The Bering Strait oceanic heat transport influences seasonal sea ice retreat and advance in the Chukchi Sea. Monitored since 1990, it depends on water temperature and factors controlling the volume transport, assumed to be local winds in the strait and an oceanic pressure difference between the Pacific and Arctic oceans (the “pressure head”). Recent work suggests that variability in the pressure head, especially during summer, relates to the strength of the zonal wind in the East Siberian Sea that raises or drops sea surface height in this area via Ekman transport. We confirm that westward winds in the East Siberian Sea relate to a broader central Arctic pattern of high sea level pressure and note that anticyclonic winds over the central Arctic Ocean also favor low September sea ice extent for the Arctic as a whole by promoting ice convergence and positive temperature anomalies. Month‐to‐month persistence in the volume transport and atmospheric circulation patterns is low, but the period 1980–2017 had a significant summertime (June–August) trend toward higher sea level pressure over the central Arctic Ocean, favoring increased transports. Some recent large heat transports are associated with high water temperatures, consistent with persistence of open water in the Chukchi Sea into winter and early ice retreat in spring. The highest heat transport recorded, October 2016, resulted from high water temperatures and ideal wind conditions yielding a record‐high volume transport. November and December 2005, the only months with southward volume (and thus heat) transports, were associated with southward winds in the strait.
- Award ID(s):
- 1927785
- NSF-PAR ID:
- 10324860
- Date Published:
- Journal Name:
- Journal of Climate
- Volume:
- 35
- Issue:
- 9
- ISSN:
- 0894-8755
- Page Range / eLocation ID:
- 2723 to 2740
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract This study uses observational and reanalysis datasets in 1980–2016 to show a close connection between a boreal autumn sea ice dipole in the Arctic Pacific sector and sea ice anomalies in the Barents Sea (BS) during the following spring. The September–October Arctic Pacific sea ice dipole variations are highly correlated with the subsequent April–May BS sea ice variations ( r = 0.71). The strong connection between the regional sea ice variabilities across the Arctic uncovers a new source of predictability for spring BS sea ice prediction at 7-month lead time. A cross-validated linear regression prediction model using the Arctic Pacific sea ice dipole with 7-month lead time is demonstrated to have significant prediction skills with 0.54–0.85 anomaly correlation coefficients. The autumn sea ice dipole, manifested as sea ice retreat in the Beaufort and Chukchi Seas and expansion in the East Siberian and Laptev Seas, is primarily forced by preceding atmospheric shortwave anomalies from late spring to early autumn. The spring BS sea ice increases are mostly driven by an ocean-to-sea ice heat flux reduction in preceding months, associated with reduced horizontal ocean heat transport into the BS. The dynamical linkage between the two regional sea ice anomalies is suggested to involve positive stratospheric polar cap anomalies during autumn and winter, with its center slowly moving toward Greenland. The migration of the stratospheric anomalies is followed in midwinter by a negative North Atlantic Oscillation–like pattern in the troposphere, leading to reduced ocean heat transport into the BS and sea ice extent increase.more » « less
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Abstract A regional data‐constrained coupled ocean‐sea ice general circulation model and its adjoint are used to investigate mechanisms controlling the volume transport variability through Bering Strait during 2002 to 2013. Comprehensive time‐resolved sensitivity maps of Bering Strait transport to atmospheric forcing can be accurately computed with the adjoint along the forward model trajectory to identify spatial and temporal scales most relevant to the strait's transport variability. The simulated Bering Strait transport anomaly is found to be controlled primarily by the wind stress on short time scales of order 1 month. Spatial decomposition indicates that on monthly time scales winds over the Bering and the combined Chukchi and East Siberian Seas are the most significant drivers. Continental shelf waves and coastally trapped waves are suggested as the dominant mechanisms for propagating information from the far field to the strait. In years with transport extrema, eastward wind stress anomalies in the Arctic sector are found to be the dominant control, with correlation coefficient of 0.94. This implies that atmospheric variability over the Arctic plays a substantial role in determining Bering Strait flow variability. The near‐linear response of the transport anomaly to wind stress allows for predictive skill at interannual time scales, thus potentially enabling skillful prediction of changes at this important Pacific‐Arctic gateway, provided that accurate measurements of surface winds in the Arctic can be obtained. The novelty of this work is the use of space and time‐resolved adjoint‐based sensitivity maps, which enable detailed dynamical, that is, causal attribution of the impacts of different forcings.
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Doi, Hideyuki (Ed.)A large volume of freshwater is incorporated in the relatively fresh (salinity ~32–33) Pacific Ocean waters that are transported north through the Bering Strait relative to deep Atlantic salinity in the Arctic Ocean (salinity ~34.8). These freshened waters help maintain the halocline that separates cold Arctic surface waters from warmer Arctic Ocean waters at depth. The stable oxygen isotope composition of the Bering Sea contribution to the upper Arctic Ocean halocline was established as early as the late 1980’s as having a δ 18 O V - SMOW value of approximately -1.1‰. More recent data indicates a shift to an isotopic composition that is more depleted in 18 O (mean δ 18 O value ~-1.5‰). This shift is supported by a data synthesis of >1400 water samples (salinity from 32.5 to 33.5) from the northern Bering and Chukchi seas, from the years 1987–2020, which show significant year-to-year, seasonal and regional variability. This change in the oxygen isotope composition of water in the upper halocline is consistent with observations of added freshwater in the Canada Basin, and mooring-based estimates of increased freshwater inflows through Bering Strait. Here, we use this isotopic time-series as an independent means of estimating freshwater flux changes through the Bering Strait. We employed a simple end-member mixing model that requires that the volume of freshwater (including runoff and other meteoric water, but not sea ice melt) flowing through Bering Strait has increased by ~40% over the past two decades to account for a change in the isotopic composition of the 33.1 salinity water from a δ 18 O value of approximately -1.1‰ to a mean of -1.5‰. This freshwater flux change is comparable with independent published measurements made from mooring arrays in the Bering Strait (freshwater fluxes rising from 2000–2500 km 3 in 2001 to 3000–3500 km 3 in 2011).more » « less
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Abstract We utilize statistically modeled ocean heat transports through the Bering Strait along with additional predictors to create skillful predictions of sea ice retreat and advance dates in the Chukchi Sea. Interannual variability of June and September oceanic heat transports through the strait can be captured using modeled Ekman transports, surface winds, and sea surface temperatures. At one‐month leads, our models can explain 41% and 79% of sea ice retreat and advance date variance with root mean squared errors (RMSEs) of 10.2 and 12.8 days respectively. At 4‐month leads, our models can respectively capture 37% and 73% of sea ice retreat and advance date variance with RMSEs of 8.5 and 14.6 days. These findings offer a path toward operational forecasts of sea ice retreat and advance dates in the Chukchi Sea in the absence of direct, readily available observations of heat transports in the strait.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1175/JCLI-D-21-0425.1
