Dr Edward Groot

Upper-tropospheric deep convective outflows during an event on 10–11 June 2019 over central Europe are analysed in ensembles of the operational Icosahedral Nonhydrostatic (ICON) numerical weather prediction model. Both a parameterised and an explicit representation of deep convective systems is studied. Near-linear response of deep convective outflow strength to net latent heating is found for parameterised convection, while different but physically coherent patterns of outflow variability are found in convection-permitting simulations at 1 km horizontal grid spacing. We investigate if the conceptual model for outflow strength proposed in our previous idealised large-eddy simulation (LES) study is able to explain the variation in outflow strength in a real-case scenario. Convective organisation and aggregation induce a non-linear increase in the magnitude of deep convective outflows with increasing net latent heating in convection-permitting simulations, consistent with the conceptual model. However, in contrast to expectations from the conceptual model, a dependence of the outflow strength on the dimensionality of convective overturning (two-dimensional versus three-dimensional) cannot be fully corroborated from the real-case simulations.

Our results strongly suggest that the interactions between gravity waves emitted by heating in individual deep convective elements within larger organised convective systems are of prime importance for the representation of divergent outflow strength from organised convection in numerical models.

A chain of processes is identified that regulates much of the spread in an ensemble of squall lines in large eddy simulations with tight initial conditions. Patterns of gravity wave propagation de-correlate and restructure the initial condition spread until a second phase of convective initiation is taking place, i.e. after 30 min of simulation time. Subsequently, variability in this convective initiation and mass overturn is associated with differences in cold pool propagation within the ensemble (propagation at 2–4 m/s.
An ensemble sensitivity analysis reveals that anomalies in squall-line-relative flow with respect to the ensemble mean are also associated with the secondary convective initiation. Downdraughts are fed with extra air by a convergence zone on the rearward flank of the updraughts. An analysis of difference growth within the ensemble shows that a substantial proportion of variability is explained by cold pool propagation contrasts during this stage (30–80 min), which is partly removed when a feature-relative perspective is taken. The patterns of coherent variability exist on the timescale of an hour and dissipate subsequently (80–100 min).

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.

The consequences of convective organisation, aggregation and convective momentum transport for upper tropospheric divergent outflows from deep convection are explored. Furthermore, the variability and predictability of these outflows is thereby connected to other aspects of dynamics and predictability of the convective systems. Different approaches to the simulation of convection are investigated, in which the conditional dependence of divergent outflow, on net latent heating rate, differs as a consequence of different methods to represent convective systems.
The theoretical understanding of the convective outflows is addressed first, by investigating a comprehensive set of idealised Large Eddy Simulations. The experiments, with four prototypes of convective systems, reveal that convective organisation and net latent heat release (convertible to precipitation rate) shape the patterns in magnitude of the divergent outflows. Dimensionality of convective outflows (2D convection versus 3D convection, or a mixed/intermediate regime) bounds an envelope of divergent outflow variability. This outcome is mostly consistent with convective outflows, if represented in older linear gravity wave models.
Investigating these convective outflows in the NWP model ICON for an event on 10th-11th of June 2019 over Central Europe, the divergent outflows in a parameterised and an explicit representations of deep convective systems are intercompared. Near-linear response of deep convective outflows to net latent heating is found in parameterised convection, while coherent patterns in variability are found in convection-permitting simulations, at 1 km horizontal grid spacing. Convective organisation and aggregation induce a non-linear increase in the magnitude deep convective outflows, with increasing net latent heating. This non-linearity is demonstrated by the confidence interval of the best fit, between power transformed net latent heating and detected magnitude of outflows. Other statistical patterns also support the representation of that pattern in the studied case. 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. To detect the representation, an ellipse fitting algorithm that describes the elongation of the intense (convective) precipitation systems is used. These signals are understandable and suggest the need of further investigation. Convective momentum transport is suggested to slightly increase the magnitude of divergent outflows, in the studied case.
In a subset of the Large Eddy Simulations, in which a so-called squall line is triggered, error or difference growth is investigated in relation to dynamics and precipitation variability, amongst others. During the two hour simulations, the first stage of convective initiation is associated with crucial gravity wave activity, which induces de-correlation between ensemble members. After an initial trigger of convection (about 20 minutes into the simulations), a second phase of convective initiation (at 30 minutes) determines much of the structure in the ensemble spread, for the next hour or so. Directly after that second phase of convective initiation, spread in cold pool acceleration is found, while cold pool propagation velocity is maintained afterwards (t=45 to t=100 minutes). Coherent flow anomalies, initiated directly after the second phase of convective initiation, are also maintained on the time scale of an hour. They dissipate after about 80 to 100 minutes simulation time. When flow is evaluated in a frame relative to cold pool edge, it is shown that error or difference growth in terms of zonal wind, within the ensemble, is substantially smaller than in the Eulerian perspective. Furthermore, feedbacks acting within the squall line are not dominating this difference growth: much of the difference is directly explained by differences in cold pool propagation. Much of the ensemble spread still maintained in the cold pool-relative framework, such as in precipitation and downdrafts, is also strongly related to the decisive second phase of convective triggering.
Looking at convective variability from a (Bayesian) perspective, conditional on precipitation rate, the often subtle threshold behaviour in convective initiation is bypassed. However, the approach demonstrates that a conditional view can shed important light on convective variability and how it is represented in NWP. Here, it shows contrasts in between idealised Large Eddy Simulations, convection-permitting NWP and deep convection parameterising NWP, where implicit assumptions on divergent convective outflows are identified. Strong coupling between dynamics, predictability and precipitation is accentuated. In representativity studies of other aspects in an NWP (e.g. microphysics, turbulence, radiation) and predictability studies, the applied conditional approach may be fruitful.

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.