</span>, and that understanding of the key processes that will lead to loss of continental <span class="hlt">ice</span> must be improved before reliable projections of <span class="hlt">sea</span>-level rise can be produced. Such projections are urgently required for effective <span class="hlt">sea</span>-defence management and coastal adaptation planning. <span class="hlt">Ice</span>2<span class="hlt">sea</span> is a consortium of European institutes and international partners seeking European funding to support an integrated scientific programme to improve understanding concerning the future glacial contribution to <span class="hlt">sea</span>-level rise. This includes improving understanding of the processes that control, past, current and future <span class="hlt">sea</span>-level rise, and generation of improved estimates of the contribution of glacial components to <span class="hlt">sea</span>-level rise over the next 200 years. The programme will include targeted studies of key processes in mountain glacier systems and <span class="hlt">ice</span> caps (e.g. Svalbard), and in <span class="hlt">ice</span> sheets in both polar regions (Greenland and Antarctica) to improve understanding of how these systems will respond to future climate change. It will include fieldwork and remote sensing studies, and develop a suite of new, cross-validated glacier and <span class="hlt">ice</span>-sheet model. <span class="hlt">Ice</span>2<span class="hlt">sea</span> will deliver these results in forms accessible to scientists, policy-makers and the general public, which will include clear presentations of the sources of uncertainty. Our aim is both, to provide improved projections of the glacial contribution to <span class="hlt">sea</span>-level rise, and to leave a legacy of improved tools and techniques that will form the basis of ongoing refinements in <span class="hlt">sea</span>-level projection. <span class="hlt">Ice</span>2<span class="hlt">sea</span> will provide exciting opportunities for many</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/6760381-iceberg-severity-off-eastern-north-america-its-relationship-sea-ice-variability-climate-change','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/6760381-iceberg-severity-off-eastern-north-america-its-relationship-sea-ice-variability-climate-change"><span>Iceberg severity off eastern North America: Its relationship to <span class="hlt">sea</span> <span class="hlt">ice</span> variability and climate change</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Marko, J.R.; Fissel, D.B.; Wadhams, P.</p> <p>1994-09-01</p> <p>Iceberg trajectory, deterioration (mass loss), and <span class="hlt">sea</span> <span class="hlt">ice</span> data are reviewed to identify the sources of observed interannual and seasonal variations in the numbers of icebergs passing south of 48[degrees]N off eastern North America. The results show the dominant role of <span class="hlt">sea</span> <span class="hlt">ice</span> in the observed variations. Important mechanisms involved include both seasonal modulation of the southerly iceberg flow by <span class="hlt">ice</span> cover control of probabilities for entrapment and decay in shallow water, and the suppression of iceberg melt/deterioration rates by high concentrations of <span class="hlt">sea</span> <span class="hlt">ice</span>. The Labrador spring <span class="hlt">ice</span> <span class="hlt">extent</span>, shown to be the critical parameter in interannual iceberg numbermore » variability, was found to be either determined by or closely correlated with midwinter Davis Strait <span class="hlt">ice</span> <span class="hlt">extents</span>. Agreement obtained between observed year-to-year and seasonal number variations with computations based upon a simple iceberg dissipation model suggests that downstream iceberg numbers are relatively insensitive to iceberg production rates and to fluctuations in southerly iceberg fluxes in areas north of Baffin Island. Past variations in the Davis Strait <span class="hlt">ice</span> index and annual <span class="hlt">ice</span> <span class="hlt">extents</span> are studied to identify trends and relationships between regional and larger-scale global climate parameters. It was found that, on decadal timescales in the post-1960 period of reasonable data quality, regional climate parameters have varied, roughly, out of phase with corresponding global and hemispheric changes. These observations are compared with expectations in terms of model results to evaluate current GCM-based capabilities for simulating recent regional behavior. 64 refs., 11 figs., 3 tabs.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=GL-2002-002288&hterms=moderating&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dmoderating','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=GL-2002-002288&hterms=moderating&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dmoderating"><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('http://adsabs.harvard.edu/abs/2016EGUGA..1810825K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1810825K"><span>Data-Driven Modeling and Prediction of 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>Kondrashov, Dmitri; Chekroun, Mickael; Ghil, Michael</p> <p>2016-04-01</p> <p>We present results of data-driven predictive analyses of <span class="hlt">sea</span> <span class="hlt">ice</span> over the main Arctic regions. Our approach relies on the Multilayer Stochastic Modeling (MSM) framework of Kondrashov, Chekroun and Ghil [Physica D, 2015] and it leads to probabilistic prognostic models of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration (SIC) anomalies on seasonal time scales. This approach is applied to monthly time series of state-of-the-art data-adaptive decompositions of SIC and selected climate variables over the Arctic. We evaluate the predictive skill of MSM models by performing retrospective forecasts with "no-look ahead" for up to 6-months ahead. It will be shown in particular that the memory effects included intrinsically in the formulation of our non-Markovian MSM models allow for improvements of the prediction skill of large-amplitude SIC anomalies in certain Arctic regions on the one hand, and of September <span class="hlt">Sea</span> <span class="hlt">Ice</span> <span class="hlt">Extent</span>, on the other. Further improvements allowed by the MSM framework will adopt a nonlinear formulation and explore next-generation data-adaptive decompositions, namely modification of Principal Oscillation Patterns (POPs) and rotated Multichannel Singular Spectrum Analysis (M-SSA).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C31B0283C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C31B0283C"><span>Change in the <span class="hlt">Extent</span> of Baffin Island's Penny <span class="hlt">Ice</span> Cap in Response to Regional Warming, 1969 - 2014</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cox, M. C.; Cormier, H. M.; Gardner, A. S.</p> <p>2014-12-01</p> <p>Glaciers are retreating globally in response to warmer atmospheric temperatures, adding large volumes of melt water to the world's oceans. The largest glacierized region and present-day contributor to <span class="hlt">sea</span> level rise outside of the massive <span class="hlt">ice</span> sheets is the Canadian Arctic. Recent work has shown that the glaciers of the southern Canadian Arctic (Baffin and Bylot Island) have experienced accelerated rates of <span class="hlt">ice</span> loss in recent decades, but little is known regarding the spatial and temporal variations in rates of loss. For this study we examine in detail changes in the <span class="hlt">extent</span> of the Penny <span class="hlt">Ice</span> Cap (a proxy for <span class="hlt">ice</span> loss) between 1969 and 2014 to better understand the climatic drivers of the recently observed accelerated rates of <span class="hlt">ice</span> loss on Baffin Island. To do this, we reconstruct the <span class="hlt">extent</span> of the <span class="hlt">ice</span> cap for the year 1969 from historical maps and for the years 1985, 1995, 2010, and 2014 from Landsat 5 TM and Landsat 8 OLI imagery. We use 2009 SPOT HRS imagery and a novel <span class="hlt">extent</span> comparison algorithm to assess the accuracy of glacier <span class="hlt">extents</span> derived from Landsat imagery. Regional temperature and precipitation records were used to explain the spatial pattern of change. Due to large variation in elevations, hypsometry was also investigated as a contributor to differences in rates of change across the <span class="hlt">ice</span> cap. Preliminary results show overall retreat throughout the <span class="hlt">ice</span> cap but with regional differences in area and length change on either side of the <span class="hlt">Ice</span> Cap divide.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUOSHE14A1392Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUOSHE14A1392Z"><span>Seasonal and Interannual Variability of the Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span>: A Comparison between AO-FVCOM and Observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Y.; Chen, C.; Beardsley, R. C.; Gao, G.; Qi, J.; Lin, H.</p> <p>2016-02-01</p> <p>A high-resolution (up to 2 km), unstructured-grid, fully <span class="hlt">ice-sea</span> coupled Arctic Ocean Finite-Volume Community Ocean Model (AO-FVCOM) was used to simulate the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> over the period 1978-2014. Good agreements were found between simulated and observed <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, concentration, drift velocity and thickness, indicating that the AO-FVCOM captured not only the seasonal and interannual variability but also the spatial distribution of the <span class="hlt">sea</span> <span class="hlt">ice</span> in the Arctic in the past 37 years. Compared with other six Arctic Ocean models (ECCO2, GSFC, INMOM, ORCA, NAME and UW), the AO-FVCOM-simulated <span class="hlt">ice</span> thickness showed a higher correlation coefficient and a smaller difference with observations. An effort was also made to examine the physical processes attributing to the model-produced bias in the <span class="hlt">sea</span> <span class="hlt">ice</span> simulation. The error in the direction of the <span class="hlt">ice</span> drift velocity was sensitive to the wind turning angle; smaller when the wind was stronger, but larger when the wind was weaker. This error could lead to the bias in the near-surface current in the fully or partially <span class="hlt">ice</span>-covered zone where the <span class="hlt">ice-sea</span> interfacial stress was a major driving force.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-GSFC_20171208_Archive_e001599.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-GSFC_20171208_Archive_e001599.html"><span>Clouds Over <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2012-11-01</p> <p>Low-lying clouds over <span class="hlt">sea</span> <span class="hlt">ice</span> on the Bellingshausen <span class="hlt">Sea</span>. Credit: NASA / Maria-Jose Vinas NASA's Operation <span class="hlt">Ice</span>Bridge is an airborne science mission to study Earth's polar <span class="hlt">ice</span>. For more information about <span class="hlt">Ice</span>Bridge, visit: www.nasa.gov/icebridge NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.C43E0596D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.C43E0596D"><span>Potential Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> refuge for sustaining a remnant polar bear population (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Durner, G. M.; Amstrup, S. C.; Douglas, D. C.; Gautier, D. L.</p> <p>2010-12-01</p> <p>Polar bears depend on <span class="hlt">sea</span> <span class="hlt">ice</span> as a platform from which they capture seals. <span class="hlt">Sea</span> <span class="hlt">ice</span> availability must be spatially and temporally adequate for birth and weaning of seal pups, and to maximize seal hunting opportunities for polar bears. Projected declines in the spatial and temporal <span class="hlt">extent</span> of summer and autumn <span class="hlt">sea</span> <span class="hlt">ice</span> could potentially limit the ability of polar bears to build up body stores sufficient to maintain reproductive fitness. General circulation models, however, suggest that summer and autumn <span class="hlt">sea</span> <span class="hlt">ice</span> may persist in the shelf waters of the Canadian Archipelago and northern Greenland adjacent to the Arctic basin. While winter-formed <span class="hlt">ice</span> is important, a primary mechanism for <span class="hlt">sea</span> <span class="hlt">ice</span> accumulation in this region is by mechanical thickening of the <span class="hlt">sea</span> <span class="hlt">ice</span> facilitated by convergent forces from the Beaufort Gyre and the Transpolar Drift Stream. Collectively these areas could provide a polar bear refugium when other regions have lost the <span class="hlt">sea</span> <span class="hlt">ice</span> necessary to support viable populations. The potential for a polar bear refugium, however, must include other resource considerations. Projected declines of <span class="hlt">sea</span> <span class="hlt">ice</span> in the Northwest Passage may expose polar bears to hazards related to increase shipping and other commerce. Increasing global demands and limited opportunities elsewhere make the Arctic an increasingly attractive area for petroleum exploration. The Canadian Archipelago coincides with the Sverdrup basin, where petroleum accumulations have already been discovered but as yet are undeveloped. The Lincoln <span class="hlt">Sea</span> Basin offshore of northern Greenland has the geological possibility of significant petroleum accumulations, and northeastern Greenland is one of the most prospective areas in the Arctic for undiscovered oil. Activities associated with commerce and petroleum development could reduce the potential viability of the region as a polar bear refugium. Hence, if the goal is a sustainable (albeit reduced) polar bear population, important considerations include commerce</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017DyAtO..79...10S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017DyAtO..79...10S"><span>Sensitivity of open-water <span class="hlt">ice</span> growth and <span class="hlt">ice</span> concentration evolution in a coupled atmosphere-ocean-<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>Shi, Xiaoxu; Lohmann, Gerrit</p> <p>2017-09-01</p> <p>A coupled atmosphere-ocean-<span class="hlt">sea</span> <span class="hlt">ice</span> model is applied to investigate to what degree the area-thickness distribution of new <span class="hlt">ice</span> formed in open water affects the <span class="hlt">ice</span> and ocean properties. Two sensitivity experiments are performed which modify the horizontal-to-vertical aspect ratio of open-water <span class="hlt">ice</span> growth. The resulting changes in the Arctic <span class="hlt">sea-ice</span> concentration strongly affect the surface albedo, the ocean heat release to the atmosphere, and the <span class="hlt">sea-ice</span> production. The changes are further amplified through a positive feedback mechanism among the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>, the Atlantic Meridional Overturning Circulation (AMOC), and the surface air temperature in the Arctic, as the Fram Strait <span class="hlt">sea</span> <span class="hlt">ice</span> import influences the freshwater budget in the North Atlantic Ocean. Anomalies in <span class="hlt">sea-ice</span> transport lead to changes in <span class="hlt">sea</span> surface properties of the North Atlantic and the strength of AMOC. For the Southern Ocean, the most pronounced change is a warming along the Antarctic Circumpolar Current (ACC), owing to the interhemispheric bipolar seasaw linked to AMOC weakening. Another insight of this study lies on the improvement of our climate model. The ocean component FESOM is a newly developed ocean-<span class="hlt">sea</span> <span class="hlt">ice</span> model with an unstructured mesh and multi-resolution. We find that the subpolar <span class="hlt">sea-ice</span> boundary in the Northern Hemisphere can be improved by tuning the process of open-water <span class="hlt">ice</span> growth, which strongly influences the <span class="hlt">sea</span> <span class="hlt">ice</span> concentration in the marginal <span class="hlt">ice</span> zone, the North Atlantic circulation, salinity and Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> volume. Since the distribution of new <span class="hlt">ice</span> on open water relies on many uncertain parameters and the knowledge of the detailed processes is currently too crude, it is a challenge to implement the processes realistically into models. Based on our sensitivity experiments, we conclude a pronounced uncertainty related to open-water <span class="hlt">sea</span> <span class="hlt">ice</span> growth which could significantly affect the climate system sensitivity.</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/2016AGUFMOS43B2035W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMOS43B2035W"><span>Biogeochemical Coupling between Ocean and <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>Wang, S.; Jeffery, N.; Maltrud, M. E.; Elliott, S.; Wolfe, J.</p> <p>2016-12-01</p> <p>Biogeochemical processes in ocean and <span class="hlt">sea</span> <span class="hlt">ice</span> are tightly coupled at high latitudes. Ongoing changes in Arctic and Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> domain likely influence the coupled system, not only through physical fields but also biogeochemical properties. Investigating the system and its changes requires representation of ocean and <span class="hlt">sea</span> <span class="hlt">ice</span> biogeochemical cycles, as well as their coupling in Earth System Models. Our work is based on ACME-HiLAT, a new offshoot of the Community Earth System Model (CESM), including a comprehensive representation of marine ecosystems in the form of the Biogeochemical Elemental Cycling Module (BEC). A full vertical column <span class="hlt">sea</span> <span class="hlt">ice</span> biogeochemical module has recently been incorporated into the <span class="hlt">sea</span> <span class="hlt">ice</span> component. We have further introduced code modifications to couple key growth-limiting nutrients (N, Si, Fe), dissolved and particulate organic matter, and phytoplankton classes that are important in polar regions between ocean and <span class="hlt">sea</span> <span class="hlt">ice</span>. The coupling of ocean and <span class="hlt">sea</span> <span class="hlt">ice</span> biology-chemistry will enable representation of key processes such as the release of important climate active constituents or seeding algae from melting <span class="hlt">sea</span> <span class="hlt">ice</span> into surface waters. Sensitivity tests suggest <span class="hlt">sea</span> <span class="hlt">ice</span> and ocean biogeochemical coupling influences phytoplankton competition, biological production, and the CO2 flux. <span class="hlt">Sea</span> <span class="hlt">ice</span> algal seeding plays an important role in determining phytoplankton composition of Arctic early spring blooms, since different groups show various responses to the seeding biomass. Iron coupling leads to increased phytoplankton biomass in the Southern Ocean, which also affects carbon uptake via the biological pump. The coupling of macronutrients and organic matter may have weaker influences on the marine ecosystem. Our developments will allow climate scientists to investigate the fully coupled responses of the <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean BGC system to physical changes in polar climate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016TCry...10.1823S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016TCry...10.1823S"><span>Mapping and assessing variability in the Antarctic marginal <span class="hlt">ice</span> zone, pack <span class="hlt">ice</span> and coastal polynyas in two <span class="hlt">sea</span> <span class="hlt">ice</span> algorithms with implications on breeding success of snow petrels</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 C.; Jenouvrier, Stephanie; Campbell, G. Garrett; Barbraud, Christophe; Delord, Karine</p> <p>2016-08-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> variability within the marginal <span class="hlt">ice</span> zone (MIZ) and polynyas plays an important role for phytoplankton productivity and krill abundance. Therefore, mapping their spatial <span class="hlt">extent</span> as well as seasonal and interannual variability is essential for understanding how current and future changes in these biologically active regions may impact the Antarctic marine ecosystem. Knowledge of the distribution of MIZ, consolidated pack <span class="hlt">ice</span> and coastal polynyas in the total Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover may also help to shed light on the factors contributing towards recent expansion of the Antarctic <span class="hlt">ice</span> cover in some regions and contraction in others. The long-term passive microwave satellite data record provides the longest and most consistent record for assessing the proportion of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover that is covered by each of these <span class="hlt">ice</span> categories. However, estimates of the amount of MIZ, consolidated pack <span class="hlt">ice</span> and polynyas depend strongly on which <span class="hlt">sea</span> <span class="hlt">ice</span> algorithm is used. This study uses two popular passive microwave <span class="hlt">sea</span> <span class="hlt">ice</span> algorithms, the NASA Team and Bootstrap, and applies the same thresholds to the <span class="hlt">sea</span> <span class="hlt">ice</span> concentrations to evaluate the distribution and variability in the MIZ, the consolidated pack <span class="hlt">ice</span> and coastal polynyas. Results reveal that the seasonal cycle in the MIZ and pack <span class="hlt">ice</span> is generally similar between both algorithms, yet the NASA Team algorithm has on average twice the MIZ and half the consolidated pack <span class="hlt">ice</span> area as the Bootstrap algorithm. Trends also differ, with the Bootstrap algorithm suggesting statistically significant trends towards increased pack <span class="hlt">ice</span> area and no statistically significant trends in the MIZ. The NASA Team algorithm on the other hand indicates statistically significant positive trends in the MIZ during spring. Potential coastal polynya area and amount of broken <span class="hlt">ice</span> within the consolidated <span class="hlt">ice</span> pack are also larger in the NASA Team algorithm. The timing of maximum polynya area may differ by as much as 5 months between algorithms. These</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1248935','SCIGOV-STC'); return false;" href="https://www.osti.gov/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/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Walden, V. P.; Hudson, S. R.; Cohen, L.</p> <p></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 suitemore » 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.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ClDy...47.3301J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy...47.3301J"><span>The interaction between <span class="hlt">sea</span> <span class="hlt">ice</span> and salinity-dominated ocean circulation: implications for halocline stability and rapid changes of <span class="hlt">sea</span> <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>Jensen, Mari F.; Nilsson, Johan; Nisancioglu, Kerim H.</p> <p>2016-11-01</p> <p>Changes in the <span class="hlt">sea</span> <span class="hlt">ice</span> cover of the Nordic <span class="hlt">Seas</span> have been proposed to play a key role for the dramatic temperature excursions associated with the Dansgaard-Oeschger events during the last glacial. In this study, we develop a simple conceptual model to examine how interactions between <span class="hlt">sea</span> <span class="hlt">ice</span> and oceanic heat and freshwater transports affect the stability of an upper-ocean halocline in a semi-enclosed basin. The model represents a <span class="hlt">sea</span> <span class="hlt">ice</span> covered and salinity stratified Nordic <span class="hlt">Seas</span>, and consists of a <span class="hlt">sea</span> <span class="hlt">ice</span> component and a two-layer ocean. The <span class="hlt">sea</span> <span class="hlt">ice</span> thickness depends on the atmospheric energy fluxes as well as the ocean heat flux. We introduce a thickness-dependent <span class="hlt">sea</span> <span class="hlt">ice</span> export. Whether <span class="hlt">sea</span> <span class="hlt">ice</span> stabilizes or destabilizes against a freshwater perturbation is shown to depend on the representation of the diapycnal flow. In a system where the diapycnal flow increases with density differences, the <span class="hlt">sea</span> <span class="hlt">ice</span> acts as a positive feedback on a freshwater perturbation. If the diapycnal flow decreases with density differences, the <span class="hlt">sea</span> <span class="hlt">ice</span> acts as a negative feedback. However, both representations lead to a circulation that breaks down when the freshwater input at the surface is small. As a consequence, we get rapid changes in <span class="hlt">sea</span> <span class="hlt">ice</span>. In addition to low freshwater forcing, increasing deep-ocean temperatures promote instability and the disappearance of <span class="hlt">sea</span> <span class="hlt">ice</span>. Generally, the unstable state is reached before the vertical density difference disappears, and the temperature of the deep ocean do not need to increase as much as previously thought to provoke abrupt changes in <span class="hlt">sea</span> <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ClDy...49.1813H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ClDy...49.1813H"><span>Sensitivity of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> to the Southern Annular Mode in coupled climate models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Holland, Marika M.; Landrum, Laura; Kostov, Yavor; Marshall, John</p> <p>2017-09-01</p> <p>We assess the <span class="hlt">sea</span> <span class="hlt">ice</span> response to Southern Annular Mode (SAM) anomalies for pre-industrial control simulations from the Coupled Model Intercomparison Project (CMIP5). Consistent with work by Ferreira et al. (J Clim 28:1206-1226, 2015. doi: 10.1175/JCLI-D-14-00313.1), the models generally simulate a two-timescale response to positive SAM anomalies, with an initial increase in <span class="hlt">ice</span> followed by an eventual <span class="hlt">sea</span> <span class="hlt">ice</span> decline. However, the models differ in the cross-over time at which the change in <span class="hlt">ice</span> response occurs, in the overall magnitude of the response, and in the spatial distribution of the response. Late twentieth century Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> trends in CMIP5 simulations are related in part to different modeled responses to SAM variability acting on different time-varying transient SAM conditions. This explains a significant fraction of the spread in simulated late twentieth century southern hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> trends across the model simulations. Applying the modeled <span class="hlt">sea</span> <span class="hlt">ice</span> response to SAM variability but driven by the observed record of SAM suggests that variations in the austral summer SAM, which has exhibited a significant positive trend, have driven a modest <span class="hlt">sea</span> <span class="hlt">ice</span> decrease. However, additional work is needed to narrow the considerable model uncertainty in the climate response to SAM variability and its implications for 20th-21st century trends.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017NatCo...814991L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017NatCo...814991L"><span>On the discrepancy between observed and CMIP5 multi-model simulated Barents <span class="hlt">Sea</span> winter <span class="hlt">sea</span> <span class="hlt">ice</span> decline</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Dawei; Zhang, Rong; Knutson, Thomas R.</p> <p>2017-04-01</p> <p>This study aims to understand the relative roles of external forcing versus internal climate variability in causing the observed Barents <span class="hlt">Sea</span> winter <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> (SIE) decline since 1979. We identify major discrepancies in the spatial patterns of winter Northern Hemisphere <span class="hlt">sea</span> <span class="hlt">ice</span> concentration trends over the satellite period between observations and CMIP5 multi-model mean externally forced response. The CMIP5 externally forced decline in Barents <span class="hlt">Sea</span> winter SIE is much weaker than that observed. Across CMIP5 ensemble members, March Barents <span class="hlt">Sea</span> SIE trends have little correlation with global mean surface air temperature trends, but are strongly anti-correlated with trends in Atlantic heat transport across the Barents <span class="hlt">Sea</span> Opening (BSO). Further comparison with control simulations from coupled climate models suggests that enhanced Atlantic heat transport across the BSO associated with regional internal variability may have played a leading role in the observed decline in winter Barents <span class="hlt">Sea</span> SIE since 1979.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.C51B..01S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.C51B..01S"><span>Impacts of Declining Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span>: An International Challenge</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Serreze, M.</p> <p>2008-12-01</p> <p>As reported by the National Snow and <span class="hlt">Ice</span> Data Center in late August of 2008, Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> had already fallen to its second lowest level since regular monitoring began by satellite. As of this writing, we were closing in on the record minimum set in September of 2007. Summers may be free of <span class="hlt">sea</span> <span class="hlt">ice</span> by the year 2030. Recognition is growing that <span class="hlt">ice</span> loss will have environmental impacts that may extend well beyond the Arctic. The Arctic Ocean will in turn become more accessible, not just to tourism and commercial shipping, but to exploitation of oil wealth at the bottom of the ocean. In recognition of growing accessibility and oil operations, the United States Coast Guard set up temporary bases this summer at Barrow and Prudhoe Bay, AK, from which they conducted operations to test their readiness and capabilities, such as for search and rescue. The Canadians have been busy showing a strong Arctic presence. In August, a German crew traversed the Northwest Passage from east to west in one of their icebreakers, the Polarstern. What are the major national and international research efforts focusing on the multifaceted problem of declining <span class="hlt">sea</span> <span class="hlt">ice</span>? What are the areas of intersection, and what is the state of collaboration? How could national and international collaboration be improved? This talk will review some of these issues.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C51A0965H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C51A0965H"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Mass Reconciliation Exercise (SIMRE) for altimetry derived <span class="hlt">sea</span> <span class="hlt">ice</span> thickness data sets</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hendricks, S.; Haas, C.; Tsamados, M.; Kwok, R.; Kurtz, N. T.; Rinne, E. J.; Uotila, P.; Stroeve, J.</p> <p>2017-12-01</p> <p>Satellite altimetry is the primary remote sensing data source for retrieval of Arctic <span class="hlt">sea-ice</span> thickness. Observational data sets are available from current and previous missions, namely ESA's Envisat and CryoSat as well as NASA ICESat. In addition, freeboard results have been published from the earlier ESA ERS missions and candidates for new data products are the Sentinel-3 constellation, the CNES AltiKa mission and NASA laser altimeter successor ICESat-2. With all the different aspects of sensor type and orbit configuration, all missions have unique properties. In addition, thickness retrieval algorithms have evolved over time and data centers have developed different strategies. These strategies may vary in choice of auxiliary data sets, algorithm parts and product resolution and masking. The <span class="hlt">Sea</span> <span class="hlt">Ice</span> Mass Reconciliation Exercise (SIMRE) is a project by the <span class="hlt">sea-ice</span> radar altimetry community to bridge the challenges of comparing data sets across missions and algorithms. The ESA Arctic+ research program facilitates this project with the objective to collect existing data sets and to derive a reconciled estimate of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> mass balance. Starting with CryoSat-2 products, we compare results from different data centers (UCL, AWI, NASA JPL & NASA GSFC) at full resolution along selected orbits with independent <span class="hlt">ice</span> thickness estimates. Three regions representative of first-year <span class="hlt">ice</span>, multiyear <span class="hlt">ice</span> and mixed <span class="hlt">ice</span> conditions are used to compare the difference in thickness and thickness change between products over the seasonal cycle. We present first results and provide an outline for the further development of SIMRE activities. The methodology for comparing data sets is designed to be extendible and the project is open to contributions by interested groups. Model results of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness will be added in a later phase of the project to extend the scope of SIMRE beyond EO products.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C44B..08S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C44B..08S"><span>How will we ensure the long-term <span class="hlt">sea</span> <span class="hlt">ice</span> data record continues?</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.; Kaleschke, L.</p> <p>2017-12-01</p> <p>The multi-channel satellite passive microwave record has been of enormous benefit to the science community and society at large since the late 1970s. Starting with the launch of the Nimbus-7 Scanning Multi-Channel Microwave Radiometer (SMMR) in October 1978, and continuing with the launch of a series of Special Sensor Microwave Imagers (SSM/Is) in June 1987 by the Defense Meteorological Satellite Program (DMSP), places previously difficult to monitor year-round, such as the polar regions, came to light. Together these sensors have provided nearly 4 decades of climate data records on the state of <span class="hlt">sea</span> <span class="hlt">ice</span> cover over the ocean and snow on land. This data has also been used to map melt <span class="hlt">extent</span> on the large <span class="hlt">ice</span> sheets, timing of snow melt onset over land and <span class="hlt">sea</span> <span class="hlt">ice</span>. Application also extend well beyond the polar regions, mapping important climate variables, such as soil moisture content, oceanic wind speed, rainfall, water vapor, cloud liquid water and total precipitable water. Today the current SSMIS operational satellite (F18) is 7 years old and there is no follow-on mission planned by the DMSP. With the end of the SSMI family of Sensors, will the polar regions once again be in the dark? Other sensors that may contribute to the long-term data record include the JAXA AMSR2 (5 years old as of May 2017), the Chinese Fen-Yung-3 and the Russian Meteor-N2. Scatterometry and L-band radiometry from SMOS and NASA's SMOS may also provide some potential means of extending the <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> data record, as well as future sensors by the DoD, JAXA and ESA. However, this will require considerable effort to intercalibrate the different sensors to ensure consistency in the long-term data record. Differences in measurement approach, frequency and spatial resolution make this a non-trivial matter. The passive microwave <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> data record is one of the longest and most consistent climate data records available. It provides daily monitoring of one of the most striking changes in</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-GSFC_20171208_Archive_e000757.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-GSFC_20171208_Archive_e000757.html"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Sets New Record Winter Low</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2015-03-19</p> <p>The <span class="hlt">sea</span> <span class="hlt">ice</span> cap of the Arctic appeared to reach its annual maximum winter <span class="hlt">extent</span> on February 25, according to data from the NASA-supported National Snow and <span class="hlt">Ice</span> Data Center (NSIDC) at the University of Colorado, Boulder. At 5.61 million square miles (14.54 million square kilometers), this year’s maximum <span class="hlt">extent</span> was the smallest on the satellite record and also one of the earliest. Credit: NASA Goddard Space Flight Center NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUOSHE54B1584J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUOSHE54B1584J"><span>The interaction between <span class="hlt">sea</span> <span class="hlt">ice</span> and salinity-dominated ocean circulation: implications for halocline stability and rapid changes of <span class="hlt">sea-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>Jensen, M. F.; Nilsson, J.; Nisancioglu, K. H.</p> <p>2016-02-01</p> <p>In this study, we develop a simple conceptual model to examine how interactions between <span class="hlt">sea</span> <span class="hlt">ice</span> and oceanic heat and freshwater transports affect the stability of an upper-ocean halocline in a semi-enclosed basin. The model represents a <span class="hlt">sea-ice</span> covered and salinity stratified ocean, and consists of a <span class="hlt">sea-ice</span> component and a two-layer ocean; a cold, fresh surface layer above a warmer, more saline layer. The <span class="hlt">sea-ice</span> thickness depends on the atmospheric energy fluxes as well as the ocean heat flux. We introduce a thickness-dependent <span class="hlt">sea-ice</span> export. Whether <span class="hlt">sea</span> <span class="hlt">ice</span> stabilizes or destabilizes against a freshwater perturbation is shown to depend on the representation of the vertical mixing. In a system where the vertical diffusivity is constant, the <span class="hlt">sea</span> <span class="hlt">ice</span> acts as a positive feedback on a freshwater perturbation. If the vertical diffusivity is derived from a constant mixing energy constraint, the <span class="hlt">sea</span> <span class="hlt">ice</span> acts as a negative feedback. However, both representations lead to a circulation that breaks down when the freshwater input at the surface is small. As a consequence, we get rapid changes in <span class="hlt">sea</span> <span class="hlt">ice</span>. In addition to low freshwater forcing, increasing deep-ocean temperatures promote instability and the disappearance of <span class="hlt">sea</span> <span class="hlt">ice</span>. Generally, the unstable state is reached before the vertical density difference disappears, and small changes in temperature and freshwater inputs can provoke abrupt changes 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=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/2007AGUFM.C11B0430C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.C11B0430C"><span>Mining Existing Radar Altimetry for <span class="hlt">Sea</span> <span class="hlt">Ice</span> Freeboard and Thickness Estimates</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Childers, V. A.; Brozena, J. M.</p> <p>2007-12-01</p> <p>Although satellites can easily monitor <span class="hlt">ice</span> <span class="hlt">extent</span> and a variety of <span class="hlt">ice</span> attributes, they cannot directly measure <span class="hlt">ice</span> thickness. As a result, very few <span class="hlt">ice</span> thickness measurements exist to constrain models of Arctic climate change. We estimated <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard and thickness from X-band radar altimeter measurements collected over seven field seasons between 1992 and 1999 as part of a Naval Research Lab (NRL)-sponsored airborne geophysical survey of gravity and magnetics over the Arctic Ocean. These freeboard and thickness estimates were compared with the SCICEX <span class="hlt">ice</span> draft record and the observed thinning of the Arctic Ocean <span class="hlt">ice</span> cover during the 1990's. Our initial calculations (shown here) suggest that retrieved profiles from this radar altimeter (with uncertainty of about 5 cm) are sensitive to openings in the <span class="hlt">ice</span> cover. Thus, conversion of these profiles to <span class="hlt">ice</span> thickness adds an invaluable dataset for assessment of recent and future changes of Arctic climate. And, snow loading is a minor issue here as all the airborne surveys were conducted during mid- to late-summer when the <span class="hlt">ice</span> cover is mostly bare. The strengths of this dataset are its small antenna footprint of ~50 m and density of spatial coverage allows for detailed characterization of the field of <span class="hlt">ice</span> thickness, and it provides surveys of regions not covered by SCICEX cruises. The entire survey covers more than half the Arctic Ocean. We find that the Canadian Basin <span class="hlt">sea</span> <span class="hlt">ice</span> behavior differs from that in the Eurasian Basin and ultimately affects mean <span class="hlt">sea</span> <span class="hlt">ice</span> thickness for each basin.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1814515Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1814515Y"><span>Observed microphysical changes in Arctic mixed-phase clouds when transitioning from <span class="hlt">sea-ice</span> to open ocean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Young, Gillian; Jones, Hazel M.; Crosier, Jonathan; Bower, Keith N.; Darbyshire, Eoghan; Taylor, Jonathan W.; Liu, Dantong; Allan, James D.; Williams, Paul I.; Gallagher, Martin W.; Choularton, Thomas W.</p> <p>2016-04-01</p> <p>The Arctic <span class="hlt">sea-ice</span> is intricately coupled to the atmosphere[1]. The decreasing <span class="hlt">sea-ice</span> <span class="hlt">extent</span> with the changing climate raises questions about how Arctic cloud structure will respond. Any effort to answer these questions is hindered by the scarcity of atmospheric observations in this region. Comprehensive cloud and aerosol measurements could allow for an improved understanding of the relationship between surface conditions and cloud structure; knowledge which could be key in validating weather model forecasts. Previous studies[2] have shown via remote sensing that cloudiness increases over the marginal <span class="hlt">ice</span> zone (MIZ) and ocean with comparison to the <span class="hlt">sea-ice</span>; however, to our knowledge, detailed in-situ data of this transition have not been previously presented. In 2013, the Aerosol-Cloud Coupling and Climate Interactions in the Arctic (ACCACIA) campaign was carried out in the vicinity of Svalbard, Norway to collect in-situ observations of the Arctic atmosphere and investigate this issue. Fitted with a suite of remote sensing, cloud and aerosol instrumentation, the FAAM BAe-146 aircraft was used during the spring segment of the campaign (Mar-Apr 2013). One case study (23rd Mar 2013) produced excellent coverage of the atmospheric changes when transitioning from <span class="hlt">sea-ice</span>, through the MIZ, to the open ocean. Clear microphysical changes were observed, with the cloud liquid-water content increasing by almost four times over the transition. Cloud base, depth and droplet number also increased, whilst <span class="hlt">ice</span> number concentrations decreased slightly. The surface warmed by ~13 K from <span class="hlt">sea-ice</span> to ocean, with minor differences in aerosol particle number (of sizes corresponding to Cloud Condensation Nuclei or <span class="hlt">Ice</span> Nucleating Particles) observed, suggesting that the primary driver of these microphysical changes was the increased heat fluxes and induced turbulence from the warm ocean surface as expected. References: [1] Kapsch, M.L., Graversen, R.G. and Tjernström, M. Springtime</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JGRD..12210855K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JGRD..12210855K"><span>Vertical thermodynamic structure of the troposphere during the Norwegian young <span class="hlt">sea</span> <span class="hlt">ICE</span> expedition (N-<span class="hlt">ICE</span>2015)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kayser, Markus; Maturilli, Marion; Graham, Robert M.; Hudson, Stephen R.; Rinke, Annette; Cohen, Lana; Kim, Joo-Hong; Park, Sang-Jong; Moon, Woosok; Granskog, Mats A.</p> <p>2017-10-01</p> <p>The Norwegian young <span class="hlt">sea</span> <span class="hlt">ICE</span> (N-<span class="hlt">ICE</span>2015) expedition was designed to investigate the atmosphere-snow-<span class="hlt">ice</span>-ocean interactions in the young and thin <span class="hlt">sea</span> <span class="hlt">ice</span> regime north of Svalbard. Radiosondes were launched twice daily during the expedition from January to June 2015. Here we use these upper air measurements to study the multiple cyclonic events observed during N-<span class="hlt">ICE</span>2015 with respect to changes in the vertical thermodynamic structure, moisture content, and boundary layer characteristics. We provide statistics of temperature inversion characteristics, static stability, and boundary layer <span class="hlt">extent</span>. During winter, when radiative cooling is most effective, we find the strongest impact of synoptic cyclones. Changes to thermodynamic characteristics of the boundary layer are associated with transitions between the radiatively "clear" and "opaque" atmospheric states. In spring, radiative fluxes warm the surface leading to lifted temperature inversions and a statically unstable boundary layer. Further, we compare the N-<span class="hlt">ICE</span>2015 static stability distributions to corresponding profiles from ERA-Interim reanalysis, from the closest land station in the Arctic North Atlantic sector, Ny-Ålesund, and to soundings from the SHEBA expedition (1997/1998). We find similar stability characteristics for N-<span class="hlt">ICE</span>2015 and SHEBA throughout the troposphere, despite differences in location, <span class="hlt">sea</span> <span class="hlt">ice</span> thickness, and snow cover. For Ny-Ålesund, we observe similar characteristics above 1000 m, while the topography and <span class="hlt">ice</span>-free fjord surrounding Ny-Ålesund generate great differences below. The long-term radiosonde record (1993-2014) from Ny-Ålesund indicates that during the N-<span class="hlt">ICE</span>2015 spring period, temperatures were close to the climatological mean, while the lowest 3000 m were 1-3°C warmer than the climatology during winter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C21G1188D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C21G1188D"><span>Estimation of Melt Ponds over Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> using MODIS Surface Reflectance Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ding, Y.; Cheng, X.; Liu, J.</p> <p>2017-12-01</p> <p>Melt ponds over Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> is one of the main factors affecting variability of surface albedo, increasing absorption of solar radiation and further melting of snow and <span class="hlt">ice</span>. In recent years, a large number of melt ponds have been observed during the melt season in Arctic. Moreover, some studies have suggested that late spring to mid summer melt ponds information promises to improve the prediction skill of seasonal Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> minimum. In the study, we extract the melt pond fraction over Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> since 2000 using three bands MODIS weekly surface reflectance data by considering the difference of spectral reflectance in ponds, <span class="hlt">ice</span> and open water. The preliminary comparison shows our derived Arctic-wide melt ponds are in good agreement with that derived by the University of Hamburg, especially at the pond distribution. We analyze seasonal evolution, interannual variability and trend of the melt ponds, as well as the changes of onset and re-freezing. The melt pond fraction shows an asymmetrical growth and decay pattern. The observed melt ponds fraction is almost within 25% in early May and increases rapidly in June and July with a high fraction of more than 40% in the east of Greenland and Beaufort <span class="hlt">Sea</span>. A significant increasing trend in the melt pond fraction is observed for the period of 2000-2017. The relationship between melt pond fraction and <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> will be also discussed. Key Words: melt ponds, <span class="hlt">sea</span> <span class="hlt">ice</span>, Arctic</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA601522','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA601522"><span>Multiscale Models of Melting Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2013-09-30</p> <p>September 29, 2013 LONG-TERM GOALS <span class="hlt">Sea</span> <span class="hlt">ice</span> reflectance or albedo , a key parameter in climate modeling, is primarily determined by melt pond...and <span class="hlt">ice</span> floe configurations. <span class="hlt">Ice</span> - albedo feedback has played a major role in the recent declines of the summer Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> pack. However...understanding the evolution of melt ponds and <span class="hlt">sea</span> <span class="hlt">ice</span> albedo remains a significant challenge to improving climate models. Our research is focused on</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28607400','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28607400"><span>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> melt leads to atmospheric new particle formation.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Dall Osto, M; Beddows, D C S; Tunved, P; Krejci, R; Ström, J; Hansson, H-C; Yoon, Y J; Park, Ki-Tae; Becagli, S; Udisti, R; Onasch, T; O Dowd, C D; Simó, R; Harrison, Roy M</p> <p>2017-06-12</p> <p>Atmospheric new particle formation (NPF) and growth significantly influences climate by supplying new seeds for cloud condensation and brightness. Currently, there is a lack of understanding of whether and how marine biota emissions affect aerosol-cloud-climate interactions in the Arctic. Here, the aerosol population was categorised via cluster analysis of aerosol size distributions taken at Mt Zeppelin (Svalbard) during a 11 year record. The daily temporal occurrence of NPF events likely caused by nucleation in the polar marine boundary layer was quantified annually as 18%, with a peak of 51% during summer months. Air mass trajectory analysis and atmospheric nitrogen and sulphur tracers link these frequent nucleation events to biogenic precursors released by open water and melting <span class="hlt">sea</span> <span class="hlt">ice</span> regions. The occurrence of such events across a full decade was anti-correlated with <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>. New particles originating from open water and open pack <span class="hlt">ice</span> increased the cloud condensation nuclei concentration background by at least ca. 20%, supporting a marine biosphere-climate link through <span class="hlt">sea</span> <span class="hlt">ice</span> melt and low altitude clouds that may have contributed to accelerate Arctic warming. Our results prompt a better representation of biogenic aerosol sources in Arctic climate models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20000090513','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20000090513"><span>Update on the Greenland <span class="hlt">Ice</span> Sheet Melt <span class="hlt">Extent</span>: 1979-1999</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Abdalati, Waleed; Steffen, Konrad</p> <p>2000-01-01</p> <p>Analysis of melt <span class="hlt">extent</span> on the Greenland <span class="hlt">ice</span> sheet is updated to span the time period 1979-1999 is examined along with its spatial and temporal variability using passive microwave satellite data. In order to acquire the full record, the issue of continuity between previous passive microwave sensors (SMMR, SSM/I F-8, and SSM/I F-11), and the most recent SSM/I F-13 sensor is addressed. The F-13 Cross-polarized gradient ratio (XPGR) melt-classification threshold is determined to be -0.0154. Results show that for the 21-year record, an increasing melt trend of nearly 1 %/yr is observed, and this trend is driven by conditions on in the western portion of the <span class="hlt">ice</span> sheet, rather than the east, where melt appears to have decreased slightly. Moreover, the eruption of Mt. Pinatubo in 1991 is likely to have had some impact the melt, but not as much as previously suspected. The 1992 melt anomaly is 1.7 standard deviations from the mean. Finally, the relationship between coastal temperatures and melt <span class="hlt">extent</span> suggest an increase in surface runoff contribution to <span class="hlt">sea</span> level of 0.31 mm/yr for a 1 C temperature rise.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFM.C21B1106R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFM.C21B1106R"><span>Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span>-Atmosphere Interactions: A Self-organizing Map-based Perspective</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Reusch, D. B.</p> <p>2005-12-01</p> <p>Interactions between the ocean, <span class="hlt">sea</span> <span class="hlt">ice</span> and the atmosphere are a significant component of the dynamic nature of the Earth's climate system. Self-organizing maps (SOMs), an analysis tool from the field of artificial neural networks, have been used to study variability in Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and the West Antarctic atmospheric circulation, plus the relationship and interactions between these two systems. Self-organizing maps enable unsupervised classification of large, multivariate/multidimensional data sets, e.g., time series of the atmospheric circulation or <span class="hlt">sea-ice</span> <span class="hlt">extent</span>, into a fixed number of distinct generalized states or modes, organized spatially as a two-dimensional grid, that are representative of the input data. When applied to atmospheric data, the analysis yields a nonlinear classification of the continuum of atmospheric conditions. In contrast to principal component analysis, SOMs do not force orthogonality or require subjective rotations to produce interpretable patterns. Twenty four years (1973-96) of monthly <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> data (10 deg longitude bands; Simmonds and Jacka, 1995) were analyzed with a 30-node SOM. The resulting set of generalized patterns concisely captures the spatial and temporal variability in this data. An example of the former is variability in the longitudinal region of greatest <span class="hlt">extent</span>. The SOM patterns readily show that there are multiple spatial patterns corresponding to "greatest <span class="hlt">extent</span> conditions". Temporal variability is examined by creating frequency maps (i.e., which patterns occur most often) by month. With the annual cycle still in the data, the monthly frequency maps show a cycle moving from least <span class="hlt">extent</span>, through expansion to greatest <span class="hlt">extent</span> and back through retreat. When plotted in "SOM space", month-to-month transitions occur at different rates of change, suggesting that there is variability in the rate of change in <span class="hlt">extent</span> at different times of the year, e.g., retreat in January is faster than November. Twenty</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/28011294','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28011294"><span><span class="hlt">Sea-ice</span> eukaryotes of the Gulf of Finland, Baltic <span class="hlt">Sea</span>, and evidence for herbivory on weakly shade-adapted <span class="hlt">ice</span> algae.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Majaneva, Markus; Blomster, Jaanika; Müller, Susann; Autio, Riitta; Majaneva, Sanna; Hyytiäinen, Kirsi; Nagai, Satoshi; Rintala, Janne-Markus</p> <p>2017-02-01</p> <p>To determine community composition and physiological status of early spring <span class="hlt">sea-ice</span> organisms, we collected <span class="hlt">sea-ice</span>, slush and under-<span class="hlt">ice</span> water samples from the Baltic <span class="hlt">Sea</span>. We combined light microscopy, HPLC pigment analysis and pyrosequencing, and related the biomass and physiological status of <span class="hlt">sea-ice</span> algae with the protistan community composition in a new way in the area. In terms of biomass, centric diatoms including a distinct Melosira arctica bloom in the upper intermediate section of the fast <span class="hlt">ice</span>, dinoflagellates, euglenoids and the cyanobacterium Aphanizomenon sp. predominated in the <span class="hlt">sea-ice</span> sections and unidentified flagellates in the slush. Based on pigment analyses, the <span class="hlt">ice</span>-algal communities showed no adjusted photosynthetic pigment pools throughout the <span class="hlt">sea</span> <span class="hlt">ice</span>, and the bottom-<span class="hlt">ice</span> communities were not shade-adapted. The <span class="hlt">sea</span> <span class="hlt">ice</span> included more characteristic phototrophic taxa (49%) than did slush (18%) and under-<span class="hlt">ice</span> water (37%). Cercozoans and ciliates were the richest taxon groups, and the differences among the communities arose mainly from the various phagotrophic protistan taxa inhabiting the communities. The presence of pheophytin a coincided with an elevated ciliate biomass and read abundance in the drift <span class="hlt">ice</span> and with a high Eurytemora affinis read abundance in the pack <span class="hlt">ice</span>, indicating that ciliates and Eurytemora affinis were grazing on algae. Copyright © 2016 Elsevier GmbH. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://images.nasa.gov/#/details-PIA02456.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-PIA02456.html"><span><span class="hlt">Sea</span>Winds Wind-<span class="hlt">Ice</span> Interaction</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2000-05-07</p> <p>The figure demonstrates of the capability of the <span class="hlt">Sea</span>Winds instrument on NASA QuikScat satellite in monitoring both <span class="hlt">sea</span> <span class="hlt">ice</span> and ocean surface wind, thus helping to further our knowledge in wind-<span class="hlt">ice</span> interaction and its effect on climate change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010027899','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010027899"><span>Studies of Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Concentrations from Satellite Data and Their Applications</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.; Steffen, Konrad; Zukor, Dorothy J. (Technical Monitor)</p> <p>2001-01-01</p> <p>Large changes in the <span class="hlt">sea</span> <span class="hlt">ice</span> cover have been observed recently. Because of the relevance of such changes to climate change studies it is important that key <span class="hlt">ice</span> concentration data sets used for evaluating such changes are interpreted properly. High and medium resolution visible and infrared satellite data are used in conjunction with passive microwave data to study the true characteristics of the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover, assess errors in currently available <span class="hlt">ice</span> concentration products, and evaluate the applications and limitations of the latter in polar process studies. Cloud-free high resolution data provide valuable information about the natural distribution, stage of formation, and composition of the <span class="hlt">ice</span> cover that enables interpretation of the large spatial and temporal variability of the microwave emissivity of Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Comparative analyses of co-registered visible, infrared and microwave data were used to evaluate <span class="hlt">ice</span> concentrations derived from standard <span class="hlt">ice</span> algorithms (i.e., Bootstrap and Team) and investigate the 10 to 35% difference in derived values from large areas within the <span class="hlt">ice</span> pack, especially in the Weddell <span class="hlt">Sea</span>, Amundsen <span class="hlt">Sea</span>, and Ross <span class="hlt">Sea</span> regions. Landsat and OLS data show a predominance of thick consolidated <span class="hlt">ice</span> in these areas and show good agreement with the Bootstrap Algorithm. While direct measurements were not possible, the lower values from the Team Algorithm results are likely due to layering within the <span class="hlt">ice</span> and snow and/or surface flooding, which are known to affect the polarization ratio. In predominantly new <span class="hlt">ice</span> regions, the derived <span class="hlt">ice</span> concentration from passive microwave data is usually lower than the true percentage because the emissivity of new <span class="hlt">ice</span> changes with age and thickness and is lower than that of thick <span class="hlt">ice</span>. However, the product provides a more realistic characterization of the <span class="hlt">sea</span> <span class="hlt">ice</span> cover, and are more useful in polar process studies since it allows for the identification of areas of significant divergence and polynya</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC23D1174G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC23D1174G"><span>The Role of <span class="hlt">Sea</span> <span class="hlt">Ice</span> for Vascular Plant Dispersal 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>Geirsdottir, A.; Alsos, I. G.; Seidenkrantz, M. S.; Bennike, O.; Kirchhefer, A.; Ehrich, D.</p> <p>2015-12-01</p> <p>Plant species adapted to arctic environments are expected to go extinct at their southern margins due to climate warming whereas they may find suitable habitats on arctic islands if they are able to disperse there. Analyses of species distribution and phylogenetic data indicate both that the frequency of dispersal events is higher in the arctic than in other regions, and that the dispersal routes often follow the routes of <span class="hlt">sea</span> surface currents. Thus, it has been hypothesised that <span class="hlt">sea</span> <span class="hlt">ice</span> has played a central role in Holocene colonisation of arctic islands. Here we compile data on the first Holocene occurrence of species in East Greenland, Iceland, the Faroe Islands, and Svalbard. We then combine these records with interpretations of dispersal routes inferred from genetic data and data on geographical distributions, reconstructions of Holocene <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, and records of driftwood to evaluate the potential role <span class="hlt">sea</span> <span class="hlt">ice</span> has played in past colonisation events.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUOSHE44B1511L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUOSHE44B1511L"><span>Contribution of Increasing Glacial Freshwater Fluxes to Observed Trends in 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>Le Sommer, J.; Merino, N.; Durand, G.; Jourdain, N.; Goosse, H.; Mathiot, P.; Gurvan, M.</p> <p>2016-02-01</p> <p>Southern Ocean <span class="hlt">sea-ice</span> <span class="hlt">extent</span> has experienced an overall positive trend over recent decades. While the amplitude of this trend is open to debate, the geographical pattern of regional changes has been clearly identified by observations. Mechanisms driving changes in the Antarctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> <span class="hlt">Extent</span> (SIE) are not fully understood and climate models fail to simulate these trends. Changes in different atmospheric features such as SAM or ENSO seem to explain the observed trend of Antartic <span class="hlt">sea</span> <span class="hlt">ice</span>, but only partly, since they can not account for the actual amplitude of the observed signal. The increasing injection of freshwater due to the accelerating <span class="hlt">ice</span> discharge from Antarctica <span class="hlt">Ice</span> Sheet (AIS) during the last two decades has been proposed as another candidate to contribute to SIE trend. However, the quantity and the distribution of the extra freshwater injection were not properly constrained. Recent glaciological estimations may improve the way the glacial freshwater is injected in the model. Here, we study the role of the glacial freshwater into the observed SIE trend, using the state-of-the-art Antarctic mass loss estimations. Ocean/<span class="hlt">sea-ice</span> model simulations have been carried out with two different Antarctic freshwater scenarios corresponding to 20-years of Antarctic <span class="hlt">Ice</span> Sheet evolution. The combination of an improved iceberg model with the most recent glaciological estimations has been applied to account for the most realistic possible Antarctic freshwater evolution scenarios. Results suggest that Antarctica has contributed to almost a 30% of the observed trend in regions of the South Pacific and South East Indian sectors, but has little impact in the South Atlantic sector. We conclude that the observed SIE trend over the last decades is due to a combination of both an atmospheric forcing and the extra freshwater injection. Our results advocates that the evolution of glacial freshwater needs to be correctly represented in climate models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/20601510','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/20601510"><span>Proteorhodopsin-bearing bacteria 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>Koh, Eileen Y; Atamna-Ismaeel, Nof; Martin, Andrew; Cowie, Rebecca O M; Beja, Oded; Davy, Simon K; Maas, Elizabeth W; Ryan, Ken G</p> <p>2010-09-01</p> <p>Proteorhodopsins (PRs) are widespread bacterial integral membrane proteins that function as light-driven proton pumps. Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> supports a complex community of autotrophic algae, heterotrophic bacteria, viruses, and protists that are an important food source for higher trophic levels in <span class="hlt">ice</span>-covered regions of the Southern Ocean. Here, we present the first report of PR-bearing bacteria, both dormant and active, in Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> from a series of sites in the Ross <span class="hlt">Sea</span> using gene-specific primers. Positive PR sequences were generated from genomic DNA at all depths in <span class="hlt">sea</span> <span class="hlt">ice</span>, and these sequences aligned with the classes Alphaproteobacteria, Gammaproteobacteria, and Flavobacteria. The sequences showed some similarity to previously reported PR sequences, although most of the sequences were generally distinct. Positive PR sequences were also observed from cDNA reverse transcribed from RNA isolated from <span class="hlt">sea</span> <span class="hlt">ice</span> samples. This finding indicates that these sequences were generated from metabolically active cells and suggests that the PR gene is functional within <span class="hlt">sea</span> <span class="hlt">ice</span>. Both blue-absorbing and green-absorbing forms of PRs were detected, and only a limited number of blue-absorbing forms were found and were in the midsection of the <span class="hlt">sea</span> <span class="hlt">ice</span> profile in this study. Questions still remain regarding the protein's ecological functions, and ultimately, field experiments will be needed to establish the ecological and functional role of PRs in the <span class="hlt">sea</span> <span class="hlt">ice</span> ecosystem.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20000050208','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20000050208"><span>Active and Passive Microwave Determination of the Circulation and Characteristics of Weddell and Ross <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.; Liu, Xiang</p> <p>2000-01-01</p> <p>A combination of satellite microwave data sets are used in conjunction with ECMWF (Medium Range Weather Forecasts) and NCEP (National Center for Environment Prediction) meteorological analysis fields to investigate seasonal variability in the circulation and <span class="hlt">sea-ice</span> dynamics of the Weddell and Ross <span class="hlt">Seas</span>. Results of <span class="hlt">sea-ice</span> tracking using SSM/I (Special Sensor Microwave Imager), Scatterometer and SAR images are combined with in-situ data derived from Argos buoys and GPS drifters to validate observed drift patterns. Seasonal 3-month climatologies of <span class="hlt">ice</span> motion and drift speed variance illustrate the response of the <span class="hlt">sea-ice</span> system to seasonal forcing. A melt-detection algorithm is used to track the onset of seasonal melt, and to determine the <span class="hlt">extent</span> and duration of atmospherically-led surface melting during austral summer. Results show that wind-driven drift regulates the seasonal distribution and characteristics of <span class="hlt">sea-ice</span> and the intensity of the cyclonic Gyre circulation in these two regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19850017731&hterms=climate+exchange&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dclimate%2Bexchange','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19850017731&hterms=climate+exchange&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dclimate%2Bexchange"><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=19920073994&hterms=Parkinsons&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3DParkinsons','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19920073994&hterms=Parkinsons&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3DParkinsons"><span>Spatial patterns of increases and decreases in the length of the <span class="hlt">sea</span> <span class="hlt">ice</span> season in the north polar region, 1979-1986</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>1992-01-01</p> <p>Recently it was reported that <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extents</span> in the Northern Hemisphere showed a very slight but statistically significant decrease over the 8.8-year period of the Nimbus 7 scanning multichannel microwave radiometer (SMMR) data set. In this paper the same SMMR data are used to reveal spatial patterns in increasing and decreasing <span class="hlt">sea</span> <span class="hlt">ice</span> coverage. Specifically, the length of the <span class="hlt">ice</span> season is mapped for each full year of the SMMR data set (1979-1986), and the trends over the 8 years in these <span class="hlt">ice</span> season lengths are also mapped. These trends show considerable spatial coherence, with a shortening in the <span class="hlt">sea</span> <span class="hlt">ice</span> season apparent in much of the eastern hemisphere of the north polar <span class="hlt">ice</span> cover, particularly in the <span class="hlt">Sea</span> of Okhotsk, the Barents <span class="hlt">Sea</span>, and the Kara <span class="hlt">Sea</span>, and a lengthening of the <span class="hlt">sea</span> <span class="hlt">ice</span> season apparent in much of the western hemisphere of the north polar <span class="hlt">ice</span> cover, particularly in Davis Strait, the Labrador <span class="hlt">Sea</span>, and the Beaufort <span class="hlt">Sea</span>.</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/2017PrOce.156...17L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PrOce.156...17L"><span>Under the <span class="hlt">sea</span> <span class="hlt">ice</span>: Exploring the relationship between <span class="hlt">sea</span> <span class="hlt">ice</span> and the foraging behaviour of southern elephant seals in 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>Labrousse, Sara; Sallée, Jean-Baptiste; Fraser, Alexander D.; Massom, Robert A.; Reid, Phillip; Sumner, Michael; Guinet, Christophe; Harcourt, Robert; McMahon, Clive; Bailleul, Frédéric; Hindell, Mark A.; Charrassin, Jean-Benoit</p> <p>2017-08-01</p> <p>Investigating ecological relationships between predators and their environment is essential to understand the response of marine ecosystems to climate variability and change. This is particularly true in polar regions, where <span class="hlt">sea</span> <span class="hlt">ice</span> (a sensitive climate variable) plays a crucial yet highly dynamic and variable role in how it influences the whole marine ecosystem, from phytoplankton to top predators. For mesopredators such as seals, <span class="hlt">sea</span> <span class="hlt">ice</span> both supports a rich (under-<span class="hlt">ice</span>) food resource, access to which depends on local to regional coverage and conditions. Here, we investigate sex-specific relationships between the foraging strategies of southern elephant seals (Mirounga leonina) in winter and spatio-temporal variability in <span class="hlt">sea</span> <span class="hlt">ice</span> concentration (SIC) and coverage in East Antarctica. We satellite-tracked 46 individuals undertaking post-moult trips in winter from Kerguelen Islands to the peri-Antarctic shelf between 2004 and 2014. These data indicate distinct general patterns of <span class="hlt">sea</span> <span class="hlt">ice</span> usage: while females tended to follow the <span class="hlt">sea</span> <span class="hlt">ice</span> edge as it extended northward, the males remained on the continental shelf despite increasing <span class="hlt">sea</span> <span class="hlt">ice</span>. Seal hunting time, a proxy of foraging activity inferred from the diving behaviour, was longer for females in late autumn in the outer part of the pack <span class="hlt">ice</span>, ∼150-370 km south of the <span class="hlt">ice</span> edge. Within persistent regions of compact <span class="hlt">sea</span> <span class="hlt">ice</span>, females had a longer foraging activity (i) in the highest <span class="hlt">sea</span> <span class="hlt">ice</span> concentration at their position, but (ii) their foraging activity was longer when there were more patches of low concentration <span class="hlt">sea</span> <span class="hlt">ice</span> around their position (either in time or in space; 30 days & 50 km). The high spatio-temporal variability of <span class="hlt">sea</span> <span class="hlt">ice</span> around female positions is probably a key factor allowing them to exploit these concentrated patches. Despite lack of information on prey availability, females may exploit mesopelagic finfishes and squids that concentrate near the <span class="hlt">ice</span>-water interface or within the water column (from</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018ISPAr42.3.2419Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018ISPAr42.3.2419Z"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Drift Monitoring in the Bohai <span class="hlt">Sea</span> Based on GF4 Satellite</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhao, Y.; Wei, P.; Zhu, H.; Xing, B.</p> <p>2018-04-01</p> <p>The Bohai <span class="hlt">Sea</span> is the inland <span class="hlt">sea</span> with the highest latitude in China. In winter, the phenomenon of freezing occurs in the Bohai <span class="hlt">Sea</span> due to frequent cold wave influx. According to historical records, there have been three serious <span class="hlt">ice</span> packs in the Bohai <span class="hlt">Sea</span> in the past 50 years which caused heavy losses to our economy. Therefore, it is of great significance to monitor the drift of <span class="hlt">sea</span> <span class="hlt">ice</span> and <span class="hlt">sea</span> <span class="hlt">ice</span> in the Bohai <span class="hlt">Sea</span>. The GF4 image has the advantages of short imaging time and high spatial resolution. Based on the GF4 satellite images, the three methods of SIFT (Scale invariant feature - the transform and Scale invariant feature transform), MCC (maximum cross-correlation method) and sift combined with MCC are used to monitor <span class="hlt">sea</span> <span class="hlt">ice</span> drift and calculate the speed and direction of <span class="hlt">sea</span> <span class="hlt">ice</span> drift, the three calculation results are compared and analyzed by using expert interpretation and historical statistical data to carry out remote sensing monitoring of <span class="hlt">sea</span> <span class="hlt">ice</span> drift results. The experimental results show that the experimental results of the three methods are in accordance with expert interpretation and historical statistics. Therefore, the GF4 remote sensing satellite images have the ability to monitor <span class="hlt">sea</span> <span class="hlt">ice</span> drift and can be used for drift monitoring of <span class="hlt">sea</span> <span class="hlt">ice</span> in the Bohai <span class="hlt">Sea</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/AD1026542','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD1026542"><span>Ocean Profile Measurements During the Seasonal <span class="hlt">Ice</span> Zone Reconnaissance Surveys Ocean Profiles</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2017-01-01</p> <p>repeated ocean, <span class="hlt">ice</span>, and atmospheric measurements across the Beaufort-Chukchi <span class="hlt">sea</span> seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> zone (SIZ) utilizing US Coast Guard Arctic Domain...contributing to the rapid decline in summer <span class="hlt">ice</span> <span class="hlt">extent</span> that has occurred in recent years. The SIZ is the region between maximum winter <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and...minimum summer <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>. As such, it contains the full range of positions of the marginal <span class="hlt">ice</span> zone (MIZ) where <span class="hlt">sea</span> <span class="hlt">ice</span> interacts with open water</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>'. © 2016 The Author(s).</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('http://hdl.handle.net/2060/20080045474','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080045474"><span>Physical and Radiative Characteristic and Long-term Variability of the Okhotsk <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>Nishio, Fumihiko; Comiso, Josefino C.; Gersten, Robert; Nakayama, Masashige; Ukita, Jinro; Gasiewski, Al; Stanko, Boba; Naoki, Kazuhiro</p> <p>2008-01-01</p> <p>Much of what we know about the large scale characteristics of the Okhotsk <span class="hlt">Sea</span> <span class="hlt">ice</span> cover has been provided by <span class="hlt">ice</span> concentration maps derived from passive microwave data. To understand what satellite data represent in a highly divergent and rapidly changing environment like the Okhotsk <span class="hlt">Sea</span>, we take advantage of concurrent satellite, aircraft, and ship data acquired on 7 February and characterized the <span class="hlt">sea</span> <span class="hlt">ice</span> cover at different scales from meters to hundreds of kilometers. Through comparative analysis of surface features using co-registered data from visible, infrared and microwave channels we evaluated the general radiative and physical characteristics of the <span class="hlt">ice</span> cover as well as quantify the distribution of different <span class="hlt">ice</span> types in the region. <span class="hlt">Ice</span> concentration maps from AMSR-E using the standard sets of channels, and also only the 89 GHz channel for optimal resolution, are compared with aircraft and high resolution visible data and while the standard set provides consistent results, the 89 GHz provides the means to observe mesoscale patterns and some unique features of the <span class="hlt">ice</span> cover. Analysis of MODIS data reveals that thick <span class="hlt">ice</span> types represents about 37% of the <span class="hlt">ice</span> cover indicating that young and new <span class="hlt">ice</span> types represent a large fraction of the <span class="hlt">ice</span> cover that averages about 90% <span class="hlt">ice</span> concentration according to passive microwave data. These results are used to interpret historical data that indicate that the Okhotsk <span class="hlt">Sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and area are declining at a rapid rate of about -9% and -12 % per decade, respectively.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5371420','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5371420"><span>The frequency and <span class="hlt">extent</span> of sub-<span class="hlt">ice</span> phytoplankton blooms in the Arctic Ocean</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Horvat, Christopher; Jones, David Rees; Iams, Sarah; Schroeder, David; Flocco, Daniela; Feltham, Daniel</p> <p>2017-01-01</p> <p>In July 2011, the observation of a massive phytoplankton bloom underneath a <span class="hlt">sea</span> ice–covered region of the Chukchi <span class="hlt">Sea</span> shifted the scientific consensus that regions of the Arctic Ocean covered by <span class="hlt">sea</span> <span class="hlt">ice</span> were inhospitable to photosynthetic life. Although the impact of widespread phytoplankton blooms under <span class="hlt">sea</span> <span class="hlt">ice</span> on Arctic Ocean ecology and carbon fixation is potentially marked, the prevalence of these events in the modern Arctic and in the recent past is, to date, unknown. We investigate the timing, frequency, and evolution of these events over the past 30 years. Although <span class="hlt">sea</span> <span class="hlt">ice</span> strongly attenuates solar radiation, it has thinned significantly over the past 30 years. The thinner summertime Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> is increasingly covered in melt ponds, which permit more light penetration than bare or snow-covered <span class="hlt">ice</span>. Our model results indicate that the recent thinning of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> is the main cause of a marked increase in the prevalence of light conditions conducive to sub-<span class="hlt">ice</span> blooms. We find that as little as 20 years ago, the conditions required for sub-<span class="hlt">ice</span> blooms may have been uncommon, but their frequency has increased to the point that nearly 30% of the <span class="hlt">ice</span>-covered Arctic Ocean in July permits sub-<span class="hlt">ice</span> blooms. Recent climate change may have markedly altered the ecology of the Arctic Ocean. PMID:28435859</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170003145','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170003145"><span>Antarctic <span class="hlt">Sea-Ice</span> Freeboard and Estimated Thickness from NASA's ICESat and <span class="hlt">Ice</span>Bridge Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Yi, Donghui; Kurtz, Nathan; Harbeck, Jeremy; Manizade, Serdar; Hofton, Michelle; Cornejo, Helen G.; Zwally, H. Jay; Robbins, John</p> <p>2016-01-01</p> <p>ICESat completed 18 observational campaigns during its lifetime from 2003 to 2009. Data from all of the 18 campaign periods are used in this study. Most of the operational periods were between 34 and 38 days long. Because of laser failure and orbit transition from 8-day to 91-day orbit, there were four periods lasting 57, 16, 23, and 12 days. <span class="hlt">Ice</span>Bridge data from 2009, 2010, and 2011 are used in this study. Since 2009, there are 19 Airborne Topographic Mapper (ATM) campaigns, and eight Land, Vegetation, and <span class="hlt">Ice</span> Sensor (LVIS) campaigns over the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Freeboard heights are derived from ICESat, ATM and LVIS elevation and waveform data. With nominal densities of snow, water, and <span class="hlt">sea</span> <span class="hlt">ice</span>, combined with snow depth data from AMSR-E/AMSR2 passive microwave observation over the southern ocean, <span class="hlt">sea-ice</span> thickness is derived from the freeboard. Combined with AMSR-E/AMSR2 <span class="hlt">ice</span> concentration, <span class="hlt">sea-ice</span> area and volume are also calculated. During the 2003-2009 period, <span class="hlt">sea-ice</span> freeboard and thickness distributions show clear seasonal variations that reflect the yearly cycle of the growth and decay of the Antarctic pack <span class="hlt">ice</span>. We found no significant trend of thickness or area for the Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> during the ICESat period. <span class="hlt">Ice</span>Bridge <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard and thickness data from 2009 to 2011 over the Weddell <span class="hlt">Sea</span> and Amundsen and Bellingshausen <span class="hlt">Seas</span> are compared with the ICESat results.</p> </li> <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('http://adsabs.harvard.edu/abs/2013CliPa...9.2789S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013CliPa...9.2789S"><span>High-resolution mineral dust and <span class="hlt">sea</span> <span class="hlt">ice</span> proxy records from the Talos Dome <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>Schüpbach, S.; Federer, U.; Kaufmann, P. R.; Albani, S.; Barbante, C.; Stocker, T. F.; Fischer, H.</p> <p>2013-12-01</p> <p> <span class="hlt">ice</span> <span class="hlt">extent</span> in the Atlantic as well as the Indian Ocean sector of the Southern Ocean. In contrast, Holocene ssNa+ flux in Talos Dome is about the same as during the last interglacial, indicating that there was similar <span class="hlt">ice</span> cover present in the Ross <span class="hlt">Sea</span> area during MIS 5.5 as during the Holocene.</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('http://adsabs.harvard.edu/abs/2016CliPa..12.2195G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016CliPa..12.2195G"><span>Last Interglacial climate and <span class="hlt">sea</span>-level evolution from a coupled <span class="hlt">ice</span> sheet-climate model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Goelzer, Heiko; Huybrechts, Philippe; Loutre, Marie-France; Fichefet, Thierry</p> <p>2016-12-01</p> <p>As the most recent warm period in Earth's history with a <span class="hlt">sea</span>-level stand higher than present, the Last Interglacial (LIG, ˜ 130 to 115 kyr BP) is often considered a prime example to study the impact of a warmer climate on the two polar <span class="hlt">ice</span> sheets remaining today. Here we simulate the Last Interglacial climate, <span class="hlt">ice</span> sheet, and <span class="hlt">sea</span>-level evolution with the Earth system model of intermediate complexity LOVECLIM v.1.3, which includes dynamic and fully coupled components representing the atmosphere, the ocean and <span class="hlt">sea</span> <span class="hlt">ice</span>, the terrestrial biosphere, and the Greenland and Antarctic <span class="hlt">ice</span> sheets. In this setup, <span class="hlt">sea</span>-level evolution and climate-<span class="hlt">ice</span> sheet interactions are modelled in a consistent framework.Surface mass balance change governed by changes in surface meltwater runoff is the dominant forcing for the Greenland <span class="hlt">ice</span> sheet, which shows a peak <span class="hlt">sea</span>-level contribution of 1.4 m at 123 kyr BP in the reference experiment. Our results indicate that <span class="hlt">ice</span> sheet-climate feedbacks play an important role to amplify climate and <span class="hlt">sea</span>-level changes in the Northern Hemisphere. The sensitivity of the Greenland <span class="hlt">ice</span> sheet to surface temperature changes considerably increases when interactive albedo changes are considered. Southern Hemisphere polar and sub-polar ocean warming is limited throughout the Last Interglacial, and surface and sub-shelf melting exerts only a minor control on the Antarctic <span class="hlt">sea</span>-level contribution with a peak of 4.4 m at 125 kyr BP. Retreat of the Antarctic <span class="hlt">ice</span> sheet at the onset of the LIG is mainly forced by rising <span class="hlt">sea</span> level and to a lesser <span class="hlt">extent</span> by reduced <span class="hlt">ice</span> shelf viscosity as the surface temperature increases. Global <span class="hlt">sea</span> level shows a peak of 5.3 m at 124.5 kyr BP, which includes a minor contribution of 0.35 m from oceanic thermal expansion. Neither the individual contributions nor the total modelled <span class="hlt">sea</span>-level stand show fast multi-millennial timescale variations as indicated by some reconstructions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/5599042-airborne-gravity-measurement-over-sea-ice-western-weddel-sea','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/5599042-airborne-gravity-measurement-over-sea-ice-western-weddel-sea"><span>Airborne gravity measurement over <span class="hlt">sea-ice</span>: The western Weddel <span class="hlt">Sea</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Brozena, J.; Peters, M.; LaBrecque, J.</p> <p>1990-10-01</p> <p>An airborne gravity study of the western Weddel <span class="hlt">Sea</span>, east of the Antarctic Peninsula, has shown that floating pack-<span class="hlt">ice</span> provides a useful radar altimetric reference surface for altitude and vertical acceleration corrections surface for alititude and vertical acceleration corrections to airborne gravimetry. Airborne gravimetry provides an important alternative to satellite altimetry for the <span class="hlt">sea-ice</span> covered regions of the world since satellite alimeters are not designed or intended to provide accurate geoidal heights in areas where significant <span class="hlt">sea-ice</span> is present within the radar footprint. Errors in radar corrected airborne gravimetry are primarily sensitive to the variations in the second derivative ofmore » the <span class="hlt">sea-ice</span> reference surface in the frequency pass-band of interest. With the exception of imbedded icebergs the second derivative of the pack-<span class="hlt">ice</span> surface closely approximates that of the mean <span class="hlt">sea</span>-level surface at wavelengths > 10-20 km. With the airborne method the percentage of <span class="hlt">ice</span> coverage, the mixture of first and multi-year <span class="hlt">ice</span> and the existence of leads and pressure ridges prove to be unimportant in determining gravity anomalies at scales of geophysical and geodetic interest, provided that the <span class="hlt">ice</span> is floating and not grounded. In the Weddell study an analysis of 85 crosstrack miss-ties distributed over 25 data tracks yields an rms error of 2.2 mGals. Significant structural anomalies including the continental shelf and offsets and lineations interpreted as fracture zones recording the early spreading directions within the Weddell <span class="hlt">Sea</span> are observed in the gravity map.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C21G1192Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C21G1192Z"><span>Under <span class="hlt">Sea</span> <span class="hlt">Ice</span> phytoplankton bloom detection and contamination 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>Zeng, C.; Zeng, T.; Xu, H.</p> <p>2017-12-01</p> <p>Previous researches reported compelling <span class="hlt">sea</span> <span class="hlt">ice</span> phytoplankton bloom in Arctic, while seldom reports studied about Antarctic. Here, lab experiment showed <span class="hlt">sea</span> <span class="hlt">ice</span> increased the visible light albedo of the water leaving radiance. Even a new formed <span class="hlt">sea</span> <span class="hlt">ice</span> of 10cm thickness increased water leaving radiance up to 4 times of its original bare water. Given that phytoplankton preferred growing and accumulating under the <span class="hlt">sea</span> <span class="hlt">ice</span> with thickness of 10cm-1m, our results showed that the changing rate of OC4 estimated [Chl-a] varied from 0.01-0.5mg/m3 to 0.2-0.3mg/m3, if the water covered by 10cm <span class="hlt">sea</span> <span class="hlt">ice</span>. Going further, varying thickness of <span class="hlt">sea</span> <span class="hlt">ice</span> modulated the changing rate of estimating [Chl-a] non-linearly, thus current routine OC4 model cannot estimate under <span class="hlt">sea</span> <span class="hlt">ice</span> [Chl-a] appropriately. Besides, marginal <span class="hlt">sea</span> <span class="hlt">ice</span> zone has a large amount of mixture regions containing <span class="hlt">sea</span> <span class="hlt">ice</span>, water and snow, where is favorable for phytoplankton. We applied 6S model to estimate the <span class="hlt">sea</span> <span class="hlt">ice</span>/snow contamination on sub-pixel water leaving radiance of 4.25km spatial resolution ocean color products. Results showed that <span class="hlt">sea</span> <span class="hlt">ice</span>/snow scale effectiveness overestimated [Chl-a] concentration based on routine band ratio OC4 model, which contamination increased with the rising fraction of <span class="hlt">sea</span> <span class="hlt">ice</span>/snow within one pixel. Finally, we analyzed the under <span class="hlt">sea</span> <span class="hlt">ice</span> bloom in Antarctica based on the [Chl-a] concentration trends during 21 days after <span class="hlt">sea</span> <span class="hlt">ice</span> retreating. Regardless of those overestimation caused by <span class="hlt">sea</span> <span class="hlt">ice</span>/snow sub scale contamination, we still did not see significant under <span class="hlt">sea</span> <span class="hlt">ice</span> blooms in Antarctica in 2012-2017 compared with Arctic. This research found that Southern Ocean is not favorable for under <span class="hlt">sea</span> <span class="hlt">ice</span> blooms and the phytoplankton bloom preferred to occur in at least 3 weeks after <span class="hlt">sea</span> <span class="hlt">ice</span> retreating.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70187743','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70187743"><span>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> a major determinant in Mandt's black guillemot movement and distribution during non-breeding season</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Divoky, G.J.; Douglas, David C.; Stenhouse, I. J.</p> <p>2016-01-01</p> <p>Mandt's black guillemot (Cepphus grylle mandtii) is one of the few seabirds associated in all seasons with Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>, a habitat that is changing rapidly. Recent decreases in summer <span class="hlt">ice</span> have reduced breeding success and colony size of this species in Arctic Alaska. Little is known about the species' movements and distribution during the nine month non-breeding period (September–May), when changes in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and composition are also occurring and predicted to continue. To examine bird movements and the seasonal role of <span class="hlt">sea</span> <span class="hlt">ice</span> to non-breeding Mandt's black guillemots, we deployed and recovered (n = 45) geolocators on individuals at a breeding colony in Arctic Alaska during 2011–2015. Black guillemots moved north to the marginal <span class="hlt">ice</span> zone (MIZ) in the Beaufort and Chukchi <span class="hlt">seas</span> immediately after breeding, moved south to the Bering <span class="hlt">Sea</span> during freeze-up in December, and wintered in the Bering <span class="hlt">Sea</span> January–April. Most birds occupied the MIZ in regions averaging 30–60% <span class="hlt">sea</span> <span class="hlt">ice</span> concentration, with little seasonal variation. Birds regularly roosted on <span class="hlt">ice</span> in all seasons averaging 5 h d−1, primarily at night. By using the MIZ, with its roosting opportunities and associated prey, black guillemots can remain in the Arctic during winter when littoral waters are completely covered by <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C53C..03D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C53C..03D"><span>A Decade of High-Resolution Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Measurements from Airborne Altimetry</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Duncan, K.; Farrell, S. L.; Connor, L. N.; Jackson, C.; Richter-Menge, J.</p> <p>2017-12-01</p> <p>. Variability is linked to the geographic location and <span class="hlt">extent</span> of multi-year <span class="hlt">sea</span> <span class="hlt">ice</span>. Finally, we describe accessing our high-resolution data products at the NOAA Laboratory for Satellite Altimetry.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C21E..06L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C21E..06L"><span>Atmospheric forcing of <span class="hlt">sea</span> <span class="hlt">ice</span> leads in the Beaufort <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>Lewis, B. J.; Hutchings, J.; Mahoney, A. R.; Shapiro, L. H.</p> <p>2016-12-01</p> <p>Leads in <span class="hlt">sea</span> <span class="hlt">ice</span> play an important role in the polar marine environment where they allow heat and moisture transfer between the oceans and atmosphere and act as travel pathways for both marine mammals and ships. Examining AVHRR thermal imagery of the Beaufort <span class="hlt">Sea</span>, collected between 1994 and 2010, <span class="hlt">sea</span> <span class="hlt">ice</span> leads appear in repeating patterns and locations (Eicken et al 2005). The leads, resolved by AVHRR, are at least 250m wide (Mahoney et al 2012), thus the patterns described are for lead systems that extend up to hundreds of kilometers across the Beaufort <span class="hlt">Sea</span>. We describe how these patterns are associated with the location of weather systems relative to the coastline. Mean <span class="hlt">sea</span> level pressure and 10m wind fields from ECMWF ERA-Interim reanalysis are used to identify if particular lead patterns can be uniquely forecast based on the location of weather systems. <span class="hlt">Ice</span> drift data from the NSIDC's Polar Pathfinder Daily 25km EASE-Grid <span class="hlt">Sea</span> <span class="hlt">Ice</span> Motion Vectors indicates the role shear along leads has on the motion of <span class="hlt">ice</span> in the Beaufort Gyre. Lead formation is driven by 4 main factors: (i) coastal features such as promontories and islands influence the origin of leads by concentrating stresses within the <span class="hlt">ice</span> pack; (ii) direction of the wind forcing on the <span class="hlt">ice</span> pack determines the type of fracture, (iii) the location of the anticyclone (or cyclone) center determines the length of the fracture for certain patterns; and (iv) duration of weather conditions affects the width of the <span class="hlt">ice</span> fracture zones. Movement of the <span class="hlt">ice</span> pack on the leeward side of leads originating at promontories and islands increases, creating shear zones that control <span class="hlt">ice</span> transport along the Alaska coast in winter. . Understanding how atmospheric conditions influence the large-scale motion of the <span class="hlt">ice</span> pack is needed to design models that predict variability of the gyre and export of multi-year <span class="hlt">ice</span> to lower latitudes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014ESSD....6..367L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014ESSD....6..367L"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> in the Baltic <span class="hlt">Sea</span> - revisiting BASIS <span class="hlt">ice</span>, a historical data set covering the period 1960/1961-1978/1979</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Löptien, U.; Dietze, H.</p> <p>2014-12-01</p> <p>The Baltic <span class="hlt">Sea</span> is a seasonally <span class="hlt">ice</span>-covered, marginal <span class="hlt">sea</span> in central northern Europe. It is an essential waterway connecting highly industrialised countries. Because ship traffic is intermittently hindered by <span class="hlt">sea</span> <span class="hlt">ice</span>, the local weather services have been monitoring <span class="hlt">sea</span> <span class="hlt">ice</span> conditions for decades. In the present study we revisit a historical monitoring data set, covering the winters 1960/1961 to 1978/1979. This data set, dubbed Data Bank for Baltic <span class="hlt">Sea</span> <span class="hlt">Ice</span> and <span class="hlt">Sea</span> Surface Temperatures (BASIS) <span class="hlt">ice</span>, is based on hand-drawn maps that were collected and then digitised in 1981 in a joint project of the Finnish Institute of Marine Research (today the Finnish Meteorological Institute (FMI)) and the Swedish Meteorological and Hydrological Institute (SMHI). BASIS <span class="hlt">ice</span> was designed for storage on punch cards and all <span class="hlt">ice</span> information is encoded by five digits. This makes the data hard to access. Here we present a post-processed product based on the original five-digit code. Specifically, we convert to standard <span class="hlt">ice</span> quantities (including information on <span class="hlt">ice</span> types), which we distribute in the current and free Network Common Data Format (NetCDF). Our post-processed data set will help to assess numerical <span class="hlt">ice</span> models and provide easy-to-access unique historical reference material for <span class="hlt">sea</span> <span class="hlt">ice</span> in the Baltic <span class="hlt">Sea</span>. In addition we provide statistics showcasing the data quality. The website http://www.baltic-ocean.org hosts the post-processed data and the conversion code. The data are also archived at the Data Publisher for Earth & Environmental Science, PANGAEA (doi:10.1594/PANGAEA.832353).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C11D..02K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C11D..02K"><span>How robust is the atmospheric circulation response to Arctic <span class="hlt">sea-ice</span> loss in isolation?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kushner, P. J.; Hay, S. E.; Blackport, R.; McCusker, K. E.; Oudar, T.</p> <p>2017-12-01</p> <p>. Less robust is the part of the response that scales with low-latitude warming, which, depending on the model, can reinforce or cancel the response to <span class="hlt">sea-ice</span> loss. The <span class="hlt">extent</span> to which a "tug of war" exists between tropical and high-latitude influences on the general circulation might thus be model dependent.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70194758','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70194758"><span>Forecasting consequences of changing <span class="hlt">sea</span> <span class="hlt">ice</span> availability for Pacific walruses</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Udevitz, Mark S.; Jay, Chadwick V.; Taylor, Rebecca; Fischbach, Anthony S.; Beatty, William S.; Noren, Shawn R.</p> <p>2017-01-01</p> <p>The accelerating rate of anthropogenic alteration and disturbance of environments has increased the need for forecasting effects of environmental change on fish and wildlife populations. Models linking projections of environmental change with behavioral responses and bioenergetic effects can provide a basis for these forecasts. There is particular interest in forecasting effects of projected reductions in <span class="hlt">sea</span> <span class="hlt">ice</span> availability on Pacific walruses (Odobenus rosmarus divergens). Declining <span class="hlt">extent</span> of summer <span class="hlt">sea</span> <span class="hlt">ice</span> in the Chukchi <span class="hlt">Sea</span> has caused Pacific walruses to increase use of coastal haulouts and decrease use of more productive offshore feeding areas. Such climate-induced changes in distribution and behavior could ultimately affect the status of the population. We developed behavioral models to relate changes in <span class="hlt">sea</span> <span class="hlt">ice</span> availability to adult female walrus movements and activity levels, and adapted previously developed bioenergetics models to relate those activity levels to energy requirements and the ability to meet those requirements. We then linked these models to general circulation model projections of future <span class="hlt">ice</span> availability to forecast autumn body condition for female walruses during mid- and late-century time periods. Our results suggest that as <span class="hlt">sea</span> <span class="hlt">ice</span> becomes less available in the Chukchi <span class="hlt">Sea</span>, female walruses will spend more time in the southwestern region of that <span class="hlt">sea</span>, less time resting, and less time foraging. Median forecasted autumn body masses were 7–12% lower in future scenarios than during recent times, but posterior distributions broadly overlapped and median forecasted seasonal mass losses (15–34%) were comparable to seasonal mass losses routinely experienced by other pinnipeds. These seasonal reductions in body condition would be unlikely to result in demographic effects, but if walruses were unable to rebuild endogenous reserves while wintering in the Bering <span class="hlt">Sea</span>, cumulative effects could have implications for reproduction and survival</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017TCry...11..267D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017TCry...11..267D"><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 R.; McDonald, Adrian J.; Coggins, Jack H. J.; Rack, Wolfgang</p> <p>2017-01-01</p> <p>We investigate the impacts of strong wind events on the <span class="hlt">sea</span> <span class="hlt">ice</span> concentration within the Ross <span class="hlt">Sea</span> polynya (RSP), which may have consequences on <span class="hlt">sea</span> <span class="hlt">ice</span> formation. Bootstrap <span class="hlt">sea</span> <span class="hlt">ice</span> concentration (SIC) measurements derived from satellite SSM/I brightness temperatures are correlated with surface winds and temperatures from Ross <span class="hlt">Ice</span> Shelf automatic weather stations (AWSs) and weather models (ERA-Interim). Daily data in the austral winter period were used to classify characteristic weather regimes based on the percentiles of wind speed. For each regime a composite of a SIC anomaly was formed for the entire Ross <span class="hlt">Sea</span> region and 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 and vice versa. By analyzing <span class="hlt">sea</span> <span class="hlt">ice</span> motion vectors derived from the 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, which persist for several days after a strong wind event has ended. Strong, negative correlations are found between SIC and AWS wind speed within the RSP indicating that strong winds cause significant advection of <span class="hlt">sea</span> <span class="hlt">ice</span> in the region. We were able to partially recreate these correlations using colocated, modeled ERA-Interim wind speeds. However, large AWS and model differences are observed in the vicinity of Ross Island, where ERA-Interim underestimates wind speeds by a factor of 1.7 resulting in a significant misrepresentation of RSP processes in this area based on model data. Thus, the cross-correlation functions produced by compositing based on ERA-Interim wind speeds differed significantly from those produced with AWS wind speeds. In general the rapid decrease in SIC during a strong wind event is followed by a more gradual recovery in SIC. The SIC recovery continues over a time period greater than the average persistence of strong wind events and <span class="hlt">sea</span> <span class="hlt">ice</span> motion anomalies. This suggests that <span class="hlt">sea</span> <span class="hlt">ice</span></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/2008GeoRL..35.8501D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008GeoRL..35.8501D"><span>Calcium carbonate as ikaite crystals in 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>Dieckmann, Gerhard S.; Nehrke, Gernot; Papadimitriou, Stathys; Göttlicher, Jörg; Steininger, Ralph; Kennedy, Hilary; Wolf-Gladrow, Dieter; Thomas, David N.</p> <p>2008-04-01</p> <p>We report on the discovery of the mineral ikaite (CaCO3.6H2O) in <span class="hlt">sea-ice</span> from the Southern Ocean. The precipitation of CaCO3 during the freezing of seawater has previously been predicted from thermodynamic modelling, indirect measurements, and has been documented in artificial <span class="hlt">sea</span> <span class="hlt">ice</span> during laboratory experiments but has not been reported for natural <span class="hlt">sea-ice</span>. It is assumed that CaCO3 formation in <span class="hlt">sea</span> <span class="hlt">ice</span> may be important for a <span class="hlt">sea</span> <span class="hlt">ice</span>-driven carbon pump in <span class="hlt">ice</span>-covered oceanic waters. Without direct evidence of CaCO3 precipitation in <span class="hlt">sea</span> <span class="hlt">ice</span>, its role in this and other processes has remained speculative. The discovery of CaCO3.6H2O crystals in natural <span class="hlt">sea</span> <span class="hlt">ice</span> provides the necessary evidence for the evaluation of previous assumptions and lays the foundation for further studies to help elucidate the role of ikaite in the carbon cycle of the seasonally <span class="hlt">sea</span> <span class="hlt">ice</span>-covered regions</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940026115','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940026115"><span>The role of <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics in global climate change</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hibler, William D., III</p> <p>1992-01-01</p> <p>The topics covered include the following: general characteristics of <span class="hlt">sea</span> <span class="hlt">ice</span> drift; <span class="hlt">sea</span> <span class="hlt">ice</span> rheology; <span class="hlt">ice</span> thickness distribution; <span class="hlt">sea</span> <span class="hlt">ice</span> thermodynamic models; equilibrium thermodynamic models; effect of internal brine pockets and snow cover; model simulations of Arctic <span class="hlt">Sea</span> <span class="hlt">ice</span>; and sensitivity of <span class="hlt">sea</span> <span class="hlt">ice</span> models to climate change.</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('http://adsabs.harvard.edu/abs/2017AGUFM.C21B1124W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C21B1124W"><span>Synthesis of User Needs for Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Predictions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wiggins, H. V.; Turner-Bogren, E. J.; Sheffield Guy, L.</p> <p>2017-12-01</p> <p>Forecasting Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> on sub-seasonal to seasonal scales in a changing Arctic is of interest to a diverse range of stakeholders. However, <span class="hlt">sea</span> <span class="hlt">ice</span> forecasting is still challenging due to high variability in weather and ocean conditions and limits to prediction capabilities; the science needs for observations and modeling are extensive. At a time of challenged science funding, one way to prioritize <span class="hlt">sea</span> <span class="hlt">ice</span> prediction efforts is to examine the information needs of various stakeholder groups. This poster will present a summary and synthesis of existing surveys, reports, and other literature that examines user needs for <span class="hlt">sea</span> <span class="hlt">ice</span> predictions. The synthesis will include lessons learned from the <span class="hlt">Sea</span> <span class="hlt">Ice</span> Prediction Network (a collaborative, multi-agency-funded project focused on seasonal Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> predictions), the <span class="hlt">Sea</span> <span class="hlt">Ice</span> for Walrus Outlook (a resource for Alaska Native subsistence hunters and coastal communities, that provides reports on weather and <span class="hlt">sea</span> <span class="hlt">ice</span> conditions), and other efforts. The poster will specifically compare the scales and variables of <span class="hlt">sea</span> <span class="hlt">ice</span> forecasts currently available, as compared to what information is requested by various user groups.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5179961','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5179961"><span>Linking scales in <span class="hlt">sea</span> <span class="hlt">ice</span> mechanics</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Weiss, Jérôme; Dansereau, Véronique</p> <p>2017-01-01</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 ice’. PMID:28025300</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://eric.ed.gov/?q=kelp&id=EJ335092','ERIC'); return false;" href="https://eric.ed.gov/?q=kelp&id=EJ335092"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> and Oceanographic Conditions.</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>Oceanus, 1986</p> <p>1986-01-01</p> <p>The coastal waters of the Beaufort <span class="hlt">Sea</span> are covered with <span class="hlt">ice</span> three-fourths of the year. These waters (during winter) are discussed by considering: consolidation of coastal <span class="hlt">ice</span>; under-<span class="hlt">ice</span> water; brine circulation; biological energy; life under the <span class="hlt">ice</span> (including kelp and larger animals); food chains; and <span class="hlt">ice</span> break-up. (JN)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JGRC..121.8320Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JGRC..121.8320Z"><span>Seasonal and interannual variability of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>: A comparison between AO-FVCOM and observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Yu; Chen, Changsheng; Beardsley, Robert C.; Gao, Guoping; Qi, Jianhua; Lin, Huichan</p> <p>2016-11-01</p> <p>A high-resolution (up to 2 km), unstructured-grid, fully <span class="hlt">ice-sea</span> coupled Arctic Ocean Finite-Volume Community Ocean Model (AO-FVCOM) was used to simulate the <span class="hlt">sea</span> <span class="hlt">ice</span> in the Arctic over the period 1978-2014. The spatial-varying horizontal model resolution was designed to better resolve both topographic and baroclinic dynamics scales over the Arctic slope and narrow straits. The model-simulated <span class="hlt">sea</span> <span class="hlt">ice</span> was in good agreement with available observed <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, concentration, drift velocity and thickness, not only in seasonal and interannual variability but also in spatial distribution. Compared with six other Arctic Ocean models (ECCO2, GSFC, INMOM, ORCA, NAME, and UW), the AO-FVCOM-simulated <span class="hlt">ice</span> thickness showed a higher mean correlation coefficient of ˜0.63 and a smaller residual with observations. Model-produced <span class="hlt">ice</span> drift speed and direction errors varied with wind speed: the speed and direction errors increased and decreased as the wind speed increased, respectively. Efforts were made to examine the influences of parameterizations of air-<span class="hlt">ice</span> external and <span class="hlt">ice</span>-water interfacial stresses on the model-produced bias. The <span class="hlt">ice</span> drift direction was more sensitive to air-<span class="hlt">ice</span> drag coefficients and turning angles than the <span class="hlt">ice</span> drift speed. Increasing or decreasing either 10% in water-<span class="hlt">ice</span> drag coefficient or 10° in water-<span class="hlt">ice</span> turning angle did not show a significant influence on the <span class="hlt">ice</span> drift velocity simulation results although the <span class="hlt">sea</span> <span class="hlt">ice</span> drift speed was more sensitive to these two parameters than the <span class="hlt">sea</span> <span class="hlt">ice</span> drift direction. Using the COARE 4.0-derived parameterization of air-water drag coefficient for wind stress did not significantly influence the <span class="hlt">ice</span> drift velocity simulation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.U13C0068D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.U13C0068D"><span>Reemergence of <span class="hlt">sea</span> <span class="hlt">ice</span> cover anomalies and the role of the <span class="hlt">sea</span> <span class="hlt">ice</span>-albedo feedback in CCSM simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Deweaver, E. T.</p> <p>2008-12-01</p> <p>The dramatic <span class="hlt">sea</span> <span class="hlt">ice</span> decline of 2007 and lack of recovery in 2008 raise the question of a "tipping point" for Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>, beyond which the transition to a seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> state becomes abrupt and irreversible. The tipping point is essentially a "memory catastrophe", in which a dramatic loss of <span class="hlt">sea</span> <span class="hlt">ice</span> in one summer is "remembered" in reduced <span class="hlt">ice</span> thickness over the winter season and leads to a comparably dramatic loss the following summer. The dominant contributor to this memory is presumably the <span class="hlt">sea</span> <span class="hlt">ice</span> - albedo feedback (SIAF), in which excess insolation absorbed due to low summer <span class="hlt">ice</span> cover leads to a shorter <span class="hlt">ice</span> growth season and hence thinner <span class="hlt">ice</span>. While these dynamics are clearly important, they are difficult to quantify given the lack of long-term observations in the Arctic and the suddenness of the recent loss. Alternatively, we attempt to quantify the contribution of the SIAF to the year-to-year memory of <span class="hlt">sea</span> <span class="hlt">ice</span> cover anomalies in simulations of the NCAR Community Climate System Model (CCSM) under 20th century conditions. Lagged autocorrelation plots of <span class="hlt">sea</span> <span class="hlt">ice</span> area anomalies show that anomalies in one year tend to "reemerge" in the following year. Further experiments using a slab ocean model (SOM) are used to assess the contribution of oceanic processes to the year-to-year reemergence. This contribution is substantial, particularly in the winter season, and includes memory due to the standard mixed layer reemergence mechanism and low-frequency ocean heat transport anomalies. The contribution of the SIAF to persistence in the SOM experiment is determined through additional experiments in which the SIAF is disabled by fixing surface albedo to its climatological value regardless of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration anomalies. SIAF causes a 50% increase in the magnitude of the anomalies but a relatively small increase in their persistence. Persistence is not dramatically increased because the enhancement of shortwave flux anomalies by SIAF is compensated by stronger</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C33B1190R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C33B1190R"><span>Atmospheric Influences on the Anomalous 2016 Antarctic <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>Raphael, M. N.; Schlosser, E.; Haumann, A.</p> <p>2017-12-01</p> <p>Over the past three decades, a small but significant increase in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> (SIE) has been observed in the Antarctic. However, in 2016 there was a surprisingly early onset of the melt season. The maximum Antarctic SIE was reached in August rather than end of September, and was followed by a rapid decrease. The decline of the <span class="hlt">sea</span> <span class="hlt">ice</span> area (SIA) started even earlier, in July. The retreat of the <span class="hlt">ice</span> was particularly large in November where Antarctic SIE exhibited a negative anomaly (compared to the 1981-2010 average) of almost 2 Mio. km2, which, combined with reduced Arctic SIE, led to a distinct minimum in global SIE. And, satellite observations show that from November 2016 to February 2017, the daily Antarctic SIE has been at record low levels. We use <span class="hlt">sea</span> level pressure and geopotential height data from the ECMWF- Interim reanalysis, in conjunction with <span class="hlt">sea</span> <span class="hlt">ice</span> data obtained from the National Snow and <span class="hlt">Ice</span> Data Centre (NSIDC), to investigate possible atmospheric influences on the observed phenomena. Indications are that both the onset of the melt in July and the rapid decrease in SIA and SIE in November were triggered by atmospheric flow patterns related to a positive Zonal Wave 3 index, i.e. synoptic situations leading to strong meridional flow. Additionally the Southern Annular Mode (SAM) index reached its second lowest November value since the beginning of the satellite observations. It is likely that the SIE decrease was preconditioned by SIA decrease. Positive feedback effects led to accelerated melt and consequently to the extraordinary low November SIE.</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('https://images.nasa.gov/#/details-GSFC_20171208_Archive_e001600.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-GSFC_20171208_Archive_e001600.html"><span>Iceberg trapped in <span class="hlt">sea</span> <span class="hlt">ice</span></span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2012-11-01</p> <p>An iceberg trapped in <span class="hlt">sea</span> <span class="hlt">ice</span> in the Amundsen <span class="hlt">Sea</span>, seen from the <span class="hlt">Ice</span>Bridge DC-8 during the Getz 07 mission on Oct. 27. Credit: NASA / Maria-Jose Vinas NASA's Operation <span class="hlt">Ice</span>Bridge is an airborne science mission to study Earth's polar <span class="hlt">ice</span>. For more information about <span class="hlt">Ice</span>Bridge, visit: www.nasa.gov/icebridge NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram</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/2015JGRC..120..471M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRC..120..471M"><span>Drivers of inorganic carbon dynamics in first-year <span class="hlt">sea</span> <span class="hlt">ice</span>: A model study</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moreau, Sébastien; Vancoppenolle, Martin; Delille, Bruno; Tison, Jean-Louis; Zhou, Jiayun; Kotovitch, Marie; Thomas, David N.; Geilfus, Nicolas-Xavier; Goosse, Hugues</p> <p>2015-01-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> is an active source or a sink for carbon dioxide (CO2), although to what <span class="hlt">extent</span> is not clear. Here, we analyze CO2 dynamics within <span class="hlt">sea</span> <span class="hlt">ice</span> using a one-dimensional halothermodynamic <span class="hlt">sea</span> <span class="hlt">ice</span> model including gas physics and carbon biogeochemistry. The <span class="hlt">ice</span>-ocean fluxes, and vertical transport, of total dissolved inorganic carbon (DIC) and total alkalinity (TA) are represented using fluid transport equations. Carbonate chemistry, the consumption, and release of CO2 by primary production and respiration, the precipitation and dissolution of ikaite (CaCO3·6H2O) and <span class="hlt">ice</span>-air CO2 fluxes, are also included. The model is evaluated using observations from a 6 month field study at Point Barrow, Alaska, and an <span class="hlt">ice</span>-tank experiment. At Barrow, results show that the DIC budget is mainly driven by physical processes, wheras brine-air CO2 fluxes, ikaite formation, and net primary production, are secondary factors. In terms of <span class="hlt">ice</span>-atmosphere CO2 exchanges, <span class="hlt">sea</span> <span class="hlt">ice</span> is a net CO2 source and sink in winter and summer, respectively. The formulation of the <span class="hlt">ice</span>-atmosphere CO2 flux impacts the simulated near-surface CO2 partial pressure (pCO2), but not the DIC budget. Because the simulated <span class="hlt">ice</span>-atmosphere CO2 fluxes are limited by DIC stocks, and therefore <2 mmol m-2 d-1, we argue that the observed much larger CO2 fluxes from eddy covariance retrievals cannot be explained by a <span class="hlt">sea</span> <span class="hlt">ice</span> direct source and must involve other processes or other sources of CO2. Finally, the simulations suggest that near-surface TA/DIC ratios of ˜2, sometimes used as an indicator of calcification, would rather suggest outgassing.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1711342M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1711342M"><span>Drivers of inorganic carbon dynamics in first-year <span class="hlt">sea</span> <span class="hlt">ice</span>: A model study</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moreau, Sébastien; Vancoppenolle, Martin; Delille, Bruno; Tison, Jean-Louis; Zhou, Jiayun; Kotovich, Marie; Thomas, David; Geilfus, Nicolas-Xavier; Goosse, Hugues</p> <p>2015-04-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> is an active source or a sink for carbon dioxide (CO2), although to what <span class="hlt">extent</span> is not clear. Here, we analyze CO2 dynamics within <span class="hlt">sea</span> <span class="hlt">ice</span> using a one-dimensional halo-thermodynamic <span class="hlt">sea</span> <span class="hlt">ice</span> model including gas physics and carbon biogeochemistry. The <span class="hlt">ice</span>-ocean fluxes, and vertical transport, of total dissolved inorganic carbon (DIC) and total alkalinity (TA) are represented using fluid transport equations. Carbonate chemistry, the consumption and release of CO2 by primary production and respiration, the precipitation and dissolution of ikaite (CaCO3•6H2O) and <span class="hlt">ice</span>-air CO2 fluxes, are also included. The model is evaluated using observations from a 6-month field study at Point Barrow, Alaska and an <span class="hlt">ice</span>-tank experiment. At Barrow, results show that the DIC budget is mainly driven by physical processes, wheras brine-air CO2 fluxes, ikaite formation, and net primary production, are secondary factors. In terms of <span class="hlt">ice</span>-atmosphere CO2 exchanges, <span class="hlt">sea</span> <span class="hlt">ice</span> is a net CO2 source and sink in winter and summer, respectively. The formulation of the <span class="hlt">ice</span>-atmosphere CO2 flux impacts the simulated near-surface CO2 partial pressure (pCO2), but not the DIC budget. Because the simulated <span class="hlt">ice</span>-atmosphere CO2 fluxes are limited by DIC stocks, and therefore < 2 mmol m-2 day-1, we argue that the observed much larger CO2 fluxes from eddy covariance retrievals cannot be explained by a <span class="hlt">sea</span> <span class="hlt">ice</span> direct source and must involve other processes or other sources of CO2. Finally, the simulations suggest that near surface TA/DIC ratios of ~2, sometimes used as an indicator of calcification, would rather suggest outgassing.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014ESSDD...7..419L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014ESSDD...7..419L"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> in the Baltic <span class="hlt">Sea</span> - revisiting BASIS <span class="hlt">ice</span>, a~historical data set covering the period 1960/1961-1978/1979</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Löptien, U.; Dietze, H.</p> <p>2014-06-01</p> <p>The Baltic <span class="hlt">Sea</span> is a seasonally <span class="hlt">ice</span>-covered, marginal <span class="hlt">sea</span>, situated in central northern Europe. It is an essential waterway connecting highly industrialised countries. Because ship traffic is intermittently hindered by <span class="hlt">sea</span> <span class="hlt">ice</span>, the local weather services have been monitoring <span class="hlt">sea</span> <span class="hlt">ice</span> conditions for decades. In the present study we revisit a historical monitoring data set, covering the winters 1960/1961. This data set, dubbed Data Bank for Baltic <span class="hlt">Sea</span> <span class="hlt">Ice</span> and <span class="hlt">Sea</span> Surface Temperatures (BASIS) <span class="hlt">ice</span>, is based on hand-drawn maps that were collected and then digitised 1981 in a joint project of the Finnish Institute of Marine Research (today Finish Meteorological Institute (FMI)) and the Swedish Meteorological and Hydrological Institute (SMHI). BASIS <span class="hlt">ice</span> was designed for storage on punch cards and all <span class="hlt">ice</span> information is encoded by five digits. This makes the data hard to access. Here we present a post-processed product based on the original five-digit code. Specifically, we convert to standard <span class="hlt">ice</span> quantities (including information on <span class="hlt">ice</span> types), which we distribute in the current and free Network Common Data Format (NetCDF). Our post-processed data set will help to assess numerical <span class="hlt">ice</span> models and provide easy-to-access unique historical reference material for <span class="hlt">sea</span> <span class="hlt">ice</span> in the Baltic <span class="hlt">Sea</span>. In addition we provide statistics showcasing the data quality. The website <a href="www.baltic-ocean.org"target="_blank">www.baltic-ocean.org<a/> hosts the post-prossed data and the conversion code. The data are also archived at the Data Publisher for Earth & Environmental Science PANGEA (<a href="http://dx.doi.org/"target="_blank">doi:10.1594/PANGEA.832353<a/>).</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('http://adsabs.harvard.edu/abs/2011AGUFM.C41D0434C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.C41D0434C"><span><span class="hlt">Ice</span> Sheet and <span class="hlt">Sea</span> <span class="hlt">Ice</span> Observations from Unmanned Aircraft Systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Crocker, R. I.; Maslanik, J. A.</p> <p>2011-12-01</p> <p>A suite of sensors has been assembled to map <span class="hlt">ice</span> sheet and <span class="hlt">sea</span> <span class="hlt">ice</span> surface topography with fine-resolution from small unmanned aircraft systems (UAS). This payload is optimized to provide coincident surface elevation and imagery data, and with its low cost and ease of reproduction, it has the potential to become a widely-distributed observational resource to complement polar manned-aircraft and satellite missions. To date, it has been deployed to map <span class="hlt">ice</span> sheet elevations near Jakobshavn Isbræ in Greenland, and to measure <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard and roughness in Fram Strait off the coast of Svalbard. Data collected during these campaigns have facilitate a detailed assessment of the system's surface elevation measurement accuracy, and provide a glimpse of the summer 2009 Fram Strait <span class="hlt">sea</span> <span class="hlt">ice</span> conditions. These findings are presented, along with a brief overview of our future Arctic UAS operations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ESASP.739E..42Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ESASP.739E..42Z"><span>Techniques for <span class="hlt">Sea</span> <span class="hlt">Ice</span> Characteristics Extraction and <span class="hlt">Sea</span> <span class="hlt">Ice</span> Monitoring Using Multi-Sensor Satellite Data in the Bohai <span class="hlt">Sea</span>-Dragon 3 Programme Final Report (2012-2016)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Xi; Zhang, Jie; Meng, Junmin</p> <p>2016-08-01</p> <p>The objectives of Dragon-3 programme (ID: 10501) are to develop methods for classification <span class="hlt">sea</span> <span class="hlt">ice</span> types and retrieving <span class="hlt">ice</span> thickness based on multi-sensor data. In this final results paper, we give a briefly introduction for our research work and mainly results. Key words: the Bohai <span class="hlt">Sea</span> <span class="hlt">ice</span>, <span class="hlt">Sea</span> <span class="hlt">ice</span>, optical and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JGRC..120..647F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRC..120..647F"><span>The refreezing of melt ponds 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>Flocco, Daniela; Feltham, Daniel L.; Bailey, Eleanor; Schroeder, David</p> <p>2015-02-01</p> <p>The presence of melt ponds on the surface of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> significantly reduces its albedo, inducing a positive feedback leading to <span class="hlt">sea</span> <span class="hlt">ice</span> thinning. While the role of melt ponds in enhancing the summer melt of <span class="hlt">sea</span> <span class="hlt">ice</span> is well known, their impact on suppressing winter freezing of <span class="hlt">sea</span> <span class="hlt">ice</span> has, hitherto, received less attention. Melt ponds freeze by forming an <span class="hlt">ice</span> lid at the upper surface, which insulates them from the atmosphere and traps pond water between the underlying <span class="hlt">sea</span> <span class="hlt">ice</span> and the <span class="hlt">ice</span> lid. The pond water is a store of latent heat, which is released during refreezing. Until a pond freezes completely, there can be minimal <span class="hlt">ice</span> growth at the base of the underlying <span class="hlt">sea</span> <span class="hlt">ice</span>. In this work, we present a model of the refreezing of a melt pond that includes the heat and salt balances in the <span class="hlt">ice</span> lid, trapped pond, and underlying <span class="hlt">sea</span> <span class="hlt">ice</span>. The model uses a two-stream radiation model to account for radiative scattering at phase boundaries. Simulations and related sensitivity studies suggest that trapped pond water may survive for over a month. We focus on the role that pond salinity has on delaying the refreezing process and retarding basal <span class="hlt">sea</span> <span class="hlt">ice</span> growth. We estimate that for a typical <span class="hlt">sea</span> <span class="hlt">ice</span> pond coverage in autumn, excluding the impact of trapped ponds in models overestimates <span class="hlt">ice</span> growth by up to 265 million km3, an overestimate of 26%.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C51C1002M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C51C1002M"><span>Ramifications of a potential gap in passive microwave data for the long-term <span class="hlt">sea</span> <span class="hlt">ice</span> climate record</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Meier, W.; Stewart, J. S.</p> <p>2017-12-01</p> <p>The time series of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and <span class="hlt">extent</span> from passive microwave sensors is one of the longest satellite-derived climate records and the significant decline in Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> is one of the most iconic indicators of climate change. However, this continuous and consistent record is under threat due to the looming gap in passive microwave sensor coverage. The record started in late 1978 with the launch of the Scanning Multichannel Microwave Radiometer (SMMR) and has continued with a series of Special Sensor Microwave Imager (SSMI) and Special Sensor Microwave Imager and Sounder (SSMIS) instruments on U.S. Defense Meteorological Satellite Program (DMSP) satellites. The data from the different sensors are intercalibrated at the algorithm level by adjusting algorithm coefficients so that the output <span class="hlt">sea</span> <span class="hlt">ice</span> data is as consistent as possible between the older and the newer sensor. A key aspect in constructing the time series is to have at least two sensors operating simultaneously so that data from the older and newer sensor can be obtained from the same locations. However, with recent losses of the DMSP F19 and F20, the remaining SSMIS sensors are all well beyond their planned mission lifetime. This means that risk of failure is not small and is increasing with each day of operation. The newest passive microwave sensor, the JAXA Advanced Microwave Scanning Radiometer-2 (AMSR2), is a potential contributor to the time series (though it too is now beyond it's planned 5-year mission lifetime). However, AMSR2's larger antenna and higher spatial resolution presents a challenge in integrating its data with the rest of the <span class="hlt">sea</span> <span class="hlt">ice</span> record because the <span class="hlt">ice</span> edge is quite sensitive to the sensor resolution, which substantially affects the total <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and area estimates. This will need to be adjusted for if AMSR2 is used to continue the time series. Here we will discuss efforts at NSIDC to integrate AMSR2 estimates into the <span class="hlt">sea</span> <span class="hlt">ice</span> climate record if needed. We</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19900033480&hterms=Ross+1986&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DRoss%2B1986','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19900033480&hterms=Ross+1986&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DRoss%2B1986"><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/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/24832800','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24832800"><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="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Ewert, Marcela; Deming, Jody W</p> <p>2013-03-28</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.</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/2017AGUFM.C24B..06D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C24B..06D"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Structure and Texture over Four Decades Using Landsat Archive Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Doulgeris, A. P.; Scambos, T.; Tiampo, K. F.</p> <p>2017-12-01</p> <p>Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover is a sensitive indicator of Arctic climate change, and has shown dramatic changes in recent decades, having thinned by 70% ( 3.5 m to 1.2 m between 1980 and 2015). Age distribution of the <span class="hlt">ice</span> has changed in a similar fashion, with over 90% of the <span class="hlt">ice</span> older than 5 winters now lost relative to 1985. To date, most of the data have been based on the continuous passive microwave record that began in 1978, which has 25 km grid resolution, or on SAR imagery with somewhat less frequent, less continuous observations. Landsat image data exist for the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> region north of Alaska and the MacKenzie River Delta area in Canada, the Canadian Archipelago, and Baffin Bay, extending back over 40 years. Resolution of the earliest Landsat MSS data is 56-70 m per pixel, and after 1984 many additional images at 30 m resolution are available. This 40+ year time period is used to investigate long-term changes in <span class="hlt">sea</span> <span class="hlt">ice</span> properties, such as comparing image-based snapshots with the trend in seasonal <span class="hlt">extents</span> today, as well as more novel properties like <span class="hlt">sea</span> <span class="hlt">ice</span> roughness, lead structure and texture. The proposed study will initially investigate Landsat image analysis techniques to extract quantitative measures of <span class="hlt">ice</span> roughness, lead fraction and perhaps morphological measures like lead linearity (which potentially indicate strength and compression history within the <span class="hlt">ice</span>), and to explore these measures over the 40+ year time frame.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014TCry....8.1469R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014TCry....8.1469R"><span>Temporal dynamics of ikaite in experimental <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>Rysgaard, S.; Wang, F.; Galley, R. J.; Grimm, R.; Notz, D.; Lemes, M.; Geilfus, N.-X.; Chaulk, A.; Hare, A. A.; Crabeck, O.; Else, B. G. T.; Campbell, K.; Sørensen, L. L.; Sievers, J.; Papakyriakou, T.</p> <p>2014-08-01</p> <p>Ikaite (CaCO3 · 6H2O) is a metastable phase of calcium carbonate that normally forms in a cold environment and/or under high pressure. Recently, ikaite crystals have been found in <span class="hlt">sea</span> <span class="hlt">ice</span>, and it has been suggested that their precipitation may play an important role in air-<span class="hlt">sea</span> CO2 exchange in <span class="hlt">ice</span>-covered <span class="hlt">seas</span>. Little is known, however, of the spatial and temporal dynamics of ikaite in <span class="hlt">sea</span> <span class="hlt">ice</span>. Here we present evidence for highly dynamic ikaite precipitation and dissolution in <span class="hlt">sea</span> <span class="hlt">ice</span> grown at an outdoor pool of the <span class="hlt">Sea-ice</span> Environmental Research Facility (SERF) in Manitoba, Canada. During the experiment, ikaite precipitated in <span class="hlt">sea</span> <span class="hlt">ice</span> when temperatures were below -4 °C, creating three distinct zones of ikaite concentrations: (1) a millimeter-to-centimeter-thin surface layer containing frost flowers and brine skim with bulk ikaite concentrations of >2000 μmol kg-1, (2) an internal layer with ikaite concentrations of 200-400 μmol kg-1, and (3) a bottom layer with ikaite concentrations of <100 μmol kg-1. Snowfall events caused the <span class="hlt">sea</span> <span class="hlt">ice</span> to warm and ikaite crystals to dissolve. Manual removal of the snow cover allowed the <span class="hlt">sea</span> <span class="hlt">ice</span> to cool and brine salinities to increase, resulting in rapid ikaite precipitation. The observed ikaite concentrations were on the same order of magnitude as modeled by FREZCHEM, which further supports the notion that ikaite concentration in <span class="hlt">sea</span> <span class="hlt">ice</span> increases with decreasing temperature. Thus, varying snow conditions may play a key role in ikaite precipitation and dissolution in <span class="hlt">sea</span> <span class="hlt">ice</span>. This could have a major implication for CO2 exchange with the atmosphere and ocean that has not been accounted for previously.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/biblio/638276-sea-ice-polar-climate-ncar-csm','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/638276-sea-ice-polar-climate-ncar-csm"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> and polar climate in the NCAR CSM</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Weatherly, J.W.; Briegleb, B.P.; Large, W.G.</p> <p></p> <p>The Climate System Model (CSM) consists of atmosphere, ocean, land, and <span class="hlt">sea-ice</span> components linked by a flux coupler, which computes fluxes of energy and momentum between components. The <span class="hlt">sea-ice</span> component consists of a thermodynamic formulation for <span class="hlt">ice</span>, snow, and leads within the <span class="hlt">ice</span> pack, and <span class="hlt">ice</span> dynamics using the cavitating-fluid <span class="hlt">ice</span> rheology, which allows for the compressive strength of <span class="hlt">ice</span> but ignores shear viscosity. The results of a 300-yr climate simulation are presented, with the focus on <span class="hlt">sea</span> <span class="hlt">ice</span> and the atmospheric forcing over <span class="hlt">sea</span> <span class="hlt">ice</span> in the polar regions. The atmospheric model results are compared to analyses from themore » European Centre for Medium-Range Weather Forecasts and other observational sources. The <span class="hlt">sea-ice</span> concentrations and velocities are compared to satellite observational data. The atmospheric <span class="hlt">sea</span> level pressure (SLP) in CSM exhibits a high in the central Arctic displaced poleward from the observed Beaufort high. The Southern Hemisphere SLP over <span class="hlt">sea</span> <span class="hlt">ice</span> is generally 5 mb lower than observed. Air temperatures over <span class="hlt">sea</span> <span class="hlt">ice</span> in both hemispheres exhibit cold biases of 2--4 K. The precipitation-minus-evaporation fields in both hemispheres are greatly improved over those from earlier versions of the atmospheric GCM.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19850017730&hterms=Parkinsons+circulation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DParkinsons%2Bcirculation','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19850017730&hterms=Parkinsons+circulation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DParkinsons%2Bcirculation"><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/2017AGUFMPP54A..01S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMPP54A..01S"><span>Late Quaternary Variability of Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span>: Insights From Biomarker Proxy Records and 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>Stein, R. H.; Fahl, K.; Gierz, P.; Niessen, F.; Lohmann, G.</p> <p>2017-12-01</p> <p>Over the last about four decades, coinciding with global warming and atmospheric CO2increase, the <span class="hlt">extent</span> and thickness of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> has decreased dramatically, a decrease much more rapid than predicted by climate models. The driving forces of this change are still not fully understood. In this context, detailed paleoclimatic records going back beyond the timescale of direct observations, i.e., high-resolution Holocene records but also records representing more distant warm periods, may help to to distinguish and quantify more precisely the natural and anthropogenic greenhouse gas forcing of global climate change and related <span class="hlt">sea</span> <span class="hlt">ice</span> decrease. Here, we concentrate on <span class="hlt">sea</span> <span class="hlt">ice</span> biomarker records representing the penultimate glacial/last interglacial (MIS 6/MIS 5e) and the Holocene time intervals. Our proxy records are compared with climate model simulations using a coupled atmosphere-ocean general circulation model (AOGCM). Based on our data, polynya-type <span class="hlt">sea</span> <span class="hlt">ice</span> conditions probably occurred off the major <span class="hlt">ice</span> sheets along the northern Barents and East Siberian continental margins during late MIS 6. Furthermore, we demonstrate that even during MIS 5e, i.e., a time interval when the high latitudes have been significantly warmer than today, <span class="hlt">sea</span> <span class="hlt">ice</span> existed in the central Arctic Ocean during summer, whereas <span class="hlt">sea</span> <span class="hlt">ice</span> was significantly reduced along the Barents <span class="hlt">Sea</span> continental margin influenced by Atlantic Water inflow. Assuming a closed Bering Strait (no Pacific Water inflow) during early MIS 5, model simulations point to a significantly reduced <span class="hlt">sea</span> <span class="hlt">ice</span> cover in the central Arctic Ocean, a scenario that is however not supported by the proxy record and thus seems to be less realistic. Our Holocene biomarker proxy records from the Chukchi <span class="hlt">Sea</span> indicate that main factors controlling the millennial Holocene variability in <span class="hlt">sea</span> <span class="hlt">ice</span> are probably changes in surface water and heat flow from the Pacific into the Arctic Ocean as well as the long-term decrease in summer insolation</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.C41C0478A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.C41C0478A"><span>Controls on Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> from first-year and multi-year <span class="hlt">ice</span> survival rates</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Armour, K.; Bitz, C. M.; Hunke, E. C.; Thompson, L.</p> <p>2009-12-01</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 (MY) <span class="hlt">ice</span>. The transition to an Arctic that is populated by thinner first-year (FY) <span class="hlt">sea</span> <span class="hlt">ice</span> has important implications for future trends in area and volume. 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 FY and MY <span class="hlt">ice</span> control various aspects of the <span class="hlt">sea-ice</span> system. We demonstrate that Arctic <span class="hlt">sea-ice</span> area and volume behave approximately as first-order autoregressive processes, which allows for a simple interpretation of September <span class="hlt">sea-ice</span> in which its mean state, variability, and sensitivity to climate forcing can be described naturally in terms of the average survival rates of FY and MY <span class="hlt">ice</span>. This model, used in concert with a <span class="hlt">sea-ice</span> simulation that traces FY and MY <span class="hlt">ice</span> areas to estimate the survival rates, reveals that small trends in the <span class="hlt">ice</span> survival rates explain the decline in total Arctic <span class="hlt">ice</span> area, and the relatively larger loss of MY <span class="hlt">ice</span> area, over the period 1979-2006. Additionally, our model allows for a calculation of the persistence time scales of September area and volume anomalies. A relatively short memory time scale for <span class="hlt">ice</span> area (~ 1 year) implies that Arctic <span class="hlt">ice</span> area is nearly in equilibrium with long-term climate forcing at all times, and therefore observed trends in area are a clear indication of a changing climate. A longer memory time scale for <span class="hlt">ice</span> volume (~ 5 years) suggests that volume can be out of equilibrium with climate forcing for long periods of time, and therefore trends in <span class="hlt">ice</span> volume are difficult to distinguish from its natural variability. With our reduced model, we demonstrate the connection between memory time scale and sensitivity to climate forcing, and discuss the implications that a changing memory time scale has on the trajectory of <span class="hlt">ice</span> area and volume in a warming climate. Our findings indicate that it is unlikely that a “tipping point” in September <span class="hlt">ice</span> area and volume will be</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_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><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_22 --> <div id="page_23" 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_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</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="441"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1989JGR....9418195J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1989JGR....9418195J"><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://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jacobs, S. S.; Comiso, J. C.</p> <p>1989-12-01</p> <p>We have investigated 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, 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% during winter with little month-to-month or 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. Newly identified Pennell and Ross Passage polynyas near the continental shelf break appear to be maintained in part by divergence above a submarine bank and by upwelling of warmer water near the slope front. Warmer subsurface water enters the shelf region year-round and will retard <span class="hlt">ice</span> growth and enhance heat flux to the atmosphere when entrained in the strong winter vertical circulation. Temperatures at 125-m depth on a mooring near the Ross <span class="hlt">Ice</span> Shelf during July 1984 averaged 0.15°C above freezing, sufficient to support a vertical heat flux above 100 W/m2. Monthly average subsurface ocean temperatures along the Ross <span class="hlt">Ice</span> Shelf lag the air temperature cycle and begin to rise several weeks before spring <span class="hlt">ice</span> breakout. The coarse SMMR resolution and dynamic <span class="hlt">ice</span> shelf coastlines can compromise the use of microwave <span class="hlt">sea</span> <span class="hlt">ice</span> data near continental boundaries.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUOSME12B..03L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUOSME12B..03L"><span>Under the <span class="hlt">Sea</span> <span class="hlt">Ice</span>: Exploration of the Relationships Between <span class="hlt">Sea</span> <span class="hlt">Ice</span> Patterns and Foraging Movements of a Marine Predator in 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>Labrousse, S.; Sallee, J. B.; Fraser, A. D.; Massom, R. A.; Reid, P.; Sumner, M.; Guinet, C.; Harcourt, R.; Bailleul, F.; Hindell, M.; Charrassin, J. B.</p> <p>2016-02-01</p> <p>Investigating ecological relationships between top predators and their environment is essential to understand the response of marine ecosystems to climate variability. Specifically, variability and changes in <span class="hlt">sea</span> <span class="hlt">ice</span>, which is known as an important habitat for marine ecosystems, presents complex patterns in East Antarctic. The impact for ecosystems of such changes of their habitat is however still unknown. Acting as an ecological double-edged sword, <span class="hlt">sea</span> <span class="hlt">ice</span> can impede access to marine resources while harboring a rich ecosystem during winter. Here, we investigated which type of <span class="hlt">sea</span> <span class="hlt">ice</span> habitat is used by male and female southern elephant seals during winter and examine if and how the spatio-temporal variability of <span class="hlt">sea</span> <span class="hlt">ice</span> concentration (SIC) influence their foraging strategies. We also examined over a 10 years time-series the impact of SIC and <span class="hlt">sea</span> <span class="hlt">ice</span> advance anomaly on foraging activity. To do this, we studied 46 individuals equipped with Satellite linked data recorders between 2004 and 2014, undertaking post-moult trips in winter from Kerguelen to the peri-Antarctic shelf. The general patterns of <span class="hlt">sea</span> <span class="hlt">ice</span> use by males and females are clearly distinct; while females tended to follow the <span class="hlt">sea</span> <span class="hlt">ice</span> edge as it extended northward, males remained on the continental shelf. Female foraging activity was higher in late autumn in the outer part of the pack <span class="hlt">ice</span> in concentrated SIC and spatially stable. They remained in areas of variable SIC over time and low persistence. The seal hunting time, a proxy of foraging activity inferred from the diving behaviour, was much higher during earlier advance of <span class="hlt">sea</span> <span class="hlt">ice</span> over female time-series. The females were possibly taking advantage of the <span class="hlt">ice</span> algal autumn bloom sustaining krill and an under <span class="hlt">ice</span> ecosystem without being trapped in <span class="hlt">sea</span> <span class="hlt">ice</span>. Males foraging activity increased when they remained deep inside <span class="hlt">sea</span> <span class="hlt">ice</span> over the shelf using variable SIC in time and space, presumably in polynyas or flaw leads between fast and pack <span class="hlt">ice</span>. This strategy</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('https://ntrs.nasa.gov/search.jsp?R=20140008940&hterms=parkinson&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dparkinson','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20140008940&hterms=parkinson&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dparkinson"><span>On the 2012 Record Low Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Cover: Combined Impact of Preconditioning and an August Storm</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.; Comiso, Josefino C.</p> <p>2013-01-01</p> <p>A new record low Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> for the satellite era, 3.4 x 10(exp 6) square kilometers, was reached on 13 September 2012; and a new record low <span class="hlt">sea</span> <span class="hlt">ice</span> area, 3.01 x 10(exp 6) square kilometers was reached on the same date. Preconditioning through decades of overall <span class="hlt">ice</span> reductions made the <span class="hlt">ice</span> pack more vulnerable to a strong storm that entered the central Arctic in early August 2012. The storm caused the separation of an expanse of 0.4 x 10(exp 6) square kilometers of <span class="hlt">ice</span> that melted in total, while its removal left the main pack more exposed to wind and waves, facilitating the main pack's further decay. Future summer storms could lead to a further acceleration of the decline in the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover and should be carefully monitored.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70040743','USGSPUBS'); return false;" href="https://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://adsabs.harvard.edu/abs/2016AGUFM.C43B0753X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C43B0753X"><span><span class="hlt">Sea</span> <span class="hlt">Ice</span> Freeboard and Thickness from the 2013 <span class="hlt">Ice</span>Bridge ATM and DMS Data in Ross <span class="hlt">Sea</span>, Antarctica</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xie, H.; Tian, L.; Tang, J.; Ackley, S. F.</p> <p>2016-12-01</p> <p>In November (20, 21, 27, and 28) 2013, NASA's <span class="hlt">Ice</span>Bridge mission flew over the Ross <span class="hlt">Sea</span>, Antarctica and collected important <span class="hlt">sea</span> <span class="hlt">ice</span> data with the ATM and DMS for the first time. We will present our methods to derive the local <span class="hlt">sea</span> level and total freeboard for <span class="hlt">ice</span> thickness retrieval from these two datasets. The methods include (1) leads classification from DMS data using an automated lead detection method, (2) potential leads from the reflectance of less than 0.25 from the ATM laser shots of L1B data, (3) local <span class="hlt">sea</span> level retrieval based on these qualified ATM laser shots (L1B) within the DMS-derived leads (after outliers removal from the mean ± 2 standard deviation of these ATM elevations), (4) establishment of an empirical equation of local <span class="hlt">sea</span> level as a function of distance from the starting point of each <span class="hlt">Ice</span>Bridge flight, (5) total freeboard retrieval from the ATM L2 elevations by subtracting the local <span class="hlt">sea</span> level derived from the empirical equation, and (6) <span class="hlt">ice</span> thickness retrieval. The <span class="hlt">ice</span> thickness derived from this method will be analyzed and compared with ICESat data (2003-2009) and other available data for the same region at the similar time period. Possible change and potential reasons will be identified and discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19910017264','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19910017264"><span>Marine record of late quaternary glacial-interglacial fluctuations in the Ross <span class="hlt">Sea</span> and evidence for rapid, episodic <span class="hlt">sea</span> level change due to marine <span class="hlt">ice</span> sheet collapse</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Anderson, John B.</p> <p>1991-01-01</p> <p>Some of the questions to be addressed by <span class="hlt">Sea</span>RISE include: (1) what was the configuration of the West Antarctic <span class="hlt">ice</span> sheet during the last glacial maximum; (2) What is its configuration during a glacial minimum; and (3) has it, or any marine <span class="hlt">ice</span> sheet, undergone episodic rapid mass wasting. These questions are addressed in terms of what is known about the history of the marine <span class="hlt">ice</span> sheet, specifically in Ross <span class="hlt">Sea</span>, and what further studies are required to resolve these problems. A second question concerns the <span class="hlt">extent</span> to which disintegration of marine <span class="hlt">ice</span> sheets may result in rises in <span class="hlt">sea</span> level that are episodic in nature and extremely rapid, as suggested by several glaciologists. Evidence that rapid, episodic <span class="hlt">sea</span> level changes have occurred during the Holocene is also reviewed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C53B0776L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C53B0776L"><span>Dynamics of landfast <span class="hlt">sea</span> <span class="hlt">ice</span> near Jangbogo Antarctic Research Station observed by SAR interferometry</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, H.; Han, H.</p> <p>2015-12-01</p> <p>Landfast <span class="hlt">sea</span> <span class="hlt">ice</span> is a type of <span class="hlt">sea</span> <span class="hlt">ice</span> adjacent to the coast and immobile for a certain period of time. It is important to analyze the temporal and spatial variation of landfast <span class="hlt">ice</span> because it has significant influences on marine ecosystem and the safe operation of icebreaker vessels. However, it has been a difficult task for both remote sensing and in situ observation to discriminate landfast <span class="hlt">ice</span> from other types of <span class="hlt">sea</span> <span class="hlt">ice</span>, such as pack <span class="hlt">ice</span>, and also to understand the dynamics and internal strss-strain of fast <span class="hlt">ice</span>. In this study, we identify landfast <span class="hlt">ice</span> and its annual variation in Terra Nova Bay (74° 37' 4"S, 164° 13' 7"E), East Antarctica, where Jangbogo Antarctic Research Station has recently been constructed in 2014, by using Interferometric Synthetic Aperture Radar (InSAR) technology. We generated 38 interferograms having temporal baselines of 1-9 days out of 62 COSMO-SkyMed SAR images over Terra Nova Bay obtained from December 2010 to January 2012. Landfast <span class="hlt">ice</span> began to melt in November 2011 when air temperature raised above freezing point but lasted more than two month to the end of the study period in January 2012. No meaningful relationship was found between <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> and wind and current. Glacial strain (~67cm/day) is similar to tidal strain (~40 cm) so that they appear similar in one-day InSAR. As glacial stress is cumulative while tidal stress is oscillatory, InSAR images with weekly temporal baseline (7~9 days) revealed that a consistent motion of Campbell Glacier Tongue (CGT) is pushing the <span class="hlt">sea</span> <span class="hlt">ice</span> continuously to make interferometric fringes parallel to the glacier-<span class="hlt">sea</span> <span class="hlt">ice</span> contacts. Glacial interferometric fringe is parallel to the glacier-<span class="hlt">sea</span> <span class="hlt">ice</span> contact lines while tidal strain should be parallel to the coastlines defined by <span class="hlt">sea</span> shore and glacier tongue. DDInSAR operation removed the consistent glacial strain leaving tidal strain alone so that the response of fast <span class="hlt">ice</span> to tide can be used to deduce physical properties of <span class="hlt">sea</span> <span class="hlt">ice</span> in various</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.7072K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.7072K"><span>Statistical Prediction of <span class="hlt">Sea</span> <span class="hlt">Ice</span> Concentration over Arctic</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kim, Jongho; Jeong, Jee-Hoon; Kim, Baek-Min</p> <p>2017-04-01</p> <p>In this study, a statistical method that predict <span class="hlt">sea</span> <span class="hlt">ice</span> concentration (SIC) over the Arctic is developed. We first calculate the Season-reliant Empirical Orthogonal Functions (S-EOFs) of monthly Arctic SIC from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, which contain the seasonal cycles (12 months long) of dominant SIC anomaly patterns. Then, the current SIC state index is determined by projecting observed SIC anomalies for latest 12 months to the S-EOFs. Assuming the current SIC anomalies follow the spatio-temporal evolution in the S-EOFs, we project the future (upto 12 months) SIC anomalies by multiplying the SI and the corresponding S-EOF and then taking summation. The predictive skill is assessed by hindcast experiments initialized at all the months for 1980-2010. When comparing predictive skill of SIC predicted by statistical model and NCEP CFS v2, the statistical model shows a higher skill in predicting <span class="hlt">sea</span> <span class="hlt">ice</span> concentration and <span class="hlt">extent</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2001AGUSM...U42A04K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2001AGUSM...U42A04K"><span>A Parameter Tuning Scheme of <span class="hlt">Sea-ice</span> Model Based on Automatic Differentiation Technique</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kim, J. G.; Hovland, P. D.</p> <p>2001-05-01</p> <p>Automatic diferentiation (AD) technique was used to illustrate a new approach for parameter tuning scheme of an uncoupled <span class="hlt">sea-ice</span> model. Atmospheric forcing field of 1992 obtained from NCEP data was used as enforcing variables in the study. The simulation results were compared with the observed <span class="hlt">ice</span> movement provided by the International Arctic Buoy Programme (IABP). All of the numerical experiments were based on a widely used dynamic and thermodynamic model for simulating the seasonal <span class="hlt">sea-ice</span> chnage of the main Arctic ocean. We selected five dynamic and thermodynamic parameters for the tuning process in which the cost function defined by the norm of the difference between observed and simulated <span class="hlt">ice</span> drift locations was minimized. The selected parameters are the air and ocean drag coefficients, the <span class="hlt">ice</span> strength constant, the turning angle at <span class="hlt">ice</span>-air/ocean interface, and the bulk sensible heat transfer coefficient. The drag coefficients were the major parameters to control <span class="hlt">sea-ice</span> movement and <span class="hlt">extent</span>. The result of the study shows that more realistic simulations of <span class="hlt">ice</span> thickness distribution was produced by tuning the simulated <span class="hlt">ice</span> drift trajectories. In the tuning process, the L-BFCGS-B minimization algorithm of a quasi-Newton method was used. The derivative information required in the minimization iterations was provided by the AD processed Fortran code. Compared with a conventional approach, AD generated derivative code provided fast and robust computations of derivative information.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20080018456&hterms=Secret&qs=N%3D0%26Ntk%3DTitle%26Ntx%3Dmode%2Bmatchall%26Ntt%3DThe%2BSecret','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20080018456&hterms=Secret&qs=N%3D0%26Ntk%3DTitle%26Ntx%3Dmode%2Bmatchall%26Ntt%3DThe%2BSecret"><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('https://pubs.er.usgs.gov/publication/70017680','USGSPUBS'); return false;" href="https://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/2017AGUFMPP43A1341N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMPP43A1341N"><span>Spatial and temporal dependence on <span class="hlt">sea</span> <span class="hlt">ice</span> algae in the Chukchi <span class="hlt">Sea</span>, Arctic Ocean, inferred from bivalve stable isotopic composition</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nguyen, H. M.; Black, B.; Dunton, K. H.; von Biela, V. R.</p> <p>2017-12-01</p> <p>The Chukchi <span class="hlt">Sea</span> is one of the most productive Arctic <span class="hlt">seas</span> in the world. Around 10% of its net primary production originates from <span class="hlt">sea</span> <span class="hlt">ice</span> algae, much of which falls ungrazed to a relatively shallow (40-50m) shelf. The chlorophyll a derived from sinking <span class="hlt">ice</span> algae is thought to supports a robust macrobenthic faunal community, dominated by bivalves, which in turn supports higher trophic organisms such as Pacific walrus (Odibenus rosmarus divergens), and bearded seal (Erignathus barbatus). However, recent reductions in <span class="hlt">ice</span> <span class="hlt">extent</span> and thickness could shift primary production from under-<span class="hlt">ice</span> to open-water environment, thus reducing <span class="hlt">ice</span> algal production and delivery to benthic biota. We used stable isotope analyses on benthic bivalve samples, collected in summer between 2002 and 2015, to identify contributions of <span class="hlt">ice</span> algal production to benthic organisms and track their spatial and temporal variations. <span class="hlt">Ice</span> algae contributions were indicated by δ13C values in bivalves, as <span class="hlt">ice</span> algae are isotopically heavy compared to phytoplankton and would be reflected in consumers. This 14-yr period was marked by an 8%, decrease in Arctic <span class="hlt">ice</span> <span class="hlt">extent</span>, which was especially pronounced and spatially variable in the Chukchi <span class="hlt">Sea</span>. We examined variability in the δ13C values in the common bivalves Astarte spp., Ennucula tenuis and Macoma spp. over space and time using one-way ANOVAs with Bonferroni correction to consider the potential for variation in <span class="hlt">ice</span> algae contributions. All bivalve δ13C values were within a range (-21.84‰ to -17.62‰) that suggests some <span class="hlt">ice</span> algal contribution. Among stations, E. tenuis and Astarte spp. did not significantly differ in their individual δ13C values. In contrast, Macoma spp. had significantly enriched δ13C values at one station south of Point Hope (δ13C = -17.75‰, F5,8 = 1.211, P < 0.05) in 2015. There were no significant (P > 0.05) differences in δ13C values from year to year for samples pooled across stations within a taxon. As the only taxon</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2651250','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2651250"><span>Kelp genes reveal effects of subantarctic <span class="hlt">sea</span> <span class="hlt">ice</span> during the Last Glacial Maximum</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Fraser, Ceridwen I.; Nikula, Raisa; Spencer, Hamish G.; Waters, Jonathan M.</p> <p>2009-01-01</p> <p>The end of the Last Glacial Maximum (LGM) dramatically reshaped temperate ecosystems, with many species moving poleward as temperatures rose and <span class="hlt">ice</span> receded. Whereas reinvading terrestrial taxa tracked melting glaciers, marine biota recolonized ocean habitats freed by retreating <span class="hlt">sea</span> <span class="hlt">ice</span>. The <span class="hlt">extent</span> of <span class="hlt">sea</span> <span class="hlt">ice</span> in the Southern Hemisphere during the LGM has, however, yet to be fully resolved, with most palaeogeographic studies suggesting only minimal or patchy <span class="hlt">ice</span> cover in subantarctic waters. Here, through population genetic analyses of the widespread Southern Bull Kelp (Durvillaea antarctica), we present evidence for persistent <span class="hlt">ice</span> scour affecting subantarctic islands during the LGM. Using mitochondrial and chloroplast genetic markers (COI; rbcL) to genetically characterize some 300 kelp samples from 45 Southern Ocean localities, we reveal a remarkable pattern of recent recolonization in the subantarctic. Specifically, in contrast to the marked phylogeographic structure observed across coastal New Zealand and Chile (10- to 100-km scales), subantarctic samples show striking genetic homogeneity over vast distances (10,000-km scales), with a single widespread haplotype observed for each marker. From these results, we suggest that <span class="hlt">sea</span> <span class="hlt">ice</span> expanded further and <span class="hlt">ice</span> scour during the LGM impacted shallow-water subantarctic marine ecosystems more extensively than previously suggested. PMID:19204277</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70189305','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70189305"><span>Increased Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> drift alters adult female polar bear movements and energetics</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Durner, George M.; Douglas, David C.; Albeke, Shannon; Whiteman, John P.; Amstrup, Steven C.; Richardson, Evan; Wilson, Ryan R.; Ben-David, Merav</p> <p>2017-01-01</p> <p>Recent reductions in thickness and <span class="hlt">extent</span> have increased drift rates of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Increased <span class="hlt">ice</span> drift could significantly affect the movements and the energy balance of polar bears (Ursus maritimus) which forage, nearly exclusively, on this substrate. We used radio-tracking and <span class="hlt">ice</span> drift data to quantify the influence of increased drift on bear movements, and we modeled the consequences for energy demands of adult females in the Beaufort and Chukchi <span class="hlt">seas</span> during two periods with different <span class="hlt">sea</span> <span class="hlt">ice</span> characteristics. Westward and northward drift of the <span class="hlt">sea</span> <span class="hlt">ice</span> used by polar bears in both regions increased between 1987–1998 and 1999–2013. To remain within their home ranges, polar bears responded to the higher westward <span class="hlt">ice</span> drift with greater eastward movements, while their movements north in the spring and south in fall were frequently aided by <span class="hlt">ice</span> motion. To compensate for more rapid westward <span class="hlt">ice</span> drift in recent years, polar bears covered greater daily distances either by increasing their time spent active (7.6%–9.6%) or by increasing their travel speed (8.5%–8.9%). This increased their calculated annual energy expenditure by 1.8%–3.6% (depending on region and reproductive status), a cost that could be met by capturing an additional 1–3 seals/year. Polar bears selected similar habitats in both periods, indicating that faster drift did not alter habitat preferences. Compounding reduced foraging opportunities that result from habitat loss; changes in <span class="hlt">ice</span> drift, and associated activity increases, likely exacerbate the physiological stress experienced by polar bears in a warming Arctic.</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> <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/2017EGUGA..19.8068J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.8068J"><span><span class="hlt">Sea-ice</span> cover in the Nordic <span class="hlt">Seas</span> and the sensitivity to Atlantic water temperatures</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jensen, Mari F.; Nisancioglu, Kerim H.; Spall, Michael A.</p> <p>2017-04-01</p> <p>Changes in the <span class="hlt">sea-ice</span> cover of the Nordic <span class="hlt">Seas</span> have been proposed to play a key role for the dramatic temperature excursions associated with the Dansgaard-Oeschger events during the last glacial. However, with its proximity to the warm Atlantic water, how a <span class="hlt">sea-ice</span> cover can persist in the Nordic <span class="hlt">Seas</span> is not well understood. In this study, we apply an eddy-resolving configuration of the Massachusetts Institute of Technology general circulation model with an idealized topography to study the presence of <span class="hlt">sea</span> <span class="hlt">ice</span> in a Nordic <span class="hlt">Seas</span>-like domain. We assume an infinite amount of warm Atlantic water present in the south by restoring the southern area to constant temperatures. The <span class="hlt">sea</span>-surface temperatures are restored toward cold, atmospheric temperatures, and as a result, <span class="hlt">sea</span> <span class="hlt">ice</span> is present in the interior of the domain. However, the <span class="hlt">sea-ice</span> cover in the margins of the Nordic <span class="hlt">Seas</span>, an area with a warm, cyclonic boundary current, is sensitive to the amount of heat entering the domain, i.e., the restoring temperature in the south. When the temperature of the warm, cyclonic boundary current is high, the margins are free of <span class="hlt">sea</span> <span class="hlt">ice</span> and heat is released to the atmosphere. We show that with a small reduction in the temperature of the incoming Atlantic water, the Nordic <span class="hlt">Seas</span>-like domain is fully covered in <span class="hlt">sea</span> <span class="hlt">ice</span>. Warm water is still entering the Nordic <span class="hlt">Seas</span>, however, this happens at depths below a cold, fresh surface layer produced by melted <span class="hlt">sea</span> <span class="hlt">ice</span>. Consequently, the heat release to the atmosphere is reduced along with the eddy heat fluxes. Results suggest a threshold value in the amount of heat entering the Nordic <span class="hlt">Seas</span> before the <span class="hlt">sea-ice</span> cover disappears in the margins. We study the sensitivity of this threshold to changes in atmospheric temperatures and vertical diffusivity.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.nsf.gov/pubs/2005/nsf0539/nsf0539_5.pdf','USGSPUBS'); return false;" href="http://www.nsf.gov/pubs/2005/nsf0539/nsf0539_5.pdf"><span>Correlated declines in Pacific arctic snow and <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>Stone, Robert P.; Douglas, David C.; Belchansky, Gennady I.; Drobot, Sheldon</p> <p>2005-01-01</p> <p>Simulations of future climate suggest that global warming will reduce Arctic snow and <span class="hlt">ice</span> cover, resulting in decreased surface albedo (reflectivity). Lowering of the surface albedo leads to further warming by increasing solar absorption at the surface. This phenomenon is referred to as “temperature–albedo feedback.” Anticipation of such a feedback is one reason why scientists look to the Arctic for early indications of global warming. Much of the Arctic has warmed significantly. Northern Hemisphere snow cover has decreased, and <span class="hlt">sea</span> <span class="hlt">ice</span> has diminished in area and thickness. As reported in the Arctic Climate Impact Assessment in 2004, the trends are considered to be outside the range of natural variability, implicating global warming as an underlying cause. Changing climatic conditions in the high northern latitudes have influenced biogeochemical cycles on a broad scale. Warming has already affected the <span class="hlt">sea</span> <span class="hlt">ice</span>, the tundra, the plants, the animals, and the indigenous populations that depend on them. Changing annual cycles of snow and <span class="hlt">sea</span> <span class="hlt">ice</span> also affect sources and sinks of important greenhouse gases (such as carbon dioxide and methane), further complicating feedbacks involving the global budgets of these important constituents. For instance, thawing permafrost increases the <span class="hlt">extent</span> of tundra wetlands and lakes, releasing greater amounts of methane into the atmosphere. Variable <span class="hlt">sea</span> <span class="hlt">ice</span> cover may affect the hemispheric carbon budget by altering the ocean–atmosphere exchange of carbon dioxide. There is growing concern that amplification of global warming in the Arctic will have far-reaching effects on lower latitude climate through these feedback mechanisms. Despite the diverse and convincing observational evidence that the Arctic environment is changing, it remains unclear whether these changes are anthropogenically forced or result from natural variations of the climate system. A better understanding of what controls the seasonal distributions of snow and <span class="hlt">ice</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C31A1151B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C31A1151B"><span>Influence of <span class="hlt">sea</span> <span class="hlt">ice</span> on Arctic coasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barnhart, K. R.; Kay, J. E.; Overeem, I.; Anderson, R. S.</p> <p>2017-12-01</p> <p>Coasts form the dynamic interface between the terrestrial and oceanic systems. In the Arctic, and in much of the world, the coast is a focal point for population, infrastructure, biodiversity, and ecosystem services. A key difference between Arctic and temperate coasts is the presence of <span class="hlt">sea</span> <span class="hlt">ice</span>. Changes in <span class="hlt">sea</span> <span class="hlt">ice</span> cover can influence the coast because (1) the length of the <span class="hlt">sea</span> <span class="hlt">ice</span>-free season controls the time over which nearshore water can interact with the land, and (2) the location of the <span class="hlt">sea</span> <span class="hlt">ice</span> edge controls the fetch over which storm winds can interact with open ocean water, which in turn governs nearshore water level and wave field. We first focus on the interaction of <span class="hlt">sea</span> <span class="hlt">ice</span> and <span class="hlt">ice</span>-rich coasts. We combine satellite records of <span class="hlt">sea</span> <span class="hlt">ice</span> with a model for wind-driven storm surge and waves to estimate how changes in the <span class="hlt">sea</span> <span class="hlt">ice</span>-free season have impacted the nearshore hydrodynamic environment along Alaska's Beaufort <span class="hlt">Sea</span> Coast for the period 1979-2012. This region has experienced some of the greatest changes in both <span class="hlt">sea</span> <span class="hlt">ice</span> cover and coastal erosion rates in the Arctic: the median length of the open-water season has expanded by 90 percent, while coastal erosion rates have more than doubled from 8.7 to 19 m yr-1. At Drew Point, NW winds increase shoreline water levels that control the incision of a submarine notch, the rate-limiting step of coastal retreat. The maximum water-level setup at Drew Point has increased consistently with increasing fetch. We extend our analysis to the entire Arctic using both satellite-based observations and global coupled climate model output from the Community Earth System Model Large Ensemble (CESM-LE) project. This 30-member ensemble employs a 1-degree version of the CESM-CAM5 historical forcing for the period 1920-2005, and RCP 8.5 forcing from 2005-2100. A control model run with constant pre-industrial (1850) forcing characterizes internal variability in a constant climate. Finally, we compare observations and model results to</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_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_23 --> <div id="page_24" 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_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></li> <li><a href="#" onclick='return showDiv("page_25");'>25</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="461"> <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="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2015-09-30</p> <p>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 <span class="hlt">Sea</span> 2) Identification of...which we refer to as the FGIV dataset. Analysis of melt processes from <span class="hlt">ice</span> core and IMB data (Eicken) Through stratigraphic analysis of <span class="hlt">sea</span> <span class="hlt">ice</span>...samples that are brought back to shore were melted and used to determine profiles of salinity and stable isotope ratios. These data allow us to identify</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.C21D1155C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.C21D1155C"><span>Role of the Tropical Pacific in recent Antarctic <span class="hlt">Sea-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>Codron, F.; Bardet, D.; Allouache, C.; Gastineau, G.; Friedman, A. R.; Douville, H.; Voldoire, A.</p> <p>2017-12-01</p> <p>The recent (up to 2016) trends in Antarctic <span class="hlt">sea-ice</span> cover - a global increase masking a dipole between the Ross and Bellingshausen-Weddel <span class="hlt">seas</span> - are still not well understood, and not reproduced by CMIP5 coupled climate models. We here explore the potential role of atmospheric circulation changes around the Amundsen <span class="hlt">Sea</span>, themselves possibly forced by tropical SSTs, an explanation that has been recently advanced. As a first check on this hypothesis, we compare the atmospheric circulation trends simulated by atmospheric GCMs coupled with an ocean or with imposed SSTs (AMIP experiment from CMIP5); the latter being in theory able to reproduce changes caused by natural SST variability. While coupled models simulate in aggregate trends that project on the SAM structure, strongest in summer, the AMIP simulations add in the winter season a pronounced Amundsen <span class="hlt">Sea</span> Low signature (and a PNA signature in the northern hemisphere) both consistent with a Niña-like trend in the tropical Pacific. We then use a specific coupled GCM setup, in which surface wind anomalies over the tropical Pacific are strongly nudged towards the observed ones, including their interannual variability, but the model is free to evolve elsewhere. The two GCMs used then simulate a deepening trend in the Amundsen-<span class="hlt">Sea</span> Low in winter, and are able to reproduce a dipole in <span class="hlt">sea-ice</span> cover. Further analysis shows that the <span class="hlt">sea-ice</span> dipole is partially forced by surface heat flux anomalies in early winter - the <span class="hlt">extent</span> varying with the region and GCM used. The turbulent heat fluxes then act to damp the anomalies in late winter, which may however be maintained by <span class="hlt">ice</span>-albedo feedbacks.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1364126','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1364126"><span>CICE, The Los Alamos <span class="hlt">Sea</span> <span class="hlt">Ice</span> Model</span></a></p> <p><a target="_blank" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Hunke, Elizabeth; Lipscomb, William; Jones, Philip</p> <p></p> <p>The Los Alamos <span class="hlt">sea</span> <span class="hlt">ice</span> model (CICE) is the result of an effort to develop a computationally efficient <span class="hlt">sea</span> <span class="hlt">ice</span> component for a fully coupled atmosphere–land–ocean–<span class="hlt">ice</span> global climate model. It was originally designed to be compatible with the Parallel Ocean Program (POP), an ocean circulation model developed at Los Alamos National Laboratory for use on massively parallel computers. CICE has several interacting components: a vertical thermodynamic model that computes local growth rates of snow and <span class="hlt">ice</span> due to vertical conductive, radiative and turbulent fluxes, along with snowfall; an elastic-viscous-plastic model of <span class="hlt">ice</span> dynamics, which predicts the velocity field of themore » <span class="hlt">ice</span> pack based on a model of the material strength of the <span class="hlt">ice</span>; an incremental remapping transport model that describes horizontal advection of the areal concentration, <span class="hlt">ice</span> and snow volume and other state variables; and a ridging parameterization that transfers <span class="hlt">ice</span> among thickness categories based on energetic balances and rates of strain. It also includes a biogeochemical model that describes evolution of the <span class="hlt">ice</span> ecosystem. The CICE <span class="hlt">sea</span> <span class="hlt">ice</span> model is used for climate research as one component of complex global earth system models that include atmosphere, land, ocean and biogeochemistry components. It is also used for operational <span class="hlt">sea</span> <span class="hlt">ice</span> forecasting in the polar regions and in numerical weather prediction models.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170003146','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170003146"><span>Characterizing Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Topography Using High-Resolution <span class="hlt">Ice</span>Bridge Data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Petty, Alek; Tsamados, Michel; Kurtz, Nathan; Farrell, Sinead; Newman, Thomas; Harbeck, Jeremy; Feltham, Daniel; Richter-Menge, Jackie</p> <p>2016-01-01</p> <p>We present an analysis of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> topography using high resolution, three-dimensional, surface elevation data from the Airborne Topographic Mapper, flown as part of NASA's Operation <span class="hlt">Ice</span>Bridge mission. Surface features in the <span class="hlt">sea</span> <span class="hlt">ice</span> cover are detected using a newly developed surface feature picking algorithm. We derive information regarding the height, volume and geometry of surface features from 2009-2014 within the Beaufort/Chukchi and Central Arctic regions. The results are delineated by <span class="hlt">ice</span> type to estimate the topographic variability across first-year and multi-year <span class="hlt">ice</span> regimes.</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('https://images.nasa.gov/#/details-GSFC_20171208_Archive_e000638.html','SCIGOVIMAGE-NASA'); return false;" href="https://images.nasa.gov/#/details-GSFC_20171208_Archive_e000638.html"><span>Warming <span class="hlt">Seas</span> and Melting <span class="hlt">Ice</span> Sheets</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p>2017-12-08</p> <p><span class="hlt">Sea</span> level rise is a natural consequence of the warming of our planet. We know this from basic physics. When water heats up, it expands. So when the ocean warms, <span class="hlt">sea</span> level rises. When <span class="hlt">ice</span> is exposed to heat, it melts. And when <span class="hlt">ice</span> on land melts and water runs into the ocean, <span class="hlt">sea</span> level rises. For thousands of years, <span class="hlt">sea</span> level has remained relatively stable and human communities have settled along the planet’s coastlines. But now Earth’s <span class="hlt">seas</span> are rising. Globally, <span class="hlt">sea</span> level has risen about eight inches since the beginning of the 20th century and more than two inches in the last 20 years alone. All signs suggest that this rise is accelerating. Read more: go.nasa.gov/1heZn29 Caption: An iceberg floats in Disko Bay, near Ilulissat, Greenland, on July 24, 2015. The massive Greenland <span class="hlt">ice</span> sheet is shedding about 300 gigatons of <span class="hlt">ice</span> a year into the ocean, making it the single largest source of <span class="hlt">sea</span> level rise from melting <span class="hlt">ice</span>. Credits: NASA/Saskia Madlener NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C34A..08G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C34A..08G"><span>Seasonal thickness changes of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> north of Svalbard and implications for satellite remote sensing, ecosystem, and environmental management</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.; Rösel, A.; King, J.; Spreen, G.; Divine, D.; Eltoft, T.; Gallet, J. C.; Hudson, S. R.; Itkin, P.; Krumpen, T.; Liston, G. E.; Merkouriadi, I.; Negrel, J.; Nicolaus, M.; Polashenski, C.; Assmy, P.; Barber, D. G.; Duarte, P.; Doulgeris, A. P.; Haas, C.; Hughes, N.; Johansson, M.; Meier, W.; Perovich, D. K.; Provost, C.; Richter-Menge, J.; Skourup, H.; Wagner, P.; Wilkinson, J.; Granskog, M. A.; Steen, H.</p> <p>2016-12-01</p> <p><span class="hlt">Sea-ice</span> thickness is a crucial parameter to consider when assessing the status of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>, whether for environmental management, monitoring projects, or regional or pan-arctic assessments. Modern satellite remote sensing techniques allow us to monitor <span class="hlt">ice</span> <span class="hlt">extent</span> and to estimate <span class="hlt">sea-ice</span> thickness changes; but accurate quantifications of <span class="hlt">sea-ice</span> thickness distribution rely on in situ and airborne surveys. From January to June 2015, an international expedition (N-<span class="hlt">ICE</span>2015) took place in the Arctic Ocean north of Svalbard, with the Norwegian research vessel RV Lance frozen into drifting <span class="hlt">sea</span> <span class="hlt">ice</span>. In total, four drifts, with four different floes were made during that time. <span class="hlt">Sea-ice</span> and snow thickness measurements were conducted on all main <span class="hlt">ice</span> types present in the region, first year <span class="hlt">ice</span>, multiyear <span class="hlt">ice</span>, and young <span class="hlt">ice</span>. Measurement methods included ground and helicopter based electromagnetic surveys, drillings, hot-wire installations, snow-sonde transects, snow stakes, and <span class="hlt">ice</span> mass balance and snow buoys. <span class="hlt">Ice</span> thickness distributions revealed modal thicknesses in spring between 1.6 and 1.7 m, which is lower than reported for the region from comparable studies in 2009 (2.4 m) and 2011 (1.8 m). Knowledge about the <span class="hlt">ice</span> thickness distribution in a region is crucial to the understanding of climate processes, and also relevant to other disciplines. <span class="hlt">Sea-ice</span> thickness data collected during N-<span class="hlt">ICE</span>2015 can also give us insights into how <span class="hlt">ice</span> and snow thicknesses affect ecosystem processes. In this presentation, we will explore the influence of snow cover and ocean properties on <span class="hlt">ice</span> thickness, and the role of <span class="hlt">sea-ice</span> thickness in air-<span class="hlt">ice</span>-ocean interactions. We will also demonstrate how information about <span class="hlt">ice</span> thickness aids classification of different <span class="hlt">sea</span> <span class="hlt">ice</span> types from SAR satellite remote sensing, which has real-world applications for shipping and <span class="hlt">ice</span> forecasting, and how <span class="hlt">sea</span> <span class="hlt">ice</span> thickness data contributes to climate assessments.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018OcMod.121...76M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018OcMod.121...76M"><span>Impact of increasing antarctic glacial freshwater release on regional <span class="hlt">sea-ice</span> cover 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>Merino, Nacho; Jourdain, Nicolas C.; Le Sommer, Julien; Goosse, Hugues; Mathiot, Pierre; Durand, Gael</p> <p>2018-01-01</p> <p>The sensitivity of Antarctic <span class="hlt">sea-ice</span> to increasing glacial freshwater release into the Southern Ocean is studied in a series of 31-year ocean/<span class="hlt">sea-ice</span>/iceberg model simulations. Glaciological estimates of <span class="hlt">ice</span>-shelf melting and iceberg calving are used to better constrain the spatial distribution and magnitude of freshwater forcing around Antarctica. Two scenarios of glacial freshwater forcing have been designed to account for a decadal perturbation in glacial freshwater release to the Southern Ocean. For the first time, this perturbation explicitly takes into consideration the spatial distribution of changes in the volume of Antarctic <span class="hlt">ice</span> shelves, which is found to be a key component of changes in freshwater release. In addition, glacial freshwater-induced changes in <span class="hlt">sea</span> <span class="hlt">ice</span> are compared to typical changes induced by the decadal evolution of atmospheric states. Our results show that, in general, the increase in glacial freshwater release increases Antarctic <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>. But the response is opposite in some regions like the coastal Amundsen <span class="hlt">Sea</span>, implying that distinct physical mechanisms are involved in the response. We also show that changes in freshwater forcing may induce large changes in <span class="hlt">sea-ice</span> thickness, explaining about one half of the total change due to the combination of atmospheric and freshwater changes. The regional contrasts in our results suggest a need for improving the representation of freshwater sources and their evolution in climate models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.C13F0701S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.C13F0701S"><span>2009/2010 Eurasian Cold Winter and Loss of Arctic <span class="hlt">Sea-ice</span> over Barents/Kara <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>Shim, T.; Kim, B.; Kim, S.</p> <p>2012-12-01</p> <p>In 2009/2010 winter, a few extreme cold events and heavy snowfall occurred over central North America, north western Europe, and East Asia exerting a severe social and economic impacts. In this study, we performed modeling experiments to examine the role of substantially reduced Arctic <span class="hlt">sea-ice</span> over Barents/Kara <span class="hlt">Sea</span> on the 2009/2010 cold winters. Although several previous studies investigated cause of the extreme events and emphasized the large snow-covered area over Siberia in autumn 2009, we note that the area <span class="hlt">extent</span> of Arctic <span class="hlt">sea-ice</span> over Barents/Kara <span class="hlt">sea</span> in autumn 2009 was anomalously low and the possible impact from Arctic for the extreme cold events has not been presented. To investigate the influence from the Arctic, we designed three model runs using Community Atmosphere Model Version 3 (CAM3). Each simulation differs by the prescribed surface boundary conditions: (a) CTRL - climatological seasonal cycle of <span class="hlt">sea</span> surface temperature (SST) and <span class="hlt">sea-ice</span> concentration (SIC) are prescribed everywhere, (b) EXP_65N - SST and SIC inside the Arctic circle (north of 65°N) are replaced by 2009/2010 values. Elsewhere, the climatology is used, (c) EXP_BK - Same with (b) except that SIC and SST are fixed only over Barents/Kara <span class="hlt">Sea</span> where the <span class="hlt">sea-ice</span> area dropped significantly in 2009/2010 winter. Model results from EXP_65N and EXP_BK commonly showed a large increase of air temperature in the lower troposphere where Arctic <span class="hlt">sea-ice</span> showed a large reduction. Also, compared with the observation, model successfully captured thickened geopotential height in the Arctic and showed downstream wave propagation toward midlatitude. From the analysis, we reveal that this large dipolar Arctic-midlatitude teleconnection pattern in the upper troposphere easily propagate upward and played a role in the weakening of polar vortex. This is also confirmed in the observation. However, the timing of excitation of upward propagating wave in EXP_65N and EXP_BK were different and thus the timing of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20070016598&hterms=sea+ice+albedo&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dsea%2Bice%2Balbedo','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20070016598&hterms=sea+ice+albedo&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dsea%2Bice%2Balbedo"><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/2008AGUFMOS31C1297H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFMOS31C1297H"><span>Transient sensitivities of <span class="hlt">sea</span> <span class="hlt">ice</span> export through the Canadian Arctic Archipelago inferred from a coupled ocean/<span class="hlt">sea-ice</span> adjoint model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Heimbach, P.; Losch, M.; Menemenlis, D.; Campin, J.; Hill, C.</p> <p>2008-12-01</p> <p>The sensitivity of <span class="hlt">sea-ice</span> export through the Canadian Arctic Archipelago (CAA), measured in terms of its solid freshwater export through Lancaster Sound, to changes in various elements of the ocean and <span class="hlt">sea-ice</span> state, and to elements of the atmospheric forcing fields through time and space is assessed by means of a coupled ocean/<span class="hlt">sea-ice</span> adjoint model. The adjoint model furnishes full spatial sensitivity maps (also known as Lagrange multipliers) of the export metric to a variety of model variables at any chosen point in time, providing the unique capability to quantify major drivers of <span class="hlt">sea-ice</span> export variability. The underlying model is the MIT ocean general circulation model (MITgcm), which is coupled to a Hibler-type dynamic/thermodynamic <span class="hlt">sea-ice</span> model. The configuration is based on the Arctic face of the ECCO3 high-resolution cubed-sphere model, but coarsened to 36-km horizontal grid spacing. The adjoint of the coupled system has been derived by means of automatic differentiation using the software tool TAF. Finite perturbation simulations are performed to check the information provided by the adjoint. The <span class="hlt">sea-ice</span> model's performance in the presence of narrow straits is assessed with different <span class="hlt">sea-ice</span> lateral boundary conditions. The adjoint sensitivity clearly exposes the role of the model trajectory and the transient nature of the problem. The complex interplay between forcing, dynamics, and boundary condition is demonstrated in the comparison between the different calculations. The study is a step towards fully coupled adjoint-based ocean/<span class="hlt">sea-ice</span> state estimation at basin to global scales as part of the ECCO efforts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AcMSn..31....1Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AcMSn..31....1Z"><span>Modeling ocean wave propagation under <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>Zhao, Xin; Shen, Hayley H.; Cheng, Sukun</p> <p>2015-02-01</p> <p>Operational ocean wave models need to work globally, yet current ocean wave models can only treat <span class="hlt">ice</span>-covered regions crudely. The purpose of this paper is to provide a brief overview of <span class="hlt">ice</span> effects on wave propagation and different research methodology used in studying these effects. Based on its proximity to land or <span class="hlt">sea</span>, <span class="hlt">sea</span> <span class="hlt">ice</span> can be classified as: landfast <span class="hlt">ice</span> zone, shear zone, and the marginal <span class="hlt">ice</span> zone. All <span class="hlt">ice</span> covers attenuate wave energy. Only long swells can penetrate deep into an <span class="hlt">ice</span> cover. Being closest to open water, wave propagation in the marginal <span class="hlt">ice</span> zone is the most complex to model. The physical appearance of <span class="hlt">sea</span> <span class="hlt">ice</span> in the marginal <span class="hlt">ice</span> zone varies. Grease <span class="hlt">ice</span>, pancake <span class="hlt">ice</span>, brash <span class="hlt">ice</span>, floe aggregates, and continuous <span class="hlt">ice</span> sheet may be found in this zone at different times and locations. These types of <span class="hlt">ice</span> are formed under different thermal-mechanical forcing. There are three classic models that describe wave propagation through an idealized <span class="hlt">ice</span> cover: mass loading, thin elastic plate, and viscous layer models. From physical arguments we may conjecture that mass loading model is suitable for disjoint aggregates of <span class="hlt">ice</span> floes much smaller than the wavelength, thin elastic plate model is suitable for a continuous <span class="hlt">ice</span> sheet, and the viscous layer model is suitable for grease <span class="hlt">ice</span>. For different <span class="hlt">sea</span> <span class="hlt">ice</span> types we may need different wave <span class="hlt">ice</span> interaction models. A recently proposed viscoelastic model is able to synthesize all three classic models into one. Under suitable limiting conditions it converges to the three previous models. The complete theoretical framework for evaluating wave propagation through various <span class="hlt">ice</span> covers need to be implemented in the operational ocean wave models. In this review, we introduce the <span class="hlt">sea</span> <span class="hlt">ice</span> types, previous wave <span class="hlt">ice</span> interaction models, wave attenuation mechanisms, the methods to calculate wave reflection and transmission between different <span class="hlt">ice</span> covers, and the effect of <span class="hlt">ice</span> floe breaking on shaping the <span class="hlt">sea</span> <span class="hlt">ice</span> morphology</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GeCoA.213...17B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GeCoA.213...17B"><span>Gypsum and hydrohalite dynamics in <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 M.; Papadimitriou, Stathys; Day, Sarah J.; Kennedy, Hilary</p> <p>2017-09-01</p> <p>Mineral authigenesis from their dissolved <span class="hlt">sea</span> salt matrix is an emergent feature of <span class="hlt">sea</span> <span class="hlt">ice</span> brines, fuelled by dramatic equilibrium solubility changes in the large sub-zero temperature range of this cryospheric system on the surface of high latitude oceans. The multi-electrolyte composition of seawater results in the potential for several minerals to precipitate in <span class="hlt">sea</span> <span class="hlt">ice</span>, each affecting the in-situ geochemical properties of the <span class="hlt">sea</span> <span class="hlt">ice</span> brine system, the habitat of sympagic biota. The solubility of two of these minerals, gypsum (CaSO4 ·2H2O) and hydrohalite (NaCl · 2H2O), was investigated in high ionic strength multi-electrolyte solutions at below-zero temperatures to examine their dissolution-precipitation dynamics in the <span class="hlt">sea</span> <span class="hlt">ice</span> brine system. The gypsum dynamics in <span class="hlt">sea</span> <span class="hlt">ice</span> were found to be highly dependent on the solubilities of mirabilite and hydrohalite between 0.2 and - 25.0 ° C. The hydrohalite solubility between - 14.3 and - 25.0 ° C exhibits a sharp change between undersaturated and supersaturated conditions, and, thus, distinct temperature fields of precipitation and dissolution in <span class="hlt">sea</span> <span class="hlt">ice</span>, with saturation occurring at - 22.9 ° C. The sharp changes in hydrohalite solubility at temperatures ⩽-22.9 °C result from the formation of an <span class="hlt">ice</span>-hydrohalite aggregate, which alters the structural properties of brine inclusions in cold <span class="hlt">sea</span> <span class="hlt">ice</span>. Favourable conditions for gypsum precipitation in <span class="hlt">sea</span> <span class="hlt">ice</span> were determined to occur in the region of hydrohalite precipitation below - 22.9 ° C and in conditions of metastable mirabilite supersaturation above - 22.9 ° C (investigated at - 7.1 and - 8.2 ° C here) but gypsum is unlikely to persist once mirabilite forms at these warmer (>-22.9 °C) temperatures. The dynamics of hydrohalite in <span class="hlt">sea</span> <span class="hlt">ice</span> brines based on its experimental solubility were consistent with that derived from thermodynamic modelling (FREZCHEM code) but the gypsum dynamics derived from the code were inconsistent with that indicated by its</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.C43E0587P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.C43E0587P"><span>A Changing Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span> Cover and the Partitioning of Solar Radiation</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.; Light, B.; Polashenski, C.; Nghiem, S. V.</p> <p>2010-12-01</p> <p>Certain recent changes in the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover are well established. There has been a reduction in <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span>, an overall thinning of the <span class="hlt">ice</span> cover, reduced prevalence of perennial <span class="hlt">ice</span> with accompanying increases in seasonal <span class="hlt">ice</span>, and a lengthening of the summer melt season. Here we explore the effects of these changes on the partitioning of solar energy between reflection to the atmosphere, absorption within the <span class="hlt">ice</span>, and transmission to the ocean. The physical changes in the <span class="hlt">ice</span> cover result in less light reflected and more light absorbed in the <span class="hlt">ice</span> and transmitted to the ocean. These changes directly affect the heat and mass balance of the <span class="hlt">ice</span> as well as the amount of light available for photosynthesis within and beneath the <span class="hlt">ice</span> cover. The central driver is that seasonal <span class="hlt">ice</span> covers tend to have lower albedo than perennial <span class="hlt">ice</span> throughout the melt season, permitting more light to penetrate into the <span class="hlt">ice</span> and ocean. The enhanced light penetration increases the amount of internal melting of the <span class="hlt">ice</span> and the heat content of the upper ocean. The physical changes in the <span class="hlt">ice</span> cover mentioned above have affected both the amount and the timing of the photosynthetically active radiation (PAR) transmitted into the <span class="hlt">ice</span> and ocean, increasing transmitted PAR, particularly in the spring. A comparison of the partitioning of solar irradiance and PAR for both historical and recent <span class="hlt">ice</span> conditions will be presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=PIA04300&hterms=sea+world&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dsea%2Bworld','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=PIA04300&hterms=sea+world&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dsea%2Bworld"><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('https://www.ncbi.nlm.nih.gov/pubmed/26347538','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26347538"><span>Processes controlling surface, bottom and lateral melt of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> in a state of the art <span class="hlt">sea</span> <span class="hlt">ice</span> model.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tsamados, Michel; Feltham, Daniel; Petty, Alek; Schroeder, David; Flocco, Daniela</p> <p>2015-10-13</p> <p>We present a modelling study of processes controlling the summer melt of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> cover. We perform a sensitivity study and focus our interest on the thermodynamics at the <span class="hlt">ice</span>-atmosphere and <span class="hlt">ice</span>-ocean interfaces. We use the Los Alamos community <span class="hlt">sea</span> <span class="hlt">ice</span> 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 <span class="hlt">ice</span>-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 <span class="hlt">sea</span> <span class="hlt">ice</span> parametrizations tested in this sensitivity study introduce a wide spread in the simulated <span class="hlt">sea</span> <span class="hlt">ice</span> 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 <span class="hlt">sea</span> <span class="hlt">ice</span>, this work can serve as a guide for future research priorities. © 2015 The Author(s).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JGRD..12210820G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JGRD..12210820G"><span>Spring snow conditions on Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> north of Svalbard, during the Norwegian Young <span class="hlt">Sea</span> <span class="hlt">ICE</span> (N-<span class="hlt">ICE</span>2015) expedition</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gallet, Jean-Charles; Merkouriadi, Ioanna; Liston, Glen E.; Polashenski, Chris; Hudson, Stephen; Rösel, Anja; Gerland, Sebastian</p> <p>2017-10-01</p> <p>Snow is crucial over <span class="hlt">sea</span> <span class="hlt">ice</span> due to its conflicting role in reflecting the incoming solar energy and reducing the heat transfer so that its temporal and spatial variability are important to estimate. During the Norwegian Young <span class="hlt">Sea</span> <span class="hlt">ICE</span> (N-<span class="hlt">ICE</span>2015) campaign, snow physical properties and variability were examined, and results from April until mid-June 2015 are presented here. Overall, the snow thickness was about 20 cm higher than the climatology for second-year <span class="hlt">ice</span>, with an average of 55 ± 27 cm and 32 ± 20 cm on first-year <span class="hlt">ice</span>. The average density was 350-400 kg m-3 in spring, with higher values in June due to melting. Due to flooding in March, larger variability in snow water equivalent was observed. However, the snow structure was quite homogeneous in spring due to warmer weather and lower amount of storms passing over the field camp. The snow was mostly consisted of wind slab, faceted, and depth hoar type crystals with occasional fresh snow. These observations highlight the more dynamic character of evolution of snow properties over <span class="hlt">sea</span> <span class="hlt">ice</span> compared to previous observations, due to more variable <span class="hlt">sea</span> <span class="hlt">ice</span> and weather conditions in this area. The snowpack was isothermal as early as 10 June with the first onset of melt clearly identified in early June. Based on our observations, we estimate than snow could be accurately represented by a three to four layers modeling approach, in order to better consider the high variability of snow thickness and density together with the rapid metamorphose of the snow in springtime.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19870007787&hterms=marginal&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dmarginal','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19870007787&hterms=marginal&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dmarginal"><span>Microwave properties of <span class="hlt">sea</span> <span class="hlt">ice</span> in the marginal <span class="hlt">ice</span> zone</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.; Larson, R. W.</p> <p>1986-01-01</p> <p>Active microwave properties of summer <span class="hlt">sea</span> <span class="hlt">ice</span> were measured. Backscatter data were acquired at frequencies from 1 to 17 GHz, at angles from 0 to 70 deg from vertical, and with like and cross antenna polarizations. Results show that melt-water, snow thickness, snowpack morphology, snow surface roughness, <span class="hlt">ice</span> surface roughness, and deformation characteristics are the fundamental scene parameters which govern the summer <span class="hlt">sea</span> <span class="hlt">ice</span> backscatter response. A thick, wet snow cover dominates the backscatter response and masks any <span class="hlt">ice</span> sheet features below. However, snow and melt-water are not distributed uniformly and the stage of melt may also be quite variable. These nonuniformities related to <span class="hlt">ice</span> type are not necessarily well understood and produce unique microwave signature characteristics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.9654S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.9654S"><span>Micromechanics of <span class="hlt">sea</span> <span class="hlt">ice</span> gouge in shear zones</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sammonds, Peter; Scourfield, Sally; Lishman, Ben</p> <p>2015-04-01</p> <p>The deformation of <span class="hlt">sea</span> <span class="hlt">ice</span> is a key control on the Arctic Ocean dynamics. Shear displacement on all scales is an important deformation process in the <span class="hlt">sea</span> cover. Shear deformation is a dominant mechanism from the scale of basin-scale shear lineaments, through floe-floe interaction and block sliding in <span class="hlt">ice</span> ridges through to the micro-scale mechanics. Shear deformation will not only depend on the speed of movement of <span class="hlt">ice</span> surfaces but also the degree that the surfaces have bonded during thermal consolidation and compaction. Recent observations made during fieldwork in the Barents <span class="hlt">Sea</span> show that shear produces a gouge similar to a fault gouge in a shear zone in the crust. A range of sizes of gouge are exhibited. The consolidation of these fragments has a profound influence on the shear strength and the rate of the processes involved. We review experimental results in <span class="hlt">sea</span> <span class="hlt">ice</span> mechanics from mid-scale experiments, conducted in the Hamburg model ship <span class="hlt">ice</span> tank, simulating <span class="hlt">sea</span> <span class="hlt">ice</span> floe motion and interaction and compare these with laboratory experiments on <span class="hlt">ice</span> friction done in direct shear, and upscale to field measurement of <span class="hlt">sea</span> <span class="hlt">ice</span> friction and gouge deformation made during experiments off Svalbard. We find that consolidation, fragmentation and bridging play important roles in the overall dynamics and fit the model of Sammis and Ben-Zion, developed for understanding the micro-mechanics of rock fault gouge, to the <span class="hlt">sea</span> <span class="hlt">ice</span> problem.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013CliPa...9..969B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013CliPa...9..969B"><span>The sensitivity of the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> to orbitally induced insolation changes: a study of the mid-Holocene Paleoclimate Modelling Intercomparison Project 2 and 3 simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Berger, M.; Brandefelt, J.; Nilsson, J.</p> <p>2013-04-01</p> <p>In the present work the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> in the mid-Holocene and the pre-industrial climates are analysed and compared on the basis of climate-model results from the Paleoclimate Modelling Intercomparison Project phase 2 (PMIP2) and phase 3 (PMIP3). The PMIP3 models generally simulate smaller and thinner <span class="hlt">sea-ice</span> <span class="hlt">extents</span> than the PMIP2 models both for the pre-industrial and the mid-Holocene climate. Further, the PMIP2 and PMIP3 models all simulate a smaller and thinner Arctic summer <span class="hlt">sea-ice</span> cover in the mid-Holocene than in the pre-industrial control climate. The PMIP3 models also simulate thinner winter <span class="hlt">sea</span> <span class="hlt">ice</span> than the PMIP2 models. The winter <span class="hlt">sea-ice</span> <span class="hlt">extent</span> response, i.e. the difference between the mid-Holocene and the pre-industrial climate, varies among both PMIP2 and PMIP3 models. Approximately one half of the models simulate a decrease in winter <span class="hlt">sea-ice</span> <span class="hlt">extent</span> and one half simulates an increase. The model-mean summer <span class="hlt">sea-ice</span> <span class="hlt">extent</span> is 11 % (21 %) smaller in the mid-Holocene than in the pre-industrial climate simulations in the PMIP2 (PMIP3). In accordance with the simple model of Thorndike (1992), the <span class="hlt">sea-ice</span> thickness response to the insolation change from the pre-industrial to the mid-Holocene is stronger in models with thicker <span class="hlt">ice</span> in the pre-industrial climate simulation. Further, the analyses show that climate models for which the Arctic <span class="hlt">sea-ice</span> responses to increasing atmospheric CO2 concentrations are similar may simulate rather different <span class="hlt">sea-ice</span> responses to the change in solar forcing between the mid-Holocene and the pre-industrial. For two specific models, which are analysed in detail, this difference is found to be associated with differences in the simulated cloud fractions in the summer Arctic; in the model with a larger cloud fraction the effect of insolation change is muted. A sub-set of the mid-Holocene simulations in the PMIP ensemble exhibit open water off the north-eastern coast of Greenland in summer, which can provide a fetch for surface</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_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_24 --> <div id="page_25" 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_21");'>21</a></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_24");'>24</a></li> <li class="active"><span>25</span></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="481"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMPP54A..03P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMPP54A..03P"><span>Late Holocene <span class="hlt">sea</span> <span class="hlt">ice</span> conditions in 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, C.; O'Regan, M.; Rattray, J. E.; Hutchinson, D. K.; Cronin, T. M.; Gemery, L.; Barrientos, N.; Coxall, H.; Smittenberg, R.; Semiletov, I. P.; Jakobsson, M.</p> <p>2017-12-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> in the Arctic Ocean has been in steady decline in recent decades and, based on satellite data, the retreat is most pronounced in the Chukchi and Beaufort <span class="hlt">seas</span>. Historical observations suggest that the recent changes were unprecedented during the last 150 years, but for a longer time perspective, we rely on the geological record. For this study, we analyzed sediment samples from two piston cores from Herald Canyon in the Chukchi <span class="hlt">Sea</span>, collected during the 2014 SWERUS-C3 Arctic Ocean Expedition. The Herald Canyon is a local depression across the Chukchi Shelf, and acts as one of the main pathways for Pacific Water to the Arctic Ocean after entering through the narrow and shallow Bering Strait. The study site lies at the modern-day seasonal <span class="hlt">sea</span> <span class="hlt">ice</span> minimum edge, and is thus an ideal location for the reconstruction of past <span class="hlt">sea</span> <span class="hlt">ice</span> variability. Both sediment cores contain late Holocene deposits characterized by high sediment accumulation rates (100-300 cm/kyr). Core 2-PC1 from the shallow canyon flank (57 m water depth) is 8 meter long and extends back to 4200 cal yrs BP, while the upper 3 meters of Core 4-PC1 from the central canyon (120 mwd) cover the last 3000 years. The chronologies of the cores are based on radiocarbon dates and the 3.6 ka Aniakchak CFE II tephra, which is used as an absolute age marker to calculate the marine radiocarbon reservoir age. Analysis of biomarkers for <span class="hlt">sea</span> <span class="hlt">ice</span> and surface water productivity indicate stable <span class="hlt">sea</span> <span class="hlt">ice</span> conditions throughout the entire late Holocene, ending with an abrupt increase of phytoplankton sterols in the very top of both sediment sequences. The shift is accompanied by a sudden increase in coarse sediments (> 125 µm) and a minor change in δ13Corg. We interpret this transition in the top sediments as a community turnover in primary producers from <span class="hlt">sea</span> <span class="hlt">ice</span> to open water biota. Most importantly, our results indicate that the ongoing rapid <span class="hlt">ice</span> retreat in the Chukchi <span class="hlt">Sea</span> of recent decades was unprecedented during the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMPP11B1783E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMPP11B1783E"><span>An unusual early Holocene diatom event north of the Getz <span class="hlt">Ice</span> Shelf (Amundsen <span class="hlt">Sea</span>): Implications for West Antarctic <span class="hlt">Ice</span> Sheet development</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Esper, O.; Gersonde, R.; Hillenbrand, C.; Kuhn, G.; Smith, J.</p> <p>2011-12-01</p> <p>Modern global change affects not only the polar north but also, and to increasing <span class="hlt">extent</span>, the southern high latitudes, especially the Antarctic regions covered by the West Antarctic <span class="hlt">Ice</span> Sheet (WAIS). Consequently, knowledge of the mechanisms controlling past WAIS dynamics and WAIS behaviour at the last deglaciation is critical to predict its development in a future warming world. Geological and palaeobiological information from major drainage areas of the WAIS, like the Amundsen <span class="hlt">Sea</span> Embayment, shed light on the history of the WAIS glaciers. Sediment records obtained from a deep inner shelf basin north of Getz <span class="hlt">Ice</span> Shelf document a deglacial warming in three phases. Above a glacial diamicton and a sediment package barren of microfossils that document sediment deposition by grounded <span class="hlt">ice</span> and below an <span class="hlt">ice</span> shelf or perennial <span class="hlt">sea</span> <span class="hlt">ice</span> cover (possibly fast <span class="hlt">ice</span>), respectively, a sediment section with diatom assemblages dominated by <span class="hlt">sea</span> <span class="hlt">ice</span> taxa indicates <span class="hlt">ice</span> shelf retreat and seasonal <span class="hlt">ice</span>-free conditions. This conclusion is supported by diatom-based summer temperature reconstructions. The early retreat was followed by a phase, when exceptional diatom ooze was deposited around 12,500 cal. years B.P. [1]. Microscopical inspection of this ooze revealed excellent preservation of diatom frustules of the species Corethron pennatum together with vegetative Chaetoceros, thus an assemblage usually not preserved in the sedimentary record. Sediments succeeding this section contain diatom assemblages indicating rather constant Holocene cold water conditions with seasonal <span class="hlt">sea</span> <span class="hlt">ice</span>. The deposition of the diatom ooze can be related to changes in hydrographic conditions including strong advection of nutrients. However, sediment focussing in the partly steep inner shelf basins cannot be excluded as a factor enhancing the thickness of the ooze deposits. It is not only the presence of the diatom ooze but also the exceptional preservation and the species composition of the diatom assemblage</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014SPIE.9299E..03J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014SPIE.9299E..03J"><span>Development of <span class="hlt">sea</span> <span class="hlt">ice</span> monitoring with aerial remote sensing technology</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jiang, Xuhui; Han, Lei; Dong, Liang; Cui, Lulu; Bie, Jun; Fan, Xuewei</p> <p>2014-11-01</p> <p>In the north China <span class="hlt">Sea</span> district, <span class="hlt">sea</span> <span class="hlt">ice</span> disaster is very serious every winter, which brings a lot of adverse effects to shipping transportation, offshore oil exploitation, and coastal engineering. In recent years, along with the changing of global climate, the <span class="hlt">sea</span> <span class="hlt">ice</span> situation becomes too critical. The monitoring of <span class="hlt">sea</span> <span class="hlt">ice</span> is playing a very important role in keeping human life and properties in safety, and undertaking of marine scientific research. The methods to monitor <span class="hlt">sea</span> <span class="hlt">ice</span> mainly include: first, shore observation; second, icebreaker monitoring; third, satellite remote sensing; and then aerial remote sensing monitoring. The marine station staffs use relevant equipments to monitor the <span class="hlt">sea</span> <span class="hlt">ice</span> in the shore observation. The icebreaker monitoring means: the workers complete the test of the properties of <span class="hlt">sea</span> <span class="hlt">ice</span>, such as density, salinity and mechanical properties. MODIS data and NOAA data are processed to get <span class="hlt">sea</span> <span class="hlt">ice</span> charts in the satellite remote sensing means. Besides, artificial visual monitoring method and some airborne remote sensors are adopted in the aerial remote sensing to monitor <span class="hlt">sea</span> <span class="hlt">ice</span>. Aerial remote sensing is an important means in <span class="hlt">sea</span> <span class="hlt">ice</span> monitoring because of its strong maneuverability, wide watching scale, and high resolution. In this paper, several methods in the <span class="hlt">sea</span> <span class="hlt">ice</span> monitoring using aerial remote sensing technology are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19760039698&hterms=1103&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3D%2526%25231103','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19760039698&hterms=1103&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3D%2526%25231103"><span>Beaufort <span class="hlt">Sea</span> <span class="hlt">ice</span> zones as delineated by microwave imagery</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Campbell, W. J.; Gloersen, P.; Webster, W. J.; Wilheit, T. T.; Ramseier, R. O.</p> <p>1976-01-01</p> <p>Microwave and infrared data were obtained from a research aircraft over the Beaufort <span class="hlt">Sea</span> <span class="hlt">ice</span> from the shoreline of Harrison Bay northward to a latitude of almost 81 deg N. The data acquired were compared with microwave data obtained on the surface at an approximate position of 75 deg N, 150 deg W. Over this north-south transect of the polar <span class="hlt">ice</span> canopy it was discovered that the <span class="hlt">sea</span> <span class="hlt">ice</span> could be divided into five distinct zones. The shorefast <span class="hlt">sea</span> <span class="hlt">ice</span> was found to consist uniformly of first-year <span class="hlt">sea</span> <span class="hlt">ice</span>. The second zone was found to be a mixture of first-year <span class="hlt">sea</span> <span class="hlt">ice</span>, medium size multiyear floes, and many recently refrozen leads, polynyas, and open water; considerable shearing activity was evident in this zone. The third zone was a mixture of first-year and multiyear <span class="hlt">sea</span> <span class="hlt">ice</span> which had a uniform microwave signature. The fourth zone was found to be a mixture of first-year <span class="hlt">sea</span> <span class="hlt">ice</span> and medium-to-large size multiyear floes which was similar in composition to the second zone. The fifth zone was almost exclusively multiyear <span class="hlt">ice</span> extending to the North Pole.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014TCD.....8.1517K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014TCD.....8.1517K"><span>About uncertainties in <span class="hlt">sea</span> <span class="hlt">ice</span> thickness retrieval from satellite radar altimetry: results from the ESA-CCI <span class="hlt">Sea</span> <span class="hlt">Ice</span> ECV Project Round Robin Exercise</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kern, S.; Khvorostovsky, K.; Skourup, H.; Rinne, E.; Parsakhoo, Z. S.; Djepa, V.; Wadhams, P.; Sandven, S.</p> <p>2014-03-01</p> <p>One goal of the European Space Agency Climate Change Initiative <span class="hlt">sea</span> <span class="hlt">ice</span> Essential Climate Variable project is to provide a quality controlled 20 year long data set of Arctic Ocean winter-time <span class="hlt">sea</span> <span class="hlt">ice</span> thickness distribution. An important step to achieve this goal is to assess the accuracy of <span class="hlt">sea</span> <span class="hlt">ice</span> thickness retrieval based on satellite radar altimetry. For this purpose a data base is created comprising <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard derived from satellite radar altimetry between 1993 and 2012 and collocated observations of snow and <span class="hlt">sea</span> <span class="hlt">ice</span> freeboard from Operation <span class="hlt">Ice</span> Bridge (OIB) and CryoSat Validation Experiment (CryoVEx) air-borne campaigns, of <span class="hlt">sea</span> <span class="hlt">ice</span> draft from moored and submarine Upward Looking Sonar (ULS), and of snow depth from OIB campaigns, Advanced Microwave Scanning Radiometer aboard EOS (AMSR-E) and the Warren Climatology (Warren et al., 1999). An inter-comparison of the snow depth data sets stresses the limited usefulness of Warren climatology snow depth for freeboard-to-thickness conversion under current Arctic Ocean conditions reported in other studies. This is confirmed by a comparison of snow freeboard measured during OIB and CryoVEx and snow freeboard computed from radar altimetry. For first-year <span class="hlt">ice</span> the agreement between OIB and AMSR-E snow depth within 0.02 m suggests AMSR-E snow depth as an appropriate alternative. Different freeboard-to-thickness and freeboard-to-draft conversion approaches are realized. The mean observed ULS <span class="hlt">sea</span> <span class="hlt">ice</span> draft agrees with the mean <span class="hlt">sea</span> <span class="hlt">ice</span> draft computed from radar altimetry within the uncertainty bounds of the data sets involved. However, none of the realized approaches is able to reproduce the seasonal cycle in <span class="hlt">sea</span> <span class="hlt">ice</span> draft observed by moored ULS satisfactorily. A sensitivity analysis of the freeboard-to-thickness conversion suggests: in order to obtain <span class="hlt">sea</span> <span class="hlt">ice</span> thickness as accurate as 0.5 m from radar altimetry, besides a freeboard estimate with centimetre accuracy, an <span class="hlt">ice</span>-type dependent <span class="hlt">sea</span> <span class="hlt">ice</span> density is as mandatory</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1991JGR....9618411L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1991JGR....9618411L"><span><span class="hlt">Sea</span> <span class="hlt">ice</span> ridging in the eastern Weddell <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>Lytle, V. I.; Ackley, S. F.</p> <p>1991-10-01</p> <p>In August 1986, <span class="hlt">sea</span> <span class="hlt">ice</span> ridge heights and spatial frequency in the eastern Weddell <span class="hlt">Sea</span> were measured using a ship-based acoustical sounder. Using a minimum ridge sail height of 0.75 m, a total of 933 ridges were measured along a track length of 415 km. The ridge frequency varied from 0.4 to 10.5 ridges km-1. The mean height of the ridges was found to be about 1.1 m regardless of the ridge frequency. These results are compared to other ridging statistics from the Ross <span class="hlt">Sea</span> and found to be similar. Comparison with Arctic data, however, indicates that the height and frequency of the ridges are considerably less in the Weddell <span class="hlt">Sea</span> than in the Arctic. Whereas in the Arctic the mean ridge height tends to increase with the ridge frequency, we found that this was not the case in the Weddell <span class="hlt">Sea</span>, where the mean ridge height remained constant irrespective of the ridge frequency. Estimates of the contribution of deformed <span class="hlt">ice</span> to the total <span class="hlt">ice</span> thickness are generally low except for a single 53-km section where the ridge frequency increased by an order of magnitude. This resulted in an increase in the equivalent mean <span class="hlt">ice</span> thickness due to ridging from 0.04 m in the less deformed areas to 0.45 m in the highly deformed section. These values were found to be consistent with values obtained from drilled profile lines during the same cruise.</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="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1982-11-01</p> <p>First, the natural range of temperatures at which <span class="hlt">sea</span> <span class="hlt">ice</span> exists is just a few degrees off its melting point. In fact, <span class="hlt">sea</span> <span class="hlt">ice</span> normally is only...surface of lakes and <span class="hlt">seas</span>. If <span class="hlt">ice</span> sank into its melt, as do most solids, there would be a tendency for natural water bodies to freeze completely to...I I I -c 1 I II I I 02 b . Figure 1. Structure of <span class="hlt">ice</span> I. The fact that ordinary <span class="hlt">ice</span> is such an open, low density solid also suggests that</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C21A0652H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C21A0652H"><span>NWS Alaska <span class="hlt">Sea</span> <span class="hlt">Ice</span> Program: Operations, Customer Support & Challenges</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Heim, R.; Schreck, M. B.</p> <p>2016-12-01</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/2012AGUFMOS13H..02E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMOS13H..02E"><span><span class="hlt">Sea-ice</span> information co-management: Planning for sustainable multiple uses of <span class="hlt">ice</span>-covered <span class="hlt">seas</span> in a rapidly changing Arctic</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Eicken, H.; Lovecraft, A. L.</p> <p>2012-12-01</p> <p>A thinner, less extensive and more mobile summer <span class="hlt">sea-ice</span> cover is a major element and driver of Arctic Ocean change. Declining summer <span class="hlt">sea</span> <span class="hlt">ice</span> presents Arctic stakeholders with substantial challenges and opportunities from the perspective of sustainable ocean use and derivation of <span class="hlt">sea-ice</span> or ecosystem services. <span class="hlt">Sea-ice</span> use by people and wildlife as well as its role as a major environmental hazard focuses the interests and concerns of indigenous hunters and Arctic coastal communities, resource managers and the maritime industry. In particular, rapid <span class="hlt">sea-ice</span> change and intensifying offshore industrial activities have raised fundamental questions as to how best to plan for and manage multiple and increasingly overlapping ocean and <span class="hlt">sea</span> <span class="hlt">ice</span> uses. The western North American Arctic - a region that has seen some of the greatest changes in <span class="hlt">ice</span> and ocean conditions in the past three decades anywhere in the North - is the focus of our study. Specifically, we examine the important role that relevant and actionable <span class="hlt">sea-ice</span> information can play in allowing stakeholders to evaluate risks and reconcile overlapping and potentially competing interests. Our work in coastal Alaska suggests that important prerequisites to address such challenges are common values, complementary bodies of expertise (e.g., local or indigenous knowledge, engineering expertise, environmental science) and a forum for the implementation and evaluation of a <span class="hlt">sea-ice</span> data and information framework. Alongside the International Polar Year 2007-08 and an associated boost in Arctic Ocean observation programs and platforms, there has been a movement towards new governance bodies that have these qualities and can play a central role in guiding the design and optimization of Arctic observing systems. To help further the development of such forums an evaluation of the density and spatial distribution of institutions, i.e., rule sets that govern ocean use, as well as the use of scenario planning and analysis can serve as</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C13E..07L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C13E..07L"><span>EM Bias-Correction for <span class="hlt">Ice</span> Thickness and Surface Roughness Retrievals over Rough Deformed <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>Li, L.; Gaiser, P. W.; Allard, R.; Posey, P. G.; Hebert, D. A.; Richter-Menge, J.; Polashenski, C. M.</p> <p>2016-12-01</p> <p>The very rough ridge <span class="hlt">sea</span> <span class="hlt">ice</span> accounts for significant percentage of total <span class="hlt">ice</span> areas and even larger percentage of total volume. The commonly used Radar altimeter surface detection techniques are empirical in nature and work well only over level/smooth <span class="hlt">sea</span> <span class="hlt">ice</span>. Rough <span class="hlt">sea</span> <span class="hlt">ice</span> surfaces can modify the return waveforms, resulting in significant Electromagnetic (EM) bias in the estimated surface elevations, and thus large errors in the <span class="hlt">ice</span> thickness retrievals. To understand and quantify such <span class="hlt">sea</span> <span class="hlt">ice</span> surface roughness effects, a combined EM rough surface and volume scattering model was developed to simulate radar returns from the rough <span class="hlt">sea</span> <span class="hlt">ice</span> `layer cake' structure. A waveform matching technique was also developed to fit observed waveforms to a physically-based waveform model and subsequently correct the roughness induced EM bias in the estimated freeboard. This new EM Bias Corrected (EMBC) algorithm was able to better retrieve surface elevations and estimate the surface roughness parameter simultaneously. In situ data from multi-instrument airborne and ground campaigns were used to validate the <span class="hlt">ice</span> thickness and surface roughness retrievals. For the surface roughness retrievals, we applied this EMBC algorithm to co-incident LiDAR/Radar measurements collected during a Cryosat-2 under-flight by the NASA <span class="hlt">Ice</span>Bridge missions. Results show that not only does the waveform model fit very well to the measured radar waveform, but also the roughness parameters derived independently from the LiDAR and radar data agree very well for both level and deformed <span class="hlt">sea</span> <span class="hlt">ice</span>. For <span class="hlt">sea</span> <span class="hlt">ice</span> thickness retrievals, validation based on in-situ data from the coordinated CRREL/NRL field campaign demonstrates that the physically-based EMBC algorithm performs fundamentally better than the empirical algorithm over very rough deformed <span class="hlt">sea</span> <span class="hlt">ice</span>, suggesting that <span class="hlt">sea</span> <span class="hlt">ice</span> surface roughness effects can be modeled and corrected based solely on the radar return waveforms.</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="http://www.dtic.mil/">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://hdl.handle.net/2060/20150004436','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150004436"><span><span class="hlt">Sea-Ice</span> Freeboard Retrieval Using Digital Photon-Counting Laser Altimetry</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.; Brunt, Kelly M.; Ruth, Julia M.; Kuhn, John M.; Connor, Laurence N.; Walsh, Kaitlin M.</p> <p>2015-01-01</p> <p>Airborne and spaceborne altimeters provide measurements of <span class="hlt">sea-ice</span> elevation, from which <span class="hlt">sea-ice</span> freeboard and thickness may be derived. Observations of the Arctic <span class="hlt">ice</span> pack by satellite altimeters indicate a significant decline in <span class="hlt">ice</span> thickness, and volume, over the last decade. NASA's <span class="hlt">Ice</span>, Cloud and land Elevation Satellite-2 (ICESat-2) is a next-generation laser altimeter designed to continue key <span class="hlt">sea-ice</span> observations through the end of this decade. An airborne simulator for ICESat-2, the Multiple Altimeter Beam Experimental Lidar (MABEL), has been deployed to gather pre-launch data for mission development. We present an analysis of MABEL data gathered over <span class="hlt">sea</span> <span class="hlt">ice</span> in the Greenland <span class="hlt">Sea</span> and assess the capabilities of photon-counting techniques for <span class="hlt">sea-ice</span> freeboard retrieval. We compare freeboard estimates in the marginal <span class="hlt">ice</span> zone derived from MABEL photon-counting data with coincident data collected by a conventional airborne laser altimeter. We find that freeboard estimates agree to within 0.03m in the areas where <span class="hlt">sea-ice</span> floes were interspersed with wide leads, and to within 0.07m elsewhere. MABEL data may also be used to infer <span class="hlt">sea-ice</span> thickness, and when compared with coincident but independent <span class="hlt">ice</span> thickness estimates, MABEL <span class="hlt">ice</span> thicknesses agreed to within 0.65m or better.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28586523','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28586523"><span>Increased Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> drift alters adult female polar bear movements and energetics.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Durner, George M; Douglas, David C; Albeke, Shannon E; Whiteman, John P; Amstrup, Steven C; Richardson, Evan; Wilson, Ryan R; Ben-David, Merav</p> <p>2017-09-01</p> <p>Recent reductions in thickness and <span class="hlt">extent</span> have increased drift rates of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Increased <span class="hlt">ice</span> drift could significantly affect the movements and the energy balance of polar bears (Ursus maritimus) which forage, nearly exclusively, on this substrate. We used radio-tracking and <span class="hlt">ice</span> drift data to quantify the influence of increased drift on bear movements, and we modeled the consequences for energy demands of adult females in the Beaufort and Chukchi <span class="hlt">seas</span> during two periods with different <span class="hlt">sea</span> <span class="hlt">ice</span> characteristics. Westward and northward drift of the <span class="hlt">sea</span> <span class="hlt">ice</span> used by polar bears in both regions increased between 1987-1998 and 1999-2013. To remain within their home ranges, polar bears responded to the higher westward <span class="hlt">ice</span> drift with greater eastward movements, while their movements north in the spring and south in fall were frequently aided by <span class="hlt">ice</span> motion. To compensate for more rapid westward <span class="hlt">ice</span> drift in recent years, polar bears covered greater daily distances either by increasing their time spent active (7.6%-9.6%) or by increasing their travel speed (8.5%-8.9%). This increased their calculated annual energy expenditure by 1.8%-3.6% (depending on region and reproductive status), a cost that could be met by capturing an additional 1-3 seals/year. Polar bears selected similar habitats in both periods, indicating that faster drift did not alter habitat preferences. Compounding reduced foraging opportunities that result from habitat loss; changes in <span class="hlt">ice</span> drift, and associated activity increases, likely exacerbate the physiological stress experienced by polar bears in a warming Arctic. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5008214','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5008214"><span>Arctic marine mammal population status, <span class="hlt">sea</span> <span class="hlt">ice</span> habitat loss, and conservation recommendations for the 21st century</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Stern, Harry; Kovacs, Kit M.; Lowry, Lloyd; Moore, Sue E.; Regehr, Eric V.; Ferguson, Steven H.; Wiig, Øystein; Boveng, Peter; Angliss, Robyn P.; Born, Erik W.; Litovka, Dennis; Quakenbush, Lori; Lydersen, Christian; Vongraven, Dag; Ugarte, Fernando</p> <p>2015-01-01</p> <p>Abstract Arctic marine mammals (AMMs) are icons of climate change, largely because of their close association with <span class="hlt">sea</span> <span class="hlt">ice</span>. However, neither a circumpolar assessment of AMM status nor a standardized metric of <span class="hlt">sea</span> <span class="hlt">ice</span> habitat change is available. We summarized available data on abundance and trend for each AMM species and recognized subpopulation. We also examined species diversity, the <span class="hlt">extent</span> of human use, and temporal trends in <span class="hlt">sea</span> <span class="hlt">ice</span> habitat for 12 regions of the Arctic by calculating the dates of spring <span class="hlt">sea</span> <span class="hlt">ice</span> retreat and fall <span class="hlt">sea</span> <span class="hlt">ice</span> advance from satellite data (1979–2013). Estimates of AMM abundance varied greatly in quality, and few studies were long enough for trend analysis. Of the AMM subpopulations, 78% (61 of 78) are legally harvested for subsistence purposes. Changes in <span class="hlt">sea</span> <span class="hlt">ice</span> phenology have been profound. In all regions except the Bering <span class="hlt">Sea</span>, the duration of the summer (i.e., reduced <span class="hlt">ice</span>) period increased by 5–10 weeks and by >20 weeks in the Barents <span class="hlt">Sea</span> between 1979 and 2013. In light of generally poor data, the importance of human use, and forecasted environmental changes in the 21st century, we recommend the following for effective AMM conservation: maintain and improve comanagement by local, federal, and international partners; recognize spatial and temporal variability in AMM subpopulation response to climate change; implement monitoring programs with clear goals; mitigate cumulative impacts of increased human activity; and recognize the limits of current protected species legislation. PMID:25783745</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29704449','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29704449"><span>Contribution of <span class="hlt">sea</span> <span class="hlt">ice</span> microbial production to Antarctic benthic communities is driven by <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics and composition of functional guilds.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wing, Stephen R; Leichter, James J; Wing, Lucy C; Stokes, Dale; Genovese, Sal J; McMullin, Rebecca M; Shatova, Olya A</p> <p>2018-04-28</p> <p>Organic matter produced by the <span class="hlt">sea</span> <span class="hlt">ice</span> microbial community (SIMCo) is an important link between <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics and secondary production in near-shore food webs of Antarctica. <span class="hlt">Sea</span> <span class="hlt">ice</span> conditions in McMurdo Sound were quantified from time series of MODIS satellite images for Sept. 1 through Feb. 28 of 2007-2015. A predictable <span class="hlt">sea</span> <span class="hlt">ice</span> persistence gradient along the length of the Sound and evidence for a distinct change in <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics in 2011 were observed. We used stable isotope analysis (δ 13 C and δ 15 N) of SIMCo, suspended particulate organic matter (SPOM) and shallow water (10-20 m) macroinvertebrates to reveal patterns in trophic structure of, and incorporation of organic matter from SIMCo into, benthic communities at eight sites distributed along the <span class="hlt">sea</span> <span class="hlt">ice</span> persistence gradient. Mass-balance analysis revealed distinct trophic architecture among communities and large fluxes of SIMCo into the near-shore food web, with the estimates ranging from 2 to 84% of organic matter derived from SIMCo for individual species. Analysis of patterns in density, and biomass of macroinvertebrate communities among sites allowed us to model net incorporation of organic matter from SIMCo, in terms of biomass per unit area (g/m 2 ), into benthic communities. Here, organic matter derived from SIMCo supported 39 to 71 per cent of total biomass. Furthermore, for six species, we observed declines in contribution of SIMCo between years with persistent <span class="hlt">sea</span> <span class="hlt">ice</span> (2008-2009) and years with extensive <span class="hlt">sea</span> <span class="hlt">ice</span> breakout (2012-2015). Our data demonstrate the vital role of SIMCo in ecosystem function in Antarctica and strong linkages between <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics and near-shore secondary productivity. These results have important implications for our understanding of how benthic communities will respond to changes in <span class="hlt">sea</span> <span class="hlt">ice</span> dynamics associated with climate change and highlight the important role of shallow water macroinvertebrate communities as sentinels of change for the Antarctic marine</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.C52B..01B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.C52B..01B"><span>How Vulnerable is Perennial <span class="hlt">Sea</span> <span class="hlt">Ice</span>? Insights from Earth's Late Cenozoic Natural Experiments (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Brigham-Grette, J.; Polyak, L. V.; Caissie, B.; Sharko, C. J.; Petsch, S.</p> <p>2010-12-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> is an important component of the climate system. Yet, reconstructions of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> conditions reflecting glacial and interglacial change over the past 3 million years are almost nonexistent. Our work to evaluate the <span class="hlt">sea</span> <span class="hlt">ice</span> and <span class="hlt">sea</span> surface temperature record of the Bering Strait region builds on a review of the <span class="hlt">sea</span> <span class="hlt">ice</span> history of the pan-Arctic. The best estimates of <span class="hlt">sea</span> <span class="hlt">ice</span> make use of indirect proxies based on reconstructions of treeline, <span class="hlt">sea</span> surface temperatures, depositional systems, and the ecological preferences of extant marine microfossil species. The development of new proxies of past <span class="hlt">sea</span> <span class="hlt">ice</span> <span class="hlt">extent</span> including microfossil assemblages (diatoms, ostracodes) and biomarker proxies (IP25) show promise for quantifying seasonal concentrations of <span class="hlt">sea</span> <span class="hlt">ice</span> cover on centennial to millennial timescales. Using both marine and terrestrial information, periods of restricted <span class="hlt">sea</span> <span class="hlt">ice</span> and <span class="hlt">ice</span>-free Arctic conditions can be inferred for parts of the late Cenozoic. The Arctic Ocean borderlands contain clear stratigraphic evidence for forested conditions at intervals over the past 50 million years, recording the migration of treeline from High Arctic coastal locations within the Canadian Archipelago. Metasequoia forests of the peak Eocene gave way to a variety of biomass-rich circumarctic redwood forests by 46 Ma. Between 23 and 16 Ma, cool-temperate metasequoia forests dominated NE Alaska and the Yukon while mixed conifer-hardwood forests (similar to those of modern southern maritime Canada and New England) dominated the central Canadian Archipelago. By 16 Ma, these forests gave way to larch and spruce. From 5 to 3 Ma the braid plains of the Beaufort Fm were dominated by over 100 vascular plants including pine and birch, while other locations remained dominated by spruce and larch. Boreal conditions across northern Greenland and arctic Alaska are consistent with the presence of bivalve Arctica islandica in marine sediments capping the Beaufort Formation on Meighen</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C41A0644M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C41A0644M"><span>Modelling of <span class="hlt">Sea</span> <span class="hlt">Ice</span> Thermodynamics and Biogeochemistry during the N-<span class="hlt">ICE</span>2015 Expedition in the Arctic Ocean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Meyer, A.; Duarte, P.; Mork Olsen, L.; Kauko, H.; Assmy, P.; Rösel, A.; Itkin, P.; Hudson, S. R.; Granskog, M. A.; Gerland, S.; Sundfjord, A.; Steen, H.; Jeffery, N.; Hunke, E. C.; Elliott, S.; Turner, A. K.</p> <p>2016-12-01</p> <p>Changes in the <span class="hlt">sea</span> <span class="hlt">ice</span> regime of the Arctic Ocean over the last decades from a thick perennial multiyear <span class="hlt">ice</span> to a first year <span class="hlt">ice</span> have been well documented. These changes in the <span class="hlt">sea</span> <span class="hlt">ice</span> regime will affect feedback mechanisms between the <span class="hlt">sea</span> <span class="hlt">ice</span>, atmosphere and ocean. Here we evaluate the performance of the Los Alamos <span class="hlt">Sea</span> <span class="hlt">Ice</span> Model (CICE), a state of the art <span class="hlt">sea</span> <span class="hlt">ice</span> model, to predict <span class="hlt">sea</span> <span class="hlt">ice</span> physical and biogeochemical properties at time scales of a few weeks. We also identify the most problematic prognostic variables and what is necessary to improve their forecast. The availability of a complete data set of forcing collected during the Norwegian Young <span class="hlt">sea</span> <span class="hlt">Ice</span> (N-<span class="hlt">ICE</span>-2015) expedition north of Svalbard opens the possibility to properly test CICE. Oceanographic, atmospheric, <span class="hlt">sea</span> <span class="hlt">ice</span>, snow, and biological data were collected above, on, and below the <span class="hlt">ice</span> using R/V Lance as the base for the <span class="hlt">ice</span> camps that were drifting south towards the Fram Strait. Over six months, four different drifts took place, from the Nansen Basin, through the marginal <span class="hlt">ice</span> zone, to the open ocean. Obtained results from the model show a good performance regarding <span class="hlt">ice</span> thickness, salinity and temperature. Nutrients and <span class="hlt">sea</span> <span class="hlt">ice</span> algae are however not modelled as accurately. We hypothesize that improvements in biogeochemical modeling may be achieved by complementing brine drainage with a diffusion parameterization and biogeochemical modeling with the introduction of an explicit formulation to forecast chlorophyll and regulate photosynthetic efficiency.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12.6312M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.6312M"><span>Comparative study of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> response from NEMO-LIM3 to two different atmospheric forcings</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Massonnet, Francois; Fichefet, Thierry; Goosse, Hugues; Mathiot, Pierre; König Beatty, Christof; Vancoppenolle, Martin</p> <p>2010-05-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> plays a key role within the climate system as it is, e.g., an efficient barrier to transfers of heat, mass and momentum between atmosphere and ocean. In order to simulate the observed <span class="hlt">sea</span> <span class="hlt">ice</span> state, global Ocean General Circulation Models (OGCMs) must benefit from good quality atmospheric forcings. NEMO-LIM3 is one of those OGCMs. This model results from the coupling of the <span class="hlt">sea</span> <span class="hlt">ice</span> model LIM3 with the ocean model OPA. So far, the NCEP/NCAR reanalysis dataset (2-m atmospheric temperatures and 10-m wind speeds) has been used jointly with monthly climatologies of relative humidity, cloudiness and precipitation to set up and calibrate NEMO-LIM3. Clear biases in model outputs have been tentatively attributed to this forcing. Here, we investigate the consequences of using the ERA-40-based DFS4 forcing on an ORCA1 configuration (1° resolution), with focus on the Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>. Using an adequate metric, we measure the discrepancies between the simulations resulting from the respective forcings. A particular attention is paid to the <span class="hlt">sea</span> <span class="hlt">ice</span> features along Siberia at the beginning of the 80s, as previous NEMO-LIM3 runs with the NCEP/NCAR forcing exhibit a significant overestimation of <span class="hlt">ice</span> <span class="hlt">extent</span> in this area during this time period.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA549401','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA549401"><span>Arctic <span class="hlt">Sea</span> <span class="hlt">Ice</span>: Using Airborne Topographic Mapper Measurements (ATM) to Determine <span class="hlt">Sea</span> <span class="hlt">Ice</span> Thickness</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2011-05-10</p> <p>Track Distance (Km) E le v a ti o n ( m ) ATM Elevation Profile Elevation 18 Figure 13: Geoid shape of earth’s equipotential surface , which is...inferred for the region between successive leads. Therefore, flying over a lead in the <span class="hlt">ice</span> is very important for determining the exact <span class="hlt">sea</span> surface elevation...inferred for the region between successive leads. Therefore, flying over a lead in the <span class="hlt">ice</span> is very important for determining the exact <span class="hlt">sea</span> surface</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.6676H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.6676H"><span>Scaling properties of Arctic <span class="hlt">sea</span> <span class="hlt">ice</span> deformation in high-resolution viscous-plastic <span class="hlt">sea</span> <span class="hlt">ice</span> models and satellite observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hutter, Nils; Losch, Martin; Menemenlis, Dimitris</p> <p>2017-04-01</p> <p><span class="hlt">Sea</span> <span class="hlt">ice</span> models with the traditional viscous-plastic (VP) rheology and very high grid resolution can resolve leads and deformation rates that are localised along Linear Kinematic Features (LKF). In a 1-km pan-Arctic <span class="hlt">sea</span> <span class="hlt">ice</span>-ocean simulation, the small scale <span class="hlt">sea-ice</span> deformations in the Central Arctic are evaluated with a scaling analysis in relation to satellite observations of the Envisat Geophysical Processor System (EGPS). A new coupled scaling analysis for data on Eulerian grids determines the spatial and the temporal scaling as well as the coupling between temporal and spatial scales. The spatial scaling of the modelled <span class="hlt">sea</span> <span class="hlt">ice</span> deformation implies multi-fractality. The spatial scaling is also coupled to temporal scales and varies realistically by region and season. The agreement of the spatial scaling and its coupling to temporal scales with satellite observations and models with the modern elasto-brittle rheology challenges previous results with VP models at coarse resolution where no such scaling was found. The temporal scaling analysis, however, shows that the VP model does not fully resolve the intermittency of <span class="hlt">sea</span> <span class="hlt">ice</span> deformation that is observed in satellite data.</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_21");'>21</a></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_24");'>24</a></li> <li class="active"><span>25</span></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_25 --> <div class="footer-extlink text-muted" style="margin-bottom:1rem; text-align:center;">Some links on this page may take you to non-federal websites. 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