Soil organic carbon models need independent time-series validation for reliable prediction

Communications Earth & Environment

Julia Le Noë

Stefano Manzoni

Rose Abramoff

Tobias Bölscher

Elisa Bruni

Rémi Cardinael

Philippe Ciais

Claire Chenu

Hugues Clivot

Delphine Derrien

Fabien Ferchaud

Patricia Garnier

Daniel Goll

Gwenaëlle Lashermes

Manuel Martin

Daniel Rasse

Frédéric Rees

Julien Sainte-Marie

Elodie Salmon

Marcus Schiedung

Josh Schimel

William Wieder

Samuel Abiven

Pierre Barré

Lauric Cécillon

Bertrand Guenet

Carbon cycle

Climate-change ecology

Databases

2023-05-08

10.1038/s43247-023-00830-5

https://www.nature.com/articles/s43247-023-00830-5

Numerical models are crucial to understand and/or predict past and future soil organic carbon dynamics. For those models aiming at prediction, validation is a critical step to gain confidence in projections. With a comprehensive review of ~250 models, we assess how models are validated depending on their objectives and features, discuss how validation of predictive models can be improved. We find a critical lack of independent validation using observed time series. Conducting such validations should be a priority to improve the model reliability. Approximately 60% of the models we analysed are not designed for predictions, but rather for conceptual understanding of soil processes. These models provide important insights by identifying key processes and alternative formalisms that can be relevant for predictive models. We argue that combining independent validation based on observed time series and improved information flow between predictive and conceptual models will increase reliability in predictions.

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