superparameterization

Superparameterization is a technique used in atmospheric modeling to represent subgrid-scale physical processes, such as clouds and convection, with higher resolution than the main model grid. Instead of parameterizing these processes based on bulk properties of the grid cell, a small, high-resolution model is embedded within each grid cell of the larger, coarser model. This embedded model then simulates the detailed evolution of these subgrid-scale phenomena.

The primary advantage of superparameterization is its ability to capture the complex interactions and feedbacks within subgrid-scale processes more realistically. Traditional parameterizations often simplify these interactions, potentially leading to biases in climate and weather forecasts. By using a higher-resolution model for these specific components, superparameterization aims to improve the accuracy of simulations, particularly for phenomena that are sensitive to their internal structure and dynamics.

However, superparameterization comes with a significant computational cost. Running a high-resolution model within each grid cell of a global or regional model requires substantially more computing power. This has historically limited its widespread application. Despite the computational challenges, ongoing research and advancements in computing power are making superparameterization an increasingly viable and promising approach for improving the representation of key atmospheric processes in climate and weather models.