Observations of exponential wave attenuation in Antarctic sea ice during the PIPERS campaign

Kohout, Alison L.; Smith, Madison; Roach, Lettie A.; Williams, Guy D.; Montiel, Fabien; Williams, Michael

doi:10.1017/aog.2020.36

Cited by 44 publications

(76 citation statements)

References 15 publications

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“…A threshold is nevertheless necessary, which is however derived from a distribution that distinguish regions of low variability, which are more typical of the inner pack regions, from the more variable MIZ regions. This implies that MIZ conditions can also be found, in agreement with the observed penetration of waves deep into the pack ice (Kohout et al, 2014(Kohout et al, , 2015Stopa et al, 2018;Massom et al, 2018;Vichi et al, 2019;Kohout et al, 2020). The absolute probability value of exceeding a given threshold of variability depends on the chosen value of the indicator, but it does not modify the relative pattern between the regions.…”

Section: Discussionsupporting

confidence: 75%

A statistical definition of the Antarctic marginal ice zone

Vichi

2021

Preprint

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Abstract. The marginal ice zone (MIZ) is a transitional region between the open ocean and pack ice. This region is circumpolar in the Antarctic, with different sea ice types depending on the season and the sector of the Southern Ocean. The MIZ extent have traditionally been inferred from satellite-derived sea-ice concentration (SIC, one of the essential climate variables), using the 15–80 % range as indicative of sea ice with MIZ characteristics. This proxy has been proven effective in the Arctic, where there is a good correspondence between sea-ice type and sea-ice cover. It is less reliable in the Southern Ocean, where sea-ice type is less linked to the concentration value, since wave penetration and free drift conditions have been reported with 100 % cover. I propose an alternative definition of the MIZ that is based on statistical properties of the SIC and its spatial and temporal variability. The indicator is derived from the standard deviation of daily SIC anomalies, which is often employed in the climate sciences. The use of a monthly climatological mean as the baseline allows to capture changes due to both the seasonal advancement/retreat and the local weather-driven variability typical of less consolidated sea-ice conditions. This method has been tested on the available climate data records to derive maps of the MIZ distribution over the year. It reconciles the discordant seasonal extent estimates using the SIC threshold, which is now independent of the used algorithm. This indicator also allows to derive the climatological probability of exceeding a certain threshold of SIC variability, which can be used for ship navigation, design of observational networks and for testing the skills of sea-ice models in forecasting or climate mode.

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Section: Discussionsupporting

confidence: 75%

A statistical definition of the Antarctic marginal ice zone

Vichi

2021

Preprint

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“…The observed waves attenuated exponentially as they propagated into the pack ice, with attenuation rates dependent on sea-ice fraction. Additional analyses and greater details are given by Kohout and others (in press, this issue).…”

Section: Resultsmentioning

confidence: 99%

“…Fourteen wave buoys (WIIOS: Waves-In-Ice Observation Systems) were deployed in the marginal ice zone (Kohout and others, in press, this issue). These buoys measure wave motions every 15 min using a high-precision inertial motion unit.…”

Section: Approach and Methodsmentioning

confidence: 99%

Sea-ice production and air/ice/ocean/biogeochemistry interactions in the Ross Sea during the PIPERS 2017 autumn field campaign

et al. 2020

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The Ross Sea is known for showing the greatest sea-ice increase, as observed globally, particularly from 1979 to 2015. However, corresponding changes in sea-ice thickness and production in the Ross Sea are not known, nor how these changes have impacted water masses, carbon fluxes, biogeochemical processes and availability of micronutrients. The PIPERS project sought to address these questions during an autumn ship campaign in 2017 and two spring airborne campaigns in 2016 and 2017. PIPERS used a multidisciplinary approach of manned and autonomous platforms to study the coupled air/ice/ocean/biogeochemical interactions during autumn and related those to spring conditions. Unexpectedly, the Ross Sea experienced record low sea ice in spring 2016 and autumn 2017. The delayed ice advance in 2017 contributed to (1) increased ice production and export in coastal polynyas, (2) thinner snow and ice cover in the central pack, (3) lower sea-ice Chl-a burdens and differences in sympagic communities, (4) sustained ocean heat flux delaying ice thickening and (5) a melting, anomalously southward ice edge persisting into winter. Despite these impacts, airborne observations in spring 2017 suggest that winter ice production over the continental shelf was likely not anomalous.

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“…The empirical formula of IC4M4 (Kohout et al, 2014) assumes the attenuation is a function of only H s as shown in Supplementary Text 1. Recently, Kohout et al (2020) suggested attenuation considers wave period and ice concentration in addition to H s , based on the observation with < 0.5m ice thickness in the Antarctic Sea. In the future, implementation of this model in WW3 is expected to further improve the accuracy of simulation in thin ice thickness areas such as SO.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Wave-Ice Parameterization Models in WAVEWATCH III® Along the Coastal Area of the Sea of Okhotsk During Winter

Iwasaki

Otsuka

2021

Front. Mar. Sci.

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Ocean surface waves tend to be attenuated by interaction with sea ice. In this study, six sea ice models in the third-generation wave model WAVEWATCH III® (WW3) were used to estimate wave fields over the Sea of Okhotsk (SO). The significant wave height (Hs) and mean wave period (Tm) derived from the models were evaluated with open ocean and ice-covered conditions, using SO coastal area buoy observations. The models were validated for a period of 3 years, 2008–2010. Additionally, the impact of sea ice on wave fields was demonstrated by model experiments with and without sea ice. In the open ocean condition, the root-mean square error (RMSE) and correlation coefficient for hourly Hs are 0.3 m and 0.92, and for hourly Tm 0.97 s and 0.8. In contrast, for the ice-covered condition, the averaged RMSE and correlation coefficient from all models are 0.44 m (1.6 s) and 0.8 (0.6) for Hs (Tm), respectively. Therefore, except for the bias, the accuracy of model results for the ice-covered condition is lower than for the open water condition. However, there is a significant difference between the six sea ice models. For Hs, the empirical formula whereby attenuation depends on the frequency relatively agrees with the buoy observation. For Tm, the empirical formula that is a function of Hs is better than those of other simulations. In addition, the simulations with sea ice drastically improved the wave field bias in coastal areas compared to the simulations without sea ice. Moreover, sea ice changed the monthly Hs (Tm) by more than 1 m (3 s) in the northwestern part of the SO, which has a high ice concentration.

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Observations of exponential wave attenuation in Antarctic sea ice during the PIPERS campaign

Cited by 44 publications

References 15 publications

A statistical definition of the Antarctic marginal ice zone

A statistical definition of the Antarctic marginal ice zone

Sea-ice production and air/ice/ocean/biogeochemistry interactions in the Ross Sea during the PIPERS 2017 autumn field campaign

Evaluation of Wave-Ice Parameterization Models in WAVEWATCH III® Along the Coastal Area of the Sea of Okhotsk During Winter

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