The extreme warmth of particular intervals of geologic history cannot be simulated with climate models, which are constrained by the geologic proxy record to relatively modest increases in atmospheric carbon dioxide levels. Recent recognition that biological productivity controls the abundance of cloud condensation nuclei (CCN) in the unpolluted atmosphere provides a solution to this problem. Our climate simulations show that reduced biological productivity (low CCN abundance) provides a substantial amplification of CO2-induced warming by reducing cloud lifetimes and reflectivity. If the stress of elevated temperatures did indeed suppress marine and terrestrial ecosystems during these times, this long-standing climate enigma may be solved.

10.1126/science.1153883

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Citations (148)

A time-series analysis software package (Acycle) for paleoclimate research and education

Mingsong Li Linda A. Hinnov Lee R. Kump

A comprehensive, but simple-to-use software package for analysis of time-series has been developed for paleoclimate research and education. Acycle runs either in the MATLAB environment or as a stand-alone application on Windows and Macintosh OS X, and is available free of charge. Acycle provides prewhitening procedures with multiple options available to track or remove secular trends, and integrates various power spectral analysis approaches for detection and tracking of periodic signals. Acycle also provides a toolbox that evaluates astronomical signals in paleoclimate series, and estimates the most likely sedimentation rate by maximizing the correlation coefficient between power spectra of an astronomical solution and a paleoclimate series. Sedimentary noise models for sea-level variations are also included. Many of the functions are specific to cyclostratigraphy and astrochronology, and are not found in standard, statistical packages. For a demonstration, Acycle is applied to a paleoclimate series (iron content) from Core BH9/05 in the Paleogene Central Basin of Svalbard that samples the Paleocene-Eocene Thermal Maximum (PETM). Acycle detects significant astronomical forcing of the iron series and demonstrates a relatively stable sedimentation rate during and after the PETM event.

Paleoclimatology

10.1002/essoar.10500099.1

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Spatial patterns of climate change across the Paleocene–Eocene Thermal Maximum

Proceedings of the National Academy of Sciences (2022)

Jessica E. Tierney Jiang Zhu Mingsong Li Andy Ridgwell Gregory J. Hakim

The Paleocene–Eocene Thermal Maximum (PETM; 56 Ma) is one of our best geological analogs for understanding climate dynamics in a “greenhouse” world. However, proxy data representing the event are only available from select marine and terrestrial sedimentary sequences that are unevenly distributed across Earth’s surface, limiting our view of the spatial patterns of climate change. Here, we use paleoclimate data assimilation (DA) to combine climate model and proxy information and create a spatially complete reconstruction of the PETM and the climate state that precedes it (“PETM-DA”). Our data-constrained results support strong polar amplification, which in the absence of an extensive cryosphere, is related to temperature feedbacks and loss of seasonal snow on land. The response of the hydrological cycle to PETM warming consists of a narrowing of the Intertropical Convergence Zone, off-equatorial drying, and an intensification of seasonal monsoons and winter storm tracks. Many of these features are also seen in simulations of future climate change under increasing anthropogenic emissions. Since the PETM-DA yields a spatially complete estimate of surface air temperature, it yields a rigorous estimate of global mean temperature change (5.6 ∘ C; 5.4 ∘ C to 5.9 ∘ C, 95% CI) that can be used to calculate equilibrium climate sensitivity (ECS). We find that PETM ECS was 6.5 ∘ C (5.7 ∘ C to 7.4 ∘ C, 95% CI), which is much higher than the present-day range. This supports the view that climate sensitivity increases substantially when greenhouse gas concentrations are high.

Paleoclimatology

Intertropical Convergence Zone

Climate state

Transient climate simulation

10.1073/pnas.2205326119

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Citations (88)

7.9 Terrestrial Environments

Frontiers in earth sciences (2012)

Lee R. Kump Kalle Kirsimäe Victor A. Melezhik Martin Brasier Anthony E. Fallick

Terrestrial ecosystem

Biota