In this paper we are interested in identifying insightful changes in climate observations series, through outlier detection techniques. Discords are outliers that cover a certain length instead of being a single point in the time series. The choice of the length can be critical, leading to works on computing variable length discords. This increases the number of discords, with potential overlapping, subsumption and reduced insightful results. In this work we introduce a hybrid approach to rank variable length discords and extract the most prominent ones, that can yield more impactful results. We propose a ranking function over extracted variable length discords that accounts for contained point anomalies. We investigate the combination of pattern wise anomaly detection, through the Matrix Profile paradigm, with two different point wise anomaly detectors. We experimented with MAD and PROPHET algorithms based on different concepts to extract point anomalies. We tested our approach on climate observations, representing monthly runoff time series between 1902 and 2005 over the West African region. Experimental results indicate that PROPHET combined with the Matrix Profile method, yields more qualitative rankings, through an extraction of higher values of extreme events within the variable length discords.