Open AccessMultimodel Forecasting of Precipitation at Subseasonal Timescales Over the Southwest Tropical Pacific
Author(s) -
Specq Damien,
Batté Lauriane,
Déqué Michel,
Ardilouze Constantin
Publication year - 2020
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2019ea001003
Subject(s) - predictability , madden–julian oscillation , climatology , forecast skill , initialization , precipitation , environmental science , el niño southern oscillation , hindcast , quantitative precipitation forecast , meteorology , mathematics , computer science , statistics , geography , geology , convection , programming language
Multimodel ensemble (MME) reforecasts of rainfall at subseasonal time scales in the southwest tropical Pacific are constructed using six models (BoM, CMA, ECCC, ECMWF, Météo‐France, and UKMO) from the Subseasonal‐to‐Seasonal (S2S) database by member pooling. These reforecasts are verified at each grid point of the 110°E to 200°E; 30°S to 0° domain for the 1996–2013 DJF period. The evaluation is based on correlation and on the ROC skill score of the upper quintile of precipitation for both weekly targets and Weeks 3–4 outlook. Confirming previous results at the seasonal time scales, the MME reaches the highest skill and also improves the reliability of probabilistic forecasts. However, an equivalent ensemble size comparison between the MME and the individual models shows that the better performance of the MME compared to the best individual models is significantly related to the larger ensemble size of the MME. Forecast skill is then explained in light of potential sources of predictability by evaluating the performance of the models depending on the initial ENSO and MJO state. While the role of ENSO on predictability is quite consistent with its related rainfall anomalies, the role of the MJO is more ambiguous and strongly depends on the location: An initialization in active MJO conditions does not necessarily imply better forecasts. This influence of ENSO and the MJO on predictability does not change when switching from individual models to the MME.