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

Improving model prediction of soil NO emissions through Bayesian calibration.

The biogeochemical processes that lead to the production of NO in arable soils are controlled by temporally and spatially varying drivers. The need for prediction of soil NO emissions across scales means that agroecosystem biogeochemistry models are widely used to simulate NO emissions. Due to the parameter-dense nature of agroecosystem models their parameters have to be calibrated according to the soil and climatic conditions of the intended area of application.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N O emissions.

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N O) emissions for wheat, maize, rice and temperate grasslands.