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Activity Number: 259 - SPEED: Environmetrics: Spatio-Temporal and Other Models
Type: Contributed
Date/Time: Monday, July 30, 2018 : 3:05 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #332772
Title: Evaluating Proxy Influence and Reconstruction Skill in Data Assimilation Based Climate Field Reconstructions Using Extremal Depth
Author(s): Trevor Harris* and Bo Li and Nathan Steiger and Jason Smerdon and Justin Jacobs
Companies: University of Illinois at Urbana-Champaign, Statistics and University of Illinois at Urbana-Champaign and Columbia University, Lamont-Doherty Earth Observatory and Columbia University, Lamont-Doherty Earth Observatory and Sandia National Laboratories
Keywords: paleoclimate; data assimilation; spatiotemporal; functional data; extremal depth; simultaneous inference

Climate Field Reconstructions (CFRs) estimate spatiotemporal fields of climate variables using proxy records of past climate variability. CFRs with seasonal to annual resolution spanning the last several millennia have been a particular focus of the paleoclimatic community and have emerged as important tools for studying the mechanisms of climate variability. Data Assimilation (DA) has recently been developed as a new CFR method that optimally combines model simulations with proxy networks to produce ensemble estimates of past temperature and hydroclimate fields, along with associated dynamical climate variables. By treating climate fields as noisy observations of a smooth functional process, we rigorously compare DA ensembles against the model-simulated prior and observational data using Extremal Depth (ED). ED is a functional data analysis method for ranking the depth of functions within a functional cloud. ED rankings provide a direct method for highlighting areas where a climate field diverges from the ensemble via central regions. These comparisons allow us to measure the influence of different proxies on the ensembles as well as the reconstruction skill of DA.

Authors who are presenting talks have a * after their name.

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