Intraseasonal Variability and Synergistic Effects on Extreme Temperature Variations in Eastern China during the 2023–2024 Winter
DOI:
https://doi.org/10.70917/jcc-2026-001Keywords:
Extreme Temperature Variations, Intraseasonal Oscillation, Eastern China, Synergistic EffectAbstract
In the context of global warming, abrupt transitions between extreme temperature states (extreme temperature variability events) pose severe threats to both ecosystems and socioeconomic systems. However, previous studies have primarily focused on the regulatory effects of atmospheric intraseasonal oscillation (ISO) on extreme temperatures from the perspective of a single scale, leaving the synergistic driving mechanisms of multi-scale ISO on extreme temperature variability poorly understood. This study investigates the synergistic influence mechanisms of 10–30-day and 30–60-day ISO on extreme temperature variability events in eastern China. Utilizing ERA5 reanalysis data from 1979 to 2024, the record-breaking extreme winter of 2023–2024 is employed as a representative case study to systematically elucidate these mechanisms. The results indicate that the winter of 2023–2024 was marked by a record-high surface air temperature variance, accompanied by two prominent extreme temperature variation processes. Wavelet and empirical orthogonal function (EOF) analyses reveal that both ISOs were anomalously strong during this winter and exhibited significant positive correlations with temperature variance, with the 10–30-day ISO playing a more dominant role. Phase evolution analysis demonstrates that phase-locking between the leading 10–30-day modes precedes extreme temperature events by approximately 5 days, whereas the 30–60-day ISO modulates the monthly-scale persistent warm or cold backgrounds. Dynamic diagnosis shows that the ISO-related wave activity flux drives the evolution of key circulation systems, such as the Ural blocking high and the East Asian trough, thereby governing the abrupt temperature reversals. Thermodynamic budget analysis highlights the dominant role of diabatic heating, particularly in the northern key region, where its 10–30-day component is crucial for rapid temperature reversals. Critically, a synergistic effect significantly amplifies the intensity and frequency of extreme temperature variations when both ISOs are in positive phases concurrently. This study advances the understanding of the multi-scale dynamical mechanisms underlying extreme temperature variability and provides a scientific foundation for the extended-range forecasting of such events. Moreover, the findings offer critical insights for enhancing regional climate resilience and informing risk mitigation strategies for major urban clusters in eastern China.
References
1. Alexander, L. V., Zhang, X., Peterson, T. C., et al. (2006). Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research: Atmospheres, 111(D5). doi:10.1029/2005JD006290
2. Ault, T. R., Henebry, G. M., Beurs, K. M. d., et al. (2013). The False Spring of 2012, Earliest in North American Record. Eos, Transactions American Geophysical Union, 94, 181-182. doi:10.1002/2013EO200001
3. Blunden, J., & Boyer, T. (2022). State of the Climate in 2021. Bulletin of the American Meteorological Society. doi:10.1175/2022BAMSStateoftheClimate.1
4. Bretherton, C. S., Widmann, M., Dymnikov, V. P., et al. (1999). The Effective Number of Spatial Degrees of Freedom of a Time-Varying Field. Journal of Climate, 12(7), 1990-2009. doi:10.1175/1520-0442(1999)012<1990:TENOSD>2.0.CO;2
5. Cohen, J., Screen, J. A., Furtado, J. C., et al. (2014). Recent Arctic amplification and extreme mid-latitude weather. Nature Geoscience, 7(9), 627-637. doi:10.1038/ngeo2234
6. Donat, M., Alexander, L. V., Yang, H., et al. (2013). Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset. Journal of Geophysical Research: Atmospheres, 118(5), 2098-2118. doi:10.1002/jgrd.50150
7. Doocy, S., Daniels, A., Packer, C., et al. (2013). The human impact of earthquakes: a historical review of events 1980-2009 and systematic literature review. PLoS currents, 5, ecurrents. dis. 67bd14fe457f451db450b5433a5438ee5420fb5833. doi:10.1371/currents.dis.67bd14fe457f1db0b5433a8ee20fb833
8. Duchon, C. E. (1979). Lanczos Filtering in One and Two Dimensions. J. Appl. Meteor, 18(8), 1016-1022. doi:10.1175/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2
9. Fischer, E. M., & Knutti, R. (2015). Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nature Climate Change, 5(6), 560-564. doi:10.1038/nclimate2617
10. Francis, J. A., Skific, N., Vavrus, S. J., et al. (2022). Measuring "Weather Whiplash" Events in North America: A New Large‐Scale Regime Approach. Journal of Geophysical Research. Atmospheres, 127(17). doi:10.1029/2024JD042635
11. Francis, J. A., Skific, N., & Zobel, Z. (2023). Weather whiplash events in Europe and North Atlantic assessed as continental-scale atmospheric regime shifts. npj Climate and Atmospheric Science, 6(1), 216. doi:10.1038/s41612-023-00542-9
12. Frich, P., Alexander, L. V., Della-Marta, P., et al. (2002). Observed coherent changes in climatic extremes during the second half of the twentieth century. Climate Research, 19(3), 193-212. doi:10.3354/cr019193
13. Holton, J. R., & Hakim, G. J. (2013). An introduction to dynamic meteorology (Vol. 88): Academic press.
14. Huang, J. (2004). Method of Meteorological Statistic Analysis and Forecasting. In: China Meteorological Press: Beijing, China.
15. Kloster, D. P., Morzillo, A. T., & Volin, J. C. (2019). A national and local media perspective on responsibility for and solutions to storm-related power outages in the northeastern United States. Environmental Hazards, 18(3), 228-245. doi:10.1080/17477891.2018.1541782
16. Laucelli, D., Rajani, B., Kleiner, Y., et al. (2014). Study on relationships between climate-related covariates and pipe bursts using evolutionary-based modelling. Journal of Hydroinformatics, 16(4), 743-757. doi:10.2166/hydro.2013.111
17. Legg, S. (2021). IPCC, 2021: Climate Change 2021: The Physical Science Basis. Interaction (Melbourne), 49(4).
18. Ma, S., & Zhu, C. (2019). Extreme cold wave over East Asia in January 2016: A possible response to the larger internal atmospheric variability induced by Arctic warming. Journal of Climate, 32(4), 1203-1216. doi:10.1175/JCLI-D-18-0234.1
19. Ma, S., & Zhu, C. (2021). Atmospheric circulation regime causing winter temperature whiplash events in North China. International Journal of Climatology. doi:10.1002/joc.6706
20. Marino, G. P., Kaiser, D. P., Gu, L., et al. (2011). Reconstruction of false spring occurrences over the southeastern United States, 1901–2007: an increasing risk of spring freeze damage? Environmental Research Letters, 6(2), 024015. doi:10.1088/1748-9326/6/2/024015
21. Russell, E. N., Loikith, P. C., Ajibade, I., et al. (2024). The meteorology and impacts of the September 2020 Western United States extreme weather event. Weather and Climate Extremes, 43, 100647. doi:10.1016/j.wace.2024.100647
22. Shepherd, T. G. (2016). Effects of a warming Arctic. Science, 353(6303), 989-990. doi:10.1126/science.aag0998
23. Sheridan, S. C., & Lee, C. C. (2018). Temporal trends in absolute and relative extreme temperature events across North America. Journal of Geophysical Research: Atmospheres, 123(21), 11,889-811,898. doi:10.1029/2018JD029150
24. Smith, E. T., & Sheridan, S. C. (2020). Where Do Cold Air Outbreaks Occur, and How Have They Changed Over Time? Geophysical Research Letters, 47(13). doi:10.1029/2020GL087422
25. Takaya, K., & Nakamura, H. (2001). A formulation of a phase-independent wave-activity flux for stationary and migratory quasigeostrophic eddies on a zonally varying basic flow. Journal of the Atmospheric Sciences, 58(6), 608-627. doi:10.1175/1520-0469(2001)058<0608:AFOAPI>2.0.CO;2
26. Tan, X., Wu, X., Huang, Z., et al. (2023). Increasing global precipitation whiplash due to anthropogenic greenhouse gas emissions. Nature Communications, 14(1), 2796. doi:10.1038/s41467-023-38510-9
27. Vandentorren, S., Suzan, F., Medina, S., et al. (2004). Mortality in 13 French cities during the August 2003 heat wave. American journal of public health, 94(9), 1518-1520. doi:10.2105/AJPH.94.9.1518
28. Vicedo-Cabrera, A. M., Forsberg, B., Tobias, A., et al. (2016). Associations of inter-and intraday temperature change with mortality. American journal of epidemiology, 183(4), 286-293. doi:10.1093/aje/kwv205
29. Wallace, J. M., Held, I. M., Thompson, D. W., et al. (2014). Global warming and winter weather. Science, 343(6172), 729-730. doi:10.1126/science.343.6172.729
30. Wang, H., Li, J., Zheng, F., et al. (2024). The synergistic effect of the preceding winter mid-latitude North Atlantic and summer tropical eastern Indian Ocean SST on summer extreme heat events in northern China. Weather and Climate Extremes, 44, 100660. doi:10.1016/j.wace.2024.100660
31. Wei, F. (2007). Modern Climate Statistical Diagnosis and Prediction Techniques, 2nd ed.
32. Whitehurst, L., & Slevin, C. (2020). From heat to snow: Rocky Mountains see 60-degree plunge. Greenwire(Sep.9).
33. Wilson, H. F., Casson, N. J., Glenn, A. J., et al. (2019). Landscape controls on nutrient export during snowmelt and an extreme rainfall runoff event in northern agricultural watersheds. Journal of environmental quality, 48(4), 841-849. doi:10.2134/jeq2018.07.0278
34. Wu, H., & Wu, L. (2005). Diagnosis and Prediction Method of Climate Variability. In: China Meteorological Press: Beijing, China.
35. Yang, S., & Li, T. (2016). Intraseasonal variability of air temperature over the mid-high latitude Eurasia in boreal winter. Climate Dynamics, 47(7), 2155-2175. doi:10.1007/s00382-015-2956-8
36. Yang, S., Wu, B., zhang, R., et al. (2014). Propagation of Low-Frequency Oscillation over Eurasian Mid–High Latitude in Winter and Its Association with the Eurasian Teleconnection Pattern. Chinese Journal of Atmospheric Sciences. doi:10.3878/j.issn.1006-9895.2013.12181.
37. Yang, S., Zhu, Z., Cui, J., et al. (2019). Regulation of the intraseasonal oscillation over mid-to-high latitude Eurasia on winter surface air temperature over China. Dynamics of Atmospheres and Oceans, 86, 63-72. doi:10.1016/j.dynatmoce.2019.03.007
38. Ye, Y., Qian, C., Dai, A., et al. (2025). Attribution of a record-breaking cold event in the historically warmest year of 2023 and assessing future risks. npj Climate and Atmospheric Science, 8(1), 14. doi:10.1038/s41612-024-00886-w
39. Zhang, B., Wang, S., Zscheischler, J., et al. (2023). Higher exposure of poorer people to emerging weather whiplash in a warmer world. Geophysical Research Letters, 50(21), e2023GL105640. doi:10.1029/2023GL105640
40. Zou, Y., Wang, Y., Zhang, Y., et al. (2017). Arctic sea ice, Eurasia snow, and extreme winter haze in China. Science Advances, 3(3), e1602751. doi:10.1126/sciadv.1602751
41. Zscheischler, J., & Seneviratne, S. I. (2017). Dependence of drivers affects risks associated with compound events. Science Advances, 3(6), e1700263. doi:10.1126/sciadv.1700263
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Guangxin He, Junru Li, Boqi Liu, Jingjing Duan, Jingjia Luo (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
