How modelers model: the overlooked social and human dimensions in model intercomparison studies

Fabrizio Albanito* (Corresponding Author), David McBey, Matthew Tom Harrison, Pete Smith, Fiona Ehrhardt, Gianni Bellocchi, Lorenzo Brilli, Marco Carozzi, Karen Christie, Jordi Doltra, Christopher D Dorich, Luca Doro, Peter Grace, Brian Grant, Joël Léonard, Mark Liebig, Cameron Ludemann, Raphaël Martin, Elizabeth Meier, Rachelle MeyerMassimiliano De Antoni Migliorati, Vasileios Myrgiotis, Sylvie Recous, Renáta Sándor, Val O. Snow, Jean François Soussana, Ward N Smith, Nuala Fitton, Arti Bhatia

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
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There is a growing realisation that the complexity of model ensemble studies depends not only the models used, but also on the experience and approach used by modellers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to
investigate the rationale applied by modellers in a model ensemble study where twelve process-based different biogeochemical model types were compared across five successive calibration stages. The modellers shared a common level of agreement about the importance of the variables used to initialise their models for calibration. However, we found inconsistency among modellers when judging the importance of input variables across the different calibration stages. The level of subjective weighting attributed by modellers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as fertilisation rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks were statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, net ecosystem exchange, varied significantly according to the length of the modeller’s experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modellers, with cognitive bias in “trial-and-error” calibration routines. Our study highlights that overlooked human and social attributes is critical in the outcomes of modelling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterisation are important, we contend that (1) the modeller’s assumptions on the extent to which
parameters should be altered, and (2) modeller perceptions of the importance of model parameters, are just as critical in obtaining a quality model calibration as numerical or analytical details.
Original languageEnglish
Pages (from-to)13485–13498
Number of pages14
JournalEnvironmental Science & Technology
Issue number18
Early online date2 Sept 2022
Publication statusPublished - 20 Sept 2022

Bibliographical note

Fabrizio Albanito gratefully acknowledges funding from RETINA project (NERC, NE/V003259/1) and the FACCE-JPI projects: CN-MIP, Models4Farmers and MACSUR. This study was coordinated by the Integrative Research Group of the Global Research Alliance (GRA) on agricultural GHGs and was supported by five research projects (CN‐MIP, Models4Pastures, MACSUR, COMET‐Global and
MAGGNET), which received funding by a multi-partner call on agricultural greenhouse gas research of the Joint Programming Initiative ‘FACCE’ through its national financing bodies. The study falls within the thematic area of the French government IDEX-ISITE initiative (reference: 16-IDEX-0001;project CAP 20-25)

Data Availability Statement

Supporting Information
The Supporting Information is available free of charge at


  • Model ensembles
  • biogeochemical models
  • multi-criteria decision making
  • model calibration
  • model intercomparison
  • climate change
  • greenhouse gases
  • soil carbon
  • AgMIP


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