Influences of Anthropogenic Forcing on the Exceptionally Warm August 2022 over the Eastern Tibetan Plateau

Jianping Duan State Key Laboratory of Earth Surface and Ecological Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China;

Search for other papers by Jianping Duan in
Current site
Google Scholar
PubMed
Close
,
Haoxin Zhang State Key Laboratory of Severe Weather, and Institute of Tibetan Plateau Meteorology, Chinese Academy of Meteorological Sciences, Beijing, China;

Search for other papers by Haoxin Zhang in
Current site
Google Scholar
PubMed
Close
,
Dongnan Jian Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, China;

Search for other papers by Dongnan Jian in
Current site
Google Scholar
PubMed
Close
,
Cunde Xiao State Key Laboratory of Earth Surface and Ecological Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China;

Search for other papers by Cunde Xiao in
Current site
Google Scholar
PubMed
Close
,
Fengqi Hao State Key Laboratory of Earth Surface and Ecological Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China;

Search for other papers by Fengqi Hao in
Current site
Google Scholar
PubMed
Close
,
Hongzhou Zhu Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China;

Search for other papers by Hongzhou Zhu in
Current site
Google Scholar
PubMed
Close
,
Fraser C. Lott Met Office Hadley Centre, Exeter, United Kingdom

Search for other papers by Fraser C. Lott in
Current site
Google Scholar
PubMed
Close
, and
Peter A. Stott Met Office Hadley Centre, Exeter, United Kingdom

Search for other papers by Peter A. Stott in
Current site
Google Scholar
PubMed
Close
Open access

Phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations suggest that the extremely warm August over the Tibetan Plateau in 2022 could not occur without human influences, which corresponds to a new normal during 2070–2100.

© 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: Jianping Duan, duanjp@bnu.edu.cn

Phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations suggest that the extremely warm August over the Tibetan Plateau in 2022 could not occur without human influences, which corresponds to a new normal during 2070–2100.

© 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: Jianping Duan, duanjp@bnu.edu.cn

1. Introduction

The Tibetan Plateau is the highest and largest plateau on Earth, and there are plentiful glaciers and cold-environment animals, plants, and infrastructures (e.g., the Qinghai–Tibet railway). Anomalously high surface air temperature (SAT) has serious impacts on the regional ecosystem and infrastructures. An exceptionally warm event hit the eastern Tibetan Plateau (ETP) in August 2022 (Figs. 1a–d; Figs. ES1a–f in the online supplemental material) and drew great attention and concern from the scientific community. Especially, the anomalous magnitude of August mean minimum temperature (Tmin) on the ETP in 2022 was much greater than the surrounding areas, and new records were set in many meteorological stations (Figs. 1a,b). This exceptionally warm event linked to an anticyclonic circulation over the ETP intensified by the anomalous strong and westward western Pacific subtropical high (WPSH) as well as the enhanced South Asian high (SAH) in August 2022 (Fig. 1e, Figs. ES1g,i,j). The WPSH in August 2022 extended westward about 50° more than its average during 1961–90, and the western ridge reached over the Tibetan Plateau. In addition to the analyses of atmospheric circulation pattern, it is also essential to clarify the influences of anthropogenic forcing on the circulation pattern, the event likelihood, and the future risk of such events. These results can add to the scientific basis for the adaptation planning of the Tibetan Plateau to such anomalous warm events. Previous studies indicated the critical role of human influence on the recent record-breaking warm events in summer in the other parts of China (Ma et al. 2017; Chen et al. 2019; Ren et al. 2020; Zhou et al. 2020; Wang et al. 2021; Li et al. 2022). In this study, we concentrate on attributions and risk estimations of warm events as instances when the August Tmin was higher than the one observed on the ETP in 2022.

Fig. 1.
Fig. 1.

(a) Station-recorded anomalies, (b) rank of August 2022 Tmin, and (c) regionally station-averaged series of August Tmin during 1961–2022 in the study area. (d) Return periods and 95% confidence intervals for the observed August 2022 Tmin. (e) Anomalies of 500-hPa geopotential height field (Z500; gpm) and August Tmin of 2022 derived from the ERA5 dataset. (f) August Tmin anomaly distributions for months with high (light blue) and low (light gray) correlations with the reference Z500 pattern. The black box in (a), (b), and (e) indicates the study area. All the anomalies are with respect to the 1961–90 climatology. The thick red and cyan 5860-gpm isolines indicate the western ridge of the WPSH in 2022 and its average during 1961–90, respectively.

Citation: Bulletin of the American Meteorological Society 105, 7; 10.1175/BAMS-D-23-0179.1

2. Data and methods

Observed August SAT data and geopotential height field (Z500) and horizontal wind data at 500 hPa from ERA5 (Hersbach et al. 2020) were used to analyze the August warm event on the ETP in 2022. Simulated August Tmin and Z500 data from four climate models that participated in phase 6 of the Coupled Model Intercomparison Project (CMIP6; Eyring et al. 2016; Table ES1) with [anthropogenic and natural (ALL)] and without [natural-only (NAT)] anthropogenic forcing in the historical period (1961–2014/2020) and the future (2015/2021–2100) were used to perform the attribution analysis and risk estimation (please see the supplemental material for detailed information). Considering that the ALL and NAT simulations during the period 2015–20 were obtained using the shared socioeconomic pathway (SSP2-4.5) scenario (Gillett et al. 2021; He et al. 2023; Tang et al. 2023), we extended all the ALL and NAT simulations used in this study into 2100 utilizing the corresponding SSP2-4.5 experiments to assess future changes in the warm event. In view of the great difference of spatial resolution between the ERA5 data (0.25° × 0.25°) and model simulations (Table ES1), both the ERA5 and model simulations data were interpolated to a 0.5° × 0.5° grid (a compromise way) from their original grids and the results are generally robust against different spatial resolutions used to interpolate. Based on the observation display (Figs. 1a–d; Figs. ES1a–f), the extreme warm August event on the ETP was defined using the regionally station-averaged anomalies of August Tmin from 110 meteorological stations located within 28.6°–39°N, 90.2°–102.7°E (for detailed information, please see the supplemental material). The value in 2022 (the highest one, 4.85°C with respect to 1961–90 climatology) was chosen as the threshold for observations. For CMIP6 model simulations, area-averaged anomalies of August Tmin over the study region were calculated for the period 1961–2100. Based on the availability of observations, the period 1961–2022 was chosen as the historical period. To ensure a large sample size for the generalized extreme value (GEV) distribution, the periods 2007–37 and 2070–2100 (30 simulations × 31 years) were chosen as the present and the future, respectively. The GEV distribution was utilized to fit the distributions of both observed and simulated data. A two-sided Kolmogorov–Smirnoff (K–S) test was used to test if the distributions of the historical simulations and observations are from the same population. To make simulations and observations comparable, we constructed GEV distributions using the samples from simulations and derived the thresholds for the event in simulations with the same return period as the year 2022 in observation (Duan et al. 2021). Anthropogenic influences on the likelihood of frequency of the 2022-like event are estimated using the “risk ratio” (RR) (National Academies of Sciences, Engineering, and Medicine 2016; Sun et al. 2014). The RR was defined as P1/P0, where P0 is the probability of event in ensembles without anthropogenic forcing and P1 is that for ensembles with anthropogenic forcing. Confidence intervals of the return period were estimated using empirical data by employing bootstrapping with 1000 resamples. The linkage of the warm event to the circulation pattern was explored by analyzing the pattern correlation coefficients between the reference (i.e., the observed one, Fig. 1e) and simulated August Z500 values on the ETP corresponding to the August 2022 warm event (Christidis and Stott 2022). The probability of anthropogenic influences on the highly correlated circulation pattern [has a correlation coefficient greater than 0.6 with the reference one (Fig. 2e)] was further estimated based on August Z500s on the ETP derived from NAT and ALL experiments with a large sample size (Christidis et al. 2013, 2018; Christidis and Stott 2022). To estimate the 5%–95% uncertainty range of the probabilities, resamples were randomly executed 10 000 times for each experiment and the corresponding probabilities are recalculated.

Fig. 2.
Fig. 2.

(a) Comparison of the GEV-fitted probability distributions between the observed and the simulated August Tmin during 1961–2022. GEV-fitted probability distributions of August Tmin derived from ALL and NAT simulations in (b) the historical period, (d) the present, and (f) the future. (c) The increased risk ratio of extreme events in the present and the future relative to the historical period. (e) The estimated likelihood of the circulation pattern similar to the reference one under the NAT and ALL forcings in the present and under the ALL forcing in the future. The dotted line in (b), (d), and (f) indicates the threshold used. The upper and lower dashes in (c) and (e) show the bootstrapped 5%–95% uncertainty range.

Citation: Bulletin of the American Meteorological Society 105, 7; 10.1175/BAMS-D-23-0179.1

3. Results

Observed records show a record-breaking warm of August Tmin in the study region and a greater anomalous magnitude than the surrounding areas (Figs. 1a–c). The return period of the August 2022 warm event in observation was about once in 134 years (Fig. 1d). Although there are differences among simulations of the four CMIP6 models and simulations from the MIROC6 model which underestimates the observations obviously, the ensemble simulations of the other three models provide a realistic representation of both the observed trend and the probability density function (PDF) (Fig. ES2). The Kolmogorov–Smirnoff test shows that there is no significant difference between the August 2022 Tmin distributions derived from the ensemble simulations and observations in the historical period 1961–2022 (p = 0.66), indicating that a formal attribution analysis can be reasonably conducted using the ensemble simulations of the three CMIP6 models. The threshold at 5.61°C with the same return period of observation is derived from ensemble of the ALL simulations for attribution (Fig. ES2d).

The GEV distributions of the ensemble simulations in the historical period, the present, and the future all show that the August Tmin is generally greater from the PDFs derived from the ALL forcing than the PDFs derived from the NAT forcing (Figs. 2b,d,f). The likelihoods of frequency of a 2022-like warm event in the historical period, the present, and the future calculated from the ensemble simulations with anthropogenic influence are 0.0075, 0.0573, and 0.4375 (i.e., P1 = 0.0075, 0.0573, and 0.4375), respectively, while without anthropogenic influence are all zero (i.e., P0 = 0). Therefore, the RR is infinite for all the three periods. This suggests that the extreme warm August on the ETP similar to 2022 was impossible to occur without human influences in both the historical period and the future. With the influence of anthropogenic forcing, the likelihood of frequency of extreme warm August on the ETP similar to the 2022 event is increasing, and it corresponds to a new normal by the end of this century (Figs. 2b,d,f; Figs. ES2d–f). Such situation was also found in the extreme warm October in South Korea in 2021 (Kim et al. 2022). The risk of such a warm event on the ETP has increased 7.7 times in the present (confidence interval: 3.7–22.2) relative to the historical period, and the risk could increase 58.5 times in the future (confidence interval: 29.2–167.1) (Fig. 2c).

Moreover, the pattern correlation between the referenced and the simulated August circulation (80 simulations × 31 years) in the study area indicates that the August warm events only occur in the highly correlated circulation pattern (Fig. 1f). This implies the dependence of such warm events on the reference-like circulation pattern. Anthropogenic forcing has increased the probability of the reference-like circulation pattern (80 simulations × 31 years, p = 0.2) compared to the natural world (80 simulations × 31 years, p = 0.15) in the present, and such probability by the end of the century (80 simulations × 31 years, p = 0.24) might be 20% more likely than the present (Fig. 2e).

4. Conclusions

The anomalous warming of August Tmin on the ETP in 2022 was record breaking. The warm event was favored by the exceptionally westward WPSH and the enhanced SAH. A deep high pressure system from the middle troposphere to the upper troposphere appeared in August 2022 over the ETP contributing to the anomalous downward motion (Dao and Chu 1964; Yun et al. 2015; Wei et al. 2019; Sun et al. 2023; Zhou et al. 2023) and induced air warming by the adiabatic heating during compression of sinking air (Black et al. 2004; Wang et al. 2023; Zhang et al. 2023). Attributions based on CMIP6 ensemble simulations indicate that such a warm event was impossible to occur without anthropogenic forcing, while it corresponds to a new normal by the end of this century.

Acknowledgments.

This research was supported by the Key Project of MoST (2022YFF0801903), China, and the National Natural Science Foundation of China (Grant 41875113). Jian was supported by the scientific research fund of Chengdu University of Information Technology (KYTZ202122). Fraser Lott and Peter Stott were supported by Met Office Climate Science for Service Partnership (CSSP) China project under the International Science Partnerships Fund (ISPF). We thank Nikolaos Christidis for his suggestions and comments on the manuscript.

References

  • Black, E., M. Blackburn, G. Harrison, B. Hoskins, and J. Methven, 2004: Factors contributing to the summer 2003 European heatwave. Weather, 59, 217223, https://doi.org/10.1256/wea.74.04.

    • Search Google Scholar
    • Export Citation
  • Chen, Y., and Coauthors, 2019: Anthropogenic warming has substantially increased the likelihood of July 2017–like heat waves over central Eastern China. Bull. Amer. Meteor. Soc., 100, S91S95, https://doi.org/10.1175/BAMS-D-18-0087.1.

    • Search Google Scholar
    • Export Citation
  • Christidis, N., and P. A. Stott, 2022: The extremely wet May of 2021 in the United Kingdom. Bull. Amer. Meteor. Soc., 103, E2912E2916, https://doi.org/10.1175/BAMS-D-22-0108.1.

    • 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
  • Christidis, N., A. Ciavarella, and P. A. Stott, 2018: Different ways of framing event attribution questions: The example of warm and wet winters in the United Kingdom similar to 2015/16. J. Climate, 31, 48274845, https://doi.org/10.1175/JCLI-D-17-0464.1.

    • Search Google Scholar
    • Export Citation
  • Dao, S.-Y., and F.-K. Chu, 1964: The 100-mb flow patterns in Southern Asia in summer and its relation to the advance and retreat of the west-Pacific subtropical anticyclone over the Far East. Acta Meteor. Sin., 4, 387396.

    • Search Google Scholar
    • Export Citation
  • Duan, J., and Coauthors, 2021: Anthropogenic influences on the extreme cold surge of early Spring 2019 over the southeastern Tibetan Plateau. Bull. Amer. Meteor. Soc., 102, S111S116, https://doi.org/10.1175/BAMS-D-20-0215.1.

    • Search Google Scholar
    • Export Citation
  • 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, 2021: Constraining human contributions to observed warming since the pre-industrial period. Nat. Climate Change, 11, 207212, https://doi.org/10.1038/s41558-020-00965-9.

    • Search Google Scholar
    • Export Citation
  • He, Y., K. Yang, Y. Ren, M. Zou, X. Yuan, and W. Tang, 2023: Causes of the extremely low solar radiation in the 2021 growing season over southeastern Tibetan Plateau and its impact on vegetation growth. Bull. Amer. Meteor. Soc., 104, E359E366, https://doi.org/10.1175/BAMS-D-22-0122.1.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • Kim, Y.-H., S.-K. Min, D.-H. Cha, Y.-H. Byun, F. C. Lott, and P. A. Stott, 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
  • Li, W., Z. H. Jiang, L. Z. X. Li, J.-J. Luo, and P. M. Zhai, 2022: Detection and attribution of changes in summer compound hot and dry events over northeastern China with CMIP6 models. J. Meteor. Res., 36, 3748, https://doi.org/10.1007/s13351-022-1112-8.

    • Search Google Scholar
    • Export Citation
  • Ma, S., T. 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.

    • Search Google Scholar
    • Export Citation
  • National Academies of Sciences, Engineering, and Medicine, 2016: Attribution of Extreme Weather Events in the Context of Climate Change. National Academies Press, 186 pp.

    • Search Google Scholar
    • Export Citation
  • Ren, L. W., and Coauthors, 2020: Anthropogenic influences on the persistent night-time heat wave in summer 2018 over northeast China. Bull. Amer. Meteor. Soc., 101, S83S88, https://doi.org/10.1175/BAMS-D-19-0152.1.

    • Search Google Scholar
    • Export Citation
  • Sun, B., H. Wang, Y. Huang, Z. Yin, B. Zhou, and M. Duan, 2023: Characteristics and causes of the hot-dry climate anomalies in China during summer of 2022 (in Chinese). Trans. Atmos. Sci., 46 (1), 18, https://doi.org/10.13878/j.cnki.dqkxxb.20220916003.

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

    • Search Google Scholar
    • Export Citation
  • Tang, H., J. Wang, Y. Chen, S. F. B. Tett, Y. Sun, L. Cheng, S. Sparrow, and B. Dong, 2023: Human contribution to the risk of 2021 northwestern Pacific concurrent marine and terrestrial summer heat. Bull. Amer. Meteor. Soc., 104, E673E679, https://doi.org/10.1175/BAMS-D-22-0238.1.

    • Search Google Scholar
    • Export Citation
  • Wang, C., J. Zheng, W. Lin, and Y. Wang, 2023: Unprecedented heatwave in western North America during late June of 2021: Roles of atmospheric circulation and global warming. Adv. Atmos. Sci., 40, 1428, https://doi.org/10.1007/s00376-022-2078-2.

    • Search Google Scholar
    • Export Citation
  • Wang, J., and Coauthors, 2021: Anthropogenic emissions and urbanization increase risk of compound hot extremes in cities. Nat. Climate Change, 11, 10841089, https://doi.org/10.1038/s41558-021-01196-2.

    • Search Google Scholar
    • Export Citation
  • Wei, W., R. Zhang, M. Wen, S. Yang, and W. Li, 2019: Dynamic effect of the South Asian high on the interannual zonal extension of the western North Pacific subtropical high. Int. J. Climatol., 39, 53675379, https://doi.org/10.1002/joc.6160.

    • Search Google Scholar
    • Export Citation
  • Yun, K.-S., K.-J. Ha, S.-W. Yeh, B. Wang, and B. Xiang, 2015: Critical role of boreal summer North Pacific subtropical highs in ENSO transition. Climate Dyn., 44, 19791992, https://doi.org/10.1007/s00382-014-2193-6.

    • Search Google Scholar
    • Export Citation
  • Zhang, X., and Coauthors, 2023: Increased impact of heat domes on 2021-like heat extremes in North America under global warming. Nat. Commun., 14, 1690, https://doi.org/10.1038/s41467-023-37309-y.

    • Search Google Scholar
    • Export Citation
  • Zhou, B., S. Hu, J. Peng, D. Li, L. Ma, Z. Zheng, and G. Feng, 2023: The extreme heat wave in China in August 2022 related to extreme northward movement of the eastern center of SAH. Atmos. Res., 293, 106918, https://doi.org/10.1016/j.atmosres.2023.106918.

    • Search Google Scholar
    • Export Citation
  • Zhou, C. L., D. L. Chen, K. C. Wang, A. G. Dai, and D. Qi, 2020: Conditional attribution of the 2018 summer extreme heat over northeast China: Roles of urbanization, global warming, and warming-induced circulation changes. Bull. Amer. Meteor. Soc., 101, S71S76, https://doi.org/10.1175/BAMS-D-19-0197.1.

    • Search Google Scholar
    • Export Citation

Supplementary Materials

Save
  • Black, E., M. Blackburn, G. Harrison, B. Hoskins, and J. Methven, 2004: Factors contributing to the summer 2003 European heatwave. Weather, 59, 217223, https://doi.org/10.1256/wea.74.04.

    • Search Google Scholar
    • Export Citation
  • Chen, Y., and Coauthors, 2019: Anthropogenic warming has substantially increased the likelihood of July 2017–like heat waves over central Eastern China. Bull. Amer. Meteor. Soc., 100, S91S95, https://doi.org/10.1175/BAMS-D-18-0087.1.

    • Search Google Scholar
    • Export Citation
  • Christidis, N., and P. A. Stott, 2022: The extremely wet May of 2021 in the United Kingdom. Bull. Amer. Meteor. Soc., 103, E2912E2916, https://doi.org/10.1175/BAMS-D-22-0108.1.

    • 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
  • Christidis, N., A. Ciavarella, and P. A. Stott, 2018: Different ways of framing event attribution questions: The example of warm and wet winters in the United Kingdom similar to 2015/16. J. Climate, 31, 48274845, https://doi.org/10.1175/JCLI-D-17-0464.1.

    • Search Google Scholar
    • Export Citation
  • Dao, S.-Y., and F.-K. Chu, 1964: The 100-mb flow patterns in Southern Asia in summer and its relation to the advance and retreat of the west-Pacific subtropical anticyclone over the Far East. Acta Meteor. Sin., 4, 387396.

    • Search Google Scholar
    • Export Citation
  • Duan, J., and Coauthors, 2021: Anthropogenic influences on the extreme cold surge of early Spring 2019 over the southeastern Tibetan Plateau. Bull. Amer. Meteor. Soc., 102, S111S116, https://doi.org/10.1175/BAMS-D-20-0215.1.

    • Search Google Scholar
    • Export Citation
  • 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, 2021: Constraining human contributions to observed warming since the pre-industrial period. Nat. Climate Change, 11, 207212, https://doi.org/10.1038/s41558-020-00965-9.

    • Search Google Scholar
    • Export Citation
  • He, Y., K. Yang, Y. Ren, M. Zou, X. Yuan, and W. Tang, 2023: Causes of the extremely low solar radiation in the 2021 growing season over southeastern Tibetan Plateau and its impact on vegetation growth. Bull. Amer. Meteor. Soc., 104, E359E366, https://doi.org/10.1175/BAMS-D-22-0122.1.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • Kim, Y.-H., S.-K. Min, D.-H. Cha, Y.-H. Byun, F. C. Lott, and P. A. Stott, 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
  • Li, W., Z. H. Jiang, L. Z. X. Li, J.-J. Luo, and P. M. Zhai, 2022: Detection and attribution of changes in summer compound hot and dry events over northeastern China with CMIP6 models. J. Meteor. Res., 36, 3748, https://doi.org/10.1007/s13351-022-1112-8.

    • Search Google Scholar
    • Export Citation
  • Ma, S., T. 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.

    • Search Google Scholar
    • Export Citation
  • National Academies of Sciences, Engineering, and Medicine, 2016: Attribution of Extreme Weather Events in the Context of Climate Change. National Academies Press, 186 pp.

    • Search Google Scholar
    • Export Citation
  • Ren, L. W., and Coauthors, 2020: Anthropogenic influences on the persistent night-time heat wave in summer 2018 over northeast China. Bull. Amer. Meteor. Soc., 101, S83S88, https://doi.org/10.1175/BAMS-D-19-0152.1.

    • Search Google Scholar
    • Export Citation
  • Sun, B., H. Wang, Y. Huang, Z. Yin, B. Zhou, and M. Duan, 2023: Characteristics and causes of the hot-dry climate anomalies in China during summer of 2022 (in Chinese). Trans. Atmos. Sci., 46 (1), 18, https://doi.org/10.13878/j.cnki.dqkxxb.20220916003.

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

    • Search Google Scholar
    • Export Citation
  • Tang, H., J. Wang, Y. Chen, S. F. B. Tett, Y. Sun, L. Cheng, S. Sparrow, and B. Dong, 2023: Human contribution to the risk of 2021 northwestern Pacific concurrent marine and terrestrial summer heat. Bull. Amer. Meteor. Soc., 104, E673E679, https://doi.org/10.1175/BAMS-D-22-0238.1.

    • Search Google Scholar
    • Export Citation
  • Wang, C., J. Zheng, W. Lin, and Y. Wang, 2023: Unprecedented heatwave in western North America during late June of 2021: Roles of atmospheric circulation and global warming. Adv. Atmos. Sci., 40, 1428, https://doi.org/10.1007/s00376-022-2078-2.

    • Search Google Scholar
    • Export Citation
  • Wang, J., and Coauthors, 2021: Anthropogenic emissions and urbanization increase risk of compound hot extremes in cities. Nat. Climate Change, 11, 10841089, https://doi.org/10.1038/s41558-021-01196-2.

    • Search Google Scholar
    • Export Citation
  • Wei, W., R. Zhang, M. Wen, S. Yang, and W. Li, 2019: Dynamic effect of the South Asian high on the interannual zonal extension of the western North Pacific subtropical high. Int. J. Climatol., 39, 53675379, https://doi.org/10.1002/joc.6160.

    • Search Google Scholar
    • Export Citation
  • Yun, K.-S., K.-J. Ha, S.-W. Yeh, B. Wang, and B. Xiang, 2015: Critical role of boreal summer North Pacific subtropical highs in ENSO transition. Climate Dyn., 44, 19791992, https://doi.org/10.1007/s00382-014-2193-6.

    • Search Google Scholar
    • Export Citation
  • Zhang, X., and Coauthors, 2023: Increased impact of heat domes on 2021-like heat extremes in North America under global warming. Nat. Commun., 14, 1690, https://doi.org/10.1038/s41467-023-37309-y.

    • Search Google Scholar
    • Export Citation
  • Zhou, B., S. Hu, J. Peng, D. Li, L. Ma, Z. Zheng, and G. Feng, 2023: The extreme heat wave in China in August 2022 related to extreme northward movement of the eastern center of SAH. Atmos. Res., 293, 106918, https://doi.org/10.1016/j.atmosres.2023.106918.

    • Search Google Scholar
    • Export Citation
  • Zhou, C. L., D. L. Chen, K. C. Wang, A. G. Dai, and D. Qi, 2020: Conditional attribution of the 2018 summer extreme heat over northeast China: Roles of urbanization, global warming, and warming-induced circulation changes. Bull. Amer. Meteor. Soc., 101, S71S76, https://doi.org/10.1175/BAMS-D-19-0197.1.

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

    (a) Station-recorded anomalies, (b) rank of August 2022 Tmin, and (c) regionally station-averaged series of August Tmin during 1961–2022 in the study area. (d) Return periods and 95% confidence intervals for the observed August 2022 Tmin. (e) Anomalies of 500-hPa geopotential height field (Z500; gpm) and August Tmin of 2022 derived from the ERA5 dataset. (f) August Tmin anomaly distributions for months with high (light blue) and low (light gray) correlations with the reference Z500 pattern. The black box in (a), (b), and (e) indicates the study area. All the anomalies are with respect to the 1961–90 climatology. The thick red and cyan 5860-gpm isolines indicate the western ridge of the WPSH in 2022 and its average during 1961–90, respectively.

  • Fig. 2.

    (a) Comparison of the GEV-fitted probability distributions between the observed and the simulated August Tmin during 1961–2022. GEV-fitted probability distributions of August Tmin derived from ALL and NAT simulations in (b) the historical period, (d) the present, and (f) the future. (c) The increased risk ratio of extreme events in the present and the future relative to the historical period. (e) The estimated likelihood of the circulation pattern similar to the reference one under the NAT and ALL forcings in the present and under the ALL forcing in the future. The dotted line in (b), (d), and (f) indicates the threshold used. The upper and lower dashes in (c) and (e) show the bootstrapped 5%–95% uncertainty range.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 236 236 236
PDF Downloads 161 161 161