Science.gov

Sample records for amsr-e ice concentration

  1. Assessment of the AMSR-E Sea Ice Concentration Product at the Ice Edge Using RADARSAT-1 and MODIS Imagery

    NASA Technical Reports Server (NTRS)

    Henrichs, John F.; Cavalieri, Donald J.; Markus, Thorsten

    2006-01-01

    Imagery from the C-band synthetic aperture radar (SAR) aboard RADARSAT-1 and the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to evaluate the performance of the Advanced Microwave Scanning Radiometer- EOS (AMSR-E) ice concentration product near the sea ice edge in the Bering Sea for four days during March 2003, which is concurrent with the AMSR-Ice03 field/aircraft campaign. The AMSR-E products were observed to perform very well in identifying open-water and pack-ice areas, although the AMSR-E products occasionally underestimate ice concentration in areas with thin ice. The position of the ice edge determined from AMSR-E data using a 15% concentration threshold was found to be, on average, within one AMSR-E grid square (12.5 km) of the ice edge determined from the SAR data, with the AMSR-E edge tending to be outside the SAR-derived edge

  2. Assessment of the AMSR-E Sea Ice Concentration Product at the Ice Edge Using RADARSAT-1 and MODIS Imagery

    NASA Technical Reports Server (NTRS)

    Henrichs, John F.; Cavalieri, Donald J.; Markus, Thorsten

    2006-01-01

    Imagery from the C-band synthetic aperture radar (SAR) aboard RADARSAT-1 and the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to evaluate the performance of the Advanced Microwave Scanning Radiometer- EOS (AMSR-E) ice concentration product near the sea ice edge in the Bering Sea for four days during March 2003, which is concurrent with the AMSR-Ice03 field/aircraft campaign. The AMSR-E products were observed to perform very well in identifying open-water and pack-ice areas, although the AMSR-E products occasionally underestimate ice concentration in areas with thin ice. The position of the ice edge determined from AMSR-E data using a 15% concentration threshold was found to be, on average, within one AMSR-E grid square (12.5 km) of the ice edge determined from the SAR data, with the AMSR-E edge tending to be outside the SAR-derived edge

  3. Assessment of EOS Aqua AMSR-E Arctic Sea Ice Concentrations using Landsat-7 and Airborne Microwave Imagery

    NASA Technical Reports Server (NTRS)

    Cavalieri, Donald J.; Markus, Thorsten; Hall, Dorothy K.; Gasiewski, Albin J.; Klein, Marian; Ivanoff, Alvaro

    2006-01-01

    An assessment of Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) sea ice concentrations under winter conditions using ice concentrations derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) imagery obtained during the March 2003 Arctic sea ice validation field campaign is presented. The National Oceanic and Atmospheric Administration Environmental Technology Laboratory's Airborne Polarimetric Scanning Radiometer Measurements, which were made from the National Aeronautics and Space Administration P 3B aircraft during the campaign, were used primarily as a diagnostic tool to understand the comparative results and to suggest improvements to the AMSR-E ice concentration algorithm. Based on the AMSR-E/ETM+ comparisons, a good overall agreement with little bias (approx. 1%) for areas of first year and young sea ice was found. Areas of new ice production result in a negative bias of about 5% in the AMSR-E ice concentration retrievals, with a root mean square error of 8%. Some areas of deep snow also resulted in an underestimate of the ice concentration (approx. 10%). For all ice types combined and for the full range of ice concentrations, the bias ranged from 0% to 3%, and the rms errors ranged from 1% to 7%, depending on the region. The new-ice and deep-snow biases are expected to be reduced through an adjustment of the new-ice and ice-type C algorithm tie points.

  4. Estimation of melt pond fraction over high-concentration Arctic sea ice using AMSR-E passive microwave data

    NASA Astrophysics Data System (ADS)

    Tanaka, Yasuhiro; Tateyama, Kazutaka; Kameda, Takao; Hutchings, Jennifer K.

    2016-09-01

    Melt pond fraction (MPF) on sea ice is an important factor for ice-albedo feedback throughout the Arctic Ocean. We propose an algorithm to estimate MPF using satellite passive microwave data in this study. The brightness temperature (TB) data obtained from the Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) were compared to the ship-based MPF in the Beaufort Sea and Canadian Arctic Archipelago. The difference between the TB at horizontal and vertical polarizations of 6.9 and 89.0 GHz (MP06H-89V), respectively, depends on the MPF. The correlation between MP06H-89V and ship-based MPF was higher than that between ship-based MPF and two individual channels (6.9 and 89.0 GHz of horizontal and vertical polarizations, respectively). The MPF determined with the highest resolution channel, 89.0 GHz (5 km × 5 km), provides spatial information with more detail than the 6.9 GHz channel. The algorithm estimates the relative fraction of ice covered by water (1) over areas where sea ice concentration is higher than 95%, (2) during late summer, and (3) in areas with low atmospheric humidity. The MPF estimated from AMSR-E data (AMSR-E MPF) in early summer was underestimated at lower latitudes and overestimated at higher latitudes, compared to the MPF obtained from the Moderate Resolution Image Spectrometer (MODIS MPF). The differences between AMSR-E MPF and MODIS MPF were less than 5% in most the regions and the periods. Our results suggest that the proposal algorithm serves as a basis for building time series of MPF in regions of consolidated ice pack.

  5. EOS Aqua AMSR-E Arctic Sea Ice Validation Program

    NASA Technical Reports Server (NTRS)

    Cavalieri, D. J.; Markus, T.; Gasiewski, A.; Klein, M.; Maslanik, J.; Sturm, M.; Stroeve, J.; Heinrichs, J.

    2004-01-01

    A coordinated Arctic sea ice validation field campaign using the NASA Wallops P-3B aircraft was successfully completed in March 2003. This campaign was part of the program for validating the Earth Observing System (EOS) Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sea ice products. The AMSR-E, designed and built by the Japanese National Space Development Agency for NASA, was launched May 4,2002 on the EOS Aqua spacecraft. The AMSR-E sea ice products include sea ice concentration, sea ice temperature, and snow depth on sea ice. The primary instrument on the P-3B aircraft was the NOAA ETL Polarimetric Scanning Radiometer (PSR) covering the same frequencies and polarizations as the AMSR-E. This paper describes the objectives of each of the seven flights, the Arctic regions overflown, and the coordination among satellite, aircraft, and surface-based measurements. Two of the seven aircraft flights were coordinated with scientists making surface measurements of snow and ice properties including sea ice temperature and snow depth on sea ice at a study area near Barrow, AK and at a Navy ice camp located in the Beaufort Sea. The remaining flights covered portions of the Bering Sea ice edge, the Chukchi Sea, and Norton Sound. Comparisons among the satellite and aircraft PSR data sets are presented.

  6. March 2003 EOS Aqua AMSR-E Arctic Sea Ice Field Campaign

    NASA Technical Reports Server (NTRS)

    Cavalieri, Donald J.; Markus, Thorsten; Maslanik, James A.; Sturm, Matthew; Lobl, Elena

    2006-01-01

    An overview of the March 2003 coordinated sea ice field campaign in the Alaskan Arctic is presented with reference to the papers in this special section. This campaign is part of the program to validate the Aqua Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sea ice products. Standard AMSR-E sea ice products include sea ice concentration, sea ice temperature, and snow depth on sea ice. The validation program consists of three elements, namely: 1) satellite data comparisons; 2) coordinated satellite/aircraft surface measurements; and 3) modeling and sensitivity analyses. Landsat-7 and RADARSAT observations were used in comparative studies with the retrieved AMSR-E sea ice concentrations. The aircraft sensors provided high-resolution microwave imagery of the surface, atmospheric profiles of temperature and humidity, and digital records of sea ice conditions. When combined with in situ measurements, aircraft data were used to validate the AMSR-E sea ice temperature and snow-depth products. The modeling studies helped interpret the field-data comparisons, provided insight on the limitations of the AMSR-E sea ice algorithms, and suggested potential improvements to the AMSR-E retrieval algorithms.

  7. Extracting tidal variability of sea ice concentration from AMSR-E passive microwave single-swath data: a case study of the Ross Sea

    NASA Astrophysics Data System (ADS)

    Mack, Stefanie; Padman, Laurie; Klinck, John

    2013-02-01

    Abstract The periodic divergence of stress applied by ocean tidal currents to sea <span class="hlt">ice</span> affects the time-averaged <span class="hlt">ice</span> <span class="hlt">concentration</span> (Cice) and heat and freshwater fluxes at the ocean surface. We demonstrate that, at sufficiently high latitudes, tidal variability in Cice can be extracted from single-swath data from the Advanced Microwave Scanning Radiometer-EOS (<span class="hlt">AMSR-E</span>) satellite passive microwave sensor, although time intervals between swaths are irregular. For the northwest Ross Sea where tidal currents are large, tidal divergence is the dominant cause of Cice variability in winter, with a range of ±0.2 about a mean of ~0.8. Daily-averaged Cice values vary from >0.9 at neap tides to ~0.7 at spring tides. Variability at the fundamental tidal periods is about half that expected from an inverse barotropic tide model for the Ross Sea, suggesting that the measured tidal signal in Cice may be used to diagnose sea <span class="hlt">ice</span> mechanical properties and <span class="hlt">ice</span>/ocean coupling.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20040120981','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20040120981"><span>EOS Aqua <span class="hlt">AMSR-E</span> Arctic Sea <span class="hlt">Ice</span> Validation Program: Arctic2003 Aircraft Campaign Flight Report</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cavalieri, D. J.; Markus,T.</p> <p>2003-01-01</p> <p>In March 2003 a coordinated Arctic sea <span class="hlt">ice</span> validation field campaign using the NASA Wallops P-3B aircraft was successfully completed. This campaign was part of the program for validating the Earth Observing System (EOS) Aqua Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) sea <span class="hlt">ice</span> products. The <span class="hlt">AMSR-E</span>, designed and built by the Japanese National Space Development Agency for NASA, was launched May 4, 2002 on the EOS Aqua spacecraft. The <span class="hlt">AMSR-E</span> sea <span class="hlt">ice</span> products to be validated include sea <span class="hlt">ice</span> <span class="hlt">concentration</span>, sea <span class="hlt">ice</span> temperature, and snow depth on sea <span class="hlt">ice</span>. This flight report describes the suite of instruments flown on the P-3, the objectives of each of the seven flights, the Arctic regions overflown, and the coordination among satellite, aircraft, and surface-based measurements. Two of the seven aircraft flights were coordinated with scientists making surface measurements of snow and <span class="hlt">ice</span> properties including sea <span class="hlt">ice</span> temperature and snow depth on sea <span class="hlt">ice</span> at a study area near Barrow, AK and at a Navy <span class="hlt">ice</span> camp located in the Beaufort Sea. Two additional flights were dedicated to making heat and moisture flux measurements over the St. Lawrence Island polynya to support ongoing air-sea-<span class="hlt">ice</span> processes studies of Arctic coastal polynyas. The remaining flights covered portions of the Bering Sea <span class="hlt">ice</span> edge, the Chukchi Sea, and Norton Sound.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20070034825','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20070034825"><span>Trends in the Sea <span class="hlt">Ice</span> Cover Using Enhanced and Compatible <span class="hlt">AMSR-E</span>, SSM/I and SMMR Data</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.; Nishio, Fumihiko</p> <p>2007-01-01</p> <p>Arguably, the most remarkable manifestation of change in the polar regions is the rapid decline (of about -10 %/decade) in the Arctic perennial <span class="hlt">ice</span> cover. Changes in the global sea <span class="hlt">ice</span> cover, however, are more modest, being slightly positive in the Southern Hemisphere and slightly negative in the Northern Hemisphere, the significance of which has not been adequately assessed because of unknown errors in the satellite historical data. We take advantage of the recent and more accurate <span class="hlt">AMSR-E</span> data to evaluate the true seasonal and interannual variability of the sea <span class="hlt">ice</span> cover, assess the accuracy of historical data, and determine the real trend. Consistently derived <span class="hlt">ice</span> <span class="hlt">concentrations</span> from <span class="hlt">AMSR-E</span>, SSM/I, and SMMR data were analyzed and a slight bias is observed between <span class="hlt">AMSR-E</span> and SSM/I data mainly because of differences in resolution. Analysis of the combine SMMR, SSM/I and <span class="hlt">AMSR-E</span> data set, with the bias corrected, shows that the trends in extent and area of sea <span class="hlt">ice</span> in the Arctic region is -3.4 +/- 0.2 and -4.0 +/- 0.2 % per decade, respectively, while the corresponding values for the Antarctic region is 0.9 +/- 0.2 and 1.7 .+/- 0.3 % per decade. The higher resolution of the <span class="hlt">AMSR-E</span> provides an improved determination of the location of the <span class="hlt">ice</span> edge while the SSM/I data show an <span class="hlt">ice</span> edge about 6 to 12 km further away from the <span class="hlt">ice</span> pack. Although the current record of <span class="hlt">AMSR-E</span> is less than 5 years, the data can be utilized in combination with historical data for more accurate determination of the variability and trends in the <span class="hlt">ice</span> cover.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20070021414&hterms=surface+roughness&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dsurface%2Broughness','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20070021414&hterms=surface+roughness&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dsurface%2Broughness"><span>Impact of Surface Roughness on <span class="hlt">AMSR-E</span> Sea <span class="hlt">Ice</span> Products</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Stroeve, Julienne C.; Markus, Thorsten; Maslanik, James A.; Cavalieri, Donald J.; Gasiewski, Albin J.; Heinrichs, John F.; Holmgren, Jon; Perovich, Donald K.; Sturm, Matthew</p> <p>2006-01-01</p> <p>This paper examines the sensitivity of Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) brightness temperatures (Tbs) to surface roughness by a using radiative transfer model to simulate <span class="hlt">AMSR-E</span> Tbs as a function of incidence angle at which the surface is viewed. The simulated Tbs are then used to examine the influence that surface roughness has on two operational sea <span class="hlt">ice</span> algorithms, namely: 1) the National Aeronautics and Space Administration Team (NT) algorithm and 2) the enhanced NT algorithm, as well as the impact of roughness on the <span class="hlt">AMSR-E</span> snow depth algorithm. Surface snow and <span class="hlt">ice</span> data collected during the AMSR-<span class="hlt">Ice</span>03 field campaign held in March 2003 near Barrow, AK, were used to force the radiative transfer model, and resultant modeled Tbs are compared with airborne passive microwave observations from the Polarimetric Scanning Radiometer. Results indicate that passive microwave Tbs are very sensitive even to small variations in incidence angle, which can cause either an over or underestimation of the true amount of sea <span class="hlt">ice</span> in the pixel area viewed. For example, this paper showed that if the sea <span class="hlt">ice</span> areas modeled in this paper mere assumed to be completely smooth, sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> were underestimated by nearly 14% using the NT sea <span class="hlt">ice</span> algorithm and by 7% using the enhanced NT algorithm. A comparison of polarization ratios (PRs) at 10.7,18.7, and 37 GHz indicates that each channel responds to different degrees of surface roughness and suggests that the PR at 10.7 GHz can be useful for identifying locations of heavily ridged or rubbled <span class="hlt">ice</span>. Using the PR at 10.7 GHz to derive an "effective" viewing angle, which is used as a proxy for surface roughness, resulted in more accurate retrievals of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> for both algorithms. The <span class="hlt">AMSR-E</span> snow depth algorithm was found to be extremely sensitive to instrument calibration and sensor viewing angle, and it is concluded that more work is needed to investigate the sensitivity of the gradient ratio at 37 and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20070021414&hterms=impact+factor&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dimpact%2Bfactor','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20070021414&hterms=impact+factor&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dimpact%2Bfactor"><span>Impact of Surface Roughness on <span class="hlt">AMSR-E</span> Sea <span class="hlt">Ice</span> Products</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Stroeve, Julienne C.; Markus, Thorsten; Maslanik, James A.; Cavalieri, Donald J.; Gasiewski, Albin J.; Heinrichs, John F.; Holmgren, Jon; Perovich, Donald K.; Sturm, Matthew</p> <p>2006-01-01</p> <p>This paper examines the sensitivity of Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) brightness temperatures (Tbs) to surface roughness by a using radiative transfer model to simulate <span class="hlt">AMSR-E</span> Tbs as a function of incidence angle at which the surface is viewed. The simulated Tbs are then used to examine the influence that surface roughness has on two operational sea <span class="hlt">ice</span> algorithms, namely: 1) the National Aeronautics and Space Administration Team (NT) algorithm and 2) the enhanced NT algorithm, as well as the impact of roughness on the <span class="hlt">AMSR-E</span> snow depth algorithm. Surface snow and <span class="hlt">ice</span> data collected during the AMSR-<span class="hlt">Ice</span>03 field campaign held in March 2003 near Barrow, AK, were used to force the radiative transfer model, and resultant modeled Tbs are compared with airborne passive microwave observations from the Polarimetric Scanning Radiometer. Results indicate that passive microwave Tbs are very sensitive even to small variations in incidence angle, which can cause either an over or underestimation of the true amount of sea <span class="hlt">ice</span> in the pixel area viewed. For example, this paper showed that if the sea <span class="hlt">ice</span> areas modeled in this paper mere assumed to be completely smooth, sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> were underestimated by nearly 14% using the NT sea <span class="hlt">ice</span> algorithm and by 7% using the enhanced NT algorithm. A comparison of polarization ratios (PRs) at 10.7,18.7, and 37 GHz indicates that each channel responds to different degrees of surface roughness and suggests that the PR at 10.7 GHz can be useful for identifying locations of heavily ridged or rubbled <span class="hlt">ice</span>. Using the PR at 10.7 GHz to derive an "effective" viewing angle, which is used as a proxy for surface roughness, resulted in more accurate retrievals of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> for both algorithms. The <span class="hlt">AMSR-E</span> snow depth algorithm was found to be extremely sensitive to instrument calibration and sensor viewing angle, and it is concluded that more work is needed to investigate the sensitivity of the gradient ratio at 37 and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20070008103','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20070008103"><span>EOS Aqua <span class="hlt">AMSR-E</span> Arctic Sea-<span class="hlt">Ice</span> Validation Program: Arctic2006 Aircraft Campaign Flight Report</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cavalieri, D. J.; Markus, T.</p> <p>2006-01-01</p> <p>In March 2006, a coordinated Arctic sea-<span class="hlt">ice</span> validation field campaign using the NASA Wallops P-3B aircraft was successfully completed. This campaign was the second Alaskan Arctic field campaign for validating the Earth Observing System (EOS) Aqua Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) sea-<span class="hlt">ice</span> products. The first campaign was completed in March 2003. The <span class="hlt">AMSR-E</span>, designed and built by the Japanese Space Agency for NASA, was launched May 4, 2002 on the EOS Aqua spacecraft. The <span class="hlt">AMSR-E</span> sea-<span class="hlt">ice</span> products to be validated include sea-<span class="hlt">ice</span> <span class="hlt">concentration</span>, sea-<span class="hlt">ice</span> temperature, and snow depth on sea <span class="hlt">ice</span>. The focus of this campaign was on the validation of snow depth on sea <span class="hlt">ice</span> and sea-<span class="hlt">ice</span> temperature. This flight report describes the suite of instruments flown on the P-3, the objectives of each of the six flights, the Arctic regions overflown, and the coordination among satellite, aircraft, and surface-based measurements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AnGla..46..409K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AnGla..46..409K"><span>Polynya Signature Simulation Method polynya area in comparison to <span class="hlt">AMSR-E</span> 89 GHz sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span> in the Ross Sea and off the Adélie Coast, Antarctica, for 2002-05: first results</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kern, Stefan; Spreen, Gunnar; Kaleschke, Lars; de La Rosa, Sara; Heygster, Georg</p> <p>2007-10-01</p> <p>The Polynya Signature Simulation Method (PSSM) is applied to Special Sensor Microwave/Imager observations from different Defense Meteorological Satellite Program spacecraft for 2002-05 to analyze the polynya area in the Ross Sea (Ross <span class="hlt">Ice</span> Shelf polynya (RISP) and Terra Nova Bay polynya (TNBP)) and off the Adélie Coast (Mertz Glacier polynya (MGP)), Antarctica, on a sub-daily scale. The RISP and the MGP exhibit similar average total polynya areas. Major area changes (>10 000 km2; TNPB: >2000 km2) occur over a range of 2-3 to 20 days in all regions. Sub-daily area changes are largest for the MGP (5800 km2) and smallest for the TNBP (800 km2), underlining the persistence of the forcing of the latter. ARTIST sea-<span class="hlt">ice</span> (ASI) algorithm <span class="hlt">concentration</span> maps obtained using 89 GHz Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) data are compared to PSSM maps, yielding convincing agreement in the average, similarly detailed winter polynya distribution. Average ASI algorithm <span class="hlt">ice</span> <span class="hlt">concentrations</span> take values of 25-40% and 65-80% for the PSSM open-water and thin-<span class="hlt">ice</span> class, respectively. The discrepancy with expected values (0% and 100%) can be explained by the different spatial resolution and frequency used by the methods. A new land mask and a mask to flag icebergs are introduced. Comparison of PSSM maps with thermal <span class="hlt">ice</span> thickness based on AVHRR infrared temperature and ECMWF ERA-40 data suggests an upper thickness limit for the PSSM thin-<span class="hlt">ice</span> class of 20-25 cm.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.youtube.com/watch?v=DYLbDb8Y8Tw','SCIGOVIMAGE-NASA'); return false;" href="http://www.youtube.com/watch?v=DYLbDb8Y8Tw"><span>2008 Arctic Sea <span class="hlt">Ice</span> from <span class="hlt">AMSR-E</span></span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p></p> <p>Sea <span class="hlt">ice</span> is frozen seawater floating on the surface of the ocean. Some sea <span class="hlt">ice</span> is semi-permanent, persisting from year to year, and some is seasonal, melting and refreezing from season to season. Th...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010022375','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010022375"><span>EOS Aqua <span class="hlt">AMSR-E</span> Sea <span class="hlt">Ice</span> Validation Program: Meltpond2000 Flight Report</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cavalieri, Donald J.</p> <p>2000-01-01</p> <p>This flight report describes the field component of Meltpond2000, the first in a series of Arctic and Antarctic aircraft campaigns planned as part of NASA's Earth Observing System Aqua sea <span class="hlt">ice</span> validation program for the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>). This prelaunch Arctic field campaign was carried out between June 25 and July 6, 2000 from Thule, Greenland, with the objective of quantifying the errors incurred by the <span class="hlt">AMSR-E</span> sea <span class="hlt">ice</span> algorithms resulting from the presence of melt ponds. A secondary objective of the mission was to develop a microwave capability to discriminate between melt ponds and seawater using low-frequency microwave radiometers. Meltpond2000 was a multiagency effort involving personnel from the Navy, NOAA, and NASA. The field component of the mission consisted of making five 8-hour flights from Thule Air Base with a Naval Air Warfare Center P-3 aircraft over portions of Baffin Bay and the Canadian Arctic. The aircraft sensors were provided and operated by the Microwave Radiometry Group of NOAA's Environmental TechnologyLaboratory. A Navy <span class="hlt">ice</span> observer from the National <span class="hlt">Ice</span> Center provided visual documentation of surface <span class="hlt">ice</span> conditions during each of the flights. Two of the five flights were coordinated with Canadian scientists making surface measurements of melt ponds at an <span class="hlt">ice</span> camp located near Resolute Bay, Canada. Coordination with the Canadians will provide additional information on surface characteristics and will be of great value in the interpretation of the aircraft and high-resolution satellite data sets.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020013322','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020013322"><span>EOS Aqua <span class="hlt">AMSR-E</span> Sea <span class="hlt">Ice</span> Validation Program: Meltpond 2000 Flight Report</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cavalieri, Donald J.</p> <p>2000-01-01</p> <p>This flight report describes the field component of Meltpond2000, the first in a series of Arctic and Antarctic aircraft campaigns planned as part of NASA's Earth Observing System Aqua sea <span class="hlt">ice</span> validation program for the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>). This prelaunch Arctic field campaign was carried out between June 25 and July 6, 2000 from Thule, Greenland, with the objective of quantifying the errors incurred by the <span class="hlt">AMSR-E</span> sea <span class="hlt">ice</span> algorithms resulting from the presence of melt ponds. A secondary objective of the mission was to develop a microwave capability to discriminate between melt ponds and seawater using low-frequency microwave radiometers. Meltpond2000 was a multiagency effort involving personnel from the Navy, National Oceanic and Atmospheric Administration (NOAA), and NASA. The field component of the mission consisted of making five eight-hour flights from Thule Air Base with a Naval Air Warfare Center P-3 aircraft over portions of Baffin Bay and the Canadian Arctic. The aircraft sensors were provided and operated by the Microwave Radiometry Group of NOAA's Environmental Technology Laboratory. A Navy <span class="hlt">ice</span> observer from the National <span class="hlt">Ice</span> Center provided visual documentation of surface <span class="hlt">ice</span> conditions during each of the flights. Two of the five flights were coordinated with Canadian scientists making surface measurements of melt ponds at an <span class="hlt">ice</span> camp located near Resolute Bay, Canada. Coordination with the Canadians will provide additional information on surface characteristics and will be of great value in the interpretation of the aircraft and high-resolution satellite data sets.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120012935','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120012935"><span>A Comparison of Snow Depth on Sea <span class="hlt">Ice</span> Retrievals Using Airborne Altimeters and an <span class="hlt">AMSR-E</span> Simulator</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cavalieri, D. J.; Marksu, T.; Ivanoff, A.; Miller, J. A.; Brucker, L.; Sturm, M.; Maslanik, J. A.; Heinrichs, J. F.; Gasiewski, A.; Leuschen, C.; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20120012935'); toggleEditAbsImage('author_20120012935_show'); toggleEditAbsImage('author_20120012935_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20120012935_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20120012935_hide"></p> <p>2011-01-01</p> <p>A comparison of snow depths on sea <span class="hlt">ice</span> was made using airborne altimeters and an Advanced Microwave Scanning Radiometer for the Earth Observing System (<span class="hlt">AMSR-E</span>) simulator. The data were collected during the March 2006 National Aeronautics and Space Administration (NASA) Arctic field campaign utilizing the NASA P-3B aircraft. The campaign consisted of an initial series of coordinated surface and aircraft measurements over Elson Lagoon, Alaska and adjacent seas followed by a series of large-scale (100 km ? 50 km) coordinated aircraft and <span class="hlt">AMSR-E</span> snow depth measurements over portions of the Chukchi and Beaufort seas. This paper focuses on the latter part of the campaign. The P-3B aircraft carried the University of Colorado Polarimetric Scanning Radiometer (PSR-A), the NASA Wallops Airborne Topographic Mapper (ATM) lidar altimeter, and the University of Kansas Delay-Doppler (D2P) radar altimeter. The PSR-A was used as an <span class="hlt">AMSR-E</span> simulator, whereas the ATM and D2P altimeters were used in combination to provide an independent estimate of snow depth. Results of a comparison between the altimeter-derived snow depths and the equivalent <span class="hlt">AMSR-E</span> snow depths using PSR-A brightness temperatures calibrated relative to <span class="hlt">AMSR-E</span> are presented. Data collected over a frozen coastal polynya were used to intercalibrate the ATM and D2P altimeters before estimating an altimeter snow depth. Results show that the mean difference between the PSR and altimeter snow depths is -2.4 cm (PSR minus altimeter) with a standard deviation of 7.7 cm. The RMS difference is 8.0 cm. The overall correlation between the two snow depth data sets is 0.59.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20080015852','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080015852"><span>Determination of <span class="hlt">Ice</span> Water Path in <span class="hlt">Ice</span>-over-Water Cloud Systems Using Combined MODIS and <span class="hlt">AMSR-E</span> Measurements</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Huang, Jianping; Minnis, Patrick; Lin, Bing; Yi, Yuhong; Fan, T.-F.; Sun-Mack, Sunny; Ayers, J. K.</p> <p>2006-01-01</p> <p>To provide more accurate <span class="hlt">ice</span> cloud properties for evaluating climate models, the updated version of multi-layered cloud retrieval system (MCRS) is used to retrieve <span class="hlt">ice</span> water path (IWP) in <span class="hlt">ice</span>-over-water cloud systems over global ocean using combined instrument data from the Aqua satellite. The liquid water path (LWP) of lower layer water clouds is estimated from the Advanced Microwave Scanning Radiometer for EOS (<span class="hlt">AMSR-E</span>) measurements. With the lower layer LWP known, the properties of the upper-level <span class="hlt">ice</span> clouds are then derived from Moderate Resolution Imaging Spectroradiometer measurements by matching simulated radiances from a two-cloud layer radiative transfer model. Comparisons with single-layer cirrus systems and surface-based radar retrievals show that the MCRS can significantly improve the accuracy and reduce the over-estimation of optical depth and <span class="hlt">ice</span> water path retrievals for <span class="hlt">ice</span> over-water cloud systems. During the period from December 2004 through February 2005, the mean daytime <span class="hlt">ice</span> cloud optical depth and IWP for overlapped <span class="hlt">ice</span>-over-water clouds over ocean from Aqua are 7.6 and 146.4 gm(sup -2), respectively, significantly less than the initial single layer retrievals of 17.3 and 322.3 gm(sup -2). The mean IWP for actual single-layer clouds was 128.2 gm(sup -2).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003AGUFM.H32B0548M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003AGUFM.H32B0548M"><span>Advanced Microwave Scanning Radiometer - Earth Observing System (<span class="hlt">AMSR-E</span>) Validation Data Management at the National Snow and <span class="hlt">Ice</span> Data Center (NSIDC) Distributed Active Archive Center (DAAC)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Marquis, M. C.; Paserba, A. M.</p> <p>2003-12-01</p> <p>The National Snow and <span class="hlt">Ice</span> Data Center (NSIDC) Distributed Active Archive Center (DAAC) is supporting the Advanced Microwave Scanning Radiometer - Earth Observing System (<span class="hlt">AMSR-E</span>) validation activity. NSIDC has designed and developed a web portal to data and information collected during NASA's <span class="hlt">AMSR-E</span> Validation Program: (http://nsidc.org/data/amsr_validation/.) The <span class="hlt">AMSR-E</span> validation experiments address three disciplines: soil moisture, rainfall and cryospheric validation campaigns. This poster describes all these experiments (past, present and future). NSIDC provides documentation, e.g., user guides, as well as metadata documents (DIFS) submitted to the Global Change Master Directory (GCMD), for all the <span class="hlt">AMSR-E</span> validation experiments. NSIDC further supports the validation activities by collaborating with the <span class="hlt">AMSR-E</span> Science Investigator-led Processing System (SIPS) to provide scientists in the field (e.g., Arctic and Antarctic ship and flight campaigns) with quick, easy access to <span class="hlt">AMSR-E</span> data for their validation experiments. NSIDC provides subsets of reformatted data in a manner most convenient to the validation scientists while they conduct their experiments. The <span class="hlt">AMSR-E</span> is a mission instrument launched aboard NASA's Aqua Satellite on 4 May 2002. The Aqua mission provides a multi-disciplinary study of the Earth's atmospheric, oceanic, cryospheric, and land processes and their relationship to global change. With six instruments aboard, the Aqua Satellite will travel in a polar, sun-synchronous orbit. NSIDC will archive and distribute all <span class="hlt">AMSR-E</span> products, including Levels 1A, 2, and 3 data. Users can order Level-1A <span class="hlt">AMSR-E</span> data beginning 19 June 2003 and Level-2A data beginning 01 September 2003. Other products will be available in March 2004.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23387184','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23387184"><span>[The arctic sea <span class="hlt">ice</span> refractive index retrieval based on satellite <span class="hlt">AMSR-E</span> observations].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Chen, Han-Yue; Bi, Hai-Bo; Niu, Zheng</p> <p>2012-11-01</p> <p>The refractive index of sea <span class="hlt">ice</span> in the polar region is an important geophysical parameter. It is needed as a vital input for some numerical climate models and is helpful to classifying sea <span class="hlt">ice</span> types. In the present study, according to Hong Approximation (HA), we retrieved the arctic sea <span class="hlt">ice</span> refractive index at 6.9, 10.7, 23, 37, and 89 GHz in different arctic climatological conditions. The refractive indices of wintertime first year (FY) sea <span class="hlt">ice</span> and summertime <span class="hlt">ice</span> were derived with average values of 1.78 - 1.75 and 1.724 - 1.70 at different frequencies respectively, which are consistent with previous studies. However, for multiyear (MY) <span class="hlt">ice</span>, the results indicated relatively large bias between modeled results since 10.7 GHz. At a higher frequency, there is larger MY <span class="hlt">ice</span> refractive index difference. This bias is mainly attributed to the volume scattering effect on MY microwave radiation due to emergence of massive small empty cavities after the brine water in MY <span class="hlt">ice</span> is discharged into sea. In addition, the retrieved sea <span class="hlt">ice</span> refractive indices can be utilized to classify <span class="hlt">ice</span> types (for example, the winter derivation at 89 GHz), to identify coastal polynyas (winter retrieval at 6.9 GHz), and to outline the areal extent of significantly melting marginal sea <span class="hlt">ice</span> zone (MIZ) (summer result at 6.9 GHz). The investigation of this study suggests an effective tool of passive microwave remote sensing in monitoring sea <span class="hlt">ice</span> refractive index variability.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li class="active"><span>1</span></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_1 --> <div id="page_2" 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_1");'>1</a></li> <li class="active"><span>2</span></li> <li><a href="#" onclick='return showDiv("page_3");'>3</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><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="21"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011DSRII..58.1092O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011DSRII..58.1092O"><span>Intercomparisons of Antarctic sea <span class="hlt">ice</span> types from visual ship, RADARSAT-1 SAR, Envisat ASAR, QuikSCAT, and <span class="hlt">AMSR-E</span> satellite observations in the Bellingshausen Sea</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ozsoy-Cicek, Burcu; Kern, Stefan; Ackley, Stephen F.; Xie, Hongjie; Tekeli, Ahmet E.</p> <p>2011-05-01</p> <p>Antarctic Sea <span class="hlt">Ice</span> Processes and Climate (ASPeCt) visual ship-based observations were conducted in the Bellingshausen Sea during the Sea <span class="hlt">Ice</span> Mass Balance in the Antarctic (SIMBA) cruise in austral spring 2007. A total of 59 ASPeCt observations are compared to coincident satellite active and passive microwave data. Envisat and RADARSAT-1 C-Band HH-polarization radar backscatter values (called NRCS henceforth) are derived on km-scales for six individual <span class="hlt">ice</span> types and <span class="hlt">ice</span> type mixtures. C-Band HH-polarized and Ku-Band VV-polarized NRCS are extracted on several 10 km-scale areas from coincident Envisat, RADARSAT-1, and QuikSCAT radar images for areas primarily covered with multiyear, deformed first-year, and undeformed young <span class="hlt">ice</span>, as well as <span class="hlt">ice</span> of the marginal <span class="hlt">ice</span> zone (MIZ). The C-Band NRCS permits distinction between first-year, MIZ, and undeformed young <span class="hlt">ice</span>. However, NRCS of the multiyear <span class="hlt">ice</span> zone overlaps with that of the other <span class="hlt">ice</span> zones and types. Ku-Band NRCS obtained for the same <span class="hlt">ice</span> types permits discrimination of the first-year <span class="hlt">ice</span> zone only. Obtained NRCS agree with those of previous studies and suggest a high degree of deformation and considerable potential for flooding for the first-year <span class="hlt">ice</span> case. In comparison to large scale NRCS, <span class="hlt">AMSR-E</span> snow depth values form two clearly separated clusters, one for 0.24-0.35 m depth (first-year <span class="hlt">ice</span> zone) and one for 0.36-0.54 m depth (multiyear <span class="hlt">ice</span> zone). However, a comparison to ASPeCt observations suggests a remarkable underestimation of the snow depth by <span class="hlt">AMSR-E</span> in the multiyear-first-year-<span class="hlt">ice</span> transition zone and for first-year cake <span class="hlt">ice</span>. Nevertheless, a fusion of the coarse <span class="hlt">AMSR-E</span> snow depth ranges for interior pack <span class="hlt">ice</span> regions with radar imagery at large scale, appears promising for mapping the major zones (MIZ and Pack <span class="hlt">Ice</span>) and <span class="hlt">ice</span> types (first-year and multiyear) of Antarctic sea <span class="hlt">ice</span> on a circumpolar basis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20080032364&hterms=exports&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dexports','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20080032364&hterms=exports&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dexports"><span>Summer Sea <span class="hlt">Ice</span> Motion from the 18 GHz Channel of <span class="hlt">AMSR-E</span> and the Exchange of Sea <span class="hlt">Ice</span> between the Pacific and Atlantic Sectors</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kwok, Ronald</p> <p>2008-01-01</p> <p>We demonstrate that sea <span class="hlt">ice</span> motion in summer can be derived reliably from the 18GHz channel of the <span class="hlt">AMSR-E</span> instrument on the EOS Aqua platform. The improved spatial resolution of this channel with its lower sensitivity to atmospheric moisture seems to have alleviated various issues that have plagued summer motion retrievals from shorter wavelength observations. Two spatial filters improve retrieval quality: one reduces some of the microwave signatures associated with synoptic-scale weather systems and the other removes outliers. Compared with daily buoy drifts, uncertainties in motion are approx.3-4 km/day. Using the daily motion fields, we examine five years of summer <span class="hlt">ice</span> area exchange between the Pacific and Atlantic sectors of the Arctic Ocean. With the sea-level pressure patterns during the summer of 2006 and 2007 favoring the export of sea <span class="hlt">ice</span> into the Atlantic Sector, the regional outflow is approx.21% and approx.15% of the total sea <span class="hlt">ice</span> retreat in the Pacific sector.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20080032364&hterms=export&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dexport','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20080032364&hterms=export&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dexport"><span>Summer Sea <span class="hlt">Ice</span> Motion from the 18 GHz Channel of <span class="hlt">AMSR-E</span> and the Exchange of Sea <span class="hlt">Ice</span> between the Pacific and Atlantic Sectors</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kwok, Ronald</p> <p>2008-01-01</p> <p>We demonstrate that sea <span class="hlt">ice</span> motion in summer can be derived reliably from the 18GHz channel of the <span class="hlt">AMSR-E</span> instrument on the EOS Aqua platform. The improved spatial resolution of this channel with its lower sensitivity to atmospheric moisture seems to have alleviated various issues that have plagued summer motion retrievals from shorter wavelength observations. Two spatial filters improve retrieval quality: one reduces some of the microwave signatures associated with synoptic-scale weather systems and the other removes outliers. Compared with daily buoy drifts, uncertainties in motion are approx.3-4 km/day. Using the daily motion fields, we examine five years of summer <span class="hlt">ice</span> area exchange between the Pacific and Atlantic sectors of the Arctic Ocean. With the sea-level pressure patterns during the summer of 2006 and 2007 favoring the export of sea <span class="hlt">ice</span> into the Atlantic Sector, the regional outflow is approx.21% and approx.15% of the total sea <span class="hlt">ice</span> retreat in the Pacific sector.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040081367&hterms=arctic+temperature&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Darctic%2Btemperature','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040081367&hterms=arctic+temperature&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Darctic%2Btemperature"><span>EOS Aqua <span class="hlt">AMSR-E</span> Arctic Sea <span class="hlt">Ice</span> Validation Program: Intercomparison Between Modeled and Measured Sea <span class="hlt">Ice</span> Brightness Temperatures</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Stroeve, J.; Markus, T.; Cavalieri, D. J.; Maslanik, J.; Sturm, M.; Henrichs, J.; Gasiewski, A.; Klein, M.</p> <p>2004-01-01</p> <p>During March 2003, an extensive field campaign was conducted near Barrow, Alaska to validate AQUA Advanced Microwave Scanning Radiometer (AMSR) sea <span class="hlt">ice</span> products. Field, airborne and satellite data were collected over three different types of sea <span class="hlt">ice</span>: 1) first year <span class="hlt">ice</span> with little deformation, 2) first year <span class="hlt">ice</span> with various amounts of deformation and 3) mixed first year <span class="hlt">ice</span> and multi-year <span class="hlt">ice</span> with various degrees of deformation. The validation plan relies primarily on comparisons between satellite, aircraft flights and ground-based measurements. Although these efforts are important, key aspects such as the effects of atmospheric conditions, snow properties, surface roughness, melt processes, etc on the sea <span class="hlt">ice</span> algorithms are not sufficiently well understood or documented. To improve our understanding of these effects, we combined the detailed, in-situ data collection from the 2003 field campaign with radiance modeling using a radiative transfer model to simulate the top of the atmosphere AMSR brightness temperatures. This study reports on the results of the simulations for a variety of snow and <span class="hlt">ice</span> types and compares the results with the National Oceanographic and Atmospheric Administration Environmental Technology Laboratory Polarimetric Scanning Radiometer (NOAA) (ETL) (PSR) microwave radiometer that was flown on the NASA P-3.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUFM.H31A1400S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.H31A1400S"><span><span class="hlt">AMSR-E</span>/Aqua Gridded Brightness Temperatures</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Savoie, M.; Brodzik, M. J.; Knowles, K.</p> <p>2006-12-01</p> <p>Passive microwave brightness temperature data are a major component in many geophysical models and algorithms. For many researchers, a major difficulty in using these data is transforming the satellite swath data into a model-friendly, gridded format. Two new data sets and improvements to a toolkit at the National Snow and <span class="hlt">Ice</span> Data Center (NSIDC) will help scientists incorporate these data into their research. We have produced "<span class="hlt">AMSR-E</span>/Aqua Daily EASE-Grid Brightness Temperatures" from the Advanced Microwave Scanning Radiometer - Earth Observing System (<span class="hlt">AMSR-E</span>) instrument aboard NASA's Earth Observing System (EOS) Aqua satellite. This data set will complement and extend NSIDC's existing EASE-grid brightness temperature data sets, with new data beginning June 2002 and continuing throughout the life-cycle of the instrument. Additionally, in order to respond to user demand for quarter-degree data, we are distributing "<span class="hlt">AMSR-E</span>/Aqua Daily Global Quarter-Degree Gridded Brightness Temperatures" also spanning the <span class="hlt">AMSR-E</span> time period. Researchers whose needs are not met by the above data sets can create customized grids with our <span class="hlt">AMSR-E</span> Swath to Grid Toolkit. Recent improvements to the toolkit allow subsetted swath data as input, greatly reducing the initial data volume required to produce customized grids.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.5966A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.5966A"><span>Use of high frequency radiometer and altimeter on board AMSU-B, <span class="hlt">AMSR-E</span> and Altika/SARAL for observations of the Antarctic <span class="hlt">ice</span> sheet surface.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Adodo, Fifi; Picard, Ghislain; Remy, Frederique</p> <p>2016-04-01</p> <p>Snow surface properties quickly evolved according to local weather conditions, therefore are climate change indicator. These snow surface properties such as grain size, density, accumulation rate etc... are very important for evaluation and monitoring of the impact of global warming on the polar <span class="hlt">ice</span> sheet. In order to retrieve these snowpack properties, we explore the high frequency microwave radiometer variable( Brightness Temperature (Tb)) on the Antarctic <span class="hlt">ice</span> sheet on-board AMSU-B , <span class="hlt">AMSR-E</span> in combination with the ALTIKA altimeter (37GHz) waveform parameters (Backscatter coefficient, Trailing edge Slope(TeS) and Leading edge Width(LeW)). We compare the radiometer brightness temperature to calculations with the DMRT- ML radiative transfer model which simulates brightness temperature in vertical and horizontal polarizations. With some assumptions, this combination allows a good retrieval of snowpack properties. We showed positive trend of the grains size on the Antarctic plateau especially at Dome C during the two last decades. This work will provide a higher accuracy of the estimation of snowpack surfaces properties and contribute to monitoring the <span class="hlt">ice</span> sheet surface mass balance, well constraining of meteorological and glaciological models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=GL-2002-001275&hterms=2002&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3D2002','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=GL-2002-001275&hterms=2002&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3D2002"><span><span class="hlt">AMSR-E</span> First Light Images</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>The National Space Development Agency of Japan's (NASDA) Advanced Microwave Scanning Radiometer for the Earth Observing System (<span class="hlt">AMSR-E</span>), onboard NASA's Aqua spacecraft, began sending high quality data on June 1, 2002. Initial (uncalibrated) data gathered from the <span class="hlt">AMSR-E</span> has delivered impressive pictures of the planet's sea surface temperature from the 6.9 Ghz vertical polarization channel (top image) and brightness temperatures (bottom image) from the 89.0 Ghz vertical and horizontal polarization channels and the 23.8 Ghz vertical polarization channel, averaged over the 3-day period June 2-4, 2002. The sea surface temperature image is indicative of the high level of detail the microwave imager will routinely provide even in the presence of substantial cloud cover. In the brightness temperature image, <span class="hlt">ice</span> and snow cover in white and yellow, desert areas in shades of green, other land areas in dark colors, and oceans in shades of blue. Images courtesy <span class="hlt">AMSR-E</span> Science Team, National Space Development Agency of Japan</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20060044186&hterms=arctic+ice+loss&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Darctic%2Bice%2Bloss','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20060044186&hterms=arctic+ice+loss&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Darctic%2Bice%2Bloss"><span>Improvements in the estimates of <span class="hlt">ice</span> thickness and production in the Chukchi Sea polynyas derived from <span class="hlt">AMSR-E</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Martin, Seelye; Drucker, Robert; Kwok, Ronald; Holt, Benjamin</p> <p>2005-01-01</p> <p>For January-March 2003, we use 12.5-km resolution Advanced Microwave Scanning Radiometer (AMSR) data for the first time in a comparison with Synthetic Aperture Radar (SAR) and Special Sensor Microwave/Imager (SSM/I) data to study two Chukchi coast polynyas, one consisting of many, the other of only a few 25-km SSM/I pixels. Within these polynyas, the <span class="hlt">ice</span> thicknesses are derived separately from the SMM/I 37-GHz and AMSR 36-GHz channels; the heat fluxes are derived by combining thicknesses with meteorological data. Comparison with ScanSAR data shows that for the large polynya, because AMSR provides better resolution of the surrounding coastline and first-year <span class="hlt">ice</span>, the AMSR heat losses are greater than the SSM/I; for the small polynya, AMSR measures its variability even when its area is order of a single SSM/I pixel. This means that AMSR permits more accurate calculation of polynya heat losses, yielding the potential of improved estimates of Arctic polynya productivity.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20080036090&hterms=sea+boundaries&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dsea%2Bboundaries','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20080036090&hterms=sea+boundaries&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dsea%2Bboundaries"><span>Ross Sea Polynyas: Response of <span class="hlt">Ice</span> <span class="hlt">Concentration</span> Retrievals to Large Areas of Thin <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kwok, R.; Comiso, J. C.; Martin, S.; Drucker, R.</p> <p>2007-01-01</p> <p>For a 3-month period between May and July of 2005, we examine the response of the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) Enhanced NASA Team 2 (NT2) and <span class="hlt">AMSR-E</span> Bootstrap (ABA) <span class="hlt">ice</span> <span class="hlt">concentration</span> algorithms to large areas of thin <span class="hlt">ice</span> of the Ross Sea polynyas. Coincident Envisat Synthetic Aperture Radar (SAR) coverage of the region during this period offers a detailed look at the development of the polynyas within several hundred kilometers of the <span class="hlt">ice</span> front. The high-resolution imagery and derived <span class="hlt">ice</span> motion fields show bands of polynya <span class="hlt">ice</span>, covering up to approximately 105 km(sup 2) of the Ross Sea, that are associated with wind-forced advection. In this study, <span class="hlt">ice</span> thickness from <span class="hlt">AMSR-E</span> 36 GHz polarization information serves as the basis for examination of the response. The quality of the thickness of newly formed sea <span class="hlt">ice</span> (<10 cm) from <span class="hlt">AMSR-E</span> is first assessed with thickness estimates derived from <span class="hlt">ice</span> surface temperatures from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. The effect of large areas of thin <span class="hlt">ice</span> in lowering the <span class="hlt">ice</span> <span class="hlt">concentration</span> estimates from both NT2/ABA approaches is clearly demonstrated. Results show relatively robust relationships between retrieved <span class="hlt">ice</span> <span class="hlt">concentrations</span> and thin <span class="hlt">ice</span> thickness estimates that differ between the two algorithms. These relationships define the approximate spatial coincidence of <span class="hlt">ice</span> <span class="hlt">concentration</span> and thickness isopleths. Using the 83% (ABA) and 91% (NT2) isopleths as polynya boundaries, we show that the computed coverage compares well with that using the estimated 10-cm thickness contour. The thin <span class="hlt">ice</span> response characterized here suggests that in regions with polynyas, the retrieval results could be used to provide useful geophysical information, namely thickness and coverage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20080036090&hterms=concentration&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dconcentration','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20080036090&hterms=concentration&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dconcentration"><span>Ross Sea Polynyas: Response of <span class="hlt">Ice</span> <span class="hlt">Concentration</span> Retrievals to Large Areas of Thin <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kwok, R.; Comiso, J. C.; Martin, S.; Drucker, R.</p> <p>2007-01-01</p> <p>For a 3-month period between May and July of 2005, we examine the response of the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) Enhanced NASA Team 2 (NT2) and <span class="hlt">AMSR-E</span> Bootstrap (ABA) <span class="hlt">ice</span> <span class="hlt">concentration</span> algorithms to large areas of thin <span class="hlt">ice</span> of the Ross Sea polynyas. Coincident Envisat Synthetic Aperture Radar (SAR) coverage of the region during this period offers a detailed look at the development of the polynyas within several hundred kilometers of the <span class="hlt">ice</span> front. The high-resolution imagery and derived <span class="hlt">ice</span> motion fields show bands of polynya <span class="hlt">ice</span>, covering up to approximately 105 km(sup 2) of the Ross Sea, that are associated with wind-forced advection. In this study, <span class="hlt">ice</span> thickness from <span class="hlt">AMSR-E</span> 36 GHz polarization information serves as the basis for examination of the response. The quality of the thickness of newly formed sea <span class="hlt">ice</span> (<10 cm) from <span class="hlt">AMSR-E</span> is first assessed with thickness estimates derived from <span class="hlt">ice</span> surface temperatures from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. The effect of large areas of thin <span class="hlt">ice</span> in lowering the <span class="hlt">ice</span> <span class="hlt">concentration</span> estimates from both NT2/ABA approaches is clearly demonstrated. Results show relatively robust relationships between retrieved <span class="hlt">ice</span> <span class="hlt">concentrations</span> and thin <span class="hlt">ice</span> thickness estimates that differ between the two algorithms. These relationships define the approximate spatial coincidence of <span class="hlt">ice</span> <span class="hlt">concentration</span> and thickness isopleths. Using the 83% (ABA) and 91% (NT2) isopleths as polynya boundaries, we show that the computed coverage compares well with that using the estimated 10-cm thickness contour. The thin <span class="hlt">ice</span> response characterized here suggests that in regions with polynyas, the retrieval results could be used to provide useful geophysical information, namely thickness and coverage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020023454','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020023454"><span>Global Climate Monitoring with the EOS PM-Platform's Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>)</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Spencer, Roy W.</p> <p>2002-01-01</p> <p>The Advanced Microwave Scanning 2 Radiometer (<span class="hlt">AMSR-E</span>) is being built by NASDA to fly on NASA's PM Platform (now called Aqua) in December 2000. This is in addition to a copy of AMSR that will be launched on Japan's ADEOS-II satellite in 2001. The AMSRs improve upon the window frequency radiometer heritage of the SSM/I and SMMR instruments. Major improvements over those instruments include channels spanning the 6.9 GHz to 89 GHz frequency range, and higher spatial resolution from a 1.6 m reflector (<span class="hlt">AMSR-E</span>) and 2.0 m reflector (ADEOS-II AMSR). The ADEOS-II AMSR also will have 50.3 and 52.8 GHz channels, providing sensitivity to lower tropospheric temperature. NASA funds an <span class="hlt">AMSR-E</span> Science Team to provide algorithms for the routine production of a number of standard geophysical products. These products will be generated by the <span class="hlt">AMSR-E</span> Science Investigator-led Processing System (SIPS) at the Global Hydrology Resource Center (GHRC) in Huntsville, Alabama. While there is a separate NASDA-sponsored activity to develop algorithms and produce products from AMSR, as well as a Joint (NASDA-NASA) AMSR Science Team 3 activity, here I will review only the <span class="hlt">AMSR-E</span> Team's algorithms and how they benefit from the new capabilities that <span class="hlt">AMSR-E</span> will provide. The US Team's products will be archived at the National Snow and <span class="hlt">Ice</span> Data Center (NSIDC).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009TCry....3....1O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009TCry....3....1O"><span>Antarctic summer sea <span class="hlt">ice</span> <span class="hlt">concentration</span> and extent: comparison of ODEN 2006 ship observations, satellite passive microwave and NIC sea <span class="hlt">ice</span> charts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ozsoy-Cicek, B.; Xie, H.; Ackley, S. F.; Ye, K.</p> <p>2009-02-01</p> <p>Antarctic sea <span class="hlt">ice</span> cover has shown a slight increase (<1%/decade) in overall observed <span class="hlt">ice</span> extent as derived from satellite mapping from 1979 to 2008, contrary to the decline observed in the Arctic regions. Spatial and temporal variations of the Antarctic sea <span class="hlt">ice</span> however remain a significant problem to monitor and understand, primarily due to the vastness and remoteness of the region. While satellite remote sensing has provided and has great future potential to monitor the variations and changes of sea <span class="hlt">ice</span>, uncertainties remain unresolved. In this study, the National <span class="hlt">Ice</span> Center (NIC) <span class="hlt">ice</span> edge and the <span class="hlt">AMSR-E</span> (Advanced Microwave Scanning Radiometer-Earth Observing System) <span class="hlt">ice</span> extent are examined, while the ASPeCt (Antarctic Sea <span class="hlt">Ice</span> Process and Climate) ship observations from the Oden expedition in December 2006 are used as ground truth to verify the two products during Antarctic summer. While there is a general linear trend between ASPeCt and <span class="hlt">AMSR-E</span> <span class="hlt">ice</span> <span class="hlt">concentration</span> estimates, there is poor correlation (R2=0.41) and <span class="hlt">AMSR-E</span> tends to underestimate the low <span class="hlt">ice</span> <span class="hlt">concentrations</span>. We also found that the NIC sea <span class="hlt">ice</span> edge agrees well with ship observations, while the <span class="hlt">AMSR-E</span> shows the <span class="hlt">ice</span> edge further south, consistent with its poorer detection of low <span class="hlt">ice</span> <span class="hlt">concentrations</span>. The northward extent of the <span class="hlt">ice</span> edge at the time of observation (NIC) had mean values varying from 38 km to 102 km greater on different days for the area as compared with the <span class="hlt">AMSR-E</span> sea <span class="hlt">ice</span> extent. For the circumpolar area as a whole in the December period examined, <span class="hlt">AMSR-E</span> therefore may underestimate the area inside the <span class="hlt">ice</span> edge at this time by up to 14% or, 1.5 million km2 less area, compared to the NIC <span class="hlt">ice</span> charts. Preliminary comparison of satellite scatterometer data however, suggests better resolution of low <span class="hlt">concentrations</span> than passive microwave, and therefore better agreement with ship observations and NIC charts of the area inside the <span class="hlt">ice</span> edge during Antarctic summer. A reanalysis data set for Antarctic</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010124072','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010124072"><span>Comparison of DMSP SSM/I and Landsat 7 ETM+ Sea <span class="hlt">Ice</span> <span class="hlt">Concentrations</span> During Summer Melt</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cavalieri, Donald J.; Markus, Thorsten; Ivanoff, Alvaro; Koblinsky, Chester J. (Technical Monitor)</p> <p>2001-01-01</p> <p>As part of NASA's EOS Aqua sea <span class="hlt">ice</span> validation program for the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>), Landsat 7 Enhanced Thematic Mapper (ETM+) images were acquired to develop a sea <span class="hlt">ice</span> <span class="hlt">concentration</span> data set with which to validate <span class="hlt">AMSR-E</span> sea <span class="hlt">ice</span> <span class="hlt">concentration</span> retrievals. The standard <span class="hlt">AMSR-E</span> Arctic sea <span class="hlt">ice</span> <span class="hlt">concentration</span> product will be obtained with the enhanced NASA Team (NT2) algorithm. The goal of this study is to assess the accuracy to which the NT2 algorithm, using DMSP Special Sensor Microwave Imager radiances, retrieves sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> under summer melt conditions. Melt ponds are currently the largest source of error in the determination of Arctic sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> with satellite passive microwave sensors. To accomplish this goal, Landsat 7 ETM+ images of Baffin Bay were acquired under clear sky conditions on the 26th and 27th of June 2000 and used to generate high-resolution sea <span class="hlt">ice</span> <span class="hlt">concentration</span> maps with which to compare the NT2 retrievals. Based on a linear regression analysis of 116 25-km samples, we find that overall the NT2 retrievals agree well with the Landsat <span class="hlt">concentrations</span>. The regression analysis yields a correlation coefficient of 0.98. In areas of high melt ponding, the NT2 retrievals underestimate the sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> by about 12% compared to the Landsat values.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008TCD.....2..623O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008TCD.....2..623O"><span>Antarctic summer sea <span class="hlt">ice</span> <span class="hlt">concentration</span> and extent: comparison of ODEN 2006 ship observations, satellite passive microwave and NIC sea <span class="hlt">ice</span> charts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ozsoy-Cicek, B.; Xie, H.; Ackley, S. F.; Ye, K.</p> <p>2008-07-01</p> <p>Antarctic sea <span class="hlt">ice</span> cover has shown a slight increase in overall observed <span class="hlt">ice</span> extent as derived from satellite mapping from 1979 to 2008, contrary to the decline observed in the Arctic regions. Spatial and temporal variations of the Antarctic sea <span class="hlt">ice</span> however remain a significant problem to monitor and understand, primarily due to the vastness and remoteness of the region. While satellite remote sensing has provided and has great future potential to monitor the variations and changes of sea <span class="hlt">ice</span>, uncertainties remain unresolved. In this study, the National <span class="hlt">Ice</span> Center (NIC) <span class="hlt">ice</span> edge and the <span class="hlt">AMSR-E</span> (Advanced Microwave Scanning Radiometer Earth Observing System) <span class="hlt">ice</span> extent are examined, while the ASPeCt (Antarctic Sea <span class="hlt">Ice</span> Process and Climate) ship observations from the Oden expedition in December 2006 are used as ground truth to verify the two products during Antarctic summer. While there is a general linear trend between ASPeCt and <span class="hlt">AMSR-E</span> <span class="hlt">ice</span> <span class="hlt">concentration</span> estimates, there is poor correlation (R2=0.41) and <span class="hlt">AMSR-E</span> tends to underestimate the low <span class="hlt">ice</span> <span class="hlt">concentrations</span>. We also found that the NIC sea <span class="hlt">ice</span> edge agrees well with ship observations, while the <span class="hlt">AMSR-E</span> shows the <span class="hlt">ice</span> edge further south, consistent with its poorer detection of low <span class="hlt">ice</span> <span class="hlt">concentrations</span>. The northward extent of the <span class="hlt">ice</span> edge at the time of observation (NIC) had mean values varying from 38 km to 102 km greater on different days for the area as compared with the <span class="hlt">AMSR-E</span> sea <span class="hlt">ice</span> extent. For the circumpolar area as a whole in the December period examined, <span class="hlt">AMSR-E</span> therefore underestimates the area inside the <span class="hlt">ice</span> edge at this time by up to 14% or, 1.5 million km2 less area, compared to the NIC <span class="hlt">ice</span> charts. These differences alone can account for more than half of the purported sea <span class="hlt">ice</span> loss between the pre 1960s and the satellite era suggested earlier from comparative analysis of whale catch data with satellite derived data. Preliminary comparison of satellite scatterometer data suggests better resolution of low</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20000014066','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20000014066"><span>Global Climate Monitoring with the Eos Pm-Platform's Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>)</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Spencer, Roy W.</p> <p>2000-01-01</p> <p>The Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) is being built by NASDA to fly on NASA's PM Platform (now called "Aqua") in December 2000. This is in addition to a copy of AMSR that will be launched on Japan's ADEOS-11 satellite in 2001. The AMSRs improve upon the window frequency radiometer heritage of the SSM[l and SMMR instruments. Major improvements over those instruments include channels spanning the 6.9 GHz to 89 GHz frequency range, and higher spatial resolution from a 1.6 m reflector (<span class="hlt">AMSR-E</span>) and 2.0 m reflector (ADEOS-11 AMSR). The ADEOS-11 AMSR also will have 50.3 and 52.8 GHz channels, providing sensitivity to lower tropospheric temperature. NASA funds an <span class="hlt">AMSR-E</span> Science Team to provide algorithms for the routine production of a number of standard geophysical products. These products will be generated by the <span class="hlt">AMSR-E</span> Science Investigator-led Processing System (SIPS) at the Global Hydrology Resource Center (GHRC) in Huntsville, Alabama. While there is a separate NASDA-sponsored activity to develop algorithms and produce products from AMSR, as well as a Joint (NASDA-NASA) AMSR Science Team activity, here I will review only the <span class="hlt">AMSR-E</span> Team's algorithms and how they benefit from the new capabilities that <span class="hlt">AMSR-E</span> will provide. The U.S. Team's products will be archived at the National Snow and <span class="hlt">Ice</span> Data Center (NSIDC). Further information about <span class="hlt">AMSR-E</span> can be obtained at http://www.jzhcc.msfc.nasa.Vov/AMSR.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2001AGUFMIP22B0703L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2001AGUFMIP22B0703L"><span>An End-to-End Description of the Data Flow of <span class="hlt">AMSR-E</span> and GLAS Data Products: Product Generation Through Product Delivery to Users</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lutz, B. J.; Marquis, M.</p> <p>2001-12-01</p> <p>The Aqua and ICESat missions are components of the Earth Observing System (EOS). The Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) instrument will fly on the Aqua satellite planned for launch in Spring 2002. <span class="hlt">AMSR-E</span> is a passive microwave instrument, modified from the AMSR instrument, which will be deployed on the Japanese Advanced Earth Observing Satellite-II (ADEOS-II). <span class="hlt">AMSR-E</span> will observe the atmosphere, land, oceans, and cryosphere, yielding measurements of precipitation, cloud water, water vapor, surface wetness, sea surface temperatures, oceanic wind speed, sea <span class="hlt">ice</span> <span class="hlt">concentrations</span>, snow depth, and snow water content. The Geoscience Laser Altimeter System (GLAS) instrument will fly aboard the ICESat satellite scheduled for launch in Summer 2002. This instrument will measure <span class="hlt">ice</span>-sheet topography and temporal changes in topography; cloud heights, planetary boundary heights and aerosol vertical structure; and land and water topography. The GLAS and <span class="hlt">AMSR-E</span> teams have both chosen to utilize Science Investigator-led Processing Systems (SIPS) to process their respective EOS data products. The SIPS facilities are funded by the Earth Science Data and Information System (ESDIS) Project at NASA's Goddard Space Flight Center and operated under the direction of a science team leader. The SIPS capitalize upon the scientific expertise of the science teams and the distributed processing capabilities of their institutions. The SIPS are charged with routine production of their respective EOS data products for archival at a Distributed Active Archive Center (DAAC). The National Snow and <span class="hlt">Ice</span> Data Center (NSIDC) DAAC in Boulder, Colorado will archive all <span class="hlt">AMSR-E</span> and GLAS data products. The NSIDC DAAC will distribute these data products to users throughout the world. The SIPS processing flows of both teams are rather complex. The <span class="hlt">AMSR-E</span> SIPS is composed of three separate processing facilities (Japan, California, and Alabama). The ICESat SIPS is composed of one main processing center</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=309920','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=309920"><span>SCA transfer from <span class="hlt">AMSR-E</span> to AMSR2</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The current <span class="hlt">AMSR-E</span> soil moisture product distributed through NSIDC is developed using the Normalized Polarization Difference (NPD) algorithm. Several validation exercises have showed performance issues with the NPD-based <span class="hlt">AMSR-E</span> product. This motivated us to re-examine the NPR approach and outline po...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20000092059','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20000092059"><span>Collaboration on Development and Validation of the <span class="hlt">AMSR-E</span> Snow Water Equivalent Algorithm</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Armstrong, Richard L.</p> <p>2000-01-01</p> <p>The National Snow and <span class="hlt">Ice</span> Data Center (NSIDC) has produced a global SMMR and SSM/I Level 3 Brightness Temperature data set in the Equal Area Scalable Earth (EASE) Grid for the period 1978 to 2000. Processing of current data is-ongoing. The EASE-Grid passive microwave data sets are appropriate for algorithm development and validation prior to the launch of <span class="hlt">AMSR-E</span>. Having the lower frequency channels of SMMR (6.6 and 10.7 GHz) and the higher frequency channels of SSM/I (85.5 GHz) in the same format will facilitate the preliminary development of applications which could potentially make use of similar frequencies from <span class="hlt">AMSR-E</span> (6.9, 10.7, 89.0 GHz).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040121122&hterms=Radiofrequency&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3DRadiofrequency','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040121122&hterms=Radiofrequency&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3DRadiofrequency"><span>Evaluation of the <span class="hlt">AMSR-E</span> Data Calibration Over Land</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Njoku, E.; Chan, T.; Crosson, W.; Limaye, A.</p> <p>2004-01-01</p> <p>Land observations by the Advanced Microwave Scanning Radiometer for the Earth Observing System (<span class="hlt">AMSR-E</span>), particularly of soil and vegetation moisture changes, have numerous applications in hydrology, ecology and climate. Quantitative retrieval of soil and vegetation parameters relies on accurate calibration of the brightness temperature measurements. Analyses of the spectral and polarization characteristics of early versions of the <span class="hlt">AMSR-E</span> data revealed significant calibration biases over land at 6.9 GHz. The biases were estimated and removed in the current archived version of the data Radiofrequency interference (RFI) observed at 6.9 GHz is more difficult to quanti@ however. A calibration analysis of <span class="hlt">AMSR-E</span> data over land is presented in this paper for a complete annual cycle from June 2002 through September 2003. The analysis indicates the general high quality of the data for land applications (except for RFI), and illustrates seasonal trends of the data for different land surface types and regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C53B0773Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C53B0773Y"><span>Improving Multiyear <span class="hlt">Ice</span> <span class="hlt">Concentration</span> Estimates with Reanalysis Air Temperatures</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ye, Y.; Shokr, M.; Heygster, G.; Spreen, G.</p> <p>2015-12-01</p> <p>Multiyear <span class="hlt">ice</span> (MYI) characteristics can be retrieved from passive or active microwave remote sensing observations. One of the algorithms that combine both of observations to identify partial <span class="hlt">concentrations</span> of <span class="hlt">ice</span> types (including MYI) is the Environment Canada's <span class="hlt">Ice</span> <span class="hlt">Concentration</span> Extractor (ECICE). However, cycles of warm/cold air temperature trigger wet-refreeze cycles of the snow cover on MYI <span class="hlt">ice</span> surface. Under wet snow conditions, anomalous brightness temperature and backscatter, similar to those of first year <span class="hlt">ice</span> (FYI) are observed. This leads to misidentification of MYI as being FYI, hence decreasing the estimated MYI <span class="hlt">concentration</span> suddenly. The purpose of this study is to introduce a correction scheme to restore the MYI <span class="hlt">concentration</span> under this condition. The correction is based on air temperature records. It utilizes the fact that the warm spell in autumn lasts for a short period of time (a few days). The correction is applied to MYI <span class="hlt">concentration</span> results from ECICE using an input of combined QuikSCAT and <span class="hlt">AMSR-E</span> data; acquired over the Arctic region in a series of autumn seasons from 2003 to 2008. The correction works well by replacing anomalous MYI <span class="hlt">concentrations</span> with interpolated ones. For September of the six years, it introduces over 0.1×106 km2 MYI area except for 2005. Due to the regional effect of the warm air spells, the correction could be important in the operational applications where small and meso scale <span class="hlt">ice</span> <span class="hlt">concentrations</span> are crucial.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li class="active"><span>2</span></li> <li><a href="#" onclick='return showDiv("page_3");'>3</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><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_2 --> <div id="page_3" 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_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="41"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H33C1341M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H33C1341M"><span>Finalizing the Goddard Profiling 2010 (GPROF2010) Rainfall Algorithm for <span class="hlt">AMSR-E</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Meyers, P. C.; Wang, N.; Ferraro, R.; Kummerow, C. D.; Randel, D.; Jelenak, Z.; Chang, P. S.</p> <p>2012-12-01</p> <p>The Goddard Profiling 2010 algorithm (GPROF2010) has been finalized for the <span class="hlt">AMSR-E</span> instrument. The empirical GPROF2010 land algorithm was developed for TRMM/TMI, which observes slightly different central frequencies than <span class="hlt">AMSR-E</span>. A linear transfer function was developed to convert <span class="hlt">AMSR-E</span> brightness temperatures to their corresponding TMI frequency for raining and non-raining pixels using collocated brightness temperature and TRMM Precipitation Radar (PR) measurements. Previous versions of the algorithm screen for <span class="hlt">ice</span>, snow, and desert used a series of empirical procedures which could lead to false detection of raining pixels. The latest GPROF algorithm prefaces the heritage screening procedures by referencing annual desert and monthly snow climatologies to screen pixels where rain retrievals are unreliable. A sufficiently long time series of satellite and ground-based observations from the Interactive Multisensor Snow and <span class="hlt">Ice</span> Mapping System (IMS) and <span class="hlt">AMSR-E</span> allowed for the creation of a medium resolution (0.25°x0.25°) climatology of monthly snow and <span class="hlt">ice</span> cover to identify locations where precipitation was likely. The scattering signature of rain and snow over <span class="hlt">ice</span> is not well defined due to complex emissivity signals dependent on snow depth, age, and melting, such that using a static climatology was a more stable approach to defining surface types. The annual desert climatology was extracted from the International Geosphere/Biosphere Programme surface type dataset. GPROF2010 was validated using the National Mosaic & Multi-Sensor QPE (NMQ) next generation QPE (Q2) data which blends radar, satellite, and rain gauge data to estimate surface rain rates over CONUS. GPROF2010 typically identifies raining systems over non-mountainous terrain, where Q2 estimates are more accurate, although the spatial extent of rain is usually underestimated. Distribution of rain rates above 1 mm/hr are consistent between GPROF2010 and Q2 estimates, however GPROF2010 rarely calculates rain</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 Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> 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 sea <span class="hlt">ice</span> cover properties (<span class="hlt">concentration</span>, extent, and area) through the use of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> (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 extent and area), and to improve sea <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 (<span class="hlt">AMSR-E</span>) 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 <span class="hlt">AMSR-E</span> 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('http://adsabs.harvard.edu/abs/2006AGUFM.B41C0206S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.B41C0206S"><span>Global Wetland Monitoring with <span class="hlt">AMSR-E</span> Passive Microwave Radiometry</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schroeder, R.; McDonald, K.; Podest, E.; Heimann, M.; Zimmermann, R.</p> <p>2006-12-01</p> <p>Methane is the most potent green house gas in Earth's atmosphere. Recent findings have raised wide concern as to whether living plants have a significant role in producing large amounts of methane. Although such findings may contradict the common understanding of many atmospheric scientists, laboratory studies have demonstrated that it is not clear how accurately natural methane production can be measured. Our study investigates the impact of natural wetlands on variations in methane out-gassing within a global modeling construct. At a first step, we utilize newly available passive microwave measurements from the <span class="hlt">AMSR-E</span> radiometer to observe Earth's largest wetland regions and to monitor their seasonal behavior. A remote sensing technique is presented that exploits the temporal variability of daily <span class="hlt">AMSR-E</span> brightness temperature observations to detect changes in water distribution that control inundation patterns for large wetlands in Siberia, North America, and the Amazon Basin susceptible to strong seasonal shifts in surface water retention or precipitation amounts. Initial results demonstrate that our method can be applied directly and without any tuning applied to the input remote sensing signal, though careful evaluation of our product with in-situ information remains to be carried out. This work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=10531&hterms=Turquoise&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DTurquoise','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=10531&hterms=Turquoise&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DTurquoise"><span>Record Sea <span class="hlt">Ice</span> Minimum</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>2007-01-01</p> <p>Arctic sea <span class="hlt">ice</span> reached a record low in September 2007, below the previous record set in 2005 and substantially below the long-term average. This image shows the Arctic as observed by the Advanced Microwave Scanning Radiometer for EOS (<span class="hlt">AMSR-E</span>) aboard NASA's Aqua satellite on September 16, 2007. In this image, blue indicates open water, white indicates high sea <span class="hlt">ice</span> <span class="hlt">concentration</span>, and turquoise indicates loosely packed sea <span class="hlt">ice</span>. The black circle at the North Pole results from an absence of data as the satellite does not make observations that far north. Three contour lines appear on this image. The red line is the 2007 minimum, as of September 15, about the same time the record low was reached, and it almost exactly fits the sea <span class="hlt">ice</span> observed by <span class="hlt">AMSR-E</span>. The green line indicates the 2005 minimum, the previous record low. The yellow line indicates the median minimum from 1979 to 2000.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=10531&hterms=sea+ice&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dsea%2Bice','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=10531&hterms=sea+ice&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dsea%2Bice"><span>Record Sea <span class="hlt">Ice</span> Minimum</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>2007-01-01</p> <p>Arctic sea <span class="hlt">ice</span> reached a record low in September 2007, below the previous record set in 2005 and substantially below the long-term average. This image shows the Arctic as observed by the Advanced Microwave Scanning Radiometer for EOS (<span class="hlt">AMSR-E</span>) aboard NASA's Aqua satellite on September 16, 2007. In this image, blue indicates open water, white indicates high sea <span class="hlt">ice</span> <span class="hlt">concentration</span>, and turquoise indicates loosely packed sea <span class="hlt">ice</span>. The black circle at the North Pole results from an absence of data as the satellite does not make observations that far north. Three contour lines appear on this image. The red line is the 2007 minimum, as of September 15, about the same time the record low was reached, and it almost exactly fits the sea <span class="hlt">ice</span> observed by <span class="hlt">AMSR-E</span>. The green line indicates the 2005 minimum, the previous record low. The yellow line indicates the median minimum from 1979 to 2000.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..11.1959A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.1959A"><span>Validation of simulated sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span> from sea <span class="hlt">ice</span>-ocean models and polynya classification methods in the Laptev Sea area using satellite data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Adams, S.; Willmes, S.; Heinemann, G.</p> <p>2009-04-01</p> <p>The Laptev Sea represents one of the most significant areas of net <span class="hlt">ice</span> production in the Arctic. Most of the <span class="hlt">ice</span> production takes place in a polynya forming at the fast <span class="hlt">ice</span> edge during strong offshore wind conditions. The simulation of these polynya events is a challenge for current sea <span class="hlt">ice</span>-ocean models, and validation of simulated sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span> is necessary for model improvements. High-quality data sets of sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> from remote sensing data are covering the period from 1978 to the present. These data sets are well suited for the validation of model results of sea <span class="hlt">ice</span>-ocean models. Based on the brightness temperature observations obtained from the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>), the ARTIST (Arctic Radiation and Turbulence Interaction Study) Sea <span class="hlt">Ice</span> (ASI) algorithm is used to calculate mean daily sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span>. Here we use <span class="hlt">AMSR-E</span> data for the validation of sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span> in the Laptev Sea, which are simulated by the coupled sea <span class="hlt">ice</span>-ocean models North Atlantic - Arctic Ocean - Sea-<span class="hlt">Ice</span> Model (NAOSIM) and Finite Element Sea <span class="hlt">Ice</span> Ocean Model (FESOM). The general distribution of the sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span>, the simulation of the polynya events and the position of polynyas are examined for the period October 2007 to April 2008. In addition, the polynya signature simulation method (PSSM) was applied to classify open water, thin <span class="hlt">ice</span> and thick <span class="hlt">ice</span>. The results of the validation show that the simulated distributions of the sea-<span class="hlt">ice</span> fields show similar structures, but an underestimation of sea <span class="hlt">ice</span> <span class="hlt">concentration</span>. The simulation of the polynya-events from the two models agrees reasonably well with satellite data. However, because of the absent fast <span class="hlt">ice</span> edge in both models, the position of the polynyas is shifted to the coast line. Therefore it would be necessary to include the fast <span class="hlt">ice</span> edge for simulating polynyas at the right position. Further investigations about the position of the polynyas will be performed with simulation</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=302358','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=302358"><span><span class="hlt">AMSR-E</span>-Based soil moisture retrieval algorithms and transferability to AMSR2</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>The launch of the Advanced Microwave Scanning Radiometer on NASA’s Earth Observing System Aqua satellite (<span class="hlt">AMSR-E</span>) in June of 2002 has led to major advancements in the routine global mapping of soil moisture. The wide availability of <span class="hlt">AMSR-E</span> data has promoted development of a number of global soil mo...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20080032384&hterms=statistics&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dstatistics','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20080032384&hterms=statistics&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dstatistics"><span>Global Survey and Statistics of Radio-Frequency Interference in <span class="hlt">AMSR-E</span> Land Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Njoku, Eni G.; Ashcroft, Peter; Chan, Tsz K.; Li, Li</p> <p>2005-01-01</p> <p>Radio-frequency interference (RFI) is an increasingly serious problem for passive and active microwave sensing of the Earth. To satisfy their measurement objectives, many spaceborne passive sensors must operate in unprotected bands, and future sensors may also need to operate in unprotected bands. Data from these sensors are likely to be increasingly contaminated by RFI as the spectrum becomes more crowded. In a previous paper we reported on a preliminary investigation of RFI observed over the United States in the 6.9-GHz channels of the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) on the Earth Observing System Aqua satellite. Here, we extend the analysis to an investigation of RFI in the 6.9- and 10.7-GHz <span class="hlt">AMSR-E</span> channels over the global land domain and for a one-year observation period. The spatial and temporal characteristics of the RFI are examined by the use of spectral indices. The observed RFI at 6.9 GHz is most densely <span class="hlt">concentrated</span> in the United States, Japan, and the Middle East, and is sparser in Europe, while at 10.7 GHz the RFI is <span class="hlt">concentrated</span> mostly in England, Italy, and Japan. Classification of RFI using means and standard deviations of the spectral indices is effective in identifying strong RFI. In many cases, however, it is difficult, using these indices, to distinguish weak RFI from natural geophysical variability. Geophysical retrievals using RFI-filtered data may therefore contain residual errors due to weak RFI. More robust radiometer designs and continued efforts to protect spectrum allocations will be needed in future to ensure the viability of spaceborne passive microwave sensing.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20080032384&hterms=Statistics&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DStatistics','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20080032384&hterms=Statistics&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DStatistics"><span>Global Survey and Statistics of Radio-Frequency Interference in <span class="hlt">AMSR-E</span> Land Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Njoku, Eni G.; Ashcroft, Peter; Chan, Tsz K.; Li, Li</p> <p>2005-01-01</p> <p>Radio-frequency interference (RFI) is an increasingly serious problem for passive and active microwave sensing of the Earth. To satisfy their measurement objectives, many spaceborne passive sensors must operate in unprotected bands, and future sensors may also need to operate in unprotected bands. Data from these sensors are likely to be increasingly contaminated by RFI as the spectrum becomes more crowded. In a previous paper we reported on a preliminary investigation of RFI observed over the United States in the 6.9-GHz channels of the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) on the Earth Observing System Aqua satellite. Here, we extend the analysis to an investigation of RFI in the 6.9- and 10.7-GHz <span class="hlt">AMSR-E</span> channels over the global land domain and for a one-year observation period. The spatial and temporal characteristics of the RFI are examined by the use of spectral indices. The observed RFI at 6.9 GHz is most densely <span class="hlt">concentrated</span> in the United States, Japan, and the Middle East, and is sparser in Europe, while at 10.7 GHz the RFI is <span class="hlt">concentrated</span> mostly in England, Italy, and Japan. Classification of RFI using means and standard deviations of the spectral indices is effective in identifying strong RFI. In many cases, however, it is difficult, using these indices, to distinguish weak RFI from natural geophysical variability. Geophysical retrievals using RFI-filtered data may therefore contain residual errors due to weak RFI. More robust radiometer designs and continued efforts to protect spectrum allocations will be needed in future to ensure the viability of spaceborne passive microwave sensing.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUSM.H24A..01C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUSM.H24A..01C"><span>Assessment of Errors in <span class="hlt">AMSR-E</span> Derived Soil Moisture</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Champagne, C.; McNairn, H.; Berg, A.; de Jeu, R. A.</p> <p>2009-05-01</p> <p>Soil moisture derived from passive microwave satellites provides information at a coarse spatial scale, but with temporally frequent, global coverage that can be used for monitoring applications over agricultural regions. Passive microwave satellites measure surface brightness temperature, which is largely a function of vegetation water content (which is directly related to the vegetation optical depth), surface temperature and surface soil moisture at low frequencies. Retrieval algorithms for global soil moisture data sets by necessity require limited site-specific information to derive these parameters, and as such may show variations in local accuracy. The objective of this study is to examine the errors in passive microwave soil moisture data over agricultural sites in Canada to provide guidelines on data quality assessment for using these data sets in monitoring applications. Global gridded soil moisture was acquired from the <span class="hlt">AMSR-E</span> satellite using the Land Parameter Retrieval Model, LPRM (Owe et al., 2008). The LPRM model derives surface soil moisture through an iterative optimization procedure using a polarization difference index to estimate vegetation optical depth and surface dielectric constant using frequencies at 6.9 and 10.7 GHz. The LPRM model requires no a-priori information on surface conditions, but retrieval errors are expected to increase as the amount of open water and dense vegetation within each pixel increases (Owe et al., 2008) Satellite-derived LPRM soil moisture values were used to assess changes in soil moisture retrieval accuracy over the 2007 growing season for a largely agricultural site near Guelph (Ontario), Canada. Accuracy was determined by validating LPRM soil moisture against a network of 16 in-situ monitoring sites distributed at the pixel scale for <span class="hlt">AMSR-E</span>. Changes in squared error, and pairwise correlation coefficient between satellite and in-situ surface soil moisture were assessed against changes in satellite orbit and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006cosp...36.2221L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006cosp...36.2221L"><span>Assimilating <span class="hlt">AMSR-E</span> data for soil moisture estimation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, X.; Koike, T.</p> <p></p> <p>In the last decade we see a blooming of developing and applying of land data assimilation systems LDAS This technique by integrating both in situ and remote sensing data into the dynamics of land surface model is capable of producing the evolution of land surface state such as soil moisture soil temperature and snow water equivalent in physical and spatiotemporal consistence In this paper we introduce a few numerical experiments of assimilating the Advanced Microwave Scanning Radiometer <span class="hlt">AMSR-E</span> brightness temperature data by using the LDAS we have developed The data assimilation method being used is the ensemble Kalman filter which is a Monte Carlo based sequential filter method The land model is the JMA Japan Meteorological Administration new SiB which originates from the Simple Biosphere SiB model but is reformulated with explicit snow and soil freeze thaw processes The observation operators are radiative transfer models of soil We used the semi-empirical Q h model in this study The system was tested using many observations collected during CEOP Coordinated Enhanced Observation Period a Global Energy and Water Experiment particularly at a semi-arid region site Mongolia and a cold region site Tibet-east The results showed that 1 The system can estimate land surface variables i e soil moisture soil temperature and snow much more reasonable than free-loop modeling 2 From the view point of remote sensing the soil moisture and temperature profiles can be retrieved successfully with the aid of additional information</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20170005809&hterms=Stewart&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3DStewart','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20170005809&hterms=Stewart&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3DStewart"><span>Operational Implementation of Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> Estimates from the AMSR2 Sensor</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Meier, Walter N.; Stewart, J. Scott; Liu, Yinghui; Key, Jeffrey; Miller, Jeffrey A.</p> <p>2017-01-01</p> <p>An operation implementation of a passive microwave sea <span class="hlt">ice</span> <span class="hlt">concentration</span> algorithm to support NOAA's operational mission is presented. The NASA team 2 algorithm, previously developed for the NASA advanced microwave scanning radiometer for the Earth observing system (<span class="hlt">AMSR-E</span>) product suite, is adapted for operational use with the JAXA AMSR2 sensor through several enhancements. First, the algorithm is modified to process individual swaths and provide <span class="hlt">concentration</span> from the most recent swaths instead of a 24-hour average. A latency (time since observation) field and a 24-hour <span class="hlt">concentration</span> range (maximum-minimum) are included to provide indications of data timeliness and variability. <span class="hlt">Concentration</span> from the Bootstrap algorithm is a secondary field to provide complementary sea <span class="hlt">ice</span> information. A quality flag is implemented to provide information on interpolation, filtering, and other quality control steps. The AMSR2 <span class="hlt">concentration</span> fields are compared with a different AMSR2 passive microwave product, and then validated via comparison with sea <span class="hlt">ice</span> <span class="hlt">concentration</span> from the Suomi visible and infrared imaging radiometer suite. This validation indicates the AMSR2 <span class="hlt">concentrations</span> have a bias of 3.9% and an RMSE of 11.0% in the Arctic, and a bias of 4.45% and RMSE of 8.8% in the Antarctic. In most cases, the NOAA operational requirements for accuracy are met. However, in low-<span class="hlt">concentration</span> regimes, such as during melt and near the <span class="hlt">ice</span> edge, errors are higher because of the limitations of passive microwave sensors and the algorithm retrieval.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003AGUFM.C42B..03K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003AGUFM.C42B..03K"><span>Testing <span class="hlt">AMSR-E</span> Snow Retrievals With Cold Lands Processes Experiment Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kelly, R. E.; Chang, A. T.; Foster, J. L.; Hall, D. K.; Stankov, B. B.; Gasiewaski, A. J.</p> <p>2003-12-01</p> <p>Snow cover is an important component of the global hydrological cycle. Not only does it constitute a critical seasonal and long-term storage factor but it also affects global climate mass and energy dynamics. Accurate estimation of snow mass is important, therefore, for effective characterization of the hydrological cycle at different space and time scales. Satellite passive microwave observations have been used to estimate global snow depth and snow water equivalent (SWE) since 1979. However, during this time the instruments available have been able to observe snow mass over spatial domains only at regional scales; finer scale observations have not been possible. The Advanced Microwave Scanning Radiometer - EOS launched in 2002 aboard NASA's Aqua platform, has improved spatial resolution capabilities compared with previous passive microwave instruments and, potentially, can be used to estimate snow depth and SWE with increased accuracy at the regional scale. The Cold Lands Processes Experiment (CLPX) provides an opportunity to examine the spatial variability of snow mass and to determine controls on its observability by <span class="hlt">AMSR-E</span> in complex and simple terrains. This paper investigates the characteristics of <span class="hlt">AMSR-E</span> retrievals of SWE in the CLPX study domain. Level 2A <span class="hlt">AMSR-E</span> estimates of SWE at the 25 x 25 km Equal Area Scaleable grid (EASE grid) are used in the study. The three Mesocell Study Areas (MSA), North Park, Fraser and Rabbit Ears Pass, are used as the spatial framework and data from the intensive observing periods during February and March 2003 are used for testing the <span class="hlt">AMSR-E</span> retrievals. Field measurements of SWE from the ground campaign are compared with <span class="hlt">AMSR-E</span> estimates. In North Park MSA, extensive SWE field surveys were conducted within the entire 25 x 25 km MSA to enable the characterization of SWE variability at the <span class="hlt">AMSR-E</span> grid scale. For the other two MSAs, only intensive study area measurements were made and these data cannot be used to represent the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMGC51E0795Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMGC51E0795Z"><span>Extracting Microwave Emissivity Characteristics over City using <span class="hlt">AMSR-E</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, T.; Zhang, L.; Jiang, L.; Li, Y.</p> <p>2010-12-01</p> <p>The spectrums of different land types are very important in the application of remote sensing. Different spectrums of different land types can be used in surface classification, change detection, and so on. The microwave emissivity over land is the foundation of land parameters retrieval using passive microwave remote sensing. It depends on land type due to different objects’ structure, moisture and roughness on the earth. It has shown that the land surface microwave emissivity contributed to atmosphere temperature and moisture retrieval. Meanwhile, it depends on land type, vegetation cover, and moisture et al.. There are many researches on microwave emissivity of various land types, such as bare soil, vegetation, snow, but city was less mentioned [1]. However, with the development of society, the process of urbanization accelerated quickly. The area of city expanded fast and the fraction of city area increased in one microwave pixel, especially in The North China Plain (about 30%). The passive microwave pixel containing city has impact on satellite observation and surface parameters retrieval then. So it is essential to study the emissivity of city in order to improve the accuracy of land surface parameters retrieval from passive microwave remote sensing. To study the microwave emissivity of city, some ‘pure’ city pixels were selected according to IGBP classification data, which was defined the fraction cover of city is larger than 85%. The city emissivity was calculated using <span class="hlt">AMSR-E</span> L2A brightness temperature and GLDAS land surface temperature data at different frequencies and polarizations over 2008 in China. Then the seasonal variation was analyzed along the year. Finally, the characteristic of city emissivity were compared with some meteorological data, seeking the relationship between city emissivity and climatic factors. The results have shown that the emissivity of city was different for different frequencies. It increased with the frequency becoming</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.youtube.com/watch?v=at1N3uSvAmM','SCIGOVIMAGE-NASA'); return false;" href="http://www.youtube.com/watch?v=at1N3uSvAmM"><span>Arctic Sea <span class="hlt">Ice</span> Maximum 2011</span></a></p> <p><a target="_blank" href="https://images.nasa.gov/">NASA Image and Video Library</a></p> <p></p> <p></p> <p><span class="hlt">AMSR-E</span> Arctic Sea <span class="hlt">Ice</span>: September 2010 to March 2011: Scientists tracking the annual maximum extent of Arctic sea <span class="hlt">ice</span> said that 2011 was among the lowest <span class="hlt">ice</span> extents measured since satellites began ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C33B0789Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C33B0789Z"><span>Antarctic Snowmelt Detected by Diurnal Variations of <span class="hlt">AMSR-E</span> Brightness Temperature</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zheng, L.; Zhou, C.</p> <p>2016-12-01</p> <p>The ascending passes and descending passes of <span class="hlt">AMSR-E</span> observed the Antarctic in afternoon (the warmest period) and midnight respectively. The diurnal variations of vertical polarized 36GHz brightness temperature (Tb) was served as a thaw-freeze index (TFI) to detect snowmelt in Antarctic <span class="hlt">ice</span> sheet from 2002 to 2011. A set of controlled experiments based on microwave emission model of layered snow (MEMLS) was used to model the changes of the Tb at 36 GHz in vertical polarization when the snow is about to melt. The simulations show that TFI in thicker and denser snow pack with higher exponential correlation lengths is more sensitive to the emergence of liquid water, and 10K can be a threshold value of TFI to recognize melting snow. Assuming that there is no snowmelt in the pixels beyond 3500m, the potential melting area in Antarctica was mapping in advance in order to eliminating the misjudgments of rocky pixels based on the changes of TFI in the places that never melts . The analysis of snowmelt suggested the Antarctic <span class="hlt">ice</span> sheet begin to melt in November and almost totally refreeze in late March of the next year, the daily average melting area turns out to be approximately normally distributed. The annual cumulative melting area showed considerable fluctuations and a slight drop of 5.24×104km2 per year. The cumulative melting area from 2002 to 2011 in Antarctica was 2.44×106 km2. The annual melting days and persistent duration in sustained melting area had an annual decrease of 0.81d/yr and 0.64 d/yr respectively. Antarctic snowmelt showed temporal and spatial decreases from 2002 to 2011.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20070035107&hterms=Wegener+Alfred&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DWegener%252C%2BAlfred','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20070035107&hterms=Wegener+Alfred&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DWegener%252C%2BAlfred"><span>ARISE (Antarctic Remote <span class="hlt">Ice</span> Sensing Experiment) in the East 2003: Validation of Satellite-derived Sea-<span class="hlt">ice</span> Data Product</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Massom, Robert A.; Worby, Anthony; Lytle, Victoria; Markus, Thorsten; Allison, Ian; Scambos, Theodore; Enomoto, Hiroyuki; Tateyama, Kazutaka; Haran, Terence; Comiso, Josefino C.; Pfaffling, Andreas; Tamura, Takeshi; Muto, Atsuhiro; Kanagaratnam, Pannir; Giles, Barry; Young, Neal; Hyland, Glenn; Key, Erica</p> <p>2006-01-01</p> <p>Preliminary results are presented from the first validation of geophysical data products (<span class="hlt">ice</span> <span class="hlt">concentration</span>, snow thickness on sea <span class="hlt">ice</span> (h(sub s) and <span class="hlt">ice</span> temperature (T(sub i))fr om the NASA EOS Aqua <span class="hlt">AMSR-E</span> sensor, in East Antarctica (in September-October 2003). The challenge of collecting sufficient measurements with which to validate the coarse-resolution <span class="hlt">AMSR-E</span> data products adequately was addressed by means of a hierarchical approach, using detailed in situ measurements, digital aerial photography and other satellite data. Initial results from a circumnavigation of the experimental site indicate that, at least under cold conditions with a dry snow cover, there is a reasonably close agreement between satellite- and aerial-photo-derived <span class="hlt">ice</span> <span class="hlt">concentrations</span>, i.e. 97.2+/-.6% for NT2 and 96.5+/-2.5% for BBA algorithms vs 94.3% for the aerial photos. In general, the <span class="hlt">AMSR-E</span> <span class="hlt">concentration</span> represents a slight overestimate of the actual <span class="hlt">concentration</span>, with the largest discrepancies occurring in regions containing a relatively high proportion of thin <span class="hlt">ice</span>. The <span class="hlt">AMSR-E</span> <span class="hlt">concentrations</span> from the NT2 and BBA algorithms are similar on average, although differences of up to 5% occur in places, again related to thin-<span class="hlt">ice</span> distribution. The <span class="hlt">AMSR-E</span> <span class="hlt">ice</span> temperature (T(sub i)) product agrees with coincident surface measurements to approximately 0.5 C in the limited dataset analyzed. Regarding snow thickness, the AMSR h(sub s) retrieval is a significant underestimate compared to in situ measurements weighted by the percentage of thin <span class="hlt">ice</span> (and open water) present. For the case study analyzed, the underestimate was 46% for the overall average, but 23% compared to smooth-<span class="hlt">ice</span> measurements. The spatial distribution of the <span class="hlt">AMSR-E</span> h(sub s) product follows an expected and consistent spatial pattern, suggesting that the observed difference may be an offset (at least under freezing conditions). Areas of discrepancy are identified, and the need for future work using the more extensive dataset is</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20070035107&hterms=Wegener+Alfred&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DWegener%252C%2BAlfred','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20070035107&hterms=Wegener+Alfred&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DWegener%252C%2BAlfred"><span>ARISE (Antarctic Remote <span class="hlt">Ice</span> Sensing Experiment) in the East 2003: Validation of Satellite-derived Sea-<span class="hlt">ice</span> Data Product</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Massom, Robert A.; Worby, Anthony; Lytle, Victoria; Markus, Thorsten; Allison, Ian; Scambos, Theodore; Enomoto, Hiroyuki; Tateyama, Kazutaka; Haran, Terence; Comiso, Josefino C.; <a style="text-decoration: none; " href="javascript:void(0); " onClick="displayelement('author_20070035107'); toggleEditAbsImage('author_20070035107_show'); toggleEditAbsImage('author_20070035107_hide'); "> <img style="display:inline; width:12px; height:12px; " src="images/arrow-up.gif" width="12" height="12" border="0" alt="hide" id="author_20070035107_show"> <img style="width:12px; height:12px; display:none; " src="images/arrow-down.gif" width="12" height="12" border="0" alt="hide" id="author_20070035107_hide"></p> <p>2006-01-01</p> <p>Preliminary results are presented from the first validation of geophysical data products (<span class="hlt">ice</span> <span class="hlt">concentration</span>, snow thickness on sea <span class="hlt">ice</span> (h(sub s) and <span class="hlt">ice</span> temperature (T(sub i))fr om the NASA EOS Aqua <span class="hlt">AMSR-E</span> sensor, in East Antarctica (in September-October 2003). The challenge of collecting sufficient measurements with which to validate the coarse-resolution <span class="hlt">AMSR-E</span> data products adequately was addressed by means of a hierarchical approach, using detailed in situ measurements, digital aerial photography and other satellite data. Initial results from a circumnavigation of the experimental site indicate that, at least under cold conditions with a dry snow cover, there is a reasonably close agreement between satellite- and aerial-photo-derived <span class="hlt">ice</span> <span class="hlt">concentrations</span>, i.e. 97.2+/-.6% for NT2 and 96.5+/-2.5% for BBA algorithms vs 94.3% for the aerial photos. In general, the <span class="hlt">AMSR-E</span> <span class="hlt">concentration</span> represents a slight overestimate of the actual <span class="hlt">concentration</span>, with the largest discrepancies occurring in regions containing a relatively high proportion of thin <span class="hlt">ice</span>. The <span class="hlt">AMSR-E</span> <span class="hlt">concentrations</span> from the NT2 and BBA algorithms are similar on average, although differences of up to 5% occur in places, again related to thin-<span class="hlt">ice</span> distribution. The <span class="hlt">AMSR-E</span> <span class="hlt">ice</span> temperature (T(sub i)) product agrees with coincident surface measurements to approximately 0.5 C in the limited dataset analyzed. Regarding snow thickness, the AMSR h(sub s) retrieval is a significant underestimate compared to in situ measurements weighted by the percentage of thin <span class="hlt">ice</span> (and open water) present. For the case study analyzed, the underestimate was 46% for the overall average, but 23% compared to smooth-<span class="hlt">ice</span> measurements. The spatial distribution of the <span class="hlt">AMSR-E</span> h(sub s) product follows an expected and consistent spatial pattern, suggesting that the observed difference may be an offset (at least under freezing conditions). Areas of discrepancy are identified, and the need for future work using the more extensive dataset is</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUFM.C13A..06A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.C13A..06A"><span><span class="hlt">AMSR-E</span> Algorithm for Snowmelt Onset Detection in Subarctic Heterogeneous Terrain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Apgar, J. D.; Ramage, J. M.; McKenney, R. A.; Kopczynski, S. E.; Haight, S. L.; Maltais, P.</p> <p>2006-12-01</p> <p>Snowmelt onset in the mixed terrain of the upper Yukon River basin, Canada, can be derived from brightness temperatures (Tb) obtained by the Advanced Microwave Scanning Radiometer for EOS (<span class="hlt">AMSR-E</span>) on NASA's Aqua satellite. <span class="hlt">AMSR-E</span>, with a resolution of 14 x 8 km2 for the 36.5 GHz frequency and two to four observations per day, improves upon the twice-daily coverage (EASE-Grid) and 37 x 28 km2 spatial resolution of the Special Sensor Microwave Imager (SSM/I). The onset of melt within a snowpack causes an increase in the daytime 36.5 GHz vertically polarized Tb as well as a shift to high diurnal amplitude variations (DAV) as the snow melts during the day and refreezes at night. The higher temporal and spatial resolutions, as well as the timing of data acquisition, make <span class="hlt">AMSR-E</span> more sensitive than SSM/I to sub-daily Tb oscillations, resulting in DAV that show a greater daily range compared to SSM/I. Using ground- based surface temperature and snowpack wetness to verify satellite interpretations, the snowmelt thresholds of Tb > 246 K and DAV > ±10 K developed for use with SSM/I have been adjusted for detecting melt onset with <span class="hlt">AMSR-E</span> to Tb > 252 K and DAV > ±18 K. <span class="hlt">AMSR-E</span> derived snowmelt onset correspond with SSM/I observations in the small subarctic Wheaton River basin (~60°08'05"N, ~134°53'45"W) through the 2004 and 2005 spring transitions. Snowpack wetness measurements collected in the Wheaton basin during the spring of 2005 relate well with temporally-corresponding <span class="hlt">AMSR-E</span> Tb and the established snowmelt thresholds. The enhanced resolution of <span class="hlt">AMSR-E</span> is more effective than SSM/I at delineating both spatial and temporal snowmelt dynamics in the heterogeneous terrain of the upper Yukon River basin. The use of this <span class="hlt">AMSR-E</span> snowmelt onset algorithm in other areas of the subarctic will ultimately allow for a more thorough examination of the impact of spring snowmelt upon basin hydrology and regional climate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=270985','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=270985"><span>Aqua <span class="hlt">AMSR-E</span> soil moisture retrieval: Evaluation and potential Algorithm improvement</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Global estimates of soil moisture derived from the Advanced Microwave Scanning Radiometer on Aqua (<span class="hlt">AMSR-E</span>) have been an invaluable resource over the past decade for a broad spectrum of research and applications that include global hydrology, agriculture, and climate and weather forecasting. NASA, as...</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_3 --> <div id="page_4" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="61"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20170002034&hterms=algorithms&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dalgorithms','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20170002034&hterms=algorithms&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dalgorithms"><span>A New Operational Snow Retrieval Algorithm Applied to Historical <span class="hlt">AMSR-E</span> Brightness Temperatures</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tedesco, Marco; Jeyaratnam, Jeyavinoth</p> <p>2016-01-01</p> <p>Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (<span class="hlt">AMSR-E</span>) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical <span class="hlt">AMSR-E</span> data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current <span class="hlt">AMSR-E</span> operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the <span class="hlt">AMSR-E</span> algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20170002034&hterms=temperature&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dtemperature','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20170002034&hterms=temperature&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dtemperature"><span>A New Operational Snow Retrieval Algorithm Applied to Historical <span class="hlt">AMSR-E</span> Brightness Temperatures</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tedesco, Marco; Jeyaratnam, Jeyavinoth</p> <p>2016-01-01</p> <p>Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (<span class="hlt">AMSR-E</span>) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical <span class="hlt">AMSR-E</span> data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current <span class="hlt">AMSR-E</span> operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the <span class="hlt">AMSR-E</span> algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUSM.H33C..03L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUSM.H33C..03L"><span>Validation of Retrieved Soil Moisture From <span class="hlt">AMSR-E</span> Brightness Temperatures Over the SMEX02 Domain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Laymon, C.; Crosson, W.; Limaye, A.</p> <p>2005-05-01</p> <p>A coupled hydrologic/radiobrightness model (H/RM) is utilized to estimate brightness temperatures at C and X bands and associated soil moisture for validation of soil moisture retrieved from the Advanced Microwave Scanning Radiometer for the Earth Observing System (<span class="hlt">AMSR-E</span>). This study is focused on the SMEX02 study area in central Iowa. The oversampling of the <span class="hlt">AMSR-E</span> instrument is exploited in an optimization algorithm to deconvolve the brightness temperature observations for each EASEgrid cell. In so doing, the influence from adjacent grid cells is much less than with conventional spatial analysis schemes. The high spatial and temporal resolution of H/RM modeling relative to <span class="hlt">AMSR-E</span> observations permits a statistical assessment of subgrid-scale characteristics. These results are combined with a sensitivity study of parameters used in the radiobrightness model to derive error statistics for the <span class="hlt">AMSR-E</span> retrievals. In addition, we examine other sources of operational validation errors, such as, a) the errors associated with using limited gravimetric soil moisture data or point-scale measurements of soil moisture from network stations to estimate footprint-scale mean soil moisture, b) the errors associated with asynchronous sampling times, and c) the relationship between surface moisture (~1 cm) and profile moisture. These analyses are necessary to characterize the accuracy of the AMSR data products at EASE-grid scale. Although <span class="hlt">AMSR-E</span> C band brightness temperatures are contaminated with radio frequency interference over the continental U.S., H/RM-estimated C and X band brightness temperatures are examined comparatively to reevaluate the value of C band retrievals elsewhere.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.4452G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.4452G"><span>Arctic sea <span class="hlt">ice</span> <span class="hlt">concentration</span> observed with SMOS during summer</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gabarro, Carolina; Martinez, Justino; Turiel, Antonio</p> <p>2017-04-01</p> <p>The Arctic Ocean is under profound transformation. Observations and model predictions show dramatic decline in sea <span class="hlt">ice</span> extent and volume [1]. A retreating Arctic <span class="hlt">ice</span> cover has a marked impact on regional and global climate, and vice versa, through a large number of feedback mechanisms and interactions with the climate system [2]. The launch of the Soil Moisture and Ocean Salinity (SMOS) mission, in 2009, marked the dawn of a new type of space-based microwave observations. Although the mission was originally conceived for hydrological and oceanographic studies [3,4], SMOS is also making inroads in the cryospheric sciences by measuring the thin <span class="hlt">ice</span> thickness [5,6]. SMOS carries an L-band (1.4 GHz), passive interferometric radiometer (the so-called MIRAS) that measures the electromagnetic radiation emitted by the Earth's surface, at about 50 km spatial resolution, continuous multi-angle viewing, large wide swath (1200-km), and with a 3-day revisit time at the equator, but more frequently at the poles. A novel radiometric method to determine sea <span class="hlt">ice</span> <span class="hlt">concentration</span> (SIC) from SMOS is presented. The method uses the Bayesian-based Maximum Likelihood Estimation (MLE) approach to retrieve SIC. The advantage of this approach with respect to the classical linear inversion is that the former takes into account the uncertainty of the tie-point measured data in addition to the mean value, while the latter only uses a mean value of the tie-point data. When thin <span class="hlt">ice</span> is present, the SMOS algorithm underestimates the SIC due to the low opacity of the <span class="hlt">ice</span> at this frequency. However, using a synergistic approach with data from other satellite sensors, it is possible to obtain accurate thin <span class="hlt">ice</span> thickness estimations with the Bayesian-based method. Despite its lower spatial resolution relative to SSMI or <span class="hlt">AMSR-E</span>, SMOS-derived SIC products are little affected by the atmosphere and the snow (almost transparent at L-band). Moreover L-band measurements are more robust in front of the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H21I0824X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H21I0824X"><span>Atmospheric and forest decoupling from <span class="hlt">AMSR-E</span> passive microwave brightness temperature observations in snow-covered regions over North America</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xue, Y.; Forman, B. A.</p> <p>2014-12-01</p> <p>Remotely-sensed measurements from space-borne instrumentation have been extensively utilized in order to quantify snow water equivalent (SWE) across the globe, primarily in the form of SWE retrievals derived from passive microwave (PMW) brightness temperature (Tb) measurements. However, the application of these SWE retrieval products is largely limited by wet snow, deep snow, overlying vegetation, depth hoar, <span class="hlt">ice</span> crusts, sub-grid scale lake <span class="hlt">ice</span>, snow stratigraphy, and snow morphology. Alternatively, PMW Tb can be integrated directly (i.e., without the need of a SWE retrieval algorithm) into a land surface model as part of a Tb data assimilation (DA) framework. However, it is worthwhile to first decouple non-SWE related signals from the Tb observations prior to assimilation of the SWE-related Tb information. This study addresses two significant sources of SWE-related uncertainties using the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) PMW Tb observations. Namely, atmospheric and overlying forest effects are decoupled using relatively simple radiative transfer models. Comparisons against independent Tb measurements collected during airborne PMW Tb surveys highlight the effectiveness of <span class="hlt">AMSR-E</span> Tb measurements decoupling with the eventual goal of enhancing estimated SWE as part of a PMW Tb data assimilation framework into an advanced land surface model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27924293','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27924293"><span>Use of <span class="hlt">AMSR-E</span> microwave satellite data for land surface characteristics and snow cover variation.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Boori, Mukesh Singh; Ferraro, Ralph R; Choudhary, Komal; Kupriyanov, Alexander</p> <p>2016-12-01</p> <p>This data article contains data related to the research article entitled "Global land cover classification based on microwave polarization and gradient ratio (MPGR)" [1] and "Microwave polarization and gradient ratio (MPGR) for global land surface phenology" [2]. This data article presents land surface characteristics and snow cover variation information from sensors like EOS Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>). This data article use the HDF Explorer, Matlab, and ArcGIS software to process the pixel latitude, longitude, snow water equivalent (SWE), digital elevation model (DEM) and Brightness Temperature (BT) information from <span class="hlt">AMSR-E</span> satellite data to provide land surface characteristics and snow cover variation data in all-weather condition at any time. This data information is useful to discriminate different land surface cover types and snow cover variation, which is turn, will help to improve monitoring of weather, climate and natural disasters.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20110023835&hterms=relative+measurement+error&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Drelative%2Bmeasurement%2Berror','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20110023835&hterms=relative+measurement+error&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Drelative%2Bmeasurement%2Berror"><span>Subsurface Emission Effects in <span class="hlt">AMSR-E</span> Measurements: Implications for Land Surface Microwave Emissivity Retrieval</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Galantowicz, John F.; Moncet, Jean-Luc; Liang, Pan; Lipton, Alan E.; Uymin, Gennady; Prigent, Catherine; Grassotti, Christopher</p> <p>2011-01-01</p> <p>An analysis of land surface microwave emission time series shows that the characteristic diurnal signature associated with subsurface emission in sandy deserts carry over to arid and semi-arid region worldwide. Prior work found that diurnal variation of Special Sensor Microwave/Imager (SSM/I) brightness temperatures in deserts was small relative to International Satellite Cloud Climatology Project land surface temperature (LST) variation and that the difference varied with surface type and was largest in sand sea regions. Here we find more widespread subsurface emission effects in Advanced Microwave Scanning Radiometer-EOS (<span class="hlt">AMSR-E</span>) measurements. The <span class="hlt">AMSR-E</span> orbit has equator crossing times near 01:30 and 13 :30 local time, resulting in sampling when near-surface temperature gradients are likely to be large and amplifying the influence of emission depth on effective emitting temperature relative to other factors. <span class="hlt">AMSR-E</span> measurements are also temporally coincident with Moderate Resolution Imaging Spectroradiometer (MODIS) LST measurements, eliminating time lag as a source of LST uncertainty and reducing LST errors due to undetected clouds. This paper presents monthly global emissivity and emission depth index retrievals for 2003 at 11, 19, 37, and 89 GHz from <span class="hlt">AMSR-E</span>, MODIS, and SSM/I time series data. Retrieval model fit error, stability, self-consistency, and land surface modeling results provide evidence for the validity of the subsurface emission hypothesis and the retrieval approach. An analysis of emission depth index, emissivity, precipitation, and vegetation index seasonal trends in northern and southern Africa suggests that changes in the emission depth index may be tied to changes in land surface moisture and vegetation conditions</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20090017698&hterms=inclusion+sport&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dinclusion%2Bsport','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20090017698&hterms=inclusion+sport&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dinclusion%2Bsport"><span>Combined MODIS/<span class="hlt">AMSR-E</span> SST Composites for Regional Weather Applications</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Jedlovec, Gary; Vazquez, Jorge; Armstrong, Ed; Haines, Stephanie</p> <p>2009-01-01</p> <p>Recent applications of a high resolution MODlS composite SST product have clearly shown the importance of developing high-resolution SST data sets for coastal applications and modeling. In general, coupling between the oceans and atmospheres has been closely linked to SST gradients and fronts, indicating a need for high resolution SSTs, specifically in the areas of large gradients associated with coastal regions. Thus an accurate determination of SST gradients has become critical for determining the appropriate air-sea coupling and the influence on ocean modeling. Recent research is focused on improving the accuracy and spatial coverage of the current operational MODIS SST composite product provided by the Short-term Prediction Research and Transition (SPORT) project and distributed to the community. GHRSST-PP MODlS data and microwave <span class="hlt">AMSR-E</span> data are being combined to produce composite data sets for both the West Coast and East Coast of the United States, including the Gulf of Mexico. The use of 1 km MODIS data has explicit advantages over other SST products including its global coverage and high resolution. The <span class="hlt">AMSR-E</span> data will reduce the latency of the composites. A strategy for utilizing the error characteristics contained in the GHRSST data has been developed. This strategy will include using the error characteristics directly to calculate weights in the SST composites, uncertainty maps based on the composite biases and RMS errors, and latency products calculated in the compositing process. Recent accomplishments include the development of an enhanced compositing approach based on the error-weighted combination of recent clear MODIS SST values, where the error contributions come from measurement error, potential cloud contamination, and data latency sources. Future plans call for the inclusion of <span class="hlt">AMSR-E</span> SST values with appropriate weights based upon measurement accuracy, MODIS-<span class="hlt">AMSR-E</span> SST bias, and latency.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JHyd..540...26D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JHyd..540...26D"><span>Assimilation of <span class="hlt">AMSR-E</span> snow water equivalent data in a spatially-lumped snow model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dziubanski, David J.; Franz, Kristie J.</p> <p>2016-09-01</p> <p>Accurately initializing snow model states in hydrologic prediction models is important for estimating future snowmelt, water supplies, and flooding potential. While ground-based snow observations give the most reliable information about snowpack conditions, they are spatially limited. In the north-central USA, there are no continual observations of hydrologically critical snow variables. Satellites offer the most likely source of spatial snow data, such as the snow water equivalent (SWE), for this region. In this study, we test the impact of assimilating SWE data from the Advanced Microwave Scanning Radiometer - Earth Observing System (<span class="hlt">AMSR-E</span>) instrument into the US National Weather Service (NWS) SNOW17 model for seven watersheds in the Upper Mississippi River basin. The SNOW17 is coupled with the NWS Sacramento Soil Moisture Accounting (SACSMA) model, and both simulated SWE and discharge are evaluated. The ensemble Kalman filter (EnKF) assimilation framework is applied and updating occurs on a daily cycle for water years 2006-2011. Prior to assimilation, <span class="hlt">AMSR-E</span> data is bias corrected using data from the National Operational Hydrologic Remote Sensing Center (NOHRSC) airborne snow survey program. An average <span class="hlt">AMSR-E</span> SWE bias of -17.91 mm was found for the study basins. SNOW17 and SAC-SMA model parameters from the North Central River Forecast Center (NCRFC) are used. Compared to a baseline run without assimilation, the SWE assimilation improved discharge for five of the seven study sites, in particular for high discharge magnitudes associated with snow melt runoff. SWE and discharge simulations suggest that the SNOW17 is underestimating SWE and snowmelt rates in the study basins. Deep snow conditions and periods of snowmelt may have introduced error into the assimilation due to difficulty obtaining accurate brightness temperatures under these conditions. Overall results indicate that the <span class="hlt">AMSR-E</span> data and EnKF are viable and effective solutions for improving simulations</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20110023835&hterms=microwaves+work&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dmicrowaves%2Bwork','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20110023835&hterms=microwaves+work&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dmicrowaves%2Bwork"><span>Subsurface Emission Effects in <span class="hlt">AMSR-E</span> Measurements: Implications for Land Surface Microwave Emissivity Retrieval</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Galantowicz, John F.; Moncet, Jean-Luc; Liang, Pan; Lipton, Alan E.; Uymin, Gennady; Prigent, Catherine; Grassotti, Christopher</p> <p>2011-01-01</p> <p>An analysis of land surface microwave emission time series shows that the characteristic diurnal signature associated with subsurface emission in sandy deserts carry over to arid and semi-arid region worldwide. Prior work found that diurnal variation of Special Sensor Microwave/Imager (SSM/I) brightness temperatures in deserts was small relative to International Satellite Cloud Climatology Project land surface temperature (LST) variation and that the difference varied with surface type and was largest in sand sea regions. Here we find more widespread subsurface emission effects in Advanced Microwave Scanning Radiometer-EOS (<span class="hlt">AMSR-E</span>) measurements. The <span class="hlt">AMSR-E</span> orbit has equator crossing times near 01:30 and 13 :30 local time, resulting in sampling when near-surface temperature gradients are likely to be large and amplifying the influence of emission depth on effective emitting temperature relative to other factors. <span class="hlt">AMSR-E</span> measurements are also temporally coincident with Moderate Resolution Imaging Spectroradiometer (MODIS) LST measurements, eliminating time lag as a source of LST uncertainty and reducing LST errors due to undetected clouds. This paper presents monthly global emissivity and emission depth index retrievals for 2003 at 11, 19, 37, and 89 GHz from <span class="hlt">AMSR-E</span>, MODIS, and SSM/I time series data. Retrieval model fit error, stability, self-consistency, and land surface modeling results provide evidence for the validity of the subsurface emission hypothesis and the retrieval approach. An analysis of emission depth index, emissivity, precipitation, and vegetation index seasonal trends in northern and southern Africa suggests that changes in the emission depth index may be tied to changes in land surface moisture and vegetation conditions</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.3944X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.3944X"><span>A blending snow cover data base on MODIS and <span class="hlt">AMSR-E</span> snow cover in Qinghai-Tibet Plateau</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xiaohua, H.; Wang, J.; Che, T.; Dai, L. Y.</p> <p>2012-04-01</p> <p>The algorithms of MODIS Terra and MODIS Aqua versions of the snow products have been developed by the NASA National Snow and <span class="hlt">Ice</span> Data Center (NSIDC). The MODIS global snow-cover products have been available through the NSIDC Distributed Active Archive Center (DAAC) since February 24, 2000 to Terra and July 4, 2002 to Aqua. The MODIS snow-cover maps represent a potential improvement relative to hemispheric-scale snow maps that are available today mainly because of the improved spatial resolution and snow/cloud discrimination capabilities of MODIS, and the frequent global coverage. In China, the snow distribution is different to other regions. Their accuracy on Qinghai-Tibet Plateau (QTP), however, has not yet been established. There are some drawbacks about NSIDC global snow cover products on QTP: 1. The characteristics of snow depth distribution on QTP: Thin, discontinuous. Our research indicated the MODIS snow-cover products underestimated the snow cover area in QTP. 2. The daily snow cover product from MODIS-Terra and Aqua can include the data gaps. 3. The snow products can separate snow from most obscuring clouds. However, there are still many cloud pixels in daily snow cover products. The study developed a new blending daily snow cover algorithm through improving the NSIDC snow algorithms and combining MODIS and <span class="hlt">AMSR-E</span> data in QTP. The new snow cover products will provide daily snow cover at 500-m resolution in QTP. The new snow cover algorithm employs a grouped-criteria technique using the Normalized Difference Snow Index (NDSI) and other spectral threshold tests and image fusion technology to identify and classify snow on a pixel-by-pixel basis. The usefulness of the NDSI is based on the fact that snow and <span class="hlt">ice</span> are considerably more reflective in the visible than in the shortwave IR part of the spectrum, and the reflectance of most clouds remains high in the short-wave IR, while the reflectance of snow is low. We propose a set of three steps, based on a</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 Sea-<span class="hlt">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 sea <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 sea <span class="hlt">ice</span>, combined with snow depth data from <span class="hlt">AMSR-E</span>/AMSR2 passive microwave observation over the southern ocean, sea-<span class="hlt">ice</span> thickness is derived from the freeboard. Combined with <span class="hlt">AMSR-E</span>/AMSR2 <span class="hlt">ice</span> <span class="hlt">concentration</span>, sea-<span class="hlt">ice</span> area and volume are also calculated. During the 2003-2009 period, sea-<span class="hlt">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 sea <span class="hlt">ice</span> during the ICESat period. <span class="hlt">Ice</span>Bridge sea <span class="hlt">ice</span> freeboard and thickness data from 2009 to 2011 over the Weddell Sea and Amundsen and Bellingshausen Seas are compared with the ICESat results.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JGRD..122.3330J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JGRD..122.3330J"><span>Inversion of <span class="hlt">AMSR-E</span> observations for land surface temperature estimation: 1. Methodology and evaluation with station temperature</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jiménez, C.; Prigent, C.; Ermida, S. L.; Moncet, J.-L.</p> <p>2017-03-01</p> <p>Inversions of the Earth Observation Satellite (EOS) Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) brightness temperatures (Tbs) to derive the land surface temperature (Ts) are presented based on building a global transfer function by neural networks trained with <span class="hlt">AMSR-E</span> Tbs and retrieved microwave Ts*. The only required inputs are the Tbs and monthly climatological emissivities, minimizing the dependence on ancillary data. The inversions are accompanied by a coarse estimation of retrieval uncertainty, an estimate of the quality of the retrieval, and a series of flags to signal difficult inversion situations. For ˜75% of the land surface the root-mean-square difference (RMSD) between the training target Ts* and the neural network retrieved Ts is below 2.8 K. The RMSD when comparing with the Moderate Resolution Imaging Spectroradiometer (MODIS) clear-sky Ts is below 3.9 K for the same conditions. Over 10 ground stations, <span class="hlt">AMSR-E</span> and MODIS Ts were compared with the in situ data. Overall, MODIS agrees better with the station Ts than <span class="hlt">AMSR-E</span> (all-station mean RMSD of 2.4 K for MODIS and 4.0 for <span class="hlt">AMSR-E</span>), but <span class="hlt">AMSR-E</span> provides a larger number of Ts estimates by being able to measure under cloudy conditions, with an approximated ratio of 3 to 1 over the analyzed stations. At many stations the RMSD of the <span class="hlt">AMSR-E</span> clear and cloudy sky are comparable, highlighting the ability of the microwave inversions to provide Ts under most atmospheric conditions. Closest agreement with the in situ Ts happens for stations with dense vegetation, where <span class="hlt">AMSR-E</span> emissivity is less varying.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011PolSc...5..104S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011PolSc...5..104S"><span>A GIS approach to estimating interannual variability of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> in the Dumont d’Urville Sea near Terre Adélie from 2003 to 2009</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Smith, Martina B.; Labat, Jean-Philippe; Fraser, Alexander D.; Massom, Robert A.; Koubbi, Philippe</p> <p>2011-08-01</p> <p>A Geographic Information System (GIS)-based investigation into the interannual variability of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> was conducted in the Dumont d’Urville Sea off the Terre Adélie coastline, south of 65°S and between 139 and 146°E. Sea <span class="hlt">ice</span> <span class="hlt">concentration</span> data derived from Advanced Microwave Scanning Radiometer-EOS (<span class="hlt">AMSR-E</span>) data were analysed for the period 2003 to 2009. Sea <span class="hlt">ice</span> <span class="hlt">concentration</span> was found to be least variable in three regions, namely the Buchanan Bay/Watt Bay region (143-145°E), along ∼65.5°S (west of 144.5°E), and the Adélie Bank northeast of Dumont d’Urville near 66°S, 140.5°E. The remaining areas had relatively high interannual variability, in particular the Adélie Basin (∼66°S, ∼140°E) and the outer fringe of the Mertz Glacier Polynya (MGP). In general, higher sea <span class="hlt">ice</span> <span class="hlt">concentration</span> conditions were experienced in the west of the study area (i.e., where annual fast <span class="hlt">ice</span> recurs), and open water dominated in the MGP and in the northeast. The years 2007-2009 experienced greater persistence of higher sea <span class="hlt">ice</span> <span class="hlt">concentration</span> than earlier years. This study provides a baseline for assessing changes in the regional sea <span class="hlt">ice</span> regime that may occur since the calving of the Mertz Glacier in February 2010.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C41D0720R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C41D0720R"><span>Detection of Rain-on-Snow (ROS) Events Using the Advanced Microwave Scanning Radiometer-Earth Observing System (<span class="hlt">AMSR-E</span>) and Weather Station Observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ryan, E. M.; Brucker, L.; Forman, B. A.</p> <p>2015-12-01</p> <p>During the winter months, the occurrence of rain-on-snow (ROS) events can impact snow stratigraphy via generation of large scale <span class="hlt">ice</span> crusts, e.g., on or within the snowpack. The formation of such layers significantly alters the electromagnetic response of the snowpack, which can be witnessed using space-based microwave radiometers. In addition, ROS layers can hinder the ability of wildlife to burrow in the snow for vegetation, which limits their foraging capability. A prime example occurred on 23 October 2003 in Banks Island, Canada, where an ROS event is believed to have caused the deaths of over 20,000 musk oxen. Through the use of passive microwave remote sensing, ROS events can be detected by utilizing observed brightness temperatures (Tb) from <span class="hlt">AMSR-E</span>. Tb observed at different microwave frequencies and polarizations depends on snow properties. A wet snowpack formed from an ROS event yields a larger Tb than a typical dry snowpack would. This phenomenon makes observed Tb useful when detecting ROS events. With the use of data retrieved from <span class="hlt">AMSR-E</span>, in conjunction with observations from ground-based weather station networks, a database of estimated ROS events over the past twelve years was generated. Using this database, changes in measured Tb following the ROS events was also observed. This study adds to the growing knowledge of ROS events and has the potential to help inform passive microwave snow water equivalent (SWE) retrievals or snow cover properties in polar regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.C41B0644K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.C41B0644K"><span>A Comparison of Snow Depth from GPS-Interferometry vs. <span class="hlt">AMSR-E</span>/AMSR-2</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kim, E. J.; Patel, H.; Wu, A.; Braun, J.; Small, E. E.; Larson, K. M.</p> <p>2013-12-01</p> <p>The validation of snow data products from satellite sensors has perennially faced the challenge of finding station data that is both widely distributed and possessing sufficient spatial density to provide accurate validation statistics. Point data from ground truth stations have been widely used despite the orders-of-magnitude scale mismatch as well as the insufficient spatial density; this has persisted mainly due to the lack of better alternatives. We evaluate a novel source of validation data that uses ground-based GPS networks, by comparison against snow data products from the <span class="hlt">AMSR-E</span> sensor aboard the Aqua satellite and now the AMSR2 sensor aboard the GCOM-W1satellite. There are three advantageous features of this approach. First, the GPS networks already exist. Second, the thousands of sites are widely distributed spatially and have a higher spatial density than other stations currently used for snow validation. And third, the observed area of the GPS technique approaches 10,000 m2--much larger than point-scale station observations (1 to 10 m2). Evaluating the new GPS approach along with the more well-known <span class="hlt">AMSR-E</span>/AMSR-2 snow depth product will provide a baseline for exploiting this potentially large new validation data source in a variety of remote sensing science studies. This work is based on recent advances in GPS techniques by Larson et al that have allowed geodetic quality GPS instrumentation to be used to measure changes in soil moisture and snow depth in the region surrounding GPS antennas. These retrievals relate observed changes in ground reflected GPS signals to changes in either the soil conditions around the antenna or the depth of snow at the site. In this paper, we will focus solely on snow depth. Observations from several GPS stations from the Plate Boundary Observatory (PBO), operating in varied locations in the western United States have been compared with <span class="hlt">AMSR-E</span> and/or AMSR-2 snow retrievals. These sites span a range of climates (especially</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19990078515&hterms=bootstrap&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dbootstrap','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990078515&hterms=bootstrap&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dbootstrap"><span>Enhanced Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> and <span class="hlt">Ice</span> Temperature Algorithms for AMSR</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.; Manning, Will; Gersten, Robert</p> <p>1998-01-01</p> <p>Accurate quantification of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> and <span class="hlt">ice</span> temperature from satellite passive microwave data is important because they provide the only long term, spatially detailed and consistent data set needed to study the climatology of the polar regions. Sea <span class="hlt">ice</span> <span class="hlt">concentration</span> data are used to derive large-scale daily <span class="hlt">ice</span> extents that are utilized in trend analysis of the global sea <span class="hlt">ice</span> cover. They are also used to quantify the amount of open water and thin <span class="hlt">ice</span> in polynya and divergence regions which together with <span class="hlt">ice</span> temperatures are in turn needed to estimate vertical heat and salinity fluxes in these regions. Sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> have been derived from the NASA Team and Bootstrap algorithms while a separate technique for deriving <span class="hlt">ice</span> temperature has been reported. An integrated technique that will utilizes most of the channels of AMSR (Advanced Microwave Scanning Radiometer) has been developed. The technique uses data from the 6 GHz and 37 GHz channels at vertical polarization obtain an initial estimate of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> and <span class="hlt">ice</span> temperature. The derived <span class="hlt">ice</span> temperature is then utilized to estimate the emissivities for the corresponding observations at all the other channels. A procedure for calculating the <span class="hlt">ice</span> <span class="hlt">concentration</span> similar to the Bootstrap technique is then used but with variables being emissivities instead of brightness temperatures to minimizes errors associated with spatial changes in <span class="hlt">ice</span> temperatures within the <span class="hlt">ice</span> pack. Comparative studies of <span class="hlt">ice</span> <span class="hlt">concentration</span> results with those from other algorithms, including the original Bootstrap algorithm and those from high resolution satellite visible and infrared data will be presented. Also, results from a simulation study that demonstrates the effectiveness of the technique in correcting for spatial variations in <span class="hlt">ice</span> temperatures will be shown. The <span class="hlt">ice</span> temperature results are likewise compared with satellite infrared and buoy data with the latter adjusted to account for the effects of the snow</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H43H1623C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H43H1623C"><span>Intercomparison of AMSR2 and <span class="hlt">AMSR-E</span> Soil Moisture Retrievals with MERRA-L data set over Australia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cho, E.; Choi, M.; Su, C. H.; Ryu, D.; Kim, H.; Jacobs, J. M.</p> <p>2015-12-01</p> <p>Soil moisture is an important variable in the hydrological cycle on the land surface and plays an essential role in hydrological and meteorological processes. The Advanced Microwave Scanning Radiometer for Earth Observing System (<span class="hlt">AMSR-E</span>) sensor on board the Aqua satellite offered valuable soil moisture data set from June 2002 and October 2011 and has been used in a wide range of applications. However, the <span class="hlt">AMSR-E</span> sensor stopped operation from 4 October 2011 due to a problem with its antenna. <span class="hlt">AMSR-E</span> was replaced by the Advanced Microwave Scanning Radiometer 2 (AMSR2) on the Global Climate Change Observation Mission 1 - Water (GCOM-W1) satellite in May 2012. Assessment of AMSR2 soil moisture retrievals as compared to <span class="hlt">AMSR-E</span> has not yet been extensively evaluated. This task is critical if AMSR2 soil moisture products are used as a continuous dataset continuing the legacy of <span class="hlt">AMSR-E</span>. The purpose of this study is to inter-compare AMSR2 and <span class="hlt">AMSR-E</span> microwave based soil moisture over Australia, mediated by using model-based soil moisture data set to determine statistically similar inter-comparison periods from time periods of the individual sensors. This work use NASA-VUA AMSR2 and <span class="hlt">AMSR-E</span> based soil moisture products derived by the Land Parameter Retrieval Model (LPRM) and the modelled soil moisture from NASA's MERRA-L (Modern Era Retrospective-analysis for Research and Applications-Land) re-analysis. The satellite soil moisture products are compared against the MERRA-L using traditional metrics, and the random errors in individual products are estimated using lagged instrumental variable regression analysis. Generally, the results demonstrate that the two satellite-based soil moisture retrievals have reasonable agreement with MERRA-L soil moisture data set. The error differences are notable, with the zonal error statistics are higher for AMSR2 in all climate zones, though the error maps of AMSR2 and <span class="hlt">AMSR-E</span> are spatially similar over the Australia regions. This study leads</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JHyd..553...88L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JHyd..553...88L"><span>Performance of SMAP, <span class="hlt">AMSR-E</span> and LAI for weekly agricultural drought forecasting over continental United States</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, Di; Mishra, Ashok K.; Yu, Zhongbo; Yang, Chuanguo; Konapala, Goutam; Vu, Tue</p> <p>2017-10-01</p> <p>This study applied support vector machines (SVMs) and data assimilation (DA) methods to investigate the performance of in-situ and remotely sensed products (i.e., leaf area index (LAI), <span class="hlt">AMSR-E</span> and SMAP soil moisture retrievals) for near-real time agricultural drought forecasting for in-situ stations located in continental United States (CONUS). The agricultural drought was quantified using soil water deficit index (SWDI) derived based on available soil moisture and basic soil water parameters. It is observed that SVMs or SVM-DA with limited meteorological variables as inputs able to forecast SWDI at most of the in-situ stations up to 1-2-week lead time. Addition of remotely sensed products (i.e., LAI, <span class="hlt">AMSR-E</span>, SMAP) either individually or simultaneously as inputs to SVMs can able to improve SWDI forecast at most of the stations where the strong relationship exists between LAI (and/or <span class="hlt">AMSR-E</span>, SMAP) with SWDI. Such improvement can persist up to 2-4-week lead time at some of the stations. But the efficiency tends to decrease with the increase in lead time. The addition of both LAI and SMAP (<span class="hlt">AMSR-E</span>) performs better than the independent addition of LAI or SMAP (<span class="hlt">AMSR-E</span>). Typically, the performance of drought prediction varies at local scales; therefore it is difficult to generalize our findings at regional scale.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ChJOL.tmp..117L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ChJOL.tmp..117L"><span>A comparison of Argo nominal surface and near-surface temperature for validation of <span class="hlt">AMSR-E</span> SST</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, Zenghong; Chen, Xingrong; Sun, Chaohui; Wu, Xiaofen; Lu, Shaolei</p> <p>2016-06-01</p> <p>Satellite SST (sea surface temperature) from the Advanced Microwave Scanning Radiometer for the Earth Observing System (<span class="hlt">AMSR-E</span>) is compared with in situ temperature observations from Argo profiling floats over the global oceans to evaluate the advantages of Argo NST (near-surface temperature: water temperature less than 1 m from the surface). By comparing Argo nominal surface temperature (~5 m) with its NST, a diurnal cycle caused by daytime warming and nighttime cooling was found, along with a maximum warming of 0.08±0.36°C during 14:00-15:00 local time. Further comparisons between Argo 5-m temperature/Argo NST and <span class="hlt">AMSR-E</span> SST retrievals related to wind speed, columnar water vapor, and columnar cloud water indicate warming biases at low wind speed (<5 m/s) and columnar water vapor >28 mm during daytime. The warming tendency is more remarkable for <span class="hlt">AMSR-E</span> SST/Argo 5-m temperature compared with <span class="hlt">AMSR-E</span> SST/Argo NST, owing to the effect of diurnal warming. This effect of diurnal warming events should be excluded before validation for microwave SST retrievals. Both <span class="hlt">AMSR-E</span> nighttime SST/Argo 5-m temperature and nighttime SST/Argo NST show generally good agreement, independent of wind speed and columnar water vapor. From our analysis, Argo NST data demonstrated their advantages for validation of satellite-retrieved SST.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_4 --> <div id="page_5" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="81"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ChJOL..35..712L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ChJOL..35..712L"><span>A comparison of Argo nominal surface and near-surface temperature for validation of <span class="hlt">AMSR-E</span> SST</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, Zenghong; Chen, Xingrong; Sun, Chaohui; Wu, Xiaofen; Lu, Shaolei</p> <p>2017-05-01</p> <p>Satellite SST (sea surface temperature) from the Advanced Microwave Scanning Radiometer for the Earth Observing System (<span class="hlt">AMSR-E</span>) is compared with in situ temperature observations from Argo profiling floats over the global oceans to evaluate the advantages of Argo NST (near-surface temperature: water temperature less than 1 m from the surface). By comparing Argo nominal surface temperature ( 5 m) with its NST, a diurnal cycle caused by daytime warming and nighttime cooling was found, along with a maximum warming of 0.08±0.36°C during 14:00-15:00 local time. Further comparisons between Argo 5-m temperature/Argo NST and <span class="hlt">AMSR-E</span> SST retrievals related to wind speed, columnar water vapor, and columnar cloud water indicate warming biases at low wind speed (<5 m/s) and columnar water vapor >28 mm during daytime. The warming tendency is more remarkable for <span class="hlt">AMSR-E</span> SST/Argo 5-m temperature compared with <span class="hlt">AMSR-E</span> SST/Argo NST, owing to the effect of diurnal warming. This effect of diurnal warming events should be excluded before validation for microwave SST retrievals. Both <span class="hlt">AMSR-E</span> nighttime SST/Argo 5-m temperature and nighttime SST/Argo NST show generally good agreement, independent of wind speed and columnar water vapor. From our analysis, Argo NST data demonstrated their advantages for validation of satellite-retrieved SST.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C13D0859R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C13D0859R"><span>Glacier Melt Detection in Complex Terrain Using New <span class="hlt">AMSR-E</span> Calibrated Enhanced Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System 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>Ramage, J. M.; Brodzik, M. J.; Hardman, M.</p> <p>2016-12-01</p> <p>Passive microwave (PM) 18 GHz and 36 GHz horizontally- and vertically-polarized brightness temperatures (Tb) channels from the Advanced Microwave Scanning Radiometer for EOS (<span class="hlt">AMSR-E</span>) have been important sources of information about snow melt status in glacial environments, particularly at high latitudes. PM data are sensitive to the changes in near-surface liquid water that accompany melt onset, melt intensification, and refreezing. Overpasses are frequent enough that in most areas multiple (2-8) observations per day are possible, yielding the potential for determining the dynamic state of the snow pack during transition seasons. <span class="hlt">AMSR-E</span> Tb data have been used effectively to determine melt onset and melt intensification using daily Tb and diurnal amplitude variation (DAV) thresholds. Due to mixed pixels in historically coarse spatial resolution Tb data, melt analysis has been impractical in <span class="hlt">ice</span>-marginal zones where pixels may be only fractionally snow/<span class="hlt">ice</span> covered, and in areas where the glacier is near large bodies of water: even small regions of open water in a pixel severely impact the microwave signal. We use the new enhanced-resolution Calibrated Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record product's twice daily obserations to test and update existing snow melt algorithms by determining appropriate melt thresholds for both Tb and DAV for the CETB 18 and 36 GHz channels. We use the enhanced resolution data to evaluate melt characteristics along glacier margins and melt transition zones during the melt seasons in locations spanning a wide range of melt scenarios, including the Patagonian Andes, the Alaskan Coast Range, and the Russian High Arctic icecaps. We quantify how improvement of spatial resolution from the original 12.5 - 25 km-scale pixels to the enhanced resolution of 3.125 - 6.25 km improves the ability to evaluate melt timing across boundaries and transition zones in diverse glacial environments.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.H14C..04L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H14C..04L"><span>Monitoring flood events using the <span class="hlt">AMSR-E</span> based Polarization Variation Index</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lacava, T.; Temimi, M.; Coviello, I.; Faruolo, M.; Khanbilvardi, R.; Pergola, N.; Tramutoli, V.; Wang, D.</p> <p>2011-12-01</p> <p>Floods are, by a large margin, the most costly of natural hazards in terms of both human lives and property damage. Improving the accuracy and lead time of flood monitoring/forecasts is critical to reduce agricultural, social, and human costs of flooding. In the last years several remote sensing-based approaches for flood risk monitoring/forecast have been proposed. Among them, the ones using microwave data have appeared to be the most appropriate, mainly because of their all-day and all weather capabilities, as well as to their sensitivity to water presence into the soil. It is well known that monitoring soil moisture variations is fundamental within dynamic hydrologic and climatic models, and particularly to assess the probability of occurrence (and the relative intensity) of drought or flood events. For a near real time flood-risk monitoring, data at high temporal resolution are essential, even if this leads to low spatial resolutions. This is the case of data achievable from passive microwave sensors, aboard meteorological satellites, that could be particularly usefull for flood risk monitoring especially over large river basins. Among the presently available radiometers useful for such aim, the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) aboard EOS Aqua since June 2002, is one of the most appropriate to the scope furnishing global daily observations of land surface at six frequency bands (dual polarized). Most of these bands are suitable for flood monitoring thanks to the good sensitivity to the soil moisture presence. Soil moisture measurements have been already retrieved by exploiting such a sensor. In particular, the Polarization Ratio ((PR=(Tbv - Tbh)/(Tbv + Tbh)), which takes into account the different behaviors of bare and wet soil in terms of emissivity at vertical (V) and horizontal (H) polarization, has been largely used. Polarization ratio, in fact, normalizes out the surface temperature, leaving a quantity that is primarily dependent on soil</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.6111M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.6111M"><span>A Round Robin evaluation of <span class="hlt">AMSR-E</span> soil moisture retrievals</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mittelbach, Heidi; Hirschi, Martin; Nicolai-Shaw, Nadine; Gruber, Alexander; Dorigo, Wouter; de Jeu, Richard; Parinussa, Robert; Jones, Lucas A.; Wagner, Wolfgang; Seneviratne, Sonia I.</p> <p>2014-05-01</p> <p>Large-scale and long-term soil moisture observations based on remote sensing are promising data sets to investigate and understand various processes of the climate system including the water and biochemical cycles. Currently, the ESA Climate Change Initiative for soil moisture develops and evaluates a consistent global long-term soil moisture data set, which is based on merging passive and active remotely sensed soil moisture. Within this project an inter-comparison of algorithms for <span class="hlt">AMSR-E</span> and ASCAT Level 2 products was conducted separately to assess the performance of different retrieval algorithms. Here we present the inter-comparison of <span class="hlt">AMSR-E</span> Level 2 soil moisture products. These include the public data sets from University of Montana (UMT), Japan Aerospace and Space Exploration Agency (JAXA), VU University of Amsterdam (VUA; two algorithms) and National Aeronautics and Space Administration (NASA). All participating algorithms are applied to the same <span class="hlt">AMSR-E</span> Level 1 data set. Ascending and descending paths of scaled surface soil moisture are considered and evaluated separately in daily and monthly resolution over the 2007-2011 time period. Absolute values of soil moisture as well as their long-term anomalies (i.e. removing the mean seasonal cycle) and short-term anomalies (i.e. removing a five weeks moving average) are evaluated. The evaluation is based on conventional measures like correlation and unbiased root-mean-square differences as well as on the application of the triple collocation method. As reference data set, surface soil moisture of 75 quality controlled soil moisture sites from the International Soil Moisture Network (ISMN) are used, which cover a wide range of vegetation density and climate conditions. For the application of the triple collocation method, surface soil moisture estimates from the Global Land Data Assimilation System are used as third independent data set. We find that the participating algorithms generally display a better</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C13C0849P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C13C0849P"><span>Comparing the Accuracy of AMSRE, AMSR2, SSMI and SSMIS Satellite Radiometer <span class="hlt">Ice</span> <span class="hlt">Concentration</span> Products with One-Meter Resolution Visible Imagery 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>Peterson, E. R.; Stanton, T. P.</p> <p>2016-12-01</p> <p>Determining <span class="hlt">ice</span> <span class="hlt">concentration</span> in the Arctic is necessary to track significant changes in sea <span class="hlt">ice</span> edge extent. Sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> are also needed to interpret data collected by in-situ instruments like buoys, as the amount of <span class="hlt">ice</span> versus water in a given area determines local solar heating. <span class="hlt">Ice</span> <span class="hlt">concentration</span> products are now routinely derived from satellite radiometers including the Advanced Microwave Scanning Radiometer for the Earth Observing System (<span class="hlt">AMSR-E</span>), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Special Sensor Microwave Imager (SSMI), and the Special Sensor Microwave Imager/Sounder (SSMIS). While these radiometers are viewed as reliable to monitor long-term changes in sea <span class="hlt">ice</span> extent, their accuracy should be analyzed, and compared to determine which radiometer performs best over smaller features such as melt ponds, and how seasonal conditions affect accuracy. Knowledge of the accuracy of radiometers at high resolution can help future researchers determine which radiometer to use, and be aware of radiometer shortcomings in different <span class="hlt">ice</span> conditions. This will be especially useful when interpreting data from in-situ instruments which deal with small scale measurements. In order to compare these passive microwave radiometers, selected high spatial resolution one-meter resolution Medea images, archived at the Unites States Geological Survey, are used for ground truth comparison. Sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> are derived from these images in an interactive process, although estimates are not perfect ground truth due to exposure of images, shadowing and cloud cover. 68 images are retrieved from the USGS website and compared with 9 useable, collocated SSMI, 33 SSMIS, 36 AMSRE, and 14 AMSR2 <span class="hlt">ice</span> <span class="hlt">concentrations</span> in the Arctic Ocean. We analyze and compare the accuracy of radiometer instrumentation in differing <span class="hlt">ice</span> conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040079837&hterms=Wegener+Alfred&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DWegener%252C%2BAlfred','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040079837&hterms=Wegener+Alfred&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DWegener%252C%2BAlfred"><span>Validation of EOS Aqua AMSR Sea <span class="hlt">Ice</span> Products for East Antarctica</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Massom, Rob; Lytle, Vicky; Allison, Ian; Worby, Tony; Markus, Thorsten; Scambos, Ted; Haran, Terry; Enomoto, Hiro; Tateyama, Kazu; Pfaffling, Andi</p> <p>2004-01-01</p> <p>This paper presents results from <span class="hlt">AMSR-E</span> validation activities during a collaborative international cruise onboard the RV Aurora Australis to the East Antarctic sea <span class="hlt">ice</span> zone (64-65 deg.S, 110-120 deg.E) in the early Austral spring of 2003. The validation strategy entailed an IS-day survey of the statistical characteristics of sea <span class="hlt">ice</span> and snowcover over a Lagrangian grid 100 x 50 km in size (demarcated by 9 drifting <span class="hlt">ice</span> beacons) i.e. at a scale representative of Ah4SR pixels. <span class="hlt">Ice</span> conditions ranged h m consolidated first-year <span class="hlt">ice</span> to a large polynya offshore from Casey Base. Data sets collected include: snow depth and snow-<span class="hlt">ice</span> interface temperatures on 24 (?) randomly-selected floes in grid cells within a 10 x 50 km area (using helicopters); detailed snow and <span class="hlt">ice</span> measurements at 13 dedicated <span class="hlt">ice</span> stations, one of which lasted for 4 days; time-series measurements of snow temperature and thickness at selected sites; 8 aerial photography and thermal-IR radiometer flights; other satellite products (SAR, AVHRR, MODIS, MISR, ASTER and Envisat MERIS); <span class="hlt">ice</span> drift data; and ancillary meteorological (ship-based, meteorological buoys, twice-daily radiosondes). These data are applied to a validation of standard <span class="hlt">AMSR-E</span> <span class="hlt">ice</span> <span class="hlt">concentration</span>, snowcover thickness and <span class="hlt">ice</span>-temperature products. In addition, a validation is carried out of <span class="hlt">ice</span>-surface skin temperature products h m the NOAA AVHRR and EOS MODIS datasets.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20100005033&hterms=impact+surroundings&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dimpact%2Bsurroundings','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20100005033&hterms=impact+surroundings&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dimpact%2Bsurroundings"><span>Evaluation of Enhanced High Resolution MODIS/<span class="hlt">AMSR-E</span> SSTs and the Impact on Regional Weather Forecast</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schiferl, Luke D.; Fuell, Kevin K.; Case, Jonathan L.; Jedlovec, Gary J.</p> <p>2010-01-01</p> <p>Over the last few years, the NASA Short-term Prediction Research and Transition (SPoRT) Center has been generating a 1-km sea surface temperature (SST) composite derived from retrievals of the Moderate Resolution Imaging Spectroradiometer (MODIS) for use in operational diagnostics and regional model initialization. With the assumption that the day-to-day variation in the SST is nominal, individual MODIS passes aboard the Earth Observing System (EOS) Aqua and Terra satellites are used to create and update four composite SST products each day at 0400, 0700, 1600, and 1900 UTC, valid over the western Atlantic and Caribbean waters. A six month study from February to August 2007 over the marine areas surrounding southern Florida was conducted to compare the use of the MODIS SST composite versus the Real-Time Global SST analysis to initialize the Weather Research and Forecasting (WRF) model. Substantial changes in the forecast heat fluxes were seen at times in the marine boundary layer, but relatively little overall improvement was measured in the sensible weather elements. The limited improvement in the WRF model forecasts could be attributed to the diurnal changes in SST seen in the MODIS SST composites but not accounted for by the model. Furthermore, cloud contamination caused extended periods when individual passes of MODIS were unable to update the SSTs, leading to substantial SST latency and a cool bias during the early summer months. In order to alleviate the latency problems, the SPoRT Center recently enhanced its MODIS SST composite by incorporating information from the Advanced Microwave Scanning Radiometer-EOS (<span class="hlt">AMSR-E</span>) instruments as well as the Operational Sea Surface Temperature and Sea <span class="hlt">Ice</span> Analysis. These enhancements substantially decreased the latency due to cloud cover and improved the bias and correlation of the composites at available marine point observations. While these enhancements improved upon the modeled cold bias using the original MODIS SSTs</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20100005033&hterms=evaluation+impact&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Devaluation%2Bimpact','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20100005033&hterms=evaluation+impact&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Devaluation%2Bimpact"><span>Evaluation of Enhanced High Resolution MODIS/<span class="hlt">AMSR-E</span> SSTs and the Impact on Regional Weather Forecast</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schiferl, Luke D.; Fuell, Kevin K.; Case, Jonathan L.; Jedlovec, Gary J.</p> <p>2010-01-01</p> <p>Over the last few years, the NASA Short-term Prediction Research and Transition (SPoRT) Center has been generating a 1-km sea surface temperature (SST) composite derived from retrievals of the Moderate Resolution Imaging Spectroradiometer (MODIS) for use in operational diagnostics and regional model initialization. With the assumption that the day-to-day variation in the SST is nominal, individual MODIS passes aboard the Earth Observing System (EOS) Aqua and Terra satellites are used to create and update four composite SST products each day at 0400, 0700, 1600, and 1900 UTC, valid over the western Atlantic and Caribbean waters. A six month study from February to August 2007 over the marine areas surrounding southern Florida was conducted to compare the use of the MODIS SST composite versus the Real-Time Global SST analysis to initialize the Weather Research and Forecasting (WRF) model. Substantial changes in the forecast heat fluxes were seen at times in the marine boundary layer, but relatively little overall improvement was measured in the sensible weather elements. The limited improvement in the WRF model forecasts could be attributed to the diurnal changes in SST seen in the MODIS SST composites but not accounted for by the model. Furthermore, cloud contamination caused extended periods when individual passes of MODIS were unable to update the SSTs, leading to substantial SST latency and a cool bias during the early summer months. In order to alleviate the latency problems, the SPoRT Center recently enhanced its MODIS SST composite by incorporating information from the Advanced Microwave Scanning Radiometer-EOS (<span class="hlt">AMSR-E</span>) instruments as well as the Operational Sea Surface Temperature and Sea <span class="hlt">Ice</span> Analysis. These enhancements substantially decreased the latency due to cloud cover and improved the bias and correlation of the composites at available marine point observations. While these enhancements improved upon the modeled cold bias using the original MODIS SSTs</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003AGUFM.H32B0568H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003AGUFM.H32B0568H"><span>An Assessment of the Use of <span class="hlt">AMSR</span> <span class="hlt">E</span> 10 GHz Data for Soil Moisture Estimation in SMEX02</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hsu, A. Y.; Jackson, T. J.; O'Neill, P. E.</p> <p>2003-12-01</p> <p>The launch of the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) on board the NASA EOS Aqua Satellite has drawn much interest from the scientific community that has been waiting for a low frequency spaceborne microwave radiometer (< 10 GHz) capable of measuring soil moisture. The <span class="hlt">AMSR-E</span> instrument was developed by the National Space Development Agency of Japan (NASDA) and makes dual-polarized microwave measurements at six frequencies: 6.9, 10.7, 18.7, 23.8, 36.5, and 89 GHz. Early examinations of <span class="hlt">AMSR-E</span> measurements have shown evidence of extensive Radio-Frequency Interference (RFI) in the 6.9 GHz channels, especially over the continental U.S. Due to the contamination of 6.9 GHz data by RFI, it may be necessary to use the next lowest frequency, 10.7 GHz, for soil moisture retrieval. This frequency has been available on the TRMM Microwave Imager for several years; however, the TRMM sensor only provides data between 38 N to 38 S in latitude whereas <span class="hlt">AMSR-E</span> provides global coverage. We examined the impact of alternative frequencies on soil moisture retrieval using data from the Soil Moisture Experiments in 2002 (SMEX02). SMEX02 took place in Walnut Creek Watershed and surrounding region of Iowa from June 24 to July 12. The experiment focused on microwave remote sensing of soil moisture in an agricultural setting. Land cover in the Walnut Creek Watershed consists of a patchwork of corn and soybean fields, with some isolated forested zones. This presents a challenge to soil moisture retrieval using <span class="hlt">AMSR-E</span> 10 GHz data. Extensive vegetation sampling was conducted during SMEX02 to provide information to estimate vegetation parameters required by retrieval algorithm. The maps of <span class="hlt">AMSR-E</span> 10 GHz data over the SMEX02 area from July 2 to 13 show the decrease of brightness temperature (TB) due to precipitation, although the range is not as profound as expected at L band. The Normalized Difference Polarization Index (NDPI), defined as (TBv-TBh)/(TBv+TBh), computed for various</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110007071','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110007071"><span>Potential for Monitoring Snow Cover in Boreal Forests by Combining MODIS Snow Cover and <span class="hlt">AMSR-E</span> SWE Maps</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Riggs, George A.; Hall, Dorothy K.; Foster, James L.</p> <p>2009-01-01</p> <p>Monitoring of snow cover extent and snow water equivalent (SWE) in boreal forests is important for determining the amount of potential runoff and beginning date of snowmelt. The great expanse of the boreal forest necessitates the use of satellite measurements to monitor snow cover. Snow cover in the boreal forest can be mapped with either the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Advanced Microwave Scanning Radiometer for EOS (<span class="hlt">AMSR-E</span>) microwave instrument. The extent of snow cover is estimated from the MODIS data and SWE is estimated from the <span class="hlt">AMSR-E</span>. Environmental limitations affect both sensors in different ways to limit their ability to detect snow in some situations. Forest density, snow wetness, and snow depth are factors that limit the effectiveness of both sensors for snow detection. Cloud cover is a significant hindrance to monitoring snow cover extent Using MODIS but is not a hindrance to the use of the <span class="hlt">AMSR-E</span>. These limitations could be mitigated by combining MODIS and <span class="hlt">AMSR-E</span> data to allow for improved interpretation of snow cover extent and SWE on a daily basis and provide temporal continuity of snow mapping across the boreal forest regions in Canada. The purpose of this study is to investigate if temporal monitoring of snow cover using a combination of MODIS and <span class="hlt">AMSR-E</span> data could yield a better interpretation of changing snow cover conditions. The MODIS snow mapping algorithm is based on snow detection using the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to enhance snow detection in dense vegetation. (Other spectral threshold tests are also used to map snow using MODIS.) Snow cover under a forest canopy may have an effect on the NDVI thus we use the NDVI in snow detection. A MODIS snow fraction product is also generated but not used in this study. In this study the NDSI and NDVI components of the snow mapping algorithm were calculated and analyzed to determine how they changed</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFM.H13J..04B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFM.H13J..04B"><span>Long-Term Evaluation of the <span class="hlt">AMSR-E</span> Soil Moisture Product Over the Walnut Gulch Watershed, AZ</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bolten, J. D.; Jackson, T. J.; Lakshmi, V.; Cosh, M. H.; Drusch, M.</p> <p>2005-12-01</p> <p>The Advanced Microwave Scanning Radiometer -Earth Observing System (<span class="hlt">AMSR-E</span>) was launched aboard NASA's Aqua satellite on May 4th, 2002. Quantitative estimates of soil moisture using the <span class="hlt">AMSR-E</span> provided data have required routine radiometric data calibration and validation using comparisons of satellite observations, extended targets and field campaigns. The currently applied NASA EOS Aqua ASMR-E soil moisture algorithm is based on a change detection approach using polarization ratios (PR) of the calibrated <span class="hlt">AMSR-E</span> channel brightness temperatures. To date, the accuracy of the soil moisture algorithm has been investigated on short time scales during field campaigns such as the Soil Moisture Experiments in 2004 (SMEX04). Results have indicated self-consistency and calibration stability of the observed brightness temperatures; however the performance of the moisture retrieval algorithm has been poor. The primary objective of this study is to evaluate the quality of the current version of the <span class="hlt">AMSR-E</span> soil moisture product for a three year period over the Walnut Gulch Experimental Watershed (150 km2) near Tombstone, AZ; the northern study area of SMEX04. This watershed is equipped with hourly and daily recording of precipitation, soil moisture and temperature via a network of raingages and a USDA-NRCS Soil Climate Analysis Network (SCAN) site. Surface wetting and drying are easily distinguished in this area due to the moderately-vegetated terrain and seasonally intense precipitation events. Validation of <span class="hlt">AMSR-E</span> derived soil moisture is performed from June 2002 to June 2005 using watershed averages of precipitation, and soil moisture and temperature data from the SCAN site supported by a surface soil moisture network. Long-term assessment of soil moisture algorithm performance is investigated by comparing temporal variations of moisture estimates with seasonal changes and precipitation events. Further comparisons are made with a standard soil dataset from the European</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.8603S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.8603S"><span>Estimation of mass changes caused by vegetation using <span class="hlt">AMSR-E</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schnitzer, S.; Abelen, S.; Menzel, A.; Seitz, F.</p> <p>2012-04-01</p> <p>Vegetation is one of the most important compartments in the global ecosystem, influencing soil, water balance, atmosphere, and the climate in general. Even though this is a known fact, large areas of rain forest are still destroyed for wood production or by food industries increasing their agricultural areas for soy production and stock farming. But also wild fires devastate large amounts to vegetation. Therefore, it is essential to monitor the changes of vegetation globally. In our study we address the question how big the mass changes in vegetation are. We observe the following sources of changes: 1) wild fires, 2) clear cut and 3) seasonal variations of vegetation. For the first two items we consider only forest areas where the biggest mass variations are taking place. The third point takes the entire range of vegetation classes into account. In order to observe vegetation globally we use remote sensing data from the sensor <span class="hlt">AMSR-E</span> (Advanced Microwave Scanning Radiometer - EOS) aboard of Nasa's Aqua satellite. This sensor provides data from 2002 until 2011. The data include information about the vegetation water content and are therefore ideal for our purpose. We validate our results with the help of additional databases listings, on e.g. large fire events, from literature as well as from in-situ data. The talk is concluded with a global map of hotspots of big vegetation mass changes and their triggers.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20150023295&hterms=concentration&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dconcentration','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20150023295&hterms=concentration&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dconcentration"><span>Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> and Extent</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Comiso, Josefino C.</p> <p>2014-01-01</p> <p>Among the most seasonal and most dynamic parameters on the surface of the Earth is sea <span class="hlt">ice</span> which at any one time covers about 3-6% of the planet. In the Northern Hemisphere, sea <span class="hlt">ice</span> grows in extent from about 6 x 10(exp 6) sq km to 16 x 10(exp 6) sq km, while in the Southern Hemisphere, it grows from about 3 x 10(exp 6) sq km to about 19 x 10(exp 6) sq km (Comiso, 2010; Gloersen et al., 1992). Sea <span class="hlt">ice</span> is up to about 2-3 m thick in the Northern Hemisphere and about 1 m thick in the Southern Hemisphere (Wadhams, 2002), and compared to the average ocean depth of about 3 km, it is a relatively thin, fragile sheet that can break due to waves and winds or melt due to upwelling of warm water. Being constantly advected by winds, waves, and currents, sea <span class="hlt">ice</span> is very dynamic and usually follows the directions of the many gyres in the polar regions. Despite its vast expanse, the sea <span class="hlt">ice</span> cover was previously left largely unstudied and it was only in recent years that we have understood its true impact and significance as related to the Earths climate, the oceans, and marine life.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20150023295&hterms=sea+ice&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dsea%2Bice','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20150023295&hterms=sea+ice&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dsea%2Bice"><span>Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> and Extent</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Comiso, Josefino C.</p> <p>2014-01-01</p> <p>Among the most seasonal and most dynamic parameters on the surface of the Earth is sea <span class="hlt">ice</span> which at any one time covers about 3-6% of the planet. In the Northern Hemisphere, sea <span class="hlt">ice</span> grows in extent from about 6 x 10(exp 6) sq km to 16 x 10(exp 6) sq km, while in the Southern Hemisphere, it grows from about 3 x 10(exp 6) sq km to about 19 x 10(exp 6) sq km (Comiso, 2010; Gloersen et al., 1992). Sea <span class="hlt">ice</span> is up to about 2-3 m thick in the Northern Hemisphere and about 1 m thick in the Southern Hemisphere (Wadhams, 2002), and compared to the average ocean depth of about 3 km, it is a relatively thin, fragile sheet that can break due to waves and winds or melt due to upwelling of warm water. Being constantly advected by winds, waves, and currents, sea <span class="hlt">ice</span> is very dynamic and usually follows the directions of the many gyres in the polar regions. Despite its vast expanse, the sea <span class="hlt">ice</span> cover was previously left largely unstudied and it was only in recent years that we have understood its true impact and significance as related to the Earths climate, the oceans, and marine life.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1818103Q','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1818103Q"><span>Statistical Analysis of the Correlation between Microwave Emission Anomalies and Seismic Activity Based on <span class="hlt">AMSR-E</span> Satellite Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>qin, kai; Wu, Lixin; De Santis, Angelo; Zhang, Bin</p> <p>2016-04-01</p> <p>Pre-seismic thermal IR anomalies and ionosphere disturbances have been widely reported by using the Earth observation system (EOS). To investigate the possible physical mechanisms, a series of detecting experiments on rock loaded to fracturing were conducted. Some experiments studies have demonstrated that microwave radiation energy will increase under the loaded rock in specific frequency and the feature of radiation property can reflect the deformation process of rock fracture. This experimental result indicates the possibility that microwaves are emitted before earthquakes. Such microwaves signals are recently found to be detectable before some earthquake cases from the brightness temperature data obtained by the microwave-radiometer Advanced Microwave-Scanning Radiometer for the EOS (<span class="hlt">AMSR-E</span>) aboard the satellite Aqua. This suggested that <span class="hlt">AMSR-E</span> with vertical- and horizontal-polarization capability for six frequency bands (6.925, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz) would be feasible to detect an earthquake which is associated with rock crash or plate slip. However, the statistical analysis of the correlation between satellite-observed microwave emission anomalies and seismic activity are firstly required. Here, we focus on the Kamchatka peninsula to carry out a statistical study, considering its high seismicity activity and the dense orbits covering of <span class="hlt">AMSR-E</span> in high latitudes. 8-years (2003-2010) <span class="hlt">AMSR-E</span> microwave brightness temperature data were used to reveal the spatio-temporal association between microwave emission anomalies and 17 earthquake events (M>5). Firstly, obvious spatial difference of microwave brightness temperatures between the seismic zone at the eastern side and the non-seismic zone the western side within the Kamchatka peninsula are found. Secondly, using both vertical- and horizontal-polarization to extract the temporal association, it is found that abnormal changes of microwave brightness temperatures appear generally 2 months before the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUSM.H51C..02G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUSM.H51C..02G"><span>Large Scale Evaluation of <span class="hlt">AMSR-E</span> Soil Moisture Products Based on Ground Soil Moisture Network Measurements</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gruhier, C.; de Rosnay, P.; Richaume, P.; Kerr, Y.; Rudiger, C.; Boulet, G.; Walker, J. P.; Mougin, E.; Ceschia, E.; Calvet, J.</p> <p>2007-05-01</p> <p>This paper presents an evaluation of <span class="hlt">AMSR-E</span> (Advanced Microwave Scanning Radiometer for EOS) soil moisture products, based on a comparison with three ground soil moisture networks. The selected ground sites are representative of various climatic, hydrologic and environmental conditions in temperate and semi-arid areas. They are located in the south-west of France, south-east of Australia and the Gourma region of the Sahel. These sites were respectively implemented in the framework of the projects SMOSREX (Surface Monitoring Of Soil Reservoir Experiment), SASMAS/GoREx (Scaling and Assimilation of Soil Moisture and Streamflow in the Goulburn River Experimental catchment) and AMMA (African Monsoon Multidisciplinary Analysis). In all cases, the arrangement of the soil moisture measuring sites was specifically designed to address the validation of remotely sensed soil moisture in the context of the preparation of the SMOS (Soil Moisture and Ocean Salinity) project. For the purpose of this study, 25km <span class="hlt">AMSR-E</span> products were used, including brightness temperatures at 6.9 and 10.7 GHz, and derived soil moisture. The study is focused on the year 2005. It is based on ground soil moisture network measurements from 4 stations for SMOSREX extended to the SUDOUEST project of CESBIO, 12 stations for GoRex, and 4 stations for AMMA. Temporal and spatial features of soil moisture variability and stability is a critical issue to be addressed for remotely sensed soil moisture validation. While ground measurements provide information on soil moisture dynamics at local scale and high temporal resolution (hourly), satellite measurements are sparser in time (up to several days), but cover a larger region (25km x 25km for <span class="hlt">AMSR-E</span>). First, a statistical analysis, including mean relative difference and Spearman rank, is conducted for the three soil moisture networks. This method is mainly based on the approach proposed by Cosh et al. (2004) for the purpose of the use of ground networks for</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009JGRD..11420104D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009JGRD..11420104D"><span>An EKF assimilation of <span class="hlt">AMSR-E</span> soil moisture into the ISBA land surface scheme</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Draper, C. S.; Mahfouf, J.-F.; Walker, J. P.</p> <p>2009-10-01</p> <p>An Extended Kalman Filter (EKF) for the assimilation of remotely sensed near-surface soil moisture into the Interactions between Surface, Biosphere, and Atmosphere (ISBA) model is described. ISBA is the land surface scheme in Météo-France's Aire Limitée Adaptation Dynamique développement InterNational (ALADIN) Numerical Weather Prediction (NWP) model, and this work is directed toward providing initial conditions for NWP. The EKF is used to assimilate near-surface soil moisture observations retrieved from C-band Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) brightness temperatures into ISBA. The EKF can translate near-surface soil moisture observations into useful increments to the root-zone soil moisture. If the observation and model soil moisture errors are equal, the Kalman gain for the root-zone soil moisture is typically 20-30%, resulting in a mean net monthly increment for July 2006 of 0.025 m3 m-3 over ALADIN's European domain. To test the benefit of evolving the background error, the EKF is compared to a Simplified EKF (SEKF), in which the background errors at the time of the analysis are constant. While the Kalman gains for the EKF and SEKF are derived from different model processes, they produce similar soil moisture analyses. Despite this similarity, the EKF is recommended for future work where the extra computational expense can be afforded. The method used to rescale the near-surface soil moisture data to the model climatology has a greater influence on the analysis than the error covariance evolution approach, highlighting the importance of developing appropriate methods for rescaling remotely sensed near-surface soil moisture data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H31D1141L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H31D1141L"><span><span class="hlt">AMSR-E</span> satellite observed rainforest responses during the 2005 and 2010 Amazon droughts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, Y.; McCabe, M. F.; Evans, J. P.; de Jeu, R.</p> <p>2012-12-01</p> <p>The Amazon rainforest plays an important role in the global hydrological cycle and effects the climate at both regional and global scales. Continuous large-scale deforestation and associated fire activities have been an issue of international significance over the last two decades, while in more recent times two historic droughts impacted vast areas of the Amazon in 2005 and 2010. These interrelated issues have considerable ecological response, and underline the need for a consistent observation system to comprehensively characterize the rainforest dynamics. Satellite-based remote sensing offer the only viable approach at monitoring the vegetation state at large space and time scales. Most studies examining these issues have used the Enhanced Vegetation Index (EVI), a vegetation greenness index with characteristics similar to NDVI derived from remote optical sensors. Like any optical observation, EVI is affected by water vapor, clouds and aerosols in the atmosphere, and there has been some debate about their influence on inferred vegetation changes during the recent droughts over the Amazon. Unlike optical remote sensing, passive microwave observations are not affected by such atmospheric phenomena and can measure both leaf and woody components of aboveground living biomass through a retrieved property termed the vegetation optical depth (VOD), by virtue of the sensitivity of passive microwave emissions to water in the environment. The microwave-based vegetation record therefore provides a unique opportunity to examine forest response during the 2005 and 2010 drought that is complementary to the vegetation index analysis. In this study, we apply the VOD data retrieved from the Advanced Microwave Scanning Radiometer for EOS (<span class="hlt">AMSR-E</span>), rainfall data from Tropical Rainfall Measuring Mission (TRMM), water level data obtained from in situ measurements and fire observations based on the MODIS instrument to investigate large scale forest dynamics. Results indicate that the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/48381','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/48381"><span>NO adsorption on <span class="hlt">ice</span> at low <span class="hlt">concentrations</span></span></a></p> <p><a target="_blank" href="http://www.fs.usda.gov/treesearch/">Treesearch</a></p> <p>Richard A. Sommerfeld; Martha H. Conklin; S. Kay Laird</p> <p>1992-01-01</p> <p>To better understand the properties of <span class="hlt">ice</span> surfaces at different temperatures, the adsorption of a relatively insoluble gas, NO, was studied using a continuous-flow column experiment. Adsorption isotherms for NO on the surface of <span class="hlt">ice</span> were measured for a temperature range of-1 to -70°C and a <span class="hlt">concentration</span> range of 10 to 250 ppbv. Very little adsorption was measured;...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20110023836&hterms=land+temperature+water&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dland%2Btemperature%2Bnear%2Bwater','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20110023836&hterms=land+temperature+water&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dland%2Btemperature%2Bnear%2Bwater"><span>Land Surface Microwave Emissivities Derived from <span class="hlt">AMSR-E</span> and MODIS Measurements with Advanced Quality Control</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Moncet, Jean-Luc; Liang, Pan; Galantowicz, John F.; Lipton, Alan E.; Uymin, Gennady; Prigent, Catherine; Grassotti, Christopher</p> <p>2011-01-01</p> <p>A microwave emissivity database has been developed with data from the Advanced Microwave Scanning Radiometer-EOS (<span class="hlt">AMSR-E</span>) and with ancillary land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same Aqua spacecraft. The primary intended application of the database is to provide surface emissivity constraints in atmospheric and surface property retrieval or assimilation. An additional application is to serve as a dynamic indicator of land surface properties relevant to climate change monitoring. The precision of the emissivity data is estimated to be significantly better than in prior databases from other sensors due to the precise collocation with high-quality MODIS LST data and due to the quality control features of our data analysis system. The accuracy of the emissivities in deserts and semi-arid regions is enhanced by applying, in those regions, a version of the emissivity retrieval algorithm that accounts for the penetration of microwave radiation through dry soil with diurnally varying vertical temperature gradients. These results suggest that this penetration effect is more widespread and more significant to interpretation of passive microwave measurements than had been previously established. Emissivity coverage in areas where persistent cloudiness interferes with the availability of MODIS LST data is achieved using a classification-based method to spread emissivity data from less-cloudy areas that have similar microwave surface properties. Evaluations and analyses of the emissivity products over homogeneous snow-free areas are presented, including application to retrieval of soil temperature profiles. Spatial inhomogeneities are the largest in the vicinity of large water bodies due to the large water/land emissivity contrast and give rise to large apparent temporal variability in the retrieved emissivities when satellite footprint locations vary over time. This issue will be dealt with in the future by</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_5 --> <div id="page_6" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="101"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20110023836&hterms=Quality+Control&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DQuality%2BControl','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20110023836&hterms=Quality+Control&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DQuality%2BControl"><span>Land Surface Microwave Emissivities Derived from <span class="hlt">AMSR-E</span> and MODIS Measurements with Advanced Quality Control</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Moncet, Jean-Luc; Liang, Pan; Galantowicz, John F.; Lipton, Alan E.; Uymin, Gennady; Prigent, Catherine; Grassotti, Christopher</p> <p>2011-01-01</p> <p>A microwave emissivity database has been developed with data from the Advanced Microwave Scanning Radiometer-EOS (<span class="hlt">AMSR-E</span>) and with ancillary land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same Aqua spacecraft. The primary intended application of the database is to provide surface emissivity constraints in atmospheric and surface property retrieval or assimilation. An additional application is to serve as a dynamic indicator of land surface properties relevant to climate change monitoring. The precision of the emissivity data is estimated to be significantly better than in prior databases from other sensors due to the precise collocation with high-quality MODIS LST data and due to the quality control features of our data analysis system. The accuracy of the emissivities in deserts and semi-arid regions is enhanced by applying, in those regions, a version of the emissivity retrieval algorithm that accounts for the penetration of microwave radiation through dry soil with diurnally varying vertical temperature gradients. These results suggest that this penetration effect is more widespread and more significant to interpretation of passive microwave measurements than had been previously established. Emissivity coverage in areas where persistent cloudiness interferes with the availability of MODIS LST data is achieved using a classification-based method to spread emissivity data from less-cloudy areas that have similar microwave surface properties. Evaluations and analyses of the emissivity products over homogeneous snow-free areas are presented, including application to retrieval of soil temperature profiles. Spatial inhomogeneities are the largest in the vicinity of large water bodies due to the large water/land emissivity contrast and give rise to large apparent temporal variability in the retrieved emissivities when satellite footprint locations vary over time. This issue will be dealt with in the future by</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.C31A0601D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.C31A0601D"><span>Snow Water Equivalent estimation from <span class="hlt">AMSR-E</span> data based on priori snow properties in Xinjiang province of China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dai, L.; Che, T.</p> <p>2011-12-01</p> <p>A novel snow water equivalent (SWE) retrieval algorithm from <span class="hlt">AMSR-E</span> data is established based on priori snow conditions (such as snow grain size and density, as well as the stratigraphy of snow) in Xinjiang Province of China. Within the retrieval algorithm, a microwave radiative transfer model (i.e. MEMLS) is used to simulate the brightness temperature (TB) datasets at 18 and 36 GHz under all kinds of snow conditions including snow grain size, density, depth, and stratigraphy (namely, the snow layering). Therefore, a series of relationships between snow depth and the difference of TB at these two frequencies can be obtained based on different snow grain size, density and stratigraphic conditions. These snow conditions were measured along a fixed route of this study area for a complete snow season. Furthermore, a layering scheme was established based on the snow depth (estimated by existing SD algorithm in priori), while the depth of each layer and its grain size and density were parameterized according to the age of snow cover. Finally, the SWE can be calculated by snow depth and its density. SWE retrieved by this new algorithm at seven meteorological stations in Xinjiang Province from 2003 to 2008 are compared to two existing SWE products from NSIDC (National snow and <span class="hlt">ice</span> data center) and WESTDC (Environmental and Ecological Science Data Center for West China). Three groups of root-mean-squared error (RMSE) and mean error (ME) are calculated between observed SWE and the three estimated SWE(s), respectively (Table 1). The three groups of RMSE have the same tendency of increasing with the increase of mean SWE, while the RMSE(s) from the new algorithm and from the WESTDC are much less and ME(s) are much closer to zero than from the NSIDC at all seven stations (see Table 1). The RMSE(s) from the new algorithm are less than from the WESTDC at seven stations, and ME(s) are closer to zero at five stations. At the other two stations, the ME(s) are -2.02mm and -3.05mm</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JHyd..547...67L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JHyd..547...67L"><span>Performance of <span class="hlt">AMSR_E</span> soil moisture data assimilation in CLM4.5 model for monitoring hydrologic fluxes at global scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, Di; Mishra, Ashok K.</p> <p>2017-04-01</p> <p>In this study, we evaluated the performance of community land surface model (CLM4.5) to simulate the hydrologic fluxes, such as, soil moisture (SM), evapotranspiration (ET) and runoff with (without) remote sensing data assimilation. The Advanced Microwave Scanning Radiometer-Earth Observing System (<span class="hlt">AMSR_E</span>) daily SM (both ascending and descending) are incorporated into the CLM4.5 model using data assimilation (DA) technique. The GLDAS data is used to validate the <span class="hlt">AMSR_E</span> SM data and evaluate the performance of CLM4.5 simulations. The <span class="hlt">AMSR_E</span> SM data are rescaled to meet the resolution of CLM4.5 model. By assimilating the <span class="hlt">AMSR_E</span> SM data into the CLM4.5 model can improve the SM simulations, especially over the climate transition zones in Africa, East Australia, South South America, Southeast Asia, and East North America in summer season. The Local Ensemble Kalman Filter (LEnKF) technique improves the performance of CLM4.5 model compared to the directly substituted method. The improvement in ET and surface runoff simulations from CLM4.5 model assimilated with <span class="hlt">AMSR_E</span> SM data shares similar spatial patterns with SM.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A33F0222H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A33F0222H"><span>Evaluation of cloud microphysics by the NICAM with CloudSat, CALIPSO, and <span class="hlt">AMSR-E</span> data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hashino, T.; Satoh, M.; Hagihara, Y.; Kubota, T.; Matsui, T.; Nasuno, T.; Okamoto, H.</p> <p>2012-12-01</p> <p>Global satellite observation provides valuable information not only to the retrievals of physical quantities relevant to aerosol and clouds but also to the evaluation of these simulated by GCMs and cloud resolving models. We discuss effective ways to use passive microwave measurements in the evaluation of cloud and precipitation fields simulated by a global cloud-resolving model. The passive microwave observations are made with more satellites than the active microwave counterparts and over longer time period. Since the microwave brightness temperature is sensitive to total amount of water vapor and hydrometeors in a column, they complement the active measurements. Also, it is important to understand the relation between the two measurements for cases when only passive microwave observation is available. The outputs from the Nonhydrostatic Icosahedral Atmospheric Model (NICAM, [2]) are run through a satellite signal simulator (Joint Simulator for Satellite Sensors) to simulate CloudSAT/CALIPSO/<span class="hlt">AMSR-E</span> data. This study uses a merged dataset for CloudSAT and CALIPSO ([1]). In the presentation, conditioning the radar signals on the brightness temperature of <span class="hlt">AMSR-E</span> and relation between vertical profiles of the radar and the brightness temperature will be discussed. References [1] Y. Hagihara, H. Okamoto, and R. Yoshida, J. Geophys. Res. 115, D00H33 (2010). [2] M. Satoh, T. Matsuno, H. Tomita, H. Miura, T. Nasuno, and S. Iga, J. Comput. Phys., 227, 3486 (2008).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70026403','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70026403"><span>Comparison of <span class="hlt">AMSR-E</span> and SSM/I snow parameter retrievals over the Ob river 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>Mognard, N.M.; Grippa, M.; LeToan, T.; Kelly, R.E.J.; Chang, A.T.C.; Josberger, E.G.</p> <p>2004-01-01</p> <p>Passive microwave observations from the Advanced Microwave Scanning Radiometer - EOS (<span class="hlt">AMSR-E</span>) and from the Special Sensor Microwave Imager (SSM/I) are used to analyse the evolution of the snow pack in the Ob river basin during the snow season of 2002-03. The Ob river is the biggest Russian river with respect to its watershed area (2 975 000 km2). The Ob originates in the Altai mountains and flows northward across the vast West Siberian lowland towards the Arctic Ocean. The majority of snow cover is contained in the lowlands rather than in mountainous regions and persists for six months or more. During the snow season, surface air temperatures are very cold. Therefore, the combination of cold dry snow and large areas of uniform topography is ideal for snowpack extent and water equivalent retrievals from passive microwave observations. The thermal gradient through the snow pack is estimated and used to model the growth of the snow grain size and to compute the evolution of the passive microwave derived snow depth over the region. A comparison between the <span class="hlt">AMSR-E</span> and SSM/I estimates is performed and the differences between the snow parameters from the two satellite instruments are analysed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.4861L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.4861L"><span>Estimating mountainous snow water equivalent via ensemble Kalman filtering with improved <span class="hlt">AMSR-E</span> observation and model representation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Dongyue; Durand, Michael; Margulis, Steven</p> <p>2013-04-01</p> <p>Snowpack in the Sierra Nevada mountain ranges serves as a critical water resource and an important climate indicator. Accurately estimating snow water equivalent (SWE) and melt timing in the Sierra has both civil and scientific merits. Passive microwave remote sensing (PM) contains spatial-continuous SWE information, however its coarse resolution makes it difficult or impossible for direct SWE retrieval in mountainous regions. Physical process based land surface model (LSM) could be set up at high spatial resolution to simulate SWE, nevertheless biased meteorological forcing usually lead to significant biased and uncertain modeling results. Assimilating PM into LSM could combine the advantages of both sides, offering a path to a better SWE characterization in mountainous area. In this study, downscaled PM measurements are assimilated into a high-resolution model through Ensemble Kalman Filter (EnKF) to estimate SWE in the Sierra. The study is carried out at Kern Basin where SWE are highly variable as a result of complex terrain. SSiB3 and MEMLS are coupled to provide priori prediction. NLDAS2 data are used to force the coupled model, bias in NLDAS2 has been pre-removed via Bayesian reconstruction. <span class="hlt">AMSR-E</span> brightness temperature (Tb) is assimilated into the model to reduce the model error and uncertainty. The novelty of this study is that both modeling and PM measurements, which are two SWE information resources in EnKF, have been enhanced to contribute more signals. As there is more information in both model prediction and measurement, it is reasonable to expect an even better posterior SWE than previous EnKF PM assimilations. In this study, rather than using EASE-Grid Tb, the Tb data is obtained by processing raw <span class="hlt">AMSR-E</span> 37GHz V-pol observed Tb at its native footprint resolution (L2A) of 14 km x 8 km, which is 1/6 of the size of an EASE-Grid cell. Preliminary results show this effective rise in data resolution makes L2A Tb contains three times more information about</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27879839','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27879839"><span>Temporal Variability Corrections for Advanced Microwave Scanning Radiometer E (<span class="hlt">AMSR-E</span>) Surface Soil Moisture: Case Study in Little River Region, Georgia, U.S.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Choi, Minha; Jacobs, Jennifer M</p> <p>2008-04-14</p> <p>Statistical correction methods, the Cumulative Distribution Function (CDF) matching technique and Regional Statistics Method (RSM) are applied to adjust the limited temporal variability of Advanced Microwave Scanning Radiometer E (<span class="hlt">AMSR-E</span>) data using the Common Land Model (CLM). The temporal variability adjustment between CLM and <span class="hlt">AMSR-E</span> data was conducted for annual and seasonal periods for 2003 in the Little River region, GA. The results showed that the statistical correction techniques improved <span class="hlt">AMSR-E</span>'s limited temporal variability as compared to ground-based measurements. The regression slope and intercept improved from 0.210 and 0.112 up to 0.971 and -0.005 for the non-growing season. The R² values also modestly improved. The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) products were able to identify periods having an attenuated microwave brightness signal that are not likely to benefit from these statistical correction techniques.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H21I0836F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H21I0836F"><span>Transferability and prediction of AMSR2 brightness temperatures over snow-covered land based on <span class="hlt">AMSR-E</span> brightness temperatures and machine learning algorithms</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Forman, B. A.</p> <p>2014-12-01</p> <p>Recent studies [Forman et al., 2013, IEEE; Forman and Reichle, 2014, IEEE] demonstrated the capability of machine learning (ML) algorithms to predict passive microwave (PMW) brightness temperatures (Tb) over snow-covered land as measured by the Advanced Microwave Sounding Radiometer (<span class="hlt">AMSR-E</span>). The results presented here investigate the transferability of these techniques using <span class="hlt">AMSR-E</span> to predict Tb observations as measured by AMSR2. In other words, can historical <span class="hlt">AMSR-E</span> Tb observations be used to train a ML algorithm in order to predict Tb observations collected by AMSR2 at some point in the future? The NASA Catchment Land Surface Model is first used to characterize snowpack conditions. Next, the ML algorithm is trained on the 9-year record of PMW Tb observations collected by <span class="hlt">AMSR-E</span>. An additional experiment where the ML algorithm was trained on a split-sample of the 2-year record of AMSR2 PMW Tb observations was conducted for comparison. Results suggest one ML technique - the support vector machine - when trained on <span class="hlt">AMSR-E</span> observations can sufficiently reproduce AMSR2 Tb in forested and non-forested regions during both the snow accumulation and snow ablation phases of the snow season. These results suggest transferability of machine learning from the <span class="hlt">AMSR-E</span> sensor to other data records with comparable frequency and polarization characteristics. The eventual goal is to use a ML algorithm as an observation operator within an ensemble-based data assimilation framework where model estimates will be merged with PMW Tb observations in order to improve snow water equivalent (SWE) estimates across regional and continental scales.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.usgs.gov/dds/dds27/','USGSPUBS'); return false;" href="https://pubs.usgs.gov/dds/dds27/"><span>Monthly average polar sea-<span class="hlt">ice</span> <span class="hlt">concentration</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>Schweitzer, Peter N.</p> <p>1995-01-01</p> <p>The data contained in this CD-ROM depict monthly averages of sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> in the modern polar oceans. These averages were derived from the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I) instruments aboard satellites of the U.S. Air Force Defense Meteorological Satellite Program from 1978 through 1992. The data are provided as 8-bit images using the Hierarchical Data Format (HDF) developed by the National Center for Supercomputing Applications.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016FrES...10..195F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016FrES...10..195F"><span>Detection of radio-frequency interference signals from <span class="hlt">AMSR-E</span> data over the United States with snow cover</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Feng, Chengcheng; Zou, Xiaolei; Zhao, Juan</p> <p>2016-06-01</p> <p>Radio Frequency Interference (RFI) causes severe contamination to passive and active microwave sensing observations and corresponding retrieval products. RFI signals should be detected and filtered before applying the microwave data to retrieval and data assimilation. It is difficult to detect RFI over land surfaces covered by snow because of the scattering effect of snow surface. The double principal component analysis (DPCA) method is adopted in this study, and its ability in identifying RFI signals in <span class="hlt">AMSR-E</span> data over snow covered regions is investigated. Results show that the DPCA method can detect RFI signals effectively in spite of the impact of snow scattering, and the detected RFI signals persistent over time. Compared to other methods, such as PCA and normalized PCA, DPCA is more robust and suitable for operational application.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.6082W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.6082W"><span>Improving Global Soil Moisture Retrievals from <span class="hlt">AMSR-E</span> through Enhanced Radiative Transfer Modeling and Parameter Calibration</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, Eric; Pan, Ming</p> <p>2013-04-01</p> <p>Accurate retrieval of soil moisture from satellites is always a challenge. A soil moisture retrieval product has been produced at Princeton University for last a few years from various passive microwave sensors (e. g. Advanced Microwave Scanning Radiometer - Earth Observing System: <span class="hlt">AMSR-E</span>; TRMM Microwave Imager: TMI) by inverting a single-channel single-polarization (10 GHz Horizontal polarization) forward model (Land Surface Microwave Emission Model: LSMEM). Various characteristics are noticed in this product, such as regions of zero or saturation soil moisture retrievals, indicating an under-performing forward model. Additionally, an analysis of the data set reveals a number of problems related to the vegetation optical depth (VOD) parameter and some soil parameters. To improve the VOD estimation, the previous monthly vegetation parameter (previously static from year to year) is replaced with a dynamic VOD estimation module developed at University of Montana, which derives VOD from multiple microwave channels available on <span class="hlt">AMSR-E</span> or TRMM. To improve the soil parameters, the surface roughness and soil texture parameters are calibrated to match the forward model predicted brightness temperature against the satellite observations, using one year of surface soil moisture from the VIC LSM. The new improved retrieval system that now utilizes multiple microwave channels significantly reduces the forward model bias and produces much more reasonable soil moisture estimates. Areas of active rainfall, snow cover, thick vegetation, and RFI are screened out using the microwave observations from the same platform. The new retrievals are compared to the uncalibrated LSMEM retrievals and are also assessed using soil moisture data from the NRCS SCAN and NCDC soil moisture networks.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100010244','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100010244"><span>Results from Assimilating <span class="hlt">AMSR-E</span> Soil Moisture Estimates into a Land Surface Model Using an Ensemble Kalman Filter in the Land Information System</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Blankenship, Clay B.; Crosson, William L.; Case, Jonathan L.; Hale, Robert</p> <p>2010-01-01</p> <p>Improve simulations of soil moisture/temperature, and consequently boundary layer states and processes, by assimilating <span class="hlt">AMSR-E</span> soil moisture estimates into a coupled land surface-mesoscale model Provide a new land surface model as an option in the Land Information System (LIS)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=235394','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=235394"><span>The Impact of Standing Water and Irrigation on <span class="hlt">AMSR-E</span> Sensitivity to Soil Moisture over the NAFE'06 Experiment Area</span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p><span class="hlt">AMSR-E</span> sensitivity to soil moisture and its accuracy have been studied over a wide variety of surface conditions and weather regimes using both in situ measured data and aircraft derived soil moisture estimates. Several extensive soil moisture field campaigns involving ground and air-borne component...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=306433','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=306433"><span>Remote monitoring of soil moisture using passive microwave-based technologies – theoretical basic and overview of selected algorithms for <span class="hlt">AMSR-E</span></span></a></p> <p><a target="_blank" href="https://www.ars.usda.gov/research/publications/find-a-publication/">USDA-ARS?s Scientific Manuscript database</a></p> <p></p> <p></p> <p>Satellite-based passive microwave remote sensing has been shown to be a valuable tool in mapping and monitoring global soil moisture. The Advanced Microwave Scanning Radiometer on the Aqua platform (<span class="hlt">AMSR-E</span>) has made significant contributions to this application. As the result of agency and individua...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1913097K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1913097K"><span>Improved method for sea <span class="hlt">ice</span> age computation based on combination of sea <span class="hlt">ice</span> drift and <span class="hlt">concentration</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Korosov, Anton; Rampal, Pierre; Lavergne, Thomas; Aaboe, Signe</p> <p>2017-04-01</p> <p>Sea <span class="hlt">Ice</span> Age is one of the components of the Sea <span class="hlt">Ice</span> ECV as defined by the Global Climate Observing System (GCOS) [WMO, 2015]. It is an important climate indicator describing the sea <span class="hlt">ice</span> state in addition to sea <span class="hlt">ice</span> <span class="hlt">concentration</span> (SIC) and thickness (SIT). The amount of old/thick <span class="hlt">ice</span> 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 <span class="hlt">ice</span> age climate data record [Tschudi, et al. 2015], based on Maslanik et al. [2011] provided by National Snow and <span class="hlt">Ice</span> Data Center (NSIDC) [http://nsidc.org/data/docs/daac/nsidc0611-sea-<span class="hlt">ice</span>-age/]. The sea <span class="hlt">ice</span> age algorithm [Fowler et al., 2004] is using satellite-derived <span class="hlt">ice</span> drift for Lagrangian tracking of individual <span class="hlt">ice</span> parcels (12-km grid cells) defined by areas of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> > 15% [Maslanik et al., 2011], i.e. sea <span class="hlt">ice</span> extent, according to the NASA Team algorithm [Cavalieri et al., 1984]. This approach has several drawbacks. (1) Using sea <span class="hlt">ice</span> extent instead of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> leads to overestimation of the amount of older <span class="hlt">ice</span>. (2) The individual <span class="hlt">ice</span> parcels are not advected uniformly over (long) time. This leads to undersampling in areas of consistent <span class="hlt">ice</span> divergence. (3) The end product grid cells are assigned the age of the oldest <span class="hlt">ice</span> parcel within that cell, and the frequency distribution of the <span class="hlt">ice</span> age is not taken into account. In addition, the base sea <span class="hlt">ice</span> 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 <span class="hlt">ice</span> drifter trajectories and wind-driven "free-drift" motion during summer. This results in a significant overestimate of old-<span class="hlt">ice</span> content, incorrect shape of the old-<span class="hlt">ice</span> pack, and lack of information about the <span class="hlt">ice</span> age distribution within the grid cells. We</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010PhDT........23O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010PhDT........23O"><span>Estimation of Antarctic sea <span class="hlt">ice</span> properties using surface and space borne data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ozsoy Cicek, Burcu</p> <p></p> <p>) 2007 Antarctic cruise) with coincident satellite active and passive microwave data. We combined visual ship-based observations of sea-<span class="hlt">ice</span> and snow properties during SIMBA with coincident active and passive microwave satellite data with the aims to (a) derive typical radar backscatter ranges for observed sea-<span class="hlt">ice</span> types and <span class="hlt">ice</span> type mixtures, (b) improve our knowledge about the radar backscatter of different <span class="hlt">ice</span> types in the Bellingshausen Sea at early-middle spring, (c) interpret <span class="hlt">AMSR-E</span> snow depth over these <span class="hlt">ice</span> types, and (d) identify the potential of the investigated active microwave signatures for a synergy with <span class="hlt">AMSR-E</span> data to eventually improve the snow depth retrieval. Chapter 4 presents the validation of remote sensing measurements of <span class="hlt">ice</span> extent and <span class="hlt">concentration</span> with ASPeCt ship-based <span class="hlt">ice</span> observations, conducted during the SIMBA and the Sea <span class="hlt">Ice</span> Physics and Ecosystem eXperiment (SIPEX) International Polar Year (IPY) cruises (Sept--Oct 2007). First, the total sea <span class="hlt">ice</span> cover around the entire continent was determined for 2007--2008 from Advanced Microwave Scanning Radiometer-Earth Observing System (<span class="hlt">AMSR-E</span>) passive microwave and National <span class="hlt">Ice</span> Center (NIC) charts. Second, Antarctic Sea <span class="hlt">Ice</span> Processes and Climate (ASPeCt) ship observations from the SIMBA and SIPEX expeditions in the austral end of winter--beginning of spring 2007 are used as ground truth to verify the <span class="hlt">AMSR-E</span> sea <span class="hlt">ice</span> <span class="hlt">concentration</span> product provided by both the Enhanced NASA Team Algorithm (NT2) and Bootstrap Basic Algorithm (BBA). Chapter 5 presents supplemental analysis related to the baseline thickness of Antarctic sea <span class="hlt">ice</span> on a circumpolar basis from field measurements. In this part, our objectives were (1) Develop statistical relationships between surface elevation (snow freeboard), <span class="hlt">ice</span> elevation (<span class="hlt">ice</span> freeboard) and mean sea <span class="hlt">ice</span> thickness using previous and newly obtained Antarctic sea <span class="hlt">ice</span> profiles and examine these relationships for any consistent regional trends, (2) Derive sea <span class="hlt">ice</span> thickness from</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1710250R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1710250R"><span>Long time series of soil moisture obtained using neural networks: application to <span class="hlt">AMSR-E</span> and SMOS</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rodriguez-Fernandez, Nemesio J.; Kerr, Yann H.; de Jeu, Rcihard A. M.; van der Schalie, Robin; Wigneron, Jean Pierre; Ayaari, Amen al; Dolman, Han; Drusch, Matthias; Mecklenburg, Sussane</p> <p>2015-04-01</p> <p>The Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure soil moisture (hereafter SM) from space. The instrument on-board SMOS is a L-band aperture synthesis radiometer, with full-polarization and multi-angular capabilities (Mecklenburg et al. 2012). The operational SM retrieval algorithm is based on a physical model (Kerr et al. 2012). In addition, Rodriguez-Fernandez et al. (2014) have recently implemented an inverse model based in neural networks using the approach of Aires & Prigent (2006), which consists in training the neural networks with numerical weather prediction models (ECMWF, Balsamo et al. 2009). In the context of an ESA funded project (de Jeu et al, this conference, session CL 5.7), we have studied this neural network approach to create a consistent soil moisture dataset from 2003 to 2014 using NASA/JAXA Advanced Scanning Microwave Radiometer (<span class="hlt">AMSR-E</span>) and ESA SMOS radiometers as input data. Two neural networks algorithms have been defined and optimized using <span class="hlt">AMSR-E</span> or SMOS as input data in the periods 2003-Oct 2011 and 2010-2014, respectively. The two missions overlapping period has been used to demonstrate the consistency of the SM dataset produced with both algorithms by comparing monthly averages of SM and by comparing with time series of in situ measurements at selected locations and other SM products such as the SMOS operational SM, ECMWF model SM, and <span class="hlt">AMSR-E</span> LPRM SM (Owe et al. 2008). Finally, the long time series of SM obtained with neural networks will be compared to in-situ measurements and ECMWF ERA-Interim SM at selected locations. This long-term soil moisture dataset can be used for hydrological and climate applications and it is the first step towards a longer dataset which will include additional sensors. References Aires, F. & Prigent, C. Toward a new generation of satellite surface products? Journal of Geophysical Research: Atmospheres (1984--2012), Wiley Online Library, 2006, 11</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014SPIE.9240E..03B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014SPIE.9240E..03B"><span>Sea-<span class="hlt">ice</span> distribution and variability in the East Greenland Sea, 2003-13</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Boccolari, Mauro; Guerrieri, Lorenzo; Parmiggiani, Fiorigi</p> <p>2014-10-01</p> <p>This study presents an analysis of the sea-<span class="hlt">ice</span> area time series for the East Greenland Sea for the period January 2003 - December 2013. The data used are a subset of the Arctic Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> data set derived from the observations of the passive microwave sensors <span class="hlt">AMSR-E</span> and AMSR-2 and produced, on a daily basis, by the Inst. of Environ. Physics of the University of Bremen. The area of interest goes, approximately, from 57°N to 84°N and from 53°W to 15°E. On the basis of previous studies, the parameter Sea <span class="hlt">Ice</span> Area as the sum of all pixels whose sea <span class="hlt">ice</span> <span class="hlt">concentration</span> is above 70%, was introduced for measuring sea-<span class="hlt">ice</span> extent. A first survey of the Greenland Sea data set showed a large anomaly in year 2012; this anomaly, clearly linked with the transition period from <span class="hlt">AMSR-E</span> to AMSR-2 when re-sampled SSM/I data were used, was partially corrected with a linear regression procedure. The correlation between monthly mean Sea <span class="hlt">Ice</span> Area and other geophysical parameters, like air temperature, surface wind and cloud cover, was further investigated. High anti-correlation coefficients between air temperature, at sea level and in five different tropospheric layers, and observed <span class="hlt">ice</span> cover is confirmed. Our analysis shows that the strong decline of Arctic sea-<span class="hlt">ice</span> area in the last 10 years is not observed in the East Greenland Sea; this implies that large reductions have occurred in the Canadian and Russian Arctic. This result confirms a hypothesis recently postulated to explain the different sea-<span class="hlt">ice</span> decline in the Arctic and Antarctic regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19780051440&hterms=canada+fisheries&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dcanada%2Bfisheries','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19780051440&hterms=canada+fisheries&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dcanada%2Bfisheries"><span>Time-dependence of sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> and multiyear <span class="hlt">ice</span> fraction in the Arctic Basin</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</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>Microwave images of sea <span class="hlt">ice</span> obtained by Nimbus-5 and the NASA CV-990 airborne laboratory are used to determine the time variation of the sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> and multiyear <span class="hlt">ice</span> fraction within the pack <span class="hlt">ice</span> in the Arctic Basin. The images, constructed from data acquired from the electrically scanned microwave radiometer, are analyzed for four seasons during 1973-1975. Observations indicate significant variations in the sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> in the spring, late fall, and early winter. Sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span> as low as 50% were detected in large areas in the interior of the Arctic polar sea-<span class="hlt">ice</span> pack. The applicability of passive-microwave remote sensing for monitoring the time dependence of sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> is considered.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.2137C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.2137C"><span>Evaluation of <span class="hlt">AMSR-E</span> Soil Moisture products and model simulations against observations from a Tibetan SMTMS network</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Yingying; Yang, Kun; Qin, Jun; Zhao, Long</p> <p>2013-04-01</p> <p>Soil moisture is a key parameter in the land-atmosphere interactions over the Tibetan Plateau (TP), which plays an essential role in the Asian monsoon processes. Validation of satellite observed and/or modeled surface soil moisture is a particularly challenging work due to the scale issues. Additional challenge in this area is the harsh environment and heavy workload to establish a Soil Moisture and Temperature Measurement System (SMTMS) network. In this presentation, a multi-scale soil moisture and temperature monitoring network, consisting of 55 SMTMS stations, has been established in central TP within a 1°×1° area. Firstly, the station-averaged surface soil moisture data from the network are employed to evaluate four retrieved products from the Advanced Microwave Scanning Radiometer-Earth Observing System (<span class="hlt">AMSR-E</span>), including the National Aeronautics and Space Administration (NASA) standard soil moisture product, the Japan Aerospace Exploration Agency (JAXA) standard soil moisture product, and both the C-band and X-band soil moisture products developed using the land parameter retrieval model (LPRM). The statistic metrics indicate that none of four <span class="hlt">AMSR-E</span> products provides reliable estimates in the unfrozen season, in terms of the mission requirement of the root mean square error (RMSE) < 0.06 m3m-3. These algorithms either much overestimate soil moisture or much underestimate it, although some of them can reflect the soil moisture dynamic range, indicating that the retrieval algorithms have much space to be improved for the cold semi-arid regions. Then, the station-averaged soil moisture observations are used to evaluate four modeled outputs by the Global Land Data Assimilation System (GLDAS). These land surface models (LSMs) are Community Land Model (CLM), Noah model, VIC model and MOSAIC model. The statistic results indicate that four GLDAS models tend to systematically underestimate the surface soil moisture (0-5 cm) while well simulate the soil moisture</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/2016TCry...10.2275T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016TCry...10.2275T"><span>The EUMETSAT sea <span class="hlt">ice</span> <span class="hlt">concentration</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>Tonboe, Rasmus T.; Eastwood, Steinar; Lavergne, Thomas; Sørensen, Atle M.; Rathmann, Nicholas; Dybkjær, Gorm; Toudal Pedersen, Leif; Høyer, Jacob L.; Kern, Stefan</p> <p>2016-09-01</p> <p>An Arctic and Antarctic sea <span class="hlt">ice</span> area and extent dataset has been generated by EUMETSAT's Ocean and Sea <span class="hlt">Ice</span> Satellite Application Facility (OSISAF) using the record of microwave radiometer data from NASA's Nimbus 7 Scanning Multichannel Microwave radiometer (SMMR) and the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager and Sounder (SSMIS) satellite sensors. The dataset covers the period from October 1978 to April 2015 and updates and further developments are planned for the next phase of the project. The methodology for computing the sea <span class="hlt">ice</span> <span class="hlt">concentration</span> 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 <span class="hlt">ice</span>, and water emission characteristics and inter-sensor differences/biases; and (3) a hybrid sea <span class="hlt">ice</span> <span class="hlt">concentration</span> algorithm using the Bristol algorithm over <span class="hlt">ice</span> and the Bootstrap algorithm in frequency mode over open water. A new sea <span class="hlt">ice</span> <span class="hlt">concentration</span> uncertainty algorithm has been developed to estimate the spatial and temporal variability in sea <span class="hlt">ice</span> <span class="hlt">concentration</span> retrieval accuracy. A comparison to US National <span class="hlt">Ice</span> Center sea <span class="hlt">ice</span> charts from the Arctic and the Antarctic shows that <span class="hlt">ice</span> <span class="hlt">concentrations</span> are higher in the <span class="hlt">ice</span> charts than estimated from the radiometer data at intermediate sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> between open water and 100 % <span class="hlt">ice</span>. The sea <span class="hlt">ice</span> <span class="hlt">concentration</span> climate data record is available for download at <a href=" http://www.osi-saf.org"target="_blank">www.osi-saf.org</a>, including documentation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('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 Sea <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 Sea <span class="hlt">ice</span> cover has been provided by <span class="hlt">ice</span> <span class="hlt">concentration</span> maps derived from passive microwave data. To understand what satellite data represent in a highly divergent and rapidly changing environment like the Okhotsk Sea, we take advantage of concurrent satellite, aircraft, and ship data acquired on 7 February and characterized the sea <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> <span class="hlt">concentration</span> maps from <span class="hlt">AMSR-E</span> 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> <span class="hlt">concentration</span> according to passive microwave data. These results are used to interpret historical data that indicate that the Okhotsk Sea <span class="hlt">ice</span> extent 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('http://adsabs.harvard.edu/abs/2005AGUFM.C33B1132S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFM.C33B1132S"><span>A New Look at the Northern Hemisphere Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stern, H.; Fetterer, F.; Fowler, C.</p> <p>2005-12-01</p> <p>Arctic sea <span class="hlt">ice</span> extent has decreased over the past 25 years by 7% in winter and 17% in summer, with record or near-record summer lows since 2002. These estimates come from satellite passive microwave (PM) data collected since 1979. In this study we use a new data set of Northern Hemisphere sea <span class="hlt">ice</span> <span class="hlt">concentration</span>, derived from weekly operational <span class="hlt">ice</span> charts (1972-2004) produced by the U.S. National <span class="hlt">Ice</span> Center (NIC), to re-examine regional variability and trends in Arctic sea <span class="hlt">ice</span> area and extent. The <span class="hlt">ice</span> charts through 1994 were quality-checked and converted to EASE-Grid format by the Arctic Climatology Project (2000). Charts for 1995-2004 were converted to EASE-Grid by us. Source data used by NIC to create the charts included visible and infrared satellite imagery, active radar imagery, PM data, aerial reconnaissance, ship and shore observations, buoys, model output, information from foreign <span class="hlt">ice</span> services, and climatology. The PM data were incorporated into the charts only when all other forms of data were not available. We divide the Arctic and sub-Arctic seas into regions and compare chart-derived monthly sea <span class="hlt">ice</span> <span class="hlt">concentration</span> in each region to that derived from PM data. We find that the <span class="hlt">ice</span> charts give a more realistic depiction of the <span class="hlt">ice</span> edge, the marginal <span class="hlt">ice</span> zone, and coastal areas. The PM data have the advantage of being available as a daily product rather than weekly or biweekly. The <span class="hlt">ice</span> charts for 1995-2004 also include <span class="hlt">concentrations</span> of multiyear <span class="hlt">ice</span>, first-year <span class="hlt">ice</span>, and new <span class="hlt">ice</span>. We present results of our analysis of these data, as well as calculations of the duration of the <span class="hlt">ice</span> season in each region, and the variability of the <span class="hlt">ice</span> edge.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUFM.C21A1122S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.C21A1122S"><span>A New Look at the Northern Hemisphere Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stern, H.; Fetterer, F.; Fowler, C.</p> <p>2006-12-01</p> <p>Arctic sea <span class="hlt">ice</span> extent has decreased over the past 25 years by 7% in winter and 17% in summer, with record or near-record summer lows since 2002. These estimates come from satellite passive microwave (PM) data collected since 1979. In this study we use a new data set of Northern Hemisphere sea <span class="hlt">ice</span> <span class="hlt">concentration</span>, derived from weekly operational <span class="hlt">ice</span> charts (1972-2004) produced by the U.S. National <span class="hlt">Ice</span> Center (NIC), to re- examine regional variability and trends in Arctic sea <span class="hlt">ice</span> area and extent. The <span class="hlt">ice</span> charts through 1994 were quality-checked and converted to EASE-Grid format by the Arctic Climatology Project (2000). Charts for 1995-2004 were converted to EASE-Grid by us. Source data used by NIC to create the charts included visible and infrared satellite imagery, active radar imagery, PM data, aerial reconnaissance, ship and shore observations, buoys, model output, information from foreign <span class="hlt">ice</span> services, and climatology. The PM data were incorporated into the charts only when all other forms of data were not available. We divide the Arctic and sub-Arctic seas into regions and compare chart-derived monthly sea <span class="hlt">ice</span> <span class="hlt">concentration</span> in each region to that derived from PM data. We find that the <span class="hlt">ice</span> charts give a more realistic depiction of the <span class="hlt">ice</span> edge, the marginal <span class="hlt">ice</span> zone, and coastal areas. The PM data have the advantage of being available as a daily product rather than weekly or biweekly. The <span class="hlt">ice</span> charts for 1995-2004 also include <span class="hlt">concentrations</span> of multiyear <span class="hlt">ice</span>, first-year <span class="hlt">ice</span>, and new <span class="hlt">ice</span>. We present results of our analysis of these data, as well as calculations of the duration of the <span class="hlt">ice</span> season in each region, and the variability of the <span class="hlt">ice</span> edge.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.3621T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.3621T"><span>Determination of <span class="hlt">ice</span> <span class="hlt">concentration</span> from SSM/I data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tikhonov, Vasily; Repina, Irina; Raev, Mikhael; Sharkov, Evgeny; Boyarsky, Dmitry; Komarova, Natalia; Alexeeva, Tatiana; Ivanov, Vladimir</p> <p>2013-04-01</p> <p>At present there are about ten different algorithms of SSM/I data processing for generation of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> charts. Due to various reasons, for instance: specific laws of radiation - physical substance interaction, the task of satellite data interpretation and generation of <span class="hlt">ice</span> <span class="hlt">concentration</span> charts is not successfully resolved yet. <span class="hlt">Ice</span> <span class="hlt">concentration</span>, evaluated on the basis of passive microwave data is sensitive to mixed calibration, change of sensor properties, assignment of land-ocean boundaries. Conventional algorithms of sea <span class="hlt">ice</span> parameters determination on the basis of SSM/I data take into account empirical relationships and tuning coefficients, but sometimes on the cost of loosing physical background of processes. We present the model of interaction of radiation with sliced strata. Initial parameters of the model are actual properties of <span class="hlt">ice</span> and snow. This allows using this model for interpretation of remote sensing data on sea <span class="hlt">ice</span>. New algorithm of <span class="hlt">ice</span> <span class="hlt">concentration</span> determination from SSM/I data is introduced. The algorithm is built on the basis of electrodynamic model of radiation properties of <span class="hlt">ice</span> and snow cover. The model takes into account actual physical parameters of <span class="hlt">ice</span> and snow and does not use any empirical and tuning coefficients. We present comparison of evaluated <span class="hlt">concentration</span> of Arctic sea <span class="hlt">ice</span> on the basis of developed algorithm with direct visual measurements from icebreakers and with results of other models. Developed algorithm is free of drawbacks, which exist in conventional methods. It allows making high quality determination of the state of the Arctic sea <span class="hlt">ice</span> cover.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.C31A0591D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.C31A0591D"><span>Comparison of the <span class="hlt">AMSR-E</span> and SSM/I SWE Estimates in the Major Afghanistan Watersheds</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Daly, S. F.; Vuyovich, C.; Deeb, E. J.; Baldwin, T. B.; Newman, S. D.</p> <p>2011-12-01</p> <p>The <span class="hlt">AMSR-E</span> and SSM/I SWE estimates for the major Afghanistan watersheds are compared on a watershed total and pixel-by-pixel basis. Satellite remote sensing provides the only estimates of snow conditions in Afghanistan as virtually no ground observations are available. Although the Afghanistan terrain is mountainous, satellite remote sensing is particularly applicable to this area because most watersheds have little or no forest cover. We show that systematic and long term differences exist between the total SWE volumes determined by each satellite system for many but not all the watersheds. We show the influence of pixel elevation, slope, aspect and other factors on the SWE differences. The possible impacts of differences between the algorithms and native resolutions of each satellite system are discussed. The trends in SWE difference distribution with elevation throughout the winter season are described. We explore the ability to estimate the total watershed SWE difference based on the distribution of pixel elevation, slope, aspect and other factors within the watershed and its use as a means of developing confidence intervals of the consistency of SWE estimates when compared with historical observations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23705420','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23705420"><span>[The relationship between the variation rate of MODIS land surface temperature and <span class="hlt">AMSR-E</span> soil moisture and its application to downscaling].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wang, An-Qi; Xie, Chao; Shi, Jian-Cheng; Gong, Hui-Li</p> <p>2013-03-01</p> <p>Using <span class="hlt">AMSR-E</span> soil moisture, MODIS land surface temperature (Ts) and vegetation index product, the authors discuss the relationship between the variation rate of land surface temperature and surface soil moisture. Selecting the plains region of central United States as the study area, the authors propose the distribution triangle of the variation rate of land surface temperature and soil moisture. In the present paper, temperature variation and vegetation index (TVVI), a new index containing the information of temperature variation and vegetation, is introduced. The authors prove that TVVI and soil moisture show a steady relationship of exponential function; and build a quantitative model of soil moisture(SM) and instantaneous surface temperature variation (VTs). The authors later achieve downscaling of <span class="hlt">AMSR-E</span> soil moisture data, through the above stated functional relationships and high-resolution MODIS data. Comparison with measured data on ground surface indicates that this method of downscaling is of high precision</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20070038189','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20070038189"><span>Physical and Radiative Characteristics and Long Term Variability of the Okhotsk Sea <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>2007-01-01</p> <p>Much of what we know about the large scale characteristics of the Okhotsk Sea <span class="hlt">ice</span> cover comes from <span class="hlt">ice</span> <span class="hlt">concentration</span> maps derived from passive microwave data. To understand what these satellite data represents in a highly divergent and rapidly changing environment like the Okhotsk Sea, we analyzed concurrent satellite, aircraft, and ship data and characterized the sea <span class="hlt">ice</span> cover at different scales from meters to tens of kilometers. Through comparative analysis of surface features using co-registered data from visible, infrared and microwave channels we evaluated how the general radiative and physical characteristics of the <span class="hlt">ice</span> cover changes as well as quantify the distribution of different <span class="hlt">ice</span> types in the region. <span class="hlt">Ice</span> <span class="hlt">concentration</span> maps from <span class="hlt">AMSR-E</span> 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> represent a large fraction of the lice cover that averages about 90% <span class="hlt">ice</span> <span class="hlt">concentration</span>, according to passive microwave data. A rapid decline of -9% and -12 % per decade is observed suggesting warming signals but further studies are required because of aforementioned characteristics and because the length of the <span class="hlt">ice</span> season is decreasing by only 2 to 4 days per decade.</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> <span class="hlt">concentration</span> evolution in a coupled atmosphere-ocean-sea <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-sea <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 sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> strongly affect the surface albedo, the ocean heat release to the atmosphere, and the sea-<span class="hlt">ice</span> production. The changes are further amplified through a positive feedback mechanism among the Arctic sea <span class="hlt">ice</span>, the Atlantic Meridional Overturning Circulation (AMOC), and the surface air temperature in the Arctic, as the Fram Strait sea <span class="hlt">ice</span> import influences the freshwater budget in the North Atlantic Ocean. Anomalies in sea-<span class="hlt">ice</span> transport lead to changes in sea 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-sea <span class="hlt">ice</span> model with an unstructured mesh and multi-resolution. We find that the subpolar sea-<span class="hlt">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 sea <span class="hlt">ice</span> <span class="hlt">concentration</span> in the marginal <span class="hlt">ice</span> zone, the North Atlantic circulation, salinity and Arctic sea <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 sea <span class="hlt">ice</span> growth which could significantly affect the climate system sensitivity.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUFM.C41B0202S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUFM.C41B0202S"><span>A New Look at the Northern Hemisphere Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stern, H.; Fowler, C.; Fetterer, F.</p> <p>2004-12-01</p> <p>It is widely recognized that the Arctic sea <span class="hlt">ice</span> cover has been shrinking over the past 25 years. Our knowledge of hemispheric sea <span class="hlt">ice</span> <span class="hlt">concentration</span> and extent comes almost entirely from satellite passive microwave (PM) data collected since 1978. In this study, we use a new data set of Northern Hemisphere sea <span class="hlt">ice</span> <span class="hlt">concentration</span>, derived from weekly operational <span class="hlt">ice</span> charts spanning more than three decades (1972-2003), to re-examine the regional variability and trends in sea <span class="hlt">ice</span> area and extent. The <span class="hlt">ice</span> charts from the U.S. National <span class="hlt">Ice</span> Center have been converted to EASE-Grid format. Source data for the charts include visible and infrared satellite imagery, active radar imagery, PM data, aerial reconnaissance, ship and shore observations, buoys, model output, information from foreign <span class="hlt">ice</span> services, and climatology. The PM data are used only when all other forms of data are not available. Thus we have a unique gridded data set that is largely independent of the popular PM products that are widely used by the sea <span class="hlt">ice</span> community. We divided the Arctic and sub-Arctic seas into regions and created monthly time series of sea <span class="hlt">ice</span> area and extent for each region. We also obtained the monthly NASA Team sea <span class="hlt">ice</span> <span class="hlt">concentration</span> products. We re-gridded these to the same EASE-Grid format as the charts, and computed time series of sea <span class="hlt">ice</span> area and extent for the same regions. We present comparisons of the regional differences and trends seen in the two data sets. We explain the differences based on the source data used in the charts, and the emissivity of sea <span class="hlt">ice</span> as detected by the PM instruments. Future work with the <span class="hlt">ice</span> chart data set includes analysis of multiyear, first-year, and new <span class="hlt">ice</span> <span class="hlt">concentrations</span> in order to understand the recent record-low summer <span class="hlt">ice</span> minima; duration of the <span class="hlt">ice</span> season, as an indicator of climate change; and analysis of the modes of variability of the <span class="hlt">ice</span> edge, in order to develop a predictive capability for sea <span class="hlt">ice</span> extent.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010SPIE.7841E..03D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010SPIE.7841E..03D"><span>Cross-platform calibration of SMMR, SSM/I and <span class="hlt">AMSR-E</span> passive microwave brightness temperature</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dai, Liyun; Che, Tao</p> <p>2010-11-01</p> <p>The long time series of passive microwave satellite data (SMMR, SSM/I and <span class="hlt">AMSR-E</span>) have provided important information about the earth surface science and climate research in the past three decades. Due to the update of satellite-based radiometers and their platforms, some systematic parameters are different, and there are biases among brightness temperature in different periods, which lead to inaccuracy of some parameters' retrieval. In order to obtain consistent brightness temperature datasets, and provide convenience for the researchers using these data, it is necessary to calibrate the brightness temperature from different sensors. Considering the difference between the variance of brightness temperature from different sensors on cold and warm region at the cross time, this paper analyzed the brightness temperature on the cold and warm region respectively. On the cold region, because the diurnal temperature variation is very small, the influence on brightness temperature caused by difference of the satellites overpass time during the overlap period can be ignored. The brightness temperature data at 18GHz and 37GHz channels of Nimbus-7 and 19GHz, 37GHz channels of DMSP on the Antarctic or the Greenland glacier during the overlap period were analyzed. On the warm region, due to the reason that the daily variance of temperature contributes a lot to the difference of brightness temperature from different sensors during the overlap period, the diurnal cycle of surface temperature on the Sahara desert region was analyzed, and base on it, the influence of temperature to brightness temperature was eliminated. Finally, considering the two regions, the cross coefficients of calibration were estimated.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012MsT.........12B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012MsT.........12B"><span>Potentiel des donnees <span class="hlt">AMSR-E</span> et RADARSAT-2 pour le suivi des cycles de gel/degel du sol dans des zones agricoles au Canada</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>B-Rousseau, Louis-Philippe</p> <p></p> <p>Soil freezing and thawing processes are of particular importance for agricultural areas. For example, frozen soils can increase the runoff during snowmelt in the spring. Freezing and thawing also have a direct influence on the sowing and harvesting dates, as well as on the crop yield. A better understanding of those phenomena is therefore important, and several researchers focused on this topic in the past. Due to its sensitivity to changes in the state of water, microwave remote sensing is an appropriate tool for that purpose. The main objective of this study is to monitor soil freezing and thawing processes using <span class="hlt">AMSR-E</span> and RADARSAT-2 polarimetric data acquired over an agricultural area located near Saskatoon (Saskatchewan). With <span class="hlt">AMSR-E</span> data, the goals are to compare different combinations of frequencies for the spectral gradient's algorithm regarding their capacity for detecting frozen soils, and to analyze the temporal dynamics of the brightness temperature in order to find a new indicator of soil freezing. As for RADARSAT-2 data, several polarimetric parameters and techniques are tested in order to identify soil freezing. For the first part concerning <span class="hlt">AMSR-E</span> data, a global precision for the discrimination of frozen and thawed soils higher than 90% was obtained with the spectral gradient's algorithm, for the combinations including high (18.7 and 36.5 GHz) and low (6.9 and 10.7 GHz) frequencies as well as for the one using only high frequencies. It is shown that, for the combination based on the 18.7 and 36.5 GHz frequencies, results are improved when a negative threshold is used for the spectral gradient. When high and low <span class="hlt">AMSR-E</span> frequencies are combined, a null threshold is on the contrary appropriate, which constitutes an operational advantage. A new algorithm for detecting frozen soils, based on a thresholding approach applied to the spectral gradient of polarization difference and the brightness temperature at 36.5 GHz, was also proposed. The performances</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70012715','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70012715"><span>Time-dependence of sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> 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 sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> 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 sea <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 sea <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 sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> (divergence). These observations indicate significant variations in the sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> in the spring, late fall and early winter. In addition, deep in the interior of the Arctic polar sea-<span class="hlt">ice</span> pack, heretofore unobserved large areas, several hundred kilometers in extent, of sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span> 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/2012AGUFM.A33M0324R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A33M0324R"><span>Using Regional Validation from SuomiNet, <span class="hlt">AMSR-e</span>, and NWP Re-analysis to Assess the Precipitable Water Vapor from AIRS and CrIS for Detecting Extreme Weather Events</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Roman, J.; Knuteson, R. O.; Ackerman, S. A.; Revercomb, H. E.; Smith, W.; Weisz, E.</p> <p>2012-12-01</p> <p>The IPCC 4th Assessment found that changes in extreme events, such as droughts, heat waves, and flooding, has occurred and the frequency of such events is expected to increase. Precipitable Water Vapor (PWV) is defined as the amount of liquid water that would be produced if all of the water vapor in an atmospheric column were condensed. It is a very useful parameter for forecasters to determine atmospheric stability and the probability of convection and severe weather forecast using Numerical Weather Prediction (NWP) models, making it critical for determining the occurrence of extreme events. The <span class="hlt">AMSR-E</span> sensor on the NASA Aqua platform has produced a long record of PWV over <span class="hlt">ice</span>-free ocean areas while the Atmospheric Infrared Sounder (AIRS) on the NASA Aqua satellite was the first of a new generation of satellite sensors that provided the capability to retrieve water vapor profiles at high vertical resolution and good absolute accuracy over both ocean and land areas using the same algorithm. The operational follow-on to the AIRS is the Cross-track Infrared Sounder (CrIS) successfully launched on the Suomi NPP satellite on 28 October 2011. The CrIS, along with ATMS, will provide the U.S. component of the joint U.S./European operational weather satellite system. A long record of observations from copies of these sensors is anticipated from this new network of advanced IR sounders. Among other atmospheric observables, the NASA AIRS science team has produced a global dataset of PWV beginning in September 2002 that is approaching ten years in length. This paper investigates the accuracy of satellite retrieved PWV climatology's. Validation data used is from the ground based GPS network (SuomiNet) and the conventional meteorological network as represented in NWP reanalysis products. The purpose of this study is to compare the retrievals of PWV from NASA's AIRS global gridded satellite products to our independent UW satellite retrievals, as well as compare NASA AIRS and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H31F1253T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H31F1253T"><span>Using Large-Scale Precipitation to Validate <span class="hlt">AMSR-E</span> Satellite Soil Moisture Estimates by Means of Mutual Information</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tuttle, S. E.; Salvucci, G.</p> <p>2013-12-01</p> <p>Validation of remotely sensed soil moisture is complicated by the difference in scale between remote sensing footprints and traditional ground-based soil moisture measurements. To address this issue, a new method was developed to evaluate the useful information content of remotely sensed soil moisture data using only large-scale precipitation (i.e. without modeling). Under statistically stationary conditions [Salvucci, 2001], precipitation conditionally averaged according to soil moisture (denoted E[P|S]) results in a sigmoidal shape in a manner that reflects the dependence of drainage, runoff, and evapotranspiration on soil moisture. However, errors in satellite measurement and algorithmic conversion of satellite data to soil moisture can degrade this relationship. Thus, remotely sensed soil moisture products can be assessed by the degree to which the natural sigmoidal relationship is preserved. The metric of mutual information was used as an error-dependent measure of the strength of the sigmoidal relationship, calculated from a two-dimensional histogram of soil moisture versus precipitation estimated using Gaussian mixture models. Three <span class="hlt">AMSR-E</span> algorithms (VUA-NASA [Owe et al., 2001], NASA [Njoku et al., 2003], and U. Montana [Jones & Kimball, 2010]) were evaluated with the method for a nine-year period (2002-2011) over the contiguous United States at ¼° latitude-longitude resolution, using precipitation from the North American Land Data Assimilation System (NLDAS). The U. Montana product resulted in the highest mutual information for 57% of the region, followed by VUA-NASA and NASA at 40% and 3%, respectively. Areas where the U. Montana product yielded the maximum mutual information generally coincided with low vegetation biomass and flatter terrain, while the VUA-NASA product contained more useful information in more rugged and highly vegetated areas. Additionally, E[P|S] curves resulting from the Gaussian mixture method can potentially be decomposed into</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19860019345','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19860019345"><span>Entrainment, transport and <span class="hlt">concentration</span> of meteorites in polar <span class="hlt">ice</span> sheets</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Drewry, D. J.</p> <p>1986-01-01</p> <p>Glaciers and <span class="hlt">ice</span> sheets act as slow-moving conveyancing systems for material added to both their upper and lower surfaces. Because the transit time for most materials is extremely long the <span class="hlt">ice</span> acts as a major global storage facility. The effects of horizontal and vertical motions on the flow patterns of Antarctic <span class="hlt">ice</span> sheets are summarized. The determination of the source areas of meteorites and their transport paths is a problem of central importance since it relates not only directly to <span class="hlt">concentration</span> mechanisms but also to the wider issues in glaciology and meteorites. The <span class="hlt">ice</span> and snow into which a meteorite falls, and which moves with it to the <span class="hlt">concentration</span> area, encodes information about the infall area. The principle environmental conditions being former elevation, temperature (also related to elevation), and age of the <span class="hlt">ice</span>. This encoded information could be used to identify the infall area.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20030110723&hterms=stranding&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dstranding','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20030110723&hterms=stranding&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dstranding"><span>The Broken Belt: Meteorite <span class="hlt">Concentrations</span> on Stranded <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>Harvey, R. P.</p> <p>2003-01-01</p> <p>Since the first Antarctic meteorite <span class="hlt">concentrations</span> were discovered more than 25 years ago, many theories regarding the role of iceflow in the production of meteorite <span class="hlt">concentrations</span> have been put forward, and most agree on the basic principles. These models suggest that as the East Antarctic icesheet flows toward the margins of the continent, meteorites randomly located within the volume of <span class="hlt">ice</span> are transported toward the icesheet margin. Where mountains or subsurface obstructions block glacial flow, diversion of <span class="hlt">ice</span> around or over an obstruction reduces horizontal <span class="hlt">ice</span> movement rates adjacent to the barriers and creates a vertical (upward) component of movement. If local mechanisms for <span class="hlt">ice</span> loss (ablation) exist at such sites, an equilibrium surface will develop according to the balance between <span class="hlt">ice</span> supply and loss, and the cargo of meteorites is exhumed on a blue <span class="hlt">ice</span> surface. The result is a conceptual conveyor belt bringing meteorite-bearing volumes of <span class="hlt">ice</span> from the interior of the continent to stagnant or slowmoving surfaces where <span class="hlt">ice</span> is then lost and a precious cargo is left as a lag deposit. Cassidy et al. provides an excellent overview of how this model has been adapted to several Antarctic stranding surfaces.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20030110723&hterms=conveyor+belt&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dconveyor%2Bbelt','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20030110723&hterms=conveyor+belt&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dconveyor%2Bbelt"><span>The Broken Belt: Meteorite <span class="hlt">Concentrations</span> on Stranded <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>Harvey, R. P.</p> <p>2003-01-01</p> <p>Since the first Antarctic meteorite <span class="hlt">concentrations</span> were discovered more than 25 years ago, many theories regarding the role of iceflow in the production of meteorite <span class="hlt">concentrations</span> have been put forward, and most agree on the basic principles. These models suggest that as the East Antarctic icesheet flows toward the margins of the continent, meteorites randomly located within the volume of <span class="hlt">ice</span> are transported toward the icesheet margin. Where mountains or subsurface obstructions block glacial flow, diversion of <span class="hlt">ice</span> around or over an obstruction reduces horizontal <span class="hlt">ice</span> movement rates adjacent to the barriers and creates a vertical (upward) component of movement. If local mechanisms for <span class="hlt">ice</span> loss (ablation) exist at such sites, an equilibrium surface will develop according to the balance between <span class="hlt">ice</span> supply and loss, and the cargo of meteorites is exhumed on a blue <span class="hlt">ice</span> surface. The result is a conceptual conveyor belt bringing meteorite-bearing volumes of <span class="hlt">ice</span> from the interior of the continent to stagnant or slowmoving surfaces where <span class="hlt">ice</span> is then lost and a precious cargo is left as a lag deposit. Cassidy et al. provides an excellent overview of how this model has been adapted to several Antarctic stranding surfaces.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1811757R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1811757R"><span>Melt ponds and marginal <span class="hlt">ice</span> zone from new algorithm of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> retrieval</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Repina, Irina; Tikhonov, Vasiliy; Komarova, Nataliia; Raev, Mikhail; Sharkov, Evgeniy</p> <p>2016-04-01</p> <p>Studies of spatial and temporal properties of sea <span class="hlt">ice</span> distribution in polar regions help to monitor global environmental changes and reveal their natural and anthropogenic factors, as well as make forecasts of weather, marine transportation and fishing conditions, assess perspectives of mineral mining on the continental shelf, etc. Contact methods of observation are often insufficient to meet the goals, very complicated technically and organizationally and not always safe for people involved. Remote sensing techniques are believed to be the best alternative. Its include monitoring of polar regions by means of passive microwave sensing with the aim to determine spatial distribution, types, thickness and snow cover of <span class="hlt">ice</span>. However, the algorithms employed today to retrieve sea <span class="hlt">ice</span> characteristics from passive microwave sensing data for different reasons give significant errors, especially in summer period and also near <span class="hlt">ice</span> edges and in cases of open <span class="hlt">ice</span>. A new algorithm of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> retrieval in polar regions from satellite microwave radiometry data is discussed. Beside estimating sea <span class="hlt">ice</span> <span class="hlt">concentration</span>, the algorithm makes it possible to indicate <span class="hlt">ice</span> areas with melting snow and melt ponds. Melt ponds are an important element of the Arctic climate system. Covering up to 50% of the surface of drifting <span class="hlt">ice</span> in summer, they are characterized by low albedo values and absorb several times more incident shortwave radiation than the rest of the snow and <span class="hlt">ice</span> cover. The change of melt ponds area in summer period 1987-2015 is investigated. The marginal <span class="hlt">ice</span> zone (MIZ) is defined as the area where open ocean processes, including specifically ocean waves, alter significantly the dynamical properties of the sea <span class="hlt">ice</span> cover. Ocean wave fields comprise short waves generated locally and swell propagating from the large ocean basins. Depending on factors like wind direction and ocean currents, it may consist of anything from isolated, small and large <span class="hlt">ice</span> floes drifting over a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.5568R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.5568R"><span><span class="hlt">Concentrated</span> englacial shear over rigid basal <span class="hlt">ice</span>, West Antarctica: implications for modelling and <span class="hlt">ice</span> sheet flow</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ross, Neil; Siegert, Martin</p> <p>2014-05-01</p> <p> sheet flow. Our observations suggest that, in parts of the onset zone of the Institute <span class="hlt">Ice</span> Stream, the flow of the <span class="hlt">ice</span> sheet effectively ignores the basal topography. Instead, enhanced <span class="hlt">ice</span> flow responds to a pseudo-bed, with internal deformation <span class="hlt">concentrated</span> and terminating at an englacial rheological interface between the upper <span class="hlt">ice</span> sheet column and the massive basal <span class="hlt">ice</span>. Although we cannot entirely rule out basal accretion as the cause of the strong englacial interface and thick basal layer, discrete englacial shearing acting to realign <span class="hlt">ice</span> crystals, may be the best explanation for the basal unit in this case. Our results demonstrate that we will, at the very least, need to adapt numerical models of those parts of the <span class="hlt">ice</span> sheet with extensive and thick basal <span class="hlt">ice</span> units, and that we may even need to carefully reconsider existing schematic models of <span class="hlt">ice</span> flow, to incorporate processes associated with <span class="hlt">concentrated</span> englacial shear.</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/2014ACP....14.5433H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014ACP....14.5433H"><span>Contribution of pollen to atmospheric <span class="hlt">ice</span> nuclei <span class="hlt">concentrations</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hader, J. D.; Wright, T. P.; Petters, M. D.</p> <p>2014-06-01</p> <p>Recent studies have suggested that the <span class="hlt">ice</span>-nucleating ability of some types of pollen is derived from non-proteinaceous macromolecules. These macromolecules may become dispersed by the rupturing of the pollen grain during wetting and drying cycles in the atmosphere. If true, this mechanism might prove to be a significant source of <span class="hlt">ice</span> nuclei (IN) <span class="hlt">concentrations</span> when pollen is present. Here we test this hypothesis by measuring ambient IN <span class="hlt">concentrations</span> from the beginning to the end of the 2013 pollen season in Raleigh, North Carolina, USA. Air samples were collected using a swirling aerosol collector twice per week and the solutions were analysed for <span class="hlt">ice</span> nuclei activity using a droplet freezing assay. Rainwater samples were collected at times when pollen grain number <span class="hlt">concentrations</span> were near their maximum value and analysed with the drop-freezing assay to compare the potentially enhanced IN <span class="hlt">concentrations</span> measured near the ground with IN <span class="hlt">concentrations</span> found aloft. Ambient <span class="hlt">ice</span> nuclei spectra, defined as the number of <span class="hlt">ice</span> nuclei per volume of air as a function of temperature, are inferred from the aerosol collector solutions. No general trend was observed between ambient pollen grain counts and observed IN <span class="hlt">concentrations</span>, suggesting that <span class="hlt">ice</span> nuclei multiplication via pollen grain rupturing and subsequent release of macromolecules was not prevalent for the pollen types and meteorological conditions typically encountered in the southeastern US. A serendipitously sampled collection after a downpour provided evidence for a rain-induced IN burst with an observed IN <span class="hlt">concentration</span> of approximately 30 per litre, a 30-fold increase over background <span class="hlt">concentrations</span> at -20 °C. The onset temperature of freezing for these particles was approximately -12 °C, suggesting that the <span class="hlt">ice</span>-nucleating particles were biological in origin.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015TCry....9.1735P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015TCry....9.1735P"><span>Improving Arctic sea <span class="hlt">ice</span> edge forecasts by assimilating high horizontal resolution sea <span class="hlt">ice</span> <span class="hlt">concentration</span> data into the US Navy's <span class="hlt">ice</span> forecast systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Posey, P. G.; Metzger, E. J.; Wallcraft, A. J.; Hebert, D. A.; Allard, R. A.; Smedstad, O. M.; Phelps, M. W.; Fetterer, F.; Stewart, J. S.; Meier, W. N.; Helfrich, S. R.</p> <p>2015-08-01</p> <p>This study presents the improvement in <span class="hlt">ice</span> edge error within the US Navy's operational sea <span class="hlt">ice</span> forecast systems gained by assimilating high horizontal resolution satellite-derived <span class="hlt">ice</span> <span class="hlt">concentration</span> products. Since the late 1980's, the <span class="hlt">ice</span> forecast systems have assimilated near real-time sea <span class="hlt">ice</span> <span class="hlt">concentration</span> derived from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI and then SSMIS). The resolution of the satellite-derived product was approximately the same as the previous operational <span class="hlt">ice</span> forecast system (25 km). As the sea <span class="hlt">ice</span> forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy sea <span class="hlt">ice</span> forecast system (Arctic Cap Nowcast/Forecast System - ACNFS) went into operations with a horizontal resolution of ~ 3.5 km at the North Pole. A method of blending <span class="hlt">ice</span> <span class="hlt">concentration</span> observations from the Advanced Microwave Scanning Radiometer (AMSR2) along with a sea <span class="hlt">ice</span> mask produced by the National <span class="hlt">Ice</span> Center (NIC) has been developed, resulting in an <span class="hlt">ice</span> <span class="hlt">concentration</span> product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended <span class="hlt">ice</span> <span class="hlt">concentration</span> product. The daily <span class="hlt">ice</span> edge locations from model hindcast simulations were compared against independent observed <span class="hlt">ice</span> edge locations. ACNFS initialized using the high resolution blended <span class="hlt">ice</span> <span class="hlt">concentration</span> data product decreased predicted <span class="hlt">ice</span> edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended sea <span class="hlt">ice</span> <span class="hlt">concentration</span> product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in <span class="hlt">ice</span> edge location over a system using the SSMIS sea <span class="hlt">ice</span> <span class="hlt">concentration</span> product alone. This paper describes the technique used to create the blended sea <span class="hlt">ice</span> <span class="hlt">concentration</span> product and the significant improvements in <span class="hlt">ice</span> edge forecasting in both of the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013ACPD...1331673H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ACPD...1331673H"><span>Contribution of pollen to atmospheric <span class="hlt">ice</span> nuclei <span class="hlt">concentrations</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hader, J. D.; Wright, T. P.; Petters, M. D.</p> <p>2013-12-01</p> <p>Recent studies have suggested that the <span class="hlt">ice</span> nucleating ability of some types of pollen is derived from non-proteinaceous macromolecules. These macromolecules may become dispersed by the rupturing of the pollen sac during wetting and drying cycles in the atmosphere. If true, this mechanism might prove to be a significant source of <span class="hlt">ice</span> nuclei (IN) <span class="hlt">concentrations</span> when pollen are present. Here we test this hypothesis by measuring ambient IN <span class="hlt">concentrations</span> from the beginning to the end of the 2013 pollen season in Raleigh, North Carolina, USA. Air samples were collected using a swirling aerosol collector twice per week and the solutions were analysed for <span class="hlt">ice</span> nuclei activity using a droplet freezing assay. Rainwater samples were collected at the peak of the pollen season and analysed with the drop freezing assay to compare the potentially enhanced IN <span class="hlt">concentrations</span> measured near the ground with IN <span class="hlt">concentrations</span> found aloft. Ambient <span class="hlt">ice</span> nuclei spectra, defined as the number of <span class="hlt">ice</span> nuclei per volume of air as a function of temperature, are inferred from the aerosol collector solutions. No general trend was observed between ambient pollen counts and observed IN <span class="hlt">concentrations</span>, suggesting that <span class="hlt">ice</span> nuclei multiplication via pollen sac rupturing and subsequent release of macromolecules was not prevalent for the pollen types and meteorological conditions typically encountered in the Southeastern US. A serendipitously sampled collection after a downpour provided evidence for a rain-induced IN burst with an observed IN <span class="hlt">concentration</span> of approximately 30 per litre, a 30-fold increase over background <span class="hlt">concentrations</span> at -20 °C. The onset temperature of freezing for these particles was approximately -12 °C, suggesting that the <span class="hlt">ice</span> nucleating particles were biological in origin. The magnitude of the IN burst was significantly larger than previously observed, providing additional evidence to merit further investigation of a self-regulated feedback cycle between the atmosphere and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015OcDyn..65.1353P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015OcDyn..65.1353P"><span>The implementation of sea <span class="hlt">ice</span> model on a regional high-resolution scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Prasad, Siva; Zakharov, Igor; Bobby, Pradeep; McGuire, Peter</p> <p>2015-09-01</p> <p>The availability of high-resolution atmospheric/ocean forecast models, satellite data and access to high-performance computing clusters have provided capability to build high-resolution models for regional <span class="hlt">ice</span> condition simulation. The paper describes the implementation of the Los Alamos sea <span class="hlt">ice</span> model (CICE) on a regional scale at high resolution. The advantage of the model is its ability to include oceanographic parameters (e.g., currents) to provide accurate results. The sea <span class="hlt">ice</span> simulation was performed over Baffin Bay and the Labrador Sea to retrieve important parameters such as <span class="hlt">ice</span> <span class="hlt">concentration</span>, thickness, ridging, and drift. Two different forcing models, one with low resolution and another with a high resolution, were used for the estimation of sensitivity of model results. Sea <span class="hlt">ice</span> behavior over 7 years was simulated to analyze <span class="hlt">ice</span> formation, melting, and conditions in the region. Validation was based on comparing model results with remote sensing data. The simulated <span class="hlt">ice</span> <span class="hlt">concentration</span> correlated well with Advanced Microwave Scanning Radiometer for EOS (<span class="hlt">AMSR-E</span>) and Ocean and Sea <span class="hlt">Ice</span> Satellite Application Facility (OSI-SAF) data. Visual comparison of <span class="hlt">ice</span> thickness trends estimated from the Soil Moisture and Ocean Salinity satellite (SMOS) agreed with the simulation for year 2010-2011.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013TCD.....7.1141K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013TCD.....7.1141K"><span>Cyclone impact on sea <span class="hlt">ice</span> in the central Arctic Ocean: a statistical study</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kriegsmann, A.; Brümmer, B.</p> <p>2013-03-01</p> <p>This study investigates the impact of cyclones on the Arctic Ocean sea <span class="hlt">ice</span> for the first time in a statistical manner. We apply the coupled <span class="hlt">ice</span>-ocean model NAOSIM which is forced by the ECMWF analyses for the period 2006-2008. Cyclone position and radius detected in the ECMWF data are used to extract fields of wind, <span class="hlt">ice</span> drift, and <span class="hlt">concentration</span> from the <span class="hlt">ice</span>-ocean model. Composite fields around the cyclone centre are calculated for different cyclone intensities, the four seasons, and different regions of the Arctic Ocean. In total about 3500 cyclone events are analyzed. In general, cyclones reduce the <span class="hlt">ice</span> <span class="hlt">concentration</span> on the order of a few percent increasing towards the cyclone centre. This is confirmed by independent <span class="hlt">AMSR-E</span> satellite data. The reduction increases with cyclone intensity and is most pronounced in summer and on the Siberian side of the Arctic Ocean. For the Arctic <span class="hlt">ice</span> cover the impact of cyclones has climatologic consequences. In winter, the cyclone-induced openings refreeze so that the <span class="hlt">ice</span> mass is increased. In summer, the openings remain open and the <span class="hlt">ice</span> melt is accelerated via the positive albedo feedback. Strong summer storms on the Siberian side of the Arctic Ocean may have been important reasons for the recent <span class="hlt">ice</span> extent minima in 2007 and 2012.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014TCry....8..303K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014TCry....8..303K"><span>Cyclone impact on sea <span class="hlt">ice</span> in the central Arctic Ocean: a statistical study</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kriegsmann, A.; Brümmer, B.</p> <p>2014-02-01</p> <p>This study investigates the impact of cyclones on the Arctic Ocean sea <span class="hlt">ice</span> for the first time in a statistical manner. We apply the coupled <span class="hlt">ice</span>-ocean model NAOSIM which is forced by the ECMWF analyses for the period 2006-2008. Cyclone position and radius detected in the ECMWF data are used to extract fields of wind, <span class="hlt">ice</span> drift, and <span class="hlt">concentration</span> from the <span class="hlt">ice</span>-ocean model. Composite fields around the cyclone centre are calculated for different cyclone intensities, the four seasons, and different sub-regions of the Arctic Ocean. In total about 3500 cyclone events are analyzed. In general, cyclones reduce the <span class="hlt">ice</span> <span class="hlt">concentration</span> in the order of a few percent increasing towards the cyclone centre. This is confirmed by independent <span class="hlt">AMSR-E</span> satellite data. The reduction increases with cyclone intensity and is most pronounced in summer and on the Siberian side of the Arctic Ocean. For the Arctic <span class="hlt">ice</span> cover the cumulative impact of cyclones has climatologic consequences. In winter, the cyclone-induced openings refreeze so that the <span class="hlt">ice</span> mass is increased. In summer, the openings remain open and the <span class="hlt">ice</span> melt is accelerated via the positive albedo feedback. Strong summer storms on the Siberian side of the Arctic Ocean may have been important contributions to the recent <span class="hlt">ice</span> extent minima in 2007 and 2012.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1509046','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1509046"><span>Determinants of nitrogen dioxide <span class="hlt">concentrations</span> in indoor <span class="hlt">ice</span> skating rinks.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Levy, J I; Lee, K; Yanagisawa, Y; Hutchinson, P; Spengler, J D</p> <p>1998-01-01</p> <p>OBJECTIVES: The combination of poor ventilation and fuel-powered <span class="hlt">ice</span> resurfacers has resulted in elevated nitrogen dioxide (NO2) <span class="hlt">concentrations</span> in many indoor <span class="hlt">ice</span> skating rinks. This study examined the factors influencing <span class="hlt">concentrations</span> and the effects of various engineering controls in <span class="hlt">ice</span> rinks with different resurfacer fuels. METHODS: Indoor NO2 <span class="hlt">concentrations</span> were measured in 19 enclosed <span class="hlt">ice</span> skating rinks over 3 winters by means of passive samplers, with 1-week average measurements during the first winter pilot study and single-day working-hour measurements in the final 2 winters. Personal exposures to drivers also were assessed during the last winter. RESULTS: Rinks in which propane-fueled resurfacers were used had a daily mean indoor NO2 <span class="hlt">concentration</span> of 206 ppb, compared with 132 ppb for gasoline-fueled and 37 ppb for electric-powered resurfacers. Engineering controls, such as increased ventilation and resurfacer tuning, reduced NO2 <span class="hlt">concentrations</span> by 65% on average, but outcomes varied widely, and <span class="hlt">concentrations</span> increased in subsequent months. CONCLUSIONS: Electric <span class="hlt">ice</span> resurfacers, increased ventilation, or emission control systems are recommended to protect the health of workers and patrons, with surveillance programs proposed to track implementation and maintain an observer effect. PMID:9842374</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.3302Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.3302Z"><span>Quantification of sea <span class="hlt">ice</span> production in Weddell Sea polynyas</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zentek, Rolf; Heinemann, Günther; Paul, Stephan; Stulic, Lukrecia; Timmermann, Ralph</p> <p>2017-04-01</p> <p>The regional climate model COSMO-CLM was used to perform simulations the Weddell Sea region in Antarctica for the time period 2002-2015 with the focus on atmosphere-ocean-sea <span class="hlt">ice</span> interactions. The original model was adapted to polar regions by the use of a thermodynamic sea <span class="hlt">ice</span> module with snow cover and an temperature-dependent albedo scheme for sea <span class="hlt">ice</span>. The recently published topography RTopo2 was used. The model was run with nesting in ERA-Interim data in a forecast mode. Sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> were taken from satellite measurements (<span class="hlt">AMSR-E</span>, SSMI/S, AMSR2) and were updated daily to allow for a close-to-reality hindcast. Simulations were done with 15 km resolution for the whole period 2002-2015 with the goal to force the sea-<span class="hlt">ice</span> ocean model FESOM. In a second step a 5 km simulation was one-way nested for the winter period (April - September) 2002-2015 to allow for a better quantification of sea <span class="hlt">ice</span> production in the Weddell Sea. Estimates of sea <span class="hlt">ice</span> production and comparisons of the results to remote sensing data will be presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUSM.A33A..02L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUSM.A33A..02L"><span>Deposition <span class="hlt">Ice</span> Nuclei <span class="hlt">Concentration</span> at Different Temperatures and Supersaturations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>López, M. L.; Avila, E.</p> <p>2013-05-01</p> <p><span class="hlt">Ice</span> formation is one of the main processes involved in the initiation of precipitation. Some aerosols serve to nucleate <span class="hlt">ice</span> in clouds. They are called <span class="hlt">ice</span> nuclei (IN) and they are generally solid particles, insoluble in water. At temperatures warmer than about -36°C the only means for initiation of the <span class="hlt">ice</span> phase in the atmosphere involves IN, and temperature and supersaturation required to activate IN are considered as key information for the understanding of primary <span class="hlt">ice</span> formation in clouds. The objective of this work is to quantify the IN <span class="hlt">concentration</span> at ground level in Córdoba City, Argentina, under the deposition mode, that is to say that <span class="hlt">ice</span> deposits on the IN directly from the vapor phase. It happens when the environment is supersaturated with respect to <span class="hlt">ice</span> and subsaturated with respect to liquid water. <span class="hlt">Ice</span> nuclei <span class="hlt">concentrations</span> were measured in a cloud chamber placed in a cold room with temperature control down to -35°C. The operating temperature was varied between -15°C and -30°C. <span class="hlt">Ice</span> supersaturation was ranged between 2 and 20 %. In order to quantify the number of <span class="hlt">ice</span> particles produced in each experiment, a dish containing a supercooled solution of cane sugar, water and glycerol was placed on the floor of the cloud chamber. The activated IN grew at the expense of vapor until <span class="hlt">ice</span> crystals were formed and these then fell down onto the sugar solution. Once there, these crystals could grow enough to be counted easily with a naked eye after a period of about three minutes, when they reach around 2 mm in diameter. In order to compare the present results with previously reported results, the data were grouped in three different ranges of supersaturation: the data with supersaturations between 2 and 8 %, the data with supersaturations between 8 and 14% and the data with supersaturations between 14 and 20 %. In the same way, in order to analize the behavior of IN <span class="hlt">concentration</span> with supersaturation, the data were grouped for three different temperatures, the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A33C0228P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A33C0228P"><span>Contribution of pollen to atmospheric <span class="hlt">ice</span> nuclei <span class="hlt">concentrations</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Petters, M. D.; Hader, J.; Wright, T.; McMeeking, G. R.</p> <p>2013-12-01</p> <p>Primary biological aerosol particles (PBAP) contribute to the <span class="hlt">concentrations</span> of <span class="hlt">ice</span> nuclei (IN) in the atmosphere. Laboratory studies have shown that pollen grains, a subset of PBAP, can serve as immersion mode <span class="hlt">ice</span> nuclei at temperatures ranging from -9 to -25 deg C. At the peak of the pollen season pollen <span class="hlt">concentrations</span> can reach surface-level <span class="hlt">concentrations</span> exceeding 1 per liter of air. Furthermore, previous studies have suggested that the <span class="hlt">ice</span> nucleating ability of some types of pollen is derived from non-proteinaceous macromolecules, which may become dispersed by the rupturing of the pollen sac during wetting and drying cycles. If true, this mechanism is expected to produce highly elevated IN <span class="hlt">concentrations</span> at temperatures warmer than -25 deg C. Here we test this hypothesis by measuring ambient IN <span class="hlt">concentrations</span> from the beginning to the end of the 2013 pollen season in Raleigh, North Carolina. Raleigh is surrounded by a dense mixed hardwood forest composed primarily of oak, hickory, and pine species. Air samples were collected using a swirling aerosol collector twice per week and the solution was analyzed for <span class="hlt">ice</span> nuclei activity using a droplet freezing assay setup. Rainwater samples were collected during rain events at the peak of the pollen season and analyzed with the drop freezing assay to compare the potentially enhanced IN <span class="hlt">concentrations</span> measured near the ground with IN <span class="hlt">concentrations</span> found aloft. Raw freezing spectra were used to probe the freezing activity of both abundant and rare IN contained in sample liquids by analysis of drops that had varying degrees of preconcentration and size (~50 to ~650 μm). Extreme value statistics is used to collapse the raw freezing data into a single <span class="hlt">ice</span> nuclei spectrum, defined as number of <span class="hlt">ice</span> nuclei per volume of air as a function of temperature, that spans ~6 orders of magnitude in IN <span class="hlt">concentration</span>. For a selected number of samples, <span class="hlt">concentrations</span> of biological and non-biological ambient aerosol and particles are</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040081247&hterms=qualitative+data&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dqualitative%2Bdata','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040081247&hterms=qualitative+data&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dqualitative%2Bdata"><span>Comparisons of Arctic In-Situ Snow and <span class="hlt">Ice</span> Data with Airborne Passive Microwave Measurements</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Markus, T.; Cavalien, D. J.; Gasiewski, A.; Sturm, M.; Klein, M.; Maslanik, J.; Stroeve, J.; Heinrichs, J.; Holmgren, J.; Irisov, V.</p> <p>2004-01-01</p> <p>As part of the <span class="hlt">AMSR-E</span> sea <span class="hlt">ice</span> validation campaign in March 2003, aircraft flights over the Arctic sea <span class="hlt">ice</span> were coordinated with ground measurements of snow and sea <span class="hlt">ice</span> properties. The surface-based measurements were in the vicinity of Barrow, AK, and at a Navy <span class="hlt">ice</span> camp located in the Beaufort Sea. The NASA P-3 aircraft was equipped with the NOAA ETL PSR microwave radiometer that has the same frequencies as the <span class="hlt">AMSR-E</span> sensor. The goal was to validate the standard <span class="hlt">AMSR-E</span> products <span class="hlt">ice</span> temperature and snow depth on sea <span class="hlt">ice</span>. Ground measurements are the only way to validate these parameters. The higher spatial resolution of the PSR instrument (between 30 and 500 m, depending on altitude) enables a better comparison between ground measurements and microwave data because of the expected smaller spatial variability. Maps of PSR data can then be used for further down-scaling to <span class="hlt">AMSR-E</span> pixel areas. Initial results show a good qualitative agreement between the in-situ snow depths and the PSR data. Detailed studies are underway and latest results will be presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040081247&hterms=etl&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Detl','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040081247&hterms=etl&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Detl"><span>Comparisons of Arctic In-Situ Snow and <span class="hlt">Ice</span> Data with Airborne Passive Microwave Measurements</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Markus, T.; Cavalien, D. J.; Gasiewski, A.; Sturm, M.; Klein, M.; Maslanik, J.; Stroeve, J.; Heinrichs, J.; Holmgren, J.; Irisov, V.</p> <p>2004-01-01</p> <p>As part of the <span class="hlt">AMSR-E</span> sea <span class="hlt">ice</span> validation campaign in March 2003, aircraft flights over the Arctic sea <span class="hlt">ice</span> were coordinated with ground measurements of snow and sea <span class="hlt">ice</span> properties. The surface-based measurements were in the vicinity of Barrow, AK, and at a Navy <span class="hlt">ice</span> camp located in the Beaufort Sea. The NASA P-3 aircraft was equipped with the NOAA ETL PSR microwave radiometer that has the same frequencies as the <span class="hlt">AMSR-E</span> sensor. The goal was to validate the standard <span class="hlt">AMSR-E</span> products <span class="hlt">ice</span> temperature and snow depth on sea <span class="hlt">ice</span>. Ground measurements are the only way to validate these parameters. The higher spatial resolution of the PSR instrument (between 30 and 500 m, depending on altitude) enables a better comparison between ground measurements and microwave data because of the expected smaller spatial variability. Maps of PSR data can then be used for further down-scaling to <span class="hlt">AMSR-E</span> pixel areas. Initial results show a good qualitative agreement between the in-situ snow depths and the PSR data. Detailed studies are underway and latest results will be presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AtmEn.140..381P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AtmEn.140..381P"><span>Recent increase in Antarctic Peninsula <span class="hlt">ice</span> core uranium <span class="hlt">concentrations</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Potocki, Mariusz; Mayewski, Paul A.; Kurbatov, Andrei V.; Simões, Jefferson C.; Dixon, Daniel A.; Goodwin, Ian; Carleton, Andrew M.; Handley, Michael J.; Jaña, Ricardo; Korotkikh, Elena V.</p> <p>2016-09-01</p> <p>Understanding the distribution of airborne uranium is important because it can result in both chemical and radiological toxicity. <span class="hlt">Ice</span> cores offer the most robust reconstruction of past atmospheric levels of toxic substances. Here we present the first sub-annually dated, continuously sampled <span class="hlt">ice</span> core documenting change in U levels in the Southern Hemisphere. The <span class="hlt">ice</span> core was recovered from the Detroit Plateau, northern Antarctic Peninsula, in 2007 by a joint Brazilian-Chilean-US team. It displays a significant increase in U <span class="hlt">concentration</span> that coincides with reported mining activities in the Southern Hemisphere, notably Australia. Raw U <span class="hlt">concentrations</span> in the Detroit Plateau <span class="hlt">ice</span> core increased by as much as 102 between the 1980s and 2000s accompanied by increased variability in recent years. Decadal mean U <span class="hlt">concentrations</span> increased by a factor of ∼3 from 1980 to 2007, reaching a mean of 205 pg/L from 2000 to 2007. The fact that other terrestrial source dust elements such as Ce, La, Pr, and Ti do not show a similar increase and that the increased U <span class="hlt">concentrations</span> are enriched above natural crustal levels, supports an anthropogenic source for the U as opposed to a change in atmospheric circulation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/15738219','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/15738219"><span>Physical properties of <span class="hlt">ice</span> cream containing milk protein <span class="hlt">concentrates</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Alvarez, V B; Wolters, C L; Vodovotz, Y; Ji, T</p> <p>2005-03-01</p> <p>Two milk protein <span class="hlt">concentrates</span> (MPC, 56 and 85%) were studied as substitutes for 20 and 50% of the protein content in <span class="hlt">ice</span> cream mix. The basic mix formula had 12% fat, 11% nonfat milk solids, 15% sweetener, and 0.3% stabilizer/emulsifier blend. Protein levels remained constant, and total solids were compensated for in MPC mixes by the addition of polydextrose. Physical properties investigated included apparent viscosity, fat globule size, melting rate, shape retention, and freezing behavior using differential scanning calorimetry. Milk protein <span class="hlt">concentrate</span> formulations had higher mix viscosity, larger amount of fat destabilization, narrower <span class="hlt">ice</span> melting curves, and greater shape retention compared with the control. Milk protein <span class="hlt">concentrates</span> did not offer significant modifications of <span class="hlt">ice</span> cream physical properties on a constant protein basis when substituted for up to 50% of the protein supplied by nonfat dry milk. Milk protein <span class="hlt">concentrates</span> may offer <span class="hlt">ice</span> cream manufacturers an alternative source of milk solids non-fat, especially in mixes reduced in lactose or fat, where higher milk solids nonfat are needed to compensate other losses of total solids.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2966089','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2966089"><span>Type I Antifreeze Proteins Enhance <span class="hlt">Ice</span> Nucleation above Certain <span class="hlt">Concentrations</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>Wilson, Peter W.; Osterday, Katie E.; Heneghan, Aaron F.; Haymet, Anthony D. J.</p> <p>2010-01-01</p> <p>In this study, we examined the effects that antifreeze proteins have on the supercooling and <span class="hlt">ice</span>-nucleating abilities of aqueous solutions. Very little information on such nucleation currently exists. Using an automated lag time apparatus and a new analysis, we show several dilution series of Type I antifreeze proteins. Our results indicate that, above a <span class="hlt">concentration</span> of ∼8 mg/ml, <span class="hlt">ice</span> nucleation is enhanced rather than hindered. We discuss this unexpected result and present a new hypothesis outlining three components of polar fish blood that we believe affect its solution properties in certain situations. PMID:20837472</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2001JGR...10615065R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2001JGR...10615065R"><span><span class="hlt">Ice</span> particles in stratiform clouds in the Arctic and possible mechanisms for the production of high <span class="hlt">ice</span> <span class="hlt">concentrations</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rangno, Arthur L.; Hobbs, Peter V.</p> <p>2001-07-01</p> <p>The presence of <span class="hlt">ice</span> particles in clouds affects precipitation processes, the radiative properties of the clouds, and the derivation of cloud properties from remote sensing measurements. High <span class="hlt">ice</span> particle <span class="hlt">concentrations</span> occur often in slightly to moderately supercooled clouds in the Arctic. This paper combines data collected in a common type of <span class="hlt">ice</span>-producing arctic cloud (stratocumulus) with calculations based on laboratory experiments to elucidate mechanisms that might be responsible for the <span class="hlt">ice</span>. <span class="hlt">Ice</span> splinters produced during riming could account for the relatively high <span class="hlt">concentrations</span> of <span class="hlt">ice</span> particles in clouds that encompass temperatures between -2.5°C and -8°C. However, it has generally been assumed that <span class="hlt">ice</span> splinters grow into pristine <span class="hlt">ice</span> crystal habits, whereas detailed measurements in an arctic stratocumulus cloud showed that only 32% of the <span class="hlt">ice</span> particles were pristine crystals (needles, sheaths, short columns, and plates) and 10% were broken pieces of needles or sheaths. Thirty-seven percent of the <span class="hlt">ice</span> particles were not identifiable crystal types, 20% were frozen drops, and 1% were aggregates and graupel. The large numbers of unidentifiable <span class="hlt">ice</span> particles could have originated from the fragmentation of delicate <span class="hlt">ice</span> crystals and the shattering of some drops during freezing in free fall. These two mechanisms may also play a role in the production of relatively high <span class="hlt">ice</span> particle <span class="hlt">concentrations</span> in moderately supercooled arctic clouds that lie outside of the temperature zone where <span class="hlt">ice</span> splinter production by riming occurs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JGRC..121.8511L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JGRC..121.8511L"><span>Sea <span class="hlt">ice</span> algae chlorophyll a <span class="hlt">concentrations</span> derived from under-<span class="hlt">ice</span> spectral radiation profiling platforms</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lange, Benjamin A.; Katlein, Christian; Nicolaus, Marcel; Peeken, Ilka; Flores, Hauke</p> <p>2016-12-01</p> <p>Multiscale sea <span class="hlt">ice</span> algae observations are fundamentally important for projecting changes to sea <span class="hlt">ice</span> ecosystems, as the physical environment continues to change. In this study, we developed upon previously established methodologies for deriving sea <span class="hlt">ice</span>-algal chlorophyll a <span class="hlt">concentrations</span> (chl a) from spectral radiation measurements, and applied these to larger-scale spectral surveys. We conducted four different under-<span class="hlt">ice</span> spectral measurements: irradiance, radiance, transmittance, and transflectance, and applied three statistical approaches: Empirical Orthogonal Functions (EOF), Normalized Difference Indices (NDI), and multi-NDI. We developed models based on <span class="hlt">ice</span> core chl a and coincident spectral irradiance/transmittance (N = 49) and radiance/transflectance (N = 50) measurements conducted during two cruises to the central Arctic Ocean in 2011 and 2012. These reference models were ranked based on two criteria: mean robustness R2 and true prediction error estimates. For estimating the biomass of a large-scale data set, the EOF approach performed better than the NDI, due to its ability to account for the high variability of environmental properties experienced over large areas. Based on robustness and true prediction error, the three most reliable models, EOF-transmittance, EOF-transflectance, and NDI-transmittance, were applied to two remotely operated vehicle (ROV) and two Surface and Under-<span class="hlt">Ice</span> Trawl (SUIT) spectral radiation surveys. In these larger-scale chl a estimates, EOF-transmittance showed the best fit to <span class="hlt">ice</span> core chl a. Application of our most reliable model, EOF-transmittance, to an 85 m horizontal ROV transect revealed large differences compared to published biomass estimates from the same site with important implications for projections of Arctic-wide <span class="hlt">ice</span>-algal biomass and primary production.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19920040056&hterms=PASSIVE+FILTER&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DPASSIVE%2BFILTER','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19920040056&hterms=PASSIVE+FILTER&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DPASSIVE%2BFILTER"><span>Effects of weather on the retrieval of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> and <span class="hlt">ice</span> type from passive microwave data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Maslanik, J. A.</p> <p>1992-01-01</p> <p>Effects of wind, water vapor, and cloud liquid water on <span class="hlt">ice</span> <span class="hlt">concentration</span> and <span class="hlt">ice</span> type calculated from passive microwave data are assessed through radiative transfer calculations and observations. These weather effects can cause overestimates in <span class="hlt">ice</span> <span class="hlt">concentration</span> and more substantial underestimates in multi-year <span class="hlt">ice</span> percentage by decreasing polarization and by decreasing the gradient between frequencies. The effect of surface temperature and air temperature on the magnitudes of weather-related errors is small for <span class="hlt">ice</span> <span class="hlt">concentration</span> and substantial for multiyear <span class="hlt">ice</span> percentage. The existing weather filter in the NASA Team Algorithm addresses only weather effects over open ocean; the additional use of local open-ocean tie points and an alternative weather correction for the marginal <span class="hlt">ice</span> zone can further reduce errors due to weather. <span class="hlt">Ice</span> <span class="hlt">concentrations</span> calculated using 37 versus 18 GHz data show little difference in total <span class="hlt">ice</span> covered area, but greater differences in intermediate <span class="hlt">concentration</span> classes. Given the magnitude of weather-related errors in <span class="hlt">ice</span> classification from passive microwave data, corrections for weather effects may be necessary to detect small trends in <span class="hlt">ice</span> covered area and <span class="hlt">ice</span> type for climate studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20080040137&hterms=global+model+service&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dglobal%2Bmodel%2Bservice','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20080040137&hterms=global+model+service&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dglobal%2Bmodel%2Bservice"><span>Comparison of NASA Team2 and AES-York <span class="hlt">Ice</span> <span class="hlt">Concentration</span> Algorithms Against Operational <span class="hlt">Ice</span> Charts From the Canadian <span class="hlt">Ice</span> Service</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shokr, Mohammed; Markus, Thorsten</p> <p>2006-01-01</p> <p><span class="hlt">Ice</span> <span class="hlt">concentration</span> retrieved from spaceborne passive-microwave observations is a prime input to operational sea-<span class="hlt">ice</span>-monitoring programs, numerical weather prediction models, and global climate models. Atmospheric Environment Service (AES)- York and the Enhanced National Aeronautics and Space Administration Team (NT2) are two algorithms that calculate <span class="hlt">ice</span> <span class="hlt">concentration</span> from Special Sensor Microwave/Imager observations. This paper furnishes a comparison between <span class="hlt">ice</span> <span class="hlt">concentrations</span> (total, thin, and thick types) output from NT2 and AES-York algorithms against the corresponding estimates from the operational analysis of Radarsat images in the Canadian <span class="hlt">Ice</span> Service (CIS). A new data fusion technique, which incorporates the actual sensor's footprint, was developed to facilitate this study. Results have shown that the NT2 and AES-York algorithms underestimate total <span class="hlt">ice</span> <span class="hlt">concentration</span> by 18.35% and 9.66% <span class="hlt">concentration</span> counts on average, with 16.8% and 15.35% standard deviation, respectively. However, the retrieved <span class="hlt">concentrations</span> of thin and thick <span class="hlt">ice</span> are in much more discrepancy with the operational CIS estimates when either one of these two types dominates the viewing area. This is more likely to occur when the total <span class="hlt">ice</span> <span class="hlt">concentration</span> approaches 100%. If thin and thick <span class="hlt">ice</span> types coexist in comparable <span class="hlt">concentrations</span>, the algorithms' estimates agree with CIS'S estimates. In terms of <span class="hlt">ice</span> <span class="hlt">concentration</span> retrieval, thin <span class="hlt">ice</span> is more problematic than thick <span class="hlt">ice</span>. The concept of using a single tie point to represent a thin <span class="hlt">ice</span> surface is not realistic and provides the largest error source for retrieval accuracy. While AES-York provides total <span class="hlt">ice</span> <span class="hlt">concentration</span> in slightly more agreement with CIS'S estimates, NT2 provides better agreement in retrieving thin and thick <span class="hlt">ice</span> <span class="hlt">concentrations</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20080040137&hterms=national+weather+service&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dnational%2Bweather%2Bservice','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20080040137&hterms=national+weather+service&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dnational%2Bweather%2Bservice"><span>Comparison of NASA Team2 and AES-York <span class="hlt">Ice</span> <span class="hlt">Concentration</span> Algorithms Against Operational <span class="hlt">Ice</span> Charts From the Canadian <span class="hlt">Ice</span> Service</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shokr, Mohammed; Markus, Thorsten</p> <p>2006-01-01</p> <p><span class="hlt">Ice</span> <span class="hlt">concentration</span> retrieved from spaceborne passive-microwave observations is a prime input to operational sea-<span class="hlt">ice</span>-monitoring programs, numerical weather prediction models, and global climate models. Atmospheric Environment Service (AES)- York and the Enhanced National Aeronautics and Space Administration Team (NT2) are two algorithms that calculate <span class="hlt">ice</span> <span class="hlt">concentration</span> from Special Sensor Microwave/Imager observations. This paper furnishes a comparison between <span class="hlt">ice</span> <span class="hlt">concentrations</span> (total, thin, and thick types) output from NT2 and AES-York algorithms against the corresponding estimates from the operational analysis of Radarsat images in the Canadian <span class="hlt">Ice</span> Service (CIS). A new data fusion technique, which incorporates the actual sensor's footprint, was developed to facilitate this study. Results have shown that the NT2 and AES-York algorithms underestimate total <span class="hlt">ice</span> <span class="hlt">concentration</span> by 18.35% and 9.66% <span class="hlt">concentration</span> counts on average, with 16.8% and 15.35% standard deviation, respectively. However, the retrieved <span class="hlt">concentrations</span> of thin and thick <span class="hlt">ice</span> are in much more discrepancy with the operational CIS estimates when either one of these two types dominates the viewing area. This is more likely to occur when the total <span class="hlt">ice</span> <span class="hlt">concentration</span> approaches 100%. If thin and thick <span class="hlt">ice</span> types coexist in comparable <span class="hlt">concentrations</span>, the algorithms' estimates agree with CIS'S estimates. In terms of <span class="hlt">ice</span> <span class="hlt">concentration</span> retrieval, thin <span class="hlt">ice</span> is more problematic than thick <span class="hlt">ice</span>. The concept of using a single tie point to represent a thin <span class="hlt">ice</span> surface is not realistic and provides the largest error source for retrieval accuracy. While AES-York provides total <span class="hlt">ice</span> <span class="hlt">concentration</span> in slightly more agreement with CIS'S estimates, NT2 provides better agreement in retrieving thin and thick <span class="hlt">ice</span> <span class="hlt">concentrations</span>.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_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/2017EGUGA..1914345V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1914345V"><span>Reconstruction of Aerosol <span class="hlt">Concentration</span> and Composition from Glacier <span class="hlt">Ice</span> Cores</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vogel, Alexander; Dällenbach, Kaspar; El-Haddad, Imad; Wendl, Isabel; Eichler, Anja; Schwikowski, Margit</p> <p>2017-04-01</p> <p>Reconstruction of the <span class="hlt">concentration</span> and composition of natural aerosol in an undisturbed atmosphere enables the evaluation of the understanding of aerosol-climate effects, which is currently based on highly uncertain emission inventories of the biosphere under pre-industrial conditions. Understanding of the natural state of the pre-industrial atmosphere and evaluating the atmospheric perturbations by anthropogenic emissions, and their potential feedbacks, is essential for accurate model predictions of the future climate (Boucher et al., 2013). Here, we present a new approach for the chemical characterization of the organic fraction preserved in cold-glacier <span class="hlt">ice</span> cores. From this analysis historic trends of atmospheric organic aerosols are reconstructed, allowing new insights on organic aerosol composition and mass in the pre-industrial atmosphere, which can help to improve climate models through evaluation of our current understanding of aerosol radiative effects. We present results from a proof-of-principal study, analyzing an 800 year <span class="hlt">ice</span> core record from the Lomonosovfonna glacier <span class="hlt">ice</span> core, drilled in 2009 in Svalbard, Norway, using a setup that has until then only been applied on offline measurements of aerosol filter extracts (Dällenbach et al., 2016): The melted <span class="hlt">ice</span> was nebulized and dried, such that aerosols are formed from the soluble and insoluble organic and inorganic compounds that are preserved in the <span class="hlt">ice</span>. To improve the sensitivity, the aerosol stream was then enriched by the application of an online aerosol <span class="hlt">concentrator</span>, before the aerosol was analyzed by electron ionization within a high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). We were able to demonstrate that this setup is a quantitative method toward nitrate and sulfate when internal inorganic standards of NH415NO3 and (NH4)234SO4 are added to the sample. Comparison between AMS and IC measurements of nitrate and sulfate resulted in an excellent agreement. The analysis of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1916057A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1916057A"><span><span class="hlt">Ice</span> nucleating particle <span class="hlt">concentration</span> during a combustion aerosol event</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Adams, Mike; O'Sullivan, Daniel; Porter, Grace; Sanchez-Marroquin, Alberto; Tarn, Mark; Harrison, Alex; McQuaid, Jim; Murray, Benjamin</p> <p>2017-04-01</p> <p>The formation of <span class="hlt">ice</span> in supercooled clouds is important for cloud radiative properties, their lifetime and the formation of precipitation. Cloud water droplets can supercool to below -33oC, but in the presence of <span class="hlt">Ice</span> Nucleating Particles (INPs) freezing can be initiated at much higher temperatures. The <span class="hlt">concentration</span> of atmospheric aerosols that are active as INPs depends on a number of factors, such as temperature and aerosol composition and <span class="hlt">concentration</span>. However, our knowledge of which aerosol types serve as INPs is limited. For example, there has been much discussion over whether aerosol from combustion processes are important as INP. This is significant because combustion aerosol have increased in <span class="hlt">concentration</span> dramatically since pre-industrial times and therefore have the potential to exert a significant anthropogenic impact on clouds and climate. In this study we made measurements of INP <span class="hlt">concentrations</span> in Leeds over a specific combustion aerosol event in order to test if there was a correlation between INP <span class="hlt">concentrations</span> and combustion aerosol. The combustion aerosol event was on the 5th November which is a major bonfire and firework event celebrated throughout the UK. During the event we observed a factor of five increase in aerosol and a factor of 10 increase in black carbon, but observed no significant increase in INP <span class="hlt">concentration</span>. This implies that black carbon and combustion aerosol did not compete with the background INP during this event.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012ACPD...1217295G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012ACPD...1217295G"><span>In-situ aircraft observations of <span class="hlt">ice</span> <span class="hlt">concentrations</span> within clouds over the Antarctic Peninsula and Larsen <span class="hlt">Ice</span> Shelf</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Grosvenor, D. P.; Choularton, T. W.; Lachlan-Cope, T.; Gallagher, M. W.; Crosier, J.; Bower, K. N.; Ladkin, R. S.; Dorsey, J. R.</p> <p>2012-07-01</p> <p>In-situ aircraft observations of <span class="hlt">ice</span> crystal <span class="hlt">concentrations</span> in Antarctic clouds are presented for the first time. Orographic, layer and wave clouds around the Antarctic Peninsula and Larsen <span class="hlt">Ice</span> shelf regions were penetrated by the British Antarctic Survey's Twin Otter Aircraft, which was equipped with modern cloud physics probes. The clouds studied were mostly in the free troposphere and hence <span class="hlt">ice</span> crystals blown from the surface are unlikely to have been a major source for the <span class="hlt">ice</span> phase. The temperature range covered by the experiments was 0 to -21°C. The clouds were found to contain supercooled liquid water in most regions and at heterogeneous <span class="hlt">ice</span> formation temperatures <span class="hlt">ice</span> crystal <span class="hlt">concentrations</span> (60 s averages) were often less than 0.07 l-1, although values up to 0.22 l-1 were observed. Estimates of observed aerosol <span class="hlt">concentrations</span> were used as input into the DeMott et al., 2010 <span class="hlt">ice</span> nuclei (IN) parameterisation. The observed <span class="hlt">ice</span> crystal number <span class="hlt">concentrations</span> were generally in broad agreement with the IN predictions, although on the whole the predicted values were higher. Possible reasons for this are discussed and include the lack of IN observations in this region with which to characterise the parameterisation, and/or problems in relating <span class="hlt">ice</span> <span class="hlt">concentration</span> measurements to IN <span class="hlt">concentrations</span>. Other IN parameterisations significantly overestimated the number of <span class="hlt">ice</span> particles. Generally <span class="hlt">ice</span> particle <span class="hlt">concentrations</span> were much lower than found in clouds in middle latitudes for a given temperature. Higher <span class="hlt">ice</span> crystal <span class="hlt">concentrations</span> were sometimes observed at temperatures warmer than -9 °C, with values of several per litre reached. These were attributable to secondary <span class="hlt">ice</span> particle production by the Hallett Mossop process. Even in this temperature range it was observed that there were regions with little or no <span class="hlt">ice</span> that were dominated by supercooled liquid water. It is likely that in some cases this was due to a lack of seeding <span class="hlt">ice</span> crystals to act as rimers to initiate</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012ACP....1211275G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012ACP....1211275G"><span>In-situ aircraft observations of <span class="hlt">ice</span> <span class="hlt">concentrations</span> within clouds over the Antarctic Peninsula and Larsen <span class="hlt">Ice</span> Shelf</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Grosvenor, D. P.; Choularton, T. W.; Lachlan-Cope, T.; Gallagher, M. W.; Crosier, J.; Bower, K. N.; Ladkin, R. S.; Dorsey, J. R.</p> <p>2012-12-01</p> <p>In-situ aircraft observations of <span class="hlt">ice</span> crystal <span class="hlt">concentrations</span> in Antarctic clouds are presented for the first time. Orographic, layer and wave clouds around the Antarctic Peninsula and Larsen <span class="hlt">Ice</span> shelf regions were penetrated by the British Antarctic Survey's Twin Otter aircraft, which was equipped with modern cloud physics probes. The clouds studied were mostly in the free troposphere and hence <span class="hlt">ice</span> crystals blown from the surface are unlikely to have been a major source for the <span class="hlt">ice</span> phase. The temperature range covered by the experiments was 0 to -21 °C. The clouds were found to contain supercooled liquid water in most regions and at heterogeneous <span class="hlt">ice</span> formation temperatures <span class="hlt">ice</span> crystal <span class="hlt">concentrations</span> (60 s averages) were often less than 0.07 l-1, although values up to 0.22 l-1 were observed. Estimates of observed aerosol <span class="hlt">concentrations</span> were used as input into the DeMott et al. (2010) <span class="hlt">ice</span> nuclei (IN) parameterisation. The observed <span class="hlt">ice</span> crystal number <span class="hlt">concentrations</span> were generally in broad agreement with the IN predictions, although on the whole the predicted values were higher. Possible reasons for this are discussed and include the lack of IN observations in this region with which to characterise the parameterisation, and/or problems in relating <span class="hlt">ice</span> <span class="hlt">concentration</span> measurements to IN <span class="hlt">concentrations</span>. Other IN parameterisations significantly overestimated the number of <span class="hlt">ice</span> particles. Generally <span class="hlt">ice</span> particle <span class="hlt">concentrations</span> were much lower than found in clouds in middle latitudes for a given temperature. Higher <span class="hlt">ice</span> crystal <span class="hlt">concentrations</span> were sometimes observed at temperatures warmer than -9 °C, with values of several per litre reached. These were attributable to secondary <span class="hlt">ice</span> particle production by the Hallett Mossop process. Even in this temperature range it was observed that there were regions with little or no <span class="hlt">ice</span> that were dominated by supercooled liquid water. It is likely that in some cases this was due to a lack of seeding <span class="hlt">ice</span> crystals to act as rimers to initiate</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ESASP.740E.339K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ESASP.740E.339K"><span>Baltic Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> Estimation Using Sentinel-1 SAR and Microwave Radiometer Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Karvonen, Juha</p> <p>2016-08-01</p> <p>Sea <span class="hlt">ice</span> <span class="hlt">concentration</span> (SIC) is an important sea <span class="hlt">ice</span> parameter in environmental research, weather and <span class="hlt">ice</span> forecasting and for navigation. We have developed a method for estimation of the Baltic Sea SIC using SENTINEL-1 SAR data and AMSR-2 microwave radiometer (MWR). Here we present the method and first results of January 2016. <span class="hlt">Ice</span> <span class="hlt">concentration</span> of FMI daily <span class="hlt">ice</span> charts has been used as reference data in this study.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JGRC..120.8327H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRC..120.8327H"><span>Short-term sea <span class="hlt">ice</span> forecasting: An assessment of <span class="hlt">ice</span> <span class="hlt">concentration</span> and <span class="hlt">ice</span> drift forecasts using the U.S. Navy's Arctic Cap Nowcast/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>Hebert, David A.; Allard, Richard A.; Metzger, E. Joseph; Posey, Pamela G.; Preller, Ruth H.; Wallcraft, Alan J.; Phelps, Michael W.; Smedstad, Ole Martin</p> <p>2015-12-01</p> <p>In this study the forecast skill of the U.S. Navy operational Arctic sea <span class="hlt">ice</span> forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1-7 day forecasts of Arctic sea <span class="hlt">ice</span> and ocean conditions. Many quantities are forecast by ACNFS; the most commonly used include <span class="hlt">ice</span> <span class="hlt">concentration</span>, <span class="hlt">ice</span> thickness, <span class="hlt">ice</span> velocity, sea surface temperature, sea surface salinity, and sea surface velocities. <span class="hlt">Ice</span> <span class="hlt">concentration</span> forecast skill is compared to a persistent <span class="hlt">ice</span> state and historical sea <span class="hlt">ice</span> climatology. Skill scores are focused on areas where <span class="hlt">ice</span> <span class="hlt">concentration</span> changes by ±5% or more, and are therefore limited to primarily the marginal <span class="hlt">ice</span> zone. We demonstrate that ACNFS forecasts are skilful compared to assuming a persistent <span class="hlt">ice</span> state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for forecasts up to 102 h. Modeled <span class="hlt">ice</span> drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended <span class="hlt">ice</span> <span class="hlt">concentration</span> derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and <span class="hlt">Ice</span> Mapping System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short-term forecast skill and <span class="hlt">ice</span> edge location compared to the independently derived National <span class="hlt">Ice</span> Center <span class="hlt">Ice</span> Edge product.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.9553L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.9553L"><span>Global Scale Simultaneous Retrieval of Smoothened Vegetation Optical Depth and Surface Roughness Parameter using <span class="hlt">AMSR-E</span> X-band Observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lanka, Karthikeyan; Pan, Ming; Konings, Alexandra; Piles, María; D, Nagesh Kumar; Wood, Eric</p> <p>2017-04-01</p> <p>Traditionally, passive microwave retrieval algorithms such as Land Parameter Retrieval Model (LPRM) estimate simultaneously soil moisture and Vegetation Optical Depth (VOD) using brightness temperature (Tb) data. The algorithm requires a surface roughness parameter which - despite implications - is generally assumed to be constant at global scale. Due to inherent noise in the satellite data and retrieval algorithm, the VOD retrievals are usually observed to be highly fluctuating at daily scale which may not occur in reality. Such noisy VOD retrievals along with spatially invariable roughness parameter may affect the quality of soil moisture retrievals. The current work aims to smoothen the VOD retrievals (with an assumption that VOD remains constant over a period of time) and simultaneously generate, for the first time, global surface roughness map using multiple descending X-band Tb observations of <span class="hlt">AMSR-E</span>. The methodology utilizes Tb values under a moving-time-window-setup to estimate concurrently the soil moisture of each day and a constant VOD in the window. Prior to this step, surface roughness parameter is estimated using the complete time series of Tb record. Upon carrying out the necessary sensitivity analysis, the smoothened VOD along with soil moisture retrievals is generated for the 10-year duration of <span class="hlt">AMSR-E</span> (2002-2011) with a 7-day moving window using the LPRM framework. The spatial patterns of resulted global VOD maps are in coherence with vegetation biomass and climate conditions. The VOD results also exhibit a smoothening effect in terms of lower values of standard deviation. This is also evident from time series comparison of VOD and LPRM VOD retrievals without optimization over moving windows at several grid locations across the globe. The global surface roughness map also exhibited spatial patterns that are strongly influenced by topography and land use conditions. Some of the noticeable features include high roughness over mountainous regions and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ACP....16.2185H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ACP....16.2185H"><span>Unexpectedly high ultrafine aerosol <span class="hlt">concentrations</span> above East Antarctic sea <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>Humphries, R. S.; Klekociuk, A. R.; Schofield, R.; Keywood, M.; Ward, J.; Wilson, S. R.</p> <p>2016-02-01</p> <p>Better characterisation of aerosol processes in pristine, natural environments, such as Antarctica, have recently been shown to lead to the largest reduction in uncertainties in our understanding of radiative forcing. Our understanding of aerosols in the Antarctic region is currently based on measurements that are often limited to boundary layer air masses at spatially sparse coastal and continental research stations, with only a handful of studies in the vast sea-<span class="hlt">ice</span> region. In this paper, the first observational study of sub-micron aerosols in the East Antarctic sea <span class="hlt">ice</span> region is presented. Measurements were conducted aboard the icebreaker Aurora Australis in spring 2012 and found that boundary layer condensation nuclei (CN3) <span class="hlt">concentrations</span> exhibited a five-fold increase moving across the polar front, with mean polar cell <span class="hlt">concentrations</span> of 1130 cm-3 - higher than any observed elsewhere in the Antarctic and Southern Ocean region. The absence of evidence for aerosol growth suggested that nucleation was unlikely to be local. Air parcel trajectories indicated significant influence from the free troposphere above the Antarctic continent, implicating this as the likely nucleation region for surface aerosol, a similar conclusion to previous Antarctic aerosol studies. The highest aerosol <span class="hlt">concentrations</span> were found to correlate with low-pressure systems, suggesting that the passage of cyclones provided an accelerated pathway, delivering air masses quickly from the free troposphere to the surface. After descent from the Antarctic free troposphere, trajectories suggest that sea-<span class="hlt">ice</span> boundary layer air masses travelled equatorward into the low-albedo Southern Ocean region, transporting with them emissions and these aerosol nuclei which, after growth, may potentially impact on the region's radiative balance. The high aerosol <span class="hlt">concentrations</span> and their transport pathways described here, could help reduce the discrepancy currently present between simulations and observations of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1610440J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1610440J"><span>Sea <span class="hlt">ice</span> <span class="hlt">concentration</span> and sea <span class="hlt">ice</span> drift for the Arctic summer using C- and L-band SAR</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Johansson, Malin; Berg, Anders; Eriksson, Leif</p> <p>2014-05-01</p> <p>The decreasing amount of sea <span class="hlt">ice</span> and changes from multi-year <span class="hlt">ice</span> to first year <span class="hlt">ice</span> within the Arctic Ocean opens up for increased maritime activities. These activities include transportation, fishing and tourism. One of the major threats for the shipping is the presence of sea <span class="hlt">ice</span>. Should an oil spill occur, the search and rescue is heavily dependent on constant updates of sea <span class="hlt">ice</span> movements, both to enable a safer working environment and to potentially prevent the oil from reaching the sea <span class="hlt">ice</span>. It is therefore necessary to have accurate and updated sea <span class="hlt">ice</span> charts for the Arctic Ocean during the entire year. During the melt season that <span class="hlt">ice</span> is subject to melting conditions making satellite observations of sea <span class="hlt">ice</span> more difficult. This period coincides with the peak in marine shipping activities and therefore requires highly accurate sea <span class="hlt">ice</span> <span class="hlt">concentration</span> estimates. Synthetic Aperture Radar (SAR) are not hindered by clouds and do not require daylight. The continuous record and high temporal resolution makes C-band data preferable as input data for operational sea <span class="hlt">ice</span> mapping. However, with C-band SAR it is sometimes difficult to distinguish between a wet sea <span class="hlt">ice</span> surface and surrounding open water. L-band SAR has a larger penetration depth and has been shown to be less sensitive to less sensitive than C-band to the melt season. Inclusion of L-band data into sea chart estimates during the melt season in particular could therefore improve sea <span class="hlt">ice</span> monitoring. We compare sea <span class="hlt">ice</span> <span class="hlt">concentration</span> melt season observations using Advanced Land Observing Satellite (ALOS) L-band images with Envisat ASAR C-band images. We evaluate if L-band images can be used to improve separation of wet surface <span class="hlt">ice</span> from open water and compare with results for C-band.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110023007','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110023007"><span>Improving Simulated Soil Moisture Fields Through Assimilation of <span class="hlt">AMSR-E</span> Soil Moisture Retrievals with an Ensemble Kalman Filter and a Mass Conservation Constraint</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Li, Bailing; Toll, David; Zhan, Xiwu; Cosgrove, Brian</p> <p>2011-01-01</p> <p>Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of soil moisture in a footprint area, and can be used to reduce model bias (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce model bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate the actual value of Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) soil moisture retrievals to improve the mean of simulated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C11B0377L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C11B0377L"><span>The melting sea <span class="hlt">ice</span> of Arctic polar cap in the summer solstice month and the role of ocean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, S.; Yi, Y.</p> <p>2014-12-01</p> <p>The Arctic sea <span class="hlt">ice</span> is becoming smaller and thinner than climatological standard normal and more fragmented in the early summer. We investigated the widely changing Arctic sea <span class="hlt">ice</span> using the daily sea <span class="hlt">ice</span> <span class="hlt">concentration</span> data. Sea <span class="hlt">ice</span> data is generated from brightness temperature data derived from the sensors: Defense Meteorological Satellite Program (DMSP)-F13 Special Sensor Microwave/Imagers (SSM/Is), the DMSP-F17 Special Sensor Microwave Imager/Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer - Earth Observing System (<span class="hlt">AMSR-E</span>) instrument on the NASA Earth Observing System (EOS) Aqua satellite. We tried to figure out appearance of arctic sea <span class="hlt">ice</span> melting region of polar cap from the data of passive microwave sensors. It is hard to explain polar sea <span class="hlt">ice</span> melting only by atmosphere effects like surface air temperature or wind. Thus, our hypothesis explaining this phenomenon is that the heat from deep undersea in Arctic Ocean ridges and the hydrothermal vents might be contributing to the melting of Arctic sea <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJAEO..45..221M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJAEO..45..221M"><span>DisPATCh as a tool to evaluate coarse-scale remotely sensed soil moisture using localized in situ measurements: Application to SMOS and <span class="hlt">AMSR-E</span> data in Southeastern Australia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Malbéteau, Yoann; Merlin, Olivier; Molero, Beatriz; Rüdiger, Christoph; Bacon, Stephan</p> <p>2016-03-01</p> <p>Validating coarse-scale satellite soil moisture data still represents a big challenge, notably due to the large mismatch existing between the spatial resolution (> 10 km) of microwave radiometers and the representativeness scale (several m) of localized in situ measurements. This study aims to examine the potential of DisPATCh (Disaggregation based on Physical and Theoretical scale Change) for validating SMOS (Soil Moisture and Ocean Salinity) and <span class="hlt">AMSR-E</span> (Advanced Microwave Scanning Radiometer-Earth observation system) level-3 soil moisture products. The ∽40-50 km resolution SMOS and <span class="hlt">AMSR-E</span> data are disaggregated at 1 km resolution over the Murrumbidgee catchment in Southeastern Australia during a one year period in 2010-2011, and the satellite products are compared with the in situ measurements of 38 stations distributed within the study area. It is found that disaggregation improves the mean difference, correlation coefficient and slope of the linear regression between satellite and in situ data in 77%, 92% and 94% of cases, respectively. Nevertheless, the downscaling efficiency is lower in winter than during the hotter months when DisPATCh performance is optimal. Consistently, better results are obtained in the semi-arid than in a temperate zone of the catchment. In the semi-arid Yanco region, disaggregation in summer increases the correlation coefficient from 0.63 to 0.78 and from 0.42 to 0.71 for SMOS and <span class="hlt">AMSR-E</span> in morning overpasses and from 0.37 to 0.63 and from 0.47 to 0.73 for SMOS and <span class="hlt">AMSR-E</span> in afternoon overpasses, respectively. DisPATCh has strong potential in low vegetated semi-arid areas where it can be used as a tool to evaluate coarse-scale remotely sensed soil moisture by explicitly representing the sub-pixel variability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19890009426&hterms=PASSIVE+FILTER&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DPASSIVE%2BFILTER','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19890009426&hterms=PASSIVE+FILTER&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DPASSIVE%2BFILTER"><span>Estimating sea <span class="hlt">ice</span> <span class="hlt">concentration</span> from satellite passive microwave data and a physical model</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Rothrock, D. A.; Thomas, D. R.</p> <p>1988-01-01</p> <p>Sea <span class="hlt">ice</span> remote sensing and estimation of <span class="hlt">concentrations</span> of each of several <span class="hlt">ice</span> types from passive microwave satellite data is described. The approach is based on the Kalman filter; it incorporates surface temperature, <span class="hlt">ice</span> advection, and <span class="hlt">ice</span> deformation data derived from drifting buoys and uses the whole temporal microwave record to make a smoothed estimate of <span class="hlt">ice</span> <span class="hlt">concentration</span>. The method allows resolution of previously ambiguous surface types. An example using time histories of two SMMR measurements to resolve the fractional areas of four surface types: open water, first-year, second-year and older multiyear <span class="hlt">ice</span> is shown.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFM.U21A0801M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFM.U21A0801M"><span>Towards a satellite-based sea <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. N.; Fetterer, F.; Stroeve, J.; Cavalieri, D.; Parkinson, C.; Comiso, J.; Weaver, R.</p> <p>2005-12-01</p> <p>Sea <span class="hlt">ice</span> plays an important role in the Earth's climate through its influence on the surface albedo, heat and moisture transfer between the ocean and the atmosphere, and the thermohaline circulation. Satellite data reveal that since 1979, summer Arctic sea <span class="hlt">ice</span> has, overall, been declining at a rate of almost 8%/decade, with recent summers (beginning in 2002) being particularly low. The receding sea <span class="hlt">ice</span> is having an effect on wildlife and indigenous peoples in the Arctic, and concern exists that these effects may become increasingly severe. Thus, a long-term, ongoing climate data record of sea <span class="hlt">ice</span> is crucial for tracking the changes in sea <span class="hlt">ice</span> and for assessing the significance of long-term trends. Since the advent of passive microwave satellite instruments in the early 1970s, sea <span class="hlt">ice</span> has been one of the most consistently monitored climate parameters. There is now a 27+ year record of sea <span class="hlt">ice</span> extent and <span class="hlt">concentration</span> from multi-channel passive microwave radiometers that has undergone inter-sensor calibration and other quality controls to ensure consistency throughout the record. Several algorithms have been developed over the years to retrieve sea <span class="hlt">ice</span> extent and <span class="hlt">concentration</span> and two of the most commonly used algorithms, the NASA Team and Bootstrap, have been applied to the entire SMMR-SSM/I record to obtain a consistent time series. These algorithms were developed at NASA Goddard Space Flight Center and are archived at the National Snow and <span class="hlt">Ice</span> Data Center. However, the complex surface properties of sea <span class="hlt">ice</span> affect the microwave signature, and algorithms can yield ambiguous results; no single algorithm has been found to work uniformly well under all sea <span class="hlt">ice</span> conditions. Thus there are ongoing efforts to further refine the algorithms and the time series. One approach is to develop data fusion methods to optimally combine sea <span class="hlt">ice</span> fields from two or more algorithms. Another approach is to take advantage of the improved capabilities of JAXA's <span class="hlt">AMSR-E</span> sensor on NASA's Aqua</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 Sea-<span class="hlt">Ice</span> <span class="hlt">Concentration</span> 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>A significant rise in summer temperatures over the past several decades has led to widespread retreat of the Greenland <span class="hlt">Ice</span> Sheet (GIS) margin and surrounding sea <span class="hlt">ice</span>. Recent observations from geodetic stations and GRACE show that <span class="hlt">ice</span> mass loss progressed from South Greenland up to Northwest Greenland by 2005 (Khan et al., 2010). Observations from meteorological stations at the U.S. Thule Air Force Base, remote sensing platforms, and climate reanalyses indicate a 3.5C mean annual warming in the Thule region and a 44% decrease in summer (JJAS) sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span> in Baffin Bay from 1980-2010. Mean annual precipitation near Thule increased by 12% over this interval, with the majority of the increase occurring in fall (SON). To improve projections of future <span class="hlt">ice</span> loss and sea-level rise in a warming climate, we are currently developing multi-proxy records (lake sediment cores, <span class="hlt">ice</span> cores, glacial geologic data, glaciological models) of Holocene climate variability and cryospheric response in NW Greenland, with a focus on past warm periods. As part of our efforts to develop a millennial-length <span class="hlt">ice</span> core paleoclimate record from the Thule region, we collected and analyzed snow pit samples and short firn cores (up to 20 m) from the coastal region of the GIS (2Barrel site; 76.9317 N, 63.1467 W) and the summit of North <span class="hlt">Ice</span> Cap (76.938 N, 67.671 W) in 2011 and 2012, respectively. The 2Barrel <span class="hlt">ice</span> core was sampled using a continuous <span class="hlt">ice</span> core melting system at Dartmouth, and subsequently analyzed for major anion and trace element <span class="hlt">concentrations</span> and stable water isotope ratios. Here we show that the 2Barrel <span class="hlt">ice</span> core spanning 1990-2010 records a 25% increase in mean annual snow accumulation, and is positively correlated (r = 0.52, p<0.01) with ERA-Interim precipitation. The 2Barrel annual sea-salt Na <span class="hlt">concentration</span> is strongly correlated (r = 0.5-0.8, p<0.05) with summer and fall sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span> in northern Baffin Bay near Thule (Figure 1). We hypothesize that the positive</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A51I0194A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A51I0194A"><span>Spatial and Temporal Variation of Boundary Layer Lapse Rate and Cloud-top-height Observed from MODIS, CALIPSO and <span class="hlt">AMSR-E</span> over Eastern Pacific</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Adhikari, L.; Xie, F.; Winning, T.</p> <p>2015-12-01</p> <p>The strong free tropospheric subsidence and the cool sea surface temperatures over the subtropical eastern Pacific Ocean often lead to a shallow and cloudy planetary boundary layer (PBL) capped by a strong inversion. These low PBL clouds are crucial for understanding the ocean-atmosphere interaction and the cloud-radiation feedback processes. However, accurate identification/representation of these clouds remains a key challenge in both satellite observations and global climate model simulations. Specifically, the cloud transition from the near-shore stratocumulus to trade-cumulus remains a huge challenge in climate models and warrants high-quality PBL observations from space. The MODIS collection 6 cloud top height vastly improves the global PBL cloud top heights (CTH) compared to collection 5. However, the MODIS collection 6 CTH still shows systematic higher CTH than CALIPSO in the subtropical subsidence region, which is likely due to the underestimation of lapse rate. This study presents the seasonal climatology of PBL lapse rate derived from multi-year CALIPSO with co-incident MODIS CTT and <span class="hlt">AMSR-E</span> SST measurements. The lapse rate climatology is validated by the high-resolution radiosonde observations and then used to derive the CTH from MODIS measurements. Comparison of the new lapse rate based MODIS CTH with CALIPSO CTH will be presented. The PBL height derived from the COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) GPS radio occultation (RO) will be used to evaluate the MODIS CTH as an independent dataset. The discrepancies over the transition from stratus to trade-cumuli regions (broken clouds) will also be discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1817411S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1817411S"><span>Modeling the relative contributions of secondary <span class="hlt">ice</span> formation processes to <span class="hlt">ice</span> crystal number <span class="hlt">concentrations</span> within mixed-phase clouds</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sullivan, Sylvia; Hoose, Corinna; Nenes, Athanasios</p> <p>2016-04-01</p> <p>Measurements of in-cloud <span class="hlt">ice</span> crystal number <span class="hlt">concentrations</span> can be three or four orders of magnitude greater than the in-cloud <span class="hlt">ice</span> nuclei number <span class="hlt">concentrations</span>. This discrepancy can be explained by various secondary <span class="hlt">ice</span> formation processes, which occur after initial <span class="hlt">ice</span> nucleation, but the relative importance of these processes, and even the exact physics of each, is still unclear. A simple bin microphysics model (2IM) is constructed to investigate these knowledge gaps. 2IM extends the time-lag collision parameterization of Yano and Phillips, 2011 to include rime splintering, <span class="hlt">ice-ice</span> aggregation, and droplet shattering and to incorporate the aspect ratio evolution as in Jensen and Harrington, 2015. The relative contribution of the secondary processes under various conditions are shown. In particular, temperature-dependent efficiencies are adjusted for <span class="hlt">ice-ice</span> aggregation versus collision around -15°C, when rime splintering is no longer active, and the effect of aspect ratio on the process weighting is explored. The resulting simulations are intended to guide secondary <span class="hlt">ice</span> formation parameterizations in larger-scale mixed-phase cloud schemes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1996AnGeo..14.1192L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1996AnGeo..14.1192L"><span>Pyramidal <span class="hlt">ice</span> crystal scattering phase functions and <span class="hlt">concentric</span> halos</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, C.; Jonas, P. R.; Saunders, C. P. R.</p> <p>1996-11-01</p> <p>Phase functions have been calculated using the Monte Carlo/geometric ray tracing method for single hexagonal pyramidal <span class="hlt">ice</span> crystals (such as solid and hollow bullets) randomly oriented in space and horizontal plane, in order to study the <span class="hlt">concentric</span> halo formations. Results from three dimensional model calculations show that 9° halo can be as bright as the common 22° halo for pyramidal angle of 28°, and the 18°, 20°, 24° and 35° halos cannot be seen due to the strong 22° halo domination in the scattering phase function between 18° and 35°. For solid pyramidal <span class="hlt">ice</span> crystals randomly oriented horizontally, the 35° arc can be produced and its intensity depends on the incident ray solar angle and the particle aspect ratio. Acknowledgements. The work done by P. Henelius and E. Vilenius in programme development is gratefully acknowledged. Topical Editor D. Alcayde thanks I. Pryse and A. Vallance-Jones for their help in evaluating this paper.--> Correspondence to: T. Nygrén--></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020074657','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020074657"><span>Sensitivity of Lunar Resource Economic Model to Lunar <span class="hlt">Ice</span> <span class="hlt">Concentration</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Blair, Brad; Diaz, Javier</p> <p>2002-01-01</p> <p>Lunar Prospector mission data indicates sufficient <span class="hlt">concentration</span> of hydrogen (presumed to be in the form of water <span class="hlt">ice</span>) to form the basis for lunar in-situ mining activities to provide a source of propellant for near-Earth and solar system transport missions. A model being developed by JPL, Colorado School of Mines, and CSP, Inc. generates the necessary conditions under which a commercial enterprise could earn a sufficient rate of return to develop and operate a LEO propellant service for government and commercial customers. A combination of Lunar-derived propellants, L-1 staging, and orbital fuel depots could make commercial LEO/GEO development, inter-planetary missions and the human exploration and development of space more energy, cost, and mass efficient.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.A22A..08J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.A22A..08J"><span>Physical processes controlling the evolution of <span class="hlt">ice</span> <span class="hlt">concentration</span> in cirrus clouds</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jensen, E. J.; Pfister, L.</p> <p>2011-12-01</p> <p>Several past studies have compared measured cirrus <span class="hlt">ice</span> <span class="hlt">concentrations</span> with calculations based on nucleation theory. However, such calculations only indicate the peak <span class="hlt">ice</span> <span class="hlt">concentrations</span> occurring just after nucleation events. Various cloud processes (e.g., differential sedimentation, entrainment, dispersion, and aggregation) conspire to reduce mean <span class="hlt">ice</span> <span class="hlt">concentrations</span> as the cloud evolves. Here, we use both a one-dimensional cloud model and a three-dimensional cloud-resolving model to evaluate the impact of these processes on the evolution of <span class="hlt">ice</span> <span class="hlt">concentration</span> through the lifecycle of cirrus clouds. Results are compared statistically with recent airborne measurements of <span class="hlt">ice</span> <span class="hlt">concentration</span> in the midlatitude and tropical uppermost troposphere. We will show that mean <span class="hlt">ice</span> <span class="hlt">concentrations</span> are reduced substantially by processes occurring after nucleation events, and this issue should be taken into consideration when comparing with observations that necessarily represent a range of cloud ages.</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/2011TRACE..22...63T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011TRACE..22...63T"><span>Continuous Production of <span class="hlt">Ice</span> Slurry by Control of Solute <span class="hlt">Concentration</span> with Ultrasonic Vibration</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tada, Yukio; Takimoto, Akira; Miyamoto, Tomoaki; Mikami, Hiroko; Hayashi, Yujiro</p> <p></p> <p>A method to making <span class="hlt">ice</span> slurry is one of key technology for cold-energy Storage system. This study has been conducted to clarify continuous production of <span class="hlt">ice</span> slurry by utilizing constitutional supercooling promoted by mixing of two aqueous solutions whose solute <span class="hlt">concentrations</span> are different. In this technique, fine <span class="hlt">ice</span> crystals are made under volume-catalyzed nucleation without heat transfer surface. In the experiments, cooled sucrose solution and water were mixed in the cylindrical vessel, and ultrasonic vibration was applied to promote nucleation in the supercooled solution. It was found that the <span class="hlt">ice</span> making process is classified into three characteristic patterns; stable <span class="hlt">ice</span> making, <span class="hlt">ice</span> making in stratified <span class="hlt">concentration</span> layer due to defect in solute-mixing, and no <span class="hlt">ice</span> making due to no supercooling by mixing. The characteristics of <span class="hlt">ice</span> making were discussed with the mixing ratio and total flow rate of solutions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20070018253&hterms=klein&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAuthor-Name%26N%3D0%26No%3D60%26Ntt%3Dklein','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20070018253&hterms=klein&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAuthor-Name%26N%3D0%26No%3D60%26Ntt%3Dklein"><span>Microwave Signatures of Snow on Sea <span class="hlt">Ice</span>: Observations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Markus, Thorsten; Cavalieri, Donald J.; Gasiewski, Albin J.; Klein, Marian; Maslanik, James A.; Powell, Dylan C.; Stankov, B. Boba; Stroeve, Julienne C.; Sturm, Matthew</p> <p>2006-01-01</p> <p>Part of the Earth Observing System Aqua Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) Arctic sea <span class="hlt">ice</span> validation campaign in March 2003 was dedicated to the validation of snow depth on sea <span class="hlt">ice</span> and <span class="hlt">ice</span> temperature products. The difficulty with validating these two variables is that neither can currently be measured other than in situ. For this reason, two aircraft flights on March 13 and 19,2003, were dedicated to these products, and flight lines were coordinated with in situ measurements of snow and sea <span class="hlt">ice</span> physical properties. One flight was in the vicinity of Barrow, AK, covering Elson Lagoon and the adjacent Chukchi and Beaufort Seas. The other flight was farther north in the Beaufort Sea (about 73 N, 147.5 W) and was coordinated with a Navy <span class="hlt">ice</span> camp. The results confirm the <span class="hlt">AMSR-E</span> snow depth algorithm and its coefficients for first-year <span class="hlt">ice</span> when it is relatively smooth. For rough first-year <span class="hlt">ice</span> and for multiyear <span class="hlt">ice</span>, there is still a relationship between the spectral gradient ratio of 19 and 37 GHz, but a different set of algorithm coefficients is necessary. Comparisons using other <span class="hlt">AMSR-E</span> channels did not provide a clear signature of sea <span class="hlt">ice</span> characteristics and, hence, could not provide guidance for the choice of algorithm coefficients. The limited comparison of in situ snow-<span class="hlt">ice</span> interface and surface temperatures with 6-GHz brightness temperatures, which are used for the retrieval of <span class="hlt">ice</span> temperature, shows that the 6-GHz temperature is correlated with the snow-<span class="hlt">ice</span> interface temperature to only a limited extent. For strong temperature gradients within the snow layer, it is clear that the 6-GHz temperature is a weighted average of the entire snow layer.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120013478','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120013478"><span>Variability and Anomalous Trends in the Global Sea <span class="hlt">Ice</span> Cover</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Comiso, Josefino C.</p> <p>2012-01-01</p> <p> MODIS, <span class="hlt">AMSR-E</span> and SSM/I data reveal that the sea <span class="hlt">ice</span> production rate at the coastal polynyas along the Ross <span class="hlt">Ice</span> Shelf has been increasing since 1992. This also means that the salinization rate and the formation of bottom water in the region are going up as well. Simulation studies indicate that the stronger production rate is likely associated with the ozone hole that has caused a deepening of the lows in the West Antarctic region and therefore stronger winds off the Ross <span class="hlt">Ice</span> Shelf. Stronger winds causes larger coastal polynyas near the shelf and hence an enhanced <span class="hlt">ice</span> production in the region during the autumn and winter period. Results of analysis of temperature data from MODIS and <span class="hlt">AMSR-E</span> shows that the area and <span class="hlt">concentration</span> of the sea <span class="hlt">ice</span> cover are highly correlated with surface temperature for both the Arctic and Antarctic, especially in the seasonal regions where the correlation coefficients are about 0.9. Abnormally high sea surface temperatures (SSTs) and surface <span class="hlt">ice</span> temperatures (SITs) were also observed in 2007 and 2011when drastic reductions in the summer <span class="hlt">ice</span> cover occurred, This phenomenon is consistent with the expected warming of the upper layer of the Arctic Ocean on account of <span class="hlt">ice</span>-albedo feedback. Changes in atmospheric circulation are also expected to have a strong influence on the sea <span class="hlt">ice</span> cover but the results of direct correlation analyses of the sea <span class="hlt">ice</span> cover with the Northern and the Southern Annular Mode indices show relatively weak correlations, This might be due in part to the complexity of the dynamics of the system that can be further altered by some phenomena like the Antarctic Circumpolar Wave and extra polar processes like the El Nino Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (POD),</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015TCD.....9.2543Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015TCD.....9.2543Y"><span>The benefit of using sea <span class="hlt">ice</span> <span class="hlt">concentration</span> satellite data products with uncertainty estimates in summer sea <span class="hlt">ice</span> data assimilation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yang, Q.; Losch, M.; Losa, S.; Jung, T.; Nerger, L.; Lavergne, T.</p> <p>2015-04-01</p> <p>We present sensitivity experiments in which the Ocean and Sea <span class="hlt">Ice</span> Satellite Application Facility (OSISAF) near-real time sea <span class="hlt">ice</span> <span class="hlt">concentration</span> data and the recently released Sea <span class="hlt">Ice</span> Climate Change Initiative (SICCI) data are assimilated during summer. The data assimilation system uses the MIT general circulation model (MITgcm) and a local Singular Evolutive Interpolated Kalman (LSEIK) filter. Atmospheric forcing uncertainties are modelled by using atmospheric ensemble forcing which is taken from the UK Met Office (UKMO) system available through the TIGGE (THORPEX Interactive Grand Global Ensemble) database. When a constant data uncertainty is assumed, the assimilation of SICCI <span class="hlt">concentrations</span> outperforms the assimilation of OSISAF data in both <span class="hlt">concentration</span> and thickness forecasts. This is probably because the SICCI data retrieval uses an improved processing algorithms and methodologies. For the assimilation of SICCI data, using the observation uncertainties that are provided with the data improves the ensemble mean state of <span class="hlt">ice</span> <span class="hlt">concentration</span> compared to using constant data errors, but does not improve the <span class="hlt">ice</span> thickness. This is caused by a mismatch between the SICCI <span class="hlt">concentration</span> and the modelled physical <span class="hlt">ice</span> <span class="hlt">concentration</span>. To account for this mismatch the SICCI product should feature larger uncertainties in summer. Consistently, thickness forecasts can be improved by raising the minimum observation uncertainty to inflate the underestimated data error and ensemble spread.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ACP....1612411Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ACP....1612411Z"><span>What controls the low <span class="hlt">ice</span> number <span class="hlt">concentration</span> in the upper troposphere?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhou, Cheng; Penner, Joyce E.; Lin, Guangxing; Liu, Xiaohong; Wang, Minghuai</p> <p>2016-10-01</p> <p>Cirrus clouds in the tropical tropopause play a key role in regulating the moisture entering the stratosphere through their dehydrating effect. Low <span class="hlt">ice</span> number <span class="hlt">concentrations</span> ( < 200 L-1) and high supersaturations (150-160 %) have been observed in these clouds. Different mechanisms have been proposed to explain these low <span class="hlt">ice</span> number <span class="hlt">concentrations</span>, including the inhibition of homogeneous freezing by the deposition of water vapour onto pre-existing <span class="hlt">ice</span> crystals, heterogeneous <span class="hlt">ice</span> formation on glassy organic aerosol <span class="hlt">ice</span> nuclei (IN), and limiting the formation of <span class="hlt">ice</span> number from high-frequency gravity waves. In this study, we examined the effect from three different representations of updraft velocities, the effect from pre-existing <span class="hlt">ice</span> crystals, the effect from different water vapour deposition coefficients (α = 0.1 or 1), and the effect of 0.1 % of the total secondary organic aerosol (SOA) particles acting as IN. Model-simulated <span class="hlt">ice</span> crystal numbers are compared against an aircraft observational dataset.Including the effect from water vapour deposition on pre-existing <span class="hlt">ice</span> particles can effectively reduce simulated in-cloud <span class="hlt">ice</span> number <span class="hlt">concentrations</span> for all model setups. A larger water vapour deposition coefficient (α = 1) can also efficiently reduce <span class="hlt">ice</span> number <span class="hlt">concentrations</span> at temperatures below 205 K, but less so at higher temperatures. SOA acting as IN is most effective at reducing <span class="hlt">ice</span> number <span class="hlt">concentrations</span> when the effective updraft velocities are moderate ( ˜ 0.05-0.2 m s-1). However, the effects of including SOA as IN and using (α = 1) are diminished when the effect from pre-existing <span class="hlt">ice</span> is included.When a grid-resolved large-scale updraft velocity ( < 0.1 m s-1) is used, the <span class="hlt">ice</span> nucleation parameterization with homogeneous freezing only or with both homogeneous freezing and heterogeneous nucleation is able to generate low <span class="hlt">ice</span> number <span class="hlt">concentrations</span> in good agreement with observations for temperatures below 205 K as long as the pre-existing <span class="hlt">ice</span> effect is</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ACPD...1535907Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ACPD...1535907Z"><span>What controls the low <span class="hlt">ice</span> number <span class="hlt">concentration</span> in the upper troposphere?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhou, C.; Penner, J. E.; Lin, G.; Liu, X.; Wang, M.</p> <p>2015-12-01</p> <p>Cirrus clouds in the tropical tropopause play a key role in regulating the moisture entering the stratosphere through their dehydrating effect. Low <span class="hlt">ice</span> number <span class="hlt">concentrations</span> (< 200 L-1) and high supersaturations (150-160 %) have been observed in these clouds. Different mechanisms have been proposed to explain these low <span class="hlt">ice</span> number <span class="hlt">concentrations</span>, including the inhibition of homogeneous freezing by the deposition of water vapour onto pre-existing <span class="hlt">ice</span> crystals, heterogeneous <span class="hlt">ice</span> formation on glassy organic aerosol <span class="hlt">ice</span> nuclei (IN), and limiting the formation of <span class="hlt">ice</span> number from high frequency gravity waves. In this study, we examined the effect from three different representations of updraft velocities, the effect from pre-existing <span class="hlt">ice</span> crystals, the effect from different water vapour deposition coefficients (α = 0.1 or 1), and the effect of 0.1 % of the total secondary organic aerosol (SOA) particles acting as IN. Model simulated <span class="hlt">ice</span> crystal numbers are compared against an aircraft observational dataset. Including the effect from water vapour deposition on pre-existing <span class="hlt">ice</span> particles can effectively reduce simulated in-cloud <span class="hlt">ice</span> number <span class="hlt">concentrations</span> for all model set-ups. A larger water vapour deposition coefficient (α = 1) can also efficiently reduce <span class="hlt">ice</span> number <span class="hlt">concentrations</span> at temperatures below 205 K but less so at higher temperatures. SOA acting as IN are most effective at reducing <span class="hlt">ice</span> number <span class="hlt">concentrations</span> when the effective updraft velocities are moderate (∼ 0.05-0.2 m s-1). However, the effects of including SOA as IN and using (α = 1) are diminished when the effect from pre-existing <span class="hlt">ice</span> is included. When a grid resolved large-scale updraft velocity (< 0.1 m s-1) is used, the <span class="hlt">ice</span> nucleation parameterization with homogeneous freezing only or with both homogeneous freezing and heterogeneous nucleation is able to generate low <span class="hlt">ice</span> number <span class="hlt">concentrations</span> in good agreement with observations for temperatures below 205 K as long as the pre-existing <span class="hlt">ice</span> effect is</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ACP....1611367N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ACP....1611367N"><span>Cloud <span class="hlt">ice</span> caused by atmospheric mineral dust - Part 1: Parameterization of <span class="hlt">ice</span> nuclei <span class="hlt">concentration</span> in the NMME-DREAM model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nickovic, Slobodan; Cvetkovic, Bojan; Madonna, Fabio; Rosoldi, Marco; Pejanovic, Goran; Petkovic, Slavko; Nikolic, Jugoslav</p> <p>2016-09-01</p> <p>Dust aerosols are very efficient <span class="hlt">ice</span> nuclei, important for heterogeneous cloud glaciation even in regions distant from desert sources. A new generation of <span class="hlt">ice</span> nucleation parameterizations, including dust as an <span class="hlt">ice</span> nucleation agent, opens the way towards a more accurate treatment of cold cloud formation in atmospheric models. Using such parameterizations, we have developed a regional dust-atmospheric modelling system capable of predicting, in real time, dust-induced <span class="hlt">ice</span> nucleation. We executed the model with the added <span class="hlt">ice</span> nucleation component over the Mediterranean region, exposed to moderate Saharan dust transport, over two periods lasting 15 and 9 days, respectively. The model results were compared against satellite and ground-based cloud-<span class="hlt">ice</span>-related measurements, provided by SEVIRI (Spinning Enhanced Visible and InfraRed Imager) and the CNR-IMAA Atmospheric Observatory (CIAO) in Potenza, southern Italy. The predicted <span class="hlt">ice</span> nuclei <span class="hlt">concentration</span> showed a reasonable level of agreement when compared against the observed spatial and temporal patterns of cloud <span class="hlt">ice</span> water. The developed methodology permits the use of <span class="hlt">ice</span> nuclei as input into the cloud microphysics schemes of atmospheric models, assuming that this approach could improve the predictions of cloud formation and associated precipitation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19870020588','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19870020588"><span>Satellite-derived <span class="hlt">ice</span> data sets no. 2: Arctic monthly average microwave brightness temperatures and sea <span class="hlt">ice</span> <span class="hlt">concentrations</span>, 1973-1976</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.; Comiso, J. C.; Zwally, H. J.</p> <p>1987-01-01</p> <p>A summary data set for four years (mid 70's) of Arctic sea <span class="hlt">ice</span> conditions is available on magnetic tape. The data include monthly and yearly averaged Nimbus 5 electrically scanning microwave radiometer (ESMR) brightness temperatures, an <span class="hlt">ice</span> <span class="hlt">concentration</span> parameter derived from the brightness temperatures, monthly climatological surface air temperatures, and monthly climatological sea level pressures. All data matrices are applied to 293 by 293 grids that cover a polar stereographic map enclosing the 50 deg N latitude circle. The grid size varies from about 32 X 32 km at the poles to about 28 X 28 km at 50 deg N. The <span class="hlt">ice</span> <span class="hlt">concentration</span> parameter is calculated assuming that the field of view contains only open water and first-year <span class="hlt">ice</span> with an <span class="hlt">ice</span> emissivity of 0.92. To account for the presence of multiyear <span class="hlt">ice</span>, a nomogram is provided relating the <span class="hlt">ice</span> <span class="hlt">concentration</span> parameter, the total <span class="hlt">ice</span> <span class="hlt">concentration</span>, and the fraction of the <span class="hlt">ice</span> cover which is multiyear <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110008454','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110008454"><span>Freeboard, Snow Depth and Sea-<span class="hlt">Ice</span> Roughness in East Antarctica from In Situ and Multiple Satellite Data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Markus, Thorsten; Masson, Robert; Worby, Anthony; Lytle, Victoria; Kurtz, Nathan; Maksym, Ted</p> <p>2011-01-01</p> <p>In October 2003 a campaign on board the Australian icebreaker Aurora Australis had the objective to validate standard Aqua Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) sea-<span class="hlt">ice</span> products. Additionally, the satellite laser altimeter on the <span class="hlt">Ice</span>, Cloud and land Elevation Satellite (ICESat) was in operation. To capture the large-scale information on the sea-<span class="hlt">ice</span> conditions necessary for satellite validation, the measurement strategy was to obtain large-scale sea-<span class="hlt">ice</span> statistics using extensive sea-<span class="hlt">ice</span> measurements in a Lagrangian approach. A drifting buoy array, spanning initially 50 km 100 km, was surveyed during the campaign. In situ measurements consisted of 12 transects, 50 500 m, with detailed snow and <span class="hlt">ice</span> measurements as well as random snow depth sampling of floes within the buoy array using helicopters. In order to increase the amount of coincident in situ and satellite data an approach has been developed to extrapolate measurements in time and in space. Assuming no change in snow depth and freeboard occurred during the period of the campaign on the floes surveyed, we use buoy <span class="hlt">ice</span>-drift information as well as daily estimates of thin-<span class="hlt">ice</span> fraction and rough-<span class="hlt">ice</span> vs smooth-<span class="hlt">ice</span> fractions from <span class="hlt">AMSR-E</span> and QuikSCAT, respectively, to estimate kilometer-scale snow depth and freeboard for other days. The results show that ICESat freeboard estimates have a mean difference of 1.8 cm when compared with the in situ data and a correlation coefficient of 0.6. Furthermore, incorporating ICESat roughness information into the <span class="hlt">AMSR-E</span> snow depth algorithm significantly improves snow depth retrievals. Snow depth retrievals using a combination of <span class="hlt">AMSR-E</span> and ICESat data agree with in situ data with a mean difference of 2.3 cm and a correlation coefficient of 0.84 with a negligible bias.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JGRD..121.6927Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JGRD..121.6927Y"><span>Sensitivity of Arctic warming to sea <span class="hlt">ice</span> <span class="hlt">concentration</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yim, Bo Young; Min, Hong Sik; Kim, Baek-Min; Jeong, Jee-Hoon; Kug, Jong-Seong</p> <p>2016-06-01</p> <p>We examine the sensitivity of Arctic amplification (AA) to background sea <span class="hlt">ice</span> <span class="hlt">concentration</span> (SIC) under greenhouse warming by analyzing the data sets of the historical and Representative <span class="hlt">Concentration</span> Pathway 8.5 runs of the Coupled Model Intercomparison Project Phase 5. To determine whether the sensitivity of AA for a given radiative forcing depends on background SIC state, we examine the relationship between the AA trend and mean SIC on moving 30 year windows from 1960 to 2100. It is found that the annual mean AA trend varies depending on the mean SIC condition. In particular, some models show a highly variable AA trend in relation to the mean SIC clearly. In these models, the AA trend tends to increase until the mean SIC reaches a critical level (i.e., 20-30%), and the maximum AA trend is almost 3 to 5 times larger than the trend in the early stage of global warming (i.e., 50-60%, 60-70%). However, the AA trend tends to decrease after that. Further analysis shows that the sensitivity of AA trend to mean SIC condition is closely related to the feedback processes associated with summer surface albedo and winter turbulent heat flux in the Arctic Ocean.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ESDD....6.2137S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ESDD....6.2137S"><span>Importance of open-water <span class="hlt">ice</span> growth and <span class="hlt">ice</span> <span class="hlt">concentration</span> evolution: a study based on FESOM-ECHAM6</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shi, X.; Lohmann, G.</p> <p>2015-10-01</p> <p>A newly developed global climate model FESOM-ECHAM6 with an unstructured mesh and high resolution 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. A sensitivity experiment is performed which reduces the horizontal-to-vertical aspect ratio of open-water <span class="hlt">ice</span> growth. The resulting decrease in the Arctic winter sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> strongly reduces the surface albedo, enhances the ocean heat release to the atmosphere, and increases the sea-<span class="hlt">ice</span> production. Furthermore, our simulations show a positive feedback mechanism among the Arctic sea <span class="hlt">ice</span>, the Atlantic Meridional Overturning Circulation (AMOC), and the surface air temperature in the Arctic, as the sea <span class="hlt">ice</span> transport affects the freshwater budget in regions of deep water formation. A warming over Europe, Asia and North America, associated with a negative anomaly of Sea Level Pressure (SLP) over the Arctic (positive phase of the Arctic Oscillation (AO)), is also simulated by the model. For the Southern Ocean, the most pronounced change is a warming along the Antarctic Circumpolar Current (ACC), especially for the Pacific sector. Additionally, a series of sensitivity tests are performed using an idealized 1-D thermodynamic model to further investigate the influence of the open-water <span class="hlt">ice</span> growth, which reveals similar results in terms of the change of sea <span class="hlt">ice</span> and ocean temperature. In reality, the distribution of new <span class="hlt">ice</span> on open water relies on many uncertain parameters, for example, surface albedo, wind speed and ocean currents. Knowledge of the detailed processes is currently too crude for those processes to be implemented realistically into models. Our sensitivity experiments indicate a pronounced uncertainty related to open-water sea <span class="hlt">ice</span> growth which could significantly affect the climate system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016TCry...10.2217K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016TCry...10.2217K"><span>The impact of melt ponds on summertime microwave brightness temperatures and sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kern, Stefan; Rösel, Anja; Toudal Pedersen, Leif; Ivanova, Natalia; Saldo, Roberto; Tage Tonboe, Rasmus</p> <p>2016-09-01</p> <p>Sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span> derived from satellite microwave brightness temperatures are less accurate during summer. In the Arctic Ocean the lack of accuracy is primarily caused by melt ponds, but also by changes in the properties of snow and the sea-<span class="hlt">ice</span> surface itself. We investigate the sensitivity of eight sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> retrieval algorithms to melt ponds by comparing sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> with the melt-pond fraction. We derive gridded daily sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span> from microwave brightness temperatures of summer 2009. We derive the daily fraction of melt ponds, open water between <span class="hlt">ice</span> floes, and the <span class="hlt">ice</span>-surface fraction from contemporary Moderate Resolution Spectroradiometer (MODIS) reflectance data. We only use grid cells where the MODIS sea-<span class="hlt">ice</span> <span class="hlt">concentration</span>, which is the melt-pond fraction plus the <span class="hlt">ice</span>-surface fraction, exceeds 90 %. For one group of algorithms, e.g., Bristol and Comiso bootstrap frequency mode (Bootstrap_f), sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span> are linearly related to the MODIS melt-pond fraction quite clearly after June. For other algorithms, e.g., Near90GHz and Comiso bootstrap polarization mode (Bootstrap_p), this relationship is weaker and develops later in summer. We attribute the variation of the sensitivity to the melt-pond fraction across the algorithms to a different sensitivity of the brightness temperatures to snow-property variations. We find an underestimation of the sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> by between 14 % (Bootstrap_f) and 26 % (Bootstrap_p) for 100 % sea <span class="hlt">ice</span> with a melt-pond fraction of 40 %. The underestimation reduces to 0 % for a melt-pond fraction of 20 %. In presence of real open water between <span class="hlt">ice</span> floes, the sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> is overestimated by between 26 % (Bootstrap_f) and 14 % (Bootstrap_p) at 60 % sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> and by 20 % across all algorithms at 80 % sea-<span class="hlt">ice</span> <span class="hlt">concentration</span>. None of the algorithms investigated performs best based on our investigation of data from summer 2009. We suggest that those algorithms which are</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014TCry....8.1639K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014TCry....8.1639K"><span>A sea <span class="hlt">ice</span> <span class="hlt">concentration</span> estimation algorithm utilizing radiometer and SAR data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Karvonen, J.</p> <p>2014-09-01</p> <p>We have studied the possibility of combining the high-resolution synthetic aperture radar (SAR) segmentation and <span class="hlt">ice</span> <span class="hlt">concentration</span> estimated by radiometer brightness temperatures. Here we present an algorithm for mapping a radiometer-based <span class="hlt">concentration</span> value for each SAR segment. The <span class="hlt">concentrations</span> are estimated by a multi-layer perceptron (MLP) neural network which has the AMSR-2 (Advanced Microwave Scanning Radiometer 2) polarization ratios and gradient ratios of four radiometer channels as its inputs. The results have been compared numerically to the gridded Finnish Meteorological Institute (FMI) <span class="hlt">ice</span> chart <span class="hlt">concentrations</span> and high-resolution AMSR-2 ASI (ARTIST Sea <span class="hlt">Ice</span>) algorithm <span class="hlt">concentrations</span> provided by the University of Hamburg and also visually to the AMSR-2 bootstrap algorithm <span class="hlt">concentrations</span>, which are given in much coarser resolution. The differences when compared to FMI daily <span class="hlt">ice</span> charts were on average small. When compared to ASI <span class="hlt">ice</span> <span class="hlt">concentrations</span>, the differences were a bit larger, but still small on average. According to our comparisons, the largest differences typically occur near the <span class="hlt">ice</span> edge and sea-land boundary. The main advantage of combining radiometer-based <span class="hlt">ice</span> <span class="hlt">concentration</span> estimation and SAR segmentation seems to be a more precise estimation of the boundaries of different <span class="hlt">ice</span> <span class="hlt">concentration</span> zones.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20070035051','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20070035051"><span>The Influence of Sea <span class="hlt">Ice</span> on Primary Production in the Southern Ocean: A Satellite Perspective</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Smith, Walker O., Jr.; Comiso, Josefino C.</p> <p>2007-01-01</p> <p>Sea <span class="hlt">ice</span> in the Southern Ocean is a major controlling factor on phytoplankton productivity and growth, but the relationship is modified by regional differences in atmospheric and oceanographic conditions. We used the phytoplankton biomass (binned at 7-day intervals), PAR and cloud cover data from SeaWiFS, <span class="hlt">ice</span> <span class="hlt">concentrations</span> data from SSM/I and <span class="hlt">AMSR-E</span>, and sea-surface temperature data from AVHRR, in combination with a vertically integrated model to estimate primary productivity throughout the Southern Ocean (south of 60"s). We also selected six areas within the Southern Ocean and analyzed the variability of the primary productivity and trends through time, as well as the relationship of sea <span class="hlt">ice</span> to productivity. We found substantial interannual variability in productivity from 1997 - 2005 in all regions of the Southern Ocean, and this variability appeared to be driven in large part by <span class="hlt">ice</span> dynamics. The most productive regions of Antarctic waters were the continental shelves, which showed the earliest growth, the maximum biomass, and the greatest areal specific productivity. In contrast, no large, sustained blooms occurred in waters of greater depth (> 1,000 m). We suggest that this is due to the slightly greater mixed layer depths found in waters off the continental shelf, and that the interactive effects of iron and irradiance (that is, increased iron requirements in low irradiance environments) result in the limitation of phytoplankton biomass over large regions of the Southern Ocean.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016OcMod.105...60L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016OcMod.105...60L"><span>Impact of surface wind biases on the Antarctic sea <span class="hlt">ice</span> <span class="hlt">concentration</span> budget in 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>Lecomte, O.; Goosse, H.; Fichefet, T.; Holland, P. R.; Uotila, P.; Zunz, V.; Kimura, N.</p> <p>2016-09-01</p> <p>We derive the terms in the Antarctic sea <span class="hlt">ice</span> <span class="hlt">concentration</span> budget from the output of three models, and compare them to observations of the same terms. Those models include two climate models from the 5th Coupled Model Intercomparison Project (CMIP5) and one ocean-sea <span class="hlt">ice</span> coupled model with prescribed atmospheric forcing. Sea <span class="hlt">ice</span> drift and wind fields from those models, in average over April-October 1992-2005, all exhibit large differences with the available observational or reanalysis datasets. However, the discrepancies between the two distinct <span class="hlt">ice</span> drift products or the two wind reanalyses used here are sometimes even greater than those differences. Two major findings stand out from the analysis. Firstly, large biases in sea <span class="hlt">ice</span> drift speed and direction in exterior sectors of the sea <span class="hlt">ice</span> covered region tend to be systematic and consistent with those in winds. This suggests that sea <span class="hlt">ice</span> errors in these areas are most likely wind-driven, so as errors in the simulated <span class="hlt">ice</span> motion vectors. The systematic nature of these biases is less prominent in interior sectors, nearer the coast, where sea <span class="hlt">ice</span> is mechanically constrained and its motion in response to the wind forcing more depending on the model rheology. Second, the intimate relationship between winds, sea <span class="hlt">ice</span> drift and the sea <span class="hlt">ice</span> <span class="hlt">concentration</span> budget gives insight on ways to categorize models with regard to errors in their <span class="hlt">ice</span> dynamics. In exterior regions, models with seemingly too weak winds and slow <span class="hlt">ice</span> drift consistently yield a lack of <span class="hlt">ice</span> velocity divergence and hence a wrong wintertime sea <span class="hlt">ice</span> growth rate. In interior sectors, too slow <span class="hlt">ice</span> drift, presumably originating from issues in the physical representation of sea <span class="hlt">ice</span> dynamics as much as from errors in surface winds, leads to wrong timing of the late winter <span class="hlt">ice</span> retreat. Those results illustrate that the applied methodology provides a valuable tool for prioritizing model improvements based on the <span class="hlt">ice</span> <span class="hlt">concentration</span> budget-<span class="hlt">ice</span> drift biases-wind biases</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1813447B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1813447B"><span>Observational uncertainty of Arctic sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> significantly affects seasonal climate forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bunzel, Felix; Notz, Dirk; Baehr, Johanna; Müller, Wolfgang; Fröhlich, Kristina</p> <p>2016-04-01</p> <p>We examine how the choice of a particular satellite-retrieved sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> dataset used for initialising seasonal climate forecasts impacts the prediction skill of Arctic sea-<span class="hlt">ice</span> area and Northern hemispheric 2-meter air temperatures. To do so, we performed two assimilation runs with the Max Planck Institute Earth System Model (MPI-ESM) from 1979 to 2012, where atmospheric and oceanic parameters as well as sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> were assimilated using Newtonian relaxation. The two assimilation runs differ only in the sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> dataset used for assimilating sea <span class="hlt">ice</span>. In the first run, we use sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span> as derived by the NASA-Team algorithm, while in the second run we use sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span> as derived from the Bootstrap algorithm. A major difference between these two sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> data products involves the treatment of melt ponds. While for both products melt ponds appear as open water in the raw satellite data, the Bootstrap algorithm more strongly attempts to offset this systematic bias by synthetically increasing the retrieved <span class="hlt">ice</span> <span class="hlt">concentration</span> during summer months. For each year of the two assimilation runs we performed a 10-member ensemble of hindcast experiments starting on 1 May and 1 November with a hindcast length of 6 months. For hindcasts started in November, initial differences in Arctic sea-<span class="hlt">ice</span> area and surface temperature decrease rapidly throughout the freezing period. For hindcasts started in May, initial sea-<span class="hlt">ice</span> area differences increase over time. By the end of the melting period, this causes significant differences in 2-meter air temperature of regionally more than 3°C. Hindcast skill for surface temperatures over Europe and North America is higher with Bootstrap initialization during summer and with NASA Team initialisation during winter. This implies that the choice of the sea-<span class="hlt">ice</span> data product and, thus, the observational uncertainty also affects forecasts of teleconnections that depend on Northern</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014cosp...40E1409K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014cosp...40E1409K"><span>Evaluation of the operational SAR based Baltic sea <span class="hlt">ice</span> <span class="hlt">concentration</span> products</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Karvonen, Juha</p> <p></p> <p>Sea <span class="hlt">ice</span> <span class="hlt">concentration</span> is an important <span class="hlt">ice</span> parameter both for weather and climate modeling and sea <span class="hlt">ice</span> navigation. We have developed an fully automated algorithm for sea <span class="hlt">ice</span> <span class="hlt">concentration</span> retrieval using dual-polarized ScanSAR wide mode RADARSAT-2 data. RADARSAT-2 is a C-band SAR instrument enabling dual-polarized acquisition in ScanSAR mode. The swath width for the RADARSAT-2 ScanSAR mode is about 500 km, making it very suitable for operational sea <span class="hlt">ice</span> monitoring. The polarization combination used in our <span class="hlt">concentration</span> estimation is HH/HV. The SAR data is first preprocessed, the preprocessing consists of geo-rectification to Mercator projection, incidence angle correction fro both the polarization channels. and SAR mosaicking. After preprocessing a segmentation is performed for the SAR mosaics, and some single-channel and dual-channel features are computed for each SAR segment. Finally the SAR <span class="hlt">concentration</span> is estimated based on these segment-wise features. The algorithm is similar as introduced in Karvonen 2014. The <span class="hlt">ice</span> <span class="hlt">concentration</span> is computed daily using a daily RADARSAT-2 SAR mosaic as its input, and it thus gives the <span class="hlt">concentration</span> estimated at each Baltic Sea location based on the most recent SAR data at the location. The algorithm has been run in an operational test mode since January 2014. We present evaluation of the SAR-based <span class="hlt">concentration</span> estimates for the Baltic <span class="hlt">ice</span> season 2014 by comparing the SAR results with gridded the Finnish <span class="hlt">Ice</span> Service <span class="hlt">ice</span> charts and <span class="hlt">ice</span> <span class="hlt">concentration</span> estimates from a radiometer algorithm (AMSR-2 Bootstrap algorithm results). References: J. Karvonen, Baltic Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> Estimation Based on C-Band Dual-Polarized SAR Data, IEEE Transactions on Geoscience and Remote Sensing, in press, DOI: 10.1109/TGRS.2013.2290331, 2014.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.A21E..01D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.A21E..01D"><span>Recent Field Measurements of <span class="hlt">Ice</span> Nuclei <span class="hlt">Concentration</span> Relation to Aerosol Properties</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>DeMott, P. J.; Sullivan, R. C.; McMeeking, G.; Prenni, A. J.; Hill, T. C.; Franc, G. D.; Sullivan, A. P.; Garcia, E.; Tobo, Y.; Prather, K. A.; Suski, K.; Cazorla, A.; Anderson, J. R.; Kreidenweis, S. M.</p> <p>2011-12-01</p> <p>It is expected that atmospheric variability of <span class="hlt">ice</span> nuclei <span class="hlt">concentrations</span> is governed by a variety of factors related to aerosol physical and chemical properties. Not all particles contribute equally due to the special nature of <span class="hlt">ice</span> nuclei. The "size requirement" of <span class="hlt">ice</span> nuclei (Pruppacher and Klett, 1997), partly related to the typical aerosol compositions known to act as <span class="hlt">ice</span> nuclei (e.g., mineral dust particles, certain biological particles), leads to the relation of <span class="hlt">ice</span> nuclei number <span class="hlt">concentrations</span> to larger aerosol <span class="hlt">concentrations</span> in some cases, but we emphasize here the additional relation to aerosol chemistry. Recent atmospheric <span class="hlt">ice</span> nuclei measurements focused on biomass burning, mineral dust, pollution and biological particles will be discussed to highlight new assessment of their source contributions on the basis of physical, chemical and biological analysis. Pruppacher, H. R., and J. D. Klett, 1997: Microphysics of Clouds and Precipitation (2nd Edition), Kluwer Academic Press, Dordrecht, 954 pp.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014P%26SS...91...60F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014P%26SS...91...60F"><span>Amazonian mid- to high-latitude glaciation on Mars: Supply-limited <span class="hlt">ice</span> sources, <span class="hlt">ice</span> accumulation patterns, and <span class="hlt">concentric</span> crater fill glacial flow and <span class="hlt">ice</span> sequestration</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fastook, James L.; Head, James W.</p> <p>2014-02-01</p> <p><span class="hlt">Concentric</span> crater fill (CCF) occurs in the interior of impact craters in mid- to high latitudes on Mars and is interpreted to have formed by glacial <span class="hlt">ice</span> flow and debris covering. We use the characteristics and orientation of deposits comprising CCF, the thickness of pedestal deposits in mid- to high-latitude pedestal craters (Pd), the volumes of the current polar caps, and information about regional slopes and <span class="hlt">ice</span> rheology to address questions about (1) the maximum thickness of regional <span class="hlt">ice</span> deposits during the Late Amazonian, (2) the likelihood that these deposits flowed regionally, (3) the geological regions and features most likely to induce <span class="hlt">ice</span>-flow, and (4) the locations and environments in which <span class="hlt">ice</span> is likely to have been sequestered up to the present. We find that regional <span class="hlt">ice</span> flow under Late Amazonian climate conditions requires <span class="hlt">ice</span> thicknesses exceeding many hundreds of meters for slopes typical of the vast majority of the surface of Mars, a thickness for the mid-latitudes that is well in excess of the total volume available from polar <span class="hlt">ice</span> reservoirs. This indicates that although conditions for mid- to high-latitude glaciation may have persisted for tens to hundreds of millions of years, the process is “supply limited”, with a steady state reached when the polar <span class="hlt">ice</span> cap water <span class="hlt">ice</span> supply becomes exhausted. Impact craters are by far the most abundant landform with associated slopes (interior wall and exterior rim) sufficiently high to induce glacial <span class="hlt">ice</span> flow under Late Amazonian climate conditions, and topographic slope data show that Amazonian impact craters have been clearly modified, undergoing crater interior slope reduction and floor shallowing. We show that these trends are the predictable response of <span class="hlt">ice</span> deposition and preferential accumulation and retention in mid- to high-latitude crater interiors during episodes of enhanced spin-axis obliquity. We demonstrate that flow from a single episode of an inter-crater terrain layer comparable to Pedestal</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 Sea <span class="hlt">Ice</span> <span class="hlt">Concentrations</span> 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 sea <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> <span class="hlt">concentration</span> 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 sea <span class="hlt">ice</span> cover, assess errors in currently available <span class="hlt">ice</span> <span class="hlt">concentration</span> 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 sea <span class="hlt">ice</span>. Comparative analyses of co-registered visible, infrared and microwave data were used to evaluate <span class="hlt">ice</span> <span class="hlt">concentrations</span> 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 Sea, Amundsen Sea, and Ross Sea 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> <span class="hlt">concentration</span> 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 sea <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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_10 --> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=10539&hterms=Arctic+Ocean&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DArctic%2BOcean','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=10539&hterms=Arctic+Ocean&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DArctic%2BOcean"><span>Record Arctic Sea <span class="hlt">Ice</span> Loss in 2007</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>2007-01-01</p> <p>This image of the Arctic was produced from sea <span class="hlt">ice</span> observations collected by the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) Instrument on NASA's Aqua satellite on September 16, overlaid on the NASA Blue Marble. The image captures <span class="hlt">ice</span> conditions at the end of the melt season. Sea <span class="hlt">ice</span> (white, image center) stretches across the Arctic Ocean from Greenland to Russia, but large areas of open water were apparent as well. In addition to record melt, the summer of 2007 brought an <span class="hlt">ice</span>-free opening though the Northwest Passage that lasted several weeks. The Northeast Passage did not open during the summer of 2007, however, as a substantial tongue of <span class="hlt">ice</span> remained in place north of the Russian coast. According to the National Snow and <span class="hlt">Ice</span> Data Center (NSIDC), on September 16, 2007, sea <span class="hlt">ice</span> extent dropped to 4.13 million square kilometers (1.59 million square miles)--38 percent below average and 24 percent below the 2005 record.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=10539&hterms=sea+ice&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dsea%2Bice','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=10539&hterms=sea+ice&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dsea%2Bice"><span>Record Arctic Sea <span class="hlt">Ice</span> Loss in 2007</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>2007-01-01</p> <p>This image of the Arctic was produced from sea <span class="hlt">ice</span> observations collected by the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) Instrument on NASA's Aqua satellite on September 16, overlaid on the NASA Blue Marble. The image captures <span class="hlt">ice</span> conditions at the end of the melt season. Sea <span class="hlt">ice</span> (white, image center) stretches across the Arctic Ocean from Greenland to Russia, but large areas of open water were apparent as well. In addition to record melt, the summer of 2007 brought an <span class="hlt">ice</span>-free opening though the Northwest Passage that lasted several weeks. The Northeast Passage did not open during the summer of 2007, however, as a substantial tongue of <span class="hlt">ice</span> remained in place north of the Russian coast. According to the National Snow and <span class="hlt">Ice</span> Data Center (NSIDC), on September 16, 2007, sea <span class="hlt">ice</span> extent dropped to 4.13 million square kilometers (1.59 million square miles)--38 percent below average and 24 percent below the 2005 record.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20030096002','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20030096002"><span>Sea <span class="hlt">Ice</span> Surface Temperature Product from the Moderate Resolution Imaging Spectroradiometer (MODIS)</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hall, Dorothy K.; Key, Jeffrey R.; Casey, Kimberly A.; Riggs, George A.; Cavalieri, Donald J.</p> <p>2003-01-01</p> <p>Global sea <span class="hlt">ice</span> products are produced from the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) on board both the Terra and Aqua satellites. Daily sea <span class="hlt">ice</span> extent and <span class="hlt">ice</span>-surface temperature (IST) products are available at 1- and 4-km resolution. Validation activities have been undertaken to assess the accuracy of the MODIS IST product at the South Pole station in Antarctica and in the Arctic Ocean using near-surface air-temperature data from a meteorological station and drifting buoys. Results from the study areas show that under clear skies, the MODIS ISTs are very close to those of the near-surface air temperatures with a bias of -1.1 and -1.2 K, and an uncertainty of 1.6 and 1.7 K, respectively. It is shown that the uncertainties would be reduced if the actual temperature of the <span class="hlt">ice</span> surface were reported instead of the near-surface air temperature. It is not possible to get an accurate IST from MODIS in the presence of even very thin clouds or fog, however using both the Advanced Microwave Scanning Radiometer-EOS (<span class="hlt">AMSR-E</span>) and the MODIS on the Aqua satellite, it may be possible to develop a relationship between MODIS-derived IST and <span class="hlt">ice</span> temperature derived from the <span class="hlt">AMSR-E</span>. Since the <span class="hlt">AMSR-E</span> measurements are generally unaffected by cloud cover, they may be used to complement the MODIS IST measurements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28241114','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28241114"><span>A Novel Approach To Improve the Efficiency of Block Freeze <span class="hlt">Concentration</span> Using <span class="hlt">Ice</span> Nucleation Proteins with Altered <span class="hlt">Ice</span> Morphology.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jin, Jue; Yurkow, Edward J; Adler, Derek; Lee, Tung-Ching</p> <p>2017-03-22</p> <p>Freeze <span class="hlt">concentration</span> is a separation process with high success in product quality. The remaining challenge is to achieve high efficiency with low cost. This study aims to evaluate the potential of using <span class="hlt">ice</span> nucleation proteins (INPs) as an effective method to improve the efficiency of block freeze <span class="hlt">concentration</span> while also exploring the related mechanism of <span class="hlt">ice</span> morphology. Our results show that INPs are able to significantly improve the efficiency of block freeze <span class="hlt">concentration</span> in a desalination model. Using this experimental system, we estimate that approximately 50% of the energy cost can be saved by the inclusion of INPs in desalination cycles while still meeting the EPA standard of drinking water (<500 ppm). Our investigative tools for <span class="hlt">ice</span> morphology include optical microscopy and X-ray computed tomography imaging analysis. Their use indicates that INPs promote the development of a lamellar structured <span class="hlt">ice</span> matrix with larger hydraulic diameters, which facilitates brine drainage and contains less brine entrapment as compared to control samples. These results suggest great potential for applying INPs to develop an energy-saving freeze <span class="hlt">concentration</span> method via the alteration of <span class="hlt">ice</span> morphology.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/1167648','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/1167648"><span><span class="hlt">Ice</span> <span class="hlt">Concentration</span> Retrieval in Stratiform Mixed-phase Clouds Using Cloud Radar Reflectivity Measurements and 1D <span class="hlt">Ice</span> Growth Model Simulations</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Zhang, Damao; Wang, Zhien; Heymsfield, Andrew J.; Fan, Jiwen; Luo, Tao</p> <p>2014-10-01</p> <p>Measurement of <span class="hlt">ice</span> number <span class="hlt">concentration</span> in clouds is important but still challenging. Stratiform mixed-phase clouds (SMCs) provide a simple scenario for retrieving <span class="hlt">ice</span> number <span class="hlt">concentration</span> from remote sensing measurements. The simple <span class="hlt">ice</span> generation and growth pattern in SMCs offers opportunities to use cloud radar reflectivity (Ze) measurements and other cloud properties to infer <span class="hlt">ice</span> number <span class="hlt">concentration</span> quantitatively. To understand the strong temperature dependency of <span class="hlt">ice</span> habit and growth rate quantitatively, we develop a 1-D <span class="hlt">ice</span> growth model to calculate the <span class="hlt">ice</span> diffusional growth along its falling trajectory in SMCs. The radar reflectivity and fall velocity profiles of <span class="hlt">ice</span> crystals calculated from the 1-D <span class="hlt">ice</span> growth model are evaluated with the Atmospheric Radiation Measurements (ARM) Climate Research Facility (ACRF) ground-based high vertical resolution radar measurements. Combining Ze measurements and 1-D <span class="hlt">ice</span> growth model simulations, we develop a method to retrieve the <span class="hlt">ice</span> number <span class="hlt">concentrations</span> in SMCs at given cloud top temperature (CTT) and liquid water path (LWP). The retrieved <span class="hlt">ice</span> <span class="hlt">concentrations</span> in SMCs are evaluated with in situ measurements and with a three-dimensional cloud-resolving model simulation with a bin microphysical scheme. These comparisons show that the retrieved <span class="hlt">ice</span> number <span class="hlt">concentrations</span> are within an uncertainty of a factor of 2, statistically.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/16303165','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/16303165"><span>A method for determination of methyl chloride <span class="hlt">concentration</span> in air trapped in <span class="hlt">ice</span> cores.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Saito, Takuya; Yokouchi, Yoko; Aoki, Shuji; Nakazawa, Takakiyo; Fujii, Yoshiyuki; Watanabe, Okitsugu</p> <p>2006-05-01</p> <p>A method for measuring the <span class="hlt">concentration</span> of methyl chloride (CH3Cl) in air trapped in an <span class="hlt">ice</span> core was developed. The method combines the air extraction by milling the <span class="hlt">ice</span> core samples under vacuum and the analysis of the extracted air with a cryogenic preconcentration/gas chromatograph/mass spectrometry system. The method was applied to air from Antarctic <span class="hlt">ice</span> core samples estimated to have been formed in the pre-industrial and/or early industrial periods. The overall precision of the method deduced from duplicate <span class="hlt">ice</span> core analyses was estimated to be better than +/-20 pptv. The measured CH3Cl <span class="hlt">concentration</span> of 528+/-26 pptv was similar to the present-day <span class="hlt">concentration</span> in the remote atmosphere as well as the CH3Cl <span class="hlt">concentration</span> over the past 300 years obtained from Antarctic firn air and <span class="hlt">ice</span> core analyses.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/1013155','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/1013155"><span>Seasonal comparisons of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> estimates derived from SSM/I, OKEAN and RADARSAT data</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Belchansky, G.I.; Douglas, D.C.</p> <p>2002-01-01</p> <p>The SSM/I microwave satellite radiometer and its predecessor SMMR are primary sources of information for global sea-<span class="hlt">ice</span> and climate studies. However, comparisons of SSM/I, LANDSAT, AVHRR and ERS-1 SAR have shown substantial seasonal and regional differences in their estimates of sea <span class="hlt">ice</span> <span class="hlt">concentration</span>. To evaluate these differences, we compared SSM/I estimates of sea <span class="hlt">ice</span> coverage derived with the NASA Team and Bootstrap algorithms to estimates made using RADARSAT, and OKEAN-01 satellite sensor data. The study area included the Barents, Kara Sea, Laptev Sea, and adjacent parts of the Arctic Ocean, during October 1995 through October 1999. <span class="hlt">Ice</span> <span class="hlt">concentration</span> estimates from spatially and temporally near-coincident imagery were calculated using independent algorithms for each sensor type. The OKEAN algorithm implemented the satellite's two-channel active (radar) and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter. The RADARSAT algorithm utilized a segmentation approach of the measured radar backscatter, and the SSM/I <span class="hlt">ice</span> <span class="hlt">concentrations</span> were derived at National Snow and <span class="hlt">Ice</span> Data Center (NSIDC) using the NASA Team and Bootstrap algorithms. Seasonal and monthly differences between SSM/I, OKEAN, and RADARSAT <span class="hlt">ice</span> <span class="hlt">concentrations</span> were calculated and compared. Overall, total sea <span class="hlt">ice</span> <span class="hlt">concentration</span> estimates derived independently from near-coincident RADARSAT, OKEAN-01 and SSM/I satellite imagery demonstrated mean differences of less than 5.5 % (SD < 9.5%) during the winter period. Differences between the SSM/I NASA Team and the SSM/I Bootstrap <span class="hlt">concentrations</span> were no more than 3.1 % (SD < 5.4%) during this period. RADARSAT and OKEAN-01 data both yielded higher total <span class="hlt">ice</span> <span class="hlt">concentrations</span> than the NASA Team and the Bootstrap algorithms. The Bootstrap algorithm yielded higher total <span class="hlt">ice</span> <span class="hlt">concentrations</span> than the NASA Team algorithm. Total <span class="hlt">ice</span> <span class="hlt">concentrations</span> derived from OKEAN-01 and SSM/I satellite imagery were</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PalOc..30.1525F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PalOc..30.1525F"><span>First records of winter sea <span class="hlt">ice</span> <span class="hlt">concentration</span> in the southwest Pacific sector of the Southern Ocean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ferry, Alexander J.; Crosta, Xavier; Quilty, Patrick G.; Fink, David; Howard, William; Armand, Leanne K.</p> <p>2015-11-01</p> <p>We use a Generalized Additive Model (GAM) to provide the first winter sea <span class="hlt">ice</span> <span class="hlt">concentration</span> record from two cores located within the southwest Pacific sector of the Southern Ocean. To compliment the application of GAM, a time series analysis on satellite records of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> data was used to extend the standard 13.25 year time series used for paleoceanography. After comparing GAM sea <span class="hlt">ice</span> estimates with previously published paleo sea <span class="hlt">ice</span> data we then focus on a new paleo winter sea <span class="hlt">ice</span> record for marine sediment core E27-23 (59°37.1'S, 155°14.3'E), allowing us to provide a more comprehensive view of winter sea <span class="hlt">ice</span> dynamics for the southwest Pacific Ocean. The paleo winter sea <span class="hlt">ice</span> <span class="hlt">concentration</span> estimates provide the first suggestion that winter sea <span class="hlt">ice</span> within the southwestern Pacific might have expanded during the Antarctic Cold Reversal. Throughout the Holocene, core E27-23 documents millennial scale variability in paleo winter sea <span class="hlt">ice</span> coverage within the southwest Pacific. Holocene winter sea <span class="hlt">ice</span> expansion may have resulted from the Laurentide <span class="hlt">Ice</span> Sheet deglaciation, increased intensity of the westerly winds, as well as a northern migration of the Subtropical and/or Sub-Antarctic Fronts. Brief consideration is given to the development of a paleo summer sea <span class="hlt">ice</span> proxy. We conclude that there is no evidence that summer sea <span class="hlt">ice</span> ever existed at core sites SO136-111 and E27-23 over the last 220 and 52,000 years, respectively.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUSM.C43A..20M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUSM.C43A..20M"><span>Snow and <span class="hlt">Ice</span> Products from the Aqua, Terra, and ICESat Satellites at the National Snow and <span class="hlt">Ice</span> Data Center</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.; Marquis, M.; Kaminski, M.; Armstrong, R.; Brodzik, M.</p> <p>2004-05-01</p> <p>The National Snow and <span class="hlt">Ice</span> Data Center (NSIDC) at the University of Colorado, Boulder - one of eight NASA Distributed Active Archive Centers (DAACs) - archives and distributes several products from sensors on the suite of NASA Earth Observing System (EOS) satellites. These include the sun-synchronous polar-orbiting Aqua (launched 4 May 2002) and Terra (launched 18 December 1999) platforms and the <span class="hlt">Ice</span>, Cloud, and land Elevation Satellite (ICESat) (launched 12 January 2003). The Advanced Microwave Scanning Radiometer-EOS (<span class="hlt">AMSR-E</span>) is a multi-channel passive microwave radiometer on Aqua (http://nsidc.org/daac/amsr/). <span class="hlt">AMSR-E</span> Level 3 snow products are produced in EASE-Grid format for both the Northern and Southern Hemisphere and are available as daily, 5-day, and monthly fields. Daily <span class="hlt">AMSR-E</span> Level 3 sea <span class="hlt">ice</span> products are produced on a polar stereographic projection at gridded spatial resolutions of 6.25 km, 12.5 km and 25 km. Since April 2004, these products have been available for public distribution from NSIDC. The Moderate-resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua is a 36-channel visible/infrared sensor that produces a consistent long-term time series of fully-automated, quality-controlled data. Level 2 swath products are available for both snow cover and sea <span class="hlt">ice</span>. Daily and 8-day Level 3 gridded snow cover products are available with estimates of snow extent and albedo at 500m resolution, along with daily Level 3 gridded sea <span class="hlt">ice</span> products with estimates for sea <span class="hlt">ice</span> extent and <span class="hlt">ice</span> surface temperature at 1 km resolution. These products are currently available from NSIDC (http://nsidc.org/daac/modis/). The Geoscience Laser Altimeter System (GLAS) is the sole instrument on ICESat. The standard GLAS Level 2 <span class="hlt">ice</span> sheet altimetry product contains the <span class="hlt">ice</span> sheet elevation and elevation distribution calculated from algorithms fine-tuned for <span class="hlt">ice</span> sheet returns. The standard GLAS Level 2 sea <span class="hlt">ice</span> altimetry product contains the sea <span class="hlt">ice</span> freeboard and sea <span class="hlt">ice</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27997388','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27997388"><span>Notable increases in nutrient <span class="hlt">concentrations</span> in a shallow lake during seasonal <span class="hlt">ice</span> growth.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Fang, Yang; Changyou, Li; Leppäranta, Matti; Xiaonghong, Shi; Shengnan, Zhao; Chengfu, Zhang</p> <p>2016-12-01</p> <p>Nutrients may be eliminated from <span class="hlt">ice</span> when liquid water is freezing, resulting in enhanced <span class="hlt">concentrations</span> in the unfrozen water. The nutrients diluted from the <span class="hlt">ice</span> may contribute to accumulated <span class="hlt">concentrations</span> in sediment during winter and an increased risk of algae blooms during the following spring and summer. The objective of this study was to evaluate the influence of <span class="hlt">ice</span> cover on nitrogen (N) and phosphorus (P) <span class="hlt">concentrations</span> in the water and sediment of a shallow lake, through an examination of Ulansuhai Lake, northern China, from the period of open water to <span class="hlt">ice</span> season in 2011-2013. The N and P <span class="hlt">concentrations</span> were between two and five times higher, and between two and eight times higher, than in unfrozen lakes, respectively. As the <span class="hlt">ice</span> thickness grew, contents of total N and total P showed C-shaped profiles in the <span class="hlt">ice</span>, and were lower in the middle layer and higher in the bottom and surface layers. Most of the nutrients were released from the <span class="hlt">ice</span> to liquid water. The results confirm that <span class="hlt">ice</span> can cause the nutrient <span class="hlt">concentrations</span> in water and sediment during winter to increase dramatically, thereby significantly impacting on processes in the water environment of shallow lakes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16...56A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16...56A"><span>Global-scale evaluation of two satellite-based passive microwave soil moisture data sets (SMOS and <span class="hlt">AMSR-E</span>) with respect to modelled estimates</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alyaari, Amen; Wigneron, Jean-Pierre; Ducharne, Agnes; Govind, Ajit; Kerr, Yann; Bitar, Ahmad Al; Jeu, Richard De.; Rosnay, Patricia; Albergel, Clement; Sabater, Joaquin; Moisy, Christophe</p> <p>2014-05-01</p> <p>Global Level-3 surface soil moisture (SSM) maps from the passive microwave Soil Moisture and Ocean Salinity satellite (SMOS)have been recently released. To further improve the Level-3 retrieval algorithm, evaluation of the accuracy of the spatio-temporal variability of the SMOS Level 3 products (referred to as SMOSL3) is necessary. In this study, a comparative analysis of SMOSL3 with a SSM product derived from the observations of the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>) computed by implementing the Land Parameter Retrieval Model (LPRM) algorithm, referred to as AMSRM, is presented. The comparison of both products (SMOSL3 and AMSRM)was made against SSM products produced by a numerical weather prediction system (SM-DAS-2) at ECMWF (European Centre for Medium-Range Weather Forecasts) for the 03/2010-09/2011 period at the global scale. The latter product was considered here as a "reference" product for the inter-comparison of the SMOSL3 and AMSRM products. Three statistical criteria were used for the evaluation, the correlation coefficient (R), the root-mean-squared difference (RMSD), and the bias. Global maps of these criteria were averaged, taking into account vegetation information in terms of biome types and Leaf Area Index (LAI). We found that both the SMOSL3 and AMSRM products captured well the spatio-temporal variability of the SM-DAS-2 SSM products in most of the biomes. In general, the AMSRM products overestimated (i.e., wet bias) while the SMOSL3 products underestimated (i.e., dry bias) SSM in comparison to the SM-DAS-2 SSM products. In term of correlation values, the SMOSL3 products were found to better capture the SSM temporal dynamics in highly vegetated biomes ("Tropical humid", "Temperate Humid", etc.) Whereas best results for AMSRM were obtained over arid and semi-arid biomes ("Desert temperate", "Desert tropical", etc.). When removing the seasonal cycles in the SSM time variations to compute anomaly values, better correlation with the SM</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20010095015&hterms=arctic+ice+melt&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Darctic%2Bice%2Bmelt','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20010095015&hterms=arctic+ice+melt&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Darctic%2Bice%2Bmelt"><span>Correlation Studies of Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> with Surface Temperature and Meltponding</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Comiso, J. C.; Zukor, Dorothy J. (Technical Monitor)</p> <p>2001-01-01</p> <p>The spatial and temporal variability of sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> derived from passive microwave data is studied in conjunction with co-registered high resolution infrared and visible satellite data. Cloud free infrared and visible data provide surface temperature and large scale surface characteristics, respectively, that can be used to better understand regional and seasonal fluctuations in <span class="hlt">ice</span> <span class="hlt">concentrations</span>. Results from correlation analysis of <span class="hlt">ice</span> <span class="hlt">concentration</span> versus surface temperature data show the intuitively expected negative relationship but the strength in the relationship is unexpectedly very strong. In the Antarctic, the correlation is consistently very high spatially when yearly anomalies are used, and not so high in some areas when seasonal anomalies are used, especially during spring and summer. In the monthly anomalies, the correlation is also good, especially in dynamically active regions. The expanse in the anomalies in surface temperature are shown to go way beyond the sea <span class="hlt">ice</span> regions into the open ocean and continental areas, suggesting strong atmospheric forcing. Weak correlations are normally found in highly consolidated areas, where large changes in temperature do not cause large changes in <span class="hlt">ice</span> <span class="hlt">concentration</span> on a short term, and in open ocean polynya areas, where the change in <span class="hlt">ice</span> <span class="hlt">concentration</span> may be cause by melt from the underside of the <span class="hlt">ice</span>. In the Arctic, strong correlations between surface temperature and <span class="hlt">ice</span> <span class="hlt">concentration</span> are evident for all seasons except during the summer. In the summer, factors such as meltponding, surface wetness, and <span class="hlt">ice</span> breakup, as detected by high resolution visible data, contributes to larger uncertainties in the determination of <span class="hlt">ice</span> <span class="hlt">concentration</span> and the lack of good correlation of the variables.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20010095015&hterms=Negative+correlation&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DNegative%2Bcorrelation','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20010095015&hterms=Negative+correlation&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DNegative%2Bcorrelation"><span>Correlation Studies of Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> with Surface Temperature and Meltponding</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Comiso, J. C.; Zukor, Dorothy J. (Technical Monitor)</p> <p>2001-01-01</p> <p>The spatial and temporal variability of sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> derived from passive microwave data is studied in conjunction with co-registered high resolution infrared and visible satellite data. Cloud free infrared and visible data provide surface temperature and large scale surface characteristics, respectively, that can be used to better understand regional and seasonal fluctuations in <span class="hlt">ice</span> <span class="hlt">concentrations</span>. Results from correlation analysis of <span class="hlt">ice</span> <span class="hlt">concentration</span> versus surface temperature data show the intuitively expected negative relationship but the strength in the relationship is unexpectedly very strong. In the Antarctic, the correlation is consistently very high spatially when yearly anomalies are used, and not so high in some areas when seasonal anomalies are used, especially during spring and summer. In the monthly anomalies, the correlation is also good, especially in dynamically active regions. The expanse in the anomalies in surface temperature are shown to go way beyond the sea <span class="hlt">ice</span> regions into the open ocean and continental areas, suggesting strong atmospheric forcing. Weak correlations are normally found in highly consolidated areas, where large changes in temperature do not cause large changes in <span class="hlt">ice</span> <span class="hlt">concentration</span> on a short term, and in open ocean polynya areas, where the change in <span class="hlt">ice</span> <span class="hlt">concentration</span> may be cause by melt from the underside of the <span class="hlt">ice</span>. In the Arctic, strong correlations between surface temperature and <span class="hlt">ice</span> <span class="hlt">concentration</span> are evident for all seasons except during the summer. In the summer, factors such as meltponding, surface wetness, and <span class="hlt">ice</span> breakup, as detected by high resolution visible data, contributes to larger uncertainties in the determination of <span class="hlt">ice</span> <span class="hlt">concentration</span> and the lack of good correlation of the variables.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1919037D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1919037D"><span>Operational multisensor sea <span class="hlt">ice</span> <span class="hlt">concentration</span> algorithm utilizing Sentinel-1 and AMSR2 data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dinessen, Frode</p> <p>2017-04-01</p> <p>The Norwegian <span class="hlt">Ice</span> Service provide <span class="hlt">ice</span> charts of the European part of the Arctic every weekday. The charts are produced from a manually interpretation of satellite data where SAR (Synthetic Aperture Radar) data plays a central role because of its high spatial resolution and Independence of cloud cover. A new chart is produced every weekday and the charts are distributed through the CMEMS portal. After the launch of Sentinel-1A and B the number of available SAR data have significant increased making it difficult to utilize all the data in a manually process. This in combination with a user demand for a more frequent update of the <span class="hlt">ice</span> conditions, also during the weekends, have made it important to focus the development on utilizing the high resolution Sentinel-1 data in an automatic sea <span class="hlt">ice</span> <span class="hlt">concentration</span> analysis. The algorithm developed here is based on a multi sensor approach using an optimal interpolation to combine sea <span class="hlt">ice</span> <span class="hlt">concentration</span> products derived from Sentinel-1 and passive microwave data from AMSR2. The Sentinel-1 data is classified with a Bayesian SAR classification algorithm using data in extra wide mode dual polarization (HH/HV) to separate <span class="hlt">ice</span> and water in the full 40x40 meter spatial resolution. From the classification of <span class="hlt">ice</span>/water the sea <span class="hlt">ice</span> <span class="hlt">concentration</span> is estimated by calculating amount of <span class="hlt">ice</span> within an area of 1x1 km. The AMSR2 sea <span class="hlt">ice</span> <span class="hlt">concentration</span> are produced as part of the EUMETSAT Ocean and Sea <span class="hlt">Ice</span> Satellite Application Facility (OSI SAF) project and utilize the 89 GHz channel to produce a <span class="hlt">concentration</span> product with a 3km spatial resolution. Results from the automatic classification will be presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AdSpR..56..119K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AdSpR..56..119K"><span>Evaluation of the operational SAR based Baltic Sea <span class="hlt">ice</span> <span class="hlt">concentration</span> products</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Karvonen, Juha</p> <p>2015-07-01</p> <p>Sea <span class="hlt">ice</span> <span class="hlt">concentration</span> is an important <span class="hlt">ice</span> parameter both for weather and climate modeling and sea <span class="hlt">ice</span> navigation. We have developed an fully automated algorithm for sea <span class="hlt">ice</span> <span class="hlt">concentration</span> retrieval using dual-polarized ScanSAR wide mode RADARSAT-2 data. RADARSAT-2 is a C-band SAR (Synthetic Aperture Radar) instrument enabling dual-polarized acquisition in ScanSAR mode. The swath width for the RADARSAT-2 ScanSAR mode is about 500 km, making it very suitable for operational sea <span class="hlt">ice</span> monitoring. The polarization combination used in our <span class="hlt">concentration</span> estimation is HH/HV. The SAR data is first preprocessed; the preprocessing consists of geo-rectification to Mercator projection, incidence angle correction for both the polarization channels, and SAR mosaicking. After preprocessing a segmentation is performed for the SAR mosaics, and some features are computed for each SAR segment. Finally the SAR <span class="hlt">concentration</span> is estimated based on these segment-wise features. The algorithm is basically similar as introduced in Karvonen 2014. The <span class="hlt">ice</span> <span class="hlt">concentration</span> is computed daily using a daily RADARSAT-2 SAR mosaic as its input, and it thus gives the <span class="hlt">concentration</span> estimated at each grid cell (pixel) based on the most recent SAR data at the location. The algorithm has been run in an operational test mode since January 2014. We present evaluation of the SAR-based <span class="hlt">concentration</span> estimates for the Baltic <span class="hlt">ice</span> season 2014 by comparing the SAR results with gridded Finnish Meteorological Institute (FMI) <span class="hlt">ice</span> charts and <span class="hlt">ice</span> <span class="hlt">concentration</span> estimates from a radiometer algorithm.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUFM.C52A..01C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.C52A..01C"><span>Global Sea <span class="hlt">Ice</span> Charting at the National <span class="hlt">Ice</span> Center</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Clemente-Colon, P.</p> <p>2006-12-01</p> <p> Special Sensor Microwave/Imager (SSM/I) provide better temporal and synoptic view of the Polar Regions albeit at a significantly reduced spatial resolution compared to that of active sensors. Automated SSM/I algorithms provide for near-real time production of sea <span class="hlt">ice</span> parameters including <span class="hlt">concentration</span> and multiyear fraction. The retrieval of sea <span class="hlt">ice</span> drift vectors from passive microwave with SSM/I is operational and it is being also implemented using the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>), a research instrument available aboard the Aqua satellite. As in the case of QuikSCAT, NIC is also exploring the use of sea <span class="hlt">ice</span> parameters retrieved from WindSat and the synergy of these observations with the WindSat wind vectors off the MIZ through a newly formed sea <span class="hlt">ice</span> algorithm working group.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A13B0322P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A13B0322P"><span>What Controls the Low <span class="hlt">Ice</span> Number <span class="hlt">Concentration</span> in the Upper Tropical Troposphere?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Penner, J.; Zhou, C.; Lin, G.; Liu, X.; Wang, M.</p> <p>2015-12-01</p> <p>Cirrus clouds in the tropical tropopause play a key role in regulating the moisture entering the stratosphere through their dehydrating effect. Low <span class="hlt">ice</span> number <span class="hlt">concentrations</span> and high supersaturations were frequently were observed in these clouds. However, low <span class="hlt">ice</span> number <span class="hlt">concentrations</span> are inconsistent with cirrus cloud formation based on homogeneous freezing. Different mechanisms have been proposed to explain this discrepancy, including the inhibition of homogeneous freezing by pre-existing <span class="hlt">ice</span> crystals and/or glassy organic aerosol heterogeneous <span class="hlt">ice</span> nuclei (IN) and limiting the formation of <span class="hlt">ice</span> number from high frequency gravity waves. In this study, we examined the effect from three different parameterizations of in-cloud updraft velocities, the effect from pre-existing <span class="hlt">ice</span> crystals, the effect from different water vapor deposition coefficients (α=0.1 or 1), and the effect from 0.1% of secondary organic aerosol (SOA) acting as glassy heterogeneous <span class="hlt">ice</span> nuclei (IN) in CAM5. Model simulated <span class="hlt">ice</span> crystal numbers are compared against an aircraft observational dataset. Using grid resolved large-scale updraft velocity in the <span class="hlt">ice</span> nucleation parameterization generates <span class="hlt">ice</span> number <span class="hlt">concentrations</span> in better agreement with observations for temperatures below 205K while using updraft velocities based on the model-generated turbulence kinetic energy generates <span class="hlt">ice</span> number <span class="hlt">concentrations</span> in better agreement with observations for temperatures above 205K. A larger water vapor deposition coefficient (α=1) can efficiently reduce the <span class="hlt">ice</span> number at temperatures below 205K but less so at higher temperatures. Glassy SOA IN are most effective at reducing the <span class="hlt">ice</span> number <span class="hlt">concentrations</span> when the effective in-cloud updraft velocities are moderate (~0.05-0.2 m s-1). Including the removal of water vapor on pre-existing <span class="hlt">ice</span> can also effectively reduce the <span class="hlt">ice</span> number and diminish the effects from the additional glassy SOA heterogeneous IN. We also re-evaluate whether IN seeding in cirrus cloud is</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.A13A0203M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.A13A0203M"><span>Measurements of <span class="hlt">ice</span> nuclei <span class="hlt">concentrations</span> and compositions in the maritime tropics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McMeeking, G. R.; Danielczok, A.; Bingemer, H.; Klein, H.; Hill, T. C.; Franc, G. D.; Martinez, M.; Venero, I.; Mayol-Bracero, O. L.; Ardon-Dryer, K.; Levin, Z.; Anderson, J.; Twohy, C. H.; Toohey, D. W.; DeMott, P. J.</p> <p>2011-12-01</p> <p>Tropical maritime cumulus clouds represent an important component of the global water cycle, but the relative roles of primary and secondary <span class="hlt">ice</span> production in these clouds are poorly understood. Heterogeneous <span class="hlt">ice</span> nuclei (IN) are responsible for <span class="hlt">ice</span> initiation in towering tropical cumulus clouds, so information regarding their abundance, distribution, source compositions and dependence on cloud temperature is crucial to understanding the <span class="hlt">ice</span> production processes. Here we present recent measurements of <span class="hlt">ice</span> nuclei (IN) <span class="hlt">concentrations</span> measured from ground-based and airborne (NSF/NCAR C-130) platforms during the <span class="hlt">Ice</span> in Clouds-Tropical experiment, which took place in July 2011 over the Caribbean Sea near St. Croix in the US Virgin Islands. IN measurement techniques included airborne ambient and cloud particle residual measurements using a continuous flow diffusion chamber and off-line analysis of samples collected from the aircraft and two ground sites located on the island of Puerto Rico. Off-line measurements of IN <span class="hlt">concentrations</span> included analysis by the Frankfurt <span class="hlt">Ice</span> Nuclei Deposition FreezinG Experiment (FRIDGE) system and drop freezing via two methods of particles collected from filter samples. The measurement period included some periods with a strong Saharan dust influence that resulted in higher IN <span class="hlt">concentrations</span> compared to clean maritime conditions. First analysis of IN physical, chemical and biological composition, and investigation of relationships between IN <span class="hlt">concentrations</span> and total aerosol <span class="hlt">concentrations</span>, composition and size are also presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..1910408V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..1910408V"><span>Contribution of Feldspar and Marine Organic aerosols to global <span class="hlt">ice</span> nucleating particles <span class="hlt">concentrations</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vergara Temprado, Jesús; Murray, Benjamin J.; Wilson, Theodore W.; O'Sullivan, Daniel; Browse, Jo; Pringle, Kirsty J.; Ardon-Dryer, Karin; Bertram, Allan K.; Burrows, Susannah; Cerbunis, Darius; DeMott, Paul J.; Mason, Ryan H.; O'Dowd, Colin D.; Rinaldi, Matteo; Carslaw, Ken S.</p> <p>2017-04-01</p> <p><span class="hlt">Ice</span> nucleating particles (INP) are aerosol particles that can heterogeneously freeze supercooled liquid water in the mixed-phase range of temperatures where water can exist in both liquid and <span class="hlt">ice</span> states (0 to -37 oC). They affect the amount of <span class="hlt">ice</span> and liquid water in mixed-phase clouds changing many of their properties. Climate models tend to represent their effect by parameterizing their atmospheric <span class="hlt">concentration</span> as function of temperature or temperature and aerosol loading. However, different aerosol species nucleate <span class="hlt">ice</span> with different abilities affecting the <span class="hlt">concentrations</span> in different parts of the world. Representing these differences in models can lead to a better representation of mixed-phase clouds and <span class="hlt">ice</span> processes affecting the radiative flux and the climate sensitivity of climate models. Here, we present the simulated <span class="hlt">concentrations</span> of K-feldspar and marine organic aerosols using a global aerosol model, and then estimate the contribution of these species to INP <span class="hlt">concentrations</span> across the globe using laboratory developed parameterizations of their <span class="hlt">ice</span> nucleating ability. We show that these two species combined perform better at predicting global observations of INP than typically used parameterizations. Biases appear mainly in terrestrial environments at high temperatures, which might be caused by a relevant missing source of INP in our model. This work is a step forward in our understanding of how INP are distributed and what species are needed to be included in models in order to improve the representation of heterogeneous <span class="hlt">ice</span> nucleation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AdWR...98..122C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AdWR...98..122C"><span>Using machine learning to produce near surface soil moisture estimates from deeper in situ records at U.S. Climate Reference Network (USCRN) locations: Analysis and applications to <span class="hlt">AMSR-E</span> satellite validation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Coopersmith, Evan J.; Cosh, Michael H.; Bell, Jesse E.; Boyles, Ryan</p> <p>2016-12-01</p> <p>Surface soil moisture is a critical parameter for understanding the energy flux at the land atmosphere boundary. Weather modeling, climate prediction, and remote sensing validation are some of the applications for surface soil moisture information. The most common in situ measurement for these purposes are sensors that are installed at depths of approximately 5 cm. There are however, sensor technologies and network designs that do not provide an estimate at this depth. If soil moisture estimates at deeper depths could be extrapolated to the near surface, in situ networks providing estimates at other depths would see their values enhanced. Soil moisture sensors from the U.S. Climate Reference Network (USCRN) were used to generate models of 5 cm soil moisture, with 10 cm soil moisture measurements and antecedent precipitation as inputs, via machine learning techniques. Validation was conducted with the available, in situ, 5 cm resources. It was shown that a 5 cm estimate, which was extrapolated from a 10 cm sensor and antecedent local precipitation, produced a root-mean-squared-error (RMSE) of 0.0215 m3/m3. Next, these machine-learning-generated 5 cm estimates were also compared to <span class="hlt">AMSR-E</span> estimates at these locations. These results were then compared with the performance of the actual in situ readings against the <span class="hlt">AMSR-E</span> data. The machine learning estimates at 5 cm produced an RMSE of approximately 0.03 m3/m3 when an optimized gain and offset were applied. This is necessary considering the performance of <span class="hlt">AMSR-E</span> in locations characterized by high vegetation water contents, which are present across North Carolina. Lastly, the application of this extrapolation technique is applied to the ECONet in North Carolina, which provides a 10 cm depth measurement as its shallowest soil moisture estimate. A raw RMSE of 0.028 m3/m3 was achieved, and with a linear gain and offset applied at each ECONet site, an RMSE of 0.013 m3/m3 was possible.</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/2016EGUGA..18.5024A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.5024A"><span>European Marine Background <span class="hlt">Ice</span> Nucleating Particle <span class="hlt">concentrations</span> Measured at the Mace Head Station, Ireland.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Atkinson, James; Kanji, Zamin A.; Ovadnevaite, Jurgita; Ceburnis, Darius; O'Dowd, Colin</p> <p>2016-04-01</p> <p><span class="hlt">Ice</span> formation is an important process which controls cloud microphysical properties and can be critical in the creation of precipitation, therefore influencing the hydrological cycle and energy budget of the Earth. <span class="hlt">Ice</span> Nucleating Particles (INP) can greatly increase the temperature and rate of <span class="hlt">ice</span> formation, but the sources and geographical distributions of these particles is not well understood. Mace Head in Ireland is a coastal site on the north eastern edge of Europe with prevailing winds generally from the Atlantic Ocean with little continental influence. Observations of INP <span class="hlt">concentration</span> from August 2015 using the Horizontal <span class="hlt">Ice</span> Nucleation Chamber (HINC) at temperature of -30 C are presented. Correlations between the INP and meteorological conditions and aerosol compositions are made, as well as comparisons with commonly used INP <span class="hlt">concentration</span> parameterisations. Observed INP <span class="hlt">concentrations</span> are generally low, suggesting that oceanic sources in this region do not contribute significant numbers of INP to the global distribution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28060386','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28060386"><span><span class="hlt">Concentrations</span> of a triplet excited state are enhanced in illuminated <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>Chen, Zeyuan; Anastasio, Cort</p> <p>2017-01-25</p> <p>Photochemical reactions influence the fates and lifetimes of organic compounds in snow and <span class="hlt">ice</span>, both through direct photoreactions and via photoproduced transient species such as hydroxyl radical (˙OH) and, perhaps, triplet excited states of organic compounds (i.e., triplets). While triplets can be important oxidants in atmospheric drops and surface waters, little is known of this class of oxidants in frozen samples. To investigate this, we examined the photoreaction of phenol with the triplet state of 3,4-dimethoxybenzaldehyde ((3)DMB*), a product from biomass combustion, in illuminated laboratory <span class="hlt">ices</span>. Our results show that the rate of phenol loss due to (3)DMB* is, on average, increased by a factor of 95 ± 50 in <span class="hlt">ice</span> compared to the equivalent liquid sample. We find that this experimentally measured freeze <span class="hlt">concentration</span> factor, FEXP, is independent of total solute <span class="hlt">concentration</span> and temperature, in contrast to what is expected from a liquid-like region whose composition follows freezing point depression. We also find that FEXP for triplets is independent of pH, although the rates of phenol loss increase with decreasing pH in both solution and <span class="hlt">ice</span>. The enhancement in the rate of phenol loss in/on <span class="hlt">ice</span> indicates that <span class="hlt">concentrations</span> of triplet excited states are enhanced in <span class="hlt">ice</span> relative to solution and suggests that this class of oxidants might be a significant sink for organics in snow and <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040031526&hterms=bootstrap&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dbootstrap','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040031526&hterms=bootstrap&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dbootstrap"><span>Using Satellite-derived <span class="hlt">Ice</span> <span class="hlt">Concentration</span> to Represent Antarctic Coastal Polynyas in Ocean Climate Models</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Stoessel, Achim; Markus, Thorsten</p> <p>2003-01-01</p> <p>The focus of this paper is on the representation of Antarctic coastal polynyas in global <span class="hlt">ice</span>-ocean general circulation models (OGCMs), in particular their local, regional, and high-frequency behavior. This is verified with the aid of daily <span class="hlt">ice</span> <span class="hlt">concentration</span> derived from satellite passive microwave data using the NASATeam 2 (NT2) and the bootstrap (BS) algorithms. Large systematic regional and temporal discrepancies arise, some of which are related to the type of convection parameterization used in the model. An attempt is made to improve the fresh-water flux associated with melting and freezing in Antarctic coastal polynyas by ingesting (assimilating) satellite <span class="hlt">ice</span> <span class="hlt">concentration</span> where it comes to determining the thermodynamics of the open-water fraction of a model grid cell. Since the NT2 coastal open-water fraction (polynyas) tends to be less extensive than the simulated one in the decisive season and region, assimilating NT2 coastal <span class="hlt">ice</span> <span class="hlt">concentration</span> yields overall reduced net freezing rates, smaller formation rates of Antarctic Bottom Water, and a stronger southward flow of North Atlantic Deep Water across 30 S. Enhanced net freezing rates occur regionally when NT2 coastal <span class="hlt">ice</span> <span class="hlt">concentration</span> is assimilated, concomitant with a more realistic <span class="hlt">ice</span> thickness distribution and accumulation of High-Salinity Shelf Water. Assimilating BS rather than NT2 coastal <span class="hlt">ice</span> <span class="hlt">concentration</span>, the differences to the non-assimilated simulation are generally smaller and of opposite sign. This suggests that the model reproduces coastal <span class="hlt">ice</span> <span class="hlt">concentration</span> in closer agreement with the BS data than with the NT2 data, while more realistic features emerge when NT2 data are assimilated.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040031526&hterms=Antarctic+climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DAntarctic%2Bclimate','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040031526&hterms=Antarctic+climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DAntarctic%2Bclimate"><span>Using Satellite-derived <span class="hlt">Ice</span> <span class="hlt">Concentration</span> to Represent Antarctic Coastal Polynyas in Ocean Climate Models</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Stoessel, Achim; Markus, Thorsten</p> <p>2003-01-01</p> <p>The focus of this paper is on the representation of Antarctic coastal polynyas in global <span class="hlt">ice</span>-ocean general circulation models (OGCMs), in particular their local, regional, and high-frequency behavior. This is verified with the aid of daily <span class="hlt">ice</span> <span class="hlt">concentration</span> derived from satellite passive microwave data using the NASATeam 2 (NT2) and the bootstrap (BS) algorithms. Large systematic regional and temporal discrepancies arise, some of which are related to the type of convection parameterization used in the model. An attempt is made to improve the fresh-water flux associated with melting and freezing in Antarctic coastal polynyas by ingesting (assimilating) satellite <span class="hlt">ice</span> <span class="hlt">concentration</span> where it comes to determining the thermodynamics of the open-water fraction of a model grid cell. Since the NT2 coastal open-water fraction (polynyas) tends to be less extensive than the simulated one in the decisive season and region, assimilating NT2 coastal <span class="hlt">ice</span> <span class="hlt">concentration</span> yields overall reduced net freezing rates, smaller formation rates of Antarctic Bottom Water, and a stronger southward flow of North Atlantic Deep Water across 30 S. Enhanced net freezing rates occur regionally when NT2 coastal <span class="hlt">ice</span> <span class="hlt">concentration</span> is assimilated, concomitant with a more realistic <span class="hlt">ice</span> thickness distribution and accumulation of High-Salinity Shelf Water. Assimilating BS rather than NT2 coastal <span class="hlt">ice</span> <span class="hlt">concentration</span>, the differences to the non-assimilated simulation are generally smaller and of opposite sign. This suggests that the model reproduces coastal <span class="hlt">ice</span> <span class="hlt">concentration</span> in closer agreement with the BS data than with the NT2 data, while more realistic features emerge when NT2 data are assimilated.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017TCry...11.1987G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017TCry...11.1987G"><span>New methodology to estimate Arctic sea <span class="hlt">ice</span> <span class="hlt">concentration</span> from SMOS combining brightness temperature differences in a maximum-likelihood estimator</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gabarro, Carolina; Turiel, Antonio; Elosegui, Pedro; Pla-Resina, Joaquim A.; Portabella, Marcos</p> <p>2017-08-01</p> <p>Monitoring sea <span class="hlt">ice</span> <span class="hlt">concentration</span> is required for operational and climate studies in the Arctic Sea. Technologies used so far for estimating sea <span class="hlt">ice</span> <span class="hlt">concentration</span> have some limitations, for instance the impact of the atmosphere, the physical temperature of <span class="hlt">ice</span>, and the presence of snow and melting. In the last years, L-band radiometry has been successfully used to study some properties of sea <span class="hlt">ice</span>, remarkably sea <span class="hlt">ice</span> thickness. However, the potential of satellite L-band observations for obtaining sea <span class="hlt">ice</span> <span class="hlt">concentration</span> had not yet been explored. In this paper, we present preliminary evidence showing that data from the Soil Moisture Ocean Salinity (SMOS) mission can be used to estimate sea <span class="hlt">ice</span> <span class="hlt">concentration</span>. Our method, based on a maximum-likelihood estimator (MLE), exploits the marked difference in the radiative properties of sea <span class="hlt">ice</span> and seawater. In addition, the brightness temperatures of 100 % sea <span class="hlt">ice</span> and 100 % seawater, as well as their combined values (polarization and angular difference), have been shown to be very stable during winter and spring, so they are robust to variations in physical temperature and other geophysical parameters. Therefore, we can use just two sets of tie points, one for summer and another for winter, for calculating sea <span class="hlt">ice</span> <span class="hlt">concentration</span>, leading to a more robust estimate. After analysing the full year 2014 in the entire Arctic, we have found that the sea <span class="hlt">ice</span> <span class="hlt">concentration</span> obtained with our method is well determined as compared to the Ocean and Sea <span class="hlt">Ice</span> Satellite Application Facility (OSI SAF) dataset. However, when thin sea <span class="hlt">ice</span> is present (<span class="hlt">ice</span> thickness ≲ 0.6 m), the method underestimates the actual sea <span class="hlt">ice</span> <span class="hlt">concentration</span>. <br"> Our results open the way for a systematic exploitation of SMOS data for monitoring sea <span class="hlt">ice</span> <span class="hlt">concentration</span>, at least for specific seasons. Additionally, SMOS data can be synergistically combined with data from other sensors to monitor pan-Arctic sea <span class="hlt">ice</span> conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016DSRII.131..160C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016DSRII.131..160C"><span>Effect of elevated CO2 <span class="hlt">concentration</span> on microalgal communities in Antarctic pack <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>Coad, Thomas; McMinn, Andrew; Nomura, Daiki; Martin, Andrew</p> <p>2016-09-01</p> <p>Increased anthropogenic CO2 emissions are causing changes to oceanic pH and CO2 <span class="hlt">concentrations</span> that will impact many marine organisms, including microalgae. Phytoplankton taxa have shown mixed responses to these changes with some doing well while others have been adversely affected. Here, the photosynthetic response of sea-<span class="hlt">ice</span> algal communities from Antarctic pack <span class="hlt">ice</span> (brine and infiltration microbial communities) to a range of CO2 <span class="hlt">concentrations</span> (400 ppm to 11,000 ppm in brine algae experiments, 400 ppm to 20,000 ppm in the infiltration <span class="hlt">ice</span> algae experiment) was investigated. Incubations were conducted as part of the Sea-<span class="hlt">Ice</span> Physics and Ecosystem Experiment II (SIPEX-2) voyage, in the austral spring (September-November), 2012. In the brine incubations, maximum quantum yield (Fv/Fm) and relative electron transfer rate (rETRmax) were highest at ambient and 0.049% (experiment 1) and 0.19% (experiment 2) CO2 <span class="hlt">concentrations</span>, although, Fv/Fm was consistently between 0.53±0.10-0.68±0.01 across all treatments in both experiments. Highest rETRmax was exhibited by brine cultures exposed to ambient CO2 <span class="hlt">concentrations</span> (60.15). In a third experiment infiltration <span class="hlt">ice</span> algal communities were allowed to melt into seawater modified to simulate the changed pH and CO2 <span class="hlt">concentrations</span> of future springtime <span class="hlt">ice</span>-edge conditions. Ambient and 0.1% CO2 treatments had the highest growth rates and Fv/Fm values but only the highest CO2 <span class="hlt">concentration</span> produced a significantly lower rETRmax. These experiments, conducted on natural Antarctic sea-<span class="hlt">ice</span> algal communities, indicate a strong level of tolerance to elevated CO2 <span class="hlt">concentrations</span> and suggest that these communities might not be adversely affected by predicted changes in CO2 <span class="hlt">concentration</span> over the next century.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C11B0359R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C11B0359R"><span>Arctic Sea <span class="hlt">Ice</span> Extent and <span class="hlt">Concentrations</span> are Overpredicted by a Regional Coupled Ocean - Sea <span class="hlt">Ice</span> Model as Compared to 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>Reddy, T.</p> <p>2014-12-01</p> <p>It is well known that the environment of the Arctic Ocean is currently changing rapidly as a result of anthropogenic inputs of greenhouse gases into the atmosphere. The loss of sea <span class="hlt">ice</span> is one of the most dramatic and impactful aspects of change. Warming is amplified due to the open water albedo feedback effect. <span class="hlt">Ice</span> is more reflective than ocean, creating a positive feedback with negative consequences. Here I compare our knowledge of sea <span class="hlt">ice</span> in the Arctic Ocean using data from two satellites (SSM/I "NASA-team" and "Bootstrap" algorithms and MODIS MOD29 sea <span class="hlt">ice</span> extent) to a coupled numerical model of ocean circulation and sea <span class="hlt">ice</span> (Regional Ocean Modeling System, ROMS). I run the model from 1987 through 2012. Most years, the summer <span class="hlt">ice</span> extent near Greenland and Norway is fairly well simulated, but in the Alaskan and Canadian Region, satellite data show <span class="hlt">ice</span> retreating dramatically from the coast, well into the Arctic basin. This retreat is not captured in the model. During the sea <span class="hlt">ice</span> minima in September, <span class="hlt">ice</span> extent from satellite data is approximately 72% of modeled <span class="hlt">ice</span> extent (for a ten-year average from 2003 - 2012). I focus on extent, <span class="hlt">concentrations</span>, and thicknesses, three of the main <span class="hlt">ice</span> characteristics described in ROMS. The results of this model vs. satellite data comparison show that the model simulates the presence of much more <span class="hlt">ice</span>, both a larger extent and higher <span class="hlt">concentrations</span>. This overestimation of <span class="hlt">ice</span> is common among many of the models presented in the literature. Changes in <span class="hlt">ice</span> thicknesses are a major concern as multi-year <span class="hlt">ice</span> is being lost at an accelerating pace. <span class="hlt">Ice</span> thickness is a good proxy for <span class="hlt">ice</span> age, and young <span class="hlt">ice</span> is much more susceptible to melting than older <span class="hlt">ice</span>. Many polar ocean models specify <span class="hlt">ice</span> <span class="hlt">concentrations</span> and thicknesses instead of modeling them. This is problematic at present due to limited data available for <span class="hlt">ice</span> thickness. For this reason, it is key to develop models like ROMS in order to improve understanding of the Arctic ocean - <span class="hlt">ice</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014cosp...40E2731R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014cosp...40E2731R"><span>A new algorithm to measure sea <span class="hlt">ice</span> <span class="hlt">concentration</span> from passive microwave remote sensing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Repina, Irina; Sharkov, Evgeniy; Komarova, Nataliya; Raev, Mikhail; Tikhonov, Vasilii; Boyarskiy, Dmitriy</p> <p></p> <p>Studies of spatial and temporal properties of sea <span class="hlt">ice</span> distribution in polar regions help to monitor global environmental changes and reveal their natural and anthropogenic factors, as well as make forecasts of weather, marine transportation and fishing conditions, assess perspectives of mineral mining on the continental shelf, etc. Contact methods of observation are often insufficient to meet the goals, very complicated technically and organizationally and not always safe for people involved. Remote sensing techniques are believed to be the best alternative. Its include monitoring of polar regions by means of passive microwave sensing with the aim to determine spatial distribution, types, thickness and snow cover of <span class="hlt">ice</span>. However, the algorithms employed today to retrieve sea <span class="hlt">ice</span> characteristics from passive microwave sensing data for different reasons give significant errors, especially in summer period and also near <span class="hlt">ice</span> edges and in cases of open <span class="hlt">ice</span>. One of the error sources is the current practice of using empirical dependencies and adjustment coefficients for the retrieval of <span class="hlt">ice</span> characteristics and neglecting the physics of the process. We discuss an electrodynamic model of the sea surface - sea <span class="hlt">ice</span> - snow cover - atmosphere system developed with account taken of physical and structural properties of the ambient. Model calculations of <span class="hlt">ice</span> brightness temperature in different <span class="hlt">concentrations</span> and snow covers are in good agreement with SSM/I measurement data. On the base of this model we develop a new algorithm for the retrieval of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> from passive microwave sensing data - Variation Arctic Sea <span class="hlt">Ice</span> Algorithm (VASIA). In contrast to the well-known techniques (NASA TEAM, Bootstrap, ASI, NORSEX et al), it takes into account the real physical parameters of <span class="hlt">ice</span>, snow and open water rather than empirical and adjustment coefficients. Satellite data were provided by the POLE-RT-Fields SSM/I and SSMIS data collection for polar regions retrieved from the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/508187','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/508187"><span>Changes in Arctic sea <span class="hlt">ice</span> <span class="hlt">concentration</span>, 1988 to 1994, as detected using image trend analysis</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Derksen, C.; Piwowar, J.; Sokol, J.; LeDrew, E.</p> <p>1997-08-01</p> <p>Arctic sea <span class="hlt">ice</span> may prove to be an early indicator of global climatic change because of its high sensitivity to overlying air temperature and the predicted amplified response of the polar regions to a changing climate. Long term investigations into temporal change in variables such as Arctic sea <span class="hlt">ice</span> <span class="hlt">concentration</span> will prove to be even more significant as the accumulation of passive microwave remote sensing data continues. Hypertemporal image analysis techniques, of which image trend analysis is just one, provide methods for investigating spatial and temporal data trends through long image sequences. This study utilizes a Special Sensor Microwave/Imager dataset of monthly mean and anomaly sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> to investigate changes in Arctic sea <span class="hlt">ice</span> cover over seven years. The construction of a three dimensional hypertemporal image cube of the monthly data allows trends in <span class="hlt">ice</span> <span class="hlt">concentration</span> to be isolated through a linear regression analysis of all image pixels through the time series. The slope of the trend line for each pixel is assigned to a new image to show areas of negative and positive change through time. Use of monthly anomaly data deseasonalizes and removes autocorrelation from the mean monthly averages. The interpretation of results at a regional scale with no artificial boundaries applied highlights the spatial distribution of temporal trends in <span class="hlt">ice</span> <span class="hlt">concentration</span> within the natural climate system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016SPIE.9999E..0TS','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016SPIE.9999E..0TS"><span>Analysis on long-term variability of sea <span class="hlt">ice</span> albedo and its relationship with sea <span class="hlt">ice</span> <span class="hlt">concentration</span> over Antarctica</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Seo, Minji; Kim, Hyun-cheol; Seong, Noh-hun; Kwon, Chaeyoung; Kim, Honghee; Han, Kyung-Soo</p> <p>2016-10-01</p> <p>Sea <span class="hlt">ice</span> is an important factor for understanding Antarctic climate change. Especially, annual change of sea <span class="hlt">ice</span> shows different trend in Antarctica and Arctic. This different variation need to continuously observe the Polar Regions. Sea <span class="hlt">Ice</span> Albedo (SIA) and Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> (SIC) are an indicator of variation on sea <span class="hlt">ice</span>. In addition, albedo is key parameter to understand the energy budget in Antarctica. This being so, it is important to analyze long-term variation of the two factors for observing of change of Antarctic environment. In this study, we analyzed long-term variability of SIC and SIA to understand the changes of sea <span class="hlt">ice</span> over Antarctic and researched the relationship with two factors. We used the SIA data at The Satellite Application Facility on Climate Monitoring (CM SAF) and the SIC data provided by Ocean and Sea <span class="hlt">Ice</span> Satellite Application Facility (OSI-SAF) from 1982 to 2009. The study period was selected to Antarctic summer season due to polar nights. We divided study periods into two terms, Nov-Dec(ND) and Jan-Feb(JF) in order to reflect the characteristics of sea <span class="hlt">ice</span> area, which minimum extend occurred in September and maximum extend occurred in February. We analyzed the correlation between SIA and SIC. As a results, two variables have a strong positive correlation (each correlation coefficients are 0.91 in Nov-Dec and 0.90 in Jan-Feb). We performed time series analysis using linear regression to understand the spatial and temporal tendency of SIA and SIC. As a results, SIA and SIC have a same spatial trend such as Weddle sea and Ross sea sections show the positive trend and Bellingshausen Amundsen sea shows the negative trend of two factors. Moreover, annual SIA change rate is 0.26% 0.04% yr-1 over section where represent positive trend during two study periods. And annual SIA change rate is - 0.14 - 0.25 % yr-1 of in the other part where represent negative trend during two study periods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.8635M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.8635M"><span>Influence of Arctic sea-<span class="hlt">ice</span> and greenhouse gas <span class="hlt">concentration</span> change on the West African Monsoon.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Monerie, Paul-Arthur; Oudar, Thomas; Sanchez-Gomez, Emilia; Terray, Laurent</p> <p>2016-04-01</p> <p>The Sahelian precipitation are projected to increase in the CNRM-CM5 coupled climate model due to a strengthening of the land-Sea temperature gradient, the increase in the North Atlantic temperature and the deepening of the Heat Low. Arctic Sea-<span class="hlt">Ice</span> loss impacts the low-level atmospheric circulation through a decrease in the northward heat transport. Some authors have linked the sea-<span class="hlt">ice</span> loss to a poleward shift of the InterTropical Convergence Zone. Within the CMIP5 models the effect of these mechanisms are not distinguishable and it is difficult to understand the effect of the Arctic sea-<span class="hlt">ice</span> loss on the West African Monsoon so far. We performed several sensitivity experiments with the CNRM-CM5 coupled climate models by modifying the arctic sea-<span class="hlt">ice</span> extent and/or the greenhouse gas <span class="hlt">concentration</span>. We then investigated separately the impact of Arctic sea-<span class="hlt">ice</span> loss and greenhouse gas <span class="hlt">concentration</span> increases on the West African Monsoon. The increase in greenhouse gas explains the northward shift and the strengthening of the monsoon. Its effect is stronger with a sea-<span class="hlt">ice</span> free Arctic that leads to an increase in North Atlantic temperature and in Sahelian precipitation at the end of the rainy season (September-October). We argue that the decrease in sea-<span class="hlt">ice</span> extent, in the context of the global warming, may moistens the Sahel during the rainy season by changing the pressure, winds and moisture fluxes at low-level.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20090035004&hterms=cirrus+clouds&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dcirrus%2Bclouds','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20090035004&hterms=cirrus+clouds&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dcirrus%2Bclouds"><span>Understanding <span class="hlt">Ice</span> Supersaturation, Particle Growth, and Number <span class="hlt">Concentration</span> in Cirrus Clouds</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Comstock, Jennifer M.; Lin, Ruei-Fong; Starr, David O'C.; Yang, Ping</p> <p>2008-01-01</p> <p>Many factors control the <span class="hlt">ice</span> supersaturation and microphysical properties in cirrus clouds. We explore the effects of dynamic forcing, <span class="hlt">ice</span> nucleation mechanisms, and <span class="hlt">ice</span> crystal growth rate on the evolution and distribution of water vapor and cloud properties in nighttime cirrus clouds using a one-dimensional cloud model with bin microphysics and remote sensing measurements obtained at the Department of Energy's Atmospheric Radiation Measurement (ARM) Climate Research Facility located near Lamont, OK. We forced the model using both large-scale vertical ascent and, for the first time, mean mesoscale velocity derived from radar Doppler velocity measurements. Both heterogeneous and homogeneous nucleation processes are explored, where a classical theory heterogeneous scheme is compared with empirical representations. We evaluated model simulations by examining both bulk cloud properties and distributions of measured radar reflectivity, lidar extinction, and water vapor profiles, as well as retrieved cloud microphysical properties. Our results suggest that mesoscale variability is the primary mechanism needed to reproduce observed quantities. Model sensitivity to the <span class="hlt">ice</span> growth rate is also investigated. The most realistic simulations as compared with observations are forced using mesoscale waves, include fast <span class="hlt">ice</span> crystal growth, and initiate <span class="hlt">ice</span> by either homogeneous or heterogeneous nucleation. Simulated <span class="hlt">ice</span> crystal number <span class="hlt">concentrations</span> (tens to hundreds particles per liter) are typically two orders of magnitude smaller than previously published results based on aircraft measurements in cirrus clouds, although higher <span class="hlt">concentrations</span> are possible in isolated pockets within the nucleation zone.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20090035004&hterms=concentration&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dconcentration','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20090035004&hterms=concentration&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dconcentration"><span>Understanding <span class="hlt">Ice</span> Supersaturation, Particle Growth, and Number <span class="hlt">Concentration</span> in Cirrus Clouds</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Comstock, Jennifer M.; Lin, Ruei-Fong; Starr, David O'C.; Yang, Ping</p> <p>2008-01-01</p> <p>Many factors control the <span class="hlt">ice</span> supersaturation and microphysical properties in cirrus clouds. We explore the effects of dynamic forcing, <span class="hlt">ice</span> nucleation mechanisms, and <span class="hlt">ice</span> crystal growth rate on the evolution and distribution of water vapor and cloud properties in nighttime cirrus clouds using a one-dimensional cloud model with bin microphysics and remote sensing measurements obtained at the Department of Energy's Atmospheric Radiation Measurement (ARM) Climate Research Facility located near Lamont, OK. We forced the model using both large-scale vertical ascent and, for the first time, mean mesoscale velocity derived from radar Doppler velocity measurements. Both heterogeneous and homogeneous nucleation processes are explored, where a classical theory heterogeneous scheme is compared with empirical representations. We evaluated model simulations by examining both bulk cloud properties and distributions of measured radar reflectivity, lidar extinction, and water vapor profiles, as well as retrieved cloud microphysical properties. Our results suggest that mesoscale variability is the primary mechanism needed to reproduce observed quantities. Model sensitivity to the <span class="hlt">ice</span> growth rate is also investigated. The most realistic simulations as compared with observations are forced using mesoscale waves, include fast <span class="hlt">ice</span> crystal growth, and initiate <span class="hlt">ice</span> by either homogeneous or heterogeneous nucleation. Simulated <span class="hlt">ice</span> crystal number <span class="hlt">concentrations</span> (tens to hundreds particles per liter) are typically two orders of magnitude smaller than previously published results based on aircraft measurements in cirrus clouds, although higher <span class="hlt">concentrations</span> are possible in isolated pockets within the nucleation zone.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015OcMod..93...22B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015OcMod..93...22B"><span>Assimilation of sea surface temperature, sea <span class="hlt">ice</span> <span class="hlt">concentration</span> and sea <span class="hlt">ice</span> drift in a model of the Southern Ocean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barth, Alexander; Canter, Martin; Van Schaeybroeck, Bert; Vannitsem, Stéphane; Massonnet, François; Zunz, Violette; Mathiot, Pierre; Alvera-Azcárate, Aida; Beckers, Jean-Marie</p> <p>2015-09-01</p> <p>Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating sea surface temperature, sea <span class="hlt">ice</span> <span class="hlt">concentration</span> and sea <span class="hlt">ice</span> drift. In the following it is also shown how surface winds in the Southern Ocean can be improved using sea <span class="hlt">ice</span> drift estimated from infrared radiometers. Such satellite observations are available since the late seventies and have the potential to improve the wind forcing before more direct measurements of winds over the ocean are available using scatterometry in the late nineties. The model results are compared to the assimilated data and to independent measurements (the World Ocean Database 2009 and the mean dynamic topography based on observations). The overall improvement of the assimilation is quantified, in particular the impact of the assimilation on the representation of the polar front is discussed. Finally a method to identify model errors in the Antarctic sea <span class="hlt">ice</span> area is proposed based on Model Output Statistics techniques using a series of potential predictors. This approach provides new directions for model improvements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.5746B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5746B"><span>Assimilation of sea surface temperature, sea <span class="hlt">ice</span> <span class="hlt">concentration</span> and sea <span class="hlt">ice</span> drift in a model of the Southern Ocean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barth, Alexander; Canter, Martin; Van Schaeybroeck, Bert; Vannitsem, Stéphane; Massonnet, François; Zunz, Violette; Mathiot, Pierre; Alvera-Azcárate, Aida; Beckers, Jean-Marie</p> <p>2015-04-01</p> <p>Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating sea surface temperature, sea <span class="hlt">ice</span> <span class="hlt">concentration</span> and sea <span class="hlt">ice</span> drift. In the following it is also shown how surface winds in the Southern Ocean can be improved using sea <span class="hlt">ice</span> drift estimated from infrared radiometers. Such satellite observations are available since the late seventies and have the potential to improve the wind forcing before more direct measurements of winds over the ocean are available using scatterometry in the late nineties. The model results are compared to the assimilated data and to independent measurements (the World Ocean Database 2009 and the mean dynamic topography based on observations). The overall improvement of the assimilation is quantified, in particular the impact of the assimilation on the representation of the polar front is discussed. Finally a method to identify model errors in the Antarctic sea <span class="hlt">ice</span> area is proposed based on Model Output Statistics techniques using a series of potential predictors. This approach provides new directions for model improvements.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016Sci...353.1427S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016Sci...353.1427S"><span>A Pleistocene <span class="hlt">ice</span> core record of atmospheric O2 <span class="hlt">concentrations</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stolper, D. A.; Bender, M. L.; Dreyfus, G. B.; Yan, Y.; Higgins, J. A.</p> <p>2016-09-01</p> <p>The history of atmospheric O2 partial pressures (PO2) is inextricably linked to the coevolution of life and Earth’s biogeochemical cycles. Reconstructions of past PO2 rely on models and proxies but often markedly disagree. We present a record of PO2 reconstructed using O2/N2 ratios from ancient air trapped in <span class="hlt">ice</span>. This record indicates that PO2 declined by 7 per mil (0.7%) over the past 800,000 years, requiring that O2 sinks were ~2% larger than sources. This decline is consistent with changes in burial and weathering fluxes of organic carbon and pyrite driven by either Neogene cooling or increasing Pleistocene erosion rates. The 800,000-year record of steady average carbon dioxide partial pressures (PCO2) but declining PO2 provides distinctive evidence that a silicate weathering feedback stabilizes PCO2 on million-year time scales.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20020054184&hterms=Ice+Age&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3DIce%2BAge','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20020054184&hterms=Ice+Age&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3DIce%2BAge"><span><span class="hlt">Concentrating</span> Antarctic Meteorites on Blue <span class="hlt">ice</span> Fields: The Frontier Mountain Meteorite Trap</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Sandford, Scott A.; DeVincenzi, D. (Technical Monitor)</p> <p>2002-01-01</p> <p>The collection of meteorites in Antarctica has greatly stimulated advancement in the field of meteoritics by providing the community with significant numbers of rare and unique meteorites types and by yielding large numbers of meteorites that sample older infall epochs (Grady et al., 1998). The majority of Antarctic meteorites are found on blue <span class="hlt">ice</span> fields, where they are thought to be <span class="hlt">concentrated</span> by wind and glacial drift (cf. Cassidy et al., 1992). The basic "<span class="hlt">ice</span> flow model" describes the <span class="hlt">concentration</span> of meteorites by the stagnation or slowing of <span class="hlt">ice</span> as it moves against a barrier located in a zone with low snow accumulation. However, our limited knowledge of the details of the actual <span class="hlt">concentration</span> mechanisms prevents establishing firm conclusions concerning the past meteorite flux from the Antarctic record (Zolensky, 1998). The terrestrial ages of Antarctic meteorites indicate that their <span class="hlt">concentration</span> occurs on time scales of tens to hundreds of thousands of years (Nishiizumi et al., 1989). It is a challenge to measure a mechanism that operates so slowly, and since such time scales can span more than one glacial epoch one cannot assume that the snow accumulation rates, <span class="hlt">ice</span> velocities and directions, etc. that are measured today are representative of those extant over the age of the trap. Testing the basic "<span class="hlt">ice</span> flow model" therefore requires the careful measurement of meteorite locations, glacialogical <span class="hlt">ice</span> flow data, <span class="hlt">ice</span> thicknesses, bedrock and surface topology, <span class="hlt">ice</span> ablation and snow accumulation rates, and mass transport by wind over an extended period of time in a location where these quantities can be interpreted in the context of past glacialogical history.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1814425F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1814425F"><span>Comparing modelled and measured <span class="hlt">ice</span> crystal <span class="hlt">concentrations</span> in orographic clouds during the INUPIAQ campaign</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Farrington, Robert; Connolly, Paul J.; Lloyd, Gary; Bower, Keith N.; Flynn, Michael J.; Gallagher, Martin W.; Field, Paul R.; Dearden, Chris; Choularton, Thomas W.; Hoyle, Chris</p> <p>2016-04-01</p> <p>At temperatures between -35°C and 0°C, the presence of insoluble aerosols acting as <span class="hlt">ice</span> nuclei (IN) is the only way in which <span class="hlt">ice</span> can nucleate under atmospheric conditions. Previous field and laboratory campaigns have suggested that mineral dust present in the atmosphere act as IN at temperatures warmer than -35°C (e.g. Sassen et al. 2003); however, the cause of <span class="hlt">ice</span> nucleation at temperatures greater than -10°C is less certain. In-situ measurements of aerosol properties and cloud micro-physical processes are required to drive the improvement of aerosol-cloud processes in numerical models. As part of the <span class="hlt">Ice</span> NUcleation Process Investigation and Quantification (INUPIAQ) project, two field campaigns were conducted in the winters of 2013 and 2014 (Lloyd et al. 2014). Both campaigns included measurements of cloud micro-physical properties at the summit of Jungfraujoch in Switzerland (3580m asl), using cloud probes, including the Two-Dimensional Stereo Hydrometeor Spectrometer (2D-S), the Cloud Particle Imager 3V (CPI-3V) and the Cloud Aerosol Spectrometer with Depolarization (CAS-DPOL). The first two of these probes measured significantly higher <span class="hlt">ice</span> number <span class="hlt">concentrations</span> than those observed in clouds at similar altitudes from aircraft. In this contribution, we assess the source of the high <span class="hlt">ice</span> number <span class="hlt">concentrations</span> observed by comparing in-situ measurements at Jungfraujoch with WRF simulations applied to the region around Jungfraujoch. During the 2014 field campaign the model simulations regularly simulated <span class="hlt">ice</span> particle <span class="hlt">concentrations</span> that were 3 orders of magnitude per litre less than the observed <span class="hlt">ice</span> number <span class="hlt">concentration</span>, even taking into account the aerosol properties measured upwind. WRF was used to investigate a number of potential sources of the high <span class="hlt">ice</span> crystal <span class="hlt">concentrations</span>, including: an increased <span class="hlt">ice</span> nucleating particle (INP) <span class="hlt">concentration</span>, secondary <span class="hlt">ice</span> multiplication and the advection of surface <span class="hlt">ice</span> or snow crystals into the clouds. It was found that the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016TCry...10..761Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016TCry...10..761Y"><span>Brief communication: The challenge and benefit of using sea <span class="hlt">ice</span> <span class="hlt">concentration</span> satellite data products with uncertainty estimates in summer sea <span class="hlt">ice</span> data assimilation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yang, Qinghua; Losch, Martin; Losa, Svetlana N.; Jung, Thomas; Nerger, Lars; Lavergne, Thomas</p> <p>2016-04-01</p> <p>Data assimilation experiments that aim at improving summer <span class="hlt">ice</span> <span class="hlt">concentration</span> and thickness forecasts in the Arctic are carried out. The data assimilation system used is based on the MIT general circulation model (MITgcm) and a local singular evolutive interpolated Kalman (LSEIK) filter. The effect of using sea <span class="hlt">ice</span> <span class="hlt">concentration</span> satellite data products with appropriate uncertainty estimates is assessed by three different experiments using sea <span class="hlt">ice</span> <span class="hlt">concentration</span> data of the European Space Agency Sea <span class="hlt">Ice</span> Climate Change Initiative (ESA SICCI) which are provided with a per-grid-cell physically based sea <span class="hlt">ice</span> <span class="hlt">concentration</span> uncertainty estimate. The first experiment uses the constant uncertainty, the second one imposes the provided SICCI uncertainty estimate, while the third experiment employs an elevated minimum uncertainty to account for a representation error. Using the observation uncertainties that are provided with the data improves the ensemble mean forecast of <span class="hlt">ice</span> <span class="hlt">concentration</span> compared to using constant data errors, but the thickness forecast, based on the sparsely available data, appears to be degraded. Further investigating this lack of positive impact on the sea <span class="hlt">ice</span> thicknesses leads us to a fundamental mismatch between the satellite-based radiometric <span class="hlt">concentration</span> and the modeled physical <span class="hlt">ice</span> <span class="hlt">concentration</span> in summer: the passive microwave sensors used for deriving the vast majority of the sea <span class="hlt">ice</span> <span class="hlt">concentration</span> satellite-based observations cannot distinguish ocean water (in leads) from melt water (in ponds). New data assimilation methodologies that fully account or mitigate this mismatch must be designed for successful assimilation of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> satellite data in summer melt conditions. In our study, thickness forecasts can be slightly improved by adopting the pragmatic solution of raising the minimum observation uncertainty to inflate the data error and ensemble spread.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.8815M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.8815M"><span>Closure between <span class="hlt">ice</span>-nucleating particle and <span class="hlt">ice</span> crystal number <span class="hlt">concentrations</span> in <span class="hlt">ice</span> clouds embedded in Saharan dust: Lidar observation during the BACCHUS Cyprus 2015 campaign</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mamouri, Rodanthi-Elisavet; Ansmann, Albert; Bühl, Johannes; Engelmann, Ronny; Baars, Holger; Nisantzi, Argyro; Hadjimitsis, Diofantos; Atkinson, James; Kanji, Zamin; Vrekoussis, Michalis; Sciare, Jean; Mihalopoulos, Nikos</p> <p>2016-04-01</p> <p>For the first time, we compare <span class="hlt">ice</span>-nucleating particle number <span class="hlt">concentration</span> (INPC) derived from polarization lidar (Mamouri and Ansmann, 2015) with <span class="hlt">ice</span> crystal number <span class="hlt">concentrations</span> (ICNC) in <span class="hlt">ice</span> cloud layers embedded in the observed Saharan dust layers (at heights above 6 km and corresponding temperatures from -20 to -40°C). ICNC is estimated from the respective cirrus extinction profiles obtained with the same polarization lidar in combination with Doppler lidar measurements of the <span class="hlt">ice</span> crystal sedimentation speed from which the mean size of the crystals can be estimated. Good agreement between INPC and ICNC was obtained for two case studies of the BACCHUS Cyprus 2015 field campaign with focus on INPC profiling. The campaign was organized by the Cyprus Institute, Nicosia, where a lidar was deployed. Additionaly, observations of AERONET and EALINET Lidar stations during the BACCHUS Cyprus 2015 field campaign, performed by Cyprus University of Technology in Limassol. Both, INPC and ICNC were found in the range from 10-50 1/L. Lidar-derived INPC values were also compared with in-situ INPC measurements (Horizontal <span class="hlt">Ice</span> Nucleation Chamber, HINC, ETH Zurich, deployed at Agia Marina, at 500 m a.s.l., 30 km west of the lidar site). Reasonable and partly good agreement (during dust events) was found between the two retrievals. The findings of these closure studies corroborate the applicability of available INPC parameterization schemes (DeMott et al., 2010, 2015) implemented in the lidar retrieval scheme, and more generally INPC profiling by using active remote sensing (at ground and in space with CALIPSO and EarthCARE lidars).</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_12 --> <div id="page_13" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="241"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70024509','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70024509"><span>Seasonal comparisons of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> estimates derived from SSM/I, OKEAN, and RADARSAT data</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Belchansky, G.I.; Douglas, D.C.</p> <p>2002-01-01</p> <p>The Special Sensor Microwave Imager (SSM/I) microwave satellite radiometer and its predecessor SMMR are primary sources of information for global sea <span class="hlt">ice</span> and climate studies. However, comparisons of SSM/I, Landsat, AVHRR, and ERS-1 synthetic aperture radar (SAR) have shown substantial seasonal and regional differences in their estimates of sea <span class="hlt">ice</span> <span class="hlt">concentration</span>. To evaluate these differences, we compared SSM/I estimates of sea <span class="hlt">ice</span> coverage derived with the NASA Team and Bootstrap algorithms to estimates made using RADARSAT, and OKEAN-01 satellite sensor data. The study area included the Barents Sea, Kara Sea, Laptev Sea, and adjacent parts of the Arctic Ocean, during October 1995 through October 1999. <span class="hlt">Ice</span> <span class="hlt">concentration</span> estimates from spatially and temporally near-coincident imagery were calculated using independent algorithms for each sensor type. The OKEAN algorithm implemented the satellite's two-channel active (radar) and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter. The RADARSAT algorithm utilized a segmentation approach of the measured radar backscatter, and the SSM/I <span class="hlt">ice</span> <span class="hlt">concentrations</span> were derived at National Snow and <span class="hlt">Ice</span> Data Center (NSIDC) using the NASA Team and Bootstrap algorithms. Seasonal and monthly differences between SSM/I, OKEAN, and RADARSAT <span class="hlt">ice</span> <span class="hlt">concentrations</span> were calculated and compared. Overall, total sea <span class="hlt">ice</span> <span class="hlt">concentration</span> estimates derived independently from near-coincident RADARSAT, OKEAN-01, and SSM/I satellite imagery demonstrated mean differences of less than 5.5% (S.D. <9.5%) during the winter period. Differences between the SSM/I NASA Team and the SSM/I Bootstrap <span class="hlt">concentrations</span> were no more than 3.1% (S.D. <5.4%) during this period. RADARSAT and OKEAN-01 data both yielded higher total <span class="hlt">ice</span> <span class="hlt">concentrations</span> than the NASA Team and the Bootstrap algorithms. The Bootstrap algorithm yielded higher total <span class="hlt">ice</span> <span class="hlt">concentrations</span> than the NASA Team algorithm. Total <span class="hlt">ice</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170006487','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170006487"><span>High <span class="hlt">Ice</span> Water <span class="hlt">Concentrations</span> in the 19 August 2015 Coastal Mesoconvective System</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Proctor, Fred H.; Harrah, Steven; Switzer, George F.; Strickland, Justin K.; Hunt, Patricia J.</p> <p>2017-01-01</p> <p>During August 2015, NASA's DC-8 research aircraft was flown into High <span class="hlt">Ice</span> Water Content (HIWC) events as part of a three-week campaign to collect airborne radar data and to obtain measurements from microphysical probes. Goals for this flight campaign included improved characterization of HIWC events, especially from an airborne radar perspective. This paper focuses on one of the flight days, in which a coastal mesoscale convective system (MCS) was investigated for HIWC conditions. The system appears to have been maintained by bands of convection flowing in from the Gulf of Mexico. These convective bands were capped by a large cloud canopy, which masks the underlying structure if viewed from an infrared sensing satellite. The DC-8 was equipped with an IsoKinetic Probe that measured <span class="hlt">ice</span> <span class="hlt">concentrations</span> of up to 2.3 g m(exp -3) within the cloud canopy of this system. Sustained measurements of <span class="hlt">ice</span> crystals with <span class="hlt">concentrations</span> exceeding 1 g m(exp -3) were encountered for up to ten minutes of flight time. Airborne Radar reflectivity factors were found to be weak within these regions of high <span class="hlt">ice</span> water <span class="hlt">concentrations</span>, suggesting that Radar detection of HIWC would be a challenging endeavor. This case is then investigated using a three-dimensional numerical cloud model. Profiles of <span class="hlt">ice</span> water <span class="hlt">concentrations</span> and radar reflectivity factor demonstrate similar magnitudes and scales between the flight measurements and model simulation. Also discussed are recent modifications to the numerical model's <span class="hlt">ice</span>-microphysics that are based on measurements during the flight campaign. The numerical model and its updated <span class="hlt">ice</span>-microphysics are further validated with a simulation of a well-known case of a supercell hailstorm measured during the Cooperative Convective Precipitation Experiment. Differences in HIWC between the continental supercell and the coastal MCS are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/944767','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/944767"><span>Understanding <span class="hlt">Ice</span> Supersaturation, Particle Growth, and Number <span class="hlt">Concentration</span> in Cirrus Clouds</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Comstock, Jennifer M.; Lin, Ruei-Fong; Starr, David O.; Yang, P.</p> <p>2008-12-10</p> <p>Many factors control the <span class="hlt">ice</span> supersaturation and microphysical properties in cirrus clouds. We explore the effects of dynamic forcing, <span class="hlt">ice</span> nucleation mechanisms, and <span class="hlt">ice</span> crystal growth rate on the evolution and distribution of water vapor and cloud properties in cirrus clouds using a detailed microphysical model and remote sensing measurements obtained at the Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate Research Facility located near Lamont, OK. To help understand dynamic scales important in cirrus formation, we force the model using both large-scale forcing derived using ARM variational analysis, and mean mesoscale velocity derived from radar Doppler velocity measurements. Both heterogeneous and homogeneous nucleation processes are explored, where we have implemented a rigorous classical theory heterogeneous nucleation scheme to compare with empirical representations. We evaluate model simulations by examining both bulk cloud properties and distributions of measured radar reflectivity, lidar extinction, and water vapor profiles, as well as retrieved cloud microphysical properties. This approach allows for independent verification of both the large and small particle modes of the particle size distribution. Our results suggest that mesoscale variability is the primary mechanism needed to reproduce observed quantities, while nucleation mechanism is secondary. Slow <span class="hlt">ice</span> crystal growth tends to overestimate the number of small <span class="hlt">ice</span> crystals, but does not seem to influence bulk properties such as <span class="hlt">ice</span> water path and cloud thickness. The most realistic simulations as compared with observations are forced using mesoscale waves, include fast <span class="hlt">ice</span> crystal growth, and initiate <span class="hlt">ice</span> by either homogeneous or heterogeneous nucleation. <span class="hlt">Ice</span> crystal number <span class="hlt">concentrations</span> on the order of 10-100 L-1 produce results consistent with both lidar and radar observations during a cirrus event observed on 7 December 1999, which has an optical depth range typical of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014CliPa..10.1659G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014CliPa..10.1659G"><span>Dating a tropical <span class="hlt">ice</span> core by time-frequency analysis of ion <span class="hlt">concentration</span> depth profiles</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gay, M.; De Angelis, M.; Lacoume, J.-L.</p> <p>2014-09-01</p> <p><span class="hlt">Ice</span> core dating is a key parameter for the interpretation of the <span class="hlt">ice</span> archives. However, the relationship between <span class="hlt">ice</span> depth and <span class="hlt">ice</span> age generally cannot be easily established and requires the combination of numerous investigations and/or modelling efforts. This paper presents a new approach to <span class="hlt">ice</span> core dating based on time-frequency analysis of chemical profiles at a site where seasonal patterns may be significantly distorted by sporadic events of regional importance, specifically at the summit area of Nevado Illimani (6350 m a.s.l.), located in the eastern Bolivian Andes (16°37' S, 67°46' W). We used ion <span class="hlt">concentration</span> depth profiles collected along a 100 m deep <span class="hlt">ice</span> core. The results of Fourier time-frequency and wavelet transforms were first compared. Both methods were applied to a nitrate <span class="hlt">concentration</span> depth profile. The resulting chronologies were checked by comparison with the multi-proxy year-by-year dating published by de Angelis et al. (2003) and with volcanic tie points. With this first experiment, we demonstrated the efficiency of Fourier time-frequency analysis when tracking the nitrate natural variability. In addition, we were able to show spectrum aliasing due to under-sampling below 70 m. In this article, we propose a method of de-aliasing which significantly improves the core dating in comparison with annual layer manual counting. Fourier time-frequency analysis was applied to <span class="hlt">concentration</span> depth profiles of seven other ions, providing information on the suitability of each of them for the dating of tropical Andean <span class="hlt">ice</span> cores.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000038166&hterms=bootstrap&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dbootstrap','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000038166&hterms=bootstrap&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dbootstrap"><span>Passive Microwave Algorithms for Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span>: A Comparison of Two 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.; Cavalieri, Donald J.; Parkinson, Claire L.; Gloersen, Per</p> <p>1997-01-01</p> <p>The most comprehensive large-scale characterization of the global sea <span class="hlt">ice</span> cover so far has been provided by satellite passive microwave data. Accurate retrieval of <span class="hlt">ice</span> <span class="hlt">concentrations</span> from these data is important because of the sensitivity of surface flux(e.g. heat, salt, and water) calculations to small change in the amount of open water (leads and polynyas) within the polar <span class="hlt">ice</span> packs. Two algorithms that have been used for deriving <span class="hlt">ice</span> <span class="hlt">concentrations</span> from multichannel data are compared. One is the NASA Team algorithm and the other is the Bootstrap algorithm, both of which were developed at NASA's Goddard Space Flight Center. The two algorithms use different channel combinations, reference brightness temperatures, weather filters, and techniques. Analyses are made to evaluate the sensitivity of algorithm results to variations of emissivity and temperature with space and time. To assess the difference in the performance of the two algorithms, analyses were performed with data from both hemispheres and for all seasons. The results show only small differences in the central Arctic in but larger disagreements in the seasonal regions and in summer. In some ares in the Antarctic, the Bootstrap technique show <span class="hlt">ice</span> <span class="hlt">concentrations</span> higher than those of the Team algorithm by as much as 25%; whereas, in other areas, it shows <span class="hlt">ice</span> <span class="hlt">concentrations</span> lower by as much as 30%. The The differences in the results are caused by temperature effects, emissivity effects, and tie point differences. The Team and the Bootstrap results were compared with available Landsat, advanced very high resolution radiometer (AVHRR) and synthetic aperture radar (SAR) data. AVHRR, Landsat, and SAR data sets all yield higher <span class="hlt">concentrations</span> than the passive microwave algorithms. Inconsistencies among results suggest the need for further validation studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000038166&hterms=PASSIVE+FILTER&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DPASSIVE%2BFILTER','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000038166&hterms=PASSIVE+FILTER&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DPASSIVE%2BFILTER"><span>Passive Microwave Algorithms for Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span>: A Comparison of Two 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.; Cavalieri, Donald J.; Parkinson, Claire L.; Gloersen, Per</p> <p>1997-01-01</p> <p>The most comprehensive large-scale characterization of the global sea <span class="hlt">ice</span> cover so far has been provided by satellite passive microwave data. Accurate retrieval of <span class="hlt">ice</span> <span class="hlt">concentrations</span> from these data is important because of the sensitivity of surface flux(e.g. heat, salt, and water) calculations to small change in the amount of open water (leads and polynyas) within the polar <span class="hlt">ice</span> packs. Two algorithms that have been used for deriving <span class="hlt">ice</span> <span class="hlt">concentrations</span> from multichannel data are compared. One is the NASA Team algorithm and the other is the Bootstrap algorithm, both of which were developed at NASA's Goddard Space Flight Center. The two algorithms use different channel combinations, reference brightness temperatures, weather filters, and techniques. Analyses are made to evaluate the sensitivity of algorithm results to variations of emissivity and temperature with space and time. To assess the difference in the performance of the two algorithms, analyses were performed with data from both hemispheres and for all seasons. The results show only small differences in the central Arctic in but larger disagreements in the seasonal regions and in summer. In some ares in the Antarctic, the Bootstrap technique show <span class="hlt">ice</span> <span class="hlt">concentrations</span> higher than those of the Team algorithm by as much as 25%; whereas, in other areas, it shows <span class="hlt">ice</span> <span class="hlt">concentrations</span> lower by as much as 30%. The The differences in the results are caused by temperature effects, emissivity effects, and tie point differences. The Team and the Bootstrap results were compared with available Landsat, advanced very high resolution radiometer (AVHRR) and synthetic aperture radar (SAR) data. AVHRR, Landsat, and SAR data sets all yield higher <span class="hlt">concentrations</span> than the passive microwave algorithms. Inconsistencies among results suggest the need for further validation studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/1025075','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/1025075"><span>Effects of <span class="hlt">ice</span> number <span class="hlt">concentration</span> on dynamics of a shallow mixed-phase stratiform cloud</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Ovchinnikov, Mikhail; Korolev, Alexei; Fan, Jiwen</p> <p>2011-09-17</p> <p>Previous modeling studies have shown a high sensitivity of simulated properties of mixed-phase clouds to <span class="hlt">ice</span> number <span class="hlt">concentration</span>, Ni, with many models losing their ability to maintain the liquid phase as Ni increases. Although models differ widely at what Ni the mixed-phase cloud becomes unstable, the transition from a mixed-phase to an <span class="hlt">ice</span> only cloud in many cases occurs over a narrow range of <span class="hlt">ice</span> <span class="hlt">concentration</span>. To gain better understanding of this non-linear model behavior, in this study, we analyze simulations of a mixed-phase stratiform Artic cloud observed on 26 April 2008 during recent Indirect and Semi-Direct Aerosol Campaign (ISDAC). The BASE simulation, in which Ni is constrained to match the measured value, produces a long-lived cloud in a quasi steady state similar to that observed. The simulation without the <span class="hlt">ice</span> (NO_<span class="hlt">ICE</span>) produces a comparable but slightly thicker cloud because more moisture is kept in the mixed layer due to lack of precipitation. When Ni is quadrupled relative to BASE (HI_<span class="hlt">ICE</span>), the cloud starts loosing liquid water almost immediately and the liquid water path is reduced by half in less than two hours. The changes in liquid water are accompanied by corresponding reduction in the radiative cooling of the layer and a slow down in the vertical mixing, confirming the important role of interactions among microphysics, radiation and dynamics in this type of clouds. Deviations of BASE and HI_<span class="hlt">ICE</span> from NO_<span class="hlt">ICE</span> are used to explore the linearity of the model response to variation in Ni. It is shown that at early stages, changes in liquid and <span class="hlt">ice</span> water as well as in radiative cooling/heating rates are proportional to the Ni change, while changes in the vertical buoyancy flux are qualitatively different in HI_<span class="hlt">ICE</span> compared to BASE. Thus, while the positive feedback between the liquid water path and radiative cooling of the cloud layer is essential for glaciation of the cloud at higher Ni, the non-linear (with respect to Ni) reduction in positive</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/1353334','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/1353334"><span>Contribution of feldspar and marine organic aerosols to global <span class="hlt">ice</span> nucleating particle <span class="hlt">concentrations</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Vergara-Temprado, Jesús; Murray, Benjamin J.; Wilson, Theodore W.; O'Sullivan, Daniel; Browse, Jo; Pringle, Kirsty J.; Ardon-Dryer, Karin; Bertram, Allan K.; Burrows, Susannah M.; Ceburnis, Darius; DeMott, Paul J.; Mason, Ryan H.; O'Dowd, Colin D.; Rinaldi, Matteo; Carslaw, Ken S.</p> <p>2017-01-01</p> <p><p><span class="hlt">Ice</span>-nucleating particles (INPs) are known to affect the amount of <span class="hlt">ice</span> in mixed-phase clouds, thereby influencing many of their properties. The atmospheric INP <span class="hlt">concentration</span> changes by orders of magnitude from terrestrial to marine environments, which typically contain much lower <span class="hlt">concentrations</span>. Many modelling studies use parameterizations for heterogeneous <span class="hlt">ice</span> nucleation and cloud <span class="hlt">ice</span> processes that do not account for this difference because they were developed based on INP measurements made predominantly in terrestrial environments without considering the aerosol composition. Errors in the assumed INP <span class="hlt">concentration</span> will influence the simulated amount of <span class="hlt">ice</span> in mixed-phase clouds, leading to errors in top-of-atmosphere radiative flux and ultimately the climate sensitivity of the model. Here we develop a global model of INP <span class="hlt">concentrations</span> relevant for mixed-phase clouds based on laboratory and field measurements of <span class="hlt">ice</span> nucleation by K-feldspar (an <span class="hlt">ice</span>-active component of desert dust) and marine organic aerosols (from sea spray). The simulated global distribution of INP <span class="hlt">concentrations</span> based on these two species agrees much better with currently available ambient measurements than when INP <span class="hlt">concentrations</span> are assumed to depend only on temperature or particle size. Underestimation of INP <span class="hlt">concentrations</span> in some terrestrial locations may be due to the neglect of INPs from other terrestrial sources. Our model indicates that, on a monthly average basis, desert dusts dominate the contribution to the INP population over much of the world, but marine organics become increasingly important over remote oceans and they dominate over the Southern Ocean. However, day-to-day variability is important. Because desert dust aerosol tends to be sporadic, marine organic aerosols dominate the INP population on many days per month over much of the mid- and high-latitude Northern Hemisphere. This study advances our understanding of which aerosol species need to be included in order to</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ACP....17.3637V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ACP....17.3637V"><span>Contribution of feldspar and marine organic aerosols to global <span class="hlt">ice</span> nucleating particle <span class="hlt">concentrations</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vergara-Temprado, Jesús; Murray, Benjamin J.; Wilson, Theodore W.; O'Sullivan, Daniel; Browse, Jo; Pringle, Kirsty J.; Ardon-Dryer, Karin; Bertram, Allan K.; Burrows, Susannah M.; Ceburnis, Darius; DeMott, Paul J.; Mason, Ryan H.; O'Dowd, Colin D.; Rinaldi, Matteo; Carslaw, Ken S.</p> <p>2017-03-01</p> <p><span class="hlt">Ice</span>-nucleating particles (INPs) are known to affect the amount of <span class="hlt">ice</span> in mixed-phase clouds, thereby influencing many of their properties. The atmospheric INP <span class="hlt">concentration</span> changes by orders of magnitude from terrestrial to marine environments, which typically contain much lower <span class="hlt">concentrations</span>. Many modelling studies use parameterizations for heterogeneous <span class="hlt">ice</span> nucleation and cloud <span class="hlt">ice</span> processes that do not account for this difference because they were developed based on INP measurements made predominantly in terrestrial environments without considering the aerosol composition. Errors in the assumed INP <span class="hlt">concentration</span> will influence the simulated amount of <span class="hlt">ice</span> in mixed-phase clouds, leading to errors in top-of-atmosphere radiative flux and ultimately the climate sensitivity of the model. Here we develop a global model of INP <span class="hlt">concentrations</span> relevant for mixed-phase clouds based on laboratory and field measurements of <span class="hlt">ice</span> nucleation by K-feldspar (an <span class="hlt">ice</span>-active component of desert dust) and marine organic aerosols (from sea spray). The simulated global distribution of INP <span class="hlt">concentrations</span> based on these two species agrees much better with currently available ambient measurements than when INP <span class="hlt">concentrations</span> are assumed to depend only on temperature or particle size. Underestimation of INP <span class="hlt">concentrations</span> in some terrestrial locations may be due to the neglect of INPs from other terrestrial sources. Our model indicates that, on a monthly average basis, desert dusts dominate the contribution to the INP population over much of the world, but marine organics become increasingly important over remote oceans and they dominate over the Southern Ocean. However, day-to-day variability is important. Because desert dust aerosol tends to be sporadic, marine organic aerosols dominate the INP population on many days per month over much of the mid- and high-latitude Northern Hemisphere. This study advances our understanding of which aerosol species need to be included in order to</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19890049148&hterms=utopia&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dutopia','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19890049148&hterms=utopia&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dutopia"><span><span class="hlt">Concentric</span> crater fill on Mars - An aeolian alternative to <span class="hlt">ice</span>-rich mass wasting</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Zimbelman, J. R.; Clifford, S. M.; Williams, S. H.</p> <p>1989-01-01</p> <p><span class="hlt">Concentric</span> crater fill, a distinctive martian landform represented by a <span class="hlt">concentric</span> pattern of surface undulations confined within a crater rim, has been interpreted as an example of <span class="hlt">ice</span>-enhanced regolith creep at midlatitudes (e.g., Squyres and Carr, 1986). Theoretical constraints on the stability and mobility of ground <span class="hlt">ice</span> limit the applicability of an <span class="hlt">ice</span>-rich soil in effectively mobilizing downslope movement at latitudes poleward of + or - 30 deg, where <span class="hlt">concentric</span> crater fill is observed. High-resolution images of <span class="hlt">concentric</span> crater fill material in the Utopia Planitia region (45 deg N, 271 deg W) show it to be an eroded, multiple-layer deposit. Layering should not be preserved if the crater fill material moved by slow deformation throughout its thickness, as envisioned in the <span class="hlt">ice</span>-enhanced creep model. Multiple layers are also exposed in the plains material surrounding the craters, indicating a recurrent depositional process that was at least regional in extent. Mantling layers are observed in high-resolution images of many other locations around Mars, suggesting that deposition occurred on a global scale and was not limited to the Utopia Planitia region. It is suggested that an aeolian interpretation for the origin and modification of <span class="hlt">concentric</span> crater fill material is most consistent with morphologic and theoretical constraints.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1989LPSC...19..397Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1989LPSC...19..397Z"><span><span class="hlt">Concentric</span> crater fill on Mars - an aeolian alternative to <span class="hlt">ice</span>-rich mass wasting</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zimbelman, J. R.; Clifford, S. M.; Williams, S. H.</p> <p></p> <p><span class="hlt">Concentric</span> crater fill, a distinctive martian landform represented by a <span class="hlt">concentric</span> pattern of surface undulations confined within a crater rim, has been interpreted as an example of <span class="hlt">ice</span>-enhanced regolith creep at midlatitudes (e.g., Squyres and Carr, 1986). Theoretical constraints on the stability and mobility of ground <span class="hlt">ice</span> limit the applicability of an <span class="hlt">ice</span>-rich soil in effectively mobilizing downslope movement at latitudes poleward of + or - 30 deg, where <span class="hlt">concentric</span> crater fill is observed. High-resolution images of <span class="hlt">concentric</span> crater fill material in the Utopia Planitia region (45 deg N, 271 deg W) show it to be an eroded, multiple-layer deposit. Layering should not be preserved if the crater fill material moved by slow deformation throughout its thickness, as envisioned in the <span class="hlt">ice</span>-enhanced creep model. Multiple layers are also exposed in the plains material surrounding the craters, indicating a recurrent depositional process that was at least regional in extent. Mantling layers are observed in high-resolution images of many other locations around Mars, suggesting that deposition occurred on a global scale and was not limited to the Utopia Planitia region. It is suggested that an aeolian interpretation for the origin and modification of <span class="hlt">concentric</span> crater fill material is most consistent with morphologic and theoretical constraints.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.A21F0162C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.A21F0162C"><span>Measurements of Atmospheric <span class="hlt">Ice</span> Nuclei <span class="hlt">Concentrations</span> at Two Canadian Sites: Downtown Toronto and Whistler, British Columbia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Corbin, J. C.; Leaitch, W. R.; Evans, G. J.; MacDonald, A.; Abbatt, J.</p> <p>2010-12-01</p> <p>The subset of atmospheric aerosol particles termed <span class="hlt">ice</span> nuclei (IN) facilitate heterogeneous <span class="hlt">ice</span> formation by lowering the energy barrier to <span class="hlt">ice</span> formation, thus allowing <span class="hlt">ice</span> clouds to form at temperatures above the homogeneous freezing threshold. Though <span class="hlt">ice</span> plays a major role in initiating precipitation globally, the composition and distribution of IN in the atmosphere remains poorly understood. In order to investigate potential anthropogenic contributions to atmospheric <span class="hlt">ice</span> nucleation, we measured IN <span class="hlt">concentrations</span> on a major road in Toronto, ON, using the University of Toronto Continuous Flow Diffusion Chamber (UT-CFDC). The majority of measurements were conducted close to 95% relative humidity (RH) with respect to water, but full RH scans to conditions above supersaturation with respect to liquid water were also performed. Simultaneous measurements of aerosol size (APS, SMPS) and chemical composition (Aerosol Time-of-Flight Mass Spectrometer, ATOFMS) allow us to investigate the relationship of IN to varying aerosol types. The number of IN observed was highly variable, ranging from 0-87/L. These urban data will be contrasted with similar data obtained in the coniferous forest of Whistler Mountain, BC. An intense biogenic secondary aerosol event observed at Whistler is used to estimate an upper limit for IN from organic aerosol formed from monoterpene oxidation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/22364554','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/22364554"><span>PHOTOCHEMISTRY OF POLYCYCLIC AROMATIC HYDROCARBONS IN COSMIC WATER <span class="hlt">ICE</span>: THE ROLE OF PAH IONIZATION AND <span class="hlt">CONCENTRATION</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Cook, Amanda M.; Mattioda, Andrew L.; Roser, Joseph; Bregman, Jonathan; Bouwman, Jordy; Linnartz, Harold</p> <p>2015-01-20</p> <p>Infrared spectroscopic studies of ultraviolet (UV) irradiated, water-rich, cosmic <span class="hlt">ice</span> analogs containing small polycyclic aromatic hydrocarbons (PAHs) are described. The irradiation studies of anthracene:H{sub 2}O, pyrene:H{sub 2}O, and benzo[ghi]perylene:H{sub 2}O <span class="hlt">ices</span> (14 K) at various <span class="hlt">concentrations</span> reported by Bouwman et al. are extended. While aromatic alcohols and ketones have been reported in residues after irradiated PAH:H{sub 2}O <span class="hlt">ices</span> were warmed to 270 K, it was not known if they formed during <span class="hlt">ice</span> irradiation or during warm-up when reactants interact as H{sub 2}O sublimes. Recent work has shown that they form in low temperature <span class="hlt">ice</span>. Using DFT computed IR spectra to identify photoproducts and PAH cations, we tentatively identify the production of specific alcohols [PAH(OH) {sub n} ] and quinones [PAH(O) {sub n} ] for all PAH:H{sub 2}O <span class="hlt">ices</span> considered here. Little evidence is found for hydrogenation at 14 K, consistent with the findings of Gudipati and Yang. Addition of O and OH to the parent PAH is the dominant photochemical reaction, but PAH erosion to smaller PAHs (producing CO{sub 2} and H{sub 2}CO) is also important. DFT spectra are used to assess the contribution of PAH-related species to interstellar absorption features from 5 to 9 μm. The case is made that PAH cations are important contributors to the C2 component and PAH(OH) {sub n} and PAH(O) {sub n} to the C5 component described by Boogert et al. Thus, interstellar <span class="hlt">ices</span> should contain neutral and ionized PAHs, alcohols, ketones and quinones at the ∼2%-4% level relative to H{sub 2}O. PAHs, their photoproducts, and ion-mediated processes should therefore be considered when modeling interstellar <span class="hlt">ice</span> processes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ApJ...799...14C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ApJ...799...14C"><span>Photochemistry of Polycyclic Aromatic Hydrocarbons in Cosmic Water <span class="hlt">Ice</span>: The Role of PAH Ionization and <span class="hlt">Concentration</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cook, Amanda M.; Ricca, Alessandra; Mattioda, Andrew L.; Bouwman, Jordy; Roser, Joseph; Linnartz, Harold; Bregman, Jonathan; Allamandola, Louis J.</p> <p>2015-01-01</p> <p>Infrared spectroscopic studies of ultraviolet (UV) irradiated, water-rich, cosmic <span class="hlt">ice</span> analogs containing small polycyclic aromatic hydrocarbons (PAHs) are described. The irradiation studies of anthracene:H2O, pyrene:H2O, and benzo[ghi]perylene:H2O <span class="hlt">ices</span> (14 K) at various <span class="hlt">concentrations</span> reported by Bouwman et al. are extended. While aromatic alcohols and ketones have been reported in residues after irradiated PAH:H2O <span class="hlt">ices</span> were warmed to 270 K, it was not known if they formed during <span class="hlt">ice</span> irradiation or during warm-up when reactants interact as H2O sublimes. Recent work has shown that they form in low temperature <span class="hlt">ice</span>. Using DFT computed IR spectra to identify photoproducts and PAH cations, we tentatively identify the production of specific alcohols [PAH(OH) n ] and quinones [PAH(O) n ] for all PAH:H2O <span class="hlt">ices</span> considered here. Little evidence is found for hydrogenation at 14 K, consistent with the findings of Gudipati & Yang. Addition of O and OH to the parent PAH is the dominant photochemical reaction, but PAH erosion to smaller PAHs (producing CO2 and H2CO) is also important. DFT spectra are used to assess the contribution of PAH-related species to interstellar absorption features from 5 to 9 μm. The case is made that PAH cations are important contributors to the C2 component and PAH(OH) n and PAH(O) n to the C5 component described by Boogert et al. Thus, interstellar <span class="hlt">ices</span> should contain neutral and ionized PAHs, alcohols, ketones and quinones at the ~2%-4% level relative to H2O. PAHs, their photoproducts, and ion-mediated processes should therefore be considered when modeling interstellar <span class="hlt">ice</span> processes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011JGRD..116.0T06O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011JGRD..116.0T06O"><span>Effects of <span class="hlt">ice</span> number <span class="hlt">concentration</span> on dynamics of a shallow mixed-phase stratiform cloud</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ovchinnikov, Mikhail; Korolev, Alexei; Fan, Jiwen</p> <p>2011-01-01</p> <p>Previous modeling studies have shown high sensitivity of mixed-phase clouds to <span class="hlt">ice</span> number <span class="hlt">concentration</span>, Ni, with simulated clouds often transitioning from mixed-phase to <span class="hlt">ice</span>-only regime within a narrow range of Ni. To better understand the mechanisms behind this transition, we analyze several simulations of a mixed-phase stratiform Arctic cloud observed on 26 April 2008 during the Indirect and Semi-Direct Aerosol Campaign (ISDAC). In the BASE run, Ni is constrained to match the measured value and a persistent mixed-phase cloud is formed, with properties similar to those observed. When Ni is quadrupled (HI_<span class="hlt">ICE</span>) the liquid water path is reduced by half within two hours. The changes in liquid water are accompanied by diminishing radiative cooling and slowing vertical mixing, exposing complex interactions among microphysics, radiation and dynamics. Deviations of BASE and HI_<span class="hlt">ICE</span> from a simulation without <span class="hlt">ice</span> are used to explore the linearity of the model response to variation in Ni. It is shown that early changes in cloud condensate amount and radiative cooling rate are proportional to Ni, while changes in the vertical buoyancy flux and dynamics are qualitatively different in HI_<span class="hlt">ICE</span> compared to BASE. The nonlinear (with respect to Ni) reduction in buoyancy flux drives the initial response of the mixed layer dynamics to the appearance of <span class="hlt">ice</span> and subsequently determines the sustainability of liquid water in the cloud in this case. Two additional sensitivity experiments link the decreased buoyancy production to the latent heat release from the depositional <span class="hlt">ice</span> growth while confirming the importance of the cloud-radiation feedback.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003JGRD..108.4687R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003JGRD..108.4687R"><span>Impact of observed sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> on the Southern Hemisphere extratropical atmospheric circulation in summer</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.</p> <p>2003-11-01</p> <p>The response of the Southern Hemisphere extratropical, atmospheric circulation to extremes of sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> in summer is investigated using a fully coupled climate model. Maximum and minimum sea-<span class="hlt">ice</span> extremes were obtained from satellite-derived data, and a 12-month climatology for each case was created. Two 10-year simulations for each scenario were completed, and the results for the southern summer are compared. At the surface the sea-<span class="hlt">ice</span> extremes directly affected the temperatures around Antarctica and through these the latitudinal surface temperature and pressure gradients. The midlatitude surface westerlies are weaker in the maximum scenario, while the polar easterlies expand farther north. In the middle troposphere the zonal jet strengthens slightly and shifts equatorward in the maximum scenario. Comparisons of the sea level pressure field and the 500 hPa geopotential height field for the two scenarios show that the leading mode of circulation variability, the Southern Hemisphere Annular Mode, tends toward positive polarity (lower than normal geopotential heights over Antarctica) in the minimum sea-<span class="hlt">ice</span> scenario. This tendency is associated with local differences in surface pressure gradient and temperature and large scale dynamic responses to extremes of sea-<span class="hlt">ice</span> <span class="hlt">concentration</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010018558','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010018558"><span>The Impact of Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> Accuracies on Climate Model Simulations with the GISS GCM</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.; Rind, David; Healy, Richard J.; Martinson, Douglas G.; Zukor, Dorothy J. (Technical Monitor)</p> <p>2000-01-01</p> <p>The Goddard Institute for Space Studies global climate model (GISS GCM) is used to examine the sensitivity of the simulated climate to sea <span class="hlt">ice</span> <span class="hlt">concentration</span> specifications in the type of simulation done in the Atmospheric Modeling Intercomparison Project (AMIP), with specified oceanic boundary conditions. Results show that sea <span class="hlt">ice</span> <span class="hlt">concentration</span> uncertainties of +/- 7% can affect simulated regional temperatures by more than 6 C, and biases in sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> of +7% and -7% alter simulated annually averaged global surface air temperatures by -0.10 C and +0.17 C, respectively, over those in the control simulation. The resulting 0.27 C difference in simulated annual global surface air temperatures is reduced by a third, to 0.18 C, when considering instead biases of +4% and -4%. More broadly, least-squares fits through the temperature results of 17 simulations with <span class="hlt">ice</span> <span class="hlt">concentration</span> input changes ranging from increases of 50% versus the control simulation to decreases of 50% yield a yearly average global impact of 0.0107 C warming for every 1% <span class="hlt">ice</span> <span class="hlt">concentration</span> decrease, i.e., 1.07 C warming for the full +50% to -50% range. Regionally and on a monthly average basis, the differences can be far greater, especially in the polar regions, where wintertime contrasts between the +50% and -50% cases can exceed 30 C. However, few statistically significant effects are found outside the polar latitudes, and temperature effects over the non-polar oceans tend to be under 1 C, due in part to the specification of an unvarying annual cycle of sea surface temperatures. The +/- 7% and 14% results provide bounds on the impact (on GISS GCM simulations making use of satellite data) of satellite-derived <span class="hlt">ice</span> <span class="hlt">concentration</span> inaccuracies, +/- 7% being the current estimated average accuracy of satellite retrievals and +/- 4% being the anticipated improved average accuracy for upcoming satellite instruments. Results show that the impact on simulated temperatures of imposed <span class="hlt">ice</span> <span class="hlt">concentration</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMED41A0821T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMED41A0821T"><span>Assessing Methanesulfonate as a Proxy for Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> Using Firn Cores from the Disko Bay Region of Greenland</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thadhani, E.; Fernandopulle, G. C.; Rhodes, C. T.; Galls, D.; Bingham, M.</p> <p>2016-12-01</p> <p>The Jakobshavn Glacier, responsible for draining upwards of 7% of the Greenland <span class="hlt">Ice</span> Sheet (GIS) into Disko Bay, has experienced terminal retreat in recent decades. High sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> have been heavily implicated in the blocking of glacier calving, thereby reducing glacial retreat. However, the satellite imagery used to evaluate levels of sea <span class="hlt">ice</span> only spans the past few decades, thus establishing the necessity for a valid proxy to generate records father into the past. Methanesulfonate (MSA) is an oxidation product of dimethyl sulfide (DMS), a chemical released by phytoplankton. As phytoplankton population increases with decreased sea <span class="hlt">ice</span> <span class="hlt">concentration</span>, MSA <span class="hlt">concentrations</span> should shift directly with the phytoplankton population and inversely with sea <span class="hlt">ice</span> <span class="hlt">concentration</span>.The objective of this study was to evaluate MSA as a natural proxy for sea <span class="hlt">ice</span> variability.Two meters of firn core were drilled from an <span class="hlt">ice</span> cap on the Nuussuaq Peninsula, near Disko Bay in April 2014. The core was processed into 3 cm resolution samples for measurement of oxygen isotope ratios by cavity ring down spectroscopy and MSA <span class="hlt">concentrations</span> by ion chromatography. Sea <span class="hlt">Ice</span> <span class="hlt">concentrations</span> at three locations around Disko Bay were calculated using IDRISI software (a GIS) and satellite imagery. Peaks in the oxygen isotope ratio, which has stable seasonal variation with maximum levels in warmer months, were correlated with the likewise seasonal peaks in the percent of the grid uncovered by sea <span class="hlt">ice</span> (Figure 1). With these corresponding peaks, Taylor series approximation allowed each sample depth to be dated. We subsequently compared MSA <span class="hlt">concentrations</span> to sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> at known dates for three different potential source areas around Disko Bay within 25km by 25km grids. In the grid below Disko Island, peaks in MSA and the inverse of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> occurred simultaneously (Figure 2, r=0.4). The parallel peaks in MSA, oxygen isotope and inverse sea <span class="hlt">ice</span> <span class="hlt">concentration</span> indicate the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19920035391&hterms=Self+Defense&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DSelf%2BDefense','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19920035391&hterms=Self+Defense&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DSelf%2BDefense"><span>Aircraft active and passive microwave validation of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> from the Defense Meteorological Satellite Program special sensor microwave imager</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cavalieri, D. J.; Crawford, J. P.; Drinkwater, M. R.; Eppler, D. T.; Farmer, L. D.; Jentz, R. R.; Wackerman, C. C.</p> <p>1991-01-01</p> <p>Results are presented of a series of coordinate special sensor microwave imager (SSM/I) underflights that were carried out during March 1988 with NASA and Navy aircraft over portions of the Bering, Beaufort, and Chukchi seas. NASA DC-8 AMMR data from Bering Sea <span class="hlt">ice</span> edge crossings were used to verify that the <span class="hlt">ice</span> edge location, defined as the position of the initial <span class="hlt">ice</span> bands encountered by the aircraft, corresponds to an SSM/I <span class="hlt">ice</span> <span class="hlt">concentration</span> of 15 percent. Direct comparison of SSM/I and aircraft <span class="hlt">ice</span> <span class="hlt">concentrations</span> for regions having at least 80 percent aircraft coverage reveals that the SSM/I total <span class="hlt">ice</span> <span class="hlt">concentration</span> is lower on average by 2.4 +/-2.4 percent. For multiyear <span class="hlt">ice</span>, NASA and Navy flights across the Beaufort and Chukchi seas show that the SSM/I algorithm correctly maps the large-scale distribution of multiyear <span class="hlt">ice</span>: the zone of first-year <span class="hlt">ice</span> off the Alaskan coast, the large areas of mixed first-year and multiyear <span class="hlt">ice</span>, and the region of predominantly multiyear <span class="hlt">ice</span> north of the Canadian archipelago.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19920035391&hterms=defense&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Ddefense','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19920035391&hterms=defense&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Ddefense"><span>Aircraft active and passive microwave validation of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> from the Defense Meteorological Satellite Program special sensor microwave imager</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Cavalieri, D. J.; Crawford, J. P.; Drinkwater, M. R.; Eppler, D. T.; Farmer, L. D.; Jentz, R. R.; Wackerman, C. C.</p> <p>1991-01-01</p> <p>Results are presented of a series of coordinate special sensor microwave imager (SSM/I) underflights that were carried out during March 1988 with NASA and Navy aircraft over portions of the Bering, Beaufort, and Chukchi seas. NASA DC-8 AMMR data from Bering Sea <span class="hlt">ice</span> edge crossings were used to verify that the <span class="hlt">ice</span> edge location, defined as the position of the initial <span class="hlt">ice</span> bands encountered by the aircraft, corresponds to an SSM/I <span class="hlt">ice</span> <span class="hlt">concentration</span> of 15 percent. Direct comparison of SSM/I and aircraft <span class="hlt">ice</span> <span class="hlt">concentrations</span> for regions having at least 80 percent aircraft coverage reveals that the SSM/I total <span class="hlt">ice</span> <span class="hlt">concentration</span> is lower on average by 2.4 +/-2.4 percent. For multiyear <span class="hlt">ice</span>, NASA and Navy flights across the Beaufort and Chukchi seas show that the SSM/I algorithm correctly maps the large-scale distribution of multiyear <span class="hlt">ice</span>: the zone of first-year <span class="hlt">ice</span> off the Alaskan coast, the large areas of mixed first-year and multiyear <span class="hlt">ice</span>, and the region of predominantly multiyear <span class="hlt">ice</span> north of the Canadian archipelago.</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('https://www.ncbi.nlm.nih.gov/pubmed/22624355','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22624355"><span>[Characteristics of carbonaceous aerosol <span class="hlt">concentration</span> in snow and <span class="hlt">ice</span> of glaciers in Tianshan Mountains].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wang, Sheng-Jie; Zhang, Ming-Jun; Wang, Fei-Teng; Li, Zhong-Qin</p> <p>2012-03-01</p> <p>The snow and <span class="hlt">ice</span> samples, collected at Glacier No. 1 at the headwaters of Urumqi River (UG1) and Glacier No. 51 at Haxilegen of Kuytun River (HG51) in 2002 and 2004, were analyzed for organic carbon (OC) and element carbon (EC) by thermal/ optical reflectance (TOR). The spatio-temporal characteristics and environmental significance of OC and EC <span class="hlt">concentration</span> were discussed in details. The <span class="hlt">concentration</span> order of total carbon (TC) was: snowpack of west branch on UG1 (1 943 ng x g(-1)) > snowpack of east branch on UG1 (989 ng x g(-1)) > snowpack of HG51 (150 ng x g(-1)) > glacier <span class="hlt">ice</span> of east branch on UG1 (77 ng x g(-1)), and the <span class="hlt">concentration</span> order of OC and EC lay similar as TC. The <span class="hlt">concentration</span> of OC and EC in snowpack of Tianshan Mountains were 557 ng x g(-1) and 188 ng x g(-1), respectively. <span class="hlt">Concentration</span> peak of carbonaceous aerosol usually appeared near the dust layer at the bottom section of snowpack, but the some sudden events could increase the <span class="hlt">concentration</span> in the surface snow. Because of the seasonality of carbon emission (e. g. heating and agricultural activities) and transportation (e. g. atmospheric circulation), the <span class="hlt">concentration</span> of carbonaceous aerosol increased from July to November with fluctuations. Difference on the order of magnitude might exist between the <span class="hlt">concentration</span> in snow (firn) and glacier <span class="hlt">ice</span>, which was influenced by the glacier surroundings, sampling situation and other factors. EC on the surface snow affected the albedo significantly, and an average albedo reduction of 0.22 in the wavelength of 300-700 nm was simulated by SNICAR (snow, <span class="hlt">ice</span>, and aerosol radiative) model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19870060026&qs=N%3D0%26Ntk%3DTitle%26Ntx%3Dmode%2Bmatchall%26Ntt%3DB','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19870060026&qs=N%3D0%26Ntk%3DTitle%26Ntx%3Dmode%2Bmatchall%26Ntt%3DB"><span>Shuttle Imaging Radar B (SIR-B) Weddell Sea <span class="hlt">ice</span> observations - A comparison of SIR-B and scanning multichannel microwave radiometer <span class="hlt">ice</span> <span class="hlt">concentrations</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Martin, Seelye; Holt, Benjamin; Cavalieri, Donald J.; Squire, Vernon</p> <p>1987-01-01</p> <p><span class="hlt">Ice</span> <span class="hlt">concentrations</span> over the Weddell Sea were studied using SIR-B data obtained during the October 1984 mission, with special attention given to the effect of ocean waves on the radar return at the <span class="hlt">ice</span> edge. Sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> were derived from the SIR-B data using two image processing methods: the classification scheme at JPL and the manual classification method at Scott Polar Research Institute (SPRI), England. The SIR <span class="hlt">ice</span> <span class="hlt">concentrations</span> were compared with coincident <span class="hlt">concentrations</span> from the Nimbus-7 SMMR. For <span class="hlt">concentrations</span> greater than 40 percent, which was the smallest <span class="hlt">concentration</span> observed jointly by SIR-B and the SMMR, the mean difference between the two data sets for 12 points was 2 percent. A comparison between the JPL and the SPRI SIR-B algorithms showed that the algorithms agree to within 1 percent in the interior <span class="hlt">ice</span> pack, but the JPL algorithm gives slightly greater <span class="hlt">concentrations</span> at the <span class="hlt">ice</span> edge (due to the fact that the algorithm is affected by the wind waves in these areas).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015TCD.....9.2339P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015TCD.....9.2339P"><span>Assimilating high horizontal resolution sea <span class="hlt">ice</span> <span class="hlt">concentration</span> data into the US Navy's <span class="hlt">ice</span> forecast systems: Arctic Cap Nowcast/Forecast System (ACNFS) and the Global Ocean Forecast System (GOFS 3.1)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Posey, P. G.; Metzger, E. J.; Wallcraft, A. J.; Hebert, D. A.; Allard, R. A.; Smedstad, O. M.; Phelps, M. W.; Fetterer, F.; Stewart, J. S.; Meier, W. N.; Helfrich, S. R.</p> <p>2015-04-01</p> <p>This study presents the improvement in the US Navy's operational sea <span class="hlt">ice</span> forecast systems gained by assimilating high horizontal resolution satellite-derived <span class="hlt">ice</span> <span class="hlt">concentration</span> products. Since the late 1980's, the <span class="hlt">ice</span> forecast systems have assimilated near real-time sea <span class="hlt">ice</span> <span class="hlt">concentration</span> derived from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI and then SSMIS). The resolution of the satellite-derived product was approximately the same as the previous operational <span class="hlt">ice</span> forecast system (25 km). As the sea <span class="hlt">ice</span> forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy sea <span class="hlt">ice</span> forecast system (Arctic Cap Nowcast/Forecast System - ACNFS) went into operations with a horizontal resolution of ~3.5 km at the North Pole. A method of blending <span class="hlt">ice</span> <span class="hlt">concentration</span> observations from the Advanced Microwave Scanning Radiometer (AMSR2) along with a sea <span class="hlt">ice</span> mask produced by the National <span class="hlt">Ice</span> Center (NIC) has been developed resulting in an <span class="hlt">ice</span> <span class="hlt">concentration</span> product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended <span class="hlt">ice</span> <span class="hlt">concentration</span> product. The daily <span class="hlt">ice</span> edge locations from model hindcast simulations were compared against independent observed <span class="hlt">ice</span> edge locations. ACNFS initialized using the high resolution blended <span class="hlt">ice</span> <span class="hlt">concentration</span> data product decreased predicted <span class="hlt">ice</span> edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended sea <span class="hlt">ice</span> <span class="hlt">concentration</span> product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in <span class="hlt">ice</span> edge location over a system using the SSMIS sea <span class="hlt">ice</span> <span class="hlt">concentration</span> product alone. This paper describes the technique used to create the blended sea <span class="hlt">ice</span> <span class="hlt">concentration</span> product and the significant improvements to both of the Navy's sea <span class="hlt">ice</span> forecasting systems.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.C43B0668R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.C43B0668R"><span>Recent Increase in Elemental Carbon <span class="hlt">Concentration</span> and Deposition in a Svalbard <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>Ruppel, M.; Isaksson, E. D.; Ström, J.; Svensson, J.; Beaudon, E.; Korhola, A.</p> <p>2013-12-01</p> <p>Black carbon (BC) is an aerosol produced by incomplete combustion of biomass and fossil fuels. Due to its strong light absorption it warms the atmosphere. Climate effects of BC are intensified in the Arctic where its deposition on snow and <span class="hlt">ice</span> decreases surface albedo, causing earlier spring melt and associated feedbacks. Despite the significant role of BC in Arctic climate warming, there is little information on its <span class="hlt">concentrations</span> and climate effects in the Arctic in time periods preceding direct observational data. Here we present first results on BC (here operationally defined as elemental carbon (EC)) <span class="hlt">concentrations</span> and deposition on a Svalbard (European Arctic) glacier (Holtedahlfonna) from 1700 to 2004. The inner part of a 125 m deep <span class="hlt">ice</span> core was melted, filtered and analyzed for apparent elemental carbon using a thermal optical method. EC <span class="hlt">concentrations</span> (μg L-1) and the deposition (mg m-2 yr-1) were generally low in the pre-industrial era. <span class="hlt">Concentrations</span> peaked around 1910 and again around 1950, whereas only the 1910 peak was recorded in the EC deposition, followed by decreasing deposition values. Strikingly, both EC <span class="hlt">concentration</span> and deposition started to increase rapidly from the 1970s until 2004. This rise is not seen in any thus far published European or Arctic <span class="hlt">ice</span> core, and it seems to contradict atmospheric BC measurements from the Arctic which indicate decreasing atmospheric BC <span class="hlt">concentrations</span> since the beginning of the observations at the end of 1980s. However, the magnitude of the measured <span class="hlt">concentrations</span> is in accordance with previous <span class="hlt">ice</span> core EC measurements from the European Alps and a BC <span class="hlt">concentration</span> and deposition peak around 1910 has also been recorded in Greenland <span class="hlt">ice</span> cores. Work is continuing to disentangle the cause of the increasing EC values in the recent decades suggested by the present <span class="hlt">ice</span> core. Contribution from any local sources has been ruled out. Back trajectory modeling is carried out to establish the EC source areas. The present</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/7120760','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/7120760"><span>Investigation of Antarctic sea <span class="hlt">ice</span> <span class="hlt">concentration</span> by means of selected algorithms. Final report, 1991-1992</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Lomax, A.S.</p> <p>1992-05-08</p> <p>Changes in areal extent and <span class="hlt">concentration</span> of sea <span class="hlt">ice</span> around Antarctica may serve as sensitive indicators of global warming. A comparison study was conducted between the outputs of the three main algorithms currently in use (NASA Team, Comisco, and NORSEX) and a sea-<span class="hlt">ice</span> model (Fine Resolution Antarctic Model). Data from the DMSP Special Sensor Microwave/Imager (SSM/I) were used as input algorithms for the time frame July, 1987 to June, 1990. Large disparities are apparent when comparing the NASA algorithm with the Comisco and NORSEX algorithms. Very large differences, some higher than 30 per cent, exist in the marginal <span class="hlt">ice</span> zones, along the coast, and in the Weddell and Ross Seas Heat fluxes through recurring polynyas were calculated to quantify further differences in the algorithms; however, no conclusive patterns were apparent. No significant change in the extent or area of the <span class="hlt">ice</span> pack occurred from July, 1987 through June, 1990. Antarctic Ocean, Antarctic regions, Global warming, Sea <span class="hlt">ice</span>-Antarctic regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.A23E..05D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.A23E..05D"><span>Investigating and parameterizing physical, chemical, and thermodynamic dependencies of <span class="hlt">ice</span> nuclei <span class="hlt">concentrations</span> (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Demott, P. J.; Prenni, A. J.; Sullivan, R. C.; Liu, X.; Kreidenweis, S. M.; Carpenter, J. M.; Branson, M.; Moehler, O.; Glen, A.; Brooks, S. D.</p> <p>2010-12-01</p> <p>Recent observations of atmospheric <span class="hlt">ice</span> nuclei (IN) <span class="hlt">concentrations</span> have been compiled into a new parameterization that relates IN abundance to temperature and the size distribution of ambient aerosols (DeMott et al. 2010). We show cases of positive performance of the proposed parameterization in predicting <span class="hlt">ice</span> formation in case studies of cloud formation and evolution, and also show consistency with new <span class="hlt">ice</span> nuclei data not used in the development of the parameterization. Nevertheless, atmospheric evidence also suggests remaining critical needs which, when met, are expected to lead to improvement in the parameterization. First, there is a need for new ambient data at modestly supercooled temperatures, including cases of high total particle <span class="hlt">concentrations</span>. Second, observations are needed to quantify the influence of varied aerosol source regions/compositions and aerosol aging on IN activity. The size-dependence in the parameterization probably reflects the dominance of dust and biological particles as IN, but this must be tested directly, and the possibilities of additional IN types such as black carbon should be further explored. With respect to the role of atmospheric processing, much of our present conceptualization of aging impacts on <span class="hlt">ice</span> nuclei has come from recent laboratory studies. Therefore, we also explore here the consistency of laboratory data collected for untreated and processed mineral dust particles with atmospheric data collected for more general <span class="hlt">ice</span> nuclei populations, and suggest observational strategies for studying aging effects in the atmosphere. DeMott, P.J., A. J. Prenni, X. Liu, M. D. Petters, C H. Twohy, M. S. Richardson, T. Eidhammer, S. M. Kreidenweis, and D. C. Rogers, 2010: Predicting global atmospheric <span class="hlt">ice</span> nuclei distributions and their impacts on climate, Accepted to Proc. Natnl. Acad. Sci., 107 (25), 11217-11222.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014ISPAr.XL8...21C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014ISPAr.XL8...21C"><span>Diurnal Difference Vegetation Water Content (ddVWC) of Advance Microwave Scanning Radiometer-Earth Observing System (<span class="hlt">AMSR-E</span>) for assessment of crop water stress at regional level</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chakraborty, A.; Sesha Sai, M. V. R.</p> <p>2014-11-01</p> <p>Advance Microwave Scanning Radiometer - Earth Observing System (<span class="hlt">AMSR-E</span>) derived Vegetation Water Content (VWC) at predawn (01:30 LST, descending pass) and afternoon (13:30 LST; ascending pass) were used to assess crop water stress condition over the selected meteorological subdivisions of India. The temporal profile of Normalized Difference Vegetation Index (NDVI) was used to study the progression of crop growth. The Diurnal Difference Vegetation Water Content (ddVWC) was found to be sensitive to rainfall patterns (wet/dry spell) particularly in moderate to full crop cover condition (NDVI > 0.4). The ddVWC was found to be significantly (p = 0.05) correlated with the rainfall over the rainfed regions. The ddVWC was further characterized to represent different categories of crop water stress considering irrigated flooded rice crop as a benchmark. Inter year comparative analysis of temporal variations of the ddVWC revealed its capability to differentiate normal (2005) and sub-normal years (2008 and 2009) in term of intensity and persistence of crop water stress. Spatio-temporal patterns of ddVWC could capture regional progression of crop water stress at high temporal resolution in near real time.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JASTP.109...37O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JASTP.109...37O"><span>Evidence of the solar Gleissberg cycle in the nitrate <span class="hlt">concentration</span> in polar <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>Ogurtsov, M. G.; Oinonen, M.</p> <p>2014-03-01</p> <p>Two sets of nitrate (NO3-) <span class="hlt">concentration</span> data, obtained from Central Greenland and East Antarctic (Dronning Maud Land) <span class="hlt">ice</span> cores, were analyzed statistically. Distinct century-scale (50-150 yr) variability was revealed in both data sets during AD 1576-1990. It was found that century-type variation in Greenland and Antarctic nitrate correlates fairly significantly with the corresponding Gleissberg cycle: (a) in sunspot number over 1700-1970 AD; (b) in 10Be <span class="hlt">concentration</span> in Central and South Greenland over 1576-1970 AD. Thus, presence of century-scale relationship between polar nitrate and solar activity was confirmed over the last 4 centuries. That proves that NO3- <span class="hlt">concentration</span> in polar <span class="hlt">ice</span> caps could serve as indicator of long-term solar variability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19900044612&hterms=ice+melt&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dice%2Bmelt','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19900044612&hterms=ice+melt&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dice%2Bmelt"><span>Investigation of the effects of summer melt on the calculation of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> using active and passive microwave data</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.; Burns, Barbara A.; Onstott, Robert G.</p> <p>1990-01-01</p> <p>The effects of <span class="hlt">ice</span> surface melt on microwave signatures and errors in the calculation of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> are examined, using active and passive microwave data sets from the Marginal <span class="hlt">Ice</span> Zone Experiment aircraft flights in the Fram Strait region. Consideration is given to the possibility of using SAR to supplement passive microwave data to unambiguously discriminate between open water areas and ponded floes. Coincident active multichannel microwave radiometer and SAR measurements of individual floes are used to describe the effects of surface melt on sea <span class="hlt">ice</span> <span class="hlt">concentration</span> calculations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19980076134&hterms=causes+parkinson&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dcauses%2Bparkinson','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19980076134&hterms=causes+parkinson&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dcauses%2Bparkinson"><span>Arctic and Antarctic Sea <span class="hlt">Ice</span> <span class="hlt">Concentrations</span> from Multichannel Passive-Microwave Satellite Data Sets: User's Guide</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.; Gloersen, Per; Zwally, H. Jay</p> <p>1997-01-01</p> <p>Satellite multichannel passive-microwave sensors have provided global radiance measurements with which to map, monitor, and study the Arctic and Antarctic polar sea <span class="hlt">ice</span> covers. The data span over 18 years (as of April 1997), starting with the launch of the Scanning Multichannel Microwave Radiometer (SMMR) on NASA's SeaSat A and Nimbus 7 in 1978 and continuing with the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI) series beginning in 1987. It is anticipated that the DMSP SSMI series will continue into the 21st century. The SSMI series will be augmented by new, improved sensors to be flown on Japanese and U.S. space platforms. This User's Guide provides a description of a new sea <span class="hlt">ice</span> <span class="hlt">concentration</span> data set generated from observations made by three of these multichannel sensors. The data set includes gridded daily <span class="hlt">ice</span> <span class="hlt">concentrations</span> (every-other-day for the SMMR data) for both the north and south polar regions from October 26, 1978 through September 30, 1995, with the one exception of a 6-week data gap from December 3, 1987 through January 12, 1988. The data have been placed on two CD-ROMs that include a ReadMeCD file giving the technical details on the file format, file headers, north and south polar grids, ancillary data sets, and directory structure of the CD-ROM. The CD-ROMS will be distributed by the National Snow and <span class="hlt">Ice</span> Data Center in Boulder, CO.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19980076134&hterms=Parkinson&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3DParkinson','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19980076134&hterms=Parkinson&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3DParkinson"><span>Arctic and Antarctic Sea <span class="hlt">Ice</span> <span class="hlt">Concentrations</span> from Multichannel Passive-Microwave Satellite Data Sets: User's Guide</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.; Gloersen, Per; Zwally, H. Jay</p> <p>1997-01-01</p> <p>Satellite multichannel passive-microwave sensors have provided global radiance measurements with which to map, monitor, and study the Arctic and Antarctic polar sea <span class="hlt">ice</span> covers. The data span over 18 years (as of April 1997), starting with the launch of the Scanning Multichannel Microwave Radiometer (SMMR) on NASA's SeaSat A and Nimbus 7 in 1978 and continuing with the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI) series beginning in 1987. It is anticipated that the DMSP SSMI series will continue into the 21st century. The SSMI series will be augmented by new, improved sensors to be flown on Japanese and U.S. space platforms. This User's Guide provides a description of a new sea <span class="hlt">ice</span> <span class="hlt">concentration</span> data set generated from observations made by three of these multichannel sensors. The data set includes gridded daily <span class="hlt">ice</span> <span class="hlt">concentrations</span> (every-other-day for the SMMR data) for both the north and south polar regions from October 26, 1978 through September 30, 1995, with the one exception of a 6-week data gap from December 3, 1987 through January 12, 1988. The data have been placed on two CD-ROMs that include a ReadMeCD file giving the technical details on the file format, file headers, north and south polar grids, ancillary data sets, and directory structure of the CD-ROM. The CD-ROMS will be distributed by the National Snow and <span class="hlt">Ice</span> Data Center in Boulder, CO.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19940007290&hterms=Ice+cover+Arctic+Ocean&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DIce%2Bcover%2BArctic%2BOcean','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19940007290&hterms=Ice+cover+Arctic+Ocean&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DIce%2Bcover%2BArctic%2BOcean"><span>Summer Arctic <span class="hlt">ice</span> <span class="hlt">concentrations</span> and characteristics from SAR and SSM/I data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Comiso, Joey C.; Kwok, Ron</p> <p>1993-01-01</p> <p>The extent and <span class="hlt">concentration</span> of the Summer minima provide indirect information about the long term ability of the perennial portion of the <span class="hlt">ice</span> pack to survive the Arctic atmosphere and ocean system. Both active and passive microwave data were used with some success for monitoring the <span class="hlt">ice</span> cover during the Summer, but they both suffer from similar problems caused by the presence of meltponding, surface wetness, flooding, and freeze/thaw cycles associated with periodic changes in surface air temperatures. A comparative analysis of <span class="hlt">ice</span> conditions in the Arctic region using coregistered ERS-1 SAR (Synthetic Aperture Radar) and SSM/I (Special Sensor Microwave/Imager) data was made. The analysis benefits from complementary information from the two systems, the good spatial resolution of SAR data, and the good time resolution of and global coverage by SSM/I data. The results show that in many areas <span class="hlt">ice</span> <span class="hlt">concentrations</span> derived from SAR data are significantly different (usually higher) than those derived from passive microwave data. Additional insights about surface conditions can be inferred depending on the nature of the discrepancies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18..639C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18..639C"><span>Robustness of the large-scale modes of variability of winter Arctic sea <span class="hlt">ice</span> <span class="hlt">concentration</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Close, Sally; Houssais, Marie-Noëlle; Herbaut, Christophe</p> <p>2016-04-01</p> <p>The dominant mode of variability of Arctic winter sea <span class="hlt">ice</span> <span class="hlt">concentration</span> has previously been suggested to be represented by a double-dipole structure, with the loading pattern of the first empirical orthogonal mode having phase of one sign in the Sea of Okhotsk and Barents Sea and opposing sign in the Labrador and Bering Seas. In this study, we build on this previous work, examining the robustness of the primary modes of large-scale variability of the winter sea <span class="hlt">ice</span> <span class="hlt">concentration</span> in the Arctic based on the satellite data record. We find that the double-dipole structure does not emerge as a robust mode of variability: rather, the primary mode can be considered as a tripole, explaining significant variability only in the Sea of Okhotsk, Barents and Bering Seas. In contrast, the Labrador Sea emerges in isolation in the second empirical orthogonal mode. The relative magnitude of the poles of variability in the empirical orthogonal function loading patterns are sensitive to the detrending of the data; however, the isolation of the variability of the Labrador Sea <span class="hlt">ice</span> remains a robust feature. We find that there is no significant interannual-scale co-variability amongst the sea <span class="hlt">ice</span> areas of the four seas comprising the double-dipole after low-frequency variability has been removed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.4259W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.4259W"><span>Immersion freezing in <span class="hlt">concentrated</span> solution droplets for a variety of <span class="hlt">ice</span> nucleating particles</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wex, Heike; Kohn, Monika; Grawe, Sarah; Hartmann, Susan; Hellner, Lisa; Herenz, Paul; Welti, Andre; Lohmann, Ulrike; Kanji, Zamin; Stratmann, Frank</p> <p>2016-04-01</p> <p>The measurement campaign LINC (Leipzig <span class="hlt">Ice</span> Nucleation counter Comparison) was conducted in September 2015, during which <span class="hlt">ice</span> nucleation measurements as obtained with the following instruments were compared: - LACIS (Leipzig Aerosol Cloud Interaction Simulator, see e.g. Wex et al., 2014) - PIMCA-PINC (Portable Immersion Mode Cooling Chamber together with PINC) - PINC (Portable <span class="hlt">Ice</span> Nucleation Chamber, Chou et al., 2011) - SPIN (SPectrometer for <span class="hlt">Ice</span> Nuclei, Droplet Measurement Technologies) While LACIS and PIMCA-PINC measured immersion freezing, PINC and SPIN varied the super-saturation during the measurements and collected data also for relative humidities below 100% RHw. A suite of different types of <span class="hlt">ice</span> nucleating particles were examined, where particles were generated from suspensions, subsequently dried and size selected. For the following samples, data for all four instruments are available: K-feldspar, K-feldspar treated with nitric acid, Fluka-kaolinite and birch pollen. Immersion freezing measurements by LACIS and PIMCA-PINC were in excellent agreement. Respective parameterizations from these measurement were used to model the <span class="hlt">ice</span> nucleation behavior below water vapor saturation, assuming that the process can be described as immersion freezing in <span class="hlt">concentrated</span> solutions. This is equivalent to simply including a <span class="hlt">concentration</span> dependent freezing point depression in the immersion freezing parameterization, as introduced for coated kaolinite particles in Wex et al. (2014). Overall, measurements performed below water vapor saturation were reproduced by the model, and it will be discussed in detail, why deviations were observed in some cases. Acknowledgement: Part of this work was funded by the DFG Research Unit FOR 1525 INUIT, grant WE 4722/1-2. Literature: Chou, C., O. Stetzer, E. Weingartner, Z. Juranyi, Z. A. Kanji, and U. Lohmann (2011), <span class="hlt">Ice</span> nuclei properties within a Saharan dust event at the Jungfraujoch in the Swiss Alps, Atmos. Chem. Phys., 11(10), 4725</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUFM.U23A..08S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUFM.U23A..08S"><span>``Pre-Vostok'' Greenhouse Gas <span class="hlt">Concentrations</span> Reconstructed From the EPICA Dome C <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>Stocker, T. F.; Siegenthaler, U.; Spahni, R.; Chappellaz, J.; Fischer, H.</p> <p>2004-12-01</p> <p>The new deep <span class="hlt">ice</span> core recovered from Dome Concordia in the framework of EPICA, the European Project of <span class="hlt">Ice</span> Coring in Antarctica, contains a continuous climate history of the past 740,000 years [EPICA Community Members, 2004]. We present the current status of measurements of CO2, CH4 and N2O on air trapped in the bubbles of the Dome C <span class="hlt">ice</span> core. CO2 is measured using laser absorption spectroscopy on samples of less than 10 g of <span class="hlt">ice</span> which are mechanically crushed or milled. CH4 and N2O are extracted using a melt-refreeze technique and then measured by gas chromatography. The <span class="hlt">ice</span> core contains an uncontaminated climate record down to Marine Isotope Stage 14 (MIS 14) as verified by a consistent gas age/<span class="hlt">ice</span> age difference determined at terminations V and VI. CO2 and CH4 results from MIS 11 show that the normal levels of greenhouse gases prevailed during this exceptionally long interglacial. This demonstrates that the length of the interglacial was not due to exceptionally high greenhouse gas levels. MIS 13 and earlier interglacials, however, show significantly colder interglacials. In addition, the glacials are shorter which results in a more balanced sequence of cold and warm phases. Measurements of the greenhouse gas <span class="hlt">concentrations</span> are central in understanding the mechanisms in the climate system which cause the significant change of character of the <span class="hlt">ice</span> age cycles at around 400 kyr BP. We will present greenhouse gas measurements covering the first of the "pre-Vostok" interglacials from MIS 11 to MIS 14 (410 to 550 kyr BP) for CO2, and from MIS 11 to MIS 16 (410 to 620 kyr BP) for CH4. These measurements will resolve the "EPICA Challenge" [Wolff et al., 2004] put out to modelers to predict the expected greenhouse gas levels prior to 400 kyr BP based on the knowledge of the orbital parameters, and known paleoclimatic proxies (sea level from marine sediment records, dust load and isotopic <span class="hlt">concentration</span> of precipitation in Antarctica from the EPICA Dome C <span class="hlt">ice</span> core</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014A%26A...562A..22C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014A%26A...562A..22C"><span>Photochemistry of PAHs in cosmic water <span class="hlt">ice</span>. The effect of <span class="hlt">concentration</span> on UV-VIS spectroscopy and ionization efficiency</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cuylle, Steven H.; Allamandola, Louis J.; Linnartz, Harold</p> <p>2014-02-01</p> <p>Context. Observations and models show that polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in the interstellar medium. Like other molecules in dense clouds, PAHs accrete onto interstellar dust grains, where they are embedded in an <span class="hlt">ice</span> matrix dominated by water. In the laboratory, mixed molecular <span class="hlt">ices</span> (not containing PAHs) have been extensively studied using Fourier transform infrared absorption spectroscopy. Experiments including PAHs in <span class="hlt">ices</span> have started, however, the <span class="hlt">concentrations</span> used are typically much higher than the <span class="hlt">concentrations</span> expected for interstellar <span class="hlt">ices</span>. Optical spectroscopy offers a sensitive alternative. Aims: We report an experimental study of the effect PAH <span class="hlt">concentration</span> has on the electronic spectra and the vacuum UV (VUV) driven processes of PAHs in water-rich <span class="hlt">ices</span>. The goal is to apply the outcome to cosmic <span class="hlt">ices</span>. Methods: Optical spectroscopic studies allow us to obtain in-situ and quasi real-time electronic solid state spectra of two prototypical PAHs (pyrene and coronene) embedded in water <span class="hlt">ice</span> under VUV photoprocessing. The study is carried out on PAH:H2O <span class="hlt">concentrations</span> in the range of 1:30 000 to pure PAH, covering the temperature range from 12 to 125 K. Results: PAH <span class="hlt">concentration</span> strongly influences the efficiency of PAH cation formation. At low <span class="hlt">concentrations</span>, ionization efficiencies are over 60% dropping to about 15% at 1:1000. Increasing the PAH <span class="hlt">concentration</span> reveals spectral broadening in neutral and cation PAH spectra attributed to PAH clustering inside the <span class="hlt">ice</span>. At the PAH <span class="hlt">concentrations</span> expected for interstellar <span class="hlt">ices</span>, some 10 to 20% may be present as cations. The presence of PAHs in neutral and ion form will add distinctive absorption bands to cosmic <span class="hlt">ice</span> optical spectra and this may serve as a tool to determine PAH <span class="hlt">concentrations</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23785068','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23785068"><span>Multi-plate freeze <span class="hlt">concentration</span>: Recovery of solutes occluded in the <span class="hlt">ice</span> and determination of thawing time.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Gulfo, Rafael; Auleda, Josep M; Moreno, Fabián Leonardo; Ruiz, Yolanda; Hernández, Eduard; Raventós, Mercè</p> <p>2014-09-01</p> <p>The retention of solutes in the <span class="hlt">ice</span> formed in a falling-film freeze <span class="hlt">concentrator</span> (multi-plate freeze-<span class="hlt">concentrator</span>) was analysed. Solutions of fructose, glucose and sucrose and a simulated juice with initial <span class="hlt">concentrations</span> of 5, 10, 15 and 20 °Brix were freeze <span class="hlt">concentrated</span>. The <span class="hlt">ice</span> produced in the four steps of the process retains solutes at levels of 1.0-8.8 °Brix (expressed as solute mass fraction in the <span class="hlt">ice</span>). The recovery of these solutes during thawing can increase overall system efficiency. All thawing steps were carried out dividing the sample in 10 fractions at 20 ℃. The first thawed fractions showed solute <span class="hlt">concentrations</span> that were 1.9-3.3 times higher than the mean solute mass fraction in the <span class="hlt">ice</span>, while the last fractions of <span class="hlt">ice</span> showed very low levels of retained solutes, less than 0.2 times the mean solute mass fraction in the <span class="hlt">ice</span>. It was found that fractionated thawing can recover most of the solute content in the <span class="hlt">ice</span>. The procedure presented in the present study allows the determination of the solute <span class="hlt">concentration</span> achieved in the various thawing fractions and predicts the thawing time required for a given form factor, melting temperature and initial solute mass fraction in the <span class="hlt">ice</span>. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150011077','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150011077"><span>Verification of a New NOAA/NSIDC Passive Microwave Sea-<span class="hlt">Ice</span> <span class="hlt">Concentration</span> Climate Record</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Meier, Walter N.; Peng, Ge; Scott, Donna J.; Savoie, Matt H.</p> <p>2014-01-01</p> <p>A new satellite-based passive microwave sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> product developed for the National Oceanic and Atmospheric Administration (NOAA)Climate Data Record (CDR) programme is evaluated via comparison with other passive microwave-derived estimates. The new product leverages two well-established <span class="hlt">concentration</span> algorithms, known as the NASA Team and Bootstrap, both developed at and produced by the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC). The sea <span class="hlt">ice</span> estimates compare well with similar GSFC products while also fulfilling all NOAA CDR initial operation capability (IOC) requirements, including (1) self describing file format, (2) ISO 19115-2 compliant collection-level metadata,(3) Climate and Forecast (CF) compliant file-level metadata, (4) grid-cell level metadata (data quality fields), (5) fully automated and reproducible processing and (6) open online access to full documentation with version control, including source code and an algorithm theoretical basic document. The primary limitations of the GSFC products are lack of metadata and use of untracked manual corrections to the output fields. Smaller differences occur from minor variations in processing methods by the National Snow and <span class="hlt">Ice</span> Data Center (for the CDR fields) and NASA (for the GSFC fields). The CDR <span class="hlt">concentrations</span> do have some differences from the constituent GSFC <span class="hlt">concentrations</span>, but trends and variability are not substantially different.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22053829','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22053829"><span>Control of <span class="hlt">ice</span> chromatographic retention mechanism by changing temperature and dopant <span class="hlt">concentration</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tasaki, Yuiko; Okada, Tetsuo</p> <p>2011-12-15</p> <p>A liquid phase coexists with solid water <span class="hlt">ice</span> in a typical binary system, such as NaCl-water, in the temperature range between the freezing point and the eutectic point (t(eu)) of the system. In <span class="hlt">ice</span> chromatography with salt-doped <span class="hlt">ice</span> as the stationary phase, both solid and liquid phase can contribute to solute retention in different fashions; that is, the solid <span class="hlt">ice</span> surface acts as an adsorbent, while a solute can be partitioned into the liquid phase. Thus, both adsorption and partition mechanisms can be utilized for <span class="hlt">ice</span> chromatographic separation. An important feature in this approach is that the liquid phase volume can be varied by changing the temperature and the <span class="hlt">concentration</span> of a salt incorporated into the <span class="hlt">ice</span> stationary phase. Thus, we can control the relative contribution from the partition mechanism in the entire retention because the liquid phase volume can be estimated from the freezing depression curve. Separation selectivity can thereby be modified. The applicability of this concept has been confirmed for the solutes of different adsorption and partition abilities. The predicted retention based on thermodynamics basically agrees well with the corresponding experimental retention. However, one important inconsistency has been found. The calculation predicts a step-like discontinuity of the solute retention at t(eu) because the phase diagram suggests that the liquid phase abruptly appears at t(eu) when the temperature increases. In contrast, the corresponding experimental plots are continuous over the wider range including the subeutectic temperatures. This discrepancy is explained by the existence of the liquid phase below t(eu). A difference between predicted and measured retention factors allows the estimation of the volume of the subeutectic liquid phase.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/18349223','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/18349223"><span>High hydrostatic pressure modification of whey protein <span class="hlt">concentrate</span> for improved body and texture of lowfat <span class="hlt">ice</span> cream.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lim, S-Y; Swanson, B G; Ross, C F; Clark, S</p> <p>2008-04-01</p> <p>Previous research demonstrated that application of high hydrostatic pressure (HHP), particularly at 300 MPa for 15 min, can enhance foaming properties of whey protein <span class="hlt">concentrate</span> (WPC). The purpose of this research was to determine the practical impact of HHP-treated WPC on the body and texture of lowfat <span class="hlt">ice</span> cream. Washington State University (WSU)-WPC was produced by ultrafiltration of fresh separated whey received from the WSU creamery. Commercial whey protein <span class="hlt">concentrate</span> 35 (WPC 35) powder was reconstituted to equivalent total solids as WSU-WPC (8.23%). Three batches of lowfat <span class="hlt">ice</span> cream mix were produced to contain WSU-WPC without HHP, WSU-WPC with HHP (300 MPa for 15 min), and WPC 35 without HHP. All lowfat <span class="hlt">ice</span> cream mixes contained 10% WSU-WPC or WPC 35. Overrun and foam stability of <span class="hlt">ice</span> cream mixes were determined after whipping for 15 min. <span class="hlt">Ice</span> creams were produced using standard <span class="hlt">ice</span> cream ingredients and processing. The hardness of <span class="hlt">ice</span> creams was determined with a TA-XT2 texture analyzer. Sensory evaluation by balanced reference duo-trio test was carried out using 52 volunteers. The <span class="hlt">ice</span> cream mix containing HHP-treated WSU-WPC exhibited the greatest overrun and foam stability, confirming the effect of HHP on foaming properties of whey proteins in a complex system. <span class="hlt">Ice</span> cream containing HHP-treated WSU-WPC exhibited significantly greater hardness than <span class="hlt">ice</span> cream produced with untreated WSU-WPC or WPC 35. Panelists were able to distinguish between <span class="hlt">ice</span> cream containing HHP-treated WSU-WPC and <span class="hlt">ice</span> cream containing untreated WPC 35. Improvements of overrun and foam stability were observed when HHP-treated whey protein was used at a <span class="hlt">concentration</span> as low as 10% (wt/wt) in <span class="hlt">ice</span> cream mix. The impact of HHP on the functional properties of whey proteins was more pronounced than the impact on sensory properties.</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/2015AGUFM.C11C0785F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C11C0785F"><span>Estimating Trapped Gas <span class="hlt">Concentrations</span> as Bubbles Within Lake <span class="hlt">Ice</span> Using Ground Penetrating Radar</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fantello, N.; Parsekian, A.; Walter Anthony, K. M.</p> <p>2015-12-01</p> <p>Climate warming is currently one of the most important issues that we are facing. The degradation of permafrost beneath thermokarst lakes has been associated with enhanced methane emissions and it presents a positive feedback to climate warming. Thermokarst lakes release methane to the atmosphere mainly by ebullition (bubbling) but there are a large number of uncertainties regarding the magnitude and variability of these emissions. Here we present a methodology to estimate the amount of gas released from thermokarst lakes through ebullition using ground-penetrating radar (GPR). This geophysical technique is well suited for this type of problem because it is non-invasive, continuous, and requires less effort and time than the direct visual inspection. We are studying GPR data collected using 1.2 GHz frequency antennas in Brooklyn Lake, Laramie, WY, in order to quantify the uncertainties in the method. Although this is not a thermokarst lake, gas bubbles are trapped in the <span class="hlt">ice</span> and spatial variability in bubble <span class="hlt">concentration</span> within the <span class="hlt">ice</span> is evident. To assess the variability in bulk physical properties of the <span class="hlt">ice</span> due to bubbles, we gathered GPR data from different types of <span class="hlt">ice</span>. We compared the velocity of the groundwave and reflection obtained from radargrams, and found on each case a larger value for the groundwave velocity suggesting a non-homogeneous medium and that the <span class="hlt">concentration</span> of bubbles is prone to be near the surface instead of at greater depths. We use a multi-phase dielectric-mixing model to estimate the amount of gas present in a sample of volume of <span class="hlt">ice</span> and found an uncertainty in relative permittivity (estimated using reflection velocity) of 0.0294, which translates to an uncertainty of 1.1% in gas content; and employing groundwave velocity we found 0.0712 and 2.9%, respectively. If locations of gas seeps in lakes could be detected and quantified using GPR along with field measurements, this could help to constrain future lake-source carbon gas</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23772704','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23772704"><span>Effects of locust bean gum and mono- and diglyceride <span class="hlt">concentrations</span> on particle size and melting rates of <span class="hlt">ice</span> cream.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Cropper, S L; Kocaoglu-Vurma, N A; Tharp, B W; Harper, W J</p> <p>2013-06-01</p> <p>The objective of this study was to determine how varying <span class="hlt">concentrations</span> of the stabilizer, locust bean gum (LBG), and different levels of the emulsifier, mono- and diglycerides (MDGs), influenced fat aggregation and melting characteristics of <span class="hlt">ice</span> cream. <span class="hlt">Ice</span> creams were made containing MDGs and LBG singly and in combination at <span class="hlt">concentrations</span> ranging between 0.0% to 0.14% and 0.0% to 0.23%, respectively. Particle size analysis, conducted on both the mixes and <span class="hlt">ice</span> cream, and melting rate testing on the <span class="hlt">ice</span> cream were used to determine fat aggregation. No significant differences (P < 0.05) were found between particle size values for experimental <span class="hlt">ice</span> cream mixes. However, higher <span class="hlt">concentrations</span> of both LBG and MDG in the <span class="hlt">ice</span> creams resulted in values that were larger than the control. This study also found an increase in the particle size values when MDG levels were held constant and LBG amounts were increased in the <span class="hlt">ice</span> cream. <span class="hlt">Ice</span> creams with higher <span class="hlt">concentrations</span> of MDG and LBG together had the greatest difference in the rate of melting than the control. The melting rate decreased with increasing LBG <span class="hlt">concentrations</span> at constant MDG levels. These results illustrated that fat aggregation may not only be affected by emulsifiers, but that stabilizers may play a role in contributing to the destabilization of fat globules. © 2013 Institute of Food Technologists®</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ACPD...1529125H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ACPD...1529125H"><span>Unexpectedly high ultrafine aerosol <span class="hlt">concentrations</span> above East Antarctic sea-<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>Humphries, R. S.; Klekociuk, A. R.; Schofield, R.; Keywood, M.; Ward, J.; Wilson, S. R.</p> <p>2015-10-01</p> <p>The effect of aerosols on clouds and their radiative properties is one of the largest uncertainties in our understanding of radiative forcing. A recent study has concluded that better characterisation of pristine, natural aerosol processes leads to the largest reduction in these uncertainties. Antarctica, being far from anthropogenic activities, is an ideal location for the study of natural aerosol processes. Aerosol measurements in Antarctica are often limited to boundary layer air-masses at spatially sparse coastal and continental research stations, with only a handful of studies in the sea <span class="hlt">ice</span> region. In this paper, the first observational study of sub-micron aerosols in the East Antarctic sea <span class="hlt">ice</span> region is presented. Measurements were conducted aboard the <span class="hlt">ice</span>-breaker Aurora Australis in spring 2012 and found that boundary layer condensation nuclei (CN3) <span class="hlt">concentrations</span> exhibited a five-fold increase moving across the Polar Front, with mean Polar Cell <span class="hlt">concentrations</span> of 1130 cm-3 - higher than any observed elsewhere in the Antarctic and Southern Ocean region. The absence of evidence for aerosol growth suggested that nucleation was unlikely to be local. Air parcel trajectories indicated significant influence from the free troposphere above the Antarctic continent, implicating this as the likely nucleation region for surface aerosol, a similar conclusion to previous Antarctic aerosol studies. The highest aerosol <span class="hlt">concentrations</span> were found to correlate with low pressure systems, suggesting that the passage of cyclones provided an accelerated pathway, delivering air-masses quickly from the free-troposphere to the surface. After descent from the Antarctic free troposphere, trajectories suggest that sea <span class="hlt">ice</span> boundary layer air-masses travelled equator-ward into the low albedo Southern Ocean region, transporting with them emissions and these aerosol nuclei where, after growth, may potentially impact on the region's radiative balance. The high aerosol <span class="hlt">concentrations</span> and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFM.A53B1146B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.A53B1146B"><span>Singlet Molecular Oxygen on <span class="hlt">Ice</span>: Rates of Formation and Steady State <span class="hlt">Concentrations</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bower, J. P.; Anastasio, C.</p> <p>2007-12-01</p> <p>Singlet molecular oxygen (1O2*), the first electronically excited state of molecular oxygen, reacts rapidly with certain types of environmental pollutants such as furans, phenols, and polycyclic aromatic hydrocarbons (PAHs). Its formation requires the absorption of light by a chromophore (a.k.a. sensitizer), which subsequently transfers energy to ground state molecular oxygen. In the environment, 1O2* chemistry has been studied primarily in the aqueous phase, such as in surface waters or cloud and fog drops. In this work, we expand our current understanding by investigating the rate of formation (Rf) and steady state <span class="hlt">concentration</span> ([1O2*]) of 1O2* on <span class="hlt">ice</span>. To investigate 1O2* kinetics, we use a chemical probe technique in which photoformed 1O2* reacts with furfuryl alcohol (FFA). To generate 1O2*, we illuminated frozen samples containing a sensitizer (Rose Bengal, RB) at 549 nm. The <span class="hlt">concentration</span> of total solutes in each sample was controlled using sodium sulfate (Na2SO4). Following illumination, the decay of FFA was measured using high performance liquid chromatography (HPLC). <span class="hlt">Ice</span> tests were conducted at 253, 263, and 268 K. Liquid tests for comparison were conducted at 278 K. Results showed dramatically faster (~104) FFA decay on <span class="hlt">ice</span> than in liquid samples prepared from the same solutions, in agreement with the calculated solute <span class="hlt">concentration</span> factor in the quasi-liquid layer (QLL) on <span class="hlt">ice</span> compared to bulk solution. Varying the <span class="hlt">concentration</span> of RB resulted in similar changes in both Rf and [1O2*], with magnitudes of change close to those expected. Changing temperature and total solutes, both of which control the volume of the QLL on <span class="hlt">ice</span>, revealed two model regimes: FFA as a major (1) or minor (2) sink of 1O2*. Experimental results from the former regime show good agreement with expected values for both Rf and [1O2*]. Experiments in the later regime are currently in progress. We will also discuss the potential implications of 1O2* to the chemistry of naturally</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.2924G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.2924G"><span>Detection and Analysis of High <span class="hlt">Ice</span> <span class="hlt">Concentration</span> Events and Supercooled Drizzle from IAGOS Commercial Aircraft</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gallagher, Martin; Baumgardner, Darrel; Lloyd, Gary; Beswick, Karl; Freer, Matt; Durant, Adam</p> <p>2016-04-01</p> <p>Hazardous encounters with high <span class="hlt">ice</span> <span class="hlt">concentrations</span> that lead to temperature and airspeed sensor measurement errors, as well as engine rollback and flameout, continue to pose serious problems for flight operations of commercial air carriers. Supercooled liquid droplets (SLD) are an additional hazard, especially for smaller commuter aircraft that do not have sufficient power to fly out of heavy <span class="hlt">icing</span> conditions or heat to remove the <span class="hlt">ice</span>. New regulations issued by the United States and European regulatory agencies are being implemented that will require aircraft below a certain weight class to carry sensors that will detect and warn of these types of <span class="hlt">icing</span> conditions. Commercial aircraft do not currently carry standard sensors to detect the presence of <span class="hlt">ice</span> crystals in high <span class="hlt">concentrations</span> because they are typical found in sizes that are below the detection range of aircraft weather radar. Likewise, the sensors that are currently used to detect supercooled water do not respond well to drizzle-sized drops. Hence, there is a need for a sensor that can fill this measurement void. In addition, the forecast models that are used to predict regions of <span class="hlt">icing</span> rely on pilot observations as the only means to validate the model products and currently there are no forecasts for the prevalence of high altitude <span class="hlt">ice</span> crystals. Backscatter Cloud Probes (BCP) have been flying since 2011 under the IAGOS project on six Airbus commercial airliners operated by Lufthansa, Air France, China Air, Iberia and Cathay Pacific, and measure cloud droplets, <span class="hlt">ice</span> crystals and aerosol particles larger than 5 μm. The BCP can detect these particles and measures an optical equivalent diameter (OED) but is not able to distinguish the type of particle, i.e. whether they are droplets, <span class="hlt">ice</span> crystals, dust or ash. However, some qualification can be done based on measured temperature to discriminate between liquid water and <span class="hlt">ice</span>. The next generation BCP (BCPD, Backscatter Cloud Probe with polarization detection) is</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010JGRD..11523213B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010JGRD..11523213B"><span>Sensitivity of the global distribution of cirrus <span class="hlt">ice</span> crystal <span class="hlt">concentration</span> to heterogeneous freezing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barahona, D.; Rodriguez, J.; Nenes, A.</p> <p>2010-12-01</p> <p>This study presents the sensitivity of global <span class="hlt">ice</span> crystal number <span class="hlt">concentration</span>, Nc, to the parameterization of heterogeneous <span class="hlt">ice</span> nuclei (IN). Simulations are carried out with the NASA Global Modeling Initiative chemical and transport model coupled to an analytical <span class="hlt">ice</span> microphysics parameterization. Heterogeneous freezing is described using nucleation spectra derived from theoretical considerations and empirical data for dust, black carbon, ammonium sulfate, and glassy aerosol as IN precursors. When competition between homogeneous and heterogeneous freezing is considered, global mean Nc vary by up to a factor of twenty depending on the heterogeneous freezing spectrum used. IN effects on Nc strongly depend on dust and black carbon <span class="hlt">concentrations</span> and are strongest under conditions of weak updraft and high temperature. Regardless of the heterogeneous spectrum used, dust is an important contributor of IN over large regions of the Northern Hemisphere. Black carbon however exhibits appreciable effects on Nc when the freezing fraction is greater than 1%. Compared to in situ observations, Nc is overpredicted at temperatures below 205 K, even if a fraction of liquid aerosol is allowed to act as glassy IN. Assuming that cirrus formation is forced by weak updraft addressed this overprediction but promoted heterogeneous freezing effects to the point where homogeneous freezing is inhibited for IN <span class="hlt">concentrations</span> as low as 1 L-1. Chemistry and dynamics must be considered to explain cirrus characteristics at low temperature. Only cloud formation scenarios where competition between homogeneous and heterogeneous freezing is the dominant feature would result in maximum supersaturation levels consistent with observations.</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 sea <span class="hlt">ice</span> thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea <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 sea <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 sea <span class="hlt">ice</span> thickness distribution. An important step to achieve this goal is to assess the accuracy of sea <span class="hlt">ice</span> thickness retrieval based on satellite radar altimetry. For this purpose a data base is created comprising sea <span class="hlt">ice</span> freeboard derived from satellite radar altimetry between 1993 and 2012 and collocated observations of snow and sea <span class="hlt">ice</span> freeboard from Operation <span class="hlt">Ice</span> Bridge (OIB) and CryoSat Validation Experiment (CryoVEx) air-borne campaigns, of sea <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 (<span class="hlt">AMSR-E</span>) 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 <span class="hlt">AMSR-E</span> snow depth within 0.02 m suggests <span class="hlt">AMSR-E</span> snow depth as an appropriate alternative. Different freeboard-to-thickness and freeboard-to-draft conversion approaches are realized. The mean observed ULS sea <span class="hlt">ice</span> draft agrees with the mean sea <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 sea <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 sea <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 sea <span class="hlt">ice</span> density is as mandatory</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008JGRC..113.6008N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008JGRC..113.6008N"><span>Bulk heat transfer coefficient in the <span class="hlt">ice</span>-upper ocean system in the <span class="hlt">ice</span> melt season derived from <span class="hlt">concentration</span>-temperature relationship</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nihashi, Sohey; Ohshima, Kay I.</p> <p>2008-06-01</p> <p>The bulk heat transfer coefficient in the <span class="hlt">ice</span>-upper ocean system (Kb) in the <span class="hlt">ice</span> melt season is estimated by a new method at 18 areas that cover much of the Antarctic seasonal <span class="hlt">ice</span> zone. The method is based on a model in which <span class="hlt">ice</span> melting is caused only by heat input through open water and is treated in a bulk fashion in the <span class="hlt">ice</span>-upper ocean system. Kb is estimated by fitting a convergent curve derived from the model to an observed <span class="hlt">ice</span> <span class="hlt">concentration</span>-temperature plot (CT-plot). Estimated Kb is 1.15 ± 0.72 × 10-4 m s-1 on average. If Kb can be expressed by the product of the heat transfer coefficient (ch) and the friction velocity (uτ), ch is 0.0113 ± 0.0055. This value is about two times larger than that estimated at the <span class="hlt">ice</span> bottom. The relationship between Kb and the geostrophic wind speed (Uw), which is roughly proportional to uτ, shows a significant positive correlation, as expected. Further, Kb seems more likely to be proportional to the square or cube of Uw rather than a linear relationship. Since Kb estimated from our method is associated with <span class="hlt">ice</span> melting in a bulk fashion in the <span class="hlt">ice</span>-upper ocean system, this relationship likely indicates both the mixing process of heat in the upper ocean (proportional to uτ3) and the local heat transfer process at the <span class="hlt">ice</span>-ocean interface (proportional to uτ).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017TCry...11...47D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017TCry...11...47D"><span>Satellite microwave assessment of Northern Hemisphere lake <span class="hlt">ice</span> phenology from 2002 to 2015</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Du, Jinyang; Kimball, John S.; Duguay, Claude; Kim, Youngwook; Watts, Jennifer D.</p> <p>2017-01-01</p> <p>A new automated method enabling consistent satellite assessment of seasonal lake <span class="hlt">ice</span> phenology at 5 km resolution was developed for all lake pixels (water coverage ≥ 90 %) in the Northern Hemisphere using 36.5 GHz H-polarized brightness temperature (Tb) observations from the Advanced Microwave Scanning Radiometer for EOS and Advanced Microwave Scanning Radiometer 2 (<span class="hlt">AMSR-E</span>/2) sensors. The lake phenology metrics include seasonal timing and duration of annual <span class="hlt">ice</span> cover. A moving t test (MTT) algorithm allows for automated lake <span class="hlt">ice</span> retrievals with daily temporal fidelity and 5 km resolution gridding. The resulting <span class="hlt">ice</span> phenology record shows strong agreement with available ground-based observations from the Global Lake and River <span class="hlt">Ice</span> Phenology Database (95.4 % temporal agreement) and favorable correlations (R) with alternative <span class="hlt">ice</span> phenology records from the Interactive Multisensor Snow and <span class="hlt">Ice</span> Mapping System (R = 0.84 for water clear of <span class="hlt">ice</span> (WCI) dates; R = 0.41 for complete freeze over (CFO) dates) and Canadian <span class="hlt">Ice</span> Service (R = 0.86 for WCI dates; R = 0.69 for CFO dates). Analysis of the resulting 12-year (2002-2015) <span class="hlt">AMSR-E</span>/2 <span class="hlt">ice</span> record indicates increasingly shorter <span class="hlt">ice</span> cover duration for 43 out of 71 (60.6 %) Northern Hemisphere lakes examined, with significant (p < 0.05) regional trends toward earlier <span class="hlt">ice</span> melting for only five lakes. Higher-latitude lakes reveal more widespread and larger trends toward shorter <span class="hlt">ice</span> cover duration than lower-latitude lakes, consistent with enhanced polar warming. This study documents a new satellite-based approach for rapid assessment and regional monitoring of seasonal <span class="hlt">ice</span> cover changes over large lakes, with resulting accuracy suitable for global change studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2000NIMPB.172..847S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2000NIMPB.172..847S"><span>7Be and 10Be <span class="hlt">concentrations</span> in recent firn and <span class="hlt">ice</span> at Law Dome, Antarctica</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Smith, A. M.; Fink, D.; Child, D.; Levchenko, V. A.; Morgan, V. I.; Curran, M.; Etheridge, D. M.; Elliott, G.</p> <p>2000-10-01</p> <p>Over the past three years, the Australian National Tandem for Applied Research (ANTARES) AMS facility at ANSTO has been expanding its sample preparation and measurement capability, particularly for 10Be, 26Al and 36Cl. During this time, ANSTO has continued its collaboration with the AAD and CSIRO Atmospheric Research on the measurement of cosmogenic isotopes from Law Dome, Antarctica. This research program has been supported by the construction of a dedicated geochemistry laboratory for the processing of <span class="hlt">ice</span> and rock samples for the preparation of AMS targets. Here we present our first results for 10Be <span class="hlt">concentrations</span> measured in <span class="hlt">ice</span> cores from three sites at Law Dome and describe the sample processing protocol and aspects of the AMS measurement procedure. These sites are characterised by an eightfold difference in accumulation rate with a common precipitation source. In combination with an established <span class="hlt">ice</span> chronology, this has enabled some preliminary findings concerning the relationship between the snow accumulation rate and the measured 10Be <span class="hlt">concentration</span> for Law Dome during recent times. Additionally, we present 7Be and 10Be/ 7Be measurements made for a few surface snow samples from Law Dome and Australia.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AtmEn..89..683K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AtmEn..89..683K"><span>Recent increase in Ba <span class="hlt">concentrations</span> as recorded in a South Pole <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>Korotkikh, Elena V.; Mayewski, Paul A.; Dixon, Daniel; Kurbatov, Andrei V.; Handley, Michael J.</p> <p>2014-06-01</p> <p>Here we present high-resolution (∼9.4 samples/year) records of Ba <span class="hlt">concentrations</span> for the period from 1541 to 1999 A.D. obtained from an <span class="hlt">ice</span> core recovered at the South Pole (US ITASE-02-6) site. We note a significant increase in Ba <span class="hlt">concentration</span> (by a factor of ∼23) since 1980 A.D. The Ba crustal enrichment factor (EFc) values rise from ∼3 before 1980 A.D. to ∼32 after 1980 A.D. None of the other measured major and trace elements reveal such significant increases in <span class="hlt">concentrations</span> and EFc values. Comparison with previously reported Antarctic Ba records suggests that significant increases in Ba <span class="hlt">concentrations</span> at South Pole since 1980 A.D. are most likely caused by local source pollution. The core was collected in close proximity to Amundsen-Scott South Pole Station; therefore activities at the station, such as diesel fuel burning and intense aircraft activity, most likely caused the observed increase in Ba <span class="hlt">concentrations</span> and its EFc values in the South Pole <span class="hlt">ice</span> core record.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ACPD...1516697L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ACPD...1516697L"><span>Influence of the ambient humidity on the <span class="hlt">concentration</span> of natural deposition <span class="hlt">ice</span> nuclei</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>López, M. L.; Ávila, E. E.</p> <p>2015-06-01</p> <p>This study reports measurements of deposition <span class="hlt">ice</span> nuclei (IN) <span class="hlt">concentration</span> at ground level during the period July-December 2014 in Córdoba, Argentina. The measurements were carried out at temperature of -25 °C and at 15 % supersaturation over <span class="hlt">ice</span>. They were performed on days with different thermodynamic conditions, including rainy days. The effect of the relative humidity at ground level (RHamb) on the IN <span class="hlt">concentration</span> was analyzed. The number of IN activated varied from 1 -1 at RHamb of 25 % to 30 L-1 at RHamb of 90 %. In general, a linear trend between the IN <span class="hlt">concentration</span> and the RHamb was found, suggesting that this variable must be related to the ability of the aerosols acting as IN. These results are consistent with previous results. From the backward trajectories analysis, it was found that the link between IN <span class="hlt">concentration</span> and RHamb is independent of the origin of the air masses. The role of nucleation occurring in pores and cavities was discussed as possible mechanism to explain the increase on the IN <span class="hlt">concentration</span> during high ambient relative humidity events.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUFM.C41A0189M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUFM.C41A0189M"><span>A Record of Dissolved Metal <span class="hlt">Concentrations</span> in the Lena River During the Period of <span class="hlt">Ice</span> Breakup</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Monson, O. D.; Guay, C. K.; Holmes, R. M.; Zhulidov, A. V.</p> <p>2004-12-01</p> <p>The PARTNERS project is a 5-year research program (2002-2007) funded by the Arctic System Science Program of the U.S. National Science Foundation. The objective of the PARTNERS project is to measure several biogeochemical parameters in the six largest rivers that drain the watershed of the Arctic Ocean (Yenisey, Lena, Ob, Mackenzie, Yukon, and Kolyma) as a means to study the origins and fates of continental runoff. As part of the PARTNERS field program for 2004, samples were collected on the Lena River in the spring (May-June) during the period of peak discharge and <span class="hlt">ice</span> breakup. Samples were collected from the bank at the town of Zhigansk (66.75 N, 23.38 E) once daily from May 28th through June 7th, 2004. The river was completely <span class="hlt">ice</span> covered at the beginning of this period. The river level rose dramatically each day until <span class="hlt">ice</span> breakup, which occurred on May 30th. Following breakup, the river level began to drop steadily. Visual observation of daily water samples indicated a darkening of the tannic brown color of the river water as discharge levels increased up until breakup, suggesting an increase in DOC <span class="hlt">concentrations</span> associated with the peak discharge and <span class="hlt">ice</span> breakup period. Water samples for metals analyses were syringe filtered in the field through 0.45 um polypropylene and 0.02 um Anotop filter discs and acidified under clean conditions upon return to the laboratory. The samples were analyzed by high-resolution ICPMS for a suite of metals including Ba, Cd, Ce, Co, Cr, Cs, Cu, Fe, Li, Mn, Mo, Ni, Pb, Rb, Re, Sr, Tl, U, V, and Zn. Here we report the results from these analyses as a daily time series of metal <span class="hlt">concentrations</span> bracketing the <span class="hlt">ice</span> breakup and peak discharge events. During this relatively short amount of time, significant fluctuations in metal <span class="hlt">concentrations</span> were observed, which are likely related to concurrent fluctuations in DOC <span class="hlt">concentrations</span> and other changes in river chemistry occurring during this dynamic period of the annual hydrologic cycle in</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013ACPD...13.1767H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ACPD...13.1767H"><span>High <span class="hlt">concentrations</span> of biological aerosol particles and <span class="hlt">ice</span> nuclei during and after rain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Huffman, J. A.; Pöhlker, C.; Prenni, A. J.; DeMott, P. J.; Mason, R. H.; Robinson, N. H.; Fröhlich-Nowoisky, J.; Tobo, Y.; Després, V. R.; Garcia, E.; Gochis, D. J.; Harris, E.; Müller-Germann, I.; Ruzene, C.; Schmer, B.; Sinha, B.; Day, D. A.; Andreae, M. O.; Jimenez, J. L.; Gallagher, M.; Kreidenweis, S. M.; Bertram, A. K.; Pöschl, U.</p> <p>2013-01-01</p> <p>Bioaerosols are relevant for public health and may play an important role in the climate system, but their atmospheric abundance, properties and sources are not well understood. Here we show that the <span class="hlt">concentration</span> of airborne biological particles in a forest ecosystem increases dramatically during rain and that bioparticles are closely correlated with atmospheric <span class="hlt">ice</span> nuclei (IN). The greatest increase of bioparticles and IN occurred in the size range of 2-6 μm, which is characteristic for bacterial aggregates and fungal spores. By DNA analysis we found high diversities of airborne bacteria and fungi, including human and plant pathogens (mildew, smut and rust fungi, molds, Enterobacteraceae, Pseudomonadaceae). In addition to known bacterial and fungal IN (Pseudomonas sp., Fusarium sporotrichioides), we discovered two species of IN-active fungi that were not previously known as biological <span class="hlt">ice</span> nucleators (Isaria farinosa and Acremonium implicatum). Our findings suggest that atmospheric bioaerosols, IN and rainfall are more tightly coupled than previously assumed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.5514H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.5514H"><span>High <span class="hlt">concentrations</span> of biological aerosol particles and <span class="hlt">ice</span> nuclei during and after rain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Huffman, J. Alex; Pöhlker, Christopher; Prenni, Anthony; DeMott, Paul; Mason, Ryan; Robinson, Niall; Fröhlich-Nowoisky, Janine; Tobo, Yutaka; Després, Viviane; Garcia, Elvin; Gochis, David; Sinha, Bärbel; Day, Douglas; Andreae, Meinrat; Jimenez, Jose; Gallagher, Martin; Kreidenweis, Sonia; Bertram, Allan; Pöschl, Ulrich</p> <p>2013-04-01</p> <p>Bioaerosols are relevant for public health and may play an important role in the climate system, but their atmospheric abundance, properties and sources are not well understood. Here we show that the <span class="hlt">concentration</span> of airborne biological particles in a forest ecosystem increases dramatically during rain and that bioparticles are closely correlated with atmospheric <span class="hlt">ice</span> nuclei (IN). The greatest increase of bioparticles and IN occurred in the size range of 2-6 µm, which is characteristic for bacterial aggregates and fungal spores. By DNA analysis we found high diversities of airborne bacteria and fungi, including human and plant pathogens (mildew, smut and rust fungi, molds, Enterobacteraceae, Pseudomonadaceae). In addition to known bacterial and fungal IN (Pseudomonas sp., Fusarium sporotrichioides), we discovered two species of IN-active fungi that were not previously known as biological <span class="hlt">ice</span> nucleators (Isaria farinosa and Acremonium implicatum). Our findings suggest that atmospheric bioaerosols, IN and rainfall are more tightly coupled than previously assumed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820009689','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820009689"><span>Satellite-derived <span class="hlt">ice</span> data sets no. 1: Antarctic monthly average microwave brightness temperatures and sea-<span class="hlt">ice</span> <span class="hlt">concentrations</span>, 1973 - 1976</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.</p> <p>1981-01-01</p> <p>A summary data set concerning 4 years of Antarctic sea-<span class="hlt">ice</span> conditions was created and is available on magnetic tape. The data were derived from electrically scanning microwave radiometer brightness temperatures and were mapped into a polar stereographic grid enclosing the 50 deg S latitude circle. The grid size varies from about 32 by 32 sq km at the poles to about 28 by 28 sq km at 50 deg S. The microwave brightness temperatures of Antarctic sea <span class="hlt">ice</span> are predominantly characteristic of first-year <span class="hlt">ice</span> with an emissivity of 0.92 at 19 GHz frequency. Sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> were calculated from the brightness temperature data for each grid element with an algorithm that uses an emissivity value of 0.92 and an <span class="hlt">ice</span> physical temperature estimate from climatological surface air temperatures. Monthly, multiyear monthly, and yearly maps of brightness temperatures and sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> were created for the 4 years, except for 7 months for which useable data were insufficient.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.P21B1223L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.P21B1223L"><span>Laboratory measurements of <span class="hlt">ice</span> tensile strength dependence on density and <span class="hlt">concentration</span> of silicate and polymer impurities at low temperatures</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Litwin, K. L.; Beyeler, J. D.; Polito, P. J.; Zygielbaum, B. R.; Sklar, L. S.; Collins, G. C.</p> <p>2009-12-01</p> <p>The tensile strength of <span class="hlt">ice</span> bedrock on Titan should strongly influence the effectiveness of the erosional processes responsible for carving the extensive fluvial drainage networks and other surface features visible in images returned by the Cassini and Huygens probes. Recent measurements of the effect of temperature on the tensile strength of low-porosity, polycrystalline <span class="hlt">ice</span>, without impurities, suggest that <span class="hlt">ice</span> bedrock at the Titan surface temperature of 93 K may be as much as five times stronger than <span class="hlt">ice</span> at terrestrial surface temperatures. However, <span class="hlt">ice</span> bedrock on Titan and other outer solar system bodies may have significant porosity, and impurities such silicates or polymers are possible in such <span class="hlt">ices</span>. In this laboratory investigation we are exploring the dependence of tensile strength on the density and <span class="hlt">concentration</span> of impurities, for polycrystalline <span class="hlt">ice</span> across a wide range of temperatures. We use the Brazilian tensile splitting test to measure strength, and control temperature with dry <span class="hlt">ice</span> and liquid nitrogen. The 50 mm diameter <span class="hlt">ice</span> cores are made from a log-normally distributed seed crystal mixture with a median size of 1.4 mm. To control <span class="hlt">ice</span> density and porosity we vary the packing density of the seed grains in core molds and vary the degree of saturation of the matrix with added near-freezing distilled water. We also vary <span class="hlt">ice</span> density by blending in a similarly-sized mixture of angular fragments of two types of impurities, a fine-grained volcanic rock and a polyethylene polymer. Because both types of impurities have greater tensile strength than <span class="hlt">ice</span> at Earth surface temperatures, we expect higher <span class="hlt">concentrations</span> of impurities to correlate with increased strength for <span class="hlt">ice</span>-rock and <span class="hlt">ice</span>-polymer mixtures. However, at the ultra-cold temperatures of the outer planets, we expect significant divergence in the temperature dependence of <span class="hlt">ice</span> tensile strength for the various mixtures and resulting densities. These measurements will help constrain the range of possible</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19950048007&hterms=bootstrap&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dbootstrap','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19950048007&hterms=bootstrap&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dbootstrap"><span>Arctic sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> from special sensor microwave imager and advanced very high resolution radiometer satellite data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Emery, W. J.; Fowler, C.; Maslanik, J.</p> <p>1994-01-01</p> <p>Nearly coincident data from the special sensor microwave imager (SSM/I) and the advanced very high resolution radiometer (AVHRR) are used to compute and compare Arctic sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> for different regions and times of the year. To help determine overall accuracies and to highlight sources of differences between passive microwave, optical wavelength, and thermal wavelength data, <span class="hlt">ice</span> <span class="hlt">concentrations</span> are estimated using two operational SSM/I <span class="hlt">ice</span> <span class="hlt">concentration</span> algorithms and with visible- and thermal-infrared wavelength AVHRR data. All algorithms capture the seasonal patterns of <span class="hlt">ice</span> growth and melt. The ranges of differences fall within the general levels of uncertainty expected for each method and are similar to previous accuracy estimates. The estimated <span class="hlt">ice</span> <span class="hlt">concentrations</span> are all highly correlated, with uniform biases, although differences between individual pairs of observations can be large. On average, the NASA Team algorithm yielded 5% higher <span class="hlt">ice</span> <span class="hlt">concentrations</span> than the Bootstrap algorithm, while during nonmelt periods the two SSM/I algorithms agree to within 0.5%. These seasonal differences are consistent with the ways that the 19-GHz and 37-GHz microwave channels are used in the algorithms. When compared to the AVHRR-derived <span class="hlt">ice</span> <span class="hlt">concentrations</span>, the Team-algorithm results are more similar on average in terms of correlation and mean differences. However, the Team algorithm underestimates <span class="hlt">concentrations</span> relative to the AVHRR output by 6% during cold months and overestimates by 3% during summer. Little seasonal difference exists between the Bootstrap and AVHRR results, with a mean difference of about 5%. Although the mean differences are less between the SSM/I-derived <span class="hlt">concentrations</span> and <span class="hlt">concentrations</span> estimated using AVHRR channel 1, the correlations appear substantially better between the SSM/I data and <span class="hlt">concentrations</span> derived from AVHRR channel 4, particularly for the Team algorithm output.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19950048007&hterms=high+resolution+melt&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dhigh%2Bresolution%2Bmelt','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19950048007&hterms=high+resolution+melt&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dhigh%2Bresolution%2Bmelt"><span>Arctic sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> from special sensor microwave imager and advanced very high resolution radiometer satellite data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Emery, W. J.; Fowler, C.; Maslanik, J.</p> <p>1994-01-01</p> <p>Nearly coincident data from the special sensor microwave imager (SSM/I) and the advanced very high resolution radiometer (AVHRR) are used to compute and compare Arctic sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> for different regions and times of the year. To help determine overall accuracies and to highlight sources of differences between passive microwave, optical wavelength, and thermal wavelength data, <span class="hlt">ice</span> <span class="hlt">concentrations</span> are estimated using two operational SSM/I <span class="hlt">ice</span> <span class="hlt">concentration</span> algorithms and with visible- and thermal-infrared wavelength AVHRR data. All algorithms capture the seasonal patterns of <span class="hlt">ice</span> growth and melt. The ranges of differences fall within the general levels of uncertainty expected for each method and are similar to previous accuracy estimates. The estimated <span class="hlt">ice</span> <span class="hlt">concentrations</span> are all highly correlated, with uniform biases, although differences between individual pairs of observations can be large. On average, the NASA Team algorithm yielded 5% higher <span class="hlt">ice</span> <span class="hlt">concentrations</span> than the Bootstrap algorithm, while during nonmelt periods the two SSM/I algorithms agree to within 0.5%. These seasonal differences are consistent with the ways that the 19-GHz and 37-GHz microwave channels are used in the algorithms. When compared to the AVHRR-derived <span class="hlt">ice</span> <span class="hlt">concentrations</span>, the Team-algorithm results are more similar on average in terms of correlation and mean differences. However, the Team algorithm underestimates <span class="hlt">concentrations</span> relative to the AVHRR output by 6% during cold months and overestimates by 3% during summer. Little seasonal difference exists between the Bootstrap and AVHRR results, with a mean difference of about 5%. Although the mean differences are less between the SSM/I-derived <span class="hlt">concentrations</span> and <span class="hlt">concentrations</span> estimated using AVHRR channel 1, the correlations appear substantially better between the SSM/I data and <span class="hlt">concentrations</span> derived from AVHRR channel 4, particularly for the Team algorithm output.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27344387','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27344387"><span>Whey protein phospholipid <span class="hlt">concentrate</span> and delactosed permeate: Applications in caramel, <span class="hlt">ice</span> cream, and cake.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Levin, M A; Burrington, K J; Hartel, R W</p> <p>2016-09-01</p> <p>Whey protein phospholipid <span class="hlt">concentrate</span> (WPPC) and delactosed permeate (DLP) are 2 coproducts of cheese whey processing that are currently underutilized. Past research has shown that WPPC and DLP can be used together as a functional dairy ingredient in foods such as <span class="hlt">ice</span> cream, soup, and caramel. However, the scope of the research has been limited to a single WPPC supplier. The variability of the composition and functionality of WPPC was previously studied. The objective of this research was to expand on the previous study and examine the potential applications of WPPC and DLP blends in foods. In <span class="hlt">ice</span> cream, WPPC was added as a natural emulsifier to replace synthetic emulsifiers. The WPPC decreased the amount of partially coalesced fat and increased the drip-through rate. In caramel, DLP and WPPC replaced sweetened condensed skim milk and lecithin. Cold flow increased significantly, and hardness and stickiness decreased. In cake, DLP and WPPC were added as a total replacement of eggs, with no change in yield, color, or texture. Overall, WPPC and DLP can be utilized as functional dairy ingredients at a lower cost in <span class="hlt">ice</span> cream and cake but not in chewy caramel.</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/2012AGUFMPP22A..08M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMPP22A..08M"><span>Recent and past dust <span class="hlt">concentrations</span> and fluxes from a developing array of Antarctic <span class="hlt">ice</span> cores</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McConnell, J. R.; Anschütz, H.; Baggenstos, D.; Das, S. B.; Isaksson, E. D.; Lawrence, R.; Layman, L.; Maselli, O.; Severinghaus, J. P.; Sigl, M.; Petit, J. R.; Grente, B.</p> <p>2012-12-01</p> <p>Continental dust is an important component of climate forcing, both because of its interaction with incoming solar and outgoing long wave radiation and because of its impact on albedo when deposited on bright surfaces such as fresh snow. Continental dust may also play an important role in ocean fertilization and carbon sequestration. Because the lifetime of dust aerosol in the atmosphere is only on the order of days to weeks, spatial and temporal variability in <span class="hlt">concentrations</span> and fluxes is high and understanding of recent and long term changes is limited. Here we present and discuss detailed continuous, high depth resolution measurements of a range of dust proxies in a developing array of Antarctic <span class="hlt">ice</span> cores. Included are traditional proxies such as non-sea-salt (nss) calcium and insoluble particle number and size distribution as well as less traditional proxies such as aluminum, vanadium, manganese, rare earth elements, and nss uranium which together provide important insights into how dust sources and transport may have changed in the past. The array includes a number of new shallow <span class="hlt">ice</span> core records from East and West Antarctica spanning recent centuries to millennia, as well as Last Glacial Maximum to early Holocene records from the deep WAIS Divide and Taylor Glacier Horizontal <span class="hlt">ice</span> cores.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ISPAr41B8..459C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ISPAr41B8..459C"><span>Forecasting Antarctic Sea <span class="hlt">Ice</span> <span class="hlt">Concentrations</span> Using Results of Temporal Mixture Analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chi, Junhwa; Kim, Hyun-Cheol</p> <p>2016-06-01</p> <p>Sea <span class="hlt">ice</span> <span class="hlt">concentration</span> (SIC) data acquired by passive microwave sensors at daily temporal frequencies over extended areas provide seasonal characteristics of sea <span class="hlt">ice</span> dynamics and play a key role as an indicator of global climate trends; however, it is typically challenging to study long-term time series. Of the various advanced remote sensing techniques that address this issue, temporal mixture analysis (TMA) methods are often used to investigate the temporal characteristics of environmental factors, including SICs in the case of the present study. This study aims to forecast daily SICs for one year using a combination of TMA and time series modeling in two stages. First, we identify temporally meaningful sea <span class="hlt">ice</span> signatures, referred to as temporal endmembers, using machine learning algorithms, and then we decompose each pixel into a linear combination of temporal endmembers. Using these corresponding fractional abundances of endmembers, we apply a autoregressive model that generally fits all Antarctic SIC data for 1979 to 2013 to forecast SIC values for 2014. We compare our results using the proposed approach based on daily SIC data reconstructed from real fractional abundances derived from a pixel unmixing method and temporal endmember signatures. The proposed method successfully forecasts new fractional abundance values, and the resulting images are qualitatively and quantitatively similar to the reference data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.3976A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.3976A"><span>Lidar observations of <span class="hlt">ice</span>-nucleating particle (INPC) and <span class="hlt">ice</span> crystal number (ICNC) <span class="hlt">concentrations</span>: height-resolved INPC-ICNC closure studies in mixed-phase altocumulus layers</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ansmann, Albert; Bühl, Johannes; Mamouri, Rodanthi-Elisavet; Engelmann, Ronny; Seifert, Patric; Nisantzi, Argyro; Hadjimitsis, Diofantos; Sciare, Jean</p> <p>2017-04-01</p> <p>During the six-week Cyprus-2015 field campaign in March and April 2015, conducted in the framework of the BACCHUS project (Impact of Biogenic versus Anthropogenic emissions on Clouds and Climate: towards a Holistic UnderStanding, collaborative project of the seventh EU framework programme, ENV.2013.6.1-2), we observed the evolution of extended liquid-water altocumulus fields with subsequent heterogeneous <span class="hlt">ice</span> formation. The altocumulus layers developed in aged Saharan dust layers between 3.5 km (-20°C) and 7.5 km height (-35°C cloud top temperature). We observed such altocumulus developments on 12 days. By applying our recently developed polarization-lidar method we estimated the <span class="hlt">ice</span>-nucleating particle <span class="hlt">concentration</span> (INPC, immersion freezing) at cloud level (before the clouds developed and after their dissolution). Simultaneously performed Doppler lidar observations of the terminal velocities of falling <span class="hlt">ice</span> crystals in virga below the shallow altocumulus layers allowed us to estimate the <span class="hlt">ice</span> crystal number <span class="hlt">concentration</span> (ICNC) of the falling <span class="hlt">ice</span> crystals. In this retrieval, a realistic <span class="hlt">ice</span> crystal size distribution has to be assumed. In addition, the volume extinction coefficient of the <span class="hlt">ice</span> crystals has to be known (to obtain the total <span class="hlt">ice</span> crystal <span class="hlt">concentration</span>), and is obtained from the polarization lidar observations by using classical backscatter or Raman lidar retrieval methods. We assume that all <span class="hlt">ice</span> crystals, which nucleated in the 300-500 m thick altocumulus layers, grow fast (according to the literature to about 100 µm size within 1 minute) and immediately fall out of the main shallow cloud layer so that the derived ICNC values provide us with the number of nucleated <span class="hlt">ice</span> crystals as a function of cloud top temperature and given INP conditions. Based on this unique observational approach we investigated, to our knowledge for the first time, the consistency between the INPC and ICNC in mixed-phase clouds. We found reasonable agreement between INPC</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MNRAS.470.4564J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MNRAS.470.4564J"><span>The catalytic role of water in the photochemistry of ammonia <span class="hlt">ice</span>: from diluted to <span class="hlt">concentrated</span> phase</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jonusas, Mindaugas; Krim, Lahouari</p> <p>2017-10-01</p> <p>Using infrared spectroscopy as an in situ probe for reactions occurring in the solid phase, we investigated the influence of water molecules on the photochemistry of ammonia <span class="hlt">ices</span>. Experiments were carried out in diluted and <span class="hlt">concentrated</span> phases and between 3 and 130 K. We showed that the photolysis of NH3-H2O (2 per cent of H2O) <span class="hlt">ices</span> using continuous radiation from 115 to 400 nm produces NH2OH as the main photoproduct, but also that such a photoinduced reaction strongly depends on both the initial <span class="hlt">ice</span> temperature and the environment where the primary reactants NH3 and H2O are trapped. Our experimental results highlight the catalytic role played by H2O molecules in enhancing the formation yield of NH2 during the photolysis process through the NH3 + OH → NH2 + H2O hydrogen abstraction reaction, which is only favored at low temperatures in the range of 3-60 K. During heating of such irradiated ammonia-water <span class="hlt">ices</span>, the amount of NH2OH keeps rising while that of NH2, is greatly reduced only from 70 K onwards. These behaviours are attributed to the competition that occurs between NH2 formation from the NH3 + OH reaction and its consumption from the NH2 + OH radical recombination. These results might explain the variable abundances of NH2 and NH3 provided by previous astronomical observations, where the NH2/NH3 ratio ranges from 0.02 to 0.5 depending on the regions of the interstellar medium that were analysed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.B13D0545S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.B13D0545S"><span>Sensitivity of Oxygen Isotopes of Sulfate in <span class="hlt">Ice</span> Cores to Past Changes in Atmospheric Oxidant <span class="hlt">Concentrations</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sofen, E. D.; Alexander, B.; Kunasek, S. A.; Mickley, L.; Murray, L. T.; Kaplan, J. O.</p> <p>2009-12-01</p> <p>The oxygen isotopic composition (Δ17O) of sulfate from <span class="hlt">ice</span> cores allows for a quantitative assessment of the past oxidative capacity of the atmosphere, which has implications for the lifetime of pollutants (e.g. CO) and greenhouse gases (e.g. CH4), and changes in the sulfur budget on various timescales. Using Δ17O of sulfate measurements from the WAIS-Divide, Antarctica and Site-A, Greenland <span class="hlt">ice</span> cores as constraints, we use the GEOS-Chem global three-dimensional chemical transport model to study changes in the <span class="hlt">concentrations</span> of OH, O3, and H2O2 and their impact on sulfate Δ17O between the preindustrial and present-day. The Greenland <span class="hlt">ice</span> core sulfate oxygen isotope observations are insensitive to changes in oxidant <span class="hlt">concentrations</span> on the preindustrial-industrial timescale due to the rising importance of metal catalyzed S(IV) oxidation in mid- to high-northern latitudes resulting from anthropogenic metal emissions. The small change in Antarctic <span class="hlt">ice</span> core sulfate Δ17O observations on this timescale is consistent with simultaneous increases in boundary layer O3 (32%) and H2O2 (49%) <span class="hlt">concentrations</span> in the Southern Hemisphere, which have opposing effects on the sulfate O-isotope anomaly. Sulfate Δ17O is insensitive to the relatively small (-12%) decrease in Southern Hemisphere OH <span class="hlt">concentrations</span> on this timescale due to the dominance of in-cloud versus gas-phase formation of sulfate in the mid-to-high southern latitudes. We find that the fraction of sulfate formed globally through gas-phase oxidation has not changed substantially between preindustrial and present times, however the total amount of sulfate formed in the gas-phase has nearly quadrupled due to rising anthropogenic emissions of sulfur dioxide. Measurements over a glacial-interglacial cycle from the Vostok core indicate dramatic changes in the Δ17O of sulfate on this timescale, which provide a strong constraint for glacial-era atmospheric chemistry modeling efforts. We will present preliminary results of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.C23A0482H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.C23A0482H"><span>Modeled methanesulfonic acid (MSA) <span class="hlt">concentrations</span> in Antarctica: the influence of meteorology in explaining modern versus LGM differences in <span class="hlt">ice</span> cores</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hezel, P. J.; Alexander, B.; Bitz, C. M.; Steig, E. J.</p> <p>2011-12-01</p> <p>Methanesulfonic acid (MSA) <span class="hlt">concentrations</span> measured in <span class="hlt">ice</span> cores in Antarctica for the last glacial maximum (LGM) are higher than modern day <span class="hlt">concentrations</span> on the East Antarctic Plateau (Vostok), but are lower than modern <span class="hlt">concentrations</span> in West Antarctica (Siple Dome). MSA <span class="hlt">concentrations</span> measured in <span class="hlt">ice</span> cores have been interpreted as an indicator of both local sea <span class="hlt">ice</span> extent (via modulation of dimethylsulfide (DMS) emissions) and regional circulation on decadal time scales, but there has been no assessment of the importance of these two processes in determining MSA <span class="hlt">concentrations</span> on glacial-interglacial time scales. Explanations for the modern - LGM MSA differences at Vostok invoke increased DMS emissions caused by increased dust fertilization in the LGM (Legrand et al., 1991). Saltzman et al. (2006) show that the MSA measurements at Siple Dome do not corroborate stronger DMS emissions in the Pacific sector during the LGM. We use the GEOS-Chem chemical transport model forced with GISS-ModelE meteorology from modern and LGM boundary conditions to simulate Antarctic MSA <span class="hlt">concentrations</span>. We estimate the contribution of transport and precipitation to the modern-LGM difference at each location. Changes in DMS emissions, sea <span class="hlt">ice</span> extent, and oxidant <span class="hlt">concentrations</span> are evaluated as additional important factors in explaining modern versus LGM MSA <span class="hlt">concentrations</span> in Antarctic <span class="hlt">ice</span> cores.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMPP13A2058W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMPP13A2058W"><span>Major Ion <span class="hlt">concentrations</span> in the new NEEM <span class="hlt">ice</span> core in Greenland</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wegner, A.; Azuma, K. G.; Hirabayashi, M.; Schmidt, K.; Hansson, M.; Twarloh, B.</p> <p>2012-12-01</p> <p>The drilling of the new deep <span class="hlt">ice</span> core in NEEM (77.45°N 51.06°W) was terminated in 2010. Using a continuous flow analysis system (CFA), discrete samples were filled and analyzed for major ion <span class="hlt">concentrations</span> (Na, K, Mg, Ca, Cl, SO_4 and NO_3) using Ion Chromatography (IC). The samples were measured at Alfred Wegener Institute for Polar and Marine Research (Germany) and National Institute of Polar Research (Japan). Here we present preliminary results of the major Ion <span class="hlt">concentrations</span>. We found highest variations in <span class="hlt">concentrations</span> of Calcium and Magnesium which are mainly originating from terrestrial sources with <span class="hlt">concentrations</span> between 5-10 ppb and 4 ppb during the Holocene compared to 800 ppb and 80 ppb during the LGM. This is in line with measurements of particulate dust <span class="hlt">concentrations</span>. Sulphate <span class="hlt">concentrations</span> closely follow DO events and vary between 25 ppb during the Holocene and ~400 ppb during the LGM. Sodium <span class="hlt">concentrations</span> vary between ~ 8 ppb during the Holocene and up to 100 ppb during the LGM. We discuss influences of changes in the source areas and atmospheric transport intensity on the different time scales.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.C13B0567O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.C13B0567O"><span>First Measurements of Osmium <span class="hlt">Concentration</span> and Isotopic Composition in a Summit, 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.; Sharma, M.; Hawley, R. L.; Courville, Z.</p> <p>2010-12-01</p> <p>Osmium (Os) is one of the rarer elements in the environment and therefore one of the most difficult to accurately measure, but its isotopically distinctive crustal, mantle-derived, and extra-terrestrial sources make it a valuable geochemical tracer. Recent state-of-the-art analyses of precipitation, river water, and ocean water samples from around the world have revealed elevated <span class="hlt">concentrations</span> of Os with a characteristically low (unradiogenic) Os isotopic signature (187Os/188Os). This unusual low Os isotopic signal has been interpreted as evidence for widespread Os pollution due to the smelting of Platinum Group Element (PGE) sulfide ores for use in automobile catalytic converters. However, an environmental time series of Os <span class="hlt">concentrations</span> and isotopic composition spanning the pre-industrial to modern era has not previously been developed to evaluate changes in atmospheric Os sources through time. Here we present the first measurements of Os <span class="hlt">concentration</span> and isotopic composition (to our knowledge) in a 100 m-long <span class="hlt">ice</span> core collected from Summit, Greenland, spanning from ca. 1700 to 2010 AD. Due to the extremely low Os <span class="hlt">concentrations</span> in snow (10-15 g/g), these analyses have only recently become possible with advances in Thermal Ionization Mass Spectrometry (TIMS) and ultra-clean analytical procedures. Initial results indicate that the 187Os/188Os of Greenland snow was unradiogenic (187Os/188Os = 0.13-0.15) for at least several periods over the past 300 years, including both pre-anthropogenic and modern times. Os <span class="hlt">concentrations</span> in the Summit <span class="hlt">ice</span> core are relatively high (11-52 pg/kg) compared to previously measured precipitation in North America, Europe, Asia and Antarctic sea <span class="hlt">ice</span> (0.35-23 pg/kg). The low (unradiogenic) isotopic composition are consistent with extraterrestrial (cosmic dust and meteorites; 187Os/188Os = 0.13) and possibly volcanic (187Os/188Os = 0.15-0.6) Os sources, although the Os isotopic composition of volcanic emissions is poorly constrained</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.6668G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.6668G"><span>Variations of ion <span class="hlt">concentrations</span> in the deep <span class="hlt">ice</span> core and surface snow at NEEM, Greenland</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Goto-Azuma, K.; Wegner, A.; Hansson, M.; Hirabayashi, M.; Kuramoto, T.; Miyake, T.; Motoyama, H.; NEEM Aerosol Consortium members</p> <p>2012-04-01</p> <p>Discrete samples were collected from the CFA (Continuous Flow Analysis) melt fractions during the field campaign carried out at NEEM, Greenland in 2009-2011, and were distributed to different laboratories. Ionic species were analyzed at National Institute of Polar Research (Japan) and Alfred Wegener Institute for Polar and Marine Research (Germany). Here we present and compare the ion <span class="hlt">concentration</span> data obtained by both institutes. Most of the ions show good agreement between the two institutes. As is indicated with the CFA data (Bigler and the NEEM Aerosol Consortium members, EGU 2012), ion chromatograph data also display that calcium and sodium, mainly originated from terrestrial dust and sea-salt, respectively, show large variations associated with Dansgaard-Oeschger (DO) events. Chloride, fluoride, sulfate, sodium, potassium and magnesium also show such variations, as has been already reported for other Greenland <span class="hlt">ice</span> cores. New ion data obtained from the NEEM deep core also show large variability of oxalate and phosphate <span class="hlt">concentrations</span> during DO events. Acetate, which is thought to be mainly derived from biomass burning, as is oxalate, appears to show variability associated with DO events, but to a lesser extent. On the other hand, nitrate, ammonium and methanesulfonate do not show such variations. Together with ion data from the deep <span class="hlt">ice</span> core, we present those from the pits dug during the NEEM field campaign to discuss seasonal variations of ionic species. The seasonal and millennial scale variations of ions are thought to be caused by changes in atmospheric circulation and source strength.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25851700','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25851700"><span>Small molecule <span class="hlt">ice</span> recrystallization inhibitors enable freezing of human red blood cells with reduced glycerol <span class="hlt">concentrations</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Capicciotti, Chantelle J; Kurach, Jayme D R; Turner, Tracey R; Mancini, Ross S; Acker, Jason P; Ben, Robert N</p> <p>2015-04-08</p> <p>In North America, red blood cells (RBCs) are cryopreserved in a clinical setting using high glycerol <span class="hlt">concentrations</span> (40% w/v) with slow cooling rates (~1°C/min) prior to storage at -80°C, while European protocols use reduced glycerol <span class="hlt">concentrations</span> with rapid freezing rates. After thawing and prior to transfusion, glycerol must be removed to avoid intravascular hemolysis. This is a time consuming process requiring specialized equipment. Small molecule <span class="hlt">ice</span> recrystallization inhibitors (IRIs) such as β-PMP-Glc and β-pBrPh-Glc have the ability to prevent <span class="hlt">ice</span> recrystallization, a process that contributes to cellular injury and decreased cell viability after cryopreservation. Herein, we report that addition of 110 mM β-PMP-Glc or 30 mM β-pBrPh-Glc to a 15% glycerol solution increases post-thaw RBC integrity by 30-50% using slow cooling rates and emphasize the potential of small molecule IRIs for the preservation of cells.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4389209','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4389209"><span>Small Molecule <span class="hlt">Ice</span> Recrystallization Inhibitors Enable Freezing of Human Red Blood Cells with Reduced Glycerol <span class="hlt">Concentrations</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>Capicciotti, Chantelle J.; Kurach, Jayme D. R.; Turner, Tracey R.; Mancini, Ross S.; Acker, Jason P.; Ben, Robert N.</p> <p>2015-01-01</p> <p>In North America, red blood cells (RBCs) are cryopreserved in a clinical setting using high glycerol <span class="hlt">concentrations</span> (40% w/v) with slow cooling rates (~1°C/min) prior to storage at −80°C, while European protocols use reduced glycerol <span class="hlt">concentrations</span> with rapid freezing rates. After thawing and prior to transfusion, glycerol must be removed to avoid intravascular hemolysis. This is a time consuming process requiring specialized equipment. Small molecule <span class="hlt">ice</span> recrystallization inhibitors (IRIs) such as β-PMP-Glc and β-pBrPh-Glc have the ability to prevent <span class="hlt">ice</span> recrystallization, a process that contributes to cellular injury and decreased cell viability after cryopreservation. Herein, we report that addition of 110 mM β-PMP-Glc or 30 mM β-pBrPh-Glc to a 15% glycerol solution increases post-thaw RBC integrity by 30-50% using slow cooling rates and emphasize the potential of small molecule IRIs for the preservation of cells. PMID:25851700</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1210513D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1210513D"><span>Estimation of <span class="hlt">ice</span> thickness on large lakes from passive microwave and radar altimeter data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Duguay, Claude; Kang, Kyung-Kuk; Kouraev, Alexei; Mercier, Franck</p> <p>2010-05-01</p> <p>Lake <span class="hlt">ice</span> grows steadily between the end of freeze-up period and the onset of break-up period as a result of the thermodynamics of freezing water as well as dynamic <span class="hlt">ice</span> motion on the surface. In thermodynamic thickening, the conductive heat flow controls the <span class="hlt">ice</span> growth rate and the <span class="hlt">ice</span> thickness, and the <span class="hlt">ice</span> thickens downward as a result of heat loss at the top of the <span class="hlt">ice</span> cover. There has been some demonstration of the potential of brightness temperature from passive microwave airborne radiometers to estimate <span class="hlt">ice</span> thickness. The value of passive microwave and radar altimeter data from current satellite missions merits to be examined in this respect. The major objective of this study was estimate <span class="hlt">ice</span> thickness from brightness temperature (TB) at 10.65 and 18.70 GHz from <span class="hlt">AMSR-E</span> channels and the 19.35 GHz frequency channel from SSM/I on large lakes of the Northern Hemisphere (e.g. Great Bear Lake, Great Slave Lake, Lake Baikal). The evolution of horizontally and vertically polarized TB derived from <span class="hlt">AMSR-E</span> level 2A raw brightness temperature and EASE Grid Level-3 SSM/I products was compared with <span class="hlt">ice</span> thicknesses obtained with a previously validated thermodynamic lake <span class="hlt">ice</span> model and in situ observations over the course of seven winter seasons (2002 and 2009), as well as with recent estimates from the Jason-2 Ku-band radar altimeter data (since 2008). Results show that both passive microwave and radar altimeter data acquired in the 10-19 GHz frequency range offer a promising means for estimating <span class="hlt">ice</span> thickness from large northern lakes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24081467','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24081467"><span>Effect of freezing rate and dendritic <span class="hlt">ice</span> formation on <span class="hlt">concentration</span> profiles of proteins frozen in cylindrical vessels.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Rodrigues, Miguel A; Miller, Maria A; Glass, Matt A; Singh, Satish K; Johnston, Keith P</p> <p>2011-04-01</p> <p>The process of freezing protein solutions can perturb the conformation of the protein and potentially lead to aggregate formation during long-term storage in the frozen state. Radial macroscopic freeze <span class="hlt">concentration</span> and temperature profiles for bovine serum albumin (BSA) solutions in small cylindrical stainless steel vessels were determined for various freezing rates. The measured <span class="hlt">concentrations</span> of both BSA and immunoglobulin G2, as well as trehalose in sampled <span class="hlt">ice</span> sections, increased by up to twofold to threefold toward the bottom and radial center for slow freezing rates produced in stagnant air freezers. The <span class="hlt">concentration</span> and temperature profiles result in density gradients that transport solutes by convective flow. For faster external cooling by either forced convection of air or a liquid coolant, the increased freezing rate raised the <span class="hlt">ice</span> front velocity resulting in enhanced dendritic <span class="hlt">ice</span> growth. The <span class="hlt">ice</span> trapped the solutes more effectively before they were removed from the <span class="hlt">ice</span> front by diffusion and convection, resulting in more uniform solute <span class="hlt">concentration</span> profiles. The dynamic temperature profiles from multiple radial thermocouples were consistent with the independently measured freeze <span class="hlt">concentration</span> profiles. The ability to control the protein <span class="hlt">concentration</span> profile in the frozen state offers the potential to improve stability of protein in long-term frozen storage. Copyright © 2010 Wiley-Liss, Inc.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/12201518','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/12201518"><span>Effect of double homogenization and whey protein <span class="hlt">concentrate</span> on the texture of <span class="hlt">ice</span> cream.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Ruger, P R; Baer, R J; Kasperson, K M</p> <p>2002-07-01</p> <p><span class="hlt">Ice</span> cream samples were made with a mix composition of 11% milk fat, 11% milk solids-not-fat, 13% sucrose, 3% corn syrup solids (36 dextrose equivalent), 0.28% stabilizer blend, or 0.10% emulsifier and vanilla extract. Mixes were high temperature short time pasteurized at 80 degrees C for 25 s, homogenized at 141 kg/cm2 pressure on the first stage and 35 kg/cm2 pressure on the second, and cooled to 3 degrees C. The study included six treatments from four batches of mix. Mix from batch one contained 0.10% emulsifier. Half of this batch (treatment 1), was subsequently frozen and the other half (upon exiting the pasteurizer) was reheated to 60 degrees C, rehomogenized at 141 kg/cm2 pressure on the first stage and 35 kg/cm2 pressure on the second (treatment 2), and cooled to 3 degrees C. Mix from batch two contained 0.28% stabilizer blend. Half of this batch was used as the control (treatment 3), the other half upon exiting the pasteurizer was reheated to 60 degrees C, rehomogenized at 141 kg/cm2 pressure on the first stage and 35 kg/cm2 pressure on the second (treatment 4), and cooled to 3 degrees C. Batch three, containing 0.10% emulsifier and 1% whey protein <span class="hlt">concentrate</span> substituted for 1% nonfat dry milk, upon exiting the pasteurizer was reheated to 60 degrees C, rehomogenized at 141 kg/cm2 pressure on the first stage and 35 kg/cm2 pressure on the second (treatment 5), and cooled to 3 degrees C. Batch four, containing 0.28% stabilizer blend and 1% whey protein <span class="hlt">concentrate</span> substituted for 1% nonfat dry milk, upon exiting the pasteurizer was reheated to 60 degrees C, rehomogenized at 141 kg/ cm2 pressure on the first stage and 35 kg/cm2 pressure on the second (treatment 6), and cooled to 3 degrees C. Consistency was measured by flow time through a pipette. Flow time of treatment 3 was greater than all treatments, and the flow times of treatments 4 and 6 were greater than treatments 1, 2, and 5. Flow time was increased in <span class="hlt">ice</span> cream mix by the addition of stabilizer</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.6297D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.6297D"><span>Interactions between Arctic sea <span class="hlt">ice</span> drift, <span class="hlt">concentration</span> and thickness modeled by NEMO-LIM3 at different resolutions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Docquier, David; Massonnet, François; Raulier, Jonathan; Lecomte, Olivier; Fichefet, Thierry</p> <p>2016-04-01</p> <p>Sea <span class="hlt">ice</span> <span class="hlt">concentration</span> and thickness have substantially decreased in the Arctic since the beginning of the satellite era. As a result, mechanical strength has decreased allowing more fracturing and leading to increased sea <span class="hlt">ice</span> drift. However, recent studies have highlighted that the interplay between sea <span class="hlt">ice</span> thermodynamics and dynamics is poorly represented in contemporary global climate model (GCM) simulations. Thus, the considerable inter-model spread in terms of future sea <span class="hlt">ice</span> extent projections could be reduced by better understanding the interactions between drift, <span class="hlt">concentration</span> and thickness. This study focuses on the results coming from the global coupled ocean-sea <span class="hlt">ice</span> model NEMO-LIM3 between 1979 and 2012. Three different simulations are forced by the Drakkar Forcing Set (DFS) 5.2 and run on the global tripolar ORCA grid at spatial resolutions of 0.25, 1° and 2°. The relation between modeled sea <span class="hlt">ice</span> drift, <span class="hlt">concentration</span> and thickness is further analyzed, compared to observations and discussed in the framework of the above-mentioned poor representation. It is proposed as a process-based metric for evaluating model performance. This study forms part of the EU Horizon 2020 PRIMAVERA project aiming at developing a new generation of advanced and well-evaluated high-resolution GCMs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016APJAS..52..467K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016APJAS..52..467K"><span>Development of statistical seasonal prediction models of Arctic Sea <span class="hlt">Ice</span> <span class="hlt">concentration</span> using CERES absorbed 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>Kim, Yoojin; Kim, Ha-Rim; Choi, Yong-Sang; Kim, WonMoo; Kim, Hye-Sil</p> <p>2016-11-01</p> <p>Statistical seasonal prediction models for the Arctic sea <span class="hlt">ice</span> <span class="hlt">concentration</span> (SIC) were developed for the late summer (August-October) when the downward trend is dramatic. The absorbed solar radiation (ASR) at the top of the atmosphere in June has a significant seasonal leading role on the SIC. Based on the lagged ASR-SIC relationship, two simple statistical models were established: the Markovian stochastic and the linear regression models. Crossvalidated hindcasts of SIC from 1979 to 2014 by the two models were compared with each other and observation. The hindcasts showed general agreement between the models as they share a common predictor, ASR in June and the observed SIC was well reproduced, especially over the relatively thin-<span class="hlt">ice</span> regions (of one- or multi-year sea <span class="hlt">ice</span>). The robust predictability confirms the functional role of ASR in the prediction of SIC. In particular, the SIC prediction in October was quite promising probably due to the pronounced icealbedo feedback. The temporal correlation coefficients between the predicted SIC and the observed SIC were 0.79 and 0.82 by the Markovian and regression models, respectively. Small differences were observed between the two models; the regression model performed slightly better in August and September in terms of temporal correlation coefficients. Meanwhile, the prediction skills of the Markovian model in October were higher in the north of Chukchi, the East Siberian, and the Laptev Seas. A strong non-linear relationship between ASR in June and SIC in October in these areas would have increased the predictability of the Markovian model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.2692W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.2692W"><span><span class="hlt">Ice</span> nucleation of Snomax® particles below water vapor saturation: immersion freezing in <span class="hlt">concentrated</span> solution droplets</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wex, Heike; Kanji, Zamin A.; Boose, Yvonne; Beyer, Alexander; Henning, Silvia; Augustin-Bauditz, Stefanie</p> <p>2015-04-01</p> <p>Heterogeneous <span class="hlt">ice</span> nucleation has received an increasing amount of interest in the past years, as it initiates the <span class="hlt">ice</span> phase in mixed phase clouds (MPCs) and, to some extent, also in cirrus clouds. The presence of <span class="hlt">ice</span> influences cloud radiative properties and, for mixed phase clouds, also the formation of precipitation. Immersion freezing is thought to be the most important mechanism through which <span class="hlt">ice</span> formation could take place in MPCs. Here, we examine the <span class="hlt">ice</span> nucleation activity of biological <span class="hlt">ice</span> nucleating particles (INP) derived from bacteria, namely, particles generated from Snomax® suspensions, both above and below water vapor saturation. During a measurement campaign in Leipzig, <span class="hlt">ice</span> nucleation measurements were conducted with PINC (Portable <span class="hlt">Ice</span> Nucleus Counter, Chou et al., 2011) and LACIS (Leipzig Aerosol Cloud Interaction Simulator, see e.g. Wex et al., 2014a). Immersion freezing measurements from PINC and LACIS were in agreement in the temperature regime for which both instruments operate reliably. Here, we will show that measurements done below water vapour saturation and above the deliquescence relative humidity of the Snomax® particles follow what would be expected for immersion freezing in <span class="hlt">concentrated</span> solutions, similar to what was suggested for coated kaolinite particles in Wex et al. (2014b). Additionally, some measurements reported in the literature that were done in the water vapour sub-saturated regime will be evaluated based on the assumption made above, showing that at least some of the <span class="hlt">ice</span> nucleation which previously was ascribed to deposition <span class="hlt">ice</span> nucleation rather follows the behavior of immersion freezing in <span class="hlt">concentrated</span> solutions. Literature: Chou, C., O. Stetzer, E. Weingartner, Z. Juranyi, Z. A. Kanji, and U. Lohmann (2011), <span class="hlt">Ice</span> nuclei properties within a Saharan dust event at the Jungfraujoch in the Swiss Alps, Atmos. Chem. Phys., 11(10), 4725-4738, doi:10.5194/acp-11-4725-2011. Wex, H. et al. (2014a) Intercomparing different devices</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.C21A0655Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.C21A0655Z"><span>Assimilation of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> data in the Arctic via DART/CICE5 in the CESM1</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.; Bitz, C. M.; Anderson, J. L.; Collins, N.; Hendricks, J.; Hoar, T. J.; Raeder, K.</p> <p>2016-12-01</p> <p>Arctic sea <span class="hlt">ice</span> cover has been experiencing significant reduction in the past few decades. Climate models predict that the Arctic Ocean may be <span class="hlt">ice</span>-free in late summer within a few decades. Better sea <span class="hlt">ice</span> prediction is crucial for regional and global climate prediction that are vital to human activities such as maritime shipping and subsistence hunting, as well as wildlife protection as animals face habitat loss. The physical processes involved with the persistence and re-emergence of sea <span class="hlt">ice</span> cover are found to extend the predictability of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> (SIC) and thickness at the regional scale up to several years. This motivates us to investigate sea <span class="hlt">ice</span> predictability stemming from initial values of the sea <span class="hlt">ice</span> cover. Data assimilation is a useful technique to combine observations and model forecasts to reconstruct the states of sea <span class="hlt">ice</span> in the past and provide more accurate initial conditions for sea <span class="hlt">ice</span> prediction. This work links the most recent version of the Los Alamos sea <span class="hlt">ice</span> model (CICE5) within the Community Earth System Model version 1.5 (CESM1.5) and the Data Assimilation Research Testbed (DART). The linked DART/CICE5 is ideal to assimilate multi-scale and multivariate sea <span class="hlt">ice</span> observations using an ensemble Kalman filter (EnKF). The study is focused on the assimilation of SIC data that impact SIC, sea <span class="hlt">ice</span> thickness, and snow thickness. The ensemble sea <span class="hlt">ice</span> model states are constructed by introducing uncertainties in atmospheric forcing and key model parameters. The ensemble atmospheric forcing is a reanalysis product generated with DART and the Community Atmosphere Model (CAM). We also perturb two model parameters that are found to contribute significantly to the model uncertainty in previous studies. This study applies perfect model observing system simulation experiments (OSSEs) to investigate data assimilation algorithms and post-processing methods. One of the ensemble members of a CICE5 free run is chosen as the truth. Daily synthetic</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C41D0756R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C41D0756R"><span>An Intercomparison of Predicted Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> from Global Ocean Forecast System & Arctic Cap Nowcast/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>Rosemond, K.</p> <p>2015-12-01</p> <p>The objective of this research is to provide an evaluation of improvements in marginal <span class="hlt">ice</span> zone (MIZ) and pack <span class="hlt">ice</span> estimations from the Global Ocean Forecast System (GOFS) model compared to the current operational model, the Arctic Cap Nowcast/Forecast System (ACNFS). This will be determined by an intercomparison between the subjectively estimated operational <span class="hlt">ice</span> <span class="hlt">concentration</span> data from the National <span class="hlt">Ice</span> Center (NIC) MIZ analysis and the <span class="hlt">ice</span> <span class="hlt">concentration</span> estimates from GOFS and ACNFS. This will help ascertain which nowcast from the models compares best to the NIC operational data stream needed for vessel support. It will also provide a quantitative assessment of GOFS and ACNFS performance and be used in the Operational Evaluation (OPEVAL) report from the NIC to NRL. The intercomparison results are based on statistical evaluations through a series of map overlays from both models ACNFS, GOFS with the NIC's MIZ data. All data was transformed to a common grid and difference maps were generated to determine which model had the greatest difference compared to the MIZ <span class="hlt">ice</span> <span class="hlt">concentrations</span>. This was provided daily for both the freeze-up and meltout seasons. Results indicated the GOFS model surpassed the ACNFS model, however both models were comparable. These results will help US Navy and NWS Anchorage <span class="hlt">ice</span> forecasters understand model biases and know which model guidance is likely to provide the best estimate of future <span class="hlt">ice</span> conditions.The objective of this research is to provide an evaluation of improvements in marginal <span class="hlt">ice</span> zone (MIZ) and pack <span class="hlt">ice</span> estimations from the Global Ocean Forecast System (GOFS) model compared to the current operational model, the Arctic Cap Nowcast/Forecast System (ACNFS). This will be determined by an intercomparison between the subjectively estimated operational <span class="hlt">ice</span> <span class="hlt">concentration</span> data from the National <span class="hlt">Ice</span> Center (NIC) MIZ analysis and the <span class="hlt">ice</span> <span class="hlt">concentration</span> estimates from GOFS and ACNFS. This will help ascertain which nowcast from the models</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28708127','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28708127"><span>An active bacterial community linked to high chl-a <span class="hlt">concentrations</span> in Antarctic winter-pack <span class="hlt">ice</span> and evidence for the development of an anaerobic sea-<span class="hlt">ice</span> bacterial community.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Eronen-Rasimus, Eeva; Luhtanen, Anne-Mari; Rintala, Janne-Markus; Delille, Bruno; Dieckmann, Gerhard; Karkman, Antti; Tison, Jean-Louis</p> <p>2017-10-01</p> <p>Antarctic sea-<span class="hlt">ice</span> bacterial community composition and dynamics in various developmental stages were investigated during the austral winter in 2013. Thick snow cover likely insulated the <span class="hlt">ice</span>, leading to high (<4 μg l(-1)) chlorophyll-a (chl-a) <span class="hlt">concentrations</span> and consequent bacterial production. Typical sea-<span class="hlt">ice</span> bacterial genera, for example, Octadecabacter, Polaribacter and Glaciecola, often abundant in spring and summer during the sea-<span class="hlt">ice</span> algal bloom, predominated in the communities. The variability in bacterial community composition in the different <span class="hlt">ice</span> types was mainly explained by the chl-a <span class="hlt">concentrations</span>, suggesting that as in spring and summer sea <span class="hlt">ice</span>, the sea-<span class="hlt">ice</span> bacteria and algae may also be coupled during the Antarctic winter. Coupling between the bacterial community and sea-<span class="hlt">ice</span> algae was further supported by significant correlations between bacterial abundance and production with chl-a. In addition, sulphate-reducing bacteria (for example, Desulforhopalus) together with odour of H2S were observed in thick, apparently anoxic <span class="hlt">ice</span>, suggesting that the development of the anaerobic bacterial community may occur in sea <span class="hlt">ice</span> under suitable conditions. In all, the results show that bacterial community in Antarctic sea <span class="hlt">ice</span> can stay active throughout the winter period and thus possible future warming of sea <span class="hlt">ice</span> and consequent increase in bacterial production may lead to changes in bacteria-mediated processes in the Antarctic sea-<span class="hlt">ice</span> zone.</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/2013ACP....13.6151H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ACP....13.6151H"><span>High <span class="hlt">concentrations</span> of biological aerosol particles and <span class="hlt">ice</span> nuclei during and after rain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Huffman, J. A.; Prenni, A. J.; DeMott, P. J.; Pöhlker, C.; Mason, R. H.; Robinson, N. H.; Fröhlich-Nowoisky, J.; Tobo, Y.; Després, V. R.; Garcia, E.; Gochis, D. J.; Harris, E.; Müller-Germann, I.; Ruzene, C.; Schmer, B.; Sinha, B.; Day, D. A.; Andreae, M. O.; Jimenez, J. L.; Gallagher, M.; Kreidenweis, S. M.; Bertram, A. K.; Pöschl, U.</p> <p>2013-07-01</p> <p>Bioaerosols are relevant for public health and may play an important role in the climate system, but their atmospheric abundance, properties, and sources are not well understood. Here we show that the <span class="hlt">concentration</span> of airborne biological particles in a North American forest ecosystem increases significantly during rain and that bioparticles are closely correlated with atmospheric <span class="hlt">ice</span> nuclei (IN). The greatest increase of bioparticles and IN occurred in the size range of 2-6 μm, which is characteristic for bacterial aggregates and fungal spores. By DNA analysis we found high diversities of airborne bacteria and fungi, including groups containing human and plant pathogens (mildew, smut and rust fungi, molds, Enterobacteriaceae, Pseudomonadaceae). In addition to detecting known bacterial and fungal IN (Pseudomonas sp., Fusarium sporotrichioides), we discovered two species of IN-active fungi that were not previously known as biological <span class="hlt">ice</span> nucleators (Isaria farinosa and Acremonium implicatum). Our findings suggest that atmospheric bioaerosols, IN, and rainfall are more tightly coupled than previously assumed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.P51B1736S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.P51B1736S"><span>Optimal Electromagnetic (EM) Geophysical Techniques to Map the <span class="hlt">Concentration</span> of Subsurface <span class="hlt">Ice</span> and Adsorbed Water on Mars and the Moon</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stillman, D. E.; Grimm, R. E.</p> <p>2013-12-01</p> <p>Water <span class="hlt">ice</span> is ubiquitous in our Solar System and is a probable target for planetary exploration. Mapping the lateral and vertical <span class="hlt">concentration</span> of subsurface <span class="hlt">ice</span> from or near the surface could determine the origin of lunar and martian <span class="hlt">ice</span> and quantify a much-needed resource for human exploration. Determining subsurface <span class="hlt">ice</span> <span class="hlt">concentration</span> on Earth is not trivial and has been attempted previously with electrical resistivity tomography (ERT), ground penetrating radar (GPR), airborne EM (AEM), and nuclear magnetic resonance (NMR). These EM geophysical techniques do not actually detect <span class="hlt">ice</span>, but rather the absence of unfrozen water. This causes a non-unique interpretation of frozen and dry subsurface sediments. This works well in the arctic because most locations are not dry. However, for planetary exploration, liquid water is exceedingly rare and subsurface mapping must discriminate between an <span class="hlt">ice</span>-rich and a dry subsurface. Luckily, nature has provided a unique electrical signature of <span class="hlt">ice</span>: its dielectric relaxation. The dielectric relaxation of <span class="hlt">ice</span> creates a temperature and frequency dependence of the electrical properties and varies the relative dielectric permittivity from ~3.1 at radar frequencies to >100 at low frequencies. On Mars, sediments smaller than silt size can hold enough adsorbed unfrozen water to complicate the measurement. This is because the presence of absorbed water also creates frequency-dependent electrical properties. The dielectric relaxation of adsorbed water and <span class="hlt">ice</span> can be separated as they have different shapes and frequency ranges as long as a spectrum spanning the two relaxations is measured. The volume <span class="hlt">concentration</span> of <span class="hlt">ice</span> and adsorbed water is a function of the strength of their relaxations. Therefore, we suggest that capacitively-coupled dielectric spectroscopy (a.k.a. spectral induced polarization or complex resistivity) can detect the <span class="hlt">concentration</span> of both <span class="hlt">ice</span> and adsorbed water in the subsurface. To prove this concept we have collected</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/6000520','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/6000520"><span>Sea <span class="hlt">ice</span> terminology</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Not Available</p> <p>1980-09-01</p> <p>A group of definitions of terms related to sea <span class="hlt">ice</span> is presented, as well as a graphic representation of late winter <span class="hlt">ice</span> zonation of the Beaufort Sea Coast. Terms included in the definition list are belt, bergy bit, bight, brash <span class="hlt">ice</span>, calving, close pack <span class="hlt">ice</span>, compacting, compact pack <span class="hlt">ice</span>, <span class="hlt">concentration</span>, consolidated pack <span class="hlt">ice</span>, crack, diffuse <span class="hlt">ice</span> edge, fast <span class="hlt">ice</span>, fast-<span class="hlt">ice</span> boundary, fast-<span class="hlt">ice</span> edge, first-year <span class="hlt">ice</span>, flaw, flaw lead, floe, flooded <span class="hlt">ice</span>, fractured, fractured zone, fracturing, glacier, grey <span class="hlt">ice</span>, grey-white <span class="hlt">ice</span>, growler, hummock, iceberg, iceberg tongue, <span class="hlt">ice</span> blink, <span class="hlt">ice</span> boundary, <span class="hlt">ice</span> cake, <span class="hlt">ice</span> edge, <span class="hlt">ice</span> foot, <span class="hlt">ice</span> free, <span class="hlt">ice</span> island, <span class="hlt">ice</span> shelf, large fracture, lead, medium fracture, multiyear <span class="hlt">ice</span>, nilas, old <span class="hlt">ice</span>, open pack <span class="hlt">ice</span>, open water, pack <span class="hlt">ice</span>, polar <span class="hlt">ice</span>, polynya, puddle, rafted <span class="hlt">ice</span>, rafting, ram, ridge, rotten <span class="hlt">ice</span>, second-year <span class="hlt">ice</span>, shearing, shore lead, shore polynya, small fracture, strip, tabular berg, thaw holes, very close pack <span class="hlt">ice</span>, very open pack <span class="hlt">ice</span>, water sky, young coastal <span class="hlt">ice</span>, and young <span class="hlt">ice</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AdAtS..34..509Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AdAtS..34..509Y"><span>Observational evidence of high <span class="hlt">ice</span> <span class="hlt">concentration</span> in a shallow convective cloud embedded in stratiform cloud over North China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yang, Jiefan; Lei, Hengchi; Hou, Tuanjie</p> <p>2017-04-01</p> <p>In this study we observed the microphysical properties, including the vertical and horizontal distributions of <span class="hlt">ice</span> particles, liquid water content and <span class="hlt">ice</span> habit, in different regions of a slightly supercooled stratiform cloud. Using aircraft instrument and radar data, the cloud top temperature was recorded as higher than -15°C, behind a cold front, on 9 September 2015 in North China. During the flight sampling, the high <span class="hlt">ice</span> number <span class="hlt">concentration</span> area was located in the supercooled part of a shallow convective cloud embedded in a stratiform cloud, where the ambient temperature was around -3°C. In this area, the maximum number <span class="hlt">concentrations</span> of particles with diameter greater than 100 μm and 500 μm ( N 100 and N 500) exceeded 300 L-1 and 30 L-1, respectively, and were related to large supercooled water droplets with diameter greater than 24 μm derived from cloud-aerosol spectrometer probe measurements. The <span class="hlt">ice</span> particles types in this region were predominantly columnar, needle, graupel, and some freezing drops, suggesting that the occurrence of high <span class="hlt">ice</span> number <span class="hlt">concentrations</span> was likely related to the Hallett-Mossop mechanism, although many other <span class="hlt">ice</span> multiplication processes cannot be totally ruled out. The maximum <span class="hlt">ice</span> number <span class="hlt">concentration</span> obtained during the first penetration was around two to three orders of magnitude larger than that predicted by the Demott and Fletcher schemes when assuming the cloud top temperature was around -15°C. During the second penetration conducted within the stratiform cloud, N 100 and N 500 decreased by a factor of five to ten, and the presence of columnar and needle-like crystals became very rare.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.A31F3086K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A31F3086K"><span><span class="hlt">Ice</span> Nucleation of Snomax® Particles below Water Vapor Saturation: Immersion Freezing in <span class="hlt">Concentrated</span> Solution Droplets</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kanji, Z. A.; Boose, Y.; Augustin, S.; Wex, H.</p> <p>2014-12-01</p> <p>Heterogeneous <span class="hlt">ice</span> nucleation in the atmosphere is important and has received an increasing amount of interest in the past years, as it initiates the <span class="hlt">ice</span> phase in mixed phase clouds and, to some extent, also in cirrus clouds. The presence of <span class="hlt">ice</span> influences cloud radiative properties and, for mixed phase clouds, also the formation of precipitation and cloud lifetime. Immersion freezing has been in the focus of <span class="hlt">ice</span> nucleation research in recent years. Here, we examine <span class="hlt">ice</span> nucleation activity of biological <span class="hlt">ice</span> nuclei (IN) derived from bacteria, namely of particles generated from a suspensions of Snomax®, both above and below water vapor saturation. Measurements were done with PINC (Portable <span class="hlt">Ice</span> Nucleus Counter, Chou et al., 2011) during a measurement campaign at LACIS (Leipzig Aerosol Cloud Interaction Simulator, see e.g. Wex et al., 2014) in Leipzig. Immersion freezing measurements from PINC and LACIS were in agreement in the temperature regime for which both instruments operate reliably. Here, we will show that measurements done below water vapor saturation follow what would be expected for immersion freezing in <span class="hlt">concentrated</span> solutions, similar to what was suggested for coated kaolinite particles in Wex et al. (2014). Chou, C., O. Stetzer, E. Weingartner, Z. Juranyi, Z. A. Kanji, and U. Lohmann (2011), <span class="hlt">Ice</span> nuclei properties within a Saharan dust event at the Jungfraujoch in the Swiss Alps, Atmos. Chem. Phys., 11(10), 4725-4738, doi:10.5194/acp-11-4725-2011. Wex, H., P. J. DeMott, Y. Tobo, S. Hartmann, M. Rösch, T. Clauss, L. Tomsche, D. Niedermeier, and F. Stratmann (2014), Kaolinite particles as <span class="hlt">ice</span> nuclei: learning from the use of different kaolinite samples and different coatings, Atmos. Chem. Phys., 14, doi:10.5194/acp-14-5529-2014.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1113752H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1113752H"><span>L-band radiometry for sea <span class="hlt">ice</span> applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Heygster, G.; Hedricks, S.; Mills, P.; Kaleschke, L.; Stammer, D.; Tonboe, R.</p> <p>2009-04-01</p> <p> Peake (1976). This expression was used by Menashi et al. (1993) to derive the thickness of sea <span class="hlt">ice</span> from UHF (0.6 GHz) radiometer. Second, retrieval algorithms for sea <span class="hlt">ice</span> parameters with emphasis on <span class="hlt">ice</span>-water discrimination from L-band observations considering the specific SMOS observations modes and geometries are investigated. A modified Menashi model with the permittivity depending on brine volume and temperature suggests a thickness sensitivity of up to 150 cm for low salinity (multi year or brackish) sea <span class="hlt">ice</span> at low temperatures. At temperatures approaching the melting point the thickness sensitivity reduces to a few centimetres. For first year <span class="hlt">ice</span> the modelled thickness sensitivity is roughly half a meter. Runs of the model MEMLS with input data generated from a 1-d thermodynamic sea <span class="hlt">ice</span> model lead to similar conclusio. The results of the forward model may strongly vary with the input microphysical details. E.g. if the permittivity is modelled to depend in addition on the sea <span class="hlt">ice</span> thickness as supported by several former field campaigns for thin <span class="hlt">ice</span>, the model predictions change strongly. Prior to the launch of SMOS, an important source of observational data is the SMOS Sea-<span class="hlt">Ice</span> campaign held near Kokkola, Finland, March 2007 conducted as an add-on of the POL-<span class="hlt">ICE</span> campaign. Co-incident L-band observations taken with the EMIRAD instrument of the Technical University of Denmark, <span class="hlt">ice</span> thickness values determined from the EM bird of AWI and in situ observations during the campaign are combined. Although the campaign data are to be use with care, for selected parts of the flights the sea <span class="hlt">ice</span> thickness can be retrieved correctly. However, as the instrumental conditions and calibration were not optimal, more in situ data, preferably from the Arctic, will be needed before drawing clear conclusions about a future the sea <span class="hlt">ice</span> thickness product based on SMOS data. Use of additional information from other microwave sensors like <span class="hlt">AMSR-E</span> might be needed to constrain the conditions, e</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/AD1004224','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD1004224"><span>Improving Arctic Sea <span class="hlt">Ice</span> Edge Forecasts by Assimilating High Horizontal Resolution Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> Data into the US Navy’s <span class="hlt">Ice</span> Forecast Systems</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2016-06-13</p> <p>5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 6. AUTHOR(S) 7...Currently, the Navy uses ACNFS to predict conditions in all <span class="hlt">ice</span>-covered areas poleward of 40◦ N, with a grid resolution of approximately 3.5 km at the...resolution near the pole) and assim- ilated to create the initial conditions for each ACNFS and GOFS 3.1 model run. Once assimilated, sea <span class="hlt">ice</span> concentra</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19900047009&hterms=Nimbus&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3DNimbus','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19900047009&hterms=Nimbus&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3DNimbus"><span>Observations of winter polynyas and fractures using NOAA AVHRR TIR images and Nimbus-7 SMMR sea <span class="hlt">ice</span> <span class="hlt">concentration</span> charts</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Dey, B.; Feldman, Uri</p> <p>1989-01-01</p> <p>The validity of the daily sea <span class="hlt">ice</span> <span class="hlt">concentration</span> charts obtained from Nimbus-7 SMMR data is tested against thermal IR NOAA AVHRR-4 images with a 1.1 x 1.1 km resolution. Polynyas and fractures recorded in both data sets are compared for the same times over the same areas. Although the coarser resolution of SMMR data leads to a loss of detail, it is found that the <span class="hlt">ice</span> <span class="hlt">concentration</span> charts provide valuable data on openings in the <span class="hlt">ice</span>. The SMMR data on the location, orientation, and size of polynyas and fractures are shown to be in good agreement with the AVHRR data. Also, the results suggest that the location and orientation of plynyas and fractures are related to the direction of surface winds.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015TCry....9...37K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015TCry....9...37K"><span>The impact of snow depth, snow density and <span class="hlt">ice</span> density on sea <span class="hlt">ice</span> thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea <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>2015-01-01</p> <p>We assess different methods and input parameters, namely snow depth, snow density and <span class="hlt">ice</span> density, used in freeboard-to-thickness conversion of Arctic sea <span class="hlt">ice</span>. This conversion is an important part of sea <span class="hlt">ice</span> thickness retrieval from spaceborne altimetry. A data base is created comprising sea <span class="hlt">ice</span> freeboard derived from satellite radar altimetry between 1993 and 2012 and co-locate observations of total (sea <span class="hlt">ice</span> + snow) and sea <span class="hlt">ice</span> freeboard from the Operation <span class="hlt">Ice</span> Bridge (OIB) and CryoSat Validation Experiment (CryoVEx) airborne campaigns, of sea <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 (<span class="hlt">AMSR-E</span>) and the Warren climatology (Warren et al., 1999). We compare the different data sets in spatiotemporal scales where satellite radar altimetry yields meaningful results. An inter-comparison of the snow depth data sets emphasizes the limited usefulness of Warren climatology snow depth for freeboard-to-thickness conversion under current Arctic Ocean conditions reported in other studies. We test different freeboard-to-thickness and freeboard-to-draft conversion approaches. The mean observed ULS sea <span class="hlt">ice</span> draft agrees with the mean sea <span class="hlt">ice</span> draft derived from radar altimetry within the uncertainty bounds of the data sets involved. However, none of the approaches are able to reproduce the seasonal cycle in sea <span class="hlt">ice</span> draft observed by moored ULS. A sensitivity analysis of the freeboard-to-thickness conversion suggests that sea <span class="hlt">ice</span> density is as important as snow depth.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28811497','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28811497"><span>Vertical ocean heat redistribution sustaining sea-<span class="hlt">ice</span> <span class="hlt">concentration</span> trends in the Ross Sea.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lecomte, Olivier; Goosse, Hugues; Fichefet, Thierry; de Lavergne, Casimir; Barthélemy, Antoine; Zunz, Violette</p> <p>2017-08-15</p> <p>Several processes have been hypothesized to explain the slight overall expansion of Antarctic sea <span class="hlt">ice</span> over the satellite observation era, including externally forced changes in local winds or in the Southern Ocean's hydrological cycle, as well as internal climate variability. Here, we show the critical influence of an ocean-sea-<span class="hlt">ice</span> feedback. Once initiated by an external perturbation, it may be sufficient to sustain the observed sea-<span class="hlt">ice</span> expansion in the Ross Sea, the region with the largest and most significant expansion. We quantify the heat trapped at the base of the ocean mixed layer and demonstrate that it is of the same order of magnitude as the latent heat storage due to the long-term changes in sea-<span class="hlt">ice</span> volume. The evidence thus suggests that the recent <span class="hlt">ice</span> coverage increase in the Ross Sea could have been achieved through a reorganization of energy within the near-surface <span class="hlt">ice</span>-ocean system.The mechanisms responsible for the overall expansion of Antarctic sea-<span class="hlt">ice</span> in recent decades remain unclear. Here, using observations and model results, the authors show that <span class="hlt">ice</span>-ocean feedbacks, triggered by an external perturbation, could be responsible for changes in sea-<span class="hlt">ice</span> extent observed in the Ross Sea.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AtmEn.127....1H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AtmEn.127....1H"><span>Variations of <span class="hlt">ice</span> nuclei <span class="hlt">concentration</span> induced by rain and snowfall within a local forested site in Japan</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hara, Kazutaka; Maki, Teruya; Kobayashi, Fumihisa; Kakikawa, Makiko; Wada, Masashi; Matsuki, Atsushi</p> <p>2016-02-01</p> <p>Biological <span class="hlt">ice</span> nuclei (IN) such as certain species of bacteria and fungi are believed to have impacts on <span class="hlt">ice</span> nucleation in mixed-phase clouds at temperatures warmer than -15 °C. Recent studies have indicated that rain is closely related to increases of biological IN in the near-surface atmosphere. However, variations of IN <span class="hlt">concentrations</span> during rain and snowfall have not been compared. In the present study, field measurements of atmospheric IN were carried out under fine, cloudy, rain and snow at a local forested site in Japan. IN <span class="hlt">concentrations</span> at -7 °C in spring were dramatically increased by rain, and <span class="hlt">concentrations</span> associated with rain (0.86-2.2 m-3) were greater than 2.6 times higher than the mean <span class="hlt">concentration</span> during fine weather (0.33 m-3). In winter, <span class="hlt">concentrations</span> associated with rain (1.6 to >5.7 m-3) were also higher than those under cloudy sky (1.1 m-3), but increases were not observed during snowfall (0.21-0.4 m-3). Detectable IN <span class="hlt">concentrations</span> associated with rain considerably decreased after heat treatment at 90 °C, indicating that IN increased during rain were likely biological substances such as heat-sensitive <span class="hlt">ice</span> nucleation active proteins. Consequently, different types of precipitation may have varying effects on IN <span class="hlt">concentration</span> associated with biological substances.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/17833720','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/17833720"><span><span class="hlt">Ice</span> crystal <span class="hlt">concentration</span> in cumulus clouds: influence of the drop spectrum.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Mossop, S C; Hallett, J</p> <p>1974-11-15</p> <p>Secondary <span class="hlt">ice</span> crystals are thrown off when supercooled cloud drops are captured and freeze on a moving target in a cloud at -5 degrees C. The rate of production of these <span class="hlt">ice</span> crystals is proportional to the rate of accretion of drops of the diameter >/=24 micrometers.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140013408','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140013408"><span>A Long-Term and Reproducible Passive Microwave Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> Data Record for Climate Studies and Monitoring</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Peng, G.; Meier, W. N.; Scott, D. J.; Savoie, M. H.</p> <p>2013-01-01</p> <p>A long-term, consistent, and reproducible satellite-based passive microwave sea <span class="hlt">ice</span> <span class="hlt">concentration</span> climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea <span class="hlt">ice</span> <span class="hlt">concentration</span> data are on the National Snow and <span class="hlt">Ice</span> Data Center (NSIDC) polar stereographic grid with nominal 25 km × 25 km grid cells in both the Southern and Northern Hemisphere polar regions from 9 July 1987 to 31 December 2007. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea <span class="hlt">ice</span> <span class="hlt">concentration</span> CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The data set, along with detailed data processing steps and error source information, can be found at http://dx.doi.org/10.7265/N5B56GN3.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/20338422','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/20338422"><span>Short communication: low-fat <span class="hlt">ice</span> cream flavor not modified by high hydrostatic pressure treatment of whey protein <span class="hlt">concentrate</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Chauhan, J M; Lim, S-Y; Powers, J R; Ross, C F; Clark, S</p> <p>2010-04-01</p> <p>The purpose of this study was to examine flavor binding of high hydrostatic pressure (HHP)-treated whey protein <span class="hlt">concentrate</span> (WPC) in a real food system. Fresh Washington State University (WSU, Pullman) WPC, produced by ultrafiltration of separated Cheddar cheese whey, was treated at 300 MPa for 15 min. Commercial WPC 35 powder was reconstituted to equivalent total solids as WSU WPC (8.23%). Six batches of low-fat <span class="hlt">ice</span> cream were produced: A) HHP-treated WSU WPC without diacetyl; B) and E) WSU WPC with 2 mg/L of diacetyl added before HHP; C) WSU WPC with 2 mg/L of diacetyl added after HHP; D) untreated WSU WPC with 2 mg/L of diacetyl; and F) untreated commercial WPC 35 with 2 mg/L of diacetyl. The solution of WSU WPC or commercial WPC 35 contributed 10% to the mix formulation. <span class="hlt">Ice</span> creams were produced by using standard <span class="hlt">ice</span> cream ingredients and processes. Low-fat <span class="hlt">ice</span> creams containing HHP-treated WSU WPC and untreated WSU WPC were analyzed using headspace-solid phase microextraction-gas chromatography. Sensory evaluation by balanced reference duo-trio test was carried out using 50 untrained panelists in 2 sessions on 2 different days. The headspace-solid phase microextraction-gas chromatography analysis revealed that <span class="hlt">ice</span> cream containing HHP-treated WSU WPC had almost 3 times the <span class="hlt">concentration</span> of diacetyl compared with <span class="hlt">ice</span> cream containing untreated WSU WPC at d 1 of storage. However, diacetyl was not detected in <span class="hlt">ice</span> creams after 14 d of storage. Eighty percent of panelists were able to distinguish between low-fat <span class="hlt">ice</span> creams containing untreated WSU WPC with and without diacetyl, confirming panelists' ability to detect diacetyl. However, panelists were not able to distinguish between low-fat <span class="hlt">ice</span> creams containing untreated and HHP-treated WSU WPC with diacetyl. These results show that WPC diacetyl-binding properties were not enhanced by 300-MPa HHP treatment for 15 min, indicating that HHP may not be suitable for such applications.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFMPP31E..01C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFMPP31E..01C"><span>Greenhouse Gas <span class="hlt">Concentration</span> Records Extended Back to 800,000 Years From the EPICA Dome C <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>Chappellaz, J.; Luethi, D.; Loulergue, L.; Barnola, J.; Bereiter, B.; Blunier, T.; Jouzel, J.; Lefloch, M.; Lemieux, B.; Masson-Delmotte, V.; Raynaud, D.; Schilt, A.; Siegenthaler, U.; Spahni, R.; Stocker, T.</p> <p>2007-12-01</p> <p>The deep <span class="hlt">ice</span> core recovered from Dome Concordia in the framework of EPICA, the European Project for <span class="hlt">Ice</span> Coring in Antarctica, has extended the record of Antarctic climate history back to 800,000 years [Jouzel et al., 2007]. We present the current status of measurements of CO2, CH4 and N2O on air trapped in the bubbles of the Dome C <span class="hlt">ice</span> core. CO2 is measured in two laboratories using different techniques (laser absorption spectroscopy or gas chromatography on samples of 8 and 40 g of <span class="hlt">ice</span> which are mechanically crushed or milled, respectively). CH4 and N2O are extracted using a melt-refreeze technique and then measured by gas chromatography (in two laboratories for CH4). The greenhouse gas <span class="hlt">concentrations</span> have now been measured on the lowest 200 m of the Dome C core, going back to Marine Isotope Stage 20 (MIS 20) as verified by a consistent gas age/<span class="hlt">ice</span> age difference determined at termination IX [Jouzel et al., 2007]. The atmospheric CO2 <span class="hlt">concentration</span> mostly lagged the Antarctic temperature with a rather strong correlation throughout the eight and a half glacial cycles, but with significantly lower CO2 values between 650 and 750 kyr BP. Its lowest level ever measured in <span class="hlt">ice</span> cores (172 ppmv) is observed during MIS 16 (minimum centered at 667 kyr BP according to the EDC3 chronology) redetermining the natural span of CO2 to 172-300 ppmv. With 2245 individual measurements, the CH4 <span class="hlt">concentration</span> is now reconstructed over 800,000 years from a single core, with an average time resolution of 380 years. Spectral analyses of the CH4 signal show an increasing contribution of precession during the last four climatic cycles compared with the four older ones, suggesting an increasing impact of low latitudes sources/sinks. Millennial scale features in this very detailed signal allows us to compare their occurrence with <span class="hlt">ice</span> volume reconstructions and the isotopic composition of precipitation over the East Antarctic plateau. N2O is still affected by glaciological artefacts involving</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMPP21A1309G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMPP21A1309G"><span>Late Quaternary Advance and Retreat of an East Antarctic <span class="hlt">Ice</span> Shelf System: Insights from Sedimentary Beryllium-10 <span class="hlt">Concentrations</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Guitard, M. E.; Shevenell, A.; Domack, E. W.; Rosenheim, B. E.; Yokoyama, Y.</p> <p>2014-12-01</p> <p>Observed retreat of Antarctica's marine-based glaciers and the presence of warm (~2°C) modified Circumpolar Deep Water on Antarctica's continental shelves imply ocean temperatures may influence Antarctic cryosphere stability. A paucity of information regarding Late Quaternary East Antarctic cryosphere-ocean interactions makes assessing the variability, timing, and style of deglacial retreat difficult. Marine sediments from Prydz Bay, East Antarctica contain hemipelagic siliceous mud and ooze units (SMO) alternating with glacial marine sediments. The record suggests Late Quaternary variability of local outlet glacier systems, including the Lambert Glacier/Amery <span class="hlt">Ice</span> Shelf system that drains 15% of the East Antarctic <span class="hlt">Ice</span> Sheet. We present a refined radiocarbon chronology and beryllium-10 (10Be) record of Late Quaternary depositional history in Prydz Channel, seaward of the Amery <span class="hlt">Ice</span> Shelf system, which provides insight into the timing and variability of this important outlet glacier system. We focus on three piston cores (NBP01-01, JPC 34, 35, 36; 750 m water depth) that contain alternating SMO and granulated units uninterrupted by glacial till; the record preserves a succession of glacial marine deposits that pre-date the Last Glacial Maximum. We utilize the ramped pyrolysis preparatory method to improve the bulk organic carbon 14C-based chronology for Prydz Channel. To determine if the SMO intervals reflect open water conditions or sub-<span class="hlt">ice</span> shelf advection, we measured sedimentary 10Be <span class="hlt">concentrations</span>. Because <span class="hlt">ice</span> cover affects 10Be pathways through the water column, sedimentary <span class="hlt">concentrations</span> should provide information on past depositional environments in Prydz Channel. In Prydz Channel sediments, 10Be <span class="hlt">concentrations</span> are generally higher in SMO units and lower in glacial units, suggesting Late Quaternary fluctuations in the Amery <span class="hlt">Ice</span> Shelf. Improved chronologic constraints indicate that these fluctuations occurred on millennial timescales during the Last Glacial</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A22A..06K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A22A..06K"><span>A new single-moment microphysics scheme for cloud-resolving models using observed dependence of <span class="hlt">ice</span> <span class="hlt">concentration</span> on temperature.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Khairoutdinov, M.</p> <p>2015-12-01</p> <p>The representation of microphysics, especially <span class="hlt">ice</span> microphysics, remains one of the major uncertainties in cloud-resolving models (CRMs). Most of the cloud schemes use the so-called bulk microphysics approach, in which a few moments of such distributions are used as the prognostic variables. The System for Atmospheric Modeling (SAM) is the CRM that employs two such schemes. The single-moment scheme, which uses only mass for each of the water phases, and the two-moment scheme, which adds the particle <span class="hlt">concentration</span> for each of the hydrometeor category. Of the two, the single-moment scheme is much more computationally efficient as it uses only two prognostic microphysics variables compared to ten variables used by the two-moment scheme. The efficiency comes from a rather considerable oversimplification of the microphysical processes. For instance, only a sum of the liquid and icy cloud water is predicted with the temperature used to diagnose the mixing ratios of different hydrometeors. The main motivation for using such simplified microphysics has been computational efficiency, especially in the applications of SAM as the super-parameterization in global climate models. Recently, we have extended the single-moment microphysics by adding only one additional prognostic variable, which has, nevertheless, allowed us to separate the cloud <span class="hlt">ice</span> from liquid water. We made use of some of the recent observations of <span class="hlt">ice</span> microphysics collected at various parts of the world to parameterize several aspects of <span class="hlt">ice</span> microphysics that have not been explicitly represented before in our sing-moment scheme. For example, we use the observed broad dependence of <span class="hlt">ice</span> <span class="hlt">concentration</span> on temperature to diagnose the <span class="hlt">ice</span> <span class="hlt">concentration</span> in addition to prognostic mass. Also, there is no artificial separation between the pristine <span class="hlt">ice</span> and snow, often used by bulk models. Instead we prescribed the <span class="hlt">ice</span> size spectrum as the gamma distribution, with the distribution shape parameter controlled by the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUFM.U43B0868P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.U43B0868P"><span>Measurements of Bacterial <span class="hlt">Concentrations</span> on a Millimeter Scale in <span class="hlt">Ice</span> Cores With a Scanning Laser Fluorescence Spectrometer</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Price, P.; Rohde, R. A.; Bramall, N. E.; Bay, R. C.</p> <p>2006-12-01</p> <p>We report non-destructive detection of variability on a mm depth scale in the organic content of <span class="hlt">ice</span> cores at NICL, as determined by the fluorescence spectrum measured by a Targeted Ultraviolet Chemical Sensor (TUCS). Many of the spectra we obtained are consistent with the amino acid tryptophan, a strongly fluorescing constituent in microbes. Identification with native fluorescence of microbes is supported by previous measurements of varying microbial <span class="hlt">concentration</span> in samples from selected regions of the GISP2 core (Tung et al., 2005; 2006) that are consistent with our observations at the same depths. Sub-mm depth resolution was achieved and structure at this scale was observed. At each depth the fluorescence emission spectrum was measured at 5 wavelengths using 20-nm narrow band filters plus a long pass channel. The spectrum of microbes was calibrated by making lab measurements of fluorescence of various species and is distinguishable from mineral dust and metals due to differences in spectral shape. In bulk <span class="hlt">ice</span> samples from 3 depths in the GISP2 core, where a table of methane <span class="hlt">concentrations</span> (Ed Brook, unpublished) had shown several excesses above the atmospheric contribution, Tung et al. (2005) found 10-fold excesses of microbial <span class="hlt">concentrations</span> at 2954 m and 3036 m and a 3-fold excess at 3018 m. In the present work we found strong, rapidly varying organic signals at all three depths. At 3018 m the peak value was much stronger than that obtained by Brook and occurred in the core section below the one he studied. Since he measured methane at several-meter depth intervals, and since we found the microbial excesses to be <span class="hlt">concentrated</span> in 0.3 m intervals, we conclude that of order 30 microbe-rich regions may be present in GISP2. The 3 microbe-rich depths found by Tung et al. (2005) were less than 90 m above the basal <span class="hlt">ice</span> at 3041-3053 m. The large fluctuations in apparent tryptophan <span class="hlt">concentrations</span> that we found at 2954, 3018, and 3036 m are consistent with microbe</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JQSRT.198...68B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JQSRT.198...68B"><span>The application of the boundary element method in BEM++ to small extreme Chebyshev <span class="hlt">ice</span> particles and the remote detection of the <span class="hlt">ice</span> crystal number <span class="hlt">concentration</span> of small atmospheric <span class="hlt">ice</span> particles</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Baran, Anthony J.; Groth, Samuel P.</p> <p>2017-09-01</p> <p>The measurement of the shape and size distributions of small atmospheric <span class="hlt">ice</span> particles (i.e. less than about 100 μm in size) is still an unresolved problem in atmospheric physics. This paper is composed of two parts, each addressing one of these measurements. In the first part, we report on an application of a new open-source electromagnetic boundary element method (BEM) called ;BEM++; to characterise the shape of small <span class="hlt">ice</span> particles through the simulation of the two-dimensional (2D) light scattering patterns of extreme Chebyshev <span class="hlt">ice</span> particles. Previous electromagnetic studies of Chebyshev particles have <span class="hlt">concentrated</span> upon high Chebyshev orders, but with low Chebyshev deformation parameters. Here, we extend such studies by <span class="hlt">concentrating</span> on the 2D light scattering properties of Chebyshev particles with extreme deformation parameters, up to 0.5, and with Chebyshev orders up to 16, at a size parameter of 15, in a fixed orientation. The results demonstrate the applicability of BEM++ to the study of the electromagnetic scattering properties of extreme particles and the usefulness of measuring the light scattering patterns of particles in 2D to mimic the scattering behaviours of highly irregular particles, such as dendritic atmospheric <span class="hlt">ice</span> or hazardous biological and/or aerosol particles. In the second part, we demonstrate the potential application of remotely sensed very-high-resolution brightness temperature measurements of optically thin cirrus between wavelengths of about 8.0 and 12.0 μm to resolve the current atmospheric physics issue of determining the number <span class="hlt">concentration</span> of small <span class="hlt">ice</span> particles with size less than about 100 μm.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ACPD...1020607S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ACPD...1020607S"><span>The sensitivity of the oxygen isotopes of <span class="hlt">ice</span> core sulfate to changing oxidant <span class="hlt">concentrations</span> since the preindustrial</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sofen, E. D.; Alexander, B.; Kunasek, S. A.</p> <p>2010-08-01</p> <p>Changes in tropospheric oxidant <span class="hlt">concentrations</span> since preindustrial times have implications for the ozone radiative forcing, lifetimes of reduced trace gases, aerosol formation, and human health but are highly uncertain. Measurements of the triple oxygen isotopes of sulfate in <span class="hlt">ice</span> cores (described by Δ17OSO4 = δ17O - 0.52 × δ18O) provide one of the few constraints on paleo-oxidants. We use the GEOS-Chem global atmospheric chemical transport model to simulate changes in oxidant <span class="hlt">concentrations</span> and the Δ17OSO4 between 1850 and 1990 to assess the sensitivity of Δ17OSO4 measurements in Greenland and Antarctic <span class="hlt">ice</span> cores to changing tropospheric oxidant <span class="hlt">concentrations</span>. The model indicates a 42% increase in the <span class="hlt">concentration</span> of global mean tropospheric O3, a 10% decrease in OH, and a 58% increase in H2O2 between the preindustrial and present. Modeled Δ17OSO4 is consistent with measurements from <span class="hlt">ice</span> core and aerosol samples. Model results indicate that the observed decrease in the Arctic Δ17OSO4 in spite of increasing O3 is due to the combined effects of increased sulfate formation by O2 catalyzed by anthropogenic transition metals and increased cloud water acidity. In Antarctica, the Δ17OSO4 is sensitive to relative changes of oxidant <span class="hlt">concentrations</span>, but in a nonlinear fashion. Sensitivity studies explore the uncertainties in preindustrial emissions of oxidant precursors.</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/2017BGeo...14.2407H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017BGeo...14.2407H"><span>Impact of ocean acidification on Arctic phytoplankton blooms and dimethyl sulfide <span class="hlt">concentration</span> under simulated <span class="hlt">ice</span>-free and under-<span class="hlt">ice</span> conditions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hussherr, Rachel; Levasseur, Maurice; Lizotte, Martine; Tremblay, Jean-Éric; Mol, Jacoba; Thomas, Helmuth; Gosselin, Michel; Starr, Michel; Miller, Lisa A.; Jarniková, Tereza; Schuback, Nina; Mucci, Alfonso</p> <p>2017-05-01</p> <p>In an experimental assessment of the potential impact of Arctic Ocean acidification on seasonal phytoplankton blooms and associated dimethyl sulfide (DMS) dynamics, we incubated water from Baffin Bay under conditions representing an acidified Arctic Ocean. Using two light regimes simulating under-<span class="hlt">ice</span> or subsurface chlorophyll maxima (low light; low PAR and no UVB) and <span class="hlt">ice</span>-free (high light; high PAR + UVA + UVB) conditions, water collected at 38 m was exposed over 9 days to 6 levels of decreasing pH from 8.1 to 7.2. A phytoplankton bloom dominated by the centric diatoms Chaetoceros spp. reaching up to 7.5 µg chlorophyll a L-1 took place in all experimental bags. Total dimethylsulfoniopropionate (DMSPT) and DMS <span class="hlt">concentrations</span> reached 155 and 19 nmol L-1, respectively. The sharp increase in DMSPT and DMS <span class="hlt">concentrations</span> coincided with the exhaustion of NO3- in most microcosms, suggesting that nutrient stress stimulated DMS(P) synthesis by the diatom community. Under both light regimes, chlorophyll a and DMS <span class="hlt">concentrations</span> decreased linearly with increasing proton <span class="hlt">concentration</span> at all pH levels tested. <span class="hlt">Concentrations</span> of DMSPT also decreased but only under high light and over a smaller pH range (from 8.1 to 7.6). In contrast to nano-phytoplankton (2-20 µm), pico-phytoplankton ( ≤ 2 µm) was stimulated by the decreasing pH. We furthermore observed no significant difference between the two light regimes tested in term of chlorophyll a, phytoplankton abundance and taxonomy, and DMSP and DMS net <span class="hlt">concentrations</span>. These results show that ocean acidification could significantly decrease the algal biomass and inhibit DMS production during the seasonal phytoplankton bloom in the Arctic, with possible consequences for the regional climate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27664024','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27664024"><span>Freezing and glass transitions upon cooling and warming and <span class="hlt">ice/freeze-concentration</span>-solution morphology of emulsified aqueous citric acid.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bogdan, Anatoli; Molina, Mario J; Tenhu, Heikki</p> <p>2016-12-01</p> <p>Although freeze-induced phase separation and the <span class="hlt">ice</span>/FCS (freeze-<span class="hlt">concentration</span> solution) morphology of aqueous solutions play an important role in fields ranging from life sciences and biotechnology to geophysics and high-altitude <span class="hlt">ice</span> clouds, their understanding is far from complete. Herein, using differential scanning calorimetry (DSC) and optical cryo-microscope (OC-M), we have studied the freezing and glass transition behavior and the <span class="hlt">ice</span>/FCS morphology of emulsified 10-60wt% CA (citric acid) solutions in the temperature region of ∼308and153K. We have obtained a lot of new result which are understandable and unclear. The most essential understandable results are as follows: (i) similar to bulk CA/H2O, emulsified CA/H2O also freezes upon cooling and warming and (ii) the <span class="hlt">ice</span>/FCS morphology of frozen drops smaller than ∼3-4μm is less ramified than that of frozen bulk solutions. Unclear results, among others, are as follows: (i) in contrast to bulk solutions, which produce one freezing event, emulsified CA/H2O produces two freezing events and (ii) in emulsions, drop <span class="hlt">concentration</span> is not uniform. Our results demonstrate that DSC thermograms and OC-M images/movies are mutually supplementary and allow us to extract important information which cannot be gained when DSC and OC-M techniques are used alone. Copyright © 2016 Elsevier B.V. All rights reserved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.usgs.gov/of/2007/1047/srp/srp029/of2007-1047srp029.pdf','USGSPUBS'); return false;" href="https://pubs.usgs.gov/of/2007/1047/srp/srp029/of2007-1047srp029.pdf"><span>Sea <span class="hlt">ice</span> <span class="hlt">concentration</span> temporal variability over the Weddell Sea and its relationship with tropical sea surface temperature</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Barreira, S.; Compagnucci, R.</p> <p>2007-01-01</p> <p>Principal Components Analysis (PCA) in S-Mode (correlation between temporal series) was performed on sea <span class="hlt">ice</span> monthly anomalies, in order to investigate which are the main temporal patterns, where are the homogenous areas located and how are they related to the sea surface temperature (SST). This analysis provides 9 patterns (4 in the Amundsen and Bellingshausen Seas and 5 in the Weddell Sea) that represent the most important temporal features that dominated sea <span class="hlt">ice</span> <span class="hlt">concentration</span> anomalies (SICA) variability in the Weddell, Amundsen and Bellingshausen Seas over the 1979-2000 period. Monthly Polar Gridded Sea <span class="hlt">Ice</span> <span class="hlt">Concentrations</span> data set derived from satellite information generated by NASA Team algorithm and acquired from the National Snow and <span class="hlt">Ice</span> Data Center (NSIDC) were used. Monthly means SST are provided by the National Center for Environmental Prediction reanalysis. The first temporal pattern series obtained by PCA has its homogeneous area located at the external region of the Weddell and Bellingshausen Seas and Drake Passage, mostly north of 60°S. The second region is centered in 30°W and located at the southeast of the Weddell. The third area is localized east of 30°W and north of 60°S. South of the first area, the fourth PC series has its homogenous region, between 30° and 60°W. The last area is centered at 0° W and south of 60°S. Correlation charts between the five Principal Components series and SST were performed. Positive correlations over the Tropical Pacific Ocean were found for the five PCs when SST series preceded SICA PC series. The sign of the correlation could relate the occurrence of an El Niño/Southern Oscillation (ENSO) warm (cold) event with posterior positive (negative) anomalies of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> over the Weddell Sea.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19920035390&hterms=Self+Defense&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DSelf%2BDefense','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19920035390&hterms=Self+Defense&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DSelf%2BDefense"><span>NASA team algorithm for sea <span class="hlt">ice</span> <span class="hlt">concentration</span> retrieval from Defense Meteorological Satellite Program special sensor microwave imager - Comparison with Landsat satellite imagery</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Steffen, Konrad; Schweiger, Axel</p> <p>1991-01-01</p> <p>The present study describes the validation of the the NASA team algorithm for the determination of sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> from the Defense Meteorological Satellite Program special sensor microwave imager (SSM/I). A total of 28 cloud-free Landsat scenes were selected to permit validation of the passive microwave <span class="hlt">ice</span> <span class="hlt">concentration</span> algorithm for a range of <span class="hlt">ice</span> <span class="hlt">concentrations</span> and <span class="hlt">ice</span> types. The sensitivity of the NASA team algorithm to the selection of locally and seasonally adjusted algorithm parameters is discussed. Mean absolute differences between SSM/I and Landsat <span class="hlt">ice</span> <span class="hlt">concentrations</span> are within 1 percent during fall using local and global tie points (standard deviations of the difference are +/-3.1 and +/-6.2 percent, respectively). In areas with greater amounts of nilas and young <span class="hlt">ice</span>, the NASA team algorithm was found to underestimate <span class="hlt">ice</span> <span class="hlt">concentrations</span> by as much as 9 percent. It is inferred that the standard deviation between SSM/I and Landsat <span class="hlt">ice</span> <span class="hlt">concentrations</span> decreases from +/-7 to +/-5 percent with local tie points compared to the global ones for spring and fall.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19920035390&hterms=defense&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Ddefense','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19920035390&hterms=defense&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Ddefense"><span>NASA team algorithm for sea <span class="hlt">ice</span> <span class="hlt">concentration</span> retrieval from Defense Meteorological Satellite Program special sensor microwave imager - Comparison with Landsat satellite imagery</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Steffen, Konrad; Schweiger, Axel</p> <p>1991-01-01</p> <p>The present study describes the validation of the the NASA team algorithm for the determination of sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> from the Defense Meteorological Satellite Program special sensor microwave imager (SSM/I). A total of 28 cloud-free Landsat scenes were selected to permit validation of the passive microwave <span class="hlt">ice</span> <span class="hlt">concentration</span> algorithm for a range of <span class="hlt">ice</span> <span class="hlt">concentrations</span> and <span class="hlt">ice</span> types. The sensitivity of the NASA team algorithm to the selection of locally and seasonally adjusted algorithm parameters is discussed. Mean absolute differences between SSM/I and Landsat <span class="hlt">ice</span> <span class="hlt">concentrations</span> are within 1 percent during fall using local and global tie points (standard deviations of the difference are +/-3.1 and +/-6.2 percent, respectively). In areas with greater amounts of nilas and young <span class="hlt">ice</span>, the NASA team algorithm was found to underestimate <span class="hlt">ice</span> <span class="hlt">concentrations</span> by as much as 9 percent. It is inferred that the standard deviation between SSM/I and Landsat <span class="hlt">ice</span> <span class="hlt">concentrations</span> decreases from +/-7 to +/-5 percent with local tie points compared to the global ones for spring and fall.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AtmRe.188...11L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AtmRe.188...11L"><span>An observational study of atmospheric <span class="hlt">ice</span> nuclei number <span class="hlt">concentration</span> during three fog-haze weather periods in Shenyang, northeastern China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Liguang; Zhou, Deping; Wang, Yangfeng; Hong, Ye; Cui, Jin; Jiang, Peng</p> <p>2017-05-01</p> <p>Characteristics of <span class="hlt">ice</span> nuclei (IN) number <span class="hlt">concentrations</span> during three fog-haze weather periods from November 2010 to January 2012 in Shenyang were presented in this paper. A static diffusion chamber was used and sampling of IN aerosols was conducted using a membrane filter method. Sampling membrane filter processing conditions were unified in the activation temperature at - 15 °C under conditions of 20% <span class="hlt">ice</span> supersaturation and 3% water supersaturation. The variations of natural IN number <span class="hlt">concentrations</span> in different weather conditions were investigated. The relations between the meteorological factors and the IN number <span class="hlt">concentrations</span> were analyzed, and relationships between pollutants and IN number <span class="hlt">concentrations</span> were also studied. The results showed that mean IN number <span class="hlt">concentration</span> were 38.68 L- 1 at - 20 °C in Shenyang, for all measurements. Mean IN number <span class="hlt">concentrations</span> are higher during haze days (55.92 L- 1 at - 20 °C) and lower after rain. Of all meteorological factors, wind speed, boundary stability, and airflow direction appeared to influence IN number <span class="hlt">concentrations</span>. IN number <span class="hlt">concentrations</span> were positively correlated with particulate matters PM1, PM2.5, and PM10 during haze weather.</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 Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> 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>Hydrospheric Sciences, NASA/Goddard Space Flight Center for their advice and guidance. The brightness temperature gridded data used in the project were...Polynyas have significant impacts on the biological and physical phenomena of the polar regions. Polynyas within the <span class="hlt">ice</span> pack are analogous to oases in...1976. Stevens, D., Sea <span class="hlt">ice</span>, FRAM News, 1, 2, 1991. Stewart, F.H., The distribution and ecological significance of major recurring polynyas, pp. 22-34</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011ACP....11.3459S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011ACP....11.3459S"><span>The effect of sea <span class="hlt">ice</span> loss on sea salt aerosol <span class="hlt">concentrations</span> and the radiative balance 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>Struthers, H.; Ekman, A. M. L.; Glantz, P.; Iversen, T.; Kirkevåg, A.; Mårtensson, E. M.; Seland, Ø.; Nilsson, E. D.</p> <p>2011-04-01</p> <p>Understanding Arctic climate change requires knowledge of both the external and the local drivers of Arctic climate as well as local feedbacks within the system. An Arctic feedback mechanism relating changes in sea <span class="hlt">ice</span> extent to an alteration of the emission of sea salt aerosol and the consequent change in radiative balance is examined. A set of idealized climate model simulations were performed to quantify the radiative effects of changes in sea salt aerosol emissions induced by prescribed changes in sea <span class="hlt">ice</span> extent. The model was forced using sea <span class="hlt">ice</span> <span class="hlt">concentrations</span> consistent with present day conditions and projections of sea <span class="hlt">ice</span> extent for 2100. Sea salt aerosol emissions increase in response to a decrease in sea <span class="hlt">ice</span>, the model results showing an annual average increase in number emission over the polar cap (70-90° N) of 86 × 106 m-2 s-1 (mass emission increase of 23 μg m-2 s-1). This in turn leads to an increase in the natural aerosol optical depth of approximately 23%. In response to changes in aerosol optical depth, the natural component of the aerosol direct forcing over the Arctic polar cap is estimated to be between -0.2 and -0.4 W m-2 for the summer months, which results in a negative feedback on the system. The model predicts that the change in first indirect aerosol effect (cloud albedo effect) is approximately a factor of ten greater than the change in direct aerosol forcing although this result is highly uncertain due to the crude representation of Arctic clouds and aerosol-cloud interactions in the model. This study shows that both the natural aerosol direct and first indirect effects are strongly dependent on the surface albedo, highlighting the strong coupling between sea <span class="hlt">ice</span>, aerosols, Arctic clouds and their radiative effects.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C13A0803S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C13A0803S"><span>Separating Continental Mineral Dust from Cosmic Dust using Platinum Group Element <span class="hlt">Concentrations</span> and Osmium Isotopes in Ancient Polar <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>Seo, J. H.; Jackson, B.; Osterberg, E. C.; Sharma, M.</p> <p>2015-12-01</p> <p>The platinum group element (PGEs: Pt, Pd, Rh, Ir, Os, and Ru) accumulation in ancient polar archives have been argued to trace cosmic dust and "smoke" from larger meteors but the PGE <span class="hlt">concentration</span> data lack specificity. For example, the extent to which the terrestrial volcanism/dust has contributed to the PGE inventory of polar <span class="hlt">ice</span> cannot be readily evaluated. Since the Os isotope compositions (187Os/188Os ratio) of the terrestrial and extraterrestrial sources are distinctly different from each other, the PGE <span class="hlt">concentrations</span> when combined with Os isotope composition have the potential to untangle contributions from these sources. Platinum group element <span class="hlt">concentration</span> determinations in polar <span class="hlt">ice</span> cores are highly challenging due to their extremely low <span class="hlt">concentrations</span> (down to 10-15 g/g or fg/g). Here, a new procedure is presented that allows PGEs and Os isotope compositions to be determined from a ~50 g sample of polar <span class="hlt">ice</span>. Decontaminated <span class="hlt">ice</span>-melt is spiked with 101Ru, 106Pd, 190Os, 191Ir, and 198Pt and frozen at -20 °C in quartz-glass ampoules. A mixture of purified HNO3 and H2O2 is then added and the sample is heated to 300 °C at 128bar using a High Pressure Asher. This allows all spikes to be equilibrated with the sample PGEs and all Os species are oxidized to OsO4. The resulting OsO4 is extracted using distillation, purified, and measured using negative thermal ionization mass spectrometry. PGEs are then separated and purified using two stage column chromatography and their <span class="hlt">concentrations</span> determined by isotope dilution using a triple quadruople inductively coupled plasma mass spectrometer coupled to an Apex de-solvation nebulizer. The developed method was applied to modern Greenland firn and snow. The PGE <span class="hlt">concentrations</span> of the firn are 4.0 fg/g for Ir, 20 fg/g for Ru, 590 fg/g for Pt, 38 fg/g for Pd, and 1.3 fg/g for Os, while those of the snow are 3.0 fg/g for Ir, 53 fg/g for Ru, 360 fg/g for Pt, 32 fg/g for Pd, and 0.4 fg/g for Os, respectively. A comparison</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ACP....16..927L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ACP....16..927L"><span>Influence of the ambient humidity on the <span class="hlt">concentration</span> of natural deposition-mode <span class="hlt">ice</span>-nucleating particles</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>López, M. L.; Ávila, E. E.</p> <p>2016-01-01</p> <p>This study reports measurements of deposition-mode <span class="hlt">ice</span>-nucleating particle (INP) <span class="hlt">concentrations</span> at ground level during the period July-December 2014 in Córdoba, Argentina. Ambient air was sampled into a cloud chamber where the INP <span class="hlt">concentration</span> was measured at a temperature of -25 °C and a 15 % supersaturation over <span class="hlt">ice</span>. Measurements were performed on days with different thermodynamic conditions, including rainy days. The effect of the relative humidity at ground level (RHamb) on the INP <span class="hlt">concentration</span> was analyzed. The number of INPs activated varied from 1 L-1 at RHamb of 25 % to 30 L-1 at RHamb of 90 %. In general, a linear trend between the INP <span class="hlt">concentration</span> and the RHamb was found, suggesting that this variability must be related to the effectiveness of the aerosols acting as INPs. From the backward trajectories analysis, it was found that the link between INP <span class="hlt">concentration</span> and RHamb is independent of the origin of the air masses. The role of biological INPs and nucleation occurring in pores and cavities was discussed as a possible mechanism to explain the increase of the INP <span class="hlt">concentration</span> during high ambient relative humidity events. This work provides valuable measurements of deposition-mode INP <span class="hlt">concentrations</span> from the Southern Hemisphere where INP data are sparse so far.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016CliPa..12.1829K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016CliPa..12.1829K"><span>The effect of greenhouse gas <span class="hlt">concentrations</span> and <span class="hlt">ice</span> sheets on the glacial AMOC in a coupled 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>Klockmann, Marlene; Mikolajewicz, Uwe; Marotzke, Jochem</p> <p>2016-09-01</p> <p>Simulations with the Max Planck Institute Earth System Model (MPI-ESM) are used to study the sensitivity of the AMOC and the deep-ocean water masses during the Last Glacial Maximum to different sets of forcings. Analysing the individual contributions of the glacial forcings reveals that the <span class="hlt">ice</span> sheets cause an increase in the overturning strength and a deepening of the North Atlantic Deep Water (NADW) cell, while the low greenhouse gas (GHG) <span class="hlt">concentrations</span> cause a decrease in overturning strength and a shoaling of the NADW cell. The effect of the orbital configuration is negligible. The effects of the <span class="hlt">ice</span> sheets and the GHG reduction balance each other in the deep ocean so that no shoaling of the NADW cell is simulated in the full glacial state. Experiments in which different GHG <span class="hlt">concentrations</span> with linearly decreasing radiative forcing are applied to a setup with glacial <span class="hlt">ice</span> sheets and orbital configuration show that GHG <span class="hlt">concentrations</span> below the glacial level are necessary to cause a shoaling of the NADW cell with respect to the pre-industrial state in MPI-ESM. For a pCO2 of 149 ppm, the simulated overturning state and the deep-ocean water masses are in best agreement with the glacial state inferred from proxy data. Sensitivity studies confirm that brine release and shelf convection in the Southern Ocean are key processes for the shoaling of the NADW cell. Shoaling occurs only when Southern Ocean shelf water contributes significantly to the formation of Antarctic Bottom Water.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GeCoA.209..233H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GeCoA.209..233H"><span>Constraining the recent history of the perennially <span class="hlt">ice</span>-covered Lake Bonney, East Antarctica using He, Kr and Xe <span class="hlt">concentrations</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hall, Chris M.; Castro, Maria Clara; Kenig, Fabien; Doran, Peter T.</p> <p>2017-07-01</p> <p>Lake Bonney is a perennially <span class="hlt">ice</span>-covered lake in the McMurdo Dry Valleys (MDVs) that has long been studied in order to provide constraints on the paleoclimate of West Antarctica. The lake is divided into two lobes, West Lake Bonney (WLB) and East Lake Bonney (ELB) that are separated by a narrow ridge. The two lobes currently receive surface melt water during austral summers from glacier-fed ephemeral streams and this meltwater enters the lake via a narrow ring, or moat, of liquid water that forms around the lake during summer. The West Lobe also receives water from direct input of melt water from Taylor glacier and saline water from irregular subglacial discharge. Here, we combine previously published He data from Lake Bonney with new Kr and Xe <span class="hlt">concentration</span> data to examine the signatures of water recharge via the seasonal moat and these data are used to constrain a model for He, Kr and Xe transport within both WLB and ELB over about the last 5000-6000 yrs. A detailed numerical simulation is presented that combines diffusive transport of noble gases within the stratified water column of Lake Bonney, along with <span class="hlt">ice</span> ablation at the top of the <span class="hlt">ice</span> cover, partitioning of noble gases between water and <span class="hlt">ice</span>, plus exchange of noble gases between WLB and ELB. Results strongly suggest that open moats have only operated for about 2-3 centuries within the last millennium. These results are corroborated by the high <span class="hlt">concentration</span> of He, especially within WLB, which points to a history of <span class="hlt">ice</span> cover with no open moats operating for both lobes for at least about 5 millennia. In addition, the distribution of He, Kr and Xe suggest that a significant rise of the water level of Lake Bonney associated with a warmer period may have been interrupted by a roughly 4-5 century long cold period during which the moats were not large enough to allow air saturated water into the lake, with this cold period ending about one century ago. In addition, during this cold period, there is evidence for</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JPRS..130..122B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JPRS..130..122B"><span>Validation of Suomi-NPP VIIRS sea <span class="hlt">ice</span> <span class="hlt">concentration</span> with very high-resolution satellite and airborne camera imagery</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Baldwin, Daniel; Tschudi, Mark; Pacifici, Fabio; Liu, Yinghui</p> <p>2017-08-01</p> <p>Two independent VIIRS-based Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> (SIC) products are validated against SIC as estimated from Very High Spatial Resolution Imagery for several VIIRS overpasses. The 375 m resolution VIIRS SIC from the Interface Data Processing Segment (IDPS) SIC algorithm is compared against estimates made from 2 m DigitalGlobe (DG) WorldView-2 imagery and also against estimates created from 10 cm Digital Mapping System (DMS) camera imagery. The 750 m VIIRS SIC from the Enterprise SIC algorithm is compared against DG imagery. The IDPS vs. DG comparisons reveal that, due to algorithm issues, many of the IDPS SIC retrievals were falsely assigned <span class="hlt">ice</span>-free values when the pixel was clearly over <span class="hlt">ice</span>. These false values increased the validation bias and RMS statistics. The IDPS vs. DMS comparisons were largely over <span class="hlt">ice</span>-covered regions and did not demonstrate the false retrieval issue. The validation results show that products from both the IDPS and Enterprise algorithms were within or very close to the 10% accuracy (bias) specifications in both the non-melting and melting conditions, but only products from the Enterprise algorithm met the 25% specifications for the uncertainty (RMS).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20090038693','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20090038693"><span>Estimation of Sea <span class="hlt">Ice</span> Thickness Distributions through the Combination of Snow Depth and Satellite Laser Altimetry Data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kurtz, Nathan T.; Markus, Thorsten; Cavalieri, Donald J.; Sparling, Lynn C.; Krabill, William B.; Gasiewski, Albin J.; Sonntag, John G.</p> <p>2009-01-01</p> <p>Combinations of sea <span class="hlt">ice</span> freeboard and snow depth measurements from satellite data have the potential to provide a means to derive global sea <span class="hlt">ice</span> thickness values. However, large differences in spatial coverage and resolution between the measurements lead to uncertainties when combining the data. High resolution airborne laser altimeter retrievals of snow-<span class="hlt">ice</span> freeboard and passive microwave retrievals of snow depth taken in March 2006 provide insight into the spatial variability of these quantities as well as optimal methods for combining high resolution satellite altimeter measurements with low resolution snow depth data. The aircraft measurements show a relationship between freeboard and snow depth for thin <span class="hlt">ice</span> allowing the development of a method for estimating sea <span class="hlt">ice</span> thickness from satellite laser altimetry data at their full spatial resolution. This method is used to estimate snow and <span class="hlt">ice</span> thicknesses for the Arctic basin through the combination of freeboard data from ICESat, snow depth data over first-year <span class="hlt">ice</span> from <span class="hlt">AMSR-E</span>, and snow depth over multiyear <span class="hlt">ice</span> from climatological data. Due to the non-linear dependence of heat flux on <span class="hlt">ice</span> thickness, the impact on heat flux calculations when maintaining the full resolution of the ICESat data for <span class="hlt">ice</span> thickness estimates is explored for typical winter conditions. Calculations of the basin-wide mean heat flux and <span class="hlt">ice</span> growth rate using snow and <span class="hlt">ice</span> thickness values at the 70 m spatial resolution of ICESat are found to be approximately one-third higher than those calculated from 25 km mean <span class="hlt">ice</span> thickness values.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28298529','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28298529"><span>Deep-sea coral evidence for lower Southern Ocean surface nitrate <span class="hlt">concentrations</span> during the last <span class="hlt">ice</span> age.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wang, Xingchen Tony; Sigman, Daniel M; Prokopenko, Maria G; Adkins, Jess F; Robinson, Laura F; Hines, Sophia K; Chai, Junyi; Studer, Anja S; Martínez-García, Alfredo; Chen, Tianyu; Haug, Gerald H</p> <p>2017-03-28</p> <p>The Southern Ocean regulates the ocean's biological sequestration of CO2 and is widely suspected to underpin much of the <span class="hlt">ice</span> age decline in atmospheric CO2 <span class="hlt">concentration</span>, but the specific changes in the region are debated. Although more complete drawdown of surface nutrients by phytoplankton during the <span class="hlt">ice</span> ages is supported by some sediment core-based measurements, the use of different proxies in different regions has precluded a unified view of Southern Ocean biogeochemical change. Here, we report measurements of the (15)N/(14)N of fossil-bound organic matter in the stony deep-sea coral Desmophyllum dianthus, a tool for reconstructing surface ocean nutrient conditions. The central robust observation is of higher (15)N/(14)N across the Southern Ocean during the Last Glacial Maximum (LGM), 18-25 thousand years ago. These data suggest a reduced summer surface nitrate <span class="hlt">concentration</span> in both the Antarctic and Subantarctic Zones during the LGM, with little surface nitrate transport between them. After the <span class="hlt">ice</span> age, the increase in Antarctic surface nitrate occurred through the deglaciation and continued in the Holocene. The rise in Subantarctic surface nitrate appears to have had both early deglacial and late deglacial/Holocene components, preliminarily attributed to the end of Subantarctic iron fertilization and increasing nitrate input from the surface Antarctic Zone, respectively.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PNAS..114.3352W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PNAS..114.3352W"><span>Deep-sea coral evidence for lower Southern Ocean surface nitrate <span class="hlt">concentrations</span> during the last <span class="hlt">ice</span> age</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Xingchen Tony; Sigman, Daniel M.; Prokopenko, Maria G.; Adkins, Jess F.; Robinson, Laura F.; Hines, Sophia K.; Chai, Junyi; Studer, Anja S.; Martínez-García, Alfredo; Chen, Tianyu; Haug, Gerald H.</p> <p>2017-03-01</p> <p>The Southern Ocean regulates the ocean’s biological sequestration of CO2 and is widely suspected to underpin much of the <span class="hlt">ice</span> age decline in atmospheric CO2 <span class="hlt">concentration</span>, but the specific changes in the region are debated. Although more complete drawdown of surface nutrients by phytoplankton during the <span class="hlt">ice</span> ages is supported by some sediment core-based measurements, the use of different proxies in different regions has precluded a unified view of Southern Ocean biogeochemical change. Here, we report measurements of the 15N/14N of fossil-bound organic matter in the stony deep-sea coral Desmophyllum dianthus, a tool for reconstructing surface ocean nutrient conditions. The central robust observation is of higher 15N/14N across the Southern Ocean during the Last Glacial Maximum (LGM), 18-25 thousand years ago. These data suggest a reduced summer surface nitrate <span class="hlt">concentration</span> in both the Antarctic and Subantarctic Zones during the LGM, with little surface nitrate transport between them. After the <span class="hlt">ice</span> age, the increase in Antarctic surface nitrate occurred through the deglaciation and continued in the Holocene. The rise in Subantarctic surface nitrate appears to have had both early deglacial and late deglacial/Holocene components, preliminarily attributed to the end of Subantarctic iron fertilization and increasing nitrate input from the surface Antarctic Zone, respectively.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AIPC.1531..103K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AIPC.1531..103K"><span>Retrieval of volcanic ash and <span class="hlt">ice</span> cloud physical properties together with gas <span class="hlt">concentration</span> from IASI measurements using the AVL model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kochenova, S.; De Mazière, M.; Kumps, N.; Vandenbussche, S.; Kerzenmacher, T.</p> <p>2013-05-01</p> <p>Observation and tracking of volcanic aerosols are important for preventing possible aviation hazards and determining the influence of aerosols on climate. The useful information primary includes the <span class="hlt">concentration</span>, particle size and altitude of aerosol load. Moreover, volcanic eruptions are usually accompanied by strong emissions of SO2 and enhanced <span class="hlt">concentrations</span> of H2O in the atmosphere. Volcanic ash particles can also catalyze the formation of <span class="hlt">ice</span> clouds by serving as cloud nuclei. Hyperspectral infrared sounders, such as IASI (Infrared Atmospheric Sounding Interferometer), have proven to be powerful tools for capturing volcanic aerosol and <span class="hlt">ice</span> cloud signatures and enhanced volcanic gas <span class="hlt">concentrations</span>. Information on atmospheric constituents is extracted from such hyperspectral measurements with the help of radiative transfer (RT) codes capable of solving both direct and inverse RT problems. We will demonstrate the retrieval of aerosol and <span class="hlt">ice</span> cloud physical properties together with gas <span class="hlt">concentration</span> from IASI measurements with the help of the AVL RT model. AVL is one of the 'code combination packages' which are becoming more and more popular in the scientific domain. It consists of several codes, each of which handles a specific set of physics-related tasks. The codes function smoothly as a whole due to the use of a special interface. AVL is perfectly suitable (i) to model the propagation of UV-visible-IR radiation through a coupled atmosphere-surface system for a wide range of atmospheric, spectral and geometrical conditions; and (ii) to retrieve vertical gas profiles and aerosol <span class="hlt">concentration</span> through the use of its embedded retrieval algorithm on the basis of an optimal estimation method (OEM). The retrievals are performed for IASI measurements (radiance, Level 1C product) carried out over Eyjafjallajökull volcano, Iceland, in April 2010.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.A21A0107K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.A21A0107K"><span>Recent Increase in Black Carbon <span class="hlt">Concentrations</span> from a Mt. Everest <span class="hlt">Ice</span> Core Spanning 1860-2000 AD</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kaspari, S.; Schwikowski, M.; Gysel, M.; Mayewski, P. A.; Kang, S.; Hou, S.</p> <p>2009-12-01</p> <p>Black carbon produced by the incomplete combustion of biomass, coal and diesel fuels can significantly contribute to climate change by altering the Earth’s radiative balance. Black carbon in the atmosphere absorbs light and causes atmospheric heating, whereas black carbon deposited on snow and <span class="hlt">ice</span> can significantly reduce the surface albedo, resulting in rapid melting of snow and <span class="hlt">ice</span>. Historical records of black carbon <span class="hlt">concentration</span> and distribution in the atmosphere are needed to determine the role of black carbon in climate change, however most studies have relied on estimated inventories based on wood and/or fossil fuel consumption data. Reconstructing black carbon <span class="hlt">concentrations</span> in Asia is particularly important because this region has some of the largest black carbon sources globally, which negatively impact climate, water resources, agriculture and human health. We analyzed a Mt. Everest <span class="hlt">ice</span> core for black carbon using a single particle soot photometer (SP2). The high-resolution black carbon data demonstrates strong seasonality, with peak <span class="hlt">concentrations</span> during the winter-spring, and low <span class="hlt">concentrations</span> during the summer monsoon season. Black carbon <span class="hlt">concentrations</span> from 1975-2000 relative to 1860-1975 have increased approximately threefold, and the timing of this increase is consistent with black carbon emission inventory data from South Asia. It is notable that there is no increasing trend in iron (used as a proxy for dust) since 1860. This is significant because it suggests that if the recent retreat of glaciers in the region is due, at least in part, to the effect of impurities on snow albedo, the reduced albedo is due to changes in black carbon emissions, not dust.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70026040','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70026040"><span>Oxygen-18 <span class="hlt">concentrations</span> in recent precipitation and <span class="hlt">ice</span> cores on the Tibetan Plateau</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Tian, L.; Yao, T.; Schuster, P.F.; White, J.W.C.; Ichiyanagi, K.; Pendall, Elise; Pu, J.; Yu, W.</p> <p>2003-01-01</p> <p>A detailed study of the climatic significance of ??18O in precipitation was completed on a 1500 km southwest-northeast transect of the Tibetan Plateau in central Asia. Precipitation samples were collected at four meteorological stations for up to 9 years. This study shows that the gradual impact of monsoon precipitation affects the spatial variation of ??18O-T relationship along the transect. Strong monsoon activity in the southern Tibetan Plateau results in high precipitation rates and more depleted heavy isotopes. This depletion mechanism is described as a precipitation "amount effect" and results in a poor ??18O-T relationship at both seasonal and annual scales. In the middle of the Tibetan Plateau, the effects of the monsoon are diminished but continue to cause a reduced correlation of ??18O and temperature at the annual scale. At the monthly scale, however, a significant ??18O-T relationship does exist. To the north of the Tibetan Plateau beyond the extent of the effects of monsoon precipitation, ??18O in precipitation shows a strong temperature dependence. ??18O records from two shallow <span class="hlt">ice</span> cores and historic air temperature data were compared to verify the modern ??18O-T relationship. ??18O in Dunde <span class="hlt">ice</span> core was positively correlated with air temperature from a nearby meteorological station in the north of the plateau. The ??18O variation in an <span class="hlt">ice</span> core from the southern Plateau, however, was inversely correlated with precipitation amount at a nearby meteorological station and also the accumulation record in the <span class="hlt">ice</span> core. The long-term variation of ??18O in the <span class="hlt">ice</span> core record in the monsoon regions of the southern Tibetan Plateau suggest past monsoon seasons were probably more expansive. It is still unclear, however, how changes in large-scale atmosphere circulation might influence summer monsoon precipitation on the Tibetan Plateau.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20050179461','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20050179461"><span>Sea <span class="hlt">Ice</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Parkinson, Claire L.; Cavalieri, Donald J.</p> <p>2005-01-01</p> <p>Sea <span class="hlt">ice</span> covers vast areas of the polar oceans, with <span class="hlt">ice</span> 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 <span class="hlt">ice</span> 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 <span class="hlt">ice</span> 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 <span class="hlt">ice</span> covers, and many studies suggest possible connections between the <span class="hlt">ice</span> 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 <span class="hlt">ice</span> coverage in the Arctic and increased <span class="hlt">ice</span> coverage in the Antarctic from late 1978 through the end of 2003, with the Antarctic <span class="hlt">ice</span> increases following marked decreases in the Antarctic <span class="hlt">ice</span> during the 1970s. For a detailed picture of the seasonally varying <span class="hlt">ice</span> cover at the start of the 21st century, this chapter includes <span class="hlt">ice</span> <span class="hlt">concentration</span> 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 <span class="hlt">ice</span> covers from the 1970s through 2003.</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/2013AtmEn..75..188B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AtmEn..75..188B"><span>Measuring a 10,000-fold enhancement of singlet molecular oxygen (1O2*) <span class="hlt">concentration</span> on illuminated <span class="hlt">ice</span> relative to the corresponding liquid solution</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bower, Jonathan P.; Anastasio, Cort</p> <p>2013-08-01</p> <p>Much attention has focused on the highly reactive hydroxyl radical in the oxidation of trace organic compounds on snow and <span class="hlt">ice</span> (and subsequent release of volatile organics to the atmospheric boundary layer) but other oxidants are likely also important in this processing. Here we examine the <span class="hlt">ice</span> chemistry of singlet molecular oxygen (1O2*), which can be significant in atmospheric water drops but has not been examined in <span class="hlt">ice</span> or snow. To examine 1O2* on <span class="hlt">ice</span> we illuminate laboratory <span class="hlt">ices</span> containing Rose Bengal (RB) as the source of 1O2*, furfuryl alcohol (FFA) as the probe, and Na2SO4 to control the total solute <span class="hlt">concentration</span>. We find that the 1O2*-mediated loss of FFA (and, thus, the 1O2* <span class="hlt">concentration</span>) is up to 11,000 times greater on <span class="hlt">ice</span> than in the equivalent liquid sample at the same photon flux. We attribute this large increase in the 1O2* steady-state <span class="hlt">concentration</span> to the freeze-<span class="hlt">concentration</span> of solutes into liquid-like regions (LLRs) in/on <span class="hlt">ice</span>: compared to the initial solution, in the LLRs of <span class="hlt">ice</span> the sources for 1O2* are highly <span class="hlt">concentrated</span>, while the <span class="hlt">concentration</span> of the dominant sink for 1O2* (i.e., water) remains largely unchanged. Similar to results expected in liquid solution, rates of FFA loss in <span class="hlt">ice</span> depend on both the initial sensitizer <span class="hlt">concentration</span> and temperature, providing evidence that these reactions occur in LLRs. However, we find that the enhancement in 1O2* <span class="hlt">concentrations</span> on <span class="hlt">ice</span> does not follow predictions from freezing-point depression, likely because experiments were conducted below the eutectic temperature for sodium sulfate, where all of the salt should have precipitated. We also explore a method for separating 1O2* and rad OH contributions to FFA oxidation in laboratory <span class="hlt">ices</span> and show its application to two natural snow samples. We find that 1O2* <span class="hlt">concentrations</span> in these snows are approximately 100 times higher than observed in polluted, mid-latitude fog waters, showing that the enhancement of 1O2* on <span class="hlt">ice</span> is environmentally relevant and that</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ESASP.740E.336B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ESASP.740E.336B"><span>Singular Value Decomposition Analysis of Cloud Fraction Cover and Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> over the Arctic Region, 1982-2009</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Boccolari, Mauro; Parmiggiani, Flavio</p> <p>2016-08-01</p> <p>In this study, the coupled spatial and temporal variability between seasonal data of Cloud Fraction Cover (CFC) and Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> (SIC) in the Arctic Ocean for the 1982-2009 period were investigated by using the Singular Value Decomposition (SVD) method.The spatial patterns of CFCs related to the first mode of co-variability, identify the maximum covariance, for all seasons, in the Barents Sea and in the Arctic Ocean Canada, while the SIC and 'found in the Chukchi Sea in autumn (in according to the maximum sea <span class="hlt">ice</span> melting) and the Barents sea during both the winter and spring.CFC spatial patterns related to the first mode of co- variability, locate maximum covariance, for all seasons, in the Barents Sea and in the Canadian side of the Arctic Ocean, while for SIC is found in the Chukchi Sea during autumn (corresponding to the maximum sea <span class="hlt">ice</span> melting) and in the Barents Sea during both the winter and spring.Finally, the correlation between the seasonal time series of expansion coefficients derived from the SVD analysis, for both CFC and SIC fields, with the seasonal time series of some relevant climate indices for the Arctic (NAO, AO, PDO and PNA). Statistically significant values for both fields were found during summer with the AO, and during autumn with the PNA.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMPP11C2034H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMPP11C2034H"><span>Constraining Recent Climate History Using Helium, Krypton and Xenon <span class="hlt">Concentrations</span> in the Perennially <span class="hlt">Ice</span>-Covered Lake Bonney, 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>Hall, C. M.; Castro, C.; Kenig, F. P. H.; Peter, D.</p> <p>2016-12-01</p> <p>Lake Bonney is a perennially <span class="hlt">ice</span>-covered lake in the McMurdo Dry Valleys (MDVs) that has long been studied to provide constraints on the paleoclimate of West Antarctica. The lake is divided into two lobes, West Lake Bonney (WLB) and East Lake Bonney (ELB) that are separated by a narrow ridge. The two lobes currently receive surface melt water during austral summers from glacier-fed ephemeral streams and this meltwater enters the lake via a narrow ring, or moat, of liquid water that forms around the lake during summer. The West Lobe also receives water from direct input of melt water from Taylor glacier and saline water from irregular subglacial discharge.Here, we combine previously published He data from Lake Bonney [1, 2] with new Kr and Xe <span class="hlt">concentration</span> data to examine the signatures of water recharge via the seasonal moat and these data are used to constrain a model for He, Kr and Xe transport within both WLB and ELB over about the last 5000-6000 yrs. A detailed numerical simulation combines diffusive transport of noble gases within the stratified water column of Lake Bonney, along with <span class="hlt">ice</span> ablation at the top of the <span class="hlt">ice</span> cover, partitioning of noble gases between water and <span class="hlt">ice</span>, plus exchange of noble gases between WLB and ELB. Results strongly suggest that open moats have only operated for about 2 to 3 centuries within the last millennium. These results are corroborated by the high <span class="hlt">concentration</span> of He, especially within WLB, which points to a history of <span class="hlt">ice</span> cover with no open moats operating for both lobes for at least about 5 millennia. In addition, the distribution of He, Kr and Xe suggests that a significant rise of the water level of Lake Bonney associated with a warmer period may have been interrupted by a roughly 4 century long cold period during which the moats were not deep enough to allow air saturated water into the lake, with this cold period ending about one century ago. In the last century Lake Bonney levels have been rising, pointing either to more</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20080047000&hterms=exports&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dexports','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20080047000&hterms=exports&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dexports"><span>Baffin Bay <span class="hlt">Ice</span> Drift and Export: 2002-2007</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kwok, Ron</p> <p>2007-01-01</p> <p>Multiyear estimates of sea <span class="hlt">ice</span> drift in Baffin Bay and Davis Strait are derived for the first time from the 89 GHz channel of the <span class="hlt">AMSR-E</span> instrument. Uncertainties in the drift estimates, assessed with Envisat <span class="hlt">ice</span> motion, are approximately 2-3 km/day. A persistent atmospheric trough, between the coast of Greenland and Baffin Island, drives the prevailing southward drift pattern with average daily displacements in excess of 18-20 km during winter. Over the 5-year record, the <span class="hlt">ice</span> export ranges between 360 and 675 x 10(exp 3) km(exp 2), with an average of 530 x 10(exp 3) km(exp 2). Sea <span class="hlt">ice</span> area inflow from the Nares Strait, Lancaster Sound and Jones Sound potentially contribute up to a third of the net area outflow while <span class="hlt">ice</span> production at the North Water Polynya contributes the balance. Rough estimates of annual volume export give approximately 500-800 km(exp 3). Comparatively, these are approximately 70% and approximately 30% of the annual area and Strait.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20080047000&hterms=export&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dexport','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20080047000&hterms=export&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dexport"><span>Baffin Bay <span class="hlt">Ice</span> Drift and Export: 2002-2007</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kwok, Ron</p> <p>2007-01-01</p> <p>Multiyear estimates of sea <span class="hlt">ice</span> drift in Baffin Bay and Davis Strait are derived for the first time from the 89 GHz channel of the <span class="hlt">AMSR-E</span> instrument. Uncertainties in the drift estimates, assessed with Envisat <span class="hlt">ice</span> motion, are approximately 2-3 km/day. A persistent atmospheric trough, between the coast of Greenland and Baffin Island, drives the prevailing southward drift pattern with average daily displacements in excess of 18-20 km during winter. Over the 5-year record, the <span class="hlt">ice</span> export ranges between 360 and 675 x 10(exp 3) km(exp 2), with an average of 530 x 10(exp 3) km(exp 2). Sea <span class="hlt">ice</span> area inflow from the Nares Strait, Lancaster Sound and Jones Sound potentially contribute up to a third of the net area outflow while <span class="hlt">ice</span> production at the North Water Polynya contributes the balance. Rough estimates of annual volume export give approximately 500-800 km(exp 3). Comparatively, these are approximately 70% and approximately 30% of the annual area and Strait.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.C51A0542R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.C51A0542R"><span>The Increase of the <span class="hlt">Ice</span>-free Season as Further Indication of the Rapid Decline of the Arctic sea <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>Rodrigues, J.</p> <p>2008-12-01</p> <p>The unprecedented depletion of sea <span class="hlt">ice</span> in large sectors of the Arctic Ocean in the summer of 2007 has been the subject of many publications which highlight the spectacular disappearance of the sea <span class="hlt">ice</span> at the time of minimum <span class="hlt">ice</span> cover or emphasise the losses at very high latitudes. However, minimum values can be strongly affected by specific circumstances occurring in a comparatively short time interval. The unusually clear skies and the presence of a particular wind pattern over the Arctic Ocean may partly explain the record minimum attained in September 2007. In this contribution, instead of limiting ourselves to the September minimum or the March maximum, we consider the <span class="hlt">ice</span> conditions throughout the year, opting for a less used, and hopefully more convenient approach. We chose as variables to describe the evolution of the sea <span class="hlt">ice</span> situation in the Arctic Ocean and peripheral seas in the 1979-2007 period the length of the <span class="hlt">ice</span>- free season (LIFS) and the inverse sea <span class="hlt">ice</span> index (ISII). The latter is a quantity that measures the degree of absence of sea <span class="hlt">ice</span> in a year and varies between zero (when there is a perennial <span class="hlt">ice</span> cover) and one (when there is open water all year round). We used sea <span class="hlt">ice</span> <span class="hlt">concentration</span> data obtained from passive microwave satellite imagery and processed with the Bootstrap algorithm for the SMMR and SSM/I periods, and with the Enhanced NASA Team algorithm for the <span class="hlt">AMSR-E</span> period. From a linear fit of the observed data, we found that the average LIFS in the Arctic went from 118 days in the late 1970s to 148 days in 2006, which represents an average rate of increase of 1.1 days/year. In the period 2001-2007 the LIFS increased monotonically at an average rate of 5.5 days/year, in good agreement with the general consensus that the Arctic sea <span class="hlt">ice</span> is currently in an accelerated decline. We also found that 2007 was the longest <span class="hlt">ice</span>- free season on record (168 days). The ISII also reached a maximum in 2007 . We also investigated what happened at the regional</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70012918','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70012918"><span><span class="hlt">Concentrations</span> and source areas of <span class="hlt">ice</span> nuclei in the Alaskan atmosphere.</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Fountain, A.G.; Ohtake, T.</p> <p>1985-01-01</p> <p>The results indicate a seasonal variation in nucleus <span class="hlt">concentration</span> with a winter minimum and a north-to-south trend in the increasing average <span class="hlt">concentration</span>. Some episodes of high <span class="hlt">concentrations</span> were correlated with 500 mb isobaric transport from Eurasia and 700 mb cyclogenesis over Alaska. These results suggest that local nucleus sources play a dominant role in the seasonal variation, while some individual episodes are caused by external or regional influences. -from Authors</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2790017','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2790017"><span>Measurement of the size of intracellular <span class="hlt">ice</span> crystals in mouse oocytes using a melting point depression method and the influence of intracellular solute <span class="hlt">concentrations</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>Han, Xu; Critser, John K.</p> <p>2009-01-01</p> <p>Characterization of intracellular <span class="hlt">ice</span> formed during the cooling procedures of cells significantly benefits the development and optimization design of cryopreservation or cryosurgery techniques. In this study, we investigated the influence of the <span class="hlt">concentration</span> of extracellular non-permeable and permeable solutes on the melting points of the intracellular <span class="hlt">ice</span> in mouse oocytes using cryomicroscopy. The results showed that the melting points of the intracellular <span class="hlt">ice</span> are always lower than the extracellular <span class="hlt">ice</span>. Based on this observation and the Gibbs-Thomson relation, we established a physical model to calculate the size of intracellular <span class="hlt">ice</span> crystals and described its relationship with the <span class="hlt">concentrations</span> of intracellular permeating solutes and macromolecules. This model predicts that the increased <span class="hlt">concentration</span> of macromolecules in cells, by increasing the extracellular non-permeating solute <span class="hlt">concentration</span>, can significantly lower the required <span class="hlt">concentration</span> of permeable solutes for intracellular vitrification. The prediction was tested through the cryomicroscopic observation of the co-existence of intracellular vitrification and extracellular crystallization during cooling at 100°C/min when the extracellular solutions contain 5 molal (m) ethylene glycol and 0.3 to 0.6 m NaCl. PMID:19729005</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19729005','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19729005"><span>Measurement of the size of intracellular <span class="hlt">ice</span> crystals in mouse oocytes using a melting point depression method and the influence of intracellular solute <span class="hlt">concentrations</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Han, Xu; Critser, John K</p> <p>2009-12-01</p> <p>Characterization of intracellular <span class="hlt">ice</span> formed during the cooling procedures of cells significantly benefits the development and optimization design of cryopreservation or cryosurgery techniques. In this study, we investigated the influence of the <span class="hlt">concentration</span> of extracellular non-permeable and permeable solutes on the melting points of the intracellular <span class="hlt">ice</span> in mouse oocytes using cryomicroscopy. The results showed that the melting points of the intracellular <span class="hlt">ice</span> are always lower than the extracellular <span class="hlt">ice</span>. Based on this observation and the Gibbs-Thomson relation, we established a physical model to calculate the size of intracellular <span class="hlt">ice</span> crystals and described its relationship with the <span class="hlt">concentrations</span> of intracellular permeating solutes and macromolecules. This model predicts that the increased <span class="hlt">concentration</span> of macromolecules in cells, by increasing the extracellular non-permeating solute <span class="hlt">concentration</span>, can significantly lower the required <span class="hlt">concentration</span> of permeable solutes for intracellular vitrification. The prediction was tested through the cryomicroscopic observation of the co-existence of intracellular vitrification and extracellular crystallization during cooling at 100 degrees C/min when the extracellular solutions contain 5 molal (m) ethylene glycol and 0.3 to 0.6m NaCl.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27794227','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27794227"><span>Sea <span class="hlt">ice</span>-associated decline in body condition leads to increased <span class="hlt">concentrations</span> of lipophilic pollutants in polar bears (Ursus maritimus) from Svalbard, Norway.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tartu, Sabrina; Bourgeon, Sophie; Aars, Jon; Andersen, Magnus; Polder, Anuschka; Thiemann, Gregory W; Welker, Jeffrey M; Routti, Heli</p> <p>2017-01-15</p> <p>Global climate changes are magnified in the Arctic and are having an especially dramatic effect on the spatial and temporal distribution and the thickness traits of sea <span class="hlt">ice</span>. Decline of Arctic sea <span class="hlt">ice</span> may lead to qualitative and/or quantitative changes in diet and reduced body condition (i.e. adipose tissue stores) of <span class="hlt">ice</span>-associated apex predators such as polar bears (Ursus maritimus). This may further affect their tissue <span class="hlt">concentrations</span> of lipophilic pollutants. We determined how variations in adipose tissue stores associated to both breeding status and spatial changes in sea <span class="hlt">ice</span> conditions and diet influence <span class="hlt">concentrations</span> and biotransformation of lipophilic persistent organic pollutants (POPs). We collected 112 blood and fat samples from female polar bears (Ursus maritimus) of different breeding status (alone, with cubs of the year, or with yearlings) during two seasons (April and September) in 2012 and 2013 at three locations of Svalbard, Norway, with contrasted sea <span class="hlt">ice</span> conditions. We inferred diet from nitrogen and carbon stable isotope ratios in red blood cells and fatty acid composition in adipose tissue. Relative to diet, body condition, which was negatively related to sea <span class="hlt">ice</span> extent at both temporal and spatial scales, was the most important predictor for <span class="hlt">concentrations</span> of POPs in plasma and fat, whereas diet showed a minor influence. Additionally, fatter females were more efficient at biotransforming PCBs than were leaner ones. Breeding status influenced the <span class="hlt">concentrations</span> of less lipophilic compounds such as β-hexachlorocyclohexane, which were lower in females with yearlings, probably due to excretion into milk and subsequent offloading to young. In conclusion, our results indicate that declining sea <span class="hlt">ice</span> indirectly leads to increased <span class="hlt">concentrations</span> of lipophilic pollutants in polar bears mediated through reduced feeding opportunities and declining body condition rather than changes in diet composition.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002DSRI...49.2163K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002DSRI...49.2163K"><span>High <span class="hlt">concentrations</span> of exopolymeric substances in Arctic winter sea <span class="hlt">ice</span>: implications for the polar ocean carbon cycle and cryoprotection of diatoms</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Krembs, C.; Eicken, H.; Junge, K.; Deming, J. W.</p> <p>2002-12-01</p> <p>Exopolymeric substances (EPS) produced by microorganisms play important roles in various aquatic, porous, and extreme environments. Only recently has their occurrence in sea <span class="hlt">ice</span> been considered. We used macroscopic and microscopic approaches to study the content and possible ecological role of EPS in wintertime fast <span class="hlt">ice</span> near Barrow, Alaska (71°20' N, 156°40' W). Using Alcian blue staining of melted <span class="hlt">ice</span> samples, we observed high <span class="hlt">concentrations</span> of EPS in all samples examined, ranging from 0.79 to 7.71 mg xanthan gum equivalents (XGEQV) l -1. Areal conversions to carbon equivalents yielded 1.5-1.9 g C m -2 <span class="hlt">ice</span> in March and 3.3-4.0 g C m -2 in May (when the <span class="hlt">ice</span> was thicker). Although EPS did not correlate with macronutrient or pigment data, the latter analyses indicated ongoing or recent biological activity in the <span class="hlt">ice</span> within temperature horizons of -11°C to -9°C and warmer. EPS correlated positively with bacterial abundance (although no functional relationship could be deduced) and with dissolved organic carbon (DOC) <span class="hlt">concentrations</span>. Ratios of EPS/DOC decreased at colder temperatures within the core, arguing against physical conversion of DOC to EPS during freezing. When sea-<span class="hlt">ice</span> segments were maintained at representative winter temperatures (-5°C,-15°C and -25°C) for 3-14 months, the total EPS content increased significantly at rates of 5-47 μg XGEQV l -1 d -1, similar to published rates of EPS production by diatoms. Microscopic images of <span class="hlt">ice</span>-core sections at these very cold temperatures, using a recently developed non-invasive method, revealed diatoms sequestered in spacious brine pockets, intact autofluorescent chloroplasts in 47% of the (pennate) diatoms observed, and indications of mucus in diatom-containing pores. The high <span class="hlt">concentrations</span> of EPS detected in these winter <span class="hlt">ice</span> cores represent a previously unrecognized form of organic matter that may contribute significantly to polar ocean carbon cycles, not only within the <span class="hlt">ice</span> but after springtime release into</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011E%26PSL.307..334S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011E%26PSL.307..334S"><span>On the suitability of partially clathrated <span class="hlt">ice</span> for analysis of <span class="hlt">concentration</span> and δ 13C of palaeo-atmospheric CO 2</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schaefer, Hinrich; Lourantou, Anna; Chappellaz, Jérôme; Lüthi, Dieter; Bereiter, Bernhard; Barnola, Jean-Marc</p> <p>2011-07-01</p> <p>The stable carbon isotopic signature of carbon dioxide (δ 13CO 2) measured in the air occlusions of polar <span class="hlt">ice</span> provides important constraints on the carbon cycle in past climates. In order to exploit this information for previous glacial periods, one must use deep, clathrated <span class="hlt">ice</span>, where the occluded air is preserved not in bubbles but in the form of air hydrates. Therefore, it must be established whether the original atmospheric δ 13CO 2 signature can be reconstructed from clathrated <span class="hlt">ice</span>. We present a comparative study using coeval bubbly <span class="hlt">ice</span> from Berkner Island and <span class="hlt">ice</span> from the bubble-clathrate transformation zone (BCTZ) of EPICA Dome C (EDC). In the EDC samples the gas is partitioned into clathrates and remaining bubbles as shown by erroneously low and scattered CO 2 <span class="hlt">concentration</span> values, presenting a worst-case test for δ 13CO 2 reconstructions. Even so, the reconstructed atmospheric δ 13CO 2 values show only slightly larger scatter. The difference to data from coeval bubbly <span class="hlt">ice</span> is statistically significant. However, the 0.16‰ magnitude of the offset is small for practical purposes, especially in light of uncertainty from non-uniform corrections for diffusion related fractionation that could contribute to the discrepancy. Our results are promising for palaeo-atmospheric studies of δ 13CO 2 using a ball mill dry extraction technique below the BCTZ of <span class="hlt">ice</span> cores, where gas is not subject to fractionation into microfractures and between clathrate and bubble reservoirs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24268400','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24268400"><span>Physicochemical, bioactive, and sensory properties of persimmon-based <span class="hlt">ice</span> cream: technique for order preference by similarity to ideal solution to determine optimum <span class="hlt">concentration</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Karaman, Safa; Toker, Ömer Said; Yüksel, Ferhat; Çam, Mustafa; Kayacier, Ahmed; Dogan, Mahmut</p> <p>2014-01-01</p> <p>In the present study, persimmon puree was incorporated into the <span class="hlt">ice</span> cream mix at different <span class="hlt">concentrations</span> (8, 16, 24, 32, and 40%) and some physicochemical (dry matter, ash, protein, pH, sugar, fat, mineral, color, and viscosity), textural (hardness, stickiness, and work of penetration), bioactive (antiradical activity and total phenolic content), and sensory properties of samples were investigated. The technique for order preference by similarity to ideal solution approach was used for the determination of optimum persimmon puree <span class="hlt">concentration</span> based on the sensory and bioactive characteristics of final products. Increase in persimmon puree resulted in a decrease in the dry matter, ash, fat, protein contents, and viscosity of <span class="hlt">ice</span> cream mix. Glucose, fructose, sucrose, and lactose were determined to be major sugars in the <span class="hlt">ice</span> cream samples including persimmon and increase in persimmon puree <span class="hlt">concentration</span> increased the fructose and glucose content. Better melting properties and textural characteristics were observed for the samples with the addition of persimmon. Magnesium, K, and Ca were determined to be major minerals in the samples and only K <span class="hlt">concentration</span> increased with the increase in persimmon content. Bioactive properties of <span class="hlt">ice</span> cream samples improved and, in general, acetone-water extracts showed higher bioactivity compared with ones obtained using methanol-water extracts. The technique for order preference by similarity to ideal solution approach showed that the most preferred sample was the <span class="hlt">ice</span> cream containing 24% persimmon puree.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.C11B..05N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.C11B..05N"><span>Freeze <span class="hlt">concentration</span> effects on <span class="hlt">ice</span> (photo) chemical kinetics investigated by UV-Vis spectroscopy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Newberg, J. T.; Arble, C.; Zhang, J.</p> <p>2013-12-01</p> <p>We will describe the setup of a fiber coupled UV-Vis spectrometer to investigate the chemistry and photochemistry of aqueous solutions before and after freezing. The photochemical degradation of pyranine at the isosbestic point was investigated. Direct photochemical degradation was minor compared to indirect degradation through hydroxyl radical (OH) attack at room temperature. At -10 C indirect OH degradation was increased relative to room temperature studies, and has been attributed to the freeze <span class="hlt">concentration</span> effect. The reaction of bromate with bromide in the presence of acid to form molecular bromine was investigated. Upon freezing the formation rate of bromine significantly increases, which we attribute to the freeze <span class="hlt">concentration</span> effect.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.C53A0650J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.C53A0650J"><span>Black Carbon <span class="hlt">Concentrations</span> from ~1850-1980 from a High-Resolution <span class="hlt">Ice</span> Core from Geladandong, Central Tibetan Plateau</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jenkins, M.; Kaspari, S.; Kang, S.; Grigholm, B. O.; Mayewski, P. A.</p> <p>2011-12-01</p> <p>Black carbon (BC), produced by the incomplete combustion of fossil and bio-fuels, is estimated to be the second largest contributor to global warming behind CO2; when deposited on snow and <span class="hlt">ice</span> BC reduces albedos, potentially enhancing surface melt and glacial retreat. The study of BC's past and present variability is imperative in order to better understand and estimate its potential impact on climate and water resources. This is especially important in the Himalaya/Tibetan Plateau, a region that provides fresh water to over a billion people and where BC's climatic effects are estimated to be the largest (Flanner et al., 2007; Ramanathan and Carmichael, 2008). To more accurately constrain BC's past variability in this sensitive region, an <span class="hlt">ice</span> core recovered in 2005 from Mt. Geladandong (5800 m a.s.l.) on the central Tibetan Plateau was analyzed for BC at high resolution using a Single Particle Soot Photometer (SP2). Results indicate that 1) average BC <span class="hlt">concentrations</span> at this location are higher than at other locations closer to BC sources and analyzed by the same method (Mt. Everest by Kaspari et al., 2011 and Muztagh Ata by Wang et al., in prep), and 2) BC exists in peak <span class="hlt">concentrations</span> high enough (>10 μg/L) to cause a >1% reduction in surface albedo at the sampling location (Ming et al., 2009; Hadley et al., 2010). Potential causes of the higher BC <span class="hlt">concentrations</span> at the Geladandong site include lower annual precipitation and the mechanical trapping and <span class="hlt">concentration</span> of BC caused by surface melt and/or sublimation (Conway et al., 1996; Huang et al., 2011). Preliminary dating (Grigholm et al., in prep) has dated the top of the core to ~1980, suggesting that annual mass loss at the site has removed the upper portion of the record. This supports the findings of Kehrwald et al. (2008) who reported that glaciers below ~6050 m a.s.l. in the Himalaya/Tibetan Plateau are losing mass annually. Presented here is the record of BC on the central Tibetan Plateau over the time</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.C11A0748A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C11A0748A"><span>Seasonal Variation and Controls on Subglacial Riverine CO2 <span class="hlt">Concentrations</span> From a Small Catchment, West Greenland <span class="hlt">Ice</span> Sheet</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Andrews, G.; Jacobson, A. D.</p> <p>2015-12-01</p> <p>Previous research has suggested that subglacial discharge from the Greenland <span class="hlt">Ice</span> Sheet (GrIS) may have the potential to be a significant source of CO2 to the atmosphere in a warming world (Ryu and Jacobson, 2011). To trace the flux, sources of, and controls on subglacial CO2, we sampled the Akuliarusiarsuup Kuua River subglacial portal, which receives water from the Isunnguata and Russell Glaciers, west GrIS, six times throughout June - August, 2014. Additionally, we sampled two nearby supraglacial streams. We present preliminary data on pCO2 values, DIC and DOC <span class="hlt">concentrations</span>, major cation and anion <span class="hlt">concentrations</span>, δ13CDIC isotopes, as well as ∆14C-DIC and -DOCisotopes. Waters emerging from the subglacial portal are 2 - 2.5x supersaturated in CO2 with respect to atmospheric equilibrium. pCO2 values rise from ~700 to 1000ppm between June and July then return to ~700ppm in August. Although subglacial pCO2 and ∆14CDIC values vary, throughout the summer they exhibit similar trends as contemporaneous supraglacial stream values, suggesting that subglacial CO2 is at least partially derived from supraglacial meltwater which has accessed the <span class="hlt">ice</span> sheet base through moulins and crevasses. δ13CDIC isotopes of supraglacial streams are highly depleted (-24‰), suggesting that CO2 is sourced from microbial respiration of surficial organic carbon. Subglacial portal δ13CDIC isotopes are also relatively depleted (-17‰) but are sufficiently different relative to supraglacial streams so as to require an additional δ13CDIC enriched source. A strong correlation (R2 = 0.89, n= 6) between subglacial Ca+Mg <span class="hlt">concentrations</span> and alkalinity (≈ HCO3) suggests that the additional source of DIC to these waters is dissolution of carbonate. Finally, the correlation (R2 = 0.55, n = 6) between subglacial pCO2 and ∆14CDOC values suggest that one control on variable CO2 <span class="hlt">concentrations</span> throughout the melt season is the age, and presumably, the lability, of organic carbon available to</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ACP....15.2313B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ACP....15.2313B"><span>Persistent after-effects of heavy rain on <span class="hlt">concentrations</span> of <span class="hlt">ice</span> nuclei and rainfall suggest a biological cause</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bigg, E. K.; Soubeyrand, S.; Morris, C. E.</p> <p>2015-03-01</p> <p>Rainfall is one of the most important aspects of climate, but the extent to which atmospheric <span class="hlt">ice</span> nuclei (IN) influence its formation, quantity, frequency, and location is not clear. Microorganisms and other biological particles are released following rainfall and have been shown to serve as efficient IN, in turn impacting cloud and precipitation formation. Here we investigated potential long-term effects of IN on rainfall frequency and quantity. Differences in IN <span class="hlt">concentrations</span> and rainfall after and before days of large rainfall accumulation (i.e., key days) were calculated for measurements made over the past century in southeastern and southwestern Australia. Cumulative differences in IN <span class="hlt">concentrations</span> and daily rainfall quantity and frequency as a function of days from a key day demonstrated statistically significant increasing logarithmic trends (R2 > 0.97). Based on observations that cumulative effects of rainfall persisted for about 20 days, we calculated cumulative differences for the entire sequence of key days at each site to create a historical record of how the differences changed with time. Comparison of pre-1960 and post-1960 sequences most commonly showed smaller rainfall totals in the post-1960 sequences, particularly in regions downwind from coal-fired power stations. This led us to explore the hypothesis that the increased leaf surface populations of IN-active bacteria due to rain led to a sustained but slowly diminishing increase in atmospheric <span class="hlt">concentrations</span> of IN that could potentially initiate or augment rainfall. This hypothesis is supported by previous research showing that leaf surface populations of the <span class="hlt">ice</span>-nucleating bacterium Pseudomonas syringae increased by orders of magnitude after heavy rain and that microorganisms become airborne during and after rain in a forest ecosystem. At the sites studied in this work, aerosols that could have initiated rain from sources unrelated to previous rainfall events (such as power stations) would</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19185125','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19185125"><span>An ultra-clean technique for accurately analysing Pb isotopes and heavy metals at high spatial resolution in <span class="hlt">ice</span> cores with sub-pg g(-1) Pb <span class="hlt">concentrations</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Burn, Laurie J; Rosman, Kevin J R; Candelone, Jean-Pierre; Vallelonga, Paul; Burton, Graeme R; Smith, Andrew M; Morgan, Vin I; Barbante, Carlo; Hong, Sungmin; Boutron, Claude F</p> <p>2009-02-23</p> <p>Measurements of Pb isotope ratios in <span class="hlt">ice</span> containing sub-pg g(-1) <span class="hlt">concentrations</span> are easily compromised by contamination, particularly where limited sample is available. Improved techniques are essential if Antarctic <span class="hlt">ice</span> cores are to be analysed with sufficient spatial resolution to reveal seasonal variations due to climate. This was achieved here by using stainless steel chisels and saws and strict protocols in an ultra-clean cold room to decontaminate and section <span class="hlt">ice</span> cores. Artificial <span class="hlt">ice</span> cores, prepared from high purity water were used to develop and refine the procedures and quantify blanks. Ba and In, two other important elements present at pg g(-1) and fg g(-1) <span class="hlt">concentrations</span> in Polar <span class="hlt">ice</span>, were also measured. The final blank amounted to 0.2+/-0.2 pg of Pb with (206)Pb/(207)Pb and (208)Pb/(207)Pb ratios of 1.16+/-0.12 and 2.35+/-0.16, respectively, 1.5+/-0.4 pg of Ba and 0.6+/-2.0 fg of In, most of which probably originates from abrasion of the steel saws by the <span class="hlt">ice</span>. The procedure was demonstrated on a Holocene Antarctic <span class="hlt">ice</span> core section and was shown to contribute blanks of only approximately 5%, approximately 14% and approximately 0.8% to monthly resolved samples with respective Pb, Ba and In <span class="hlt">concentrations</span> of 0.12 pg g(-1), 0.3 pg g(-1) and 2.3 fg g(-1). Uncertainties in the Pb isotopic ratio measurements were degraded by only approximately 0.2%.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003EAEJA....12815H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003EAEJA....12815H"><span>Data sets for snow cover monitoring and modelling from the National Snow and <span class="hlt">Ice</span> Data Center</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Holm, M.; Daniels, K.; Scott, D.; McLean, B.; Weaver, R.</p> <p>2003-04-01</p> <p>A wide range of snow cover monitoring and modelling data sets are pending or are currently available from the National Snow and <span class="hlt">Ice</span> Data Center (NSIDC). In-situ observations support validation experiments that enhance the accuracy of remote sensing data. In addition, remote sensing data are available in near-real time, providing coarse-resolution snow monitoring capability. Time series data beginning in 1966 are valuable for modelling efforts. NSIDC holdings include SMMR and SSM/I snow cover data, MODIS snow cover extent products, in-situ and satellite data collected for NASA's recent Cold Land Processes Experiment, and soon-to-be-released ASMR-E passive microwave products. The <span class="hlt">AMSR-E</span> and MODIS sensors are part of NASA's Earth Observing System flying on the Terra and Aqua satellites Characteristics of these NSIDC-held data sets, appropriateness of products for specific applications, and data set access and availability will be presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012ACP....12..727G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012ACP....12..727G"><span>On the observation of unusual high <span class="hlt">concentration</span> of small chain-like aggregate <span class="hlt">ice</span> crystals and large <span class="hlt">ice</span> water contents near the top of a deep convective cloud during the CIRCLE-2 experiment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gayet, J.-F.; Mioche, G.; Bugliaro, L.; Protat, A.; Minikin, A.; Wirth, M.; Dörnbrack, A.; Shcherbakov, V.; Mayer, B.; Garnier, A.; Gourbeyre, C.</p> <p>2012-01-01</p> <p>During the CIRCLE-2 experiment carried out over Western Europe in May 2007, combined in situ and remote sensing observations allowed to describe microphysical and optical properties near-top of an overshooting convective cloud (11 080 m/-58 °C). The airborne measurements were performed with the DLR Falcon aircraft specially equipped with a unique set of instruments for the extensive in situ cloud measurements of microphysical and optical properties (Polar Nephelometer, FSSP-300, Cloud Particle Imager and PMS 2-D-C) and nadir looking remote sensing observations (DLR WALES Lidar). Quasi-simultaneous space observations from MSG/SEVIRI, CALIPSO/CALIOP-WFC-IIR and CloudSat/CPR combined with airborne RASTA radar reflectivity from the French Falcon aircraft flying above the DLR Falcon depict very well convective cells which overshoot by up to 600 m the tropopause level. Unusual high values of the <span class="hlt">concentration</span> of small <span class="hlt">ice</span> particles, extinction, <span class="hlt">ice</span> water content (up to 70 cm-3, 30 km-1 and 0.5 g m-3, respectively) are experienced. The mean effective diameter and the maximum particle size are 43 μm and about 300 μm, respectively. This very dense cloud causes a strong attenuation of the WALES and CALIOP lidar returns. The SEVIRI retrieved parameters confirm the occurrence of small <span class="hlt">ice</span> crystals at the top of the convective cell. Smooth and featureless phase functions with asymmetry factors of 0.776 indicate fairly uniform optical properties. Due to small <span class="hlt">ice</span> crystals the power-law relationship between <span class="hlt">ice</span> water content (IWC) and radar reflectivity appears to be very different from those usually found in cirrus and anvil clouds. For a given equivalent reflectivity factor, IWCs are significantly larger for the overshooting cell than for the cirrus. Assuming the same prevalent microphysical properties over the depth of the overshooting cell, RASTA reflectivity profiles scaled into <span class="hlt">ice</span> water content show that retrieved IWC up to 1 g m-3 may be observed near the cloud top</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('http://adsabs.harvard.edu/abs/2015AGUFM.A13D0371W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A13D0371W"><span>Impacts of alternative fuels in aviation on microphysical aerosol properties and predicted <span class="hlt">ice</span> nuclei <span class="hlt">concentration</span> at aircraft cruise altitude</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Weinzierl, B.; D'Ascoli, E.; Sauer, D. N.; Kim, J.; Scheibe, M.; Schlager, H.; Moore, R.; Anderson, B. E.; Ullrich, R.; Mohler, O.; Hoose, C.</p> <p>2015-12-01</p> <p>In the past decades air traffic has been substantially growing affecting air quality and climate. According to the International Civil Aviation Authority (ICAO), in the next few years world passenger and freight traffic is expected to increase annually by 6-7% and 4-5%, respectively. One possibility to reduce aviation impacts on the atmosphere and climate might be the replacement of fossil fuels by alternative fuels. However, so far the effects of alternative fuels on particle emissions from aircraft engines and their ability to form contrails remain uncertain. To study the effects of alternative fuels on particle emissions and the formation of contrails, the Alternative Fuel Effects on Contrails and Cruise Emissions (ACCESS) field experiment was conducted in California. In May 2014, the DLR Falcon 20 and the NASA HU-25 jet aircraft were instrumented with an extended aerosol and trace gas payload probing different types of fuels including JP-8 and JP-8 blended with HEFA (Hydroprocessed Esters and Fatty Acids) while the NASA DC8 aircraft acted as the source aircraft for ACCESS-2. Emission measurements were taken in the DC8 exhaust plumes at aircraft cruise level between 9-12 km altitude and at distances between 50 m and 20 km behind the DC8 engines. Here, we will present results from the ACCESS-2 aerosol measurements which show a 30-60% reduction of the non-volatile (mainly black carbon) particle number <span class="hlt">concentration</span> in the aircraft exhaust for the HEFA-blend compared to conventional JP-8 fuel. Size-resolved particle emission indices show the largest reductions for larger particle sizes suggesting that the HEFA blend contains fewer and smaller black carbon particles. We will combine the airborne measurements with a parameterization of deposition nucleation developed during a number of <span class="hlt">ice</span> nucleation experiments at the AIDA chamber in Karlsruhe and discuss the impact of alternative fuels on the abundance of potential <span class="hlt">ice</span> nuclei at cruise conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1992Metic..27Q.298T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1992Metic..27Q.298T"><span>Proposed <span class="hlt">Ice</span> Flow, Given 200m and 400m Additional <span class="hlt">Ice</span> in the Allan Hills Region, Antarctica: Implications for Meteorite <span class="hlt">Concentration</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Traub-Metlay, S.; Cassidy, W. A.</p> <p>1992-07-01</p> <p>The Allan Hills-David Glacier region contains some of the most highly populated meteorite stranding surfaces in Antarctica. Nearly 2000 meteorites have to date been collected from the icefields associated with the Allan Hills, and nearly 1500 from areas around Elephant Moraine. While much attention has been focused on the current geological and glaciological conditions of these stranding surfaces, less work has been done concerning what they may have looked like in the past, when <span class="hlt">ice</span> thicknesses may have been greater. In this study, conjectural maps of the current Allan Hills area with 200 meters and 400 meters of additional <span class="hlt">ice</span> cover each are analyzed for probable regional and local <span class="hlt">ice</span> flow patterns. A dramatic decrease in <span class="hlt">ice</span> thickness over a relatively brief period of time could result either from climatic change or a geologically rapid regional uplift. Delisle and Sievers (1991) noted that the valley between the Allan Hills Main Icefield and the Allan Hills resembles a half-graben resulting from east-west extensional tectonics, and that the mesa-like bedrock features associated with the Near Western and Mid Western Icefields resemble fault blocks. They concluded that the Allan Hills area icefields may have become active stranding surfaces as a result of a regional uplift within the past 1-2 million years, assuming a current rate of uplift in the Allan Hills region of ~100 meters/million years. Whether the cause was climatic or tectonic, generalized maps of current <span class="hlt">ice</span> contours plus 400 and 200 meters <span class="hlt">ice</span> may provide views of what the Allan Hills region looked like just before activation of the modern meteorite stranding surfaces (Figs. 1 and 2). At an <span class="hlt">ice</span> thickness greater by 400 meters, <span class="hlt">ice</span> could flow smoothly over the Allan Hills and would drain down to the Mawson Glacier via the Odell Glacier, east of the Allan Hills; down the Manhaul Bay depression between the east and west arms of Allan Hills; and down the half-graben discovered by Delisle and Sievers</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20070016601&hterms=Ecological+zones&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DEcological%2Bzones','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20070016601&hterms=Ecological+zones&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DEcological%2Bzones"><span>Variations in the Sea <span class="hlt">Ice</span> Edge and the Marginal <span class="hlt">Ice</span> Zone on Different Spatial Scales as Observed from Different Satellite Sensor</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Markus, Thorsten; Henrichs, John</p> <p>2006-01-01</p> <p>The Marginal sea <span class="hlt">Ice</span> Zone (MIZ) and the sea <span class="hlt">ice</span> edge are the most dynamic areas of the sea <span class="hlt">ice</span> cover. Knowledge of the sea <span class="hlt">ice</span> edge location is vital for routing shipping in the polar regions. The <span class="hlt">ice</span> edge is the location of recurrent plankton blooms, and is the habitat for a number of animals, including several which are under severe ecological threat. Polar lows are known to preferentially form along the sea <span class="hlt">ice</span> edge because of induced atmospheric baroclinicity, and the <span class="hlt">ice</span> edge is also the location of both vertical and horizontal ocean currents driven by thermal and salinity gradients. Finally, sea <span class="hlt">ice</span> is both a driver and indicator of climate change and monitoring the position of the <span class="hlt">ice</span> edge accurately over long time periods enables assessment of the impact of global and regional warming near the poles. Several sensors are currently in orbit that can monitor the sea <span class="hlt">ice</span> edge. These sensors, though, have different spatial resolutions, different limitations, and different repeat frequencies. Satellite passive microwave sensors can monitor the <span class="hlt">ice</span> edge on a daily or even twice-daily basis, albeit with low spatial resolution - 25 km for the Special Sensor Microwave Imager (SSM/I) or 12.5 km for the Advanced Microwave Scanning Radiometer (<span class="hlt">AMSR-E</span>). Although special methods exist that allow the detection of the sea <span class="hlt">ice</span> edge at a quarter of that nominal resolution (PSSM). Visible and infrared data from the Advanced Very High Resolution Radiometer (AVHRR) and from the Moderate Resolution Imaging Spectroradiometer (MODIS) provide daily coverage at 1 km and 250 m, respectively, but the surface observations me limited to cloud-free periods. The Landsat 7 Enhanced Thematic Mapper (ETM+) has a resolution of 15 to 30 m but is limited to cloud-free periods as well, and does not provide daily coverage. Imagery from Synthetic Aperture Radar (SAR) instruments has resolutions of tens of meters to 100 m, and can be used to distinguish open water and sea <span class="hlt">ice</span> on the basis of surface</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.C13A0591U','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.C13A0591U"><span>The effect of acidified sample storage time on the determination of trace element <span class="hlt">concentration</span> in <span class="hlt">ice</span> cores by ICP-SFMS</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Uglietti, C.; Gabrielli, P.; Lutton, A.; Olesik, J.; Thompson, L. G.</p> <p>2012-12-01</p> <p>Trace elements in micro-particles entrapped in <span class="hlt">ice</span> cores are a valuable proxy of past climate and environmental variations. Inductively coupled plasma sector field mass spectrometry (ICP-SFMS) is generally recognized as a sensitive and accurate technique for the quantification of ultra-trace element <span class="hlt">concentrations</span> in <span class="hlt">ice</span> cores. Usually, ICP-SFMS analyses of <span class="hlt">ice</span> core samples are performed by melting and acidifying aliquots. Acidification is important to transfer trace elements from particles into solution by partial and/or complete dissolution. Only elements in solution and in sufficiently small particles will be vaporized and converted to elemental ions in the plasma for detection by ICP-SFMS. However, experimental results indicate that differences in acidified sample storage time at room temperature may lead to the recovery of different trace element fractions. Moreover, different lithologies of the relatively abundant crustal material entrapped in the <span class="hlt">ice</span> matrix could also influence the fraction of trace elements that are converted into elemental ions in the plasma. These factors might affect the determination of trace elements <span class="hlt">concentrations</span> in <span class="hlt">ice</span> core samples and hamper the comparison of results obtained from <span class="hlt">ice</span> cores from different locations and/or epochs. In order to monitor the transfer of elements from particles into solution in acidified melted <span class="hlt">ice</span> core samples during storage, a test was performed on sections from nine <span class="hlt">ice</span> cores retrieved from low latitude drilling sites around the world. When compared to <span class="hlt">ice</span> cores from polar regions, these samples are characterized by a relative high content of micro-particles that may leach trace elements into solution differently. Of the nine <span class="hlt">ice</span> cores, five are from the Tibetan Plateau (Dasuopu, Guliya, Naimonanyi, Puruogangri and Dunde), two from the Andes (Quelccaya and Huascaran), one from Africa (Kilimanjaro) and one from the Eastern Alps (Ortles). These samples were decontaminated by triple rinsing, melted and</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 Sea-<span class="hlt">Ice</span> Freeboard and Estimated Thickness in the Weddell Sea, 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>Sea-<span class="hlt">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 (<span class="hlt">AMSR-E</span>) data and nominal densities of snow, water and sea <span class="hlt">ice</span>, to estimate sea-<span class="hlt">ice</span> thickness. Sea-<span class="hlt">ice</span> freeboard and thickness distributions show clear seasonal variations that reflect the yearly cycle of growth and decay of the Weddell Sea (Antarctica) pack <span class="hlt">ice</span>. During October-November, sea <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 sea <span class="hlt">ice</span> melts away and the sea-<span class="hlt">ice</span> pack is mainly distributed in the west Weddell Sea; 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 sea-<span class="hlt">ice</span> extent 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://adsabs.harvard.edu/abs/2015AGUFMPP34A..02E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMPP34A..02E"><span>Separating the Effects of Northern Hemisphere <span class="hlt">Ice</span>-Sheets, CO2 <span class="hlt">Concentrations</span> and Orbital Parameters on Global Precipitation During the Late Pleistocene Glacial Cycles</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Elison Timm, O.; Friedrich, T.; Timmermann, A.; Ganopolski, A.</p> <p>2015-12-01</p> <p>Global-scale changes in the hydrological cycle have been reconstructed in many parts of the world using various archives of proxy information. The signals found in proxies allow us to study the complex response of the global hydrological cycle to the combined forcing and feedback mechanisms. However, it remains a challenge to attribute the observed variations to specific causes, in particular, it is difficult to distinguish CO2 and <span class="hlt">ice</span>-sheet response in time series. Here, we present new results from a set of transient paleoclimate simulation of the last eight glacial cycles (784,000 years) using accelerated forcing. In order to isolate the <span class="hlt">ice</span>-sheet forcing from the CO2 -driven response and orbital forcing, we made use of additional transient experiments with varying forcing combinations covering the last 408,000 years: (a) keeping CO2 <span class="hlt">concentrations</span> constant, (b) keeping the <span class="hlt">ice</span>-sheet fixed, (c) orbital forcing only. The simulations show that orbital forcing has strongest impact in the tropical and subtropical regions. The northern hemisphere <span class="hlt">ice</span>-sheets stamp a characteristic spatial footprint on the global precipitation variability. The <span class="hlt">ice</span>-sheets mainly affect the extratropical northern hemisphere, but the cone of influence extends further into the North African monsoon regions, and to a weaker extent into the Asian monsoon. In an attempt to validate our model-specific results we compared our results with existing hydrological paleo proxy records. Despite the growing number of proxy archives, the aim to identify the <span class="hlt">ice</span>-sheet influence in spatially limited networks of proxy time series remains as challenge. More records that cover at least two full glacial cycles could significantly increase the signal separation. In conclusion, our results suggest that the northern hemisphere <span class="hlt">ice</span>-sheets played an important role in modulating the global hydrological cycle.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JMetR..31..468Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JMetR..31..468Z"><span>Physical processes responsible for the interannual variability of sea <span class="hlt">ice</span> <span class="hlt">concentration</span> in Arctic in boreal autumn since 1979</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Lei; Li, Tim</p> <p>2017-06-01</p> <p>Arctic sea <span class="hlt">ice</span> <span class="hlt">concentration</span> (ASIC) in boreal autumn exhibits prominent interannual variability since 1979. The physical mechanism responsible for the year-to-year variation of ASIC is investigated through observational data analyses and idealized numerical modeling. It is found that the ASIC interannual variability is closely associated with the anomalous meridional circulations over the Northern Hemisphere, which is further linked with the tropical sea surface temperature (SST) forcing. A tropics-wide SST cooling anomaly leads to an enhanced meridional SST gradient to the north of the equator in boreal summer, generating strengthened and northward shifting Hadley circulation over the Northern Hemisphere. Consequently, the meridional circulations are enhanced and pushed poleward, leading to an enhanced descending motion at the North Pole, surrounded by an ascending motion anomaly; the surface outflow turns into easterly anomalies, opposing the mean-state winds. As a result, positive cloudiness and weakened surface wind speed emerge, which reduce ASIC through changes in the surface latent heat flux and the downward longwave radiation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1811856R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1811856R"><span>A new high-precision technique for measurement of N2O <span class="hlt">concentration</span> in polar <span class="hlt">ice</span> cores with small amount of samples</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ryu, Yeongjun; Yang, Ji-Woong; Ahn, Jinho</p> <p>2016-04-01</p> <p>Nitrous oxide, one of the major greenhouse gases, has about 300 times higher GWP for 100 years, although its mixing ratio is a thousand time less than that of CO2. Since N2O has important roles in biogeochemical nitrogen cycles, atmospheric ozone destruction, and long term scale climate feedback, it is crucial to comprehend the underlying mechanisms that lead changes in global inventories of greenhouse gases in the past. Because previous data from <span class="hlt">ice</span> core studies have large uncertainty of 5 ppbv with relatively low temporal resolutions, they are not sufficient for interpreting centennial to multi-centennial variations. Here we present a new high-precision technique for measuring N2O <span class="hlt">concentration</span> of ancient air occluded in <span class="hlt">ice</span> cores. We use a wet extraction method (melting-refreezing method) to extract gas from the <span class="hlt">ice</span> core, and GC-ECD to determine N2O <span class="hlt">concentration</span>. The optimized setting for GC-ECD permits high sensitivity for N2O, and minimizes volume of <span class="hlt">ice</span> core sample that is requisite to get reliable results. Here we present preliminary results that we obtained from 15 ~ 20 g of <span class="hlt">ice</span> core samples. The values for solubility correction is measured by an additional melting-refreezing process. The amount of correction is about 3 ppbv for 329.88 ppbv N2O standard gas air (calibrated from NOAA) with an uncertainty of < 1 ppbv. We also compare the results with those from a dry extraction method for validation, and present preliminary results from Styx <span class="hlt">ice</span> core, Antarctica. The updated results will be presented at the meeting.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23619025','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23619025"><span>Effect of cooling rate and cryoprotectant <span class="hlt">concentration</span> on intracellular <span class="hlt">ice</span> formation of small abalone (Haliotis diversicolor) eggs.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Yang, Chiang-Yi; Yeh, Yu-Hui Flora; Lee, Po-Ting; Lin, Ta-Te</p> <p>2013-08-01</p> <p>The intracellular <span class="hlt">ice</span> formation (IIF) behavior of Haliotis diversicolor (small abalone) eggs is investigated in this study, in relation to controlling the cooling rate and the <span class="hlt">concentration</span> of dimethyl sulfoxide (DMSO). The IIF phenomena are monitored under a self-developed thermoelectric cooling (TEC) cryomicroscope system which can achieve accurate temperature control without the use of liquid nitrogen. The accuracy of the isothermal and ramp control is within ±0.5 °C. The IIF results indicate that the IIF of small abalone eggs is well suppressed at cooling rates of 1.5, 3, 7 and 12 °C/min with 2.0, 2.5, 3.0 and 4.0M DMSO in sea water. As 2.0M DMSO in sea water is the minimum <span class="hlt">concentration</span> that has sufficient IIF suppression, it is selected as the suspension solution for the cryopreservation of small abalone eggs in order to consider the solution's toxicity effect. Moreover, IIF characteristics of the cumulative probability of IIF temperature distribution are shown to be well fitted by the Weibull probabilistic distribution. According to our IIF results and the Weibull distribution parameters, we conclude that cooling at 1.5 °C/min from 20 to -50 °C with 2.0M DMSO in sea water is more feasible than other combinations of cooling rates and DMSO <span class="hlt">concentrations</span> in our experiments. Applying this protocol and observing the subsequent osmotic activity, 48.8% of small abalone eggs are osmotically active after thawing. In addition, the higher the cooling rate, the less chance of osmotically active eggs. A separate fertility test experiment, with a cryopreservation protocol of 1.5 °C/min cooling rate and 2.0M DMSO in sea water, achieves a hatching rate of 23.7%. This study is the first to characterize the IIF behavior of small abalone eggs in regard to the cooling rate and the DMSO <span class="hlt">concentration</span>. The Weibull probabilistic model fitting in this study is an approach that can be applied by other researchers for effective cryopreservation variability estimation and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1412530S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1412530S"><span>The role of Antarctic <span class="hlt">Ice</span> Shelves in Holocene CO2 <span class="hlt">concentrations</span>: A study using the University of Victoria Earth System 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>Simmons, C.; Mysak, L. A.; Matthews, H. D.</p> <p>2012-04-01</p> <p>The University of Victoria Earth System Climate Model (version v.9) is used to investigate carbon cycle dynamics from the Last Glacial Maximum to the present, with a particular emphasis on recreating the Holocene carbon cycle from 8000 to 150 years before present (BP). Without the explicit representation of peatlands, coral reefs and land-use change, the UVic model simulation of the natural carbon cycle over the above Holocene period is characterized by a decline in the atmospheric CO2 <span class="hlt">concentration</span>, from 260 to around 250 ppm, in contrast to the increase from 260 to 280 ppm actually observed during this period. However, these results are highly sensitive to the configuration of land <span class="hlt">ice</span> shelves near Antarctica, with more extensive land <span class="hlt">ice</span> leading to deeper vertical circulation in the Southern Ocean, less Antarctic-generated bottom waters globally, and a higher atmospheric CO2 <span class="hlt">concentration</span> (260 ppm) at 150 yr BP. The remaining observed 20 ppm CO2 increase was likely caused by a combination of changes in land use, ocean circulation and ocean chemistry associated with coral reef migration between 8000 and 150 yr BP. The 5-8 ppm contribution of <span class="hlt">ice</span> shelf extent may well be an important contributor to the higher analogue CO2 levels during the Holocene interglacial, as current data and reconstructions suggests that these <span class="hlt">ice</span> shelves are indeed more extensive today than during many previous interglacial periods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1512562R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1512562R"><span>Two-way coupled <span class="hlt">ice</span> sheet-earth system simulations: Consequences of raising CO2 <span class="hlt">concentration</span> for Greenland and the interacting climate system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rodehacke, Christian; Vizcaino, Miren; Mikolajewicz, Uwe</p> <p>2013-04-01</p> <p>The observed distinct warming in the Arctic and the northward flow of tropical water masses seem to trigger enhanced melting of the Greenland <span class="hlt">ice</span> sheet, which adds more fresh water into the ambient ocean. A continuation of the observed accelerated melting during the last decade would stabilize the water column in the adjacent deep water formation sides. With our fully coupled <span class="hlt">ice</span> sheet-earth system model we approach the questions if this weakens the formation of deep water masses and reduces the thermohaline driven meridional overturning circulation (MOC). We have performed idealized future projections to investigate the response of the interaction under raising atmospheric carbon dioxide <span class="hlt">concentration</span> with our two-way coupled <span class="hlt">ice</span> sheet-earth system model system. We will present the building blocks of our fully coupled system, which includes a physical based calculation of the <span class="hlt">ice</span> sheet's surface mass balance and <span class="hlt">ice</span> sheet-ocean interaction; The ESM instead is subject to orographic changes and receives fresh water fluxes, for example. Since the behavior of an <span class="hlt">ice</span> sheet in the near future is controlled by both the external forcing and by its initial conditions, we have performed Latin Hyper Cube (LHC) simulations with the <span class="hlt">ice</span> sheet model over more than one glacial-interglacial cycle utilizing standard techniques to obtain a reasonable initial state. According to several quantities the best performing LHC member is exposed afterwards to boundary conditions determined from energy balance calculations again obtained from simulated forcing fields. Finally the fully coupled system is brought into a quasi-equilibrium under pre-industrial conditions before idealized scenarios have been started. In contrast to commonly used strategies, our coupled <span class="hlt">ice</span> sheet inherits the memory of a glacial cycle simulations obtain exclusively from ESM fields. Furthermore we use a mass conserving scheme, do neither apply flux corrections nor utilize anomaly coupling. Under different CO2</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA551010','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA551010"><span>Real-time Data Assimilation of Satellite Derived <span class="hlt">Ice</span> <span class="hlt">Concentration</span> into the Arctic Cap Nowcast/Forecast System (ACNFS)</span></a></p> <p><a target="_blank" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2011-09-01</p> <p>North America , Technology Solutions Group Stennis Space Center, MS 39529 USA M.W. Phelps Jacobs Engineering Stennis Space Center, MS 39529 USA...precipitation rates (i.e., snowfall ); a model of <span class="hlt">ice</span> dynamics that predicts the velocity field of the <span class="hlt">ice</span> pack based on a model of the material strength of the...the Data Assimilation and Model Evaluation Experiments North Atlantic data, the International Bathymetric Chart of the Arctic Ocean data, the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.8599P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.8599P"><span>Microwat : a new Earth Explorer mission proposal to measure the Sea surface Temperature and the Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Prigent, Catherine; Aires, Filipe; Heygster, Georg</p> <p>2017-04-01</p> <p>Ocean surface characterization from satellites is required to understand, monitor and predict the general circulation of the ocean and atmosphere. With more than 70% global cloud coverage at any time, visible and infrared satellite observations only provide limited information. The polar regions are particularly vulnerable to the climate changes and are home to complex mesoscale mechanisms that are still poorly understood. They are also under very persis- tent cloudiness. Passive microwave observations can provide surface information such as Sea Surface Temperature (SST) and Sea <span class="hlt">Ice</span> <span class="hlt">Concentration</span> (SIC) regardless of the cloud cover, but up to now they were limited in spatial resolution. Here, we propose a passive microwave conically scanning imager, MICROWAT, in a polar orbit, for the retrieval of the SST and SIC, with a spatial resolution of 15km. It observes at 6 and 10GHz, with low-noise dual polarization receivers, and a foldable mesh antenna of 5m-diameter. Furthermore, MICROWAT will fly in tandem with MetOp-SG B to benefit from the synergy with scatterometers (SCA) and microwave imagers (MWI). MICROWAT will provide global SST estimates, twice daily, regardless of cloud cover, with an accuracy of 0.3K and a spatial resolution of 15km. The SIC will be derived with an accuracy of 3%. With its unprecedented "all weather" accurate SST and SIC at 15km, MICROWAT will provide the atmospheric and oceanic forecasting sys- tems with products compatible with their increasing spatial resolution and complexity, with impact for societal applications. It will also answer fundamental science questions related to the ocean, the atmosphere and their interactions. * Prigent, Aires, Bernardo, Orlhac, Goutoule, Roquet, & Donlon, Analysis of the potential and limitations of microwave radiometry for the retrieval of sea surface temperature: Definition</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/AD1013757','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD1013757"><span>Wave-<span class="hlt">ice</span> Interaction and 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>2015-09-30</p> <p><span class="hlt">concentrations</span> at the time. Any <span class="hlt">ice</span> present appeared to be fragments of deformed <span class="hlt">ice</span> (ridges), as most of the level <span class="hlt">ice</span> had melted . Figure 1: Final...the buoys had approached the edge of the melting pack <span class="hlt">ice</span> , from late August, when the vast floes had already fragmented due to dynamics and...1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Wave- <span class="hlt">ice</span> interaction and the Marginal <span class="hlt">Ice</span> Zone Prof</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19842493','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19842493"><span><span class="hlt">Ice</span> growth in supercooled solutions of a biological "antifreeze", AFGP 1-5: an explanation in terms of adsorption rate for the <span class="hlt">concentration</span> dependence of the freezing point.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Knight, C A; DeVries, A L</p> <p>2009-07-21</p> <p>It is widely accepted, and we agree, that the lowering of the temperature at which <span class="hlt">ice</span> can grow in a water solution of one of the biological antifreezes is a result of adsorption of the antifreeze molecules at the <span class="hlt">ice</span> surface. However, how this can produce a well-defined "freezing point" that varies with the solution <span class="hlt">concentration</span> has remained problematical. The results of a series of measurements of <span class="hlt">ice</span> growing in supercooled solutions of an effective antifreeze are reported and interpreted in terms of this fundamental problem. It seemed that the solution of the problem would have to rely upon adsorption rate, because that appeared to be the only way for the <span class="hlt">concentration</span> in solution to be so important. The crystal growth results are most unusual, and appear to confirm this. The growth rates over a wide range of antifreeze <span class="hlt">concentration</span> in solution (about 0.05 to 9 mg ml(-1)) are zero from the thermodynamic freezing point down to the "non-equilibrium" freezing point, where there is a very sudden increase to a plateau value that then remains about constant as the supercooling is increased by about 2 degrees C. The plateau values of growth rate are faster than those from pure water at the lower-supercooling ends of the plateaus, but slower at higher supercooling, until the growth rate starts rising toward that from pure water. These plateau values of growth rate increase markedly with increasing <span class="hlt">concentration</span> of the antifreeze in solution. Along with these changes there are complex changes in the growth orientations, from c-axis spicules in the plateaus to those more characteristic of growth from pure water at greater supercooling. We conclude that the non-equilibrium freezing point is determined by the adsorption rate. It is the warmest temperature at which the <span class="hlt">ice</span> growth rate on the basal plane (where the antifreeze does not adsorb) is fast enough to prevent the area of basal face on a growing <span class="hlt">ice</span> crystal from becoming too small to grow, which is determined in</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27503397','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27503397"><span>Metrics for monitoring cancer inequities: residential segregation, the Index of <span class="hlt">Concentration</span> at the Extremes (<span class="hlt">ICE</span>), and breast cancer estrogen receptor status (USA, 1992-2012).</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Krieger, Nancy; Singh, Nakul; Waterman, Pamela D</p> <p>2016-09-01</p> <p>To address the paucity of evidence on residential segregation and cancer, we explored their relationship using a new metric: the Index of <span class="hlt">Concentration</span> at the Extremes (<span class="hlt">ICE</span>). We focused on breast cancer estrogen receptor (ER) status, a biomarker associated with survival and, etiologically, with social and economic privilege. We obtained data from the 13 registry group of US Surveillance, Epidemiology, and End Results (SEER) program for 1992-2012 on all women aged 25-84 who were diagnosed with primary invasive breast cancer (n = 516,382). We appended to each case's record her annual county median household income quintile and the quintile for her annual county value for <span class="hlt">ICE</span> measures for income (≤20th vs. ≥80th household income quintile), race/ethnicity (black vs. white), and income plus race/ethnicity (low-income black vs. high-income white). The odds of being ER+ versus ER- were estimated in relation to the county-level income and <span class="hlt">ICE</span> measures, adjusting for relevant covariates. Women in the most privileged versus deprived county quintile for household income and for all three <span class="hlt">ICE</span> measures had a 1.1- to 1.3-fold increased odds (95 % confidence intervals excluding 1) of having an ER+ tumor. These results were robust to adjustment for age at diagnosis, cancer registry, tumor characteristics (tumor stage, si