Sea ice thickness, freeboard, and snow depth products from Operation  IceBridge airborne data

Kurtz, N. T.; Farrell, S. L.; Studinger, M.; Galin, N.; Harbeck, J. P.; Lindsay, R. W.; Onana, V.; Panzer, B.; Sonntag, J. G.

doi:10.5194/tc-7-1035-2013

Cited by 256 publications

(316 citation statements)

References 53 publications

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“…Airborne lidar [e.g., Kurtz et al, 2013], and drone-based photogrammetric methods [e.g., Nolan et al, 2015] can provide the additional spatial coverage required for broader-scale aggregate estimates. Achieving the centimeter-scale vertical accuracies necessary to detect the small changes induced by precipitation and blowing snow processes we show here are a challenge for airborne platforms.…”

Section: Discussionmentioning

confidence: 99%

Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice

et al. 2016

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Snow distribution over sea ice is an important control on sea ice physical and biological processes. We combine measurements of the atmospheric boundary layer and blowing snow on an Antarctic sea ice floe with terrestrial laser scanning to characterize a typical storm and its influence on the spatial patterns of snow distribution at resolutions of 1–10 cm over an area of 100 m × 100 m. The pre‐storm surface exhibits multidirectional elongated snow dunes formed behind aerodynamic obstacles. Newly deposited dunes are elongated parallel to the predominant wind direction during the storm. Snow erosion and deposition occur over 62% and 38% of the area, respectively. Snow deposition volume is more than twice that of erosion (351 m3 versus 158 m3), resulting in a modest increase of 2 ± 1 cm in mean snow depth, indicating a small net mass gain despite large mass relocation. Despite significant local snow depth changes due to deposition and erosion, the statistical distributions of elevation and the two‐dimensional correlation functions remain similar to those of the pre‐storm surface. Pre‐storm and post‐storm surfaces also exhibit spectral power law relationships with little change in spectral exponents. These observations suggest that for sea ice floes with mature snow cover features under conditions similar to those observed in this study, spatial statistics and scaling properties of snow surface morphology may be relatively invariant. Such an observation, if confirmed for other ice types and conditions, may be a useful tool for model parameterizations of the subgrid variability of sea ice surfaces.

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Section: Discussionmentioning

confidence: 99%

Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice

et al. 2016

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“…Details of the full algorithm methodology are described in Kurtz et al (2013). Briefly, the algorithm is an empirical method that selects the a-s interface using a combined peak and threshold method.…”

Section: Existing Nsidc Product (Nsidc)mentioning

confidence: 99%

Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

et al. 2017

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Abstract. Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air-snow (a-s) and snow-ice (s-i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two groundbased campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice.

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“…first-year ice but discard measurements in lower latitude regions (Ricker et al, 2014;Hendricks et al, 2016), and NASA produce a blended snow depth dataset by also including satellite, reanalysis, and airborne estimates (Kurtz et al, 2013). Each institution also processes the radar returns differently.…”

Section: Ice Thickness Distribution From Cryosat-2mentioning

confidence: 99%

New insight from CryoSat-2 sea ice thickness for sea ice modelling

Feltham¹,

Schröder²,

Tsamados³

2018

Preprint

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Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone 10 simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of 15 positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50% of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does 20 not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

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Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data

Cited by 256 publications

References 53 publications

Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice

Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice

Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

New insight from CryoSat-2 sea ice thickness for sea ice modelling

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