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Volume 14, issue 6 | Copyright
Clim. Past, 14, 947-967, 2018
https://doi.org/10.5194/cp-14-947-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 29 Jun 2018

Research article | 29 Jun 2018

Assessing the performance of the BARCAST climate field reconstruction technique for a climate with long-range memory

Tine Nilsen1, Johannes P. Werner2, Dmitry V. Divine3,1, and Martin Rypdal1 Tine Nilsen et al.
  • 1Department of Mathematics and Statistics, UiT – The Arctic University of Norway, 9037 Tromsø, Norway
  • 2Bjerknes Centre for Climate Research, 5020 Bergen, Norway
  • 3Norwegian Polar Institute, Fram Centre, 9296 Tromsø, Norway

Abstract. The skill of the state-of-the-art climate field reconstruction technique BARCAST (Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time) to reconstruct temperature with pronounced long-range memory (LRM) characteristics is tested. A novel technique for generating fields of target data has been developed and is used to provide ensembles of LRM stochastic processes with a prescribed spatial covariance structure. Based on different parameter setups, hypothesis testing in the spectral domain is used to investigate if the field and spatial mean reconstructions are consistent with either the fractional Gaussian noise (fGn) process null hypothesis used for generating the target data, or the autoregressive model of order 1 (AR(1)) process null hypothesis which is the assumed temporal evolution model for the reconstruction technique. The study reveals that the resulting field and spatial mean reconstructions are consistent with the fGn process hypothesis for some of the tested parameter configurations, while others are in better agreement with the AR(1) model. There are local differences in reconstruction skill and reconstructed scaling characteristics between individual grid cells, and the agreement with the fGn model is generally better for the spatial mean reconstruction than at individual locations. Our results demonstrate that the use of target data with a different spatiotemporal covariance structure than the BARCAST model assumption can lead to a potentially biased climate field reconstruction (CFR) and associated confidence intervals.

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The BARCAST climate field 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 not only reflect the statistics of the real climate, but they may very well be affected by the manipulation of the data.
The BARCAST climate field reconstruction method is tested using synthetic data experiments. It...
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