Selection of optimal proxy locations for temperature field reconstructions using evolutionary algorithms

Fernando Jaume-Santero,Ricardo García-Herrera,Sancho Salcedo-Sanz,David Barriopedro,Natalia Calvo

doi:10.1038/s41598-020-64459-6

Fernando Jaume-Santero, Ricardo García-Herrera + Show 3 more

Open Access

https://doi.org/10.1038/s41598-020-64459-6

Copy DOI

Abstract

In the Era of exponential data generation, increasing the number of paleoclimate records to improve climate field reconstructions might not always be the best strategy. By using pseudo-proxies from different model ensembles, we show how biologically-inspired artificial intelligence can be coupled with different reconstruction methods to minimize the spatial bias induced by the non-homogeneous distribution of available proxies. The results indicate that small subsets of records situated over representative locations can outperform the reconstruction skill of the full proxy network, even in more realistic pseudo-proxy experiments and observational datasets. These locations highlight the importance of high-latitude regions and major teleconnection areas to reconstruct annual global temperature fields and their responses to external forcings and internal variability. However, low frequency temperature variations such as the transition between the Medieval Climate Anomaly and the Little Ice Age are better resolved by records situated at lower latitudes. According to our idealized experiments a careful selection of proxy locations should be performed depending on the targeted time scale of the reconstructed field.

Highlights

In the Era of exponential data generation, increasing the number of paleoclimate records to improve climate field reconstructions might not always be the best strategy
To better constrain this bias, other sources of uncertainty are avoided by using the Community Earth System Model Last Millennium Ensemble[19] (CESM-LME) as a surrogated reality, where pseudo-proxies matching the locations of the PAGES-2k archive are artificially generated. For these idealized conditions of the PAGES-2k proxy network we generate global Climate Field Reconstructions (CFRs) of annual temperature for the last millennium simulation of the CESM-LME that are biased by the uneven distribution of real proxies
CFR techniques[20] are coupled with an evolutionary algorithm to explore if optimized subsets of PAGES-2k locations can be used instead without sacrificing the reconstruction skill. These pseudo-proxy experiments allow us to address the following questions in the perfectly known model’s world: Can we quantify the spatial bias due to the uneven distribution of records? Do we need all available records of the PAGES-2k network to maximize the skill of global temperature field reconstructions of the last millennium? If not, how many records are required to reconstruct the temperature of the last millennium without degrading the skill? Can we find a subset of PAGES-2k proxy locations that reduces the spatial bias of the full-proxy network?

Summary

Introduction

In the Era of exponential data generation, increasing the number of paleoclimate records to improve climate field reconstructions might not always be the best strategy. Annual temperature global patterns of the first full-forcing CESM-LME member simulation spanning the 850– 2005 period of the Common Era (CE) are chosen as the target fields to reconstruct from pseudo-proxies[21] at the 569 grid-points matching the locations of the PAGES-2k archive.

Results

Conclusion