Leader: Bernadette Sloyan
Activity that provides the underpinning science to climate predictability.
A dynamical prediction consists of an ensemble of forecasts produced by integrating a climate model forward in time from a set of observation-based initial conditions. As the forecast range increases, processes in the ocean become increasingly important and the sparseness, non-uniformity and secular change in sub-surface ocean observations is a challenge to initialization and validation of the forecasting system. Recent studies, suggest improvement in forecast skill when sub-surface ocean information is used as part of the initialization. However, to date, most multi-year prediction systems generally only assimilate SSTs, thus ignoring the potential information held in the other satellite data and sub-surface observational data.
To be able to provide useful multi-year to -decadal forecasts we have to understand the dynamical processes in the ocean and atmosphere that govern the predictability of the climate system. There remain many unanswered question on the fundamental nature and drivers of the production of ocean variance with the potential to remain coherent on the multi-year to –decadal time scale, and how these anomalous ocean signals re-emerge at the ocean-atmosphere boundary. It is also critical to understand the teleconnections processes in the atmosphere that transmit these signals from the ocean-atmosphere boundary to the land regions. Thus, it is combination of improved dynamical understanding of both the ocean and atmosphere and the coupling and teleconnections between them that will lead to the development of skillful decadal forecasts.
The Observations and Processes activity will provide satellite and in situ observational ocean data sets for initialising the ocean model of the forecasting system, provide with held data set and produce data products for model validation and, provide process resolving observations to inform model physics assessments and improvements. The Processes theme will use the observational data and run model perturbation experiments targeted to understand the dynamics of growing modes of variability in the ocean and assess whether these provide improved forecast skill. We will identify key ocean processes that drive multi-year variability in the ocean and assess what is their impact on climate variability and forecast potential.