Multi-modelling predictions show high uncertainty of required carbon input changes to reach a 4‰ target
Item
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Title
Multi-modelling predictions show high uncertainty of required carbon input changes to reach a 4‰ target
European Journal of Soil Science
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Creator
Elisa Bruni
Claire Chenu
Rose Z. Abramoff
Guido Baldoni
Dietmar Barkusky
Hugues Clivot
Yuanyuan Huang
Thomas Kätterer
Dorota Pikuła
Heide Spiegel
Iñigo Virto
Bertrand Guenet
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Subject
4 per 1000 initiative
European targets
agriculture
carbon sequestration
climate change
multi-modelling
soil organic carbon
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Abstract
Soils store vast amounts of carbon (C) on land, and increasing soil organic carbon (SOC) stocks in already managed soils such as croplands may be one way to remove C from the atmosphere, thereby limiting subsequent warming. The main objective of this study was to estimate the amount of additional C input needed to annually increase SOC stocks by 4‰ at 16 long-term agricultural experiments in Europe, including exogenous organic matter (EOM) additions. We used an ensemble of six SOC models and ran them under two configurations: (1) with default parametrization and (2) with parameters calibrated site-by-site to fit the evolution of SOC stocks in the control treatments (without EOM). We compared model simulations and analysed the factors generating variability across models. The calibrated ensemble was able to reproduce the SOC stock evolution in the unfertilised control treatments. We found that, on average, the experimental sites needed an additional 1.5 ± 1.2 Mg C ha−1 year−1 to increase SOC stocks by 4‰ per year over 30 years, compared to the C input in the control treatments (multi-model median ± median standard deviation across sites). That is, a 119% increase compared to the control. While mean annual temperature, initial SOC stocks and initial C input had a significant effect on the variability of the predicted C input in the default configuration (i.e., the relative standard deviation of the predicted C input from the mean), only water-related variables (i.e., mean annual precipitation and potential evapotranspiration) explained the divergence between models when calibrated. Our work highlights the challenge of increasing SOC stocks in agriculture and accentuates the need to increasingly lean on multi-model ensembles when predicting SOC stock trends and related processes. To increase the reliability of SOC models under future climate change, we suggest model developers to better constrain the effect of water-related variables on SOC decomposition. Highlights The feasibility of the 4‰ target was studied at 16 long-term agricultural experiments. An ensemble of soil organic carbon models was used to estimate the uncertainty of the predictions. On average across the sites, carbon input had to increase by 119% compared to initial conditions. High uncertainty of the simulations was mainly driven by water-related variables.
