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The impact of boreal surface thermal anomalies on January Central Asian moist vortex

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Abstract

Central Asian vortex (CAV) is a kind of typical regional cyclonic vortex in the North Hemisphere and its important component, Central Asian moist vortex (CAMV), is one of the key systems causing Central Asian heavy precipitation. This study focused on the main characteristics and interannual forcing sources of January CAMV, which has led to rapid increase of Central Asian heavy snowfall during several decades, mainly discussing about the individual and cooperative effect of boreal surface thermal anomalies on CAMVs. The results indicate that the January CAMV demonstrates significantly interannual variability in its annual activity days, moreover, this interannual variability has been experiencing decadal enhancement since 2004, which explains the decadal increase in contemporaneous Central Asian heavy snowfall amount. The “northern moist vortex” (NMV) featuring the low vortex in the northern Central Asia accompanying the European and Tibetan Plateau Ridge, represents the typical circulation configuration of January CAMV. The interannual frequency of NMV is highly related to preceding North Pacific Victoria mode (VM), mid-latitude North Atlantic sea surface temperature (MNA) as well as the land-sea temperature contrast (LSTC) between Africa and eastern North Atlantic. Among them, VM and MNA could respectively stimulate circumglobal and Atlantic-Eurasian wave train structures into northern Central Asia. While, LSTC could excite anomalous lower-level cyclonic circulation over the Mediterranean entrance, accelerating and lifting the entrance of African-Asian jet stream via the way of “eddy-driven jet stream”, thereby strengthening the docking of the African-Asian jet stream with the North Atlantic jet stream and enhancing the waveguide effect of two jet streams. When these three surface thermal factors coexist on the interannual scale, VM tends to play a leading role for contributing more than 50% to NMVs in three factors. While, MNA and LSTC play unneglected roles of “relay” to further modulate NMVs, and the cooperative effect of multiple surface thermal anomalies is the key to impacting January CAMVs.

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Data availability

The CPC global daily precipitation dataset can be obtained from NOAA (ftp://ftp.cdc.noaa.gov/Datasets/cpc_global_precip/). The ERA-Interim reanalysis dataset is collected by the ECMWF (https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-interim). The Optimal Interpolation Sea Surface Temperature V2 dataset is available from NOAA (https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html).

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Acknowledgements

The authors would like to thank the support by National Natural Science Foundation of China (Grant Numbers: 42175048, 42205085), Special Projects for Innovation and Development (Grant Number: CXFZ2024J002), China Yangtze Power Co., Ltd. Project (Grant Number: 2423020054), Guizhou New Meteorological Technology Co., Ltd. Project (Grant Number: 2024-N69), National Key Research and Development Program of China (Grant Number: 2018YFC1505603) and China Meteorological Administration (CMA) Youth Innovation Group (CMA2024QN06). We are also very grateful to the editor and anonymous reviewers for their careful review and valuable comments, which led to substantial improvement of this work.

Funding

This research was jointly supported by National Natural Science Foundation of China (Grant Numbers: 42175048, 42205085), Special Projects for Innovation and Development (Grant Number: CXFZ2024J002), China Yangtze Power Co., Ltd. Project (Grant Number: 2423020054), Guizhou New Meteorological Technology Co., Ltd. Project (Grant Number: 2024-N69), National Key Research and Development Program of China (Grant Number: 2018YFC1505603) and China Meteorological Administration (CMA) Youth Innovation Group (CMA2024QN06).

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Authors and Affiliations

  1. China Meteorological Administration Key Laboratory for Climate Prediction Studies, National Climate Center, Beijing, 100081, China

    Zhiheng Chen & Zongjian Ke

  2. Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Joint International Research Laboratory of Climate and Environment Change (ILCEC)/ Collaborative Innovation Center On Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Jie Zhang

  3. Beijing Meteorological Service, Beijing, 100089, China

    Xinyi Yang

  4. School of Physical Science and Technology, Yangzhou University, Yangzhou, 225009, China

    Qianrong Ma

  5. Suzhou Meteorological Bureau, Suzhou, 215131, China

    Jiahao Lu

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Appendix

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Fig. 17

Fig. 17
figure 17

Schematic diagram of the objective method to identify the vortex a, cold core b, and cold trough c at a grid point (i, j). Stars denote the centers. The solid curve denotes geopotential height contour and the dotted curves denote temperature contours (Chen et al. 2023)

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Chen, Z., Zhang, J., Ke, Z. et al. The impact of boreal surface thermal anomalies on January Central Asian moist vortex. Clim Dyn 62, 7625–7649 (2024). https://doi.org/10.1007/s00382-024-07298-9

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