Diurnal Self-Aggregation

Abstract:

Convective self-aggregation can give rise to cloud clusters developing over timescales of weeks over a constant temperature sea surface. In reality, sea surface temperatures do oscillate diurnally, affecting the atmospheric state and influencing rain rates significantly. Over continents, surface temperatures vary more strongly. Through cloud-resolving numerical experiments, we show that qualitatively different dynamics emerge from modest surface temperature oscillations: while the spatial distribution of rainfall is homogeneous during the first day, already on the second day, the rain field is firmly structured. In later days, this clustering becomes stronger and alternates from day to day. We find that these features are robust to changes in resolution, domain size, and mean surface temperature, but can be removed by a reduction of the amplitude of diurnal surface temperature oscillation, suggesting that there is a transition from a random to a clustered state. We find maximal clustering at a scale of l_max ≈ 180 km, which we relate to the emergence of mesoscale convective systems. At l_max, rainfall is strongly enhanced and far exceeds the rainfall expected at random. To interpret the transition to clustering, we use simple conceptual modelling. Our modeling captures the transition, which is driven by the formation of mesoscale convective systems and brings about day-to-day moisture oscillations. Our results may help clarify how continental extremes build up and how cloud clustering over the tropical ocean could emerge as an instance of spontaneous symmetry breaking at timescales much faster than in conventional radiative-convective equilibrium self-aggregation.