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

Brief communication:Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates

Pan-Arctic sea ice thickness has been monitored over recent decades by satellite radar altimeters such as CryoSat-2, which emits Ku-band radar waves that are assumed in publicly available sea ice thickness products to penetrate overlying snow and scatter from the ice–snow interface. Here we examine two expressions for the time delay caused by slower radar wave propagation through the snow layer and related assumptions concerning the time evolution of overlying snow density. Two conventional treatments introduce systematic underestimates of up to 15 cm into ice thickness estimates and up to 10 cm into thermodynamic growth rate estimates over multi-year ice in winter. Correcting these biases would impact a wide variety of model projections, calibrations, validations and reanalyses

A metamaterial frequency-selective super-absorber that has absorbing cross section significantly bigger than the geometric cross section

Using the idea of transformation optics, we propose a metamaterial device that serves as a frequency-selective super-absorber, which consists of an absorbing core material coated with a shell of isotropic double negative metamaterial. For a fixed volume, the absorption cross section of the super-absorber can be made arbitrarily large at one frequency. The double negative shell serves to amplify the evanescent tail of the high order incident cylindrical waves, which induces strong scattering and absorption. Our conclusion is supported by both analytical Mie theory and numerical finite element simulation. Interesting applications of such a device are discussed.Comment: 16 pages, 5 figure

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

Increasing Multiyear Sea Ice Loss in the Beaufort Sea: A New Export Pathway for the Diminishing Multiyear Ice Cover of the Arctic Ocean

Historically, multiyear sea ice (MYI) covered a majority of the Arctic and circulated through the Beaufort Gyre for years. However, increased ice melt in the Beaufort Sea during the early 2000s was proposed to have severed this circulation. Constructing a regional MYI budget from 1997 to 2021 reveals that MYI import into the Beaufort Sea has increased year-round, yet less MYI now survives through summer and is transported onwards in the Gyre. Annual average MYI loss quadrupled over the study period and increased from ∼7% to ∼33% of annual Fram Strait MYI export, while the peak in 2018 (385,000 km2) was similar in magnitude to Fram Strait MYI export. The ice-albedo feedback coupled with the transition toward younger thinner MYI is responsible for the increased MYI loss. MYI transport through the Beaufort Gyre has not been severed, but it has been reduced so severely to prevent it from being redistributed throughout the Arctic Ocean

Investigations into Frost Flower Physical Characteristics and the C-Band Scattering Response

A dedicated study on the physical characteristics and C-band scattering response of frost-flower-covered sea ice was performed in an artificial sea ice mesocosm over a 36-h period in January 2017. Meteorological conditions were observed and recorded automatically at the facility when the sea ice grew and frost flowers formed while the C-band scattering measurements were conducted continuously over a range of incidence angles. Surface roughness was characterized using a LiDAR. During the experiment, frost flowers did not initially form on the extremely smooth ice surface even though suitable meteorological conditions prevailed during their development (low air temperature, low near-surface wind speed, and high near-surface relative humidity). This provides evidence that both the presence of (i) liquid brine at the surface and (ii) raised nodules as nucleation points are required to enable frost flower initiation. As the ice thickened, we observed that raised nodules gradually appeared, frost flowers formed, and flowers subsequently spread to cover the surface over a six-hour period. In contrast to previous experiments, the frost flower layer did not become visibly saturated with liquid brine. The C-band scattering measurements exhibited increases as high as 14.8 dB (vertical polarization) in response to the frost flower formation with low incidence angles (i.e., 25°) showing the largest dynamic range. Co-polarization ratios responded to the physical and thermodynamic changes associated with the frost flower formation process. Our results indicate that brine expulsion at the sea ice surface and frost flower salination can have substantial temporal variability, which can be detected by scatterometer time-series measurements. This work contributes towards the operational satellite image interpretation for Arctic waters by improving our understanding of the highly variable C-band microwave scattering properties of young sea ice types

Improved Arctic Sea Ice Freeboard Retrieval From Satellite Altimetry Using Optimized Sea Surface Decorrelation Scales

A growing number of studies are concluding that the resilience of the Arctic sea ice cover in a warming climate is essentially controlled by its thickness. Satellite radar and laser altimeters have allowed us to routinely monitor sea ice thickness across most of the Arctic Ocean for several decades. However, a key uncertainty remaining in the sea ice thickness retrieval is the error on the sea surface height (SSH) which is conventionally interpolated at ice floes from a limited number of lead observations along the altimeter's orbital track. Here, we use an objective mapping approach to determine sea surface height from all proximal lead samples located on the orbital track and from adjacent tracks within a neighborhood of 30–220 (mean 105) km. The patterns of the SSH signal's zonal, meridional, and temporal decorrelation length scales are obtained by analyzing the covariance of historic CryoSat-2 Arctic lead observations, which match the scales obtained from an equivalent analysis of high-resolution sea ice-ocean model fields. We use these length scales to determine an optimal SSH and error estimate for each sea ice floe location. By exploiting leads from adjacent tracks, we can increase the sea ice radar freeboard precision estimated at orbital crossovers by up to 20%. In regions of high SSH uncertainty, biases in CryoSat-2 radar freeboard can be reduced by 25% with respect to coincident airborne validation data. The new method is not restricted to a particular sensor or mode, so it can be generalized to all present and historic polar altimetry missions

Sub-kilometre scale distribution of snow depth on Arctic sea ice from Soviet drifting stations

The sub-kilometre scale distribution of snow depth on Arctic sea ice impacts atmosphere-ice fluxes of energy and mass, and is of importance for satellite estimates of sea-ice thickness from both radar and lidar altimeters. While information about the mean of this distribution is increasingly available from modelling and remote sensing, the full distribution cannot yet be resolved. We analyse 33 539 snow depth measurements from 499 transects taken at Soviet drifting stations between 1955 and 1991 and derive a simple statistical distribution for snow depth over multi-year ice as a function of only the mean snow depth. We then evaluate this snow depth distribution against snow depth transects that span first-year ice to multiyear ice from the MOSAiC, SHEBA and AMSR-Ice field campaigns. Because the distribution can be generated using only the mean snow depth, it can be used in the downscaling of several existing snow depth products for use in flux modelling and altimetry studies