Ensemble superparameterization versus stochastic parameterization: A comparison of model uncertainty representation in tropical weather prediction

• Published in: Journal of advances in modeling earth systems Vol. 9; no. 2; pp. 1231 - 1250
• Main Authors: Subramanian, Aneesh C., Palmer, Tim N.
• Format: Journal Article
• Language: English
• Published: Washington John Wiley & Sons, Inc 01-06-2017
• Subjects: Atmospheric precipitations; Climate; Climate models; Convection; Cyclones; Dynamics; ensemble forecasts; Fields; Frameworks; Global climate; Growth; MJO; Modelling; Moist convection; moist static energy; Parameterization; Perturbations; Physics; Precipitation; Rainfall; Reliability; Tropical climate; Tropical convection; Tropical convective systems; tropical weather; Tropical winds; Turbulent fluxes; Uncertainty; Weather forecasting; Wind; Winds
• ISSN: 1942-2466; 1942-2466
• DOI: 10.1002/2016MS000857

Stochastic schemes to represent model uncertainty in the European Centre for Medium‐Range Weather Forecasts (ECMWF) ensemble prediction system has helped improve its probabilistic forecast skill over the past decade by both improving its reliability and reducing the ensemble mean error. The largest uncertainties in the model arise from the model physics parameterizations. In the tropics, the parameterization of moist convection presents a major challenge for the accurate prediction of weather and climate. Superparameterization is a promising alternative strategy for including the effects of moist convection through explicit turbulent fluxes calculated from a cloud‐resolving model (CRM) embedded within a global climate model (GCM). In this paper, we compare the impact of initial random perturbations in embedded CRMs, within the ECMWF ensemble prediction system, with stochastically perturbed physical tendency (SPPT) scheme as a way to represent model uncertainty in medium‐range tropical weather forecasts. We especially focus on forecasts of tropical convection and dynamics during MJO events in October–November 2011. These are well‐studied events for MJO dynamics as they were also heavily observed during the DYNAMO field campaign. We show that a multiscale ensemble modeling approach helps improve forecasts of certain aspects of tropical convection during the MJO events, while it also tends to deteriorate certain large‐scale dynamic fields with respect to stochastically perturbed physical tendencies approach that is used operationally at ECMWF.
Plain Language Summary
Probabilistic weather forecasts, especially for tropical weather, is still a significant challenge for global weather forecasting systems. Expressing uncertainty along with weather forecasts is important for informed decision making. Hence, we explore the use of a relatively new approach in using super‐parameterization, where a cloud resolving model is embedded within a global model, in probabilistic tropical weather forecasts at medium range. We show that this approach helps improve modeling uncertainty in forecasts of certain features such as precipitation magnitude and location better, but forecasts of tropical winds are not necessarily improved.
Key Points
Ensemble superparameterization is used for model uncertainty representation in probabilistic weather prediction
We compare probabilistic forecasts with stochastic parameterization versus ensemble superparameterization for tropical convective systems
Nature of convective error growth in a multiscale ensemble modeling framework is studied