Anthropogenic Influence on 2022 June Extreme Rainfall over the Pearl River Basin

Xinru Liu School of Mathematics and Statistics, Central South University, Changsha, China;

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Hang Jie School of Mathematics and Statistics, Central South University, Changsha, China;

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Yulin Zou School of Mathematics and Statistics, Central South University, Changsha, China;

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Shengjun Liu School of Mathematics and Statistics, Central South University, Changsha, China;

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Yamin Hu Guangdong Climate Center, Guangdong, China;

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Shuyi Liu School of Mathematics and Statistics, Central South University, Changsha, China;

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Dangfu Yang Shenzhen Research Institute, Hong Kong Polytechnic University, Shenzhen, China;

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Liang Zhao State Key Laboratory of Numerical Modeling for Atmosphere Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Jian He Guangdong Climate Center, Guangdong, China;

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Abstract

According to HadGEM3 (CMIP6) models, anthropogenic forcing reduced the probability of 2022-like June mean precipitation by about 32% (15%) and increased 5-day rainfall extreme probability by about 1.8 (1.3) times.

© 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: Shengjun Liu, shjliu.cg@csu.edu.cn; Yamin Hu, huym@gd121.cn

Abstract

According to HadGEM3 (CMIP6) models, anthropogenic forcing reduced the probability of 2022-like June mean precipitation by about 32% (15%) and increased 5-day rainfall extreme probability by about 1.8 (1.3) times.

© 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: Shengjun Liu, shjliu.cg@csu.edu.cn; Yamin Hu, huym@gd121.cn

During early to mid-June of 2022, the Pearl River basin (PRB) in China experienced a severe flooding event. The total precipitation in June reached 357 mm, marking a 65% increase compared to the typical annual average. This extreme rainfall event affected 6.489 million people, caused the collapse of over 9,200 houses, damaged crops in an area of 288.4 thousand hectares, and resulted in direct economic losses of 27.82 billion yuan (3.89 billion U.S. dollars; www.mem.gov.cn/xw/yjglbgzdt/202301/t20230112_440396.shtml). As such, this extended rainy event was defined as one of the top 10 extreme weather and climate events over China in 2022 by the China Meteorological Administration (CMA).

It has been found that human activities since the preindustrial period can influence extreme rainfall in East Asia, particularly by exacerbating short-term extreme rainfall (Dong et al. 2021; Paik et al. 2020). Numerous studies have investigated the influence of anthropogenic forcing on extreme rainfall events, revealing their effects on both the probability and intensity of heavy precipitation (Zhou et al. 2021; Yang et al. 2022; Qian et al. 2022; Lu et al. 2022). Furthermore, it has been demonstrated that anthropogenic influences can change the likelihood of persistent heavy rainfall and daily extremes (Zhang et al. 2020; Hu et al. 2023). This study aims to explore the human influence on the changing risk of 5-day precipitation extreme and monthly precipitation in the PRB.

Data and methods

We analyzed daily rainfall observations (1961–2022) from ∼2,400 stations provided by the CMA. Among these, we focused on 275 stations in the PRB region (red box in Figs. 1a,b; 21.31°–26.49°N, 102.14°–115.53°E). First, the multistation-averaged precipitation across these 275 stations is computed. The standardized monthly mean precipitation (MMPA) and the maxima of consecutive 5-day precipitation (Rx5day) anomalies, which are calculated concerning the June climatological period of 1961–2005, reached almost 1.65 standard deviations for MMPA and 2.64 standard deviations for Rx5day; these values are utilized as thresholds for the subsequent attribution analysis.

Fig. 1.
Fig. 1.

(a),(b) Observed anomaly of monthly mean precipitation (MMPA) and the maxima of consecutive 5-day (Rx5day) in June 2022 over China. The red box (21.31°–26.49°N, 102.14°–115.53°E) marks the study region of the Pearl River basin (PRB). The inset map in the bottom-right corner represents the South China Sea region. (c),(d) Regional-averaged June MMPA and Rx5day in PRB during 1961–2022 for observations (red) and model simulations under ALL forcing in HadGEM3(orange) and CMIP6 (blue). The orange and blue shadings show the minimum-to-maximum spreads of model simulations under ALL forcing in HadGEM3 and CMIP6. (e),(f) GEV fit (red line) of observations with 90% confidence intervals (black dashed line). The crosses are estimated from the empirical distributions of the observations with the red square denoting the 2022 event.

Citation: Bulletin of the American Meteorological Society 105, 2; 10.1175/BAMS-D-23-0132.1

The HadGEM3-GA6-N216 (referred to as HadGEM3 hereafter) model and the multimodel ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6) were both applied to investigate the role of anthropogenic forcings on the changing risks of the 2022-like precipitation extremes over the PRB. The HadGEM3 model at an N216 resolution of 0.56° × 0.83° outputs includes all-forced simulations (historical) conditioned on the observed sea surface temperatures (SST) and sea ice (Rayner et al. 2003) and natural simulations (historicalNat) with anthropogenic signals removed from observed SSTs and with the external forcings during the preindustrial period (Ciavarella et al. 2018). Both historical and historicalNat ensembles consist of 15 members during the historical period (1961–2013), and 525 members for 2022.

For CMIP6, climate simulation experiments of historical, ScenarioMIP, and the DAMIP (NAT, AER, GHG) are used (Zhou et al. 2019). Specifically, historical runs reflect observed atmospheric composition changes due to both anthropogenic and natural forcings, whereas DAMIP-Nat only considers the forcings of natural factors. DAMIP-AER and DAMIP-GHG respectively consider aerosol forcing and greenhouse gas forcing. It is important to note that due to the coupled nature of CMIP6 model simulations, we were unable to directly represent the year 2022. Instead, we selected a period around the target year to serve as the representation of 2022. As historical simulations in CMIP6 ended in 2014, we combined them with SSP2-4.5 future scenario simulations from 2015 to 2026 and used them as ALL forcing (ALL). However, NAT simulations (DAMIP) ended in 2020, resulting in the absence of NAT future scenarios. To ensure robust probability distribution function (PDF) estimates, we utilized a 15-yr window (2006–2020) in ALL, GHG, AA, and NAT as ALL2022, GHG2022, AA2022, NAT2022 in CMIP6/DAMIP simulations. We used a total of 40 members from 11 models (Table S1 in the online supplemental material; https://doi.org/10.1175/BAMS-D-23-0132.2); this cohort of members was subsequently employed for conducting attribution analysis. Note that for each model, the baseline period of 1961–2005 was constructed from its ensemble mean response to ALL forcing and used for all the simulations to maintain impacts from the external forcing (Hu et al. 2023).

We used risk ratio (RR) to quantify anthropogenic contribution to changes in event probabilities (Stott et al. 2016). For both MMPA and Rx5day, the exceedance probability values of the 2022-like extreme events are estimated from the generalized extreme value (GEV) (Jenkinson 1955) fitted PDF of precipitation anomalies. The probability of events with equivalent or heavier precipitation than the June 2022 event in the simulations under ALL, NAT, GHG, and AER forcings was defined as PALL, PNAT, PGHG, and PAER, respectively. The change in probability of a 2022-like event in the factual and counterfactual climates can thus be expressed as RRALL= PALL/PNAT. Similarly, we used RRGHG = PGHG/PNAT and RRAER = PAER/PNAT to denote the ratio of the probability under only GHG or AER forcings to that without human interference. The 90% confidence interval (90% CI) was obtained by using 1,000 bootstrap resampling (Christidis et al. 2013).

Results

In June 2022, over half of the stations (150 stations) in the PRB have positive MMPA anomalies (Fig. 1a). The regional-mean MMPA anomaly deviates by 1.65 standard deviations relative to the 1961–2005 climatology (Fig. 1c), ranking as the seventh highest anomaly since 1961 and expected to recur once every 9 years (Fig. 1e). However, observed Rx5day exhibits even greater extremes, with approximately 84% of the stations having positive rainfall anomalies during the corresponding period (Fig. 1b). Such high values corresponded to a 1-in-32-yr event (Fig. 1f). The substantial discrepancy in severity and return period between these two extreme precipitation indicators highlights the need for in-depth analysis of this persistent heavy precipitation phenomenon.

We present the performance of both the HadGEM3 and CMIP6 models in capturing the observed variability (Figs. 1c,d). Besides the application of quantile–quantile (QQ) plots, a two-sided Kolmogorov–Smirnov test with a significance level of 0.05 (Hodges 1958) was employed. The findings indicate a strong concordance between the PDF fitted using the GEV distribution for the simulations and that derived from the observed data (Fig. S1). Consequently, we can assert that both the HadGEM3 and CMIP6 models have demonstrated their reliability in attributing the extreme precipitation event that occurred in June 2022 over the PRB and that the GEV distribution can reasonably model the anomalies of both MMPA and Rx5day.

The RR values of HadGEM3 and CMIP6 forcing on 2022 extreme events are calculated (Table S2). For MMPA, the corresponding probability is 13.0% (11.0%–14.7%) under NAT forcing and 8.8% (7.1%–10.3%) under ALL forcing, with a RRALL of 0.68 (0.53–0.84) in HadGEM3 simulations (Fig. 2a). Similarly, in CMIP6 simulations, the estimated occurrence probability decreased from 9.7% (8.1%–11.3%) in NAT to 8.2% (6.8%–9.7%) in ALL, with a RRALL of 0.85 (0.66–1.08). Meanwhile, we estimate the corresponding RRGHG and RRAER to be 1.61 (1.31–1.96) and 0.28 (0.19–0.38), respectively. Moreover, the changes in return periods also demonstrate that the monthly extreme precipitation occurs less frequently due to anthropogenic influences, with a 1-in-10-yr event in NAT and a 1-in-14yr event in ALL in HadGEM3 simulations.

Fig. 2.
Fig. 2.

Attribution results. (left) The MMPA results and (right) the Rx5day results. (a),(b) Best estimates and the 90% confidence intervals of risk ratios and exceedance probabilities. A dashed line separates risk ratios (to the left) and exceedance probabilities (to the right). Error bars and boxes mark the 5%–95% uncertainty ranges estimated via the bootstrapping method (N = 1,000). Black dashed boxes correspond to the HadGEM3 run (labels with the “H” prefix), while the others represent the CMIP6 run. (c),(d) GEV fits for HadGEM3 under all-forced (blue) and naturally forced (orange) simulations. Black dashed lines indicate the observed threshold values in June 2022, i.e., 1.54, and 2.43 for MMPA and Rx5day, respectively. (e),(f) Return periods fitted from HadGEM3. (g)–(j) As in (c)–(f), but for CMIP6 based on ensembles of ALL (blue), NAT (orange), GHG (gray), and AER (green).

Citation: Bulletin of the American Meteorological Society 105, 2; 10.1175/BAMS-D-23-0132.1

For Rx5day, the ALL distributions show a shift toward more intense events as compared to the NAT climate in both HadGEM3 and CMIP6, which indicates an increase in the probability of Rx5day like that in June 2022 in response to anthropogenic forcings (Figs. 2d,f). It can also be found from Fig. 2b that PALL exceeds PNAT, resulting in RRALL greater than 1 and contrasting with the conclusion of the MMPA. Further comparison of forced experiments reveals that GHG and AER exhibit probabilities exceeding the observed threshold (2.64) at 6.5% (5.5%–7.6%) and 0.7% (0.4%–1.2%), respectively. Figure 2j shows that GHG has a return period of only 17 years, while the return period of AER is 145 years.

Anthropogenic forcings have varying impacts on extreme precipitation at different temporal scales. Under the influence of greenhouse gases, atmospheric water vapor increases (Allen and Ingram 2002; Trenberth et al. 2003). When warmer air holds more moisture, it can lead to increased atmospheric instability and the formation of more intense rainstorms. Additionally, changes in atmospheric circulation patterns can enhance the transport of moisture and the convergence of air masses, further contributing to heavier precipitation events. The impact of aerosols is mainly related to the radiative forcing effect and cloud condensation nuclei (Guo et al. 2023). Owing to aerosol radiation interaction and aerosol–cloud interaction, there is a reduction in downward shortwave radiation. Over a longer time scale, increased aerosol loading suppresses monthly precipitation through radiative forcing in the PRB region (Lau et al. 2017). However, on the Rx5day scale, the combination of enhanced greenhouse gases and local atmospheric thermodynamics and dynamics intensifies precipitation events (Paik et al. 2020). It is important to note that CMIP6 data show that aerosols predominantly decrease precipitation during both June and Rx5day periods, with a more pronounced effect in June (Figs. S2b,d). In contrast, greenhouse gases consistently increase precipitation across these scales, exerting a stronger influence on Rx5day precipitation (Figs. S2f,h). Aerosols induce abnormal northerly winds over the PRB on a monthly scale, while GHG-induced Rx5day precipitation is associated with anomalous cyclonic circulations and low pressure systems. Moreover, on a monthly scale, aerosols have a greater inhibitory effect on precipitation compared to the enhancing effect of greenhouse gases. However, this relationship reverses on the Rx5day scale (Figs. S2j,l).

Conclusions

We find that in the HadGEM3 and CMIP6 models, anthropogenic forcings have opposing contributions to the probabilities of MMPA and Rx5day in the current climate. Anthropogenic forcings have reduced the exceedance probability of the 2022 June MMPA event in the Pearl River basin, China, while increasing that of the Rx5day event by a factor of about 1.8 (1.3) in HadGEM3 (CMIP6) simulations. Our study was conducted using two sets of ensemble simulations, and the attribution results from both HadGEM3 and CMIP6 models were consistent.

Further disentangling the contributions of greenhouse gases and anthropogenic aerosols on the risks of MMPA and Rx5day, greenhouse gas forcing is estimated to induce an increase in the exceedance probability by 61% and 150%, while anthropogenic aerosol forcing has a decreased contribution.

Acknowledgments.

This study was funded by the National Natural Science Foundation of China (U2142205), Guangdong Major Project of Basic and Applied Basic Research (2020B0301030004), Special Fund of China Meteorological Administration for Innovation and Development (CXFZ2023J027), and Special Fund for Forecasters of China Meteorological Administration (CMAYBY2020-094).

References

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    • Search Google Scholar
    • Export Citation
  • Yang, Y., and Coauthors, 2022: Abrupt emissions reductions during COVID-19 contributed to record summer rainfall in China. Nat. Commun., 13, 959, https://doi.org/10.1038/s41467-022-28537-9.

    • Search Google Scholar
    • Export Citation
  • Zhang, W., and Coauthors, 2020: Anthropogenic influence on 2018 summer persistent heavy rainfall in central western China [in “Explaining Extreme Events of 2018 from a Climate Perspective”]. Bull. Amer. Meteor. Soc., 101, S65S70, https://doi.org/10.1175/BAMS-D-19-0147.1.

    • Search Google Scholar
    • Export Citation
  • Zhou, T., L. Ren, and W. Zhang, 2021: Anthropogenic influence on extreme meiyu rainfall in 2020 and its future risk. Sci. China Earth Sci., 64, 16331644, https://doi.org/10.1007/s11430-020-9771-8.

    • Search Google Scholar
    • Export Citation
  • Zhou, T., L.-W. Zou, and X.-L. Chen, 2019: Commentary on the Coupled Model Intercomparison Project phase 6 (CMIP6). Climate Change Res., 15, 445456, https://doi.org/10.12006/j.issn.1673-1719.2019.193.

    • Search Google Scholar
    • Export Citation

Supplementary Materials

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  • Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and the hydrologic cycle. Nature, 419, 224232, https://doi.org/10.1038/nature01092.

    • 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
  • Ciavarella, 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
  • Dong, S., Y. Sun, C. Li, X. Zhang, S.-K. Min, and Y.-H. Kim, 2021: Attribution of extreme precipitation with updated observations and CMIP6 simulations. J. Climate, 34, 871881, https://doi.org/10.1175/JCLI-D-19-1017.1.

    • Search Google Scholar
    • Export Citation
  • Guo, Y., B. Dong, and J. Zhu, 2023: Anthropogenic impacts on changes in summer extreme precipitation over China during 1961–2014: Roles of greenhouse gases and anthropogenic aerosols. Climate Dyn., 60, 26332643, https://doi.org/10.1007/s00382-022-06453-4.

    • Search Google Scholar
    • Export Citation
  • Hodges, J., Jr., 1958: The significance probability of the Smirnov two-sample test. Ark. Mat., 3, 469486, https://doi.org/10.1007/BF02589501.

    • Search Google Scholar
    • Export Citation
  • Hu, T., Y. Sun, X. Zhang, and D. Wang, 2023: Anthropogenic influence on the 2021 wettest September in northern China [in “Explaining Extreme Events from a Climate Perspective”]. Bull. Amer. Meteor. Soc., 104, E243E248, https://doi.org/10.1175/BAMS-D-22-0156.1.

    • Search Google Scholar
    • Export Citation
  • Jenkinson, A. F., 1955: The frequency distribution of the annual maximum (or minimum) values of meteorological elements. Quart. J. Roy. Meteor. Soc., 81, 158171, https://doi.org/10.1002/qj.49708134804.

    • Search Google Scholar
    • Export Citation
  • Lau, W. K., K. M. Kim, and L. R. Leung, 2017: Changing circulation structure and precipitation characteristics in Asian monsoon regions: Greenhouse warming vs. aerosol effects. Geosci. Lett., 4, 28, https://doi.org/10.1186/s40562-017-0094-3.

    • Search Google Scholar
    • Export Citation
  • Lu, C., Y. Sun, and X. Zhang, 2022: The 2020 record-breaking meiyu in the Yangtze River valley of China: The role of anthropogenic forcing and atmospheric circulation [in “Explaining Extreme Events of 2020 from a Climate Perspective”]. Bull. Amer. Meteor. Soc., 103, S98S104, https://doi.org/10.1175/BAMS-D-21-0161.1.

    • Search Google Scholar
    • Export Citation
  • Paik, S., S.-K. Min, X. Zhang, M. G. Donat, A. D. King, and Q. Sun, 2020: Determining the anthropogenic greenhouse gas contribution to the observed intensification of extreme precipitation. Geophys. Res. Lett., 47, e2019GL086875, https://doi.org/10.1029/2019GL086875.

    • Search Google Scholar
    • Export Citation
  • Qian, C., Y. Ye, W. Zhang, and T. Zhou, 2022: Heavy rainfall event in mid-August 2020 in southwestern China: Contribution of anthropogenic forcings and atmospheric circulation [in “Explaining Extreme Events of 2020 from a Climate Perspective”]. Bull. Amer. Meteor. Soc., 103, S111S117, https://doi.org/10.1175/BAMS-D-21-0233.1.

    • Search Google Scholar
    • Export Citation
  • Rayner, N., 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
  • Stott, P. A., and Coauthors, 2016: Attribution of extreme weather and climate-related events. Wiley Interdiscip. Rev.: Climate Change, 7, 2341, https://doi.org/10.1002/wcc.380.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bull. Amer. Meteor. Soc., 84, 12051218, https://doi.org/10.1175/BAMS-84-9-1205.

    • Search Google Scholar
    • Export Citation
  • Yang, Y., and Coauthors, 2022: Abrupt emissions reductions during COVID-19 contributed to record summer rainfall in China. Nat. Commun., 13, 959, https://doi.org/10.1038/s41467-022-28537-9.

    • Search Google Scholar
    • Export Citation
  • Zhang, W., and Coauthors, 2020: Anthropogenic influence on 2018 summer persistent heavy rainfall in central western China [in “Explaining Extreme Events of 2018 from a Climate Perspective”]. Bull. Amer. Meteor. Soc., 101, S65S70, https://doi.org/10.1175/BAMS-D-19-0147.1.

    • Search Google Scholar
    • Export Citation
  • Zhou, T., L. Ren, and W. Zhang, 2021: Anthropogenic influence on extreme meiyu rainfall in 2020 and its future risk. Sci. China Earth Sci., 64, 16331644, https://doi.org/10.1007/s11430-020-9771-8.

    • Search Google Scholar
    • Export Citation
  • Zhou, T., L.-W. Zou, and X.-L. Chen, 2019: Commentary on the Coupled Model Intercomparison Project phase 6 (CMIP6). Climate Change Res., 15, 445456, https://doi.org/10.12006/j.issn.1673-1719.2019.193.

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

    (a),(b) Observed anomaly of monthly mean precipitation (MMPA) and the maxima of consecutive 5-day (Rx5day) in June 2022 over China. The red box (21.31°–26.49°N, 102.14°–115.53°E) marks the study region of the Pearl River basin (PRB). The inset map in the bottom-right corner represents the South China Sea region. (c),(d) Regional-averaged June MMPA and Rx5day in PRB during 1961–2022 for observations (red) and model simulations under ALL forcing in HadGEM3(orange) and CMIP6 (blue). The orange and blue shadings show the minimum-to-maximum spreads of model simulations under ALL forcing in HadGEM3 and CMIP6. (e),(f) GEV fit (red line) of observations with 90% confidence intervals (black dashed line). The crosses are estimated from the empirical distributions of the observations with the red square denoting the 2022 event.

  • Fig. 2.

    Attribution results. (left) The MMPA results and (right) the Rx5day results. (a),(b) Best estimates and the 90% confidence intervals of risk ratios and exceedance probabilities. A dashed line separates risk ratios (to the left) and exceedance probabilities (to the right). Error bars and boxes mark the 5%–95% uncertainty ranges estimated via the bootstrapping method (N = 1,000). Black dashed boxes correspond to the HadGEM3 run (labels with the “H” prefix), while the others represent the CMIP6 run. (c),(d) GEV fits for HadGEM3 under all-forced (blue) and naturally forced (orange) simulations. Black dashed lines indicate the observed threshold values in June 2022, i.e., 1.54, and 2.43 for MMPA and Rx5day, respectively. (e),(f) Return periods fitted from HadGEM3. (g)–(j) As in (c)–(f), but for CMIP6 based on ensembles of ALL (blue), NAT (orange), GHG (gray), and AER (green).

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