Detailed microphysics in a Lagrangian cloud model

The turbulent behaviour of clouds is responsible for many of the uncertainties in weather and climate models, in particular when it comes to the timing and intensity of precipitation. Weather and climate models are too coarse to resolve the details of the interactions between clouds and their environment and also have a much simplified representation of microphysical processes, such as the growth of cloud drops and the formation of rain, snow and ice. Such processes can be studied in a great amount of detail in so-called Large Eddy Models, where the interaction between clouds and their environment is largely resolved (grid spacing is less than 100 m). Large Eddy Models have been very useful for understanding turbulence in clouds, but when it comes to microphysical processes they are often still reliant on simple descriptions. For example, such a description may only know about the amount of liquid water in a model grid cell, and needs to make many assumptions about how this is divided between smaller and bigger drops.

In order to address this problem, it is possible to use so-called bin microphysics schemes, where the amount of condensate associated with drops of different size categories is prognosed. This approach has been successfully implemented in the Met Office Large Eddy Model, but is too computationally expensive for many applications where a high resolution is needed. Moreover, traditional Large Eddy Models, which are formulated in a Eulerian framework (they perform bookkeeping on grid cells), suffer from spurious mixing, which makes results very sensitive to resolution.

One of the fundamental problems here is that microphysical processes are essentially Lagrangian: they happen along trajectories of the flow (for small particles) or along fall trajectories (for bigger particles, influenced by the ambient wind). We have recently developed a new code, MPIC (moist parcel in cell), which deals with the dynamics of clouds in an essentially Lagrangian framework, i.e. by advecting parcels of fluid (Christiansen 1973, Dritschel et al. 2016). This code does not suffer from spurious mixing, and has been shown to compare well to traditional Large Eddy Models. MPIC also reduces computational cost when the same resolution is used, and we expect these computational advantages to be even bigger for a bin microphysics scheme. A simple way to think about it is this: rather than separately moving around liquid water corresponding to 50-100 droplet size categories from grid cell to grid cell, we move one parcel which contains a bin size distribution and only need to change the parcel's location during the advection process. Similarly, when parcels mix and split this can be done by simple summations and divisions.

The student will make the new MPIC model suitable for studies of realistic atmospheric clouds by changing its thermodynamical formulation and integrating a bin microphysics scheme into the model. This work will be done in collaboration with David Dritschel at the University of St Andrews, where much of the development of MPIC took place. We will first look into the growth of cloud droplets that move along with the flow, but would later also like to consider the formation of rain. We expect this work to lead to the publication of a number of articles that will tell us more about the details of the formation of large cloud drops, which are important for rain formation. In particular, we are interested in the trajectories of such drops, which can be determined in a consistent way in the MPIC framework.