Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

Inconsistent recognition of uncertainty in studies of climate change impacts on forests


Petr,  M.
External Organizations;

Vacchiano,  G.
External Organizations;

Thom,  D.
External Organizations;

Mairota,  P.
External Organizations;

Kautz,  M.
External Organizations;

Goncalves,  L. M. S.
External Organizations;

Yousefpour,  R.
External Organizations;

Kaloudis,  S.
External Organizations;


Reyer,  Christopher P. O.
Potsdam Institute for Climate Impact Research;

External Ressource
No external resources are shared
Fulltext (public)

(Publisher version), 2MB

Supplementary Material (public)
There is no public supplementary material available

Petr, M., Vacchiano, G., Thom, D., Mairota, P., Kautz, M., Goncalves, L. M. S., Yousefpour, R., Kaloudis, S., Reyer, C. P. O. (2019): Inconsistent recognition of uncertainty in studies of climate change impacts on forests. - Environmental Research Letters, 14, 11, 113003.

Cite as: https://publications.pik-potsdam.de/pubman/item/item_23330
Background. Uncertainty about climate change impacts on forests can hinder mitigation and adaptation actions. Scientific enquiry typically involves assessments of uncertainties, yet different uncertainty components emerge in different studies. Consequently, inconsistent understanding of uncertainty among different climate impact studies (from the impact analysis to implementing solutions) can be an additional reason for delaying action. In this review we (a) expanded existing uncertainty assessment frameworks into one harmonised framework for characterizing uncertainty, (b) used this framework to identify and classify uncertainties in climate change impacts studies on forests, and (c) summarised the uncertainty assessment methods applied in those studies. Methods. We systematically reviewed climate change impact studies published between 1994 and 2016. We separated these studies into those generating information about climate change impacts on forests using models –'modelling studies', and those that used this information to design management actions—'decision-making studies'. We classified uncertainty across three dimensions: nature, level, and location, which can be further categorised into specific uncertainty types. Results. We found that different uncertainties prevail in modelling versus decision-making studies. Epistemic uncertainty is the most common nature of uncertainty covered by both types of studies, whereas ambiguity plays a pronounced role only in decision-making studies. Modelling studies equally investigate all levels of uncertainty, whereas decision-making studies mainly address scenario uncertainty and recognised ignorance. Finally, the main location of uncertainty for both modelling and decision-making studies is within the driving forces—representing, e.g. socioeconomic or policy changes. The most frequently used methods to assess uncertainty are expert elicitation, sensitivity and scenario analysis, but a full suite of methods exists that seems currently underutilized. Discussion & Synthesis. The misalignment of uncertainty types addressed by modelling and decision-making studies may complicate adaptation actions early in the implementation pathway. Furthermore, these differences can be a potential barrier for communicating research findings to decision-makers.