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Juliette Blanchet and Victor Mélèse

Abstract

This article proposes a statistical framework for assessing the multiscale severity of a given storm at a given location. By severity we refer to the rareness of the storm event, as measured by the return period. Rather than focusing on predetermined spatiotemporal scales, we consider a model assessing the return period of a storm event observed across the continuum of durations and areas around a focus location. We develop a Bayesian intensity–duration–area–frequency model based on extreme value distribution and space–time scale invariance hypotheses. The model allows us to derive an analytical expression of the return period for any duration and area, while the Bayesian framework allows us by construction to assess the related uncertainties. We apply this framework to high-resolution radar–rain gauge reanalysis data covering a mountainous region of southern France during the autumns 2008–15 and comprising 50 rain events. We estimate the model at two grid points located a few kilometers apart on either side of the mountain crest, considering spatiotemporal scales ranging over 3–48 h and 1–2025 km2. We show that at all scales and for all significant events, the return period uncertainties are skewed to the right, evidencing the need of considering uncertainty to avoid systematic risk underestimation. We also reveal the large variability of the storm severity both at short distance and across scales, due to both the natural variability of rainfall and the mask effect induced by the mountain crest.

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Juliette Blanchet and Jean-Dominique Creutin

Abstract

We propose a new approach to explain multiday rainfall accumulation over a French Alpine watershed using large-scale atmospheric predictors based on analogy. The classical analogy framework associates a rainfall cumulative distribution function (CDF) with a given atmospheric situation from the precipitation accumulations yielded by the closest situations. The analogy may apply to single-day or multiday sequences of pressure fields. The proposed approach represents a paradigm shift in analogy. It relies on the similarity of the local topology mapping the pressure field sequences, somehow forgetting the pressure fields per se. This topology is summarized by the way the sequences of pressure fields resemble their neighbors (dimensional predictors) and how fast they evolve in time (dynamical predictors). Although some information—and hence predictability—is expected to be lost when compared with classical analogy, this approach provides new insight on the atmospheric features generating rainfall CDFs. We apply both approaches to geopotential heights over western Europe in view of assessing 3-day rainfall accumulations over the Isère River catchment at Grenoble, France. Results show that dimensional predictors are the most skillful features for predicting 3-day rainfall—bringing alone 60% of the predictability of the classical analogy approach—whereas the dynamical predictors are less explicative. These results open new directions of research that the classical analogy approach cannot handle. They show, for instance, that both dry sequences and strong rainfall sequences are associated with singular 500-hPa geopotential shapes acting as local attractors—a way of explaining the change in rainfall CDFs in a changing climate.

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Guillaume Evin, Benoit Hingray, Juliette Blanchet, Nicolas Eckert, Samuel Morin, and Deborah Verfaillie

Abstract

The quantification of uncertainty sources in ensembles of climate projections obtained from combinations of different scenarios and climate and impact models is a key issue in climate impact studies. The small size of the ensembles of simulation chains and their incomplete sampling of scenario and climate model combinations makes the analysis difficult. In the popular single-time ANOVA approach for instance, a precise estimate of internal variability requires multiple members for each simulation chain (e.g., each emission scenario–climate model combination), but multiple members are typically available for a few chains only. In most ensembles also, a precise partition of model uncertainty components is not possible because the matrix of available scenario/models combinations is incomplete (i.e., projections are missing for many scenario–model combinations). The method we present here, based on data augmentation and Bayesian techniques, overcomes such limitations and makes the statistical analysis possible for single-member and incomplete ensembles. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates. This approach is illustrated for projections of regional precipitation and temperature for four mountain massifs in France. It is applicable for any kind of ensemble of climate projections, including those produced from ad hoc impact models.

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