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Model benchmarking is important for evaluating land surface model performance and guiding model improvements. Emerging model benchmark approaches (eg, functional relationship benchmarks) show promise in providing insights into both model responses to environmental forcings and simulated ecosystem processes. However, the subjective choices made in the selection and application of observational benchmarks can have a large influence on inferred model skill. Therefore, a systematic assessment of the impact of functional benchmarking choices on inferences of model skill is a needed component of robust model evaluation efforts. The International Land Model Benchmarking (ILAMB) tool is used here to test the influence the choice of observational benchmarks has on inferred model skill across the Arctic-Boreal region, as this region represents a potential key tipping point in Earth's climate system. We …