Variations in the Frequency of Winter Extreme Cold Days in Northern China and Possible Causalities

Chaoxia Yuan Key Laboratory of Meteorological Disaster of Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

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Wenmao Li Key Laboratory of Meteorological Disaster of Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

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Abstract

Weather and climate extremes often cause devastating disasters and motivate research to improve their understanding. Here, the spatiotemporal variations in the frequency of winter extreme cold days (WECDs) in northern China (NC) were investigated. The results show that the first EOF mode reflects the spatially consistent anomalies and explains 39% of the total variance. The second EOF mode represents the east–west contrasted anomalies and explains 17% of the total variance. In the winter, when the negative Arctic Oscillation (AO) co-occurs with the stronger Siberian high (SH), cold air from the polar region is more easily advected southward, causing the increased number of WECDs in all of NC. The co-occurrence of negative AO and stronger SH may be related to excessive snow in the preceding autumn over northeastern Eurasia through the local positive feedback between the snow and surface air temperatures and the tropospheric–stratospheric interaction. On the other hand, in the winter when the negative AO co-occurs with the weaker SH, eastern NC is still under the influence of cold-air intrusion from the polar region relevant to the negative AO, whereas western NC is impacted by the anomalous warm-air advection related to the weaker SH. The weakening of the SH may be caused by the El Niño teleconnection. The reproducibility of the observed variations and possible causalities of WECDs in NC by 10 coupled models participating in CMIP5 has been assessed. All the models can reproduce the observed dominant EOF modes. The relevant large-scale circulation anomalies and possible causalities can also be well simulated by the multimodel ensemble.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chaoxia Yuan, chaoxia.yuan@nuist.edu.cn

Abstract

Weather and climate extremes often cause devastating disasters and motivate research to improve their understanding. Here, the spatiotemporal variations in the frequency of winter extreme cold days (WECDs) in northern China (NC) were investigated. The results show that the first EOF mode reflects the spatially consistent anomalies and explains 39% of the total variance. The second EOF mode represents the east–west contrasted anomalies and explains 17% of the total variance. In the winter, when the negative Arctic Oscillation (AO) co-occurs with the stronger Siberian high (SH), cold air from the polar region is more easily advected southward, causing the increased number of WECDs in all of NC. The co-occurrence of negative AO and stronger SH may be related to excessive snow in the preceding autumn over northeastern Eurasia through the local positive feedback between the snow and surface air temperatures and the tropospheric–stratospheric interaction. On the other hand, in the winter when the negative AO co-occurs with the weaker SH, eastern NC is still under the influence of cold-air intrusion from the polar region relevant to the negative AO, whereas western NC is impacted by the anomalous warm-air advection related to the weaker SH. The weakening of the SH may be caused by the El Niño teleconnection. The reproducibility of the observed variations and possible causalities of WECDs in NC by 10 coupled models participating in CMIP5 has been assessed. All the models can reproduce the observed dominant EOF modes. The relevant large-scale circulation anomalies and possible causalities can also be well simulated by the multimodel ensemble.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chaoxia Yuan, chaoxia.yuan@nuist.edu.cn
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