Constraining variability of the Atlantic Meridional Overturning Circulation at 25N from historical observations, 1980 to 2005

Abstract

The Atlantic Meridional Overturning Circulation (MOC) plays an important role in
maintaining western Europe’s moderate climate. Although expected to weaken under
increasing greenhouse gas concentrations, the magnitude of the response remains
uncertain. Monitoring of the Atlantic MOC strength at 25ºN commenced in 2004 as part
of the UK Rapid Climate Change programme. Prior to this, detection of such a slowing
of the MOC and its associated meridional heat transport was inhibited by the temporally
sparse observations of its strength. This thesis aims to extend the time series of Atlantic
MOC at 25ºN back to 1980 using historical observations, thus constraining the
magnitude of its recent interannual variability.
Analysis of transatlantic hydrographic sections at 25ºN show the annual average MOC
to have varied in strength by approximately 25% over the last 50 years, with weaker
overturning in 2004 than previously computed. Temporal resolution of the dataset is
increased through decomposition of the Atlantic MOC at this latitude into its Ekman
component computed from wind stress climatology, transport of the Florida Current
from cable observations and mid-ocean geostrophic transport from CTD or moored
temperature and pressure derived boundary dynamic height profiles. The resulting time
series constrain interannual fluctuations of the Ekman, Florida Straits and mid-ocean
baroclinic geostrophic transports at ± 1, ± 2, and ± 3 Sv respectively between 1980 and
2005. In addition to interannual fluctuations in strength, evidence is found for a 2 to 3
Sv weakening of the MOC between 1980 and 2005. The underlying changes of
increased mid-ocean southward transport of thermocline waters and decreased
southward flow of lower North Atlantic Deep Water are consistent in sign, although of
reduced magnitude, with the transatlantic hydrographic sections observations. This
thesis presents and discusses these findings along with the relative merits of different
datasets for detecting such variability.

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