Based on the NECP/NCAR reanalysis dataset, the associations between the number of cold days (NCD) over East Asia (100–150° E, 25–55° N) and Arctic Oscillation (AO)/Arctic warming during 1956–2015 are explored. The results show the NCD was closely associated with AO during 1956–1990 and Arctic warming during 1991–2015. It reveals NCD over East Asia showed a downward trend and a significantly negative correlation with AO in the previous stage, while it presented an upward trend and notably positive association with Arctic warming in the later period. Meanwhile the increase in the earlier-stage AO will often be accompanied by the weakness of the Siberian high (SH), the Ural Mountains Blocking high (UBH), and the East Asian trough (EAT), and a “positive–negative–positive” wave band exist in the upper troposphere, which is linked with weakened northerly wind over East Asia. All these anomalies are unfavorable for the southward transportation of cold air, eventually leading to the decrease in NCD over East Asia. Additionally, when the near-surface temperature over the Arctic rises in the later period, on the one hand, SH reinforces and further results in more NCD over East Asia; on the other hand, the 1000–500 hPa thickness field displays a “north positive–south negative” pattern, which is conducive to the deceleration of the westerlies at mid-latitudes over Eurasia, and further bring about the enhancement of EAT and UBH, favoring the southward intrusion of cold air, finally, more NCD are generated.
The past decade has witnessed a rapid decline in the Arctic sea ice and therefore has raised a rising demand for sea ice forecasts. In this study, based on an analysis of long-term Arctic summer sea ice concentration (SIC) and global sea surface temperature (SST) datasets, a physical–empirical (PE) partial least squares regression (PLSR) model is presented in order to predict the summer SIC variability around the key areas of the Arctic shipping route. First, the main SST modes closely associated with sea ice anomalies are found by the PLSR method. Then, a prediction model is reasonably established on the basis of these PLSR modes. We investigate the performance of the PE PLSR model by examining its reproducibility of the seasonal SIC variability. Results show that the proposed model turns out promising prediction reliability and accuracy for Arctic summer SIC change, thus providing a reference for the further study of Arctic SIC variability and global climate change.
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