Recent Advances in China on the Predictability of Weather and Climate

This article summarizes the progress made in predictability studies of weather and climate in recent years in China, with a main focus on advances in methods to study error growth dynamics and reduce uncertainties in the forecasting of weather and climate. Specifically, it covers (a) advances in methods to study weather and climate predictability dynamics, especially those in nonlinear optimal perturbation methods associated with initial errors and model errors and their applications to ensemble forecasting and target observations, (b) new data assimilation algorithms for initialization of predictions and novel assimilation approaches to neutralize the combined effects of initial and model errors for weather and climate, (c) applications of new statistical approaches to climate predictions, and (d) studies on meso- to small-scale weather system predictability dynamics. Some of the major frontiers and challenges remaining in predictability studies are addressed in this context.