Articles | Volume 17, issue 4
https://doi.org/10.5194/gmd-17-1765-2024
https://doi.org/10.5194/gmd-17-1765-2024
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

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Cited articles

Barboza, L., Li, B., Tingley, M. P., and Viens, F. G.: Reconstructing past temperatures from natural proxies and estimated climate forcings using short- and long-memory models, Ann. Appl. Stat., 8, 1966–2001, 2014. a
Christiansen, B., Schmith, T., and Thejll, P.: A surrogate ensemble study of climate reconstruction methods: Stochasticity and robustness, J. Climate, 22, 951–976, https://doi.org/10.1175/2008JCLI2301.1, 2009. a
Clement, A., Bellomo, K., Murphy, L. N., Cane, M. A., Mauritsen, T., Rädel, G., and Stevens, B.: The Atlantic Multidecadal Oscillation without a role for ocean circulation, Science, 350, 320–324, https://doi.org/10.1126/science.aab3980, 2015. a
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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.