Arctic sea ice, Eurasia snow, and extreme winter haze in China

Yufei Zou, Yuhang Wang, Yuzhong Zhang, Ja Ho Koo

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154 Citations (Scopus)


The East China Plains (ECP) region experienced the worst haze pollution on record for January in 2013. We show that the unprecedented haze event is due to the extremely poor ventilation conditions, which had not been seen in the preceding three decades. Statistical analysis suggests that the extremely poor ventilation conditions are linked to Arctic sea ice loss in the preceding autumn and extensive boreal snowfall in the earlier winter. We identify the regional circulation mode that leads to extremely poor ventilation over the ECP region. Climate model simulations indicate that boreal cryospheric forcing enhances the regional circulation mode of poor ventilation in the ECP region and provides conducive conditions for extreme haze such as that of 2013. Consequently, extreme haze events in winter will likely occur at a higher frequency in China as a result of the changing boreal cryosphere, posing difficult challenges for winter haze mitigation but providing a strong incentive for greenhouse gas emission reduction.

Original languageEnglish
Article numbere1602751
JournalScience Advances
Issue number3
Publication statusPublished - 2017 Mar

Bibliographical note

Funding Information:
We acknowledge high-performance computing support from Yellowstone ( provided by NCAR’s Computational and Information Systems Laboratory (CISL) and sponsored by the NSF. We thank the NOAA/Office of Oceanic and Atmospheric Research/Earth System Research Laboratory Physical Sciences Division (Boulder, CO, USA) for providing NCEP reanalysis data on the website at, the Met Office Hadley Centre for the HadISST data set, the NCDC for the GSOD data set, and the Global Snow Lab at Rutgers University for gridded SCE data. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in table S6 of this paper) for producing their model output and making it available. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We also thank Y. Peings, X. Li, and Z. Xie for helpful discussion on statistical data analysis and climate sensitivity experiments.

Publisher Copyright:
2017 © The Authors, some rights reserved.

All Science Journal Classification (ASJC) codes

  • General


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