4.6 Article

North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part II: Inter-annual to decadal variability

期刊

OCEAN MODELLING
卷 97, 期 -, 页码 65-90

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.ocemod.2015.11.007

关键词

Global ocean - sea-ice modelling; Ocean model comparisons; Atmospheric forcing; Inter-annual to decadal variability and mechanisms; Atlantic meridional overturning circulation variability; Variability in the North Atlantic

资金

  1. U. S. National Science Foundation (NSF)
  2. NSF
  3. U. S. Department of Energy
  4. NOAA Climate Program Office under Climate Variability and Predictability Program [NA090AR4310163, NA130AR4310138, NA13OAR4310136]
  5. NSF Collaborative Research EaSM2 [OCE-1243015]
  6. Department of Climate Change and Energy Efficiency
  7. Bureau of Meteorology
  8. CSIRO
  9. National Computational Infrastructure facility at the Australian National University
  10. Helmholtz Climate Initiative REKLIM (Regional Climate Change) project.
  11. Research Council of Norway through the EarthClim [207711/E10]
  12. NOTUR/NorStore projects
  13. Centre for Climate Dynamics at the Bjerknes Centre for Climate Research
  14. Italian Ministry of Education, University, and Research
  15. Italian Ministry of Environment, Land, and Sea under the GEMINA project
  16. Russian Science Foundation [14-27-00126]
  17. Co-Operative Project RACE - Regional Atlantic Circulation and Global Change
  18. German Federal Ministry for Education and Research (BMBF) [03F0651B]
  19. BNP-Paribas foundation via the PRECLIDE project under the CNRS research convention [30023488]
  20. Ocean Model Development Panel
  21. NERC [noc010010] Funding Source: UKRI
  22. Division Of Ocean Sciences [1243015] Funding Source: National Science Foundation
  23. Natural Environment Research Council [noc010010] Funding Source: researchfish

向作者/读者索取更多资源

Simulated inter-annual to decadal variability and trends in the North Atlantic for the 1958-2007 period from twenty global ocean - sea-ice coupled models are presented. These simulations are performed as contributions to the second phase of the Coordinated Ocean-ice Reference Experiments (CORE-II). The study is Part II of our companion paper (Danabasoglu et al., 2014) which documented the mean states in the North Atlantic from the same models. A major focus of the present study is the representation of Atlantic meridional overturning circulation (AMOC) variability in the participating models. Relationships between AMOC variability and those of some other related variables, such as subpolar mixed layer depths, the North Atlantic Oscillation (NAO), and the Labrador Sea upper-ocean hydrographic properties, are also investigated. In general, AMOC variability shows three distinct stages. During the first stage that lasts until the mid-to late-1970s, AMOC is relatively steady, remaining lower than its long-term (1958-2007) mean. Thereafter, AMOC intensifies with maximum transports achieved in the mid-to late-1990s. This enhancement is then followed by a weakening trend until the end of our integration period. This sequence of low frequency AMOC variability is consistent with previous studies. Regarding strengthening of AMOC between about the mid-1970s and the mid-1990s, our results support a previously identified variability mechanism where AMOC intensification is connected to increased deep water formation in the subpolar North Atlantic, driven by NAO-related surface fluxes. The simulations tend to show general agreement in their temporal representations of, for example, AMOC, sea surface temperature (SST), and subpolar mixed layer depth variabilities. In particular, the observed variability of the North Atlantic SSTs is captured well by all models. These findings indicate that simulated variability and trends are primarily dictated by the atmospheric datasets which include the influence of ocean dynamics from nature superimposed onto anthropogenic effects. Despite these general agreements, there are many differences among the model solutions, particularly in the spatial structures of variability patterns. For example, the location of the maximum AMOC variability differs among the models between Northern and Southern Hemispheres. (C) 2015 Elsevier Ltd. All rights reserved.

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