Preprints
https://doi.org/10.5194/cp-2022-5
https://doi.org/10.5194/cp-2022-5
 
31 Jan 2022
31 Jan 2022
Status: a revised version of this preprint is currently under review for the journal CP.

Evaluation of statistical climate reconstruction methods based on pseudoproxy experiments using linear and machine learning methods

Zeguo Zhang, Sebastian Wagner, Marlene Klockmann, and Eduardo Zorita Zeguo Zhang et al.
  • Helmholtz Zentrum Hereon, Institute of Coastal Systems, 21502 Geesthacht, Germany

Abstract. Three different climate field reconstruction (CFR) methods employed to reconstruct North Atlantic-European (NAE) and Northern Hemisphere (NH) summer season temperature over the past millennium from proxy records are tested in the framework of pseudoproxy experiments derived from three climate simulations with Earth System Models. Two of these methods are traditional multivariate linear methods (Principal Components Regression, PCR and Canonical Correlation Analysis, CCA), whereas the third method (Bidirectional Long-Short-Term Memory Neural Network, Bi-LSTM) belongs to the category of machine learning methods. The Bi-LSTM method does not need to assume linear and temporally stable relationships between the underlying proxy network and the targeted climate field, in contrast to PCR and CCA. In addition, Bi-LSTM incorporates information on the serial correlation of the time series. All three methods tested herein achieve reasonable reconstruction performance in both spatial and temporal scale. Generally, the reconstruction skill is higher in regions with denser proxy coverage, but reconstruction skill is also achieved in proxy-free areas due to climate teleconnections. All three CFR methodologies generally tend to more strongly underestimate the target temperature variations as more noise is introduced into the pseudoproxies. The Bi-LSTM method tested in our experiments shows relatively worse reconstruction skills compared to PCR and CCA, yet it brings some encouraging results on capturing extreme cooling climate signals. This indicates that this nonlinear CFR method could be a potential methodology for past climate extremes analysis.

Zeguo Zhang 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 cp-2022-5', Anonymous Referee #1, 14 Mar 2022
  • RC2: 'Comment on cp-2022-5', Anonymous Referee #2, 29 Mar 2022

Zeguo Zhang et al.

Zeguo Zhang et al.

Viewed

Total article views: 627 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
444 165 18 627 6 6
  • HTML: 444
  • PDF: 165
  • XML: 18
  • Total: 627
  • BibTeX: 6
  • EndNote: 6
Views and downloads (calculated since 31 Jan 2022)
Cumulative views and downloads (calculated since 31 Jan 2022)

Viewed (geographical distribution)

Total article views: 588 (including HTML, PDF, and XML) Thereof 588 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 Sep 2022
Download
Short summary
We employed a machine learning method, Bidirectional Long-Short-Term Memory Neural Network, Bi-LSTM for the first time for past temperature field reconstructions, the nonlinear Bi-LSTM method tested in our experiments shows relatively worse reconstruction skills compared to PCR and CCA, yet it brings some encouraging results on capturing extreme cooling climate signals. This indicates that this nonlinear method could be a potential methodology for past climate extremes analysis.