Parameterization of mixed layer eddies. III: Implementation and impact in global ocean climate simulations

Fox‐Kemper, Baylor; Danabasoglu, Gökhan; Ferrari, Raffaele; Griffies, Stephen M.; Hallberg, Robert; Holland, Marika M.; Maltrud, Mathew; Peacock, Synte; Samuels, Bonita L.

doi:10.1016/j.ocemod.2010.09.002

Cited by 281 publications

(292 citation statements)

References 84 publications

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“…Moreover, there is no apparent relationship between stratification, MLD, and the vertical mixing parameterisation. In particular, the parameterisation designed by Fox-Kemper et al (2011) to improve the mixed layer representation (present in the models marked with a black bullet point, Table 2) does not consistently result in better performances than those models without the parameterisation. This lack of relationship between MLD biases and vertical mixing parameterisation was already found by Huang et al (2014) for the summer MLD.…”

Section: Heat and Saltmentioning

confidence: 95%

North Atlantic deep water formation and AMOC in CMIP5 models

Heuzé¹

2017

Ocean Sci.

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Abstract. Deep water formation in climate models is indicative of their ability to simulate future ocean circulation, carbon and heat uptake, and sea level rise. Present-day temperature, salinity, sea ice concentration and ocean transport in the North Atlantic subpolar gyre and Nordic Seas from 23 CMIP5 (Climate Model Intercomparison Project, phase 5) models are compared with observations to assess the biases, causes and consequences of North Atlantic deep convection in models. The majority of models convect too deep, over too large an area, too often and too far south. Deep convection occurs at the sea ice edge and is most realistic in models with accurate sea ice extent, mostly those using the CICE model. Half of the models convect in response to local cooling or salinification of the surface waters; only a third have a dynamic relationship between freshwater coming from the Arctic and deep convection. The models with the most intense deep convection have the warmest deep waters, due to a redistribution of heat through the water column. For the majority of models, the variability of the Atlantic Meridional Overturning Circulation (AMOC) is explained by the volumes of deep water produced in the subpolar gyre and Nordic Seas up to 2 years before. In turn, models with the strongest AMOC have the largest heat export to the Arctic. Understanding the dynamical drivers of deep convection and AMOC in models is hence key to realistically forecasting Arctic oceanic warming and its consequences for the global ocean circulation, cryosphere and marine life.

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Section: Heat and Saltmentioning

confidence: 95%

North Atlantic deep water formation and AMOC in CMIP5 models

Heuzé¹

2017

Ocean Sci.

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“…Mesoscale eddy transport uses the method developed by Ferrari et al (2010), modifying the isoneutral method developed by Gent and McWilliams (1990). The restratification effect of submesoscale eddies uses the theory developed by FoxKemper et al (2008) and implementation by Fox-Kemper et al (2011). The horizontal viscosity uses the anisotropic scheme of Large et al (2001) for better representation of equatorial currents.…”

Section: Ocean Componentmentioning

confidence: 99%

GEOS-5 seasonal forecast system

et al. 2017

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Since June 2013, GEOS-5 forecasts of the Arctic sea-ice distribution were provided to the Sea-Ice Outlook project. The seasonal forecast output data includes surface fields, atmospheric and ocean fields, as well as sea ice thickness and area, and soil moisture variables. The current paper aims to document the characteristics of the GEOS-5 seasonal forecast system and to highlight forecast biases and skills of selected variables (sea surface temperature, air temperature at 2 m, precipitation and sea ice extent) to be used as a benchmark for the future GMAO seasonal forecast systems and to facilitate comparison with other global seasonal forecast systems. Once a month, sea surface temperature from a suite of 11 ensemble forecasts is contributed to the North American Multi-Model Ensemble (NMME) consensus project, which compares and distributes seasonal forecasts of ENSO events. Keywords Global forecast · Seasonal prediction · NMME · GEOS-5 · ENSO · Forecast skill AbbreviationsThis paper is a contribution to the special collection on the North American Multi-Model Ensemble (NMME) seasonal prediction experiment. The special collection focuses on documenting the use of the NMME system database for research ranging from predictability studies, to multi-model prediction evaluation and diagnostics, to emerging applications of climate predictability for subseasonal to seasonal predictions.This special issue is coordinated by Annarita Mariotti (NOAA), Heather Archambault (NOAA), Jin Huang (NOAA), Ben Kirtman (University of Miami) and Gabriele Villarini (University of Iowa). Electronic supplementary materialThe online version of this article

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“…Their dynamics and resulting impact on vertical mixing are not properly understood or accounted for in the numerical models. Recent progress includes the promising implementation by Fox-Kemper et al (2011), however, the application in the Arctic, under sea ice, merits further research.…”

Section: Sub-mesoscale Eddies Fronts and Other Processesmentioning

confidence: 99%

Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review

et al. 2014

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Abstract. The Arctic climate system includes numerous highly interactive small-scale physical processes in the atmosphere, sea ice, and ocean. During and since the International Polar Year 2007-2009, significant advances have been made in understanding these processes. Here, these recent advances are reviewed, synthesized, and discussed. In atmospheric physics, the primary advances have been in cloud physics, radiative transfer, mesoscale cyclones, coastal, and fjordic processes as well as in boundary layer processes and surface fluxes. In sea ice and its snow cover, advances have been made in understanding of the surface albedo and its relationships with snow properties, the internal structure of sea ice, the heat and salt transfer in ice, the formation of superimposed ice and snow ice, and the small-scale dynamics of sea ice. For the ocean, significant advances have been related to exchange processes at the ice-ocean interface, diapycnal mixing, double-diffusive convection, tidal currents and diurnal resonance. Despite this recent progress, some of these small-scale physical processes are still not sufficiently understood: these include wave-turbulence interactions in the atmosphere and ocean, the exchange of heat and salt at the ice-ocean interface, and the mechanical weakening of sea ice. Many other processes are reasonably well understood as stand-alone processes but the challenge is to understand their interactions with and impacts and feedbacks on other processes. Uncertainty in the parameterization of small-scale processes continues to be among the greatest challenges facing climate modelling, particularly in high latitudes. Further improvements in parameterization require new year-round field campaigns on the Arctic sea ice, closely combined with satellite remote sensing studies and numerical model experiments.

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Parameterization of mixed layer eddies. III: Implementation and impact in global ocean climate simulations

Cited by 281 publications

References 84 publications

North Atlantic deep water formation and AMOC in CMIP5 models

North Atlantic deep water formation and AMOC in CMIP5 models

GEOS-5 seasonal forecast system

Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review

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