Utilizing Distributed Acoustic Sensing to Characterize Microseismicity within Arctic Sea Ice in the Beaufort Sea
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Abbott, Robert↗
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Impact of cloud cover on Arctic sea ice surface melt
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RADARSAT C-ICE↗
C-Band Backscatter Across a Snow Line on Sea Ice
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CRREL ice sea ice ice sheet backscatter↗
Multiphase Reactive Transport and Platelet Ice Accretion in the Sea Ice of McMurdo Sound, Antarctica
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J. J. Buffo↗
Sea Ice Concentration and Extent
Among the most seasonal and most dynamic parameters on the surface of the Earth is sea ice which at any one time covers about 3-6% of the planet. In the Northern Hemisphere, sea ice 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 ice 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 ice is very dynamic and usually follows the directions of the many gyres in the polar regions. Despite its vast expanse, the sea ice 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.
sea ice extent↗
Remote sensing of sea ice thickness by a combined spatial and frequency domain interferometer : formulations, instrument design & development
The thickness of Arctic sea ice plays a critical role in Earth's climate and ocean circulation. An accurate measurement of this parameter on synoptic scales at regular intervals would enable characterization of this important component for the understanding of ocean circulation and the global heat balance. Presented in this paper is a low frequency VHF interferometer technique and associated radar instrument design to measure sea ice thickness based on the use of backscatter correlation functions. The sea ice medium is represented as a multi-layered medium consisting of snow, seaice and sea water, with the interfaces between layers characterized as rough surfaces. This technique utilizes the correlation of two radar waves of different frequencies and incident and observation angles, scattered from the sea ice medium. The correlation functions relate information about the sea ice thickness. Inversion techniques such as the genetic algorithm, gradient descent, and least square methods, are used to derive sea ice thickness from the phase information related by the correlation functions.
remote sensing↗
Microwave remote sensing of sea ice
The long term objectives are: (1) to understand the physics of the multispectral microwave radiative characteristics of sea ice as it goes through different phases; (2) to improve characterization of sea ice cover using satellite microwave sensors; and (3) to study ice/ocean physical and biological processes associated with polynya formations and variability of the marginal sea ice region. Two field experiments were conducted to pursue these objectives. One involved measurements of radiative and physical characteristics of sea ice from a ship during a 3-month long cruise through the Weddell Sea ice pack during the Austral winter of 1986. The other involved similar measurements from two aircrafts and a submarine over the Central Arctic and Greenland Sea region. Preliminary results have already led to an enhanced understanding of the microwave signatures of pancake ice, nilas, first year ice, multiyear ice and effects of snow cover. Coastal and deep ocean polynyas and their role in bottom water formation and ocean circulation were studied using a time series of ice images from SMMR. An unsupervised cluster analysis of Arctic sea ice using SMMR and THIR emissivity and brightness temperature data was implemented. The analysis indicates the existence of several unique and persistent clusters in the Central Arctic region during winter and that the sum of the area of these clusters excluding those of first year ice is about 20 percent less than minimum ice cover area inferred from a previous summer data. This result is consistent with saline surface for some multiyear ice floes as observed during MIZEZ and suggests that a significant fraction of multiyear ice floes in the Arctic have first year ice signatures.
Comiso, J. C.↗
Passive Microwave Arctic Sea Ice Melt Onset Dates From the Advanced Horizontal Range Algorithm 1979 - 2022
The onset of the summer melt season is a key stage of the Arctic sea ice seasonal cycle and is an indicator of climate change. Surface melting of the bare or snow-covered sea ice is detected using passive microwave satellite observations. The data set presented here is a 44 year record of Arctic sea ice annual melt onset (MO) dates for 1979–2022 produced using an updated version of the Advanced Horizontal Range Algorithm (AHRA). This data product contains annual maps of the sea ice MO date and a set of descriptive statistics summarizing the data. This paper describes a new update of the AHRA methodology, now AHRA V5, including key changes to the algorithm starting date and sea ice mask methodology to improve estimates of early-season MO dates especially near the sea ice periphery. AHRA V5 data are suitable for monitoring trends in Arctic and regional sea ice MO dates and for process studies of atmosphere-sea ice interactions during the early spring and summer months.
Angela C. Bliss↗
Wide-Band Radar for Measuring Thickness of Sea Ice
A wide-band penetrating radar system for measuring the thickness of sea ice is under development. The need for this or a similar system arises as follows: Spatial and temporal variations in the thickness of sea ice are important indicators of heat fluxes between the ocean and atmosphere and, hence, are important indicators of climate change in polar regions. A remote-sensing system that could directly measure the thickness of sea ice over a wide thickness range from aboard an aircraft or satellite would be of great scientific value. Obtaining thickness measurements over a wide region at weekly or monthly time intervals would contribute significantly to understanding of changes in the spatial distribution and of the mass balance of sea ice. A prototype of the system was designed on the basis of computational simulations directed toward understanding what signal frequencies are needed to satisfy partly competing requirements to detect both bottom and top ice surfaces, obtain adequate penetration despite high attenuation in the lossy sea-ice medium, and obtain adequate resolution, all over a wide thickness range. The prototype of the system is of the frequency-modulation, continuous-wave (FM-CW) type. At a given time, the prototype functions in either of two frequency-band/operational-mode combinations that correspond to two thickness ranges: a lower-frequency (50 to 250 MHz) mode for measuring thickness greater than about 1 m, and a higher frequency (300 to 1,300 MHz) mode for measuring thickness less than about 1 m. The bandwidth in the higher-frequency (lesser-thickness) mode is adequate for a thickness resolution of 15 cm; the bandwidth in the lower-frequency (greater-thickness) mode is adequate for a thickness resolution of 75 cm. Although a thickness resolution of no more than 25 cm is desired for scientific purposes, the 75-cm resolution was deemed acceptable for the purpose of demonstrating feasibility. The prototype was constructed as a modified version of a 500-to-2,000-MHz FM-CW radar system developed previously for mapping near-surface internal layers of the Greenland ice sheet. The prototype included two sets of antennas: one for each frequency-band/mode. For Arctic and Antarctic field tests, the prototype was mounted on a sled that was towed across the ice. The Arctic field test was performed in the lower-frequency mode on ice ranging in thickness from 1 to 4 m. In the analysis of the results of the Arctic field test, a comparison of the radar-determined ice thicknesses with actual ice thicknesses yielded an overall mean difference of 14 cm and standard deviation of 30 cm. The Antarctic field test was performed in the higher-frequency mode; analysis of the results led to the conclusion that this mode is useful for measuring thicknesses between 0.5 and 1 m. Several modifications have been conceived for implementation in further development toward an improved practical system: The system would function in a single frequency-band/mode (100 to 1,200 MHz) that would afford a resolution of about 15 cm. There would be a single antenna system that would be optimized for the entire 100-to-1,000-MHz frequency band. To enable ice-thickness surveys over larger areas, the system would be made capable of operating aboard a low-flying aircraft that could be either piloted or robotic. Data-processing techniques to deconvolve the system response have been developed on the basis of impulse-response measurements over a calm ocean. Implementation of these techniques in the system would enable correction for imperfections of the system and would thereby increase the effective sensitivity of the system.
Gogineni, Prasad↗
Numerical Simulation of North Atlantic Sea Ice Variability, 1951 - 1980
A two-level dynamic-thermodynamic sea ice model is used to simulate the growth, drift and decay of sea ice in the Northern Hemisphere during a 30-year period, 1951 to 1980. The model is run with a daily timestep on a 222 km grid and is forced by interanually varying fields of geostrophic wind and temperature-derived thermodynamic fluxes. The objective is a quantitative description of large-scale sea ice variability in terms of the dynamic and thermodynamic processes responsible for the fluctuations, especially in the North Atlantic where sea ice represents a substantial input of fresh water. The fields of ice velocity and thickness contain strong seasonal as well as interannual variability. The mean drift pattern results in thicknesses of 4 to 5 m offshore of northern Canada and Greenland, while winter thicknesses of approximately 2 m are typical of Alaskan. Eurasian and East Greenland coastal waters. The 30-year mean fields are characterized by ecessive ice in the North Atlantic during winter and by a summer retreat that is more rapid than observed.
Walsh, J. E.↗
Intersensor Calibration Between F13 SSMI and F17 SSMIS for Global Sea Ice Data Records
An intercalibration between F13 Special Sensor Microwave Imager (SSMI) and F17 Special Sensor Microwave Imager Sounder (SSMIS) sea ice extents and areas for a full year of overlap was undertaken preparatory to extending the 1979-2007 NASA Goddard Space Flight Center (GSFC) NASA Team algorithm time series of global sea ice extents and areas. The 1979- 2007 time series was created from Scanning Multichannel Microwave Radiometer (SMMR) and SSMI data. After intercalibration, the yearly mean F17 and F13 difference in Northern Hemisphere sea ice extents is -0.0156%, with a standard deviation of the differences of 0.6204%, and the yearly mean difference in Northern Hemisphere sea ice areas is 0.5433%, with a standard deviation of 0.3519%. For the Southern Hemisphere, the yearly mean difference in sea ice extents is 0.0304% +/- 0.4880%, and the mean difference in sea ice areas is 0.1550% +/- 0.3753%. This F13/F17 intercalibration enables the extension of the 28-year 1979-2007 SMMR/SSMI sea ice time series for as long as there are stable F17 SSMIS brightness temperatures available.
Cavalieri, Donald J.↗
Intersensor Calibration Between F13 SSMI and F17 SSMIS for Global Sea Ice Data Records
An intercalibration between F13 Special Sensor Microwave Imager (SSMI) and F17 Special Sensor Microwave Imager Sounder (SSMIS) sea ice extents and areas for a full year of overlap was undertaken preparatory to extending the 1979-2007 NASA Goddard Space Flight Center (GSFC) NASA Team algorithm time series of global sea ice extents and areas. The 1979- 2007 time series was created from Scanning Multichannel Microwave Radiometer (SMMR) and SSMI data. After intercalibration, the yearly mean F17 and F13 difference in Northern Hemisphere sea ice extents is -0.0156%, with a standard deviation of the differences of 0.6204%, and the yearly mean difference in Northern Hemisphere sea ice areas is 0.5433%, with a standard deviation of 0.3519%. For the Southern Hemisphere, the yearly mean difference in sea ice extents is 0.0304% 0.4880%, and the mean difference in sea ice areas is 0.1550% 0.3753%. This F13/F17 intercalibration enables the extension of the 28-year 1979-2007 SMMR/SSMI sea ice time series for as long as there are stable F17 SSMIS brightness temperatures available.
Cavalieri, Donald J.↗
Recent Antarctic Sea Ice Extent Variability and Its Relation to Atmospheric Circulation in Reanalyses and Seasonal Forecasts
Sea ice is considered an important indicator of climate change. Interest has focused on the trend towards reduced sea ice cover in the Arctic Ocean, particularly during summer months. In the Southern Hemisphere, sea ice has also received attention due to a trend towards increasing cover, in opposition to the Northern Hemisphere and to climate model projections. This all changed in the austral spring 2016, when the Southern Ocean sea ice cover dropped dramatically, and has remained consistently at below average values up to the present. Here I review some of the issues and perceived causes of the 2016 sea ice reduction. Several studies have suggested a change in surface winds, which would allow warmer waters to move upwards in the water column and towards the surface. Results from the GMAO (Global Modeling and Assimilation Office) ocean data assimilation system are consistent with this hypothesis, and indicate a significant warming of subsurface waters occurred in the winter of 2016, and has remained in place.
Polar↗
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 Team 2 Sea Ice Concentration Algorithm Retrieval Uncertainty
Satellite microwave radiometers are widely used to estimate sea ice cover properties (concentration, extent, and area) through the use of sea ice concentration (IC) algorithms. Rare are the algorithms providing associated IC uncertainty estimates. Algorithm uncertainty estimates are needed to assess accurately global and regional trends in IC (and thus extent and area), and to improve sea ice predictions on seasonal to interannual timescales using data assimilation approaches. This paper presents a method to provide relative IC uncertainty estimates using the enhanced NASA Team (NT2) IC algorithm. The proposed approach takes advantage of the NT2 calculations and solely relies on the brightness temperatures (TBs) used as input. NT2 IC and its associated relative uncertainty are obtained for both the Northern and Southern Hemispheres using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) TB. NT2 IC relative uncertainties estimated on a footprint-by-footprint swath-by-swath basis were averaged daily over each 12.5-km grid cell of the polar stereographic grid. For both hemispheres and throughout the year, the NT2 relative uncertainty is less than 5%. In the Southern Hemisphere, it is low in the interior ice pack, and it increases in the marginal ice 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 ice types associated with deep snow and new ice. Seasonal variations in uncertainty show larger values in summer as a result of melt conditions and greater atmospheric contributions. Our analysis also includes an evaluation of the NT2 algorithm sensitivity to AMSR-E sensor noise. There is a 60% probability that the IC does not change (to within the computed retrieval precision of 1%) due to sensor noise, and the cumulated probability shows that there is a 90% chance that the IC varies by less than +/-3%. We also examined the daily IC variability, which is dominated by sea ice drift and ice formation/melt. Daily IC variability is the highest, year round, in the MIZ (often up to 20%, locally 30%). The temporal and spatial distributions of the retrieval uncertainties and the daily IC variability is expected to be useful for algorithm intercomparisons, climate trend assessments, and possibly IC assimilation in models.
Sea Ice↗
Changes in atmospheric circulation amplify extreme snowfall fueled by Arctic sea ice loss over high-latitude land
Arctic sea-ice retreat has been linked to increased winter precipitation and heavy snowfall over land, likely due to a combination of enhanced evaporation from ice-free Arctic marginal seas (AMS) and changes in atmospheric circulation. However, their relative roles and contributions remain uncertain. Here, we show that a greater proportion of AMS evaporative moisture reached high-latitude land during the cold seasons from 1980–1989 to 2012–2021. Atmospheric circulation changes added an additional 13 % increase in the AMS moisture contribution, accounting for 11 % of the total increase in AMS-sourced land precipitation. Notably, 46 % of the increase in AMS-sourced extreme snowfall is attributed to circulation-driven landward moisture transport, representing an 84 % increase beyond the effect of enhanced AMS evaporation alone. Further analysis indicates that both the rise in Arctic moisture and the atmospheric circulation shifts are primarily driven by anthropogenic forcing. These findings highlight how atmospheric circulation changes amplify extreme snowfall fueled by AMS evaporation, underscoring the synergistic effects of Arctic sea ice loss and circulation change on high-latitude winter precipitation.
Anthropogenic forcing↗
The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis
The NASA Eulerian Snow On Sea Ice Model (NESOSIM) is a new, open-source snow budget model that is currentlyconfigured to produce daily estimates of the depth and density of snow on sea ice across the Arctic Ocean throughthe accumulation season. NESOSIM has been developed in a three-dimensional Eulerian framework and includes two(vertical) snow layers and several simple parameterizations (accumulation, wind packing, advection_divergence, blowingsnow lost to leads) to represent key sources and sinks of snow on sea ice. The model is forced with daily inputs of snowfall and near-surface winds (from reanalyses), sea ice concentration (from satellite passive microwave data) and sea ice drift (from satellite feature tracking) during the accumulation season (August through April). In this study, we present the NESOSIM formulation, calibration efforts, sensitivity studies and validation efforts across an Arctic Ocean domain (100 km horizontal resolution). The simulated snow depth and density are calibrated with in situ data collected on drifting ice stations during the 1980s. NESOSIM shows strong agreement with the in situ seasonal cycles of snow depth and density, and shows good (moderate) agreement with the regional snow depth (density) distributions. NESOSIM is run for a contemporary period (2000 to 2015), with the results showing strong sensitivity to the reanalysis derived snowfall forcing data, with the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the Japanese Meteorological Agency 55-year reanalysis (JRA-55) forced snow depths generally higher than ERA-Interim,and the Arctic System Reanalysis (ASR) generally lower. We also generate and force NESOSIM with a consensus "median" daily snowfall dataset from these reanalyses.The results are compared against snow depth estimates derived from NASA's Operation IceBridge (OIB) snow radar data from 2009 to 2015, showing moderate_strong correlations and root mean squared errors of 10 cm depending on the OIB snow depth product analyzed, similar to the comparisons between OIB snow depths and the commonly used modified Warren snow depth climatology. Potential improvements to this initial NESOSIM formulation are discussed in the hopes of improving the accuracy and reliability of these simulated snow depths and densities.
Petty, Alek A.↗
A Laboratory Study of the Effect of Frost Flowers on C Band Radar Backscatter from Sea Ice
C band images of Arctic sea ice taken by the ERS 1 synthetic aperture radar show transitory regions of enhanced radar backscatter from young sea ice. Published field observations associate this increase with frost flower growth and the capture of blowing snow by the flowers. To investigate the first part of this phenomenon, we carried out a laboratory experiment on the response of C band radar backscatter to frost flowers growing on the surface of newly formed saline ice. The experiment took place in a 5 m by 7 m by 1.2 m deep saline water pool located in a two-story indoor refrigerated facility at the Cold Regions Research and Engineering Laboratory. Sodium chloride ice was grown in this pool at an air temperature of -28 C. The frost flowers first appeared on the ice surface as dendrites and then changed to needles as the ice sheet grew thicker and the surface temperatures became colder. The frost flowers reached to a height of 10-15 mm, and beneath each cluster of frost flowers a slush layer formed to a thickness of approximately 4 mm. Far-field radar measurements of the backscatter from the ice were made at incident angles from 20 to 40 deg and at approximately 6-hour intervals throughout the 3-day period of the experiment. A backscatter minimum occurred early in the flower growth at the time coincident with an abrupt doubling in the ice surface salinity. Once the full flower coverage was achieved, we removed first the crystal flowers and then the slush layer from the ice surface. The results for these cases show that the crystals have little impact on the backscatter, while the underlying slush patches yield a backscatter increase of 3-5 dB over that of bare ice. The laboratory results suggest that this relative backscatter increase of approximately 5 dB can be used as an index to mark the full area coverage of frost flowers.
Nghiem, S. V.↗