Attributing changes in Hurricane Dorian’s hazards to climate change

Kevin A Reed, Stony Brook University, School of Marine and Atmospheric Sciences, Stony Brook, United States, Michael F Wehner, Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, United States, Alyssa M Stansfield, Stony Brook University, School of Marine and Atmospheric Science, Stony Brook, NY, United States and Colin M. Zarzycki, Pennsylvania State University, Department of Meteorology and Atmospheric Science, University Park, United States
Changes in extreme events, like Hurricane Dorian, are a primary way in which climate change directly impacts society. In recent years, significant advances have been made in attribution frameworks to help quantify climate change impacts on individual tropical cyclones. Here we present the results of the conditional attribution methodology, previously developed and tested in the Community Earth System Model, as applied to Hurricane Dorian. In particular, a variable-resolution configuration of the Community Atmosphere Model (CAM) with a high-resolution nest over the North Atlantic Ocean is used to quantify the impact of human-induced climate change on the characteristics of Hurricane Dorian. Short 7-day ensemble hindcasts are initialized at various times in advance of Hurricane Dorian’s landfall in the Bahamas. The CAM hindcasts are initialized with atmospheric and ocean surface analyses from NOAA’s Global Forecast System, representing the “actual” ensemble, as well as with the climate change signal removed from the observed air temperature, specific humidity, and sea surface temperature initial conditions, representing the “counterfactual” ensemble. A comparison and statistical analysis of the actual and counterfactual ensembles allows for an assessment of the impact of climate change on Hurricane Dorian’s rainfall, intensity and storm size with direct consequences for the Bahamas and the East Coast of the U.S. This work is part of a growing effort in the scientific community to refine the application of attribution frameworks for quantification of the impact of climate change on devastating extreme climate and weather events.