Evaluating Proxy Influence in Assimilated Paleoclimate Reconstructions: Testing the Exchangeability of Two Ensembles of Spatial Processes

• Activity Number: 562 - Statistical Methods for Multivariate Spatial and Spatio-Temporal Models with Application to the Environment
• Type: Invited
• Date/Time: Thursday, August 6, 2020 : 3:00 PM to 4:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Bo Li* and Trevor Harris and Nathan Steiger and Jason Smerdon and Naveen Narisetty and Derek Tucker
• Companies: University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign and Columbia University and Columbia University and University of Illinois at Urbana-Champaign and Sandia National Lab
• Keywords: climate field reconstructions; data assimilation; functional depth; spatial fields

Abstract

Climate field reconstructions (CFR) attempt to estimate spatiotemporal fields of climate variables in the past using climate proxies. While many different CFR products and methods exist, Data Assimilation (DA) methods are a recent and promising new means of deriving CFRs that optimally fuse large collections of proxies with climate model information. Despite the growing application of DA-based CFRs, little is understood about how much the assimilated proxies change the statistical properties of the climate model data. We propose a robust and computationally efficient method, based on functional data depth, to evaluate the differences in the distributions of two spatiotemporal processes. We apply our test to study global and regional proxy influence in DA-based CFRs by comparing the background and analysis states. We find that the analysis states are significantly altered from the climate-model-based background states due to the assimilation of paleoclimate proxies. Moreover, the difference between the analysis and background states increases as the number of assimilated prox- ies increases, even in regions far beyond proxy collection sites.