Predictability of weather and climate

Upper-tropospheric outflow is analysed in cloud-resolving large-eddy simulations. Thereby, the role of convective organization, latent heating, and other factors in upper-tropospheric divergent-outflow variability from deep convection is diagnosed using a set of more than 80 large-eddy simulations because the outflows are thought to be an important feedback from (organized) deep convection to large-scale atmospheric flows; perturbations in those outflows may sometimes propagate into larger-scale perturbations.
Upper-tropospheric divergence is found to be controlled by net latent heating and convective organization. At low precipitation rates isolated convective cells have a stronger mass divergence than squall lines. The squall line divergence is the weakest (relative to the net latent heating) when the outflow is purely 2D in the case of an infinite-length squall line. At high precipitation rates the mass divergence discrepancy between the various modes of convection reduces. Hence, overall, the magnitude of divergent outflow is explained by the latent heating and the dimensionality of the outflow, which together create a non-linear relation.

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.