Natural Climate Oscillations May Counteract Red Sea Warming over the Coming Decades

Georgios Krokos, Vassilis P. Papadopoulos, Sarantis S. Sofianos, Hernando Ombao, Patryk Dybczak, Ibrahim Hoteit

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Recent reports of warming trends in the Red Sea raise concerns about the response of the basin's fragile ecosystem under an increasingly warming climate. Using a variety of available Sea Surface Temperature (SST) datasets, we investigate the evolution of Red Sea SST in relation to natural climate variability. Analysis of long-term SST datasets reveals a sequence of alternating positive and negative trends, with similar amplitudes and a periodicity of nearly 70 years associated with the Atlantic Multidecadal Oscillation (AMO). High warming rates reported recently appear to be a combined effect of global warming and a positive phase of natural SST oscillations. Over the next decades, the SST trend in the Red Sea purely related to global warming is expected to be counteracted by the cooling AMO phase. Regardless of the current positive trends, projections incorporating long-term natural oscillations suggest a possible decreasing effect on SST in the near future.
Original languageEnglish (US)
Pages (from-to)3454-3461
Number of pages8
JournalGeophysical Research Letters
Volume46
Issue number6
DOIs
StatePublished - Mar 28 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): REP/1/3268-01-01
Acknowledgements: The study was supported by King Abdullah University of Science and Technology (KAUST) under the "Virtual Red Sea Initiative", Award Number REP/1/3268-01-01. We acknowledge all data providers that made their datasets available for this study, and specifically thank the UK MetOffice Ocean for providing the OSTIA and HadISST products, NASA for providing the AVHRR-OI data, and NOAA for providing the ERSST data and the AMO time series.The SSA analysis was performed with routines developed in Matlab by Eric Breitenberger. We are also grateful to Dionysios Raitsos, John Gittings and Sabique Langodan for the valuable discussions during the preparation of the manuscript.

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