Climate and parameter sensitivity and induced uncertainties in carbon stock projections for European forests (using LPJ-GUESS 4.0)
(2022) In Geoscientific Model Development 15(16). p.6495-6519- Abstract
Understanding uncertainties and sensitivities of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyze sensitivities (change in model outputs per unit change in inputs) and uncertainties (changes in model outputs scaled to uncertainty in inputs) of vegetation dynamics under climate change, projected by a state-of-the-art dynamic vegetation model (LPJ-GUESS v4.0) across European forests (the species Picea abies, Fagus sylvatica and Pinus sylvestris), considering uncertainties of both model parameters and environmental drivers. We find that projected forest carbon fluxes are most sensitive to photosynthesis-, water-, and mortality-related parameters, while... (More)
Understanding uncertainties and sensitivities of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyze sensitivities (change in model outputs per unit change in inputs) and uncertainties (changes in model outputs scaled to uncertainty in inputs) of vegetation dynamics under climate change, projected by a state-of-the-art dynamic vegetation model (LPJ-GUESS v4.0) across European forests (the species Picea abies, Fagus sylvatica and Pinus sylvestris), considering uncertainties of both model parameters and environmental drivers. We find that projected forest carbon fluxes are most sensitive to photosynthesis-, water-, and mortality-related parameters, while predictive uncertainties are dominantly induced by environmental drivers and parameters related to water and mortality. The importance of environmental drivers for predictive uncertainty increases with increasing temperature. Moreover, most of the interactions of model inputs (environmental drivers and parameters) are between environmental drivers themselves or between parameters and environmental drivers. In conclusion, our study highlights the importance of environmental drivers not only as contributors to predictive uncertainty in their own right but also as modifiers of sensitivities and thus uncertainties in other ecosystem processes. Reducing uncertainty in mortality-related processes and accounting for environmental influence on processes should therefore be a focus in further model development.
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- author
- Oberpriller, Johannes ; Herschlein, Christine ; Anthoni, Peter LU ; Arneth, Almut LU ; Krause, Andreas ; Rammig, Anja LU ; Lindeskog, Mats LU ; Olin, Stefan LU and Hartig, Florian
- organization
- publishing date
- 2022-08-30
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Geoscientific Model Development
- volume
- 15
- issue
- 16
- pages
- 25 pages
- publisher
- Copernicus GmbH
- external identifiers
-
- scopus:85137845903
- ISSN
- 1991-959X
- DOI
- 10.5194/gmd-15-6495-2022
- language
- English
- LU publication?
- yes
- id
- 7b744595-8661-48e1-b1ba-7272e6acd1b2
- date added to LUP
- 2022-12-02 08:57:37
- date last changed
- 2022-12-02 08:57:37
@article{7b744595-8661-48e1-b1ba-7272e6acd1b2,
abstract = {{<p>Understanding uncertainties and sensitivities of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyze sensitivities (change in model outputs per unit change in inputs) and uncertainties (changes in model outputs scaled to uncertainty in inputs) of vegetation dynamics under climate change, projected by a state-of-the-art dynamic vegetation model (LPJ-GUESS v4.0) across European forests (the species Picea abies, Fagus sylvatica and Pinus sylvestris), considering uncertainties of both model parameters and environmental drivers. We find that projected forest carbon fluxes are most sensitive to photosynthesis-, water-, and mortality-related parameters, while predictive uncertainties are dominantly induced by environmental drivers and parameters related to water and mortality. The importance of environmental drivers for predictive uncertainty increases with increasing temperature. Moreover, most of the interactions of model inputs (environmental drivers and parameters) are between environmental drivers themselves or between parameters and environmental drivers. In conclusion, our study highlights the importance of environmental drivers not only as contributors to predictive uncertainty in their own right but also as modifiers of sensitivities and thus uncertainties in other ecosystem processes. Reducing uncertainty in mortality-related processes and accounting for environmental influence on processes should therefore be a focus in further model development.</p>}},
author = {{Oberpriller, Johannes and Herschlein, Christine and Anthoni, Peter and Arneth, Almut and Krause, Andreas and Rammig, Anja and Lindeskog, Mats and Olin, Stefan and Hartig, Florian}},
issn = {{1991-959X}},
language = {{eng}},
month = {{08}},
number = {{16}},
pages = {{6495--6519}},
publisher = {{Copernicus GmbH}},
series = {{Geoscientific Model Development}},
title = {{Climate and parameter sensitivity and induced uncertainties in carbon stock projections for European forests (using LPJ-GUESS 4.0)}},
url = {{http://dx.doi.org/10.5194/gmd-15-6495-2022}},
doi = {{10.5194/gmd-15-6495-2022}},
volume = {{15}},
year = {{2022}},
}