1. On the Flow Through Bering Strait:
A Synthesis of Model Results and Observations
Jaclyn Clement Kinney1, Wieslaw Maslowski1, Mike Steele2, Yevgeny Aksenov3, Beverly de Cuevas3, Jaromir Jakacki4, An Nguyen5, Robert Osinski1, Rebecca Woodgate2, Jinlun Zhang2
1Naval Postgraduate School, Monterey, CA
2Applied Physics Laboratory, Univ. Washington, Seattle, WA
3National Oceanography Centre, Southampton, UK
4Institute of Oceanology, Polish Academy of Sciences, Sopot, Poland
5Jet Propulsion Laboratory, Pasadena, CA
Sponsored by NSF/AOMIP
IPY Oslo Science Conference, Norway
fix numbers

2. Importance of Bering Strait:
only Pacific connection to the Chukchi Sea and greater Arctic Ocean
political boundary restricts access
challenge to observationalists and modelers
important for maintenance of Arctic Ocean halocline, ice edge position, transports through Fram Strait, and freshwater budgets in the Nordic Seas
MISR visible image
Bering Strait is 85 km wide, m deep
Connects the Bering Sea and Pacific water with the Chukchi Sea and the greater Arctic Ocean
Political boundary between the US and Russia restricts access
Ice floes with deep drafts are a threat to moored instruments
Challenge to modelers because it is so narrow and shallow
Many global models either have a closed Bering Strait or instead us some type of prescribed conditions
A closed Bering Strait has been shown to have an affect on ice edge position in the Bering Sea, oceanic and sea ice transports through Fram Strait, and the freshwater budget of the Greenland and Norwegian Seas and Atlantic Ocean
So therefore we believe that properly representing this strait is important to the larger picture
Primary goal of this work:
Intercomparison of the volume, heat and freshwater fluxes through Bering Strait.
Courtesy NASA/JPL

3. Observations in Bering Strait
Observational results provided by UW (Woodgate et al.)
moored instruments placed ~10 m above the bottom (A1, A2, A3)
velocity, T, S: eastern channel and north of the strait (since 1991)
very limited access to the western channel of Bering Strait – Russian waters (intermittent data during )
fluxes extrapolated from point measurements (e.g. Woodgate et al., 2005)
additional moorings added thanks to recent collaboration between the US and Russia (light blue dots; data no yet publicly available)
data used a reference temperature of -1.9oC for heat fluxes, so we are attempting to do the same (UW models currently cannot have a T < -1.8oC)
get figure of high res array – no available data yet for model validation

4. Participating modeling groups
UW models:
PIOMAS (
BESTMAS (
POP (ocean) and TED (ice)
NCEP/NCAR reanalysis forcing
horizontal resolution is ~22 km (PIOMAS) and ~7 km (BESTMAS), 30 vertical levels
new BESTMAS run with min. ocean depth of 15 m (instead of 30 m) and widened Bering Strait
ECCO2 (
MITgcm (ocean) and (ice)
Japanese 25-year ReAnalysis (JRA-25) forcing
18km horizontal resolution, 50 vertical levels
10 m minimum ocean depth
ORCA (
ORCA025L64-N102 - NEMO framework (ocean) and LIM2 sea ice model: VP and 2-layer Semtner thermodynamics
6-hourly ECMWF/CORE2-based DRAKAR Forcing Set (DFS3) forcing
Horizontal grid: Global Arakawa C tri-polar, min 6 km (zonal) X 3.1 km (meridional), max 27.8 km, 64 vertical levels
25 m minimum ocean depth
NPS (NAME) (
based on POP (ocean) and Zhang-Hibler (ice); transitioning to CICE (ice)
daily ECMWF forcing
~9km horizontal resolution, 45 vertical levels, 10 m minimum ocean depth
Calculations of heat and freshwater fluxes
data used a reference temperature of -1.9oC for heat fluxes, so we are attempting to do the same with the model output
freshwater fluxes are referenced to 34.8

5. Long-term mean (1979-2004) velocity across Bering Strait from 5 models
NPS model result suggest winter shear in vertical velocity due to the presence of sea ice on top
significant horizontal shear in all models
some vertical shear
highest velocity in eastern channel

6. Monthly mean Velocity from models and observations
(East)
Velocity from ~10 m above the seafloor A2 location in the deep part of the Eastern Channel
Correlation coefficients between modeled and observed velocities range between 0.69 – 0.77
all modeled velocities are significantly correlated with the observed near-bottom velocity at A2. the correlation coefficients range from 0.69 to 0.77
range of correlation coefficients is 0.22 – 0.72 for A3
Gleb’s stuff (more data-constrained estimate from Pantaleev’s Inverse? model)

7. Comparison of model and data long-term means of velocity and volume transport
model/data
mean velocity
mean volume transport
BESTMAS
34.0
0.72
NPS
34.1
0.65
data
24.8
0.8+
ORCA
43.2
1.33
ECCO2
39.9
1.07
PIOMAS
29.5
0.79
Higher resolution models (BESTMAS and NPS) have lower mean velocities and volume transport
Lower resolution models tend to have higher mean velocities and volume transport

8. Volume, heat, and freshwater transports for model sections across Bering Strait
data mean ( ) = 0.80 Sv
important to get volume flux correct in order to estimate heat and freshwater fluxes, as they depend on volume
data mean ( ) = 6 TW
data mean ( ) = 54 mSv

9. Annual Cycles of volume, heat and freshwater transport
strong annual cycles, as is typical of arctic seas
data shows the strongest annual cycle of volume
most models have a similarly-shaped annual cycle
heat flux peaks in August and is near zero in winter
freshwater peaks in mid-summer
Arctic seas tend to have strong annual cycles of temperature and salinity and, often, of volume transport. The Bering Strait region is no different in this respect. Volume transport peaks is highest in summer (May - July) and lowest in winter (December – March; Fig. 8). When comparing amongst the models and the data, we see that the data has the strongest annual cycle, with a range of 0.4 to 1.3 Sv. ORCA’s annual cycle of volume shows more local maxima and minima than any of the others. PIOMAS, BESTMAS, ECCO2, and NPS models have similar annual cycles to the data, however they are not as strong. This may be related to the fact that a seasonal cycle at a single point, as in data, may have more variability then a sectional average, as in models.
The heat flux annual cycle is very strong and peaks in August for all of the models (Fig. 8). (Data was not available for the heat flux annual cycle. Rebecca, could we still get it?) However, the models do not agree on the magnitude of the summertime peak, which ranges between 15 to over 40 TW. The heat flux is near zero for December – April. The models with the highest resolutions (BESTMAS and NPS) show lower peaks in the summertime heat flux ( TW), while the lower resolution models have higher heat fluxes.
Freshwater transport is important to the Chukchi Sea and the greater Arctic Ocean due to the importance of maintaining the halocline and sea ice formation. This halocline separates the upper mixed layer from the deeper, warmer Atlantic layer. A disturbance in the halocline could allow Atlantic water to rise into the mixed layer and possibly melt sea ice. Annual cycles of freshwater transport through Bering Strait are similar for PIOMAS, BESTMAS, and NPS, with peaks in the summer (June – August) and lowest in winter (December – April). The freshwater transport maxima for these models is between mSv in July. Annual cycles for the global models (ECCO2 and ORCA) have somewhat similar shapes, however they transport more freshwater (up to 115 mSv in summer and more than 60 mSv in winter) to the north.

10. Comparison of the March mean velocity between the 9-km and 2
Comparison of the March mean velocity between the 9-km and 2.3-km NPS model runs
1/12o (9km) NPS model
1/48o (2.3km) NPS model
going to 2km does not qualitiv change the structure of the flow and property transport across Bering Strait
shift in velocity core vice mooring location
2km allows for a frictional layer
going to 2km does not qualitatively change the structure of the flow and property transport across Bering Strait, however the flow is more realistic in high resolution
the range of velocity at the mooring location ~10 above the bottom can be large

11. Conclusions and future work
At present, uncertainties (?%) remain with accurate estimation of volume transport through Bering Strait
Other property transports will be affected by uncertainty in volume transport
High resolution models produce lower values of the transport, while lower resolution models produce higher values
Moving to higher horizontal resolution (2km) does not qualitatively change the structure of the flow and property transport across Bering Strait, however smaller scale features are better represented
An expanded array of observations of vertical and horizontal structure and their temporal evolution in Bering Strait is needed to better constrain models and narrow uncertainty
this is currently underway, with more moorings in the strait
availability of processed data for model intercomparison
Recent observational results utilizing AVHRR-derived SSTs show an increase in 2007 temperatures that was not reflected in the near-bottom temperature signal from moorings
this changed the heat transport estimates
might there be a similar situation for velocity estimates, if there were more surface layer observations?
model results would suggest this to be true
, especially on the western side and in the upper water column

14. recent temperature and associated heat flux increase in 2007
Moored observations (near-bottom) combined with AVHRR SSTs
still missing velocity at surface/upper walter colulmn
Woodgate et al., 2010 GRL
recent temperature and associated heat flux increase in 2007
(due to including AVHRR SST in estimates) – how would other surface estimates change total fluxes?

15. a velocity minimum West of Fairway Rock
Velocity contours from current meter data (Coachman et al., 1975)
highest speeds tend to be found in the eastern channel
the pattern of horizontal shear in the Bering Strait seems to be relatively invariant
a velocity minimum West of Fairway Rock
several cases of local flow reversal in the upper layer

16. Comparison of the 2 methods of volume transport calculation
full model cross-section utilizing all points in horizontal and vertical
velocity near-bottom at A2 location multiplied by cross-sectional area of 2.6 km2 (method used in Woodgate et al., 2005 GRL)
correlation between NPS vol and NPS vol at 10m above bottom A2 = 0.98
Pantaleev presentation at AOMIP meeting Oct using reconstructed circulation method said that the best place to put a mooring is in the eastern channel (A2)…correlation coefficient is .94 (it is .88 for A3)
The observational method yields a volume transport that is 25% higher than the full model cross-section. (Clement et al., 2005 DSRII)
Using the observational method for other model results yields a volume transport that may be higher or lower than the full model cross-section…however it is never the same.