Assessment of the probability of extreme weather events and their potential effects in large conurbations

The likelihood of occurrence of extreme high-temperature run events is estimated for different values of the event intensity and persistence from very long synthetic time series of daily maximum temperatures generated by Monte Carlo simulations using a first-order autoregressive or Markov model. A theoretical analysis reveals a higher relative sensitivity of the simulated extreme event probabilities to changes in the variability of climate than to changes in its mean state. Moreover, this sensitivity relatively increases at a nonlinear rate the more extreme the event. The developed probabilistic model is applied in order to derive local scenarios of extreme high-temperature run events for a large conurbation like the city of Berlin assuming both arbitrary hypothetical and physically based new climate states described by changes in the model parameters (e.g. the mean, the standard deviation and the first-order autocorrelation of the daily maximum temperature time series). As a consequence of a 1.7°C increase in the mean as well as a 19% increase in the temperature variability in July as predicted by the climate model ECHAM_1/LSG assuming an unrestricted future increase in the global atmospheric concentration of climate relevant greenhouse gases according to the IPCC Scenario A (“Business as usual”) the intensity as well as the persistence of extreme high-temperature run events will rise considerably up to the end of the next century. In particular, intense hot spells characterized by at least five consecutive daily maximum temperatures equaling or exceeding 33°C are expected to occur every eight years under the new climate conditions compared to a current repetition time of about 47 years. The potential environmental effects might be a significant increase in the heat-stress-related morbidity and mortality rate, an aggravation of the summer smog situation and a destabilization of the urban ecosystems.