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Detailed microphysics in a Lagrangian cloud model

Prof. Alan Blyth (SEE), Dr. Steven Böing (SEE), Prof David Dritschel (University of St Andrews), Dr. Zhiqiang Cui (SEE)

Project partner(s): University of St. Andrews

Contact email: A.M.Blyth@leeds.ac.uk

We are looking for an enthusiastic student to work on adapting an exciting new Lagrangian cloud model, called the Moist Parcel In Cell (MPIC) model, to the simulation of realistic atmospheric clouds. The work will also involve implementing a bin-microphysics scheme in the model, which will be used to address the role of high-liquid water regions in the warm rain formation process. A poor representation of this processes is currently leading to substantial biases in Numerical Weather Prediction and climate models.

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.

A particularly difficult problem that is hard to address using current approaches is the effect of entrainment and turbulence on the development of rain drops in warm clouds. This so-called warm rain process is important because (a) heavy rain falls from warm clouds in most parts of the world, but particularly in the tropics, where parametrisations of convection and rainfall are known to suffer from significant biases; and(b) the raindrops formed from the warm rain process (by collision and coalescence) can play a critical role in the subsequent formation and development of ice and precipitation in cold clouds.

In order to address the problem of representing the cloud drops, it is possible to use so-called bin microphysics schemes, where the amount of condensate associated with cloud 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 work will be done in collaboration with David Dritschel at the University of St Andrews, where much of the development of MPIC took place. It will involve changing MPICs thermodynamical formulation and integrating a bin microphysics scheme into the model. Once this has been done, we will first look into the growth and evaporation of cloud droplets that move along with the flow. Of particular interest here are the effects of mixing on droplets of different sizes (homogeneous/heterogeneous mixing). We expect this work to lead to the publication of several 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.

MPIC will also allow us to study to what extent bigger droplets in the centre of the cloud are shielded from the effects of evaporation. At a later stage, we would also like to consider the effects of precipitation.

Figure 1: a comparison between the moisture fields for an idealised cloud in the MONC and MPIC model. The top row shows MPIC at 643 and MONC at 1283 whereas the bottom row uses shows MPIC at 1283 and MONC at 2563. MPIC can generate some of the more detailed features at the flow at a lower resolution.

Objectives

In this project, you will work with leading scientists at Leeds and St Andrews to demonstrate the potential of the new MPIC model to study the interplay between mixing and cloud microphysics.

In particular, the studentship will involve:

  1. Adapting the formulation of the MPIC model to the simulation of clouds in scenarios based on realistic atmospheric conditions and implementing a bin-microphysics scheme in the MPIC model. Here, we can build on fact that bin-microphysics is already available in two models that we already have experience with (the Met Office Large Eddy Model and MAC3, see e.g. Hill et al 2008 and Huang et al. 2008). This work will be done in collaboration with Steven Böing and Zhiqiang Cui, who have expertise on MPIC and bin microphysics schemes, respectively.
  2. Studying the formation of high-liquid water regions in cumulus clouds in a Lagrangian framework. Here, we will consider if these parcels mostly have their origin below cloud base and to what extent these parcels are shielded from mixing with the environment outside the cloud. The much more accurate representation of mixing on the smallest scales in MPIC will play a key role in addressing these questions.
  3. Depending on the interests of the student, the framework can be extended to study scenarios where the number of aerosols in the entrained air from the environment is important. This number is typically much lower than in the air that enters the cloud at its base, but this is not always taken into account in idealised studies. We wonder how sensitive rain formation is to the vertical distribution of aerosols. Another extension would focus on cases where the formation of precipitation plays a key role, using recently developed Lagrangian approaches to precipitation (e.g. Arabas and Shima 2013, Riechelmann et al. 2013). This extension would allow us to consider for the role of the rain droplet size spectrum in the formation of cold outflows below cloud base. Finally, we may also use MPIC to study the role of turbulent collisions in creating larger drops (see e.g. Wyszogrodzki et al. 2013). Turbulence is thought to significantly enhance the rate of warm rain formation. MPIC has a more detailed representation of turbulent regions than other models, which will be important as the processes involved are very non-linear.

Figure 2: the graphical interface of the MPIC model shows the location of parcels that were initiated in a rising thermal.

Potential for high impact outcome

MPIC presents a radically different approach to the simulation of atmospheric moist processes, and will be able to teach us about the interplay between mixing processes and microphysics in a way that is not possible in traditional models. Previous work by both Alan Blyth and Steven Böing has shown the usefulness of Lagrangian analysis for understanding cloud processes and turbulence (e.g. Lasher-Trapp et al 2005, Cooper et al 2013, Böing et al 2014). MPIC takes this approach a step further by allowing for a Lagrangian analysis which is fully consistent with the model dynamics. This may be particularly important since the high liquid water regions which are crucial for rain formation are barely resolved in traditional models. The student would be the first to demonstrate the viability of the MPIC approach for microphysical analysis. The method will enable us to identify processes that contribute to the substantial precipitation and cloud biases in Numerical Weather Prediction and climate models. A better understanding of these processes is becoming increasingly important as the resolution of these models increases. The MPIC model also has potential for usage as an embedded model in fine-scale forecasting or climate studies. We expect the project to generate several publications in leading journals.

Training

The School of Earth and Environment is one of the leading centres for atmospheric research in the world. There is a leading group in cloud physics and a relatively new, growing group in radar meteorology. There is considerable expertise in using and designing sophisticated models. A number of models suitable for examining processes on multiple scales is available and used by research scientists and PhD students. The School has a group of around 40 atmospheric-science research students. This studentship would form a part of the active and stimulating research environment. The school specifically provides an excellent programme of NERC approved skills training for research students, as well as a number of subject specific activities including the Arran Summer School and Masters-level taught modules. There is also a stimulating programme of internal and external seminars and research group meetings to provide a broader background in atmospheric research.

As the MPIC model was developed as a collaboration between the supervisors at the University of Leeds and the University of St Andrews, the supervisors will together be able to provide the student with practical training.

Student profile

The student should have an interest in the interaction between cloud physics and fluid dynamics and a background in mathematics or physics. The candidate will have some experience with programming and knowledge of scientific computing would be desirable.

References

Arabas, Sylwester, and Shin-Ichiro Shima. "Large-eddy simulations of trade wind cumuli using particle-based microphysics with Monte Carlo coalescence." Journal of the Atmospheric Sciences 70.9 (2013): 2768-2777.
Christiansen, I. P. "Numerical simulation of hydrodynamics by the method of point vortices." Journal of Computational Physics 13.3 (1973): 363-379.
Böing, Steven J., et al. "On the deceiving aspects of mixing diagrams of deep cumulus convection." Journal of the Atmospheric Sciences 71.1 (2014): 56-68.
Cooper, William A., Sonia G. Lasher-Trapp, and Alan M. Blyth. "The influence of entrainment and mixing on the initial formation of rain in a warm cumulus cloud." Journal of the Atmospheric Sciences 70.6 (2013): 1727-1743.
Dritschel, D, Böing, S, Blyth, A. and Parker, D, “A parcel-based model of moist convection”, manuscript in preparation.
Hill, A. A., S. Dobbie, and Y. Yin. "The impact of aerosols on non‐precipitating marine stratocumulus. I: Model description and prediction of the indirect effect." Quarterly Journal of the Royal Meteorological Society 134.634 (2008): 1143-1154.
Huang, Yahui, et al. "The development of ice in a cumulus cloud over southwest England." New Journal of Physics 10.10 (2008): 105021.
Lasher‐Trapp, Sonia G., William A. Cooper, and Alan M. Blyth. "Broadening of droplet size distributions from entrainment and mixing in a cumulus cloud." Quarterly Journal of the Royal Meteorological Society 131.605 (2005): 195-220.
Riechelmann, T., Y. Noh, and S. Raasch. "A new method for large-eddy simulations of clouds with Lagrangian droplets including the effects of turbulent collision." New Journal of Physics 14.6 (2012): 065008.
Wyszogrodzki, A. A., et al. "Turbulent collision-coalescence in maritime shallow convection." Atmospheric Chemistry and Physics 13.16 (2013): 8471-8487.

Related undergraduate subjects:

  • Environmental science
  • Mathematics
  • Meteorology
  • Physics