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The Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2) mission has collected surface elevation measurements for over 5 years. ICESat‐2 carries an instrument that emits laser light at 532 nm, and ice and snow absorb weakly at this wavelength. Previous modeling studies found that melting snow could induce significant bias to altimetry signals, but there is no formal assessment on ICESat‐2 acquisitions during the melting season. We performed two case studies over the Greenland Ice Sheet to quantify bias in ICESat‐2 signals over snow: one to validate Airborne Topographic Mapper (ATM) data against Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS‐NG) grain sizes, and a second to estimate ICESat‐2 bias relative to ATM. We used snow optical grain sizes derived from ATM and AVIRIS‐NG to attribute altimetry bias to snowpack properties. For the first case study, the mean and standard deviation of optical grain sizes were 340 ± 65 µm (AVIRIS‐NG) and 670 ± 420 µm (ATM). A mean altimetry bias of 4.81 ± 1.76 cm was found for ATM, with larger biases linked to increases in grain size. In the second case study, we found a mean grain size of 910 ± 381 µm and biases of 6.42 ± 1.77 cm (ICESat‐2) and 9.82 ± 0.97 cm (ATM). The grain sizes and densities needed to recreate biases with a model are uncommon in nature, so we propose that additional surface attributes must be considered to characterize ICESat‐2 bias over snow. The altimetry biases are within the accuracy requirements of the ICESat‐2 mission, but we cannot rule out more significant errors over coarse‐grained snow. Plain Language Summary: The Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2) mission has been used to measure changes in land ice, vegetation cover, and sea ice, and there is growing interest to use ICESat‐2 for snow science. ICESat‐2 uses a green laser to estimate the elevation of the Earth's surface, and recent work has shown that snow can introduce errors in green laser measurements such as ICESat‐2. In this study, we used ICESat‐2 and airborne data to (a) identify errors in the ICESat‐2 data and (b) link the errors to snow properties over the Greenland Ice Sheet. We found that ICESat‐2 errors are linked to two snow properties: the size of individual snow grains and the density of the snow. Two retrieval methods were used to estimate snow grain size, though the methods show significant differences when large snow grains are observed. Snow grain size and density are insufficient to fully explain ICESat‐2 biases, so we propose that other factors also play a role. The ICESat‐2 errors are within the accuracy requirements of ICESat‐2, but more significant errors may be possible during the Northern Hemisphere melting season. Key Points: Very large snow grains and densities are needed to explain centimeter‐level differences between ICESat‐2 and airborne lidarBiases from snow grain size and other factors may be influenced by different height measurement methods between ICESat‐2 and airborne lidarSnow grain size retrievals strongly differ between airborne lidar and AVIRIS‐NG over regions with coarse‐grained snow [ABSTRACT FROM AUTHOR]

In recent decades, Greenland's peripheral glaciers have experienced large‐scale mass loss, resulting in a substantial contribution to sea level rise. While their total area of Greenland ice cover is relatively small (4%), their mass loss is disproportionally large compared to the Greenland ice sheet. Satellite altimetry from Ice, Cloud, and land Elevation Satellite (ICESat) and ICESat‐2 shows that mass loss from Greenland's peripheral glaciers increased from 27.2 ± 6.2 Gt/yr (February 2003–October 2009) to 42.3 ± 6.2 Gt/yr (October 2018–December 2021). These relatively small glaciers now constitute 11 ± 2% of Greenland's ice loss and contribute to global sea level rise. In the period October 2018–December 2021, mass loss increased by a factor of four for peripheral glaciers in North Greenland. While peripheral glacier mass loss is widespread, we also observe a complex regional pattern where increases in precipitation at high altitudes have partially counteracted increases in melt at low altitude. Plain Language Summary: The Arctic is warming more rapidly than the rest of the world. This warming has had an especially profound impact on Greenland's ice cover. Only 4% of Greenland's ice cover are small peripheral glaciers that are distinct from the ice sheet proper. Despite comprising this relatively small area, these small peripheral glaciers are responsible for 11% of the ice loss associated with Greenland's recent sea level rise contribution. Using the satellite laser platforms Ice, Cloud, and land Elevation Satellite (ICESat) and ICESat‐2, we estimate that ice loss from these Greenland glaciers increased from 27 ± 6 Gt/yr (2003–2009) to 42 ± 6 Gt/yr (2018–2021). We find that the largest acceleration in ice loss is in North Greenland, where we observe ice loss to increase by a factor of four between 2003 and 2021. In some areas, it appears that recent increases in snowfall at high altitudes have partially counteracted recent increases in melt at low altitudes. While many recent Greenland ice loss assessments have focused on only the ice sheet, the recent sharp increase in ice loss from small peripheral glaciers highlights the importance of accurately monitoring Greenland's small peripheral glaciers. These small peripheral glaciers appear poised to play an outsized role in Greenland ice loss for decades to come. Key Points: Greenland peripheral glacier mass loss increased from 27.2 Gt/yr (February 2003–October 2009) to 42.3 Gt/yr (October 2018–December 2021Mass loss increased by a factor of four for peripheral glaciers in North Greenland during February 2003–December 2021Enhanced precipitation in northeast Greenland during October 2018–December 2021 resulted in ice thickening at higher elevations [ABSTRACT FROM AUTHOR]

The Advanced Topographic Laser Altimeter System (ATLAS) was launched aboard the Ice Cloud and land‐Elevation Satellite‐2 (ICESat‐2) satellite in September 2018. ATLAS is a single wavelength (532 nm) lidar system designed to acquire high resolution measurements of the earth's surface while also obtaining atmospheric backscatter from molecules, clouds, and aerosols. Because ATLAS is optimized for altimetry, the atmospheric data acquired is unique in many respects and requires non‐standard analysis techniques. For example, the high repetition rate laser limits the vertical extent of the profiles to just 14 km and causes atmospheric scattering from above 15 km to be added to the scattering in the lower 0–14 km profile. In addition, the limited vertical range of the acquired profiles renders it difficult to compute the magnitude of the solar background and hinders the application of standard calibration techniques. Despite these limitations, methods have been developed to successfully produce data products that have value to the atmospheric community for cloud and aerosol research and are currently available at the National Snow and Ice Data Center (NSIDC). In this paper we describe the ICESat‐2 atmospheric channel and the methods used to process the ATLAS raw photon count data to obtain calibrated backscatter and higher level products such as layer heights and type, blowing snow, column optical depth and apparent surface reflectance. Plain Language Summary: ICESat‐2 is a polar orbiting satellite equipped with a high repetition rate laser that fires green pulses of light to earth 10,000 times per second. The main objective of ICESat‐2 is the high‐resolution measurement of the height of the earth's surface, with emphasis on the change in elevation of ice sheets that cover most of Greenland and Antarctica. In addition to surface elevation, ICESat‐2 also obtains information on the vertical structure of the atmosphere including the height and thickness of clouds and aerosols. The atmospheric measurements are important for climate studies and because they extend the data record begun by other earth‐orbiting satellite lidars like Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) which has been acquiring atmospheric data since 2006 and is nearing the end of its life. The creation of a long record of cloud and aerosol observations is very important for detecting changes that may be occurring due to humanity's influence on the climate system. Key Points: ICESat‐2 is a satellite lidar that acquires atmospheric backscatter profiles from 0 to 14 km every 280 m along the satellite trackThe high repetition rate laser creates unusual problems for processing the raw data to higher level data productsProducts such as calibrated backscatter, cloud and aerosol layer heights, column optical depth and blowing snow are described and shown [ABSTRACT FROM AUTHOR]

Surface melting occurs during summer on the Antarctic and Greenland ice sheets, but the volume of stored surface meltwater has been difficult to quantify due to a lack of accurate depth estimates. NASA's ICESat‐2 laser altimeter brings a new capability: photons penetrate water and are reflected from both the water and the underlying ice; the difference provides a depth estimate. ICESat‐2 sampled Amery Ice Shelf on January 2, 2019 and showed double returns from surface depressions, indicating meltwater. For four melt features, we compared depth estimates from eight algorithms: six based on ICESat‐2 and two from coincident Landsat‐8 and Sentinel‐2 imagery. All algorithms successfully identified surface water at the same locations. Algorithms based on ICESat‐2 produced the most accurate depths; the image‐based algorithms underestimated depths (by 30%–70%). This implies that ICESat‐2 depths can be used to tune image‐based algorithms, moving us closer to quantifying stored meltwater volumes across Antarctica and Greenland. Plain Language Summary: Summer surface melting on Antarctica's ice shelves is a small component of overall ice sheet mass loss but can be important for individual ice shelves and may increase as the climate warms. However, the volume of meltwater has been difficult to monitor because depth estimates are challenging. NASA's ICESat‐2 laser altimetry mission brings a new capability to this problem. ICESat‐2 532 nm photons (green light) are able to pass through water and reflect from both the water surface and the underlying ice surface; the difference in elevation provides meltwater depth estimates. In this pilot study, we compared depths from eight algorithms (six ICESat‐2 and two image based) over four Amery Ice Shelf meltwater lakes for an ICESat‐2 pass in early January 2019. The ICESat‐2 algorithms all produced more reliable depth estimates, and the image‐based algorithms underestimated the depths. This implies that ICESat‐2 water depths can be used to tune image‐based depth retrieval algorithms, enabling improved performance and allowing us to estimate more accurately how much surface melt is stored in melt ponds on the ice sheets each summer. Key Points: ICESat‐2 photons penetrate surface melt lakes and reflect from both the water surface and the underlying ice, providing depth estimatesWe compared depths from eight algorithms (six ICESat‐2 and two image‐based) for four lakes present on Amery Ice Shelf in January 2019Depths from ICESat‐2 were more accurate than from imagery (30%–70% too low); merging these data will improve estimates ice‐sheet wide [ABSTRACT FROM AUTHOR]

Ice-sheet mass balance and ice behaviour have been effectively monitored remotely by space-borne laser ranging technology, i.e. satellite laser altimetry, and/or satellite gravimetry. ICES atmission launched in 2003 has pioneered laser altimetry providing a large amount of elevation data related to ice sheet change with high spatial and temporal resolution. ICESat-2, the successor to the ICESat mission, was launched in 2018, continuing the legacy of its predecessor. This paper presents an overview of the satellite laser altimetry and a review of Greenland ice sheet change estimated from ICESat data and compared against estimates derived from satellite gravimetry, i.e. changes of the Earth’s gravity field obtained from the GRACE data. In addition to that, it provides an insight into the characteristics and possibilities of ice sheet monitoring with renewed mission ICESat-2, which was compared against ICESat for the examination of ice height changes on the Jakobshavn glacier. ICESat comparison (2004–2008) shows that an average elevation change in different areas on Greenland varies up to ±0.60 m yr−1. Island’s coastal southern regions are most affected by ice loss, while inland areas record near-balance state. In the same period, gravity anomaly measurements showed negative annual mass balance trends in coastal regions ranging from a few cm up to -0.36 m yr-1 w.e. (water equivalent), while inland records show slightly positive trends. According to GRACE observations, in the following years (2009–2017), negative annual mass balance trends on the coast continued. [ABSTRACT FROM AUTHOR]

Supraglacial lakes are widespread on the surface of the Antarctic Ice Sheet and Greenland Ice Sheet due to global warming. Estimating the location and depth of supraglacial lakes is critical for evaluating the impact of surface meltwater on ice dynamics. The accuracy of traditional image-based methods is limited, especially when supraglacial lakes are deep. Recently, Ice, Clouds, and land Elevation Satellite-2 data have been used to develop algorithms to measure the depth of supraglacial lakes. The ICESat-2-based methods are currently either semiautomated or sensitive to noise, which makes them less efficient and less accurate in detecting supraglacial lakes. In this study, we develop an automated algorithm for the retrieval of the location and depth of supraglacial lakes using ICESat-2 ATL03 data. We investigated 10 supraglacial lakes as case studies over Antarctica and Greenland (4 lakes on the Amery Ice Shelf, Antarctica and 6 lakes in southwestern Greenland). The results show that our algorithm can detect supraglacial lakes at depths up to 8.25 m. The RMSE for the lake depth retrievals in Antarctica is approximately 0.30 m and that of the lakes in Greenland is approximately 0.32 m. Our algorithm reveals high robustness and accuracy, especially in shallow areas. Additionally, the fully automated algorithm does not require prior knowledge and guarantees efficiency when there is an extensive study area. This result implies that our algorithm has the potential to address large amounts of data and may even be useful to estimate meltwater volumes across the surface of the entire ice sheet. • Developed an automated algorithm to retrieve the location and depth of supraglacial lakes based on ICESat-2 ATL03 data. • The algorithm presents better performance in both accuracy and robustness than other automated algorithm. • Our work provides an efficient solution for accurately estimating large-scale supraglacial lake depth from ATL03 data. [ABSTRACT FROM AUTHOR]

In this study we present and validate a simple empirical method to obtain bathymetry profiles using the geolocated photon data from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission, which was launched by NASA in September 2018. The satellite carries the Advanced Topographic Laser Altimeter System (ATLAS), which is a lidar that can detect single photons and calculate their bounce point positions. ATLAS uses a green laser, causing some of the photons to penetrate the air–water interface. Under the right conditions and in shallow waters (<40 m), these photons are reflected back to ATLAS after interaction with the ocean bottom. Using ICESat-2 data from four different overflights above the Heron Reef, Australia, a comparison with SDB data showed a median absolute deviation of approximately 18 cm and Root Mean Square Errors (RMSEs) down to 28 cm. Crossovers between two different overflights above the Heron Reef showed a median absolute difference of 13 cm. For an area north-west of Sisimiut, Greenland, the comparison was done with multibeam echo sounding data, with RMSEs down to between 35 cm, and correspondingly showed median absolute deviations between 33 and 49 cm. The proposed method works well under good conditions with clear waters such as in the Great Barrier Reef; however, for more difficult areas a more advanced machine learning technique should be investigated in order to find an automated method that can distinguish between bathymetry and other signals and noise. [ABSTRACT FROM AUTHOR]