Atlantic hurricanes and associated insurance loss potentials in future climate scenarios: limitations of high-resolution AGCM simulations

• Main Authors: Thomas F. Stocker, Marc Wüest, David N. Bresch, Akio Kitoh, Hiroyuki Murakami, Sabine Kleppek, Christoph C. Raible
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2012-01-01
• Series: Tellus: Series A, Dynamic Meteorology and Oceanography
• Subjects: Loss potential; hurricanes; future scenarios; hurricane identification method
• Online Access: http://www.tellusa.net/index.php/tellusa/article/view/15672/pdf
• ISSN: 0280-6495; 1600-0870

Potential future changes in tropical cyclone (TC) characteristics are among the more serious regional threats of global climate change. Therefore, a better understanding of how anthropogenic climate change may affect TCs and how these changes translate in socio-economic impacts is required. Here, we apply a TC detection and tracking method that was developed for ERA-40 data to time-slice experiments of two atmospheric general circulation models, namely the fifth version of the European Centre model of Hamburg model (MPI, Hamburg, Germany, T213) and the Japan Meteorological Agency/ Meteorological research Institute model (MRI, Tsukuba city, Japan, TL959). For each model, two climate simulations are available: a control simulation for present-day conditions to evaluate the model against observations, and a scenario simulation to assess future changes. The evaluation of the control simulations shows that the number of intense storms is underestimated due to the model resolution. To overcome this deficiency, simulated cyclone intensities are scaled to the best track data leading to a better representation of the TC intensities. Both models project an increased number of major hurricanes and modified trajectories in their scenario simulations. These changes have an effect on the projected loss potentials. However, these state-of-the-art models still yield contradicting results, and therefore they are not yet suitable to provide robust estimates of losses due to uncertainties in simulated hurricane intensity, location and frequency.