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Synchronous tropical and polar temperature evolution in the Eocene.

Marlow J Cramwinckel1, Matthew Huber2, Ilja J Kocken3

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Reconstructing Eocene climates with high atmospheric carbon dioxide (CO2) helps predict future climate change. This study reveals parallel tropical and deep-ocean temperature shifts, driven by greenhouse gases, not ocean circulation.

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Area of Science:

  • Palaeoclimatology
  • Climate Modeling
  • Geochemistry

Background:

  • Understanding past warm climates with high atmospheric CO2 is vital for future climate change projections.
  • The Eocene epoch (56-34 million years ago) was the warmest in 66 million years, but continuous tropical temperature data is scarce.
  • Previous studies established Eocene polar warmth, highlighting the need for tropical temperature records to quantify global climate evolution.

Observation:

  • A new continuous Eocene equatorial sea surface temperature record was created using biomarker palaeothermometry from Atlantic Ocean sediments.
  • This record, combined with existing data, forms a 26-million-year multi-proxy stack of Eocene tropical climate.
  • Tropical and deep-ocean temperatures showed parallel changes, influenced by long-term trends and short-lived events.

Findings:

  • Greenhouse gas forcing, not ocean circulation changes, is identified as the primary driver of Eocene climate.
  • A consistent linear relationship between tropical and deep-ocean temperatures suggests a stable polar amplification factor during the Eocene.
  • Quantitative climate model comparisons estimate Eocene global average temperatures were significantly higher than preindustrial levels (e.g., 29°C in early Eocene vs. 14.4°C preindustrial).

Implications:

  • The findings support greenhouse gas forcing as the main driver of Eocene climate dynamics.
  • A constant polar amplification factor implies predictable temperature increases at higher latitudes relative to the tropics.
  • Eocene Earth system sensitivity estimates align with high-end previous assessments, offering crucial data for climate models and future climate predictions.