@misc{klockmann_gaussian_process_2021, 
author={Klockmann, M., Zorita, E.},
title={Gaussian Process Regression - A tool for improved climate index reconstructions},
year={2021},
howpublished = {conference lecture: Virtual;},
doi = {https://doi.org/10.5194/egusphere-egu21-4457},
abstract = {We implement the machine-learning method GPR for climate index reconstruction with the goal of preserving the amplitude of past climate variability. To test the framework in a controlled environment, we create pseudo-proxies from a coupled climate model simulation of the past 2000 years. In our test environment, the GPR strongly improves the reconstruction of the AMV with respect to a multi-linear Principal Component Regression. The amplitude of reconstructed variability is very close to the true variability even if non-climatic noise is added to the pseudo-proxies. In addition, the framework can directly take into account known proxy uncertainties and fit data-sets with a variable number of records in time. Thus, the GPR framework seems to be a highly suitable tool for robust and improved climate index reconstructions.},
note = {Online available at: \url{https://doi.org/10.5194/egusphere-egu21-4457} (DOI). Klockmann, M.; Zorita, E.: Gaussian Process Regression - A tool for improved climate index reconstructions. EGU General Assembly 2021. Virtual, 2021. DOI: 10.5194/egusphere-egu21-4457}}