On the control of subantarctic stratification by the ocean circulation

Small, R. Justin; DuVivier, Alice K.; Long, Matthew C.; Grooms, Ian; Large, William G.

doi:10.1007/s00382-020-05473-2

Cited by 12 publications

(11 citation statements)

References 76 publications

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“…Implicit in this view is a densification of SAZ waters by these fluxes, which drive the deep convection leading to SAMW production. However, the origin and renewal of SAZ waters has received less attention, except for a few modeling studies illustrating the contribution of along-ACC advection from subtropical-gyre western boundaries, specifically the Agulhas current (Small et al, 2021;Wang et al, 2014). Our analysis supports the generality of this finding, by attributing the subsurface salinity maximum characterizing SAZ deep-convection regions (DuVivier et al, 2018) to advection of warm, saline waters of subtropical origin.…”

Section: Discussionsupporting

confidence: 69%

“…Therefore, the properties of SAMW are a consequence of the processes which, acting over the previous annual cycle, determine winter mixed-layer characteristics. Such processes include air-sea fluxes (Holte et al, 2012;McCartney, 1977), northward Ekman transport of cool, fresh upper-ocean waters across the ACC (Rintoul & England, 2002), eddy-induced cross-frontal exchanges (Herraiz-Borreguero & Rintoul, 2010;Holte et al, 2013;Sallée et al, 2008) and advection of subtropical waters by the gyre-and mesoscale circulation (Small et al, 2021;Wang et al, 2014). Quantifying the variable interplay between these contributions is key to understand the sensitivity of SAMW formation rates and properties to climate variability (Gao et al, 2018;Meijers et al, 2019;Naveira Garabato et al, 2009;Portela et al, 2020;Qu et al, 2020;Rintoul & England, 2002), as well as the implications for oceanic heat (Gao et al, 2018) and carbon (Hauck et al, 2013) storage and the fertilization of low-latitude oceans (Ayers & Strutton, 2013).…”

mentioning

confidence: 99%

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Subtropical contribution to Subantarctic Mode Waters

Fernández-Castro

Mazloff

Williams

et al. 2022

Preprint

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<p>Subantarctic Mode Waters (SAMW), forming in the deep winter mixed layers in the Subantarctic Zone (SAZ) to the north of the Antarctic Circumpolar Current (ACC), connect the ocean thermocline with the atmosphere, contributing to ocean carbon and heat uptake and transporting high-latitude nutrients northward, to fuel primary production at low latitudes. Many aspects of SAMW formation are poorly understood due to the data scarcity during Austral winter. Here, we use biogeochemical Argo float observations to investigate the seasonal development, origin and significance of a subsurface salinity maximum in the SAMW formation regions. This conspicuous feature develops every summer in the seasonal thermocline of the SAMW formation regions as a consequence of the advection along the ACC of warmer and saltier waters from the western boundaries of the subtropical gyres, in particular the Agulhas Return current. The salinity maximum acts as a gatekeeper for SAMW ventilation, since it controls the seasonal evolution of stratification at the base of the mixed layer, modulating its rate of deepening during autumn and winter and re-stratifying the SAMW pool when winter mixing ceases. We also show that the subtropical influx, often overlooked, is key to understand the variability of SAMW properties, since it represents a leading order term in the heat and salt budgets at the formation regions. Finally, the analysis of the nitrate seasonal cycle at the SAMW formation regions as recorded by the Argo floats, revealed that the seasonal salinity increase goes along with a decrease in the concentration of this nutrient, as a consequence of the advection of subtropical waters containing low preformed nitrate. These results suggest that nutrient concentration in SAMW is controlled not only by the rate of upwelling of high-nutrient waters south of the ACC and the degree of biological drawdown during their northward transit, as frequently assumed, but also by the influx of subtropical waters, pointing to previously overlooked feedbacks in the redistribution of nutrients between high and low latitudes.</p>

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Section: Discussionsupporting

confidence: 69%

mentioning

confidence: 99%

Subtropical contribution to Subantarctic Mode Waters

Fernández-Castro

Mazloff

Williams

et al. 2022

Preprint

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“…The mixed layer depth simulation in CESM2 is therefore of interest in the context of understanding large‐scale biogeochemical distributions and the strength of the biological pump. Notably, the mixed layer depth in the model manifests as a result of interactions between the vertical mixing scheme (Large et al., 1994) and both parameterized (e.g., Danabasoglu et al., 2010; Fox‐Kemper et al., 2008; Gent & Mcwilliams, 1990) and resolved transport controlling stratification (Small et al., 2020). Figure 1 shows winter and summer distributions of mixed layer depth in CESM2 historical simulations compared with an observational estimate.…”

Section: Resultsmentioning

confidence: 99%

“…Wintertime mixed layer depths are too shallow in critical deepwater formation regions along the Antarctic margin and in the Subantarctic along the northern flank of the Antarctic Circumpolar Current (ACC) (Figure 1f). These latter biases are likely attributable to the representation of horizontal advection and insufficient transport of warm, salty subtropical waters into the ACC region (Small et al., 2020). The biases in the Subantarctic likely restrict Southern Ocean uptake of transient tracers (next section) (e.g., Terhaar et al., 2021).…”

Section: Resultsmentioning

confidence: 99%

Simulations With the Marine Biogeochemistry Library (MARBL)

Long

Moore

Lindsay

et al. 2021

J Adv Model Earth Syst

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The Marine Biogeochemistry Library (MARBL) is a prognostic ocean biogeochemistry model that simulates marine ecosystem dynamics and the coupled cycles of carbon, nitrogen, phosphorus, iron, silicon, and oxygen. MARBL is a component of the Community Earth System Model (CESM); it supports flexible ecosystem configuration of multiple phytoplankton and zooplankton functional types; it is also portable, designed to interface with multiple ocean circulation models. Here, we present scientific documentation of MARBL, describe its configuration in CESM2 experiments included in the Coupled Model Intercomparison Project version 6 (CMIP6), and evaluate its performance against a number of observational data sets. The model simulates present‐day air‐sea CO2 flux and many aspects of the carbon cycle in good agreement with observations. However, the simulated integrated uptake of anthropogenic CO2 is weak, which we link to poor thermocline ventilation, a feature evident in simulated chlorofluorocarbon distributions. This also contributes to larger‐than‐observed oxygen minimum zones. Moreover, radiocarbon distributions show that the simulated circulation in the deep North Pacific is extremely sluggish, yielding extensive oxygen depletion and nutrient trapping at depth. Surface macronutrient biases are generally positive at low latitudes and negative at high latitudes. CESM2 simulates globally integrated net primary production (NPP) of 48 Pg C yr−1 and particulate export flux at 100 m of 7.1 Pg C yr−1. The impacts of climate change include an increase in globally integrated NPP, but substantial declines in the North Atlantic. Particulate export is projected to decline globally, attributable to decreasing export efficiency associated with changes in phytoplankton community composition.

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“…Perhaps the sharp background meridional salinity gradient highlights horizontally advective MLD anomalies better than the temperature structure, which is more strongly coupled to the atmosphere. Conversely, perhaps the impacts of vertically variable (i.e., sheared) horizontal advection of salinity more directly influences aseasonal MLD variability than temperature via the upper‐ocean stratification budget (there is evidence for this effect during winter in the Southern Ocean [DuVivier et al., 2018 ; Small et al., 2020 ]). However, we find it difficult to draw conclusions about the underlying physics from the input sensitivities without a more detailed investigation, which is left for future work.…”

Section: Resultsmentioning

confidence: 99%

Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth From Dense Satellite and Sparse In Situ Observations

Foster

Gagne

2021

J Adv Model Earth Syst

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The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere‐ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal (<90 $< 90$ day) MLD variability. Yet, many multivariate observations‐based analyses include implicit modeled subseasonal MLD variability. One analysis method is optimal interpolation of in situ data, but the interior analysis can be improved by leveraging surface data with regression or variational approaches. Here, we demonstrate how machine learning methods and satellite sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid‐latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2° gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We validate our methodology by applying it to ocean model simulations. We find that incorporating sea surface data through a machine learning model improves the performance of spatiotemporal MLD variability estimation compared to optimal interpolation of Argo observations alone. These preliminary results are a promising first step for the application of machine learning to MLD prediction.

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On the control of subantarctic stratification by the ocean circulation

Cited by 12 publications

References 76 publications

Subtropical contribution to Subantarctic Mode Waters

Subtropical contribution to Subantarctic Mode Waters

Simulations With the Marine Biogeochemistry Library (MARBL)

Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth From Dense Satellite and Sparse In Situ Observations

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