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Michael E. Mann, Scott Rutherford, Eugene Wahl, and Caspar Ammann

Abstract

Two widely used statistical approaches to reconstructing past climate histories from climate “proxy” data such as tree rings, corals, and ice cores are investigated using synthetic “pseudoproxy” data derived from a simulation of forced climate changes over the past 1200 yr. These experiments suggest that both statistical approaches should yield reliable reconstructions of the true climate history within estimated uncertainties, given estimates of the signal and noise attributes of actual proxy data networks.

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Scott D. Rutherford, Michael E. Mann, Eugene Wahl, and Caspar Ammann

Abstract

Smerdon et al. report two errors in the climate model grid data used in previous pseudoproxy-based climate reconstruction experiments that do not impact the main conclusions of those works. The errors did not occur in subsequent works and therefore have no impact on the results presented therein. Results presented here for the Climate System Model (CSM) using multiple pseudoproxy noise realizations show that the quantitative differences between the incorrect and corrected results are within the expected variability of the noise realizations. It should also be made clear that the climate reconstruction method used in Smerdon et al. to illustrate the nature of the errors, the Regularized Expectation Maximization method with Ridge Regression (RegEM-Ridge), is known to produce climate reconstructions with considerable variance loss and has been superseded by RegEM-TTLS (TTLS indicates truncated total least squares).

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Michael E. Mann, Scott Rutherford, Eugene Wahl, and Caspar Ammann
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Michael E. Mann, Scott Rutherford, Eugene Wahl, and Caspar Ammann
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Scott D. Rutherford, Michael E. Mann, Caspar M. Ammann, and Eugene R. Wahl

Abstract

In a recent paper, Christiansen et al. compared climate reconstruction methods using surrogate ensembles from a coupled general circulation model and pseudoproxies. Their results using the regularized expectation maximization method with truncated total least squares (RegEM-TTLS) appear inconsistent with previous studies. Results presented here show that the poor performance of RegEM-TTLS in Christiansen et al. is due to 1) their use of the nonhybrid method compared to the hybrid method; 2) a stagnation tolerance that is too large and does not permit the solution to stabilize, which is compounded in another paper by Christiansen et al. by the introduction of an inappropriate measure of stagnation; and 3) their use of a truncation parameter that is too large. Thus, the poor performance of RegEM-TTLS in both Christiansen et al. papers is due to poor implementation of the method rather than to shortcomings inherent to the method.

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