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Exploring climate model data

Assessment of climate impact on a red-listed moss (Bryophyta) species


Assess the impacts of climate change on a vulnerable species at a specific forest site.
This Use Case is not functionally implemented in this portal.


Forest production alters and puts pressure on forest ecosystems. To monitor the status of forest ecosystems a number of vulnerable species are used as indicators. In addition to the pressure from the forest production as such, there might be additional pressure induced from climate changes. To assess the possible climate change impact on the sporophyte stage of this particular red-listed indicator species an impact model driven by climate data had been developed.


Lars Bärring, SMHI Rossby Centre, Sweden.


Climate scientists (SMHI Rossby Centre), ecological scientists (Swedish University of Agricultural Sciences).

Data needs

  • Observed meteorological data from the closely located research station. The impact model requires monthly/seasonal temperature and precipitation, but for the calibration/statistical downscaling daily data is needed.
  • An ensemble of regional climate model (RCM) data statistically calibrated/downscaled to the meteorological station closest to the field site.

Typical course of events

  • Initial discussions, selection of a test regional climate scenario for driving the impact model. Assessment of first results.
  • Assessment of differences among the regional climate change simulations with respect to the reference ERA-40 driven simulation. Assessment of the statistical calibration/downscaling method with respect to the station data. Downscaling the extracted model data using the DBS method (Yang et al., 2010a,b).
  • Discussion of different ways to use the ensemble to gain insight into various aspects and sources of uncertainty.
  • Running the impact model using the downscaled scenario data.
  • Writing reports for scientific journals and thesis.

Support to users

As a research collaboration user support is an integral part of the collaboration.

Requested flexibility

Because of the interactive nature of the whole activity substantial flexibility was built in the process.

Alternative course of events


References for the Use Case

  • Yang, W., Bárdossy, A. and Caspary, H-J. 2010a: Downscaling daily precipitation time series using a combined circulation- and regression-based approach. Theoretical and Applied Climatology, 10.1007/s00704-010-0272-0.
  • Yang, W., Andreásson, J., Graham, LP., Olsson, J., Rosberg, J. and Wetterhall, F. 2010b: Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies. Hydrology Research, 41.3–4, 211-228.
  • Ruete, A., Yang, W., Bärring, L., Stenseth, N.C. & Snäll, T., 2012: Disentangling effects of uncertainties on population projections: climate change impact on an epixylic bryophyte. Proceedings of the Royal Society B. Published online 28 March 2012. doi: 10.1098/rspb.2012.0428.
  • Ruete, A., 2012: Population viability analysis under environmental change: development of Bayesian tools. Acta Universitatis agriculturae Sueciae, (ISSN: 1652-6880); 2012:22. Doctoral dissertation, Department of Ecology, Swedish University of Agricultural Sciences. Uppsala, Swden. 62 pp.

File format(s)

NetCDF, ascii

Software used

  • CDO for data extraction.
  • R for statistical analyses and graphical display.



Sources and cascade of uncertainty

  • Uncertainties on emission scenarios are dealt with by using different emission scenarios.
  • Uncertainties related to choice of GCM are dealt with by using several GCMs.
  • Uncertainties related to initial conditions are dealt with by using one GCM runs only differing by their initial conditions.
  • Uncertainties related to the RCM formulation is to some extent dealt with by relating the performance of the employed RCM (RCA3) to other RCMs that were participating in the ENSEMBLE project.

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.