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https://doi.org/10.5194/egusphere-2025-3850
https://doi.org/10.5194/egusphere-2025-3850
21 Aug 2025
 | 21 Aug 2025

Machine Learning Reveals Strong Grid-Scale Dependence in the Satellite Nd–LWP Relationship

Matthew W. Christensen, Andrew Geiss, Adam C. Varble, and Po-Lun Ma

Abstract. The relationship between cloud droplet number concentration (Nd) and liquid water path (LWP) is highly uncertain yet crucial for determining the impact of aerosol-cloud interactions (ACI) on Earth's radiation budget. The Nd-LWP relationship is examined using a machine learning (ML) random forest model applied to five years of satellite data at grid resolutions ranging from 10° to 0.05° in 12 distinct regions. In the subtropics, the shape of the Nd-LWP relationship switches from an inverted-V at 1° grid-resolution to an "M" shape at 0.1° resolution with decreased dlnLWPdlnNd sensitivity. Tropical and midlatitude regions generally show a more positive sensitivity. Cloud sampling and filtering also influence this slope, wherein the exclusion of thin clouds, as commonly performed to reduce retrieval uncertainty, leads to strongly negative sensitivity across all regions. Precipitation is primarily responsible for driving the strength of the sensitivity, with strong positive slopes in raining clouds and negative and/or neutral responses found in non-raining clouds. A new method to compute radiative forcing from the ML model shows a robust Twomey radiative forcing across all regions and grid resolutions. However, LWP and cloud fraction rapid adjustments, which are ~50 % or smaller than the Twomey effect, decrease to negligible values with higher spatial resolution data. As Earth system models move toward higher spatial resolutions in the future, evaluating the LWP and CF adjustment contributions to the radiative forcing budget at these finer resolutions will be essential for evaluation and model development.

Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Matthew W. Christensen, Andrew Geiss, Adam C. Varble, and Po-Lun Ma

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Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-3850', Anonymous Referee #1, 24 Sep 2025
  • RC2: 'Comment on egusphere-2025-3850', Anonymous Referee #2, 25 Sep 2025
Matthew W. Christensen, Andrew Geiss, Adam C. Varble, and Po-Lun Ma
Matthew W. Christensen, Andrew Geiss, Adam C. Varble, and Po-Lun Ma

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Short summary
We used satellite data and machine learning to better understand how tiny particles in the atmosphere affect clouds and their brightness. At higher spatial resolution, we discovered a new “M”-shaped pattern in the relationship between cloud water and droplet concentration unlike the inverted-V shape observed at coarsely gridded scales. Cloud water increases more with droplet concentration when rain is present. These findings support the development of next-generation atmospheric models.
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