A multi-resolution approach to global ocean modeling

Ringler, Todd D.; Petersen, Mark; Higdon, Robert L.; Jacobsen, Doug; Jones, Philip W.; Maltrud, Mathew

doi:10.1016/j.ocemod.2013.04.010

Cited by 291 publications

(311 citation statements)

References 59 publications

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“…FESOM1.4 (Wang et al, 2014), SELFE (Zhang and Baptista, 2008) and SCHISM (Zhang et al, 2016). In contrast, MPAS (Ringler et al, 2013) is based on hexagonal meshes using a finite volume approach. Danilov (2013) provides an account of the issues of unstructured mesh modelling, and what is clear from that review is that selecting a solution approach or grid arrangement, for example, on the basis of a lack of computational modes or formal accuracy is far from straightforward, and must be left to detailed investigations in idealised and realistic cases.…”

Section: Options For Multiscale Modellingmentioning

confidence: 99%

“…the finite element model FESOM1.4 was reported to be ∼ 10 times slower than comparable structured grid models (Wang et al, 2014) and experience with the FV-COM in the northwest European shelf suggest this model is ∼ 5 times slower than NEMO. More recent work suggests a very different picture: MPAS quotes a penalty of 3.4 compared with POP (Ringler et al, 2013) and the finite volume FESOM2 code reports a through-put 5 times faster than FESOM1.4. The comparison of this model with two finer-resolution structured grid models in Table 3 suggests this model is, if anything, more efficient.…”

Section: Scalability and Efficiency Of Ocean Modelsmentioning

confidence: 99%

“…S is a factor for models that need unstructured meshes, which, following the discussion above, we take to range from 3.4 (e.g. Ringler et al, 2013) to 1 (parity between structured and unstructured cases). Here, we focus on resource rather than turnaround time (SYPD) and, without the scalability and time-stepping improvements identified above, an increased resource may only be utilisable by waiting longer for a finer-resolution model to finish.…”

Section: The Comparative Cost Of Ocean Modelsmentioning

confidence: 99%

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Prospects for improving the representation of coastal and shelf seas in global ocean models

et al. 2017

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Abstract. Accurately representing coastal and shelf seas in global ocean models represents one of the grand challenges of Earth system science. They are regions of immense societal importance through the goods and services they provide, hazards they pose and their role in global-scale processes and cycles, e.g. carbon fluxes and dense water formation. However, they are poorly represented in the current generation of global ocean models. In this contribution, we aim to briefly characterise the problem, and then to identify the important physical processes, and their scales, needed to address this issue in the context of the options available to resolve these scales globally and the evolving computational landscape.We find barotropic and topographic scales are well resolved by the current state-of-the-art model resolutions, e.g. nominal 1/12 • , and still reasonably well resolved at 1/4 • ; here, the focus is on process representation. We identify tides, vertical coordinates, river inflows and mixing schemes as four areas where modelling approaches can readily be transferred from regional to global modelling with substantial benefit. In terms of finer-scale processes, we find that a 1/12 • global model resolves the first baroclinic Rossby radius for only ∼ 8 % of regions < 500 m deep, but this increases to ∼ 70 % for a 1/72 • model, so resolving scales globally requires substantially finer resolution than the current state of the art.We quantify the benefit of improved resolution and process representation using 1/12 • global-and basin-scale northern North Atlantic nucleus for a European model of the ocean (NEMO) simulations; the latter includes tides and a k-ε vertical mixing scheme. These are compared with global stratification observations and 19 models from CMIP5. In terms of correlation and basin-wide rms error, the high-resolution models outperform all these CMIP5 models. The model with tides shows improved seasonal cycles compared to the highresolution model without tides. The benefits of resolution are particularly apparent in eastern boundary upwelling zones.To explore the balance between the size of a globally refined model and that of multiscale modelling options (e.g. finite element, finite volume or a two-way nesting approach), we consider a simple scale analysis and a conceptual grid refining approach. We put this analysis in the context of evolving computer systems, discussing model turnaround time, scalability and resource costs. Using a simple cost model compared to a reference configuration (taken to be a 1/4 • global model in 2011) and the increasing performance of the UK Research Councils' computer facility, we estimate an unstructured mesh multiscale approach, resolving process scales down to 1.5 km, would use a comparable share of the computer resource by 2021, the two-way nested multiscale approach by 2022, and a 1/72 • global model by 2026. However, we also note that a 1/12 • global model would not have a comparable computational cost to a 1 • global model in 2017 until 2027. Hence, we conc...

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Section: Options For Multiscale Modellingmentioning

confidence: 99%

Section: Scalability and Efficiency Of Ocean Modelsmentioning

confidence: 99%

Section: The Comparative Cost Of Ocean Modelsmentioning

confidence: 99%

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Prospects for improving the representation of coastal and shelf seas in global ocean models

et al. 2017

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“…More are in active development (e.g. Ringler et al, 2013) and the number of unstructured models joining these efforts -that directly contribute to reports compiled by the Intergovernmental Panel on Climate Change (IPCC) -is likely to grow. Similarly, on smaller scales, the geometric flexibility of unstructured discretisations is being applied to reduce the need for nesting models, and to accurately apply forcings or coupling physics (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…As models include a greater range of spatial scales, more computational effort is required to optimise the discretisation before a simulation proceeds (e.g. the actively developed MPAS models, Ringler et al (2013), strongly optimise their hexagonal prism based mesh discretisation). An increasing number of geometric degrees of freedom demand the meshing process is broken up over multiple parallel threads (as demonstrated in , just as simulation models have evolved to run in parallel.…”

Section: Introductionmentioning

confidence: 99%

Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models

Candy

Pietrzak

2018

Geosci. Model Dev.

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Abstract. The approaches taken to describe and develop spatial discretisations of the domains required for geophysical simulation models are commonly ad hoc, model-or application-specific, and under-documented. This is particularly acute for simulation models that are flexible in their use of multi-scale, anisotropic, fully unstructured meshes where a relatively large number of heterogeneous parameters are required to constrain their full description. As a consequence, it can be difficult to reproduce simulations, to ensure a provenance in model data handling and initialisation, and a challenge to conduct model intercomparisons rigorously. This paper takes a novel approach to spatial discretisation, considering it much like a numerical simulation model problem of its own. It introduces a generalised, extensible, selfdocumenting approach to carefully describe, and necessarily fully, the constraints over the heterogeneous parameter space that determine how a domain is spatially discretised. This additionally provides a method to accurately record these constraints, using high-level natural language based abstractions that enable full accounts of provenance, sharing, and distribution. Together with this description, a generalised consistent approach to unstructured mesh generation for geophysical models is developed that is automated, robust and repeatable, quick-to-draft, rigorously verified, and consistent with the source data throughout. This interprets the description above to execute a self-consistent spatial discretisation process, which is automatically validated to expected discrete characteristics and metrics.Library code, verification tests, and examples available in the repository at https://github.com/shingleproject/ Shingle. Further details of the project presented at http:// shingleproject.org.

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Sea level in ocean reanalyses and tide gauges

2014

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[1] Previous studies have noted the presence of interannual to multidecadal variability in tide gauge sea level records which is correlated with meteorological variability and which can overwhelm the signal associated with global sea level rise. This study examines the usefulness of using a set of seven ocean reanalysis and synthesis products in studies of sea level variability by comparing the tide gauges and reanalysis products at a representative set of 87 tide gauge station locations. The comparison is carried out for both a half-century base period and a century long-extended period. Treating the set of products as an ensemble of realizations obtained using different techniques, the results show generally good agreement for the half-century period with ensemble average correlations of 0.57 and RMS differences of 2.2 cm, reducing to a correlation of 0.5 for the extended period. A significant fraction of the difference between tide gauge sea level and product sea level is associated with meteorological forcing. These results support the conclusion that much of the interannual to multidecadal variability that appears in the tide gauge records is meteorologically driven. This suggests that ocean products have potential to be used to isolate this variability from the signal associated with the underlying global sea level rise.

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A multi-resolution approach to global ocean modeling

Cited by 291 publications

References 59 publications

Prospects for improving the representation of coastal and shelf seas in global ocean models

Prospects for improving the representation of coastal and shelf seas in global ocean models

Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models

Sea level in ocean reanalyses and tide gauges

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