<p>Outliers detection generally aims at identifying extreme events and insightful changes in climate behavior. One important type of outlier is pattern outlier also called discord, where the outlier pattern detected covers a time interval instead of a single point in the time series. Machine learning contributes many algorithms and methods in this field especially unsupervised algorithms for different types of data time series. In a first submitted paper, we have investigated discord detection applied to climate-related impact observations. We have introduced the prominent discord notion, a contextual concept that derives a set of insightful discords by identifying dependencies among variable length discords, and which is ordered based on the number of discords they subsume. </p><p>Following this study, here we propose a ranking function based on the length of the first subsumed discord and the total length of the prominent discord, and make use of the powerful matrix profile technique. Preliminary results show that our approach, applied to monthly runoff timeseries between 1902 and 2005 over West Africa, detects both the emergence of long term change with the associated former climate regime, and the regional driest decade (1982-1992) of the 20th century (i.e. climate extreme event). In order to demonstrate the genericity and multiple insights gained by our method, we go further by evaluating the approach on other impact (e.g. crop data, fires, water storage) and climate (precipitation and temperature) observations, to provide similar results on different variables, extract relationships among them and identify what constitutes a prominent discord in such cases. A further step will consist in evaluating our methodology on climate and impact historical simulations, to determine if prominent discords highlighted in observations can be captured in climate and impact models.</p>

<p>As global warming is projected to intensify according to model simulations, a large range of resulting impacts and stressors is expected during the 21st century. Severe impacts are particularly projected in vulnerable regions such as West Africa, where local populations largely rely on livestock systems as their main food production and income source. As climate change threatens livestock systems in various ways, here we assess how regional livestock could be exposed to cumulated and cross-sectoral climate stressors during the upcoming decades. A set of eight major risk indicators that may affect livestock is assessed and illustrate changes in food availability, heat stress, flood and drought risks. Corresponding simulations are analysed from the largest multi-model climate-related impact simulations database ISIMIP.</p><p>Under the RCP8.5 scenario, we find that a large part of West Africa will experience at least 5 to 6 cumulated cross-sectoral climate stressors before the 2030s, including amplified severe heat stress conditions and flood risks. Consequently, about 30% of total west african livestock will be affected by these cumulated stressors, with highest exposures shown for sheeps and cattles (respectively 39% and 38% of their total regional density). Multi-model means show that these species will be first exposed to significant intensification of severe heat stress conditions from early 2020s, then to more flood risks from 2030s. This study brings new quantifications that could help policy makers to prioritize decisions to prepare local populations to face multiple climate-related impacts.</p>