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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Verification of a New NOAA/NSIDC Passive Microwave Sea-Ice Concentration Climate Record

A new satellite-based passive microwave sea-ice concentration 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 concentration 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 ice 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 Ice Data Center (for the CDR fields) and NASA (for the GSFC fields). The CDR concentrations do have some differences from the constituent GSFC concentrations, but trends and variability are not substantially different.

Passive Microwave↗
NASA NTRS2016

How Will Sea Ice Loss Affect the Greenland Ice Sheet? On the Puzzling Features of Greenland Ice-Core Isotopic Composition

The modern cryosphere, Earth's frozen water regime, is in fast transition. Greenland ice cores show how fast theses changes can be, presenting evidence of up to 15 C warming events over timescales of less than a decade. These events, called Dansgaard/Oeschger (D/O) events, are believed to be associated with rapid changes in Arctic sea ice, although the underlying mechanisms are still unclear. The modern demise of Arctic sea ice may, in turn, instigate abrupt changes on the Greenland Ice Sheet. The Arctic Sea Ice and Greenland Ice Sheet Sensitivity (Ice2Ice Chttps://ice2ice.b.uib.noD) initiative, sponsored by the European Research Council, seeks to quantify these past rapid changes to improve our understanding of what the future may hold for the Arctic. Twenty scientists gathered in Copenhagen as part of this initiative to discuss the most recent observational, technological, and model developments toward quantifying the mechanisms behind past climate changes in Greenland. Much of the discussion focused on the causes behind the changes in stable water isotopes recorded in ice cores. The participants discussed sources of variability for stable water isotopes and framed ways that new studies could improve understanding of modern climate. The participants also discussed how climate models could provide insights into the relative roles of local and nonlocal processes in affecting stable water isotopes within the Greenland Ice Sheet. Presentations of modeling results showed how a change in the source or seasonality of precipitation could occur not only between glacial and modern climates but also between abrupt events. Recent fieldwork campaigns illustrate an important role of stable isotopes in atmospheric vapor and diffusion in the final stable isotope signal in ice. Further, indications from recent fieldwork campaigns illustrate an important role of stable isotopes in atmospheric vapor and diffusion in the final stable isotope signal in ice. This feature complicates the quantitative interpretation of ice core signals but also makes the stable ice isotope signal a more robust regional indicator of climate, speakers noted. Meeting participants agreed that to further our understanding of these relationships, we need more process-focused field and laboratory campaigns.

Greenland↗
NASA NTRS2002

Simulating Snow Over Sea Ice In Climate Models

We have evaluated two methods of simulating the seasonal cycle of snow over sea ice in and around the Arctic: The NCAR global climate model CCM3, with its standard snow hydrology, and the snow pack model SNTHERM, forced with hourly atmospheric output from CCM3. A new dataset providing dates for the onset of snow melt over Arctic sea ice provides a means for assessing basin-wide how well the models simulate melt onset, but contains no information on how long it then takes for all the snow to melt. Use of data from the SHEBA site provides very detailed information on the behavior of the snow before and during the melt season, but only for a very limited area. Russian drift data provide climatological data on the seasonal cycle of snow water equivalent and snow density, over multi-year sea ice in the central Arctic basin. These datasets are used to compare the two modeling methods, and to see if use of the more physically-realistic SNTHERM provides any significant improvements. Conclusions obtained so far include: 1. Both CCM3 and CCM3/SNTHERM do a good job overall of matching the onset of snow melt dataset; although CCM3/SNTHERM consistently trends to underestimate the date and CCM3 to overestimate it. 2. SHEBA and ice drift data for the Arctic show that CCM3/ SNTHERM does a better job than CCM3 at simulating the total melt period. 3. Ice drift snow density and accumulation data suggest that while providing superior results, CCM3/SNTHERM may still suffer from overly vigorous melting. 4. Both the large-scale atmospheric forcing and snow pack physical processes are important in proper simulation of the snow seasonal cycle. Ongoing work includes further diagnosis of CCM3/SNTHERM, use of more observational datasets, especially from marginal seas in the pan-Arctic, and full coupling of SNTHERM into CCM3 (work to date has all been off-line simulations).

Arnold, James E.↗
NASA NTRS2007

The Influence of Sea Ice on Primary Production in the Southern Ocean: A Satellite Perspective

Sea ice 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, ice concentrations data from SSM/I and AMSR-E, 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 ice 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 ice 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.

Smith, Walker O., Jr.↗
NASA NTRS1983

Arctic Sea ice by passive microwave observations from the Nimbus-5 Satellite

The results of a dynamic/thermodynamic numerical model of Arctic sea ice are compared with satellite images from the Nimbus 5 electrically scanning microwave radiometer. The model combines aspects of two previous sea ice models those of Parkinson and Washington and Ling, Rasmussen, and Campbell. A solid/fluid model basically follows the formulation of the Parkinson and Washington model with the addition of the constitutive equation and equation of state from the Ling model. The Parkinson and Washington model simulates the seasonal cycle of sea ice thicknesses and concentrations with a horizontal resolution of roughly 200 km and a timestep of 8 hours. The thermodynamics are calculated through energy balances at the interfaces between ice and air, water and ice, and water and air. The ice dynamics are calculated through a momentum equation balancing air stress, water stress, dynamic topography, and Coriolis force, with an adjustment for internal ice resistance.

Campbell, W. J.↗
NASA NTRS1989

Arctic sea ice 1973-1987 - Seasonal, regional, and interannual variability

The seasonal, regional, and interannual variations in the Northern-Hemisphere sea-ice cover were investigated for the time period 1973-1987, using data derived from the Nimbus-5 ESMR and the Nimbus-7 SMMR. The records show an average seasonal cycle of sea-ice cover, ranging from a summer minimum of 8.5 x 10 to the 6th sq km in September to a winter maximum of 15 x 10 to the 6th sq km in March, with considerable interannual variability found both regionally and hemispherically. Some regions (e.g., the Baffin Bay/Davis Strait, were found to exhibit upward trends in sea-ice extents, while other regions (e.g., the Kara Sea and the Barents Sea) exhibited downward trend. However, the record for the Northern Hemisphere overall showed no significant trend over the 1973-1987 period, giving no evidence of consistent warming or cooling of the north polar region.

Parkinson, Claire L.↗
NASA NTRS1996

The Character of the Beaufort Sea Ice Cover From Active and Passive Microwave Observations

By definition, ice which survives the summer is classified as multiyear ice. Thus, multiyear ice concentration during the winter should be nearly equivalent to the ice concentration during the previous summer's minima. This condition provides a reasonable criterion for evaluation of ice concentration and ice type retrieval algorithms from remote sensing datasets. From SSM/I data, the NASA Team algorithm estimates the multiyear, first-year and total ice concentrations throughout the sea ice season using combinations of the polarization and spectral gradient ratios.

Beaufort Sea↗
NASA NTRS2001

Arctic Sea Ice Export Through Fram Strait and Atmospheric Planetary Waves

A link is found between the variability of Arctic sea ice export through Ram Strait and the phase of the longest atmospheric planetary wave (zonal wave 1) in SLP for the period 1958-1997. Previous studies have identified a link between From Strait ice export and the North Atlantic Oscillation (NAO), but this link has been described as unstable because of a lack of consistency over time scales longer than the last two decades. Inconsistent and low correlations are also found between From Strait ice export and the Arctic Oscillation (AD) index. This paper shows that the phase of zonal wave 1 explains 60% - 70% of the simulated From Strait ice export variance over the Goodyear period 1958 - 1997. Unlike the NAB and AD links, these high variances are consistent for both the first and second halves of the Goodyear period. This consistency is attributed to the sensitivity of the wave I phase at high latitudes to the presence of secondary low pressure systems in the Barents Sea that serve to drive sea ice southward through From Strait. These results provide further evidence that the phase of zonal wave 1 in SLP at high latitudes drives regional as well as hemispheric low frequency Arctic Ocean and sea ice variability.

Cavalieri, Donald J.↗
NASA NTRS2020

Influence of Sea-Ice Anomalies on Antarctic Precipitation Using Source Attribution in the Community Earth System Model

We conduct sensitivity experiments using a general circulation model that has an explicit water source tagging capability forced by prescribed composites of pre-industrial sea-ice concentrations (SICs) and corresponding sea surface temperatures (SSTs) to understand the impact of sea-ice anomalies on regional evaporation, moisture transport and source–receptor relationships for Antarctic precipitation in the absence of anthropogenic forcing. Surface sensible heat fluxes, evaporation and column-integrated water vapor are larger over Southern Ocean (SO) areas with lower SICs. Changes in Antarctic precipitation and its source attribution with SICs have a strong spatial variability. Among the tagged source regions, the Southern Ocean (south of 50∘ S) contributes the most (40 %) to the Antarctic total precipitation, followed by more northerly ocean basins, most notably the South Pacific Ocean (27%), southern Indian Ocean (16 %) and South Atlantic Ocean (11 %). Comparing two experiments prescribed with high and low pre-industrial SICs, respectively, the annual mean Antarctic precipitation is about 150 Gt yr−1 (or 6 %) more in the lower SIC case than in the higher SIC case. This difference is larger than the model-simulated interannual variability in Antarctic precipitation (99 Gt yr−1). The contrast in contribution from the Southern Ocean, 102 Gt yr−1, is even more significant compared to the interannual variability of 35 Gt yr−1 in Antarctic precipitation that originates from the Southern Ocean. The horizontal transport pathways from individual vapor source regions to Antarctica are largely determined by large-scale atmospheric circulation patterns. Vapor from lower-latitude source regions takes elevated pathways to Antarctica. In contrast, vapor from the Southern Ocean moves southward within the lower troposphere to the Antarctic continent along moist isentropes that are largely shaped by local ambient conditions and coastal topography. This study also highlights the importance of atmospheric dynamics in affecting the thermodynamic impact of sea-ice anomalies associated with natural variability on Antarctic precipitation. Our analyses of the seasonal contrast in changes of basin-scale evaporation, moisture flux and precipitation suggest that the impact of SIC anomalies on regional Antarctic precipitation depends on dynamic changes that arise from SIC–SST perturbations along with internal variability. The latter appears to have a more significant effect on the moisture transport in austral winter than in summer.

Wang, Hailong↗
NASA NTRS2018

Melt Pond Conditions on Declining Arctic Sea Ice Over 1979-2016: Model Development, Validation, and Results

A melt pond (MP) distribution equation has been developed and incorporated into the Marginal Ice-Zone Modeling and Assimilation System to simulate Arctic MPs and sea ice over 1979-2016. The equation differs from previous MP models and yet benefits from previous studies for MP parameterizations as well as a range of observations for model calibration. Model results show higher magnitude of MP volume per unit ice area and area fraction in most of the Canada Basin and the East Siberian Sea and lower magnitude in the central Arctic. This is consistent with Moderate Resolution Imaging Spectroradiometer observations, evaluated with Measurements of Earth Data for Environmental Analysis (MEDEA) data, and closely related to top ice melt per unit ice area. The model simulates a decrease in the total Arctic sea ice volume and area, owing to a strong increase in bottom and lateral ice melt. The sea ice decline leads to a strong decrease in the total MP volume and area. However, the Arctic-averaged MP volume per unit ice area and area fraction show weak, statistically insignificant downward trends, which is linked to the fact that MP water drainage per unit ice area is increasing. It is also linked to the fact that MP volume and area decrease relatively faster than ice area. This suggests that overall the actual MP conditions on ice have changed little in the past decades as the ice cover is retreating in response to Arctic warming, thus consistent with the Moderate Resolution Imaging Spectroradiometer observations that show no clear trend in MP area fraction over 2000-2011.

sea ice↗
NASA NTRS1980

Sea ice simulations based on fields generated by the GLAS GCM

The GLAS General Circulation Model (GCM) was applied to the four-month simulation of the thermodynamic part of the Parkinson-Washington sea ice model using atmospheric boundary conditions. The sea ice thickness and distribution were predicted for the Jan. 1-Apr. 30 period using the GCM-fields of solar and infrared radiation, specific humidity and air temperature at the surface, and snow accumulation; the sensible heat and evaporative surface fluxes were consistent with the ground temperatures produced by the ice model and the air temperatures determined by the atmospheric concept. It was concluded that the Parkinson-Washington sea ice model results in acceptable ice concentrations and thicknesses when used with GLAS GCM for the Jan.-Apr. period suggesting the feasibility of fully coupled ice-atmosphere simulations with these two approaches.

Parkinson, C. L.↗
NASA NTRS1979

The response of the Goddard general circulation model to sea ice boundary conditions

The effect of variation in the location of Arctic sea ice boundaries on the model's mean monthly climatology was examined. When sea ice boundaries were at their maximum extent the differences resulted in the January-February climatology. Sea level pressure was higher over the Barents Sea, in the Davis Strait, and in the Sea of Okhotsk. Pressure was lower by as much as 8 mb in the North Atlantic between Iceland and the British Isles, and in the Gulf of Alaska. Pressure rises in the eastern subtropical regions of the North Atlantic and North Pacific accompanied pressure falls in the Gulf of Alaska and Icelandic region. Geopotential heights at 500 mb were more than 100 gpm lower in the Bering Sea, and more than 120 gpm lower in the Icelandic region. Zonally averaged temperatures were cooler by 4 deg C below 3800 mb between 50 deg and 70 deg N with little change elsewhere. Zonally averaged geopotentials were lower by as much as 70 gpm in the mid-troposphere between 50/-70 deg N and zonal winds increased by as much as 3 m s in the mid-troposphere between 35/-50 deg N.

Herman, G.↗
NASA NTRS2006

Interferometric System for Measuring Thickness of Sea Ice

The cryospheric advanced sensor (CAS) is a developmental airborne (and, potentially, spaceborne) radar-based instrumentation system for measuring and mapping the thickness of sea ice. A planned future version of the system would also provide data on the thickness of snow covering sea ice. Frequent measurements of the thickness of polar ocean sea ice and its snow cover on a synoptic scale are critical to understanding global climate change and ocean circulation.

Hussein, Ziad↗
NASA NTRS2006

Upwelling of Arctic pycnocline associated with shear motion of sea ice

High-resolution radar imagery shows that the dynamic response of winter sea ice to gradients in large-scale surface wind stress is often localized along quasi-linear fractures hundreds of kilometers long. Relative shearing motion across these narrow fractures can exceed 10 cm/s. In one event recorded during the drift of the SHEBA ice camp, we observed an intense zone of pycnocline upwelling (approx.14 m) associated with significant shear motion near the camp, while upward turbulent heat flux in the ocean boundary layer reached nearly 400 W/sq m, an order of magnitude greater than at any other time during the year-long drift. We attribute the upwelling to Ekman pumping associated with concentrated ice shear. Over the entire Arctic Ocean sea ice cover, this process could be responsible for significant heat exchange between the cold surface layer and warmer subsurface water at the ubiquitous fractures resulting from large-scale atmosphere-ice interactions.

McPhee, M. G.↗
NASA NTRS1992

Interannual variability of monthly Southern Ocean sea ice distributions

The interannual variability of the Southern-Ocean sea-ice distributions was mapped and analyzed using data from Nimbus-5 ESMR and Nimbus-7 SMMR, collected from 1973 to 1987. The set of 12 monthly maps obtained reveals many details on spatial variability that are unobtainable from time series of ice extents. These maps can be used as baseline maps for comparisons against future Southern Ocean sea ice distributions. The maps are supplemented by more detailed maps of the frequency of ice coverage, presented in this paper for one month within each of the four seasons, and by the breakdown of these results to the periods covered individually by each of the two passive-microwave imagers.

Parkinson, Claire L.↗
NASA NTRS2006

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

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.

Cavalieri, Donald J.↗