Towards multi-resolution global climate modeling with ECHAM6–FESOM. Part I: model formulation and mean climate

Sidorenko, Dmitry; Rackow, Thomas; Jung, Thomas; Semmler, Tido; Barbi, Dirk; Danilov, Sergey; Dethloff, Klaus; Dorn, Wolfgang; Fieg, Kerstin; Goessling, Helge; Handorf, Dörthe; Harig, Sven; Hiller, Wolfgang; Juricke, Stephan; Lösch, Martin; Schröter, Jens; Sein, Dmitry V.; Wang, Qiang

doi:10.1007/s00382-014-2290-6

Cited by 169 publications

(185 citation statements)

References 94 publications

(158 reference statements)

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“…This coupled atmosphere-ocean-sea ice model has been shown to be of comparable performance in simulating present-day climate and its variability to state-of-the-art coupled climate models that took part in phase 5 of the Coupled Model Intercomparison Project (CMIP5) (Sidorenko et al 2015;Rackow et al 2015, manuscript submitted to Climate Dyn. ).…”

Section: Methodology a Model Setupmentioning

confidence: 99%

“…Altogether we have a 100-member CTL and a 100-member RED ensemble. In these ensemble simulations the enforcement of the global flux conservation as described in Sidorenko et al (2015) has been switched off. This has been done to avoid possible spurious teleconnections associated with the correction of the global flux.…”

Section: Methodology a Model Setupmentioning

confidence: 99%

“…We use ECHAM6 in the standard resolution of T63L47 corresponding to about 200 km horizontally with 47 vertical levels up to 0.01 hPa (about 80 km) coupled to FESOM with a horizontal ocean grid resolution between 25 and 150 km and 46 vertical levels as defined in Sidorenko et al (2015). The coupling software used is OASIS3-MCT (Valcke et al 2013).…”

Section: Methodology a Model Setupmentioning

confidence: 99%

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Seasonal Atmospheric Responses to Reduced Arctic Sea Ice in an Ensemble of Coupled Model Simulations

et al. 2016

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Arctic sea ice decline is expected to continue throughout the twenty-first century as a result of increased greenhouse gas concentrations. Here we investigate the impact of a strong Arctic sea ice decline on the atmospheric circulation and low pressure systems in the Northern Hemisphere through numerical experimentation with a coupled climate model. More specifically, a large ensemble of 1-yr-long integrations, initialized on 1 June with Arctic sea ice thickness artificially reduced by 80%, is compared to corresponding unperturbed control experiments. The sensitivity experiment shows an ice-free Arctic from July to October; during autumn the largest near-surface temperature increase of about 15 K is found in the central Arctic, which goes along with a reduced meridional temperature gradient, a decreased jet stream, and a southward shifted Northern Hemisphere storm track; and the near-surface temperature response in winter and spring reduces substantially due to relatively fast sea ice growth during the freezing season. Changes in the maximum Eady growth rate are generally below 5% and hardly significant, with reduced vertical wind shear and reduced vertical stability counteracting each other. The reduced vertical wind shear manifests itself in a decrease of synoptic activity by up to 10% and shallower cyclones while the reduced vertical stability along with stronger diabatic heating due to more available moisture may be responsible for the stronger deepening rates and thus faster cyclone development once a cyclone starts to form. Furthermore, precipitation minus evaporation decreases over the Arctic because the increase in evaporation outweighs that for precipitation, with implications for the ocean stratification and hence ocean circulation.

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Section: Methodology a Model Setupmentioning

confidence: 99%

Section: Methodology a Model Setupmentioning

confidence: 99%

Section: Methodology a Model Setupmentioning

confidence: 99%

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Seasonal Atmospheric Responses to Reduced Arctic Sea Ice in an Ensemble of Coupled Model Simulations

et al. 2016

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“…FESOM, Sidorenko et al, 2014). This approach provides the full provenance, reproducibility, and complete constraining descriptions of the significantly more complex spatial discretisations supported by these models.…”

Section: Self-validationmentioning

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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“…This study also aims at detecting the possibly differing impacts in the more complex coupled system compared with uncoupled models. To this end, the coupled atmosphere-ocean-sea ice-land surface system ECHAM6-FESOM is used in a configuration with relatively high resolution in the Arctic ocean [34]. More specifically, single climate simulations are conducted to analyse the impact of the stochastic parametrization on the mean state of the system.…”

mentioning

confidence: 99%

Influence of stochastic sea ice parametrization on climate and the role of atmosphere–sea ice–ocean interaction

Juricke

Jung

2014

Phil. Trans. R. Soc. A.

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Towards multi-resolution global climate modeling with ECHAM6–FESOM. Part I: model formulation and mean climate

Cited by 169 publications

References 94 publications

Seasonal Atmospheric Responses to Reduced Arctic Sea Ice in an Ensemble of Coupled Model Simulations

Seasonal Atmospheric Responses to Reduced Arctic Sea Ice in an Ensemble of Coupled Model Simulations

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

Influence of stochastic sea ice parametrization on climate and the role of atmosphere–sea ice–ocean interaction

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