Precipitating convection profoundly affects the global energy and water balance of our planet by the release of latent heat and the transport of momentum, heat and moisture. Convective clouds are a frequent occurrence over many regions of the Earth. They take various forms, from isolated convective cells to large-scale convective complexes. Convective clouds have the ability to strongly interact with their environment, being the land surface, the surrounding air or previous clouds. This ability makes an understanding of their lifecycle and a prediction of their evolution challenging.

My research combines models at all resolutions to better understand and improve the representation of precipitating convection in atmospheric models. I tackle the convection problem both from a small-scale process level perspective, trying to better understand the processes that control the lifecycle of convection, and from a large-scale more climatic perspective, trying to understand the importance of specific convective features for the climate system. As tool I routinely use large-eddy simulations with resolution O(100 m) where convection is fully explicit, convection-permitting models with resolution O(1-10 km) where convection is partly explicit and coarser resolution models with parameterized convection. My research also often makes use of idealized simulations before testing the so developed theories in more realistic set-ups.

 

 

Coupling between convection and the land surface

The preferred occurrence of convective clouds over the deforested region of the Amazon rainforest, the alignment of clouds along coastlines, or, in more general terms, the differences in the precipitation diurnal cycle between land and ocean, are all manifestations of the interactions that exist between convection and the land surface. I'm especially interested to better understand (i)  what are the main mechanisms that control such interactions, (ii) under which conditions is the land surface especially important for the development of convection and (iii) what is the role of the representation of convection on the results. In an earlier study (Hohenegger et al. 2009) we for instance found that precipitation in a convection-permitting model is favored over drier soils, whereas it is favored over wetter soils in a coarse-resolution model using a convective parameterization. Large-eddy simulations with fully explicit convection and coupled to a land surface scheme offer new possibilities to investigate such issues.

 

Further reading (e.g.)

Hohenegger C., L. Schlemmer and L. Silvers, 2015: Coupling of convection and circulation at various resolutions. Tellus A, 67, 26678

Rieck M., C. Hohenegger and P. Gentine, 2015: The effect of moist convection on thermally induced mesoscale circulations. Quart. J. Roy. Meteor. Soc., 14, 2418-2428.

Hohenegger C., P. Brockhaus, C. S. Bretherton, and C. Schär, 2009: The soil-moisture precipitation feedback in simulations with explicit and parameterized convection. J. Climate22, 5003-5020.

 

 

How to form deep convective clouds?

On a typical summer day, the first clouds to appear are shallow cumuli with tops below 4 km, followed  by congesti (4-8 km) and finally deep cumulonimbi. The main mechanisms that control this transition and especially promote the formation of the deeper clouds are still debated. Two of these mechanisms, namely the role of cold pools and the role of moistening by congestus clouds, have been investigated. Although our day-to-day experience seems to suggest that deeper clouds grow out of shallower clouds, the idea that previous clouds moisten the atmosphere thus allowing a gradual deepening of convection is incompatible with the observed fast development of deep convection (Hohenegger and Stevens 2013).

 

Further reading (e.g.)

Schlemmer L and C. Hohenegger, 2014: The formation of wider and deeper clouds as a result of cold-pool dynamics. J. Atmos. Sci., 71, 2842-2858.

Hohenegger C. and B. Stevens, 2013: Preconditioning deep convection with cumulus congestus. J. Atmos. Sci., 70, 448-464.

 

 

Convective self-aggregation

When large-eddy simulation models are run at kilometer resolution in a radiative convective equilibrium set-up, convective clouds often show the peculiar behavior of self-aggregating. The initially randomly distributed convective clouds begin to merge with each other and end up forming one big clump of convection. Several studies have proposed mechanisms to explain this process of organization, but using fixed SST. Here I'm interested in better understanding the role of having an interactive surface on the self-aggregation of convection. The set-up of radiative convective equilibrium is also well suited to compare model simulations at different resolutions and to investigate parameterization choices on the development of convection. Finally in a recent study (Hohenegger and Stevens 2016) we used this set-up to investigate the role of convective organization for the climate. We found that the self-aggregation of convection was key for the climate of our idealized planet as it allowed the planet to equilibrate at a tropics-like SST.

 

Further reading (e.g.)

Hohenegger C. and B. Stevens, 2016: Coupled radiative convective equilibrium simulations with explicit and parameterized convection. J. Adv. Mod. Earth Systems, 8, 1468-1482

 

 

Model biases and parameterization development

Model biases at all scales are often associated with the representation of convection. Based on the results of  large-eddy simulations we have for instance proposed a unified shallow and deep convection scheme (Hohenegger and Bretherton 2011). Or we have been analyzing the output of CMIP5 models with a focus on the Atlantic region and adjacent land areas, a region where none of the models can reproduce observations. This analysis allowed us to isolate one possible reason for the misrepresented Atlantic ITCZ (Siongco et al. 2014).

 

Further reading (e.g.)

Siongco C., C. Hohenegger and B. Stevens, 2014:  The Atlantic ITCZ bias in CMIP5 models, Clim. Dyn., 45, 1169.

Hohenegger C. and C. S. Bretherton, 2011: Simulating deep convection with a shallow convection scheme. Atmos. Chem. Phys., 11, 10389-10406.