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Climatic Change

, Volume 91, Issue 1–2, pp 11–27 | Cite as

Detecting potential changes in the meridional overturning circulation at 26˚N in the Atlantic

  • Johanna Baehr
  • Klaus Keller
  • Jochem Marotzke
Open Access
Original Article

Abstract

We analyze the ability of an oceanic monitoring array to detect potential changes in the North Atlantic meridional overturning circulation (MOC). The observing array is ‘deployed’ into a numerical model (ECHAM5/MPI-OM), and simulates the measurements of density and wind stress at 26°N in the Atlantic. The simulated array mimics the continuous monitoring system deployed in the framework of the UK Rapid Climate Change program. We analyze a set of three realizations of a climate change scenario (IPCC A1B), in which – within the considered time-horizon of 200 years – the MOC weakens, but does not collapse. For the detection analysis, we assume that the natural variability of the MOC is known from an independent source, the control run. Our detection approach accounts for the effects of observation errors, infrequent observations, autocorrelated internal variability, and uncertainty in the initial conditions. Continuous observation with the simulated array for approximately 60 years yields a statistically significant (p < 0.05) detection with 95 percent reliability assuming a random observation error of 1 Sv (1 Sv = 106 m3 s−1). Observing continuously with an observation error of 3 Sv yields a detection time of about 90 years (with 95 percent reliability). Repeated hydrographic transects every 5 years/ 20 years result in a detection time of about 90 years/120 years, with 95 percent reliability and an assumed observation error of 3 Sv. An observation error of 3 Sv (one standard deviation) is a plausible estimate of the observation error associated with the RAPID UK 26°N array.

Keywords

Observation Error Meridional Heat Transport Atlantic Thermohaline Circulation Springer Climatic Change Unforced System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science + Business Media B.V. 2007

Authors and Affiliations

  1. 1.Max Planck Institute for MeteorologyHamburgGermany
  2. 2.Department of GeosciencesThe Pennsylvania State UniversityPhiladelphiaUSA

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