Song, Zhaoyang; Latif, Mojib; Park, Wonsun; Zhang, Yuming (2020): δ18O and Model data for enhanced Greenland surface-temperature variability during the Last Glacial Maximum [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.922864
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Abstract:
Stable oxygen isotope records from central Greenland suggest disproportionally large long-term surface-air temperature (SAT) variability during the last glacial maximum (LGM) relative to preindustrial times. Large perturbations in mean atmospheric circulation and its variability forced by extensive Northern Hemisphere ice sheet coverage has been suggested as cause for the enhanced Greenland SAT variability. Here, we assess the factors driving Greenland SAT variability during the LGM by means of dedicated climate model simulations and find remote forcing from the Pacific of critical importance. Atmospheric teleconnections from the Interdecadal Pacific Oscillation (IPO), a multidecadal oscillation of sea-surface temperature in the Pacific Ocean, strongly intensify under LGM conditions, driving enhanced surface wind variability over Greenland, which in turn amplifies SAT variability by anomalous atmospheric heat transport. A major role of the IPO in forcing Greenland SAT variability also is supported by a number of models from the Paleoclimate Modelling Intercomparison Project Phase III.
Supplement to:
Song, Zhaoyang; Latif, Mojib; Park, Wonsun; Zhang, Yuming (2020): Interdecadal Pacific Oscillation Drives Enhanced Greenland Surface Temperature Variability During the Last Glacial Maximum. Geophysical Research Letters, 47(23), https://doi.org/10.1029/2020GL088922
Project(s):
Paleo Modelling (PalMod)
Parameter(s):
| # | Name | Short Name | Unit | Principal Investigator | Method/Device | Comment |
|---|---|---|---|---|---|---|
| 1 | Description | Description | Song, Zhaoyang | |||
| 2 | Binary Object | Binary | Song, Zhaoyang | |||
| 3 | Binary Object (File Size) | Binary (Size) | Bytes | Song, Zhaoyang | ||
| 4 | Binary Object (Media Type) | Binary (Type) | Song, Zhaoyang |
License:
Creative Commons Attribution 4.0 International (CC-BY-4.0)
Size:
26 data points