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Hindcasting experiments (conducting a model forecast for a time period in\udwhich observational data are available) are being undertaken increasingly\udoften by the integrated assessment model (IAM) community, across many scales\udof models. When they are undertaken, the results are often evaluated using\udglobal aggregates or otherwise highly aggregated skill scores that mask\uddeficiencies. We select a set of deviation-based measures that can be applied\udon different spatial scales (regional versus global) to make evaluating the\udlarge number of variable–region combinations in IAMs more tractable. We also\udidentify performance benchmarks for these measures, based on the statistics\udof the observational dataset, that allow a model to be evaluated in absolute\udterms rather than relative to the performance of other models at similar\udtasks. An ideal evaluation method for hindcast experiments in IAMs would\udfeature both absolute measures for evaluation of a single experiment for a\udsingle model and relative measures to compare the results of multiple\udexperiments for a single model or the same experiment repeated across\udmultiple models, such as in community intercomparison studies. The\udperformance benchmarks highlight the use of this scheme for model evaluation\udin absolute terms, providing information about the reasons a model may\udperform poorly on a given measure and therefore identifying opportunities for\udimprovement. To demonstrate the use of and types of results possible with the\udevaluation method, the measures are applied to the results of a past hindcast\udexperiment focusing on land allocation in the Global Change Assessment Model\ud(GCAM) version 3.0. The question of how to more holistically evaluate models\udas complex as IAMs is an area for future research. We find quantitative\udevidence that global aggregates alone are not sufficient for evaluating IAMs\udthat require global supply to equal global demand at each time period, such\udas GCAM. The results of this work indicate it is unlikely that a single\udevaluation measure for all variables in an IAM exists, and therefore sector-by-sector evaluation may be necessary

Evaluation of integrated assessment model hindcast experiments: a case study of the GCAM 3.0 land use module