Using climate models to estimate the quality of global observational data sets

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Science  28 Oct 2016:
Vol. 354, Issue 6311, pp. 452-455
DOI: 10.1126/science.aaf6369

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Models and data: A two-way street

Data are used to drive models of climate and other complex systems, but is the relationship between data and models a one-way process? Massonnet et al. used climate models to assess the quality of the observations that such models use. Starting with a simple model and progressing to more complex ones, the authors show that models are better when they are assessed against the most recent, most advanced, and most independent observational references. These findings should help to evaluate the quality of observational data sets and provide guidance for more objective data set selection.

Science, this issue p. 452


Observational estimates of the climate system are essential to monitoring and understanding ongoing climate change and to assessing the quality of climate models used to produce near- and long-term climate information. This study poses the dual and unconventional question: Can climate models be used to assess the quality of observational references? We show that this question not only rests on solid theoretical grounds but also offers insightful applications in practice. By comparing four observational products of sea surface temperature with a large multimodel climate forecast ensemble, we find compelling evidence that models systematically score better against the most recent, advanced, but also most independent product. These results call for generalized procedures of model-observation comparison and provide guidance for a more objective observational data set selection.

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