Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover

Mallett, Robbie; Stroeve, Julienne; Landy, Jack; Willatt, Rosemary; Nandan, Vishnu; Liston, Glen E.

doi:10.5194/tc-15-2429-2021

Cited by 32 publications

(28 citation statements)

References 60 publications

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“…Interannual changes in snow depth provide significant regional/monthly impacts on our thickness resultsmitigating some (or in some cases all) of the impact from interannual differences in Arctic winter freeboards observed by ICESat-2. Our results provide further evidence of the importance of accurate snow representation when assessing interannual variability in winter Arctic sea ice thickness from satellite altimetry (Bunzel et al, 2018;Mallett et al, 2021). Specific regional thickness anomalies, e.g.…”

Section: Discussionsupporting

confidence: 57%

“…However, residual height biases of several centimetres are still observed between the beams as of Release 005 (updated from the analysis shown in Bagnardi et al, 2021, not shown) development. More sophisticated sea surface interpolation methods should also be explored (Landy et al, 2021). Additional algorithm development efforts are on-going towards the next data release (Release 006), expected sometime later in 2022.…”

Section: Future Workmentioning

confidence: 99%

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Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection

Petty

Keeney

Cabaj

et al. 2022

Preprint

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Abstract. Reliable basin-scale estimates of sea ice thickness are urgently needed to improve our understanding of recent changes and future projections of polar climate. Data collected by NASA’s ICESat-2 mission have provided new, high-resolution, estimates of sea ice freeboard across both hemispheres since data collection started in October 2018. These data have been used in recent work to produce estimates of winter Arctic sea ice thickness using snow loading estimates from the NASA Eulerian Snow On Sea Ice Model (NESOSIM). Here we provide an impact assessment of upgrades to both the ICESat-2 freeboard data (ATL10) and NESOSIM snow loading on estimates of winter Arctic sea ice thickness. Misclassified leads were removed from the freeboard algorithm in the third release (rel003) of ICESat-2 freeboard data, which increased freeboards in January and April 2019, and increased the fraction of low freeboards in November 2018, compared to rel002. These changes improved comparisons of sea ice thickness (lower mean biases and standard deviations, higher correlations) with monthly gridded thickness estimates produced from ESA’s CryoSat-2 (using the same input snow and ice density assumptions). Later releases (rel004 and rel005) of ICESat-2 ATL10 freeboards result in less significant changes in the freeboard distributions and thus thickness. The latest version of NESOSIM (version 1.1), forced by CloudSat-scaled ERA5 snowfall, has been re-calibrated using snow depth estimates obtained by NASA’s Operation IceBridge airborne mission. The upgrade from NESOSIM v1.0 to v1.1 results in only small changes in snow depth which have a less significant impact on thickness compared to the rel002 to rel003 freeboard changes. Finally, we present our updated monthly gridded winter Arctic sea ice thickness dataset and highlight key changes over the past three winter seasons of data collection (November 2018–April 2021). Strong differences in total winter Arctic thickness across the three winters are observed, linked to clear differences in the multiyear ice thickness at the start of each winter. Interannual changes in snow depth provide significant impacts on our thickness results on regional and seasonal scales. Our analysis of recent winter Arctic sea ice thickness variability is provided online in a novel Jupyter Book format to increase transparency and user engagement with our derived gridded monthly thickness dataset.

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Section: Discussionsupporting

confidence: 57%

Section: Future Workmentioning

confidence: 99%

Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection

Petty

Keeney

Cabaj

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Instead, here the FOAM modelled snow depth is used. Modelled snow depth has a greater spatial and temporal variability than can be obtained from a climatology, as demonstrated by Mallett et al (2021) and illustrated in Fig. 1.…”

Section: Conversion From Freeboard To Thicknessmentioning

confidence: 89%

Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)

et al. 2022

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Abstract. The feasibility of assimilating sea ice thickness (SIT) observations derived from CryoSat-2 along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var assimilation scheme, NEMOVAR, within the Met Office's global, coupled ocean–sea-ice model, Forecast Ocean Assimilation Model (FOAM). The CryoSat-2 Arctic freeboard measurements are produced by the Centre for Polar Observation and Modelling (CPOM) and are converted to SIT within FOAM using modelled snow depth. This is the first time along-track observations of SIT have been used in this way, with other centres assimilating gridded and temporally averaged observations. The assimilation leads to improvements in the SIT analysis and forecast fields generated by FOAM, particularly in the Canadian Arctic. Arctic-wide observation-minus-background assimilation statistics for 2015–2017 show improvements of 0.75 m mean difference and 0.41 m root-mean-square difference (RMSD) in the freeze-up period and 0.46 m mean difference and 0.33 m RMSD in the ice break-up period. Validation of the SIT analysis against independent springtime in situ SIT observations from NASA Operation IceBridge (OIB) shows improvement in the SIT analysis of 0.61 m mean difference (0.42 m RMSD) compared to a control without SIT assimilation. Similar improvements are seen in the FOAM 5 d SIT forecast. Validation of the SIT assimilation with independent Beaufort Gyre Exploration Project (BGEP) sea ice draft observations does not show an improvement, since the assimilated CryoSat-2 observations compare similarly to the model without assimilation in this region. Comparison with airborne electromagnetic induction (Air-EM) combined measurements of SIT and snow depth shows poorer results for the assimilation compared to the control, despite covering similar locations to the OIB and BGEP datasets. This may be evidence of sampling uncertainty in the matchups with the Air-EM validation dataset, owing to the limited number of observations available over the time period of interest. This may also be evidence of noise in the SIT analysis or uncertainties in the modelled snow depth, in the assimilated SIT observations, or in the data used for validation. The SIT analysis could be improved by upgrading the observation uncertainties used in the assimilation. Despite the lack of CryoSat-2 SIT observations available for assimilation over the summer due to the detrimental effect of melt ponds on retrievals, it is shown that the model is able to retain improvements to the SIT field throughout the summer months due to prior, wintertime SIT assimilation. This also results in regional improvements to the July modelled sea ice concentration (SIC) of 5 % RMSD in the European sector, due to slower melt of the thicker sea ice.

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“…6). The warming induced strong temperature gradients and increased vapour fluxes in the snow, which can cause stronger snow metamorphism and significantly change the snow permittivity already at above −5 • C snow temperatures (Mätzler, 1987). Also, liquid water content can increase at temperatures slightly below 0 • C, and small liquid water fractions of, e.g., 2 % strongly change the microwave loss in the snow (Hallikainen, 1986).…”

Section: Sea Ice Concentrationmentioning

confidence: 99%

MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years

et al. 2021

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Abstract. We combine satellite data products to provide a first and general overview of the physical sea ice conditions along the drift of the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition and a comparison with previous years (2005–2006 to 2018–2019). We find that the MOSAiC drift was around 20 % faster than the climatological mean drift, as a consequence of large-scale low-pressure anomalies prevailing around the Barents–Kara–Laptev sea region between January and March. In winter (October–April), satellite observations show that the sea ice in the vicinity of the Central Observatory (CO; 50 km radius) was rather thin compared to the previous years along the same trajectory. Unlike ice thickness, satellite-derived sea ice concentration, lead frequency and snow thickness during winter months were close to the long-term mean with little variability. With the onset of spring and decreasing distance to the Fram Strait, variability in ice concentration and lead activity increased. In addition, the frequency and strength of deformation events (divergence, convergence and shear) were higher during summer than during winter. Overall, we find that sea ice conditions observed within 5 km distance of the CO are representative for the wider (50 and 100 km) surroundings. An exception is the ice thickness; here we find that sea ice within 50 km radius of the CO was thinner than sea ice within a 100 km radius by a small but consistent factor (4 %) for successive monthly averages. Moreover, satellite acquisitions indicate that the formation of large melt ponds began earlier on the MOSAiC floe than on neighbouring floes.

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Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover

Cited by 32 publications

References 60 publications

Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection

Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection

Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)

MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years

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