Accurate predictions of weather and climate events have enormous social and economic values but remain to have significant uncertainties at different time and spatial scales. The difficulties and challenges may come from uncertainties in initial conditions and/or errors in the forecast models. Moreover, nonlinear processes, leading to a chaotic behavior, may lead to error growth and then the loss of predictability.
This session will focus on the studies of initial error dynamics and model error physics related to predictability. Contributions related to theoretical, numerical (from idealized models to comprehensive earth system models), and/or observational researches are greatly encouraged. The main topics could include (i) investigating the influences of the uncertainties of initial conditions and model physical processes on the predictability; in particular, analyzing the relevant initial error dynamics and model error physics; (ii) exploring approaches to estimate error growth that limits predictability for weather and climate forecasts; (iii) quantifying predictabilities of both deterministic and probabilistic approaches for different weather and climate phenomena from thunderstorms, torrential rains, tropical cyclones and typhoons to ENSO and climate change with time scales ranging from a few hours to centuries.; (iv) exploring the approaches to reduce/control the above uncertainties such as ensemble forecast, targeted (i.e., adaptive) observations, and data assimilation.