The altimetry from NASA's ICESat-2 orbiting observatory addresses science requirements that pertain to the monitoring of changes in sea ice, ice sheets, ocean circulation, and vegetation biomass. Retrievals from the ICESat-2 (IS-2) mission add to the valuable multidecadal record of elevation changes from previous and forthcoming altimetry missions for understanding geophysical processes and for climate monitoring, forecasts, and projections. Launched in September 2018, the second-​generation spaceborne lidar onboard the Advanced Topographic Laser Altimeter System (ATLAS) employs a unique photon-counting approach for profiling the surface.

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 three 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 ~6000 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.

Accurately resolving spatio-temporal variations in sea surface height across the polar oceans is key to improving our understanding of ocean circulation variability and change. Here, we examine the first 2 years (2018–2020) of Arctic Ocean sea surface height anomalies (SSHA) from the photon-counting laser altimeter onboard NASA's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). ICESat-2 SSHA estimates are compared to estimates from ESA's CryoSat-2 mission, including semisynchronous along-track measurements from the recent CRYO2ICE orbit alignment campaign. There are documented residual centimeter-scale range biases between the ICESat-2 beams (in release-003 data) and we opted for a single-beam approach in our comparisons. We find good agreement in the along-track estimates (spatial correlation coefficient >0.8 and mean differences <0.03 m) as well as in the gridded monthly SSHA estimates (temporal correlation coefficient of 0.76 and a mean difference of 0.01 m) from the two altimeters, suggesting ICESat-2 adds to the CryoSat-2 SSHA estimates.

CryoSat-2 (CS2) is the first mission equipped with a pulse-limited radar altimeter capable of operating in Synthetic Aperture Radar (SAR) Interferometric (SARIn) mode. Over ice sheets and ice caps, CS2 SARIn data have been used to retrieve surface elevations over an across-track ground “swath.” This work demonstrates that retracking multiple coherent peaks of CS2 SARIn waveforms, in combination with the interferometric phase, enables to obtain more than one valid height estimate from single SARIn waveforms over Arctic sea ice. For some SARIn waveforms, the scattering from sea ice at the satellite nadir is successfully separated from returns originating from off-nadir leads. An average bias of –1.8 cm is found for absolute sea ice elevations when using a 50% threshold retracker. It is shown that including multiple SARIn peaks and the associated phase difference in the processing does not introduce any bias on the average sea ice freeboard heights compared with the estimates from regular SAR processing schemes, while significantly increasing the number of valid sea surface height retrievals (+55%) and the number of freeboard estimates in the coastal domain and in multi-year ice regions (~3 times). This results in an average ~34% reduction of the gridded random freeboard uncertainty, corresponding to a ~20% reduction of the gridded total sea ice thickness uncertainty. The results of this work show that SARIn acquisitions over Arctic sea ice provide improved spatial coverage and denser sampling of sea level and sea ice freeboard compared with the SAR mode, with accuracy being largely driven by the retracking algorithm.

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 (1950–1989) and satellite altimetry-derived dynamic ocean topography (2004–-2019) 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 AO maxima in 1989 and 2007–08 and after an AO minimum in 2010, indicate the cyclonic mode is forced by the Arctic Oscillation (AO) with a lag of about one 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.

NASA's Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2) mission launched in September 2018 and is now providing high‐resolution surface elevation profiling across the entire globe, including the sea ice cover of the Arctic and Southern Oceans. For sea ice applications, successfully discriminating returns between sea ice and open water is key for accurately determining freeboard (the extension of sea ice above local sea level) and new information regarding the geometry of sea ice floes and leads. We take advantage of near‐coincident optical imagery obtained from the European Space Agency (ESA) Sentinel‐2 (S‐2) satellite over the Western Weddell Sea of the Southern Ocean in March 2019 and the Lincoln Sea of the Arctic Ocean in May 2019 to evaluate the surface classification scheme in the ICESat‐2 ATL07 and ATL10 sea ice products. We find a high level of agreement between the ATL07 (specular) lead classification and visible leads in the S‐2 imagery in these two coincident images across all six ICESat‐2 beams, increasing our confidence in the freeboard products and deriving new estimates of the sea ice state. The S‐2 overlays provide additional, albeit limited, evidence of the misclassification of dark leads due to clouds. Dark leads are no longer used to derive sea surface and thus freeboard as of the third release (r003) of the ICESat‐2 sea ice products. We show estimates of lead fraction and more preliminary estimates of chord length (a proxy for floe size) using two metrics for classifying sea surface (lead) segments across both the Arctic and Southern Ocean for the first winter season of data collection.

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.

Hemisphere‐wide observations of melt ponds on sea ice are needed to understand their influence on the surface radiation budget of the Arctic Ocean and to extend the satellite sea ice thickness data record. Here we present a first assessment of NASA's Ice, Cloud, and land Elevation Satellite‐2 (ICESat‐2) over individual Arctic sea ice melt ponds with different reflective properties. We use coincident high‐resolution satellite imagery from WorldView‐2 and Sentinel‐2 over different sea ice topographies to show that smooth ponds are highly reflective and can saturate the ICESat‐2 photon detection system. Rougher ponds have a more varied backscatter signal, and in some cases both the water and underlying ice surfaces are visible in photon height distributions. Characterizing the complex photon backscatter signals of melt ponds on sea ice is a first step toward automating retrievals of pond parameters such as width, melt pond fraction and depth, and improving higher‐level ICESat‐2 sea ice height and freeboard products.

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 km³ 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.