Processes driving sea ice variability in the Bering Sea in an eddying ocean/sea ice model: anomalies from the mean seasonal cycle

Li, Linghan; Miller, Arthur J.; McClean, Julie L.; Eisenman, Ian; Hendershott, Myrl C.

doi:10.1007/s10236-014-0769-7

Cited by 18 publications

(19 citation statements)

References 54 publications

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“…5). This is consistent with previous studies indicating that anomalous southeasterlies or southerlies associated with the westward shift of the Aleutian Low contributed to the increase of BTF transport (Danielson et al 2014) and the decrease of sea ice in the Bering Sea (Rodionov et al 2007;Zhang et al 2010;Li et al 2014). The sea surface density in the northwestern Bering Sea was correlated with BTF transport (Fig.…”

Section: Correlation Of Sss With Btf Transportsupporting

confidence: 81%

Relations between salinity in the northwestern Bering Sea, the Bering Strait throughflow and sea surface height in the Arctic Ocean

et al. 2017

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The correlation between transport via the Bering Strait throughflow (BTF) and sea surface salinity (SSS) in the Bering Sea has been examined mainly using an atmosphere-ocean-ice coupled climate model that has an eddy-permitting ocean component. The SSS anomaly in the northwestern Bering Sea is high from winter to spring when the BTF transport anomaly is large in the cold season. Similar features can be seen in an observation dataset and two kinds of ocean data assimilation product. BTF transport is strongly correlated with sea surface height (SSH) in the northeastern Bering Sea, the southwestern Chukchi Sea and the East Siberian Sea. The SSH along the Russian coast in the Arctic Ocean is uncorrelated with the SSH in the Bering Sea, the meaning being that the Arctic SSH affects the BTF and the SSS independently of the SSH in the Bering Sea. The low SSH along the Siberian coast is correlated with easterly wind anomalies over the Laptev Sea and north of the New Siberian Islands. The relationship between the low Siberian-coast SSH and the high SSS in the Bering Sea, however, is not confirmed in 10 years of satellite-derived SSH. Mixed-layer salt budget analysis has revealed that the high SSS anomalies are mainly caused by the increases of horizontal advective salt convergence north of 62.5°N, and by the decreases of sea-ice melting south of 62.5°N, through the strengthening of the near-surface northeastward currents. In the warm season, these two factors fade and the salinization disappears.

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Section: Correlation Of Sss With Btf Transportsupporting

confidence: 81%

Relations between salinity in the northwestern Bering Sea, the Bering Strait throughflow and sea surface height in the Arctic Ocean

et al. 2017

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“…Further work is needed to evaluate the time evolution of the anomalies from the mean seasonal cycle analyzed here, including the impacts of anomalous winds, surface energy fluxes, fluctuating currents and mesoscale eddy effects. This is the topic of a followup study (Li et al, 2014). Fig.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…This can then form a baseline for determining the controls on the interannual changes in sea ice (see Li et al, 2014) and those associated with global warming on long time scales.…”

Section: Introductionmentioning

confidence: 99%

Processes driving sea ice variability in the Bering Sea in an eddying ocean/sea ice model: Mean seasonal cycle

McClean

Miller

et al. 2014

Ocean Modelling

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a b s t r a c tThe seasonal cycle of sea ice variability in the Bering Sea, together with the thermodynamic and dynamic processes that control it, are examined in a fine resolution (1/10°) global coupled ocean/sea-ice model configured in the Community Earth System Model (CESM) framework. The ocean/sea-ice model consists of the Los Alamos National Laboratory Parallel Ocean Program (POP) and the Los Alamos Sea Ice Model (CICE). The model was forced with time-varying reanalysis atmospheric forcing for the time period [1970][1971][1972][1973][1974][1975][1976][1977][1978][1979][1980][1981][1982][1983][1984][1985][1986][1987][1988][1989]. This study focuses on the time period 1980-1989. The simulated seasonal-mean fields of sea ice concentration strongly resemble satellite-derived observations, as quantified by root-meansquare errors and pattern correlation coefficients. The sea ice energy budget reveals that the seasonal thermodynamic ice volume changes are dominated by the surface energy flux between the atmosphere and the ice in the northern region and by heat flux from the ocean to the ice along the southern ice edge, especially on the western side. The sea ice force balance analysis shows that sea ice motion is largely associated with wind stress. The force due to divergence of the internal ice stress tensor is large near the land boundaries in the north, and it is small in the central and southern ice-covered region. During winter, which dominates the annual mean, it is found that the simulated sea ice was mainly formed in the northern Bering Sea, with the maximum ice growth rate occurring along the coast due to cold air from northerly winds and ice motion away from the coast. South of St Lawrence Island, winds drive the model sea ice southwestward from the north to the southwestern part of the ice-covered region. Along the ice edge in the western Bering Sea, model sea ice is melted by warm ocean water, which is carried by the simulated Bering Slope Current flowing to the northwest, resulting in the S-shaped asymmetric ice edge. In spring and fall, similar thermodynamic and dynamic patterns occur in the model, but with typically smaller magnitudes and with season-specific geographical and directional differences.

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“…Our model study is focused on summer 2009 in the middle of a cool phase of the decadal pattern on the Bering shelf (Stabeno et al, ). Several numerical studies of the eastern Bering Sea have been undertaken (e.g., Clement et al, ; Clement Kinney et al, ; Clement Kinney & Maslowski, ; Danielson et al, ; Ezer & Oey, ; Hu & Wang, ; Li et al, ; Panteleev et al, ; Zhang et al, ), with focus on the mean circulation and seasonal and interannual variability in the volume transports, heat fluxes, ice extent, ice concentration, and ice mass. However, none of these attempted to model the entire eastern Bering Sea region at a resolution below 4 km, nor did they focus on the ANSC structure or variability at the level of the subsurface temperature maximum.…”

Section: Introductionmentioning

confidence: 99%

Connectivity of the Aleutian North Slope Current and Bering Sea Basin Waters at the Level of the Subsurface Temperature Maximum: A Modeling Study

2018

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A 2‐km resolution ocean circulation model for the Eastern Bering Sea is utilized to understand whether and where slope‐interior exchange along the path of the Aleutian North Slope Current helps maintain the subsurface temperature maximum on the 26.8‐kg/m3 isopycnal surface in the Bering Sea Basin, at approximately 300–400‐m depths. A simulation for June–October 2009 shows warmer water advecting westward on this isopycnal along the southern slope of the Aleutian Islands, through Amchitka Pass (180°W), and then eastward along the northern slope as the season progresses, reaching the Bering Canyon. However, Lagrangian particle tracking on this surface shows that very few floats released over the slope south of Amukta Pass (172°W) will end up in the Aleutian North Slope Current. The warming in the Aleutian North Slope Current at the depth of the 26.8‐kg/m3 isopycnal can be explained as resulting from a tidally enhanced downward vertical turbulent flux of warmer water which has been advected eastward in faster flowing waters at shallower depths. This hypothesis is confirmed by the heat equation term balance analysis and Lagrangian particle tracking on the 26.8‐kg/m3 surface and a shallower, 26.4‐kg/m3 surface. The model shows that the warm slope current separates into the basin near several topographical features, particularly at 178°W (just east of Amchitka Pass) and 174°W (Atka Island). North of Amukta Pass, the warmer pattern is strongly modulated at the tidal and fortnightly time scales and is associated with mixing in and transport through the pass.

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Processes driving sea ice variability in the Bering Sea in an eddying ocean/sea ice model: anomalies from the mean seasonal cycle

Abstract: A fine-resolution (1/10°) ocean/sea ice model configured in the Community Earth System Model framework is compared with observations and studied to determine the basin-scale and local balances controlling the variability of sea ice anomalies from the mean seasonal cycle in the Bering

Cited by 18 publications

References 54 publications

Relations between salinity in the northwestern Bering Sea, the Bering Strait throughflow and sea surface height in the Arctic Ocean

Relations between salinity in the northwestern Bering Sea, the Bering Strait throughflow and sea surface height in the Arctic Ocean

Processes driving sea ice variability in the Bering Sea in an eddying ocean/sea ice model: Mean seasonal cycle

Connectivity of the Aleutian North Slope Current and Bering Sea Basin Waters at the Level of the Subsurface Temperature Maximum: A Modeling Study

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