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


Farina, R and Sandor, R and Abdalla, M and Alvaro-Fuentes, J and Bechini, L and Bolinder, MA and Brilli, L and Claire, C and Clivot, H and De Antoni Migliorati, M and Di Bene, C and Dorich, CD and Ehrhardt, F and Ferchaud, F and Fitton, N and Francaviglia, R and Franko, U and Giltrap, DL and BGrant, B and Guenet, B and Harrison, MT and Kirschbaum, MUF and Kuka, K and Kulmala, L and Liski, J and McGrath, MJ and Meier, E and Menichetti, L and Moyano, Fernando and Nendel, C and Recous, S and Reibold, N and Shepherd, A and Smith, WN and Smith, P and Soussana, JF and Stella, T and Taghizadeh-Toosi, A and Tsutskikh, E and Bellocchi, G, Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils, Global Change Biology ISSN 1354-1013 (2020) [Refereed Article]

Available from 08 November 2021

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Copyright 2020 John Wiley & Sons Ltd. This is the peer reviewed version of the following article, which has been published in final form at This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.

DOI: doi:10.1111/gcb.15441


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. 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: Denmark (two sites), France, Russia, Sweden, 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). 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. Moving to Mix (37.516.7 Mg C ha‐1) and Spe (36.819.8 Mg C ha‐1) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.

Item Details

Item Type:Refereed Article
Research Division:Agricultural, Veterinary and Food Sciences
Research Group:Agriculture, land and farm management
Research Field:Agricultural systems analysis and modelling
Objective Division:Environmental Policy, Climate Change and Natural Hazards
Objective Group:Adaptation to climate change
Objective Field:Climate change adaptation measures (excl. ecosystem)
UTAS Author:Harrison, MT (Associate Professor Matthew Harrison)
ID Code:141754
Year Published:2020
Web of Science® Times Cited:1
Deposited By:TIA - Research Institute
Deposited On:2020-11-17
Last Modified:2020-12-15

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