Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific
This work addresses the challenge of reducing errors in Earth system models for climate prediction, but it appears incremental as it applies existing causal methods to a specific domain without claiming major breakthroughs.
The researchers tackled the problem of understanding heterogeneous aerosol-cloud interactions across different meteorological regimes by applying non-linear causal machine learning methods to study the effects of aerosols on cloud droplet radius, aiming to improve Earth system models that currently fail to reproduce observed relationships.
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 (r$_{e}$). 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.