Attribution of the Unprecedented 2021 October Heatwave in South Korea

Yeon-Hee Kim Division of Environmental Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea;

Search for other papers by Yeon-Hee Kim in
Current site
Google Scholar
PubMed
Close
,
Seung-Ki Min Division of Environmental Science and Engineering, Pohang University of Science and Technology, Pohang, and Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Incheon, South Korea;

Search for other papers by Seung-Ki Min in
Current site
Google Scholar
PubMed
Close
,
Dong-Hyun Cha School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea;

Search for other papers by Dong-Hyun Cha in
Current site
Google Scholar
PubMed
Close
,
Young-Hwa Byun National Institute of Meteorological Sciences, Korea Meteorological Administration, Seogwipo, South Korea;

Search for other papers by Young-Hwa Byun 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
Free access

GCM ensembles indicate that the October 2021 South Korean heatwave was extremely unlikely to occur without human influences, which corresponds to 2060s’ new normal without ambitious greenhouse gas mitigation.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR: Seung-Ki Min, skmin@postech.ac.kr

Supplemental material: 10.1175/BAMS-D-22-0124.2

GCM ensembles indicate that the October 2021 South Korean heatwave was extremely unlikely to occur without human influences, which corresponds to 2060s’ new normal without ambitious greenhouse gas mitigation.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR: Seung-Ki Min, skmin@postech.ac.kr

Supplemental material: 10.1175/BAMS-D-22-0124.2

During the early October (1–15 October) in 2021, an unseasonable hot spell struck South Korea, with station-averaged (based on 62 stations; Fig. ES1a in the online supplemental material; https://doi.org/10.1175/BAMS-D-22-0124.2) daily mean temperatures exceeding two standard deviations (σ) during 11 of the 15 days (Fig. 1a). South Korean average temperature during 1–15 October (T15days) reached 19.9°C, 3.9°C higher than 1991–2020 climatology, which corresponds to a 1-in-6,250 year event (3.6σ, Fig. 1b). All stations except one have observed record-high T15days since 1973 (Fig. ES1a) and many southern stations recorded daily maximum temperature above 30°C (KMA 2022), experiencing hot summerlike conditions.1 This extremely high T15days is a typical temperature of mid-September (Fig. 1a), indicating a delayed withdrawal of summer season by a few weeks, in line with a previous study (Park et al. 2018). The extreme October warming was partly due to a strong subtropical high developed over the East China Sea, which was related to a strong tropical convection over South China/Philippine Seas (Fig. 1d,e) with an active Madden–Julian oscillation (Hsu et al. 2020). In addition, a blocking high developed over the northern North Pacific likely contributed to the long-lasting heatwave. These resemble typical synoptic patterns driving summertime heatwaves (Yeh et al. 2018; Yeo et al. 2019; Min et al. 2020; Kim et al. 2021). Here we use daily 500 and 200 hPa geopotential height and outgoing longwave radiation (OLR) data obtained from NCEP1 reanalysis (Kalnay et al. 1996) and NOAA interpolated data2 (Liebmann and Smith 1996), respectively. When using ERA5 (Hersbach et al. 2020), results remain essentially the same.

Fig. 1.
Fig. 1.

(a) Observed time series of daily mean temperature averaged over 62 stations in Korea from 1 Sep to 31 Oct 2021. Black solid and dashed lines indicate the climatology and ±2 standard deviation of daily mean temperature, respectively (both estimated from 1991 to 2020). Pink shading indicates the first half of October. The horizontal gray line denotes daily mean temperature averaged during the first half of October (T15days). Time series of (b) T15days anomalies and (c) standard deviations (SD) of October daily mean temperatures over 1973–2021. Spatial patterns of (d) 500 hPa geopotential height (H500) anomalies and (e) OLR anomalies during the first half of October 2021. The purple and orange lines in (d) represent 5,880 gpm contour of H500 and 12,480 gpm contour of H200 observed in 2021 (solid line) and climatology (dashed line), respectively. All anomalies are relative to 1991–2020 means. Gray dots indicate record high or low values observed in 2021. Composite patterns of H500 anomalies from (f) CMIP6 and (g) HadGEM3-A ALL simulations having T15days higher than the observed 2013 value (1.9σ). Gray hatches indicate areas where more than 70% of runs show the same sign of anomalies. Note that 24 runs from 5 CMIP6 models are used for (f) due to data availability (Table ES1).

Citation: Bulletin of the American Meteorological Society 103, 12; 10.1175/BAMS-D-22-0124.1

Such summerlike weather in early October was followed by a cold snap in the mid-October3 with a sharp decrease in temperature by about 12°C (Fig. 1a and Figs. ES1c,d). The early October warming was particularly strong and long-lasting, bringing the largest October daily temperature fluctuations since 1973 (σ = 5.1°C, Fig. 1c; KMA 2022), and exerted huge socioeconomic impacts, particularly reducing crop yields. It is very important to understand and attribute this summerlike heatwave that was first observed in October to evaluate their future changes and associated impacts. This study quantifies human contribution to the 2021 October heatwave event in South Korea using two global climate model (GCM) ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6) and HadGEM3-A-N216 large-ensemble experiments. We also assess how often October 2021–like warming will occur in the future under different greenhouse gas emission scenarios using CMIP6 future scenario runs.

Data and methods

We use daily mean temperatures from 62 Korean weather stations for 1973–2021 (Fig. ES1a; 12 stations provide data from 1954). We use CMIP6 (Eyring et al. 2016) multimodel simulations for 2001–20 (11 models providing 36 ensemble members; see Table ES1) performed under historical [anthropogenic plus natural (ALL)], hist-GHG [greenhouse gas only (GHG)], and hist-nat [volcanic and solar forcing only (NAT)]. ALL simulations are extended to 2020 using corresponding SSP5-8.5 scenario runs. Future simulations under the shared socioeconomic pathways (SSP) scenarios are also used (O’Neill et al. 2016). To evaluate future changes in October-like warm extremes over South Korea in relation to carbon neutrality, outputs from the high-emission (SSP5-8.5) scenario are compared with low-emission (SSP1-2.6) scenarios in which CO2 emission is assumed to reach net zero around 2075 (Riahi et al. 2017). To increase the sample size needed to construct probability distributions, we use 20 yr periods of historical (2001–20) and future (moving windows), which gives 720 samples [20 years × 36 ensemble members (33 runs for SSP1-2.6)] for each experiment or scenario. The spatial domain for South Korea is set to 34–38°N and 125–130°E (land only).

Large-ensemble (525 members) high-resolution (0.83° × 0.56°) simulations from HadGEM3-A (Ciavarella et al. 2018; Vautard et al. 2019) are also used, which were conducted for only a single year of 2021. The historicalExt ensemble (ALL) is driven by 2021 observed sea surface temperature (SST) and sea ice concentration (SIC) boundary condition obtained from HadISST1 (Rayner et al. 2003). Observed greenhouse gases and anthropogenic aerosols have been implemented. The historicalNatExt ensemble (NAT) is driven by adjusted SST and SIC, which are obtained by removing anthropogenic warming contribution from the 2021 observations based on CMIP5 multimodel mean differences between ALL and NAT experiments (Stone and Pall 2021).

The anomalies of all CMIP6 simulations are calculated relative to 1991–2020 means of ALL simulations for each model while the 1991–2013 mean is used for HadGEM3-A due to data availability. To take account of possible model biases in temperature variability, we normalize observed and simulated T15days anomalies with respect to the corresponding interannual standard deviations estimated from the reference period. NAT and GHG experiments are normalized based on each model’s ALL values. When evaluating ALL runs in view of observations, CMIP6 and HadGEM3-A models show reasonable performances at simulating T15days variability and circulation patterns associated with the heatwaves (Figs. 1f,g; see supplementary text for details).

The probabilities of heatwave intensity (T15days) exceeding the observed threshold with and without anthropogenic forcings (PALL or PGHG and PNAT, respectively) are estimated empirically by counting the number of extreme samples and dividing it by the total number of samples. Then, the risk ratio (RR) is calculated as RRALL/NAT = PALL/PNAT or RRGHG/NAT = PGHG/PNAT (e.g., Easterling et al. 2016). To consider possible uncertainties in probability estimates and evaluate the robustness of attributable risk, we employ the different thresholds based on the second and third warmest October (1998 and 2013 observations, respectively) following previous studies (Stott et al. 2004) and also measure the 90% confidence intervals (5%–95%, CI) of RR using the “likelihood ratio method” (Paciorek et al. 2018).

Results

Figure 2a shows the probability density function (PDFs) of the normalized T15days for ALL, GHG, and NAT from CMIP6, constructed by fitting T15days to Gaussian kernel function. The probability of normalized temperature higher than the observed 2021 values (3.6σ) is 0.4% in GHG (PGHG) with no occurrence in ALL and NAT simulations (Table 1). The October 2021 warming is also found to be very rare in HadGEM3-A with PALL = 0.6% and PNAT = 0.0%. This indicates the observed heatwave in October 2021 is an extremely unusual case, which cannot be captured by the comparatively small sample size. To support that this kind of “record-shattering” event can occur today in line with a long-term detectable shift of T15days distributions responding to the global warming (Fischer et al. 2021; Thompson et al. 2022), we have compared observed and simulated long-term (1954–2021, based on 12 stations) trends in T15days (Figs. ES1b,c). The observed (statistically significant) trend is much larger than the internal variability ranges estimated from CMIP6 preindustrial control runs (Table ES1). Nevertheless, quantifying relative contribution of natural variability and warming trend remains challenging.

Fig. 2.
Fig. 2.

(a) Kernel density functions of normalized first half of October mean temperature (T15days) from CMIP6 ALL (green), GHG (red), NAT (blue), and SSP5-8.5 (for 2060s; purple). (b) RR distributions for CMIP6 ALL (green) and GHG (red) with respect to NAT for hypothetical values of T15days. Shading represents the 5%–95% confidence intervals of RR. Vertical lines in (a) and (b) indicate observed thresholds in 2021 (solid), 1998 (dashed), and 2013 (dashed). (c) Return periods of 2021 T15days in the future periods from the 2030s to the 2090s from SSP5-8.5 (dark red) and SSP1-2.6 (blue) scenarios.

Citation: Bulletin of the American Meteorological Society 103, 12; 10.1175/BAMS-D-22-0124.1

Table 1.

Probability of occurrence of the first half of October mean temperature (T15days) exceeding the observed 2021, 1998, and 2013 thresholds, and the corresponding RR values. Square brackets indicate the 5%–95% uncertainty ranges of RR.

Table 1.

Next, we have checked how often October 2021–like warming will occur in the future based on SSP5-8.5 scenario. This projection is unlikely influenced by the “hot model problem” (Hausfather et al. 2022) since the 11 CMIP6 models show a moderate climate sensitivity on average (Table ES1). Figure 2c displays the return periods for the observed T15days for the varying future 20 years. In the 2030s (2021–40), October 2021–like warming remains as a rare case that occurs once in 60 years, but its frequency increases in time and it occurs once in 2 years in the 2060s (2051–70). In the 2090s, such event is experienced almost every year. When comparing PDFs of the 2060s with historical results (Fig. 2a), it is evident that the observed 2021 T15days event is extremely rare in the historical runs, but it becomes new normal in 2060s. This manifests a summer season intrusion into October under a strong-emission scenario (cf. Park et al. 2022). In the SSP1-2.6 scenario, the return period is shortened up to 30 years in the 2050s, which remains by the end of twenty-first century, indicating that summerlike Korean heatwaves in October can be largely avoided if the SSP1-2.6 scenario is followed.

Due to the limited number of simulated events, particularly for PNAT, the RR analysis was repeated using the 1998 and 2013 observed thresholds (2.3σ and 1.9σ, respectively). In CMIP6, the probability of 1998-like extreme events increases as 1.5% and 10.0% in ALL and GHG, respectively, but 0% in NAT (Table 1). When using the 2013 observed value, probability increases further (PALL = 3.9%, PGHG = 19.0%, and PNAT = 1.0%) and corresponding RR values with respect to NAT are 4 (2.1–8.4) and 19.6 (11.0–39.5) for ALL and GHG, respectively. The lower bounds of RR are significantly larger than unity, presenting robust evidence that human influences make 2013-like event about 4 times more likely to occur. HadGEM3-A results largely support CMIP6-based results. When applying observed 1998 thresholds, PALL = 5.7% and PNAT = 0% (Table 1, Fig. ES2a). For the 2013 observed value, RR is estimated as 69 (18.7–655.8) with PALL = 13.1% and PNAT = 0.2%. Further analysis using hypothetical normalized T15days thresholds raining from 0 to 2.5σ confirms the robust influence of anthropogenic forcing on the unseasonal heat event. The resulting RRs from ALL remain around 3 for weaker observed thresholds (<1.7σ) and then increase rapidly in CMIP6 simulations (Fig. 2b), which is supported by HadGEM3-A-based results (Fig. ES2b). We have also repeated our attribution analyses for normalized H500 anomalies averaged over South Korea (30–45°N, 110–140°E) using available CMIP6 models (Table ES1) and obtained largely consistent results (Figs. ES2c,d). This confirms that anthropogenic influences have robustly increased the probability of extreme anomalous high over Korea through the long-term shift of distributions responding to the global warming, well supporting temperature-based results.

Concluding remarks

This study compares probabilities of 2021-like extremely warm October in South Korea between real and counterfactual world conditions using the datasets from CMIP6 multimodel simulations and HadGEM3-A high-resolution large ensemble simulations. Results from both GCM ensembles indicate that the exceptionally strong October 2021 heatwave was extremely unlikely to occur without human influences. It should be noted, however, that the sample size and extreme nature of the event make it difficult to draw concrete conclusions from the model simulations. When based on weaker observed thresholds, robust human influences are found to have made such warm events at least 4 times more likely. Future analysis warns that October will be merged into summer in the near future, resulting in serious socioeconomic damage in health, agriculture, energy, etc. Under the high-emission SSP5-8.5 scenario, this unseasonal warmth is expected to become the new normal, occurring every 2 years after the mid-twenty-first century, which can be avoided when carbon neutrality is achieved around 2075 following the SSP1-2.6 scenario.

Acknowledgments.

We thank three anonymous reviewers for their constructive comments. This study was supported by National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2018R1A5A1024958 and NRF2021R1A2C3007366). We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access (https://esgf-node.llnl.gov/projects/cmip6/), and the multiple funding agencies that support CMIP6 and ESGF.

References

  • BaCiavarella, A., and Coauthors , 2018: Upgrade of the HadGEM3-A based attribution system to high resolution and a new validation framework for probabilistic event attribution. Wea. Climate Extremes, 20, 932, https://doi.org/10.1016/j.wace.2018.03.003.

    • Search Google Scholar
    • Export Citation
  • Easterling, D. R., K. E. Kunkel, M. F. Wehner, and L. Sun , 2016: Detection and attribution of climate extremes in the observed record. Wea. Climate Extremes, 11, 1727, https://doi.org/10.1016/j.wace.2016.01.001.

    • 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
  • Fischer, E. M., S. Sippel, and R. Knutti , 2021: Increasing probability of record-shattering climate extremes. Nat. Climate Change, 11, 689695, https://doi.org/10.1038/s41558-021-01092-9.

    • Search Google Scholar
    • Export Citation
  • Hausfather, Z., K. Marvel, G. A. Schmidt, J. W. Nielsen-Gammon, and M. Zelinka , 2022: Climate simulations: Recognize the ‘hot model’ problem. Nature, 605, 2629, https://doi.org/10.1038/d41586-022-01192-2.

    • 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
  • Hsu, P., Y. Qian, Y. Liu, H. Murakami, and Y. Gao , 2020: Role of abnormally enhanced MJO over the western Pacific in the formation and subseasonal predictability of the record-breaking northeast Asian heatwave in the summer of 2018. J. Climate, 33, 33333349, https://doi.org/10.1175/JCLI-D-19-0337.1.

    • 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, H.-K., B.-K. Moon, M.-K. Kim, J.-Y. Park, and Y.-K. Hyun , 2021: Three distinct atmospheric circulation patterns associated with high temperature extremes in South Korea. Sci. Rep., 11, 12, 911, https://doi.org/10.1038/s41598-021-92368-9.

    • Search Google Scholar
    • Export Citation
  • KMA, 2022: Abnormal climate report 2021 (in Korean). Korean Meteorological Administration Rep., 234 pp.

  • Liebmann, B., and C. A. Smith , 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 12751277, https://doi.org/10.1175/1520-0477-77.6.1274.

    • Search Google Scholar
    • Export Citation
  • Min, S.-K., Y.-H. Kim, S.-M. Lee, S. Sparrow, S. Li, F. C. Lott, and P. A. Stott , 2020: Quantifying human impact on the 2018 summer longest heat wave in South Korea​. Bull. Amer. Meteor. Soc., 101, S103S108​, https://doi.org/10.1175/BAMS-D-19-0151.1.

    • Search Google Scholar
    • Export Citation
  • O’Neill, B. C., and Coauthors , 2016: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev., 9, 34613482, https://doi.org/10.5194/gmd-9-3461-2016.

    • Search Google Scholar
    • Export Citation
  • Paciorek, C. J., D. A. Stone, and M. F. Wehner , 2018: Quantifying statistical uncertainty in the attribution of human influence on severe weather. Wea. Climate Extremes, 20, 6980, https://doi.org/10.1016/j.wace.2018.01.002.

    • Search Google Scholar
    • Export Citation
  • Park, B.-J., Y.-H. Kim, S.-K. Min, and E.-P. Lim , 2018: Anthropogenic and natural contributions to the lengthening of the summer season in the Northern Hemisphere. J. Climate, 31, 68036819, https://doi.org/10.1175/JCLI-D-17-0643.1.

    • Search Google Scholar
    • Export Citation
  • Park, B.-J., S.-K. Min, and E. Weller , 2022: Lengthening of summer season over the Northern Hemisphere under 1.5°C and 2.0°C global warming. Environ. Res. Lett., 17, 14012, https://doi.org/10.1088/1748-9326/ac3f64.

    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan , 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Search Google Scholar
    • Export Citation
  • Riahi, K., and Coauthors , 2017: The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environ. Change, 42, 153168, https://doi.org/10.1016/j.gloenvcha.2016.05.009.

    • Search Google Scholar
    • Export Citation
  • Stone, D. A., and P. Pall , 2021: Benchmark estimate of the effect of anthropogenic emissions on the ocean surface. Int. J. Climatol., 41, 30103026, https://doi.org/10.1002/joc.7002.

    • Search Google Scholar
    • Export Citation
  • Stott, P. A., D. A. Stone, and M. R. Allen , 2004: Human contribution to the European heatwave of 2003. Nature, 432, 610614, https://doi.org/10.1038/nature03089.

    • Search Google Scholar
    • Export Citation
  • Thompson, V., and Coauthors , 2022: The 2021 western North America heat wave among the most extreme events ever recorded globally. Sci. Adv., 8, eabm6860, https://doi.org/10.1126/sciadv.abm6860.

    • Search Google Scholar
    • Export Citation
  • Vautard, R., and Coauthors , 2019: Evaluation of the HadGEM3-A simulations in view of detection and attribution of human influence on extreme events in Europe. Climate Dyn., 52, 11871210, https://doi.org/10.1007/s00382-018-4183-6.

    • Search Google Scholar
    • Export Citation
  • Yeh, S.-W., Y.-J. Won, J.-S. Hong, K.-J. Lee, M. Kwon, K.-H. Seo, and Y.-G. Ham , 2018: The record-breaking heat wave in 2016 over South Korea and its physical mechanism. Mon. Wea. Rev., 146, 14631474, https://doi.org/10.1175/MWR-D-17-0205.1.

    • Search Google Scholar
    • Export Citation
  • Yeo, S.-R., S.-W. Yeh, and W.-S. Lee , 2019: Two types of heat wave in Korea associated with atmospheric circulation pattern. J. Geophys. Res. Atmos., 124, 74987511, https://doi.org/10.1029/2018JD030170.

    • Search Google Scholar
    • Export Citation

Supplementary Materials

Save
  • BaCiavarella, A., and Coauthors , 2018: Upgrade of the HadGEM3-A based attribution system to high resolution and a new validation framework for probabilistic event attribution. Wea. Climate Extremes, 20, 932, https://doi.org/10.1016/j.wace.2018.03.003.

    • Search Google Scholar
    • Export Citation
  • Easterling, D. R., K. E. Kunkel, M. F. Wehner, and L. Sun , 2016: Detection and attribution of climate extremes in the observed record. Wea. Climate Extremes, 11, 1727, https://doi.org/10.1016/j.wace.2016.01.001.

    • 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
  • Fischer, E. M., S. Sippel, and R. Knutti , 2021: Increasing probability of record-shattering climate extremes. Nat. Climate Change, 11, 689695, https://doi.org/10.1038/s41558-021-01092-9.

    • Search Google Scholar
    • Export Citation
  • Hausfather, Z., K. Marvel, G. A. Schmidt, J. W. Nielsen-Gammon, and M. Zelinka , 2022: Climate simulations: Recognize the ‘hot model’ problem. Nature, 605, 2629, https://doi.org/10.1038/d41586-022-01192-2.

    • 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
  • Hsu, P., Y. Qian, Y. Liu, H. Murakami, and Y. Gao , 2020: Role of abnormally enhanced MJO over the western Pacific in the formation and subseasonal predictability of the record-breaking northeast Asian heatwave in the summer of 2018. J. Climate, 33, 33333349, https://doi.org/10.1175/JCLI-D-19-0337.1.

    • 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, H.-K., B.-K. Moon, M.-K. Kim, J.-Y. Park, and Y.-K. Hyun , 2021: Three distinct atmospheric circulation patterns associated with high temperature extremes in South Korea. Sci. Rep., 11, 12, 911, https://doi.org/10.1038/s41598-021-92368-9.

    • Search Google Scholar
    • Export Citation
  • KMA, 2022: Abnormal climate report 2021 (in Korean). Korean Meteorological Administration Rep., 234 pp.

  • Liebmann, B., and C. A. Smith , 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 12751277, https://doi.org/10.1175/1520-0477-77.6.1274.

    • Search Google Scholar
    • Export Citation
  • Min, S.-K., Y.-H. Kim, S.-M. Lee, S. Sparrow, S. Li, F. C. Lott, and P. A. Stott , 2020: Quantifying human impact on the 2018 summer longest heat wave in South Korea​. Bull. Amer. Meteor. Soc., 101, S103S108​, https://doi.org/10.1175/BAMS-D-19-0151.1.

    • Search Google Scholar
    • Export Citation
  • O’Neill, B. C., and Coauthors , 2016: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev., 9, 34613482, https://doi.org/10.5194/gmd-9-3461-2016.

    • Search Google Scholar
    • Export Citation
  • Paciorek, C. J., D. A. Stone, and M. F. Wehner , 2018: Quantifying statistical uncertainty in the attribution of human influence on severe weather. Wea. Climate Extremes, 20, 6980, https://doi.org/10.1016/j.wace.2018.01.002.

    • Search Google Scholar
    • Export Citation
  • Park, B.-J., Y.-H. Kim, S.-K. Min, and E.-P. Lim , 2018: Anthropogenic and natural contributions to the lengthening of the summer season in the Northern Hemisphere. J. Climate, 31, 68036819, https://doi.org/10.1175/JCLI-D-17-0643.1.

    • Search Google Scholar
    • Export Citation
  • Park, B.-J., S.-K. Min, and E. Weller , 2022: Lengthening of summer season over the Northern Hemisphere under 1.5°C and 2.0°C global warming. Environ. Res. Lett., 17, 14012, https://doi.org/10.1088/1748-9326/ac3f64.

    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan , 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Search Google Scholar
    • Export Citation
  • Riahi, K., and Coauthors , 2017: The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environ. Change, 42, 153168, https://doi.org/10.1016/j.gloenvcha.2016.05.009.

    • Search Google Scholar
    • Export Citation
  • Stone, D. A., and P. Pall , 2021: Benchmark estimate of the effect of anthropogenic emissions on the ocean surface. Int. J. Climatol., 41, 30103026, https://doi.org/10.1002/joc.7002.

    • Search Google Scholar
    • Export Citation
  • Stott, P. A., D. A. Stone, and M. R. Allen , 2004: Human contribution to the European heatwave of 2003. Nature, 432, 610614, https://doi.org/10.1038/nature03089.

    • Search Google Scholar
    • Export Citation
  • Thompson, V., and Coauthors , 2022: The 2021 western North America heat wave among the most extreme events ever recorded globally. Sci. Adv., 8, eabm6860, https://doi.org/10.1126/sciadv.abm6860.

    • Search Google Scholar
    • Export Citation
  • Vautard, R., and Coauthors , 2019: Evaluation of the HadGEM3-A simulations in view of detection and attribution of human influence on extreme events in Europe. Climate Dyn., 52, 11871210, https://doi.org/10.1007/s00382-018-4183-6.

    • Search Google Scholar
    • Export Citation
  • Yeh, S.-W., Y.-J. Won, J.-S. Hong, K.-J. Lee, M. Kwon, K.-H. Seo, and Y.-G. Ham , 2018: The record-breaking heat wave in 2016 over South Korea and its physical mechanism. Mon. Wea. Rev., 146, 14631474, https://doi.org/10.1175/MWR-D-17-0205.1.

    • Search Google Scholar
    • Export Citation
  • Yeo, S.-R., S.-W. Yeh, and W.-S. Lee , 2019: Two types of heat wave in Korea associated with atmospheric circulation pattern. J. Geophys. Res. Atmos., 124, 74987511, https://doi.org/10.1029/2018JD030170.

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

    (a) Observed time series of daily mean temperature averaged over 62 stations in Korea from 1 Sep to 31 Oct 2021. Black solid and dashed lines indicate the climatology and ±2 standard deviation of daily mean temperature, respectively (both estimated from 1991 to 2020). Pink shading indicates the first half of October. The horizontal gray line denotes daily mean temperature averaged during the first half of October (T15days). Time series of (b) T15days anomalies and (c) standard deviations (SD) of October daily mean temperatures over 1973–2021. Spatial patterns of (d) 500 hPa geopotential height (H500) anomalies and (e) OLR anomalies during the first half of October 2021. The purple and orange lines in (d) represent 5,880 gpm contour of H500 and 12,480 gpm contour of H200 observed in 2021 (solid line) and climatology (dashed line), respectively. All anomalies are relative to 1991–2020 means. Gray dots indicate record high or low values observed in 2021. Composite patterns of H500 anomalies from (f) CMIP6 and (g) HadGEM3-A ALL simulations having T15days higher than the observed 2013 value (1.9σ). Gray hatches indicate areas where more than 70% of runs show the same sign of anomalies. Note that 24 runs from 5 CMIP6 models are used for (f) due to data availability (Table ES1).

  • Fig. 2.

    (a) Kernel density functions of normalized first half of October mean temperature (T15days) from CMIP6 ALL (green), GHG (red), NAT (blue), and SSP5-8.5 (for 2060s; purple). (b) RR distributions for CMIP6 ALL (green) and GHG (red) with respect to NAT for hypothetical values of T15days. Shading represents the 5%–95% confidence intervals of RR. Vertical lines in (a) and (b) indicate observed thresholds in 2021 (solid), 1998 (dashed), and 2013 (dashed). (c) Return periods of 2021 T15days in the future periods from the 2030s to the 2090s from SSP5-8.5 (dark red) and SSP1-2.6 (blue) scenarios.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 2438 1151 88
PDF Downloads 1758 712 31