1Department of Earth Sciences, University of Southern California, Los Angeles, California, USA
2Department of Statistics, Stanford University, Palo Alto, California, USA
3Lamont-Doherty Earth Observatory of Columbia University, Palisades, New York, USA
4Department of Statistics & Department of Environmental Earth System Sciences, Stanford University, Palo Alto, California, USA
Abstract. Pseudoproxy experiments (PPEs) have become an essential framework for evaluating paleoclimate reconstruction methods. Most existing PPE studies assume constant proxy availability through time and uniform proxy quality across the pseudoproxy network. Real multi-proxy networks are, however, marked by pronounced disparities in proxy quality, and a steep decline in proxy availability back in time, either of which may have large effects on reconstruction skill. Additionally, an investigation of a real-world global multi-proxy network suggests that proxies are not exclusively indicators of local climate; rather, many are indicative of large-scale teleconnections. A suite of PPEs constructed from a millennium-length general circulation model simulation is thus designed to mimic these various real-world characteristics. The new pseudoproxy network is used to evaluate four climate field reconstruction (CFR) techniques: truncated total least square embedded within the regularized EM algorithm (RegEM-TTLS), the Mann et al. (2009) implementation of RegEM-TTLS (M09), canonical correlation analysis (CCA), and Gaussian graphical models embedded within RegEM (GraphEM). Each method's risk properties are also assessed via a 100-member noise ensemble.
Contrary to expectation, it is found that reconstruction skill does not vary monotonically with proxy availability, but rather is a function of the type of climate variability (forced events vs. internal variability). The use of realistic spatiotemporal pseudoproxy characteristics also exposes large inter-method differences. Despite the comparable fidelity in reconstructing the global mean temperature, spatial skill varies considerably between CFR techniques. Both GraphEM and CCA efficiently exploit teleconnections, and produce consistent reconstructions across the ensemble. RegEM-TTLS and M09 appear advantageous for reconstructions on highly noisy data, but are subject to larger stochastic variations across different realizations of pseudoproxy noise. Results collectively highlight the importance of designing realistic pseudoproxy networks and implementing multiple noise realizations of PPEs. The results also underscore the difficulty in finding the proper bias-variance tradeoff for jointly optimizing the spatial skill of CFRs and the fidelity of the global mean reconstructions.