Informativeness of teleconnections in frequency analysis of rainfall extremes

Abstract

We propose an effective and reproducible framework to assess the informative content of teleconnections (or climate indices) for representing and modeling the frequency regime of rainfall extremes at regional scale. Our dataset consists of 680 annual maximum series of rainfall depth, with 1 and 24 h durations, located in Northern Italy. We compute at-site time series of L-moments (i.e., the mean and the L-coefficient of variation) through sliding time windows; then we discretize the study region into tiles, where Lmoments time series are averaged. We compute the 30-years sliding mean for six teleconnections: North Atlantic Oscillation, Pacific Decadal Oscillation, East Atlantic – West Russia pattern (EA-WR), El Niño Southern Oscillation, Mediterranean Oscillation Index, and Western Mediterranean Oscillation Index (WeMOI). Then, we calculate L-moments-teleconnection Spearman correlations for single sites and for tiles with several resolutions, and retain correlations with p-values ≤ 0.05. We observe spatial patterns of strong correlation between several teleconnections and gridded Lmoments. These spatial patterns are clearly visible at various tiles’ resolutions, and may be used for setting up regional prediction models. The strongest influence is detected for the sliding mean on the WeMOI and EA-WR. Finally, we show a preliminary application of climate-informed regional frequency analysis, through a hierarchical framework, where the L-moments are modelled as functions of teleconnections. We observe high variability of teleconnection-driven predictions of rainfall percentiles, and an increase in overall goodness-of-fit of the climate-informed regional models relative to stationary models. Overall, our research suggests promising pathways for climate-informed local and regional frequency analysis of rainfall extremes, and describes a general method, that can be adapted to different geographical and climatic contexts, as well as environmental variables.