A weibull approach for improving climate model projections of tropical cyclone wind-speed distributions
Journal of Climate
American Meteorological Society
Reliable estimates of future changes in extreme weather phenomena, such as tropical cyclone maximum wind speeds, are critical for climate change impact assessments and the development of appropriate adaptation strategies. However, global and regional climate model outputs are often too coarse for direct use in these applications, with variables such as wind speed having truncated probability distributions compared to those of observations. This poses two problems: How canmodel-simulated variables best be adjusted to make themmore realistic? And how can such adjustments be used to make more reliable predictions of future changes in their distribution? This study investigates North Atlantic tropical cyclone maximum wind speeds from observations (1950- 2010) and regional climate model simulations (1995-2005 and 2045-55 at 12- and 36-km spatial resolutions). The wind speed distributions in these datasets are well represented by the Weibull distribution, albeit with different scale and shape parameters. A power-law transfer function is used to recalibrate the Weibull variables and obtain future projections of wind speeds. Two different strategies, bias correction and change factor, are tested by using 36-km model data to predict future 12-km model data (pseudo-observations). The strategies are also applied to the observations to obtain likely predictions of the future distributions of wind speeds. The strategies yield similar predictions of likely changes in the fraction of events within Saffir-Simpson categories-for example, an increase from 21% (1995-2005) to 27%-37% (2045-55) for category 3 or above events and an increase from 1.6% (1995- 2005) to 2.8%-9.8% (2045-55) for category 5 events. © 2014 American Meteorological Society.
Acknowledgments. Support for this work was provided by theWillis Research Network, the Research Program to Secure Energy for America, NSF EASM Grant S1048841, and the NCARWeather and Climate Assessment Science Program. We thank Sherrie Fredrick for extracting data, and Cindy Bruyère, James Done, and Ben Youngman for productive discussions that enhanced this research. We also thank Dr. Adam Monahan and one anonymous reviewer for their insightful comments and suggestions.
This is the final version of the article. Available from the publisher via the DOI in this record.
Open Access Article
Vol. 27, pp. 6119 - 6133