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.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-1765-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
28 Feb 2024
Development and technical paper |
| 28 Feb 2024
| 28 Feb 2024
Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space
Marlene Klockmann
CORRESPONDING AUTHOR
Institute for Coastal Systems – Analysis and Modelling, Helmholtz-Zentrum Hereon, Geesthacht, Germany
now at: Institute of Oceanography, Universität Hamburg, Hamburg, Germany
Udo von Toussaint
Max Planck Institute for Plasma Physics, Garching, Germany
Eduardo Zorita
Institute for Coastal Systems – Analysis and Modelling, Helmholtz-Zentrum Hereon, Geesthacht, Germany
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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.
Reconstructions of climate variability before the observational period rely on climate proxies...
