Detecting anthropogenic cloud perturbations with deep learning
This work addresses a critical uncertainty in climate science that impacts global temperature predictions and policy-making, though it is incremental in applying existing deep learning methods to a new domain.
The study tackled the problem of quantifying the effect of anthropogenic aerosols on cloud properties and the Earth's energy balance by using deep convolutional neural networks to detect specific cloud perturbations, providing insights into their climatic effects.
One of the most pressing questions in climate science is that of the effect of anthropogenic aerosol on the Earth's energy balance. Aerosols provide the `seeds' on which cloud droplets form, and changes in the amount of aerosol available to a cloud can change its brightness and other physical properties such as optical thickness and spatial extent. Clouds play a critical role in moderating global temperatures and small perturbations can lead to significant amounts of cooling or warming. Uncertainty in this effect is so large it is not currently known if it is negligible, or provides a large enough cooling to largely negate present-day warming by CO2. This work uses deep convolutional neural networks to look for two particular perturbations in clouds due to anthropogenic aerosol and assess their properties and prevalence, providing valuable insights into their climatic effects.