Informing Robust Functional Relationship Benchmarks: An Evaluation of the Temperature Sensitivity of Ecosystem Respiration Across the Arctic‐Boreal Region

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 Q 10 , 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.

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Publication Details

Journal
Journal of Geophysical Research Biogeosciences
Published
2026-05-01
DOI
https://doi.org/10.1029/2025jg009307
Primary Topic
Climate change and permafrost
Type
article
Field-Weighted Citation Impact
0.00

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article

Informing Robust Functional Relationship Benchmarks: An Evaluation of the Temperature Sensitivity of Ecosystem Respiration Across the Arctic‐Boreal Region

Brovkin, Logan T. Berner, A. F. Gustafson, Jon M. Wells et al.
Journal of Geophysical Research Biogeosciences
Climate change and permafrost
article

Informing Robust Functional Relationship Benchmarks: An Evaluation of the Temperature Sensitivity of Ecosystem Respiration Across the Arctic‐Boreal Region

Brovkin, Logan T. Berner, A. F. Gustafson, Jon M. Wells, Nicholas Bouskill, W. J. Riley, Gesa Meyer, Alexandra Pongrácz, David M. Lawrence, Jeralyn Poe, Goran Georgievski, Fengming Yuan, Z. A. Mekonnen, Jing Tao, Benjamin Sulman, Deborah Huntzinger, S C Swenson, Heidi Rodenhizer, Christina Schädel, Qing Zhu, Philippe Ciais, Edward A. G. Schuur, David Wårlind, Andrew H. MacDougall, Christopher Schwalm, Eleanor J. Burke, Chunjing Qiu, Yi Xi, Joe R. Melton
article en

Abstract

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 Q 10 , 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.

Journal of Geophysical Research BiogeosciencesVol. 131(5)
NSF National Center for Atmospheric Research (US), Oak Ridge National Laboratory (US), Met Office (GB), Centre National de la Recherche Scientifique (FR), Oregon State University (US), Woodwell Climate Research Center (US), Environment and Climate Change Canada (CA), University of Wisconsin–Madison (US), Lawrence Berkeley National Laboratory (US), Lund University (SE), Université de Versailles Saint-Quentin-en-Yvelines (FR), St. Francis Xavier University (CA), Northern Arizona University (US), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Laboratoire des Sciences du Climat et de l'Environnement (FR), CEA Paris-Saclay (FR), Max Planck Institute for the Science of Human History (DE), Max Planck Institute for Meteorology (DE), Potsdam Institute for Climate Impact Research (DE), East China Normal University (CN)
U.S. Department of Energy, European Research Council, Biological and Environmental Research, HORIZON EUROPE European Research Council, National Science Foundation Graduate Research Fellowship Program
Life in Land
Openalex Percentile: Top 5%
Climate change and permafrost
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