Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils

Roberta Farina* (Corresponding Author), Renáta Sándor, Mohamed Abdalla, Jorge Álvaro-Fuentes, Luca Bechini, Martin A. Bolinder, Lorenzo Brilli, Claire Chenu, Hugues Clivot, Massimiliano De Antoni Migliorati, Claudia Di Bene, Christopher D. Dorich, Fiona Ehrhardt, Fabien Ferchaud, Nuala Fitton, Rosa Francaviglia, Uwe Franko, Donna Giltrap, Brian B. Grant, Bertrand GuenetMatthew T. Harrison, Miko U. F. Kirschbaum, Katrin Kuka, Liisa Kulmala, Jari Liski, Matthew J. McGrath, Elizabeth Meier, Lorenzo Menichetti, Fernando Moyano, Claas Nendel, Sylvie Recous, Nils Reibold, Anita Shepherd, Ward N. Smith, Pete Smith, Jean-Francois Soussana, Tommaso Stella, Arezoo Taghizadeh-Toosi, Elena Tsutskikh, Gianni Bellocchi

*Corresponding author for this work

Research output: Contribution to journalArticle


Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. To do this, we evaluated SOC simulated from an ensemble of 26 process‐based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites in Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: a) calibrating decomposition parameters separately at each experimental site (Spe); b) using a generic, knowledge-based, parameterisation applicable in the Central European region (Gen); and c) using a combination of both a) and b) strategies (Mix). With this methodology, we addressed uncertainties from different modelling approaches with or without spin-up initialisation of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C ha-1 compared to the observed mean of 36.0 (±19.7) Mg C ha-1 (last observed year), indicating sufficiently reliable SOC estimates. This is important because moving to Mix (37.5±16.7 Mg C ha-1) and Spe (36.8±19.8 Mg C ha-1) provided only marginal gains in accuracy, but with these scenarios modellers would need to apply increasingly more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.
Original languageEnglish
JournalGlobal Change Biology
Publication statusAccepted/In press - 26 Oct 2020

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