A new Eulerian method to estimate “spicy” Agulhas leakage in climate models

Putrasahan, Dian; Beal, Lisa M.; Kirtman, Ben P.

doi:10.1002/2015gl064482

Cited by 12 publications

(15 citation statements)

References 39 publications

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“…This time scale is comparable to the frequency of shedding of Agulhas rings as estimated from observations (Schouten et al 2000). That all hindcast simulations share this common feature (which also comes out when using Putrasahan et al 2015 method, not shown), despite differences in spatial characteristics along Goodhopeline, is a major finding of this study. This suggests that, despite differences in Agulhas rings (size, pathway through Goodhope line, vertical structure), they all represent the dominant mechanism of variability of AL, including the coarser resolution ORCA025 and INDa0.12.…”

Section: Temporal Variability Of Agulhas Leakagesupporting

confidence: 62%

“…When using Putrasahan et al (2015) integration method, this slope increases to −2 , but there is still no change of slope at sub-annual frequencies as observed for the other models. Hence we conclude that AL as a mechanism of variability is not represented in this model.…”

Section: Temporal Variability Of Agulhas Leakagementioning

confidence: 78%

“…IPSL-CM5 NET and TOTAL transports reveal significant peaks of variability at seasonal time scale. Yet, those peaks disappear when using Putrasahan et al (2015) method of integration of AL (not shown), hence we conclude that none of the model simulations diagnosed here present a seasonality in AL.…”

Section: Temporal Variability Of Agulhas Leakagementioning

confidence: 92%

“…For these models, the method developed by Putrasahan et al (2015) based on spiciness may be more efficient at capturing AL. This method is also suggested to capture better the extent at depth of Agulhas Rings.…”

Section: Quantifying Agulhas Leakagementioning

confidence: 99%

“…These thermohaline properties can then be utilised in order to establish an integration domain over which volume transport is calculated (Rouault et al 2009). The limitation of such an approach is that by imposing water mass criteria, the leakage in eddy-rich regions may be underestimated, although similar methods have been shown to reproduce the variability well (van Sebille et al 2010b;Loveday et al 2014;Putrasahan et al 2015). The opposing method is to use a Lagrangian approach in which numerical floats are released into the eastern Agulhas Current domain, and after a certain time period the amount of floats that have crossed a certain section to the west of the retroflection defines the volume transport of the leakage.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal characteristics of Agulhas leakage: a model inter-comparison study

et al. 2016

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west of the southern tip of Africa , is used to identify features that are robust regardless of the model used and takes into account the thermohaline biases of each model. When determined by a passive tracer method, 60 % of the magnitude of Agulhas leakage is captured and more than 80 % of its temporal fluctuations, suggesting that the method is appropriate for investigating the variability of Agulhas leakage. In all simulations but one, the major driver of variability is associated with mesoscale features passing through the section. High resolution (<1/10 • ) hindcast models agree on the temporal (2-4 cycles per year) and spatial (300-500 km) scales of these features corresponding to observed Agulhas Rings. Coarser resolution models (<1/4 • ) reproduce similar time scale of variability of Agulhas leakage in spite of their difficulties in representing the Agulhas rings properties. A coarser resolution climate model (2 • ) does not resolve the spatio-temporal mechanism of variability of Agulhas leakage. Hence it is expected to underestimate the contribution of Agulhas Current System to climate variability.

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Section: Temporal Variability Of Agulhas Leakagesupporting

confidence: 62%

Section: Temporal Variability Of Agulhas Leakagementioning

confidence: 78%

Section: Temporal Variability Of Agulhas Leakagementioning

confidence: 92%

Section: Quantifying Agulhas Leakagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal characteristics of Agulhas leakage: a model inter-comparison study

et al. 2016

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A New Improved Estimation of Agulhas Leakage Using Observations and Simulations of Lagrangian Floats and Drifters

2020

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A new estimate of Agulhas leakage transport is calculated using profiling floats and drifters.Since Richardson's seminal estimate of 15 Sv in 2007, the number of floats and drifters passing through the Agulhas Current has quadrupled. Within uncertainties we find the same leakage percentages as Richardson, with 34% of drifters leaking at the surface and 21% of floats leaking at 1,000 m depth. We find that the drifters tend to follow a northward leakage pathway via the Benguela Current compared to the northwestward leakage pathway of the floats along the Agulhas Ring corridor. We simulate the isobaric and profiling behavior of the floats and drifters using two high resolution models and two offline Lagrangian tracking tools, quantifying for the first time the sampling biases associated with the observations. We find that the isobaric bias cannot be robustly simulated but likely causes an underestimate of observed leakage by one or two Sverdrups. The profiling behavior of the floats causes no significant bias in the leakage. Fitting a simulated vertical leakage profile to the observed leakage percentages from the floats and drifters and using the mean Agulhas transport observed by a moored array at 34 • S we find an improved Agulhas leakage transport of 21.3 Sv, with an estimated error of 4.7 Sv. Our new leakage transport is higher primarily because we account for leakage at depths down to 2,000 m, while Richardson considered only the top 1,000 m of the water column. Plain Language SummaryWe calculate a new estimation of Agulhas leakage using floats and drifters. Since most recent estimate of 15 Sv was calculated in 2007, we have had four times as many observations pass through the Agulhas Current. We find more drifters leak at the surface than floats at intermediate depth. The drifters follow a northward leakage passage compared to the northwestward passage of the floats. Using two models and two particle tracking tools, we mimic the behavior of these floats and drifters to better understand and quantify the sampling biases associated with these observations. Combining the results from the observations and these simulations, we calculate a new improved leakage transport of 21.3 Sv with an error of 4.7 Sv.Quantifying Agulhas leakage is difficult, with previous estimates ranging from 2 to 20 Sv (1 Sv = 1 × 10 6 m 3 s −1 ). The Cape Basin features the highest eddy kinetic energy outside a western boundary current system in the world (Chelton et al., 2011). Due to this highly turbulent nature, the mixing and stirring

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Interannual Variability of the South Atlantic Ocean Heat Content in a High‐Resolution Versus a Low‐Resolution General Circulation Model

Gronholz

Dong

Lopez

et al. 2020

Geophysical Research Letters

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High-and low-resolution coupled climate model simulations are analyzed to investigate the impact of model resolution on South Atlantic Ocean Heat Content (OHC) variability at interannual time scale and the associated physical mechanisms. In both models, ocean heat transport convergence is the main driver of OHC variability on interannual time scales. However, the origin of the meridional heat transport (MHT) convergence anomalies differs in the two models. In the high-resolution model, OHC variability is dominated by MHT from the south. This is in contrast to the low-resolution model, where OHC variability is largely controlled by MHT from the north. In the low-resolution simulation, both the Ekman and geostrophic transports contribute to the OHC variability, whereas in the high-resolution model, the geostrophic transport dominates. These differences highlight the importance of model resolution to appropriately represent ocean dynamics in the South Atlantic Ocean and associated impacts on regional and global climate. Plain Language Summary In this study we analyze heat content changes of the upper South Atlantic Ocean and the impact of model resolution on these changes. Results from two numerical simulations are compared. One simulation with high-resolution allows smaller-scale processes directly, while the other simulation with low-resolution does not. In both simulations oceanic heat transport dominates the ocean heat content changes on interannual time scale, while atmospheric fluxes play a secondary role. The heat anomalies, however, originate from different regions in the two simulations. While the oceanic heat transport from the south dominates in the high-resolution simulation, oceanic heat transport from the north dominates in the low-resolution simulation. Furthermore, wind-induced surface heat transport plays a significant role in the low-resolution while the heat transport in the high-resolution simulation is dominated by changes in the ocean density field at depth. These results suggest fundamentally different driving mechanisms between the simulations and highlight the importance of model resolution to appropriately represent ocean dynamics in the South Atlantic Ocean and associated impacts on large-scale climate.

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A new Eulerian method to estimate “spicy” Agulhas leakage in climate models

Cited by 12 publications

References 39 publications

Spatio-temporal characteristics of Agulhas leakage: a model inter-comparison study

Spatio-temporal characteristics of Agulhas leakage: a model inter-comparison study

A New Improved Estimation of Agulhas Leakage Using Observations and Simulations of Lagrangian Floats and Drifters

Interannual Variability of the South Atlantic Ocean Heat Content in a High‐Resolution Versus a Low‐Resolution General Circulation Model

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