4. Event prediction
Faculty: F. Doblas-Reyes, A. Kumar and C. Prodhomme
Participants: S. Abelen, U. J. Diasso, L.B. Diaz K. Kashinath, A. S. Pepler

The assessment of the prediction skill of extreme climate events is the first step towards an efficient application of seasonal prediction in both society and the industry. Multi-model global retrospective predictions will be used by the students to investigate the ability of current operational systems to predict the 10th and 90th percentiles of the seasonal precipitation, temperature and wind. They will compare the skill and reliability of the predictions for extreme events with the forecast quality of the seasonal averages, explore the conditional skill by stratifying the events as a function of a subset of large-scale variability modes (NAO, ENSO) and investigate how the skill and the prediction uncertainty changes as more prediction systems are added. A discussion of the relevance of predicting seasonal extreme events for different sectors is expected. Our research unit at IC3 is developing a set of R functions to perform the analyses on climate predictions that will be released via the CRAN.

Introductory material on predicting seasonal or decadal extremes: