Recent Wind-Driven Variability in Atlantic Water Mass Distribution and Meridional Overturning Circulation

Evans, Dafydd Gwyn; Toole, John M.; Forget, Gaël; Zika, Jan D.; Garabato, Alberto C. Naveira; Nurser, A. J. George; Yu, Lisan

doi:10.1175/jpo-d-16-0089.1

Cited by 45 publications

(60 citation statements)

References 49 publications

(49 reference statements)

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“…There are other interannual signals such as the coherent subtropical cooling and subpolar warming in 2010. The subtropical cooling has previously been shown to have been driven by a weak AMOC and hence heat transport at 26.5°N (Cunningham et al, ) with an important contribution driven by wind variations (Evans et al, ).…”

Section: Variabilitymentioning

confidence: 99%

“…In the subtropics the variability has been markedly different with interannual variability superimposed on a more gradual warming trend (Robson et al, ; Williams et al, ). The AMOC at 26.5°N has been monitored since 2004 by the RAPID‐MOCHA array (McCarthy et al, ) revealing interannual variability including a large, temporary weakening in winter 2009–2010, believed to be wind‐driven (Evans et al, ; McCarthy et al, ; Roberts, Waters, et al, ) that caused a cooling of the subtropics (Cunningham et al, ). The AMOC strength has also weakened since 2004, and has been found to be in a weaker state since 2008 (Smeed et al, ).…”

Section: Introductionmentioning

confidence: 99%

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The Mean State and Variability of the North Atlantic Circulation: A Perspective From Ocean Reanalyses

et al. 2019

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The observational network around the North Atlantic has improved significantly over the last few decades with subsurface profiling floats and satellite observations and the recent efforts to monitor the Atlantic Meridional Overturning Circulation (AMOC). These have shown decadal time scale changes across the North Atlantic including in heat content, heat transport, and the circulation. However, there are still significant gaps in the observational coverage. Ocean reanalyses integrate the observations with a dynamically consistent ocean model and can be used to understand the observed changes. However, the ability of the reanalyses to represent the dynamics must also be assessed. We use an ensemble of global ocean reanalyses to examine the time mean state and interannual-decadal variability of the North Atlantic ocean since 1993. We assess how well the reanalyses are able to capture processes and whether any understanding can be gained. In particular, we examine aspects of the circulation including convection, AMOC and gyre strengths, and transports. We find that reanalyses show some consistency, in particular showing a weakening of the subpolar gyre and AMOC at 50 • N from the mid-1990s until at least 2009 (related to decadal variability in previous studies), a strengthening and then weakening of the AMOC at 26.5 • N since 2000, and impacts of circulation changes on transports. These results agree with model studies and the AMOC observations at 26.5 • N since 2005. We also see less spread across the ensemble in AMOC strength and mixed layer depth, suggesting improvements as the observational coverage has improved.

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Section: Variabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Mean State and Variability of the North Atlantic Circulation: A Perspective From Ocean Reanalyses

et al. 2019

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“…The AMOC in the models is integrated from the bottom, so the simulated AMOC strength relative to the surface is defined as the difference between the maximum value (near 1,000 m) and the surface value (on the order of 1 Sv due to freshwater transport over the Atlantic at 26°N). FollowingEvans et al (2017), here the model overturning is computed just from the Eulerian mean transport, so we remove effects from parameterized mesoscale (in OM4p5) and submesoscale eddies.…”

mentioning

confidence: 99%

The GFDL Global Ocean and Sea Ice Model OM4.0: Model Description and Simulation Features

Adcroft

Anderson

Balaji

et al. 2019

J Adv Model Earth Syst

278

361

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We document the configuration and emergent simulation features from the Geophysical Fluid Dynamics Laboratory (GFDL) OM4.0 ocean/sea ice model. OM4 serves as the ocean/sea ice component for the GFDL climate and Earth system models. It is also used for climate science research and is contributing to the Coupled Model Intercomparison Project version 6 Ocean Model Intercomparison Project. The ocean component of OM4 uses version 6 of the Modular Ocean Model and the sea ice component uses version 2 of the Sea Ice Simulator, which have identical horizontal grid layouts (Arakawa C‐grid). We follow the Coordinated Ocean‐sea ice Reference Experiments protocol to assess simulation quality across a broad suite of climate‐relevant features. We present results from two versions differing by horizontal grid spacing and physical parameterizations: OM4p5 has nominal 0.5° spacing and includes mesoscale eddy parameterizations and OM4p25 has nominal 0.25° spacing with no mesoscale eddy parameterization. Modular Ocean Model version 6 makes use of a vertical Lagrangian‐remap algorithm that enables general vertical coordinates. We show that use of a hybrid depth‐isopycnal coordinate reduces the middepth ocean warming drift commonly found in pure z* vertical coordinate ocean models. To test the need for the mesoscale eddy parameterization used in OM4p5, we examine the results from a simulation that removes the eddy parameterization. The water mass structure and model drift are physically degraded relative to OM4p5, thus supporting the key role for a mesoscale closure at this resolution.

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“…A very large number of interesting regional studies is possible, bearing in mind the resolution problems near boundaries. As an example of what can be done regionally with salinity, can be seen in Wunsch and Heimbach (2013), Buckley et al (2014Buckley et al ( , 2015, Evans et al (2017), and Piecuch (2017)-all with a focus on the Atlantic Ocean.…”

Section: Regional Studiesmentioning

confidence: 99%

“…All observations have limits of sampling, random and bias errors, and finite duration. So, for example, even the revolutionary Argo datasets alone fail to adequately depict important physical processes (e.g., Evans et al 2017). The state estimate provides, in addition to the directly measured variables, all those required by or computable from a free-running general circulation model.…”

mentioning

confidence: 99%

A Dynamically Consistent, Multivariable Ocean Climatology

Fukumori

Heimbach

Ponte

et al. 2018

Bulletin of the American Meteorological Society

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A dynamically consistent 20-yr average ocean climatology based on monthly values during the years 1994–2013 has been produced from the most recent state estimate of the Estimating the Circulation and Climate of the Ocean (ECCO) project, globally, top to bottom. The estimate was produced from a least squares fit of a free-running ocean general circulation model to almost all available near-global data. Data coverage in space and time during this period is far more homogeneous than in any earlier interval and includes CTD, elephant seal, and Argo temperature and salinity profiles; sea ice coverage; full altimetric and gravity-field coverage; satellite sea surface temperatures; and the initializing meteorological coverage from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim). Dominant remaining data inhomogeneity arises from increasing coverage from the Argo profiles from about 2000 to the present. The state estimate exactly satisfies the primitive equations of the free-running Massachusetts Institute of Technology General Circulation Model (MITgcm) at all times and hence produces values satisfying the fundamental conservation laws of energy, freshwater, and so forth, permitting its use for climate change studies. Quantities such as calculated heat content depend upon all observations, not just temperature, for example, altimetric height and meteorological exchanges. Output files are publicly available in Network Common Data Form (netCDF) and MATLAB form and include hydrographic variables, three components of velocity, and pressure at all depths, as well as other variables, including inferred air–sea momentum and buoyancy fluxes, 3D mixing parameters, and sea ice cover.

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Recent Wind-Driven Variability in Atlantic Water Mass Distribution and Meridional Overturning Circulation

Cited by 45 publications

References 49 publications

The Mean State and Variability of the North Atlantic Circulation: A Perspective From Ocean Reanalyses

The Mean State and Variability of the North Atlantic Circulation: A Perspective From Ocean Reanalyses

The GFDL Global Ocean and Sea Ice Model OM4.0: Model Description and Simulation Features

A Dynamically Consistent, Multivariable Ocean Climatology

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