  Cirrus clouds are key modulators of Earth's climate. Their dependencies on
meteorological and aerosol conditions are among the largest uncertainties in
global climate models. This work uses three years of satellite and reanalysis
data to study the link between cirrus drivers and cloud properties. We use a
gradient-boosted machine learning model and a Long Short-Term Memory (LSTM)
network with an attention layer to predict the ice water content and ice
crystal number concentration. The models show that meteorological and aerosol
conditions can predict cirrus properties with $R^2 = 0.49$. Feature
attributions are calculated with SHapley Additive exPlanations (SHAP) to
quantify the link between meteorological and aerosol conditions and cirrus
properties. For instance, the minimum concentration of supermicron-sized dust
particles required to cause a decrease in ice crystal number concentration
predictions is $2 \times 10^{-4}$ mg m\textsuperscript{-3}. The last 15 hours
before the observation predict all cirrus properties.
