Sub-grid Scale Processes and Parameterization

Sub-grid Scale Processes and Parameterization

Even in high resolution models, some weather and climate processes are too small in scale to be calculated directly, and always will be, even though their impacts are important to weather and climate systems. These sub-grid scale processes, such as cumulus convection and radiative transfer of solar and longwave radiation, indirectly affect the climate and weather variables (the "resolved" variables) calculated on the grid. These indirect effects are determined by what is called "parameterization".

Graph showing growth by condensation growth and collision-coalescence growth plus Animation of collision-coalescence

Click image above to see animation.

Parameterizations are designed using observations and the laws of physics; they are not a "best guess". Take the example of a model representation of cloud microphysics. Conservation laws dictate the mass of water going in and out of a grid box. Vapor in the model condenses according to observationally based thresholds of relative humidity, forming "clouds". Physical processes lead from clouds to the formation of droplets that then fall to grid boxes below as rain or snow, also based on observational evidence from within clouds.

Thus, the parameterization of clouds, although subject to many unknowns, is based on a combination of the conservation of mass and energy and an empirical understanding of cloud formation, grounded in observations. Using those properties, modelers create parameterizations for cloud processes that operate within model grid layers when appropriate environmental conditions are met.

Parameterizations do bring with them uncertainty. Some processes are better understood than others. But this is true of all model processes. Even the numerical representation of the laws of motion comes with uncertainty. There is no unique way to write the numerical solutions for resolved processes, like motion, and they all have some error associated with them. This is equally true for both weather models and climate models. Even so, uncertainty in weather and climate models is decreasing as our understanding of climate and weather processes further improves and computing power continues to increase.