Opening and closing of the Storfjorden polynya Frank Nilsen Frank Nilsen 1,2 1,2 , Ragnheid Skogseth , Ragnheid Skogseth 1 , , Katja Weigel Katja Weigel 1 1.The University Centre in Svalbard (UNIS), 1.The University Centre in Svalbard (UNIS), Longyearbyen, Norway Longyearbyen, Norway 2.Geophysical Institute, University of Bergen, 2.Geophysical Institute, University of Bergen, Bergen, Norway Bergen, Norway
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Opening and closing of the Storfjorden polynya. Coastal Polynya Skogseth (2003), PhD thesis Storfjorden is estimated to supply 5-10% of the newly formed.
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1.The University Centre in Svalbard (UNIS), Longyearbyen, Norway1.The University Centre in Svalbard (UNIS), Longyearbyen, Norway
2.Geophysical Institute, University of Bergen, Bergen, Norway2.Geophysical Institute, University of Bergen, Bergen, Norway
Coastal Polynya
Skogseth (2003), PhD thesis
Storfjorden is estimated to supply 5-10% of the newly formed dense waters of the Arctic Ocean
The Barents Sea and Storfjorden
Storfjorden
Skogseth et al. (2005), CSR
• Is approximately 190 km long and 190 m deep at its maximum depth.
• A 120 m deep sill at about 77°N in the south.
•The basin covers an area of about 13·103 km2 with a volume of 8.5·1011 m3.
Storfjorden Polynya
Skogseth (2003), PhD thesis
Maximum Salinity observed in the deepest part of Storfjorden
Skogseth & Fer (2005), AGU Anderson et al. (2004), JGR
Polynya Area and SAR
• We utilize the polynya width model formulated by Haarpaintner et al. (2001) and Skogseth et al. (2004).
• The model calculates the area of open water and thin ice defined as the polynya area, and the area of fast and pack ice.
• The model consist of two algorithms: polynya width algorithm and an open water width algorithm.
• The polynya width algorithm needs two empirical factors as input: the opening (OF) and closing (CF) factors.
Empirical Tuning
Wind Stress Curl field (DJFM, 1970-2004)
From the Hindcast data base (met.no) with a 75 km resolution
Sea Ice Drift
waa UUUU 21
DJFMA wind stress
OF and CF correlated with the winter mean wind stress curl
DJFMA wind stress
Mechanical and thermodynamical sea ice growth
FF
fph
Fp U
Uh
W
tL
SU
dt
dW
)(
1
000,
0
1- O(1) + O(10) = CF1+ O(1) + O(0) = OF
Result Summary
Winters with low Iw
More positive curl Divergence
Winters with high Iw
More negative curl Convergence
Reproduced OF as a function of the curl at 75.4°N, 25.1°E
Total ice production (normalized)
Conclusions• Physical explanations for the empirical OF and CF
are found.• CF is dominated by thermodynamical ice growth
through consolidation of frazil ice, but mechanical ice growth determine the interannual variations.
• OF’s deviations from the free drift solution is determined by divergence and convergence in the compact ice cover outside Storfjorden.
• Annual variations in OF and CF are directly linked to the wind stress curl field and an empirical relations is found from the hindcast data base (met.no) time series.
• The polynya model gives a more realistic sea ice production when the model is run with time varying OF and CF.
The Barents Sea Ice Index Iw
Ådlandsvik & Loeng (1990), Polar Res.
The integrated winter ice covered area south of 76ºN in a zone between 25ºE and 45ºE.
Correlation between the curl (DJFM) and Iw (1970-2003)