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.

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.

Fog is a high-impact weather phenomenon affecting human activity, including aviation, transport, and health. Its prediction is a longstanding issue for weather forecast models. The success of a forecast depends on complex interactions among various meteorological and topographical parameters; even very small changes in some of these can determine the difference between thick fog and good visibility. This makes prediction of fog one of the most challenging goals for numerical weather prediction. The Local and Nonlocal Fog Experiment (LANFEX) is an attempt to improve our understanding of radiation fog formation through a combined field and numerical study. The 18-month field trial was deployed in the United Kingdom with an extensive range of equipment, including some novel measurements (e.g., dew measurement and thermal imaging). In a hilly area we instrumented flux towers in four adjacent valleys to observe the evolution of similar, but crucially different, meteorological conditions at the different sites. We correlated these with the formation and evolution of fog. The results indicate new quantitative insight into the subtle turbulent conditions required for the formation of radiation fog within a stable boundary layer. Modeling studies have also been conducted, concentrating on high-resolution forecast models and research models from 1.5-km to 100-m resolution. Early results show that models with a resolution of around 100 m are capable of reproducing the local-scale variability that can lead to the onset and development of radiation fog, and also have identified deficiencies in aerosol activation, turbulence, and cloud micro- and macrophysics, in model parameterizations.

A quantitative examination of the annual cycle in the tropical tropopause temperatures, tropical ascent, momentum balance, and wave driving is performed using ECMWF analyses to determine how the annual cycle in tropical tropopause temperatures arises. Results show that the annual cycle in tropical tropopause temperatures is driven by the annual variation in ascent and consequent dynamical (adiabatic) cooling at the tropical tropopause. Mass divergence local to the tropical tropopause has the dominant contribution to ascent near the tropical tropopause. The annual cycle in mass divergence, and the associated meridional flow, near the tropical tropopause is driven by Eliassen–Palm (EP) flux divergence, that is, wave dissipation. The EP flux divergence near the tropical tropopause is dominated by stationary waves with both the horizontal and vertical components of the EP flux contributing. However, the largest annual cycle is in the divergence of the vertical EP flux and in particular from the contribution in the vertical flux of zonal momentum. These results do not match the existing theory that the annual cycle is driven by the wave dissipation in the extratropical stratosphere, that is, the stratospheric pump. It is suggested that the annual cycle is linked to equatorial Rossby waves forced by convective heating in the tropical troposphere.