Enhancing Earth System Decision Making with Dominant Frequency State Analysis
PI: Dr Katy Sheen, University of Exeter
CIs: Dr. J Bruun, University of Exeter; Prof. Amadou Gaye from Université Cheikh Anta Diop, Dakar, Senegal; Dr Ousmane Nidaye based at The National Senegalese Agency of Civil Aviation and Meteorology (ANACIM), Senegal
Project overview: Better quantifying uncertainty in climate and weather variability is a key challenge for decision-making. For example, livelihoods in the semi-arid African Sahel rely heavily on agriculture and are highly impacted by even tiny changes in the summer rains, which are notoriously erratic and poorly predicted on timescales suitable for adaptive planning. Sahel rainfall variability is modulated by a complex medley of atmospheric, oceanic, land and dust interactions. How do we better quantify and predict the spatially varying likelihood of extreme Sahelian rainfall events in the context of these changing global climate conditions? In this study we will build on a novel approach to climate timeseries analysis called Dominant Frequency State Analysis (DFSA). DFSA identifies and quantifies the seasonal cycle, low amplitude (and typically low frequency) climate signals, trends and noise. To date it has been used on single timeseries, but in this project we will extend it to multiple sites to analyse precipitation data from across the Sahel. The output will be visually intuitive maps of Sahel rainfall climate drivers. For example, maps which quantify how the inter-annual El Niño Southern Oscillation impacts rainfallacross the Sahel. These maps will help to identify early warning predictors of Sahelian rainfall change. In future, this analysis could be combined with extreme event statistics to produce more accurate risk assessment maps of extreme drought or early or late growing season rains. Our project outputs will be developed and shared with our partners in Senegal, who currently provide advice about Sahelian rainfall variability to a range of end users, from national government to rural farming communities.