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A statistics-based reconstruction of high-resolution global terrestrial climate for the last 800,000 years.

Mario KrappRobert M BeyerStephen L EdmundsonPaul J ValdesAndrea Manica
Published in: Scientific data (2021)
Curated global climate data have been generated from climate model outputs for the last 120,000 years, whereas reconstructions going back even further have been lacking due to the high computational cost of climate simulations. Here, we present a statistically-derived global terrestrial climate dataset for every 1,000 years of the last 800,000 years. It is based on a set of linear regressions between 72 existing HadCM3 climate simulations of the last 120,000 years and external forcings consisting of CO2, orbital parameters, and land type. The estimated climatologies were interpolated to 0.5° resolution and bias-corrected using present-day climate. The data compare well with the original HadCM3 simulations and with long-term proxy records. Our dataset includes monthly temperature, precipitation, cloud cover, and 17 bioclimatic variables. In addition, we derived net primary productivity and global biome distributions using the BIOME4 vegetation model. The data are a relevant source for different research areas, such as archaeology or ecology, to study the long-term effect of glacial-interglacial climate cycles for periods beyond the last 120,000 years.
Keyphrases
  • climate change
  • high resolution
  • electronic health record
  • molecular dynamics
  • big data
  • machine learning
  • computed tomography
  • monte carlo
  • high speed