The Paleoclimate Reanalysis Project

Abstract:

Recent advances in proxy-model data assimilation have made feasible the development of global proxy-based reanalyses. Proxy-based reanalyses aim to make optimum use of both proxy and model data while presenting paleoclimate information in an accessible format—they will undoubtedly play a pivotal role in the future of paleoclimate research. In the Paleoclimate Reanalysis Project (PaleoR) we use ‘off-line’ data assimilation to constrain the CESM1(CAM5) Last Millennial Ensemble (LME) simulation with a globally distributed multivariate proxy dataset, producing a decadal resolution reanalysis of the past millennium. Discrete time periods are ‘reconstructed’ by using anomalous (±0.5σ) proxy climate signals to select an ensemble of climate state analogues from the LME. Prior to assimilation the LME simulates internal variability that is temporally inconsistent with information from the proxy archive. After assimilation the LME is highly correlated to almost all included proxy data, and dynamical relationships between modeled variables are preserved; thus providing a ‘real-world’ view of climate system evolution during the past millennium. Unlike traditional regression based approaches to paleoclimatology, PaleoR is unaffected by temporal variations in teleconnection patterns. Indices representing major modes of global ocean-atmosphere climate variability can be calculated directly from PaleoR spatial fields. PaleoR derived ENSO, SAM, and NAO indices are consistent with observations and published multiproxy reconstructions. The computational efficiency of ‘off-line’ data assimilation allows easy incorporation and evaluation of new proxy data, and experimentation with different setups and model simulations. PaleoR is our first attempt at a global paleoclimate reanalysis and we have identified several opportunities for future improvements. PaleoR spatial fields can be viewed online at http://climatefutures.mq.edu.au/research/themes/marine/paleor/.