GENESIS: Dynamics and parametrisation of deep convective triggering, maintenance and updraughts

Lead Research Organisation: University of Leeds
Department Name: School of Earth and Environment


Physically, deep convection is a key process in the atmosphere, particularly in the tropics, where it is the dominant driver of the weather as well as playing a key role in forcing global circulation. Despite this key role on the large scale, convection is inherently a relatively small-scale process with convective clouds typically being on the scale of 100's of metres to a few kms, and therefore unresolved in global numerical weather predicition (NWP) and climate models. "Parametrisation" of convection is therefore critical to accurately represent the impact of convection on the larger scale flow. This is not a simple problem, and deficiencies in current convective parametrisation schemes lead to significant model biases, the wrong diurnal cycle of convection in the tropics (with knock-on effects on rainfall and surface heating by radiation) and inadequate representation of important atmospheric circulations such as the Madden-Julien Oscillation, which are driven by convection. Two particular, and linked, problems which contribute to these deficiencies are the triggering of convection (timing, location and the stochastic nature of triggering) and the subsequent organisation of convection into larger convective systems.

The overarching aim of this project is to bring together our understanding of the various physical processes which control the triggering and organisation of deep convection, and to use this to develop a framework in which these processes can be integrated in a consistent way. Such a framework will allow these important processes to be represented in new convective parametrisation schemes in a more physically realistic and consistent manner. In particular, a physically-based convective triggering scheme should be easier to integrate into the new generation of stochastic convective parametrisation schemes which are being developed. Such schemes will also be easier to make scale-aware, i.e. to adapt with the model resolution to only parametrise the necessary sub-grid processes, while allowing the model to resolve larger-scale features of the convection. This is particularly important for the latest NWP and regional climate model simulations which are of sufficiently high resolution that they permit the explicit representation of convection, albeit rather crudely.

A further limitation of current parametrisation schemes is that they tend to be instantaneous. Where convection organised, the system has "memory", i.e. the occurrence and organisation of convection will impact on the local development of further convection. We will use techniques from other branches of fluid dynamics to understand and quantify organisation in convective systems and develop measures which can be used as the basis for new stochastic convection parametrisations.

This project will consider both internal processes and external processes. Internal processes generated by the convection itself, such as gravity waves and cold pools, play a key role in the triggering and organisation of convection. Over land external factors such as surface heterogeneity and topography also play an important role in triggering convection and controlling how it can organise. Integrating these various competing influences into a consistent framework will be a significant step forward for parametrisation schemes. Having developed the framework from studying individual processes through idealised numerical simulations with the Met Office Unified Model (MetUM) and the Met Office-NERC cloud resolving model (MONC) we will test the ideas in more realistic large-domain simulations to help quantify the important scale interactions between small scale convection and the larger scale fields.

The output of this project will be a series of generic physically-based model frameworks which can be used as components in different convective parametrisations schemes which are being proposed or developed, both within this programme and internationally

Planned Impact

Convection is directly listed in many of World Climate Research Programme grand challenges; it is key for numerical weather prediction, and it couples with many aspects of the earth system (e.g. dust, aerosol wash out, and vegetation). Improvements to convective parametrisation as a result of this research will have wide impact on uses of weather and climate models, from weather forecasts over the scale of a few days, through seasonal timescales where convection plays an important role in the monsoon and the MJO, up to climate predictions on the timescale of decades or centuries.

Key beneficiaries

1) The Met Office and other operational forecasting centres (notably our partners in Meteo-France) will be the key direct beneficiaries from improvements in convective parametrisation resulting from this research. Model improvements will lead to better forecasting of heavy convective precipitation events, more accurate representation of the diurnal cycle and convectively driven circulations in the tropics, and more accurate predictions of changes in rainfall globally from climate models.

2) Government, commercial organisations and NGOs will all benefit from improved short term forecasts of high impact convective events, allowing planning to reduce risk of flooding and more rapid response to events that do occur. Improvements in longer term seasonal and climate predictions will allow strategic planning to reduce the risk of such events or adapt to changes in rainfall over particular areas. In addition to risk reduction, improved forecasts have enormous economic benefit in terms of optimisation of a number of industries, such as power generation, transportation and retail.

3) The general public are also important end users of weather and climate forecasts and will similarly benefit from improved accuracy of forecasts. In particular being able to reduce uncertainty in climate change predictions, particularly changes in rainfall will help to convince the general public, and hence politicians, of the impact of climate change and ensure that decisions are made to reduce this impact. Improved forecasts will also have impact on the well-being of vulnerable people in the developing world, where many people are sensitive to weather and climate hazards, and are potentially able to use forecasts to make better decisions in regard to agriculture and natural hazards. Improved forecasts of hazardous weather may save lives in extreme situations, and at least provide people with longer warnings and allow them to protect property where possible.


10 25 50

publication icon
Bickle M (2021) Understanding mechanisms for trends in Sahelian squall lines: Roles of thermodynamics and shear in Quarterly Journal of the Royal Meteorological Society

publication icon
Böing S (2019) Comparison of the Moist Parcel-in-Cell (MPIC) model with large-eddy simulation for an idealized cloud in Quarterly Journal of the Royal Meteorological Society

publication icon
Böing S (2016) An object-based model for convective cold pool dynamics in Mathematics of Climate and Weather Forecasting

publication icon
Dritschel D (2018) The moist parcel-in-cell method for modelling moist convection DRITSCHEL et al. . in Quarterly Journal of the Royal Meteorological Society

publication icon
Haerter J (2019) Circling in on Convective Organization in Geophysical Research Letters

publication icon
Halliday O (2018) Forced gravity waves and the tropospheric response to convection HALLIDAY et al. . in Quarterly Journal of the Royal Meteorological Society

publication icon
Vallis G (2019) A simple system for moist convection: the Rainy-Bénard model in Journal of Fluid Mechanics

Description This project has been concerned with modelling and understanding the origins of convective clouds. This has involved a number of approaches in which we have made progress.

1. We have developed novel techniques to characterise the fluid dynamical structures which act as precursors for the genesis of convective clouds. Using these mathematical and computational methods we have begun to characterise different atmospheric environments, in terms of the clouds they will trigger. For example, in low winds there are upright plumes and thermals which efficiently initiate the clouds; in higher winds, the plumes are tilted downshear and elongated. These patterns have a significant influence on the cloud and the mixing with its environment. The structures can be included in the representation of these clouds in the next generation Met Office forecast model, in the next phase of the ParaCon programme.

2. Collaborating with colleagues from ParaCon at the University of Exeter, we devised a simple mathematical model of cloud heating in a convecting system. The model - known as "rainy-Benard", is an extension of the classic "Rayleigh-Benard" model which has been very extensively studied as a mathematical/physical system. From the extended model we can explore the mechanisms of cloud initiation, genesis and decay, and study the controls on these processes in a theoretical framework.

3. We have used machine-learning techniques to classify convective cloud fields in an "unsupervised" way, for the first time. This means that we can classify patterns of cloud organisation without being prejudiced by preconceptions of how these "should" behave. The techniques can be used to analyse the skill of models objectively.

4. We have devised a radically new cloud model, known as MPIC, in which cloud particles are tracked as they move through a model grid. This model has great potential for future cloud simulation, especially when clouds are carrying properties like chemical tracers.

5. Collaborating with the University of Copenhagen, we have devised a simple mathematical model for cloud triggering which helps explain some patterns of cloud organisation.

6. We have analysed the emission of atmospheric "gravity waves" from convective clouds, and the way in which these waves transmit the condensational heating in a cloud, into its remote environment. The solutions have answered a long-standing question in convective parametrisation, regarding the localisation of subsiding air around a cloud of rising air. The region of subsidence is initially close to the cloud, but moves outwards from its source with time in a predictable way. This enables us to characterise errors associated with simple representations of cloud convection (in which all subsidence is assumed to remain local).
Exploitation Route The outcomes of this work will be used to inform, modify and evaluate the new convective cloud scheme in the Met Office forecast models. This effort will be undertaken in the next phase of ParaCon.
Sectors Environment

Title MPIC 
Description The Moist Parcel-in-Cell (MPIC) model is a revolutionary new cloud model which has been written by Prof David Dritschel of St Andrews in close collaboration with the GENESIS project group at Leeds. 
Type Of Material Computer model/algorithm 
Year Produced 2018 
Provided To Others? Yes  
Impact The model is not yet being widely used but has great potential. 
Description Met Office 
Organisation Meteorological Office UK
Country United Kingdom 
Sector Academic/University 
PI Contribution Our research group analyses atmospheric processes in order to better represent them in the Met Office's forecast models. We also use those forecast models in our research, and evaluate their performance in order to identify the best strategies to improve the models.
Collaborator Contribution The Met Office brings its models and its datasets to the partnership, in addition to the considerable expertise of its staff. The Met Office also represent a conduit to the impact of our research for society, through its provision of operational weather and climate forecasts.
Impact Our research has influenced the Met Office strategy for model development, especially in regard to high-resolution models, and the convective parametrisation scheme. We have jointly influenced international strategy for atmospheric research and measurements.