Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 4
Articles | Volume 17, issue 4
https://doi.org/10.5194/gmd-17-1765-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-17-1765-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 4
Development and technical paper
 | 
28 Feb 2024
Development and technical paper |  | 28 Feb 2024

Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space

Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita

Viewed

Total article views: 2,473 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,895 489 89 2,473 112 70 117
  • HTML: 1,895
  • PDF: 489
  • XML: 89
  • Total: 2,473
  • Supplement: 112
  • BibTeX: 70
  • EndNote: 117
Views and downloads (calculated since 05 Apr 2022)
Cumulative views and downloads (calculated since 05 Apr 2022)

Viewed (geographical distribution)

Total article views: 2,473 (including HTML, PDF, and XML) Thereof 2,352 with geography defined and 121 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 03 Nov 2025
Download
Short summary
Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Read more
Share
Special issue
Benchmark datasets and machine learning algorithms for Earth...