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

Reyer, Christopher P.O.; Flechsig, Michael; Lasch-Born, Petra; Van Oijen, Marcel. 2016 Integrating parameter uncertainty of a process-based model in assessments of climate change effects on forest productivity. Climatic Change, 137 (3). 395-409. https://doi.org/10.1007/s10584-016-1694-1

Before downloading, please read NORA policies.

Preview

Text N514026PP.pdf - Accepted Version Download (1MB) | Preview

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1007/s10584-016-1694-1
UKCEH and CEH Sections/Science Areas:	Dise
ISSN:	0165-0009
Additional Keywords:	4C, Bayesian calibration, climate models, Europe, Monte Carlo analysis, National Forest Inventory data
NORA Subject Terms:	Ecology and Environment Meteorology and Climatology Atmospheric Sciences
Date made live:	26 Jul 2016 09:22 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/514026

Actions (login required)

View Item

Document Downloads

More statistics for this item...