 |
| Titel |
Reconstructing Paleosalinity from δ18O during the Last Glacial Maximum, Last Interglacial and Late Holocene |
| VerfasserIn |
Max Holloway, Louise Sime, Joy Singarayer, Julia Tindall, Paul Valdes |
| Konferenz |
EGU General Assembly 2015
|
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250109098
|
| Publikation (Nr.) |
 EGU/EGU2015-8977.pdf |
|
|
|
|
|
| Zusammenfassung |
| Reconstructions of salinity are used to diagnose changes in the hydrological cycle and ocean
circulation. The most widely used method of determining past salinity uses oxygen isotope
(δOw) residuals, relying on a constant relationship between δOw and salinity throughout
time.
An isotope-enabled fully coupled General Circulation Model (GCM) has been used to
assess how the relationship between δOw and surface salinity varies in response to past
climate changes. We undertake simulations of the Late Holocene (LH), Last Glacial
Maximum (LGM), and Last Interglacial (LIG) focussed on 0 ky, 21ky, and 125 ky
respectively.
The results show considerable variability in the δOw-salinity relationship, with
large differences observed between spatial and temporal δOw-salinity gradients. We
find that the largest sources of uncertainty in salinity reconstructions are caused by
changes in regional freshwater budgets, ocean circulation, and sea ice regimes. These
can cause reconstruction uncertainties exceeding 4 psu. We find that paleosalinity
reconstructions in the South Atlantic, and Indian Oceans should be most robust,
since these regions exhibit relatively constant δOw-salinity relationships across
spatial and temporal scales. Largest uncertainties will affect North Atlantic and high
latitude paleosalinity reconstructions. Finally we show that it is very difficult to
generate reliable salinity estimates for regions of dynamic oceanography, such as
the North Atlantic Current, without additional constraints. Paleosalinity is a good
example where combining models and data can help constrain the terms affecting
δOw and thus improve the interpretation of δOw in relation to past climate change. |
|
|
|
|
|
|