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Previous findings show that large-scale atmospheric circulation plays an important role in driving Arctic sea ice variability from synoptic to seasonal time scales. While some circulation patterns responsible for Barents–Kara sea ice changes have been identified in previous works, the most important patterns and the role of their persistence remain unclear. Our study uses self-organizing maps to identify nine high-latitude circulation patterns responsible for day-to-day Barents–Kara sea ice changes. Circulation patterns with a high pressure center over the Urals (Scandinavia) and a low pressure center over Iceland (Greenland) are found to be the most important for Barents–Kara sea ice loss. Their opposite-phase counterparts are found to be the most important for sea ice growth. The persistence of these circulation patterns helps explain sea ice variability from synoptic to seasonal time scales. We further use sea ice models forced by observed atmospheric fields (including the surface circulation and temperature) to reproduce observed sea ice variability and diagnose the role of atmosphere-driven thermodynamic and dynamic processes. Results show that thermodynamic and dynamic processes similarly contribute to Barents–Kara sea ice concentration changes on synoptic time scales via circulation. On seasonal time scales, thermodynamic processes seem to play a stronger role than dynamic processes. Overall, our study highlights the importance of large-scale atmospheric circulation, its persistence, and varying physical processes in shaping sea ice variability across multiple time scales, which has implications for seasonal sea ice prediction. Significance Statement: Understanding what processes lead to Arctic sea ice changes is important due to their significant impacts on the ecosystem, weather, and shipping, and hence our society. A well-known process that causes sea ice changes is atmospheric circulation variability. We further pin down what circulation patterns and underlying mechanisms matter. We identify multiple circulation patterns responsible for sea ice loss and growth to different extents. We find that the circulation can cause sea ice loss by mechanically pushing sea ice northward and bringing warm and moist air to melt sea ice. The two processes are similarly important. Our study advances understanding of the Arctic sea ice variability with important implications for Arctic sea ice prediction. [ABSTRACT FROM AUTHOR]

Information about sea ice surface topography and related deformation is crucial for studies of sea ice mass balance, sea ice modeling, and ship navigation through the ice pack. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), part of the National Aeronautics and Space Administration (NASA) Earth Observing System, has been on orbit for over 4 years, sensing the sea ice surface topography with six laser beams capable of capturing individual features such as pressure ridges. To assess the capabilities and uncertainties of ICESat-2 products, coincident high-resolution measurements of sea ice surface topography are required. During the yearlong Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the Arctic Ocean, we successfully carried out a coincident underflight of ICESat-2 with a helicopter-based airborne laser scanner (ALS), achieving an overlap of more than 100 km. Despite the comparably short data set, the high-resolution centimeter-scale measurements of the ALS can be used to evaluate the performance of ICESat-2 products. Our goal is to investigate how the sea ice surface roughness and topography are represented in different ICESat-2 products as well as how sensitive ICESat-2 products are to leads and small cracks in the ice cover. Here, we compare the ALS measurements with ICESat-2's primary sea ice height product, ATL07, and the high-fidelity surface elevation product developed by the University of Maryland (UMD). By applying a ridge-detection algorithm, we find that 16 % (4 %) of the number of obstacles in the ALS data set are found using the strong (weak) center beam in ATL07. Significantly higher detection rates of 42 % (30 %) are achieved when using the UMD product. While only one lead is indicated in ATL07 for the underflight, the ALS reveals many small, narrow, and only partly open cracks that appear to be overlooked by ATL07. [ABSTRACT FROM AUTHOR]

Information about the sea ice surface topography and related deformation are crucial for studies of sea ice mass balance, sea ice modeling, and ship navigation through the ice pack. NASA's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) has been on-orbit for nearly four years, sensing the sea ice surface topography with six laser beams capable of capturing individual features such as pressure ridges. To assess the capabilities and uncertainties of ICESat-2 products, coincident high-resolution measurements of the sea ice surface topography are required. During the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition in the Arctic Ocean, we successfully carried out a coincident underflight of ICESat-2 with a helicopter-based airborne laser scanner (ALS) achieving an overlap of more than 100 km. Despite the comparably short data set, the high resolution measurements on centimetre scales of the ALS can be used to evaluate the performance of ICESat-2 products. Our goal is to investigate how the sea ice surface roughness and topography is represented in different ICESat-2 products, and how sensitive ICESat-2 products are to leads and small cracks in the ice cover. Here we compare the ALS measurements with the ICESat-2's primary sea ice height product, ATL07, and the high-fidelity surface elevation product developed by the University of Maryland (UMD). By applying a ridge-detection algorithm, we find that 16 % (4 %) of the number of obstacles in the ALS data set are found using the strong (weak) center beam in ATL07. Significantly higher detection rates of 42 % (30 %) are achieved when using the UMD product. Only one lead is indicated in ATL07 for the underflight, while the ALS reveals mostly small, narrow and only partly open cracks that appear to be overlooked by ATL07. More research on how even small leads can be detected by ATL07 using additional validation data sets and complementing measurements, such as airborne thermal infrared imaging, would be useful to further improve the sea ice data products. [ABSTRACT FROM AUTHOR]

In the Arctic, multi-year sea ice is being rapidly replaced by seasonal sea ice. Baffin Bay, situated between Greenland and Canada, is part of the seasonal ice zone. In this study, we present a long-term multi-mission assessment (2003–2020) of spring sea ice thickness in Baffin Bay from satellite altimetry and sea ice charts. Sea ice thickness within Baffin Bay is calculated from Envisat, ICESat, CryoSat-2, and ICESat-2 freeboard estimates, alongside a proxy from the ice chart stage of development that closely matches the altimetry data. We study the sensitivity of sea ice thickness results estimated from an array of different snow depth and snow density products and methods for redistributing low-resolution snow data onto along-track altimetry freeboards. The snow depth products that are applied include a reference estimated from the Warren climatology, a passive microwave snow depth product, and the dynamic snow scheme SnowModel-LG. We find that applying snow depth redistribution to represent small-scale snow variability has a considerable impact on ice thickness calculations from laser freeboards but was unnecessary for radar freeboards. Decisions on which snow loading product to use and whether to apply snow redistribution can lead to different conclusions on trends and physical mechanisms. For instance, we find an uncertainty envelope around the March mean sea ice thickness of 13 % for different snow depth/density products and redistribution methods. Consequently, trends in March sea ice thickness from 2003–2020 range from - 23 to 17 cm per decade, depending on which snow depth/density product and redistribution method is applied. Over a longer timescale, since 1996, the proxy ice chart thickness product has demonstrated statistically significant thinning within Baffin Bay of 7 cm per decade. Our study provides further evidence for long-term asymmetrical trends in Baffin Bay sea ice thickness (with - 17.6 cm per decade thinning in the west and 10.8 cm per decade thickening in the east of the bay) since 2003. This asymmetrical thinning is consistent for all combinations of snow product and processing method, but it is unclear what may have driven these changes. [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]

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]

In this paper we develop a CryoSat-2 algorithm to retrieve the surface elevation of the air–snow interface over Antarctic sea ice. This algorithm utilizes a two-layer physical model that accounts for scattering from a snow layer atop sea ice as well as scattering from below the snow surface. The model produces waveforms that are fit to CryoSat-2 level 1B data through a bounded trust region least-squares fitting process. These fit waveforms are then used to track the air–snow interface and retrieve the surface elevation at each point along the CryoSat-2 ground track, from which the snow freeboard is computed. To validate this algorithm, we compare retrieved surface elevation measurements and snow surface radar return power levels with those from Operation IceBridge, which flew along a contemporaneous CryoSat-2 orbit in October 2011 and November 2012. Average elevation differences (standard deviations) along the flight lines (IceBridge Airborne Topographic Mapper, ATM – CryoSat-2) are found to be 0.016 cm (29.24 cm) in 2011 and 2.58 cm (26.65 cm) in 2012. The spatial distribution of monthly average pan-Antarctic snow freeboard found using this method is similar to what was observed from NASA's Ice, Cloud, and land Elevation Satellite (ICESat), where the difference (standard deviation) between October 2011–2017 CryoSat-2 mean snow freeboard and spring 2003–2007 mean freeboard from ICESat is 1.92 cm (9.23 cm). While our results suggest that this physical model and waveform fitting method can be used to retrieve snow freeboard from CryoSat-2, allowing for the potential to join laser and radar altimetry data records in the Antarctic, larger (∼30 cm) regional differences from ICESat and along-track differences from ATM do exist, suggesting the need for future improvements to the method. Snow–ice interface elevation retrieval is also explored as a potential to obtain snow depth measurements. However, it is found that this retrieval method often tracks a strong scattering layer within the snow layer instead of the actual snow–ice interface, leading to an overestimation of ice freeboard and an underestimation of snow depth in much of the Southern Ocean but with promising results in areas such as the East Antarctic sector. [ABSTRACT FROM AUTHOR]

Sea ice topography significantly impacts turbulent energy/momentum exchange, e.g., atmospheric (wind) drag, over Arctic sea ice. Unfortunately, observational estimates of this contribution to atmospheric drag variability are spatially and temporally limited. Here we present new estimates of the neutral atmospheric form drag coefficient over Arctic sea ice in early spring, using high-resolution Airborne Topographic Mapper elevation data from NASA's Operation IceBridge mission. We utilize a new three-dimensional ice topography data set and combine this with an existing parameterization scheme linking surface feature height and spacing to form drag. To be consistent with previous studies investigating form drag, we compare these results with those produced using a new linear profiling topography data set. The form drag coefficient from surface feature variability shows lower values (<0.5-1 ×10−3) in the Beaufort/Chukchi Seas, compared with higher values (>0.5-1 ×10−3) in the more deformed ice regimes of the Central Arctic (north of Greenland and the Canadian Archipelago), which increase with coastline proximity. The results show moderate interannual variability, including a strong increase in the form drag coefficient from 2013 to 2014/2015 north of the Canadian Archipelago. The form drag coefficient estimates are extrapolated across the Arctic with Advanced Scatterometer satellite radar backscatter data, further highlighting the regional/interannual drag coefficient variability. Finally, we combine the results with existing parameterizations of form drag from floe edges (a function of ice concentration) and skin drag to produce, to our knowledge, the first pan-Arctic estimates of the total neutral atmospheric drag coefficient (in early spring) from 2009 to 2015. [ABSTRACT FROM AUTHOR]

Sea-ice thickness in the Sea of Okhotsk is estimated for 2004–2008 from ICESat derived freeboard under the assumption of hydrostatic balance. Total ice thickness including snow depth (h t o t ) averaged over 2004–2008 is 95 cm. The interannual variability of h t o t is large; from 77.5 cm (2008) to 110.4 cm (2005). The mode of h t o t varies from 50–60 cm (2007 and 2008) to 70–80 cm (2005). Ice thickness derived from ICESat data is validated from a comparison with that observed by Electromagnetic Induction Instrument (EM) aboard the icebreaker Soya near Hokkaido, Japan. Annual maps of h t o t reveal that the spatial distribution of h t o t is similar every year. Ice volume of 6.3 × 1011 m3 is estimated from the ICESat derived h t o t and AMSR-E derived ice concentration. A comparison with ice area demonstrates that the ice volume cannot always be represented by the area solely, despite the fact that the area has been used as a proxy of the volume in the Sea of Okhotsk. The ice volume roughly corresponds to that of annual ice production in the major coastal polynyas estimated based on heat budget calculations. This also supports the validity of the estimation of sea-ice thickness and volume using ICESat data. [ABSTRACT FROM AUTHOR]

Data from an IceBridge Arctic campaign on April 20, 2010 with the Airborne Topographic Mapper (ATM) and the Land, Vegetation, and Ice Sensor (LVIS) in operation on the same airplane were used in this study. ATM and LVIS lidar waveforms were fitted with Gaussian curves to calculate pulsewidth, peak location, pulse amplitude, and signal baseline. For each waveform, the centroid, skewness, kurtosis, and pulse area were also calculated. Received waveform parameters, such as pulsewidth, pulse amplitude, pulse area, skewness, and kurtosis, show coherent response to variations of geophysical features along an ATM or LVIS profile. These parameters, combined with elevation, were used to identify leads in sea-ice freeboard calculation. The relationship between these parameters and sea-ice freeboard and surface features were studied by comparing the parameters with ATM and LVIS-derived freeboard and coincident Digital Mapping System images which have been used to classify sea-ice surface types such as leads, thin ice, gray ice, and thick ice. An elevation bias of more than 16 cm (peak-to-peak) as a function of laser scanner azimuth was found in the ATM data, and an empirical correction was applied; this correction will improve the ATM shot-to-shot freeboard significantly. The newly derived ATM freeboard was compared with the current IceBridge IDCSI2 freeboard product at National Snow and Ice Data Center (NSIDC) and the freeboard derived from LVIS data. Over the studied area, the mean freeboard is 0.540 $\pm$ 0.091 m for the IDCSI2 at NSIDC, 0.496 $\pm$ 0.062 m for the ATM after empirical elevation correction, and 0.509 $\pm$ 0.048 m for the LVIS. [ABSTRACT FROM PUBLISHER]

Surface elevation and roughness measurements from NASA's Ice, Cloud, and land Elevation Satellite (ICESat) are compared with high-resolution airborne laser altimeter measurements over the Arctic sea ice north of Alaska, which were taken during the March 2006 EOS Aqua Advanced Microwave Scanning Radiometer sea ice validation campaign. The comparison of the elevation measurements shows that they agree quite well with correlations of around 0.9 for individual shots and a bias of less than 2 cm. The differences are found to decrease quite rapidly when applying running means. The comparison of the roughness measurements show that there are significant differences between the two data sets, with ICESat generally having higher values. The roughness values are only moderately correlated on an individual-shot basis, but applying running means to the data significantly improves the correlations to as high as 0.9. For the conversion of the elevation measurements into snow-ice freeboard, ocean surface elevation estimates are made with the high-resolution laser altimeter data, as well as several methods using lower resolution ICESat data. Under optimum conditions, i.e., when leads that are larger than the ICESat footprint are present, the ICESat- and Airborne Topographic Mapper-derived freeboards are found to agree to within 2 cm. For other areas, ICESat tends to under- estimate the freeboard by up to 9 cm. [ABSTRACT FROM AUTHOR]

We explore current variability and future projections of winter Arctic sea ice thickness and growth using data from climate models and satellite observations. Winter ice thickness in the Community Earth System Model's Large Ensemble compares well against thickness estimates from the Pan‐Arctic Ice Ocean Modeling and Assimilation System and CryoSat‐2, despite some significant regional differences—for example, a high thickness bias in Community Earth System Model's Large Ensemble in the western Arctic. Differences across the available CryoSat‐2 thickness products hinder more robust validation efforts. We assess the importance of the negative conductive feedback of sea ice growth (thinner ice grows faster) by regressing October atmosphere/ice/ocean conditions against winter ice growth. Our regressions demonstrate the importance of a strong negative conductive feedback process in our current climate, which increases winter growth for thinner initial ice, but indicate that later in the 21st century this negative feedback is overwhelmed by variations in the fall atmosphere/ocean state. Plain language summary: In this study we explore the thickness and growth of Arctic sea ice through winter using data from climate models and satellite observations. Winter Arctic sea ice thickness in a widely used set of climate model simulations compares well against thickness estimates produced from a climate model constrained by observations and sea ice thickness estimates derived from satellite observations, although important regional differences are found. Our analysis suggests an increase in the amount of Arctic sea ice grown in winter through the coming decades, partly due to the fact thinner ice grows faster than thicker, more insulated, ice. As the Arctic warms rapidly, the strong atmosphere and ocean forcing dominates over this feedback and is projected to promote declines in sea ice growth. Key Points: Winter Arctic sea ice thickness and growth variability is explored using data from climate models and satellite observationsWe project an increase in winter Arctic sea ice growth over the coming decades, due in‐part to the presence of a negative conductive feedback associated with thinner iceThe importance of the negative feedback reduces through the end of the 21st century, as the Arctic warms significantly and promotes delays and overall declines in Arctic sea ice growth [ABSTRACT FROM AUTHOR]

The ICESat-2 (IS2) ATL07 sea ice height product provides precise and accurate measurements of sea ice height for the polar regions. Although the original ATL07 product classifies the sea ice surface into snow-covered ice and open water (dark leads and specular leads), it has two critical issues: (1) it does not distinguish thin ice (gray ice) from thick/snow-covered ice or open water and (2) it involves uncertainties in the dark lead determination. To address these issues and obtain more accurate lead fraction and freeboard estimation, in this study, we assess a data-driven machine learning approach for sea ice surface type classification from the ATL07 product. A total of 17 IS2 tracks covering the Ross Sea in February, March, September, October, and November 2019 are used in this study. For training and testing the neural network (NN) models, we label the ATL07 data into three surface types: thick/snow-covered sea ice, thin/gray sea ice, and open water based on the coincident Sentinel-2 optical images. We use six variables from the ATL07 data: background rate, photon rate, relative surface height, width of height distribution, number of laser pulses, and difference in mean and median height, with the first three variables found to be the most significant in determining surface types. Our spatiotemporal test shows that the NN models can be applied to the ATL07 dataset with ∼99% accuracy in the Ross Sea, and the beam test shows that the difference in the accuracy for three beams is negligible. While the current ATL07 product captures only ∼5% of thin ice and ∼ 81% of open water correctly, our NN models capture ∼90% of both open water and thin ice leads successfully. When we qualitatively assess the surface classification of the NN models by using the coincident optical and radar images, our NN models do not show significant misclassification issues both in the daytime and nighttime and both in the winter and summer seasons. Compared to the IS2 ATL10 sea ice freeboard product, the ability of our NN model to separate thin ice and open water from snow-covered/thick ice is significant in freeboard estimation, especially for winter and highly packed sea ice areas. Open water and thin ice classes also improve mapping lead fraction and floe size distribution. • ICESat-2 ATL07 data are labeled based on the coincident Sentinel-2 optical images. • The ATL07 product cannot distinguish thin/gray ice from sea ice or open water. • Neural network (NN) models are trained for sea ice surface classification. • Our NN models separate open water and thin ice types with >90% of accuracy. • Floe sizes and freeboard in the Ross Sea are calculated from our new NN models. [ABSTRACT FROM AUTHOR]

Two CryoSat-2 sea ice thickness products derived with independent algorithms are used to initialize a coupled ice-ocean modeling system in which a series of reanalysis studies are performed for the period of March 15, 2014–September 30, 2015. Comparisons against moored upward looking sonar, drifting ice mass balance buoy, and NASA Operation IceBridge ice thickness data show that the modeling system exhibits greatly reduced bias using the satellite-derived ice thickness data versus the operational model run without these data. The model initialized with CryoSat-2 ice thickness exhibits skill in simulating ice thickness from the initial period to up to 6 months. We find that the largest improvements in ice thickness occur over multi-year ice. Based on the data periods examined here, we find that for the 18-month study period, when compared with upward looking sonar measurements, the CryoSat-2 reanalyses show significant improvement in bias (0.47–0.75) and RMSE (0.89–1.04) versus the control run without these data (1.44 and 1.60, respectively). An ice drift comparison reveals little change in ice velocity statistics for the Pan Arctic region; however some improvement is seen during the summer/autumn months in 2014 for the Bering/Beaufort/Chukchi and Greenland/Norwegian Seas. These promising results suggest that such a technique should be used to reinitialize operational sea ice modeling systems. [ABSTRACT FROM AUTHOR]

The fine spatial resolution of the ICESat-2 (IS2) satellite altimeter allows monitoring the evolution of sea ice thickness with detailed dynamic information (e.g. ridges and leads). In this study, we first assess the ability of IS2 to estimate thermodynamic ice growth and dynamic thickening during the ice-growing season in the central Arctic Ocean. As an indicator of the thermodynamic ice growth, we use 10 thermistor string-based sea ice mass balance array (SIMBA) buoys deployed at a scale of ~50 km from the Icebreaker Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. We collect IS2 data within 20 km buffer distance from the individual buoys, and calculate the mode, median, and mean of the IS2-derived ice thickness. The IS2 modal thickness shows the least bias (−0.169 m) with the buoy ice thickness, representing level ice thickness. In addition, the increasing rate of the IS2 modal thickness is close to the thermodynamic ice growth with a small bias of −0.054 cm/day. However, the increasing rates of the IS2 median and mean thickness are greater than the thermodynamic ice growth by about 0.114 cm/day and 0.198 cm/day, respectively, because they also include ice growth caused by thickness redistribution during dynamic deformation. The dynamic contributions may account for 26.1 ± 10.3% and 34.4 ± 10.1% of the total increase of the IS2 median and mean thickness, respectively. Within a ~ 50 km radius area from the MOSAiC Central Observatory, IS2 measurements exhibit that the ridge fraction increased from <2% in November to ~4% in March (~0.029%/day of average increasing rate) and ridge height increased about 0.047 cm/day during the same period. However, lead formation does not show significant contributions to the dynamic ice thickening because leads are temporary features lasting only 2–3 days. Although there are considerable uncertainties in IS2 ice thickness estimation and IS2-buoy thickness comparison, this study emphasizes the importance of combining measurements by IS2 and SIMBA buoys to explain the regional sea ice mass balance with separating the thermodynamic and dynamic contributions. • ICESat-2 (IS2) detects increase of sea ice thickness during the ice growing season. • IS2 mode thickness represents thermodynamic ice growth with −0.054 cm/day of bias. • IS2 median and mean include 26% and 34% of dynamic contribution, respectively. • Ridge fraction within ~50 km of the buoys increased with 0.029%/day. • Ridge height within ~50 km of the buoys increased with 0.047 cm/day. [ABSTRACT FROM AUTHOR]

The Ice, Cloud, and land Elevation Satellite – 2 (ICESat-2) observatory was launched on 15 September 2018 to measure ice sheet and glacier elevation change, sea ice freeboard, and enable the determination of the heights of Earth's forests. ICESat-2's laser altimeter, the Advanced Topographic Laser Altimeter System (ATLAS) uses green (532 nm) laser light and single-photon sensitive detection to measure time of flight and subsequently surface height along each of its six beams. In this paper, we describe the major components of ATLAS, including the transmitter, the receiver and the components of the timing system. We present the major components of the ICESat-2 observatory, including the Global Positioning System, star trackers and inertial measurement unit. The ICESat-2 Level 1B data product (ATL02) provides the precise photon round-trip time of flight, among other data. The ICESat-2 Level 2A data product (ATL03) combines the photon times of flight with the observatory position and attitude to determine the geodetic location (i.e. the latitude, longitude and height) of the ground bounce point of photons detected by ATLAS. The ATL03 data product is used by higher-level (Level 3A) surface-specific data products to determine glacier and ice sheet height, sea ice freeboard, vegetation canopy height, ocean surface topography, and inland water body height. • Describes the ICESat-2 Observatory and its sole instrument: the Advanced Topographic Laser Altimeter System (ATLAS) • Presents the structure and major contents of the ICESat-2 Level 1B data product (ATL02; photon times of flight) • Presents the structure and major contents of the ICESat-2 Level 2A data product (ATL03; Global Geolocated Photons) [ABSTRACT FROM AUTHOR]