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Assessing the predictability of extreme rainfall seasons over southern Africa
Author(s) -
Landman Willem A.,
Botes Stephanie,
Goddard Lisa,
Shongwe Mxolisi
Publication year - 2005
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2005gl023965
Subject(s) - predictability , gcm transcription factors , climatology , canonical correlation , forecast skill , probabilistic logic , environmental science , meteorology , geography , statistics , mathematics , general circulation model , climate change , geology , oceanography
A model output statistics (MOS) technique is developed to investigate the potential rainfall forecast skill for extreme seasons over southern Africa. Rainfall patterns produced by the ECHAM4.5 atmospheric GCM are statistically recalibrated to regional rainfall for the seasons of September–November, December–February, March–May and June–August. Archived records of the GCM simulated fields are related to observed rainfall through a set of canonical correlation analysis (CCA) equations. Probabilistic forecast skill (RPSS and ROC) of MOS‐recalibrated simulations for 5 equi‐probable categories is assessed using a 3‐year‐out cross‐validation approach. High skill RPSS values are found for the DJF and MAM seasons. Although ROC scores for DJF and MAM are larger than 0.5 for all categories (scores less than 0.5 suggest negative skill), scores for DJF show that the extreme categories are more predictable than the inner categories and scores for MAM show that skill is mostly associated with the extremely wet category. The GCM's ability to reproduce tropical‐temperate trough variability constitutes the main source of predictability for DJF and MAM.