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

SEA ICE AND ICE SHEET SURFACE ROUGHNESS CHARACTERIZATION AND ITS EFFECTS ON BI-DIRECTIONAL REFLECTANCE

The roughness of sea ice can affect its bidirectional reflectance distribution function (BRDF) thus influencing retrieval of its physical properties from satellite. We leverage WorldView-1 and Multi-angle Imaging SpectroRadiometer (MISR) remote sensing data sets collected over sea ice and the adjacent McMurdo Ice Shelf in proximity to McMurdo station to first characterize the roughness of sea ice and other snow surfaces and then examine the effects of surface roughness on satellites images of varying spatial resolutions. First, high resolution DEMs were created from stereographic WorldView-1 image pairs with NASA stereo imagery processing tool Ames Stereo Pipeline (ASP). A variety of geomorphometric measures of roughness, including roughness index, were characterized from the high resolution DEMs. Following adequate characterization of sea ice roughness, its impact on surface reflectance derived from optical satellites spanning a range of spatial resolutions was assessed

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

Resolving Fine-Scale Surface Features on Polar Sea Ice: A First Assessment of UAS Photogrammetry Without Ground Control

Mapping landfast sea ice at a fine spatial scale is not only meaningful for geophysical study, but is also of benefit for providing information about human activities upon it. The combination of unmanned aerial systems (UAS) with structure from motion (SfM) methods have already revolutionized the current close-range Earth observation paradigm. To test their feasibility in characterizing the properties and dynamics of fast ice, three flights were carried out in the 2016–2017 austral summer during the 33rd Chinese National Antarctic Expedition (CHINARE), focusing on the area of the Prydz Bay in East Antarctica. Three-dimensional models and orthomosaics from three sorties were constructed from a total of 205 photos using Agisoft PhotoScan software. Logistical challenges presented by the terrain precluded the deployment of a dedicated ground control network; however, it was still possible to indirectly assess the performance of the photogrammetric products through an analysis of the statistics of the matching network, bundle adjustment, and Monte-Carlo simulation. Our results show that the matching networks are quite strong, given a sufficient number of feature points (mostly > 20,000) or valid matches (mostly > 1000). The largest contribution to the total error using our direct georeferencing approach is attributed to inaccuracies in the onboard position and orientation system (POS) records, especially in the vehicle height and yaw angle. On one hand, the 3D precision map reveals that planimetric precision is usually about one-third of the vertical estimate (typically 20 cm in the network centre). On the other hand, shape-only errors account for less than 5% for the X and Y dimensions and 20% for the Z dimension. To further illustrate the UAS’s capability, six representative surface features are selected and interpreted by sea ice experts. Finally, we offer pragmatic suggestions and guidelines for planning future UAS-SfM surveys without the use of ground control. The work represents a pioneering attempt to comprehensively assess UAS-SfM survey capability in fast ice environments, and could serve as a reference for future improvements

Measuring glacier surface roughness using plot-scale, close-range digital photogrammetry

Glacier roughness at sub-metre scales is an important control on the ice surface energy balance and has implications for scattering energy measured by remote-sensing instruments. Ice surface roughness is dynamic as a consequence of spatial and temporal variation in ablation. To date, studies relying on singular and/or spatially discrete two-dimensional profiles to describe ice surface roughness have failed to resolve common patterns or causes of variation in glacier surface morphology. Here we demonstrate the potential of close-range digital photogrammetry as a rapid and cost-effective method to retrieve three-dimensional data detailing plot-scale supraglacial topography. The photogrammetric approach here employed a calibrated, consumer-grade 5 Mpix digital camera repeatedly imaging a plot-scale (≤25 m2) ice surface area on Midtre Lovénbreen, Svalbard. From stereo-pair images, digital surface models (DSMs) with sub-centimetre horizontal resolution and 3 mm vertical precision were achieved at plot scales ≤4 m2. Extraction of roughness metrics including estimates of aerodynamic roughness length (z 0) was readily achievable, and temporal variations in the glacier surface topography were captured. Close-range photogrammetry, with appropriate camera calibration and image acquisition geometry, is shown to be a robust method to record sub-centimetre variations in ablating ice topography. While the DSM plot area may be limited through use of stereo-pair images and issues of obliquity, emerging photogrammetric packages are likely to overcome such limitations

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

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

Cirrus clouds

Andrew J. Heymsfield, Martina Kramer, Anna Luebke, Phil Brown, Daniel J. Cziczo, Charmaine Franklin, Ulrike Lohmann, Greg McFarquhar, Zbigniew Ulanowski and Kristof Van Trich, American Meteorological Society , January 2017, this article has been published in final form at DOI: http://dx.doi.org/10.1175/AMSMONOGRAPHS-D-16-0010.1 Published by AMS Publications © 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (http://www.ametsoc.org/PUBSCopyrightPolicy).The goal of this article is to synthesize information about what is now known about one of the three main types of clouds, cirrus, and to identify areas where more knowledge is needed. Cirrus clouds, composed of ice particles, form primarily in the upper troposphere, where temperatures are generally below -30°C. Satellite observations show that the maximum-occurrence frequency of cirrus is near the tropics, with a large latitudinal movement seasonally. In-situ measurements obtained over a wide range of cloud types, formation mechanisms, temperatures, and geographical locations indicate that the ice water content and particle size generally decrease with decreasing temperature, whereas the ice particle concentration is nearly constant or increase slightly with decreasing temperature. High ice concentrations, sometimes observed in strong updrafts , results from homogeneous nucleation. The satellite-based and in-situ measurements indicate that cirrus ice crystals typically depart from the simple, idealized geometry for smooth hexagonal shapes, indicating complexity and/or surface roughness. Their shapes significantly impact cirrus radiative properties and feedbacks to climate. Cirrus clouds, one of the most uncertain components of general circulation models (GCM), pose one of the greatest challenges in predicting the rate and geographical pattern of climate change. Improved measurements of the properties and size distributions and surface structure of small ice crystals — about 20 μm, and identifying the dominant ice nucleation process — heterogeneous versus homogeneous ice nucleation, under different cloud dynamical forcings, will lead to a better representation of their properties in GCM and in modeling their current and future effects on climate.Peer reviewe