Assessing the Impact of Lead and Floe Sampling on Arctic Sea Ice Thickness Estimates from Envisat and CryoSat‐2

Multidecadal observations of sea ice thickness, in addition to those available for extent, are key to understanding long‐term variations and trends in the amount of Arctic sea ice. The European Space Agency's Envisat (2002–2010) and CryoSat‐2 (2010–present) satellite radar altimeter missions provide a continuous 17‐year dataset with the potential to estimate sea ice thickness. However, the satellite footprints are not equal in area and so different distributions of floes and leads are sampled by each mission. Here, we compare lead and floe sampling from Envisat and CryoSat‐2 to investigate the impact of geometric sampling differences on Arctic sea ice thickness estimates. We find that Envisat preferentially samples wider, thicker sea ice floes, and that floes in less consolidated ice regions are effectively thickened by off‐nadir ranging to leads. Consequently, Envisat sea ice thicknesses that are an average of 80 cm higher than CryoSat‐2 over first‐year ice and 23 cm higher over multiyear ice. By considering the along‐track distances between lead and floe measurements, we are able to develop a sea ice thickness correction that is based on Envisat's inability to resolve discrete surfaces relative to CryoSat‐2. This is a novel, physically based approach to addressing the bias between the satellites and reduces the average thickness difference to negligible values over first‐year and multiyear ice. Finally, we evaluate our new bias‐corrected Envisat sea ice thickness product using independent airborne, moored‐buoy and submarine data. The European Space Agency's Envisat and CryoSat‐2 satellites have the potential to produce a continuous record of Arctic sea ice thickness since 2002, but this is complicated by the fact that the satellites do not sample the sea ice surface in the same way. We find that Envisat is only able to sample larger, thicker sea ice relative to CryoSat‐2, because of its poorer resolution. In this paper we account for these differences in sampling to combine Arctic sea ice thickness estimates from two the satellite missions. Applying a sea ice thickness bias correction to Envisat data reduces the ice thickness difference between Envisat and CryoSat‐2 from an average of 53.0 to 0.5 c

CryoSat instrument performance and ice product quality status

Over the past 20 years, satellite radar altimetry has shown its ability to revolutionise our understanding of the ocean and climate. Previously, these advances were largely limited to ice-free regions, neglecting large portions of the Polar Regions. Launched in 2010, the European Space Agency’s (ESA) polar-orbiting CryoSat satellite was specifically designed to measure changes in the thickness of polar sea ice and the elevation of the ice sheets and mountain glaciers. To reach this goal, the CryoSat products have to meet the highest performance standards, achieved through continual improvements of the associated Instrument Processing Facilities. Since April 2015, the CryoSat ice products are generated with Baseline-C, which represented a major processor upgrade. Several improvements were implemented in this new Baseline, most notably the release of freeboard data within the Level 2 products. The Baseline-C upgrade has brought significant improvements to the quality of Level-1B and Level-2 products relative to the previous Baseline-B products, which in turn is expected to have a positive impact on the scientific exploitation of CryoSat measurements over land ice and sea ice. This paper provides an overview of the CryoSat ice data quality assessment and evolutions, covering all quality control and calibration activities performed by ESA and its partners. Also discussed are the forthcoming evolutions of the processing chains and improvements anticipated in the next processing Baseline

Sea ice roughness overlooked as a key source of uncertainty in CryoSat-2 ice freeboard retrievals

ESA's CryoSat‐2 has transformed the way we monitor Arctic sea ice, providing routine measurements of the ice thickness with near basin‐wide coverage. Past studies have shown that uncertainties in the sea ice thickness retrievals can be introduced at several steps of the processing chain, for instance in the estimation of snow depth, and snow and sea ice densities. Here, we apply a new physical model to CryoSat‐2 which further reveals sea ice surface roughness as a key overlooked feature of the conventional retrieval process. High‐resolution airborne observations demonstrate that snow and sea ice surface topography can be better characterized by a Lognormal distribution, which varies based on the ice age and surface roughness within a CryoSat‐2 footprint, than a Gaussian distribution. Based on these observations, we perform a set of simulations for the CryoSat‐2 echo waveform over ‘virtual’ sea ice surfaces with a range of roughness and radar backscattering configurations. By accounting for the variable roughness, our new Lognormal retracker produces sea ice freeboards which compare well with those derived from NASA's Operation IceBridge airborne data and extends the capability of CryoSat‐2 to profile the thinnest/smoothest sea ice and thickest/roughest ice. Our results indicate that the variable ice surface roughness contributes a systematic uncertainty in sea ice thickness of up to 20% over first‐year ice and 30% over multi‐year ice, representing one of the principal sources of pan‐Arctic sea ice thickness uncertainty

A Facet-Based Numerical Model for Simulating SAR Altimeter Echoes From Heterogeneous Sea Ice Surfaces

Cryosat-2 has provided measurements of pan-Arctic sea ice thickness since 2010 with unprecedented spatial coverage and frequency. However, it remains uncertain how the Ku-band radar interacts with the vast range of scatterers that can be present within the satellite footprint, including sea ice with varying physical properties and multi-scale roughness, snow cover, and leads. Here, we present a numerical model designed to simulate delay-Doppler SAR (Synthetic Aperture Radar) altimeter echoes from snow-covered sea ice, such as those detected by Cryosat-2. Backscattered echoes are simulated directly from triangular facetbased models of actual sea ice topography generated from Operation IceBridge Airborne Topographic Mapper (ATM) data, as well as virtual statistical models simulated artificially. We use these waveform simulations to investigate the sensitivity of SAR altimeter echoes to variations in satellite parameters (height, pitch, roll) and sea ice properties (physical properties, roughness, presence of water). We show that the conventional Gaussian assumption for sea ice surface roughness may be introducing significant error into the Cryosat-2 waveform retracking process. Compared to a more representative lognormal surface, an echo simulated from a Gaussian surface with rms roughness height of 0.2 m underestimates the ice freeboard by 5 cm – potentially underestimating sea ice thickness by around 50 cm. We present a set of ‘ideal’ waveform shape parameters simulated for sea ice and leads to inform existing waveform classification techniques. This model will ultimately be used to improve retrievals of key sea ice properties, including freeboard, surface roughness and snow depth, from SAR altimeter observations

Community Review of Southern Ocean Satellite Data Needs

This review represents the Southern Ocean community’s satellite data needs for the coming decade. Developed through widespread engagement, and incorporating perspectives from a range of stakeholders (both research and operational), it is designed as an important community-driven strategy paper that provides the rationale and information required for future planning and investment. The Southern Ocean is vast but globally connected, and the communities that require satellite-derived data in the region are diverse. This review includes many observable variables, including sea-ice properties, sea-surface temperature, sea-surface height, atmospheric parameters, marine biology (both micro and macro) and related activities, terrestrial cryospheric connections, sea-surface salinity, and a discussion of coincident and in situ data collection. Recommendations include commitment to data continuity, increase in particular capabilities (sensor types, spatial, temporal), improvements in dissemination of data/products/uncertainties, and innovation in calibration/validation capabilities. Full recommendations are detailed by variable as well as summarized. This review provides a starting point for scientists to understand more about Southern Ocean processes and their global roles, for funders to understand the desires of the community, for commercial operators to safely conduct their activities in the Southern Ocean, and for space agencies to gain greater impact from Southern Ocean-related acquisitions and missions.The authors acknowledge the Climate at the Cryosphere program and the Southern Ocean Observing System for initiating this community effort, WCRP, SCAR, and SCOR for endorsing the effort, and CliC, SOOS, and SCAR for supporting authors’ travel for collaboration on the review. Jamie Shutler’s time on this review was funded by the European Space Agency project OceanFlux Greenhouse Gases Evolution (Contract number 4000112091/14/I-LG)

A Facet-Based Numerical Model for Simulating SAR Altimeter Echoes from Heterogeneous Sea Ice Surfaces

Cryosat-2 has provided measurements of pan-Arctic sea ice thickness since 2010 with unprecedented spatial coverage and frequency. However, it remains uncertain how the Ku-band radar interacts with the vast range of scatterers that can be present within the satellite footprint, including sea ice with varying physical properties and multi-scale roughness, snow cover, and leads. Here, we present a numerical model designed to simulate delay-Doppler SAR (Synthetic Aperture Radar) altimeter echoes from snow-covered sea ice, such as those detected by Cryosat-2. Backscattered echoes are simulated directly from triangular facetbased models of actual sea ice topography generated from Operation IceBridge Airborne Topographic Mapper (ATM) data, as well as virtual statistical models simulated artificially. We use these waveform simulations to investigate the sensitivity of SAR altimeter echoes to variations in satellite parameters (height, pitch, roll) and sea ice properties (physical properties, roughness, presence of water). We show that the conventional Gaussian assumption for sea ice surface roughness may be introducing significant error into the Cryosat-2 waveform retracking process. Compared to a more representative lognormal surface, an echo simulated from a Gaussian surface with rms roughness height of 0.2 m underestimates the ice freeboard by 5 cm – potentially underestimating sea ice thickness by around 50 cm. We present a set of ‘ideal’ waveform shape parameters simulated for sea ice and leads to inform existing waveform classification techniques. This model will ultimately be used to improve retrievals of key sea ice properties, including freeboard, surface roughness and snow depth, from SAR altimeter observations

CryoSat Ice Baseline-D validation and evolutions

The ESA Earth Explorer CryoSat-2 was launched on 8 April 2010 to monitor the precise changes in the thickness of terrestrial ice sheets and marine floating ice. To do that, CryoSat orbits the planet at an altitude of around 720 km with a retrograde orbit inclination of 92∘ and a quasi repeat cycle of 369 d (30 d subcycle). To reach the mission goals, the CryoSat products have to meet the highest quality standards to date, achieved through continual improvements of the operational processing chains. The new CryoSat Ice Baseline-D, in operation since 27 May 2019, represents a major processor upgrade with respect to the previous Ice Baseline-C. Over land ice the new Baseline-D provides better results with respect to the previous baseline when comparing the data to a reference elevation model over the Austfonna ice cap region, improving the ascending and descending crossover statistics from 1.9 to 0.1 m. The improved processing of the star tracker measurements implemented in Baseline-D has led to a reduction in the standard deviation of the point-to-point comparison with the previous star tracker processing method implemented in Baseline-C from 3.8 to 3.7 m. Over sea ice, Baseline-D improves the quality of the retrieved heights inside and at the boundaries of the synthetic aperture radar interferometric (SARIn or SIN) acquisition mask, removing the negative freeboard pattern which is beneficial not only for freeboard retrieval but also for any application that exploits the phase information from SARIn Level 1B (L1B) products. In addition, scatter comparisons with the Beaufort Gyre Exploration Project (BGEP; https://www.whoi.edu/beaufortgyre, last access: October 2019) and Operation IceBridge (OIB; Kurtz et al., 2013) in situ measurements confirm the improvements in the Baseline-D freeboard product quality. Relative to OIB, the Baseline-D freeboard mean bias is reduced by about 8 cm, which roughly corresponds to a 60 % decrease with respect to Baseline-C. The BGEP data indicate a similar tendency with a mean draft bias lowered from 0.85 to −0.14 m. For the two in situ datasets, the root mean square deviation (RMSD) is also well reduced from 14 to 11 cm for OIB and by a factor of 2 for the BGEP. Observations over inland waters show a slight increase in the percentage of good observations in Baseline-D, generally around 5 %–10 % for most lakes. This paper provides an overview of the new Level 1 and Level 2 (L2) CryoSat Ice Baseline-D evolutions and related data quality assessment, based on results obtained from analyzing the 6-month Baseline-D test dataset released to CryoSat expert users prior to the final transfer to operations

CryoSat Ice Baseline-D validation and evolutions

The ESA Earth Explorer CryoSat-2 was launched on 8 April 2010 to monitor the precise changes in the thickness of terrestrial ice sheets and marine floating ice. To do that, CryoSat orbits the planet at an altitude of around 720 km with a retrograde orbit inclination of 92∘ and a quasi repeat cycle of 369 d (30 d subcycle). To reach the mission goals, the CryoSat products have to meet the highest quality standards to date, achieved through continual improvements of the operational processing chains. The new CryoSat Ice Baseline-D, in operation since 27 May 2019, represents a major processor upgrade with respect to the previous Ice Baseline-C. Over land ice the new Baseline-D provides better results with respect to the previous baseline when comparing the data to a reference elevation model over the Austfonna ice cap region, improving the ascending and descending crossover statistics from 1.9 to 0.1 m. The improved processing of the star tracker measurements implemented in Baseline-D has led to a reduction in the standard deviation of the point-to-point comparison with the previous star tracker processing method implemented in Baseline-C from 3.8 to 3.7 m. Over sea ice, Baseline-D improves the quality of the retrieved heights inside and at the boundaries of the synthetic aperture radar interferometric (SARIn or SIN) acquisition mask, removing the negative freeboard pattern which is beneficial not only for freeboard retrieval but also for any application that exploits the phase information from SARIn Level 1B (L1B) products. In addition, scatter comparisons with the Beaufort Gyre Exploration Project (BGEP; https://www.whoi.edu/beaufortgyre, last access: October 2019) and Operation IceBridge (OIB; Kurtz et al., 2013) in situ measurements confirm the improvements in the Baseline-D freeboard product quality. Relative to OIB, the Baseline-D freeboard mean bias is reduced by about 8 cm, which roughly corresponds to a 60 % decrease with respect to Baseline-C. The BGEP data indicate a similar tendency with a mean draft bias lowered from 0.85 to −0.14 m. For the two in situ datasets, the root mean square deviation (RMSD) is also well reduced from 14 to 11 cm for OIB and by a factor of 2 for the BGEP. Observations over inland waters show a slight increase in the percentage of good observations in Baseline-D, generally around 5 %–10 % for most lakes. This paper provides an overview of the new Level 1 and Level 2 (L2) CryoSat Ice Baseline-D evolutions and related data quality assessment, based on results obtained from analyzing the 6-month Baseline-D test dataset released to CryoSat expert users prior to the final transfer to operations

Retrieving Sea Level and Freeboard in the Arctic: A Review of Current Radar Altimetry Methodologies and Future Perspectives

Spaceborne radar altimeters record echo waveforms over all Earth surfaces, but their interpretation and quantitative exploitation over the Arctic Ocean is particularly challenging. Radar returns may be from all ocean, all sea ice, or a mixture of the two, so the first task is the determination of which surface and then an interpretation of the signal to give range. Subsequently, corrections have to be applied for various surface and atmospheric effects before making a comparison with a reference level. This paper discusses the drivers for improved altimetry in the Arctic and then reviews the various approaches that have been used to achieve the initial classification and subsequent retracking over these diverse surfaces, showing examples from both LRM (low resolution mode) and SAR (synthetic aperture radar) altimeters. The review then discusses the issues concerning corrections, including the choices between using other remote-sensing measurements and using those from models or climatology. The paper finishes with some perspectives on future developments, incorporating secondary frequency, interferometric SAR and opportunities for fusion with measurements from laser altimetry or from the SMOS salinity sensor, and provides a full list of relevant abbreviations