Using Stochastically Perturbed Parameterizations to Represent Model Uncertainty. Part I: Implementation and Parameter Sensitivity

Abstract

Abstract Accurately representing model-based sources of uncertainty is essential for the development of reliable ensemble prediction systems for NWP applications. Uncertainties in discretizations, algorithmic approximations, and diabatic and unresolved processes combine to influence forecast skill in a flow-dependent way. An emerging approach designed to provide a process-level representation of these potential error sources, stochastically perturbed parameterizations (SPP), is introduced into the Canadian operational Global Ensemble Prediction System. This implementation extends the SPP technique beyond its typical application to free parameters in the physics suite by sampling uncertainty both within the dynamical core and at the formulation level using “error models” when multiple physical closures are available. Because SPP perturbs components within the model, internal consistency is ensured and conservation properties are not affected. The full SPP scheme is shown to increase ensemble spread to keep pace with error growth on a global scale. The sensitivity of the ensemble to each independently perturbed “element” is then assessed, with those responsible for the bulk of the response analyzed in more detail. Perturbations to surface exchange coefficients and the turbulent mixing length have a leading impact on near-surface statistics. Aloft, a tropically focused error model representing uncertainty in the advection scheme is found to initiate growing perturbations on the subtropical jet that lead to forecast improvements at higher latitudes. The results of Part I suggest that SPP has the potential to serve as a reliable representation of model uncertainty for ensemble NWP applications. Significance Statement Ensemble systems account for the negative impact that uncertainties in prediction models have on forecasts. Here, uncertain model parameters and algorithms are subjected to perturbations representing impact on forecast errors. By initiating error growth within the model calculations, the equally skillful members of the ensemble remain physically realistic and self-consistent, which is not guaranteed by other depictions of model error. This “stochastically perturbed parameterization” technique (SPP) comprises many small error sources, each analyzed in isolation. Each source is related to a limited set of processes, making it possible to determine how the individual perturbations affect the forecast. We conclude that SPP in the Canadian Global Ensemble Forecasting System produces realistic estimates of the impact of model uncertainties on forecast skill.