Abstract
Abstract. Spatiotemporal paleoclimate reconstructions that seek to estimate climate conditions over the last several millennia are derived from multiple climate proxy records (e.g., tree rings, ice cores, corals, and cave formations) that are heterogeneously distributed across land and marine environments. Assessing the skill of the methods used for these reconstructions is critical as a means of understanding the spatiotemporal uncertainties in the derived reconstruction products. Traditional statistical measures of skill have been applied in past applications, but they often lack formal null hypotheses that incorporate the spatiotemporal characteristics of the fields and allow for formal significance testing. More recent attempts have developed assessment metrics to evaluate the difference of the characteristics between two spatiotemporal fields. We apply these assessment metrics to results from synthetic reconstruction experiments based on multiple climate model simulations to assess the skill of four reconstruction methods. We further interpret the comparisons using analysis of empirical orthogonal functions (EOFs) that represent the noise-filtered climate field. We demonstrate that the underlying features of a targeted temperature field that can affect the performance of CFRs include the following: (i) the characteristics of the eigenvalue spectrum, namely the amount of variance captured in the leading EOFs; (ii) the temporal stability of the leading EOFs; (iii) the representation of the climate over the sampling network with respect to the global climate; and (iv) the strength of spatial covariance, i.e., the dominance of teleconnections, in the targeted temperature field. The features of climate models and reconstruction methods identified in this paper demonstrate more detailed assessments of reconstruction methods and point to important areas of testing and improving real-world reconstruction methods.
Highlights
Climate field reconstructions (CFRs) are spatially explicit estimates of past climate conditions that use layered or banded archives containing chemical, biological, or physical indicators as proxies for climate prior to the advent of instrumental records
To complement the analysis of the covariance structure skill in the El Niño–Southern Oscillation (ENSO)-teleconnected regions, we investigate the proportion of variance explained by the first five leading empirical orthogonal functions (EOFs) of the ENSO teleconnection dominant region (D2)
We have provided a comprehensive assessment of four widely applied CFR methods in terms of their skill in recovering the mean surface and covariance patterns in the targeted temperature field
Summary
Climate field reconstructions (CFRs) are spatially explicit estimates of past climate conditions that use layered or banded archives containing chemical, biological, or physical indicators as proxies for climate prior to the advent of instrumental records. Almost all skill characterizations of previous PPEs are descriptive in nature, largely employing spatial maps and global aggregates of statistics such as the mean biases in derived CFRs, correlations between the CFRs and known fields, or the root mean square error of the CFRs relative to the known fields While such comparisons are useful for evaluating the relative performance of the various CFR methods, they do not employ a formal null hypothesis that can determine whether or not the spatiotemporal differences between reconstructed fields are statistically significant. Our results demonstrate how our methods can be used to improve interpretations of uncertainties and limitations in state-of-the-art CFR methods and provide improved understanding of how specific characteristics of the real climate system may give rise to enhanced or reduced CFR performance
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