A box model of the Arctic natural variability

Ou, Hsien Wang

doi:10.1007/s00382-012-1453-6

Cited by 1 publication

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“…The negative feedback was subsequently detected via statistical analysis of observations (Strong et al 2009) and hybrid statistical-dynamical modeling (Strong and Magnusdottir 2010). In contrast, Ou (2013) proposed that the large-scale sea ice-atmosphere feedback over the Arctic was positive where, for example, negative SLP anomalies resulting from ice removal from the Barents Sea would strengthen the cyclonic circulation over the eastern Arctic, leading to further ice loss through Fram Strait.…”

Section: Introductionmentioning

confidence: 94%

A Model-Based Decomposition of the Sea Ice–Atmosphere Feedback over the Barents Sea during Winter

Liptak

Strong

2014

Journal of Climate

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The feedback between Barents Sea ice and the winter atmosphere was studied in a modeling framework by decomposing it into two sequential boundary forcing experiments. The Community Ice Code (CICE) model was initialized with anomalously high sea ice concentration (SIC) over the Barents Sea and forced with an atmosphere produced by positive SIC anomalies, and CICE was initialized with low Barents Sea SIC and forced with an atmosphere produced by negative SIC anomalies. Corresponding control runs were produced by exposing the same SIC initial conditions to climatological atmospheres, and the monthly mean sea ice response showed a positive feedback over the Barents Sea for both experiments: the atmosphere produced by positive SIC anomalies increased SIC over the Barents Sea during the winter, and the atmosphere produced by negative SIC anomalies decreased SIC. These positive feedbacks were driven primarily by thermodynamic forcing from surface longwave flux anomalies and were weakened somewhat by atmospheric temperature advection. Dynamical effects also opposed the positive feedback, with enhanced surface wind stress divergence over the Barents Sea in the high-SIC case and enhanced convergence in the low-SIC case.

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Section: Introductionmentioning

confidence: 94%