How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies

作者：Fabrizio Albanito, David McBey, Matthew Tom Harrison, Pete Smith, Fiona Ehrhardt, Arti Bhatia, Gianni Bellocchi, Lorenzo Brilli, Marco Carozzi, K Christie, Jordi Doltra, Christopher D. Dorich, Luca Doro, Peter Grace, Brian Grant, Joël Léonard, Mark A. Liebig, Cameron I. Ludemann, Raphaël Martin, Elizabeth A. Meier, Rachelle Meyer, Massimiliano De Antoni Migliorati, Vasileios Myrgiotis, Sylvie Recous, Renáta Sándor, Val Snow, Jean‐François Soussana, Ward Smith, Nuala Fitton · 发表于：Environmental Science & Technology · 年份：2022 · DOI：10.1021/acs.est.2c02023 · 被引用次数：17 · 研究领域：Climate change impacts on agriculture、Species Distribution and Climate Change、Hydrology and Watershed Management Studies

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers 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 modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers 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 the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consis...