4.3 Article

On ocean and sea ice modes of variability in the Bering Sea

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AMER GEOPHYSICAL UNION
DOI: 10.1029/2011JC007389

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  1. NOAA EcoFOCI
  2. Department of Interior Bureau of Ocean Energy Management [M07PC13368]
  3. National Science Foundation [ARC-0732771, ARC-0732431, ARC-0957985]
  4. EPSCoR
  5. Office Of The Director [0919608] Funding Source: National Science Foundation
  6. Office of Advanced Cyberinfrastructure (OAC)
  7. Direct For Computer & Info Scie & Enginr [0960175] Funding Source: National Science Foundation

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Results from a 35 year hindcast of northeast Pacific Ocean conditions are confronted with observational data collected over the Bering Sea shelf within the integration time period. Rotary power spectra of the hindcast currents near NOAA mooring site M2 site fall within the 95% confidence bounds for the observational spectra, except for a high bias in the counter-clockwise rotating component at 10 m depth in the high frequencies (periods <24 h). The model exhibits the most skill in reproducing anomalies of the integrated annual sea ice concentration and monthly subsurface (60 m depth) temperature fields, accounting for 85% and 50% of their observed variability. Analysis of the integrated ice concentration time series reveals evolution in the mean duration of ice-free waters (40 year trend of +6.8 days/decade) and changes in this parameter's variance with time. Correlation and empirical orthogonal function (EOF) analyses reveal the primary temporal-spatial patterns of variability in the temperature and salinity fields over the Bering Sea and northern Gulf of Alaska for near-surface (0-20 m) and subsurface (40-100 m) depth layers. Correlation analysis between the EOF principal components and various climate index and observed time series shows that the Pacific Decadal Oscillation, the North Pacific Gyre Oscillation, and the Bering Sea annually integrated ice area anomalies are important indices of thermohaline variability; the spatial structures of these modes give insight to their potential impacts upon the ecosystem. We identify a number of ecologically and economically important species whose temporal variability is significantly correlated with the identified spatial patterns.

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