Stochastic Parameterisations: Representing Model Uncertainty in Climate Models

Abstract:

Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the sub-grid scale motion is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale-separation in the atmosphere means that this approach is a large source of error in predictions. Over the last decade an alternative paradigm has developed: the use of stochastic techniques to represent small-scale processes. These techniques are now ubiquitous in weather and seasonal forecasting centres worldwide. I will discuss their potential to represent model structural uncertainty on climate timescales. I will present experiments from three climate models (EC-Earth, HadGEM, and the Community Earth System Model) that demonstrate the potential of using stochastic approaches to represent uncertainty in climate simulations.