Sources of predictability over the Mediterranean at seasonal time-scale: building up anempirical forecasting model

Although most operational seasonal forecasting systems are based on dynamical
models, empirical forecasting systems, built on statistical relationships between
present and future at seasonal time horizons conditions of the climate system, provide
a feasible and realistic alternative and a source of supplementary information. Here, a
new empirical model based on partial least squares regression is presented. Originally
designed as a flexible tool, the system is able to automatically select predictors from an initial pool and explore spatial fields looking for additional predictors.
the model can be run with many configurations including different predictands,
resolutions, leads and aggregation times. The model benefits from specific predictors
for the Mediterranean region unveiled in the frame of the MEDSCOPE project.  We
present here 2 sets of results: the first one from a configuration producing probabilistic forecasts of seasonal
(3 month averages) temperature and precipitation over the Mediterranean area, their verification and comparison
against a selection of state-of-the-art seasonal forecast systems based on dynamical
models in a hindcast period (1994-2015). The model is able to produce spatially
coherent anomaly patterns, and reach levels of skill comparable to those based on
dynamical models. To explore the potential of the model for producing skilful forecasts over reduced areas, a second set of results are calculated using higher resolution predictands over Iberia, again comparing its skill with that of a set of state of the art models. Examples of the model usage for evaluating the impact on skill of
certain predictor helping in the search and understanding of new sources of
predictability are also shown.