ML4WX

Climate analysis & attribution

Observing Aerosol Primary Convective Invigoration and Its Meteorological Feedback

Lin Zang, Daniel Rosenfeld, Zengxin Pan, Feiyue Mao, Yannian Zhu, Xin Lü, Wei Yu Gong

Geophysical Research Letters ·

PaperPDF

In one sentence. Neural network sensitivity analysis of observations separates aerosol primary convective invigoration (72% of cloud-top cooling) from meteorological effects in deep convection.

Abstract

Abstract Aerosols can invigorate deep convective clouds (DCCs) directly by nucleating more cloud droplets, named as Primary Aerosol Convective Invigoration (PAI). However, the covarying Meteorology‐Aerosol Invigoration (MAI) effect on DCC has been a long‐standing issue in quantifying PAI's contribution. Here, observations show that PAI causes positive feedback from DCC to meteorology, further invigorating DCC through enhanced humidity, updraft and destabilization, thereby adding to MAI. Further, PAI is separated from MAI observationally by quantifying the sensitivity of DCC properties to aerosol changes under fixed meteorology through the artificial neural network. When fine aerosol changes from the cleanest to optimal concentration (5 μg m −3 ), PAI contributes 72% ± 2% of the total aerosol‐associated cloud top cooling by 12°C, 42% ± 4% of the 30% prolonged lifetime, and 50% ± 4% of the more than doubled rainfall. This result underlines the comparable magnitudes of PAI and MAI, which have not been considered until now in weather and climate prediction.

Labels

Topic
Climate analysis & attribution, Observations & remote sensing
Type
Application study
Method
Classical ML (trees, GP, linear, kernels)
Time scale
0–6 h
Domain
Regional
Data
Satellite, Station / in-situ
Status
Research
Identifiers
DOI:10.1029/2023gl104151 OpenAlex:W4381848324
Added
2026-10-22

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