Convection, turbulence, clouds, and precipitation occur across a wide range of scales, from hundreds of kilometers to hundreds of meters, and clouds at all scales strongly influence weather. Small shallow cumulus strongly affect the planetary albedo and produce a significant amount of precipitation, while turbulence and cloud processes largely control the radiatively significant transition between stratocumulus and shallow cumulus. Deep convection produces a large fraction of the Earth’s precipitation. The stratiform clouds generated by cumulus detrainment are important radiatively. The interaction between (relatively) large- and small-scale processes is also important: the diurnal cycle of precipitation over land, which essentially all weather and climate models simulate poorly, arises through interactions among the turbulent boundary layer, shallow cumulus convection, and deep cumulus convection. Representing the interactions between turbulence, clouds, deep convection, and radiation are of key importance for predicting weather and climate.
Global models parameterize the effects of processes that occur on scales near or below the horizontal grid spacing, including turbulence, convection, and associated cloud and radiation processes. Current global forecast models use grid spacings of a few tens of kilometers; in the next few years the mesh size is expect to be less than ten kilometers. Conventional parameterizations of deep convection rely on assumptions that are fundamentally inconsistent with such high-resolution models. Smaller clouds such as shallow cumuli, however, will not be even partially resolved in the foreseeable future. Developing parameterizations that work well across a range of parameterized and explicit phenomena is a significant challenge.
Our hypothesis is that the NCEP global models can be improved by installing an integrated, self-consistent description of turbulence, clouds, deep convection, and the interactions between clouds and radiative and microphysical processes. We therefore propose a CPT to unify the representation of turbulence and SGS cloud processes and to unify the representation of SGS deep convective precipitation and grid-scale precipitation as the horizontal resolution decreases. Both of these unifications are physically based and both have been extensively tested against LES and CRM results.
We will improve the representation of small-scale phenomena by implementing a PDFbased subgrid-scale turbulence and cloudiness scheme that would replace the boundary layer turbulence scheme, the shallow convection scheme, and the cloud fraction schemes in the GFS and CFS. We will improve the treatment of deep convection by introducing a unified parameterization that scales continuously between the simulation of individual clouds when and where the grid spacing is sufficiently fine and the behavior of a conventional parameterization of deep convection when and where the grid spacing is coarse. We will improve the representation of the interactions of clouds, radiation, and microphysics in the GFS/CFS by using the additional information provided by the PDF-based SGS cloud scheme. The team will evaluate the impacts of the model upgrades with metrics used by the NCEP short-range and seasonal forecast operations.
Our proposal is relevant to the MAPP Program’s competition for Research to Advance Climate and Earth System Models. The project will help to achieve the first of the NOAA Next-Generation Strategic Plan climate objectives, an improved scientific understanding of the changing climate system and its impacts, by improving two core capabilities: understanding and modeling, and predictions and projections.