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Climate Dynamics

, Volume 47, Issue 9–10, pp 2991–3010 | Cite as

The role of local atmospheric forcing on the modulation of the ocean mixed layer depth in reanalyses and a coupled single column ocean model

  • Byju Pookkandy
  • Dietmar Dommenget
  • Nicholas Klingaman
  • Scott Wales
  • Christine Chung
  • Claudia Frauen
  • Holger Wolff
Article

Abstract

The role of local atmospheric forcing on the ocean mixed layer depth (MLD) over the global oceans is studied using ocean reanalysis data products and a single-column ocean model coupled to an atmospheric general circulation model. The focus of this study is on how the annual mean and the seasonal cycle of the MLD relate to various forcing characteristics in different parts of the world’s oceans, and how anomalous variations in the monthly mean MLD relate to anomalous atmospheric forcings. By analysing both ocean reanalysis data and the single-column ocean model, regions with different dominant forcings and different mean and variability characteristics of the MLD can be identified. Many of the global oceans’ MLD characteristics appear to be directly linked to the different atmospheric forcing characteristics at different locations. Here, heating and wind-stress are identified as the main drivers; in some, mostly coastal, regions the atmospheric salinity forcing also contributes. The annual mean MLD is more closely related to the annual mean wind-stress and the MLD seasonality is more closely related to the seasonality in heating. The single-column ocean model, however, also points out that the MLD characteristics over most global ocean regions, and in particular in the tropics and subtropics, cannot be maintained by local atmospheric forcings only, but are also a result of ocean dynamics that are not simulated in a single-column ocean model. Thus, lateral ocean dynamics are essential in correctly simulating observed MLD.

Keywords

Ocean mixed layer depth Atmospheric forcings Coupled single column ocean model Annual mean Seasonal variability Flux correction 

Notes

Acknowledgments

The authors would like to thank Australian National Computational Infrastructure, in Canberra, for providing computational platform for simulation of the ACCESS-KPP coupled model. The ARC Climate System Science (CE110001028) supported this study. Nicholas Klingaman was funded by the National Centre for Atmospheric Science-Climate, a collaborative centre of the Natural Environment Research Council, under agreement R8/H12/83/001.

Supplementary material

382_2016_3009_MOESM1_ESM.pdf (1.9 mb)
Supplementary material 1 (PDF 1931 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Byju Pookkandy
    • 1
  • Dietmar Dommenget
    • 1
  • Nicholas Klingaman
    • 2
  • Scott Wales
    • 3
  • Christine Chung
    • 4
  • Claudia Frauen
    • 5
  • Holger Wolff
    • 1
  1. 1.ARC Centre of Excellence for Climate System Science, School of Earth Atmosphere and EnvironmentMonash UniversityClaytonAustralia
  2. 2.National Centre for Atmospheric Science-Climate, Department of MeteorologyUniversity of ReadingReadingUnited Kingdom
  3. 3.ARC Centre of Excellence for Climate System Science, School of Earth SciencesUniversity of MelbourneMelbourneAustralia
  4. 4.Bureau of MeteorologyMelbourneAustralia
  5. 5.CNRM-GAME (Météo-France/CNRS)ToulouseFrance

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