The Challenge

The challenge of the CSIRO Decadal Climate Forecasting Project is to improve and advance the use of multi-year to decadal forecasts to enable Australian industries and regulators to better deal with climate variability and climate extremes. Internationally, considerable research investment is occurring with the aim of developing useful multi-year to decadal climate forecasts. This endeavour has been identified by the World Climate Research Program (WCRP) as a grand challenge in near term climate prediction with the anticipation that useful climate forecasts are attainable. This project will contribute to this challenge.

As with all research, the DCFP carries some risk, however, the rewards to the Australian community of delivering useful climate forecasts at the multi-year to decadal timescales carries potentially huge benefits for Australian industries and our ability to manage our natural assets. Success will require the project to apply our climate understanding gained through the observations, modelling and process studies to improving and advancing the use of climate forecasts. We will need to develop novel ensemble-based probabilistic climate forecast metrics similar to those currently employed in numerical weather prediction.

Probabilistic assessments will be used to characterise the forecast skill, to help identify where to direct our resources, and to evaluate the progress towards delivering better and more useful forecasts. Such assessments will also help direct the development of the climate model (i.e. identification of model bias), improve data assimilation by assessing the value of different observational data streams, and to better constrain ensemble initialisation through systematic characterisation of forecast error growth.


Our Mission

Is to improve multi-year to decadal climate forecasts by:

  1. Advancing fundamental climate research into: where does the predictability of the climate system reside, the processes that give rise to that predictability, and the critical observations that will help us to realise the potential climate predictability 
  2. Applying state-of-art ensemble data assimilation to determine the initial climate state for the forecasts
  3. Integrating climate processes with the forecasting effort in the development of the climate perturbations used in the ensemble forecasts

Advance the utility of climate forecasts by:

  1. Closely integrating verification and applications with forecasting effort (targeted evaluation linked with the application)
  2. Advancing process understanding and process verification to build confidence in the value of forecasts