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This content will become publicly available on August 16, 2026

Title: Aerosol Influences on Cloud Water: Insights From ARM EPCAPE Observations With Explainable Machine Learning
Abstract This study employs an explainable machine learning (ML) framework (XGBoost‐SHapley Additive exPlanations analysis) to investigate controlling factors on cloud liquid water path (LWP) using EPCAPE observations near the California coast. Aerosols are found to be the dominant factor explaining LWP variability, surpassing meteorological factors (MFs). By isolating aerosol effects from meteorological influences, the ML reveals a negative linear relationship between LWP and cloud droplet number concentration (Nd) in log space, likely driven by entrainment drying via evaporation‐entrainment feedback. This aligns with the negative regime of the inverted‐V relationship reported in previous studies, while no positive LWP responses are found due to a limited number of precipitating cases in EPCAPE. Furthermore, the sensitivity of LWP toNdshows a non‐linear dependence on MFs like moisture contrast between surface and free troposphere and lower‐tropospheric stability. This occurs due to the interplay between the MFs' direct effects on entrainment drying and indirect effects through LWP adjustments.  more » « less
Award ID(s):
2126098
PAR ID:
10629444
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
American Geophysical Union
Date Published:
Journal Name:
Geophysical Research Letters
Volume:
52
Issue:
15
ISSN:
0094-8276
Subject(s) / Keyword(s):
Aerosol cloud
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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