Part of Fig 1

The dominant role of the East Siberian Sea in driving the oceanic flow through the Bering Strait - conclusions from GRACE ocean mass satellite data and in situ mooring observations between 2002 and 2016

Cecilia Peralta-Ferriz and Rebecca A Woodgate

Applied Physics Laboratory, University of Washington

Submitted to Geophysical Research Letters, August 2017




HIGHLIGHTS
Ocean mass data show monthly variability in the Bering Strait throughflow to be primarily driven from the Arctic, not the Pacific.
70% of the summer flow variability is driven by oceanic sea-level changes in the East Siberian Sea, associated with westward Arctic winds.
In winter, northward Bering Strait winds and North Pacific cyclonic winds (raising sea-level over the Bering Sea Shelf) also contribute.

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Please contact Cecilia Peralta-Ferriz (ferriz@apl.washington.edu) for use of any of this material

Abstract
It is typically stated that the Pacific-to-Arctic oceanic flow through the Bering Strait (important for Arctic heat, freshwater, and nutrient budgets) is driven by local wind and a (poorly defined) far-field "pressure-head" forcing, related to sea-surface-height differences between the Pacific and the Arctic. Using monthly, Arctic-wide, ocean bottom pressure satellite data from 2002-2016 and in situ mooring data from the Bering Strait, we discover the spatial structure of this pressure-head forcing, finding that the Bering Strait throughflow variability is dominantly driven from the Arctic, specifically by sea-level change in the East Siberian Sea, in turn related to westward winds along the Arctic coasts. In the (comparatively calm) summer, this explains 70% of the Bering Strait variability, while in winter Bering Strait winds and higher sea-levels on the Bering Sea Shelf are also important. Results suggest a key Arctic role in recently observed increases in the Pacific input to the Arctic.

Polar Science Center, University of Washington, 2017

Figures
  For details, see paper

Figure 1
Fig 1

Figure 2
Fig 2

Figure 3
Fig 3
Figure 1. (a) Results for mode 1 of the EOF analysis performed with the year-round GRACE ocean bottom pressure anomalies (OBP) data, showing (left) the EOF pattern in cm/STD (color-contours) and the percentage of variance explained by the mode 1 (% on the map); and (right) time-series for the corresponding principal component, PC1 (in color); monthly mean total Bering Strait northward velocity (vvel - black line) and the pressure-head part of the Bering Strait northward velocity (PHterm - gray line), all normalized by their standard deviations (std). Correlation coefficients (R) are significant above the 95% confidence level, unless marked otherwise (ns).
(b)
As per (a), but for EOF mode 2.
(c) Maps of regression coefficients between year-round atmospheric variables (color contours - atmospheric sea-level pressure (SLP) in 0.5hPa/std intervals; scaled arrows - 925mb height winds) and (i) the normalized monthly mean total Bering Strait northward velocity (vvel); (ii) the normalized pressure-head part of the Bering Strait northward velocity (PHterm); and (iii) PC1.
(d) Maps of correlation coefficients (R) (color contours at 0.025 intervals) between year-round SLP and (i) the normalized monthly mean total Bering Strait northward velocity (vvel); (ii) the normalized pressure-head part of the Bering Strait northward velocity (PHterm); and (iii) PC1.
(e) As per (d), but for (i) northward winds (Vwinds); (ii) and (iii) eastward winds (Uwinds) instead of SLP. Throughout the figure, coastlines and isobaths (200m, 300m, 400m and 500m) are from GSHHG (Global Self-consistent, Hierarchical, High resolution Geography Database, http://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html). Only regression and correlation coefficients significant above the 95% confidence level are shown in the maps.
Figure 2. As per Figure 1, but using only summer (June, July, August) data. Note the 2nd EOF is not significant and so is not shown.
Figure 3. As per Figure 1, but using only winter (December, January, February) data.



Polar Science Center, University of Washington, 2017

We gratefully acknowledge financial support for this work from the National Science Foundation (NSF) and NASA.

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