The impact of Labrador Sea temperature and salinity variability on density and the subpolar AMOC in a decadal prediction system. Issue 23 (15th December 2016)

• Record Type: Journal Article
• Authors: Menary, Matthew B.; Hermanson, Leon; Dunstone, Nick J.
• Is Part Of: Geophysical research letters. Volume 43:Issue 23(2016)
• Page Start: 12, 217
• Page End: 12, 227
• Publication Date: 2016-12-15
• Subjects: decadal prediction -- North Atlantic -- Labrador Sea -- AMOC -- Atlantic Meridional Overturning Circulation -- subpolar gyre; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1002/2016GL070906
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: Labrador Sea density variability is important for Atlantic Meridional Overturning Circulation (AMOC) dynamics and hence decadal variability in the Atlantic. We investigate whether temperature or salinity dominate top 500 m interannual Labrador Sea density variability in gridded observations, an assimilation of the observations, and a set of multiannual hindcasts. We find that salinity dominates in the observations and assimilation. In the hindcasts salinity remains dominant for the first year but from year three these revert to the same temperature dominance seen in the underlying climate model. This is due to damping of the interannual salinity variability, possibly caused by unrealistically large convection that develops. Crucially, the hindcasts have high correlation skill in temperature/salinity throughout, but no skill in density, dynamic sea level, or the subpolar AMOC due to the incorrect drivers. This highlights the importance of correctly simulating both the sign and magnitude of temperature/salinity variability in a prediction system. Key Points: Over recent decades, gridded observations in the near‐surface Labrador Sea suggest interannual density variability is salinity driven Forecasts with a dynamical prediction model quickly switch to temperature‐driven density variability, explaining poor skill in density/AMOC This analysis framework may provide a way to assess some of the potential systematic biases in dynamical prediction systems