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final standard error of spatial modeling of air temperature in the case of the GAR
study example. Similarly, it was shown by many studies that inhomogenities can
even exceed the climate change signal in climate time series (see e.g. [AUE 07]). It
is quite easy to understand from these findings that treatment of data homogenity is
essential in the analysis of spatial or temporal variability in climate data.
Although adjustment of errors originating from different means calculations can
be performed quite easily in the case of longer time series from automatic weather
stations, adjustment of inhomogenity originating from e.g. urbanization effects of
villages is not that simple. Urbanization does not cause a sudden break in series but
instead a gradual inhomogenity trend (Figure 1.6). In the case of homogenization of
the urbanization effect, it is very useful to collect information on changing building
density and changing land-use.
Figure 1.6. Time series of annual mean urban temperature excess (relative to rural mean
1951 to 1995) based on height-reduced temperature records. The station in the densely
built-up area shows a stable temperature excess against the rural surroundings,
whereas the trend of temperature excess at the station in the urban development
area is 0.18°C per decade. Data source [BOH 98]
Another inhomogenity in climate networks results from the change of sensors of
the same type or different types. The increasing number of automatic weather
stations causes such a systematic shift of sensors. Parallel measurements with both
the old and the new sensor correctly merge the datasets of different sensors.
However, such parallel measurements are not performed on a regular basis, and
even if parallel measurements are undertaken, they are quite often undertaken over a
very short period. An example of inhomogenity from different sensors is shown in
Figure 1.7 for measurement of sunshine duration in Austria, replacing the Campbell-
Stokes sunshine autograph with the Haenni-Solar sensor.
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