Informing Robust Functional Relationship Benchmarks: An Evaluation of the Temperature Sensitivity of Ecosystem Respiration Across the Arctic‐Boreal Region.
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| Title: | Informing Robust Functional Relationship Benchmarks: An Evaluation of the Temperature Sensitivity of Ecosystem Respiration Across the Arctic‐Boreal Region. |
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| Authors: | Poe, Jeralyn1 (AUTHOR) jmp838@nau.edu, Huntzinger, Deborah1,2,3 (AUTHOR), Riley, William J.4 (AUTHOR), Wells, Jon M.5 (AUTHOR), Schuur, Edward A. G.3 (AUTHOR), Schwalm, Christopher6 (AUTHOR), Berner, Logan T.1 (AUTHOR), Rodenhizer, Heidi6 (AUTHOR), Bouskill, Nicholas J.7 (AUTHOR), Brovkin, Victor8 (AUTHOR), Burke, Eleanor J.9 (AUTHOR), Ciais, Philippe10 (AUTHOR), Georgievski, Goran8,11,12 (AUTHOR), Gustafson, Adrian13 (AUTHOR), Lawrence, David M.14 (AUTHOR), MacDougall, Andrew H.15 (AUTHOR), Mekonnen, Zelalem A.4 (AUTHOR), Melton, Joe R.16 (AUTHOR), Meyer, Gesa16 (AUTHOR), Pongracz, Alexandra13 (AUTHOR) |
| Source: | Journal of Geophysical Research. Biogeosciences. May2026, Vol. 131 Issue 5, p1-19. 19p. |
| Subject Terms: | *Temperature effect, *Carbon cycle, Model validation, Soil respiration, Arctic climate |
| Geographic Terms: | Alaska, Canada |
| Abstract: | During land model development, simulated carbon dynamics are often benchmarked against observational data sets to evaluate model performance. Functional relationship benchmarks are the relationship between a driving variable (e.g., temperature) and a response variable (e.g., ecosystem respiration) and are a promising tool for assessing model performance by evaluating modeled sensitivities to changing environmental conditions. However, observed functional relationships can be influenced by choices made during data collection and throughout the benchmarking process, impacting the inferred skill of land models. To avoid misrepresenting a model's true performance, it is necessary to systematically evaluate best practices when constructing functional relationship benchmarks. We developed a set of guidelines for constructing functional relationship benchmarks, considering the choice of data set, number of daily observations, temporal extent, and temporal resolution across Alaska and Canada over a 20‐year period from 2001 to 2020. The temperature sensitivity of ecosystem respiration from observations, evaluated through an apparent Q10, is highly variable both spatially and as a result of the data processing approach applied in the benchmark formation. When benchmarking 13 models from the Warming Permafrost Model Intercomparison Project (WrPMIP), the range in inferred model skill is substantially impacted by the choices applied in constructing functional relationship benchmarks. The inferred performance of a given model is most sensitive to the number of daily observations and temporal extent, followed by choice of benchmark data set and temporal averaging. Results from this analysis can guide the development of consistent and robust functional relationships for future model evaluation studies. Plain Language Summary: Land models are often compared against data sets to evaluate model performance, referred to as model benchmarking. However, data sets themselves can contain errors and vary based on the measurement approach, and choices made during the processing of data can influence benchmarks derived from observational data products. Here, we evaluated how ecosystem respiration responds to temperature across Alaska and Canada, and how this relationship can be influenced by the choice of data set, number of data points throughout the month, number of years, and averaging over time. We found that the relationship between ecosystem respiration and temperature varies widely according to these choices, particularly the number of daily observations and temporal extent. When applying different subsets of the data to test model performance, we found that these choices produced a large range in model scores compared with those when applying the full data set. As a result, we developed a set of best practices that can serve as a guide during model benchmarking and can provide a more accurate sense of model performance. Key Points: The range in inferred model skill is substantially impacted by the choices applied in constructing functional relationship benchmarksThe inferred performance of a given model is most sensitive to the number of daily observations and temporal extent of the benchmark data setThese results can guide the development of consistent and robust functional relationships for future model evaluation studies [ABSTRACT FROM AUTHOR] |
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| Database: | GreenFILE |
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