Reducing uncertainty of high-latitude ecosystem models through identification of key parameters
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 th...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2023-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/ace637 |
| _version_ | 1797356545083703296 |
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| author | Hannah Mevenkamp Nico Wunderling Uma Bhatt Tobey Carman Jonathan Friedemann Donges Helene Genet Shawn Serbin Ricarda Winkelmann Eugenie Susanne Euskirchen |
| author_facet | Hannah Mevenkamp Nico Wunderling Uma Bhatt Tobey Carman Jonathan Friedemann Donges Helene Genet Shawn Serbin Ricarda Winkelmann Eugenie Susanne Euskirchen |
| author_sort | Hannah Mevenkamp |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T14:28:12Z |
| format | Article |
| id | doaj.art-8cefca34caf345c1ad3d19e050651e4d |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-08T14:28:12Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-8cefca34caf345c1ad3d19e050651e4d2024-01-12T17:27:14ZengIOP PublishingEnvironmental Research Letters1748-93262023-01-0118808403210.1088/1748-9326/ace637Reducing uncertainty of high-latitude ecosystem models through identification of key parametersHannah Mevenkamp0https://orcid.org/0000-0002-7241-5374Nico Wunderling1https://orcid.org/0000-0002-3566-323XUma Bhatt2https://orcid.org/0000-0003-1056-3686Tobey Carman3https://orcid.org/0000-0003-4617-4674Jonathan Friedemann Donges4https://orcid.org/0000-0001-5233-7703Helene Genet5https://orcid.org/0000-0003-4537-9563Shawn Serbin6https://orcid.org/0000-0003-4136-8971Ricarda Winkelmann7https://orcid.org/0000-0003-1248-3217Eugenie Susanne Euskirchen8https://orcid.org/0000-0002-0848-4295University of Alaska Fairbanks, Institute of Arctic Biology , Fairbanks, AK, United States of AmericaPotsdam Institute for Climate Impact Research , Member of the Leibniz Association, Potsdam, Germany; Stockholm Resilience Centre , Stockholm, SwedenGeophysical Institute, University of Alaska Fairbanks , Fairbanks, AK, United States of AmericaUniversity of Alaska Fairbanks, Institute of Arctic Biology , Fairbanks, AK, United States of AmericaPotsdam Institute for Climate Impact Research , Member of the Leibniz Association, Potsdam, Germany; Stockholm Resilience Centre , Stockholm, SwedenUniversity of Alaska Fairbanks, Institute of Arctic Biology , Fairbanks, AK, United States of AmericaBrookhaven National Laboratory , Environmental and Climate Sciences Department, Upton, NY, United States of AmericaPotsdam Institute for Climate Impact Research , Member of the Leibniz Association, Potsdam, Germany; University of Potsdam, Institute of Physics and Astronomy , Potsdam, GermanyUniversity of Alaska Fairbanks, Institute of Arctic Biology , Fairbanks, AK, United States of AmericaClimate 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.https://doi.org/10.1088/1748-9326/ace637complex networkscausal loop diagrammodel uncertaintyArctic ecosystem and ecological databases |
| spellingShingle | Hannah Mevenkamp Nico Wunderling Uma Bhatt Tobey Carman Jonathan Friedemann Donges Helene Genet Shawn Serbin Ricarda Winkelmann Eugenie Susanne Euskirchen Reducing uncertainty of high-latitude ecosystem models through identification of key parameters Environmental Research Letters complex networks causal loop diagram model uncertainty Arctic ecosystem and ecological databases |
| title | Reducing uncertainty of high-latitude ecosystem models through identification of key parameters |
| title_full | Reducing uncertainty of high-latitude ecosystem models through identification of key parameters |
| title_fullStr | Reducing uncertainty of high-latitude ecosystem models through identification of key parameters |
| title_full_unstemmed | Reducing uncertainty of high-latitude ecosystem models through identification of key parameters |
| title_short | Reducing uncertainty of high-latitude ecosystem models through identification of key parameters |
| title_sort | reducing uncertainty of high latitude ecosystem models through identification of key parameters |
| topic | complex networks causal loop diagram model uncertainty Arctic ecosystem and ecological databases |
| url | https://doi.org/10.1088/1748-9326/ace637 |
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