The Meridional overturning circulation (MOC) is one crucial component in Earth's climate system, redistributing heat round the globe. The abyssal limb of the MOC is fed by the deep water formation near the poles. A basic requirement for any successful climate model simulation is the ability to reproduce this circulation correctly. The deep water formation itself, convection, occurs on smaller scales than the climate model grid size. Therefore the convection process needs to be parameterized. It is, however, somewhat unclear how well the parameterizations which are developed for turbulence can reproduce the deep convection and associated water mass transformations.
The convection in the Greenland Sea was studied with 1-D turbulence model GOTM and with data from three Argo floats. The model was run over the winter 2010-2011 with ERA-Interim and NCEP/NCAR atmospheric forcings and with three different mixing parameterizations, k-e, k-kL (Mellor-Yamada) and KPP. Furthermore, the effects of mesoscale spatial variations in the atmospheric forcing data were tested by running the model with forcings taken along the floats' paths (Lagrangian approach) and from the floats' median locations (Eulerian approach).
The convection was found to happen by gradual mixed layer deepening. It caused salinity decrease in the Recirculating Atlantic Water (RAW) layer just below the surface while in the deeper layers salinity and density increase was clearly visible. A slight temperature decrease was observed in whole water column above the convection depth. Atmospheric forcing had the strongest effect on the model results. ERA-interim forcing produced model output closer to the observations, but the convection begun too early with both forcings and both generated too low temperatures in the end. The salinity increase at mid-depths was controlled mainly by the RAW layer, but also atmospheric freshwater flux was found to affect the end result. Furthermore, NCEP/NCAR freshwater flux was found to be large enough (negative) to become a clear secondary driving factor for the convection. The results show that mixing parameterization mainly alters the timing of convection. KPP parameterization produced clearly too fast convection while k-e parameterization produced output which was closest to the observations. The results using Lagrangian and Eulerian approaches were ambiguous in the sense that neither of them was systematically closer to the observations. This could be explained by the errors in the reanalyzes arising from their grid size. More conclusive results could be produced with the aid of finer scale atmospheric data. The results, however, clearly indicate that atmospheric variability in scales of 100 km produces quantifiable differences in the results.
