Many numerical weather prediction models are moving towards more prognostic schemes for the prediction of ice and liquid water contents within clouds. This paper describes a large-scale cloud and precipitation scheme developed for the UK Meteorological Office's Unified Model. It uses physically based transfer equations to predict ice as a prognostic variable. We review similar schemes and then describe our new scheme, giving examples of its performance in mesoscale forecasts compared with the current operational scheme and with observations. The microphysical processes occurring in a frontal cloud are well modelled. The prediction of supercooled stratocumulus cloud is much improved.

A prognostic cloud fraction and prognostic condensate scheme has been developed for the Met Office Unified Model. This is designed to replace the scheme currently used in weather forecast and climate simulations, in which cloud fraction and liquid water content are calculated diagnostically. Such a scheme overprescribes links between cloud fraction, condensate and water vapour contents. By contrast, our new prognostic cloud fraction and prognostic condensate scheme (PC2) calculates increments to prognostic variables of liquid, ice and total cloud fractions, water vapour and liquid condensate as a result of each physical process represented in the model. (Ice condensate is already represented prognostically.) This paper provides a summary of the PC2 scheme, describes how it is implemented, and discusses its relationship with other existing cloud schemes. Key aspects of the PC2 formulation are: the consistent derivation of prognostic terms, the reversible nature of the scheme under idealised forcing scenarios, the well-behaved performance in the limit of very low and very high cloud fraction, the inclusion of ice microphysical processes, and the improved representation of cloud erosion processes. A companion paper presents the results from the scheme.

Cloud fraction, liquid and ice water contents derived from long-term radar, lidar, and microwave radiometer data are systematically compared to models to quantify and improve their performance.

[1] CloudSat radar reflectivities are simulated in the Met Office global forecast model in a manner which is consistent with the CloudSat observations. The method is described and applied in an evaluation study of the model's performance over the period December 2006 to February 2007. The study uses both statistical and case study approaches and examines the model's simulation of cloud systems globally and in three regions of contrasting weather and cloud regimes: the tropical warm pool, the North Atlantic Ocean, and the stratocumulus region off the west coast of California. In general, the model shows a good representation of the vertical structure of clouds systems, although a lack of midlevel cloud is ubiquitous. The model shows a nondrizzling cloud mode and a clearly separated drizzling mode that is not seen in the observations, independent of the geographical region. The comparisons suggest that the intensity of drizzle is too high, confirming on a global basis what recent ground-based measurements have also shown. They also suggest that the parameterization of ice cloud fraction as a monotonic function of the grid box mean ice water content is not consistent with the observations.

A prognostic cloud fraction and prognostic condensate scheme (PC2) has been developed for the Met Office Unified Model. A companion paper discussed the motivation for a new scheme and described its formulation in detail. In this paper we describe the results of climate model simulations, concentrating on the mechanisms by which the cloud and condensate predicted by the model change between the Control and new scheme. We demonstrate that the detrainment of condensate from the convection scheme directly into the large scale, as parametrized in the PC2 scheme, produces improved simulations of deep tropical cloud. We also show that the unphysical strong link between cloud fraction and condensed water content that is present in the Control scheme has been broken by using PC2, but that it is still challenging to produce optically thin cloud in a large-scale model. Shallow convection proves to be a difficult cloud type to parametrize using a prognostic scheme, although the PC2 scheme performs well. The use of increased vertical resolution, in both the Control and PC2, improved the simulation of cloud when compared to observations.