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

Surface Properties Linked to Retrieval Uncertainty of Satellite Sea-Ice Thickness with Upward-Looking Sonar Measurements.

One of the key sources of uncertainties in sea ice freeboard and thickness estimates derived from satellite radar altimetry results from changes in sea ice surface properties. In this study, we analyse this effect, comparing upward-looking sonar (ULS) measurements in the Beaufort Sea over the period 2003–2018 to sea ice draft derived from Envisat and Cryosat-2 data. We show that the sea ice draft growth underestimation observed for the most of winter seasons depends on the surface properties preconditioned by the melt intensity during the preceding summer. The comparison of sea ice draft time series in the Cryosat-2 era indicates that applying 50% retracker thresholds, used to produce the European Space Agency’s Climate Change Initiative (CCI) product, provide better agreement between satellite retrievals and ULS data than the 80% threshold that is closer to the expected physical waveform interpretation. Our results, therefore, indicate compensating error contributions in the full end-to-end sea-ice thickness processing chain, which prevents the quantification of individual factors with sea-ice thickness/draft validation data alon

Assessment of contemporary satellite sea ice thickness products for Arctic sea ice

Advances in remote sensing of sea ice over the past two decades have resulted in a wide variety of satellite-derived sea ice thickness data products becoming publicly available. Selecting the most appropriate product is challenging given end user objectives range from incorporating satellite-derived thickness information in operational activities, including sea ice forecasting, routing of maritime traffic and search and rescue, to climate change analysis, longer-term modelling, prediction and future planning. Depending on the use case, selecting the most suitable satellite data product can depend on the region of interest, data latency, and whether the data are provided routinely, for example via a climate or maritime service provider. Here we examine a suite of current sea ice thickness data products, collating key details of primary interest to end users. We assess 8 years of sea ice thickness observations derived from sensors on board the CryoSat-2 (CS2), Advanced Very-High-Resolution Radiometer (AVHRR) and Soil Moisture and Ocean Salinity (SMOS) satellites. We evaluate the satellite-only observations with independent ice draft and thickness measurements obtained from the Beaufort Gyre Exploration Project (BGEP) upward looking sonar (ULS) instruments and Operation IceBridge (OIB), respectively. We find a number of key differences among data products but find that products utilizing CS2-only measurements are reliable for sea ice thickness, particularly between ∼0.5 and 4&thinsp;m. Among data compared, a blended CS2-SMOS product was the most reliable for thin ice. Ice thickness distributions at the end of winter appeared realistic when compared with independent ice draft measurements, with the exception of those derived from AVHRR. There is disagreement among the products in terms of the magnitude of the mean thickness trends, especially in spring 2017. Regional comparisons reveal noticeable differences in ice thickness between products, particularly in the marginal seas in areas of considerable ship traffic.</p

Airborne investigation of quasi-specular Ku-band radar scattering for satellite altimetry over snow-covered Arctic sea ice

Surface-based Ku-band radar altimetry investigations indicate the radar signal is typically backscattered from well above the snow-sea ice interface. However, this would induce a bias in satellite altimeter sea ice thickness retrievals not reflected by buoy validation. Our study presents a mechanism to potentially explain this paradox: probabilistic quasi-specular radar scattering from the snow-ice interface. We introduce the theory for this mechanism before identifying it in airborne Ku-band radar observations collected over landfast first year Arctic sea ice near Eureka, Canada, in spring 2016. Based on SAR data, this study area likely represents level first year sea ice across the Arctic. Radar backscatter from the snow and ice interfaces were estimated by co-aligning laser scanner and radar observations with in situ measurements. On average, 4-5 times more radar power was scattered from the snow-ice than the air-snow interface over first-year ice. However, return power varied by up to 20 dB between consecutive radar echoes, particularly from the snow-ice interface, depending on local slope and roughness. Measured laser-radar snow depths were more accurate when radar returns were specular, but there was no systematic bias between airborne and in situ snow depths. The probability and strength of quasi-specular returns depend on the measuring height above and slope distribution of sea ice, so these findings have implications for satellite altimetry snow depth and freeboard estimates. This mechanism could explain the apparent differences in Ku-band radar penetration into snow on sea ice when observed from the range of a surface-, airborne- or satellite-based sensor

The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system

In the past decade groundbreaking new satellite observations of the Arctic sea ice cover have been made, allowing researchers to understand the state of the Arctic sea ice system in greater detail than before. The derived estimates of sea ice thickness are useful but limited in time and space. In this study the first results of a new sea ice data assimilation system are presented. Observations assimilated (in various combinations) are monthly mean sea ice thickness and monthly mean sea ice thickness distribution from CryoSat-2 and NASA daily Bootstrap sea ice concentration. This system couples the Centre for Polar Observation and Modelling's (CPOM) version of the Los Alamos Sea Ice Model (CICE) to the localised ensemble transform Kalman filter (LETKF) from the Parallel Data Assimilation Framework (PDAF) library. The impact of assimilating a sub-grid-scale sea ice thickness distribution is of particular novelty. The sub-grid-scale sea ice thickness distribution is a fundamental component of sea ice models, playing a vital role in the dynamical and thermodynamical processes, yet very little is known of its true state in the Arctic. This study finds that assimilating CryoSat-2 products for the mean thickness and the sub-grid-scale thickness distribution can have significant consequences for the modelled distribution of the ice thickness across the Arctic and particularly in regions of thick multi-year ice. The assimilation of sea ice concentration, mean sea ice thickness and sub-grid-scale sea ice thickness distribution together performed best when compared to a subset of CryoSat-2 observations held back for validation. Regional model biases are reduced: the thickness of the thickest ice in the Canadian Arctic Archipelago (CAA) is decreased, but the thickness of the ice in the central Arctic is increased. When comparing the assimilation of mean thickness with the assimilation of sub-grid-scale thickness distribution, it is found that the latter leads to a significant change in the volume of ice in each category. Estimates of the thickest ice improve significantly with the assimilation of sub-grid-scale thickness distribution alongside mean thickness

Estimating Arctic sea ice thickness and volume using CryoSat-2 radar altimeter data

Arctic sea ice is a major element of the Earth’s climate system. It acts to regulate regional heat and freshwater budgets and subsequent atmospheric and oceanic circulation across the Arctic and at lower latitudes. Satellites have observed a decline in Arctic sea ice extent for all months since 1979. However, to fully understand how changes in the Arctic sea ice cover impact on our global weather and climate, long-term and accurate observations of its thickness distribution are also required. Such observations were made possible with the launch of the European Space Agency’s (ESA’s) CryoSat-2 satellite in April 2010, which provides unparalleled coverage of the Arctic Ocean up to 88°N. Here we provide an end-to-end, comprehensive description of the data processing steps employed to estimate Northern Hemisphere sea ice thickness and subsequent volume using CryoSat-2 radar altimeter data and complementary observations. This is a sea ice processor that has been under constant development at the Centre for Polar Observation and Modelling (CPOM) since the early 1990s. We show that there is no significant bias in our satellite sea ice thickness retrievals when compared with independent measurements. We also provide a detailed analysis of the uncertainties associated with our sea ice thickness and volume estimates by considering the independent sources of error in the retrieval. Each month, the main contributors to the uncertainty are snow depth and snow density, which suggests that a crucial next step in Arctic sea ice research is to develop improved estimates of snow loading. In this paper we apply our theory and methods solely to CryoSat-2 data in the Northern Hemisphere. However, they may act as a guide to developing a sea ice processing system for satellite radar altimeter data over the Southern Hemisphere, and from other Polar orbiting missions

Kara and Barents sea ice thickness estimation based on CryoSat-2 radar altimeter and Sentinel-1 dual-polarized synthetic aperture radar

We present a method to combine CryoSat-2 (CS2) radar altimeter and Sentinel-1 synthetic aperture radar (SAR) data to obtain sea ice thickness (SIT) estimates for the Barents and Kara seas. From the viewpoint of tactical navigation, along-track altimeter SIT estimates are sparse, and the goal of our study is to develop a method to interpolate altimeter SIT measurements between CS2 ground tracks. The SIT estimation method developed here is based on the interpolation of CS2 SIT utilizing SAR segmentation and segmentwise SAR texture features. The SIT results are compared to SIT data derived from the AARI ice charts; to ORAS5, PIOMAS and TOPAZ4 ocean-sea ice data assimilation system reanalyses; to combined CS2 and Soil Moisture and Ocean Salinity (SMOS) radiometer weekly SIT (CS2SMOS SIT) charts; and to the daily MODIS (Moderate Resolution Imaging Spectro-radiometer) SIT chart. We studied two approaches: CS2 directly interpolated to SAR segments and CS2 SIT interpolated to SAR segments with mapping of the CS2 SIT distributions to correspond to SIT distribution of the PIOMAS ice model. Our approaches yield larger spatial coverage and better accuracy compared to SIT estimates based on either CS2 or SAR data alone. The agreement with modelled SIT is better than with the CS2SMOS SIT. The average differences when compared to ice models and the AARI ice chart SIT were typically tens of centimetres, and there was a significant positive bias when compared to the AARI SIT (on average 27 cm) and a similar bias (24 cm) when compared to the CS2SMOS SIT. Our results are directly applicable to the future CRISTAL mission and Copernicus programme SAR missions.Peer reviewe