In present-day climate models, subgrid-scale clouds and turbulence in the boundary layer are parameterized using different schemes for different cloud regimes. A mass-flux scheme is used to parameterize shallow cumulus clouds, an eddy-diffusivity scheme is used to parameterize subgrid-scale turbulent fluxes, and a macrophysics scheme is used to parameterize stratiform clouds. Each of these three schemes has its own drawbacks, and the framework itself of using separate schemes for separate regimes is problematic.

Here, we discuss an alternative framework for parameterizing clouds and turbulence. It is a unified approach in which all boundary layer clouds and turbulence are modeled using the same equation set. It involves estimating the probability density function (PDF) of subgrid variability in moisture, heat content, and turbulence, and then using the PDF to diagnose relevant quantities such as cloud fraction and liquid water.

We have implemented and tested our PDF parameterization in two climate models, GFDL\'s AM3 and NCAR\'s CAM model. We will discuss the merits and deficiencies of our current climate simulations based on our PDF parameterization.