Journal cover Journal topic
Climate of the Past An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/cp-2018-17
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
13 Mar 2018
Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Climate of the Past (CP) and is expected to appear here in due course.
How wrong are climate field reconstruction techniques in reconstructing a climate with long-range memory?
Tine Nilsen1, Johannes P. Werner2, and Dmitry V. Divine3,1 1Department of Mathematics and Statistics, University of Tromsø The Arctic University of Norway, Tromsø, Norway
2Bjerknes Centre for Climate Research and Department for Earth Science, University of Bergen, Bergen, Norway
3Norwegian Polar Institute, Tromsø, Norway
Abstract. The Bayesian hierarchical model BARCAST (Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time) climate field reconstruction (CFR) technique, and idealized input data are used in the pseudoproxy experiments of this study. Ensembles of targets are generated from fields of long-range memory stochastic processes using a novel approach. The range of experiment setups include input data with different levels of persistence and levels of proxy noise, but without any form of external forcing. The input data are thereby a simplistic alternative to standard target data extracted from general circulation model (GCM) simulations. Ensemble-based temperature reconstructions are generated, representing the European landmass for a millennial time period. Hypothesis testing in the spectral domain is then used to investigate if the field and spatial mean reconstructions are consistent with either the fractional Gaussian noise (fGn) null hypothesis used for generating the target data, or the autoregressive model of order one (AR(1)) null hypothesis which is the assumed temperature model for this reconstruction technique. The study reveals that the resulting field and spatial mean reconstructions are consistent with the fGn hypothesis for most of the parameter configurations. There are local differences in reconstructed scaling characteristics between individual grid cells, and a generally better agreement with the fGn model for the spatial mean reconstruction than at individual locations. The discrepancy from an fGn is most evident for the high-frequency part of the reconstructed signal, while the long-range memory is better preserved at frequencies corresponding to decadal time scales and longer. Selected experiment setups were found to give reconstructions consistent with the AR(1) model. Reconstruction skill is measured on an ensemble member basis using selected validation metrics. Despite the mismatch between the BARCAST temporal covariance model and the model of the target, the ensemble mean was in general found to be consistent with the target data, while the estimated confidence intervals are more affected by this discrepancy. Our results show that the use of target data with a different spatiotemporal covariance structure than the BARCAST model assumption can lead to a potentially biased CFR reconstruction and associated confidence intervals, because of the wrong model assumptions.
Citation: Nilsen, T., Werner, J. P., and Divine, D. V.: How wrong are climate field reconstruction techniques in reconstructing a climate with long-range memory?, Clim. Past Discuss., https://doi.org/10.5194/cp-2018-17, in review, 2018.
Tine Nilsen et al.
Tine Nilsen et al.

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Short summary
One temperature reconstruction method is tested using synthetic data experiments. It is demonstrated that the output reconstructions have altered statistical properties compared with the input data, but they are also not necessarily consistent with the model assumption of the reconstruction method. The conclusion is that the statistical properties of a reconstruction do not only reflect the statistics of the real climate, but may very well be affected by the manipulation of the data.
One temperature reconstruction method is tested using synthetic data experiments. It is...
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