Causal Directions Matter: How Environmental Factors Drive Convective Cloud Detrainment Heights

Abstract This study investigates how environmental factors influence the level of maximum detrainment (LMD) in deep convective clouds. Through a novel application of the Linear Non‐Gaussian Acyclic Model (LiNGAM), we discover causal structures between environmental variables and LMD, observed at six tropical sites operated by the Atmospheric Radiation Measurement (ARM) user facility. LiNGAM effectively identifies causal directions among variables of interest, revealing robust relationships such as those among the lifting condensation level (LCL), level of free convection (LFC), and convective inhibition (CIN), aligning with prior knowledge. Relative humidity is shown to directly influence LMD; however, this relationship exhibits strong nonlinearity and becomes difficult to detect when the contrast between oceanic and continental environments is excluded from the analysis. This study highlights the importance of establishing causal relationships before performing statistical inference.

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Publication Details

Journal
Geophysical Research Letters
Published
2025-06-30
DOI
https://doi.org/10.1029/2025gl114941
Citations
1
Primary Topic
Meteorological Phenomena and Simulations
Type
article
Field-Weighted Citation Impact
1.30

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article

Causal Directions Matter: How Environmental Factors Drive Convective Cloud Detrainment Heights

Simon H. Lee, Daniel J. Kirshbaum, Christian Philipp Lackner, Michael P. Jensen et al.
1 citations
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Meteorological Phenomena and Simulations
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article

Causal Directions Matter: How Environmental Factors Drive Convective Cloud Detrainment Heights

Simon H. Lee, Daniel J. Kirshbaum, Christian Philipp Lackner, Michael P. Jensen, Tao Zhang, Dié Wang
article en
1 citations

Abstract

Abstract This study investigates how environmental factors influence the level of maximum detrainment (LMD) in deep convective clouds. Through a novel application of the Linear Non‐Gaussian Acyclic Model (LiNGAM), we discover causal structures between environmental variables and LMD, observed at six tropical sites operated by the Atmospheric Radiation Measurement (ARM) user facility. LiNGAM effectively identifies causal directions among variables of interest, revealing robust relationships such as those among the lifting condensation level (LCL), level of free convection (LFC), and convective inhibition (CIN), aligning with prior knowledge. Relative humidity is shown to directly influence LMD; however, this relationship exhibits strong nonlinearity and becomes difficult to detect when the contrast between oceanic and continental environments is excluded from the analysis. This study highlights the importance of establishing causal relationships before performing statistical inference.

Geophysical Research LettersVol. 52(13)
Brookhaven National Laboratory (US), McGill University (CA), Stony Brook University (US)
U.S. Department of Energy, Workforce Development for Teachers and Scientists
Openalex Percentile: Top 21%
Meteorological Phenomena and Simulations
1.30
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