|Title||Predicting uncertainty in forecasts of weather and climate|
|Year of Publication||1999|
|Secondary Title||Technical Memorandum|
The predictability of weather and climate forecasts is determined by the projection of uncertainties in both initial conditions and model formulation onto flow-dependent instabilities of the chaotic climate attractor. Since it is essential to be able to estimate the impact of such uncertainties on forecast accuracy, no weather or climate prediction can be considered complete without a forecast of the associated flow-dependent predictability. The problem of predicting uncertainty can be posed in terms of Liouville equation for the growth of initial uncertainty, or a form of Fokker-Planck equation if model uncertainties are also taken into account. However, in practice, the problem is approached using ensembles of integration of comprehensive weather and climate prediction models, with explicit perturbations to both initial conditions and model formulation; the resulting ensemble of forecasts can be interpreted as a probabilistic prediction. Many of the difficulties in forecasting predictability arise from the large dimensionality of the climate system, and spectral techniques to generate ensemble perturbations have been developed. Special emphasis is placed on the use of singular-vector methods to determine the linearly-unstable component of the initial probability density function. Methods to sample uncertainties in model formulation are also described. Practical ensemble prediction systems for prediction on timescales of days (weather forecasts), seasons (including predictions of El Nino) and decades (including climate change projections) are described, and examples of resulting probabilistic forecast products shown. Methods to evaluate the skill of these probabilistic forecasts are outlined. By using ensemble forecasts as input to a simple decision-model analysis, it is shown that the probability forecasts of weather and climate have greater potential economic value than corresponding single deterministic forecasts with uncertain accuracy.