| INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
Prev
|
|
|
Characteristics of complex network of heatwaves over China |
| Xuemin Shen(沈雪敏)1, Xiaodong Hu(胡晓东)2, Aixia Feng(冯爱霞)3, Qiguang Wang(王启光)4,†, and Changgui Gu(顾长贵)1 |
1 School of Business, University of Shanghai for Science and Technology, Shanghai 200093, China;
2 College of Engineering, China Agricultural University, Beijing 100083, China;
3 National Meteorological Information Center, Beijing 100081, China;
4 China Meteorological Administration Training Center, Beijing 100081, China |
|
|
|
|
Abstract Using complex network methods, we construct undirected and directed heatwave networks to systematically analyze heatwave events over China from 1961 to 2023, exploring their spatiotemporal evolution patterns in different regions. The findings reveal a significant increase in heatwaves since the 2000s, with the average occurrence rising from approximately 3 to 5 times, and their duration increasing from 15 to around 30 days, nearly doubling. An increasing trend of "early onset and late withdrawal" of heatwaves has become more pronounced each year. In particular, eastern regions experience heatwaves that typically start earlier and tend to persist into September, exhibiting greater interannual variability compared to western areas. The middle and lower reaches of the Yangtze River and Xinjiang are identified as high-frequency heatwave areas. Complex network analysis reveals the dynamics of heatwave propagation, with degree centrality and synchronization distance indicating that the middle and lower reaches of the Yangtze River, Northeast China, and Xinjiang are key nodes in heatwave spread. Additionally, network divergence analysis shows that Xinjiang acts as a "source" area for heatwaves, exporting heat to surrounding regions, while the central region functions as a major "sink," receiving more heatwave events. Further analysis from 1994 to 2023 indicates that heatwave events exhibit stronger network centrality and more complex synchronization patterns. These results suggest that complex networks provide a refined framework for depicting the spatiotemporal dynamics of heatwave propagation, offering new avenues for studying their occurrence and development patterns.
|
Received: 05 November 2024
Revised: 23 December 2024
Accepted manuscript online: 31 December 2024
|
|
PACS:
|
89.75.Fb
|
(Structures and organization in complex systems)
|
| |
92.70.Mn
|
(Impacts of global change; global warming)
|
| |
92.70.Kb
|
(Regional climate change)
|
|
| Fund: Project supported by the National Key Research and Development Program of China (Grant Nos. 2022YFE0136000 and 2024YFC3013100), the Joint Meteorological Fund (Grant No. U2342211), and the Joint Research Project for Meteorological Capacity Improvement (Grant No. 22NLTSZ004) and the National Meteorological Information Center (Grant No. NMICJY202301). |
Corresponding Authors:
Qiguang Wang
E-mail: wangqg@cma.gov.cn
|
Cite this article:
Xuemin Shen(沈雪敏), Xiaodong Hu(胡晓东), Aixia Feng(冯爱霞), Qiguang Wang(王启光), and Changgui Gu(顾长贵) Characteristics of complex network of heatwaves over China 2025 Chin. Phys. B 34 038903
|
[1] Muri H, Tjiputra J, Otterå O H, Adakudlu M, Lauvset S K, Grini A, Schulz M, Niemeier U and Kristjánsson J E 2018 J. Clim. 31 6319
[2] Sun Y, Zhang X B, Zwiers F W, Song L C, Wan H, Hu T, Yin H and Ren G Y 2014 Nat. Clim. Change 4 1082
[3] Lu C H, Shen Y C, Li Y H, Xiang B and Qin Y J 2022 J. Meteorol. Res. 36 643
[4] Shi H B 2012 Sci. Geogr. Sin. 32 866 (in Chinese)
[5] Ding T and Qian W H 2011 Adv. Atmos. Sci. 28 322
[6] Zhai P M and Pan X H 2003 Acta Geogr. Sin. 58 1 (in Chinese)
[7] Lin X and Guan Z Y 2008 Trans. Atmos. Sci. 31 1 (in Chinese)
[8] Sun J Q, Wang H J and Yuan W 2011 Clim. Environ. Res. 16 199 (in Chinese)
[9] Dong X Y and Wu B Y 2019 J. Appl. Meteor. Sci. 30 431 (in Chinese)
[10] Li S S, Yang S N, Zhang D H and Liu X F 2015 J. Appl. Meteor. Sci. 26 545 (in Chinese)
[11] Xie J H, Yu J H, Chen H S and Hsu P C 2020 Adv. Atmos. Sci. 37 1435
[12] Zhang P, Dai E F, Wu C S and H J 2024 Clim. Dyn. 62 841
[13] Yu E T and Sun J Q 2019 Trans. Atmos. Sci. 42 46 (in Chinese)
[14] Qian W H and Lin X 2004 Clim. Res. 27 119
[15] Yan Z, Jones P D, Davies T D, Moberg A, Bergström H, Camuffo D, Cocheo C, Maugeri M, Demarée G R, Verhoeve T, Thoen E, Barriendos M, Rodríguez R, Martín-Vide J and Yang C 2002 Clim. Change 53 355
[16] Li J, Zhu Z W and Dong W J 2017 Meteorol. Atmos. Phys. 129 273
[17] Zhang Z W and Shi X H 2010 J. Qinghai Meteorol. 4 2 (in Chinese) CNKI:SUN:QHQX.0.2010-04-002
[18] Jia H and Yun S 2018 J. Catastrophol. 33 223 (in Chinese)
[19] Horton R M, Mankin J S, Lesk C, Coffel E and Raymond C 2016 Curr. Clim. Change Rep. 2 242
[20] Mondal S and Mishra A K 2021 Geophys. Res. Lett. 48 e2020GL090411
[21] An B K and Feng Y 2014 Appl. Mech. Mater. 651 1811
[22] Wang Y and Wang J F 2021 J. Forecast. 40 911
[23] Zhang P T, Ning P Y, Cao R H and Xu J W 2021 Front. Phys. 9 702064
[24] Huang S, Chen J, Li M Y, Xu Y H and Hu M B 2024 Chin. Phys. B 33 030205
[25] Hu X, Chen J X and Xia C Y 2024 Chin. Phys. B 33 100202
[26] Boers N, Bookhagen B, Marwan N, Kurths J and Marengo J 2013 Geophys. Res. Lett. 40 4386
[27] Konapala G and Mishra A 2017 J. Hydrol. 555 600
[28] Malik N, Bookhagen B, Marwan N and Kurths J 2012 Clim. Dyn. 39 971
[29] Mondal S, Mishra A K and Leung L R 2020 Geophys. Res. Lett. 47 e2020GL088185
[30] Feng A X, Gong Z Q, Wang Q G and Feng G L 2012 Theor. Appl. Climatol. 109 635
[31] Feng A X, Wang Q G, Zhang S X, Enomoto T, Gong Z Q, Hu Y Y and Feng G L 2022 Chin. Phys. B 31 049201
[32] Gong Z Q, Su H J, He S H and Feng G L 2017 J. Trop. Meteorol. 23 426
[33] Gong Z Q, Zhou L, Zhi R and Feng G L 2008 Acta Phys. Sin. 57 5351 (in Chinese)
[34] Hu H R, Gong Z Q, Wang J, Qiao P J, Liu L and Feng G L 2021 Acta Phys. Sin. 70 249201 (in Chinese)
[35] He S H, Gong Z Q, Ye F and Feng G L 2017 Acta Meteorol. Sin. 75 894 (in Chinese)
[36] Zhou L, Gong Z Q, Zhi R and Feng G L 2008 Acta Phys. Sin. 57 7380 (in Chinese)
[37] Yang J H, Song J Q and Cao X Q 2013 Acta Phys. Sin. 62 174102 (in Chinese)
[38] Hu J, Zhang Y J, Wu P and Li H J 2022 Chaos, Solitons and Fractals 154 111613
[39] Ding T, Qian W H and Yan Z W 2010 Int. J. Climatol. 30 1452
[40] Perkins-Kirkpatrick S E and Lewis S C 2020 Nat. Commun. 11 3357
[41] Perkins S E 2015 Atmos. Res. 164-165 242
[42] Alexander L V, Zhang X, Peterson T C, et al. 2006 J. Geophys. Res. Atmos. 111 D5
[43] Perkins S E, Alexander L V and Nairn J R 2012 Geophys. Res. Lett. 39 1
[44] Quiroga R Q, Kreuz T and Grassberger P 2002 Phys. Rev. E 66 041904
[45] McKinnon K A, Simpson I R and Williams A P 2024 Proc. Nat. Acad. Sci. USA 121 e2406143121
[46] Ning G C, Luo M, Zhang Q, Wang S G, Liu Z, Yang Y J, Wu S J and Zeng Z L 2021 J. Geophys. Res. Atmos. 126 e2020JD034111
[47] Kadier Z, Li Z Y, Abulikemu A, Zhu K F, Abulimiti A, An D W and Abuduaini A 2023 Remote Sens. 15 4833
[48] Zhang X Y, Liu Y, Zhu Y, Ma Q, Philippe G, Qu Y P and Yin H 2023 Hydrol. Res. 54 869
[49] Guo Y X and Fu Z T 2024 Clim. Dyn. 62 665
[50] Chen X L and Zhou T J 2018 Clim. Dyn. 51 4403
[51] Zhao R, Zhou X, Liu J, Li Y P, Huang G H and Li C H 2024 J. Hydrol. 639 131635
[52] Wu J C, Zhu Y, Liu Y, Yin H, Yuan F andWang J 2022 J. China Hydrol. 42 72 (in Chinese) |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|
