4.4 Article

Seasonality in the North Sea during the Allerod and Late Medieval Climate Optimum using bivalve sclerochronology

期刊

INTERNATIONAL JOURNAL OF EARTH SCIENCES
卷 98, 期 1, 页码 83-98

出版社

SPRINGER
DOI: 10.1007/s00531-008-0363-7

关键词

Paleoseasonality; Paleoclimate oscillations; Arctica islandica; Growth rates; Oxygen isotopes

资金

  1. German Research Foundation (DFG) [SCHO793/3]

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Seasonal temperature patterns may have changed through time in response to current global warming. However, the temporal resolution of available proxy records is not sufficient to quantify paleotemperature seasonality prior to anthropogenic forcing of the climate. In the present study, we reconstructed seasonal and inter-annual temperature patterns of the North Sea during the last 140 years, the Allerod Interglacial and the Late Medieval Climate Optimum using sclerochronological and delta O-18(aragonite) data from bivalve shells, Arctica islandica. On average, the climate during 1278-1353 AD was ca. 1.1A degrees C colder and seasonality was ca. 60% less than today. During the Allerod, long-term temperatures remained about 3.2A degrees C below present values, and absolute summer and winter anomalies were ca. -4 and -2.7A degrees C, respectively. However, seasonality was statistically indistinguishable from today. Long-term average temperatures compare well with existing data for the Late Medieval and Allerod, but detailed information on seasonality during the studied time intervals has never been presented before. Our data also demonstrated that annual instrumental and delta O-18(aragonite)-derived temperatures did not always match. This difference is explained by (1) NAO-driven salinity changes, which influence the temperature estimation from delta O-18(aragonite) and (2) food-driven changes in growth rates; portions of the shell that formed more rapidly are overrepresented in carbonate samples. Our study indicated that individual bivalve shells can open discrete, near-century long, ultra-high-resolution windows into the climate past. Such information can be vital for testing and verifying numerical climate models.

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