Using uncertainty-aware machine learning models to study aerosol-cloud interactions. (arXiv:2301.11921v1 [physics.data-an])

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.