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Emergent constraints on future precipitation changes

Author

Listed:
  • Hideo Shiogama

    (National Institute for Environmental Studies)

  • Masahiro Watanabe

    (University of Tokyo)

  • Hyungjun Kim

    (Korea Advanced Institute of Science and Technology
    Korea Advanced Institute of Science and Technology
    University of Tokyo)

  • Nagio Hirota

    (National Institute for Environmental Studies)

Abstract

Future projections of global mean precipitation change (ΔP) based on Earth-system models have larger uncertainties than projections of global mean temperature changes (ΔT)1. Although many observational constraints on ΔT have been proposed, constraints on ΔP have not been well studied2–5 and are often complicated by the large influence of aerosols on precipitation4. Here we show that the upper bound (95th percentile) of ΔP (2051–2100 minus 1851–1900, percentage of the 1980–2014 mean) is lowered from 6.2 per cent to 5.2–5.7 per cent (minimum–maximum range of sensitivity analyses) under a medium greenhouse gas concentration scenario. Our results come from the Coupled Model Intercomparison Project phase 5 and phase 6 ensembles6–8, in which ΔP for 2051–2100 is well correlated with the global mean temperature trends during recent decades after 1980 when global anthropogenic aerosol emissions were nearly constant. ΔP is also significantly correlated with the recent past trends in precipitation when we exclude the tropical land areas with few rain-gauge observations. On the basis of these significant correlations and observed trends, the variance of ΔP is reduced by 8–30 per cent. The observationally constrained ranges of ΔP should provide further reliable information for impact assessments.

Suggested Citation

  • Hideo Shiogama & Masahiro Watanabe & Hyungjun Kim & Nagio Hirota, 2022. "Emergent constraints on future precipitation changes," Nature, Nature, vol. 602(7898), pages 612-616, February.
    • Handle: RePEc:nat:nature:v:602:y:2022:i:7898:d:10.1038_s41586-021-04310-8
    DOI: 10.1038/s41586-021-04310-8
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    Cited by:

    1. Wenyu Zhou & L. Ruby Leung & Nicholas Siler & Jian Lu, 2023. "Future precipitation increase constrained by climatological pattern of cloud effect," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    2. Qing Sun & Yi Zhang & Xianghong Che & Sining Chen & Qing Ying & Xiaohui Zheng & Aixia Feng, 2022. "Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields," Agriculture, MDPI, vol. 12(11), pages 1-15, October.
    3. Timothy M. Lenton & Jesse F. Abrams & Annett Bartsch & Sebastian Bathiany & Chris A. Boulton & Joshua E. Buxton & Alessandra Conversi & Andrew M. Cunliffe & Sophie Hebden & Thomas Lavergne & Benjamin , 2024. "Remotely sensing potential climate change tipping points across scales," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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