Determining Variability in Arctic Sea Ice Pressure Ridge Topography With ICESat‐2

Duncan, Kyle; Farrell, S. L.

doi:10.1029/2022gl100272

Cited by 20 publications

(15 citation statements)

References 35 publications

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“…Towards the smoother end of the spectrum are the Kara Sea and Baffin Bay. This is consistent with the expected distribution of multi-year ice, which is more deformed and so rougher [58,59].…”

Section: Resultssupporting

confidence: 91%

Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer

2022

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Sea-ice surface roughness (SIR) is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer-melt pond extent, and being related to ice age and thickness. High-resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness does not extend over multi-decadal timescales. Launched on the Terra satellite in 1999, the NASA Multi-angle Imaging SpectroRadiometer (MISR) instrument acquires optical imagery from nine near-simultaneous camera view zenith angles. Extending on previous work to model surface roughness from specular anisotropy, a training dataset of cloud-free angular reflectance signatures and surface roughness, defined as the standard deviation of the within-pixel lidar elevations, from near-coincident operation IceBridge (OIB) airborne laser data is generated and is modelled using support vector regression (SVR) with a radial basis function (RBF) kernel selected. Blocked k-fold cross-validation is implemented to tune hyperparameters using grid optimisation and to assess model performance, with an R2 (coefficient of determination) of 0.43 and MAE (mean absolute error) of 0.041 m. Product performance is assessed through independent validation by comparison with unseen similarly generated surface-roughness characterisations from pre-IceBridge missions (Pearson’s r averaged over six scenes, r = 0.58, p < 0.005), and with AWI CS2-SMOS sea-ice thickness (Spearman’s rank, rs = 0.66, p < 0.001), a known roughness proxy. We present a derived sea-ice roughness product at 1.1 km resolution (2000–2020) over the seasonal period of OIB operation and a corresponding time-series analysis. Both our instantaneous swaths and pan-Arctic monthly mosaics show considerable potential in detecting surface-ice characteristics such as deformed rough ice, thin refrozen leads, and polynyas.

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Section: Resultssupporting

confidence: 91%

Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer

2022

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“…Mean residual effects from the three datasets have similarities in Figure 1 but also subtle differences. In all datasets, residual effects contribute to thickness increases in some areas of the Chukchi and East Siberian Sea, likely due to ridging and advection of thicker ice into them (Duncan & Farrell, 2022), and decreases in coastal Laptev Seas, likely due to divergence of ice seaward from coastal polynyas (Hoffman et al., 2019; Willmes & Heinemann, 2016), but the extent and magnitude of these effects differ. In the SLICE dataset, residual effects rarely exceed 0.2 m month −1 in the Chukchi and East Siberian Seas, whereas both PIOMAS and CESM2‐OMIP2 show much of this region exceeding 0.2 m month −1 and sometimes greater than 0.4 m month −1 .…”

Section: Methods and Resultsmentioning

confidence: 99%

“…An important difference between SLICE and both PIOMAS and CESM2‐OMIP2 is a region of significant residual thickness increase found in SLICE just north of most of the CAA and extending towards the peripheral seas, between 0.1 m month −1 and 0.2 m month −1 (Figure 1), likely due to ridging and convergence by the Beaufort Gyre and Transpolar Drift (Duncan & Farrell, 2022; Kwok & Cunningham, 2016). Neither PIOMAS nor CESM2‐OMIP2 depicts this region to be as expansive, though both show indications of this area and CESM2‐OMIP2 includes a larger region of 0–0.1 month −1 .…”

Section: Methods and Resultsmentioning

confidence: 99%

Model Mean State Sea Ice Thickness Reflects Dynamic Effect Biases: A Process Based Evaluation

Anheuser,

Liu,

Key

2024

Geophysical Research Letters

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Global climate models account for sea ice thickness by summing thermodynamic processes that affect thickness through phase change and dynamic processes that affect thicknesses through relative motion. Comparison of these individual processes with observations is essential for model interpretation and development. We utilized observational estimates of basal thermodynamic growth, overall thickness changes and their residual difference (including dynamics) to evaluate these processes in the National Center for Atmospheric Research (NCAR) Community Earth System Model 2 (CESM2) submission to the World Climate Research Program (WCRP) Ocean Model Comparison Project Phase 2 (OMIP2) and Pan‐Arctic Ice–Ocean Modeling and Assimilation System (PIOMAS). Both models exhibit a similar pattern of higher basal thermodynamic growth and lower residual effects and wintertime thickness in the central Arctic than observational estimates for 2010–2018, and vice versa in the peripheral seas. Correcting residual effect biases would ameliorate the biases in both mean thickness and basal thermodynamic growth.

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“…Sea ice height data were obtained from ICESat-2's ATL03 global geolocated photon height data (Neumann et al, 2021), which was then processed by the UMD-RDA described in Duncan and Farrell (2022) and Farrell et al (2020). Each ICESat-2 ATLAS track has six beams arranged in three pairs of strong and weak beams spaced 3.3 km apart and has a nominal along-track sampling interval of 0.7 m with a footprint of ∼11-12 m in diameter (Kwok et al, 2019;Magruder et al, 2020).…”

Section: Sea Ice Roughness and Heightmentioning

confidence: 99%

Arctic Sea Ice Topography Information From RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) Backscatter

Macdonald,

Scharien,

Duncan

et al. 2024

Geophysical Research Letters

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Sea ice topography information can be obtained from altimetry but these data are spatially and temporally limited compared to recent synthetic aperture radar (SAR) missions such as the RADARSAT Constellation Mission (RCM). We analyze the relationship between sea ice roughness and height obtained from three Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 tracks on two dates in March 2022, with RCM backscatter from 17 images in the McClintock Channel, Canadian Arctic. We analyze how this relationship varies with ice type, polarization, and incidence angle. We find particularly notable relationships between sea ice roughness and horizontal‐transmit/vertical‐receive backscatter for first‐year ice, and sea ice height and backscatter for multi‐year ice. We develop a preliminary model for winter sea ice roughness retrieval using RCM. In comparison with independent ICESat‐2 data in our study region, we find the model performs effectively at estimating a roughness distribution and key roughness statistics, and characterizes spatial variations in roughness at a sub‐kilometer scale.

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Determining Variability in Arctic Sea Ice Pressure Ridge Topography With ICESat‐2

Cited by 20 publications

References 35 publications

Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer

Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer

Model Mean State Sea Ice Thickness Reflects Dynamic Effect Biases: A Process Based Evaluation

Arctic Sea Ice Topography Information From RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) Backscatter

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