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The relationship between winter sea ice variability and the North Atlantic Oscillation (NAO) is examined for the time period
1860–2300. This study uses model output to extend recently reported observational results to multi-century time scales. Nine
ensemble members are used in two Global Climate Models with forcing evolving from pre-industrial conditions through the so-called
A1B scenario in which carbon dioxide stabilizes at 720 ppm by 2100. Throughout, the NAO generates an east-west dipole pattern
of sea ice concentration (SIC) anomalies with oppositely signed centers of action over the Labrador and Barents Seas. During
the positive polarity of the NAO, SIC increases over the Labrador Sea due to wind-driven equatorward advection of ice, and
SIC decreases over the Barents Sea due to wind-driven poleward transport of heat within the mixed layer of the ocean. Although
this NAO-driven SIC variability pattern can always be detected, it accounts for a markedly varying fraction of the total sea
ice variability depending on the strength of the forced sea ice extent trend. For the first half of the 20th century or 1990
control conditions, the NAO-driven SIC pattern accounts for almost a third of the total SIC variance. In the context of the
long term winter sea ice retreat from 1860 to 2300, the NAO-driven SIC pattern is robustly observable, but accounts for only
2% of the total SIC variance. The NAO-driven SIC dipole retreats poleward with the retreating marginal ice zone, and its Barents
Sea center of action weakens. Results presented here underscore the idea that the NAO’s influence on Arctic climate is robustly
observable, but time dependent in its form and statistical importance. 相似文献
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Climate Dynamics - During the 2012–2013 winter, the negative phase of the North Atlantic Oscillation (NAO) predominated, resulting in a cold winter over Europe and northern Asia punctuated by... 相似文献
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The variance of the North Atlantic Oscillation index (denoted N) is shown to depend on its coupling with area-averaged sea ice concentration anomalies in and around the Barents Sea (index denoted B). The observed form of this coupling is a negative feedback whereby positive N tends to produce negative B, which in turn forces negative N. The effects of this feedback in the system are examined by modifying the feedback in two modeling frameworks: a statistical vector autoregressive model (F VAR) and an atmospheric global climate model (F CAM) customized so that sea ice anomalies on the lower boundary are stochastic with adjustable sensitivity to the model??s evolving N. Experiments show that the variance of N decreases nearly linearly with the sensitivity of B to N, where the sensitivity is a measure of the negative feedback strength. Given that the sea ice concentration field has anomalies, the variance of N goes down as these anomalies become more sensitive to N. If the sea ice concentration anomalies are entirely absent, the variance of N is even smaller than the experiment with the most sensitive anomalies. Quantifying how the variance of N depends on the presence and sensitivity of sea ice anomalies to N has implications for the simulation of N in global climate models. In the physical system, projected changes in sea ice thickness or extent could alter the sensitivity of B to N, impacting the within-season variability and hence predictability of N. 相似文献
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