Circle-A: Parametrizing Convection in the Hard Grey Zone: Modelling the Interaction of Turbulent Cloud processes with Explicit Cloud Dynamics.

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"Anarchy is the mother of Order", claimed Proudhon, referring to the Anarchist movement. Many might question whether this is true of human behaviour, but it certainly is of the atmosphere. Some of the most extreme weather on earth is associated with deep convective rain storms driven by the heat released and buoyancy generated when water vapour turns into liquid cloud water or ice. These storms start out as small turbulent eddies which grow into violent thunderstorms, the most severe of which preserve their existence by a process of continual regeneration. In the right conditions, storms can organise into much larger structures such as squall lines covering hundreds of kilometres and lasting many hours. The most extreme, violent and beautiful example of organised convection is the tropical cyclone. This ever-larger scale organisation nevertheless remains, in part, controlled by processes occurring at the smallest turbulent scales. If we represent these processes poorly in models used to predict weather and climate, we severely compromise the accuracy of their predictions. Ever-increasing computer power has given us the ability to run higher resolution models that are beginning to represent individual clouds with some realism, but we are far from directly resolving those processes that control the size, intensity, number etc. of clouds. This project is aimed at substantially improving the way we represent the effect of these processes on cloud growth and dissipation in practicable weather forecast models and climate models.We can get so far by theoretical derivation from the fundamental equations of physics, but doing so raises more unanswered questions, such as how can we predict the distribution of water in a cloud, which is highly sensitive to motions within turbulent eddies, given only limited information of average properties over larger scales (e.g. a few km)? We can 'close' the problem by forming hypotheses about dependencies based upon arguments such as scaling and symmetry, but these need testing and calibrating. As part of the project we plan to run a number of ambitious reference simulations of controlled, idealized, convective flows, to provide data to test these hypotheses. We also propose developing a hierarchy of simplified representations of the turbulent flow directed specifically at cost effective modelling of deep convection. In particular, we plan to implement three schemes:1. A 'Rolls-Royce' scheme, with as little approximation as possible. 2. A highly simplified scheme similar to those commonly used but and with a fresh analysis of key parameters.3. A new scheme traceable from 1 but based on a simplification of 1 directed specifically at the representation of deep convective clouds, focussing on vertical motion and resulting condensation separately from horizontal motion.The high resolution data will be analysed much the same way that observational data would be if available, but we also plan to make use of high-resolution models is a way which could never be achieved through observation; we plan to provide information about the 'truth' directly to lower resolution simulations as they run, either turbulent fluxes or mean variables. This can be done in a number of ways to learn about which terms are most important in driving the resolved flow. One exciting and novel way is to use techniques recently developed in the Data Assimilation Research Centre to use 'observations' (in our case, reference high-resolution simulations) to determine objectively which parametrizations best represent the 'missing' terms in a model, the omission of which lead the model to diverge from the observations.