Sep 6, 2016

Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change

by
Wiel, Karin van der, Kapnick, Sarah B., Oldenborgh, Geert Jan van, Whan, Kirien, Philip, Sjoukje, Vecchi, Gabriel A., Singh, Roop K., Arrighi, Julie, Cullen, Heidi
,
Hydrology and Earth System Sciences Discussions
  • Performs a rapid attribution analysis in real-time using the best readily available observational data and high-resolution global climate model simulations to provide the necessary analysis to understand the potential role of anthropogenic climate change in the Louisiana flood of August 2016
  • Aims to show the possibility of performing rapid attribution studies when both observational and model data, and analysis methods are readily available upon the start
  • Presents a first estimate of how anthropogenic climate change has affected the likelihood of a comparable extreme precipitation event in the Central U.S. Gulf Coast
  • Stats that while the flooding event of interest triggering this study occurred in south Louisiana, for the purposes of our analysis, the study defines an extreme precipitation event by taking the spatial maximum of annual 3-day inland maximum precipitation over the region: 29–31º N, 85–95º W—referred to as the Central US Gulf Coast
  • Finds that the observed local return time of the 12–14 August precipitation event in 2016 is about 550 years (95 % confidence interval (C.I.): 450–1450)
  • Finds the probability for an event like this to happen anywhere in the region is presently 1 in 30 years (C.I. 11–110)
  • Finds that these probabilities and the intensity of extreme precipitation events of this return time have increased since 1900
  • States that a Central US Gulf Coast extreme precipitation event has effectively become more likely in 2016 than it was in 1900
  • Finds the global climate models tell a similar story, with the regional probability of 3-day extreme precipitation increasing due to anthropogenic climate change by a factor of more than a factor 1.4 in the most accurate analyses