The Antarctic sea ice cover from ICESat-2 and CryoSat-2: freeboard, snow depth, and ice thickness

Kacimi, Sahra; Kwok, R.

doi:10.5194/tc-14-4453-2020

Cited by 65 publications

(65 citation statements)

References 47 publications

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“…In the Antarctic, the October snow thickness pattern and magnitudes are reasonable based on the limited observations (e.g. Webster et al, 2018;Kacimi and Kwok, 2020). The snow thickness pattern closely matches that of the ice thickness which is in part because thicker ice is less prone to snow-ice formation and so can maintain a thicker snowpack.…”

Section: Control Sea Ice Climatesupporting

confidence: 54%

The influence of snow on sea ice as assessed from simulations of CESM2

Holland¹,

Clemens‐Sewall²,

Landrum³

et al. 2021

Preprint

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Abstract. We assess the influence of snow on sea ice in experiments using the Community Earth System Model, version 2 for a pre-industrial and a 2xCO2 climate state. In the pre-industrial climate, we find that increasing simulated snow accumulation on sea ice results in thicker sea ice and a cooler climate in both hemispheres. The sea ice mass budget response differs fundamentally between the two hemispheres. In the Arctic, increasing snow results in a decrease in both sea ice growth and sea ice melt due to the snow’s impact on conductive heat transfer and albedo, respectively. This leads to a reduced amplitude in the annual cycle of ice thickness. In the Antarctic, with increasing snow, ice growth increases due to snow-ice formation and is balanced by larger basal ice melt. In the warmer 2xCO2 climate, the Arctic sea ice sensitivity to snow depth is small and reduced relative to that of the pre-industrial climate. Whereas, in the Antarctic, the sensitivity to snow on sea ice in the 2xCO2 climate is qualitatively similar to the sensitivity in the pre-industrial climate. These results underscore the importance of accurately representing snow accumulation on sea ice in coupled earth system models, due to its impact on a number of competing processes and feedbacks.

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Section: Control Sea Ice Climatesupporting

confidence: 54%

The influence of snow on sea ice as assessed from simulations of CESM2

Holland¹,

Clemens‐Sewall²,

Landrum³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…1e) closely matches that of the ice thickness, which is in part because thicker ice is less prone to snow-ice formation and so can maintain a thicker snowpack. While observations are limited in the Antarctic, the model appears reasonable (e.g., Webster et al, 2018;Kacimi and Kwok, 2020; Fig. S2).…”

Section: Control Sea Ice Climatementioning

confidence: 75%

“…Additionally, ongoing work is needed to better understand the processes driving snow redistribution and metamorphosis, among other factors. Analysis of field data, such as from the year-long MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate; Nicolaus et al, 2021) drift campaign, and remotely sensed information from campaigns like Operation IceBridge (e.g., Koenig et al, 2010) and ICESat-2 and CryoSat-2 (e.g., Kwok et al, 2020) will help to better constrain factors controlling snow distributions and properties and can enable improved representation of those processes into climate models. This will ultimately improve predictions of these important aspects of the changing polar regions.…”

Section: Discussionmentioning

confidence: 99%

The influence of snow on sea ice as assessed from simulations of CESM2

et al. 2021

View full text Add to dashboard Cite

Abstract. We assess the influence of snow on sea ice in experiments using the Community Earth System Model version 2 for a preindustrial and a 2xCO2 climate state. In the preindustrial climate, we find that increasing simulated snow accumulation on sea ice results in thicker sea ice and a cooler climate in both hemispheres. The sea ice mass budget response differs fundamentally between the two hemispheres. In the Arctic, increasing snow results in a decrease in both congelation sea ice growth and surface sea ice melt due to the snow's impact on conductive heat transfer and albedo, respectively. These factors dominate in regions of perennial ice but have a smaller influence in seasonal ice areas. Overall, the mass budget changes lead to a reduced amplitude in the annual cycle of ice thickness. In the Antarctic, with increasing snow, ice growth increases due to snow–ice formation and is balanced by larger basal ice melt, which primarily occurs in regions of seasonal ice. In a warmer 2xCO2 climate, the Arctic sea ice sensitivity to snow depth is small and reduced relative to that of the preindustrial climate. In contrast, in the Antarctic, the sensitivity to snow on sea ice in the 2xCO2 climate is qualitatively similar to the sensitivity in the preindustrial climate. These results underscore the importance of accurately representing snow accumulation on sea ice in coupled Earth system models due to its impact on a number of competing processes and feedbacks that affect the melt and growth of sea ice.

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“…ATLAS has six beams of three pairs of strong and weak beams with 11 m footprints spaced by 0.7 m [32], providing a better spatial resolution compared to the previous ICESat mission [33]. Various studies have utilized ICESat-2 for the characterization of sea ice freeboard and thickness in the polar regions, taking advantage of this outstanding spatial resolution [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Weekly Mapping of Sea Ice Freeboard in the Ross Sea from ICESat-2

et al. 2021

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NASA’s ICESat-2 has been providing sea ice freeboard measurements across the polar regions since October 2018. In spite of the outstanding spatial resolution and precision of ICESat-2, the spatial sparsity of the data can be a critical issue for sea ice monitoring. This study employs a geostatistical approach (i.e., ordinary kriging) to characterize the spatial autocorrelation of the ICESat-2 freeboard measurements (ATL10) to estimate weekly freeboard variations in 2019 for the entire Ross Sea area, including where ICESat-2 tracks are not directly available. Three variogram models (exponential, Gaussian, and spherical) are compared in this study. According to the cross-validation results, the kriging-estimated freeboards show correlation coefficients of 0.56–0.57, root mean square error (RMSE) of ~0.12 m, and mean absolute error (MAE) of ~0.07 m with the actual ATL10 freeboard measurements. In addition, the estimated errors of the kriging interpolation are low in autumn and high in winter to spring, and low in southern regions and high in northern regions of the Ross Sea. The effective ranges of the variograms are 5–10 km and the results from the three variogram models do not show significant differences with each other. The southwest (SW) sector of the Ross Sea shows low and consistent freeboard over the entire year because of the frequent opening of wide polynya areas generating new ice in this sector. However, the southeast (SE) sector shows large variations in freeboard, which demonstrates the advection of thick multiyear ice from the Amundsen Sea into the Ross Sea. Thus, this kriging-based interpolation of ICESat-2 freeboard can be used in the future to estimate accurate sea ice production over the Ross Sea by incorporating other remote sensing data.

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The Antarctic sea ice cover from ICESat-2 and CryoSat-2: freeboard, snow depth, and ice thickness

Cited by 65 publications

References 47 publications

The influence of snow on sea ice as assessed from simulations of CESM2

The influence of snow on sea ice as assessed from simulations of CESM2

The influence of snow on sea ice as assessed from simulations of CESM2

Weekly Mapping of Sea Ice Freeboard in the Ross Sea from ICESat-2

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