Reducing uncertainty of high-latitude ecosystem models through identification of key parameters
Abstract
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),more »
- Publication Date:
- Research Org.:
- Brookhaven National Laboratory (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1993662
- Alternate Identifier(s):
- OSTI ID: 1996608
- Report Number(s):
- BNL-224658-2023-JAAM
Journal ID: ISSN 1748-9326
- Grant/Contract Number:
- SC0012704; NGEE Arctic
- Resource Type:
- Published Article
- Journal Name:
- Environmental Research Letters
- Additional Journal Information:
- Journal Name: Environmental Research Letters Journal Volume: 18 Journal Issue: 8; Journal ID: ISSN 1748-9326
- Publisher:
- IOP Publishing
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; complex networks; causal loop diagram; model uncertainty; Arctic ecosystem; ecological databases
Citation Formats
Mevenkamp, Hannah, Wunderling, Nico, Bhatt, Uma, Carman, Tobey, Friedemann Donges, Jonathan, Genet, Helene, Serbin, Shawn, Winkelmann, Ricarda, and Susanne Euskirchen, Eugenie. Reducing uncertainty of high-latitude ecosystem models through identification of key parameters. United Kingdom: N. p., 2023.
Web. doi:10.1088/1748-9326/ace637.
Mevenkamp, Hannah, Wunderling, Nico, Bhatt, Uma, Carman, Tobey, Friedemann Donges, Jonathan, Genet, Helene, Serbin, Shawn, Winkelmann, Ricarda, & Susanne Euskirchen, Eugenie. Reducing uncertainty of high-latitude ecosystem models through identification of key parameters. United Kingdom. https://doi.org/10.1088/1748-9326/ace637
Mevenkamp, Hannah, Wunderling, Nico, Bhatt, Uma, Carman, Tobey, Friedemann Donges, Jonathan, Genet, Helene, Serbin, Shawn, Winkelmann, Ricarda, and Susanne Euskirchen, Eugenie. Thu .
"Reducing uncertainty of high-latitude ecosystem models through identification of key parameters". United Kingdom. https://doi.org/10.1088/1748-9326/ace637.
@article{osti_1993662,
title = {Reducing uncertainty of high-latitude ecosystem models through identification of key parameters},
author = {Mevenkamp, Hannah and Wunderling, Nico and Bhatt, Uma and Carman, Tobey and Friedemann Donges, Jonathan and Genet, Helene and Serbin, Shawn and Winkelmann, Ricarda and Susanne Euskirchen, Eugenie},
abstractNote = {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.},
doi = {10.1088/1748-9326/ace637},
journal = {Environmental Research Letters},
number = 8,
volume = 18,
place = {United Kingdom},
year = {Thu Aug 03 00:00:00 EDT 2023},
month = {Thu Aug 03 00:00:00 EDT 2023}
}
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