UNSEEN trends: Detecting decadal changes in 100-year precipitation extremes

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Authors

Timo Kelder , Malte Muller, Louise J. Slater, Tim Marjoribanks, Robert L. Wilby, Christel Prudhomme, Patrik Bohlinger, Laura Ferranti, Thomas Nipen

Abstract

Sample sizes of observed climate extremes are typically too small to reliably constrain non-stationary behaviour. To facilitate detection of non-stationarities in 100-year precipitation values over a short period of 35 years (1981-2015), we apply the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach, by pooling ensemble members and lead times from the ECMWF seasonal prediction system SEAS5. We generate a 3500-year UNSEEN dataset of autumn 3-day extreme precipitation events across Western Norway and Svalbard. The UNSEEN ensemble shows that an event of 1.5 times the magnitude of the most severe flood episode recorded in Western Norway can arise with a return period of ~2000 years. Applying the novel UNSEEN-trends approach, we demonstrate that for Svalbard the 100-year event in 1981 could be expected to occur with a return period of around 40 years in 2015. These new insights have important implications for current design-level practices and for understanding the underlying causes of non-stationarities.

DOI

https://doi.org/10.31223/osf.io/hyxeq

Subjects

Atmospheric Sciences, Civil and Environmental Engineering, Climate, Earth Sciences, Engineering, Environmental Sciences, Hydrology, Meteorology, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics

Keywords

Precipitation, climate change, Big Data Analysis, ECMWF SEAS5, Large Ensemble Simulations, Trend Analysis

Dates

Published: 2020-05-26 01:09

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License

CC BY Attribution 4.0 International

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