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All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Impact of the Arctic Oscillation from March on summertime sea ice

Current understanding of the cold season Arctic oscillation (AO) impact on the summertime sea ice is revisited in this study by analyzing the role from each month. Earlier studies examined the prolonged AO impact using a smooth average over 1–2 seasons (e.g. December–March, December–April, March–May), ignoring large month-to-month AO variability. This study finds that the March AO is most influential on the summertime sea ice loss. First, the March AO is most highly negative-correlated with the AO in summer. Secondly, surface energy budget, sea level pressure, and low-tropospheric circulation exhibit that their time-lagged responses to the positive (negative) phase of the March AO grow with time, transitioning to the patterns associated with the negative (positive) phase of the AO that induces sea ice decrease (increase) in summer. Time evolution of the surface energy budget explains the growth of the sea ice concentration anomaly in summer, and a warming-to-cooling transition in October. The regional difference in sea ice anomaly distribution can be also explained by circulation and surface energy budget patterns. The sea ice concentration along the pan-Arctic including the Laptev, East Siberian, Chukchi, and Beaufort Sea decreases (increases) in summer in response to the positive (negative) phase of the March AO, while the sea ice to the northeast of Greenland increases (decreases). This sea ice response is better represented by the March AO than by the seasonally averaged winter AO, suggesting that the March AO can play more significant role. This study also finds that the sea ice decrease in response to the positive AO is distinctively smaller in the 20th century than in the 21st century, along with the opposite sea ice response over the Canada Basin due to circulation difference between the two periods.

Arctic Oscillation↗
NASA NTRS2022

Arctic Biogeochemical and Optical Properties of Dissolved Organic Matter Across River to Sea Gradients

Arctic landscapes are warming and becoming wetter due to changes in precipitation and the timing of snowmelt which consequently alters seasonal runoff and river discharge patterns. These changes in hydrology lead to increased mobilization and transport of terrestrial dissolved organic matter (DOM) to Arctic coastal seas where significant impacts on biogeochemical cycling can occur. Here, we present measurements of dissolved organic carbon (DOC) and chromophoric DOM (CDOM) in the Yukon River-to-Bering Sea system and two river plumes on the Alaska North Slope which flow into the Beaufort Sea. Our sampling characterized optical and biogeochemical properties of DOM during high and low river discharge periods for the Yukon River-Bering Sea system. The average DOC concentration at the multiple Yukon River mouths ranged from a high of 10.36 mg C L-1 during the ascending limb of the 2019 freshet (late May), 6.4 mg C L-1 during the descending limb of the 2019 freshet (late June), and a low of 3.86 mg C L-1 during low river discharge in August 2018. CDOM absorption coefficient at 412 nm (aCDOM(412)) averaged 8.23 m-1, 5.07 m-1, and 1.9 m-1, respectively. Several approaches to model DOC concentration based on its relationship with CDOM properties demonstrated cross-system seasonal and spatial robustness for these Arctic coastal systems despite spanning an order of magnitude decrease in DOC concentration from the lower Yukon River to the Northern Bering Sea as well as the North Slope systems. “Snapshot” fluxes of DOC and CDOM across the Yukon River Delta to Norton Sound were calculated from our measurements and modeled water fluxes forced with upstream USGS river gauge data. Our findings suggest that during high river flow, DOM reaches the delta largely unaltered by inputs or physical and biogeochemical processing and that the transformations of Yukon River DOM largely occur in the plume. However, during low summer discharge, multiple processes including local precipitation events, microbial decomposition, photochemistry, and likely others can alter the DOM properties within the lower Yukon River and Delta prior to flowing into Norton Sound.

Arctic↗
NASA NTRS2024

Sea Ice Deformation Is Not Scale Invariant Over Length Scales Greater Than A Kilometer

In March and April 2021, buoys were deployed in the Beaufort Sea, Arctic Ocean, to measure sea-ice horizontal deformation over spatial scales that had not been previously achieved. Geodetic-quality position measurements allowed measurements of strain-rate over lengths from about 200 m to 2 km. Conventional ice-drifters extended spatial coverage up to about 100 km. Past studies find there is multi-fractal behavior for horizontal sea-ice deformation from 10 to 1000 km. Our results demonstrate that such behavior does not hold when including spatial scales below 10 km. We find that sea-ice deformation is not scale in variant between the scale of individual sea-ice floes and aggregates of floes. Therefore, we cannot expect the same physical laws or forcing to describe sea-ice kinematics over these regimes, nor can we assume log-log linear behavior for mean deformation. Using this scaling behavior as a metric to validate models that resolve sea ice floes and their interactions is hence not recommended.

Jennifer K. Hutchings↗
NASA NTRS1972

Data requirements in support of the marine weather service program

Data support activities for the Marine Weather Service Program are outlined. Forecasts, cover anomolous water levels, including sea and swell, surface and breakers, and storm surge. Advisories are also provided for sea ice on the Great Lake and Cook inlet in winter, and in the Bering, Chukchi, and Beaufort Seas in summer. Attempts were made to deal with ocean currents in the Gulf Stream, areas of upwelling, and thermal structure at least down through the mixed layer.

Travers, J.↗
NASA NTRS2004

EOS Aqua AMSR-E Arctic Sea Ice Validation Program

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.

Cavalieri, D. J.↗
NASA NTRS2003

EOS Aqua AMSR-E Arctic Sea Ice Validation Program: Arctic2003 Aircraft Campaign Flight Report

In March 2003 a coordinated Arctic sea ice 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 (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 to be validated include sea ice concentration, sea ice temperature, and snow depth on sea ice. 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 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. Two additional flights were dedicated to making heat and moisture flux measurements over the St. Lawrence Island polynya to support ongoing air-sea-ice processes studies of Arctic coastal polynyas. The remaining flights covered portions of the Bering Sea ice edge, the Chukchi Sea, and Norton Sound.

Cavalieri, D. J.↗
NASA NTRS2011

Airborne Surveys of Snow Depth over Arctic Sea Ice

During the spring of 2009, an ultrawideband microwave radar was deployed as part of Operation IceBridge to provide the first cross-basin surveys of snow thickness over Arctic sea ice. In this paper, we analyze data from three approx 2000 km transects to examine detection issues, the limitations of the current instrument, and the regional variability of the retrieved snow depth. Snow depth is the vertical distance between the air \snow and snow-ice interfaces detected in the radar echograms. Under ideal conditions, the per echogram uncertainty in snow depth retrieval is approx 4 - 5 cm. The finite range resolution of the radar (approx 5 cm) and the relative amplitude of backscatter from the two interfaces limit the direct retrieval of snow depths much below approx 8 cm. Well-defined interfaces are observed over only relatively smooth surfaces within the radar footprint of approx 6.5 m. Sampling is thus restricted to undeformed, level ice. In early April, mean snow depths are 28.5 +/- 16.6 cm and 41.0 +/- 22.2 cm over first-year and multiyear sea ice (MYI), respectively. Regionally, snow thickness is thinner and quite uniform over the large expanse of seasonal ice in the Beaufort Sea, and gets progressively thicker toward the MYI cover north of Ellesmere Island, Greenland, and the Fram Strait. Snow depth over MYI is comparable to that reported in the climatology by Warren et al. Ongoing improvements to the radar system and the utility of these snow depth measurements are discussed.

Kwok, R.↗
NASA NTRS2015

Is Ice-Rafted Sediment in a North Pole Marine Record Evidence for Perennial Sea-ice Cover?

Ice-rafted sediments of Eurasian and North American origin are found consistently in the upper part (13 Ma BP to present) of the Arctic Coring Expedition (ACEX) ocean core from the Lomonosov Ridge, near the North Pole (approximately 88 degrees N). Based on modern sea-ice drift trajectories and speeds, this has been taken as evidence of the presence of a perennial sea-ice cover in the Arctic Ocean from the middle Miocene onwards. However, other high latitude land and marine records indicate a long-term trend towards cooling broken by periods of extensive warming suggestive of a seasonally ice-free Arctic between the Miocene and the present. We use a coupled sea-ice slab-ocean model including sediment transport tracers to map the spatial distribution of ice-rafted deposits in the Arctic Ocean. We use 6 hourly wind forcing and surface heat fluxes for two different climates: one with a perennial sea-ice cover similar to that of the present day and one with seasonally ice-free conditions, similar to that simulated in future projections. Model results confirm that in the present-day climate, sea ice takes more than 1 year to transport sediment from all its peripheral seas to the North Pole. However, in a warmer climate, sea-ice speeds are significantly faster (for the same wind forcing) and can deposit sediments of Laptev, East Siberian and perhaps also Beaufort Sea origin at the North Pole. This is primarily because of the fact that sea-ice interactions are much weaker with a thinner ice cover and there is less resistance to drift. We conclude that the presence of ice-rafted sediment of Eurasian and North American origin at the North Pole does not imply a perennial sea-ice cover in the Arctic Ocean, reconciling the ACEX ocean core data with other land and marine records.

Palaeoclimate↗
NASA NTRS2004

Comparisons of Arctic In-Situ Snow and Ice Data with Airborne Passive Microwave Measurements

As part of the AMSR-E sea ice validation campaign in March 2003, aircraft flights over the Arctic sea ice were coordinated with ground measurements of snow and sea ice properties. The surface-based measurements were in the vicinity of Barrow, AK, and at a Navy ice 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 AMSR-E sensor. The goal was to validate the standard AMSR-E products ice temperature and snow depth on sea ice. 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 AMSR-E 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.

Markus, T.↗
NASA NTRS1993

Sea ice feature and type identification in merged ERS-1 SAR and LANDSAT Thematic Mapper imagery

LANDSAT Thematic Mapper (TM) and ERS-1 SAR (Synthetic Aperture Radar) C band images were acquired for the same area in the Beaufort Sea, 18 Apr. 1992. The two images were co-located to the same grid (25 m resolution) and supervised classification was performed on the TM channel 3 scene in order to classify open water, nilas, grey ice, first year ice, and multiyear ice. Comparison of the LANDSAT classification and the corresponding dB values from the SAR scene showed that, under the given circumstances (high surface winds), open water/nilas/grey ice as defined by a single ice category by the SAR classifier could not be distinguished from first year ice. Surface roughening due to wind appears to be a major problem for the SAR classifier, as the range of the dB values is large enough to overlap all the other ice categories. Additional information, such as surface wind speed is necessary to overcome part of this problem.

Steffen, K.↗
NASA NTRS2011

A Comparison of Snow Depth on Sea Ice Retrievals Using Airborne Altimeters and an AMSR-E Simulator

A comparison of snow depths on sea ice was made using airborne altimeters and an Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) 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 AMSR-E 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 AMSR-E 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 AMSR-E snow depths using PSR-A brightness temperatures calibrated relative to AMSR-E 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.

Cavalieri, D. J.↗
NASA NTRS1994

Feasibility of sea ice typing with synthetic aperture radar (SAR): Merging of Landsat thematic mapper and ERS 1 SAR satellite imagery

Earth Remote-Sensing Satellite (ERS) 1 synthetic aperture radar (SAR) and Landsat thematic mapper (TM) images were acquired for the same area in the Beaufort Sea, April 16 and 18, 1992. The two image pairs were colocated to the same grid (25-m resolution), and a supervised ice type classification was performed on the TM images in order to classify ice free, nilas, gray ice, gray-white ice, thin first-year ice, medium and thick first-year ice, and old ice. Comparison of the collocated SAR pixels showed that ice-free areas can only be classified under calm wind conditions (less than 3 m/s) and for surface winds greater than 10 m/s based on the backscattering coefficient alone. This is true for pack ice regions during the cold months of the year where ice-free areas are spatially limited and where the capillary waves that cause SAR backscatter are dampened by entrained ice crystals. For nilas, two distinct backscatter classes were found at -17 dB and at -10 dB. The higher backscattering coefficient is attributed to the presence of frost flowers on light nilas. Gray and gray-white ice have a backscatter signature similar to first-year ice and therefore cannot be distinguished by SAR alone. First-year and old ice can be clearly separated based on their backscattering coefficient. The performance of the Geophysical Processor System ice classifier was tested against the Landsat derived ice products. It was found that smooth first-year ice and rough first-year ice were not significantly different in the backscatter domain. Ice concentration estimates based on ERS 1 C band SAR showed an error range of 5 to 8% for high ice concentration regions, mainly due to misclassified ice-free and smooth first-year ice areas. This error is expected to increase for areas of lower ice concentration. The combination of C band SAR and TM channels 2, 4, and 6 resulted in ice typing performance with an estimated accuracy of 90% for all seven ice classes.

Steffen, Konrad↗
NASA NTRS1973

Mapping Arctic sea ice from the Earth Resources Technology Satellite

The author has identified the following significant results. Methods of detecting ice and for distinguishing between ice and clouds are discussed, and examples of ERTS-1 data showing ice distributions in northern Hudson Bay, M'Clure Strait, the eastern Beaufort Sea, and the Greenland Sea are presented. The results of the initial analysis of ERTS-1 data indicate that the locations of ice edges and ice concentrations can be accurately mapped, and that considerable information on ice type can be derived through use of the various spectral bands. Ice features as small as 80 to 100 m width can be mapped.

Barnes, J. C.↗
DOE OSTI2026

Evaluation of geophysical and anthropogenic sources of hydroacoustic noise in the Alaskan arctic

Quantifying the ocean soundscape is crucial for ocean-based seismoacoustic monitoring; it sets a baseline for the kinds and sizes of signals that can be detected above the background noise. As sea ice recedes, human activity in and around the Arctic Ocean is increasing, elevating sound levels and heightening the urgency of monitoring. Here, seven years of passive acoustic recordings from a National Oceanographic and Atmospheric Administration hydrophone in the Beaufort Sea are analyzed, focusing on frequencies between 1 and 125 Hz, a band of interest for detecting regional earthquakes and similar events. Sound is related to geophysical and anthropogenic sources, and seasonal and intraseasonal variations in the soundscape are examined. Sea ice emerges as a keystone feature of the Arctic Ocean acoustic environment, controlling or contributing to ambient sound levels at all frequencies studied. During low-ice months, sound levels are dominated by wind and sea surface waves, and in ice-covered months, wind-driven ice noise dominates. Seismic air gun surveys are prominent during low-ice periods, with sound levels decreasing with increasing distance and bathymetric complexity along the propagation path. The implications of this baseline soundscape for event detection in the Alaskan Arctic are discussed.

Niklasson, Siobhan [Los Alamos National Laboratory↗
NASA NTRS1991

NASA, Navy, and AES/York sea ice concentration comparison of SSM/I algorithms with SAR derived values

Previous research studies have focused on producing algorithms for extracting geophysical information from passive microwave data regarding ice floe size, sea ice concentration, open water lead locations, and sea ice extent. These studies have resulted in four separate algorithms for extracting these geophysical parameters. Sea ice concentration estimates generated from each of these algorithms (i.e., NASA/Team, NASA/Comiso, AES/York, and Navy) are compared to ice concentration estimates produced from coincident high-resolution synthetic aperture radar (SAR) data. The SAR concentration estimates are produced from data collected in both the Beaufort Sea and the Greenland Sea in March 1988 and March 1989, respectively. The SAR data are coincident to the passive microwave data generated by the Special Sensor Microwave/Imager (SSM/I).

Jentz, R. R.↗
NASA NTRS2012

Field and Satellite Observations of the Formation and Distribution of Arctic Atmospheric Bromine Above a Rejuvenated Sea Ice Cover

Recent drastic reduction of the older perennial sea ice in the Arctic Ocean has resulted in a vast expansion of younger and saltier seasonal sea ice. This increase in the salinity of the overall ice cover could impact tropospheric chemical processes. Springtime perennial ice extent in 2008 and 2009 broke the half-century record minimum in 2007 by about one million km2. In both years seasonal ice was dominant across the Beaufort Sea extending to the Amundsen Gulf, where significant field and satellite observations of sea ice, temperature, and atmospheric chemicals have been made. Measurements at the site of the Canadian Coast Guard Ship Amundsen ice breaker in the Amundsen Gulf showed events of increased bromine monoxide (BrO), coupled with decreases of ozone (O3) and gaseous elemental mercury (GEM), during cold periods in March 2008. The timing of the main event of BrO, O3, and GEM changes was found to be consistent with BrO observed by satellites over an extensive area around the site. Furthermore, satellite sensors detected a doubling of atmospheric BrO in a vortex associated with a spiral rising air pattern. In spring 2009, excessive and widespread bromine explosions occurred in the same region while the regional air temperature was low and the extent of perennial ice was significantly reduced compared to the case in 2008. Using satellite observations together with a Rising-Air-Parcel model, we discover a topographic control on BrO distribution such that the Alaskan North Slope and the Canadian Shield region were exposed to elevated BrO, whereas the surrounding mountains isolated the Alaskan interior from bromine intrusion.

Nghiem, Son V.↗
NASA NTRS1973

Use of ERTS data for mapping Arctic sea ice

This investigation is to evaluate the application of ERTS data for detecting and mapping Arctic sea ice. The specific objectives are to determine the spectral bands most suitable for detecting ice, to measure the scale and types of ice features that can be detected, and to develop interpretive techniques for differentiating ice from clouds and for mapping ice concentrations. The ERTS data are being analyzed primarily for three Arctic areas, the eastern Beaufort Sea, Baffin Bay, and the Greenland Sea.

Barnes, J. C.↗
NASA NTRS1990

A multisensor approach to sea ice classification for the validation of DMSP-SSM/I passive microwave derived sea ice products

The validation of sea ice products derived from the Special Sensor Microwave Imager (SSM/I) on board a DMSP platform is examined using data from the Landsat MSS and NOAA-AVHRR sensors. Image processing techniques for retrieving ice concentrations from each type of imagery are developed and results are intercompared to determine the ice parameter retrieval accuracy of the SSM/I NASA-Team algorithm. For case studies in the Beaufort Sea and East Greenland Sea, average retrieval errors of the SSM/I algorithm are between 1.7 percent for spring conditions and 4.3 percent during freeze up in comparison with Landsat derived ice concentrations. For a case study in the East Greenland Sea, SSM/I derived ice concentration in comparison with AVHRR imagery display a mean error of 9.6 percent.

Steffen, K.↗