Abstract
Evaluating the effect of adaptation and mitigation measures is important for urban
development strategies. This can be achieved using high resolution numerical models.
However, they are computationally expensive, thus simulating a 30-year climate period is
challenging. An approach can be to simulate only a subset of days from the 30 years.
Identifying the number of days which are sufficient to represent the urban climate is the
aim of this presentation.
The presented statistical dynamical downscaling method is applied to simulate the urban
climate of Hamburg. It utilises 30-year time series from 27 weather stations in Northern
Germany and The Netherlands. For some meteorological quantities measured at these
stations, the frequency distributions have been analysed. These are compared with
artificial frequency distributions built with bootstrapping and a lower number of days. For
comparing these distributions, a skill score following Perkins et al. (2007) is further
developed, now taking into account the relationship between the quantities. The results of
this statistical dynamical downscaling method indicate that the statistics of the urban
climate of Hamburg can be simulated with a much lower number of days than the 30-year
time series.
Perkins, S. A., A. J. Pitman, N. J. Holbrook, J. McAneney (2007): Evaluation of the AR4
climate models simulated daily maximum temperature, minimum temperature and
precipitation over Australia using probability density functions, Journal of climate, 20,
4356-4376
