Climatic Change

, Volume 137, Issue 3–4, pp 395–409 | Cite as

Integrating parameter uncertainty of a process-based model in assessments of climate change effects on forest productivity

  • Christopher P. O. ReyerEmail author
  • Michael Flechsig
  • Petra Lasch-Born
  • Marcel van Oijen


The parameter uncertainty of process-based models has received little attention in climate change impact studies. This paper aims to integrate parameter uncertainty into simulations of climate change impacts on forest net primary productivity (NPP). We used either prior (uncalibrated) or posterior (calibrated using Bayesian calibration) parameter variations to express parameter uncertainty, and we assessed the effect of parameter uncertainty on projections of the process-based model 4C in Scots pine (Pinus sylvestris) stands under climate change. We compared the uncertainty induced by differences between climate models with the uncertainty induced by parameter variability and climate models together. The results show that the uncertainty of simulated changes in NPP induced by climate model and parameter uncertainty is substantially higher than the uncertainty of NPP changes induced by climate model uncertainty alone. That said, the direction of NPP change is mostly consistent between the simulations using the standard parameter setting of 4C and the majority of the simulations including parameter uncertainty. Climate change impact studies that do not consider parameter uncertainty may therefore be appropriate for projecting the direction of change, but not for quantifying the exact degree of change, especially if parameter combinations are selected that are particularly climate sensitive. We conclude that if a key objective in climate change impact research is to quantify uncertainty, parameter uncertainty as a major factor driving the degree of uncertainty of projections should be included.


Parameter Uncertainty National Forest Inventory Input Uncertainty Calibration Dataset Climate Change Impact Study 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work would not have been possible without the data and support provided by Werner Rammer (Austria), Gaby Deckmyn (Belgium), Andres Kiviste (Estonia), Annikki Mäkelä and Sanna Härkönen (Finland). We are grateful to the support of COST Actions FP0603 and FP1304 as well as the FP7 project MOTIVE (grant agreement no. 226544). We further acknowledge the help and support of our colleagues Felicitas Suckow, Tobias Pilz, Martin Gutsch, Sebastian Ostberg and Stefan Lange.

Author contribution

CR designed research, carried out simulations, analysed data and wrote the paper. MF, MvO and PLB supervised the whole process, helped with implementation of BC algorithms and interpretation of the data and contributed to paper writing.

Supplementary material

10584_2016_1694_MOESM1_ESM.pdf (1.7 mb)
ESM 1 (PDF 1.71 mb)


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Christopher P. O. Reyer
    • 1
    • 2
    Email author
  • Michael Flechsig
    • 1
  • Petra Lasch-Born
    • 1
  • Marcel van Oijen
    • 3
  1. 1.Potsdam Institute for Climate Impact ResearchPotsdamGermany
  2. 2.Department of GeographyHumboldt University BerlinBerlinGermany
  3. 3.Centre for Ecology and HydrologyCEH-EdinburghPenicuikUK

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