Divergent convective outflow in ICON deep convection-permitting and parameterised deep convection simulations

Abstract

Abstract. Upper-tropospheric deep convective outflows during an event on 10th–11th of June 2019 over Central Europe are analysed from simulation output of the operational numerical weather prediction model ICON. Both, a parameterised and an explicit representation of deep convective systems are studied. Near-linear response of deep convective outflow strength to net latent heating is found for parameterised convection, while coherent patterns in variability are found in convection-permitting simulations at 1 km horizontal grid spacing. Furthermore, three hypotheses on factors that may affect the magnitude of the convective outflow are tested in the convection-permitting configuration: organisation of convection through dimensionality of the systems, organisation of convection through aggregation and convective momentum transport. Convective organisation and aggregation induce a non-linear increase in the magnitude of deep convective outflows with increasing net latent heating, as shown by the confidence interval of the best fit between power transformed net latent heating and detected magnitude of outflows. However, mixed and weaker than expected signals are found in an attempt to detect the representation of dimensionality of the convection and its consequences for the divergent outflows with an ellipse fitting algorithm that describes the elongation of the intense (convective) precipitation systems. As opposed to expectations, convective momentum transport is identified to slightly increase the magnitude of divergent outflows in this case study.