mlsanityData to 7 Oct 2026

Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific

arXiv 2110.15084 ↗First posted 28 Oct 2021physics.ao-ph cs.LG physics.data-ancited by 6

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Abstract (arXiv)

Aerosol-cloud interactions include a myriad of effects that all begin when aerosol enters a cloud and acts as cloud condensation nuclei (CCN). An increase in CCN results in a decrease in the mean cloud droplet size (re). The smaller droplet size leads to brighter, more expansive, and longer lasting clouds that reflect more incoming sunlight, thus cooling the earth. Globally, aerosol-cloud interactions cool the Earth, however the strength of the effect is heterogeneous over different meteorological regimes. Understanding how aerosol-cloud interactions evolve as a function of the local environment can help us better understand sources of error in our Earth system models, which currently fail to reproduce the observed relationships. In this work we use recent non-linear, causal machine learning methods to study the heterogeneous effects of aerosols on cloud droplet radius.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 11 of its 41 references that are in the map. First posted on arXiv 28 Oct 2021.

Builds on

11 of 41 references.

References · 11

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Cited by year

Citing papers per year: 2022: 4; 2023: 0; 2024: 0; 2025: 2.

6 papers.

  1. 2025CausalRivers -- Scaling up benchmarking of causal discovery for real-world time-seriescited by 24
  2. 2022Towards assessing agricultural land suitability with causal machine learningcited by 11
  3. 2022Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventionscited by 5
  4. 2025CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal modelscited by 5
  5. 2022Big Earth Data and Machine Learning for Sustainable and Resilient Agriculturecited by 0
  6. 2022Using uncertainty-aware machine learning models to study aerosol-cloud interactionscited by 0