Massonnet, François; Bellprat, Omar; Guemas, Virginie; Doblas-Reyes, Francisco J (2016): Climate forecast data (Nino3.4 SST, Arctic sea ice extent) and observational references, link to files in Rdata format [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.864680,

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Supplement to: Massonnet, F et al. (2016): Using climate models to estimate the quality of global observational data sets. Science, 354(6311), 452-455, https://doi.org/10.1126/science.aaf6369

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.

This R-based dataset comprises

- Monthly-mean Sea Surface Temperature (SST) forecasts from 11 climate models, each composed of 10 members, from May to August 1993-2009

- Monthly-mean Sea Ice Extent (SIE) forecasts from 9 climate models, each composed of 10 members, from May to August 1993-2008

- SST from four observational references between May-August 1993-2009

- Arctic SIE from four observational references between May-August 1993-2008

- Spatial information on correlation, root mean square error and standard deviation for the above products.