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Published on: September 5, 2018
Agulhas leakage dynamics affects decadal variability in Atlantic overturning circulation
A Biastoch1, C W Böning, J R E Lutjeharms
1Leibniz-Institut für Meereswissenschaften, Düsternbrooker Weg 20, 24105 Kiel, Germany. abiastoch@ifm-geomar.de
Climate predictions need understanding ocean circulation dynamics. Southern Hemisphere signals from the Agulhas leakage region significantly influence decadal meridional overturning circulation (MOC) variability in the North Atlantic.
Area of Science:
- Oceanography
- Climate Science
- Geophysics
Background:
- Predicting decadal climate evolution hinges on understanding the meridional overturning circulation (MOC).
- Ocean measurement programs in the North Atlantic reveal significant intraseasonal to interannual MOC variability.
- Long-term MOC transport changes are poorly documented due to sparse historical data.
Purpose of the Study:
- To investigate the drivers of decadal MOC variability.
- To explore potential Southern Hemisphere influences on North Atlantic MOC dynamics.
- To provide a more comprehensive understanding of MOC variability for climate prediction.
Main Methods:
- Analysis of a comprehensive ocean model simulation.
- Tracking dynamic signals from the Agulhas leakage region.
- Comparing Northern and Southern Hemisphere contributions to MOC variability.
Main Results:
- Decadal MOC variability in the North Atlantic is influenced by factors beyond deep water formation in the Arctic.
- Dynamic signals from the Agulhas leakage region in the Southern Hemisphere contribute significantly to North Atlantic MOC.
- The Southern Hemisphere's contribution to MOC variability is of a similar magnitude to the Northern Hemisphere's.
Conclusions:
- Observed MOC changes require considering influences from both Northern and Southern Hemispheres.
- The Agulhas leakage region is a critical source of decadal MOC variability signals in the North Atlantic.
- Accurate climate prediction necessitates incorporating Southern Hemisphere ocean dynamics into MOC models.
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