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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. [ABSTRACT FROM AUTHOR]

A crucial step in an epidemiological study of the effects of air pollution is to accurately quantify exposure of the population. In this paper, we investigate the sensitivity of the health effects estimates associated with short-term exposure to fine particulate matter with respect to three potential metrics for daily exposure: ambient monitor data, estimated values from a deterministic atmospheric chemistry model, and stochastic daily average human exposure simulation output. Each of these metrics has strengths and weaknesses when estimating the association between daily changes in ambient exposure to fine particulate matter and daily emergency hospital admissions. Monitor data is readily available, but is incomplete over space and time. The atmospheric chemistry model output is spatially and temporally complete but may be less accurate than monitor data. The stochastic human exposure estimates account for human activity patterns and variability in pollutant concentration across microenvironments, but requires extensive input information and computation time. To compare these metrics, we consider a case study of the association between fine particulate matter and emergency hospital admissions for respiratory cases for the Medicare population across three counties in New York. Of particular interest is to quantify the impact and/or benefit to using the stochastic human exposure output to measure ambient exposure to fine particulate matter. Results indicate that the stochastic human exposure simulation output indicates approximately the same increase in the relative risk associated with emergency admissions as using a chemistry model or monitoring data as exposure metrics. However, the stochastic human exposure simulation output and the atmospheric chemistry model both bring additional information, which helps to reduce the uncertainly in our estimated risk. [ABSTRACT FROM AUTHOR]