Comparison of Physical to Numerical Mixing with Different Tracer Advection Schemes in Estuarine Environments

Kalra, Tarandeep S.; Li, Xiangyu; Warner, John C.; Geyer, W. Rockwell; Wu, Hui

doi:10.3390/jmse7100338

Cited by 17 publications

(13 citation statements)

References 49 publications

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“…A further analysis on the numerical aspects of this approach is shown in Kalra et al. (2019). For this application to the Hudson River Estuary, the numerical mixing is calculated directly and found to be small.…”

Section: Resultsmentioning

confidence: 99%

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Using Tracer Variance Decay to Quantify Variability of Salinity Mixing in the Hudson River Estuary

et al. 2020

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The salinity distribution in estuaries is controlled by the mixing of salt and fresh water, which is driven by many factors including tidal amplitude, freshwater inflow, bathymetry, wind stress, and waves. The timing and location of the mixing are dependent on stratification and shear in the water column, which vary tidally (Simpson et al., 1990) and over the spring neap cycle (Bowen & Geyer, 2003). There have been many previous approaches to identify and quantify the mechanisms of estuarine mixing. Early studies focused on constant and parameterized values of eddy diffusivity (Hansen & Rattray, 1965; Lerczak et al. 2006). However, the eddy diffusivity is not in itself a measure of estuarine mixing but rather of the turbulence that would cause mixing in the presence of a vertical gradient. Several recent studies of estuarine mixing (Burchard & Rennau, 2008; MacCready et al., 2018; Wang & Geyer, 2018) have proposed that the destruction of salinity variance provides the appropriate measure of the mixing of salt in an estuary. This destruction of scalar variance has long been addressed in context with turbulent microstructure as the quantity χ s (Nash & Moum, 2002). Not only does χ s relate directly to the irreversible process that formally represents mixing (in context with the entropy of mixing, Gregg, 1984), but it also has particular significance in context with estuaries in that the strength of the exchange flow is directly proportional to the volume integral of χ s (Mac-Cready et al., 2018).

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Section: Resultsmentioning

confidence: 99%

“…In addition, a measure of the numerical mixing was determined from the approach of Burchard and Rennau (2008). A further analysis on the numerical aspects of this approach is shown in Kalra et al (2019). For this application to the Hudson River Estuary, the numerical mixing is calculated directly and found to be small.…”

Section: Salinity Variancementioning

confidence: 99%

Using Tracer Variance Decay to Quantify Variability of Salinity Mixing in the Hudson River Estuary

et al. 2020

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“…The Generic Length Scale scheme (Umlauf & Burchard, 2003), with parameters chosen for the κ − ϵ closure (Warner et al., 2005), is used to represent unresolved turbulence. Salinity and temperature have been evolved using “Multidimensional Positive Definite Advection Transport Algorithm” known as MPDATA in ROMS (e.g., Kalra et al., 2019).…”

Section: Ocean State Ocean Modelmentioning

confidence: 99%

“…Palmer River,12. Taunton River. evolved using "Multidimensional Positive Definite Advection Transport Algorithm" known as MPDATA in ROMS (e.g., Kalra et al, 2019).…”

Section: Ocean State Ocean Modelmentioning

confidence: 99%

Consistent Predictability of the Ocean State Ocean Model Using Information Theory and Flushing Timescales

et al. 2021

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The Ocean State Ocean Model (OSOM) is an application of the Regional Ocean Modeling System spanning the Rhode Island waterways, including Narragansett Bay, Mt. Hope Bay, larger rivers, and the Block Island Shelf circulation from Long Island to Nantucket. This study discusses the physical aspects of the estuary (Narragansett and Mount Hope Bays and larger rivers) to evaluate physical circulation predictability. This estimate is intended to help decide if a forecast and prediction system is warranted, to prepare for coupling with biogeochemistry and fisheries models with widely disparate timescales, and to find the spin‐up time needed to establish the climatological circulation of the region. Perturbed initial condition ensemble simulations are combined with metrics from information theory to quantify the predictability of the OSOM forecast system–i.e., how long anomalies from different initial conditions persist. The predictability timescale in this model agrees with readily estimable timescales such as the freshwater flushing timescale evaluated using the total exchange flow (TEF) framework, indicating that the estuarine dynamics rather than chaotic transport is the dominant model behavior limiting predictions. The predictability of the OSOM is ∼7–40 days, varying with parameters, region, and season.

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“…We tested two schemes and found both accurately reproduced the online results offline, though U3C4 performed slightly better. Note, however, that online tracer advection performance itself depends on the dynamics involved; more information is available in Kalra et al (2019). Also note that MPDATA requires more runtime than U3C4 (Figure 4).…”

Section: C12 Header Filementioning

confidence: 99%

Performance of offline passive tracer advection in ROMS (v3.6, revision 904)

Thyng

Kobashi

Xomchuk

et al. 2020

Preprint

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Abstract. Offline advection schemes allow for low computational cost simulations using existing model output. This study presents the approach and assessment for passive offline tracer advection within the Regional Ocean Modeling System (ROMS). An advantage of running the code within ROMS itself is consistency in the numerics on and offline. We find that the offline tracer model is robust: after about 14 days of simulation (almost 60 advection timescales), the skill score comparing offline output to the online simulation using the TS_U3HADVECTION and TS_C4VADVECTION (3rd-order upstream horizontal advection and 4th-order centered vertical advection) tracer advection schemes is 99.6 % accurate for an offline time step 20 times larger than online, and online output saved with a period below the advection timescale. For tracer advection scheme MPDATA, accuracy is more variable with offline time step and forcing input frequency choices, but is still over 99 % for many reasonable choices. Both schemes are conservative. Important factors for maintaining high offline accuracy are: outputting from the online simulation often enough to resolve the advection timescale, forcing offline using realistic vertical salinity diffusivity values from the online simulation, and using double precision to save results.

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Comparison of Physical to Numerical Mixing with Different Tracer Advection Schemes in Estuarine Environments

Cited by 17 publications

References 49 publications

Using Tracer Variance Decay to Quantify Variability of Salinity Mixing in the Hudson River Estuary

Using Tracer Variance Decay to Quantify Variability of Salinity Mixing in the Hudson River Estuary

Consistent Predictability of the Ocean State Ocean Model Using Information Theory and Flushing Timescales

Performance of offline passive tracer advection in ROMS (v3.6, revision 904)

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