Anthropogenic Contribution to the Unprecedented 2022 Midsummer Extreme High-Temperature Event in Southern China

Chenyu Cao Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, and Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou, China;

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Xiaodan Guan Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou, China;

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Chao Li Key Laboratory of Geographic Information Science, Ministry of Education, and School of Geographic Sciences, East China Normal University, Shanghai, China;

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Zhaokui Gao Key Laboratory for Semi-Arid Climate Change, Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

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Tonghui Gu Key Laboratory for Semi-Arid Climate Change, Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

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Anthropogenic influence contributed approximately 61% to the extreme high-temperature event in southern China in midsummer 2022, according to a dynamic adjustment methodology and supported by optimal fingerprinting analysis of CMIP6 models.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR: Xiaodan Guan, guanxd@lzu.edu.cn

Anthropogenic influence contributed approximately 61% to the extreme high-temperature event in southern China in midsummer 2022, according to a dynamic adjustment methodology and supported by optimal fingerprinting analysis of CMIP6 models.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR: Xiaodan Guan, guanxd@lzu.edu.cn

From June to August 2022, extensive regions of China, particularly southern China experienced an unprecedented and prolonged extreme high-temperature event (hereafter EHT). This event lasted for 69 days, leading to record-breaking temperatures surpassing 40°C in many cities. According to records from the China Meteorological Administration (CMA), this event was the most severe in terms of duration, spatial extent, and intensity since 1961 (www.cma.gov.cn/2011xwzx/2011xqxxw/2011xqxyw/202208/t20220817_5038081.html). It exceeded the duration of the 2013 EHT by 7 days. According to the China Ministry of Emergency Management, approximately 38.305 million people were affected, and approximately 4,076 thousand hectares of crops were damaged due to the persistent high temperatures and corresponding drought conditions. The direct economic losses were estimated to be 32.8 billion RMB (www.mem.gov.cn/xw/yjglbgzdt/202209/t20220917_422674.shtml).

EHTs have emerged as significant meteorological disasters on global scale (Christidis et al. 2011; Whan et al. 2015; S. S. Wang et al. 2021; Zheng and Wang 2019). Since 2013, China has experienced frequent and severe EHTs, and this trend is projected to continue beyond 2022 (Lu et al. 2016; Sun et al. 2016; Wang et al. 2019; J. Wang et al. 2021) . Previous analyses of EHTs in various regions have primarily focused on the factors of atmospheric circulation (Lu et al. 2020), sea surface temperature (Jian et al. 2020), urbanization effects (Zhou et al. 2020), and human activities (Ma et al. 2017; Wang et al. 2020; Christidis and Scott 2022; Kim et al. 2022). However, the contribution of natural and anthropogenic factors to the unprecedented EHT in southern China in 2022 remains unclear. Consequently, we first examined the record-breaking EHT based on the observed dataset. Subsequently, a dynamic adjustment methodology was applied to extract and quantify the relative contributions of dynamic and adjusted forcings to this EHT. These findings were further supported by the optimal fingerprinting analysis.

Data and methods

The observed 2-m daily maximum temperature data which covered 1979 to present with a resolution of 0.5° × 0.5° from the Climate Prediction Center Global Unified Temperature dataset provided by the National Oceanic and Atmospheric Administration were employed to examine and analyze the attribution of EHTs. Additionally, we utilized a monthly geopotential height dataset that includes 17 vertical levels and sea level pressure dataset with a spatial resolution of 2.5° from the National Centers for Environmental Prediction (Kalnay et al. 1996) and sea surface temperature data at a resolution of 1° × 1° provided by the Met Office Hadley Centre to analyze the associated atmospheric circulations (Rayner et al. 2003; Hu et al. 2021).

In this study, EHT were defined and analyzed based on three aspects, namely, averaged tasmax during midsummer (Tasmax), count (TX90p, warm days), and intensity (TXx, the hottest day), which were compared with the base period (1991–2020) according to the extreme event indices provided by the Expert Team on Sector-Specific Climate Indices (ET-SCI; https://climpact-sci.org/indices/). To estimate the return periods of this event, the generalized extreme value (GEV) distribution was employed here.

The dynamic adjustment methodology, proposed by Wallace et al. (2012) to analyze the causes of increasing temperatures in the mid- to high latitudes of the Northern Hemisphere, was applied in this study (Guan et al. 2015a,b; Guo et al. 2018). The raw Tasmax was divided into two components based on this methodology: the dynamically induced (dyn) and radiatively induced (rad) parts. The rad parts represent the anthropogenic influence, while the dyn parts represent the influence of atmospheric circulation. More detailed descriptions of this methodology can be found in Guan et al. (2015a) and Smoliak et al. (2015).

Furthermore, we used 19 models from the Coupled Model Intercomparison Project phase 6 (CMIP6) under different simulation scenarios to verify the contribution of anthropogenic influence and estimate the fraction of attributable risk (FAR) (Allen 2003; Stott et al. 2004). The FAR is defined as FAR = 1 – (P0/P1), where P0 (P1) is the probability of exceeding Tasmax without (with) anthropogenic forcing. Due to the limit of historical simulations, the daily maximum temperature from seven models under shared socioeconomic pathways (SSP) 2–4.5 was used to extend the historical simulations, including historical (ALL) and only natural forcing (NAT) experiments (a total of 19 models contain historical and SSP2–4.5 scenarios, of which seven models contain SSP2–4.5 nat scenario). A total of 1,000 bootstrapping samples were employed to estimate 10%–90% confidence intervals (CI; Ma et al. 2023; Wang et al. 2023). All the observational, reanalysis, and simulation data were interpolated to the same spatial resolution of 1° × 1°.

Results

Severe EHT primarily occurred south of 40°N during July and August, particularly in the Yangtze River basin (indicated by the black box in Fig. 1a), as shown by spatial analyses of Tasmax, TX90p, and TXx anomalies (Figs. 1a–c). The corresponding area-averaged anomalies reached 2.54°C, 20 days, and 2.1°C, respectively, and all reached their highest levels in the past 44 years (Fig. 1d). Of particular significance is the fact that the TXx anomalies surpass the Tasmax anomalies, indicating an exceptionally severe EHT in China in 2022. Based on GEV fits, the return periods of Tasmax and TX90p were estimated to be 185 (CI: 32 ± ∞) and 117 (CI: 6 ± ∞) years, respectively (Fig. 1e and Fig. S1a in the online supplemental material; https://doi.org/10.1175/BAMS-D-23-0199.2). However, the return period for TXx exceeded 400 years, underscoring the unprecedented nature of this extreme heat event.

Fig. 1.
Fig. 1.

(a) Observed Tasmax anomaly during midsummer 2022 over southern China. The black box denotes the study region (25°–35°N, 85°–120°E). (b),(c) As in (a), but for TX90p and TXx. (d) Time series of the observed Tasmax anomaly (bars) and TX90p and TXx (lines) for the black box in (a) in midsummer from 1979 to 2022. (e) Return period of the Tasmax anomaly. The red circle represents 2022. (f) Geopotential height anomalies from 1,000 to 10 hPa in midsummer 2022. All anomalies are relative to 1991–2020. The dotted line represents the 10% confidence intervals.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-23-0199.1

The occurrence of EHT is often associated with large-scale atmospheric circulations (Fig. 1f). In midsummer 2022, a “high pressure belt” formed, connecting the eastward extension of the Iranian high and the westward extension of the western Pacific subtropical high (WPSH) at 500 hPa, contributing to the occurrence of EHT in southern China. Furthermore, an exceptionally robust South Asian high appeared at 200 hPa (Figs. S1c,d). In general, the negative dipole mode in the Indian Ocean, combined with the strong La Niña event in the equatorial Pacific led to this EHT through intensifying the South Asian high and the WPSH (Fig. S1e).

However, such severe EHT cannot be solely attributed to atmospheric circulation. To recognize the influence of anthropogenic climate change on these EHT, we employed the dynamic adjustment methodology to calculate the contribution of anthropogenic forcing (rad part) and circulation forcing (dyn part) to Tasmax. The spatial pattern of the rad part (Figs. 2a,b) exhibits a similar distribution to the raw Tasmax over southern China during midsummer 2022. On the other hand, the dyn part does not exceed 1°C across China, except for southwestern China (Fig. 2c). In accordance with earlier studies, northern China was historically characterized by severe desertification and susceptibility to the effects of climate change (Huang et al. 2016). Yu et al. (2021) utilized a high-resolution (25 km) land–atmosphere coupled Weather Research and Forecasting (WRF) regional climate model. Their findings indicate that the augmentation of vegetation has counteracted the warming induced by human activities in recent years. However, this effect appears to be less significant in humid regions in southern China, leading to a contrasting temperature anomaly between northern and southern China. Regarding regionally averaged Tasmax, the raw, rad, and dyn Tasmax are 2.54°, 1.54°, and 1.0°C during midsummer 2022, respectively. The rad and dyn components account for 60.6% and 39.4% of the raw Tasmax, respectively (Fig. 2d). According to the GEV fitting for the raw, rad, and dyn Tasmax, the FAR is estimated to be 0.99 (for raw and dyn Tasmax, relative to atmospheric circulation), which indicates that anthropogenic forcing dominated this EHT (Fig. 2e).

Fig. 2.
Fig. 2.

(a)–(c) Raw, rad, and dyn Tasmax anomalies during midsummer 2022 over southern China relative to those in 1991–2020. (d) Region-averaged raw, rad, and dyn Tasmax anomalies in midsummer from 1979 to 2022 over southern China. The solid circles represent the Tasmax in 2022. (e) CDF of the raw, rad, and dyn Tasmax. The black dotted line represents the observations for 2022.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-23-0199.1

To confirm the robustness of anthropogenic contributions to the EHT, we applied the method proposed by Sun et al. (2014, 2018) to validate the results obtained from the dynamic adjustment methodology. Figure S2a illustrates the nonoverlapping series of midsummer mean 5-yr Tasmax anomalies from 1979 to 2022 (the last point is for 2019–22). It is evident that the Tasmax under ALL (0.56°C) underestimated the observed Tasmax (0.72°C). Moreover, the results from the optimal fingerprinting analysis revealed that the scaling factors for ALL and NAT are 0.8 (CI: 0.42–1.23) and 0.297 (CI: –0.78 to 1.25), respectively, in the two-signal detection analysis, where the observed series is regressed onto ALL and NAT simultaneously. This indicates that the observed Tasmax is primarily induced by anthropogenic forcing. Although natural forcing may contribute to the occurrence of extremely high temperatures, it is not statistically significant (the confidence interval includes 0; Fig. S2b).

By examining the GEV fit of Tasmax under both ALL and NAT scenarios, we find that the probability of experiencing EHT similar to that in 2022 has increased due to anthropogenic forcing. The FAR is estimated to be 0.85 (CI: 0.78–0.9) (relative to NAT). This further supports the notion that anthropogenic factors play a significant role in the 2022-like high temperature event.

Conclusions

This study aimed to investigate the role of anthropogenic forcing in the unprecedented extreme high-temperature event that occurred in southern China during midsummer 2022 based on a dynamic adjustment methodology. It is worth noting that CPC only provided data from 1979 to the present, but longer time series data may have an impact on the recurrence period and attribution of this EHT. Our analysis revealed that 60.6% of the 2022 EHT is attributed to anthropogenic climate changes in southern China. These results were further supported by the FAR estimated through GEV fitting, implying the suitability of dynamic adjustment methods for detecting and attributing extreme weather events and assessing the contribution of anthropogenic forcing to climate change. Nevertheless, further investigation is warranted to explore the influence of external forcing on internal variability and the role of internal variability in response to external forcing. This would provide deeper insights for a more accurate attribution of extreme events.

Acknowledgments.

Chenyu Cao and Xiaodan Guan are first coauthors. This work is supported by the National Natural Science Foundation of China (42041004 and 41722502), and the Central Universities (lzujbky-2022-ct06). The authors thank the CPC, NCEP, Hadley Centre, and CMIP6 for providing accessible excellent datasets that made this study possible.

Data availability statement.

The CPC dataset is from https://psl.noaa.gov/data/gridded/data.cpc.globaltemp.html. The NCEP reanalysis dataset can be accessed from https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html. The Hadley Centre dataset is available from www.metoffice.gov.uk/hadobs/hadisst/data/download.html. And the CMIP6 outputs can be downloaded from https://esgf-node.llnl.gov/projects/cmip6/.

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Supplementary Materials

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  • Allen, M. R., 2003: Liability for climate change. Nature, 421, 891892, https://doi.org/10.1038/421891a.

  • Christidis, N., and P. A. Scott, 2022: Anthropogenic climate change and the record-high temperature of May 2020 in western Europe [in “Explaining Extreme Events of 2020 from a Climate Perspective”]. Bull. Amer. Meteor. Soc., 103, S33S37, https://doi.org/10.1175/BAMS-D-21-0128.1.

    • Search Google Scholar
    • Export Citation
  • Christidis, N., P. A. Stott, and S. J. Brown, 2011: The role of human activity in the recent warming of extremely warm daytime temperatures. J. Climate, 24, 19221930, https://doi.org/10.1175/2011JCLI4150.1.

    • Search Google Scholar
    • Export Citation
  • Guan, X. D., J. Huang, R. Guo, and P. Lin, 2015a: The role of dynamically induced variability in the recent warming trend slowdown over the Northern Hemisphere. Sci. Rep., 5, 12669, https://doi.org/10.1038/srep12669.

    • Search Google Scholar
    • Export Citation
  • Guan, X. D., J. Huang, R. Guo, H. P. Yu, P. Lin, and Y. T. Zhang, 2015b: Role of radiatively forced temperature changes in enhanced semi-arid warming in the cold season over East Asia. Atmos. Chem. Phys., 15, 13 77713 786, https://doi.org/10.5194/acp-15-13777-2015.

    • Search Google Scholar
    • Export Citation
  • Guo, R. X., X. Guan, Y. He, Z. Gan, and H. Jin, 2018: Different roles of dynamic and thermodynamic effects in enhanced semi-arid warming. Int. J. Climatol., 38, 1322, https://doi.org/10.1002/joc.5155.

    • Search Google Scholar
    • Export Citation
  • Hu, Z. Y., and Coauthors, 2021: Was the extended rainy winter 2018/19 over the middle and lower reaches of the Yangtze River driven by anthropogenic forcing [in “Explaining Extreme Events of 2019 from a Climate Perspective”]? Bull. Amer. Meteor. Soc., 102, S67S73, https://doi.org/10.1175/BAMS-D-20-0127.1.

    • Search Google Scholar
    • Export Citation
  • Huang, J. P., M. Ji, Y. Xie, S. Wang, Y. He, and J. Ran, 2016: Global semi-arid climate change over last 60 years. Climate Dyn., 46, 11311150, https://doi.org/10.1007/s00382-015-2636-8.

    • Search Google Scholar
    • Export Citation
  • Jian, Y. T., X. X. Lin, W. Zhou, M. Q. Jian, M. Y. T. Leung, and P. K. Y. Cheung, 2020: Analysis of record-high temperature over southeast coastal China in winter 2018/19: The combined effect of mid- to high-latitude circulation systems and SST forcing over the North Atlantic and tropical western Pacific. J. Climate, 33, 88138831, https://doi.org/10.1175/JCLI-D-19-0732.1.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kim, Y.-H., S.-K. Min, D.-H. Cha, Y.-H. Byun, F. C. Lott, and P. A. Scott, 2022: Attribution of the unprecedented 2021 October heatwave in South Korea. Bull. Amer. Meteor. Soc., 103, E2923E2929, https://doi.org/10.1175/BAMS-D-22-0124.1.

    • Search Google Scholar
    • Export Citation
  • Lu, C. H., Y. Sun, H. Wan, X. Zhang, and H. Yin, 2016: Anthropogenic influence on the frequency of extreme temperatures in China. Geophys. Res. Lett., 43, 65116518, https://doi.org/10.1002/2016GL069296.

    • Search Google Scholar
    • Export Citation
  • Lu, C. H., Y. Sun, N. Christidis, and P. A. Stott, 2020: Contribution of global warming and atmospheric circulation to the hottest spring in eastern China in 2018. Adv. Atmos. Sci., 37, 12851294, https://doi.org/10.1007/s00376-020-0088-5.

    • Search Google Scholar
    • Export Citation
  • Ma, S. M., T. J. Zhou, D. A. Stone, O. Angélil, and H. Shiogama, 2017: Attribution of the July–August 2013 heat event in central and eastern China to anthropogenic greenhouse gas emissions. Environ. Res. Lett., 12, 054020, https://doi.org/10.1088/1748-9326/aa69d2.

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  • Fig. 1.

    (a) Observed Tasmax anomaly during midsummer 2022 over southern China. The black box denotes the study region (25°–35°N, 85°–120°E). (b),(c) As in (a), but for TX90p and TXx. (d) Time series of the observed Tasmax anomaly (bars) and TX90p and TXx (lines) for the black box in (a) in midsummer from 1979 to 2022. (e) Return period of the Tasmax anomaly. The red circle represents 2022. (f) Geopotential height anomalies from 1,000 to 10 hPa in midsummer 2022. All anomalies are relative to 1991–2020. The dotted line represents the 10% confidence intervals.

  • Fig. 2.

    (a)–(c) Raw, rad, and dyn Tasmax anomalies during midsummer 2022 over southern China relative to those in 1991–2020. (d) Region-averaged raw, rad, and dyn Tasmax anomalies in midsummer from 1979 to 2022 over southern China. The solid circles represent the Tasmax in 2022. (e) CDF of the raw, rad, and dyn Tasmax. The black dotted line represents the observations for 2022.

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