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  Reducing uncertainty of high-latitude ecosystem models through identification of key parameters

Mevenkamp, H., Wunderling, N., Bhatt, U., Carman, T., Donges, J. F., Genet, H., Serbin, S., Winkelmann, R., Euskirchen, E. S. (2023): Reducing uncertainty of high-latitude ecosystem models through identification of key parameters. - Environmental Research Letters, 18, 084032.
https://doi.org/10.1088/1748-9326/ace637

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 Creators:
Mevenkamp, Hannah1, Author
Wunderling, Nico2, Author              
Bhatt, Uma1, Author
Carman, Tobey1, Author
Donges, Jonathan Friedemann2, Author              
Genet, Helene1, Author
Serbin, Shawn1, Author
Winkelmann, Ricarda2, Author              
Euskirchen, Eugenie Susanne1, Author
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Climate change is having significant impacts on Earth's ecosystems and carbon budgets, and in the Arctic may drive a shift from an historic carbon sink to a source. Large uncertainties in terrestrial biosphere models (TBMs) used to forecast Arctic changes demonstrate the challenges of determining the timing and extent of this possible switch. This spread in model predictions can limit the ability of TBMs to guide management and policy decisions. One of the most influential sources of model uncertainty is model parameterization. Parameter uncertainty results in part from a mismatch between available data in databases and model needs. We identify that mismatch for three TBMs, DVM-DOS-TEM, SIPNET and ED2, and four databases with information on Arctic and boreal above- and belowground traits that may be applied to model parametrization. However, focusing solely on such data gaps can introduce biases towards simple models and ignores structural model uncertainty, another main source for model uncertainty. Therefore, we develop a causal loop diagram (CLD) of the Arctic and boreal ecosystem that includes unquantified, and thus unmodeled, processes. We map model parameters to processes in the CLD and assess parameter vulnerability via the internal network structure. One important substructure, feed forward loops (FFLs), describe processes that are linked both directly and indirectly. When the model parameters are data-informed, these indirect processes might be implicitly included in the model, but if not, they have the potential to introduce significant model uncertainty. We find that the parameters describing the impact of local temperature on microbial activity are associated with a particularly high number of FFLs but are not constrained well by existing data. By employing ecological models of varying complexity, databases, and network methods, we identify the key parameters responsible for limited model accuracy. They should be prioritized for future data sampling to reduce model uncertainty.

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Language(s): eng - English
 Dates: 2023-06-302023-08-032023-08-03
 Publication Status: Finally published
 Pages: 15
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: PIKDOMAIN: RD1 - Earth System Analysis
MDB-ID: pending
Organisational keyword: FutureLab - Earth Resilience in the Anthropocene
Research topic keyword: Ice
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Tipping Elements
Regional keyword: Arctic & Antarctica
Regional keyword: North America
Model / method: Nonlinear Data Analysis
OATYPE: Gold Open Access
DOI: 10.1088/1748-9326/ace637
 Degree: -

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Title: Environmental Research Letters
Source Genre: Journal, SCI, Scopus, p3, oa
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Publ. Info: -
Pages: - Volume / Issue: 18 Sequence Number: 084032 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/150326
Publisher: IOP Publishing