The Influence of High-Frequency Atmospheric Forcing on the Circulation and Deep Convection of the Labrador Sea

Holdsworth, Amber M.; Myers, Paul G.

doi:10.1175/jcli-d-14-00564.1

Cited by 50 publications

(59 citation statements)

References 55 publications

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“…Noticeably, most of the winds with wind speed ≥10 m/s, which are commonly associated with transitory storms (Wang et al, ), are smoothed out by the low‐pass filter. The temperature peaks associated with high‐frequency atmospheric phenomena are also reduced, but the daily mean temperature is not significantly affected by the filter (Figure c) in agreement with Holdsworth and Myers (). Hence, with the purpose of highlighting the difference in the atmospheric forcing between the CONTROL and CALM simulations, we hereafter adopt “high‐frequency winds” to refer the “high‐frequency atmospheric phenomena,” which are absent in the CALM simulation.…”

Section: Methodssupporting

confidence: 86%

Assessing the Role of High‐Frequency Winds and Sea Ice Loss on Arctic Phytoplankton Blooms in an Ice‐Ocean‐Biogeochemical Model

et al. 2019

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The long‐term trend of increasing phytoplankton net primary production (NPP) in the Arctic correlates with increasing light penetration due to sea ice loss. However, recent studies suggest that enhanced stormy wind mixing may also play a significant role enhancing NPP. Here, we isolate the role of sea ice and stormy winds (hereafter high‐frequency winds) using an eddy‐permitting ice‐ocean‐biogeochemical model configured for the North Atlantic and the Arctic. In the model, the presence of high‐frequency winds stimulates nutrient upwelling by producing an earlier and longer autumn‐winter mixing period with deeper mixing layer. The early onset of autumn mixing results in nutrients being brought‐up to near‐surface waters before the light becomes the dominant limiting factor, which leads to the autumn bloom. The enhanced mixing results in higher nutrient concentrations in spring and thus a large spring bloom. The model also shows significant iron limitation in the Labrador Sea, which is intensified by high‐frequency winds. The effect of sea ice loss on NPP was found to be regionally dependent on the presence of high‐frequency winds. This numerical study suggests high‐frequency winds play significant role increasing NPP in the Arctic and sub‐Arctic by alleviating phytoplankton nutrient limitation and that the isolated effect of sea ice loss on light plays a comparatively minor role.

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Section: Methodssupporting

confidence: 86%

Assessing the Role of High‐Frequency Winds and Sea Ice Loss on Arctic Phytoplankton Blooms in an Ice‐Ocean‐Biogeochemical Model

et al. 2019

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“…Finally, previous modeling studies have shown that the deep winter MLDs in the Labrador Sea are highly sensitive to subseasonal wind forcing (Holdsworth and Myers, ; Wu et al, ). We also find that deep mixing in the Labrador Sea is highly sensitive to subseasonal winds, but we also show that other sites of deep MLDs around the globe are not especially sensitive to subseasonal winds.…”

Section: Discussionmentioning

confidence: 88%

“…However, suppressing the effects of intrinsic oceanic variability by averaging all these grid points into regional hot spots shows that the seasonal cycle of MLD is reduced in the LP scenario (relative to CTL) in all these regions (Figure ). The Labrador Sea is most notable (as in Holdsworth and Myers, ). There, the seasonal cycle amplitude exceeds 1,000 m in some locations, and the subseasonal winds are crucial to sustaining this large seasonal cycle amplitude in CTL.…”

Section: Resultsmentioning

confidence: 99%

Global Impacts of Subseasonal (<60 Day) Wind Variability on Ocean Surface Stress, Buoyancy Flux, and Mixed Layer Depth

Whitt

Nicholson²,

Carranza

2019

JGR Oceans

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Subseasonal surface wind variability strongly impacts the annual mean and subseasonal turbulent atmospheric surface fluxes. However, the impacts of subseasonal wind variability on the ocean are not fully understood. Here, we quantify the sensitivity of the ocean surface stress ( ), buoyancy flux (B), and mixed layer depth (MLD) to subseasonal wind variability in both a one-dimensional (1-D) vertical column model and a three-dimensional (3-D) global mesoscale-resolving ocean/sea ice model. The winds are smoothed by time filtering the pseudo-stresses, so the mean stress is approximately unchanged, and some important surface flux feedbacks are retained. The 1-D results quantify the sensitivities to wind variability at different time scales from 120 days to 1 day at a few sites. The 3-D results quantify the sensitivities to wind variability shorter than 60 days at all locations, and comparisons between 1-D and 3-D results highlight the importance of 3-D ocean dynamics. Globally, subseasonal winds explain virtually all of subseasonal variance, about half of subseasonal B variance but only a quarter of subseasonal MLD variance. Subseasonal winds also explain about a fifth of the annual mean MLD and a similar and spatially correlated fraction of the mean friction velocity, u * = √ | |∕ sw where sw is the density of seawater. Hence, the subseasonal MLD variance is relatively insensitive to subseasonal winds despite their strong impact on local B and variability, but the mean MLD is relatively sensitive to subseasonal winds to the extent that they modify the mean u * , and both of these sensitivities are modified by 3-D ocean dynamics. Key Points: • We quantify the global impact of subseasonal winds on ocean surface fluxes and mixed-layer depth (MLD) in a mesoscale-resolving model • Globally, subseasonal winds are responsible for about a fifth of the annual average MLD and about a quarter of the subseasonal MLD variance • The increase in the mean MLD with subseasonal winds is caused by a higher friction velocity, but other factors modify the sensitivity Supporting Information: • Supporting Information S1A few previous studies have looked into the sensitivity of the climatological MLD to subseasonal atmospheric variability in 3-D global ocean circulation models by explicitly modifying the wind forcing in ocean models using grids that do not fully resolve mesoscales. For example, Lee and Liu (2005) explore the impact of diurnal winds on the MLD and sea surface temperature using an eddy-parameterizing (nominal 1 • resolution) global ocean model. Their model is forced with daily averaged fluxes, except for the wind stresses, which are averaged over 12 hr or subsampled every 24 hr. The annual and zonal mean MLD increases by up to a maximum of about 10 m with 12-hr winds compared to subsampled 24-hr winds, and the response of the MLD is largely attributable to enhanced vertical mixing by high-frequency winds at middle-to-high latitudes. More recently, Condron and Renfrew (2013) parameterize the effects of unresolved mesoscale a...

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“…A map of the model domain with the respective horizontal grid resolution is shown in Figure a. This regional configuration has been used in the past for studying the circulation and deep convection in the Labrador Sea [ Holdsworth and Myers , ] and the spreading of Greenland freshwater in the subarctic seas [ Dukhovskoy et al ., ]. The model domain of this configuration covers the whole North Atlantic and the Nordic Sea (including the Gulf of Mexico in the west and the Mediterranean Sea in the east) with open boundaries at 20°S and the Bering Strait.…”

Section: Methodsmentioning

confidence: 99%

Temperature flux carried by individual eddies across 47°N in the Atlantic Ocean

et al. 2017

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Surface geostrophic velocity fields derived from satellite altimetry between January 1993 and April 2014 are used to detect and investigate eddies in the North Atlantic between 40°N–55°N and 60°W–10°W. Focus is on a zonal section along 47°N, roughly at the boundary between the subpolar and the subtropical gyres. Sea surface temperature data are used to quantify the temperature anomalies associated with eddies and the respective surface temperature fluxes related to these eddies. Identified eddy pathways across 47°N are related to the mean background velocity from full‐depth ship observations carried out on 11 cruises between 2003 and 2014. The analysis is repeated in two model simulations with 1/4° and 1/12° horizontal resolution, respectively, for the period 2002–2013. The analysis reveals almost 37,000 altimeter‐derived eddies with a lifetime longer than 1 week in the area. The highest number of eddies is found along the pathway of the North Atlantic Current, roughly following the 4000 m isobath, and on the Grand Banks of Newfoundland. Time series of temperature fluxes by eddies crossing 47°N reveal that single isolated eddies with large SST signatures contribute ∼25% to the surface temperature flux. Relating the observed eddies to the observed top‐to‐bottom velocity distribution at 47°N points to the existence of eddy pathways across 47°. The highest‐temperature fluxes are linked to the fastest and most pronounced current branches in the western Newfoundland Basin. While there are fewer eddies in both model simulations, the key findings are consistent between the observations and the two model simulations.

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The Influence of High-Frequency Atmospheric Forcing on the Circulation and Deep Convection of the Labrador Sea

Cited by 50 publications

References 55 publications

Assessing the Role of High‐Frequency Winds and Sea Ice Loss on Arctic Phytoplankton Blooms in an Ice‐Ocean‐Biogeochemical Model

Assessing the Role of High‐Frequency Winds and Sea Ice Loss on Arctic Phytoplankton Blooms in an Ice‐Ocean‐Biogeochemical Model

Global Impacts of Subseasonal (<60 Day) Wind Variability on Ocean Surface Stress, Buoyancy Flux, and Mixed Layer Depth

Temperature flux carried by individual eddies across 47°N in the Atlantic Ocean

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