A pseudoproxy assessment of why climate field reconstruction methods perform the way they do in time and space

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 herein 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 that represent the noise-filtered climate 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.