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Exploring climate model data

Metaphor: Spatial Population Analysis model

Delivery of climate model data from different scenario runs for the Metaphor model.

The Metaphor model is a tool for Spatial Population Analysis. With the Metaphor model the survival probability of a certain species in a certain area can be examined. The model provides insight in which parameters have the strongest effect on population viability, which is useful to find the most effective way to improve the population viability. This can be achieved by the comparison of different scenarios. The main processes in spatial population dynamics are reproduction, mortality and dispersal. METAPHOR simulates these processes over time in order to estimate long term viability of a fragmented population (a metapopulation). METAPHOR can calculate effects on metapopulation processes due to changes in landscape patterns caused by habitat redesign or management.

Currently daily averages from the KNMI weather stations are used as input data. These daily averages are shuffled in order to generate multiple generic datasets. These daily averages are also adjusted and corrected to represent a future climate scenario. One of the drawbacks of this approach is that specific events may occur multiple times, because they are still resembled in the data even after shuffling and adjustments.

Climate model input data could be a valuable addition to the Metaphor model. This would allow the model to run on predefined climate scenarios. Required input parameters for the Metaphor model are: daily precipitation, daily precipitation duration, daily temperature, daily maximum temperature and cloud cover. Daily averaged, high resolution model data from several ensemble runs is desired.

Data needs

  • Data format: ASCII files with data for certain locations. Currently the model accepts ASCII files which contains values for certain dates at a single location.
  • Different climate scenarios and multiple ensembles
  • Area and location: De Veluwe in the Netherlands
  • Projection: EPSG: 4326 (WGS84 latitude longitude)
  • Resolution: Current resolution of 100 km is already suitable, but a higher spatial resolution in the order of 10 km is desired in the long term.
  • Data is needed on local scale (habitat of species)
  • Weather extremes are important
  • Period: 1980-2100
  • Temporal resolution: daily averaged data
  • Parameters: temperature, precipitation, precipitation duration, cloud cover, evaporation, surface radiation
  • All data at the earths surface (one layer)
  • Data delivery: FTP

How to obtain the data

  1. Login and go to advanced search
  2. Select Variable 'pr' (for precipitation)
  3. Select Frequency 'day' (for daily sampling*)
  4. Select Realm 'Atmosphere'
  5. Select '1980' in from field and '2100' in to field
  6. Select files and add them to the basket
  7. Go to the basket
  8. In the basket, select a file, click on 'view'. This will lauch the ADAGUC viewer
  9. In the viewer select the parameter 'hum' (left upper side of the viewer). This will display a global map with the variables
  10. Select the magnifying glass (top of viewer) to zoom in to the Netherlands
  11. Select 'menu' (top of viewer) to select 'Retrieve data using WCS'. This will launch a window
  12. In this window:
    - Adjust the 'Format settings' to AAIGRID
    - Adjust the 'Projection settings' to EPSG 28992 (which is rijksdriehoeksstelsel)
    - Adjust the 'Bounding Box' settings by clicking the 'Get current map view bounding box'
    - Now download the data by clicking on the 'get coverage' button (right corner)
  13. Repeat this procedure for each file in the basket
  14. Repeat this procedure for precipitation duration, cloud cover, evaporation and surface radiation by selecting one of these variables in the first step.

*Daily sampling is available. Daily averaged data needs to be calculated using Transformation services, which are not available yet.

Extra steps if transformation becomes available: From step 6 go to 'Transformation' menu, select the averaging transformation, select input data (note: this is data in your basket), set the averaging parameters and run the transformation. When the transformation is finished, the result data is added to your basket. Then continue at step 7.

Sources of Uncertainty

The DataCenter will work internally on regular grids, while the source data is in a Gaussian reduced grid. The input gaussian reduced grids are converted to regular grids before data processing can take place. The Ecologists should always be able to order the original Gaussian reduced grids; but data transformation will always be applied on regular grids.

Requested flexibility

The DataCenter will provide the methods for data transformation. For the Ecologist this will be averaging to yearly averages and transforming the data to the Ecologists favourite format. On the long term higher resolution data (10x10 km) is requested.




The ENES3 project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 824084.