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Sea ice thickness is an essential variable to understand and forecast the dynamic ice cover and can be estimated by satellite altimetry. Nevertheless, it is affected by uncertainties especially from snow depth, a key parameter to derive it from ice freeboard. We developed a snow depth product based on the differences between CryoSat-2 SAR Ku and IceSat-2 laser altimeters which have different snow penetration capabilities. We compared this product to other existing snow depth products. This comparison indicates correlations all above 0.37 and reaching 0.91 relative to airborne snow radar. We estimate the snow depth uncertainties of our product to be 0.06 m on average. Combining two frequencies to simultaneously measure ice and total freeboard reduces the uncertainties in ice thickness by around a factor of two compared with a single-frequency solution combined with a snow depth climatology. The lack of in situ measurements is a limiting factor to assess the advantages of coincident CryoSat-2 and ICESat-2 acquisitions. Validation data are of major importance for upcoming missions. [ABSTRACT FROM AUTHOR]

Differential penetration of green laser light into snow and ice has long been considered a possible cause of range and thus elevation bias in laser altimeters. Over snow, ice, and water, green photons can penetrate the surface and experience multiple scattering events in the subsurface volume before being scattered back to the surface and subsequently the instrument's detector, therefore biasing the range of the measurement. Newly formed sea ice adjacent to open-water leads provides an opportunity to identify differential penetration without the need for an absolute reference surface or dual-color lidar data. We use co-located, coincident high-resolution natural-color imagery and airborne lidar data to identify surface and ice types and evaluate elevation differences between those surfaces. The lidar data reveals that apparent elevations of thin ice and finger-rafted thin ice can be several tens of centimeters below the water surface of surrounding leads, but not over dry snow. These lower elevations coincide with broadening of the laser pulse, suggesting that subsurface volume scattering is causing the pulse broadening and elevation shift. To complement our analysis of pulse shapes and help interpret the physical mechanism behind the observed elevation biases, we match the waveform shapes with a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse, the surface roughness, and the optical scattering properties of the medium. We parameterize the scattering in our model based on the scattering length Lscat , the mean distance a photon travels between isotropic scattering events. The largest scattering lengths are found for thin ice that exhibits the largest negative elevation biases, where scattering lengths of several centimeters allow photons to build up considerable range biases over multiple scattering events, indicating that biased elevations exist in lower-level Airborne Topographic Mapper (ATM) data products. Preliminary analysis of ICESat-2 ATL10 data shows that a similar relationship between subsurface elevations (restored negative freeboard) and "pulse width" is present in ICESat-2 data over sea ice, suggesting that biased elevations caused by differential penetration likely also exist in lower-level ICESat-2 data products. The spatial correlation of observed differential penetration in ATM data with surface and ice type suggests that elevation biases could also have a seasonal component, increasing the challenge of applying a simple bias correction. [ABSTRACT FROM AUTHOR]

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]

We estimate the snow depth and snow freeboard of Antarctic sea ice using a comprehensive retrieval method (referred to as CryoSat-2 Waveform Fitting for Antarctic sea ice, or CS2WFA) consisting of a physical waveform model and a waveform-fitting process that fits modeled waveforms to CryoSat-2 data. These snow depth and snow freeboard estimates are combined with snow, sea ice, and sea water density values to calculate the sea ice thickness and volume over an 11 + year span between 2010 and 2021. We first compare our snow freeboard, snow depth, and sea ice thickness estimates to other altimetry- and ship-based observations and find good agreement overall in both along-track and monthly gridded comparisons. Some discrepancies exist in certain regions and seasons that are theorized to come from both sampling biases and the differing assumptions in the retrieval methods. We then present an 11 + year time series of sea ice thickness and volume both regionally and pan-Antarctic. This time series is used to uncover intra-decadal changes in the ice cover between 2010 and 2021, showing small, competing regional thickness changes of less than 0.5 cm yr -1 in magnitude. Finally, we place these thickness estimates in the context of a longer-term, snow freeboard-derived, laser–radar sea ice thickness time series that began with NASA's Ice, Cloud, and land Elevation Satellite (ICESat) and continues with ICESat-2 and contend that reconciling and validating this longer-term, multi-sensor time series will be important in better understanding changes in the Antarctic sea ice cover. [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]

We utilize a physical waveform model and a waveform-fitting method to estimate the snow depth and snow freeboard of Antarctic sea ice from CryoSat-2, and use these estimates to calculate the sea ice thickness and volume over an 11+ year time series. We compare our snow depth and thickness estimates to other altimetry- and ship-based observations, and find good agreement overall with some discrepancies in certain regions and seasons. The time series is used to calculate trends in the data, and we find small but statistically significant negative trends in the Ross Sea autumn (-0.3 cm yr-1), the Eastern Weddell winter (-0.8 cm yr-1), and the Western Weddell autumn and annual-average (-2.6 and -1.6 cm yr-1, respectively). Significant positive trends are found in the pan-Antarctic summer (0.4 cm yr-1) and Amundsen-Bellingshausen winter and annual-average (2.3 and 0.9 cm yr-1, respectively). Though pan-Antarctic trends in sea ice thickness and volume are small between 2010–2021, we find larger-magnitude trends regionally and since 2014. We place these thickness estimates in the context of a longer-term, snow-freeboard-derived, laser-radar sea ice thickness time series that began with ICESat and continues with ICESat-2. Reconciling and validating this longer-term, multi-sensor time series will be important in better understanding changes in the Antarctic sea ice cover. [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]

We investigate wintertime extreme sea ice loss events on synoptic to subseasonal time scales over the Barents–Kara Sea, where the largest sea ice variability is located. Consistent with previous studies, extreme sea ice loss events are associated with moisture intrusions over the Barents–Kara Sea, which are driven by the large-scale atmospheric circulation. In addition to the role of downward longwave radiation associated with moisture intrusions, which is emphasized by previous studies, our analysis shows that strong turbulent heat fluxes are associated with extreme sea ice melting events, with both turbulent sensible and latent heat fluxes contributing, although turbulent sensible heat fluxes dominate. Our analysis also shows that these events are connected to tropical convective anomalies. A dipole pattern of convective anomalies with enhanced convection over the Maritime Continent and suppressed convection over the central to eastern Pacific is consistently detected about 6–10 days prior to extreme sea ice loss events. This pattern is associated with either the Madden–Julian oscillation (MJO) or El Niño–Southern Oscillation (ENSO). Composites show that extreme sea ice loss events are connected to tropical convection via Rossby wave propagation in the midlatitudes. However, tropical convective anomalies alone are not sufficient to trigger extreme sea ice loss events, suggesting that extratropical variability likely modulates the connection between tropical convection and extreme sea ice loss events. [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]

The Advanced Topographic Laser Altimetry System (ATLAS) onboard the NASA Ice, Cloud, and land Elevation Satellite‐2 (ICESat‐2) is the newest Earth observing satellite for global elevation studies. The primary objectives for ICESat‐2 follow the objectives of its predecessor, ICESat and also focus on providing cryospheric measurements to determine ice sheet mass balance, and monitor both sea ice thickness and extent. However, the global observations support secondary science objectives as well such as biomass estimation, inland water elevation, sea state height and aerosol concentrations. Since launch of ICESat‐2, ATLAS has collected more than a trillion measurements. This study provides a mission overview, a description of the operational components that enable the altimeter products for science, on‐orbit observatory performance, and assessment of the spacecraft attitude control systems that enable repeat measurements to within 10 m and pointing control within ±45 m. These metrics should be considered for ground‐based validation campaigns or science investigations. Plain Language Summary: Space‐based remote sensing provides an unequaled point of view for observing changes on Earth's surface. Collecting precise elevation data from this perspective with modern measurement technology holds promise for a wide range of science disciplines given the coverage over all surface types (e.g., land ice, sea ice, inland water, ocean, and vegetation). Over time this high quality data can not only reveal global elevations but elevation change in those regions with repeat measurements. ICESat‐2 carries a state‐of‐the‐art laser ranging system for accurate elevation measurements and utilizes onboard instrumentation and databases to help control the laser pointing enabling repeat measurements and individual geodetic (latitude and longitude) points of interest. The repeat measurements are critical for looking at elevation change over time, while the collection of elevations over specific locations on the Earth enable studies for science and validation efforts. ICESat‐2 has been on‐orbit for over two years and has collected nearly a trillion measurements. Evaluation of the repeat measurements over time indicate the satellite can point to within 10 m while the ability to collect a measurement of a single position on the surface is within the mission specification of ±45 m. Key Points: ICESat‐2 has proven the on‐orbit ability of photon‐counting lidar for precise and accurate space‐based altimetryICESat‐2 onboard attitude control systems exceed the ±45 m mission requirement for pointing controlThe satellite is able to follow reference ground tracks to provide repeat measurements within 10 m on average [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]

Twin 5-month seasonal forecast experiments are performed to predict the September 2018 mean and minimum ice extent using the fully coupled Navy Earth System Prediction Capability (ESPC). In the control run, ensemble forecasts are initialized from the operational US Navy Global Ocean Forecasting System (GOFS) 3.1 but do not assimilate ice thickness data. Another set of forecasts are initialized from the same GOFS 3.1 fields but with sea ice thickness derived from CryoSat-2 (CS2). The Navy ESPC ensemble mean September 2018 minimum sea ice extent initialized with GOFS 3.1 ice thickness was over-predicted by 0.68 M km2 (5.27 M km2) vs the ensemble forecasts initialized with CS2 ice thickness that had an error of 0.40 M km2 (4.99 M km2), a 43% reduction in error. The September mean integrated ice edge error shows a 18% improvement for the Pan-Arctic with the CS2 data vs the control forecasts. Comparison against upward looking sonar ice thickness in the Beaufort Sea reveals a lower bias and RMSE with the CS2 forecasts at all three moorings. Ice concentration at these locations is also improved, but neither set of forecasts show ice free conditions as observed at moorings A and D. [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]

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]

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]

Over Arctic sea ice, pressure ridges and floe and melt pond edges all introduce discrete obstructions to the flow of air or water past the ice and are a source of form drag. In current climate models form drag is only accounted for by tuning the air-ice and ice-ocean drag coefficients, that is, by effectively altering the roughness length in a surface drag parameterization. The existing approach of the skin drag parameter tuning is poorly constrained by observations and fails to describe correctly the physics associated with the air-ice and ocean-ice drag. Here, the authors combine recent theoretical developments to deduce the total neutral form drag coefficients from properties of the ice cover such as ice concentration, vertical extent and area of the ridges, freeboard and floe draft, and the size of floes and melt ponds. The drag coefficients are incorporated into the Los Alamos Sea Ice Model (CICE) and show the influence of the new drag parameterization on the motion and state of the ice cover, with the most noticeable being a depletion of sea ice over the west boundary of the Arctic Ocean and over the Beaufort Sea. The new parameterization allows the drag coefficients to be coupled to the sea ice state and therefore to evolve spatially and temporally. It is found that the range of values predicted for the drag coefficients agree with the range of values measured in several regions of the Arctic. Finally, the implications of the new form drag formulation for the spinup or spindown of the Arctic Ocean are discussed. [ABSTRACT FROM AUTHOR]

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]

NASA's ICESat-2 launched successfully from Vandenberg Air Force Base in California on 15 September 2018. ICESat-2 is a state-of-the-art laser altimeter that will accurately and precisely measure the Earth's surface at the centimeter scale. The mission goals for the satellite are generally associated with the polar regions where continent-scale changes in ice-sheet elevation at the centimeter scale have a large impact on mean sea level. Similarly, sea ice freeboard and freeboard changes also require centimeter-level precision. However, ICESat-2 will collect data globally and therefore the mission has a suite of data products including products for atmospheric science, vegetation canopy height, ocean elevation and inland water surface elevation. This presentation will provide an early look at ICESat-2 data, with a focus on data from the ice sheets. We will present some initial analysis, including assessments of ICESat-2 accuracy and precision based on ground-based GPS data collected near the South Pole. [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]

Understanding the causes and magnitudes of changes in the cryosphere remains a priority for Earth science research. Over the past decade, NASA's and ESA's Earth-observing satellites have documented a decrease in both the areal extent and thickness of Arctic sea ice, and an ongoing loss of grounded ice from Greenland and Antarctic ice sheets. Understanding the pace and mechanisms of these changes requires long-term observations of ice-sheet mass, sea-ice thickness, and sea-ice extent.NASA's Ice, Cloud, and land Elevation Satellite - 2 (ICESat-2) mission launched in September 2018, and has been collecting laser altimetry data since October 2018. ICESat-2 has three pairs of beams, each pair separated by about 3 km across-track with a pair spacing of 90 m. The spot size is 17 m with an along-track sampling interval of 0.7 m. This measurement concept is a result of the lessons learned from the original ICESat mission. The multi-beam approach is critical for estimating cross-track slope around the margins of Greenland and Antarctica enabling the calculation of elevation change on a seasonal basis. For sea ice, the dense spatial sampling (eliminating along-track gaps) and the small footprint size are especially useful for sea surface height measurements from, often narrow, leads which are needed for sea ice freeboard and ice thickness measurements. This presentation will include details of the on-orbit performance of ICESat-2's laser altimeter; the data collection strategy for ICESat-2; details on the main data products of interest to the cryospheric science community including surface-specific products for sea ice and grounded ice; and details on how to access and use the ICESat-2 data products. [ABSTRACT FROM AUTHOR]