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. 2023 Sep 14;10(1):624.
doi: 10.1038/s41597-023-02489-1.

A pseudoproxy emulation of the PAGES 2k database using a hierarchy of proxy system models

Feng Zhu  1 , Julien Emile-Geay  2 , Kevin J Anchukaitis  3   4 , Nicholas P McKay  5 , Samantha Stevenson  6 , Zilu Meng  7
Affiliations

A pseudoproxy emulation of the PAGES 2k database using a hierarchy of proxy system models

Feng Zhu et al. Sci Data. .

Abstract

Paleoclimate reconstructions are now integral to climate assessments, yet the consequences of using different methodologies and proxy data require rigorous benchmarking. Pseudoproxy experiments (PPEs) provide a tractable and transparent test bed for evaluating climate reconstruction methods and their sensitivity to aspects of real-world proxy networks. Here we develop a dataset that leverages proxy system models (PSMs) for this purpose, which emulates the essential physical, chemical, biological, and geological processes that translate climate signals into proxy records, making these synthetic proxies more relevant to the real world. We apply a suite of PSMs to emulate the widely-used PAGES 2k dataset, including realistic spatiotemporal sampling and error structure. A hierarchical approach allows us to produce many variants of this base dataset, isolating the impact of sampling bias in time and space, representation error, sampling error, and other assumptions. Combining these various experiments produces a rich dataset ("pseudoPAGES2k") for many applications. As an illustration, we show how to conduct a PPE with this dataset based on emerging climate field reconstruction techniques.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The PAGES 2k Phase 2 network.
Fig. 2
Fig. 2
The spatiotemporal availability of the PAGES 2k pseudoproxy network with realistic and full temporal availability.
Fig. 3
Fig. 3
The dashboard for the tree ring width record “NAm_136” in dataset “ppwn_SNRinf_rta”. The unit “NA” stands for “not applicable” as the variable is a standardized index and thus unitless. “PSD” refers to power spectral density and is in the unit of power (squared unit of the proxy variable) per year.
Fig. 4
Fig. 4
The dashboard for the maximum latewood density record “NAm_134” in dataset “ppwn_SNRinf_rta”. The unit “NA” stands for “not applicable” as the variable is a standardized index and thus unitless. “PSD” refers to power spectral density and is in the unit of power (squared unit of the proxy variable) per year.
Fig. 5
Fig. 5
The dashboard for the coral δ18O record “Ocn_075” in dataset “ppwn_SNRinf_rta”. “PSD” refers to power spectral density and is in the unit of power (squared unit of the proxy variable) per year.
Fig. 6
Fig. 6
The dashboard for the coral Sr/Ca record “Ocn_067” in dataset “ppwn_SNRinf_rta”. “PSD” refers to power spectral density and is in the unit of power (squared unit of the proxy variable) per year.
Fig. 7
Fig. 7
The dashboard for the ice core δ18O record “Arc_029” in dataset “ppwn_SNRinf_rta”. “PSD” refers to power spectral density and is in the unit of power (squared unit of the proxy variable) per year.
Fig. 8
Fig. 8
The dashboard for the lake varve thickness record “Arc_025” in dataset “ppwn_SNRinf_rta”. “PSD” refers to power spectral density and is in the unit of power (squared unit of the proxy variable) per year.
Fig. 9
Fig. 9
Flow chart of the general procedure for pseudoproxy generation.
Fig. 10
Fig. 10
Spectral analysis of the pseudoproxy records in dataset “ppwn_SNRinf_rta” by proxy type. The gray curves denote the power spectral density (PSD, in the unit of power per year, i.e., squared unit of the proxy variable per year) of the real records, while the colored curves denote that of the pseudoproxy records.
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