# Stochastic parameterization of subgrid-scale processes: A review of recent physically-based approaches

## TL;DR

This paper reviews recent physically-based stochastic parameterization methods for subgrid-scale processes in climate models, emphasizing their impact on accurately representing atmospheric variability and the importance of stability properties.

## Contribution

It provides a comprehensive review of recent approaches and demonstrates their effectiveness using a stochastic triad system relevant to climate dynamics.

## Key findings

- Stability of subgrid processes significantly affects parameterization performance.
- Physically-based stochastic methods improve climate variability representation.
- Stochastic triad system illustrates key dynamics of subgrid processes.

## Abstract

We review some recent methods of subgrid-scale parameterization used in the context of climate modeling. These methods are developed to take into account (subgrid) processes playing an important role in the correct representation of the atmospheric and climate variability. We illustrate these methods on a simple stochastic triad system relevant for the atmospheric and climate dynamics, and we show in particular that the stability properties of the underlying dynamics of the subgrid processes has a considerable impact on their performances.