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Disproportionate control on aerosol burden by light rain

Nature Geoscience volume 14, pages 72–76 (2021)Cite this article

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Abstract

Atmospheric aerosols are of great climatic and environmental importance due to their effects on the Earth’s radiative energy balance and air quality. Aerosol concentrations are strongly influenced by rainfall via wet removal. Global climate models have been used to quantify their climate and health effects. However, they commonly suffer from a well-known problem of ‘too much light rain and too little heavy rain’. The impact of simulated rainfall intensities on aerosol burden at the global scale is still unclear. Here we show that rainfall intensity has profound impacts on aerosol burden, and light rain has a disproportionate control on it. By improving the representation of convection, the light-rain (1–20 mm d−1) frequency in two state-of-the-art global climate models is reduced. As a result, the aerosol burden is increased globally, especially over the tropics and subtropics, by as much as 0.3 in aerosol optical depth in tropical rain belts. It is attributed to the dominant contribution of light rain to the accumulated wet removal by its frequent occurrence despite its weak intensity. The implication of these findings is that understanding the nature of aerosol scavenging by rainfall is critical to aerosol–climate interaction and its impact on climate.

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Fig. 1: Rainfall intensity spectrum and aerosol wet removal as functions of rain rates.
Fig. 2: Rainfall and wet removal amount.
Fig. 3: AOD at 550 nm.
Fig. 4: Changes of light-rain frequency.

Data availability

GPM data can be accessed online at https://pmm.nasa.gov/data-access/downloads/gpm. TRMM data are available from https://disc2.gesdisc.eosdis.nasa.gov/opendap/TRMM_L3/. MODIS data can be obtained at https://search.earthdata.nasa.gov and AERONET data are available at https://aeronet.gsfc.nasa.gov/new_web/data.html. The CAM and EAMv1 simulation data are provided in an open repository, Zenodo (https://doi.org/10.5281/zenodo.4259554).

Code availability

The CESM1.2.1-CAM5.3 source code can be downloaded from the CESM official website: http://www2.cesm.ucar.edu. The E3SM-EAMv1 source code is available from the E3SM official website: https://e3sm.org/. The stochastic deep-convection code is available from the corresponding author upon request.

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Acknowledgements

This work is supported by the National Key Research and Development Program of China grants 2017YFA0604000 and the National Natural Science Foundation of China grants 41975126 and 41605074. G.J.Z. is supported by the US Department of Energy (DOE), Office of Science, Biological and Environmental Research Program (BER) under award numbers DE-SC19373 and DE-SC0016504. Part of the E3SM model simulation was done during Y.W.’s visit to LLNL. Work at LLNL was performed under the auspices of the US DOE by Lawrence Livermore National Laboratory under contract no. DE-AC52-07NA27344. S.X., X.L. and Q.T. are supported by the DOE Energy Exascale Earth System Model (E3SM) project and H.-Y.M. is funded by the DOE Regional and Global Model Analysis program area (RGMA) and ASR’s Cloud-Associated Parameterizations Testbed (CAPT) project. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the US DOE under contract no. DE-AC02-05CH11231.

Author information

Authors and Affiliations

  1. Ministry of Education Key Laboratory for Earth System Modeling and Department of Earth System Science, Tsinghua University, Beijing, China

    Yong Wang, Wenwen Xia & Bin Wang

  2. Department of Atmospheric Sciences, Texas A&M University, College Station, TX, USA

    Xiaohong Liu

  3. Lawrence Livermore National Laboratory, Livermore, CA, USA

    Shaocheng Xie, Qi Tang & Hsi-Yen Ma

  4. Brookhaven National Laboratory, Upton, NY, USA

    Wuyin Lin

  5. CMA-NJU Joint Laboratory for Climate Prediction Studies, Institute for Climate and Global Change Research, School of Atmospheric Sciences, Nanjing University, Nanjing, China

    Yiquan Jiang

  6. State Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Bin Wang

  7. College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China

    Bin Wang

  8. Scripps Institution of Oceanography, La Jolla, CA, USA

    Guang J. Zhang

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Contributions

Y.W. and G.J.Z. conceived the idea and led the study. S.X. contributed to the initial research plan. Y.W. conducted the model simulations. Y.W. and W.X. performed the analysis. Y.W., G.J.Z. and S.X. interpreted the results. Y.W. and G.J.Z. wrote the paper with contributions from X.L. and S.X. All authors participated in the revision and editing of the paper.

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Correspondence to Guang J. Zhang.

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Peer review information Nature Geoscience thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: Clare Davis; Xujia Jiang.

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Extended data

Extended Data Fig. 1 Convective and large-scale rainfall intensity spectrum.

Frequency distributions of (a) convective and (b) large-scale rainfall intensity over the tropics (20°S-20°N) in the CAM5 and STOC runs respectively.

Extended Data Fig. 2 Occurrence frequency of rainfall as functions of rain rates.

Frequency distributions of (a) total, (b) convective and (c) large-scale rainfall intensity over the tropics (20°S-20°N). Results from Global Precipitation Measurement (GPM), Tropical Rainfall Measuring Mission (TRMM), EAMv1 and EAMv1_STOC are denoted by gray, black, blue and red lines respectively.

Extended Data Fig. 3 Rainfall and wet removal amount.

Amount distributions of (a) total rainfall and (b) wet removal of all aerosols by different rainfall intensities over (20°N, 50°N). GPM, TRMM, CAM5 and STOC are denoted by gray, black, blue and red lines, respectively. The total rainfall rate in the range from 0.1 to 1000 mm d−1 is logarithmically scaled, with equal bin width of \({\Delta}\ln \left( R \right) = {\Delta}R/R = 0.1\). Numbers in parentheses are regional mean aerosol wet deposition rates, which equal areas under respective curves.

Extended Data Fig. 4 Contributions of wet removal for different aerosol species.

Contributions of wet removal by different rainfall intensities to the total wet removal for (a) sulfate, (b) sea salt, (c) dust, (d) black carbon, (e) POM and (f) SOA in the CAM5 (blue) and STOC (red) runs.

Extended Data Fig. 5 Cumulative contributions of wet removal for different aerosol species.

Cumulative contributions of wet removal by different rainfall intensities to the total wet removal for (a) sulfate, (b) sea salt, (c) dust, (d) black carbon, (e) POM and (f) SOA in the CAM5 (blue) and STOC (red) runs.

Extended Data Fig. 6 Contributions of wet removal for different aerosol species.

Same as Extended Data Fig. 4, but for the EAMv1 (blue) and EAMv1_STOC (red) runs.

Extended Data Fig. 7 Cumulative contributions of wet removal for different aerosol species.

Same as Extended Data Fig. 5, but for the EAMv1 (blue) and EAMv1_STOC (red) runs.

Extended Data Fig. 8 Aerosol lifetime.

Aerosol lifetime (days) and relative changes (%) between the CAM5 and STOC simulations for different aerosol species.

Extended Data Fig. 9 Comparison of simulated aerosol optical depth with observations.

Observed and simulated AOD at AERONET sites over (a) (30°N-60°N) and (b) (60°N-90°N). Circles represent the modeled AOD average in each AERONET observed AOD bin with an interval of 0.05.

Extended Data Fig. 10 Changes in rainfall and light rain frequency.

Global distributions of changes in (a) total, (b) convective, (c) large-scale rainfall and (d) the occurrence frequency of total rainfall intensity smaller than 10 mm d−1 between the STOC and CAM5 runs. Areas in (a–c) exceeding 95% t-test confidence level are stippled.

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Supplementary Information

Supplementary Table 1 and Figs. 1–4.

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Wang, Y., Xia, W., Liu, X. et al. Disproportionate control on aerosol burden by light rain. Nat. Geosci. 14, 72–76 (2021). https://doi.org/10.1038/s41561-020-00675-z

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