Quantifying the benefits of microphysical complexity in an idealised one dimensional framework using the Factorial Method

The representation of liquid-phase microphysics is an important consideration for NWP and climate models, as it has immediate consequences for the simulation of both warm and mixed-phase cloud. In particular the onset and rate of precipitation in operational models is sensitive to the type of microphysical parameterizations used, and additionally in the case of climate models, the choice of microphysics scheme determines (to some as yet unknown extent) the ability to capture indirect effects. In terms of simulating mixed-phase cloud, it is especially prudent to ensure an adequate representation of the liquid-phase first before an eventual improved treatment of the ice phase can be considered.

The aim of this work is to explore and quantify the degree to which the level of liquid-phase sophistication in models can impact upon the simulation of warm and mixed-phase cloud. To achieve this aim, an idealised 1-D column model will be used to drive a suite of microphysics schemes consisting of incremental increases in complexity. The suite includes a bulk scheme with options for both single and dual moment treatments of liquid droplets and rain, and a bin scheme with explicit treatment of both liquid and aerosol, capable of representing multi-component aerosol in different mixing states. The use of the 1-D driver model ensures that the dynamical forcing can be easily constrained while differences in microphysical behaviour between the schemes can be easily identified. The experimental design for the 1-D simulations is based around the Factorial Method. This technique has been adopted previously in the context of cloud modelling by Teller and Levin (2008) to quantify the effects of both microphysical and atmospheric thermodynamic factors on precipitation produced by a winter time mixed-phase convective cloud. For the purpose of this study, the application of the Factorial Method is extended to consider a range of idealised cloud types (including warm shallow convection and both warm and mixed-phase stratocumulus) across a range of microphysical complexity to allow the behaviour of different schemes to be compared directly. The factors considered include prescribed updraft speed, temperature profile and cloud condensation nuclei (CCN) concentration, and additionally in the case of the bin scheme, mean dry aerosol diameter and aerosol composition.

Ultimately, for each scheme the Factorial Method analysis will establish the degree to which surface precipitation is influenced by changes in microphysical factors compared to dynamical factors. From this, the relative importance of the microphysics as a function of complexity can be determined, thus helping to decide whether certain meteorological regimes exist for which the additional expense of more complex schemes can be justified. Initial results have been produced for an idealised warm shallow convective cloud, which show that as the level of microphysical complexity is increased, the peak precipitation rate becomes larger and the onset of precipitation earlier. While all schemes show some level of sensitivity to changes in atmospheric thermodynamic conditions, the degree of sensitivity was found to be dependent on the choice of microphysics scheme; indeed the relative contribution from dynamical factors is positively correlated with microphysical complexity, suggesting that the relative effect of increasing CCN concentrations on precipitation suppression is less significant in more sophisticated schemes compared to single moment bulk treatments. Future work will concentrate on quantifying the effects of aerosol dry diameter and composition using the explicit bin scheme, to establish the range of thermodynamic conditions for which such additional factors are most important.