Seasonal to decadal variability of Arctic Ocean heat content: A model‐based analysis and implications for autonomous observing systems

Lique, Camille; Steele, Michael

doi:10.1002/jgrc.20127

Cited by 24 publications

(25 citation statements)

References 91 publications

(121 reference statements)

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“…Due to inaccuracy in diagnosing heat budget terms (e.g., caused by interpolation) and missing heat diffusion terms in our model output, the mismatch between the ocean heat content changing rate and Arctic net heat flux can have the same order of magnitude as this value. Therefore, it is hard to carry out analysis of closed heat budget in this and previous modeling studies (e.g., Lique and Steele, 2013). In the following, we try to better understand the difference of ocean heat content between the two simulations by analyzing AW passive tracers.…”

Section: Heat Content and Water Mass Sourcesmentioning

confidence: 99%

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

et al. 2018

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Abstract. In the framework of developing a global modeling system which can facilitate modeling studies on Arctic Ocean and high-to midlatitude linkage, we evaluate the Arctic Ocean simulated by the multi-resolution Finite Element Sea ice-Ocean Model (FESOM). To explore the value of using high horizontal resolution for Arctic Ocean modeling, we use two global meshes differing in the horizontal resolution only in the Arctic Ocean (24 km vs. 4.5 km). The high resolution significantly improves the model's representation of the Arctic Ocean. The most pronounced improvement is in the Arctic intermediate layer, in terms of both Atlantic Water (AW) mean state and variability. The deepening and thickening bias of the AW layer, a common issue found in coarse-resolution simulations, is significantly alleviated by using higher resolution. The topographic steering of the AW is stronger and the seasonal and interannual temperature variability along the ocean bottom topography is enhanced in the high-resolution simulation. The high resolution also improves the ocean surface circulation, mainly through a better representation of the narrow straits in the Canadian Arctic Archipelago (CAA). The representation of CAA throughflow not only influences the release of water masses through the other gateways but also the circulation pathways inside the Arctic Ocean. However, the mean state and variability of Arctic freshwater content and the variability of freshwater transport through the Arctic gateways appear not to be very sensitive to the increase in resolution employed here. By highlighting the issues that are independent of model resolution, we address that other efforts including the improvement of parameterizations are still required.

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Section: Heat Content and Water Mass Sourcesmentioning

confidence: 99%

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

et al. 2018

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“…Warm water paths are examined using winter (January–March) and summer (July–September) mean temperature maps with mean velocity vectors in the core of the AW layer (266 m) (Figures a and b) and using time series of volume transport and heat content in the AW layer (T>2°C) through nine sections across the AW pathways (purple lines in Figure a and Table ). Heat content of the warm water layer (T>2°C) in each transect was computed as in Lique and Steele []:

\int_{z 0}^{z l} ρ_{0} C_{p} false(T false(t, x, y, z false) - T_{r e f} false) d z

with

ρ_{0}

the density of ocean water, C p the ocean specific heat (4000 J.kg −1 .K −1 ), and T ref a reference temperature (here −1.8°C).…”

Section: Warm Water Layer: Inferences From Model Outputsmentioning

confidence: 99%

“…Warm Water Pathways, Volume Transports, and Heat Contents in 2015 Warm water paths are examined using winter (January-March) and summer (July-September) mean temperature maps with mean velocity vectors in the core of the AW layer (266 m) (Figures 5a and 5b) and using time series of volume transport and heat content in the AW layer (T>28C) through nine sections across the AW pathways (purple lines in Figure 3a and Table 1). Heat content of the warm water layer (T>28C) in each transect was computed as in Lique and Steele [2013]:…”

Section: 1002/2016jc012424mentioning

confidence: 99%

Atlantic waters inflow north of Svalbard: Insights from IAOOS observations and Mercator Ocean global operational system during N-ICE2015

Koenig

Provost

Villacieros-Robineau

et al. 2017

J. Geophys. Res. Oceans

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As part of the N‐ICE2015 campaign, IAOOS (Ice Atmosphere Ocean Observing System) platforms gathered intensive winter data at the entrance of Atlantic Water (AW) inflow to the Arctic Ocean north of Svalbard. These data are used to examine the performance of the 1/12° resolution Mercator Ocean global operational ice/ocean model in the marginal ice zone north of Svalbard. Modeled sea‐ice extent, ocean heat fluxes, mixed layer depths and AW mass characteristics are in good agreement with observations. Model outputs are then used to put the observations in a larger spatial and temporal context. Model outputs show that AW pathways over and around the Yermak Plateau differ in winter from summer. In winter, the large AW volume transport of the West Spitsbergen Current (WSC) (∼4 Sv) proceeds to the North East through 3 branches: the Svalbard Branch (∼0.5 Sv) along the northern shelf break of Svalbard, the Yermak Branch (∼1.1 Sv) along the western slope of the Yermak Plateau and the Yermak Pass Branch (∼2.0 Sv) through a pass in the Yermak Plateau at 80.8°N. In summer, the AW transport in the WSC is smaller (∼2 Sv) and there is no transport through the Yermak Pass. Although only eddy‐permitting in the area, the model suggests an important mesoscale activity throughout the AW flow. The large differences in ice extent between winters 2015 and 2016 follow very distinct atmospheric and oceanic conditions in the preceding summer and autumn seasons. Convection‐induced upward heat fluxes maintained the area free of ice in winter 2016.

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“…[]. In particular, the simulation has been validated in the Arctic and the exchanges with the North Atlantic subpolar gyre have been compared against various data records, which suggest that despite its relatively low resolution it resolves major components of the transport and its low‐frequency variability [ Lique et al ., , ; Lique and Steele , ]. A comparison (not shown) with SSMI‐derived sea ice cover maps provided at National Snow and Ice Data Center indicate that the sea ice simulation reproduces the timing of sea ice formation and melt in the Labrador Sea.…”

Section: Datamentioning

confidence: 99%

Composition of freshwater in the spring of 2014 on the southern Labrador shelf and slope

Benetti

Reverdin

Lique

et al. 2017

J. Geophys. Res. Oceans

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The Labrador Current is an important conduit of freshwater from the Arctic to the interior North Atlantic subpolar gyre. Here we investigate the spatial variability of the freshwater sources over the southern Labrador shelf and slope during May–June 2014. Using measurements of seawater properties such as temperature, salinity, nutrients, and oxygen isotopic composition, we estimate the respective contributions of saline water of Atlantic and Pacific origins, of brines released during sea ice formation, and of freshwater from sea ice melt and meteoric water origins. On the southern Labrador shelf, we find a large brine signal and Pacific water influence indicating a large contribution of water from the Canadian Arctic. The brine signal implies that more than 4 m of sea ice formed upstream, either in the Arctic or in Baffin Bay and the northern Labrador Sea. Over the midshelf and slope at 52°N, we find a stronger influence of slope water from the West Greenland Current with a smaller contribution of Pacific water and no brine signal. Thus, there is advection of water from the slope region to the midshelf between 55°N and 52°N. Very freshwater with high meteoric content is found close to the coast in June 2014. Observations from 1995 and 2008 suggest a higher fraction of brine and Pacific water on the shelf compared to that observed in 2014.

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Seasonal to decadal variability of Arctic Ocean heat content: A model‐based analysis and implications for autonomous observing systems

Cited by 24 publications

References 91 publications

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

Atlantic waters inflow north of Svalbard: Insights from IAOOS observations and Mercator Ocean global operational system during N-ICE2015

Composition of freshwater in the spring of 2014 on the southern Labrador shelf and slope

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Seasonal to decadal variability of Arctic Ocean heat content: A model‐based analysis and implications for autonomous observing systems

Cited by 24 publications

References 91 publications

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

Atlantic waters inflow north of Svalbard: Insights from IAOOS observations and Mercator Ocean global operational system during N-ICE2015

Composition of freshwater in the spring of 2014 on the southern Labrador shelf and slope

Contact Info

Product

Resources

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A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4