AS1.12

The self-aggregation of convection in idealised models has been widely studied. Work has been done to identify key physical mechanisms responsible for both driving and maintaining aggregation in a range of idealised radiative-convective equilibrium (RCE) models. These idealised models are typically run without any land, rotation, variation in sea-surface temperatures (SSTs), or a diurnal cycle. Due to these idealisations, a key question in the study of convective aggregation is how these convective processes and mechanisms manifest in the real-world. Several studies have tried to tackle this question by increasing the complexity of processes in the idealised models, such as SST gradients, adding a slab ocean, adding a diurnal cycle, or adding an aerosol diabatic heating perturbation. Particularly, the inclusion of interactive ocean surfaces has been shown to strongly impact the formation of aggregated clusters.

The interactions between land surfaces and aggregation are currently less well understood. Early studies have found that convective aggregation may favour land areas over oceans, and that soil moisture feedbacks can act to oppose the aggregation altogether. Thus, in this study we investigate the relationship between land, oceans, and aggregation, addressing the following questions:

• How does the inclusion of an idealised island into a global RCE model impact the aggregation of convection?
• Are the physical mechanisms responsible for the aggregation similar to those seen in land-free simulations?
• How sensitive are these results to our choice of land parameters, such as initial soil moisture, surface temperature, soil type, and land topography?

How to cite: Dingley, B., Dagan, G., Herbert, R., and Stier, P.: The impact of land-sea contrasts in the aggregation of convection, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-778, https://doi.org/10.5194/egusphere-egu22-778, 2022.

Vertical wind shear plays a key role in the organisation and intensification of Mesoscale Convective Systems (MCSs). In West Africa, the meridional temperature gradient between the hot Sahel and the humid Gulf of Guinea results in a strong easterly wind at mid-levels and south-westerlies at low-levels leading to a strong vertical wind shear. A decadal increase in vertical wind shear has recently been linked to a decadal increase in intense MCSs over the Sahel. Here, the effects of vertical wind shear on MCSs over West Africa have been investigated using a 10-year (1998 - 2007) MCS dataset. The results show that, a strong vertical wind shear is associated with long-lived, fast moving, large and cold (deep) storms with high rain-rates.  The observed cloud-top heights of storms over the oceans are closer to their level of neutral buoyancies (LNBs) compared to their land counterparts. We hypothesise that this is due to greater entrainment dilution over land compared to storms over the ocean. The difference between the observed cloud-top heights and the LNBs of land MCSs is minimised over regions of high vertical wind shear. Strong vertical wind shear results in colder brightness temperatures relative to the temperature at their LNBs. This is consistent with recent modelling work showing that shear reduces  entrainment dilution of squall-line updrafts. We conclude that, modelling the impacts of vertical shear, which are normally missed in convection parameterisations, are not only important for predictions of high impact weather, but also important for modelling the climatology of anvil heights.

How to cite: Baidu, M., Schwendike, J., Marsham, J., and Bain, C.: Observed Effects of Vertical wind shear on the intensities of Mesoscale Convective Systems in West Africa., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1898, https://doi.org/10.5194/egusphere-egu22-1898, 2022.

In this work, we present an alternative theory for the parameterization of atmospheric convection based on plumes with a realistic description. The convective source terms, which provide the feedback of the convection upon the large-scale variables are obtained by performing a sub-grid averaging, considering that the convective variables are fluctuations from the mean, large-scale, state. In this way, we do not need to consider that the large-scale is described by the environmental state, which means that we do not need to consider that the fractional area occupied by the convection is very small. Thus, our formulation can be implemented in numerical weather prediction (NWP) and climate models with high resolutions, in which case a stochastic representation for the fractional area occupied by the convection in every grid box must be considered. In our parameterization, the convective variables in each grid box are described by one round steady-state convective plume placed in the center on the box with radial profiles which respect the condition that the convective variables represent fluctuations from the mean state. Performing an integral analysis over the plume's domain in the radial direction, we obtain a system of ordinarily differential equations (ODE) which take into consideration both the buoyancy-driven and the turbulent entrainment, offering thus a more accurate description of the convective dynamics then the classic entrainment hypothesis. Moreover, the influence of the large-scale is taken into consideration at any height, not just at the cloud base as in the standard mass-flux formulation. The system of ODE that we obtain can be analytically solved between the vertical grids of the NWP or climate models if we consider that the plume radius is constant and we use the scaling argument that the buoyancy-driven entrainment scales with the inverse of the height. The radial coefficients which result from the radial integration of the plume can be obtained by prescribing the exact form of the radial profiles of the vertical velocity, scalar components and turbulent fluxes, or determined using large-eddy simulations. The closure of our model consists in prescribing the vertical velocity and the radius of the plume at the initial level, which can be considered to follow a given probability density function as in the existing stochastic parameterizations.

How to cite: Vraciu, C.: On the parameterization of atmospheric convection with a realistic plume model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2876, https://doi.org/10.5194/egusphere-egu22-2876, 2022.

The energy transfer by electromagnetic radiation drives atmospheric processes on a wide range of scales: Solar irradiance drives heat and moisture fluxes from the surface and the net radiative cooling of the upper troposphere enables deep convection. However, computing accurate radiative fluxes in atmospheric models is expensive because of the integration over the non-linear absorption spectra of the atmosphere. This integration is typically approximated using so-called correlated k-distribution or similar methods and requires a large number (~10²) of spectral quadrature points. In this study, we aim to find the lowest spectral resolution that still allows for accurate cloud field properties in large-eddy simulations of convective clouds. First, we reduce the spectral resolution of the radiation parametrization RRTMGP with an optimization algorithm that repeatedly combines adjacent quadrature points while maintaining the highest possible accuracy on a set of radiative metrics. Reduced sets of quadrature points are then tested further using three distinct sets of large-eddy simulations of convective clouds: deep convection in radiative-convective equilibrium (RCEMIP), shallow convection with precipitation shallow cumulus over the ocean (RICO), and shallow convection over land with a tight connection to the surface energy balance. We find that the spectral resolution of the radiation model, and thereby its computational costs, can be reduced by a factor three to four while retaining statistically similar cloud field properties. While this could reduce the total computational costs of an atmospheric simulation, or allow for a smaller radiation time step, it may also be a crucial step in increasing the feasibility of using 3D radiative transfer in large-eddy simulations.

How to cite: veerman, M., Pincus, R., and van Heerwaarden, C.: Accelerating simulations of convective clouds by reducing the spectral resolution of radiative transfer, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3629, https://doi.org/10.5194/egusphere-egu22-3629, 2022.

Atmospheric convection is responsible for turbulent transport of heat, moisture, and momentum and fuels the convective cloud formation. Due to lack of observations, numerical prediction models treat convection using sometimes inadequately constrained parametrization schemes. Observations of atmospheric convection to further constrain these parameterisations are notoriously difficult to obtain due to the intermittent, localized,  and turbulent character of convection. However, every day, hundreds of paragliding, hang gliding, and gliding pilots probe the convective boundary layer in hope of finding the best convective thermals. They spend years learning the art of finding and flying in the convective air, while they proudly share their flight tracks online. In this presentation we show how tracks of these engineless aircrafts can be used to sample atmospheric convection. We showcase a dataset from a paragliding championship to classify convection. We elaborate on how the international databases can be used to characterize atmospheric convection and aid building parametrizations based on machine learning.

How to cite: Palenik, J. and Spengler, T.: THERMAL: Sampling Atmospheric Convection Using Paragliders, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5352, https://doi.org/10.5194/egusphere-egu22-5352, 2022.

Measurements of shallow cumulus cloud properties at meteorological sites are of crucial importance to evaluate Large-Eddy Simulations (LES) of this cloud regime, as well as its parameterized representation in numerical weather and climate models. However, to this date, these datasets have mainly consisted of vertical, one-dimensional profile data, often sampled with remote sensing equipment such as lidar or radar. A recently explored new method for adding multi-dimensional information is to use hemispheric images from a network of multiple cameras. Such networks observe shallow cumuli in unprecedented spatial detail and at ultra-high frequency. Fisheye cameras provide a large field of view, which enables the observation of complete shallow cumulus life cycles. Camera networks can thus strongly complement the existing instruments at a site, yielding unprecedented new insights into cumulus cloud field geometry, dynamics, and evolution. One possible way to independently assess the accuracy of camera networks is to apply it to virtual cloud fields as generated with LES, acting as truth in an Observation System Simulation Experiment (OSSE). However, for this purpose virtual hemispheric camera projections of the LES cloud fields are needed.

In this study, we combine Beer-Lambert radiative transfer with an open-source Monte Carlo path-tracing code to generate such projections. The method is applied to simulate a network of multiple stereo cameras as currently installed at the Jülich Observatory for Cloud Evolution (JOYCE), Germany as part of the ongoing SOCLES project. Three-dimensional LES cloud fields for selected days are used as input. Hemispheric projections are then generated and provided to the existing algorithm for generating three-dimensional fields from actual stereo camera images. Comparing the latter to the input LES fields then allows precise error estimation. One goal is to, thus, find out how much of an arbitrary three-dimensional cloud field can, in theory, be reliably detected by a stereo camera setup. A second goal is to use this information to optimize the configuration of the stereo camera network at the site.

We find that the hemispheric path tracing projections can function well in this workflow. For the selected days we find that 81% of the reconstructable cloudy grid boxes in an LES cloud field is reconstructed by the stereo camera algorithm. Modest dependence on reconstruction tolerance is reported, while dependence on camera distance is also investigated.

How to cite: Burchart, Y., Beekmans, C., and Neggers, R.: Using atmospheric path-tracing as a simple hemispheric simulator to test stereo camera reconstructions of 3-dimensional boundary layer cloud fields, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5473, https://doi.org/10.5194/egusphere-egu22-5473, 2022.

It is well-recognized that triggering of convective cells through cold pools is key to the organization of convection, as reviewed in Zuidema et al. (2017). Yet, numerous studies have found that both the characterization and parameterization of these effects in numerical models is cumbersome - in part due to the lack of numerical convergence (Δx→ 0) achieved in typical cloud-resolving simulators. Through a comprehensive numerical convergence study, we systematically approach the Δx→ 0 limit in a set of idealized large-eddy simulations capturing key cold pool processes: propagation, merging and collision of gust fronts. We characterize at which Δx convergence is achieved for physically relevant quantities, namely accumulated upwards water mass fluxes, integrated vortical rates and gust front's group velocity.

The gust front vortical size and strength achieves convergence at Δx=100m horizontal resolution (70% drop at Δx=800m), while the probability distribution of updraft fluxes upon frontal collision, ƒ(w), appears satisfactorily resolved at Δx=50m. Interestingly, ƒ(w) exhibits self-similarity as a function of Δx, down to the coarsest case of Δx=800m. A rescaling function is derived that successfully collapses all distributions onto a common solution. Further, the positive water mass perturbation caused upon propagation and collision of the gust front appears well-captured at Δx=200m (35% drop at Δx=800m). Finally, the incidental merging of several cold pools results in a large gust front, as often found in MCS. The group velocity of this merged front is only mildly dependent on resolution (10% drop at Δx=800m), suggesting that the numerical dissipation dominates over dispersion.

The understanding gained from this analysis lays the groundwork to develop robust subgrid models for CP dynamics able to sustain their growth and combat artificial numerical dissipation and dispersion.

How to cite: Fiévet, R., Meyer, B., and Haerter, J.: When is numerical resolution high enough to resolve cold pool organization?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5874, https://doi.org/10.5194/egusphere-egu22-5874, 2022.

Large uncertainties persist with respect to the role of microphysical and other small-scale processes in high clouds and their interactions with circulation and convective processes. Moreover, the uncertainty in tropical high cloud feedback is the dominant contributor to the total cloud feedback uncertainty and is believed to be connected to the description of microphysics. A large part of changes in anvil cloud properties is due to changes in ice crystal number and their size, which in turn depend on the background amount of cloud droplet number and ice nucleating particles and the description of ice nucleation.

In this work, we use idealized radiative-convective-equilibrium and tropical limited area experiments with SAM and ICON-NWP models to explore the effect of the number of ice nucleating particles and the number of cloud droplet number concentration on the cloud feedback and climate sensitivity. Moreover, we show that cloud radiative properties are strongly dependent on ice crystal number and size, which can be adequately represented only by two-moment microphysical schemes with an interactive simulation of both hydrometeor mass and number.

How to cite: Gasparini, B. and Voigt, A.: The importance of ice nucleation for climate sensitivity in radiative-convective equilibrium, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7329, https://doi.org/10.5194/egusphere-egu22-7329, 2022.

Numerical simulations of radiative-convective equilibrium in high-resolution cloud-resolving models (CRMs) revealed the tendency of atmospheric convection to self-aggregate on periods of several weeks when the domain is sufficiently large. Nevertheless, even though CRM simulations manage to identify some of the physical mechanisms driving convective clustering, the occurrence of organization seems to be dependent on the model setup, physics and parameterizations. Robust findings from simpler, idealized models, which may reproduce some of the features of the full-physics systems, are thus beneficial to better understand the differences existing between CRMs.

To this end, we have developed a simplified two dimensional stochastic model able to predict the evolution of column total water relative humidity (CRH) in the tropical free troposphere. The model prognostic equation includes a convective moistening term, diffusive lateral transport and subsidence drying, similar to model of Craig and Mack (2013), but one novelty of the new model is that, instead of the convective moistening term as a smooth deterministic function of the background humidity, we treat convection as a point process and account for stochastic variability in the convective moistening process. Therefore the model allows experiments to use domain sizes and grid resolutions similar to those used for the idealized CRM experiments.

It is found that, depending on the chosen parameter settings, the simple model can reproduce equilibrium states of strong convective aggregation and also randomly distributed states, analogous to the CRM results. A sensitivity of the occurrence of self-organization to the initial conditions, i.e., a modest hysteresis, is also found, which also agrees with the full physics CRMs. Large ensembles of numerical experiments were performed for different values of the subsidence timescale, the moisture diffusion coefficient and the parameter that determines convective sensitivity to background humidity, as well as for a range of domain sizes and horizontal grid spacings. Using dimensional arguments, combined with empirical fits from numerical data, we define a dimensionless parameter whose value indicates whether a clustered state is likely to emerge for a given set of parameter values and experimental configurations. This quantity contains dependencies on all the model processes, while also explicitly including the domain size and resolution in an attempt to explain these latter sensitivities observed in the full-physics CRM experiments.

How to cite: Biagioli, G. and Tompkins, A. M.: Convective aggregation in idealized stochastic models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7712, https://doi.org/10.5194/egusphere-egu22-7712, 2022.

Squall lines are the consequence of the interaction of low-level shear with cold pools associated with convective downdrafts, and beyond a critical shear, squall lines tend to orient themselves. It has been shown that this orientation has the effect of reducing the incoming wind shear to the squall line and maintains equilibrium between wind shear and cold pool spreading (Abramian et al 2021).

While the mechanisms behind squall line orientation seem to be increasingly well understood, few studies have focused on the implications of this organization. Yet, Roca and Fiolleau 2020 shows that mesoscale convective systems, including squall lines, are disproportionately involved in rainfall extremes in the tropics. One may then question whether the orientation of squall lines has an impact on the rainfall extremes, and if so, how and why.

Using a CRM, we perform simulations of tropical squall lines by imposing a vertical wind shear in radiative convective equilibrium. Our results show that the extreme precipitation in the squall lines is more intense in the critical organized case. It seems that when the condensation rate increases with the shear, the precipitation efficiency decreases strongly. The critical case appears to be the most favorable compromise between these two contributions, a hypothesis that we further investigate here.

How to cite: Abramian, S., Muller, C., and Risi, C.: Investigating extreme precipitation in tropical squall lines, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9649, https://doi.org/10.5194/egusphere-egu22-9649, 2022.

Organization of convection in tropics exhibits a wide range of spatial and temporal scales on account of a complex maze of interactions between clouds, circulation, radiation, and moisture. Somewhere in that maze are also present the mechanisms responsible for the phenomenon of convective self-aggregation in idealized simulations. However, the exact role played by these self-aggregation mechanisms on organization of convection in the real world remains unclear including understanding when convection will aggregate further and when convection will disaggregate. Cloud radiative feedbacks have been found to be important for initiation and maintenance of self-aggregation in idealized modelling studies. In observations too, it has been shown that radiation varies linearly with convection across different regions in the tropics. This implies that radiative feedbacks add moist static energy (MSE) to the already moist columns (that is it favors aggregation). However, the question comes up then, why does convection disaggregate in observations despite the support from radiative feedbacks? We hypothesize that advection of moisture and moist static energy (MSE) instead is important for determining when convection aggregates or disaggregates in the real world.

We utilize a moist static energy (MSE) variance budget-based phase plane as a process oriented diagnostic tool to test our hypothesis. The phase plane is formed by taking the variance of MSE on the x-axis and time tendency of variance of MSE on the y-axis. Then, cycles of aggregation and disaggregation show up as elliptical orbits on this plane. Contributions to the MSE variance tendency from the advective terms and radiative terms can be explicitly analyzed on this phase plane. Data from idealized simulations is used to understand how self-aggregation mechanisms show up on the phase plane. The results are compared with reanalysis data to understand the differences between self-aggregation mechanisms in models and mechanisms that favor aggregation in the real world. Data from different regions is also analyzed to determine whether it’s the variations in advective terms or the radiative term that govern when convection aggregates or disaggregates in observations.

How to cite: Maithel, V. and Back, L.: Role of Moist Static Energy Advection in Evolution of Convective Aggregation in Observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11294, https://doi.org/10.5194/egusphere-egu22-11294, 2022.

Operational ground-based weather radar data provide a unique opportunity to evaluate simulations of convection in high-resolution models. This is especially advantageous for tropical regions such as India where convection is frequent and interacts with the large-scale circulation. Here, storms derived from 15 Indian operational radars are directly compared to modelled storms at two convection-permitting resolutions, 1.5 and 4.4 km, for a period of 3 weeks during the peak 2016 monsoon season. We objectively identify different morphological properties of storms for 6 regions of India, that is to say their heights, sizes, and intensities. Both model resolutions are found to simulate too much shallow convection compared to radars for all 6 regions. Modelled convection is also frequently too wide and intense, but the 1.5 km model performs noticeably better. Modelled storms also exhibit a maximum area around the freezing level, higher than observed by radars, especially at 4.4 km resolution. We discuss various potential microphysical and dynamical reasons for the major differences seen, thus demonstrating the power of radar-based evaluation of monsoon convection for the Indian region.

How to cite: Doyle, A., Stein, T., and Turner, A.: Evaluating characteristics of Indian monsoon convection in high-resolution models with ground-based weather radars, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12304, https://doi.org/10.5194/egusphere-egu22-12304, 2022.

Convective organization has been associated with extreme precipitation in the tropics. Here we investigate the impact of convective self-aggregation on extreme rainfall rates. We find that convective self-aggregation significantly increases precipitation extremes, for 3-hourly accumulations(+70%) consistent with earlier studies, but also for instantaneous rates (+30%). We show that this latter enhanced instantaneous precipitation is mainly due to the local increase in relative humidity which drives larger accretion rates and lower re-evaporation and thus a higher precipitation efficiency.

An in-depth analysis based on an adapted scaling of precipitation extremes, reveals that the dynamic contribution decreases (-25%) while the thermodynamic is slightly enhanced (+5%) with convective aggregation, leading to lower condensation rates (-20%). When the atmosphere is more organized into a moist convecting region, and a dry convection-free region, deep convective updrafts are surrounded by a warmer environment which reduces convective instability and thus the dynamic contribution. The moister boundary-layer explains the positive thermodynamic contribution. The microphysic contribution is increased by +50% with aggregation. The latter is partly due to reduced evaporation of rain falling through a moister near-cloud environment (+30%), but also to resulted larger accretion efficiency (+20%).

Thus, the change of convective organization regimes in a warming climate could lead to a significantly different evolution of tropical precipitation extremes than expected from thermodynamical considerations. Improved fundamental understanding of convective organization and its sensitivity to warming, as well as its impact on precipitation extremes, is hence crucial to achieve accurate rainfall projections in a warming climate.

How to cite: Da Silva, N., Muller, C., Shamekh, S., and Fildier, B.: Significant amplification of instantaneous extreme precipitation with convective self-aggregation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12318, https://doi.org/10.5194/egusphere-egu22-12318, 2022.