Can statistical downscaling improve the skill of Seasonal Forecast over Iberian Peninsula?

Quality and high-resolution seasonal forecast is a key aspect for planning and adapting strategies of different socio-economic sectors, providing knowledge of seasonal anomalies a few months ahead (Buontempo et al., 2018). These downscaled forecast can be used as input to impact models (e.g. crop or hydrology), developing climate services applications. However, the skill of seasonal forecasts is limited over mid-latitudes, as a consequence of the limited predictability at seasonal scale (Doblas-Reyes et al., 2013). The potential showed in postprocessing methods to improve the skill of seasonal forecast and its high case-dependent on region, season or application (Manzanas et al., 2020) have been the motivation to carry out this work.

 

A statistical downscaling method developed by AEMET (Petisco, 2008a; Petisco 2008b; Amblar, 2017) have been applied to different runs of ECMWF- SEAS5, covering winter, spring and summer periods, on a domain centered over the Iberian Peninsula. The twenty-years (1997-2016) hindcast (25-members) have been considered as model-climatological reference. A very deep evaluation process of the method had been published with satisfactory results (Hernanz et al. 2022a, d, e). The algorithm makes successive use of an analogue technique -based on a Euclidean distance- and multivariate regression to downscale maximum and minimum temperature and precipitation at daily timescale, through a selection of large-scale model circulation variables (predictors) linked to the local observed variable of interest (predictand). The method uses a high resolution observational gridded dataset developed by AEMET (Peral et al., 2017) (0.05º), covering the peninsular Spain and the Balearic Islands.

 

We have obtained results for mean seasonal temperature and accumulated seasonal precipitation for the follow metrics: the forecasted anomaly, the lower and upper forecasted probabilities and the AreaROC for lower and upper terciles. They show improved spatial detail of the probability of occurrence compared to raw SEAS5 and high values of ROC area (spatially and in percentage), allowing to conclude that at least in certain seasons and over the Iberian Peninsula, the downscaling algorithm developed by AEMET provides added value to ECMWF-S5 seasonal forecasts.