Articles | Volume 18, issue 12
https://doi.org/10.5194/cp-18-2643-2022
https://doi.org/10.5194/cp-18-2643-2022
Research article
 | 
20 Dec 2022
Research article |  | 20 Dec 2022

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

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Cited articles

Amrhein, D. E., Hakim, G. J., and Parsons, L. A.: Quantifying structural uncertainty in paleoclimate data assimilation with an application to the Last Millennium, Geophys. Res. Lett., 47, e2020GL090485, https://doi.org/10.1029/2020GL090485, 2020. 
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Short summary
A bidirectional long short-term memory (LSTM) neural network was employed for the first time for past temperature field reconstructions. The LSTM method tested in our experiments using a limited calibration and validation dataset shows worse reconstruction skills compared to traditional reconstruction methods. However, a certain degree of reconstruction performance achieved by the nonlinear LSTM method shows that skill can be achieved even when using small samples with limited datasets.