Properties and transitions of mesoscale convective organisation during EUREC4A using unsupervised learning

Abstract

<p>The representation of shallow tradewind cumulus clouds in climate models accounts for the majority of inter-model spread in climate projections,<br>highlighting an urgent need to understand these clouds better. In particular, their spatial organisation appears to cause a strong impact of their radiative properties and dynamical evolution. The precise mechanisms driving different forms of convective organisation which arise both in nature and in simulations are, however, currently unknown.</p><p>Using unsupervised learning for identifying regimes of convective organisation in the tropical Atlantic, we will show results from analysing: a) what the<br>radiative properties of different forms of organisation are, b) what atmospheric characteristics coincide with different forms of organisation and c) what transitions occur when following air-masses along Lagrangian trajectories.  Specifically, we find: a) net radiation changes significantly between different forms of organisation, b) agreement with previous studies on the importance of boundary layer wind-speed and to some degree atmospheric stability, and c) we are able to succinctly capture what transitions occur between regimes.</p>