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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Low-frequency passive-microwave observations of sea ice in the Weddell Sea

The microwave emission properties of first-year sea ice were investigated from the R/V Polarstern during the Antarctic Winter Weddell Gyre Project in 1989. Radiometer measurements were made at 611 MHz and 10 GHz and were accompanied by video and visual observations. Using the theory of radiometric emission from a layered medium, a method for deriving sea ice thickness from radiometer data is developed and tested. The model is based on an incoherent reflection process and predicts that the emissivity of saline ice increases monotonically with increasing ice thickness until saturation occurs.

Menashi, James D.↗
NASA NTRS1998

Polarimetric C-band SAR Observations of Sea Ice in the Greenland Sea

The fully polarimetric EMISAR acquired C-band radar signatures of sea ice in the Greenland Sea during a campaign in March 1995. We present maps of polarimetric signatures over an area containing various kinds of ice and discuss the use of polarimetric SAR for identification of ice types and their geophysical characteristics.

Polarimetry↗
NASA NTRSDate not supplied

The Impact of Large Scale Atmospheric and Cyclone Variability on the Sea-Ice Edge in the Labrador Sea

We study the temporal variability of the wintertime Labrador Sea ice area. The driving factors of these intraseasonal and interannual variations are related to large scale atmospheric variability and cyclone variability both of which can be characterized by the Arctic Oscillation (AO) index. We observe negative trends in the maximum sea-ice area over the past 40 years, and a positive correlation between the AO index and Labrador Sea ice area. Using satellite-derived daily ice area along with reanalysis-derived cyclones, turbulent flux, wind, humidity, air and sea temperature fields, we delve into the physical coupling mechanisms by which cyclones influence the position of the ice edge in the Labrador Sea throughout the winter.

Sea ice↗
NASA NTRS2024

Physics of the Seasonal Sea Ice Zone

The seasonal sea ice zone encompasses the region between the winter maximum and summer minimum sea ice extent. In both the Arctic and Antarctic, the majority of the ice cover can now be classified as seasonal. Here, we review the sea ice physics that governs the evolution of seasonal sea ice in the Arctic and Antarctic, spanning sea ice growth, melt, and dynamics and including interactions with ocean surface waves as well as other coupled processes. The advent of coupled wave–ice modeling and discrete-element modeling, together with improved and expanded satellite observations and field campaigns, has yielded advances in process understanding. Many topics remain in need of further investigation, including rheologies appropriate for seasonal sea ice, wave-induced sea ice fracture, welding for sea ice freeze-up, and the distribution of snow on seasonal sea ice. Future research should aim to redress biases (such as disparities in focus between the Arctic and Antarctic and between summer and winter processes) and connect observations to modeling across spatial scales.

sea ice↗
NASA NTRS1977

Scatterometer results from shorefast and floating sea ice

Multipolarized scatterometer sea ice measurements at 13.3 GHz obtained during the winter and spring of 1975-76 from a number of flight lines off the east coast of Canada were analyzed. Radar scattering coefficients, sigma, were calculated for several regions of sea ice as interpreted from aerial photographs. The variation in sigma which incidence angle is presented for HH (Horizontal transmit - Horizontal receive) and HV (Horizontal transmit - Vertical receive) polarizations for the various ice regions. The depolarization ratio (sigma HH/sigma HV) as a function of incidence angle is also given. The sea ice regions studied included shorefast ice with varying degrees of snow cover and surface roughness and several varieties of floating sea ice with different thicknesses.

Cihlar, J.↗
NASA NTRS2007

Arctic Sea Ice Parameters from AMSR-E Data using Two Techniques, and Comparisons with Sea Ice from SSM

We use two algorithms to process AMSR-E data in order to determine algorithm dependence, if any, on the estimates of sea ice concentration, ice extent and area, and trends and to evaluate how AMSR-E data compare with historical SSM/I data. The monthly ice concentrations derived from the two algorithms from AMSR-E data (the AMSR-E Bootstrap Algorithm, or ABA, and the enhanced NASA Team algorithm, or NT2) differ on average by about 1 to 3%, with data from the consolidated ice region being generally comparable for ABA and NT2 retrievals while data in the marginal ice zones and thin ice regions show higher values when the NT2 algorithm is used. The ice extents and areas derived separately from AMSR-E using these two algorithms are, however, in good agreement, with the differences (ABA-NT2) being about 6.6 x 10(exp 4) square kilometers on average for ice extents and -6.6 x 10(exp 4) square kilometers for ice area which are small compared to mean seasonal values of 10.5 x 10(exp 6) and 9.8 x 10(exp 6) for ice extent and area: respectively. Likewise, extents and areas derived from the same algorithm but from AMSR-E and SSM/I data are consistent but differ by about -24.4 x 10(exp 4) square kilometers and -13.9 x 10(exp 4) square kilometers, respectively. The discrepancies are larger with the estimates of extents than area mainly because of differences in channel selection and sensor resolutions. Trends in extent during the AMSR-E era were also estimated and results from all three data sets are shown to be in good agreement (within errors).

Comiso, Josefino C.↗
NASA NTRS2021

Weekly Mapping of Sea Ice Freeboard in the Ross Sea from ICESat-2

NASA’s ICESat-2 has been providing sea ice freeboard measurements across the polar regions since October 2018. In spite of the outstanding spatial resolution and precision of ICESat-2, the spatial sparsity of the data can be a critical issue for sea ice monitoring. This study employs a geostatistical approach (i.e., ordinary kriging) to characterize the spatial autocorrelation of the ICESat-2 freeboard measurements (ATL10) to estimate weekly freeboard variations in 2019 for the entire Ross Sea area, including where ICESat-2 tracks are not directly available. Three variogram models (exponential, Gaussian, and spherical) are compared in this study. According to the cross-validation results, the kriging-estimated freeboards show correlation coefficients of 0.56–0.57, root mean square error (RMSE) of ~0.12 m, and mean absolute error (MAE) of ~0.07 m with the actual ATL10 freeboard measurements. In addition, the estimated errors of the kriging interpolation are low in autumn and high in winter to spring, and low in southern regions and high in northern regions of the Ross Sea. The effective ranges of the variograms are 5–10 km and the results from the three variogram models do not show significant differences with each other. The southwest (SW) sector of the Ross Sea shows low and consistent freeboard over the entire year because of the frequent opening of wide polynya areas generating new ice in this sector. However, the southeast (SE) sector shows large variations in freeboard, which demonstrates the advection of thick multiyear ice from the Amundsen Sea into the Ross Sea. Thus, this kriging-based interpolation of ICESat-2 freeboard can be used in the future to estimate accurate sea ice production over the Ross Sea by incorporating other remote sensing data.

Satellite altimeter↗
NASA NTRS1995

C-Band Backscatter Measurements of Winter Sea-Ice in the Weddell Sea, Antarctica

During the 1992 Winter Weddell Gyre Study, a C-band scatterometer was used from the German ice-breaker R/V Polarstern to obtain detailed shipborne measurement scans of Antarctic sea-ice. The frequency-modulated continuous-wave (FM-CW) radar operated at 4-3 GHz and acquired like- (VV) and cross polarization (HV) data at a variety of incidence angles (10-75 deg). Calibrated backscatter data were recorded for several ice types as the icebreaker crossed the Weddell Sea and detailed measurements were made of corresponding snow and sea-ice characteristics at each measurement site, together with meteorological information, radiation budget and oceanographic data. The primary scattering contributions under cold winter conditions arise from the air/snow and snow/ice interfaces. Observations indicate so e similarities with Arctic sea-ice scattering signatures, although the main difference is generally lower mean backscattering coefficients in the Weddell Sea. This is due to the younger mean ice age and thickness, and correspondingly higher mean salinities. In particular, smooth white ice found in 1992 in divergent areas within the Weddell Gyre ice pack was generally extremely smooth and undeformed. Comparisons of field scatterometer data with calibrated 20-26 deg incidence ERS-1 radar image data show close correspondence, and indicate that rough Antarctic first-year and older second-year ice forms do not produce as distinctively different scattering signatures as observed in the Arctic. Thick deformed first-year and second-year ice on the other hand are clearly discriminated from younger undeformed ice. thereby allowing successful separation of thick and thin ice. Time-series data also indicate that C-band is sensitive to changes in snow and ice conditions resulting from atmospheric and oceanographic forcing and the local heat flux environment. Variations of several dB in 45 deg incidence backscatter occur in response to a combination of thermally-regulated parameters including sea-ice brine volume, snow and ice complex dielectric properties, and snow physical properties.

Drinkwater, M. R.↗
NASA NTRS2007

Observational Evidence of a Hemispheric-wide Ice-ocean Albedo Feedback Effect on Antarctic Sea-ice Decay

The effect of ice-ocean albedo feedback (a kind of ice-albedo feedback) on sea-ice decay is demonstrated over the Antarctic sea-ice zone from an analysis of satellite-derived hemispheric sea ice concentration and European Centre for Medium-Range Weather Forecasts (ERA-40) atmospheric data for the period 1979-2001. Sea ice concentration in December (time of most active melt) correlates better with the meridional component of the wind-forced ice drift (MID) in November (beginning of the melt season) than the MID in December. This 1 month lagged correlation is observed in most of the Antarctic sea-ice covered ocean. Daily time series of ice , concentration show that the ice concentration anomaly increases toward the time of maximum sea-ice melt. These findings can be explained by the following positive feedback effect: once ice concentration decreases (increases) at the beginning of the melt season, solar heating of the upper ocean through the increased (decreased) open water fraction is enhanced (reduced), leading to (suppressing) a further decrease in ice concentration by the oceanic heat. Results obtained fi-om a simple ice-ocean coupled model also support our interpretation of the observational results. This positive feedback mechanism explains in part the large interannual variability of the sea-ice cover in summer.

Nihashi, Sohey↗
NASA NTRS2010

ICESat Observations of Seasonal and Interannual Variations of Sea-Ice Freeboard and Estimated Thickness in the Weddell Sea, Antarctica (2003-2009)

Sea-ice freeboard heights for 17 ICESat campaign periods from 2003 to 2009 are derived from ICESat data. Freeboard is combined with snow depth from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) data and nominal densities of snow, water and sea ice, to estimate sea-ice thickness. Sea-ice freeboard and thickness distributions show clear seasonal variations that reflect the yearly cycle of growth and decay of the Weddell Sea (Antarctica) pack ice. During October-November, sea ice 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 ice melts away and the sea-ice 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-ice 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.

Yi, Donghui↗
NASA NTRS2024

Spring 2021 Sea Ice Transport in the Southern Beaufort Sea Occurred During Coastal Lead Opening Events

Winds blew a record large portion of the Arctic’s multiyear sea ice (MYI) into the southern Beaufort Sea (SBS) in winter 2021. From early March, a network of buoys from the Sea Ice Dynamic Experiment (SIDEx) tracked the MYI as it drifted across the SBS toward the Chukchi Sea. Transport was episodic as the consolidated ice pack interacted with coastal boundaries and repeatedly fractured. We investigated variability in 2021 MYI transport by relating in situ sea ice drift to remotely sensed coastal lead opening events, which have been associated with increased ice drift speeds in winter. Daily ice concentration data show ten opening events occurred throughout March and April 2021. Opening lasted 1-5 consecutive days as southeasterly winds pushed the SBS ice pack away from coastal boundaries. During opening, the ice pack abruptly accelerated and its response to wind forcing stabilized around free drift conditions, drifting at 2.1 % of wind speeds (median rate of 13.7 km d −1 ). With this efficient wind-to-ice momentum transfer, nearly all (94 %) alongshore MYI transport in March-April 2021 occurred during opening events, which comprised just half of March-April days. Only 6 % of transport occurred during the other half of days without observed opening. On these days, the ice was mostly stationary (median 1.7 km d −1 drift rate, 0.6 % of wind speeds) as northwesterly winds compressed the pack against the coast. Spatiotemporal discontinuities in ice-wind speed ratios were measured across several coastal leads that transected the SIDEx buoy network, providing direct observations of changes in ice drift regime as coastal leads opened. These results quantify the disproportionate contribution of offshore and alongshore winds in breaking the SBS ice pack away from the coast, opening leads, and driving SBS ice transport during spring 2021, highlighting the critical role these episodic events play in consolidation season sea ice drift.

Oceanography↗
NASA NTRS2006

The Use of RGPS Kinematic Data to Estimate Nonlinear Sea Ice Motion

In current simulations of the interaction between sea ice and its environment, large significance is placed on the deformation of the sea ice. Sea ice deformation is an important process in determining the sea ice thickness distribution across a wide range of space and time scales. Changes in the sea ice thickness distribution affect energy and mass fluxes between the atmosphere and ocean and also the strength of the ice. While most current ice models assume linear variation in the ice motion field to calculate strain, deformation of sea ice occurs through the opening, closing and shearing of ice along discrete linear features. New numerical models are being developed which explicitly account for discontinuities in ice motion, and the need for requisite data sets for model validation has emerged. Multiple buoy data sets, as well as satellite data, have been used to examine the movement and deformation of sea ice. Generally it has been found that the ice motion field has been represented well by buoy data, as well as satellite data over a broad range of scales. However, the underlying deformation (spatial variation in displacement) as represented by different data sets may vary. For the work presented here, sea ice motion In current simulations of the interaction between sea ice and its environment, large significance is placed on the deformation of the sea ice. Sea ice deformation is an important process in determining the sea ice thickness distribution across a wide range of space and time scales. Changes in the sea ice thickness distribution affect energy and mass fluxes between the atmosphere and ocean and also the strength of the ice. While most current ice models assume linear variation in the ice motion field to calculate strain, deformation of sea ice occurs through the opening, closing and shearing of ice along discrete linear features. New numerical models are being developed which explicitly account for discontinuities in ice motion, and the need for requisite data sets for model validation has emerged. Multiple buoy data sets, as well as satellite data, have been used to examine the movement and deformation of sea ice. Generally it has been found that the ice motion field has been represented well by buoy data, as well as satellite data over a broad range of scales. However, the underlying deformation (spatial variation in displacement) as represented by different data sets may vary. For the work presented here, sea ice motio

Pruis, M.↗
NASA NTRS2013

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

Surface mass balance (SMB) over ice sheets and snow on sea ice (SOSI) are important components of the cryosphere. Large knowledge gaps remain in scientists' abilities to monitor SMB and SOSI, including insufficient measurements and difficulties with satellite retrievals. On ice sheets, snow accumulation is the sole mass gain to SMB, and meltwater runoff can be the dominant single loss factor in extremely warm years such as 2012. SOSI affects the growth and melt cycle of the Earth's polar sea ice cover. The summer of 2012 saw the largest satellite-recorded melt area over the Greenland ice sheet and the smallest satellite-recorded Arctic sea ice extent, making this meeting both timely and relevant.

Snow↗
NASA NTRS2020

Analyzing the Impact of CryoSat-2 Ice Thickness Initialization on Seasonal Arctic Sea Ice Prediction

Twin 5-month seasonal forecast experiments are performed to predict the September 2018 minimum ice extent using the fully coupled Navy Earth System Prediction Capability (ESPC). In the control run, ensemble forecasts are initialized from the operational US Navy Global Ocean Forecasting System (GOFS) 3.1 for the ocean and sea ice but do not assimilate ice thickness data. Another set of forecasts are initialized from the same GOFS 3.1 fields but with sea ice thickness derived from CryoSat-2 (CS2). The Navy ESPC ensemble mean September 2018 minimum sea ice extent initialized with GOFS 3.1 ice thickness was over-predicted by 0.68 M sq.km (5.27 M sq.km) versus the ensemble set of forecasts initialized with CS2 ice thickness that had an error of 0.40 M sq.km (4.99 M sq.km), a 56% reduction in error. The September mean Integrated Ice Edge Error (IIEE) shows a 19% improvement for the entire Arctic with the CS2 data versus the control run. Comparison against Upward Looking Sonar (ULS) ice thickness in the Beaufort Sea reveals a lower bias and RMSE with the CS2 forecasts at all three moorings. Ice concentration at these locations is also improved, but neither set of experiments show ice free conditions as observed at moorings A and D.

time-lagged ensembles↗
NASA NTRS2012

Validation of Airborne FMCW Radar Measurements of Snow Thickness Over Sea Ice in Antarctica

Antarctic sea ice and its snow cover are integral components of the global climate system, yet many aspects of their vertical dimensions are poorly understood, making their representation in global climate models poor. Remote sensing is the key to monitoring the dynamic nature of sea ice and its snow cover. Reliable and accurate snow thickness data are currently a highly sought after data product. Remotely sensed snow thickness measurements can provide an indication of precipitation levels, predicted to increase with effects of climate change in the polar regions. Airborne techniques provide a means for regional-scale estimation of snow depth and distribution. Accurate regional-scale snow thickness data will also facilitate an increase in the accuracy of sea ice thickness retrieval from satellite altimeter freeboard estimates. The airborne data sets are easier to validate with in situ measurements and are better suited to validating satellite algorithms when compared with in situ techniques. This is primarily due to two factors: better chance of getting coincident in situ and airborne data sets and the tractability of comparison between an in situ data set and the airborne data set averaged over the footprint of the antennas. A 28-GHz frequency modulated continuous wave (FMCW) radar loaned by the Center for Remote Sensing of Ice Sheets to the Australian Antarctic Division is used to measure snow thickness over sea ice in East Antarctica. Provided with the radar design parameters, the expected performance parameters of the radar are summarized. The necessary conditions for unambiguous identification of the airsnow and snowice layers for the radar are presented. Roughnesses of the snow and ice surfaces are found to be dominant determinants in the effectiveness of layer identification for this radar. Finally, this paper presents the first in situ validated snow thickness estimates over sea ice in Antarctica derived from an FMCW radar on a helicopterborne platform.

sea ice↗
NASA NTRS2004

The Secret of the Svalbard Sea Ice Barrier

An elongated sea ice feature called the Svalbard sea ice barrier rapidly formed over an area in the Barents Sea to the east of Svalbard posing navigation hazards. The secret of its formation lies in the bottom bathymetry that governs the distribution of cold Arctic waters masses, which impacts sea ice growth on the water surface.

Atlantic waters↗