Open AccessEnsemble superparameterization versus stochastic parameterization: A comparison of model uncertainty representation in tropical weather prediction
Author(s) -
Subramanian Aneesh C.,
Palmer Tim N.
Publication year - 2017
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1002/2016ms000857
Subject(s) - madden–julian oscillation , ensemble forecasting , meteorology , numerical weather prediction , climatology , environmental science , range (aeronautics) , climate model , probabilistic logic , weather forecasting , convection , climate change , mathematics , geography , statistics , geology , oceanography , materials science , composite material
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.