22 Apr 2022
22 Apr 2022

Reconstructing Holocene temperatures in time and space using paleoclimate data assimilation

Michael P. Erb1, Nicholas P. McKay1, Nathan Steiger2,3, Sylvia Dee4, Chris Hancock1, Ruza F. Ivanovic5, Lauren J. Gregoire5, and Paul Valdes6 Michael P. Erb et al.
  • 1School of Earth and Sustainability, Northern Arizona University, Flagstaff, AZ, USA
  • 2Lamont-Doherty Earth Observatory, Columbia University, New York, NY, USA
  • 3Institute of Earth Sciences, Hebrew University, Jerusalem, Israel
  • 4Department of Earth, Environmental, and Planetary Sciences, Rice University, Houston, TX, USA
  • 5School of Earth and Environment, University of Leeds, Leeds, UK
  • 6School of Geographical Sciences, University of Bristol, Bristol, UK

Abstract. Paleoclimatic records provide valuable information about Holocene climate, revealing aspects of climate variability for a multitude of sites around the world. However, such data also possess limitations. Proxy networks are spatially uneven, seasonally biased, uncertain in time, and present a variety of challenges when used in concert to illustrate the complex variations of past climate. Paleoclimatic data assimilation provides one approach to reconstructing past climate that can account for the diverse nature of proxy records while maintaining the physics-based covariance structures simulated by climate models. Here, we use paleoclimate data assimilation to create a spatially-complete reconstruction of temperature over the past 12,000 years using proxy data from the Temperature 12k database and output from transient climate model simulations. Following the last glacial period, the reconstruction shows Holocene temperatures warming to a peak near 6,400 years ago followed by a slow cooling toward the present day, supporting a preindustrial global mean surface temperature maximum during the mid-Holocene. Sensitivity tests show that if proxies have an overlooked summer bias, some apparent mid-Holocene warmth could actually represent summer trends rather than annual mean trends. Regardless, the potential effects of proxy seasonal biases are insufficient to align reconstructed global mean temperature with the warming trends seen in transient model simulations.

Michael P. Erb et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-184', Anonymous Referee #1, 01 Jun 2022
    • AC1: 'Reply on RC1', Michael Erb, 19 Sep 2022
  • CC1: 'Comment on egusphere-2022-184', Jessica Tierney, 03 Jul 2022
    • AC3: 'Reply on CC1', Michael Erb, 22 Sep 2022
  • RC2: 'Comment on egusphere-2022-184', Anonymous Referee #2, 07 Jul 2022
    • AC2: 'Reply on RC2', Michael Erb, 19 Sep 2022

Michael P. Erb et al.

Data sets

Holocene temperature reconstruction using paleoclimate data assimilation Michael P. Erb, Nicholas P. McKay, Nathan Steiger, Sylvia Dee, Chris Hancock, Ruza F. Ivanovic, Lauren J. Gregoire, and Paul Valdes

Model code and software

Code for the Holocene reconstruction Michael P. Erb and Nathan Steiger

Michael P. Erb et al.


Total article views: 1,217 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
703 498 16 1,217 5 10
  • HTML: 703
  • PDF: 498
  • XML: 16
  • Total: 1,217
  • BibTeX: 5
  • EndNote: 10
Views and downloads (calculated since 22 Apr 2022)
Cumulative views and downloads (calculated since 22 Apr 2022)

Viewed (geographical distribution)

Total article views: 978 (including HTML, PDF, and XML) Thereof 978 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 28 Sep 2022
Short summary
To look at climate over the past 12000 years, we reconstruct spatial temperature using natural climate archives and information from model simulations. We see mild global mean warm around 6000 years ago, which differs somewhat from past reconstructions. If more of our data represents summer values, this could explains some of the observed temperature change, but it still wouldn't explain the large difference between many reconstructions and climate models over this period.