von Reumont, Jonas, Hetzinger, Steffen, Garbe-Schönberg, Dieter, Manfrino, C. and Dullo, Christian (2018) Tracking interannual to multidecadal-scale climate variability in the Atlantic Warm Pool using central Caribbean coral data. Paleoceanography and Paleoclimatology, 33 (4). pp. 395-411. DOI 10.1002/2018PA003321.

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Abstract

Atlantic Warm Pool (AWP) climate variability is subject to multiple influences of remote and local forcing. However, shortage of observational data before the mid‐20th century and of long‐term sea surface temperature (SST) and climate records has hampered the detection and investigation of decadal‐ and longer‐scale variability. We present new seasonally resolved 125‐year records of coral δ18O and Sr/Ca variations in the Central Caribbean Sea (Little Cayman, Cayman Islands; Diploria strigosa). Both geochemical proxies show decreasing long‐term trends, indicating long‐term warming. Sr/Ca indicates much stronger regional warming than large‐scale grid‐SST data, while δ18O tracks large‐scale SST changes in the AWP. Seawater δ18O variations are reconstructed, indicating a drying trend over the past century.

High spatial correlation between coral δ18O and SST in the region of the Loop Current and Gulf Stream system suggests that Little Cayman is a sensitive location for detecting past large‐scale temperature variability beyond the central Caribbean region. More specifically, our δ18O data tracks changes in North Atlantic Oscillation (NAO) variability on decadal and multidecadal time scales providing insights into the temporal and spatial nonstationarity of the NAO. A combination of our δ18O record with two coral records from different Caribbean sites reveals high spatial correspondence between coral δ18O and SST variability in the North Atlantic subtropical gyre, where few instrumental measurements and proxies are available prior to the 20th century. Our results clearly demonstrate the potential of combining proxy data to provide information from sparsely sampled areas, helping to reduce uncertainty in model‐based projections.

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