of models to adapt the management of protected areas with regard to climate
change, we follow the course of this model cascade and make various underlying
assumptions and models transparent.
5.2.1 Climate Models
Some prior steps are necessary for regional climate change impact projections.
A big challenge for earth system analysis was to understand the interplay between
human activities, greenhouse gases, and the climate system of the earth (IPCC
2007). The higher the concentration of greenhouse gases, the higher the energy of
the atmosphere is. This results in higher global mean temperatures and subsequently increased global evaporation. Here, the model cascade starts (Fig. 5.1).
Each model type builds at least partly on physically based assumptions, hypotheses
and input data from prior cascade results (Table 5.1). In the Special Emission
Scenarios (SRES; Nakic ´enovic ´ and Swart 2000) projections about the future development of earth’s population with regard to population numbers, economic development, energy sources etc. were developed. According to these scenarios (e.g. B1,
A2 etc.) concentrations of greenhouse gases are projected. This provides input for
global circulation or climate models (GCMs). These global models normally work
on a global grid (e.g. at the resolution of 50 Â 50 km
2 ).
For a park manager, a 50 Â 50 km
2 resolution is far from being useful for
management adaptation. For example, in mountainous regions with climate variables differing at a small scale a resolution of several hundred metres is required to
be helpful. Currently, there are two alternative model approaches to derive more
regional and local resolutions. It is possible to downscale the results from global
models dynamically to a resolution of 10 Â 10 km (0,08
), an option chosen by the
Max Planck Institute for Meteorology in Hamburg with the climate model REMO
(Jacob and Podzun 1997). Alternatively, statistical regional climate models scale
down trends from global climate models based on data from local weather stations
and only include the global temperature trend as a parameter. This is how regional
climate models such as STAR or WettReg work (Orlowsky et al. 2008). While the
latter reflect the local conditions more precisely, the former has the advantage of
also including new climatic conditions and long-term developments.
However, most park managers focus on living organisms, mainly plants, where
temperature and precipitation alone are not sufficient parameters to identify climate
change impacts. A more useful integrated indicator is the climatic water balance
(CWB). The CWB expresses the difference between precipitation and potential
evaporation. In the framework of the HABIT-CHANGE project, climate
change projections have been provided for all the investigation areas (Stagl et al.
submitted). In Fig. 5.2 the results are displayed via boxplots for two selected
investigation areas for three time periods: (a) For the Natural Park Bucegi
(Romania) the climate models indicate, despite the existing inter-model uncertainties, a clear trend towards a reduction of potential water availability for the
66
K. Vohland et al.
change, we follow the course of this model cascade and make various underlying
assumptions and models transparent.
5.2.1 Climate Models
Some prior steps are necessary for regional climate change impact projections.
A big challenge for earth system analysis was to understand the interplay between
human activities, greenhouse gases, and the climate system of the earth (IPCC
2007). The higher the concentration of greenhouse gases, the higher the energy of
the atmosphere is. This results in higher global mean temperatures and subsequently increased global evaporation. Here, the model cascade starts (Fig. 5.1).
Each model type builds at least partly on physically based assumptions, hypotheses
and input data from prior cascade results (Table 5.1). In the Special Emission
Scenarios (SRES; Nakic ´enovic ´ and Swart 2000) projections about the future development of earth’s population with regard to population numbers, economic development, energy sources etc. were developed. According to these scenarios (e.g. B1,
A2 etc.) concentrations of greenhouse gases are projected. This provides input for
global circulation or climate models (GCMs). These global models normally work
on a global grid (e.g. at the resolution of 50 Â 50 km
2 ).
For a park manager, a 50 Â 50 km
2 resolution is far from being useful for
management adaptation. For example, in mountainous regions with climate variables differing at a small scale a resolution of several hundred metres is required to
be helpful. Currently, there are two alternative model approaches to derive more
regional and local resolutions. It is possible to downscale the results from global
models dynamically to a resolution of 10 Â 10 km (0,08
), an option chosen by the
Max Planck Institute for Meteorology in Hamburg with the climate model REMO
(Jacob and Podzun 1997). Alternatively, statistical regional climate models scale
down trends from global climate models based on data from local weather stations
and only include the global temperature trend as a parameter. This is how regional
climate models such as STAR or WettReg work (Orlowsky et al. 2008). While the
latter reflect the local conditions more precisely, the former has the advantage of
also including new climatic conditions and long-term developments.
However, most park managers focus on living organisms, mainly plants, where
temperature and precipitation alone are not sufficient parameters to identify climate
change impacts. A more useful integrated indicator is the climatic water balance
(CWB). The CWB expresses the difference between precipitation and potential
evaporation. In the framework of the HABIT-CHANGE project, climate
change projections have been provided for all the investigation areas (Stagl et al.
submitted). In Fig. 5.2 the results are displayed via boxplots for two selected
investigation areas for three time periods: (a) For the Natural Park Bucegi
(Romania) the climate models indicate, despite the existing inter-model uncertainties, a clear trend towards a reduction of potential water availability for the
66
K. Vohland et al.