In order to assess performance, most models have been individually evaluated against a range of observations. Moreover, in the annual updates of the global carbon budgets, a model has to meet a small set of basic criteria to have its output included . More systematic benchmarking approaches have also been proposed that utilise a range of different datasets  to assess multiple simulated processes. These methods, in principle, facilitate assigning quality scores to models based on their overall performance . So far, this scoring does not yet allow a clear quality ranking of models, since individual DGVMs tend to score well for some variables and badly for others. A recent comparison of global fire-vegetation model outputs was also able to clearly identify outliers when using a formalised benchmarking and scoring approach . However, benchmarking
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Climate Change 2022: Impacts, Adaptation and Vulnerability
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