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Investigating Mesozoic Climate Trends and Sensitivities With a Large Ensemble of Climate Model Simulations.

Jan LandwehrsGeorg FeulnerStefan PetriBenjamin SamesMichael Wagreich
Published in: Paleoceanography and paleoclimatology (2021)
The Mesozoic era (∼252 to 66 million years ago) was a key interval in Earth's evolution toward its modern state, witnessing the breakup of the supercontinent Pangaea and significant biotic innovations like the early evolution of mammals. Plate tectonic dynamics drove a fundamental climatic transition from the early Mesozoic supercontinent toward the Late Cretaceous fragmented continental configuration. Here, key aspects of Mesozoic long-term environmental changes are assessed in a climate model ensemble framework. We analyze so far the most extended ensemble of equilibrium climate states simulated for evolving Mesozoic boundary conditions covering the period from 255 to 60 Ma in 5 Myr timesteps. Global mean temperatures are generally found to be elevated above the present and exhibit a baseline warming trend driven by rising sea levels and increasing solar luminosity. Warm (Triassic and mid-Cretaceous) and cool (Jurassic and end-Cretaceous) anomalies result from pCO2 changes indicated by different reconstructions. Seasonal and zonal temperature contrasts as well as continental aridity show an overall decrease from the Late Triassic-Early Jurassic to the Late Cretaceous. Meridional temperature gradients are reduced at higher global temperatures and less land area in the high latitudes. With systematic sensitivity experiments, the influence of paleogeography, sea level, vegetation patterns, pCO2, solar luminosity, and orbital configuration on these trends is investigated. For example, long-term seasonality trends are driven by paleogeography, but orbital cycles could have had similar-scale effects on shorter timescales. Global mean temperatures, continental humidity, and meridional temperature gradients are, however, also strongly affected by pCO2.
Keyphrases
  • climate change
  • human health
  • molecular dynamics
  • convolutional neural network
  • neural network
  • computed tomography
  • machine learning
  • high resolution
  • image quality
  • monte carlo
  • life cycle