On discriminating between GCM forcing configurations using Bayesian reconstructions of late-holocene temperatures

Martin Tingley, Peter F. Craigmile, Murali Haran, Bo Li, Elizabeth Mannshardt, Bala Rajaratnam

Research output: Contribution to journalArticlepeer-review


Several climate modeling groups have recently generated ensembles of last-millennium climate simulations under different forcing scenarios. These experiments represent an ideal opportunity to establish the baseline feasibility of using proxy-based reconstructions of late-Holocene climate as out-of-calibration tests of the fidelity of the general circulation models used to project future climate. This paper develops a formal statistical model for assessing the agreement between members of an ensemble of climate simulations and the ensemble of possible climate histories produced from a hierarchical Bayesian climate reconstruction. As the internal variabilities of the simulated and reconstructed climate are decoupled from one another, the comparison is between the two latent, or unobserved, forced responses. Comparisons of the spatial average of a 600-yr high northern latitude temperature reconstruction to suites of last-millennium climate simulations from the GISS-E2 and CSIRO models, respectively, suggest that the proxy-based reconstructions are able to discriminate only between the crudest features of the simulations within each ensemble. Although one of the three volcanic forcing scenarios used in the GISS-E2 ensemble results in superior agreement with the reconstruction, no meaningful distinctions can be made between simulations performed with different estimates of solar forcing or land cover changes. In the case of the CSIRO model, sequentially adding orbital, greenhouse gas, solar, and volcanic forcings to the simulations generally improves overall consensus with the reconstruction, though the distinctions are not individually significant.

Original languageEnglish (US)
Pages (from-to)8264-8281
Number of pages18
JournalJournal of Climate
Issue number20
StatePublished - 2015


  • Bayesian methods
  • Model evaluation/performance
  • Paleoclimate

ASJC Scopus subject areas

  • Atmospheric Science


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