Are Terrestrial Biosphere Models Fit for Simulating the Global Land Carbon Sink?. (4th May 2022)

• Record Type: Journal Article
• Authors: Seiler, Christian; Melton, Joe R.; Arora, Vivek K.; Sitch, Stephen; Friedlingstein, Pierre; Anthoni, Peter; Goll, Daniel; Jain, Atul K.; Joetzjer, Emilie; Lienert, Sebastian; Lombardozzi, Danica; Luyssaert, Sebastiaan; Nabel, Julia E. M. S.; Tian, Hanqin; Vuichard, Nicolas; Walker, Anthony P.; Yuan, Wenping; Zaehle, Sönke
• Is Part Of: Journal of advances in modeling earth systems. Volume 14:Number 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-04
• Subjects: biogeochemical cycles, processes, and modeling -- biosphere/atmosphere interactions -- carbon cycling; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2021MS002946
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one‐third of anthropogenic CO2 emissions during the 1959–2019 period. This sink‐estimate is produced by an ensemble of terrestrial biosphere models and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well terrestrial biosphere models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation‐based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference data sets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter‐model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties. Plain Language Summary: Earth's natural vegetation absorbs about one‐third of CO2 emissions caused by human activities. This value is produced by a group of models rather than through direct observations. Our study assesses how well models reproduce the processes that drive the CO2 exchange between land and atmosphere using a wide range of data sets that are mainly derived from field measurements and satellite images. These reference data sets are prone to errors that are not quantified in a consistent manner. To account for such errors, we first compare different reference data sets against each other. We then compare model output against reference data and assess whether the differences are comparable to the differences among the reference data sets. We conclude that the performance of models is encouraging given how uncertain reference data are, but that ample potential for improvements remains. Key Points: Differences between model and observations are often similar compared to differences between independently derived observation‐based data We quantify differences between independently derived observations to disentangle model deficiencies from observational uncertainties Future work should address biases in soil organic carbon, leaf area index, and the large spread of gross primary productivity among models