Predictability of weather and climate

Abstract:

Using a simple pedagogical model, it is shown how information about the statistical reliability of initial-value ensemble forecasts can be relevant in assessing the trustworthiness of the climate system’s response to forcing.

Although the development of seamless prediction systems is becoming increasingly common, there is still confusion regarding the relevance of information from initial-value forecasts for assessing the trustworthiness of the climate system’s response to forcing. A simple system which mimics the real climate system through its regime structure is used to illustrate this potential relevance. The more complex version of this model defines “REALITY” and a simplified version of the system represents the “MODEL”. The MODEL’s response to forcing is profoundly incorrect. However, the untrustworthiness of the MODEL’s response to forcing can be deduced from the MODEL’s initial-value unreliability. The nonlinearity of the system is crucial in accounting for this result.

Abstract:

Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this paper. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving a greater attention than 5 to 10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and to other components of the Earth system as well as the overall computational efficiency of representing model uncertainty.