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Thermodynamically enhanced precipitation extremes due to counterbalancing influences of anthropogenic greenhouse gases and aerosols

Nature Water volume 1, pages 614–625 (2023)Cite this article

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Abstract

Responses of precipitation extremes to forcings by anthropogenic greenhouse gases (GHGs) and aerosol (AER) emissions could significantly impact society and ecosystems. Although human influences on changes in precipitation extremes are detectable, how precipitation extremes have responded to human-induced climate change remains unclear. Here we apply a robust physical scaling diagnostic on reanalysis-based and simulated precipitation extremes to disentangle global and regional changes in historical precipitation extremes to thermodynamic and dynamic contributions from anthropogenic GHGs and AER forcings. The results show that, despite large spatiotemporal uncertainties of dynamic contributions to regional changes in precipitation extremes, thermodynamic effects of anthropogenic GHGs (AER) significantly increase (decrease) the intensity of precipitation extremes. Since GHG positive effects are higher than AER negative effects, the counterbalancing effects enhance global precipitation extremes from 1960 to 2014. Increasing precipitation extremes are expected to be exacerbated in the future, given that GHG warming will continue to increase while AER cooling will decrease in the coming decades.

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Fig. 1: Historical precipitation extremes and scaling.
Fig. 2: Changes in Rx1day, thermodynamic and dynamic responses under various forcings.
Fig. 3: Sensitivity of precipitation extremes with respect to global warming under various forcings.
Fig. 4: Global mean anomalies and temporal trends of Rx1day, thermodynamic and dynamic responses under various forcings.
Fig. 5: Temporal trends of regional Rx1day, thermodynamic, and dynamic responses under various forcings.
Fig. 6: Detection and attribution results for global and regional Rx1day and its thermodynamic and dynamic responses.

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Data availability

All CMIP6 simulations are accessible via https://esgf-node.llnl.gov/search/cmip6/. All reanalysis datasets are publicly available for download via the following links: ERA5 data from European Centre for Medium-Range Weather Forecasts, https://cds.climate.copernicus.eu; JRA55 available at the National Center for Atmospheric Research, https://climatedataguide.ucar.edu/climate-data/jra-55. The results of the physical scaling diagnostic for reanalyses and climate model simulations are publicly available from a Zenodo repository59: https://doi.org/10.5281/zenodo.7790872. The maps have been made by using an object-oriented matplotlib wrapper called ‘proplot’ (https://proplot.readthedocs.io).

Code availability

The Python code for the robust physical scaling diagnostic is available at https://github.com/oliverangelil/precip_extremes_scaling. All other analysis and visualization code is available in a GitHub repository https://github.com/huangzq681/Rx1day_decompose_DNA_2022.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (52179030 and 51809295, X.T., and 52179029 and 51879289, B.L.), the National Key R&D Program of China (2021YFC3001004, X.T. and X.C.), and the Guangzhou Science and Technology Plan Project (201904010097, X.T.). X.T. is also supported by the Fundamental Research Funds for the Central Universities, Sun Yat-sen University. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Zeqin Huang, Xuezhi Tan.

  2. These authors jointly supervised this work: Xuezhi Tan, Bingjun Liu.

Authors and Affiliations

  1. Center of Water Resources and Environment, School of Civil Engineering, Sun Yat-sen University, Guangzhou, PR China

    Zeqin Huang, Xuezhi Tan, Bingjun Liu & Xiaohong Chen

  2. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, PR China

    Xuezhi Tan

  3. Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, Canada

    Thian Yew Gan

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Contributions

X.T. conceived the study. X.T. and Z.H. designed the study and performed the analyses, with contributions from T.Y.G., B.L. and X.C. in interpreting the results. X.T. wrote the paper, with contributions from Z.H. and T.Y.G.

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Correspondence to Xuezhi Tan or Bingjun Liu.

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Extended data

Extended Data Fig. 1 Comparisons of Rx1day between reanalysis datasets and CMIP6 historical simulations with HadEX3 observation.

(a) Historical (1960–2014) climatology of global land HadEX3 Rx1day, with grid cells spanning at least 90% of years during the period shown (same as Fig. 1a, reproduced here for comparison convenience). (b) Distribution of pattern correlations between HadEX3 observational Rx1day with Rx1day and full scaling from each reanalysis and CMIP6 model (n = 16). The box plots show the median values (middle line) and the middle 50 percent of the data (the boxes between the first and third quartiles), with the maximum and minimum located at the whiskers, and outliers outside the whiskers. (c–h) Historical climatology of Rx1day for ERA5 (c), JRA55 (e), and multi-model ensemble mean CMIP6 simulation (g), and their difference compared with the HadEX3 observation (d, f, h).

Extended Data Fig. 2 Comparisons between HadEX3 observational Rx1day with Rx1day and full scaling from each reanalysis and CMIP6 model (n = 16) for different subregions.

(a–e) Distribution of the pattern correlations for (a) NA, (b) EU, (c) EAS, (d) SSA, and (e) AU. The box plots show the median values (middle line) and the middle 50 percent of the data (the boxes between the first and third quartiles), with the maximum and minimum located at the whiskers, and outliers outside the whiskers.

Extended Data Fig. 3 Agreement of spatial patterns of the climatology and trends between Rx1day and scaling for each reanalysis and CMIP6 model.

(a, b) Pattern correlations of the climatology (a) and trends (b) between Rx1day and scaling for each reanalysis and CMIP6 model (n = 1). Both pattern correlations for the whole globe (blue bars) and global land area (HadEX3_region, orange bars) are taken into account for each climate model. For climate models with multiple ensemble members like CanESM5 and CESM2, the bar plots show the ensemble-mean values with the error bars depicting the standard deviation.

Extended Data Fig. 4 Contributions of thermodynamic response, dynamic response, and their covariance to the interannual variance of Rx1day under various forcings.

(a–x) Spatial patterns of variance contribution from the thermodynamic response (1st column), dynamic response (2nd column), and their covariance (3rd column) for ERA5 (a–c), JRA55 (e–g), ALL forcing (i–k), GHG forcing (m–o), AER forcing (q–s), NAT forcing (u–w). The variances explained by the dynamic response (%, calculated as the ratio of the dynamic contribution to the sum of both the thermodynamic and dynamic contributions) for various forcings are shown in the 4th column. Stippling in the 3rd column indicates that more than 2/3 of the CMIP6 ensemble models agree on the sign of the covariance.

Extended Data Fig. 5 Changes in vertically integrated saturation specific humidity qs conditioned on the occurrence of precipitation extremes.

(a–f) Multi-model mean historical changes in qs for ERA5 (a), JRA55 (b), ALL forcing (c), GHG forcing (d), AER forcing (e), and NAT forcing (f).

Extended Data Fig. 6 Changes in vertically averaged vertical velocity we conditioned on the occurrence of precipitation extremes.

(a–f) Multi-model mean historical changes in we for ERA5 (a), JRA55 (b), ALL forcing (c), GHG forcing (d), AER forcing (e), and NAT forcing (f).

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Huang, Z., Tan, X., Gan, T.Y. et al. Thermodynamically enhanced precipitation extremes due to counterbalancing influences of anthropogenic greenhouse gases and aerosols. Nat Water 1, 614–625 (2023). https://doi.org/10.1038/s44221-023-00107-3

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