A reconstruction of global hydroclimate and dynamical variables over the Common Era

Nathan J. Steiger*, Jason E. Smerdon, Edward R. Cook, Benjamin I. Cook

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

94 Scopus citations


Hydroclimate extremes critically affect human and natural systems, but there remain many unanswered questions about their causes and how to interpret their dynamics in the past and in climate change projections. These uncertainties are due, in part, to the lack of long-term, spatially resolved hydroclimate reconstructions and information on the underlying physical drivers for many regions. Here we present the first global reconstructions of hydroclimate and associated climate dynamical variables over the past two thousand years. We use a data assimilation approach tailored to reconstruct hydroclimate that optimally combines 2,978 paleoclimate proxy-data time series with the physical constraints of an atmosphere - ocean climate model. The global reconstructions are annually or seasonally resolved and include two spatiotemporal drought indices, near-surface air temperature, an index of North Atlantic variability, the location of the intertropical convergence zone, and monthly Niño indices. This database, called the Paleo Hydrodynamics Data Assimilation product (PHYDA), will provide a critical new platform for investigating the causes of past climate variability and extremes, while informing interpretations of future hydroclimate projections.

Original languageAmerican English
Article number180086
JournalScientific data
StatePublished - 22 May 2018
Externally publishedYes

Bibliographical note

Funding Information:
We acknowledge the CESM1 (CAM5) Last Millennium Ensemble Community Project and the supercomputing resources provided by NSF/CISL/Yellowstone. This work was supported by the NOAA Climate and Global Change Postdoctoral Fellowship Program administered by UCAR's Visiting Scientist Programs. This work was also supported in part by the National Science Foundation under grants, AGS-1243204, AGS-1401400, AGS-1602581, AGS-1602920, and OISE-1743738. LDEO contribution number 8214. We also thank Mark Cane, Alexey Kaplan, and A. Park Williams for very helpful discussions in the development of this product.

Publisher Copyright:
© The Author(s) 2018.


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