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Using ICESat‐2 and CryoSat‐2 freeboards, we examine the variability of monthly Arctic sea ice snow depth, thickness and volume between October 2018 and April 2021. For the 3 years, satellite‐derived estimates captured a decrease in mean April snow depth (∼2.50 cm) and ice thickness (∼0.28 m) equivalent to an ice volume loss of ∼12.5%. Results show greater thinning of multiyear ice with an end‐of‐season thickness in 2021 that is lower by ∼16.1% (0.50 m), with negligible changes over first‐year ice. For the period, sea ice thickness estimates using snow depth from climatology result in thicker ice (by up to ∼0.22 m) with a smaller decrease in multiyear ice thickness (∼0.38 m). An 18‐year satellite record, since the launch of ICESat, points to a loss of ∼6,000 km3 or one‐third of the winter Arctic ice volume driven by decline in multiyear‐ice coverage in the multi‐decadal transition to a largely seasonal ice cover. Plain Language Summary: Ice thickness and volume are critical variables for assessing the evolution and response of the polar sea ice cover to a warming climate. Retrieval of sea ice thickness from altimeter freeboards (i.e., the vertical height of the floating ice and snow above the local sea level) requires knowledge of loading due to snow. Until recently, snow depth has been prescribed with a climatology based on historical field records. Using freeboard differences from ICESat‐2 and CryoSat‐2, we are now able to derive snow depth estimates. In this paper we examine the differences between climatological and satellite‐derived snow depth as well as the retrieved ice thicknesses from the two altimeter missions. Their changes for three winters between 2018 and 2021 are documented. Derived ice volume estimates are placed within the context of an 18‐year satellite record. Key Points: For 2018–2021, the decline in mean Arctic sea ice thickness of ∼0.28 m in spring is largely explained by ∼0.50 m thinning of multiyear iceSatellite‐derived snow depth estimates, when compared to climatology, provide a thinner Arctic ice thickness especially during the fallSatellite records since 2003 show a loss of one‐third of the winter sea ice volume driven by the decline in multiyear ice coverage [ABSTRACT FROM AUTHOR]

The Surface Water Ocean Topography mission (SWOT), scheduled for launch in 2021, is the first space-borne radar interferometer capable of providing wide-swath height maps of water surfaces with centimetric precision. In addition to its primary objectives in oceanography and hydrography, the SWOT instrument offers opportunities for other applications. Here, we explore the feasibility of sea ice freeboard and sea surface height retrievals in the ice-covered oceans from SWOT data. The quality of SWOT height estimates depends on the backscatter strength and number of samples used for multi-looking. We use near-nadir radar backscatter estimates from sea ice and water over the range of SWOT incidence angles to simulate SWOT height maps and assess the retrieval precision under different backscatter, surface type and roughness conditions. Unlike wind-roughened open water, the available observations suggest that backscatter over sea ice has a moderate dependence on look angle (specularity), and the backscatter of younger, flatter sea ice has a greater degree of specularity than older, more deformed and colder sea ice. To achieve a similar freeboard precision to conventional altimeters (∼3 cm) requires averaging over 15–40 km2 in the near- to mid-swath and 90–175 km2 in the far-swath for lower northern latitudes (<65°N), and 9–18 km2 in the near- to mid-swath and 30–50 km2 in the far-swath over Southern hemisphere ice. Compared to a typical altimeter grid cell used for time and area averages (∼25 km × 25 km or 625 km2), this represents an improvement in resolution of 3–70 fold between the near- and far-swath. Overall, the results suggest that SWOT has the potential to provide unique new insights in the high-latitude oceans by providing two-dimensional maps of sea ice thickness and dynamic ocean topography at higher resolution, in both space and time, than has previously been possible. [ABSTRACT FROM AUTHOR]

Arctic Ocean surface circulation change should not be viewed as the strength of the anticyclonic Beaufort Gyre. While the Beaufort Gyre is a dominant feature of average Arctic Ocean surface circulation, empirical orthogonal function analysis of dynamic height (1950–89) and satellite altimetry–derived dynamic ocean topography (2004–19) show the primary pattern of variability in its cyclonic mode is dominated by a depression of the sea surface and cyclonic surface circulation on the Russian side of the Arctic Ocean. Changes in surface circulation after Arctic Oscillation (AO) maxima in 1989 and 2007–08 and after an AO minimum in 2010 indicate the cyclonic mode is forced by the AO with a lag of about 1 year. Associated with a one standard deviation increase in the average AO starting in the early 1990s, Arctic Ocean surface circulation underwent a cyclonic shift evidenced by increased spatial-average vorticity. Under increased AO, the cyclonic mode complex also includes increased export of sea ice and near-surface freshwater, a changed path of Eurasian runoff, a freshened Beaufort Sea, and weakened cold halocline layer that insulates sea ice from Atlantic water heat, an impact compounded by increased Atlantic Water inflow and cyclonic circulation at depth. The cyclonic mode's connection with the AO is important because the AO is a major global scale climate index predicted to increase with global warming. Given the present bias in concentration of in situ measurements in the Beaufort Gyre and Transpolar Drift, a coordinated effort should be made to better observe the cyclonic mode. [ABSTRACT FROM AUTHOR]

In Release 001 and 002 of the ICESat-2 sea ice products, candidate height segments used to estimate the reference sea surface height for freeboard calculations included two surface types: specular and smooth dark leads. We found that the uncorrected photon rates, used as proxies of surface reflectance, are attenuated due to clouds resulting in the potential misclassification of sea ice as dark leads, biasing the reference sea surface height relative to those derived from the more reliable specular returns. This results in higher reference sea surface heights and lower estimated ice freeboards. The resolution of available cloud flags from the ICESat-2 atmosphere data product is too coarse to provide useful filtering at the lead segment scale. In Release 003, we have modified the surface-reference-finding algorithm so that only specular leads are used. The consequence of this change can be seen in the composites of mean freeboard of the Arctic and Southern oceans. Broadly, coverages have decreased by ∼10 –20 % because there are fewer leads (by excluding the dark leads), and the composite means have increased by 0–4 cm because of the use of more consistent specular leads. [ABSTRACT FROM AUTHOR]

We offer a view of the Antarctic sea ice cover from lidar (ICESat-2) and radar (CryoSat-2) altimetry, with retrievals of freeboard, snow depth, and ice thickness that span an 8-month winter between 1 April and 16 November 2019. Snow depths are from freeboard differences. The multiyear ice observed in the West Weddell sector is the thickest, with a mean sector thickness > 2 m. The thinnest ice is found near polynyas (Ross Sea and Ronne Ice Shelf) where new ice areas are exported seaward and entrained in the surrounding ice cover. For all months, the results suggest that ∼ 65 %–70 % of the total freeboard is comprised of snow. The remarkable mechanical convergence in coastal Amundsen Sea, associated with onshore winds, was captured by ICESat-2 and CryoSat-2. We observe a corresponding correlated increase in freeboards, snow depth, and ice thickness. While the spatial patterns in the freeboard, snow depth, and thickness composites are as expected, the observed seasonality in these variables is rather weak. This most likely results from competing processes (snowfall, snow redistribution, snow and ice formation, ice deformation, and basal growth and melt) that contribute to uncorrelated changes in the total and radar freeboards. Evidence points to biases in CryoSat-2 estimates of ice freeboard of at least a few centimeters from high salinity snow (> 10) in the basal layer resulting in lower or higher snow depth and ice thickness retrievals, although the extent of these areas cannot be established in the current data set. Adjusting CryoSat-2 freeboards by 3–6 cm gives a circumpolar ice volume of 17 900–15 600 km 3 in October, for an average thickness of ∼ 1.29–1.13 m. Validation of Antarctic sea ice parameters remains a challenge, as there are no seasonally and regionally diverse data sets that could be used to assess these large-scale satellite retrievals. [ABSTRACT FROM AUTHOR]

The Fram Strait sea ice volume export 1992-2014 is derived by combining sea ice thickness from upward looking sonars (ULS) with satellite observations of sea ice drift and area. Fram Strait is the main gate for sea ice export from the Arctic. The average yearly sea ice export is 2,400 ± 640km³. The mean and modal ULS ice thickness in Fram Strait decreased by 15% and 21% per decade, respectively, during 1990-2014. Combined with sea ice drift and area this leads to a decrease of the Arctic sea ice volume export of 27 ± 2% per decade between 1992 and 2014. Thus, for the given time period, changes in sea ice export do not drive the sea ice volume decrease in the Arctic Basin. However, for individual years like 2007 and 2012 the ice export likely has contributed to the loss of summer sea ice. Combined with PIOMAS model simulation we estimate that 14% of the total Arctic sea ice volume is exported every year through Fram Strait. This fraction of the total sea ice volume exported per year does not show a trend because the Arctic Basin ice volume is decreasing at a similar rate as the Fram Strait ice volume export. Compared to ice velocities from Acoustic Doppler Current Profiler (ADCP) the satellite ice drift shows good correspondence in variability but a negative bias. Ice volume transport estimates presented here thus should be considered a conservative estimate.We show, however, that the transport estimates are not sensitive to the exact flux gate location. [ABSTRACT FROM AUTHOR]

National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐2 (ICESat‐2) mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard, the Advanced Topographic Laser Altimeter System, is now providing routine, very high‐resolution, surface elevation data across the globe, including the Arctic and Southern oceans. In this study, we demonstrate our new processing chain for converting the along‐track ICESat‐2 sea ice freeboard product (ATL10) into sea ice thickness, focusing our initial efforts on the Arctic Ocean. For this conversion, we primarily make use of snow depth and density data from the NASA Eulerian Snow on Sea Ice Model. The coarse resolution (~100 km) snow data are redistributed onto the high‐resolution (approximately 30–100 m) ATL10 freeboards using relationships obtained from snow depth and freeboard data collected by NASA's Operation IceBridge mission. We present regional sea ice thickness distributions and highlight their seasonal evolution through our first winter season of data collection. We include ice thickness uncertainty estimates, while also acknowledging the limitations of these estimates. We generate a gridded monthly thickness product and compare this with various monthly sea ice thickness estimates obtained from European Space Agency's CryoSat‐2 satellite mission, with ICESat‐2 showing consistently lower thicknesses. Finally, we compare our February/March 2019 thickness estimates to ICESat February/March (19 February to 21 March) 2008 ice thickness estimates using the same input assumptions, which show an ~0.37 m or ~20% thinning across an inner Arctic Ocean domain in this 11‐year time period. Plain Language Summary NASA's ICESat‐2 mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard is a highly precise laser, which is now providing routine, very high‐resolution, surface height measurements across the globe, including over the Arctic and Southern oceans. In this study, we show new estimates of Arctic sea ice thickness from the first winter season of data collected by ICESat‐2. Sea ice thickness is calculated by combining the measured ICESat‐2 freeboards—the extension of sea ice above sea level—with a new snow on sea ice model. Our derived thicknesses are consistently lower than the thicknesses calculated from ESA's CryoSat‐2 data and the original ICESat mission, which ended in 2008. More work is needed to verify these new thickness estimates. [ABSTRACT FROM AUTHOR]

The Beaufort Gyre freshwater content has increased since the 1990s, potentially stabilizing in recent years. The mechanisms proposed to explain the stabilization involve either mesoscale eddy activity that opposes Ekman pumping or the reduction of Ekman pumping due to reduced sea ice–ocean surface stress. However, the relative importance of these mechanisms is unclear. Here, we present observational estimates of the Beaufort Gyre mechanical energy budget and show that energy dissipation and freshwater content stabilization by eddies increased in the late-2000s. The loss of sea ice and acceleration of ocean currents after 2007 resulted in enhanced mechanical energy input but without corresponding increases in potential energy storage. To balance the energy surplus, eddy dissipation and its role in gyre stabilization must have increased after 2007. Our results imply that declining Arctic sea ice will lead to an increasingly energetic Beaufort Gyre with eddies playing a greater role in its stabilization. The freshwater content of the Beaufort Gyre in the Western Arctic Ocean has increased in response to almost two decades of persistent anti-cyclonic winds. Here, the authors found that dramatic loss of sea ice and acceleration of surface currents after 2007 led to a net annual wind energy input to the Beaufort Gyre, and anticipate that continued sea ice decline will lead to an increasingly energetic Beaufort Gyre. [ABSTRACT FROM AUTHOR]

The airborne Operation IceBridge and spaceborne CryoSat-2 missions observe polar sea ice at different spatial and temporal scales as well as with different sensor suites. Comparison of data products from IceBridge and CryoSat-2 is complicated by the fact that they use different geophysical corrections: reference ellipsoid, geoid model, tide model, and atmospheric corrections to derive surface elevation and sea-ice freeboard. In this paper, we compare sea-ice surface elevation and freeboard using eight coincident CryoSat-2, Airborne Topographic Mapper (ATM), and Land, Vegetation, and Ice Sensor (LVIS) observations with direct IceBridge underflights of CryoSat-2 ground tracks. We apply identical geophysical corrections to CryoSat-2 and IceBridge data to eliminate elevation biases due to these differences and focus on differences due to retracker performance. The IceBridge ATM and LVIS elevation and freeboard and Snow Radar snow depth data sets are averaged to each CryoSat-2 footprint for comparison. With snow depth measurements, we are able to compare elevations and freeboards at the snow/ice interface for five different CryoSat-2 retrackers (ESA, GSFCv1, AWI, JPL, and GSFCv2) and IceBridge. The overall mean of freeboard differences between GSFCv2, ESA, AWI, JPL retrackers, and ATM are in agreement within 0.05 m. However, the five different CryoSat-2 retrackers show distinct differences in mean elevation over leads and over floes. This suggests that the physical interpretation of the different retrackers needs to be considered depending on usage, for example, elevations from CryoSat-2 retrackers need to be carefully calibrated before comparing with elevation from other satellites for long-term surface elevation trends. [ABSTRACT FROM AUTHOR]

We examine the variability of sea ice freeboard, snow depth, and ice thickness in three years (2011, 2014, and 2016) of repeat surveys of an IceBridge (OIB) transect across the Weddell Sea. Averaged over this transect, ice thickness ranges from 2:40±1:07 (2011) to 2:60±1:15m (2014) and snow depth from 35:8±11:5 (2016) to 43:6±10:2 cm (2014), suggesting a highly variable but broadly thicker ice cover compared to that inferred from drilling and ship-based measurements. Spatially, snow depth and ice thickness are higher in the more deformed ice of the westernWeddell. The impact of undersampling the thin end of the snow depth distribution on the regional statistics, due to the resolution of the snow radar, is assessed. Radar freeboards (uncompensated for snow thickness) from CryoSat-2 (CS-2) sampled along the same transect are consistently higher (by up to 8 cm) than those computed using OIB data. This suggests radar scattering that originates above the snow-ice interface, possibly due to salinity in the basal layer of the snow column. Consequently, sea ice thicknesses computed using snow depth estimates solely from differencing OIB and CS-2 freeboards (without snow radar) are therefore generally higher; mean differences in sea ice thickness along a transect are up to 0:6m higher (in 2014). This analysis is relevant to the use of differences between ICESat- 2 and CS-2 freeboards to estimate snow depth for ice thickness calculations. Our analysis also suggests that, even with these expected biases, this is an improvement over the assumption that snow depth is equal to the total freeboard, with which the underestimation of thickness could be up to a meter. Importantly, better characterization of the source of these biases is critical for obtaining improved estimates and understanding the limits of retrievals of Weddell Sea ice thickness from satellite altimeters. [ABSTRACT FROM AUTHOR]

A realistic representation of sea-ice deformation in models is important for accurate simulation of the sea-ice mass balance. Simulated sea-ice deformation from numerical simulations with 4.5, 9, and 18 km horizontal grid spacing and a viscous-plastic (VP) sea-ice rheology are compared with synthetic aperture radar (SAR) satellite observations (RGPS, RADARSAT Geophysical Processor System) for the time period 1996-2008. All three simulations can reproduce the large-scale ice deformation patterns, but small-scale seaice deformations and linear kinematic features (LKFs) are not adequately reproduced. The mean sea-ice total deformation rate is about 40% lower in all model solutions than in the satellite observations, especially in the seasonal sea-ice zone. A decrease in model grid spacing, however, produces a higher density and more localized ice deformation features. The 4.5 km simulation produces some linear kinematic features, but not with the right frequency. The dependence on length scale and probability density functions (PDFs) of absolute divergence and shear for all three model solutions show a power-law scaling behavior similar to RGPS observations, contrary to what was found in some previous studies. Overall, the 4.5 km simulation produces the most realistic divergence, vorticity, and shear when compared with RGPS data. This study provides an evaluation of high and coarse-resolution viscous-plastic sea-ice simulations based on spatial distribution, time series, and power-law scaling metrics. [ABSTRACT FROM AUTHOR]

Satellite radar altimeter (RA) measurements are important for continued monitoring of rapidly changing polar regions. In 2010, the European Space Agency launched CryoSat-2 carrying SIRAL, a Ku-band RA with objectives of determining the thickness and extent of sea ice and the topography of the ice sheets. One difficulty with Ku-band radar surveys over snow and ice is unknown penetration of RA signal into snow cover. Improving our understanding of the interactions of RA signals with snow and ice is needed to produce accurate elevation products. To this end, we developed a low-power, ultra wideband (12-18 GHz) RA for airborne surveys to provide fine resolution measurements capable of detecting both scattering from the surface and layers within sea ice and ice sheets. These measurements provide a means of identifying the dominant scattering location of lower resolution RA measurements comparable to satellite-based instruments. We generated two products: a full-bandwidth waveform (FBW) to identify scattering targets at fine resolution and a reduced bandwidth waveform (RBW) to represent conventional RA measurements. Retrackers are used to generate height estimates over various surface conditions for comparisons. Over ice sheets, the leading-edge tracker provided consistent ice-surface elevation measurements between the FBW and RBW results; however, there were significant differences between the results from the centroid tracker. Over sea ice, the location of the dominant return between the results from snow-covered sea ice is highly variable. This paper provides an overview of RA surveys in polar regions, a description of the CReSIS system, and a discussion of the results. [ABSTRACT FROM AUTHOR]

Abstract: Since 2009, subsets of declassified satellite images of Arctic sea ice have been released as literal image derived products (LIDPs) with one-meter resolution for scientific use. The collections include imagery acquired over six fixed locations in the Arctic Basin since 1999. Targeted acquisitions over drifting ice parcels, from 2009 on, provide an added dimension to the LIDP archive. This mode of acquisition is especially useful for capturing time-varying processes during the melt season. In this paper, we describe the characteristics of LIDPs, the extent of the current archive, and highlight their uses for Arctic sea ice science. At this writing, the archive of LIDPs is still quite limited, and thus this paper illustrates the potential utility of the imagery. We provide an overview of the retrieval of five sea ice parameters that would benefit significantly from the surface details afforded by the higher resolution LIDPs. They include: melt pond coverage, open water fraction, ridge height, floe size, and, openings and closings. Two other uses are suggested: measurement of lateral melt and the interpretation of radar backscatter. The intent is to motivate geophysical uses of the LIDP and future acquisitions. Results here suggest that the effective observations of sea ice parameters and especially their changes require more focused sampling strategies to address specific spatial and temporal sampling needs. Presently, acquisitions of this type of imagery at fixed locations and drifting sites are being continued, and LIDPs are added to the archive as they are released. [Copyright &y& Elsevier]

Presents an analysis of the ice-sheet structure of Lake Vostok in Antarctica. Subglacial melting and net ice loss in the north of the lake; Exchange of significant quantities of water between the base of the ice sheet and the lake waters; Net transfer of impurities from the influent basal ice to the lake water.

In the above paper [1], there is an error in Table I. The value “30” in the bottom row, fifth column should be “350.” The corrected table is provided here. [ABSTRACT FROM PUBLISHER]

A letter to the editor and a response to the article "The thinning of Arctic sea ice," by Ron Kwok and Norbert Untersteiner in the April 2011 issue is presented.

The Ice, Cloud, and land Elevation Satellite (ICESat) mission used laser altimetry measurements to determine changes in elevations of glaciers and ice sheets, as well as sea ice thickness distribution. These measurements have provided important information on the response of the cryopshere (Earth's frozen surfaces) to changes in atmosphere and ocean condition. ICESat operated from 2003 to 2009 and provided repeat altimetry measurements not only to the cryosphere scientific community but also to the ocean, terrestrial and atmospheric scientific communities. The conclusive assessment of significant ongoing rapid changes in the Earth's ice cover, in part supported by ICESat observations, has strengthened the need for sustained, high accuracy, repeat observations similar to what was provided by the ICESat mission. Following recommendations from the National Research Council for an ICESat follow-on mission, the ICESat-2 mission is now under development for planned launch in 2018. The primary scientific aims of the ICESat-2 mission are to continue measurements of sea ice freeboard and ice sheet elevation to determine their changes at scales from outlet glaciers to the entire ice sheet, and from 10s of meters to the entire polar oceans for sea ice freeboard. ICESat carried a single beam profiling laser altimeter that produced ~ 70 m diameter footprints on the surface of the Earth at ~ 150 m along-track intervals. In contrast, ICESat-2 will operate with three pairs of beams, each pair separated by about 3 km cross-track with a pair spacing of 90 m. Each of the beams will have a nominal 17 m diameter footprint with an along-track sampling interval of 0.7 m. The differences in the ICESat-2 measurement concept are a result of overcoming some limitations associated with the approach used in the ICESat mission. The beam pair configuration of ICESat-2 allows for the determination of local cross-track slope, a significant factor in measuring elevation change for the outlet glaciers surrounding the Greenland and Antarctica coasts. The multiple beam pairs also provide improved spatial coverage. The dense spatial sampling eliminates along-track measurement gaps, and the small footprint diameter is especially useful for sea surface height measurements in the often narrow leads needed for sea ice freeboard and ice thickness retrievals. The ICESat-2 instrumentation concept uses a low energy 532 nm (green) laser in conjunction with single-photon sensitive detectors to measure range. Combining ICESat-2 data with altimetry data collected since the start of the ICESat mission in 2003, such as Operation IceBridge and ESA's CryoSat-2, will yield a 15 + year record of changes in ice sheet elevation and sea ice thickness. ICESat-2 will also provide information of mountain glacier and ice cap elevations changes, land and vegetation heights, inland water elevations, sea surface heights, and cloud layering and optical thickness. [ABSTRACT FROM AUTHOR]