<p>Heavy rainfall, flooding, and landslides can cause important damage to infrastructure and the environment. By increasing the atmosphere’s capacity to hold moisture, rising temperatures have been shown to largely drive the global intensification of heavy rainfall in recent decades, consistent with established physics. Anthropogenic aerosols also affect extreme rainfall patterns through a slow response, acting as a mitigating cooling and drying factor, and a fast response involving regional cloud-aerosol interactions. Here, we identify a fourth mechanism illustrating how industrial and volcanic aerosols have altered the interhemispheric temperature contrast, shifting the tropical rainbelt, thus helping reshape intense rainfall patterns over time. We found that detecting this signal is not strongly dependent on using probability or raw-space indices, but is sensitive to the choice of observational dataset, possibly due to poorly sampled regions where satellite retrievals lack local calibration. Our analyses also provide support of possible emission inventory errors in East Asia.</p>

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Understanding the evolving patterns of extreme rainfall

  • Céline J. W. Bonfils,
  • Shiheng Duan,
  • Margot Bador,
  • Giuliana Pallotta,
  • Gavin D. Madakumbura

摘要

Heavy rainfall, flooding, and landslides can cause important damage to infrastructure and the environment. By increasing the atmosphere’s capacity to hold moisture, rising temperatures have been shown to largely drive the global intensification of heavy rainfall in recent decades, consistent with established physics. Anthropogenic aerosols also affect extreme rainfall patterns through a slow response, acting as a mitigating cooling and drying factor, and a fast response involving regional cloud-aerosol interactions. Here, we identify a fourth mechanism illustrating how industrial and volcanic aerosols have altered the interhemispheric temperature contrast, shifting the tropical rainbelt, thus helping reshape intense rainfall patterns over time. We found that detecting this signal is not strongly dependent on using probability or raw-space indices, but is sensitive to the choice of observational dataset, possibly due to poorly sampled regions where satellite retrievals lack local calibration. Our analyses also provide support of possible emission inventory errors in East Asia.