Improving seasonal forecasts for the Iberian Peninsula through climate variability modes

Accurate seasonal forecasts can be very useful for taking adaptation and prevention measures to unfavourable weather conditions, such as droughts, heatwaves, extreme precipitation events or high fire risk conditions. In a region with a very marked seasonal cycle and a high inter-annual variability of atmospheric conditions such as the Iberian Peninsula, the demand for improved seasonal forecasting becomes even more compelling.Currently operational seasonal forecasting systems have considerable scope for improvement in their ability to predict mid-latitude temperature or precipitation. However, some of these systems have shown some skill in predicting, months in advance, the phase of some modes of variability such as the North Atlantic Oscillation.Here we show that seasonal forecast of surface variables (such as 2m air temperature or total precipitation) can be improved by taking advantage of the models' skill in predicting the main modes of variability in Europe. First, a quantification of the theoretical potential for improving the forecasts is carried out using an ensemble-member weighting technique that increase the statistical weight of those members whose variability mode configuration is closer to the observed configuration. Then, we assess the actual potential for improving the forecasts by using a two steps prediction: (1) a multi-system forecast of the corresponding variability mode configuration and (2) a seasonal forecast of the surface variables in which we use the first step forecast of the variability mode configuration to weight the ensemble members. Different verification metrics were used to quantify the skill of the forecasts, both deterministic and probabilistic, all defined as in the WMO forecast guidance.The results show that ensemble-member weighting method with information on variability modes is a window of forecast opportunity that is able to improve seasonal forecasts if further research is conducted.