Reducing Uncertainty of High-Latitude Ecosystem Models Through Identification of Key Parameters

The Science

Climate change significantly impacts Earth’s ecosystems and carbon budgets. In the Arctic, this may result in a historic shift from a net carbon sink to a source. Large uncertainties in terrestrial biosphere models (TBMs) used to forecast Arctic change 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. Researchers identified a 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 parameterization. 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, researchers developed a causal loop diagram (CLD) of the Arctic and boreal ecosystem that includes unquantified, and thus unmodeled, processes.

The Impact

Even small uncertainties surrounding the amount of carbon that may be sequestered or lost from Arctic ecosystems can propagate and significantly limit the ability to make adequate policy decisions. In particular, model structural uncertainty is difficult to quantify and can be related to parameter-based uncertainty. This paper provides a framework for combining model structural and parameter-based uncertainty into one analysis and improves understanding of each model’s strengths and weaknesses. This framework will further help to ensure models are applied appropriately.

Summary

Researchers examined three ecosystem models (DVM-DOS-TEM, SIPNET, and ED2) for parameter-based uncertainty and structural considerations and developed a CLD for the Arctic and boreal ecosystem. The team mapped model parameters to processes in the CLD and assessed parameter vulnerability via the internal network structure. One important substructure, feed-forward loops (FFLs), describes 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.

Researchers found 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, the team identified key parameters responsible for limited model accuracy that should be prioritized for future data sampling to reduce model uncertainty.

Principal Investigator

Eugenie Euskirchen
University of Alaska Fairbanks
[email protected]

Program Manager

Daniel Stover
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
[email protected]

Funding

This research was supported as part of the Next-Generation Ecosystem Experiments Arctic, which is funded by the Biological and Environmental Research program within the U.S. Department of Energy’s Office of Science.

References