Using uncertainty-aware machine learning models to study aerosol-cloud interactions

TL;DR

This paper employs uncertainty-aware causal machine learning to estimate aerosol-cloud interactions from satellite data, addressing the heterogeneity and uncertainty in climate model predictions of aerosol effects on cloud properties.

Contribution

It introduces a novel application of causal machine learning with uncertainty bounds to study aerosol-cloud interactions using satellite observations.

Findings

Only one climate model plausibly reproduces the observed trend.

Uncertainty bounds depend on unmeasured factors influencing aerosol effects.

The study enhances understanding of aerosol impacts on cloud properties.

Abstract

Aerosol-cloud interactions (ACI) include various effects that result from aerosols entering a cloud, and affecting cloud properties. In general, an increase in aerosol concentration results in smaller droplet sizes which leads to larger, brighter, longer-lasting clouds that reflect more sunlight and cool the Earth. The strength of the effect is however heterogeneous, meaning it depends on the surrounding environment, making ACI one of the most uncertain effects in our current climate models. In our work, we use causal machine learning to estimate ACI from satellite observations by reframing the problem as a treatment (aerosol) and outcome (change in droplet radius). We predict the causal effect of aerosol on clouds with uncertainty bounds depending on the unknown factors that may be influencing the impact of aerosol. Of the three climate models evaluated, we find that only one plausibly recreates the trend, lending more credence to its estimate cooling due to ACI.