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Exploring climate model data

The EU CLUE and Land Use Scanner simulations

The EU CLUE and Land Use Scanner simulations for future land-use change over Europe

Goal: Delivery of climate model data from different scenario runs for the EU CLUE scanner model.

The EU CLUE and the Land Use Scanner models simulate (future) land-use change over Europe. Socio-economic and climatic changes are expected to alter the current land-use patterns in the Europe. In order to study these uncertain developments and propose adaptation and mitigation strategies to cope with the possible changes in the physical and societal environment a set of future scenarios is developed. These scenarios integrate possible socio-economic and climatic changes and are used in the Land Use Scanner model to simulate future land-use patterns. Based on these simulations sector-specific adaptation and mitigation measures can be developed in related research projects. Currently WorldClim data are used on a 10 km grid. Ensembles data (ECA&D) for current climate are used. Result data is loaded into a GIS system for further analysis.

Data needs

  • Vertical resolution: only surface layer is required
  • Spatial resolution: as high as possible (1x1 km), but lower resolution is not a problem (current model 100x100 km resolution is usable)
  • Temporal: decadal, yearly (1990, 2000, 2010) until 2030
  • Parameters:
    • Annual Mean Temperature
    • Mean Diurnal Range (Mean of monthly (max temp - min temp))
    • Max Temperature of Warmest Month
    • Min Temperature of Coldest Month
    • Annual Precipitation
    • Precipitation of Wettest Month
    • Precipitation of Driest Month
    • Total precipitation for crop/growing season
  • Projection: Lambert
  • Area: Europe (minimum is 27 EU member states
  • Data format: ASCII Grid (GIS format, usable in ARCGIS)
  • Data delivery: INSPIRE compliant OGC services

How to obtain the data

For this use case annual means have to be calculated and indices have to be derived. This functionality is not available in the current release. In one of the next releases, this functionality should be available.

Requested Flexibility

It must be possible to choose the interpolation method used. Data should be provided in Lambert projection. It must be possible to use services to process and download the data. Interpolation methods: nearest neighbor is not the suitable interpolation method for the EU CLUE scanner, this because of the sensitivity of the model for discontinuous transitions. It is better to use bilinear interpolation to transform the intervals to smoother data set. Resolution is not a mayor issue, as the spatial variability in the data is (probably) small on short distances. The effect of different scenarios will be studied.

Sources of uncertainty

Using different scenarios and using model data for both current situation (1990-2010) and future prediction (2030). It is useful to also use observational data for the current situation (1990-2010) for studying CLUE model behavior, using observational data and climate model data respectively. This to obtain insight in the uncertainty when using the CLUE model for future predictions and to gain understanding on the properties of climate model data. Note that using climate model data or observational data can lead to different predictions. This is because climate models simulate different realizations of the current conditions, which can be different from the observed data. The CLUE scanner needs to know bias in the model data and also if and which bias corrections are made.

Read more on uncertainties


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