A global multiproxy database for temperature reconstructions of the Common Era

doi:10.1038/SDATA.2017.88

Abstract:

Reproducible climate reconstructions of the Common Era (1 CE to present) are key to placing industrial-era warming into the context of natural climatic variability. Here we present a community-sourced database of temperature-sensitive proxy records from the PAGES2k initiative. The database gathers 692 records from 648 locations, including all continental regions and major ocean basins. The records are from trees, ice, sediment, corals, speleothems, documentary evidence, and other archives. They range in length from 50 to 2000 years, with a median of 547 years, while temporal resolution ranges from biweekly to centennial. Nearly half of the proxy time series are significantly correlated with HadCRUT4.2 surface temperature over the period 1850–2014. Global temperature composites show a remarkable degree of coherence between high- and low-resolution archives, with broadly similar patterns across archive types, terrestrial versus marine locations, and screening criteria. The database is suited to investigations of global and regional temperature variability over the Common Era, and is shared in the Linked Paleo Data (LiPD) format, including serializations in Matlab, R and Python.

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A global multiproxy database for temperature reconstructions of the Common Era

Citations

No evidence for globally coherent warm and cold periods over the preindustrial Common Era

Consistent multi-decadal variability in global temperature reconstructions and simulations over the Common Era.

Holocene global mean surface temperature, a multi-method reconstruction approach

Palaeoclimate constraints on the impact of 2 °C anthropogenic warming and beyond

Mid-latitude net precipitation decreased with Arctic warming during the Holocene.

References

Controlling the false discovery rate: a practical and powerful approach to multiple testing

An introduction to the bootstrap

A Simple Sequentially Rejective Multiple Test Procedure

An Introduction to the Bootstrap

Sparse inverse covariance estimation with the graphical lasso

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