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
Revised Oct 2017
Published December 2017

Citation: Peralta-Ferriz, C., & Woodgate, R. A., 2017, 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. Geophysical Research Letters, 44, 11,472-11,481. doi:10.1002/2017GL075179




HIGHLIGHTS
Ocean mass data show monthly variability in the Bering Strait throughflow to be primarily driven from the Arctic, not the Pacific.
  ~2/3rds of 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 and in situ mooring data from the Bering Strait from 2002-2016, 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 ~2/3rds of the Bering Strait variability.  In winter, local wind variability dominates the total flow, but the pressure-head term, while still correlated with the ESS-dominated sea-level pattern, is now more strongly related to Bering Sea shelf sea-level variability.

© 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) Correlation coefficients (R) between monthly mean, year-round GRACE ocean bottom pressure anomalies (OBP) data and (i) standardized (i.e., scaled by its standard deviation, std) total Bering Strait northward velocity (vvel); (ii) standardized pressure-head driven Bering Strait northward velocity (PHterm); and (iii) time-series of EOF mode 1 (PC1) shown in (b). Black box marks the region used for EOF computation.
(b) First EOF of the year-round OBP data, showing (left) the EOF pattern in cm/std and the percentage of OBP variance explained by the mode; and (right) time-series for the corresponding principal component, here PC1 (color), monthly vvel (black line) and PHterm (gray line), each standardized. Correlation coefficients (R) are significant above the 95% confidence level, unless marked otherwise (ns).
 (c) As per (b), but for second EOF.
(d) As per (a) but for correlations with SLP (instead of OBP).
 (e) As per (a), but for correlations with (i) northward winds (Vwinds); (ii) and (iii) eastward winds (Uwinds) instead of OBP.
For all maps, only correlations significant at 95% or above are shown, and coastlines and isobaths (200m, 300m, 400m and 500m) are from GSHHG (http://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html).

Figure 2. As per Figure 1, but for summer (June, July, August) data. (Note Figure (e)(ii) shows correlations with eastward (Uwind), not northward wind (Vwind)). The 2nd EOF is not significant and is not shown.

Figure 3. As per Figure 1, but for winter (December, January, February) data. Panels (a), (d) and (e) show correlations for both PC1 (iii) and PC2 (iv).


© 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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