The 2022 Record-Breaking Heat Event over the Middle and Lower Reaches of the Yangtze River: The Role of Anthropogenic Forcing and Atmospheric Circulation

Dongqian Wang Climate Studies Key Laboratory, National Climate Center, China Meteorological Administration, Beijing, China;

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Ying Sun Climate Studies Key Laboratory, National Climate Center, China Meteorological Administration, Beijing, and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

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Ting Hu Climate Studies Key Laboratory, National Climate Center, China Meteorological Administration, Beijing, China;

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Hong Yin Climate Studies Key Laboratory, National Climate Center, China Meteorological Administration, Beijing, China;

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Abstract

The anthropogenic forcing and anomalous atmospheric circulation have increased the occurrence probability of 2022-like extreme heat by approximately 62.0 and 2.6 times, respectively.

© 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: Ying Sun, sunying@cma.gov.cn

Abstract

The anthropogenic forcing and anomalous atmospheric circulation have increased the occurrence probability of 2022-like extreme heat by approximately 62.0 and 2.6 times, respectively.

© 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: Ying Sun, sunying@cma.gov.cn

In July–August (JA) of 2022, a rare heat event hit the middle and lower reaches of the Yangtze River (MLYR; 25°–35°N, 100°–122.5°E; black box in Fig. 1a), which is one of the most important economic zones in China. The number of hot days with daily maximum temperature above 35°C in the MLYR was at a historical high of 28.4 days, 3 times the 1961–90 average (Figs. 1a,b). Heat-induced drought, water shortages, energy shortages, and wildfires affected about 38.3 million people, with 4.3 million people in need of livelihood assistance (MEM 2022). China Meteorological Administration (CMA) issued a 41-day “high temperature warning” and a 12-day red warning for the first time in history (CMA 2023a). Understanding the causes of this event and projecting its potential future changes is an urgent issue for policymakers and the public.

Fig. 1.
Fig. 1.

(a),(d) Spatial distribution of observed NHD and TAS anomalies (relative to 1961–90) during July–August in 2022. The black box shows the area under investigation. (b),(e) Time series of NHD and TAS anomalies of observations (black), CMIP6 ALL forcing simulations (red) and CMIP6 NAT forcing simulations (blue) relative to 1961–90 (thick lines denote ensemble mean, and shading denotes the 5%–95% ranges of the individual model simulations; thick dashed line denotes adjusted ensemble mean). (c) Scatterplot between NHD and TAS anomalies during the period of 1961–2022. (f) Spatial distribution of ZG500 anomalies (shaded) relative to 1961–90 and correlation coefficients between ZG500 and the regional-averaged TAS anomalies (contours). The black box marks the selected region to reflect the influence of atmospheric circulation on the heat event.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-23-0152.1

Previous studies have suggested that human activity has increased the likelihood of heat waves in China (Sun et al. 2014). With continued global warming, the occurrence of rare extreme events with anomalous intensity (Chen et al. 2022) has posed additional challenges to event attribution studies, because the climate models may not be able to reproduce these rare events (Wang and Sun 2022). The high climate sensitivity of CMIP6 models (Zelinka et al. 2020) also has a potential impact on the robustness of attribution results. Additionally, the impact of anomalous circulation patterns on the extreme event has received increasing attention but to what extent the circulation has affected the extreme event remains not very clear, as the attribution results can be different if the problem is framed differently (Christidis and Stott 2015, 2022; Trenberth et al. 2015). Here we used observation-constrained model response and circulation similarity methods to investigate the role of anthropogenic forcing and atmospheric circulation in the 2022 heat event. The future changes in such an event were also projected under different emission scenarios.

Data and methods

For the observation, we used homogenized daily and monthly near-surface air temperature (TAS) at 2,419 stations (Zhu et al. 2015) for the period 1961–2022 developed by the National Meteorological Information Center (NMIC). We used the number of hot days (NHD) when daily maximum temperature is equal to or above 35°C to characterize this extreme heat event. The 35°C threshold has been used as the standard for CMA to issue high temperature blue warning (CMA 2023b) and is an important indicator for human health. We computed NHD at each station and then averaged the anomalies (relative to 1961–90 mean) onto 2.5° × 2.5° grid boxes. The July–August NHD averaged in the MLYR was finally calculated. We also used geopotential height at 500 hPa (ZG500) from NCEP–NCAR reanalysis (Kalnay et al. 1996) to investigate the impact of anomalous atmospheric circulation on the 2022 extreme heat event.

For the model results, we used TAS and ZG500 from climate models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6; Eyring et al. 2016). There were 173 runs from 31 models under historical [ALL, natural + anthropogenic (ANT)] forcing, 71 runs from 14 models under natural-only (NAT) forcing, and 148(135) runs from 31 models under SSP2–4.5(SSP5–8.5) scenarios, respectively (Table ES1 in the online supplemental material; https://doi.org/10.1175/BAMS-D-23-0152.2). To avoid too many runs from one individual model, we kept at most eight runs from each model. Because most of ALL simulations end in 2014, the model results under the SSP2–4.5 scenario were used to extend the simulations to 2027, as the NAT simulations for 2015–20 were driven by the SSP2–4.5 natural forcing agents (Gillett et al. 2016). We used preindustrial control (CTL) simulations from 31 models to estimate natural internal variability. All the model results were interpolated into the same gridded boxes as the observation at 2.5° × 2.5°, using the quadratic spline interpolation.

The baseline period of 1961–90 was constructed based on the ensemble mean for each model and used for the calculation of anomalies for all the simulations to maintain the impact of external forcing.

To estimate the anthropogenic influence on the event, we applied a two-step attribution method (Sun et al. 2014): 1) We found a close relationship between the TAS and NHD in the MLYR (correlation coefficient 0.91, Fig. 1c), so the TAS in the MLYR can be used as an indicator of 2022-like extreme heat event to conduct attribution study. 2) Because the CMIP6 models overestimated the observed TAS (Fig. 1e), we conducted a bias adjustment based on an optimal fingerprinting method (Allen and Stott 2003; Ribes et al. 2013) to correct the model temperature response to external forcing. The single- (ALL forcing) and two-signal (ANT and NAT forcing) detection analyses were conducted by regressing the observed TAS onto the models. The regression coefficients (scaling factors) were then obtained to best estimate the TAS response to external forcing. By adding the best estimates of the adjusted TAS response to ANT forcing in 2022 to the CTL simulations (representing the internal variability of climate system), the reconstructed TAS series represents a reasonable estimate for the world under anthropogenic forcing (for more detailed information, please refer to the supplemental material). 3) We calculated risk ratio (RRANT/CTL = PANT/PCTL) to quantify anthropogenic influence on the occurrence probability of the 2022-like extreme heat event, where PANT and PCTL represent the probabilities exceeding the 2022 TAS threshold in the reconstructed ANT and CTL samples, respectively.

To estimate the impact of anomalous circulation on the event, we used a circulation similarity method developed by Christidis and Stott (2015). Previous studies have shown that the strengthening and westward extension of western North Pacific subtropical high (WPSH) is the major atmospheric circulation pattern responsible for the heat event in the MLYR (Wang et al. 2016). The WPSH impact the heat event by directly causing a descending motion around its ridgeline (Kosaka et al. 2012). We found that the observed TAS over the MLYR is highly correlated (with correlation coefficient 0.74) with the WPSH in the key region of 30°–37.5°N, 115°–130°E (Fig. 1f). We compared different region choices by expanding the latitude and longitude belts by 2.5° and found no significant influence on the attribution results. The ALL results in the current climate period (11 years centered on 2022) were then divided into two groups by judging the high or low correlations between observed ZG500 and model ALL simulations: ALL-High and ALL-Low, which represent the groups with correlation coefficients above or below 0.6, respectively. The probabilities of events exceeding the 2022-like threshold value in these two groups were then calculated, and RRALL-High/ALL-low was defined as PALL-High/PALL-Low. The confidence interval was estimated with a 1,000 times Monte Carlo bootstrap (Christidis et al. 2013).

For the future projection, we used an attribution constrained method (Stott and Forest 2007) by multiplying the multimodel ensemble mean under SSP2–4.5 and SSP5–8.5 by the regression coefficients for ALL forcing detection to obtain future TAS changes. We used the occurrence probabilities of the 2022-like event in each of the future decades (e.g., 2030–39, P2030s) and current climate state (Pccs; 11 years centered on 2022) to estimate RR (RR2030s/ccs = P2030s/Pccs).

Results

In JA of 2022, the NHD and TAS anomalies in most stations (more than 70%) of the MLYR were more than two standard deviations, and more than half stations set new record since 1961 (Figs. 1a,d). The regional mean NHD and TAS anomalies over the MLYR were 18.4 days and 2.43°C, 5.3 and 4.6 standard deviations above the 1961–90 mean, respectively. A strong anomalous anticyclonic circulation controlled the region from the East China Sea to the MLYR, suppressed the development of convective activities and promoted an increase in surface solar radiation, thus promoting the continuation of the extreme heat event (Fig. 1f).

The CMIP6 models reproduced the TAS increase but the multimodel ensemble mean showed an overestimate of the linear trend (0.21°C decade–1) compared with the observed 0.12°C decade–1 (Fig. 1e). The best estimate of the scaling factor for ALL experiments based on optimal fingerprinting method was 0.77 [90% confidence interval (CI), 0.49–1.06], suggesting an overestimated temperature response in the models and the results should be scaled down to best match the observation (Fig. ES1). By multiplying the scaling factor to the multimodel mean TAS anomaly under ALL experiments, we reconstructed the TAS time series and estimated that the 2022 TAS anomaly attributed to ALL forcing was 0.96°C, while the anomaly due to internal variability was 1.47°C (Fig. 1e). The standard deviations of TAS were also calculated to evaluate model performance in simulating observed variability. The standard deviation of observed TAS was 0.66°C, and it became 0.58°C after removing adjusted ALL response. The standard deviation in the model CTL simulations was 0.48°C (5th–95th percentiles: 0.30°–0.67°C), which was slightly smaller but generally comparable to the observed standard deviation without external forcing. All these indicated that the adjusted CMIP6 model results can be used for the event attribution study in the MLYR region.

With the adjusted TAS model data, we estimated anthropogenic influence on the 2022-like events. The observed TAS anomaly (2.43°C) in 2022 was used as the threshold to calculate the probability in the models with and without external forcing. The percentages of years with TAS anomalies above 2.43°C under preindustrial conditions is 0.01% (PCTL = 0.00009), suggesting that the 2022-like extreme heat event would rarely happen in the world without any forcing. By contrast, the estimated probability is 0.57% in the reconstructed model world with ANT forcing (PANT = 0.00569). The RRANT/CTL is estimated as 62.0 (90% CI: 9.0–133.6), meaning that the anthropogenic forcing has increased the occurrence of 2022-like extreme heat event by 62.0 times (Fig. 2a).

Fig. 2.
Fig. 2.

(a) Histograms of TAS anomalies in reconstructed model experiments with and without anthropogenic influence. (b) As in (a), but in model experiments with low- and high-correlated circulation patterns with observations in 2022. (c) The estimated risk ratios of 2022-like heat event in the coming decades.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-23-0152.1

We further evaluate the impact of anomalous circulation on the heat event. The models showed a close relationship between TAS and WPSH in ALL simulations, with only 8 out of 173 members unable to reproduce the correlation well (p > 0.01). In ALL-High simulations, the probability of the 2022-like heat event was 7.05%, while it became 2.67% in ALL-Low simulations. This suggests that the appearance of anomalous WPSH circulation pattern has increased the probability of the event by 2.6 (90% CI: 1.8–3.5) times (Fig. 2b). Additionally, we noted that both PALL-Low and PALL-High may be overestimated, as the ALL simulations showed a warmer TAS than observation, but this equivalent overestimation caused by systematic bias will not have a serious impact on RR.

In the future, the rapid increase in the occurrence probability of a 2022-like extreme heat event is projected to continue (Fig. 2c). Under a medium SSP2–4.5 scenario, the probability of a 2022-like event (Pccs = 0.00396, 1-in-253-yr event in current climate state) is projected to increase 117.9 (90% CI: 78.1–166.0) times by the 2070s, meaning that the MLYR is projected to experience a 2022-like or more severe heat event almost every 2 years by the 2070s. While under a higher emission scenario (SSP5–8.5), an equal level of risk increase is projected to occur by the 2050s.

Conclusions

The extreme heat event in the MLYR in 2022 was unprecedented and has caused serious impacts on the natural ecosystem and social economy. We conducted a bias adjustment to the CMIP6 models based on optimal fingerprinting method and evaluated the influence of anthropogenic forcing and atmospheric circulation on the event. Results indicated that the anthropogenic forcing has substantially increased the occurrence probability of such heat events (62.0 times) and the anomalous pattern of WPSH circulation has increased the occurrence probability of a 2022-like event by 2.6 times. In the future, the 2022-like event was projected to become a once-in-two-year event by the 2050s under the SSP5–8.5 scenario. Compared with previous attribution studies, we found a clear anthropogenic signal in a quite small regional scale of the MLYR, indicating enhanced human influence in recent years and a great challenge to adapt to rapid global warming.

Acknowledgments.

This study was supported by the National Natural Science Foundation of China (42025503, U2342228), the National Key R&D Program of China (2018YFA0605604), and the Key Innovation Team of China Meteorological Administration “Climate Change Detection and Response” (CMA2022ZD03).

Data availability statement.

The following data are available online: the CMIP6 GCM simulations (https://esgf-node.llnl.gov/projects/cmip6/), and the homogenized station data in China (http://data.cma.cn/).

References

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    • Search Google Scholar
    • Export Citation
  • Wang, D. Q., and Y. Sun, 2022: Effects of anthropogenic forcing and atmospheric circulation on the record-breaking welt bulb heat event over southern China in September 2021. Adv. Climate Change Res., 13, 778786, https://doi.org/10.1016/j.accre.2022.11.007.

    • Search Google Scholar
    • Export Citation
  • Wang, W., W. Zhou, X. Li, X. Wang, and D. Wang, 2016: Synoptic-scale characteristics and atmospheric controls of summer heat waves in China. Climate Dyn., 46, 29232941, https://doi.org/10.1007/s00382-015-2741-8.

    • Search Google Scholar
    • Export Citation
  • Zelinka, M. D., T. A. Myers, D. T. McCoy, S. Po-Chedley, P. M. Caldwell, P. Ceppi, S. A. Klein, and K. E. Taylor, 2020: Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782.

    • Search Google Scholar
    • Export Citation
  • Zhu, Y. N., L. J. Cao, G. L. Tang, and Z. J. Zhou, 2015: Homogenization of surface relative humidity over China (in Chinese). Climate Change Res., 11, 379386, https://doi.org/10.3969/j.issn.1673-1719.2015.06.001.

    • Search Google Scholar
    • Export Citation

Supplementary Materials

Save
  • Allen, M., and P. A. Stott, 2003: Estimating signal amplitudes in optimal fingerprinting, Part I: Theory. Climate Dyn., 21, 477491, https://doi.org/10.1007/s00382-003-0313-9.

    • Search Google Scholar
    • Export Citation
  • Chen, H. P., W. Y. He, J. P. Sun, and L. F. Chen, 2022: Increases of extreme heat-humidity days endanger future populations living in China. Environ. Res. Lett., 17, 064013, https://doi.org/10.1088/1748-9326/ac69fc.

    • Search Google Scholar
    • Export Citation
  • Christidis, N., and P. A. Stott, 2015: Extreme rainfall in the United Kingdom during winter 2013/14: The role of atmospheric circulation and climate change [in “Explaining Extreme Events of 2014 from a Climate Perspective”]. Bull. Amer. Meteor. Soc., 96, S46S50, https://doi.org/10.1175/BAMS-D-15-00094.1.

    • Search Google Scholar
    • Export Citation
  • Christidis, N., and P. A. Stott, 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, A. A. Scaife, A. Arribas, G. S. Jones, D. Copsey, J. R. Knight, and W. J. Tennant, 2013: A new HadGEM3-A-based system for attribution of weather- and climate-related extreme events. J. Climate, 26, 27562783, https://doi.org/10.1175/JCLI-D-12-00169.1.

    • Search Google Scholar
    • Export Citation
  • CMA, 2023a: Top 10 weather and climate events in 2022 unveiled (in Chinese). China Meteorological Administration, accessed 9 January 2023, www.cma.gov.cn/2011xwzx/2011xqxxw/2011xqxyw/202301/t20230109_5247477.html.

  • CMA, 2023b: Measures for issuing warning of meteorological disasters of the National Meteorological Center (in Chinese). China Meteorological Administration, accessed 4 April 2023, www.cma.gov.cn/2011xwzx/2011xqxxw/2011xqxyw/202304/t20230421_5453121.html.

  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, https://doi.org/10.5194/gmd-9-1937-2016.

    • Search Google Scholar
    • Export Citation
  • Gillett, N. P., and Coauthors, 2016: The Detection and Attribution Model Intercomparison Project (DAMIP v1.0) contribution to CMIP6. Geosci. Model Dev., 9, 36853697, https://doi.org/10.5194/gmd-9-3685-2016.

    • 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
  • Kosaka, Y., J. S. Chowdary, S.-P. Xie, Y. M. Min, and J. Y. Lee, 2012: Limitations of seasonal predictability for summer climate over East Asia and the northwestern Pacific. J. Climate, 25, 75747589, https://doi.org/10.1175/JCLI-D-12-00009.1.

    • Search Google Scholar
    • Export Citation
  • MEM, 2022: China national natural disaster situation in August of 2022 (in Chinese). Ministry of Emergency Management of China, accessed 17 September 2022, www.mem.gov.cn/xw/yjglbgzdt/.

  • Ribes, A., S. Planton, and L. Terray, 2013: Application of regularised optimal fingerprinting to attribution. Part I: Method, properties and idealised analysis. Climate Dyn., 41, 28172836, https://doi.org/10.1007/s00382-013-1735-7.

    • Search Google Scholar
    • Export Citation
  • Stott, P. A., and C. E. Forest, 2007: Ensemble climate predictions using climate models and observational constraints. Philos. Trans. Roy. Soc., A365, 20292052, https://doi.org/10.1098/rsta.2007.2075.

    • Search Google Scholar
    • Export Citation
  • Sun, Y., X. B. Zhang, F. W. Zwiers, L. C. Song, H. Wan, T. Hu, H. Yin, and G. Y. Ren, 2014: Rapid increase in the risk of extreme summer heat in eastern China. Nat. Climate Change, 4, 10821085, https://doi.org/10.1038/nclimate2410.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., J. T. Fasullo, and T. G. Shepherd, 2015: Attribution of climate extreme events. Nat. Climate Change, 5, 725730, https://doi.org/10.1038/nclimate2657.

    • Search Google Scholar
    • Export Citation
  • Wang, D. Q., and Y. Sun, 2022: Effects of anthropogenic forcing and atmospheric circulation on the record-breaking welt bulb heat event over southern China in September 2021. Adv. Climate Change Res., 13, 778786, https://doi.org/10.1016/j.accre.2022.11.007.

    • Search Google Scholar
    • Export Citation
  • Wang, W., W. Zhou, X. Li, X. Wang, and D. Wang, 2016: Synoptic-scale characteristics and atmospheric controls of summer heat waves in China. Climate Dyn., 46, 29232941, https://doi.org/10.1007/s00382-015-2741-8.

    • Search Google Scholar
    • Export Citation
  • Zelinka, M. D., T. A. Myers, D. T. McCoy, S. Po-Chedley, P. M. Caldwell, P. Ceppi, S. A. Klein, and K. E. Taylor, 2020: Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782.

    • Search Google Scholar
    • Export Citation
  • Zhu, Y. N., L. J. Cao, G. L. Tang, and Z. J. Zhou, 2015: Homogenization of surface relative humidity over China (in Chinese). Climate Change Res., 11, 379386, https://doi.org/10.3969/j.issn.1673-1719.2015.06.001.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    (a),(d) Spatial distribution of observed NHD and TAS anomalies (relative to 1961–90) during July–August in 2022. The black box shows the area under investigation. (b),(e) Time series of NHD and TAS anomalies of observations (black), CMIP6 ALL forcing simulations (red) and CMIP6 NAT forcing simulations (blue) relative to 1961–90 (thick lines denote ensemble mean, and shading denotes the 5%–95% ranges of the individual model simulations; thick dashed line denotes adjusted ensemble mean). (c) Scatterplot between NHD and TAS anomalies during the period of 1961–2022. (f) Spatial distribution of ZG500 anomalies (shaded) relative to 1961–90 and correlation coefficients between ZG500 and the regional-averaged TAS anomalies (contours). The black box marks the selected region to reflect the influence of atmospheric circulation on the heat event.

  • Fig. 2.

    (a) Histograms of TAS anomalies in reconstructed model experiments with and without anthropogenic influence. (b) As in (a), but in model experiments with low- and high-correlated circulation patterns with observations in 2022. (c) The estimated risk ratios of 2022-like heat event in the coming decades.

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